**Chemical Risk**

#### **Risk Assessment of Heavy Metals Pollution in Urban Environment** Risk Assessment of Heavy Metals Pollution in Urban Environment

DOI: 10.5772/intechopen.70798

Gevorg Tepanosyan, Lilit Sahakyan, David Pipoyan and Armen Saghatelyan Gevorg Tepanosyan, Lilit Sahakyan, David Pipoyan and Armen Saghatelyan

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.70798

#### Abstract

This chapter summarizes the results of heavy metal's human health and ecological risk assessment of multipurpose ecogeochemical studies performed by the Center for Ecological-Noosphere Studies of the National Academy of Sciences of the Republic of Armenia in the young industrial cities of Yerevan and Gyumri and in an old mining region of the city of Kajaran. According to the results children non-carcinogenic risk values were greater than permissible limit of 1 indicating the possibility of an adverse health effect in the whole area of all studied cities. Among all studied elements, the riskiest were those previously identified as primary pollutants. It has also been shown that in biogeochemical provinces, where mining activities and agricultural land of rural communities are spatially juxtaposed, health risk assessment should include all possible exposure pathways. Otherwise, underestimation of possible health risk will take place. Heavy metals in soils of Yerevan and Gyumri are also an ecological risk factor and the riskiest elements having significant contribution to the overall risk and are those (Hg, Cd, and Pb) with the high level of toxicity.

Keywords: urban environment, heavy metals, pollution, soil, dust, risk assessment

### 1. Introduction

Soils and dust of urbanized and industrialized areas are a basis of environmental quality. Nevertheless, various pollutants of the environment, especially heavy metals, migrate linked to the complexes of dust particles [1] and finally accumulate in the soil layer. Moreover, heavy metals are known to be an ecological risk factor [2–4] and cause different disorders when entering into the human organism [1, 5].

In Armenia, risks estimation associated with the pollution of cities environment by heavy metals was included in the framework of environmental complex ecogeochemical studies,

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons © 2018 The Author(s). Licensee InTech. This chapter is 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.

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which were done since 1989 by The Center for Ecological-Noosphere Studies (CENS) of the National Academy of Sciences [6].

The results of the studies [7] performed by CENS showed that in the cities of Armenia, manmade activities lead to the formation of anthropogenically polluted areas, which were mainly localized in old mining regions (i.e., city of Kajaran) and relatively young industrial cities (i.e., Yerevan and Gyumri). In both cases, the differences of geochemical peculiarities and anthropogenic sources of pollution are conditioning the uniqueness of heavy metal's quantitative and qualitative features. In the city of Kajaran [8], which is the biggest mining center of country and houses the Zangezur Copper Molybdenum Combine (ZCMC), high contents of heavy metals are the result of the superposition of geogenic and anthropogenic components, whereas in the biggest industrial center of Yerevan and postindustrial city of Gyumri [9, 10], a significant input of heavy metals is mainly from anthropogenic sources of pollution. Although primary pollutants and the levels of anthropogenic contribution differ from city to city, the increased contents of heavy metals become a risk factor to urban ecosystems and human health.

The linking of monoelemental and multielemental pollution by heavy metals to the overall index of population prevalence, the rate of children's chronic illnesses, gestosis, and to the number of premature birth [11–13] were done in the end of 1990 through the collation of monoelemental and multielemental pollution levels spatial distribution maps [14] with the disease incidences. Later on, studies [8, 15, 16] targeted the sampling of biosubstrate and evaluation of the microelemental status of the organism among identified risk groups.

Nowadays, the most common and widely used human health risk assessment method is developed by the US Environmental Protection Agency [5, 17, 18]. The method is based on four basic steps, including hazard identification, exposure assessment, dose-response assessment, and risk characterization [18]. In the case of ecological risk from heavy metals, method developed by the Hakanson [4] was used repeatedly [2, 3, 9].

In this chapter, the results of human health and ecological risk assessment of heavy metals contents in Yerevan, Gyumri, and Kajaran environment are summarized.

### 2. Materials and methods

### 2.1. Study sites

Cities presented in this study are spatially located in different parts of Armenia (Figure 1). Particularly, the capital and industrial center of the country in the city of Yerevan (40�10<sup>0</sup> 39.5300N and 44�30<sup>0</sup> 45.1000E) is situated in the central part, whereas the cities of Gyumri (40�47<sup>0</sup> 6.8400N and 43�50<sup>0</sup> 29.9700E) and Kajaran (39�9<sup>0</sup> 5.2000N and 46�9<sup>0</sup> 12.0200E) in north-western and southern parts, respectively.

#### 2.1.1. The city of Yerevan

Yerevan has a total area of 223 km<sup>2</sup> and 1.06 million population (4782 persons per square km) [19]. The city is located in the intermountain trough, and the natural landscape of city territory is mainly semidesert, arid steppe, and steppe. Yerevan's area is dominated by tuffs, volcanic

Figure 1. Spatial location of cities of Yerevan, Gyumri, and Kajaran and spatial distribution of soil and dust sampling points in each city.

lavas, and quaternary sediments, and the relief of the city is represented by plains, foothills, plateaus, and the River Hrazdan Canyon. The soil (mostly brown semidesert) profile of Yerevan is rich in carbonates, and at the lower horizon, the presence of gypsum is conditioning the lack of chemical element washout, thus creating a favorable environment for heavy metal accumulation on soil profiles [7].

Pollution with heavy metals in the city environment has been observed for many decades. Particularly, heavy metals were detected during the soil surveys conducted in 1979, 1989 [7], 2002 [7, 20], and 2012 [9, 21, 22], with ecogeochemical investigations of city snow cover and leaf dust [23, 24], Hrazdan river waters [25, 26], and homegrown vegetables [27, 28].

During the Soviet Union, the main sources [7, 24, 29] of heavy metal pollution in Yerevan were enterprises such as an electric bulb plant, the aluminum plant, the Car and Worsted complex, the experimental plant of milling machines, the polygraphic complex, and typography, as well as vehicular emission.

After the collapse of the Soviet Union, the socioeconomic transformations in 1990 lead to the changes in heavy metal geochemical streams' quantitative and qualitative features as many of the abovementioned industrial plants were closed. Moreover, in 2001, leaded gasoline ceased to be used in Armenia.

Nowadays, the potential sources [9] of heavy metals in Yerevan territory are urban transport and industrial units including molybdenum concentrate smelting and processing plant, Ferroconcrete constructions plant, accumulator's production, mechanical reconstruction plants, and industrial complex of metallic covers and corks, etc.

#### 2.1.2. The city of Gyumri

which were done since 1989 by The Center for Ecological-Noosphere Studies (CENS) of the

The results of the studies [7] performed by CENS showed that in the cities of Armenia, manmade activities lead to the formation of anthropogenically polluted areas, which were mainly localized in old mining regions (i.e., city of Kajaran) and relatively young industrial cities (i.e., Yerevan and Gyumri). In both cases, the differences of geochemical peculiarities and anthropogenic sources of pollution are conditioning the uniqueness of heavy metal's quantitative and qualitative features. In the city of Kajaran [8], which is the biggest mining center of country and houses the Zangezur Copper Molybdenum Combine (ZCMC), high contents of heavy metals are the result of the superposition of geogenic and anthropogenic components, whereas in the biggest industrial center of Yerevan and postindustrial city of Gyumri [9, 10], a significant input of heavy metals is mainly from anthropogenic sources of pollution. Although primary pollutants and the levels of anthropogenic contribution differ from city to city, the increased

contents of heavy metals become a risk factor to urban ecosystems and human health.

evaluation of the microelemental status of the organism among identified risk groups.

developed by the Hakanson [4] was used repeatedly [2, 3, 9].

2. Materials and methods

29.9700E) and Kajaran (39�9<sup>0</sup>

2.1. Study sites

and 44�30<sup>0</sup>

respectively.

2.1.1. The city of Yerevan

43�50<sup>0</sup>

contents in Yerevan, Gyumri, and Kajaran environment are summarized.

The linking of monoelemental and multielemental pollution by heavy metals to the overall index of population prevalence, the rate of children's chronic illnesses, gestosis, and to the number of premature birth [11–13] were done in the end of 1990 through the collation of monoelemental and multielemental pollution levels spatial distribution maps [14] with the disease incidences. Later on, studies [8, 15, 16] targeted the sampling of biosubstrate and

Nowadays, the most common and widely used human health risk assessment method is developed by the US Environmental Protection Agency [5, 17, 18]. The method is based on four basic steps, including hazard identification, exposure assessment, dose-response assessment, and risk characterization [18]. In the case of ecological risk from heavy metals, method

In this chapter, the results of human health and ecological risk assessment of heavy metals

Cities presented in this study are spatially located in different parts of Armenia (Figure 1).

45.1000E) is situated in the central part, whereas the cities of Gyumri (40�47<sup>0</sup>

Yerevan has a total area of 223 km<sup>2</sup> and 1.06 million population (4782 persons per square km) [19]. The city is located in the intermountain trough, and the natural landscape of city territory is mainly semidesert, arid steppe, and steppe. Yerevan's area is dominated by tuffs, volcanic

39.5300N

6.8400N and

12.0200E) in north-western and southern parts,

Particularly, the capital and industrial center of the country in the city of Yerevan (40�10<sup>0</sup>

5.2000N and 46�9<sup>0</sup>

National Academy of Sciences [6].

270 Risk Assessment

Gyumri has a total area of 44.4 km2 and 117.7 thousand population (2651 persons per square km) [16]. In the city, arid steppe and mountain steppe landscapes dominated and the city territory was characterized by accumulative relief of plains, lake, and alluvial-diluvial sedimentation, sometimes mixed with lavas and tuffs. Brown and mountain steppe chernozem soils dominated in Gyumri area.

During the Soviet Union period, the potential sources of heavy metals in Gyumri were forgeand-press, universal grinding machines, instrument engineering, electrotechnical, household electrical appliances, refrigerator compressors and ferro-concrete constructions plants, microelectromotor "Strommashina" plant, and foundry of machine-tool construction plant [30], which were operated till the devastating earthquake of 1988 and did not resume after the collapse of the Soviet Union. Unfortunately, there is a lack of information about the heavy metal emission from the abovementioned plants in city territory.

Nowadays, the Gyumri and its industrial sector are in reconstruction stage and there are no significant potential sources of heavy metals. In the polluted areas identified during 2013, Gyumri ecogeochemical complex investigations [10] were mainly linked to the historical pollution.

#### 2.1.3. The city of Kajaran

The city of Kajaran has a total area of 2.74 km<sup>2</sup> , 8.4 thousand population (3066 persons per square km) [16], and is located in the valley of river Voghchi, where two types of the erosion landforms are distinguished: U-shaped river valleys in the middle and lower course of the river and V-shaped river valleys in the riverheads. Up to 1800 m, brown soils and 1800–2400 m chestnut soils predominated. The northern slope of Kajaran territory is covered with the gray mountain-forest skeletal soils [31]. The geological base of Kajaran includes volcanogenic sedimentary and intrusive rocks of the tertiary period, particularly monzonites and porphyry granites. The Kajaran sulfide copper-molybdenum deposit is timed to the monzonites, and the main ore minerals are molybdenite and chalcopyrite and the accessory minerals are pyrite, magnetite, hematite, sphalerite, tetrahedrite, bismuthine, wulfenite, vanadinite, galena, as well as native Te and Au. Besides, ore contains Re, Se, and Ag [8].

The main pollution source of Kajaran is ZCMC, including Cu-Mo opencast mine. ZCMC complex also includes ore crushing and milling, as well as ore dressing plants and active Artsvanic tailing repository. In addition, abandoned tailing repositories of Voghchi, Darazami, and Pkhrut are also significant sources of dust and heavy metals in it [8].

#### 2.2. Soils, dust, and food sampling and analysis

Soil, dust, and food sampling and pretreatment were done according to the SOPs developed in compliance with methodological guidelines [32–34], international ISO [35–38] standards, and US EPA [39] guidelines. Totally, 1356, 443, and 76 soils and 25, 22, and 15 dust have been collected in Yerevan, Gyumri, and Kajaran, respectively.

Food sampling was done and 68 samples were collected from the agricultural lands of Kajaran and rural communities located near ZCMC Artsvanic tailing repository. Soils, dust, and food samples have been placed in special clean bags for transportation and storaging purposes. Prior to the analysis, samples laboratory pretreatment was done.

The total contents of heavy metals (Table 1) were determined using X-ray fluorescence spectrometry (Innov-X 5000, USA) [40] and atomic absorption spectrometry (AAnalyst 800 AAS PE, USA).

The analysis was done in the environmental geochemistry department and at the Central Analytical Laboratory of CENS, accredited by ISO-IEC 17025.

Detailed information concerning Yerevan's, Gyumri's, and Kajaran's soils, dust, and food sampling, samples' pretreatment, and analysis can be found in a number of manuscripts [7–10, 20–23].

#### 2.3. Health risk assessment

was characterized by accumulative relief of plains, lake, and alluvial-diluvial sedimentation, sometimes mixed with lavas and tuffs. Brown and mountain steppe chernozem soils dominated

During the Soviet Union period, the potential sources of heavy metals in Gyumri were forgeand-press, universal grinding machines, instrument engineering, electrotechnical, household electrical appliances, refrigerator compressors and ferro-concrete constructions plants, microelectromotor "Strommashina" plant, and foundry of machine-tool construction plant [30], which were operated till the devastating earthquake of 1988 and did not resume after the collapse of the Soviet Union. Unfortunately, there is a lack of information about the heavy

Nowadays, the Gyumri and its industrial sector are in reconstruction stage and there are no significant potential sources of heavy metals. In the polluted areas identified during 2013, Gyumri ecogeochemical complex investigations [10] were mainly linked to the historical

square km) [16], and is located in the valley of river Voghchi, where two types of the erosion landforms are distinguished: U-shaped river valleys in the middle and lower course of the river and V-shaped river valleys in the riverheads. Up to 1800 m, brown soils and 1800–2400 m chestnut soils predominated. The northern slope of Kajaran territory is covered with the gray mountain-forest skeletal soils [31]. The geological base of Kajaran includes volcanogenic sedimentary and intrusive rocks of the tertiary period, particularly monzonites and porphyry granites. The Kajaran sulfide copper-molybdenum deposit is timed to the monzonites, and the main ore minerals are molybdenite and chalcopyrite and the accessory minerals are pyrite, magnetite, hematite, sphalerite, tetrahedrite, bismuthine, wulfenite, vanadinite, galena, as well

The main pollution source of Kajaran is ZCMC, including Cu-Mo opencast mine. ZCMC complex also includes ore crushing and milling, as well as ore dressing plants and active Artsvanic tailing repository. In addition, abandoned tailing repositories of Voghchi, Darazami,

Soil, dust, and food sampling and pretreatment were done according to the SOPs developed in compliance with methodological guidelines [32–34], international ISO [35–38] standards, and US EPA [39] guidelines. Totally, 1356, 443, and 76 soils and 25, 22, and 15 dust have been

Food sampling was done and 68 samples were collected from the agricultural lands of Kajaran and rural communities located near ZCMC Artsvanic tailing repository. Soils, dust, and food samples have been placed in special clean bags for transportation and storaging purposes.

, 8.4 thousand population (3066 persons per

metal emission from the abovementioned plants in city territory.

The city of Kajaran has a total area of 2.74 km<sup>2</sup>

as native Te and Au. Besides, ore contains Re, Se, and Ag [8].

2.2. Soils, dust, and food sampling and analysis

collected in Yerevan, Gyumri, and Kajaran, respectively.

Prior to the analysis, samples laboratory pretreatment was done.

and Pkhrut are also significant sources of dust and heavy metals in it [8].

in Gyumri area.

272 Risk Assessment

pollution.

2.1.3. The city of Kajaran

Human health risk assessment [5] was done based on the contents of HM in soils and dust of city Yerevan, Gyumri, and Kajaran. In the case of Kajaran, health risks arising from the HM content in the food products grown near the city, ZCMC query, and its tailing storages were also studied. Health risk assessment model proposed by US EPA was used. As a preferential exposure pathway of HM for humans, soil and dust ingestion was chosen.

Noncarcinogenic health effects from the soils, dust, and food heavy metals contents was assessed using the following Eqs. [5, 17, 18].

$$\text{CDI}\_{\text{ing}} \left( \frac{\text{mg}}{\text{kg day}} \right) = \frac{\text{C} \ast \text{IngR} \ast \text{EF} \ast \text{ED} \ast 10^{-6}}{\text{AT} \ast \text{BW}},\tag{1}$$

$$\text{HQ}\_{\text{ing}} = \frac{\text{CDI}\_{\text{ing}}}{\text{RfD}\_{\text{ing}}},\tag{2}$$

$$\text{HI} = \sum \text{HQ}.\tag{3}$$

where CDI is the chronic daily intake of metal, C is the element concentration in studied medium (mg/kg), EF is the exposure frequency: 350 day/year for soil and dust, ED is the


Table 1. Heavy metals determined in soils, dust, and food.

exposure duration: 30 years for adult [17] and 6 years for children [5], IngR is the ingestion rate: 100 mg/day�<sup>1</sup> for adults and 200 mg/day�<sup>1</sup> for children average time (AT) (AT = 365 � ED) [5], and average body weight (BW, kg): 70 kg for adults [17] and 15 kg in the case of children [5].

Taking into consideration the fact that unlike Yerevan and Gyumri where there is no local food production and consumption, in Kajaran, mining region's contribution of local plant-origin food in overall diet is significantly higher. Therefore, dietary intakes of heavy metals via consumption of selected vegetables and fruits may also be a risk factor to health.

Noncarcinogenic risk of heavy metals in food was assessed by the abovementioned formulae (1)–(3) using the following parameters: EF: 183 days/year for all investigated fruits and vegetables, except potato (365 days/year). ED was set to 63.6 for males and 69.7 for females based on the average life expectancy, starting from 8 years of age. IRS: food consumption rate was evaluated based on the result of standardized food frequency questionnaires filled by 200 males and females residing in Kajaran mining impact area. According to our polling survey in studied region, BW for males and females were considered to be 70 and 60 kg, respectively.

The reference doses (RfDs) of studied heavy metals were taken from RAIS and US EPA Human health risk assessment guidance [5, 17]. Only the RfD of Pb was taken from the WHO guideline [41]. Hazard index (HI-multielement) is the sum of all HQ (monoelement). When HI and/or HQ is less than one, there is no harmful effect to the health, whereas when HI and/or HQ values are greater than one, there is a possibility of adverse health effects.

To get overall adults health risk (HIsum) from soils, dust ingestion, and food consumption in Kajaran, the obtained mean values of HI were summed.

#### 2.4. Potential ecological risk assessment

Potential ecological risk assessment (PERI) was performed using the method proposed by Hakanson [4]. From the studied elements, only Hg, Cd, As, Pb, Cu, Ni, Cr, and Zn have "toxic-response" factors 40, 30, 10, 5, 5, 5, 2, and 1, respectively. Taking into consideration the fact that soils are the sink of city pollutants, ecological risk assessment was done based on the contents of heavy metals in soils. As the city of Kajaran is spatially located within the biogeochemical province, high contents of heavy metals are intrinsic to the city environment. Here, ecosystems have their own distinctive features and there is a deviation from common environmental patterns. Therefore, the city of Kajaran was excluded from the ecological risk assessment. RI was calculated using (4)–(6) formulas:

$$\mathbf{C}\_r^i = \mathbf{C}\_{topsoil}^i / \mathbf{C}\_{n\prime}^i \tag{4}$$

$$E\_r^i = T\_r^i \* \mathbb{C}\_{r'}^i \tag{5}$$

$$\text{PERI} = \sum\_{i=1}^{n} E\_{i\prime}^{\prime} \tag{6}$$

where PERI is potential ecological risk index, Ei <sup>r</sup> is PERI of single element, T<sup>i</sup> <sup>r</sup> is "toxicresponse" factor for the selected element (i.e., Hg = 40, As = 10, Pb = Cu = Ni = 5, Cr = 2, and Zn = 1), Ci <sup>r</sup> is the pollution factor of the element, Ci soil is the concentration of element in the topsoil, and C<sup>i</sup> <sup>n</sup> is the reference value of the selected element (local background [9, 10]). The PERI levels are classified as low (<150), moderate (150–300), considerable (300–600), and very high (>600) [4].

### 3. Results

exposure duration: 30 years for adult [17] and 6 years for children [5], IngR is the ingestion rate: 100 mg/day�<sup>1</sup> for adults and 200 mg/day�<sup>1</sup> for children average time (AT) (AT = 365 � ED) [5], and average body weight (BW, kg): 70 kg for adults [17] and 15 kg in the case of children [5]. Taking into consideration the fact that unlike Yerevan and Gyumri where there is no local food production and consumption, in Kajaran, mining region's contribution of local plant-origin food in overall diet is significantly higher. Therefore, dietary intakes of heavy metals via

Noncarcinogenic risk of heavy metals in food was assessed by the abovementioned formulae (1)–(3) using the following parameters: EF: 183 days/year for all investigated fruits and vegetables, except potato (365 days/year). ED was set to 63.6 for males and 69.7 for females based on the average life expectancy, starting from 8 years of age. IRS: food consumption rate was evaluated based on the result of standardized food frequency questionnaires filled by 200 males and females residing in Kajaran mining impact area. According to our polling survey in studied region, BW for males and females were considered to be 70 and 60 kg, respectively. The reference doses (RfDs) of studied heavy metals were taken from RAIS and US EPA Human health risk assessment guidance [5, 17]. Only the RfD of Pb was taken from the WHO guideline [41]. Hazard index (HI-multielement) is the sum of all HQ (monoelement). When HI and/or HQ is less than one, there is no harmful effect to the health, whereas when HI and/or

To get overall adults health risk (HIsum) from soils, dust ingestion, and food consumption in

Potential ecological risk assessment (PERI) was performed using the method proposed by Hakanson [4]. From the studied elements, only Hg, Cd, As, Pb, Cu, Ni, Cr, and Zn have "toxic-response" factors 40, 30, 10, 5, 5, 5, 2, and 1, respectively. Taking into consideration the fact that soils are the sink of city pollutants, ecological risk assessment was done based on the contents of heavy metals in soils. As the city of Kajaran is spatially located within the biogeochemical province, high contents of heavy metals are intrinsic to the city environment. Here, ecosystems have their own distinctive features and there is a deviation from common environmental patterns. Therefore, the city of Kajaran was excluded from the ecological risk assess-

topsoil=Ci

<sup>i</sup>¼<sup>1</sup> Er

<sup>n</sup>, (4)

<sup>r</sup>, (5)

<sup>r</sup> is PERI of single element, T<sup>i</sup>

<sup>i</sup> , (6)

<sup>r</sup> is "toxic-

consumption of selected vegetables and fruits may also be a risk factor to health.

HQ values are greater than one, there is a possibility of adverse health effects.

Ci <sup>r</sup> <sup>¼</sup> Ci

> Ei <sup>r</sup> <sup>¼</sup> <sup>T</sup><sup>i</sup> <sup>r</sup> ∗ C<sup>i</sup>

PERI <sup>¼</sup> <sup>X</sup><sup>n</sup>

response" factor for the selected element (i.e., Hg = 40, As = 10, Pb = Cu = Ni = 5, Cr = 2, and

Kajaran, the obtained mean values of HI were summed.

2.4. Potential ecological risk assessment

274 Risk Assessment

ment. RI was calculated using (4)–(6) formulas:

where PERI is potential ecological risk index, Ei

Health noncarcinogenic risk assessment of adults and children was performed based on the contents of studied heavy metals (Table 1) in soils and dust of the city of Yerevan and Gyumri and in soils, dust, and food in the city of Kajaran.

#### 3.1. Noncarcinogenic risk in Yerevan

The results obtained showed that in the case of Yerevan soils, monoelemental risk to adults was detected only for the contents of Pb in two sampling sites.

Multielemental noncarcinogenic risk range from 0.12 to 2.37 with the mean of 0.25, and risk was observed in four sampling sites (Figure 2). Monoelemental noncarcinogenic risk from dust heavy metals was observed in a single sampling site and is associated with the high contents of Mo. Мultielemental risk ranges from 0.02 to 1.87 with the mean of 0.2, and risk was observed in one sampling site (Figure 2) situated in the southern part of the city. For

Figure 2. Spatial distribution of soils and dust noncarcinogenic risk to children and adults health in Yerevan.

Figure 3. Spatial distribution of soils Pb and Cr noncarcinogenic risk to children in Yerevan.

both soils and dust, the observed risky sites are spatially allocated in or near the industrial units of Yerevan (Figure 1).

Children monoelement noncarcinogenic risk from soils detected for Ni, Cu, Zn, Mo, Co, and Mn in a single sampling point while for Cr and Pb risk was observed in 28 and 72 sampling sites (Figure 3), respectively. The study revealed [21] that riskiest contents of Pb in Yerevan are the result of the redistribution of historically polluted soils. HI values of soil's heavy metal contents range from 1.1 to 22.1 with the mean of 2.31, indicating an adverse health effect to children (Figure 2) in whole territory of the city. In case of dust, HQ values greater than 1 were observed from Mo, Cd, Co, and As in 1, 1, 2, and 1 sampling sites, correspondingly. Dust HI values (Figure 2) range from 0.25 to 17.45 with the mean of 1.82, and risk was detected in 12 sampling sites located in Yerevan's residential areas and near the industrial units (Figures 1 and 2).

#### 3.2. Noncarcinogenic risk in Gyumri

Noncarcinogenic risk assessment showed that in Gyumri's territory, soils and dust heavy metal's HQ and HI values were less than 1, suggesting the absence of adverse health effects to adults. Risk from the dust heavy metal contents was also not detected in case of children. Soil's heavy metal HQ values greater than 1 were detected for Cu and Pb contents in 1 and 17 sampling sites, respectively. Moreover, Pb risky sites are spatially located in residential parts of the city and near its industrial units (Figures 1 and 4). Soil's heavy metal multielemental risk in Gyumri range from 0.85 to 7.42 with the mean of 1.56, and risk was observed in 439 of 443 sampling locations (Figure 4).

Figure 4. Spatial distribution of soils, dust, and soil Pb contents noncarcinogenic risk to children in Gyumri.

#### 3.3. Noncarcinogenic risk in Kajaran

both soils and dust, the observed risky sites are spatially allocated in or near the industrial

Figure 3. Spatial distribution of soils Pb and Cr noncarcinogenic risk to children in Yerevan.

Children monoelement noncarcinogenic risk from soils detected for Ni, Cu, Zn, Mo, Co, and Mn in a single sampling point while for Cr and Pb risk was observed in 28 and 72 sampling sites (Figure 3), respectively. The study revealed [21] that riskiest contents of Pb in Yerevan are the result of the redistribution of historically polluted soils. HI values of soil's heavy metal contents range from 1.1 to 22.1 with the mean of 2.31, indicating an adverse health effect to children (Figure 2) in whole territory of the city. In case of dust, HQ values greater than 1 were observed from Mo, Cd, Co, and As in 1, 1, 2, and 1 sampling sites, correspondingly. Dust HI values (Figure 2) range from 0.25 to 17.45 with the mean of 1.82, and risk was detected in 12 sampling sites located in Yerevan's residential areas and near the industrial

Noncarcinogenic risk assessment showed that in Gyumri's territory, soils and dust heavy metal's HQ and HI values were less than 1, suggesting the absence of adverse health effects to adults. Risk from the dust heavy metal contents was also not detected in case of children. Soil's heavy metal HQ values greater than 1 were detected for Cu and Pb contents in 1 and 17 sampling sites, respectively. Moreover, Pb risky sites are spatially located in residential parts of the city and near its industrial units (Figures 1 and 4). Soil's heavy metal multielemental risk in Gyumri range from 0.85 to 7.42 with the mean of 1.56, and risk was observed in 439 of 443

units of Yerevan (Figure 1).

276 Risk Assessment

units (Figures 1 and 2).

3.2. Noncarcinogenic risk in Gyumri

sampling locations (Figure 4).

Noncarcinogenic risk assessment based on the detected contents of heavy metals in soils and dust of Kajaran territory showed that the HQ values of adults greater than one were detected only in four soil sampling sites for the contents of Mo. HI values of soil heavy metals range from 0.23 to 5.46 with the mean of 0.64 and risk was observed in seven sampling sites (Figure 5), whereas HI values of dust were all less than 1.

In the case of children, noncarcinogenic risk observed Mn, Fe, Co, Pb, Cu, and Mo in 6, 49, 18, 1, 2 and 34 sampling sites out of the 76, respectively. Soils HI values range from 2.11 to 51.0 with the mean of 5.94 and suggested an adverse health effect to children in whole area of the city. For both Fe and Mo (Figure 6), the risky sites are spatially located in the residential part of Kajaran and near the ZCMC ore crushing, milling, and ore dressing plants. Moreover, in the same areas of city, Mo poses a noncarcinogenic risk to children (7 of 15 dust samples).

Health risk assessment of food product consumption showed that HQ for Cu was more than 1 in maize, potato, and bean both for males and females, whereas for Mo, HQ range from 0.05 to 5.79 for males and 0.05 to 8.63 for females. Particularly, in carrot, potato, and

Figure 5. Spatial distribution of soils and dust noncarcinogenic risk to children and adults health in Kajaran.

onion leaf, HQ value is more than 5, which proves that risks are obvious. For maize consumption, the HQ is higher than 1 for males and females (3.94 and 4.40, respectively). None of the studied vegetables and fruits has a HQ > 1 for Ni, Cr, Zn, Pb, As, and Cd beside the case of Ni in maize for females. In case of Hg, beet and grape indicated HQ more than 1 both for males and females. From all studied elements, only Mo HI values from all studied vegetables and fruits were greater than 1, indicating an adverse health effect both for males and females.

The results of health risk assessment in Kajaran showed that HIsum were greater than 1, indicating an adverse health effect to adults from soils, dust ingestion, and food consumption. Therefore, it should be highlighted that in biogeochemical provinces where industrial activities are closely related to the agricultural lands, the risk assessment including only environmental abiotic mediums may lead to the underestimation of risk level.

Overall, heavy metals in the Yerevan, Gyumri, and Kajaran environment are a primary concern to children health. Moreover, risk assessment showed that the riskiest elements in the cities environments are those previously identified as primary pollutants.

Figure 6. Spatial distribution of soil Fe and soils and dust Mo noncarcinogenic risk to children in Kajaran.

#### 3.4. Potential ecological risk in Yerevan and Gyumri

onion leaf, HQ value is more than 5, which proves that risks are obvious. For maize consumption, the HQ is higher than 1 for males and females (3.94 and 4.40, respectively). None of the studied vegetables and fruits has a HQ > 1 for Ni, Cr, Zn, Pb, As, and Cd beside the case of Ni in maize for females. In case of Hg, beet and grape indicated HQ more than 1 both for males and females. From all studied elements, only Mo HI values from all studied vegetables and fruits were greater than 1, indicating an adverse health

Figure 5. Spatial distribution of soils and dust noncarcinogenic risk to children and adults health in Kajaran.

The results of health risk assessment in Kajaran showed that HIsum were greater than 1, indicating an adverse health effect to adults from soils, dust ingestion, and food consumption. Therefore, it should be highlighted that in biogeochemical provinces where industrial activities are closely related to the agricultural lands, the risk assessment including only environmental

Overall, heavy metals in the Yerevan, Gyumri, and Kajaran environment are a primary concern to children health. Moreover, risk assessment showed that the riskiest elements in the

effect both for males and females.

278 Risk Assessment

abiotic mediums may lead to the underestimation of risk level.

cities environments are those previously identified as primary pollutants.

In Yerevan, PERI was evaluated based on the contents of Hg, As, Pb, Cu, Ni, Cr, and Zn and the mean values of single ecological risk indices decreased in the following order: Hg> > Pb> > Cu > As > Ni > Cr > Zn. The results of Yerevan's soils potential ecological risk assessment showed (Figure 7) that PERI ranges from 53 to 5793.2 with the mean value of 425.3. The latter belongs to the considerable risk level, which was also observed in 1068 (78.8% of all samples) sampling sites. The low level (Figure 7) of ecological risk was detected in 38 (2.8% of all samples) and the moderate level in 155 (11.4% of all samples) sampling sites. The very high level of ecological risk was detected in 95 (7.0% of all samples) sampling sites. From all elements included in Yerevan soil's ecological risk assessment, significant contribution to the considerable and very high levels of PERI was mainly from the single ecological risk indices of Pb and Hg.

In the case of the city of Gyumri, PERI was evaluated based on the contents of Hg, Cd, As, Pb, Cu, and Zn, and the mean values of single ecological risk indices decreased in the following

Figure 7. Spatial distribution of potential ecological risk levels in Yerevan and Gyumri.

order: Cd> > Hg > Pb > As > Cu > Zn. PERI ranges from 48.2 to 1892 with the mean of 252, which belongs to the moderate ecological risk level. The latter was also observed in 128 (28.9% of all samples) sampling sites. The low level (Figure 7) was detected in 183 (41.3% of all samples), considerable level in 111 (25.1% of all samples), and very high level of ecological risk in 21 (4.7% of all samples) sampling sites. In Gyumri, significant contribution to the very high levels of PERI was mainly from the single ecological risk indices of Cd, Pb, and Hg.

#### 4. Conclusions

The result of human health risk assessment showed that soils multielemental noncarcinogenic risk (HI > 1) to adults observed in a few sampling sites both for Yerevan and Kajaran, while in Gyumri HI < 1. For children, noncarcinogenic risk values indicated possible adverse health effects approximately in the whole area of all studied cities. Also for dust, risks have been detected mainly for children in the cities of Yerevan and Kajaran. In Kajaran, risk assessment showed possible adverse health effects for the population from food, as well. The riskiest elements were Pb and Cr for Yerevan, Pb for Gyumri, and Mo for Kajaran. It should be stated that unlike anthropogenic contents of Pb in Yerevan and Gyumri, the high Mo concentrations in Kajaran can be the result of geogenic input as well. According to the results of PERI in cities of Yerevan and Gyumri, considerable and very high levels of ecological risk were observed and the riskiest elements were those (Pb, Hg, and Cd) included in the first group of toxicity. Both human health and ecological risk assessment results highlight the need for further detailed studies, especially in those areas with the highest level of identified risk.

### Author details

Gevorg Tepanosyan\*, Lilit Sahakyan, David Pipoyan and Armen Saghatelyan

\*Address all correspondence to: gevorg.tepanosyan@cens.am

The Center for Ecological-Noosphere Studies, NAS RA, Yerevan, Armenia

### References

order: Cd> > Hg > Pb > As > Cu > Zn. PERI ranges from 48.2 to 1892 with the mean of 252, which belongs to the moderate ecological risk level. The latter was also observed in 128 (28.9% of all samples) sampling sites. The low level (Figure 7) was detected in 183 (41.3% of all samples), considerable level in 111 (25.1% of all samples), and very high level of ecological risk in 21 (4.7% of all samples) sampling sites. In Gyumri, significant contribution to the very high

The result of human health risk assessment showed that soils multielemental noncarcinogenic risk (HI > 1) to adults observed in a few sampling sites both for Yerevan and Kajaran, while in Gyumri HI < 1. For children, noncarcinogenic risk values indicated possible adverse health effects approximately in the whole area of all studied cities. Also for dust, risks have been detected mainly for children in the cities of Yerevan and Kajaran. In Kajaran, risk assessment showed possible adverse health effects for the population from food, as well. The riskiest elements were Pb and Cr for Yerevan, Pb for Gyumri, and Mo for Kajaran. It should be stated that unlike anthropogenic contents of Pb in Yerevan and Gyumri, the high Mo concentrations in Kajaran can be the result of geogenic input as well. According to the results of PERI in cities of Yerevan and Gyumri, considerable and very high levels of ecological risk were observed and the riskiest elements were those (Pb, Hg, and Cd) included in the first group of toxicity. Both human health and ecological risk assessment results highlight the need for further

levels of PERI was mainly from the single ecological risk indices of Cd, Pb, and Hg.

Figure 7. Spatial distribution of potential ecological risk levels in Yerevan and Gyumri.

detailed studies, especially in those areas with the highest level of identified risk.

4. Conclusions

280 Risk Assessment


[23] Saghatelyan A, Sahakyan L, Belyaeva O, Maghakyan N. Studying atmospheric dust and heavy metals on urban sites through synchronous use of different methods. Journal of Atmospheric Pollution. 2014;2(1):12-16

[11] Saghatelyan AK, Kukulyan MA, Khachatryan TS. Assessment of the risk of perinatal mortality under the pollution of environment by heavy metals. In: Pollution of the

Environment by Heavy Metals. Yerevan: Yerevan: CENS NAS RA; 1996. p. 69-70

Heavy Metals. Yerevan: Yerevan: CENS NAS RA; 1996. p. 68-69

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[13] Babayan EA, Saghatelyan AK. Some health indices under condition of lead contamination of the environment and industrial areas. Medical Science of Armenia. 1999;4(39):141-151

[14] Movsesyan HS, Aroutounian RM, Sahakyan LV, Asmaryan SG, Galstyan HM, Bazikyan GK, nMartirosyan HA, Saghatelyan AK. Study of the cancer incidence in kids and juveniles in Yerevan in relation to air and soil pollution on their dwelling sites. Biological

[15] Saghatelyan AK, Sahakyan LV, Mikayelyan MG, Belyaeva OA. Ecological and geochemical analysis of risks of the impact of mining production upon sustainable development of Armenia. Vol. 5. Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya; 2010.

[16] Saghatelyan AK, Sahakyan LV, Menchinskaya OV, Zangiyeva TD, Kajtukov MZ, Uzdenova ZH. Medical geology in connection. In: Selinus O, Finkelman RB, Centeno JA, editors. Medical Geology. A Regional Synthesis. 1st ed. Netherlands: Springer Nether-

[17] United States Environmental Protection Agency. US EPA [Internet]. 1989. Available from: https://www.epa.gov/risk/risk-assessment-guidance-superfund-rags-part [Accessed: 20/

[18] Pepper I, Gerba C, Brusseau M. Environmental and Pollution Science. 2nd ed. United

[19] National Statistical Service of the Republic of Armenia. NSS RA [Internet]. 2016 [Updated: 2016]. Available from: http://armstat.am/en/ [Accessed: 20/08/2017]

[20] Sahakyan LV. Peculiarities of the dynamics of Yerevan soil pollution with heavy metals (Ag, Pb, Cu, Zn, Ni, Co, Cr, Mo) [dissertation]. CENS NAS RA: Yerevan; 2008. 148 p. Available from: http://cens.am/research/environmental-geochemistry/environmental-

[21] Tepanosyan G, Sahakyan L, Belyaeva O, Maghakyan N, Saghatelyan A. Human health risk assessment and riskiest heavy metal origin. Chemosphere 2017;184:1230-1240. DOI:

[22] Tepanosyan G, Maghakyan N, Sahakyan L, Saghatelyan A. Heavy metals pollution levels and children health risk assessment of Yerevan. Ecotoxicology and Environmental Safety.

2017;142:257-265. DOI: http://dx.doi.org/10.1016/j.ecoenv.2017.04.013


**Provisional chapter**

### **Pharmaceuticals and Personal Care Products: Risks, Challenges, and Solutions Challenges, and Solutions**

**Pharmaceuticals and Personal Care Products: Risks,** 

DOI: 10.5772/intechopen.70799

#### Zakiya Hoyett Additional information is available at the end of the chapter

Zakiya Hoyett

[38] ISO 874:1980 Fresh fruits and vegetables – Sampling. International Organization for Standardization [Internet]. 1980. Available from: https://www.iso.org/standard/5259.

[40] Method 6200. Field Portable X-Ray Fluorescence Spectrometry for the Determination of Elemental Concentrations in Soil and Sediment. US EPA [Internet]. 2007. Available from: https://www.epa.gov/hw-sw846/sw-846-test-method-6200-field-portable-x-ray-fluores-

[41] World Health Organization. Guidelines for Drinking-water Quality—Volume 1: Recom-

[39] US EPA. pdf document [Internet]. 1999. Available from: [Accessed: 20/08/2017]

mendations. 3rd ed. Geneva: World Health Organization; 2008. 668 p

cence-spectrometry-determination [Accessed: 20/08/2017]

html [Accessed: 20/08/2017]

284 Risk Assessment

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.70799

#### **Abstract**

Pharmaceuticals and personal care products (PPCPs) encompass a large class of chemical contaminants that can originate from human usage and excretions and veterinary applications. These pollutants have captured the attention of scientists, governments, and the public as several studies across the globe reveal their widespread occurrence in lowlevel concentrations in wastewater and the aquatic environment. Most of the research on PPCPs has been generated from efforts in highly developed countries, primarily North America and Europe, although investigations and reports are emerging from Southeast Asia and China. With the increased concern of potential threats triggered by the occurrence of these chemicals in the environment, environmental risk assessment (ERA) strategies for such compounds have considerably evolved over the past decade. Regulations are in effect or planned in several western nations, however, there is no global standard for conducting ERAs. As the scope of the problem evolves, substantial research will be imperative to address these contaminants and their occurrence in the environment. This chapter will discuss the evolution of the risk associated with the occurrence of PPCPs in the environment, the challenges faced by their existence here, and the colloquy about solutions to address this escalating issue.

**Keywords:** pharmaceuticals and personal care products (PPCPs), pharmaceuticals, contaminants of emerging concern (CECs), environmental risk assessment (ERA), aquatic environment

### **1. Introduction**

Anthropogenic pollutants enter surface and ground waters via a multitude of processes. Commercial activities such as manufacturing emissions, waste disposal, and accidental releases are a few examples [1]. Other practices include deliberate introduction such as

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

sewage sludge application to land, groundwater recharge, and consumer activity which involves both the excretion and purposeful disposal of a wide range of naturally occurring and anthropogenic chemicals [1, 2]. During the last few decades, the impact of chemical pollution in the water has focused almost exclusively on the conventional "priority pollutants" [3]. Priority pollutants are a group of chemicals regulated under legislation such as the Clean Water Act (CWA) of 1972 by the United States Environmental Protection Agency (US EPA) and the Water Framework Directive (2000/60/EC) (WFD), an updated version of Council Directive 76/464/EEC, by the Environment Directorate General of the European Commission (DG Environment) for the European Union (EU) [4, 5]. These pollutants are chemicals that have specific effects on organisms, comprised mainly of agricultural and industrial chemicals and their synthesis by-products [4–6]. The prioritized lists of 126 pollutants and 33 substances in the US and EU, respectively, currently include chemicals that were selected primarily because of their toxicity, persistence, and degradability, among other factors [4, 7, 8]. Chemical production rates and the frequency of occurrence in waters was also considered [4, 5].

Pharmaceuticals and personal care products (PPCPs) are among a group of chemicals termed "contaminants of emerging concern" (CECs). CECs are not necessarily new pollutants as they may have been present in the environment for several years, but their presence and significance are only now being evaluated [3]. Due to their medical properties, PPCPs have an inherent biological effect; furthermore, they behave as persistent pollutants because of their continual infusion into the aquatic ecosystem [9–11].

### **2. Risks**

Considering scientific literature dating as far back as the early 1900s, more than 130 million organic and inorganic substances had been indexed by the American Chemical Society in the Chemical Abstracts Service (CAS) Registry, which is updated daily with about 15 thousand new substances [12]. Over eight million chemicals are commercially available, but only 350 thousand are inventoried and/or regulated globally [4, 8, 12–14].

**Figure 1** shows that the majority of chemicals in commerce are "industrial" chemicals, a significant percentage of these chemicals fall into the categories of "cosmetics ingredients" and "pharmaceuticals". Collectively, these two categories contain several compounds that are potentially persistent and bioaccumulative [14]. Caffeine, nicotine, and aspirin are a few of the pharmaceutically active compounds that have been known for years to enter the environment [3]. Only more recently has it become evident that drugs and personal care products from a wide spectrum of therapeutic and consumer-use classes exist in the environment in low concentrations [15, 16]. Over 50 million pounds of antibiotics are produced annually in the United States, with approximately 60% for human use and 40% for animal agriculture, therefore, veterinary medicines contribute considerably to PPCP occurrence [17]. In addition to pharmaceuticals, compounds such as synthetic fragrances, detergents, disinfectants, and insect repellents are among the man-made chemicals that are now beginning to accumulate in the natural environment [18].

sewage sludge application to land, groundwater recharge, and consumer activity which involves both the excretion and purposeful disposal of a wide range of naturally occurring and anthropogenic chemicals [1, 2]. During the last few decades, the impact of chemical pollution in the water has focused almost exclusively on the conventional "priority pollutants" [3]. Priority pollutants are a group of chemicals regulated under legislation such as the Clean Water Act (CWA) of 1972 by the United States Environmental Protection Agency (US EPA) and the Water Framework Directive (2000/60/EC) (WFD), an updated version of Council Directive 76/464/EEC, by the Environment Directorate General of the European Commission (DG Environment) for the European Union (EU) [4, 5]. These pollutants are chemicals that have specific effects on organisms, comprised mainly of agricultural and industrial chemicals and their synthesis by-products [4–6]. The prioritized lists of 126 pollutants and 33 substances in the US and EU, respectively, currently include chemicals that were selected primarily because of their toxicity, persistence, and degradability, among other factors [4, 7, 8]. Chemical pro-

duction rates and the frequency of occurrence in waters was also considered [4, 5].

continual infusion into the aquatic ecosystem [9–11].

thousand are inventoried and/or regulated globally [4, 8, 12–14].

**2. Risks**

286 Risk Assessment

in the natural environment [18].

Pharmaceuticals and personal care products (PPCPs) are among a group of chemicals termed "contaminants of emerging concern" (CECs). CECs are not necessarily new pollutants as they may have been present in the environment for several years, but their presence and significance are only now being evaluated [3]. Due to their medical properties, PPCPs have an inherent biological effect; furthermore, they behave as persistent pollutants because of their

Considering scientific literature dating as far back as the early 1900s, more than 130 million organic and inorganic substances had been indexed by the American Chemical Society in the Chemical Abstracts Service (CAS) Registry, which is updated daily with about 15 thousand new substances [12]. Over eight million chemicals are commercially available, but only 350

**Figure 1** shows that the majority of chemicals in commerce are "industrial" chemicals, a significant percentage of these chemicals fall into the categories of "cosmetics ingredients" and "pharmaceuticals". Collectively, these two categories contain several compounds that are potentially persistent and bioaccumulative [14]. Caffeine, nicotine, and aspirin are a few of the pharmaceutically active compounds that have been known for years to enter the environment [3]. Only more recently has it become evident that drugs and personal care products from a wide spectrum of therapeutic and consumer-use classes exist in the environment in low concentrations [15, 16]. Over 50 million pounds of antibiotics are produced annually in the United States, with approximately 60% for human use and 40% for animal agriculture, therefore, veterinary medicines contribute considerably to PPCP occurrence [17]. In addition to pharmaceuticals, compounds such as synthetic fragrances, detergents, disinfectants, and insect repellents are among the man-made chemicals that are now beginning to accumulate

**Figure 1.** Estimated number and categories of chemicals in commerce registered for use in the United States over the past 30 years. Not all chemicals may be in current use. Similar proportions would be anticipated in other countries. Adapted with permission from [14]. Copyright 2006 American Chemical Society.

Increasing introduction to the marketplace of new pharmaceuticals is adding to the already large array of poorly understood chemical classes that each have distinct modes of biochemical action [1]. In the United States, legislation exists that requires an assessment of potential risk to the environment by new pharmaceutical products. Under this policy, the Food and Drug Administration (US FDA) is required to consider the environmental impacts of manufacture, use, and distribution of human drugs as well as investigational use and approvals of veterinary drugs [19, 20]. The European Commission recently published a Roadmap that acknowledges the Commission's effort toward developing a similar strategy that will address the manufacture, use and disposal of active pharmaceutical ingredients [21].

**Figure 2** illustrates the numerous pathways by which antibiotics and other PPCPs are introduced into the environment which can be both point and non-point sources [22, 23]. Municipal sewage, both treated and untreated, is the most likely route for human use drugs to enter the environment. Wastewater treatment processes achieve variable and often incomplete removal of antibiotics [24, 25]. Human pharmaceuticals are excreted from the body in urine and feces as unchanged parent compounds, metabolites or conjugated substances; furthermore, because of their polarity, water solubility and persistence some of these compounds may not be completely eliminated or transformed during sewage treatment [26, 27]. Therefore, residential and commercial healthcare facilities, specifically hospitals, are known contributors of antibiotics to municipal wastewater [2, 19, 28–30]. Additionally, the incorrect disposal of expired or unwanted medicines in the sink, toilet, or in household solid waste that is then taken to landfills contribute to the occurrence of pharmaceuticals in wastewater [31–33]. Another possible pathway begins with the disposal of unwanted illicit drugs, synthesis byproducts, raw products and intermediates into domestic sewage systems by clandestine drug operations [3, 32, 34]. Other probable entries include leakage from pipelines, tanks, waste ponds or landfills, and atmospheric deposition [35].

**Figure 2.** Source, fate, and distribution of PPCPs in the environment.

Veterinary medicines may enter the environment through a number of pathways, with terrestrial runoff from concentrated animal feeding operations (CAFOs) and wind-borne drift of agriculturally-applied antimicrobials to crops being the primary sources [32, 34, 36]. After administration, the substances may be metabolized in the animal which changes their physical, chemical and eco-toxicological properties, but even metabolites may be reconverted to their parent compounds after excretion [37, 38]. Accidental leakage or leaching from animal waste storage can also be a source. Still another major channel by which veterinary antibiotics are released into the environment is through application of manure or slurry to agricultural fields as fertilizer [34, 36, 39].

Dependent upon the chemical properties and structures of PPCPs, several processes can affect the fate and transport of these compounds in the environment. These include, but are not limited to, sorption, biotic transformation, and abiotic transformation [7, 24, 27]. Most PPCPs are water soluble and have a low volatility, although there are few that may strongly adsorb to soils and are somewhat persistent. These characteristics allow them to be easily transported and omnipresent in various aquatic environments [7, 19]. Because PPCPs can be introduced on a continual basis to the aquatic environment, they are ubiquitously present in waters; their removal or transformation by biodegradation, hydrolysis, photolysis, and other processes is continually countered by their replenishment [3].

With concentrations typically ranging from the low parts per trillion (ppt) and parts per billion (ppb) levels, several individual PPCPs or their metabolites from a variety of therapeutic classes (**Table 1**) have been detected in environmental samples from all over the world [3]. More than 80 pharmaceuticals and their metabolites have been detected in almost


**Table 1.** Chemical classes (and members) of PPCPs detected in environmental samples.

Veterinary medicines may enter the environment through a number of pathways, with terrestrial runoff from concentrated animal feeding operations (CAFOs) and wind-borne drift of agriculturally-applied antimicrobials to crops being the primary sources [32, 34, 36]. After administration, the substances may be metabolized in the animal which changes their physical, chemical and eco-toxicological properties, but even metabolites may be reconverted to their parent compounds after excretion [37, 38]. Accidental leakage or leaching from animal waste storage can also be a source. Still another major channel by which veterinary antibiotics are released into the environment is through application of manure or slurry to agricultural

Dependent upon the chemical properties and structures of PPCPs, several processes can affect the fate and transport of these compounds in the environment. These include, but are not limited to, sorption, biotic transformation, and abiotic transformation [7, 24, 27]. Most PPCPs are water soluble and have a low volatility, although there are few that may strongly adsorb to soils and are somewhat persistent. These characteristics allow them to be easily transported and omnipresent in various aquatic environments [7, 19]. Because PPCPs can be introduced on a continual basis to the aquatic environment, they are ubiquitously present in waters; their removal or transformation by biodegradation, hydrolysis, photolysis, and other processes is

fields as fertilizer [34, 36, 39].

288 Risk Assessment

continually countered by their replenishment [3].

**Figure 2.** Source, fate, and distribution of PPCPs in the environment.


**Table 2.** Representation of the global occurrence of PPCPs in WWTP effluents.

every aquatic environment in North America and Europe surface waters [33, 40–44]. A national reconnaissance study on the occurrence of pharmaceuticals, hormones, and other organic wastewater contaminants (OWCs) in United States streams found that one or more OWCs were found in 80% of the stream samples, with 82 compounds of the 95 analyzed for detected during the study [40]. In another project, source water, finished drinking water, and distribution system (tap) water from 19 United States drinking-water treatment (DWT) plants was analyzed for 51 pharmaceuticals and pharmaceutical metabolites. Targeted compounds were detected most frequently in source water with at least one compound being detected in all 19 source waters; they were also found in approximately 89% of finished drinking waters and 87% of distribution systems [45]. In yet another study conducted by the United States Geological Survey (USGS) and the Centers for Disease Control and Prevention (CDC), several compounds that were frequently detected in samples of stream water and raw-water supplies were also detected in samples collected throughout the DWT facility, indicating that these compounds resist removal through conventional water-treatment processes [46].

PPCPS have been reported in hospital wastewaters, wastewater treatment plant (WWTP) effluents, WWTP biosolids, soil, surface waters, groundwaters, sediments, biota, and drinking water [33, 40, 47–50]. Since WWTPs are considered a major source of these pollutants, several investigations of environmental loads of PPCPs examine WWTP effluents (**Table 2**) [28]. There is less documented research of PPCP occurrence in coastal or marine ecosystems. A wide distribution of clofibric acid, caffeine, and DEET in concentrations up to 19, 16, and 1.1 ng/L, respectively, was measured throughout the North Sea and along European coasts [91]. Sulfamethoxazole, carbamazepine, tamoxifen, and indomethacin were discovered in China in the Yangtze River Estuary at levels ranging from 4.2 to 159 ng/L [92]. In the United States, sulfamethoxazole was detected in at least four bays ranging in concentrations from 4.8 to 65 ng/L, while trimethoprim was found at a maximum concentration of 72.2 ng/L in Jamaica Bay, New York and 2.1 ng/L off the coast of California [93–95].

### **3. Challenges**

**Chemical class Location Concentration range (ng/L) References**

ND – 72 [51]

ND – 5911 [52–57]

ND – 126,000 [58–61]

90–320 [24, 62]

ND – 21,278 [52, 63–72]

ND – 3052 [61, 73, 74]

0.2–96 [75]

ND – 25 [60]

230–1110 [84]

160–480 [59]

495–3730 [85]

1–889 [60]

21–1287 [90]

<2–6325 [61]

<4–2050 [86–89]

ND – 253.8 [52, 53,

76–83]

Multiple pharmaceuticals North America

290 Risk Assessment

Antimicrobials/antibiotics North America

Hormonally active agents North America

Antiepileptics East Asia

Antiseptics Europe

Sun screen agents East Asia

ND: not detected.

Musks (synthetic) North America

*U.S.*

East Asia *China Japan Korea*

Europe *Finland Norway Portugal U.K.*

*U.S.*

East Asia *China Korea*

Europe *Finland Sweden U.K.*

*Canada*

East Asia *China Japan Korea*

Europe *Portugal*

*China*

*Norway*

East Asia *China Japan*

Europe *Portugal*

*China*

Europe *U.K.*

**Table 2.** Representation of the global occurrence of PPCPs in WWTP effluents.

*U.S.*

An ecological or environmental risk assessment (ERA) is defined as the means of evaluating the probabilities and magnitudes of adverse effects to human health or ecological receptors, directly or indirectly, as a result of exposure to pollutants and other anthropogenic activities [96]. ERAs are employed to estimate any potential harm that could emerge from environmental contaminants, with a known degree of certainty, using scientific methodologies. The innovation of ERAs has become necessary as improved research reveals chemicals in the environment at levels that are potentially toxic to humans and/or our valuable natural resources [11]. The specific methodology for carrying out an ERA may vary depending on the chemical being assessed, but the core principles and the key stages of the process are fundamentally the same in each case (**Figure 3**).

ERAs can be used to predict the likelihood of future adverse effects, prospective, or to evaluate the likelihood that effects are caused by past exposure to stressors, retrospective [97].

**Figure 3.** Flow chart for a general ERA process.

Examples of prospective uses include establishing drinking water goals or wastewater discharge limits. Federal and state regulatory programs also utilize prospective ERAs to reduce toxic tort liabilities and improved public relations. The government may use retrospective ERAs as a decision making tool, for example, when determining Comprehensive Environmental Response, Compensation, and Liability Act – CERCLA or Superfund – projects [11, 98, 99]. In many cases, both approaches are included in a single risk assessment. Combined retrospective and prospective risk assessments tend to be beneficial in situations where ecosystems have a history of previous impacts and/or the potential for future effects from multiple chemical, physical, or biological stressors [97].

Although the concentrations of these PPCPs generally range from the low ppt- to ppb-levels, there is increasing evidence that PPCPs may have significant impacts on natural biotic communities. There are two major concerns with the presence of low-level concentrations of pharmaceuticals in the aquatic environment: the potential toxicity of these compounds to aquatic organisms and the exposure to humans through drinking water [23, 31, 100]. Some PPCPs, such as antidepressants, birth control drugs, and other medications have been detected in fish tissue and were identified as the cause of neurological, biochemical, and physiological changes [100, 101]. Because pharmaceuticals are designed to target specific metabolic and molecular pathways in humans and animals, it is assumed that they may affect the same pathways in animals with identical or similar target organs, tissues, cells or biomolecules. Certain receptors in lower vertebrates resemble those in humans, while others are different or lacking; in these cases, dissimilar modes of actions may occur in the lower animals [102, 103].

Acute toxicity studies typically show that the concentrations of PPCPs to produce effects such as death in half of the exposed organisms (EC50) range of 25 to ≥500 mg/L; one particular example found that the chronic toxicity or median lethal dose (LC50) of furazolidone, which is largely used in medicated fish feed, at 40 mg/kg in the mosquito larvae, *Culex pipiens* [31]. In a study that tested the effects of tylosin and oxytetracycline on three species of soil fauna, neither of the substances had any effect at environmentally relevant concentrations; however, as soil ecosystems are built up by complex and linked food webs, the study concluded that it is not yet possible to exclude that indirect effects on soil fauna driven by changes in the microbial community and alteration of the decomposer system may occur [38].

Since antibiotics are specifically designed to control bacteria in plants and animals of economic interest, this obviously makes them hazardous to bacteria and other micro-organisms in the environment. There is growing concern that low level concentrations of antibiotics in the environment contribute to the emergence of strains of disease-causing bacteria that are resistant to even high doses of these drugs [23]. Current evidence supports that feeding low doses of antibiotics to livestock in an attempt to improve production efficiency has produced resistant strains of certain microorganisms. Bacterial strains evolve and become resistant to multiple antibiotics if they are continually exposed to low doses of antibiotics in the environment since the three mechanisms of gene transfer – conjugation, transduction, and transformation – all occur in the aquatic environment [104].

Streams and rivers that receive low levels of chronic antibiotic exposure can be viewed as a source and a reservoir of resistant genes as well as a means for their dispersion. In addition, if non-target organisms, such as cyanobacteria, are over-exposed to antibiotics, they may be negatively affected, which will disturb the aquatic food chain [6]. Increased bacterial resistance has been seen in waste effluent from hospitals and pharmaceutical plants indicating that the ultimate disposal of antibiotics may be a serious public health issue [23, 29]. Furthermore, individual compounds may interact synergistically or antagonistically with other chemicals present in the environment [6, 15, 16].

### **4. Solutions**

Examples of prospective uses include establishing drinking water goals or wastewater discharge limits. Federal and state regulatory programs also utilize prospective ERAs to reduce toxic tort liabilities and improved public relations. The government may use retrospective ERAs as a decision making tool, for example, when determining Comprehensive Environmental Response, Compensation, and Liability Act – CERCLA or Superfund – projects [11, 98, 99]. In many cases, both approaches are included in a single risk assessment. Combined retrospective and prospective risk assessments tend to be beneficial in situations where ecosystems have a history of previous impacts and/or the potential for future effects

Although the concentrations of these PPCPs generally range from the low ppt- to ppb-levels, there is increasing evidence that PPCPs may have significant impacts on natural biotic communities. There are two major concerns with the presence of low-level concentrations of pharmaceuticals in the aquatic environment: the potential toxicity of these compounds to aquatic organisms and the exposure to humans through drinking water [23, 31, 100]. Some PPCPs, such as antidepressants, birth control drugs, and other medications have been detected in fish tissue and were identified as the cause of neurological, biochemical, and physiological changes [100, 101]. Because pharmaceuticals are designed to target specific metabolic and molecular pathways in humans and animals, it is assumed that they may affect the same pathways in animals with identical or similar target organs, tissues, cells or biomolecules. Certain receptors in lower vertebrates resemble those in humans, while others are different or lacking; in these cases, dissimilar modes of actions may occur in the

from multiple chemical, physical, or biological stressors [97].

**Figure 3.** Flow chart for a general ERA process.

292 Risk Assessment

lower animals [102, 103].

The production and usage of most pharmaceutical and personal care products will either stabilize or increase. It is probable that the environmental load of these chemicals will follow the same trend. Although remedying this issue seems unfeasible, it cannot be regarded as a terminal quandary. Instead, tactics should be implemented to minimize their impact on the environment.

There are four major factors that determine the concentrations of drug residues reported in environmental samples: (1) frequency of use, (2) excretion of un-metabolized drugs, (3) persistence on biodegradation, and (4) the analytical method used [105]. Due to the consequential concern resulting from the detection of PPCPs in the aquatic environment, sensitive analytical techniques have been developed to investigate this new class of environmental pollutants; techniques that will have to continue to evolve in order to improve method accuracy and sensitivity [105]. Likewise, methodology must be designed to analyze compounds in combination [93].

Perhaps reform should begin with production of PPCPs, specifically pharmaceuticals. Medicinal drugs are intended to be metabolized by organisms, yet, approximately 20% or more of these compounds are excreted in their parent form or as metabolites [26, 105]. After excretion, these compounds could possibly mix with other chemicals already present in the environment or biodegradation and transformation may occur: circumstances which could produce other metabolites or by-products, conceivably leading to a substance that may be far more toxic than the parent compounds [105]. Production of pharmaceuticals that are fully absorbed or completely metabolized by the organism would be ideal; this, however, may be impractical. The responsibility then shifts from the pharmaceutical industry to the medical industry. By purposefully managing prescriptions with deep scrutiny, doctors may begin to begin to alleviate the issue through reduction of input [26].

Effective regulation of PPCPs is implausible without a global colloquy giving great consideration to the creation and installation of a well-developed, universal ERA procedure for these contaminants. Existing protocols must be expanded to adapt to the gravity of the potential impacts of these unique compounds in the environment. Implementation of a retrospective aspect to the protocol may also be necessary in the near future [93].

### **Author details**

Zakiya Hoyett

Address all correspondence to: zakiya.hoyett@gmail.com

College of Science and Technology, Florida Agricultural and Mechanical University, Tallahassee, FL, USA

### **References**


[6] Jones OAH, Voulvoulis N, Lester JN. Potential impact of pharmaceuticals on environmental health. Bulletin of the World Health Organization. 2003;**81**(10):768-769

Perhaps reform should begin with production of PPCPs, specifically pharmaceuticals. Medicinal drugs are intended to be metabolized by organisms, yet, approximately 20% or more of these compounds are excreted in their parent form or as metabolites [26, 105]. After excretion, these compounds could possibly mix with other chemicals already present in the environment or biodegradation and transformation may occur: circumstances which could produce other metabolites or by-products, conceivably leading to a substance that may be far more toxic than the parent compounds [105]. Production of pharmaceuticals that are fully absorbed or completely metabolized by the organism would be ideal; this, however, may be impractical. The responsibility then shifts from the pharmaceutical industry to the medical industry. By purposefully managing prescriptions with deep scrutiny, doctors may begin to

Effective regulation of PPCPs is implausible without a global colloquy giving great consideration to the creation and installation of a well-developed, universal ERA procedure for these contaminants. Existing protocols must be expanded to adapt to the gravity of the potential impacts of these unique compounds in the environment. Implementation of a retrospective

begin to alleviate the issue through reduction of input [26].

Address all correspondence to: zakiya.hoyett@gmail.com

**Author details**

Tallahassee, FL, USA

2009;**28**(12):2490-2494

Zakiya Hoyett

294 Risk Assessment

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**Provisional chapter**

### **Estimation of PM2.5 Trajectory Using Atmospheric Dispersion Models and GIS in the Tokyo Metropolitan Area Dispersion Models and GIS in the Tokyo Metropolitan Area**

**Estimation of PM2.5 Trajectory Using Atmospheric** 

DOI: 10.5772/intechopen.70608

Kayoko Yamamoto and Zhaoxin Yang Kayoko Yamamoto and Zhaoxin Yang Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.70608

#### **Abstract**

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302 Risk Assessment

The present study aims to use atmospheric dispersion models and geographical information system (GIS) to make estimations of the trajectory of PM2.5 (particulate matter) discharged from specific generation sources, by grasping the atmospheric concentration within the Tokyo metropolitan area in Japan. It is expected that such estimation results should contribute to the risk assessment concerning the influences of PM2.5 on human health and ecosystem. Using ADMER in the first stage, estimations of the atmospheric concentration distribution of PM2.5 throughout the entire Tokyo metropolitan area from 2009 to 2014 were conducted. As a result, areas with high atmospheric concentration of PM2.5 focused in the same area each year, and it was revealed that the entire Tokyo and Saitama had high atmospheric concentrations. Additionally, as a result of setting Tokyo the detail estimation range, it was grasped that the atmospheric concentrations are high in Shinjuku ward and Tachikawa city in Tokyo. Based on the results in the first stage, using METI-LIS in the second stage, estimations of the trajectory of PM2.5 discharged from specific generation sources were conducted in Tachikawa city. As a result, it was made clear that PM2.5 had spread within 500 m of the specific generation sources, and the atmospheric concentrations were intensively high.

**Keywords:** PM2.5 (particulate matter), estimation of trajectory, risk assessment, air pollution, atmospheric dispersion model, geographic information systems (GIS)

### **1. Introduction**

Humans produce a variety of waste due to production and consumption activities. If the amount of such waste is not so large, it can be processed through the natural purification effect. However, in addition to waste disposal increasing beyond the natural processing capacity, new types of waste materials that cannot be naturally processed are also being produced.

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. © 2018 The Author(s). Licensee InTech. This chapter is 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.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

Such pollution of the natural environment has progressed, and this has led to the disruption of ecosystem, damage to human health, as well as various pollution issues including air pollution. Worldwide, especially in Asian countries which have achieved rapid industrialization, the amount of air pollutant discharged has rapidly increased along with the expanding scale of economic activities. Though air pollutants can be lessened by exchanging fuel used by main generation sources such as automobiles and plants with fuel that is less of a burden on the environment, there have been few reduction measures. It is necessary to accurately grasp the actual condition of air pollution and take appropriate measures to handle this issue.

Based on the background mentioned above, the present study aims to use atmospheric dispersion models and geographical information system (GIS) to make estimations of the trajectory of air pollutants discharged from specific generation sources by grasping the atmospheric concentration within the Tokyo metropolitan area in Japan. As a target air pollutant for estimations in the present study, PM2.5 (particulate matter), which has been a serious concern to human health, will be discussed. The estimations in the present study will be conducted using two types of atmospheric dispersion models in two stages. In the first stage, wide-range and long-term estimations will be conducted in the entire estimation target area. By means of the estimation results, the areas with high atmospheric concentration of PM2.5 will be selected, its generation source will be investigated, and the PM2.5 trajectory will be estimated. In the second stage, by means of the estimation results of the first stage, detailed estimations in smaller areas surrounding specific generation sources with high atmospheric concentration of PM2.5 will be conducted, and its trajectory will be estimated in detail. Based on such estimation results, the information concerning the measures to reduce PM2.5 that is more effective than before can be provided, and the estimation methods of PM2.5 trajectory proposed in the present study can be used for other air pollutants as well as in other areas. Additionally, it is expected that such estimation results should contribute to the risk assessment concerning the influences of PM2.5 on human health and ecosystem.

### **2. Related work**

Regarding studies that grasped the behavior of air pollutants using atmospheric dispersion model, there have been many with dioxin as its subject. Some of the representative studies in recent years include that of Sasaki et al. [1], Teshima et al. [2], Hoa [3], Viel [4], Ripamonti et al. [5], Ashworth et al. [6], Ishii and Yamamoto [7], Sun et al. [8], and Zhang et al. [9], in which simulations of the behavior of dioxins in the atmosphere were conducted with incinerators as its generation source. Maantay et al. [10], Chen et al. [11], Kawashima et al. [12], Onofrio et al. [13], Zhou et al. [14], and Chandra et al. [15] conducted simulations of dioxins in the atmosphere. Additionally, Armitage et al. [16], Huang and Liang [17], and Zhou [18] conducted simulations of the behavior of dioxins underwater in places including the sea, canals, and lakes.

Among the related studies above, Maantay et al. [10] and Viel [4] demonstrated the effectiveness to combine atmospheric dispersion model and GIS to estimate the behavior of dioxins in the atmosphere. Additionally, Ishii et al. [7] combined two types of atmospheric dispersion models and GIS to grasp the dispersion conditions of dioxins in both wide-rage and small-range areas with high concentration and proposed a method to evaluate environmental risks.

In a similar manner, representative studies in recent years with PM2.5 as its subject include that of Mueller et al. [19], Saide et al. [20], Chen et al. [21], Solazzo et al. [22], Lee et al. [23], Saraswat et al. [24], and Rizza et al. [25], in which simulations of the atmospheric behavior of PM2.5 were conducted. Simulations of the atmospheric behavior of PM2.5 were conducted with the generation source being traffic by Lang et al. [26], daily activities by Louge et al. [27], and incinerators by Kodros et al. [28]. However, studies on Japan are very rare, whereas most focus their target on China, North America, and Europe.

In contrast with the studies mentioned above, the present study will focus on PM2.5 discharged from specific generation sources that has been seldom targeted in Japan and demonstrate the originality by proposing detailed trajectory estimation methods using both two types of atmospheric dispersion models and GIS. Additionally, by means of two-stage estimations targeting wide areas and narrow areas with high atmospheric concentration, the atmospheric concentration distribution of PM2.5 can be accurately grasped. Moreover, using both atmospheric dispersion models and GIS for the two-stage estimation method in the present study, the effectiveness is demonstrated by quantitatively and spatially grasping the dispersion conditions of PM2.5 discharged from specific generation sources. More specifically, the areas with high atmospheric concentration will be extracted by estimating the atmospheric concentration distribution of PM2.5 throughout the entire estimation target area using atmospheric dispersion models. Furthermore, in these areas, reflecting the land use by means of GIS, the trajectory of PM2.5 discharged from specific generation sources will be estimated in detail.

### **3. Estimation method**

Such pollution of the natural environment has progressed, and this has led to the disruption of ecosystem, damage to human health, as well as various pollution issues including air pollution. Worldwide, especially in Asian countries which have achieved rapid industrialization, the amount of air pollutant discharged has rapidly increased along with the expanding scale of economic activities. Though air pollutants can be lessened by exchanging fuel used by main generation sources such as automobiles and plants with fuel that is less of a burden on the environment, there have been few reduction measures. It is necessary to accurately grasp the

Based on the background mentioned above, the present study aims to use atmospheric dispersion models and geographical information system (GIS) to make estimations of the trajectory of air pollutants discharged from specific generation sources by grasping the atmospheric concentration within the Tokyo metropolitan area in Japan. As a target air pollutant for estimations in the present study, PM2.5 (particulate matter), which has been a serious concern to human health, will be discussed. The estimations in the present study will be conducted using two types of atmospheric dispersion models in two stages. In the first stage, wide-range and long-term estimations will be conducted in the entire estimation target area. By means of the estimation results, the areas with high atmospheric concentration of PM2.5 will be selected, its generation source will be investigated, and the PM2.5 trajectory will be estimated. In the second stage, by means of the estimation results of the first stage, detailed estimations in smaller areas surrounding specific generation sources with high atmospheric concentration of PM2.5 will be conducted, and its trajectory will be estimated in detail. Based on such estimation results, the information concerning the measures to reduce PM2.5 that is more effective than before can be provided, and the estimation methods of PM2.5 trajectory proposed in the present study can be used for other air pollutants as well as in other areas. Additionally, it is expected that such estimation results should contribute to the risk assessment concerning the

Regarding studies that grasped the behavior of air pollutants using atmospheric dispersion model, there have been many with dioxin as its subject. Some of the representative studies in recent years include that of Sasaki et al. [1], Teshima et al. [2], Hoa [3], Viel [4], Ripamonti et al. [5], Ashworth et al. [6], Ishii and Yamamoto [7], Sun et al. [8], and Zhang et al. [9], in which simulations of the behavior of dioxins in the atmosphere were conducted with incinerators as its generation source. Maantay et al. [10], Chen et al. [11], Kawashima et al. [12], Onofrio et al. [13], Zhou et al. [14], and Chandra et al. [15] conducted simulations of dioxins in the atmosphere. Additionally, Armitage et al. [16], Huang and Liang [17], and Zhou [18] conducted simulations of the behavior of dioxins underwater in places including the sea,

Among the related studies above, Maantay et al. [10] and Viel [4] demonstrated the effectiveness to combine atmospheric dispersion model and GIS to estimate the behavior of dioxins in the atmosphere. Additionally, Ishii et al. [7] combined two types of atmospheric

actual condition of air pollution and take appropriate measures to handle this issue.

influences of PM2.5 on human health and ecosystem.

**2. Related work**

304 Risk Assessment

canals, and lakes.

#### **3.1. Overview of atmospheric dispersion method and GIS**

With the present study, since estimations of PM2.5 trajectory are made in two stages, two types of atmospheric dispersion models will be used. In the wide-area estimations involving the entire estimation target area of the first stage, the explosion risk evaluation atmospheric dispersion model (AIST-ADMER Ver.3) by the National Institute of Advanced Industrial Science and Technology [29–31] is used. This is an atmospheric dispersion model suitable for estimating wide-ranged and long-term atmospheric concentration distribution of chemicals according to the amount of PM2.5 discharged from generation sources as well as meteorological conditions.

In the localized and detailed estimations with selected specific generation sources as the target in the second stage, the low-rise industrial source dispersion model (METI-LIS Ver.3.2.1) by the Ministry of Economy, Trade, and Industry [32–34] is used. This model estimates the atmospheric concentration of chemicals surrounding specific generation sources. Additionally, this model takes into consideration the downwash that occurs when the air current is disturbed due to buildings surrounding the specific generation sources. By entering the data on height of the buildings near the specific generation sources, the model takes into consideration the buildings' influence on chemical dispersion, and estimations of small-ranged and detailed atmospheric concentration dispersions can be made.

Additionally, the ArcGIS Ver.10.2 of ESRI will be used as GIS. Upon the estimations of PM2.5 trajectory, using GIS, an overlay analysis with the estimation results which are obtained from two types of atmospheric dispersion models and digital map data, as well as statistical processing will be conducted.

#### **3.2. Overview of estimation method**

The flow of the estimation method in the present study is as shown in **Figure 1**, and the details will be explained below.


Based on the estimation results, the areas with high atmospheric concentration are selected, and detailed estimation range, where a detailed estimation of atmospheric concentrations is conducted, is set. By using ADMER in this way, the PM2.5 trajectory of the entire estimation target area can be grasped on a macro scale, and areas with high atmospheric concentration can be set as a detailed estimation range to be confirmed.

**3.** Based on the estimation results of the first stage, the estimation target area is selected for the second stage. The meteorological data and digital map data concerning the estimation target area of the second stage are gathered and processed, and entered into the atmospheric dispersion model, METI-LIS. In the second stage, as downwash and stack-tip downwash due to buildings are taken into consideration, the PM2.5 trajectory can be accurately estimated. Additionally, the trajectory of PM2.5 discharged especially from specific generation sources is estimated.

#### **3.3. Selection of estimation target area**

For the present study, the Tokyo metropolitan area (Ibaraki, Tochigi, Gunma, Kanagawa, Saitama, Chiba, and Tokyo) was selected as the target area for the estimations of PM2.5 trajectory. **Figure 2** shows the Tokyo metropolitan area. In the estimations of the entire target area using ADMER in the first stage, the entire Tokyo metropolitan area will be the target area. In the second stage using METI-LIS for estimations of the atmospheric concentration of chemicals surrounding specific generation sources, areas with high atmospheric concentration of PM2.5 will be selected from the entire Tokyo metropolitan area based on the estimation results of the previous stage. Furthermore, the cause of high atmospheric concentrations in those areas will be considered, and the trajectory of PM2.5 discharged from specific generation sources will be estimated in detail.

Estimation of PM2.5 Trajectory Using Atmospheric Dispersion Models and GIS in the Tokyo... http://dx.doi.org/10.5772/intechopen.70608 307

**Figure 1.** Flow of the estimation method.

due to buildings surrounding the specific generation sources. By entering the data on height of the buildings near the specific generation sources, the model takes into consideration the buildings' influence on chemical dispersion, and estimations of small-ranged and detailed

Additionally, the ArcGIS Ver.10.2 of ESRI will be used as GIS. Upon the estimations of PM2.5 trajectory, using GIS, an overlay analysis with the estimation results which are obtained from two types of atmospheric dispersion models and digital map data, as well as statistical pro-

The flow of the estimation method in the present study is as shown in **Figure 1**, and the details

**1.** In the present study, the estimation target area is selected in the beginning, the data concerning PM2.5 discharged from generation sources is gathered and processed, and the

**2.** The above data concerning PM2.5 discharged from generation sources and meteorological data are entered into the atmospheric dispersion model, ADMER in the first stage, and the atmospheric concentration of PM2.5 in the entire estimation target area is estimated.

Based on the estimation results, the areas with high atmospheric concentration are selected, and detailed estimation range, where a detailed estimation of atmospheric concentrations is conducted, is set. By using ADMER in this way, the PM2.5 trajectory of the entire estimation target area can be grasped on a macro scale, and areas with high atmospheric concen-

**3.** Based on the estimation results of the first stage, the estimation target area is selected for the second stage. The meteorological data and digital map data concerning the estimation target area of the second stage are gathered and processed, and entered into the atmospheric dispersion model, METI-LIS. In the second stage, as downwash and stack-tip downwash due to buildings are taken into consideration, the PM2.5 trajectory can be accurately estimated. Additionally, the

trajectory of PM2.5 discharged especially from specific generation sources is estimated.

For the present study, the Tokyo metropolitan area (Ibaraki, Tochigi, Gunma, Kanagawa, Saitama, Chiba, and Tokyo) was selected as the target area for the estimations of PM2.5 trajectory. **Figure 2** shows the Tokyo metropolitan area. In the estimations of the entire target area using ADMER in the first stage, the entire Tokyo metropolitan area will be the target area. In the second stage using METI-LIS for estimations of the atmospheric concentration of chemicals surrounding specific generation sources, areas with high atmospheric concentration of PM2.5 will be selected from the entire Tokyo metropolitan area based on the estimation results of the previous stage. Furthermore, the cause of high atmospheric concentrations in those areas will be considered, and the trajectory of PM2.5 discharged from specific generation sources

tration can be set as a detailed estimation range to be confirmed.

atmospheric concentration dispersions can be made.

cessing will be conducted.

306 Risk Assessment

will be explained below.

**3.2. Overview of estimation method**

generation source data is prepared.

**3.3. Selection of estimation target area**

will be estimated in detail.

**Figure 2.** Tokyo metropolitan area as estimation target area.

#### **4. Gathering and processing data**

Data shown in **Table 1** are used in the present study. Generation source data and meteorological data will be entered into the atmospheric dispersion models, while measured data of


**Table 1.** List of data used.

PM2.5 will be processed into GIS data and used for spatial analysis. For data of the amount of discharged PM2.5, the data concerning the amount of discharged chemicals announced on the basis of the Pollutant Release and Transfer Register Law (PRTR Law, enacted in 2001) will be used in the present study.

According to the PRTR Law, it is necessary for the businesses themselves to grasp the amount of hazardous chemicals discharged from businesses into the environment (atmosphere, water, and soil) and included in waste substances to be released outside business facilities, and report it to the national government. Additionally, based on the above reported data and statistics, it is essential for the national government to tally and announce the amount of chemicals discharged and transferred. As businesses with the responsibility of notification according to the PRTR Law are restricted by category of business and plant, employee scale and transaction volume, the amount of chemicals discharged from the generation sources exempt from the law are estimated and announced by the national government.

### **5. Estimation in the entire estimation target area**

#### **5.1. Estimation targets**

With the entire Tokyo metropolitan area as the estimation target area in the first stage, the atmospheric concentrations of PM2.5 will be estimated using ADMER. By setting the estimation target area to 34° 50' 00'' - 37° 12' 30'' north latitude and 138° 18' 45'' - 140° 56' 15" east longitude in accordance with the Tokyo metropolitan area, the grid will be set to 5\*5 of the tertiary grid square (1 km grid) meaning a 5 km grid square units of area, and the grid number for the entire Tokyo metropolitan area will be 42\*57 (2,394). Moreover, the estimation target period for the first stage is 6 years from 2009 to 2014. This is because a period with available data concerning the estimation target area was selected.

#### **5.2. Estimation results**

PM2.5 will be processed into GIS data and used for spatial analysis. For data of the amount of discharged PM2.5, the data concerning the amount of discharged chemicals announced on the basis of the Pollutant Release and Transfer Register Law (PRTR Law, enacted in 2001) will

Digital map data Administrative division data (2012) Ministry of Land, Infrastructure,

Base map information (scale level of 2500)

atmospheric environment (2014)

Ministry of Trade, Economy, and

National Institute of Advanced Industrial Science and Technology

National Institute of Environmental

Transport, and Tourism

Industry

Studies

Tokyo weather data (2014) Japan Meteorological Agency

According to the PRTR Law, it is necessary for the businesses themselves to grasp the amount of hazardous chemicals discharged from businesses into the environment (atmosphere, water, and soil) and included in waste substances to be released outside business facilities, and report it to the national government. Additionally, based on the above reported data and statistics, it is essential for the national government to tally and announce the amount of chemicals discharged and transferred. As businesses with the responsibility of notification according to the PRTR Law are restricted by category of business and plant, employee scale and transaction volume, the amount of chemicals discharged from the generation sources exempt from the law are estimated and announced by the national

With the entire Tokyo metropolitan area as the estimation target area in the first stage, the atmospheric concentrations of PM2.5 will be estimated using ADMER. By setting the estimation target area to 34° 50' 00'' - 37° 12' 30'' north latitude and 138° 18' 45'' - 140° 56' 15" east longitude in accordance with the Tokyo metropolitan area, the grid will be set to 5\*5 of the tertiary grid square (1 km grid) meaning a 5 km grid square units of area, and the grid number for the entire Tokyo metropolitan area will be 42\*57 (2,394). Moreover, the estimation target period for the first stage is 6 years from 2009 to 2014. This is because a period with available

**5. Estimation in the entire estimation target area**

**Type Name Source**

Generation source data Data of the amount of discharged PM2.5 (2009–2014)

Meteorological data AMeDas and rainfall data for ADMER (2009–2014)

Measured data of PM2.5 Monthly and annual data of the

data concerning the estimation target area was selected.

be used in the present study.

**Table 1.** List of data used.

308 Risk Assessment

government.

**5.1. Estimation targets**

#### *5.2.1. Results for the entire estimation target area*

The atmospheric concentration distribution of PM2.5 was estimated using ADMER, and the estimation results for each year are shown in **Figure 3**. As shown in the figure, the areas with high

**Figure 3.** Atmospheric concentration distribution of PM2.5 in the Tokyo metropolitan area (2009–2014).

atmospheric concentration were focused each year, and the entire Tokyo and Saitama as well as some parts of Ibaraki, Kanagawa, and Chiba had high atmospheric concentrations of PM2.5. For Ibaraki, Kanagawa, and Chiba, an investigation of generation sources was conducted. As areas with high atmospheric concentration of PM2.5 are located in urban central parts, main generation sources are considered to be transportation including automobiles and railroad vehicles.

#### *5.2.2. Estimation results for detailed estimation range*

Based on the estimation results in the previous section, because it was made clear that Tokyo and Saitama had high atmospheric concentrations of PM2.5, Tokyo was set as the detailed estimation range, and the atmospheric concentration distribution was estimated. One reason for this is that there are many measurement stations of PM2.5, and there is an abundance of data to aid the grasping of the atmospheric concentration distribution of PM2.5. The second reason is that the atmospheric concentration of PM2.5 in Tokyo has been decreasing every year making great improvements (55% reduction of atmospheric concentration in 10 years from 2001 to 2011). However, the rate for meeting environmental standards is low, and the atmospheric concentrations of PM2.5 in Tokyo are slightly above the environmental standards (annual average of below 15 μg/m<sup>3</sup> ).

After setting the detailed estimation range to 35° 27' 30" - 35° 55' 0" north latitude and 138° 52' 30" - 140° 0' 0" east longitude in accordance with Tokyo, a grid of 100 m\*100 m is created. Then, the atmospheric concentration distribution of PM2.5 is estimated using ADMER, and the estimation results for each year are shown in **Figure 4**. From the figure, it is clear that atmospheric concentrations are high in Shinjuku ward and Tachikawa city each year.

**Figure 4.** Atmospheric concentration distribution of PM2.5 in Tokyo (2009–2014).

#### **5.3. Discussion**

atmospheric concentration were focused each year, and the entire Tokyo and Saitama as well as some parts of Ibaraki, Kanagawa, and Chiba had high atmospheric concentrations of PM2.5. For Ibaraki, Kanagawa, and Chiba, an investigation of generation sources was conducted. As areas with high atmospheric concentration of PM2.5 are located in urban central parts, main generation sources are considered to be transportation including automobiles and railroad vehicles.

Based on the estimation results in the previous section, because it was made clear that Tokyo and Saitama had high atmospheric concentrations of PM2.5, Tokyo was set as the detailed estimation range, and the atmospheric concentration distribution was estimated. One reason for this is that there are many measurement stations of PM2.5, and there is an abundance of data to aid the grasping of the atmospheric concentration distribution of PM2.5. The second reason is that the atmospheric concentration of PM2.5 in Tokyo has been decreasing every year making great improvements (55% reduction of atmospheric concentration in 10 years from 2001 to 2011). However, the rate for meeting environmental standards is low, and the atmospheric concentrations of PM2.5 in Tokyo are slightly above the environmental standards (annual average of

After setting the detailed estimation range to 35° 27' 30" - 35° 55' 0" north latitude and 138° 52' 30" - 140° 0' 0" east longitude in accordance with Tokyo, a grid of 100 m\*100 m is created. Then, the atmospheric concentration distribution of PM2.5 is estimated using ADMER, and the estimation results for each year are shown in **Figure 4**. From the figure, it is clear that

atmospheric concentrations are high in Shinjuku ward and Tachikawa city each year.

**Figure 4.** Atmospheric concentration distribution of PM2.5 in Tokyo (2009–2014).

*5.2.2. Estimation results for detailed estimation range*

below 15 μg/m<sup>3</sup>

310 Risk Assessment

).

From the estimation results in this section, it is clear that the areas with high atmospheric concentration of PM2.5 focus in the same areas each year. Especially in the entire Tokyo and Saitama, as well as in certain parts of Ibaraki, Kanagawa, and Chiba, the atmospheric concentrations were high. Moreover, as shown in **Figure 5**, in order to verify the validity of the estimation results of atmospheric concentration of PM2.5 in the present study, the levels of estimated atmospheric concentration and measured atmospheric concentration for 2014 are compared. The measured atmospheric concentration is from the environment numerical database of the National Institute for Environmental Studies.

As shown in **Figure 5**, the estimation results of atmospheric concentrations of PM2.5 in the Tokyo metropolitan area showed excellent reproducibility. With the atmospheric dispersion model, the consistency reference for the ratio of estimated and measured atmospheric concentration is set to be around ½- to 2-fold. Though the estimated atmospheric concentration in Tokyo and Saitama was above the measured atmospheric concentration level, the former for Ibaraki, Tochigi, Gunma, Kanagawa, and Chiba was below the latter. For Tokyo and Saitama, the amounts of PM2.5 discharged from businesses and plants in addition to the amount from automobiles and railroad vehicles were also large. Though the amounts of PM2.5 discharged from these generation sources are fixed according to the categories in ADMER, it could be that the estimated atmospheric concentration is higher than the measured atmospheric concentration level, as there is a possibility of the actual amount of discharged PM2.5 being less. On the other hand, in Ibaraki, Tochigi, Gunma, Kanagawa, and Chiba, the main generation sources for PM2.5 are considered to be transportation such as automobiles and

**Figure 5.** Comparison between the measured atmospheric concentration and estimated concentration in the present study in the Tokyo metropolitan area (2014).

railroad vehicles, as well as businesses and general households. Additionally, as the amounts of PM2.5 transported from other areas and naturally discharged into the environment are not taken into account, the estimated atmospheric concentration may have been lower than the measured atmospheric concentration level.

### **6. Estimations in detailed estimation target area**

#### **6.1. Estimation target**

From the estimation results of ADMER in the previous section, it was made clear that the atmospheric concentrations of PM2.5 were especially high in Shinjuku ward and Tachikawa city. Shinjuku ward is made up of some of the most prominent busy streets in Tokyo, and there are many commuters due to the many train lines. The fuel combustion from automobiles and railroads vehicles is considered to be the main cause of the high atmospheric concentration of PM2.5 in Shinjuku ward. Additionally, as there are many high-rise buildings in Shinjuku ward and these prevent air circulation and the dispersion of PM2.5, it is thought that this results in long-term high atmospheric concentration of PM2.5. In Tachikawa city, there are many commercial facilities as well as offices that are clustered together, and there are many commuters due to the many train lines like Shinjuku ward. For this reason, the PM2.5 discharged from the combustion of fuel from automobiles and railroad vehicles is considered to be one of the causes for Tachikawa city being an area with high atmospheric concentration. Moreover, business facilities and plants are considered to be main generation sources of PM2.5.

From the reasons stated above, as the estimation target area of the second stage, Tachikawa city was selected. Because PM2.5 discharged not only from fuel combustion due to automobiles and railroad vehicles, but also from specific generation sources such as business facilities and plants. Accordingly, in order to estimate the trajectory of PM2.5 discharged from specific generation sources, two areas within Tachikawa city (area A and area B) were extracted. **Figure 6** shows the distribution of specific generation sources in the estimation targets of the second stage. 80 m grid square units is set for area A and 100 m grid square units is set for area B. Additionally, to make identifying building and road placements in the estimation target area easier, the outer peripheral lines and arterial roads downloaded from the basic map information were displayed. Moreover, the estimation target period of the second stage is 2014 in which the latest data can be obtained.

#### **6.2. Estimation results**

The estimation results of atmospheric concentration distribution of PM2.5 in area A and area B using METI-LIS are shown in **Figure 7**. The estimation results are shown in 80 m grid square units for area A and 100 m grid square units in area B. From the estimation results, it is clear that PM2.5 spreads within a range of about 500 m from the specific generation sources, and the atmospheric concentration distributions are higher. In such areas, a downwash occurs due to buildings, which in turn prevents PM2.5 from spreading by the wind.

Estimation of PM2.5 Trajectory Using Atmospheric Dispersion Models and GIS in the Tokyo... http://dx.doi.org/10.5772/intechopen.70608 313

**Figure 6.** Detailed estimation target area.

#### **6.3. Discussion**

railroad vehicles, as well as businesses and general households. Additionally, as the amounts of PM2.5 transported from other areas and naturally discharged into the environment are not taken into account, the estimated atmospheric concentration may have been lower than the

From the estimation results of ADMER in the previous section, it was made clear that the atmospheric concentrations of PM2.5 were especially high in Shinjuku ward and Tachikawa city. Shinjuku ward is made up of some of the most prominent busy streets in Tokyo, and there are many commuters due to the many train lines. The fuel combustion from automobiles and railroads vehicles is considered to be the main cause of the high atmospheric concentration of PM2.5 in Shinjuku ward. Additionally, as there are many high-rise buildings in Shinjuku ward and these prevent air circulation and the dispersion of PM2.5, it is thought that this results in long-term high atmospheric concentration of PM2.5. In Tachikawa city, there are many commercial facilities as well as offices that are clustered together, and there are many commuters due to the many train lines like Shinjuku ward. For this reason, the PM2.5 discharged from the combustion of fuel from automobiles and railroad vehicles is considered to be one of the causes for Tachikawa city being an area with high atmospheric concentration. Moreover, business

From the reasons stated above, as the estimation target area of the second stage, Tachikawa city was selected. Because PM2.5 discharged not only from fuel combustion due to automobiles and railroad vehicles, but also from specific generation sources such as business facilities and plants. Accordingly, in order to estimate the trajectory of PM2.5 discharged from specific generation sources, two areas within Tachikawa city (area A and area B) were extracted. **Figure 6** shows the distribution of specific generation sources in the estimation targets of the second stage. 80 m grid square units is set for area A and 100 m grid square units is set for area B. Additionally, to make identifying building and road placements in the estimation target area easier, the outer peripheral lines and arterial roads downloaded from the basic map information were displayed. Moreover, the estimation target period of the second stage is 2014 in which the latest data can be obtained.

The estimation results of atmospheric concentration distribution of PM2.5 in area A and area B using METI-LIS are shown in **Figure 7**. The estimation results are shown in 80 m grid square units for area A and 100 m grid square units in area B. From the estimation results, it is clear that PM2.5 spreads within a range of about 500 m from the specific generation sources, and the atmospheric concentration distributions are higher. In such areas, a downwash occurs due

facilities and plants are considered to be main generation sources of PM2.5.

to buildings, which in turn prevents PM2.5 from spreading by the wind.

measured atmospheric concentration level.

**6.1. Estimation target**

312 Risk Assessment

**6.2. Estimation results**

**6. Estimations in detailed estimation target area**

In this section, the trajectory of PM2.5 discharged from specific generation sources was estimated. The measured atmospheric concentration levels show the total amount of PM2.5 discharged in the areas surrounding the measurement station. Therefore, as the measured atmospheric concentrations which indicate the amount of PM2.5 discharged from specific

**Figure 7.** Atmospheric concentration distribution of PM2.5 in Tachikawa city (2014).

generation sources do not exist, the levels of estimated atmospheric concentration and measured atmospheric concentration cannot be compared. In the vicinity of estimation target area A, though there are many buildings, it is considered to have little effect on the dispersion of PM2.5 as most are low-rise buildings. Additionally, in estimation target area B, though there are few buildings within the premises of specific generation source, because there are many surrounding buildings, this causes a downwash in the dispersion of PM2.5 which may reduce the atmospheric concentration.

### **7. Conclusion**

The conclusion of the present study can be summarized into the following four points:


### **Author details**

generation sources do not exist, the levels of estimated atmospheric concentration and measured atmospheric concentration cannot be compared. In the vicinity of estimation target area A, though there are many buildings, it is considered to have little effect on the dispersion of PM2.5 as most are low-rise buildings. Additionally, in estimation target area B, though there are few buildings within the premises of specific generation source, because there are many surrounding buildings, this causes a downwash in the dispersion of PM2.5 which may reduce

**Figure 7.** Atmospheric concentration distribution of PM2.5 in Tachikawa city (2014).

the atmospheric concentration.

314 Risk Assessment

Kayoko Yamamoto\* and Zhaoxin Yang \*Address all correspondence to: k-yamamoto@is.uec.ac.jp University of Electro-Communications, Tokyo, Japan

### **References**


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Provisional chapter

### **Risk Assessment and Prediction of Aflatoxin in Agro-Products** Risk Assessment and Prediction of Aflatoxin

DOI: 10.5772/intechopen.70792

Peiwu Li, Xiaoxia Ding, Yizhen Bai, Linxia Wu, Xiaofeng Yue and Liangxiao Zhang Peiwu Li, Xiaoxia Ding, Yizhen Bai, Linxia Wu,

Additional information is available at the end of the chapter Xiaofeng Yue and Liangxiao Zhang

http://dx.doi.org/10.5772/intechopen.70792 Additional information is available at the end of the chapter

#### Abstract

in Agro-Products

[25] Rizza U, Barnaba F, Miglietta MM, Mangia C, Liberto LD, Dionisi D, Costabile F, Grasso F, Gobbi GP. WRF-chem model simulations of a dust outbreak over the central Mediterranean and comparison with multi-sensor desert dust observations. Atmospheric

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[27] Logue JM, Lunden MM, Singer BC. Development and Application of a Physics-based Simulation Model to Investigate Residential PM2.5 Composition and Size Distribution Across the US. Ernest Orlando Lawrence Berkeley National Laboratory; Berkeley,

[28] Kodros JK, Wiedinmyer C, Ford B, Cucinotta R, Gan R, Magzamen S, Pierce JR. Global burden of mortalities due to chronic exposure to ambient PM2.5 from open combustion

[29] National Institute of Advanced Industrial Science and Technology: ADMER [Internet]. 2012. Available from: http://www.aist-riss.jp/software/admer/ja/index\_ja.html [Accessed:

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318 Risk Assessment

Manual; 2016. p. 116

Aerosol and Air Quality Research. 2013;**13**:943-956

Aflatoxin (AFT), highly toxic and carcinogenic to humans, seriously threatens consumption safety of agro-products. It is necessary to conduct risk assessment of aflatoxin contamination in agro-food products to find out critical control points (CCPs) and develop prediction, prevention and control theories and technologies. In this chapter, risk assessment and prediction of aflatoxin contamination in peanut were taken as an example. The values under the limit of detection (LOD) were replaced by zero, 1/2 LOD or LOD according to their respective proportion, and the distribution of values higher than the LOD was fitted by @RISK software. AFB1 dietary exposure was evaluated based on nonparametric probability risk assessment and margin of exposure (MOE). A risk ranking method was adopted for mycotoxins based on food risk expectation ranking. Spatial analysis of AFB1 contamination was conducted using geographic information system (GIS). Average climatic conditions were calculated by Thiessen polygon method and the relationship between AFB1 concentration and average pre-harvest climatic conditions was obtained through multiple regression. To fulfill the purposes of reducing cost, increasing efficiency, maximizing the role of risk assessment and prediction, and improving the quality and safety of agricultural products, we will continuously focus on developing advanced and integrated technologies and solutions.

Keywords: peanut, aflatoxin, dietary exposure, risk ranking, risk prediction

### 1. Introduction

Risk prediction of agro-product, especially oil and grain products, is becoming more and more important. In this chapter, risk assessment and prediction of aflatoxin (AFT) in peanuts were taken as an example. We presented the development and research progress on risk assessment and prediction of aflatoxin in agro-products in the following aspects: (1) data processing and

© 2018 The Author(s). Licensee InTech. This chapter is 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.

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

simulation methods of peanut aflatoxin contamination (determination and simulation of highly skewed data); (2) risk assessment methods (non-parametric probability risk assessment method and margin of exposure (MOE) method); (3) risk ranking method (multi-mycotoxin risk ranking method based on the expert scoring method); (4) risk prediction technologies (large-scale aflatoxin prediction based on ArcGIS) and (5) prospect of future research.

Aflatoxin (AFT) is highly toxic and carcinogenic and has been therefore classified as a Group I carcinogen by the International Agency for Research on Cancer [1]. The most important types of aflatoxins occurring naturally in agro-products are aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1) and aflatoxin G2(AFG2) [2, 3]. The total output and output per acre of peanuts always rank first of all oil crops cultivated in China. Peanuts produced in China account for about 40% of the world's peanut trade. In addition, peanuts contribute large amounts of vegetable oil, protein and vitamin E to developed countries [3–5]. Unfortunately, aflatoxin has been detected in more than 100 kinds of agro-products, especially in peanut and maize. Aflatoxin contamination might occur during the whole process of agro-products from production, storage, processing to trade, which seriously threatens consumption safety. To control aflatoxin contamination and ensure consumption safety, it is necessary to assess the risk of aflatoxin contamination in agro-products to identify critical control points (CCPs) and develop prediction, prevention and control theories and technologies for precise control in practice.

The mechanism of aflatoxin contamination is still not unclear since it is complex and multifactor dependent. Moreover, the aflatoxin contamination processes are significantly different over several consecutive years, and the contamination shows seriously skewed distribution. Among agro-products, peanuts are most seriously contaminated by aflatoxin. Since peanuts are popular food and oilseed worldwide, the prediction and control of aflatoxin contamination in peanuts are hot issues difficult to be resolved.

### 2. Data processing and simulation methods of peanut aflatoxin contamination

#### 2.1. Data processing of peanut aflatoxin contamination

According to post-harvest peanut aflatoxin data in China from 2009 to 2010, the proportion of "trace data" (below the detection limit) was over 70%, and the proportions of trace data for AFB1, AFB2, AFG1 and AFG2 were 78.3, 78.3, 98.8 and 97.2%, respectively. The aflatoxin data for the Chinese peanuts were positively skewed, and the Kolmogorov-Smirnov test proved that AFB1 and the total aflatoxin did not conform to the normal distribution, with about 90% of the aflatoxin data concentrated in the range of 0–2 μg/kg, so that the aflatoxin data for Chinese peanuts were left censored data and in line with a left skewed distribution. The "trace data" were distributed between 0 and the detection limit, which could not be accurately quantified due to the accuracy limitation of available instruments or equipment or unsatisfactory detection techniques. The presence of these trace data posed some difficulties to subsequent statistical analyses and could not be simply ignored because they had influences on the results of risk assessment.

simulation methods of peanut aflatoxin contamination (determination and simulation of highly skewed data); (2) risk assessment methods (non-parametric probability risk assessment method and margin of exposure (MOE) method); (3) risk ranking method (multi-mycotoxin risk ranking method based on the expert scoring method); (4) risk prediction technologies

Aflatoxin (AFT) is highly toxic and carcinogenic and has been therefore classified as a Group I carcinogen by the International Agency for Research on Cancer [1]. The most important types of aflatoxins occurring naturally in agro-products are aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1) and aflatoxin G2(AFG2) [2, 3]. The total output and output per acre of peanuts always rank first of all oil crops cultivated in China. Peanuts produced in China account for about 40% of the world's peanut trade. In addition, peanuts contribute large amounts of vegetable oil, protein and vitamin E to developed countries [3–5]. Unfortunately, aflatoxin has been detected in more than 100 kinds of agro-products, especially in peanut and maize. Aflatoxin contamination might occur during the whole process of agro-products from production, storage, processing to trade, which seriously threatens consumption safety. To control aflatoxin contamination and ensure consumption safety, it is necessary to assess the risk of aflatoxin contamination in agro-products to identify critical control points (CCPs) and develop prediction, prevention and control theories and technologies for precise control

The mechanism of aflatoxin contamination is still not unclear since it is complex and multifactor dependent. Moreover, the aflatoxin contamination processes are significantly different over several consecutive years, and the contamination shows seriously skewed distribution. Among agro-products, peanuts are most seriously contaminated by aflatoxin. Since peanuts are popular food and oilseed worldwide, the prediction and control of aflatoxin contamination in

According to post-harvest peanut aflatoxin data in China from 2009 to 2010, the proportion of "trace data" (below the detection limit) was over 70%, and the proportions of trace data for AFB1, AFB2, AFG1 and AFG2 were 78.3, 78.3, 98.8 and 97.2%, respectively. The aflatoxin data for the Chinese peanuts were positively skewed, and the Kolmogorov-Smirnov test proved that AFB1 and the total aflatoxin did not conform to the normal distribution, with about 90% of the aflatoxin data concentrated in the range of 0–2 μg/kg, so that the aflatoxin data for Chinese peanuts were left censored data and in line with a left skewed distribution. The "trace data" were distributed between 0 and the detection limit, which could not be accurately quantified

2. Data processing and simulation methods of peanut aflatoxin

(large-scale aflatoxin prediction based on ArcGIS) and (5) prospect of future research.

in practice.

320 Risk Assessment

contamination

peanuts are hot issues difficult to be resolved.

2.1. Data processing of peanut aflatoxin contamination

During the process of building a risk assessment model for the peanuts' aflatoxin exposure, it is important to consider how to deal with the considerable values below the detection limit.

In accordance with the previous studies, there were mainly two solutions, which were point substitution and theoretical distribution substitution.

The point substitution method has been widely used in risk assessment of chemicals, such as heavy metals or pesticides. International aflatoxin risk assessment, conducted by JECFA or EFSA, also adopted this approach. Global Environment Monitoring System-Food Contamination Monitoring and Assessment Program [6] suggested that when the proportion of non-quantified or non-detected results was greater than 60%, the value under the limit of detection (LOD) was replaced by zero or LOD to produce upper and lower boundaries; when the proportion was less than 60%, LOD/2 was chosen as the substitute to produce statistical estimates. The point substitution method was a convenient operation, but its results were relatively rough and could not be used to evaluate uncertainty and variability.

Taking the process of total aflatoxin in peanuts as an example, the values below the LOD were all replaced by 0, 1/2LOD and LOD at first to generate three data sets, respectively. Then, the percentiles were calculated and it showed the difference occurred at <65th percentile, the three alternative results of total aflatoxin approached gradually from 65th to 85th percentile, and the maximum absolute difference was reduced from 0.16 to 0.14 μg/kg and then to 0.11 μg/kg at 95th percentile. The difference was mainly from detection limit of test method for aflatoxins. So, low detection limit was the main approach for reducing the difference among three alternative methods and improving the evaluation accuracy. The optimal detection method for aflatoxin was liquid chromatography coupled with immunoaffinity chromatography, which had relatively higher sensitivity and accuracy.

The theoretical distribution substitution method was based on the characteristics of contamination data. Taking AFB1 in post-harvest peanuts as research object, the method was performed in two steps as follows. First, we sorted all data and eliminated the trace values that were lower than LOD from the entire dataset. Second, we fitted the distribution function with the values that were higher than the LOD by @RISK software, and then used the Kolmogorov-Smirnov (K-S) or Anderson-Darling (A-D) method to perform statistical tests on the fitting results. Through screening and optimization, Pearson V, Inverse Gauss and log-normal distributions were suitable to aflatoxin distribution in peanuts, and the comparison of frequency distribution and probability density indicated that Pearson V for goodness of fit was the best.

#### 2.2. Risk assessment methods

#### 2.2.1. Risk assessment based on non-parametric probability

AFB1 dietary exposure was evaluated based on a probability distribution of aflatoxin contamination and consumption in agro-products, and the results were standardized by human bodyweight. The Monte Carlo method was chosen to perform the entire simulation process by @RISK program and the uncertainty was described by 90% confidence interval or quartile. The risk posed by dietary exposure to AFB1 was modeled by the following formula: Population risk = exposure average potency; exposure (daily intake of AFB1 expressed as ng kg<sup>1</sup> bw day) = (contamination level consumption amount)/bw; average potency = 0.3 P + 0.01 (1 P), where P represents the hepatitis-B-virus surface antigen (HBsAg) prevalence rate for different age groups.

For example, AFB1 risk assessment in peanuts was conducted on the basis of dietary exposure to AFB1 and its potential to cause hepatic cancer. Based on the results of peanut aflatoxin survey in China conducted in 2009–2010, as well as peanut consumption data and average bodyweight in each age-gender group from the 2002 Chinese Residents Nutrition and Health Survey Report [7–9], dietary exposure to AFB1 was calculated and simulated by Monte Carlo. In line with the guidelines of Global Environment Monitoring System-Food Contamination Monitoring and Assessment Program [6], the values which were less than the LOD, were estimated, assuming that the proportion of non-quantified or non-detected results was more than 60% but less than 80%, the values under the LOD were substituted by zero or the LOD, which could provide a lower or higher boundary [10].

Excess risks for liver cancer incidence per year, resulting from AFB1 dietary intake through peanut consumption, were calculated from dietary exposure to AFB1 multiplied by the average AFB1 cancer potency. According to the AFB1 risk assessment report from JECFA [11], the average cancer potency was produced by setting the individual potencies of HBsAg<sup>+</sup> and HBsAg to 0.3 and 0.01 cancers/year/100,000/ng kg<sup>1</sup> bw day<sup>1</sup> , respectively. In this assessment, the age-adjusted HBsAg<sup>+</sup> prevalence rate was obtained from the 2006 National Seroepidemiological Survey report.

To evaluate potential health risk to Chinese under AFB1 exposure in food, the excess risk for liver cancer in adults was estimated based on the mean and 97.5th percentile of the contamination and consumption data. The estimated AFB1 intake from raw peanuts was between 0.11 and 5.66 ng kg<sup>1</sup> bw day<sup>1</sup> and the population risk was 0.003–0.17 cancer cases/year/100,000 from raw peanut consumption. The population risk was 0.03–2.06 cancer cases/year/100,000 from peanut oil intake of 0.84–68.8 ng kg<sup>1</sup> bw day<sup>1</sup> . These data indicated that the risk from peanut oil was 10 times or more that from raw peanuts.

#### 2.2.2. Margin of exposure (MOE) method

A "margin of exposure" was calculated from a chosen point of departure (POD) on a dose– response curve divided by the human dietary exposure estimate, which was obtained based on the benchmark dose (BMD) developed by EFSA [12, 13]. The PODs, employed to quantify an increased cancer risk, were summarized in Table 1. When the POD was determined, a smaller


Derived from animal carcinogenicity data. b Derived from Chinese epidemiological data.

2.2. Risk assessment methods

322 Risk Assessment

rate for different age groups.

epidemiological Survey report.

2.2.2. Margin of exposure (MOE) method

2.2.1. Risk assessment based on non-parametric probability

which could provide a lower or higher boundary [10].

from peanut oil intake of 0.84–68.8 ng kg<sup>1</sup> bw day<sup>1</sup>

peanut oil was 10 times or more that from raw peanuts.

HBsAg to 0.3 and 0.01 cancers/year/100,000/ng kg<sup>1</sup> bw day<sup>1</sup>

AFB1 dietary exposure was evaluated based on a probability distribution of aflatoxin contamination and consumption in agro-products, and the results were standardized by human bodyweight. The Monte Carlo method was chosen to perform the entire simulation process by @RISK program and the uncertainty was described by 90% confidence interval or quartile. The risk posed by dietary exposure to AFB1 was modeled by the following formula: Population risk = exposure average potency; exposure (daily intake of AFB1 expressed as ng kg<sup>1</sup> bw day) = (contamination level consumption amount)/bw; average potency = 0.3 P + 0.01 (1 P), where P represents the hepatitis-B-virus surface antigen (HBsAg) prevalence

For example, AFB1 risk assessment in peanuts was conducted on the basis of dietary exposure to AFB1 and its potential to cause hepatic cancer. Based on the results of peanut aflatoxin survey in China conducted in 2009–2010, as well as peanut consumption data and average bodyweight in each age-gender group from the 2002 Chinese Residents Nutrition and Health Survey Report [7–9], dietary exposure to AFB1 was calculated and simulated by Monte Carlo. In line with the guidelines of Global Environment Monitoring System-Food Contamination Monitoring and Assessment Program [6], the values which were less than the LOD, were estimated, assuming that the proportion of non-quantified or non-detected results was more than 60% but less than 80%, the values under the LOD were substituted by zero or the LOD,

Excess risks for liver cancer incidence per year, resulting from AFB1 dietary intake through peanut consumption, were calculated from dietary exposure to AFB1 multiplied by the average AFB1 cancer potency. According to the AFB1 risk assessment report from JECFA [11], the average cancer potency was produced by setting the individual potencies of HBsAg<sup>+</sup> and

ment, the age-adjusted HBsAg<sup>+</sup> prevalence rate was obtained from the 2006 National Sero-

To evaluate potential health risk to Chinese under AFB1 exposure in food, the excess risk for liver cancer in adults was estimated based on the mean and 97.5th percentile of the contamination and consumption data. The estimated AFB1 intake from raw peanuts was between 0.11 and 5.66 ng kg<sup>1</sup> bw day<sup>1</sup> and the population risk was 0.003–0.17 cancer cases/year/100,000 from raw peanut consumption. The population risk was 0.03–2.06 cancer cases/year/100,000

A "margin of exposure" was calculated from a chosen point of departure (POD) on a dose– response curve divided by the human dietary exposure estimate, which was obtained based on the benchmark dose (BMD) developed by EFSA [12, 13]. The PODs, employed to quantify an increased cancer risk, were summarized in Table 1. When the POD was determined, a smaller

, respectively. In this assess-

. These data indicated that the risk from

Table 1. Reference points/PODs derived from animal carcinogenicity and Chinese epidemiological data (ng kg�<sup>1</sup> bw day�<sup>1</sup> ).

MOE value represented a greater risk. Compared with a traditional low-dose extrapolation approach, the MOE value easily indicated what the risk level was, provided that the POD value had been defined. The MOE value would be smaller when exposure became greater. In general, a smaller MOE represented a greater risk.

Taking risk assessment of peanut aflatoxin exposure expressed by the MOE in China as an example, the MOEs were calculated on the basis of Chinese peanut aflatoxin exposure and PODs from the reported literature, which were developed based on Chinese epidemiological data by EFSA [10] or rodent experimental data by Benford et al. The relative PODs were summarized in Table 1. Here, BMDL10 (140 ng kg�<sup>1</sup> bw day�<sup>1</sup> for rodent [16] and 870 ng kg�<sup>1</sup> bw day�<sup>1</sup> for human) and BMDL1 (78 ng kg�<sup>1</sup> bw day�<sup>1</sup> for human), were introduced into the MOE calculation, which represented the 95% lower confidence limit (CL) of the BMD for a 10 or 1% increased cancer risk .

The estimated MOE values ranging from 24.1 to 1272 were higher than the results estimated by EFSA (88–483) [10] for Africa (0.2–121.4) [17]. Far lower than 10,000, would be regarded as low concern [12], and a higher MOE value implied a lower risk. The MOE values of peanuts based on the rodent data were 24.7–1272 and 2.0–167, respectively. In other words, the cancer risk, which originated from direct consumption of post-harvest peanuts or raw peanuts, was much lower than that from peanut oil. The above results were consistent with the conclusions, which were calculated on the basis of the cancer potencies of aflatoxin employed by JECFA. However changing index among two different methods was not found by now.

### 3. Risk ranking methods

The Codex Alimentarius Commission (CAC) recommended a food risk expectation ranking method. This method is usually based on literature review, authoritative data and database records of the countries that have evaluated the food hazard/consumption frequency and detected frequency information. Then the main food contamination factors are accurately identified, and the risks from different sources are compared. According to the calculated scores of the indexes, ranking of the risk will be obtained. This approach has the advantage of clear scoring criteria and that direct use of the defined scoring criteria. In 1999, Houghton et al. [18] studied the ranking of risk factors for anxiety disorders in the UK using the risk expectation approach. In 2011, the method of risk expectation was used to systematically study the ranking and change in the relative risk index of liquefied petroleum gas transportation in Mexico metropolitan area [19]. In 2014, Speybroeck et al. [20] studied the ranking of risk factors in food chain by means of sampling survey and risk expectation. Nevertheless, there are few studies on risk factor of mycotoxins in peanuts and no study on risk ranking of mycotoxins in China until now.

In order to give a reference for risk monitoring and assessment of peanut quality and safety, the risk ranking method of mycotoxins in peanuts was proposed on the basis of the food risk expectation ranking method. A total of 604 peanut samples from 8 provinces were collected. Based on the mycotoxins concentration in peanuts and maximum residue levels, hazard degrees were identified. The effective evaluation indicators were chosen, and a normalized method was established for searching, identifying and ranking peanut mycotoxin risk factors.

#### 3.1. Hazard degree identification

This study referred to the risk ranking sample tool recommended by the CAC and considered human health threats caused by the risk factors. The hazard severity and probability of occurrence were considered from the qualitative and quantitative points of view. The hazard degrees of the risk factors and risk ranking score evaluation criteria (Table 2) were identified with their toxicity, degree of difficulty in risk control, severity, social reputation, maximum amount of detection residue and detection rate considered.

According to the basic requirements of risk identification, the modified risk identification method was developed for peanut mycotoxins after several cycles of discussion, screening and expert opinion collection (Table 3).

#### 3.2. Risk factor analysis and ranking

According to the risk ranking score evaluation criteria (Table 2) and identification of peanut mycotoxin risk degrees (Table 3), in addition to the toxicities, degrees of difficulty in risk


Table 2. Identification of food hazards and risk ranking score evaluation criteria.


Table 3. Identification of peanut mycotoxin risk degrees.

clear scoring criteria and that direct use of the defined scoring criteria. In 1999, Houghton et al. [18] studied the ranking of risk factors for anxiety disorders in the UK using the risk expectation approach. In 2011, the method of risk expectation was used to systematically study the ranking and change in the relative risk index of liquefied petroleum gas transportation in Mexico metropolitan area [19]. In 2014, Speybroeck et al. [20] studied the ranking of risk factors in food chain by means of sampling survey and risk expectation. Nevertheless, there are few studies on risk factor of mycotoxins in peanuts and no study on risk ranking of

In order to give a reference for risk monitoring and assessment of peanut quality and safety, the risk ranking method of mycotoxins in peanuts was proposed on the basis of the food risk expectation ranking method. A total of 604 peanut samples from 8 provinces were collected. Based on the mycotoxins concentration in peanuts and maximum residue levels, hazard degrees were identified. The effective evaluation indicators were chosen, and a normalized method was established for searching, identifying and ranking peanut mycotoxin risk factors.

This study referred to the risk ranking sample tool recommended by the CAC and considered human health threats caused by the risk factors. The hazard severity and probability of occurrence were considered from the qualitative and quantitative points of view. The hazard degrees of the risk factors and risk ranking score evaluation criteria (Table 2) were identified with their toxicity, degree of difficulty in risk control, severity, social reputation, maximum

According to the basic requirements of risk identification, the modified risk identification method was developed for peanut mycotoxins after several cycles of discussion, screening

According to the risk ranking score evaluation criteria (Table 2) and identification of peanut mycotoxin risk degrees (Table 3), in addition to the toxicities, degrees of difficulty in risk

> Index value (score = 4)

Index value (score = 3)

Index value (score = 2)

(score = 5)

Toxicity High Relatively high Medium Low Degree of difficulty inrisk control Difficult Poor Potentially poor Capable Severity Serious Relatively serious Medium Noteworthy Social reputation Serious Relatively serious Medium Noteworthy Maximum amount of detection residue/(μg/kg) >5000 1000–5000 500–1000 0–500 Detection rate/% >10 8–10 6–8 4–6

mycotoxins in China until now.

324 Risk Assessment

3.1. Hazard degree identification

and expert opinion collection (Table 3).

3.2. Risk factor analysis and ranking

Index Index value

Table 2. Identification of food hazards and risk ranking score evaluation criteria.

amount of detection residue and detection rate considered.

control, severities, social reputations, maximum amounts of detection residue and detection rates of peanut AFB1, AFB2, AFG1, AFG2, Ochratoxin A (OTA) and Deoxynivalenol (DON) in peanuts in China, the mycotoxin risk factor scores for peanuts were calculated by Formula (1).

$$\mathbf{s} = \frac{\sum\_{i=1}^{n} \mathbf{X}\_{\text{Ai}}}{\mathbf{n}} \times \frac{\sum\_{i=1}^{n} (\mathbf{X}\_{\text{Bi}} + \mathbf{X}\_{\text{Ci}} + \mathbf{X}\_{\text{Di}} + \mathbf{X}\_{\text{Bi}} + \mathbf{X}\_{\text{Fi}})}{\mathbf{n}} = \mathbf{U}\_{\text{A}} \times (\mathbf{U}\_{\text{B}} + \mathbf{U}\_{\text{C}} + \mathbf{U}\_{\text{D}} + \mathbf{U}\_{\text{E}} + \mathbf{U}\_{\text{F}}) \tag{1}$$

S: mycotoxin risk factor scores of peanuts; XAi: mycotoxin toxicity score of sample i; XBi: mycotoxin score of the degree of difficulty in risk control of sample i; XCi: mycotoxin severity score of sample i; XDi: mycotoxin social reputation score of sample i; XEi: mycotoxin score of the maximum amount of detection residue of sample i; XFi: mycotoxin detection rate score of sample i; n: number of samples; UA: average score of mycotoxin toxicity; UB: average score of the degree of difficulty in risk control; UC: average score of severity; UD: average score of social reputation; UE: average score of the maximum amount of detection residue; UF: average score of the detection rate.

Mycotoxin risk factor scores and ranking for peanuts in China were listed in Table 4. It indicated that high attention needed to be paid to AFB1, relatively high attention needed to be paid to AFG1, moderate attention needed to be paid to AFB2 and AFG2, and low attention needed to be paid to OTA and DON.


Table 4. Mycotoxin risk ranking for peanuts in China.

### 4. Risk prediction technologies

Geographic information systems (GIS) and geostatistics can be used to describe, analyze and display spatial patterns of a wide variety of variables at any scale and, by improving resource management and revealing causal relationships among geographically variable factors, assist in real world problems [21]. Kriging, a regression technique for interpolation of spatially correlated data, is the most common geostatistical procedure for surface interpolation, can be used to locally average the weights of data from sampled locations surrounding an unsampled location based on statistical similarity to unsampled locations; it gives unbiased estimates with the estimated variance minimized. The weights are determined using semivariance analysis between sampled locations [22]. Areas in China with the highest risk of AFB1 contamination were identified by geostatistical analyses and Kriging maps. According to different locations, terrain features, climatic conditions, variety distributions and cultivation systems, the peanut planting areas in China were divided into four sections: Northeast, North, Yangtze River and South [23]. Agricultural practices including crop rotation, tillage, irrigation and fertilization, as well as the planting date, genetic resistance, soil type and climatic conditions all impact AFT contamination of peanuts before harvest [24]. Nevertheless, climatic conditions significantly influence the AFT contamination level. In serious drought and/or high temperature conditions before harvest, fungus invasion and AFT accumulation become accelerated [25, 26].

### 4.1. Spatial analysis of AFB1 contamination of peanuts in China

A total of 9741 peanut samples were collected from main produce area in China from 2009 to 2014 and on the AFB1 content of these peanut samples were analyzed. Geostatistical analyses were performed on the annual average AFB1 content to obtain the patterns of AFB1 contamination throughout China. Kriging of AFB1 showed that aflatoxin contamination of peanuts in China was a perennial problem presenting both temporal and spatial (regional) variations. Kriging interpolation of AFB1 contamination indicated a patchy distribution, which varied with the seasons. Results showed that aflatoxin contamination was almost not found in the Northeast region during the study period. And it presented significantly temporal and spatial (regional) variations in the Yangtze River Basin region and Southeast Coast region.

#### 4.2. Relationship between AFB1 contamination levels in peanuts and climatic conditions before harvest

Cole et al. found that ripe and integral peanuts exposed to simultaneously drought and heat (25.7–31.3C) stress became be prone to Aspergillus flavus invasion and AFT production in the last 4–6 weeks of the growing season [27]. A total of 2983 peanut samples were collected from 122 counties in 6 provinces of China's Yangtze River ecological region from 2009 to 2014. Based on Thiessen polygon interpolation, average precipitation and mean temperature data in 2009–2014 in the Yangtze River ecological region were calculated by the climatic conditions of 118 weather stations. In Figure 1, we found that there was less precipitation and higher daily mean temperature (around 25C) during peanut growing season (June–August) in 2013, which aggravated the AFB1 contamination. Taking Hunan province as an example, the

Figure 1. Precipitation and mean temperature of the Yangtze River ecological region during the peanuts' growing season (2009–2014).

determination coefficient (R<sup>2</sup> ) fitted by the AFB1 content with the average climatic conditions in different pre-harvest periods was obtained by multiple regression (Figure 2). Results indicated that the average precipitation and mean temperature of 1 month before harvest had a significant influence on AFB1 contamination. Moreover, Hunan and Jiangxi were greatly affected. Due to the annual and climatic variation of AFB1 contamination level, it is necessary to build a prediction model by developing a continuous and effective AFB1 monitoring program for pre-harvest peanuts during its growing season. Up to now, there had been some progress on model building in Australia and USA [28, 29].

#### 5. Prospect of future research

4. Risk prediction technologies

326 Risk Assessment

before harvest

Geographic information systems (GIS) and geostatistics can be used to describe, analyze and display spatial patterns of a wide variety of variables at any scale and, by improving resource management and revealing causal relationships among geographically variable factors, assist in real world problems [21]. Kriging, a regression technique for interpolation of spatially correlated data, is the most common geostatistical procedure for surface interpolation, can be used to locally average the weights of data from sampled locations surrounding an unsampled location based on statistical similarity to unsampled locations; it gives unbiased estimates with the estimated variance minimized. The weights are determined using semivariance analysis between sampled locations [22]. Areas in China with the highest risk of AFB1 contamination were identified by geostatistical analyses and Kriging maps. According to different locations, terrain features, climatic conditions, variety distributions and cultivation systems, the peanut planting areas in China were divided into four sections: Northeast, North, Yangtze River and South [23]. Agricultural practices including crop rotation, tillage, irrigation and fertilization, as well as the planting date, genetic resistance, soil type and climatic conditions all impact AFT contamination of peanuts before harvest [24]. Nevertheless, climatic conditions significantly influence the AFT contamination level. In serious drought and/or high temperature conditions

before harvest, fungus invasion and AFT accumulation become accelerated [25, 26].

(regional) variations in the Yangtze River Basin region and Southeast Coast region.

4.2. Relationship between AFB1 contamination levels in peanuts and climatic conditions

Cole et al. found that ripe and integral peanuts exposed to simultaneously drought and heat (25.7–31.3C) stress became be prone to Aspergillus flavus invasion and AFT production in the last 4–6 weeks of the growing season [27]. A total of 2983 peanut samples were collected from 122 counties in 6 provinces of China's Yangtze River ecological region from 2009 to 2014. Based on Thiessen polygon interpolation, average precipitation and mean temperature data in 2009–2014 in the Yangtze River ecological region were calculated by the climatic conditions of 118 weather stations. In Figure 1, we found that there was less precipitation and higher daily mean temperature (around 25C) during peanut growing season (June–August) in 2013, which aggravated the AFB1 contamination. Taking Hunan province as an example, the

A total of 9741 peanut samples were collected from main produce area in China from 2009 to 2014 and on the AFB1 content of these peanut samples were analyzed. Geostatistical analyses were performed on the annual average AFB1 content to obtain the patterns of AFB1 contamination throughout China. Kriging of AFB1 showed that aflatoxin contamination of peanuts in China was a perennial problem presenting both temporal and spatial (regional) variations. Kriging interpolation of AFB1 contamination indicated a patchy distribution, which varied with the seasons. Results showed that aflatoxin contamination was almost not found in the Northeast region during the study period. And it presented significantly temporal and spatial

4.1. Spatial analysis of AFB1 contamination of peanuts in China

The occurrence and control of aflatoxin contamination in agro-products are world wide hot issues difficult to resolve. Studies on risk monitoring, risk assessment and early risk prediction of aflatoxin in peanuts, maize and other agro-products have long been considered as an important premise for effective aflatoxin contamination control. Hence, our further efforts

Figure 2. Multiple regression determination coefficient (R<sup>2</sup> ) fitted by the AFB1 content with average precipitation and mean temperature in different periods of time (Hunan province).

should be focused on enhancing and perfecting the basic database of aflatoxin contamination, drawing geographic risk maps, developing reliable and accurate risk forecasting techniques and a forecasting system, as well as establishing a smart and low-cost platform for sustainable aflatoxin management and communication in the field and during storage, processing and transportation. Moreover, risk monitoring, simultaneous detection technologies of multimycotoxin contamination and their interaction mechanisms should also be taken into account.

#### 5.1. Characteristics and geographic risk maps for aflatoxin contamination and main toxigenic fungal population

To boost the progress of mycotoxin risk assessment, what is crucial is to enhance the basic database construction for mycotoxin contamination, especially aflatoxin, which has the highest acute and chronic toxicity among all mycotoxins. Hence, it is necessary to conduct a continuous and effective AFT monitoring program for obtaining quantitative data from different latitudes, altitudes and ecological regions in a global level via international cooperation by sampling and detecting representative fields. Precise risk maps for aflatoxin contamination will be drawn to highlight the distribution, concentration and trend of annual occurrence. Certainly, advanced and accurate analytical techniques will be an essential part of guaranteeing the quality of monitoring and data. Meanwhile, the main toxigenic fungal population database, including A. flavus and Aspergillus parasiticus, should also be determined by collecting and identifying isolates from peanuts and soil in the corresponding areas. Geographic maps of these fungi populations will be defined. The aflatoxin production, biodiversity and phylogenetic clades of these toxigenic fungi will be revealed. Because the changes in climatic conditions will lead to a shift in the fungal population and mycotoxin patterns, more attention should be paid in a climate change scenario. What is more, multi-mycotoxin cooccurrence and interactions with different fungi, such as A. flavus and Fusarium verticillioides, have been recognized as emerging problems and cannot be ignored.

From a risk assessment perspective, to determine risk maps of aflatoxin contamination and toxigenic fungal population to a global level, which was based on a continuous and effective AFT monitoring program, is a key step in risk prediction. Additionally, aflatoxin and toxigenic fungi risk maps could be used as a communication tool for stakeholders and farmers. Moreover, the maps could be provided as a tool for scientific supervision, decision support and governments' policy-making, as well as prioritization of a more targeted approach and intervention strategies, especially in high-risk zones.

#### 5.2. Building an early predictive model of aflatoxin by combining macroscopic and molecular warning technologies

should be focused on enhancing and perfecting the basic database of aflatoxin contamination, drawing geographic risk maps, developing reliable and accurate risk forecasting techniques and a forecasting system, as well as establishing a smart and low-cost platform for sustainable aflatoxin management and communication in the field and during storage, processing and transportation. Moreover, risk monitoring, simultaneous detection technologies of multimycotoxin contamination and their interaction mechanisms should also be taken into account.

) fitted by the AFB1 content with average precipitation and

5.1. Characteristics and geographic risk maps for aflatoxin contamination and main

To boost the progress of mycotoxin risk assessment, what is crucial is to enhance the basic database construction for mycotoxin contamination, especially aflatoxin, which has the highest acute and chronic toxicity among all mycotoxins. Hence, it is necessary to conduct a continuous and effective AFT monitoring program for obtaining quantitative data from different latitudes, altitudes and ecological regions in a global level via international cooperation by sampling and detecting representative fields. Precise risk maps for aflatoxin contamination will be drawn to highlight the distribution, concentration and trend of annual occurrence. Certainly, advanced and accurate analytical techniques will be an essential part of guaranteeing the quality of monitoring and data. Meanwhile, the main toxigenic fungal population

toxigenic fungal population

328 Risk Assessment

Figure 2. Multiple regression determination coefficient (R<sup>2</sup>

mean temperature in different periods of time (Hunan province).

In further studies, there is an urgent need to establish a precise and reliable forecasting system with advanced prediction methods to reflect actual occurrence of aflatoxin contamination so that we can make appropriate management and agronomic strategies especially in high-risk areas, minimize the risk of pre-harvest contamination and therefore protect public and animal health. Moreover, applying the early warning model can significantly reduce the detoxification cost. Until now, a lot of researches indicated that key environmental factors including temperature, humidity and precipitation significantly influenced fungus growth, infection as well as aflatoxin production [30–32]. And some efforts have been devoted to developing models to predict aflatoxin contamination in peanuts and maize with climatic data used as the main or only input, such as the Agricultural Production Systems Simulator (APSIM) and the CSM-CROPGRO-Peanut model [29, 33]. However, few models were actually applied to the field to predict the future aflatoxin risk or just a small region for validation and demonstration. Besides climatic data, the factors such as ecological zones, peanut varieties and microbial structures should not be ignored, which were also believed to be aflatoxin-related factors. In short, there is further work to develop a large-scale risk prediction model based on multifactors and apply it to different fields.

Firstly, advanced technologies in digital and smart agriculture are essential for effectively monitoring the fields. GIS, environmental sensors or satellite systems will be used to monitor crop conditions and abiotic factors such as humidity, temperature, precipitation, wind and sunshine in real time, in order to acquire spatial and temporal distribution information of the crops and climatic data rapidly and accurately during crop-growing seasons. These local and accurate real-time data will be directly translated for researchers and other stakeholders. And then, correlation analysis will be, respectively, carried out between the aflatoxin contamination data and these real-time data, agronomic information and toxigenic fungus population to evaluate the role and contribution of these relevant factors in the field of aflatoxin contamination risks. In particular, the role of CO2 should be taken into account, which is increasingly important in a climate change scenario. At last, a macro-scale predictive model will be built and operated with these "real-time" data as the input to obtain specific early predictions regarding the risk of aflatoxin. Appropriate management decisions and recommendations for farmers and other stakeholders will be formulated when the contamination risk is high, which is based on the output data.

In recent years, with rapid development of molecular biology, molecular prediction technologies have gradually become frontier for early warning of mycotoxins. The interactive conditions of aw temperature elevated CO2 have a significant impact on aflatoxin biosynthetic gene expression, such as the structural genes aflD and aflM and regulatory genes aflS and aflR, and the production of AFB1 [32, 34]. A physical model was built and used to relate gene expression to aw and temperature conditions to predict AFB1 production. And its relationship with the observed AFB1 production provided a good linear regression fit to the predicted production based on the model [34]. The expression data ratio of aflS/aflR has a relationship with the amount of AFB1 or AFG1. High ratios in the range between 17 and 30C corresponded to the production profile of AFG1 biosynthesis. A low ratio was observed at >30C, which was related to AFB1 biosynthesis [35]. We therefore believed that it is possible to predict the aflatoxin risk via the expression model of key genes or secondary metabolites. In our future work, we will devote to screening and identifying more effective molecular markers to make the prediction more reliable and build a molecular forecasting model.

Therefore, it is believed that effective integration of macro-scale, molecular, ecophysiological and secondary metabolite data sets could be critical in predicting the risk of aflatoxin contamination under different biotic and abiotic stress scenarios and agronomic strategies. Such combinative technologies will be beneficial to more accurate predictions of the aflatoxin risk in different regions and also the potential for new emerging toxin threats.

#### 5.3. Developing a smart platform for aflatoxin risk communication and management

A convenient and user-friendly platform, such as a mobile app, will be developed. The platform will provide key information about the crops, contamination risks or levels, recommendations, practical solutions, problem consultation and answers to farmers and other stakeholders who require suggestions for rapid and low-cost intervention. The interpretation of the output of the predictive models and recommendations will be transformed into the platform. Thus, farmers can not only obtain the growth status of the crops in the field, but also timely and cost-effective strategies for prevention or remediation of the risks during their harvest, storage, processing and transportation.

In conclusion, it is necessary to further focus on the development of advanced and integrated technologies and solutions to achieve the purposes of reducing costs, increasing efficiency, maximizing the role of risk assessment and risk prediction, and definitely improving the quality and security of agricultural products.

### Author details

evaluate the role and contribution of these relevant factors in the field of aflatoxin contamination risks. In particular, the role of CO2 should be taken into account, which is increasingly important in a climate change scenario. At last, a macro-scale predictive model will be built and operated with these "real-time" data as the input to obtain specific early predictions regarding the risk of aflatoxin. Appropriate management decisions and recommendations for farmers and other stakeholders will be formulated when the contamination risk is high, which

In recent years, with rapid development of molecular biology, molecular prediction technologies have gradually become frontier for early warning of mycotoxins. The interactive conditions of aw temperature elevated CO2 have a significant impact on aflatoxin biosynthetic gene expression, such as the structural genes aflD and aflM and regulatory genes aflS and aflR, and the production of AFB1 [32, 34]. A physical model was built and used to relate gene expression to aw and temperature conditions to predict AFB1 production. And its relationship with the observed AFB1 production provided a good linear regression fit to the predicted production based on the model [34]. The expression data ratio of aflS/aflR has a relationship with the amount of AFB1 or AFG1. High ratios in the range between 17 and 30C corresponded to the production profile of AFG1 biosynthesis. A low ratio was observed at >30C, which was related to AFB1 biosynthesis [35]. We therefore believed that it is possible to predict the aflatoxin risk via the expression model of key genes or secondary metabolites. In our future work, we will devote to screening and identifying more effective molecular markers to make

Therefore, it is believed that effective integration of macro-scale, molecular, ecophysiological and secondary metabolite data sets could be critical in predicting the risk of aflatoxin contamination under different biotic and abiotic stress scenarios and agronomic strategies. Such combinative technologies will be beneficial to more accurate predictions of the aflatoxin risk

5.3. Developing a smart platform for aflatoxin risk communication and management

A convenient and user-friendly platform, such as a mobile app, will be developed. The platform will provide key information about the crops, contamination risks or levels, recommendations, practical solutions, problem consultation and answers to farmers and other stakeholders who require suggestions for rapid and low-cost intervention. The interpretation of the output of the predictive models and recommendations will be transformed into the platform. Thus, farmers can not only obtain the growth status of the crops in the field, but also timely and cost-effective strategies for prevention or remediation of the risks during their

In conclusion, it is necessary to further focus on the development of advanced and integrated technologies and solutions to achieve the purposes of reducing costs, increasing efficiency, maximizing the role of risk assessment and risk prediction, and definitely improving the

the prediction more reliable and build a molecular forecasting model.

in different regions and also the potential for new emerging toxin threats.

harvest, storage, processing and transportation.

quality and security of agricultural products.

is based on the output data.

330 Risk Assessment

Peiwu Li1,2,3,4,5, Xiaoxia Ding1,2,3,5, Yizhen Bai1,2,5, Linxia Wu1 \*, Xiaofeng Yue1,2,3,5 and Liangxiao Zhang1,2,3,5

\*Address all correspondence to: 1060038025@qq.com

1 Oil Crops Research Institute of Chinese Academy of Agriculture Science, Wuhan, People's Republic of China

2 Laboratory of Risk Assessment for Oilseeds Products (Wuhan), Ministry of Agriculture, Wuhan, People's Republic of China

3 Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan, People's Republic of China

4 Key Laboratory of Oil Crop Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, People's Republic of China

5 Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture, Wuhan, People's Republic of China

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**Section 6**
