**Impact of Industrial Water Pollution on Rice Production in Vietnam**

Huynh Viet Khai and Mitsuyasu Yabe

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/54279

## **1. Introduction**

Vietnam has achieved the average GDP growth rate of 6.71% per year. The industrial sector has mainly contributed economic development in Vietnam, with annual growth of 12% during the period of 200-2009. In line with its industrialization and modernization policies, Vietnam has rapidly changed economic structure from agriculture base to industrial economy. The industrial and construction sector only contributed 26 percent of national GDP in 1986, but it rapidly increases to 40.3 percent in 2009.

Economic development has brought many benefits to Vietnam. Income, public transportation and, in general, quality of life have gradually improved while the percentage of people below the poverty threshold has reduced. However, there have also been many negative consequen‐ ces of rapid industrialization, particularly on agriculture and ecosystem health, because of the exploitation of natural resources and pollution. The two biggest cities in Vietnam, Ha Noi and Ho Chi Minh, have been ranked as the worst cities in Asia for dust pollution (The World Bank, 2008). Within Vietnam, Ho Chi Minh, the largest city, is at the top of the national pollution list (The World Bank, 2007). This pollution, into the air, water and land, is released by various, large industries. For instance, footwear manufacturing releases 11% of the air pollution load, 10% of the land pollution load and 6% of the water pollution load, while the plastic products manufacturing industry produces 10, 13 and 9% of the air, land and water pollution load, respectively. The main pollution sources do not necessarily come from the largest industries. The cement industry, which only has 12 factories and employs 0.5% of the provincial work‐ force, releases 24% of the air pollution load (ICEM, 2007). Similarly, the 160 paper factories employ only 0.8% of the provincial workers but contribute 14% of the water pollution load.

According to the Department of Science, Technology, and Environment of Tay Ninh, since almost all industrial zones have not installed wastewater treatment systems in Vietnam, the

© 2013 Khai and Yabe; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Khai and Yabe.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

existence of industrial wastewater contamination appears almost everywhere. Wastewater from thousands of industrial facilities in 30 industrial areas and from small factories and businesses in the basin is the main source of pollution in the Dong Nai river of Ho Chi Minh City 1 . The untreated wastewater contaminating oil from Hai Au concrete factory has been released directly into paddy fields approximately 200m3 /day and 1,500 m3 /day for Phuoc Long textile firm. However, it is difficult to know the actual damage and loss due to the contami‐ nation of untreated wastewater from industrial activities in Vietnam (Quang, 2001).

lected from 32 industrial estates in southern Vietnam to determine the factors affecting investment on wastewater treatment plants. It performed that water pollution was a seri‐ ous problem in the big industrial estates of Ho Chi Minh City, Binh Duong, Dong Nai and Ba Ria-Vung Tau Provinces, and that financial constraints and lack of space were the main reasons why many small and medium-sized enterprises did not invest in wastewa‐ ter treatment systems. Hung *et al.*(2008) studied the effects of trade liberalization on the environment, using data from the Viet Enterprise Survey of 2002 and the World Bank's Industrial Pollution Projection System. They found that trade liberalization led to greater pollution and environmental degradation but that the Vietnamese people have gradually

Impact of Industrial Water Pollution on Rice Production in Vietnam

http://dx.doi.org/10.5772/54279

63

However, because of a lack of information on the costs of pollution, national and local authorities in Vietnam have not paid much attention to pollution control measures. In this study, we review the literature on this topic and estimate the damage of rice production due to water pollution. Our findings could help governmental bodies enforce existing water pollution regulations, for example, TCVN 5945 on water pollution standards or Decree 67 on wastewater pollution charges, also help recognize and understand the failure of some of the current environmental policies in Vietnam. Our study could also provide useful information to authorities, such as the Natural Resources and Environment, and industries to manage water pollution and data for cost-benefit analyses of treatment projects in the industrial zones of

The total economic loss of rice production includes three factors. First, a reduction in crop quantity assumes that water pollution decreases rice yield. Second, a reduction in rice quality, which is measured as price, assumes that the lower price of rice in a particular region could reflect reduced rice quality due to water pollution. Third, an increase in input costs assumes that farms may attempt to compensate for the possible productivity losses by implementing activities that are capable of offsetting this possible loss but are more costly to implement. The

expectation of the profit loss is summarized by the following formula:

= -D -D - +D

Profit loss

=> = ´D + D ´ + D

p

*n*

= -

( )( ) ( )

*P PQ Q C C*

( ) ( )

= - ´D -D ´ +D ´D - -D = - - ´D +D ´ +D +D ´D

= ++

Quantity loss Quality loss Cost increase

*PQ P Q P Q P Q C C*

*PQ C P Q P Q C P Q*

*P Q PQ C* (2)

(1)

recognized the importance of environmental protection.

Vietnam.

**2. Evaluation concept**

p

*p*

 

Profit loss

There have been a number of empirical agricultural studies concerning environmental problems, such as soil degradation, wind and water erosion in the world; however, few have specifically examined the impact of industrial pollution. Bai (1988) conducted field experi‐ ments in wheat lands irrigated with wastewater from the Liangshui River, the Tonghui River and the Wanquan River. He reported that wastewater irrigation caused a reduction in wheat yield by 8–17.1%. Similar studies in the Geobeidian area of the Tonghui River and the Yizhuang area of the Lianghe River reported that yields of wheat and rice cultivated in unpolluted soils in the sewage-irrigated area decrease by about 10% of the yields obtained in clean waterirrigated areas. In the sewage-irrigated area with polluted soils, yields of wheat and rice grown reduce by 40.6% and 39% of those in clean irrigation areas.

Chang *et al.* (2001) analyzed the impact of industrial pollution on agriculture, human health and industrial activities in Chongqing. To determine the effect of sewage-irrigation, they proposed expressing yield reductions as a function of the comprehensive water pollution index. Using this approach, reductions in yield due to sewage irrigation were about 10% for wheat and 30% for rice and vegetables. To evaluate the effects of polluted water irrigation, Lindhjem (2007) compared crop quality and quantity between a wastewater-irrigated area and a clean water-irrigated area. The total loss of corn and wheat production was estimated to be RMB 360 per mu, of which RMB 285 was caused by reduction in quantity, and RMB 75 was the reduction in quality. This paper also cites the study of Song (2004) that used dose-response functions to estimate the reductions in quantity and quality of crops from polluted water irrigation. Water pollution decreased rice production by 20% and quality by about 4%.

A study by The World Bank (2007) also used dose-response functions to calculate the economic losses from crop damage caused by water pollution, in terms of both reductions in crop quantity and quality (excess pollutant levels and substandard nutritional value). The economic cost of wastewater irrigation in China was estimated to be about 7 billion RMB annually for the four major crops (wheat, corn, rice, and vegetables). Reddy and Behera (2006) evaluated the impact of water pollution on rural communities in India, in terms of agricultural produc‐ tion, human heath, and livestock, using the effects on production, replacement costs and human capital approaches. The study estimated that the total loss per household per annum due to water pollution was \$282.5, of which \$213.2 was from agriculture, \$16.3 from livestock and \$53 from human health.

There has been some studies in recent years on industrial pollution in Vietnam such as the report written by Thong and Ngoc (2004) presented a descriptive analysis of data col‐

<sup>1</sup> The speech of Dr. Trinh Le, the Institute of Tropical Technology and Environmental Protection.

lected from 32 industrial estates in southern Vietnam to determine the factors affecting investment on wastewater treatment plants. It performed that water pollution was a seri‐ ous problem in the big industrial estates of Ho Chi Minh City, Binh Duong, Dong Nai and Ba Ria-Vung Tau Provinces, and that financial constraints and lack of space were the main reasons why many small and medium-sized enterprises did not invest in wastewa‐ ter treatment systems. Hung *et al.*(2008) studied the effects of trade liberalization on the environment, using data from the Viet Enterprise Survey of 2002 and the World Bank's Industrial Pollution Projection System. They found that trade liberalization led to greater pollution and environmental degradation but that the Vietnamese people have gradually recognized the importance of environmental protection.

However, because of a lack of information on the costs of pollution, national and local authorities in Vietnam have not paid much attention to pollution control measures. In this study, we review the literature on this topic and estimate the damage of rice production due to water pollution. Our findings could help governmental bodies enforce existing water pollution regulations, for example, TCVN 5945 on water pollution standards or Decree 67 on wastewater pollution charges, also help recognize and understand the failure of some of the current environmental policies in Vietnam. Our study could also provide useful information to authorities, such as the Natural Resources and Environment, and industries to manage water pollution and data for cost-benefit analyses of treatment projects in the industrial zones of Vietnam.

## **2. Evaluation concept**

existence of industrial wastewater contamination appears almost everywhere. Wastewater from thousands of industrial facilities in 30 industrial areas and from small factories and businesses in the basin is the main source of pollution in the Dong Nai river of Ho Chi Minh

textile firm. However, it is difficult to know the actual damage and loss due to the contami‐

There have been a number of empirical agricultural studies concerning environmental problems, such as soil degradation, wind and water erosion in the world; however, few have specifically examined the impact of industrial pollution. Bai (1988) conducted field experi‐ ments in wheat lands irrigated with wastewater from the Liangshui River, the Tonghui River and the Wanquan River. He reported that wastewater irrigation caused a reduction in wheat yield by 8–17.1%. Similar studies in the Geobeidian area of the Tonghui River and the Yizhuang area of the Lianghe River reported that yields of wheat and rice cultivated in unpolluted soils in the sewage-irrigated area decrease by about 10% of the yields obtained in clean waterirrigated areas. In the sewage-irrigated area with polluted soils, yields of wheat and rice grown

Chang *et al.* (2001) analyzed the impact of industrial pollution on agriculture, human health and industrial activities in Chongqing. To determine the effect of sewage-irrigation, they proposed expressing yield reductions as a function of the comprehensive water pollution index. Using this approach, reductions in yield due to sewage irrigation were about 10% for wheat and 30% for rice and vegetables. To evaluate the effects of polluted water irrigation, Lindhjem (2007) compared crop quality and quantity between a wastewater-irrigated area and a clean water-irrigated area. The total loss of corn and wheat production was estimated to be RMB 360 per mu, of which RMB 285 was caused by reduction in quantity, and RMB 75 was the reduction in quality. This paper also cites the study of Song (2004) that used dose-response functions to estimate the reductions in quantity and quality of crops from polluted water irrigation. Water pollution decreased rice production by 20% and quality by about 4%.

A study by The World Bank (2007) also used dose-response functions to calculate the economic losses from crop damage caused by water pollution, in terms of both reductions in crop quantity and quality (excess pollutant levels and substandard nutritional value). The economic cost of wastewater irrigation in China was estimated to be about 7 billion RMB annually for the four major crops (wheat, corn, rice, and vegetables). Reddy and Behera (2006) evaluated the impact of water pollution on rural communities in India, in terms of agricultural produc‐ tion, human heath, and livestock, using the effects on production, replacement costs and human capital approaches. The study estimated that the total loss per household per annum due to water pollution was \$282.5, of which \$213.2 was from agriculture, \$16.3 from livestock

There has been some studies in recent years on industrial pollution in Vietnam such as the report written by Thong and Ngoc (2004) presented a descriptive analysis of data col‐

1 The speech of Dr. Trinh Le, the Institute of Tropical Technology and Environmental Protection.

nation of untreated wastewater from industrial activities in Vietnam (Quang, 2001).

released directly into paddy fields approximately 200m3

62 International Perspectives on Water Quality Management and Pollutant Control

reduce by 40.6% and 39% of those in clean irrigation areas.

and \$53 from human health.

. The untreated wastewater contaminating oil from Hai Au concrete factory has been

/day and 1,500 m3

/day for Phuoc Long

City 1

The total economic loss of rice production includes three factors. First, a reduction in crop quantity assumes that water pollution decreases rice yield. Second, a reduction in rice quality, which is measured as price, assumes that the lower price of rice in a particular region could reflect reduced rice quality due to water pollution. Third, an increase in input costs assumes that farms may attempt to compensate for the possible productivity losses by implementing activities that are capable of offsetting this possible loss but are more costly to implement. The expectation of the profit loss is summarized by the following formula:

$$\begin{aligned} \pi\_p &= \left(\overline{P} - \Delta P\right)\left(\overline{Q} - \Delta Q\right) - \left(\overline{C} + \Delta C\right) \\ &= \overline{P}\overline{Q} - \overline{P} \times \Delta Q - \Delta P \times \overline{Q} + \Delta P \times \Delta Q - \overline{C} - \Delta C \\ &= \left(\overline{P}\overline{Q} - \overline{C}\right) - \left(\overline{P} \times \Delta Q + \Delta P \times \overline{Q} + \Delta \overline{C}\right) + \Delta P \times \Delta Q \\ &= \qquad \pi\_n & \quad \text{Profit loss} \end{aligned} \tag{1}$$

$$\begin{aligned} \text{=} & \begin{array}{ccccc} \text{Profit loss} & = & \overline{P} \times \Lambda Q & + & \Lambda P \times \overline{Q} & + & \Lambda \overline{C} \\ & = & \text{Quantity loss} & + & \text{Quality loss} & + & \text{Cost increase} \end{array} \end{aligned} \tag{2}$$

where *πn* and *π<sup>p</sup>* are the rice profits in the non-polluted and polluted areas. Because *ΔP*×*ΔQ* is small compared with the other parts of the equation, it can be ignored and assumed to be 0.

naire and to examine the suitability of existing rice production models for the research. There are a number of studies related to rice production in Vietnam. Kompas (2004) and Linh (2007) used a stochastic production frontier to estimate the technical efficiency of rice produc‐ tion in Vietnam. Do and Bennett (2007) used a production function approach with flood duration and relative location of upstream and downstream farmers variables to estimate the cost of changing wetland management, representing the reduced income of rice production in the Mekong River Delta. The loss of rice productivity was estimated based on the differences in rice yield between upper and lower of the Tram Chim park dyke. The results showed that the rice productivity in the lowering of park dyke decreased 0.06 tons per hectare per annum, which led to the profit loss of VND 0.07 million per hectare per annum. These three studies used the Cobb-Douglas functional form of the rice production function approach. This study uses a translog functional form and does test for checking the existence of Cobb-Douglass. The

where *Y* is the rice yield of a farmer in the studied year (tones/ha), *L* is the number of labors for rice cultivation (man-days/ha), *K* is capital input (VND/ha), *I* is a vector of material inputs as seeds (kg/ha), fertilizers (kg/ha), herbicide (ml/ha) and pesticides (ml/ha), *Z* is a vector of social-economic characteristics of farmers, and *E* is a vector of farming conditions, and *F* is the

> : Quantity loss = 0 or Coef. of F = 0 : Quantity loss > 0 or Coef. of F < 0

The reduced yield of rice is defined as the difference in the average rice yield between the non-

average of labor, capital input, material inputs, social-economic characteristics, and farming

As mentioned earlier, a translog functional form is used in the study. The production functional form in the polluted and non-polluted areas is written as followed (Tim & Battese, 2005):

( )

 b 2

1 1

= =

 a

> dg

 a

*k h*

relative location of farms (polluted site = 1, non-polluted site = 0)

The test for the existence of quantity loss due to water pollution is:

polluted and polluted site. It is estimated by following equation:

where *Δ<sup>Y</sup>* is the average yield loss caused by water pollution (kg/ha); *<sup>L</sup>*¯, *<sup>K</sup>*¯, *<sup>I</sup>*

0 1 2 3 11 12 13

*K KI I Z E F*

<sup>1</sup> ln( ) ln( ) ln( ) ln( ) ln( ) ln( )ln( ) ln( )ln( ) <sup>2</sup>

 a

*Y L K I L LK LI*

=+ + + + + + +

5 4 2 2

1 1 ln( ) ln( )ln( ) ln( ) 2 2 *k k hh*

+ + + + ++ å å

 a

 a

0 1

( ) ( )

22 23 33

 a  a *H*

*Y f LKIZEF* = (, ,, , , ) (3)

Impact of Industrial Water Pollution on Rice Production in Vietnam

http://dx.doi.org/10.5772/54279

65

*<sup>H</sup>* (4)

D = *Y fLKI ZEF fLKI ZEF* ( , , , , , 0) ( , , , , , 1) = - = (5)

¯, *<sup>Z</sup>*¯, *<sup>E</sup>*¯ are the

(6)

model takes the basic form:

conditions, respectively.

aa

a

However, it is complicated to estimate quality loss through the proxy of price because there are many other unobservable factors, excepting water pollution, which affect the price of rice. Thus, the study only calculates the three elements affected by water pollution:


## **3. Empirical model**

We surveyed rice farmers in two areas with the assumption that they had the same natural environment conditions and social characteristics, and only differed with respect to pollution. One area was considered to be the polluted area, receiving wastewater from nearby industrial parks, while the other area was assumed to be the non-polluted area, being distant from sources of industrial pollutants. The productivity loss of rice production caused by water pollution was estimated by the difference in rice yield between the two regions (Translog production function approach). The similar calculation was applied for cost increase and profit loss due to water pollution by applying the methods of Cobb-Douglas cost function and translog profit function respectively.

## **3.1. Production function approach**

The production function approach is that industrial activities possibly have a negative impact on the outputs, cost and profit of producers through the effect of environment. Environment af‐ fects goods or services existing in the market through the value change of their outputs, for in‐ stance, the reduced value of fish caught because of river pollution. The production function approach is often used to estimate the effect of environment change on soil erosion, deforesta‐ tion, fisheries, the impact of air and water pollution on agriculture and so on (Bateman *et al.*, 2003)

A literature search on the production function approach in rice production Vietnam was conducted to make sure that relevant variables will be included in the farm survey question‐ naire and to examine the suitability of existing rice production models for the research. There are a number of studies related to rice production in Vietnam. Kompas (2004) and Linh (2007) used a stochastic production frontier to estimate the technical efficiency of rice produc‐ tion in Vietnam. Do and Bennett (2007) used a production function approach with flood duration and relative location of upstream and downstream farmers variables to estimate the cost of changing wetland management, representing the reduced income of rice production in the Mekong River Delta. The loss of rice productivity was estimated based on the differences in rice yield between upper and lower of the Tram Chim park dyke. The results showed that the rice productivity in the lowering of park dyke decreased 0.06 tons per hectare per annum, which led to the profit loss of VND 0.07 million per hectare per annum. These three studies used the Cobb-Douglas functional form of the rice production function approach. This study uses a translog functional form and does test for checking the existence of Cobb-Douglass. The model takes the basic form:

$$\mathcal{Y} = f(\mathcal{L}, \mathcal{K}, \mathcal{I}, \mathcal{Z}, \mathcal{E}, \mathcal{F}) \tag{3}$$

where *Y* is the rice yield of a farmer in the studied year (tones/ha), *L* is the number of labors for rice cultivation (man-days/ha), *K* is capital input (VND/ha), *I* is a vector of material inputs as seeds (kg/ha), fertilizers (kg/ha), herbicide (ml/ha) and pesticides (ml/ha), *Z* is a vector of social-economic characteristics of farmers, and *E* is a vector of farming conditions, and *F* is the relative location of farms (polluted site = 1, non-polluted site = 0)

The test for the existence of quantity loss due to water pollution is:

where *πn* and *π<sup>p</sup>* are the rice profits in the non-polluted and polluted areas. Because *ΔP*×*ΔQ* is small compared with the other parts of the equation, it can be ignored and assumed to be 0.

However, it is complicated to estimate quality loss through the proxy of price because there are many other unobservable factors, excepting water pollution, which affect the price of rice.

**•** *Quantity loss:* Water pollution causes a decrease in rice yield. The production function

**•** *Cost increase:* Since farms may aim and indeed be able to compensate for the possible productivity losses by implementing activities which are capable of offsetting this possible loss but are more costly to implement. In such circumstances, because it is not productivity which will be impacted, but production costs, cost function approach is applied to assess

**•** *Profit loss:* This is defined as total loss of net economic return estimated by the comparison of profit functions between two selected areas (one is considered as the polluted, other is the non-polluted area). The difference in rice profits of two regions is considered as total

We surveyed rice farmers in two areas with the assumption that they had the same natural environment conditions and social characteristics, and only differed with respect to pollution. One area was considered to be the polluted area, receiving wastewater from nearby industrial parks, while the other area was assumed to be the non-polluted area, being distant from sources of industrial pollutants. The productivity loss of rice production caused by water pollution was estimated by the difference in rice yield between the two regions (Translog production function approach). The similar calculation was applied for cost increase and profit loss due to water pollution by applying the methods of Cobb-Douglas cost function and translog profit

The production function approach is that industrial activities possibly have a negative impact on the outputs, cost and profit of producers through the effect of environment. Environment af‐ fects goods or services existing in the market through the value change of their outputs, for in‐ stance, the reduced value of fish caught because of river pollution. The production function approach is often used to estimate the effect of environment change on soil erosion, deforesta‐ tion, fisheries, the impact of air and water pollution on agriculture and so on (Bateman *et al.*,

A literature search on the production function approach in rice production Vietnam was conducted to make sure that relevant variables will be included in the farm survey question‐

Thus, the study only calculates the three elements affected by water pollution:

approach is used to estimate the loss of rice yield.

64 International Perspectives on Water Quality Management and Pollutant Control

the impacts of pollution in economic terms.

**3. Empirical model**

function respectively.

2003)

**3.1. Production function approach**

loss of net economic return due to industrial pollution.

$$\begin{aligned} &H\_0: \text{Quantity loss} = 0 \text{ or } \text{Coef. of F} = 0\\ &H\_1: \text{Quantity loss} > 0 \text{ or } \text{Coef. of F} < 0 \end{aligned} \tag{4}$$

The reduced yield of rice is defined as the difference in the average rice yield between the nonpolluted and polluted site. It is estimated by following equation:

$$
\Delta Y = f(\overline{L}, \overline{K}, \overline{I}, \overline{Z}, \overline{E}, F = 0) - f(\overline{L}, \overline{K}, \overline{I}, \overline{Z}, \overline{E}, F = 1) \tag{5}
$$

where *Δ<sup>Y</sup>* is the average yield loss caused by water pollution (kg/ha); *<sup>L</sup>*¯, *<sup>K</sup>*¯, *<sup>I</sup>* ¯, *<sup>Z</sup>*¯, *<sup>E</sup>*¯ are the average of labor, capital input, material inputs, social-economic characteristics, and farming conditions, respectively.

As mentioned earlier, a translog functional form is used in the study. The production functional form in the polluted and non-polluted areas is written as followed (Tim & Battese, 2005):

$$\begin{aligned} \ln(Y) &= \alpha\_0 + \alpha\_1 \ln(L) + \alpha\_2 \ln(K) + \alpha\_3 \ln(I) + \frac{1}{2} \alpha\_{11} \left(\ln(L)\right)^2 + \alpha\_{12} \ln(L) \ln(K) + \alpha\_{13} \ln(L) \ln(I) + \\ &+ \frac{1}{2} \alpha\_{22} \left(\ln(K)\right)^2 + \alpha\_{23} \ln(K) \ln(I) + \frac{1}{2} \alpha\_{33} \left(\ln(I)\right)^2 + \sum\_{k=1}^5 \beta\_k Z\_k + \sum\_{k=1}^4 \delta\_k E\_k + \gamma F \end{aligned} \tag{6}$$

where *Y, L, K, I, F* are the same as in the above equations and *Z1, Z2, Z3, Z4* are the variables of the gender (1 = male, 0 = female), the age (years), the number of school year (years), attending trainings (1 = Yes, 0 = No) of rice households, and *E1, E2, E3, E4* are the variables of serious diseases happening during the study year (1 = Yes, 0 = No), rice monoculture (1 = yes, 0 = No), soil quality (1 = fertile soil, 0 = other soils), off-farm income ratio.

Some restrictions are used to check the constant returns to scale:

$$\begin{aligned} \alpha\_1 + \alpha\_2 + \alpha\_3 &= 1 \\ \alpha\_{11} + \alpha\_{12} + \alpha\_{13} &= 0 \\ \alpha\_{12} + \alpha\_{22} + \alpha\_{23} &= 0 \\ \alpha\_{13} + \alpha\_{23} + \alpha\_{33} &= 0 \end{aligned} \tag{7}$$

The increase in input costs is defined as the difference of the average cost between heavily

where ΔC is the increase of the average cost per ha because of water pollution (VND/ha);

The Cobb-Douglas formal function is applied to estimate the cost function in the study (Tim

( , , , , , , , 1) ( , , , , , , , 0) *C CW W W W YZEF CW W W W YZEF sh f p sh f p* D = = - = (11)

*<sup>p</sup>*, *<sup>Y</sup>*¯, *<sup>Z</sup>*¯, *<sup>E</sup>*¯ are the average price of seed, herbicide, fertilizer, pesticides,

 j

*, Wp, F* are the same as in the above equation and *Z1, Z2, Z3*, are, the age

=+ + + + + + + + å å (12)

3 3

1 1

= =

 d

 g

http://dx.doi.org/10.5772/54279

67

*k h*

 b

Impact of Industrial Water Pollution on Rice Production in Vietnam

\* \*, , , , = (*W CZEF*) (13)

<sup>&</sup>gt; (14)

polluted and less polluted areas. It is estimated by following equation:

social-economic characteristics, and farming conditions, respectively.

01 2 3 4 5

p p

Hypothesis for the existence of profit loss due to water pollution is:

0 1 p p

p p

*H H*

*n p n p*

=

and non-polluted areas. It is estimated by the equation:

 j

 j

ln( ) ln( ) ln( ) ln( ) ln( ) ln( ) *<sup>s</sup> <sup>h</sup> <sup>f</sup> <sup>p</sup> k k hh*

*C W W W W Y Z EF*

 j

(years), the number of school year (years), attending trainings (1 = Yes, 0 = No) of rice house‐ holds, and *E1, E2, E3*, are serious diseases happening during the year (1 = Yes, 0 = No), rice

Net economic return is defined as revenues from rice minus the cost of producing rice. It will be identified by a profit function approach. The profit loss is estimated by the following basic

where *π\** is normalized profit defined as gross revenue minus variable cost divided by farmspecific output price, *W\** is a vector of variable input prices divided by output price, *C* is a vector of fixed factors of the farm, *Z* is a vector of social-economic characteristics of farmers, *E* is a vector of farming conditions, *F* is the relative location of farms (polluted site = 1, non-

> : or Profit Loss = 0 or Coef. of F = 0 : or Profit Loss > 0 or Coef. of F < 0

The profit loss due to water pollution is defined by the difference in profit between the polluted

monoculture (1 = yes, 0 = No), soil quality (1 = fertile soil, 0 = other soils) respectively.

*W*¯ *s*, *W*¯ *h* , *W*¯

& Battese, 2005):

jj

where *C, Ws, Wh, Wf*

profit function:

polluted site = 0).

**3.3. Profit function approach**

*f* , *W*¯

Then, the following restriction is applied to test the existence of Cobb-Douglass function:

$$
\alpha\_{11} = \alpha\_{12} = \alpha\_{13} = \alpha\_{22} = \alpha\_{23} = \alpha\_{33} = 0 \tag{8}
$$

#### **3.2. Replacement Cost (RC)**

Replacement cost approach is defined as payment for restoring original environment (unpol‐ luted state) if it has already been damaged. The costs of moving away from the polluted area suffered by the victims of environmental damage or actual spending on safeguards against environmental risks are called replacement costs (Bateman *et al.*, 2003; Winpenny, 1991). In the study written by Reddy and Behera (2006), the replacement cost method is used to estimate the damage costs of pump sets due to water pollution. In this study, farmers in the polluted areas might spend more input costs for the compensation of rice productivity loss because they directly use the polluted water for irrigation. Thus, it is assumed that the costs of farmers in polluted areas are more than those in the non-polluted areas. In this case, the replacement cost is estimated by using the cost function approach. The basic form of cost function is given by:

$$\mathbf{C} = \mathbf{C} \{ \mathbf{W}\_{s'} \mathbf{W}\_{h'} \mathbf{W}\_{f'} \mathbf{W}\_{p'} \mathbf{Y}\_{\prime} \mathbf{Z}\_{\prime} \mathbf{E}\_{\prime} \mathbf{F} \} \tag{9}$$

where *C* is the total cost of a farmer (VND/ha), *Ws* is the price of seed (VND/kg), *Wh* is the price of herbicide (VND/100ml), *Wf* is the price of fertilizers (VND/kg), *Wp* is the price of pesticides (VND/100ml), *Y* is the rice yield of a farmer in the studied year (tones/ha), *Z* is a vector of social-economic characteristics of farmers, and *E* is a vector of farming conditions, *F* is the relative location of farms (polluted site = 1, non-polluted site = 0)

The test for the existence of cost increase due to water pollution is:

$$\begin{aligned} H\_0: &\text{Cost increase} = 0 \text{ or } \text{Coef. of F} = 0\\ H\_1: &\text{Cost increase} > 0 \text{ or } \text{Coef. of F} > 0 \end{aligned} \tag{10}$$

The increase in input costs is defined as the difference of the average cost between heavily polluted and less polluted areas. It is estimated by following equation:

$$
\Delta \mathbb{C} = \mathbb{C} (\overline{\mathcal{W}}\_{s'} \, \overline{\mathcal{W}}\_{h'} \, \overline{\mathcal{W}}\_{f'} \, \overline{\mathcal{W}}\_{p'} \, \overline{Y}, \overline{Z}, \overline{E}, F = 1) - \mathbb{C} (\overline{\mathcal{W}}\_{s'} \, \overline{\mathcal{W}}\_{h'} \, \overline{\mathcal{W}}\_{f'} \, \overline{\mathcal{W}}\_{p'} \, \overline{Y}, \overline{Z}, \overline{E}, F = 0) \tag{11}
$$

where ΔC is the increase of the average cost per ha because of water pollution (VND/ha); *W*¯ *s*, *W*¯ *h* , *W*¯ *f* , *W*¯ *<sup>p</sup>*, *<sup>Y</sup>*¯, *<sup>Z</sup>*¯, *<sup>E</sup>*¯ are the average price of seed, herbicide, fertilizer, pesticides, social-economic characteristics, and farming conditions, respectively.

The Cobb-Douglas formal function is applied to estimate the cost function in the study (Tim & Battese, 2005):

$$\ln(\text{C}) = \wp\_0 + \varrho\_1 \ln(\mathcal{W}\_s) + \varrho\_2 \ln(\mathcal{W}\_h) + \wp\_3 \ln(\mathcal{W}\_f) + \varrho\_4 \ln(\mathcal{W}\_p) + \wp\_5 \ln(\mathcal{Y}) + \sum\_{k=1}^3 \beta\_k Z\_k + \sum\_{h=1}^3 \delta\_h E\_h + \chi F \tag{12}$$

where *C, Ws, Wh, Wf , Wp, F* are the same as in the above equation and *Z1, Z2, Z3*, are, the age (years), the number of school year (years), attending trainings (1 = Yes, 0 = No) of rice house‐ holds, and *E1, E2, E3*, are serious diseases happening during the year (1 = Yes, 0 = No), rice monoculture (1 = yes, 0 = No), soil quality (1 = fertile soil, 0 = other soils) respectively.

#### **3.3. Profit function approach**

where *Y, L, K, I, F* are the same as in the above equations and *Z1, Z2, Z3, Z4* are the variables of the gender (1 = male, 0 = female), the age (years), the number of school year (years), attending trainings (1 = Yes, 0 = No) of rice households, and *E1, E2, E3, E4* are the variables of serious diseases happening during the study year (1 = Yes, 0 = No), rice monoculture (1 = yes, 0 = No),

====== 0 (8)

( , , , ,,,,) *C CW W W W YZEF sh f p* = (9)

is the price of fertilizers (VND/kg), *Wp* is the price of pesticides

*<sup>H</sup>* (10)

(7)

soil quality (1 = fertile soil, 0 = other soils), off-farm income ratio.

66 International Perspectives on Water Quality Management and Pollutant Control

Some restrictions are used to check the constant returns to scale:

**3.2. Replacement Cost (RC)**

of herbicide (VND/100ml), *Wf*

++= ++= ++= ++=

aaa

aaa

aaa

11 12 13 22 23 33 aaaaaa

Then, the following restriction is applied to test the existence of Cobb-Douglass function:

Replacement cost approach is defined as payment for restoring original environment (unpol‐ luted state) if it has already been damaged. The costs of moving away from the polluted area suffered by the victims of environmental damage or actual spending on safeguards against environmental risks are called replacement costs (Bateman *et al.*, 2003; Winpenny, 1991). In the study written by Reddy and Behera (2006), the replacement cost method is used to estimate the damage costs of pump sets due to water pollution. In this study, farmers in the polluted areas might spend more input costs for the compensation of rice productivity loss because they directly use the polluted water for irrigation. Thus, it is assumed that the costs of farmers in polluted areas are more than those in the non-polluted areas. In this case, the replacement cost is estimated by using the cost function approach. The basic form of cost function is given by:

where *C* is the total cost of a farmer (VND/ha), *Ws* is the price of seed (VND/kg), *Wh* is the price

(VND/100ml), *Y* is the rice yield of a farmer in the studied year (tones/ha), *Z* is a vector of social-economic characteristics of farmers, and *E* is a vector of farming conditions, *F* is the

> : Cost increase = 0 or Coef. of F = 0 : Cost increase > 0 or Coef. of F > 0

relative location of farms (polluted site = 1, non-polluted site = 0)

The test for the existence of cost increase due to water pollution is:

0 1

*H*

aaa

Net economic return is defined as revenues from rice minus the cost of producing rice. It will be identified by a profit function approach. The profit loss is estimated by the following basic profit function:

$$
\pi^\* = \pi\left(\mathcal{W}^\*, \mathcal{C}, Z, E, F\right) \tag{13}
$$

where *π\** is normalized profit defined as gross revenue minus variable cost divided by farmspecific output price, *W\** is a vector of variable input prices divided by output price, *C* is a vector of fixed factors of the farm, *Z* is a vector of social-economic characteristics of farmers, *E* is a vector of farming conditions, *F* is the relative location of farms (polluted site = 1, nonpolluted site = 0).

Hypothesis for the existence of profit loss due to water pollution is:

$$\begin{aligned} H\_0: \pi\_n = \pi\_p \text{ or } \text{Profit Loss} = 0 \text{ or } \text{Coef. of F} = 0\\ H\_1: \pi\_n > \pi\_p \text{ or } \text{Profit Loss} > 0 \text{ or } \text{Coef. of F} < 0 \end{aligned} \tag{14}$$

The profit loss due to water pollution is defined by the difference in profit between the polluted and non-polluted areas. It is estimated by the equation:

$$
\Delta \pi^\* = \pi \left( \overline{\mathcal{W}}^\*, \overline{\mathcal{C}}, \overline{Z}, \overline{E}, F = 0 \right) - \pi \left( \overline{\mathcal{W}}^\*, \overline{\mathcal{C}}, \overline{Z}, \overline{E}, F = 1 \right) \tag{15}
$$

**Province Air index Land index Water index Overall** Ho Chi Minh city 1 1 1 1 Hanoi 5 2 2 2 HaiPhong 2 6 4 3 Binh Duong 6 3 3 4 Dong Nai 4 4 5 5 Thai Nguyen 3 5 7 6 PhuTho 7 7 6 7 Da Nang 10 9 8 8 Ba RiaVung Tau 9 8 10 9 Can Tho 8 10 9 10

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69

Note: The pollution loads released to air, land and water were estimated for all 64 provinces in Vietnam, and then

**Zones Size Main activities Water treatments** Tra Noc 1 135 ha Processing, electron, clothes No a Tra Noc 2 165 ha Machinery No a Hung Phu 1 262 ha Harbor, Store No Hung Phu 2 212 ha Machinery No Hong Bang 38.2 ha Consumer goods No a Thot Not 150 ha Processing, clothes, shoes No a

The available decision and acceptation of local authorities to evaluate the impact of environmental pollution.

There are six industrial parks in Can Tho (Table 2), which mainly comprise agricultural and fishery processing industries, clothes and consumer goods manufacturing industries. Almost none of the industrial zones and industrial corporations located near human residences have installed wastewater treatment systems. There has been little management of toxic waste or water pollution by local authorities and business. Tra Noc 1 (built in 1995) and Tra Noc 2 (built in 1999) industrial zones have only recently been acknowledged by the Department of Resources and Environment while Thot Not has been considered by Can Tho authorities to evaluate the impact of environmental pollution (Resource and Environment department of Can Tho city, 2008). As a consequence, Tra Noc 1 and 2 have released large volumes (1000s

pollution indexes were calculated and rankings were made.

*Source: Resource and Environment Department of Can Tho City (2008)*

) of various waste products directly into the river (Tuyen, 2010).

**Table 2.** The industrial zones in Can Tho city

**Table 1.** Top 10 most polluted provinces in Vietnam

*Source: ICEM, 2007*

a

m3

where *Δπ* \*is Profit loss in 1000 VND/ha. *<sup>W</sup>*¯\*, *<sup>C</sup>*¯, *<sup>Z</sup>*¯, *<sup>E</sup>*¯are the average prices of inputs, the average of the fixed factors, the social-economic characteristics of farmers, the farming conditions, respectively.

We use the translog profit functional form. The formula is given as (Rahman, 2002, Surjit & Carlos, 1981)

$$\begin{aligned} \ln \pi^\* &= \alpha\_0 + \sum\_{j=1}^4 \alpha\_j \ln \mathcal{W}\_j^\* + \frac{1}{2} \sum\_{j=1}^4 \sum\_{k=1}^4 \tau\_{jk} \ln \mathcal{W}\_j^\* \; \ln \mathcal{W}\_k^\* + \sum\_{j=1}^4 \sum\_{l=1}^6 \phi\_{jl} \ln \mathcal{W}\_j^\* \; \ln \mathcal{C}\_l + \\ &+ \sum\_{l=1}^6 \beta\_l \ln \mathcal{C}\_l + \frac{1}{2} \sum\_{l=1}^6 \sum\_{l=1}^6 \phi\_{lr} \ln \mathcal{C}\_l \ln \mathcal{C}\_t + \sum\_{m=1}^3 \varpi\_m \mathcal{Z}\_m + \sum\_{n=1}^4 \eta\_n \mathcal{E}\_n + \gamma F \end{aligned} \tag{16}$$

where *π\** is the restricted profit (total revenue minus total cost of variable inputs) normalized by price of output (P); *Wj \** is the price of the jth input (*Wj* ) normalized by the output price (P); j is the price of seed (1), the price of herbicides (2), the price of fertilizer (3), the price of pesticide (4); *Cl* is the quantity of fixed input, where l is total amount of seed used (1), total amount of herbicides used (2), total amount of fertilizer used (3), total amount of pesticides used (4), the number of man-days for rice production (5), the money of machines and services at all stages of rice production (6); *Z1, Z2, Z3* are the age (years), the number of school year (years), and attendance at training sessions (1 = Yes, 0 = No) of rice households, respectively; and *E1, E2, E3, E4* are the variables of serious disease incidence happening during the study year (1 = Yes, 0 = No), rice monoculture (1 = Yes, 0 = No), soil quality (1 = fertile soil, 0 = other soils), and offfarm income ratio, respectively.

Then, the following restriction is applied to test the existence of the Cobb-Douglass function:

$$
\sigma\_{jk} = \phi\_{jl} = \phi\_{lt} = 0 \tag{17}
$$

## **4. Study site and data description**

#### **4.1. Study site**

In the Mekong River Delta, there are approximately 33 industrial parks, which constitute 9.5% of the total industrial parks of the country. Almost all of these 33 parks have no wastewater treatment system. The industrial parks in Can Tho city have released the biggest pollution loads, and the province is ranked in the top 10 most polluted provinces in Vietnam (Table 1). Can Tho is also one of the biggest rice producers in the Mekong River Delta. Because of these reasons, Can Tho was selected as the study site.


Note: The pollution loads released to air, land and water were estimated for all 64 provinces in Vietnam, and then pollution indexes were calculated and rankings were made.

*Source: ICEM, 2007*

D = p p

68 International Perspectives on Water Quality Management and Pollutant Control

conditions, respectively.

0

pa

b

by price of output (P); *Wj*

farm income ratio, respectively.

**4.1. Study site**

**4. Study site and data description**

reasons, Can Tho was selected as the study site.

Carlos, 1981)

 \* \*, , , , 0 \*, , , , 1 (*W CZEF W CZEF* = -) p

where *Δπ* \*is Profit loss in 1000 VND/ha. *<sup>W</sup>*¯\*, *<sup>C</sup>*¯, *<sup>Z</sup>*¯, *<sup>E</sup>*¯are the average prices of inputs, the

average of the fixed factors, the social-economic characteristics of farmers, the farming

We use the translog profit functional form. The formula is given as (Rahman, 2002, Surjit &

*j j jk j k jl j l*

*W WW WC*

 hg

4 4 4 4 6

<sup>1</sup> ln \* ln \* ln \* ln \* ln \* ln

å åå åå

*j j k j l*

= = = = =

t

*C CC Z E F*

*\** is the price of the jth input (*Wj*

1 1 1 1 1

 v

where *π\** is the restricted profit (total revenue minus total cost of variable inputs) normalized

j is the price of seed (1), the price of herbicides (2), the price of fertilizer (3), the price of pesticide (4); *Cl* is the quantity of fixed input, where l is total amount of seed used (1), total amount of herbicides used (2), total amount of fertilizer used (3), total amount of pesticides used (4), the number of man-days for rice production (5), the money of machines and services at all stages of rice production (6); *Z1, Z2, Z3* are the age (years), the number of school year (years), and attendance at training sessions (1 = Yes, 0 = No) of rice households, respectively; and *E1, E2, E3, E4* are the variables of serious disease incidence happening during the study year (1 = Yes, 0 = No), rice monoculture (1 = Yes, 0 = No), soil quality (1 = fertile soil, 0 = other soils), and off-

Then, the following restriction is applied to test the existence of the Cobb-Douglass function:

In the Mekong River Delta, there are approximately 33 industrial parks, which constitute 9.5% of the total industrial parks of the country. Almost all of these 33 parks have no wastewater treatment system. The industrial parks in Can Tho city have released the biggest pollution loads, and the province is ranked in the top 10 most polluted provinces in Vietnam (Table 1). Can Tho is also one of the biggest rice producers in the Mekong River Delta. Because of these

0 *jk jl lt*

t fj

=+ + + +

6 6 6 3 4

å åå å å

<sup>1</sup> ln ln ln

 j

2

 a

2

1 1 1 1 1

*l l t m n*

= = = = =

*l l lt l t m m n n*

+ + + ++

( = ) (15)

f

= = = (17)

) normalized by the output price (P);

(16)

#### **Table 1.** Top 10 most polluted provinces in Vietnam


a The available decision and acceptation of local authorities to evaluate the impact of environmental pollution.

*Source: Resource and Environment Department of Can Tho City (2008)*

**Table 2.** The industrial zones in Can Tho city

There are six industrial parks in Can Tho (Table 2), which mainly comprise agricultural and fishery processing industries, clothes and consumer goods manufacturing industries. Almost none of the industrial zones and industrial corporations located near human residences have installed wastewater treatment systems. There has been little management of toxic waste or water pollution by local authorities and business. Tra Noc 1 (built in 1995) and Tra Noc 2 (built in 1999) industrial zones have only recently been acknowledged by the Department of Resources and Environment while Thot Not has been considered by Can Tho authorities to evaluate the impact of environmental pollution (Resource and Environment department of Can Tho city, 2008). As a consequence, Tra Noc 1 and 2 have released large volumes (1000s m3 ) of various waste products directly into the river (Tuyen, 2010).

## **4.2. Data collection**

The study region covers the area within and around Tra Noc 1 and Tra Noc 2 industrial zones, which are two of the greatest polluters in Can Tho. People living in this area have suffered various financial impacts from the pollution: reduced crop yields, the use of cattle and agricultural equipment such as pump sets, contamination of drinking water, and increased incidence of human diseases and deaths directly and indirectly caused by water pollution.

Farmers were randomly selected for interview from two areas (Phuoc Thoi and Thoi An) with similar social and natural conditions (e.g. the same social and farming culture, ethnicity, type of soil). The selection of the polluted and non-polluted area was based on their distance from industrial zones, and on the recommendation or suggestion of local authorities and farmers. Some of the villages in Phuoc Thoi are heavily polluted by wastewater from the TraNoc 1 and 2 industrial zones. The villages in Thoi An are further away from the industrial zones than Phuoc Thoi and deemed to represent a non-polluted area (see Figure 1).

The group of fourteen interviewers and three local guide persons includes ten final year students, four staffs of School of Economics and Business Administration, Can Tho University, one local authority from people's committee, and two local farmers.

The questionnaire composes four main parts. In the first and second parts, the personal and farming information of household such as address, age, gender, training and so on and the situation of environmental pollution were interviewed. The inputs and output of rice produc‐ tion were collected in the three part and income from other activities obtained in the final section of questionnaire.

**Figure 1.** Map of the Study Site

The polluted area (PhuocThoi) (1)

Limitation value (TCVN5942,1995)

(1) Measured on January 17th, 2007 (Nga *et al.*, 2008)

(4) Measured on January 27th, 2007 (Lang *et al.*, 2009)

criteria for aquatic life are specified in a separate index.

**Table 3.** Water quality of the polluted and non-polluted area

(2) The region receives wastewater directly from the industrial park.

(3) The region receives polluted water from the primary affected water source regions.

Notes:

**TSS (mg/l) COD (mg/l) NH3-N (mg/l)**

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71



(5) Values in Class A are from the surface water used for domestic water supply with appropriate treatments.

(6) Values in Class B are from the surface water used for purposes other than domestic water supply. Water quality

The household survey took 3 months to complete from January to March 2010 and was divided into two main reporting periods. The first period was called as pilot-survey in January 2010. The aims of this interview were to check and then correct the questionnaire more clearly and concisely, and to help interviewers get used to and understand the content of questionnaire. After the interviewers were trained how to ask by using questionnaire, about 30 farmers were interviewed. The revised questionnaire was used in the second period from February to March 2010. In total, 364 rice farmers, consisting of 214 farmers in the polluted and 150 farmers in the non-polluted area, were interviewed in February and March 2010. Household data were collected on household level information related to production costs and income as well as the social and economic characteristics of the farmers, and their perceived damages and losses due to water pollution.

Table 3 showed the water quality index of the polluted and non-polluted area. The concen‐ trations of Total Suspended Solids (TSS) in the water refer to the concentrations of solid particles that can be trapped by a filter. This can be a problem because high concentrations of TSS can block sunlight from reaching submerged vegetation. This causes a reduction in the photosynthesis rate, and therefore less dissolved oxygen released into the water by plants. If bottom dwelling plants are not exposed to some light, the plants stop producing oxygen and die. Chemical Oxygen Demand (COD) is the amount of oxygen used during the oxidation of organic matter and inorganic chemicals such as ammonia nitrogen (NH3-N). High COD indicates a greater pollution load.

Impact of Industrial Water Pollution on Rice Production in Vietnam http://dx.doi.org/10.5772/54279 71

**Figure 1.** Map of the Study Site


Notes:

**4.2. Data collection**

section of questionnaire.

to water pollution.

indicates a greater pollution load.

The study region covers the area within and around Tra Noc 1 and Tra Noc 2 industrial zones, which are two of the greatest polluters in Can Tho. People living in this area have suffered various financial impacts from the pollution: reduced crop yields, the use of cattle and agricultural equipment such as pump sets, contamination of drinking water, and increased incidence of human diseases and deaths directly and indirectly caused by water pollution.

Farmers were randomly selected for interview from two areas (Phuoc Thoi and Thoi An) with similar social and natural conditions (e.g. the same social and farming culture, ethnicity, type of soil). The selection of the polluted and non-polluted area was based on their distance from industrial zones, and on the recommendation or suggestion of local authorities and farmers. Some of the villages in Phuoc Thoi are heavily polluted by wastewater from the TraNoc 1 and 2 industrial zones. The villages in Thoi An are further away from the industrial zones than

The group of fourteen interviewers and three local guide persons includes ten final year students, four staffs of School of Economics and Business Administration, Can Tho University,

The questionnaire composes four main parts. In the first and second parts, the personal and farming information of household such as address, age, gender, training and so on and the situation of environmental pollution were interviewed. The inputs and output of rice produc‐ tion were collected in the three part and income from other activities obtained in the final

The household survey took 3 months to complete from January to March 2010 and was divided into two main reporting periods. The first period was called as pilot-survey in January 2010. The aims of this interview were to check and then correct the questionnaire more clearly and concisely, and to help interviewers get used to and understand the content of questionnaire. After the interviewers were trained how to ask by using questionnaire, about 30 farmers were interviewed. The revised questionnaire was used in the second period from February to March 2010. In total, 364 rice farmers, consisting of 214 farmers in the polluted and 150 farmers in the non-polluted area, were interviewed in February and March 2010. Household data were collected on household level information related to production costs and income as well as the social and economic characteristics of the farmers, and their perceived damages and losses due

Table 3 showed the water quality index of the polluted and non-polluted area. The concen‐ trations of Total Suspended Solids (TSS) in the water refer to the concentrations of solid particles that can be trapped by a filter. This can be a problem because high concentrations of TSS can block sunlight from reaching submerged vegetation. This causes a reduction in the photosynthesis rate, and therefore less dissolved oxygen released into the water by plants. If bottom dwelling plants are not exposed to some light, the plants stop producing oxygen and die. Chemical Oxygen Demand (COD) is the amount of oxygen used during the oxidation of organic matter and inorganic chemicals such as ammonia nitrogen (NH3-N). High COD

Phuoc Thoi and deemed to represent a non-polluted area (see Figure 1).

70 International Perspectives on Water Quality Management and Pollutant Control

one local authority from people's committee, and two local farmers.

(1) Measured on January 17th, 2007 (Nga *et al.*, 2008)

(2) The region receives wastewater directly from the industrial park.

(3) The region receives polluted water from the primary affected water source regions.

(4) Measured on January 27th, 2007 (Lang *et al.*, 2009)

(5) Values in Class A are from the surface water used for domestic water supply with appropriate treatments.

(6) Values in Class B are from the surface water used for purposes other than domestic water supply. Water quality criteria for aquatic life are specified in a separate index.

**Table 3.** Water quality of the polluted and non-polluted area

In the polluted area, the concentrations of TSS, COD and NH3-H in the sewer mouth, the primary affected water source and the secondary affected water source regions were mostly much higher than those of the standard water quality (see Table 3). This indicated that our selected pollution area site was heavily polluted. The concentrations of TSS, COD and NH3- N in the sewer mouth region were nearly 2-fold, over 20-fold and 13-fold higher than those of the standard water quality of class B, respectively.

**Variables Non-polluted area Polluted area t-value** *Y* 5.88 4.99 -7.31\*\*\* *P* 4,157.79 4,060.89 -3.06\*\*\* *C* 10,909.44 10,563.61 -0.84 π 13,623.91 9,759.35 -8.37\*\*\* *Cs* 224.41 206.42 -2.37\*\* *Ch* 10.43 11.70 1.33 *Cf* 475.87 463.76 -0.53 *Cp* 77.26 70.23 -1.24 *Cl* 29.03 32.95 1.39 *Cc* 3283.02 3436.20 1.06 *Ws* 5.46 5.21 -1.34 *Wh* 32.79 32.47 0.21 *Wf* 9.42 9.54 0.92 *Wp* 24.86 21.70 -1.85\* *Age* 48.04 48.99 0.81 *Education* 6.33 6.07 -0.87 *Training* 0.49 0.35 -2.72\*\*\*<sup>ψ</sup> *Mono* 0.60 0.58 -0.39<sup>ψ</sup> *Diseases* 0.40 0.42 -0.39<sup>ψ</sup> *Off-farm ratio* 0.20 0.37 4.72\*\*\*<sup>ψ</sup> *Soil* 0.63 0.75 2.35\*\*<sup>ψ</sup>

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73

Notes: \*\*\*, \*\*, \* indicate statistical significance at the 0.01, 0.05 and 0.1 level respectively

find additional work in nearby industrial parks to supplement their income.

Table 5 showed the descriptive statistics of the main variables in the rice production model for the polluted and non-polluted areas. Although soil quality in the non-polluted area was significantly (P < 0.05) lower than that in the polluted area, rice productivity and profit in the non-polluted area was significantly (P < 0.01) higher than those in the polluted area. The price of rice in the polluted area was significantly (P < 0.01) lower than that in the non-polluted area. This indicated that water pollution might have reduced crop quality, and in turn its price. The difference in the off-farm income ratio between the two areas suggests that farmers are aware of the reduced profit from rice cultivation in polluted soil, and therefore have a tendency to

Other variables measured did not significantly differ between the two regions (Table 5), except the percentage of respondents attending training. The results also showed that, on average, farmers were 48 years old, have had 6 years of education and 60 % of them grew rice in a

<sup>ψ</sup> Z-test for the equality of two proportions

monoculture.

*Source: Own estimates; data appendix available from authors.*

**Table 5.** Descriptive Statistics of Rice Production per hectare per crop

Differences in the water quality index between the polluted and non-polluted area indicate that the water quality in the non-polluted area was much higher than that in the polluted area. However, the concentrations of TSS and NH3-N in the non-polluted were slightly higher than those of the Class A standard. This may be caused by non-point source pollutants, for instance, fertilizer, herbicide and pesticide released by agricultural activities in the region.


**Table 4.** Description of variables used in rice production models

Table 4 showed the descriptions of variables in rice production models. The volumes of herbicide and pesticide used have measurement units of equivalent units of 100 ml per hectare per crop, based on farmers' reports and experts' recommendations. This is because farmers use various types of herbicides and pesticides (mixed with water or as a powder), and sometimes mix them together, which means that it is difficult to estimate exact amounts.

#### Impact of Industrial Water Pollution on Rice Production in Vietnam http://dx.doi.org/10.5772/54279 73


Notes: \*\*\*, \*\*, \* indicate statistical significance at the 0.01, 0.05 and 0.1 level respectively

<sup>ψ</sup> Z-test for the equality of two proportions

In the polluted area, the concentrations of TSS, COD and NH3-H in the sewer mouth, the primary affected water source and the secondary affected water source regions were mostly much higher than those of the standard water quality (see Table 3). This indicated that our selected pollution area site was heavily polluted. The concentrations of TSS, COD and NH3- N in the sewer mouth region were nearly 2-fold, over 20-fold and 13-fold higher than those of

Differences in the water quality index between the polluted and non-polluted area indicate that the water quality in the non-polluted area was much higher than that in the polluted area. However, the concentrations of TSS and NH3-N in the non-polluted were slightly higher than those of the Class A standard. This may be caused by non-point source pollutants, for instance,

fertilizer, herbicide and pesticide released by agricultural activities in the region.

*Ch* Total amount of herbicides used Equivalent unit of 100 ml/ha

*Cp* Total amount of pesticide used Equivalent unit of 100 ml/ha

*Soil* Soil quality 1 = fertile soil, 0 = other soils

Table 4 showed the descriptions of variables in rice production models. The volumes of herbicide and pesticide used have measurement units of equivalent units of 100 ml per hectare per crop, based on farmers' reports and experts' recommendations. This is because farmers use various types of herbicides and pesticides (mixed with water or as a powder), and sometimes mix them together, which means that it is difficult to estimate exact amounts.

rice production Thousand VND/ha

**Variable Description Unit** *Y* Total yield per hectare Ton/hectare *P* Price of rice Thousand VND/ton *C* Total cost Thousand VND/ha π Total profit Thousand VND/ha

*Cs* Total amount of seed used Kg/ha

*Cf* Total amount of fertilizer used Kg/ha

*Cl* The number of man-days for rice production day/ha

*Age* The age of respondents Years *Education* The number of school year of respondents Years *Training* Respondents attending trainings 1= Yes, 0 = No *Mono* Rice monoculture 1= Yes, 0 = No *Diseases* Diseases happening during the study year 1= Yes, 0 = No

*Off-farm ratio* The ratio of off-farm income

**Table 4.** Description of variables used in rice production models

The money of machines and services at all stages of

*Ws* Price of seed Thousand VND/kg *Wh* Price of herbicide Thousand VND/100ml *Wf* Price of fertilizer Thousand VND/kg *Wp* Price of pesticide Thousand VND/100ml

*Cc*

the standard water quality of class B, respectively.

72 International Perspectives on Water Quality Management and Pollutant Control

*Source: Own estimates; data appendix available from authors.*

**Table 5.** Descriptive Statistics of Rice Production per hectare per crop

Table 5 showed the descriptive statistics of the main variables in the rice production model for the polluted and non-polluted areas. Although soil quality in the non-polluted area was significantly (P < 0.05) lower than that in the polluted area, rice productivity and profit in the non-polluted area was significantly (P < 0.01) higher than those in the polluted area. The price of rice in the polluted area was significantly (P < 0.01) lower than that in the non-polluted area. This indicated that water pollution might have reduced crop quality, and in turn its price. The difference in the off-farm income ratio between the two areas suggests that farmers are aware of the reduced profit from rice cultivation in polluted soil, and therefore have a tendency to find additional work in nearby industrial parks to supplement their income.

Other variables measured did not significantly differ between the two regions (Table 5), except the percentage of respondents attending training. The results also showed that, on average, farmers were 48 years old, have had 6 years of education and 60 % of them grew rice in a monoculture.

## **5. Estimated results**

### **5.1. Impact of water pollution on rice productivity**

Table 6 showed the Ordinary Least Squares (OLS) result of rice production function in translog form. The variables estimated in the model were statistically significant at 1 percent level. The estimated R-square was equal to 0.64, revealing the 64 percent change of rice yield possibly explained by independent variables in the model.

1 percent level of significance 1). Thus, the null hypothesis was rejected and the study concluded

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The second test was applied to check the Cobb-Douglass formal existence of the production function. The restricted function was estimated with the null hypothesis of jointed parameters in (8) equal to 0. The computed F statistic of 1.94 was more than the critical F(21,327) of 1.91 at 1 percent level of significance 1). Thus, the null hypothesis was rejected, meaning that the translog functional form was suitably applied for the data of rice production in the study.

The results of Table 6 showed that there was no multicollinearity in the independent variables of production function because the correlations of these independent variables estimated by using the correlation matrix were less than 70 percent. The null hypothesis homoscedasticity was also accepted by using Breusch-Pagan test because the estimated LM of 49.72 was less

Table 6 showed that the rice productivity in the polluted was lower than in the non-polluted area because the coefficient of *Pollution* variable was significantly negative at 1 percent level. In addition, the study also revealed that training courses partly contributed an increase in rice

Moreover, the model also showed that farmer age (P < 0.05) and the ratio of off-farm income (P < 0.1) explained variation in rice yield. The effect of age might have been caused by declines in the health of older farmers leading to less efficient cultivation. Farmers who earned more off-farm income were associated with less profitable rice cultivation. Our interviews with the farmers in the polluted region suggested that when rice production was no longer profitable, farmers tended to sell their land as construction land or rent their land to farmers from other regions. Local farmers also attempted to secure employment in the nearby industrial parks, from which they could earn more money than compared to rice cultivation. The study also discovered that water pollution made farmers change rice cultivation and crop intensification techniques. Before their income was mainly from rice production with three rice crops per year, now they do rice farming as part-time jobs, only grow one or two crops per year and harvest rice just enough for home consumption. These possibly were the suitable explanations

The reduced productivity of rice was calculated based on findings from Table 6. After the equation (5) was used to eliminate the effects of other factors, the estimated yield in the nonpolluted area was about 5.61 tons and around 4.94 tons for the polluted region. Then, the loss of rice yield due to polluted water irrigation was estimated by subtracting the yield in the polluted from yield in the non-polluted region (equation 5). Using this approach, the estimated

Table 7 showed R-square was equal to 0.56, revealing the variation of total rice costs of 56 percent was explained by independent variables in the model. The study also showed that the multicollinearity among the independent variables in cost function did not exist because the

<sup>2</sup> of 57.34 at the level of 1 percent 2).

yield since the coefficient of *Training* variable was significantly positive.

for the negative impact of off-farm income on rice productivity.

result was about 0.67 tons per hectare per crop (5.61 tons – 4.94 tons).

**5.2. Increase in rice production cost due to water pollution**

that technology did not exhibit constant returns to scale.

than the critical *χ*<sup>36</sup>


Notes: \*\*\*, \*\*, \* indicate statistical significance at the 0.01, 0.05 and 0.1 level respectively

*Source: Own estimates; data appendix available from authors.*

**Table 6.** The OLS regression of rice production function

The study also examined the null hypothesis in (7) that there was a proportional output change when inputs in the model were varied or farms produce rice with constant returns to scale. The restricted least squares regression with the null hypothesis of constant returns to scale was estimated. The computed F statistic was 37.09 more than the critical value F (7, 327) of 2.69 at 1 percent level of significance 1). Thus, the null hypothesis was rejected and the study concluded that technology did not exhibit constant returns to scale.

**5. Estimated results**

ln*(Cf*

ln*(Cl*

*½* ln*(Cs)*

*½* ln*(Ch)*

*½* ln*(Cf )*

*½* ln*(Cp)*

*½* ln*(Cl )*

*½* ln*(Cc)*

ln*(Cs)×*ln*(Cf*

ln*(Cs)×*ln*(Cl*

ln*(Ch)×*ln*(Cf*

**5.1. Impact of water pollution on rice productivity**

74 International Perspectives on Water Quality Management and Pollutant Control

explained by independent variables in the model.

ln*(Ch)* -0.123 -0.8 ln*(Ch)×*ln*(Cl*

ln*(Cp)* -0.157 -0.59 ln*(Cf*

ln*(Cc)* 0.572 1.3 ln*(Cf*

*)* 0.851\*\* 2.55 ln*(Cf*

*<sup>2</sup>* 0.532\*\*\* 2.72 ln*(Cp)×*ln*(Cl*

Notes: \*\*\*, \*\*, \* indicate statistical significance at the 0.01, 0.05 and 0.1 level respectively

*Source: Own estimates; data appendix available from authors.*

**Table 6.** The OLS regression of rice production function

*<sup>2</sup>* 0.037 1.13 ln*(Cl*

Table 6 showed the Ordinary Least Squares (OLS) result of rice production function in translog form. The variables estimated in the model were statistically significant at 1 percent level. The estimated R-square was equal to 0.64, revealing the 64 percent change of rice yield possibly

**Variables Coef. t-value Variables Coef. t-value** ln*(Cs)* 0.696 0.87 ln*(Ch)×*ln*(Cp)* 0.007 0.7

*)* 0.465 0.84 ln*(Ch)×*ln*(Cc)* 0.008 0.35

*<sup>2</sup>* 0.000 0.03 ln*(Cp)×*ln*(Cc)* 0.029 0.71

*<sup>2</sup>* -0.011 -0.66 *Age* -0.002\*\* -2.07

*<sup>2</sup>* 0.059\* 1.81 *Education* 0.004 1.1

*<sup>2</sup>* 0.075 1.07 *Training* 0.039\*\* 2.07

*)* -0.132 -1.34 *Mono* 0.016 0.7

*))* -0.105\* -1.83 *Off-farm ratio* -0.054\* -1.93

*)* -0.025 -0.7 Constant -5.615\*\* -2.26

ln*(Cs)×*ln*(Ch)* 0.014 0.54 *Disease* -0.012 -0.67

ln*(Cs)×*ln*(Cp)* -0.057 -1.08 *Soil* 0.031 1.64

ln*(Cs)×*ln*(Cc)* -0.216\* -1.87 *Pollution* -0.127\*\*\* -6.68

R-square 0.64 Included observation 364

The study also examined the null hypothesis in (7) that there was a proportional output change when inputs in the model were varied or farms produce rice with constant returns to scale. The restricted least squares regression with the null hypothesis of constant returns to scale was estimated. The computed F statistic was 37.09 more than the critical value F (7, 327) of 2.69 at

*)×*ln*(Cl*

*)* 0.021 1.49

*)* -0.026 -0.56

*)* -0.031 -1.41

*)×*ln*(Cp)* 0.060 1.5

*)×*ln*(Cc)* -0.022 -0.27

*)×*ln*(Cc)* -0.023 -0.58

The second test was applied to check the Cobb-Douglass formal existence of the production function. The restricted function was estimated with the null hypothesis of jointed parameters in (8) equal to 0. The computed F statistic of 1.94 was more than the critical F(21,327) of 1.91 at 1 percent level of significance 1). Thus, the null hypothesis was rejected, meaning that the translog functional form was suitably applied for the data of rice production in the study.

The results of Table 6 showed that there was no multicollinearity in the independent variables of production function because the correlations of these independent variables estimated by using the correlation matrix were less than 70 percent. The null hypothesis homoscedasticity was also accepted by using Breusch-Pagan test because the estimated LM of 49.72 was less than the critical *χ*<sup>36</sup> <sup>2</sup> of 57.34 at the level of 1 percent 2).

Table 6 showed that the rice productivity in the polluted was lower than in the non-polluted area because the coefficient of *Pollution* variable was significantly negative at 1 percent level. In addition, the study also revealed that training courses partly contributed an increase in rice yield since the coefficient of *Training* variable was significantly positive.

Moreover, the model also showed that farmer age (P < 0.05) and the ratio of off-farm income (P < 0.1) explained variation in rice yield. The effect of age might have been caused by declines in the health of older farmers leading to less efficient cultivation. Farmers who earned more off-farm income were associated with less profitable rice cultivation. Our interviews with the farmers in the polluted region suggested that when rice production was no longer profitable, farmers tended to sell their land as construction land or rent their land to farmers from other regions. Local farmers also attempted to secure employment in the nearby industrial parks, from which they could earn more money than compared to rice cultivation. The study also discovered that water pollution made farmers change rice cultivation and crop intensification techniques. Before their income was mainly from rice production with three rice crops per year, now they do rice farming as part-time jobs, only grow one or two crops per year and harvest rice just enough for home consumption. These possibly were the suitable explanations for the negative impact of off-farm income on rice productivity.

The reduced productivity of rice was calculated based on findings from Table 6. After the equation (5) was used to eliminate the effects of other factors, the estimated yield in the nonpolluted area was about 5.61 tons and around 4.94 tons for the polluted region. Then, the loss of rice yield due to polluted water irrigation was estimated by subtracting the yield in the polluted from yield in the non-polluted region (equation 5). Using this approach, the estimated result was about 0.67 tons per hectare per crop (5.61 tons – 4.94 tons).

## **5.2. Increase in rice production cost due to water pollution**

Table 7 showed R-square was equal to 0.56, revealing the variation of total rice costs of 56 percent was explained by independent variables in the model. The study also showed that the multicollinearity among the independent variables in cost function did not exist because the results estimated by correlation matrix approach showed that there were no correlations in these independent variables higher than 70 percent. The result of Breusch-Pagan test per‐ formed that the estimated LM of 14.96 was less than the critical *χ*<sup>12</sup> <sup>2</sup> of 26.22 at the level of 1 percent, revealing the absence of heterscedasticity in the estimate of cost function 2).

per ha per crop for that in the non-polluted area. Cost increase was estimated by subtracting the rice cost in the non-polluted region by the rice cost in the polluted area (equation 11). Using this approach, an increase in cost due to water pollution was calculated around VND 0.97

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77

Table 8 showed the coefficients from the OLS regression of the rice profit model using the translog profit functional form (equation 16). The full model was statistically significant at the 1% level. The estimated R-square revealed that 50% of the variation in the rice profit was

Next, we tested the null hypothesis of the Cobb-Douglass functional form. The restricted function was estimated assuming the null hypothesis that the joint parameters in (17) are 0. The computed F statistic of 1.78 was more than the critical F(55,283) value of 1.57 at the 1 percent level 1). The null hypothesis was therefore rejected, which supported the use of the translog functional form in this study. The estimate of profit function also showed the absence of multicollinearity (the correlations of independent variables less than 70 percent) and of

The coefficient of *Pollution* variable representing the effect of pollution was negative and significant (P < 0.01), which confirmed that water pollution reduced the profit of rice cultiva‐ tion. The reduction in rice profit was calculated using the coefficients presented in Table 8. The estimated profit was approximately VND 9.14 million for rice cultivation in the polluted area and VND 12.34 million for that in the non-polluted area after the influences of other factors were eliminated. The loss of rice profit due to wastewater irrigation was estimated by sub‐ tracting the rice profit in the polluted region by the rice profit in the non-polluted region (equation 15). Using this approach, the loss of profit was calculated to be approximately VND

Like the results of rice yield loss, this model also performed that farmer age (P < 0.01), attending training (P < 0.01) and the ratio of off-farm income (P < 0.1) explained variation in profit.

We also used the same estimate of profit loss due to water pollution to calculate reductions in profit caused by other factors as presented in Table 9. Cultivation in non-fertile soil, instead of fertile soil, could reduce rice profit by 8.24%. Farmers whose main sources of income were from non-agricultural sectors obtained 11.45% less rice profit than those who only had an agricultural income. Participating in trainings was estimated to increase profit by 13.03%. Profit loss caused by water pollution was much higher than the profit loss caused by other factors, which demonstrates that environment pollution has a great significance for rice farmers near industrial parks. Because of this, we suggest that the Vietnamese authorities should place a greater importance on the development and implementation of pollution

Moreover, soil quality was also an important factor affecting profit (P<0.1).

<sup>2</sup> of 105.2 at the level of 1 percent

million per ha per crop (See Table 10).

**5.3. Total loss of net economic return**

heterscedasticity (Breusch-Pagan test showed the critical *χ*<sup>74</sup>

higher than the computed LM of 100.24) 2).

3.2 million per hectare per crop (see Table 10).

control policies.

explained by the model.


Notes: \*\*\*, \*\*, \* indicate statistical significance at the 0.01, 0.05 and 0.1 level respectively

*Source: Own estimates; data appendix available from authors.*

#### **Table 7.** The OLS regression of rice cost function

The coefficient of *Pollution* variable was statistically significant positive at level of 1 percent, performing rice costs in the polluted region was higher than one in the non-polluted region. Moreover, farmers, who were older, managed their production cost more highly and less efficiently, performed by the positive effect of *Age* variable on total costs at 10 percent level. The significantly positive coefficient of *Rice monoculture* variable (P < 0.01) revealed that farmers who grew rice monoculture cost more than ones who cultivated rice rotation or intercropping. Possible explanation is that the cropping system of rice monoculture decreased the fertility of soil.

Like the calculation of yield loss, cost increase due to water pollution was estimated using the coefficients performed in Table 7. After the effect of other factors were eliminated, total cost was estimated about VND 10.37 million for rice production in the polluted area and VND 9.4 per ha per crop for that in the non-polluted area. Cost increase was estimated by subtracting the rice cost in the non-polluted region by the rice cost in the polluted area (equation 11). Using this approach, an increase in cost due to water pollution was calculated around VND 0.97 million per ha per crop (See Table 10).

## **5.3. Total loss of net economic return**

results estimated by correlation matrix approach showed that there were no correlations in these independent variables higher than 70 percent. The result of Breusch-Pagan test per‐

> **Variables Coefficient t-value** ln*(Ws)* 0.195\*\*\* 3.95 ln*(Wh)* 0.021 0.84

> *)* 0.431\*\*\* 5.5 ln*(Wp)* 0.007 0.27 ln*(Y)* 0.918\*\*\* 14.79 *Age* 0.002\* 1.9 *Education* -0.008 -1.55 *Training* -0.058\*\* -2.02 *Diseases* 0.045 1.62 *Rice monoculture* 0.143\*\*\* 4.72 *Soil* -0.032 -1.08 *Pollution* 0.098\*\*\* 3.3 Constant 6.140\*\*\* 22.84

> Notes: \*\*\*, \*\*, \* indicate statistical significance at the 0.01, 0.05 and 0.1 level

The coefficient of *Pollution* variable was statistically significant positive at level of 1 percent, performing rice costs in the polluted region was higher than one in the non-polluted region. Moreover, farmers, who were older, managed their production cost more highly and less efficiently, performed by the positive effect of *Age* variable on total costs at 10 percent level. The significantly positive coefficient of *Rice monoculture* variable (P < 0.01) revealed that farmers who grew rice monoculture cost more than ones who cultivated rice rotation or intercropping. Possible explanation is that the cropping system of rice monoculture decreased

Like the calculation of yield loss, cost increase due to water pollution was estimated using the coefficients performed in Table 7. After the effect of other factors were eliminated, total cost was estimated about VND 10.37 million for rice production in the polluted area and VND 9.4

percent, revealing the absence of heterscedasticity in the estimate of cost function 2).

<sup>2</sup> of 26.22 at the level of 1

formed that the estimated LM of 14.96 was less than the critical *χ*<sup>12</sup>

76 International Perspectives on Water Quality Management and Pollutant Control

ln*(Wf*

**Statistic summary**

respectively

**Table 7.** The OLS regression of rice cost function

the fertility of soil.

R-square 0.56 Included observation 364

*Source: Own estimates; data appendix available from authors.*

Table 8 showed the coefficients from the OLS regression of the rice profit model using the translog profit functional form (equation 16). The full model was statistically significant at the 1% level. The estimated R-square revealed that 50% of the variation in the rice profit was explained by the model.

Next, we tested the null hypothesis of the Cobb-Douglass functional form. The restricted function was estimated assuming the null hypothesis that the joint parameters in (17) are 0. The computed F statistic of 1.78 was more than the critical F(55,283) value of 1.57 at the 1 percent level 1). The null hypothesis was therefore rejected, which supported the use of the translog functional form in this study. The estimate of profit function also showed the absence of multicollinearity (the correlations of independent variables less than 70 percent) and of heterscedasticity (Breusch-Pagan test showed the critical *χ*<sup>74</sup> <sup>2</sup> of 105.2 at the level of 1 percent higher than the computed LM of 100.24) 2).

The coefficient of *Pollution* variable representing the effect of pollution was negative and significant (P < 0.01), which confirmed that water pollution reduced the profit of rice cultiva‐ tion. The reduction in rice profit was calculated using the coefficients presented in Table 8. The estimated profit was approximately VND 9.14 million for rice cultivation in the polluted area and VND 12.34 million for that in the non-polluted area after the influences of other factors were eliminated. The loss of rice profit due to wastewater irrigation was estimated by sub‐ tracting the rice profit in the polluted region by the rice profit in the non-polluted region (equation 15). Using this approach, the loss of profit was calculated to be approximately VND 3.2 million per hectare per crop (see Table 10).

Like the results of rice yield loss, this model also performed that farmer age (P < 0.01), attending training (P < 0.01) and the ratio of off-farm income (P < 0.1) explained variation in profit. Moreover, soil quality was also an important factor affecting profit (P<0.1).

We also used the same estimate of profit loss due to water pollution to calculate reductions in profit caused by other factors as presented in Table 9. Cultivation in non-fertile soil, instead of fertile soil, could reduce rice profit by 8.24%. Farmers whose main sources of income were from non-agricultural sectors obtained 11.45% less rice profit than those who only had an agricultural income. Participating in trainings was estimated to increase profit by 13.03%. Profit loss caused by water pollution was much higher than the profit loss caused by other factors, which demonstrates that environment pollution has a great significance for rice farmers near industrial parks. Because of this, we suggest that the Vietnamese authorities should place a greater importance on the development and implementation of pollution control policies.


**Variables Coef. t-value Variables Coef. t-value** ln*(Wp\*)×*ln*(Ch)* -0.010 -0.15 *Soil* 0.086\* 1.75

ln*(Wp\*)×*ln*(Cp)* 0.107 1.14 *Pollution* -0.300\*\*\* -5.81

R-square 0.50 Included observation 364

**Table 8.** The OLS regression of rice profit function

*Source: Own estimates; data appendix available from authors.*

**Table 9.** Reduced profit in rice farming and key constraints

*Source: Own estimates; data appendix available from authors.*

**Table 10.** Impact of water pollution on rice production

the industrial parks.

*)* -0.164 -1.11 *Off-farm ratio* -0.126\* -1.73

*)* -0.098 -1.22 Constant -20.213 -0.60

**Factors Reduced profit**

Polluted vs. Non-polluted area 3,203 25.95 Non-fertile vs. Fertile soil 874 8.24 Non-training vs. Training 1,465 13.03 The highest off-farm vs. Zero off-farm income ratio 1,229 11.45

Table 10 summarized the total loss of rice production due to water pollution. The estimated results showed there were about 26 percent of profit loss, including around 12 percent of reduced quantity (yield loss) and 9 percent of cost increase, adversely caused by industrial water pollution. In this study, we also observed that farmers in the polluted area use water irrigation from the highest water tide level to reduce the effects of wastewater on rice produc‐ tion. This was because the farmers thought the water at the high tide level looked less polluted than the waters at other times, despite the fact that the water was always heavily polluted near

Quantity loss 0.67 tons/ha 12% Cost increase 0.97 million VND/ha 9% Total loss of net economic return 3.2 million VND/ha 26%

**(Thousand VND)**

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**Percentage of reduced profit**

**Amount Percent**

**(%)**

79

ln*(Wp\*)×*ln*(Cf*

ln*(Wp\*)×*ln*(Cl*


**Table 8.** The OLS regression of rice profit function

**Variables Coef. t-value Variables Coef. t-value** ln*(Ws\*)* -0.111 -0.02 ln*(Wp\*)×*ln*(Cc)* 0.162 0.92 ln*(Wh\*)* 1.211 0.54 ln*(Cs)* -3.393 -0.72

*\*)* -3.869 -0.71 ln*(Ch)* -0.540 -0.34

*\*)2* -1.245\*\* -1.99 ln*(Cc)* 1.432 0.37

*\*)×*ln*(Wp\*)* 0.005 0.02 ln*(Cs)×*ln*(Ch)* -0.113 -0.80

ln*(Ws\*)×*ln*(Ch)* 0.181 1.32 ln*(Cs)×*ln*(Cp)* 0.013 0.06

ln*(Ws\*)×*ln*(Cp)* -0.165 -0.96 ln*(Cs)×*ln*(Cc)* -0.149 -0.48

ln*(Ws\*)×*ln*(Cc)* 0.029 0.08 ln*(Ch)×*ln*(Cp)* 0.054 0.93

ln*(Wh\*)×*ln*(Ch)* -0.064 -0.80 ln*(Ch)×*ln*(Cc)* 0.012 0.10

ln*(Ws\*)×*ln*(Cs)* 0.533 1.01 ln*(Cp)×*ln*(Cc)* 0.192 1.45

*)* -0.609 -1.63 *Age* -0.006\*\*\* -2.63

*)* 0.106 0.47 *Training* 0.140\*\*\* 2.90

*\*)×*ln*(Cp)* -0.104 -0.45 *Education* 0.010 1.22

*\*)×*ln*(Cc)* -0.529 -1.26 *Disease* 0.016 0.35 ln*(Wp\*)×*ln*(Cs)* 0.025 0.12 *Mono* 0.003 0.06

*)*

*)*

*)×*ln*(Cl*

*½* ln*( Ws\*)2* 0.322 0.68 ln*(Cp)* -2.241 -1.16

*)* 3.341 0.99

*)* 2.890 1.51

*<sup>2</sup>* 0.338 0.61

*<sup>2</sup>* 0.012 0.18

*<sup>2</sup>* -0.117 -0.47

*<sup>2</sup>* 0.013 0.18

*<sup>2</sup>* 0.123 1.44

*<sup>2</sup>* -0.107 -0.52

*)* 0.500\* 1.73

*))* -0.250 -1.56

*)* -0.130 -1.19

*)* 0.148\*\* 2.33

*)* -0.055 -0.41

*)* -0.140\*\* -2.07

*)×*ln*(Cp)* -0.137 -1.08

*)×*ln*(Cc)* -0.568\*\* -2.46

*)×*ln*(Cc)* -0.025 -0.22

ln*(Wp\*)* -0.698 -0.29 ln*(Cf*

78 International Perspectives on Water Quality Management and Pollutant Control

*½* ln*( Wh\*)2* -0.019 -0.15 ln*(Cl*

*½* ln*( Wp\*)2* 0.223 1.58 *½* ln*(Cs)*

ln*(Ws\*)×*ln*(Wh\*)* -0.017 -0.09 *½* ln*(Ch)*

ln*(Ws\*)×*ln*(Wp\*)* -0.042 -0.22 *½* ln*(Cp)*

ln*(Wh\*)×*ln*(Wp\*)* 0.030 0.28 *½* ln*(Cc)*

ln*(Ws\*)×*ln*(Cs)* -0.648\* -1.96 ln*(Cs)×*ln*(Cf*

ln*(Wh\*)×*ln*(Cs)* -0.314 -1.62 ln*(Ch)×*ln*(Cl*

ln*(Wh\*)×*ln*(Cp)* 0.087 0.89 ln*(Cf*

*)* -0.124 -0.84 ln*(Cf*

*)* 0.115 1.41 ln*(Cf*

ln*(Wh\*)×*ln*(Cc)* 0.244 1.63 ln*(Cp)×*ln*(Cl*

*\*)×*ln*(Ch)* -0.376\* -1.83 ln*(Cl*

*\*)* -0.257 -0.50 *½* ln*(Cf*

*\*)* 0.231 0.91 *½* ln*(Cl*

*)* 0.586\*\* 2.01 ln*(Cs)×*ln*(Cl*

*)* 0.079 0.56 ln*(Ch)×*ln*(Cf*

ln*(Wf*

*½* ln*( Wf*

ln*(Ws\*)×*ln*(Wf*

ln*(Wh\*)×*ln*(Wf*

ln*(Ws\*)×*ln*(Cf*

ln*(Ws\*)×*ln*(Cl*

ln*(Wh\*)×*ln*(Cf*

ln*(Wh\*)×*ln*(Cl*

ln*(Wf*

ln*(Wf*

ln*(Wf*

ln*(Wf*

ln*(Wf*

*\*)×*ln*(Cf*

*\*)×*ln*(Cl*

ln*(Wf*


**Table 9.** Reduced profit in rice farming and key constraints

Table 10 summarized the total loss of rice production due to water pollution. The estimated results showed there were about 26 percent of profit loss, including around 12 percent of reduced quantity (yield loss) and 9 percent of cost increase, adversely caused by industrial water pollution. In this study, we also observed that farmers in the polluted area use water irrigation from the highest water tide level to reduce the effects of wastewater on rice produc‐ tion. This was because the farmers thought the water at the high tide level looked less polluted than the waters at other times, despite the fact that the water was always heavily polluted near the industrial parks.


**Table 10.** Impact of water pollution on rice production

Moreover, the use of polluted water also caused the farmers to change their cultivation management. In previous years, three rice crops were produced annually and rice cultivation was the main income source. However, because of pollution, only one or two rice crops is now cultivated in the polluted area each year, and farmers treat rice cultivation as a part-time job, producing rice sufficient only for household consumption.

but much lower than the rice reduced productivity of 20 percent calculated by Song (2004) in

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81

Economic developments that cause damage to natural resources and the environment are unsustainable. We suggest that the Vietnamese government needs to develop policies that ensure sustainable development. Similar to environmental policies in developed countries, the Vietnamese government could consider increasing the current environmental standards and raising environmental taxes. The increase of environmental taxes could not only encourage industries to apply new technologies that reduce environmental pollution, but also generate money to compensate farmers near industrial areas for the damage to their agricultural production and health and to build wastewater treatment facilities in industrial parks. Compensation could be provided directly in cash to the farmers, or indirectly by means such as funding training or activities related to new technologies and the management of agricul‐ tural inputs and expenditure. Our study showed that training helped farmers increase their profit, which might partly offset some of the losses caused by environmental pollution.

To reduce polluted water from the industrial parks, an increase in the effectiveness of imple‐ mentation of Decision 64 and Circular 07 should be recommended. A public disclosure system for the environmental performance of polluters mentioned in Article 104 of the Law on Environmental Protection (dated 2005) and Article 23 of Degree No. 80/2006ND-CP should be considered as one of the best ways to increase the efficiency of Decision 64 and Circular 07.

Article 104 requires polluters to report and publicize the information and data about the

**•** Reports on the environmental impact assessment, decision on approval for reports on the environmental impact assessment and plan for the implementation of requirements stipulated in the decision on approval for reports on the environmental impact assessment;

**•** List of and information about sources of wastes, pollutants that seem potentially harmful

**•** Areas where environment is polluted and degraded seriously and extremely seriously, areas

**•** Report on the environmental situation at the provincial level, report on environmental impact assessment by industries, fields and the national report on the environment

**•** Agencies publicizing information about the environment have to take responsibility on accuracy, honesty and objectivity of announced information before legal agencies.

Article 23 provides details and instructions on how to implement Article 104 of the Law on

**•** The Ministry of Natural Resources and Environment have responsibility for announcing

**•** It is essential to ensure unrestricted access to publicized information

Environmental Protection. These details and instructions include:

information and data about the national environment;

the study of Lindhjem (2007) and 30 percent by Chang *et al.* (2001).

environment as follows:

to people's health and environment;

in danger of the environmental pollution.

During our study, we also received reports of skin diseases on the farmers working in the polluted region. For instance, a farmer in the polluted area reported that he had suffered from skin disease 5 days per year, and the treatment cost VND 500,000. The diseases also caused the loss of 2.5 workdays, equivalent to VND 250,000. Therefore, the estimate of total economic loss is underestimated if indirect costs such as the health costs suffered by farmers are not included.

## **6. Conclusions and policy implication**

Local authorities in Vietnam have recently removed or reduced some of the environmental impact requirements to attract industrial investments to their province. Although industrial investments with low environmental standards might increase gross domestic product and create more jobs for local households, they may also bring many problems including water, air and soil pollution. This study provides an example of the negative impacts that arise from pollution by industries.

In this study, we surveyed rice farmers in two areas with the same natural environment conditions, social characteristics (e.g. the same social and farming culture, ethnicity, type of soil), and only differed with respect to pollution. One area was considered to be the polluted area, receiving wastewater from nearby industrial parks, while the other area was assumed to be the non-polluted area, being distant from sources of industrial pollu‐ tants. The productivity loss of rice production caused by water pollution was estimated by the difference in rice yield between the two regions. The similar calculation was ap‐ plied for cost increase and profit loss for using wastewater irrigation. The results showed that the yield loss of rice was about 0.67 tons per hectare per crop, VND 0.97 million for cost increase and totally 26 percent of profit loss due to water pollution. Therefore, since the study includes 214 farmers in the polluted area and these 214 farmers cultivate rice in 148 hectare as a whole, their total cost increase per crop because of water pollution could be estimated about VND 144 million (VND 0.97 \* 148ha) and approximately VND 474 million (VND 3.2 million \* 148ha) for their total net economic loss.

According to The World Bank (2007), the development of rice roots and seedlings could be influenced by using wastewater for irrigation. Polluted water irrigation causes the reduction of height, leaf area and dry matter. Decrease in leaf surface area leads to the reduction of photosynthesis. These facts have directly impact on rice production. In other words, the impacts of polluted water on rice productivity mainly reduce the number of ears unit area, number of seed per ear and seed weight. The study estimated water pollution caused yield reduction about 12 percent. This result is nearly equal to the reduced yield of 10 percent in the sewage-irrigated area in comparison with clear water-irrigated areas estimated by Bai (2004), but much lower than the rice reduced productivity of 20 percent calculated by Song (2004) in the study of Lindhjem (2007) and 30 percent by Chang *et al.* (2001).

Moreover, the use of polluted water also caused the farmers to change their cultivation management. In previous years, three rice crops were produced annually and rice cultivation was the main income source. However, because of pollution, only one or two rice crops is now cultivated in the polluted area each year, and farmers treat rice cultivation as a part-time job,

During our study, we also received reports of skin diseases on the farmers working in the polluted region. For instance, a farmer in the polluted area reported that he had suffered from skin disease 5 days per year, and the treatment cost VND 500,000. The diseases also caused the loss of 2.5 workdays, equivalent to VND 250,000. Therefore, the estimate of total economic loss is underestimated if indirect costs such as the health costs suffered by farmers are not included.

Local authorities in Vietnam have recently removed or reduced some of the environmental impact requirements to attract industrial investments to their province. Although industrial investments with low environmental standards might increase gross domestic product and create more jobs for local households, they may also bring many problems including water, air and soil pollution. This study provides an example of the negative impacts that arise from

In this study, we surveyed rice farmers in two areas with the same natural environment conditions, social characteristics (e.g. the same social and farming culture, ethnicity, type of soil), and only differed with respect to pollution. One area was considered to be the polluted area, receiving wastewater from nearby industrial parks, while the other area was assumed to be the non-polluted area, being distant from sources of industrial pollu‐ tants. The productivity loss of rice production caused by water pollution was estimated by the difference in rice yield between the two regions. The similar calculation was ap‐ plied for cost increase and profit loss for using wastewater irrigation. The results showed that the yield loss of rice was about 0.67 tons per hectare per crop, VND 0.97 million for cost increase and totally 26 percent of profit loss due to water pollution. Therefore, since the study includes 214 farmers in the polluted area and these 214 farmers cultivate rice in 148 hectare as a whole, their total cost increase per crop because of water pollution could be estimated about VND 144 million (VND 0.97 \* 148ha) and approximately VND 474

According to The World Bank (2007), the development of rice roots and seedlings could be influenced by using wastewater for irrigation. Polluted water irrigation causes the reduction of height, leaf area and dry matter. Decrease in leaf surface area leads to the reduction of photosynthesis. These facts have directly impact on rice production. In other words, the impacts of polluted water on rice productivity mainly reduce the number of ears unit area, number of seed per ear and seed weight. The study estimated water pollution caused yield reduction about 12 percent. This result is nearly equal to the reduced yield of 10 percent in the sewage-irrigated area in comparison with clear water-irrigated areas estimated by Bai (2004),

million (VND 3.2 million \* 148ha) for their total net economic loss.

producing rice sufficient only for household consumption.

80 International Perspectives on Water Quality Management and Pollutant Control

**6. Conclusions and policy implication**

pollution by industries.

Economic developments that cause damage to natural resources and the environment are unsustainable. We suggest that the Vietnamese government needs to develop policies that ensure sustainable development. Similar to environmental policies in developed countries, the Vietnamese government could consider increasing the current environmental standards and raising environmental taxes. The increase of environmental taxes could not only encourage industries to apply new technologies that reduce environmental pollution, but also generate money to compensate farmers near industrial areas for the damage to their agricultural production and health and to build wastewater treatment facilities in industrial parks. Compensation could be provided directly in cash to the farmers, or indirectly by means such as funding training or activities related to new technologies and the management of agricul‐ tural inputs and expenditure. Our study showed that training helped farmers increase their profit, which might partly offset some of the losses caused by environmental pollution.

To reduce polluted water from the industrial parks, an increase in the effectiveness of imple‐ mentation of Decision 64 and Circular 07 should be recommended. A public disclosure system for the environmental performance of polluters mentioned in Article 104 of the Law on Environmental Protection (dated 2005) and Article 23 of Degree No. 80/2006ND-CP should be considered as one of the best ways to increase the efficiency of Decision 64 and Circular 07.

Article 104 requires polluters to report and publicize the information and data about the environment as follows:


Article 23 provides details and instructions on how to implement Article 104 of the Law on Environmental Protection. These details and instructions include:

**•** The Ministry of Natural Resources and Environment have responsibility for announcing information and data about the national environment;

**•** Ministries and ministerial-level agencies, government agencies shoulder responsibility for exposing information and data about the environment in industries and areas under their management;

2) Breusch-Pagan test for heterscedasticity:

where: n is the number of observations

k is the number of restricted factors

^

and Mitsuyasu Yabe2

*<sup>i</sup>* | =*δ*˜ <sup>0</sup> + *δ*˜ <sup>1</sup>*X*1*<sup>i</sup>* + *δ*˜ <sup>2</sup>*X*2*<sup>i</sup>* + ..... + *δ*˜ *<sup>k</sup> Xki* + *v*˜*<sup>i</sup>*

Impact of Industrial Water Pollution on Rice Production in Vietnam

http://dx.doi.org/10.5772/54279

83

We would like to express our gratitude to Dr. Benoit Laplante, a long-standing resource per‐ son with EEPSEA, and Dr Herminia Francisco, a director of EEPSEA, for their invaluable comments and suggestions in shaping the structure of the study and data analyses. Our warmest thanks go to EESEA for sponsoring the study, to Mr. Joseph Arbiol in our laborato‐ ry for reading the draft of the manuscript, and to our colleagues at Can Tho University for

[1] Bai, Y. (1988). Pollution of irrigation water and its effects, Beijing, China, Beijing

[2] Bateman, I. J, Carson, R. T, Day, B, Hanemann, M, Hanley, N, Hett, T, Lee, M. J, Loomes, G, Mourato, S, Ozdemiroglu, E, Pearce, D. W, Sugden, R, & Swanson, J. (2003). Economic Valuation With Stated Preference Techniques Edward Elgar Publishing,

[3] Chang, Y, Hans, M. S, & Haakon, V. (2001). The Environmental Cost of Water Pollution in Chongqing, China. Environment and Development Economics , 6(313-333)

[4] Do, T. N, & Bennett, J. Would wet biodiversity conservation improve social welfare? A case study in Vietnam's Mekong River Delta, International Conference on Sustainable

Agriculture University Press: Beijing Agriculture University Press.

Development: Challanges and Opportunities for GMS,

*k*

is the R-Square of |*u*

**Acknowledgments**

assisting in data collection.

1 Can Tho University, Vietnam

2 Kyushu University, Japan

London.

**Author details**

Huynh Viet Khai1

**References**

*LM* =*nR* <sup>2</sup>∼*Χ* <sup>2</sup>

R2


The requirements of these above public disclosure system illustrate a new and significant approach for environmental authorities to force environmental laws and regulations in strong manner by increasing environmental awareness and permitting the large public to put pressure on polluters to solve current environmental problems. Such public disclosure requirements also create significant pressure on environmental authorities themselves as their own decision failures might also be widely recognized by such requirements. However, the implementation of these requirements in a clear, precise, and systematic manner is strongly needed.

Since water treatment facilities in these industrial parks must be built as soon as possible, the study on their cost effectiveness could be needed and seriously considered to decide whether we should build the water treatment facilities in every individual factory or for the whole industrial parks. Moreover, we suggest that the government should not use high-yield agricultural land for the construction of new industrial parks unless they include the latest pollution treatment technologies. The impact of environmental pollution should continue to be evaluated.

## **Notes**

1) Calculated by the formula *F* = (RS*SR* −RS*SU* )/ *J RSSU* / (*<sup>N</sup>* <sup>−</sup> *<sup>K</sup>*) , where RSSR and RSSU are the restricted and unrestricted sums of squared residuals, *J* is the number of restrictions, *N* is the number of observations, and K is the number of parameters in an unrestricted function.

2) Breusch-Pagan test for heterscedasticity: *LM* =*nR* <sup>2</sup>∼*Χ* <sup>2</sup> *k*

where: n is the number of observations

R2 is the R-Square of |*u* ^ *<sup>i</sup>* | =*δ*˜ <sup>0</sup> + *δ*˜ <sup>1</sup>*X*1*<sup>i</sup>* + *δ*˜ <sup>2</sup>*X*2*<sup>i</sup>* + ..... + *δ*˜ *<sup>k</sup> Xki* + *v*˜*<sup>i</sup>*

k is the number of restricted factors

## **Acknowledgments**

**•** Ministries and ministerial-level agencies, government agencies shoulder responsibility for exposing information and data about the environment in industries and areas under their

**•** Agencies in charge of the environmental protection of People's Committees at all levels bear responsibility for make information and data about the environment in the area under their

**•** Management board of economic zones, industrial parks, export processing zones, managers of manufacturing and service units accept responsibility for publicizing information and

**•** Information and data about the environment is publicized in form of books, news in

**•** Information and data about the environment is publicized in form of books, news in newspapers or post on units' websites (if any), reported in people's council meetings, announced on notice boards in residential quarter meetings, or listed in headquarters of units or headquarters of commune, ward, town people's committee where units are in

The requirements of these above public disclosure system illustrate a new and significant approach for environmental authorities to force environmental laws and regulations in strong manner by increasing environmental awareness and permitting the large public to put pressure on polluters to solve current environmental problems. Such public disclosure requirements also create significant pressure on environmental authorities themselves as their own decision failures might also be widely recognized by such requirements. However, the implementation of these requirements in a clear, precise, and systematic manner is strongly

Since water treatment facilities in these industrial parks must be built as soon as possible, the study on their cost effectiveness could be needed and seriously considered to decide whether we should build the water treatment facilities in every individual factory or for the whole industrial parks. Moreover, we suggest that the government should not use high-yield agricultural land for the construction of new industrial parks unless they include the latest pollution treatment technologies. The impact of environmental pollution should continue to

(RS*SR* −RS*SU* )/ *J*

observations, and K is the number of parameters in an unrestricted function.

unrestricted sums of squared residuals, *J* is the number of restrictions, *N* is the number of

*RSSU* / (*<sup>N</sup>* <sup>−</sup> *<sup>K</sup>*) , where RSSR and RSSU are the restricted and

**•** Publicity of information and data about the environment is stipulated as follows:

data about the environment in the area under their management;

82 International Perspectives on Water Quality Management and Pollutant Control

management;

operation.

needed.

be evaluated.

1) Calculated by the formula *F* =

**Notes**

management publicly;

newspapers or post on units' websites;

We would like to express our gratitude to Dr. Benoit Laplante, a long-standing resource per‐ son with EEPSEA, and Dr Herminia Francisco, a director of EEPSEA, for their invaluable comments and suggestions in shaping the structure of the study and data analyses. Our warmest thanks go to EESEA for sponsoring the study, to Mr. Joseph Arbiol in our laborato‐ ry for reading the draft of the manuscript, and to our colleagues at Can Tho University for assisting in data collection.

## **Author details**

Huynh Viet Khai1 and Mitsuyasu Yabe2


## **References**


[5] Hung, P. T, Tuan, B. A, & Chinh, N. T. (2008). The Impact of Trade Liberalization on Industrial Pollution: Empirical Evidence from Vietnam the Economy and Environment Program for Southeast Asia (EEPSEA).

[19] The World Bank ((2007). Cost of Pollution in China- Economic Estimates of Physical

Impact of Industrial Water Pollution on Rice Production in Vietnam

http://dx.doi.org/10.5772/54279

85

[20] The World Bank ((2008). Handling Serious Environmnetal Polluters in Vietnam:

[21] Thong, L. Q, & Ngoc, N. A. (2004). Incentives for Wastewater Management in Industrial Estates in Vietnam the Economy and Environment Program for Southeast Asia

[22] Tim, C. D. S. P, & Battese, G. E. (2005). An Introduction to Efficiency and Productivity

[23] Tuyen, B. C. (2010). Strategy of Vietnam towards addressing Environment and Climate

Review of Implementation and Recommendations.

Analysis, New York, Springer Science.

Damages.

(EEPSEA).

risks.


[19] The World Bank ((2007). Cost of Pollution in China- Economic Estimates of Physical Damages.

[5] Hung, P. T, Tuan, B. A, & Chinh, N. T. (2008). The Impact of Trade Liberalization on Industrial Pollution: Empirical Evidence from Vietnam the Economy and Environment

[6] ICEM ((2007). Analysis of Pollution from Manufacturing Sectors in Vietnam Interna‐

[7] Khai, H. V, & Yabe, M. (2011). Evaluation of the Impact of Water Pollution on Rice Production in the Mekong Delta, Vietnam. The International Journal of Environmental,

[8] Khai, H. V, & Yabe, M. (2012). Rice Yield Loss Due to Industrial Pollution in Vietnam.

[9] Kompas, T. (2004). Market Reform, Productivity and Efficiency in Vietnamese Rice Production. International and Development Economics Working Papers, Asia Pacific School of Economics and Governmnet, Australian National University, Australia.

[10] Lang, V. T, Vinh, K. Q, & Truc, N. T. T. (2009). Environmental Consequences of and Pollution Control Options for Pond "Tra" Fish Production in Thotnot District, Can Tho city, Vietnam The Economy and Environment Program for Southeast Asia (EEPSEA).

[11] Lindhjem, H. (2007). Emvironmental Economic Impact Assessment in China: Problems

[12] Linh, H. V. (2007). Efficiency of Rice Farming Households in Vietnam: A DEA With Bootstrap and Stochastic Frontier Application. University of Minnessota, Minesota,

[13] Nga, B. T, Giao, N. T, & Nu, P. V. (2008). Effects of Waste Water From Tra Noc Industrial Zone on Adjacent Rivers in Can Tho City. Scientific journal of Can Tho University , 9(2)

[14] Quang, M. N. (2001). An Evaluation of the Chemical Pollution in Vietnam.Retrieved May 19, 2011, from http://www.mekonginfo.org/mrc\_en/doclib.nsf/ 38bfa13a79f297d2c72566170044aaf9/1d952c500be72dc587256b74000703c8?OpenDocu‐

[15] Rahman, S. (2002). Profit Efficiency Among Bangladeshi Rice Farmers. Food Policy ,

[16] Reddy, V. R, & Behera, B. (2006). Impact of Water Pollution on Rural Communities: An

[17] Resource and Environment department of Can Tho city ((2008). Report on the situation

[18] Surjit, S. S, & Carlos, A. B. (1981). Estimating Farm-Level Input Demand and Wheat Supply in the Indian Punjab Using a Translog Profit Function. American Journal of

and Prospects. Environmnetal Impact Assessment Review , 27(1)

tional Centre for Environmental Management, Indooroopily, Queenland.

Program for Southeast Asia (EEPSEA).

84 International Perspectives on Water Quality Management and Pollutant Control

Cultural, Economic and Social Sustainability , 7(5)

Journal of US-China Public Administration , 9(3)

Economic Analysis. Ecological Economics , 58(3)

of Environment in Can Tho City.

Agricultural Economics , 63(2)

USA.

ment

28(5-6)


**Chapter 4**

**The Performance Evaluation of Anaerobic Methods for**

Palm oil mill effluent (POME) is an important source of inland water pollution when released into local rivers or lakes without treatment. In the process of palm oil milling, POME is generat‐ ed through sterilization of fresh oil palm fruit bunches, clarification of palm oil and effluent from hydro-cyclone operations [Borja et al.,1996a]. POME is a viscous brown liquid with fine suspended solids at pH ranging between 4 and 5 [Najafpour et al., 2006]. In general appear‐ ance, palm oil mill effluent (POME) is a yellowish acidic wastewater with fairly high polluting properties, with average of 25,000 mg/l biochemical oxygen demand (BOD), 55,250 mg/l chem‐ ical oxygen demand (COD) and 19,610 mg/l suspended solid (SS). This highly polluting waste‐ water can cause several pollution problems. Therefore, direct discharge of POME into the

Over the past 20 years, the technique available for the treatment of POME in Malaysia has been biological treatment, consisting of anaerobic, facultative and aerobic pond systems [Chooi, 1984], and [N. Ma, 1999]. Anaerobic digestion has been employed by most palm oil mills as their primary treatment of POME [Tay, 1991]. More than 85% of palm oil mills in Malaysia have adopted the ponding system for POME treatment [Ma et al., 1993], while the rest opted for open digesting tanks [Yacop et al., 2005]. These methods are regarded as a conventional POME treatment method involving long retention times and large treatment areas. High-rate anaero‐ bic bioreactors have also been applied in laboratory-scaled POME treatment such as up-flow anaerobic sludge blanket (UASB) reactors [Borja el al., 1994a]; up-flow anaerobic filtration [Borja et al., 1994b]; fluidized bed reactors [Borja et al., 1995a], [Borja et al., 1995b] and up-flow anaerobic sludge fixed-film (UASFF) reactors [Najafpour et al., 2006]. Anaerobic contact di‐ gesters Ibrahim et al. (1984) and continuous stirred tank reactors (CSTR) have also been stud‐ ied for PMOE treatment Chin (1981). Other than anaerobic digestion, POME has also been treated using membrane technology [Ahmad et al., 2006; 2007] and [Fakhru'l-Razi, 1994].

> © 2013 Abdurahman et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Abdurahman et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons

environment is not encouraged due to the high values of COD, BOD and SS.

**Palm Oil Mill Effluent (POME) Treatment: A Review**

N.H. Abdurahman, Y.M. Rosli and N.H. Azhari

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/54331

**1. Introduction**

## **The Performance Evaluation of Anaerobic Methods for Palm Oil Mill Effluent (POME) Treatment: A Review**

N.H. Abdurahman, Y.M. Rosli and N.H. Azhari

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/54331

## **1. Introduction**

Palm oil mill effluent (POME) is an important source of inland water pollution when released into local rivers or lakes without treatment. In the process of palm oil milling, POME is generat‐ ed through sterilization of fresh oil palm fruit bunches, clarification of palm oil and effluent from hydro-cyclone operations [Borja et al.,1996a]. POME is a viscous brown liquid with fine suspended solids at pH ranging between 4 and 5 [Najafpour et al., 2006]. In general appear‐ ance, palm oil mill effluent (POME) is a yellowish acidic wastewater with fairly high polluting properties, with average of 25,000 mg/l biochemical oxygen demand (BOD), 55,250 mg/l chem‐ ical oxygen demand (COD) and 19,610 mg/l suspended solid (SS). This highly polluting waste‐ water can cause several pollution problems. Therefore, direct discharge of POME into the environment is not encouraged due to the high values of COD, BOD and SS.

Over the past 20 years, the technique available for the treatment of POME in Malaysia has been biological treatment, consisting of anaerobic, facultative and aerobic pond systems [Chooi, 1984], and [N. Ma, 1999]. Anaerobic digestion has been employed by most palm oil mills as their primary treatment of POME [Tay, 1991]. More than 85% of palm oil mills in Malaysia have adopted the ponding system for POME treatment [Ma et al., 1993], while the rest opted for open digesting tanks [Yacop et al., 2005]. These methods are regarded as a conventional POME treatment method involving long retention times and large treatment areas. High-rate anaero‐ bic bioreactors have also been applied in laboratory-scaled POME treatment such as up-flow anaerobic sludge blanket (UASB) reactors [Borja el al., 1994a]; up-flow anaerobic filtration [Borja et al., 1994b]; fluidized bed reactors [Borja et al., 1995a], [Borja et al., 1995b] and up-flow anaerobic sludge fixed-film (UASFF) reactors [Najafpour et al., 2006]. Anaerobic contact di‐ gesters Ibrahim et al. (1984) and continuous stirred tank reactors (CSTR) have also been stud‐ ied for PMOE treatment Chin (1981). Other than anaerobic digestion, POME has also been treated using membrane technology [Ahmad et al., 2006; 2007] and [Fakhru'l-Razi, 1994].

© 2013 Abdurahman et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Abdurahman et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **2. Anaerobic digestion**

Anaerobic digestion is the most suitable method for the treatment of effluents containing high concentration of organic carbon such as POME [Borja et al.,1996a]. Anaerobic diges‐ tion is defined as the engineered methanogenic anaerobic decomposition of organic mat‐ ter. It involves different species of anaerobic microorganisms that degrade organic matter [Cote et al., 2006]. In the anaerobic process, the decomposition of organic and inorganic substrate is carried out in the absence of molecular oxygen. The biological conversion of the organic substrate occurs in the mixtures of primary settled and biological sludge un‐ der anaerobic condition followed by hydrolysis, acidogenesis and methanogenesis to con‐ vert the intermediate compounds into simpler end products as methane (CH4) and carbon dioxide (CO2) [Gee et al., 1994], [Guerrero et al., 1999], and [Gerardi, 2003]. Therefore, the anaerobic digestion process offers great potential for rapid disintegration of organic matter to produce biogas that can be used to generate electricity and save fos‐ sil energy [linke, 2006]. The suggested anaerobic treatment processes for POME include anaerobic suspended growth processes, attached growth anaerobic processes (immobi‐ lized cell bioreactors, anaerobic fluidized bed reactors and anaerobic filters), anaerobic blanket processes (up-flow anaerobic sludge blanket reactors and anaerobic baffled reac‐ tors), membrane separation anaerobic treatment processes and hybrid anaerobic treat‐ ment processes.

**Treatment types**

Membrane Produce consistent and good water

treated water

for land applications

Evaporation solid concentrate from process can

manufacturing

in handling toxic wastes

Aerobic Shorter retention time, more effective

be

**2.2. Anaerobic treatment methods**

*2.2.1. Anaerobic filtration*

Anaerobic low energy requirements (no

quality after treatment, smaller space required for membrane treatment plants, can disinfect

aeration),Producing methane gas as a valuable end product, generated sludge from process could be used

utilized as feed material for fertilizer

**Table 1.** Advantages and disadvantages between anaerobic and alternative treatment methods

**Advantages Disadvantages Reference**

The Performance Evaluation of Anaerobic Methods for Palm Oil Mill Effluent (POME) Treatment: A Review

conventional treatment

required For

Short membrane life, membrane fouling, expensive compared to

Long retention time, large area

conventional digesters, slow start-

High energy consumption [ MA et al., 1997]

up (granulating reactors)

High energy requirement (aeration), rate of pathogen inactivation is lower in aerobic sludge compared to anaerobic sludge, thus unsuitable for land

applications

Anaerobic digestion has existed as a technology over 100 years. It gradually evolved, from an airtight vessel and a septic tank, to a temperature controlled, completely mixed digester, and fi‐ nally to a high rate reactor, containing a density of highly active biomass. The microbiology of methane digestion has been examined intensively in the last decade. It has been established that three physiological groups of bacteria, converting hydrogen and carbon dioxide or acetate to methane. In contrast to aerobic degradation, which is mainly a single species phenomenon, anaerobic degradation proceeds as a chain process, in which several sequent organisms are in‐ volved. Anaerobic conversion of complex substrates requires the synergistic action of the mi‐ cro-organisms involved. A factor of utmost importance, in the overall process, is the partial pressure of hydrogen and the thermodynamics linked to it. This fact has been recognized and discussed by researchers [Bryant et al., 1967]; [Boone and Bryant, 1980]; [McInerney et al. 1979]; [Hickey and Switzenbaum 1988]. Anaerobic filter were favor for wastewater treatment be‐ cause (a) high substrate removal efficiency (b) it requires a smaller reactor volume which oper‐ ates on a shorter hydraulic retention times (HRT), [Borja et al., 1994b], (c) the ability to maintain

[Ahmad et al., 2006] [Metcalf et al., 2003]

89

http://dx.doi.org/10.5772/54331

[Metcalf et al., 2003] [Borja et al., 2006a]

[Jr et al., 1999] [Doble et al., 2005]

#### **2.1. Anaerobic and alternative POME treatment methods**

Currently available alternative methods for POME treatment are: aerobic treatment, membrane treatment systems and the evaporation method. The advantages and disad‐ vantages of anaerobic and alternative treatment methods are shown in Table 1. In terms of energy requirement for POME treatment operation, anaerobic digestion has a greater advantage over the other alternative methods as it does not require energy for aeration. Furthermore, anaerobic POME treatment produces methane gas (CH4) which is a valueadded product to digestion that can be utilized in the mill to gain more revenue in terms of CER. For example, the open digesting tank for POME treatment without land application, the capital cost quoted by [Gopal et al., 1986] for a palm oil mill processing 30 tons FFB/h is RM 750,000. Based on the chemical Engineering Plant Cost Index [Ul‐ rich et al., 2004] the capital cost for this system is estimated to be US 370,272 in 2006. Comparing this to the capital cost for a membrane system in POME treatment for a palm oil mill processing 36 tons FFB/h at RM 3,950,000 [Chong, 2007], it is obvious that the former anaerobic treatment has better advantage over other treatment methods in terms of capital cost. The disadvantages of anaerobic treatment are (a) long retention times and (b) long start-up period. However, the problem of long retention times can be rectified by using high-rate anaerobic bioreactors while the long start-up period can be shortened by using granulated seed sludge [McHugh et al., 2003], utilizing seed sludge from same process [Yacob et al., 2006b] or maintaining suitable ph and temperature in the high-rate anaerobic bioreactor for growth of bacteria consortia [Liu et al., 2002].


**Table 1.** Advantages and disadvantages between anaerobic and alternative treatment methods

## **2.2. Anaerobic treatment methods**

#### *2.2.1. Anaerobic filtration*

**2. Anaerobic digestion**

ment processes.

**2.1. Anaerobic and alternative POME treatment methods**

88 International Perspectives on Water Quality Management and Pollutant Control

Anaerobic digestion is the most suitable method for the treatment of effluents containing high concentration of organic carbon such as POME [Borja et al.,1996a]. Anaerobic diges‐ tion is defined as the engineered methanogenic anaerobic decomposition of organic mat‐ ter. It involves different species of anaerobic microorganisms that degrade organic matter [Cote et al., 2006]. In the anaerobic process, the decomposition of organic and inorganic substrate is carried out in the absence of molecular oxygen. The biological conversion of the organic substrate occurs in the mixtures of primary settled and biological sludge un‐ der anaerobic condition followed by hydrolysis, acidogenesis and methanogenesis to con‐ vert the intermediate compounds into simpler end products as methane (CH4) and carbon dioxide (CO2) [Gee et al., 1994], [Guerrero et al., 1999], and [Gerardi, 2003]. Therefore, the anaerobic digestion process offers great potential for rapid disintegration of organic matter to produce biogas that can be used to generate electricity and save fos‐ sil energy [linke, 2006]. The suggested anaerobic treatment processes for POME include anaerobic suspended growth processes, attached growth anaerobic processes (immobi‐ lized cell bioreactors, anaerobic fluidized bed reactors and anaerobic filters), anaerobic blanket processes (up-flow anaerobic sludge blanket reactors and anaerobic baffled reac‐ tors), membrane separation anaerobic treatment processes and hybrid anaerobic treat‐

Currently available alternative methods for POME treatment are: aerobic treatment, membrane treatment systems and the evaporation method. The advantages and disad‐ vantages of anaerobic and alternative treatment methods are shown in Table 1. In terms of energy requirement for POME treatment operation, anaerobic digestion has a greater advantage over the other alternative methods as it does not require energy for aeration. Furthermore, anaerobic POME treatment produces methane gas (CH4) which is a valueadded product to digestion that can be utilized in the mill to gain more revenue in terms of CER. For example, the open digesting tank for POME treatment without land application, the capital cost quoted by [Gopal et al., 1986] for a palm oil mill processing 30 tons FFB/h is RM 750,000. Based on the chemical Engineering Plant Cost Index [Ul‐ rich et al., 2004] the capital cost for this system is estimated to be US 370,272 in 2006. Comparing this to the capital cost for a membrane system in POME treatment for a palm oil mill processing 36 tons FFB/h at RM 3,950,000 [Chong, 2007], it is obvious that the former anaerobic treatment has better advantage over other treatment methods in terms of capital cost. The disadvantages of anaerobic treatment are (a) long retention times and (b) long start-up period. However, the problem of long retention times can be rectified by using high-rate anaerobic bioreactors while the long start-up period can be shortened by using granulated seed sludge [McHugh et al., 2003], utilizing seed sludge from same process [Yacob et al., 2006b] or maintaining suitable ph and temperature in the high-rate anaerobic bioreactor for growth of bacteria consortia [Liu et al., 2002].

Anaerobic digestion has existed as a technology over 100 years. It gradually evolved, from an airtight vessel and a septic tank, to a temperature controlled, completely mixed digester, and fi‐ nally to a high rate reactor, containing a density of highly active biomass. The microbiology of methane digestion has been examined intensively in the last decade. It has been established that three physiological groups of bacteria, converting hydrogen and carbon dioxide or acetate to methane. In contrast to aerobic degradation, which is mainly a single species phenomenon, anaerobic degradation proceeds as a chain process, in which several sequent organisms are in‐ volved. Anaerobic conversion of complex substrates requires the synergistic action of the mi‐ cro-organisms involved. A factor of utmost importance, in the overall process, is the partial pressure of hydrogen and the thermodynamics linked to it. This fact has been recognized and discussed by researchers [Bryant et al., 1967]; [Boone and Bryant, 1980]; [McInerney et al. 1979]; [Hickey and Switzenbaum 1988]. Anaerobic filter were favor for wastewater treatment be‐ cause (a) high substrate removal efficiency (b) it requires a smaller reactor volume which oper‐ ates on a shorter hydraulic retention times (HRT), [Borja et al., 1994b], (c) the ability to maintain high concentration of biomass in contact with the wastewater without affecting treatment effi‐ ciency [Reyes et al., 1999], [Wang et al. (2007)], and (d) tolerance to shock loadings [Reyes et al., 1999], [Van Der Merwe et al., 1993]. Besides, construction and operation of anaerobic filter is less expensive and small amount of suspended solids in the effluent eliminates the need of sol‐ id separation or recycle [Russo et al. 1985].

**Types of Wastewater Operating OLR range**

**(Kg COD/m3/day)**

Slaughterhouse wastewater 1.0-6.5 79.9 (91.5) 51.1

Baker's yeast factory effluent 1.8-10.0 69.0 (74.0) 65.0

Distillery wastewaters 0.42-3.4 91.0 (93.0) 63.0

() - number in bracket denotes highest COD removal efficiency. N/A- data unavailable.

based on highest % of methane production

*2.2.2. Fluidized bed reactor*

POME 1.2-11.4 94.0 (94.0) 63.0 [Borja et al.

Landfill leachate 0.76-7.63 90.8 (90.8) N/A [Wang et al.,

**Table 2.** Operating OLR range; COD removal efficiency in various wastewater treatments using anaerobic filtration

A fluidized bed reactor (FBR) is a type of reactor device that can be used to carry out a varie‐ ty of multiphase chemical reactions. Fluidized bed reactor exhibits several advantages that make it useful for treatment of high-strength wastewaters. It has very large surface areas for biomass attachment [Borja et al., 2001], [Toldra et al., 1987] enabling high OLR and short HRTs during operation [Garcia et al., 1998], [Sowmeyan et al., 2008]. Furthermore, fluidized bed has minimal problems of channeling, plugging or gas hold-up [Borja et al., 2001], [Tol‐ dra et al. 1987]. Higher up-flow velocity of raw POME is maintained for fluidized bed reac‐ tor to enable expansion of the support material bed. Biomass will then attach and grow on the support on material. In this way, biomass can be retained in the reactor. Hickey and [Switzenbaum, 1988] reported on the development of the anaerobic expanded bed process, which was found to convert dilute organic wastes to methane at low temperatures and at high organic and hydraulic loading rates. This process was being evaluated in 1988, on a 10,000 gallons per day pilot scale, consisting of an anaerobic expanded bed followed by post- treatment. [Jeris, 1987] reported on a two year experiment, testing two pilot scale anae‐ robic fluidized bed reactors, treating primary effluent. One reactor used sand as a carrier, the other granular activated carbon (GAC). Seeding experiments indicated that the GAC de‐ veloped a biofilm more quickly and had more attached biomass. In addition, better BOD re‐ moval was observed with the GAC reactor. He noted that removal efficiencies were essentially independent of organic volumetric loading rates. Over a twelve month period in

temperate climates, effluent total BOD5 values were consistently around 40 mg/l.

Investigations have been done on the application of fluidized bed to treat cutting-oil waste‐ water [Perez et al., 2007]; real textile wastewater [Sen et al., 2003]; slaughterhouse wastewa‐ ter [Toldra et al., 1987]; wine and distillery wastewater [Garcia et al. 1998], [Sowmeyan et al.,

**COD removal efficiency (%)**

The Performance Evaluation of Anaerobic Methods for Palm Oil Mill Effluent (POME) Treatment: A Review

**Highest methane composition (%)**

**Reference**

91

http://dx.doi.org/10.5772/54331

[Ruiz et al., 1997]

1994b]

[Van der et al. 1993]

[Russo et al., 1985]

2007]

Another factor of fundamental importance has been the identification of new methanogenic species, and the characterization of their physiological behaviour. Of particular interest was the determination of the substrate affinity constants of both hydrogenotrophic and acetotro‐ phic methanogens. While the first exhibit quite high substrate affinities and remove hydro‐ gen down to ppm levels, the second group appears to contain species with only low substrate affinities [Zehnder et al., 1980]; [Huser et al., 1982]. This limited substrate affinity has,, an important consequence for anaerobic wastewater treatment.

A technological advance of utmost importance in anaerobic digestion has been the develop‐ ment of methods to concentrate methanogenic biomass in the reactor, especially in very low solids concentration in the wastewater, 1 - 2%. Such higher concentration of biomass can be achieved using of autoflocculation and gravity settling as, for instance, in the UASB reactor [Lettinga et al. 1983], by attachment to a static carrier (anaerobic filter) [Henze and Harre‐ moes, 1982]; [Van Den Berg and Kennedy 1981]; [Young and McCarty 1969], by attachment to a mobile carrier (fluidized bed) [Binot et Heijnen 1984]; [Bull et al., 1984] or by growth in and on a matrix [Huysman et al., 1983]. All these different methods are in full development

Anaerobic filters have been applied to treat various types of wastewater including soybean processing wastewater [H-Q et al., 2002a], wine vinases [Nebot et al., 1995], [Perez et al., 1998 ], land fill leachate [Wang et al., 2007], municipal wastewater [Bodkhe, 2008], brewery wastewater [Leal et al., 1998], slaughterhouse wastewater [Ruiz et al., 1997], drug wastewa‐ ter [Gangagni et al., 2005], and beet sugar water [Farhadian et al., 2007]. However, filter clogging is a major drawback in the continuous operation of anaerobic filters [Bodkhe, 2008], [Jawed et al. 2000], [Parawira et al., 2006]. Clogging of anaerobic filter has only been reported in the treatment of POME at an organic loading rate (OLR) of 20 g COD/l/day [Bor‐ ja et al., 1995b] and also in the treatment of slaughterhouse wastewater at 6 g COD/l/day. This because the other studies were conducted at lower OLRs which had lower suspended solid content compared to POME. In general, anaerobic filter s are capable of treating waste‐ waters to obtain good effluent quality with at least 70% of COD removal efficiency with methane gas composition of more than 50%. Table 2 illustrates the COD removal efficiency of some treated wastewater using anaerobic filtration based on highest achievable percent‐ age of methane in the generated biogas. In terms of POME treatment, the highest COD re‐ moval efficiency recorded was 94% with 63% of methane at an OLR of 4.5 kg COD/m3/day, while overall COD removal efficiency was up to 90% with an average methane gas composi‐ tion of 60% [Borja et al., 1994b]. Investigations have been done to improve the efficiency of anaerobic filtration in wastewater treatment. [Yu et al., 2002a] found that operating at an op‐ timal recycle ratio which varies depending on OLR will enhance COD removal. However, methane percentage will be compromised with increase in optimal recycle ratio. Higher re‐ tention of biomass in the filter will also lead to a better COD removal efficiency.


**Table 2.** Operating OLR range; COD removal efficiency in various wastewater treatments using anaerobic filtration based on highest % of methane production

## *2.2.2. Fluidized bed reactor*

high concentration of biomass in contact with the wastewater without affecting treatment effi‐ ciency [Reyes et al., 1999], [Wang et al. (2007)], and (d) tolerance to shock loadings [Reyes et al., 1999], [Van Der Merwe et al., 1993]. Besides, construction and operation of anaerobic filter is less expensive and small amount of suspended solids in the effluent eliminates the need of sol‐

Another factor of fundamental importance has been the identification of new methanogenic species, and the characterization of their physiological behaviour. Of particular interest was the determination of the substrate affinity constants of both hydrogenotrophic and acetotro‐ phic methanogens. While the first exhibit quite high substrate affinities and remove hydro‐ gen down to ppm levels, the second group appears to contain species with only low substrate affinities [Zehnder et al., 1980]; [Huser et al., 1982]. This limited substrate affinity

A technological advance of utmost importance in anaerobic digestion has been the develop‐ ment of methods to concentrate methanogenic biomass in the reactor, especially in very low solids concentration in the wastewater, 1 - 2%. Such higher concentration of biomass can be achieved using of autoflocculation and gravity settling as, for instance, in the UASB reactor [Lettinga et al. 1983], by attachment to a static carrier (anaerobic filter) [Henze and Harre‐ moes, 1982]; [Van Den Berg and Kennedy 1981]; [Young and McCarty 1969], by attachment to a mobile carrier (fluidized bed) [Binot et Heijnen 1984]; [Bull et al., 1984] or by growth in and on a matrix [Huysman et al., 1983]. All these different methods are in full development

Anaerobic filters have been applied to treat various types of wastewater including soybean processing wastewater [H-Q et al., 2002a], wine vinases [Nebot et al., 1995], [Perez et al., 1998 ], land fill leachate [Wang et al., 2007], municipal wastewater [Bodkhe, 2008], brewery wastewater [Leal et al., 1998], slaughterhouse wastewater [Ruiz et al., 1997], drug wastewa‐ ter [Gangagni et al., 2005], and beet sugar water [Farhadian et al., 2007]. However, filter clogging is a major drawback in the continuous operation of anaerobic filters [Bodkhe, 2008], [Jawed et al. 2000], [Parawira et al., 2006]. Clogging of anaerobic filter has only been reported in the treatment of POME at an organic loading rate (OLR) of 20 g COD/l/day [Bor‐ ja et al., 1995b] and also in the treatment of slaughterhouse wastewater at 6 g COD/l/day. This because the other studies were conducted at lower OLRs which had lower suspended solid content compared to POME. In general, anaerobic filter s are capable of treating waste‐ waters to obtain good effluent quality with at least 70% of COD removal efficiency with methane gas composition of more than 50%. Table 2 illustrates the COD removal efficiency of some treated wastewater using anaerobic filtration based on highest achievable percent‐ age of methane in the generated biogas. In terms of POME treatment, the highest COD re‐ moval efficiency recorded was 94% with 63% of methane at an OLR of 4.5 kg COD/m3/day, while overall COD removal efficiency was up to 90% with an average methane gas composi‐ tion of 60% [Borja et al., 1994b]. Investigations have been done to improve the efficiency of anaerobic filtration in wastewater treatment. [Yu et al., 2002a] found that operating at an op‐ timal recycle ratio which varies depending on OLR will enhance COD removal. However, methane percentage will be compromised with increase in optimal recycle ratio. Higher re‐

tention of biomass in the filter will also lead to a better COD removal efficiency.

has,, an important consequence for anaerobic wastewater treatment.

id separation or recycle [Russo et al. 1985].

90 International Perspectives on Water Quality Management and Pollutant Control

A fluidized bed reactor (FBR) is a type of reactor device that can be used to carry out a varie‐ ty of multiphase chemical reactions. Fluidized bed reactor exhibits several advantages that make it useful for treatment of high-strength wastewaters. It has very large surface areas for biomass attachment [Borja et al., 2001], [Toldra et al., 1987] enabling high OLR and short HRTs during operation [Garcia et al., 1998], [Sowmeyan et al., 2008]. Furthermore, fluidized bed has minimal problems of channeling, plugging or gas hold-up [Borja et al., 2001], [Tol‐ dra et al. 1987]. Higher up-flow velocity of raw POME is maintained for fluidized bed reac‐ tor to enable expansion of the support material bed. Biomass will then attach and grow on the support on material. In this way, biomass can be retained in the reactor. Hickey and [Switzenbaum, 1988] reported on the development of the anaerobic expanded bed process, which was found to convert dilute organic wastes to methane at low temperatures and at high organic and hydraulic loading rates. This process was being evaluated in 1988, on a 10,000 gallons per day pilot scale, consisting of an anaerobic expanded bed followed by post- treatment. [Jeris, 1987] reported on a two year experiment, testing two pilot scale anae‐ robic fluidized bed reactors, treating primary effluent. One reactor used sand as a carrier, the other granular activated carbon (GAC). Seeding experiments indicated that the GAC de‐ veloped a biofilm more quickly and had more attached biomass. In addition, better BOD re‐ moval was observed with the GAC reactor. He noted that removal efficiencies were essentially independent of organic volumetric loading rates. Over a twelve month period in temperate climates, effluent total BOD5 values were consistently around 40 mg/l.

Investigations have been done on the application of fluidized bed to treat cutting-oil waste‐ water [Perez et al., 2007]; real textile wastewater [Sen et al., 2003]; slaughterhouse wastewa‐ ter [Toldra et al., 1987]; wine and distillery wastewater [Garcia et al. 1998], [Sowmeyan et al., 2008]; ice-cream wastewater [Borja et al., 1995a], [Hawkes et al., 1995]; pharmaceutical efflu‐ ent [Saravanane et al., 2001], and POME [Borja et al., 1995b]. OLR ranges and COD removal efficiencies of various wastewater treatments using fluidized bed is tabulated in Table 3. Based on Table 3, it can be concluded that anaerobic fluidized bed can typically remove at least 65% and up to more than 90% of COD. Inverse flow anaerobic fluidized bed is capable of tolerating higher OLRs compared to up-flow configuration.

actor is a methanogenic (methane-producing) digester that evolved from the anaerobic clar‐ igester. A similar but variant technology to UASB is the expanded granular sludge bed (EGSB) digester. The underlying principle of the UASB operation is to have an aerobic sludge which exhibits good settling properties [Lettinga, 1995]. So far, UASB has been ap‐ plied for the treatment of potato wastewater [Kalyuzhnyi et al., 1998], [Lettinga et al., 1980], [Parawira et al., 2006]; domestic wastewater [Barbosa et al., 1989], [Behling et al., 1997]; slaughterhouse wastewater [Sayed et al., 1984]; POME [Borja et al., 1994c]. UASB has a rela‐ tively simple design where sludge from organic matter degradation and biomass settles in the reactor. Organic matter from wastewater that comes in contact with sludge will be di‐ gested by the biomass granules. Table 4 shows some performances of wastewater treatment using UASB system. For potato wastewater treatment [Kalyuzhnyi et al., 1998] and [Para‐ wira et al., 2006] both observed foaming and sludge floatation in the UASB reactor when op‐ erating at higher OLRs (> 6.1kg COD/m3 day). The ability of UASB to tolerate higher OLR for potato wastewater investigated by [Lettinga et al., 1980] compared due to the fact that the latter two studies were conducted at laboratory scale. In general, UASB is successful in COD removal of more than 60% for most wastewater types except for ice-cream wastewater. Researcher [Hawkes et al., 1995] suggested that the lower COD removal percentage from ice-cream wastewater was due to design faults in the reactor's three phase separator and

The Performance Evaluation of Anaerobic Methods for Palm Oil Mill Effluent (POME) Treatment: A Review

high contents of milk fat that were hard to degrade.

**Types of Wastewater Operating OLR range**

**Table 4.** Performance of UASB in various wastewater treatments

(based on methanogenic

N/A – data unavailable.

reactor)

**(Kg COD/m3/day)**

Domestic sewage 3.76 74.0 69.0

Ice-cream wastewater 0.5-50 50.0 69.6

Pharmaceutical wastewater 0.27-2.0 26.0-69.0 N/A

Confectionary wastewater 1.25-2.25 66.0 N/A

POME single-stage two-stage 1.8-13.9 63.0-81.0 54.0-67.0 [Kalyuzhnyi

Sugar – beet 4.0-5.0 95.0 N/A [Lettinga et

Slaughter wastewater 7.0-11.0 55.0-85.0 65.0-75.0 [Sayed et al.

**COD removal efficiency (%)**

1.5-6.1 92.0-98.0 59.0-70.0

**Methane Composition (%)**

http://dx.doi.org/10.5772/54331

93

**Reference**

et al 1998]

[Parawira et al. 2006]

[Barbosa et al. 1989]

[Hawkes et al. 1995]

al. 1980]

[Stronach et al. 1987]

1984]

[Forster et al. 1983]


UF-upward flow; DF-downward/inverse flow.

**Table 3.** Operating OLR range; COD removal efficiency of various wastewater treatments using fluidized bed reactor

The type of support material in the fluidized bed plays an important role to determine the efficiency of the entire treatment system [Garcia et al., 1998], [Sowmeyan et al., 2008] for both inverse flow and up-flow systems. Studies using fluidized bed to treat ice-cream waste‐ water showed different COD removal efficiencies when different support materials were used. Researcher [Hawkes et al., 1995] found that fluidized bed using granular activated car‐ bon (GAC) gave about 60% COD removal while [Borja et al., 1995a] obtained 94.4% of COD removal using ovoid saponite. Thus suitable support material needs to be selected to obtain high COD removal efficiency in the system.

#### *2.2.3. Up-flow Anaerobic Sludge Blanket (UASB) reactor*

Up-flow anaerobic sludge blanket (UASB) technology, normally referred to as UASB reac‐ tor, is a form of anaerobic digester that is used in the treatment of wastewater. UASB was developed by [Lettinga et al., 1980] whereby this system has been successful in treating a wide range of industrial effluents including those with inhibitory compounds. The UASB re‐ actor is a methanogenic (methane-producing) digester that evolved from the anaerobic clar‐ igester. A similar but variant technology to UASB is the expanded granular sludge bed (EGSB) digester. The underlying principle of the UASB operation is to have an aerobic sludge which exhibits good settling properties [Lettinga, 1995]. So far, UASB has been ap‐ plied for the treatment of potato wastewater [Kalyuzhnyi et al., 1998], [Lettinga et al., 1980], [Parawira et al., 2006]; domestic wastewater [Barbosa et al., 1989], [Behling et al., 1997]; slaughterhouse wastewater [Sayed et al., 1984]; POME [Borja et al., 1994c]. UASB has a rela‐ tively simple design where sludge from organic matter degradation and biomass settles in the reactor. Organic matter from wastewater that comes in contact with sludge will be di‐ gested by the biomass granules. Table 4 shows some performances of wastewater treatment using UASB system. For potato wastewater treatment [Kalyuzhnyi et al., 1998] and [Para‐ wira et al., 2006] both observed foaming and sludge floatation in the UASB reactor when op‐ erating at higher OLRs (> 6.1kg COD/m3 day). The ability of UASB to tolerate higher OLR for potato wastewater investigated by [Lettinga et al., 1980] compared due to the fact that the latter two studies were conducted at laboratory scale. In general, UASB is successful in COD removal of more than 60% for most wastewater types except for ice-cream wastewater. Researcher [Hawkes et al., 1995] suggested that the lower COD removal percentage from ice-cream wastewater was due to design faults in the reactor's three phase separator and high contents of milk fat that were hard to degrade.

2008]; ice-cream wastewater [Borja et al., 1995a], [Hawkes et al., 1995]; pharmaceutical efflu‐ ent [Saravanane et al., 2001], and POME [Borja et al., 1995b]. OLR ranges and COD removal efficiencies of various wastewater treatments using fluidized bed is tabulated in Table 3. Based on Table 3, it can be concluded that anaerobic fluidized bed can typically remove at least 65% and up to more than 90% of COD. Inverse flow anaerobic fluidized bed is capable

> **COD removal efficiency (%)**

**Reactor configuration**

**Reference**

2001]

1995b]

1995a]

al., 2008]

[Alvarado et al., 2008]

of tolerating higher OLRs compared to up-flow configuration.

92 International Perspectives on Water Quality Management and Pollutant Control

**(Kg COD/m3/day)**

Brewery wastewater 0.5-70.0 80.0-90.0 DF

Sunflower flour effluent 0.6-9.3 80.0-93.3 UF [Borja et al.,

POME 10.0-40.0 78.0-94.0 UF [Borja et al.,

Ice-cream wastewater 3.2-15.6 94.4 UF [Borja et al.,

Distillery effluent 6.11-35.09 80.0-92.0 DF [Sowmeyan et

Real textile wastewater 0.4-5.0 78.0-89.0 UF [Sen et al., 2003]

**Table 3.** Operating OLR range; COD removal efficiency of various wastewater treatments using fluidized bed reactor

The type of support material in the fluidized bed plays an important role to determine the efficiency of the entire treatment system [Garcia et al., 1998], [Sowmeyan et al., 2008] for both inverse flow and up-flow systems. Studies using fluidized bed to treat ice-cream waste‐ water showed different COD removal efficiencies when different support materials were used. Researcher [Hawkes et al., 1995] found that fluidized bed using granular activated car‐ bon (GAC) gave about 60% COD removal while [Borja et al., 1995a] obtained 94.4% of COD removal using ovoid saponite. Thus suitable support material needs to be selected to obtain

Up-flow anaerobic sludge blanket (UASB) technology, normally referred to as UASB reac‐ tor, is a form of anaerobic digester that is used in the treatment of wastewater. UASB was developed by [Lettinga et al., 1980] whereby this system has been successful in treating a wide range of industrial effluents including those with inhibitory compounds. The UASB re‐

**Types of Wastewater Operating OLR range**

Protein production from extracted

UF-upward flow; DF-downward/inverse flow.

high COD removal efficiency in the system.

*2.2.3. Up-flow Anaerobic Sludge Blanket (UASB) reactor*


**Table 4.** Performance of UASB in various wastewater treatments

POME treatment has been successful with UASB reactor, achieving COD removal efficiency up to 98.4% with the highest operating OLR of 10.63 kg COD/m3 day [Borja et al., 1994c]. However, reactor operated under overload conditions with high volatile fatty acid content became unstable after 15 days. Due to high amount of POME discharge daily from milling process, it is necessary to operate treatment system at higher OLR. UASB reactor is advanta‐ geous for its ability to treat wastewater with high suspended solid content [Fang et al. 1994]; [Kalyuzhnyi et al., 1998] that may clog reactors with packing material and also provide higher methane production [Kalyuzhnyi et al., 1996]; [Stronach et al., 1987]. However, this reactor might face long start-up periods if seeded sludge is not granulated.

waste is solely by the movement of gas up through the solid matter and into the top of the tank; there is no external mixing. This process is highly inefficient, for it utilizes only 50 per‐ cent of the total waste volume, and requires a very long solid retention time (SRT), usually greater than 30 days; this process has been implemented in POME [Ibrahim et al., 1984]; icecream wastewater, alcohol distillery wastewater [Vlissidis et al., 1993] and fermented olive mill wastewater treatment [Hamdi et al., 1991]. Concentrated wastewaters are suitable to be treated by anaerobic contact digestion since relatively high quality effluent can be achieved [Jr et al., 1999]. In the study of fermented olive mill wastewater treatment, anaerobic contact was capable of reaching steady state more quickly compared to anaerobic filter; however, more oxygen transfer in the digester (due to mixing) causes this process to be less suitable.

The Performance Evaluation of Anaerobic Methods for Palm Oil Mill Effluent (POME) Treatment: A Review

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95

Membrane separation has been considered for anaerobic reactors but the technology is still in a development stage. Several studies on membrane anaerobic processes for the treatment of various wastewaters including POME [Fakhru'l et al., 1999] have been performed [Fakh‐ ru'l et al., 1994]; [Nagano et al., 1992]; [Pillay et al., 1994]. For example, an ultrafiltration (UF) membrane with a molecular cut-off (MWCO) of 200,000 was used by [76] for biomass/efflu‐ ent separation in conjunction with an anaerobic process for the treatment of POME. A lower operating pressure (1.5-2 bars) but a higher cross-flow velocity (2.3 m/s) was applied in this study in order to control fouling and to reduce solid deposition on the membrane surfaces. A high COD removal could be obtained in the membrane anaerobic system (MAS), but the permeate displayed a high color content with a low turbidity (less than 10 NTU), including that the color was due to dissolved solids with molecular weights lower than 200,000 g/mol. The particular organics retained in the reactor could be liquefied and decomposed because of the long solid retention time, which was independent of the HRT. The HRT was mainly influenced by the UF membrane flux rates which directly determined the volume of influent

The Kyoto Protocol is an international agreement linked to the United Nations Framework Convention on Climate Change. The major feature of the Kyoto Protocol is that it sets bind‐ ing targets for 37 industrialized countries and the European community for reducing green‐ house gas (GHG) emissions.These amount to an average of five per cent against 1990 levels

The Clean Development Mechanism (CDM), defined in Article 12 of the Protocol, allows a country with an emission-reduction or emission-limitation commitment under the Kyoto Protocol (Annex B Party) to implement an emission-reduction project in developing coun‐ tries. Such projects can earn saleable certified emission reduction (CER) credits, each equiva‐

The mechanism is seen by many as a trailblazer. It is the first global, environmental invest‐ ment and credit scheme of its kind, providing a standardized emission offset instrument,

lent to one tonne of CO2, which can be counted towards meeting Kyoto targets.

*2.2.7. Membrane separation anaerobic treatment process*

that could be fed to the reactor.

over the five-year period 2008-2012.

**2.3. Clean Development Mechanism (CDM)**

#### *2.2.4. Anaerobic contact digester*

The anaerobic contact process is a type of anaerobic digester. Anaerobic digesters are the aerobic equivalents of activated sludge process and are currently used for treating effluents from sugar processing, distilleries, citric acid and yeast production, industries producing canned vegetables, pectin, starch, meat products, etc. This process has been implemented in POME [Ibrahim et al., 1984]; ice-cream wastewater, alcohol distillery wastewater [Vlissidis et al., 1993] and fermented olive mill wastewater treatment [Hamdi et al. 1991]. Concentrat‐ ed wastewaters are suitable to be treated by anaerobic contact digestion since relatively high quality effluent can be achieved [Jr et al., 1999]. In the study of fermented olive mill waste‐ water treatment, anaerobic contact was capable of reaching steady state more quickly com‐ pared to anaerobic filter; however, more oxygen transfer in the digester (due to mixing) causes this process to be less stable.

#### *2.2.5. Continuous Stirred Tank Reactor (CSTR)*

CSTR run at steady state with continuous flow of reactants and products; the feed assumes a uniform composition throughout the reactor, exit stream has the same composition as in the tank. The mechanical agitator provides more area of contact with the biomass thus improv‐ ing gas production. In POME treatment, CSTR has been applied by a mill under Keck Seng (Malaysia) Berhad in Masai, Johor and it is apparently the only one which has been operat‐ ing continuously since early 1980's [Tong et al., 2006]. Other applications of CSTR on waste‐ water treatment include dilute dairy wastewater [Chen et al., 1996]; jam wastewater [Mohan et al., 2008] and coke wastewater [Vazquez et al., 2006].

The CSTR in Kek Seng's Palm oil mill has COD removal efficiency of approximately 83% and CSTR treating dairy wastewater has COD removal efficiency of 60%. In terms of meth‐ ane composition in generated biogas, it was found to be 62.5% for POME treatment and 22.5-76.9% for dairy wastewater treatment.

#### *2.2.6. Anaerobic contact digestion*

Presently there are three categories of anaerobic treatment systems. The first category is the conventional anaerobic digester, which includes two basic designs and another that com‐ bines the two. The standard rate digester is the most basic treatment system. It mixes the waste is solely by the movement of gas up through the solid matter and into the top of the tank; there is no external mixing. This process is highly inefficient, for it utilizes only 50 per‐ cent of the total waste volume, and requires a very long solid retention time (SRT), usually greater than 30 days; this process has been implemented in POME [Ibrahim et al., 1984]; icecream wastewater, alcohol distillery wastewater [Vlissidis et al., 1993] and fermented olive mill wastewater treatment [Hamdi et al., 1991]. Concentrated wastewaters are suitable to be treated by anaerobic contact digestion since relatively high quality effluent can be achieved [Jr et al., 1999]. In the study of fermented olive mill wastewater treatment, anaerobic contact was capable of reaching steady state more quickly compared to anaerobic filter; however, more oxygen transfer in the digester (due to mixing) causes this process to be less suitable.

## *2.2.7. Membrane separation anaerobic treatment process*

POME treatment has been successful with UASB reactor, achieving COD removal efficiency up to 98.4% with the highest operating OLR of 10.63 kg COD/m3 day [Borja et al., 1994c]. However, reactor operated under overload conditions with high volatile fatty acid content became unstable after 15 days. Due to high amount of POME discharge daily from milling process, it is necessary to operate treatment system at higher OLR. UASB reactor is advanta‐ geous for its ability to treat wastewater with high suspended solid content [Fang et al. 1994]; [Kalyuzhnyi et al., 1998] that may clog reactors with packing material and also provide higher methane production [Kalyuzhnyi et al., 1996]; [Stronach et al., 1987]. However, this

The anaerobic contact process is a type of anaerobic digester. Anaerobic digesters are the aerobic equivalents of activated sludge process and are currently used for treating effluents from sugar processing, distilleries, citric acid and yeast production, industries producing canned vegetables, pectin, starch, meat products, etc. This process has been implemented in POME [Ibrahim et al., 1984]; ice-cream wastewater, alcohol distillery wastewater [Vlissidis et al., 1993] and fermented olive mill wastewater treatment [Hamdi et al. 1991]. Concentrat‐ ed wastewaters are suitable to be treated by anaerobic contact digestion since relatively high quality effluent can be achieved [Jr et al., 1999]. In the study of fermented olive mill waste‐ water treatment, anaerobic contact was capable of reaching steady state more quickly com‐ pared to anaerobic filter; however, more oxygen transfer in the digester (due to mixing)

CSTR run at steady state with continuous flow of reactants and products; the feed assumes a uniform composition throughout the reactor, exit stream has the same composition as in the tank. The mechanical agitator provides more area of contact with the biomass thus improv‐ ing gas production. In POME treatment, CSTR has been applied by a mill under Keck Seng (Malaysia) Berhad in Masai, Johor and it is apparently the only one which has been operat‐ ing continuously since early 1980's [Tong et al., 2006]. Other applications of CSTR on waste‐ water treatment include dilute dairy wastewater [Chen et al., 1996]; jam wastewater [Mohan

The CSTR in Kek Seng's Palm oil mill has COD removal efficiency of approximately 83% and CSTR treating dairy wastewater has COD removal efficiency of 60%. In terms of meth‐ ane composition in generated biogas, it was found to be 62.5% for POME treatment and

Presently there are three categories of anaerobic treatment systems. The first category is the conventional anaerobic digester, which includes two basic designs and another that com‐ bines the two. The standard rate digester is the most basic treatment system. It mixes the

reactor might face long start-up periods if seeded sludge is not granulated.

94 International Perspectives on Water Quality Management and Pollutant Control

*2.2.4. Anaerobic contact digester*

causes this process to be less stable.

*2.2.5. Continuous Stirred Tank Reactor (CSTR)*

22.5-76.9% for dairy wastewater treatment.

*2.2.6. Anaerobic contact digestion*

et al., 2008] and coke wastewater [Vazquez et al., 2006].

Membrane separation has been considered for anaerobic reactors but the technology is still in a development stage. Several studies on membrane anaerobic processes for the treatment of various wastewaters including POME [Fakhru'l et al., 1999] have been performed [Fakh‐ ru'l et al., 1994]; [Nagano et al., 1992]; [Pillay et al., 1994]. For example, an ultrafiltration (UF) membrane with a molecular cut-off (MWCO) of 200,000 was used by [76] for biomass/efflu‐ ent separation in conjunction with an anaerobic process for the treatment of POME. A lower operating pressure (1.5-2 bars) but a higher cross-flow velocity (2.3 m/s) was applied in this study in order to control fouling and to reduce solid deposition on the membrane surfaces. A high COD removal could be obtained in the membrane anaerobic system (MAS), but the permeate displayed a high color content with a low turbidity (less than 10 NTU), including that the color was due to dissolved solids with molecular weights lower than 200,000 g/mol. The particular organics retained in the reactor could be liquefied and decomposed because of the long solid retention time, which was independent of the HRT. The HRT was mainly influenced by the UF membrane flux rates which directly determined the volume of influent that could be fed to the reactor.

## **2.3. Clean Development Mechanism (CDM)**

The Kyoto Protocol is an international agreement linked to the United Nations Framework Convention on Climate Change. The major feature of the Kyoto Protocol is that it sets bind‐ ing targets for 37 industrialized countries and the European community for reducing green‐ house gas (GHG) emissions.These amount to an average of five per cent against 1990 levels over the five-year period 2008-2012.

The Clean Development Mechanism (CDM), defined in Article 12 of the Protocol, allows a country with an emission-reduction or emission-limitation commitment under the Kyoto Protocol (Annex B Party) to implement an emission-reduction project in developing coun‐ tries. Such projects can earn saleable certified emission reduction (CER) credits, each equiva‐ lent to one tonne of CO2, which can be counted towards meeting Kyoto targets.

The mechanism is seen by many as a trailblazer. It is the first global, environmental invest‐ ment and credit scheme of its kind, providing a standardized emission offset instrument, CERs. Besides helping to reduce carbon emission to the environment, CDM has the advant‐ age to offer developing countries such as Malaysia to attract foreign investments to sustain renewable energy projects [Menon, 2002]. Thus, palm oil mills could earn carbon credits as revenue by the utilization of methane gas as renewable energy from anaerobic digestion of palm oil mill effluent. There is a lot of attention has been give to develop anaerobic treat‐ ment for POME since the implementation of CDM.

for treatment and facilities to capture biogas. However, these methods are more economical‐ ly viable and have the capacity to tolerate a wider range of OLR. High-rate bioreactors are more effective in biodegradation as shorter retention times are needed, producing higher

The Performance Evaluation of Anaerobic Methods for Palm Oil Mill Effluent (POME) Treatment: A Review

**advantages disadvantages References**

large area of land are required, making it unsuitable for factories located in the near urban and other developed areas. no facilities to capture biogas long retention time.

lower methane emission, Clogging at high OLRs, High media and support cost Unsuitable for high suspended solid wastewater

high power requirements for bed fluidization, high cost of carrier media, not suitable for high suspended solid wastewaters

Performance dependent on sludge settleability, foaming and sludge floatation at high OLRs, long start up period if granulated, seed sludge

Granulation inhibition at high volatile fatty acid concentration Lower OLRs when treating suspended solid wastewaters

Less efficient gas production at treatment volume. Less biomass

is not used

retentio

[Chooi et al. 91984]

http://dx.doi.org/10.5772/54331

97

[Borja et al., 1994b]

[Jr et al., 1997]

[Lettinga, 1995]

[Ayati et al., 2006]

Hamdi et al., 1991]

methane yield while compromising the OLR, capital and operating cost.

Anaerobically digested POME from the ponds could be used to culture algae. Cheap, simple to construct, low maintenance costs, the energy needed to operate a ponding system is

Small reactor volume Producing high quality effluent, short hydraulic times, able to tolerate shock Loadings, retain high biomass Concentration in the

Processes, very well mixed Conditions in the reactor, large Surface area for biomass

suspended solid wastewater Producing high quality effluent No media required (less cost)

compared to operating UASB or anaerobic filtration alone, problems of clogging eliminated Higher biomass retention, more Stable operation, ability to tolerate Shock loadings, suitable for diluted Wastewater.

wastewater with biomass through mixing, increased gas production compared to conventional method

**Table 6.** Advantages and disadvantages of various treatment processes for POME

**Treatment processes**

Anaerobic filtration

Ponding system Reliable and stable

minimal.

packing

Fluidized bed Most compact of all high-rate

Attachment

UASB Useful for treatment of high

UASFF Higher OLRs achievable

CSTR Provides more contact of

#### **2.4. Comparison of various anaerobic treatment methods in POME treatment**

Table 5 shows the performance of several of anaerobic digestion or treatment methods under both mesophilic and thermophilic conditions of POME. As can be seen from Table 5, the fluid‐ ized bed reactor has the ability to treat POME at very high organic loading rates; OLR with a short retention time, biogas capture is not emphasized using this process. Therefore, it can be concluded that USFF currently gives the best performance in POME treatment, achieving high COD removal efficiency and high OLR methane production at relatively short hydraulic reten‐ tion time, HRT compared to conventional and other available anaerobic treatment methods.


N/A: data unavailable.

a In terms of BOD.

**Table 5.** Performance of various anaerobic treatment methods on POME treatment

Table 6 shows the advantages and disadvantages of each anaerobic treatment method. It can clearly seen that conventional methods are lacking in terms of treatment time, area required for treatment and facilities to capture biogas. However, these methods are more economical‐ ly viable and have the capacity to tolerate a wider range of OLR. High-rate bioreactors are more effective in biodegradation as shorter retention times are needed, producing higher methane yield while compromising the OLR, capital and operating cost.

CERs. Besides helping to reduce carbon emission to the environment, CDM has the advant‐ age to offer developing countries such as Malaysia to attract foreign investments to sustain renewable energy projects [Menon, 2002]. Thus, palm oil mills could earn carbon credits as revenue by the utilization of methane gas as renewable energy from anaerobic digestion of palm oil mill effluent. There is a lot of attention has been give to develop anaerobic treat‐

Table 5 shows the performance of several of anaerobic digestion or treatment methods under both mesophilic and thermophilic conditions of POME. As can be seen from Table 5, the fluid‐ ized bed reactor has the ability to treat POME at very high organic loading rates; OLR with a short retention time, biogas capture is not emphasized using this process. Therefore, it can be concluded that USFF currently gives the best performance in POME treatment, achieving high COD removal efficiency and high OLR methane production at relatively short hydraulic reten‐ tion time, HRT compared to conventional and other available anaerobic treatment methods.

> **Hydraulic time**

1.4 97.8 54.4 [Perez et al., 2001]

2.16 80.7 20 36 [Yacop et al., 2005]

4.5 94.0 <sup>15</sup> <sup>63</sup> [Borja et al.,

**Methane composition (%)**

**Reference**

1994b]

1995b]

2006]

[Ibrahim et al., 1984]

**2.4. Comparison of various anaerobic treatment methods in POME treatment**

**COD removal efficiency (%)**

Fluidized bed 40.0 <sup>78</sup> 0.25 N/A [Borja et al.,

UASB 10.63 98.4 4 54.2 [Borja et al., 1994c] UASFF 11.58 <sup>97</sup> <sup>3</sup> 71.9 [Najafpour et al.,

CSTR 3.33 80 18 62.5 [Tong et al., 2006]

3.44 93.3 4.7 63

Table 6 shows the advantages and disadvantages of each anaerobic treatment method. It can clearly seen that conventional methods are lacking in terms of treatment time, area required

**Table 5.** Performance of various anaerobic treatment methods on POME treatment

ment for POME since the implementation of CDM.

96 International Perspectives on Water Quality Management and Pollutant Control

**Operating OLR (Kg COD/m3/day)**

**retention (days)**

Anaerobic digester

Anaerobic filtration

Anaerobic contact processa

N/A: data unavailable. a In terms of BOD.

40

Anaerobic pond


**Table 6.** Advantages and disadvantages of various treatment processes for POME

## **2.5. Factors influencing anaerobic digester performance**

Biogas coming from biomethanization or anaerobic digestion represents an attractive strat‐ egy for both biomass waste treatment and recycling and is of great interest from an environ‐ mental point of view and may benefit society by providing a clean fuel source from renewable energy. This technology is accomplished by a series of biochemical transforma‐ tions, which can be toughly separated into a first step where hydrolysis, acidification and liq‐ uefaction take a place and second step where acetate, hydrogen and carbon dioxide are transformed into biogas with methane content between 60-80%, which cover a large part of energy. Many factors govern the performance of anaerobic digesters where adequate control is required to prevent reactor failure. These factors are operating temperature, pH, mixing, nutrients for bacteria and organic loading rates into the digester.

*2.5.3. Mixing*

under different OLR and mixing conditions.

*2.5.4. Organic loading rates*

**3. Conclusions**

**Author details**

Malaysia

N.H. Abdurahman1,2, Y.M. Rosli1,2 and N.H. Azhari1,2

1 LebuhrayaTun Razak, Gambang, Kuantan, Malaysia

Distribution of bacteria, substrate, nutrients and temperature equalization by means of ade‐ quate mixing, are known to be crucial for the overall anaerobic digester (AD) process [Chap‐ man, 1989]. Several investigations show that improvements in reactor performance can be achieved when changes in mixing intensity are imposed [Angelidaki et al., 2004]. According to [Gerardi, 2003] the main advantages of mixing in AD are: minimization of solids accumu‐ lation that may restrict reactor hydraulics, reduction of scum build up, elimination of tem‐ perature stratification and maintaining close contact between substrate particles and microbial communities. In a sequential experiment [Stroot et al., 2001] studied the feasibility of co-digestion of municipal solid waste, primary sludge and waste activated sludge (WAS) under mesophilic conditions in laboratory scale continuous stirred tank reactors (CSTRs)

The Performance Evaluation of Anaerobic Methods for Palm Oil Mill Effluent (POME) Treatment: A Review

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99

Organic loading rate is defined as the application of soluble and particulate organic matter. It's typically expressed on an area basis as pounds of BOD per unit area. Various studies have shown that higher OLRs will reduce COD removal efficiency in wastewater treatment systems [Torkian et al., 2003], [Sanchez et al., 2005]. However, gas production will increase with OLR un‐ til a stage when methanogens could not work quick enough to convert acetic acid to methane.

The performance of anaerobic treatment for POME and effects of organic loading rates were thoroughly reviewed. The palm oil industry is an indisputable source of pollution in Malaysia. In order to counteract the negative impact of this source, anaerobic digestion is an advanta‐ geous method for POME treatment as it generates valuable and product that can be exchanged into revenue when registered as a clean development mechanism CDM project. Furthermore, research can be done to develop a thermophilic anaerobic bioreactor with minimal control to ease system operation. Moreover, intensity of mixing in the thermophilic range should be inves‐ tigated to obtain an optimum mixing rate that will keep microbial consortia in close proximity and at the same time improve the system efficiency. Furthermore, operation costs can be re‐

duced through utilization of biogas for heat or electricity energy generation in the plant.

2 Faculty of Chemical and Natural Resources Engineering, University Malaysia Pahang,

#### *2.5.1. Operating temperature*

One of the most important factors affecting anaerobic digestion of organic waste is tempera‐ ture. Anaerobic digestions can be developed at different temperature ranges including mes‐ ophilic temperatures (approximately 35ºC) and thermophilic temperatures ranging from 55 ºC to 60 ºC. Conventional anaerobic digestion is carried out at mesophilic temperatures (35– 37 ºC), mainly because of the lower energy requirements and better stability of the process. POME is discharged at temperatures around 80-90 oC [Zinatizadeh et al., 2006] which ac‐ tually makes treatment at both mesophilic and thermophilic temperature feasible especially in tropical countries like Malaysia. Effect of temperature on the performance of anaerobic di‐ gestion was investigated by [H-Q et al., 2002a] and found that substrate degradation rate and biogas production rate at 55 oC was higher than operation at 37 oC. Studies have re‐ ported that thermophilic digesters are able to tolerate higher OLRs and operate at shorter HRT while producing more biogas [Ahn et al., 2002], [Kim et al., 2006], and [Yilmaz et al., 2008]. However, failure to control temperature increase can result in biomass washout [Lau et al., 1997] with accumulation of volatile fatty acid due to inhibition of methanogenesis. At high temperatures, production of volatile fatty acid is higher compared to mesophilic tem‐ perature range [H-Q et al., 2002a].

#### *2.5.2. pH*

A pH (potential of Hydrogen) measurement reveals if a solution is acidic or alkaline (also base or basic). If the solution has an equal amount of acidic and alkaline molecules, the pH is consid‐ ered neutral. The microbial communities in anaerobic digesters are sensitive to pH changes and methanogens are affected to a great extend [Jr et al., 1999]. Several cases of reactor failure reported in studies of wastewater treatment are due to accumulation of high volatile fatty acid concentration, causing a drop in pH which inhibited methanogenesis Parawira et al. (2006), [Patel et al., 2002]. Thus, volatile fatty acid concentration is an important parameter to monitor to guarantee reactor performance [Buyukkamaci et al., 2004]. It was found that digester could tolerate acetic acid concentrations up to 4000 mg/l without inhibition of gas production Staf‐ ford (1982). To control the level of volatile fatty acid in the system, alkalinity has to be main‐ tained by recirculation of treated effluent [Najafpour et al., 2006], [Borja et al., 1996a] to the digester or addition of lime and bicarbonate salt [Gerardi, 2003].

## *2.5.3. Mixing*

**2.5. Factors influencing anaerobic digester performance**

98 International Perspectives on Water Quality Management and Pollutant Control

nutrients for bacteria and organic loading rates into the digester.

digester or addition of lime and bicarbonate salt [Gerardi, 2003].

*2.5.1. Operating temperature*

perature range [H-Q et al., 2002a].

*2.5.2. pH*

Biogas coming from biomethanization or anaerobic digestion represents an attractive strat‐ egy for both biomass waste treatment and recycling and is of great interest from an environ‐ mental point of view and may benefit society by providing a clean fuel source from renewable energy. This technology is accomplished by a series of biochemical transforma‐ tions, which can be toughly separated into a first step where hydrolysis, acidification and liq‐ uefaction take a place and second step where acetate, hydrogen and carbon dioxide are transformed into biogas with methane content between 60-80%, which cover a large part of energy. Many factors govern the performance of anaerobic digesters where adequate control is required to prevent reactor failure. These factors are operating temperature, pH, mixing,

One of the most important factors affecting anaerobic digestion of organic waste is tempera‐ ture. Anaerobic digestions can be developed at different temperature ranges including mes‐ ophilic temperatures (approximately 35ºC) and thermophilic temperatures ranging from 55 ºC to 60 ºC. Conventional anaerobic digestion is carried out at mesophilic temperatures (35– 37 ºC), mainly because of the lower energy requirements and better stability of the process. POME is discharged at temperatures around 80-90 oC [Zinatizadeh et al., 2006] which ac‐ tually makes treatment at both mesophilic and thermophilic temperature feasible especially in tropical countries like Malaysia. Effect of temperature on the performance of anaerobic di‐ gestion was investigated by [H-Q et al., 2002a] and found that substrate degradation rate and biogas production rate at 55 oC was higher than operation at 37 oC. Studies have re‐ ported that thermophilic digesters are able to tolerate higher OLRs and operate at shorter HRT while producing more biogas [Ahn et al., 2002], [Kim et al., 2006], and [Yilmaz et al., 2008]. However, failure to control temperature increase can result in biomass washout [Lau et al., 1997] with accumulation of volatile fatty acid due to inhibition of methanogenesis. At high temperatures, production of volatile fatty acid is higher compared to mesophilic tem‐

A pH (potential of Hydrogen) measurement reveals if a solution is acidic or alkaline (also base or basic). If the solution has an equal amount of acidic and alkaline molecules, the pH is consid‐ ered neutral. The microbial communities in anaerobic digesters are sensitive to pH changes and methanogens are affected to a great extend [Jr et al., 1999]. Several cases of reactor failure reported in studies of wastewater treatment are due to accumulation of high volatile fatty acid concentration, causing a drop in pH which inhibited methanogenesis Parawira et al. (2006), [Patel et al., 2002]. Thus, volatile fatty acid concentration is an important parameter to monitor to guarantee reactor performance [Buyukkamaci et al., 2004]. It was found that digester could tolerate acetic acid concentrations up to 4000 mg/l without inhibition of gas production Staf‐ ford (1982). To control the level of volatile fatty acid in the system, alkalinity has to be main‐ tained by recirculation of treated effluent [Najafpour et al., 2006], [Borja et al., 1996a] to the Distribution of bacteria, substrate, nutrients and temperature equalization by means of ade‐ quate mixing, are known to be crucial for the overall anaerobic digester (AD) process [Chap‐ man, 1989]. Several investigations show that improvements in reactor performance can be achieved when changes in mixing intensity are imposed [Angelidaki et al., 2004]. According to [Gerardi, 2003] the main advantages of mixing in AD are: minimization of solids accumu‐ lation that may restrict reactor hydraulics, reduction of scum build up, elimination of tem‐ perature stratification and maintaining close contact between substrate particles and microbial communities. In a sequential experiment [Stroot et al., 2001] studied the feasibility of co-digestion of municipal solid waste, primary sludge and waste activated sludge (WAS) under mesophilic conditions in laboratory scale continuous stirred tank reactors (CSTRs) under different OLR and mixing conditions.

## *2.5.4. Organic loading rates*

Organic loading rate is defined as the application of soluble and particulate organic matter. It's typically expressed on an area basis as pounds of BOD per unit area. Various studies have shown that higher OLRs will reduce COD removal efficiency in wastewater treatment systems [Torkian et al., 2003], [Sanchez et al., 2005]. However, gas production will increase with OLR un‐ til a stage when methanogens could not work quick enough to convert acetic acid to methane.

## **3. Conclusions**

The performance of anaerobic treatment for POME and effects of organic loading rates were thoroughly reviewed. The palm oil industry is an indisputable source of pollution in Malaysia. In order to counteract the negative impact of this source, anaerobic digestion is an advanta‐ geous method for POME treatment as it generates valuable and product that can be exchanged into revenue when registered as a clean development mechanism CDM project. Furthermore, research can be done to develop a thermophilic anaerobic bioreactor with minimal control to ease system operation. Moreover, intensity of mixing in the thermophilic range should be inves‐ tigated to obtain an optimum mixing rate that will keep microbial consortia in close proximity and at the same time improve the system efficiency. Furthermore, operation costs can be re‐ duced through utilization of biogas for heat or electricity energy generation in the plant.

## **Author details**

N.H. Abdurahman1,2, Y.M. Rosli1,2 and N.H. Azhari1,2

1 LebuhrayaTun Razak, Gambang, Kuantan, Malaysia

2 Faculty of Chemical and Natural Resources Engineering, University Malaysia Pahang, Malaysia

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**Chapter 5**

**Ultrasonic Membrane Anaerobic System (UMAS) for**

Palm oil mill effluent (POME) is an important source of inland water pollution when released into local rivers or lakes without treatment. The production of palm oil, however, results in the generation of large quantities of polluted wastewater commonly referred as palm oil mill efflu‐ ent (POME). In the process of palm oil milling, POME is generated through sterilization of fresh oil palm fruit bunches, clarification of palm oil and effluent from hydro-cyclone operations [1]. POME is a viscous brown liquid with fine suspended solids at pH ranging between 4 and 5 [2]. In general appearance, palm oil mill effluent (POME) is a yellowish acidic wastewater with fair‐ ly high polluting properties, with average of 25,000 mg/l biochemical oxygen demand (BOD), 55,250 mg/l chemical oxygen demand (COD) and 19,610 mg/l suspended solid (SS). This highly polluting wastewater can cause several pollution problems. Anaerobic digestion is the most suitable method for the treatment of effluents containing high concentration of organic carbon such as POME [1]. Anaerobic digestion is defined as the engineered methanogenic anaerobic decomposition of organic matter. It involves different species of anaerobic microorganisms that degrade organic matter [3]. In the anaerobic process, the decomposition of organic and inorgan‐ ic substrate is carried out in the absence of molecular oxygen. The biological conversion of the organic substrate occurs in the mixtures of primary settled and biological sludge under anaero‐ bic condition followed by hydrolysis, acidogenesis and methanogenesis to convert the inter‐ mediate compounds into simpler end products as methane (CH4) and carbon dioxide (CO2) [4], [5], and [6]. Therefore, the anaerobic digestion process offers great potential for rapid disinte‐ gration of organic matter to produce biogas that can be used to generate electricity and save fos‐ sil energy [7]. The suggested anaerobic treatment processes for POME include anaerobic suspended growth processes, attached growth anaerobic processes (immobilized cell bioreac‐ tors, anaerobic fluidized bed reactors and anaerobic filters), anaerobic blanket processes (up-

> © 2013 Abdurahman et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Abdurahman et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons

licensee InTech. This is a paper distributed under the terms of the Creative Commons

**Palm Oil Mill Effluent (POME) Treatment**

N.H. Abdurahman, N.H. Azhari and Y.M. Rosli

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/54459

**1. Introduction**

