**3. Data analysis**

In order to determine the seasonal variation and statistical significance, results are presented in tabular format. Tables 4 a and 4 b show the average concentration level of NO2, season-wise, along standard deviation (SD) values measured at different sampling sites of study.

Table 4 a shows average values of NO2 concentration in different seasons of 12 major sampling categories in urban Rawalpindi and Islamabad from November 2009 to July 2010.

Table 4 b shows the seasonal average concentration of NO2 of 12 major sampling categories in urban Rawalpindi and Islamabad from September 2010 to March 2011.

Table 5 presents NO2 concentration for each selected category, as described in study area profile, to understand the general trends of NO2 concentration levels among different catego‐ ries during the course of experimental period.



**3. Data analysis**

96 Current Air Quality Issues

**Sampling Categories**

**NO2 Conc.**

**Dual Carriage Ways (5)**

> **Old Residential Areas (5)**

**Modern Residential Areas (5)**

**Recreational Spots (9)**

**Rawalpindi**

In order to determine the seasonal variation and statistical significance, results are presented in tabular format. Tables 4 a and 4 b show the average concentration level of NO2, season-wise,

Table 4 a shows average values of NO2 concentration in different seasons of 12 major sampling

Table 4 b shows the seasonal average concentration of NO2 of 12 major sampling categories in

Table 5 presents NO2 concentration for each selected category, as described in study area profile, to understand the general trends of NO2 concentration levels among different catego‐

> **Early Spring (Feb)**

**(weekly basis) (ppb) (ppb) (ppb) (ppb) (ppb) (ppb) (ppb)**

**Major Roads (10)** 60±12.19 68±9.56 52±13.52 45±10.23 36±8.97 26±5.88 19±4.74 **Sub-roads (6)** 74±20.50 86±24.47 60±16.49 50±11.05 38±12.65 33±13.01 21±4.39 **Small Roads (3)** 55±9.78 63±4.89 47±5.57 40±8.24 31±3.40 25±4.68 18±4.81 **Public Hospital (5)** 48±18.71 63±18.40 37±0.74 29±2.29 22±2.24 18±0.79 14±0.96 **Private Hospitals (8)** 61±14.47 75±14.19 38±1.16 32±2.0.3 25±2.29 20±5.57 14±3.98 **Public EI (11)** 85±30.58 95±32.94 75±23.75 63±17.94 47±17.37 31±10.14 20±1.94 **Private EI (17)** 55±9.71 66±9.54 45±4.56 43±9.65 38±10.89 26±4.54 18±3.18

**Spring (Mar)**

87±19.78 98±26.87 63±12.29 53±6.49 44±10.64 22±4.22 18±1.91

83±15.24 95±16.09 55±13.32 51±6.66 37±6.44 26±2.54 19±1.05

65±20.07 73±14.89 69±24.49 59±12.55 36±7.13 28±5.08 21±2.61

75±38.40 87±40.76 62±36.39 56±21.88 43±19.97 31±11.12 19±2.37

**Commercial Area (2)** 75±0.83 82±17 61±6.69 51±7.11 36±4.29 21±6.20 18±4.78 **Bus Stops (9)** 74±20.26 83±31.47 69±33.78 58±17 39±17.32 28±8.41 20±5.25

**Mild Summer (April)**

**Summer (Pre-Monsoon) (May to June)**

**Monsoon (July to August)**

along standard deviation (SD) values measured at different sampling sites of study.

categories in urban Rawalpindi and Islamabad from November 2009 to July 2010.

urban Rawalpindi and Islamabad from September 2010 to March 2011.

**Winter (Dec to Jan)**

ries during the course of experimental period.

**Mild Winter (Nov)**


**Table 4.** (a): Seasonal mean values of NO2 from November 2009 to July 2010 (b): Seasonal mean values of NO2 from September 2010 to March 2011

In Table 5 most of the sampling sites of study area showed nearly similar average concentration from month of November 2009 to March 2011. Maximum concentration of NO2 shown on dual carriage ways.

The possible cause of such elevated levels of NO2 concentration is extensive increase in number of vehicles, increase in population, busy roads, fuel inefficient vehicles, driving ways, and traffic jams. Gilbert reported that NO2 is considerably related to both the distance from the nearest highway and the traffic count on the nearest highway [20].

The rest of the categories showed nearly the same average concentration. Major roads and subroads showed average NO2 concentration levels of 53.56 ppb and 51.78 ppb, respectively. Subroads, bus stops, recreational spots, and educational institutions showed similar concentration levels of approx. 51 ppb.


**Table 5.** Average NO2 concentration levels in twin cities from November 2009 to March 2011

**Sampling Categories**

98 Current Air Quality Issues

**NO2 Conc.**

**Modern Residential Areas (5)**

September 2010 to March 2011

levels of approx. 51 ppb.

carriage ways.

**Islamabad**

**Twin Cities**

**Mild Winter (Nov)**

**Winter (Dec to Jan)**

**Early Spring (Feb)**

**(weekly basis) (ppb) (ppb) (ppb) (ppb) (ppb) (ppb) (ppb)**

**Dual Carriage Ways (3)** 31±5.72 50±11.40 82±21.11 99±32.70 67±19.78 49±12.84 **Major Roads (3)** 22±0.80 37±1.93 53±4.33 65±0.30 44±2.30 33±2.00 **Sub-Roads (4)** 26±2.48 39±4.60 54±5.74 65±4.08 46±3.02 35±9.17 **Small Roads (3)** 30±5.94 41±4.12 54±4.24 63±6.67 47±2.60 38±2.79 **Public Hospitals (3)** 22±2.14 34±2.66 45±0.80 60±1.41 45±0.22 34±0.82 **Private Hospitals (1)** 22 31 40 55 38 30

**Public EI (5)** 31±7.41 40±3.60 52±7.94 64±8.39 46±9.34 37±9.62 **Private EI (6)** 29±11.58 41±8.65 54±10.14 63±7.03 47±7.93 36±9.17

32±7.86 49±11.70 66±20.07 75±16.16 60±19.16 48±16.53

**Old Residential Areas (5)** 27±2.97 61±14.74 84±14.18 95±16.51 58±12.41 48±10.06

**Commercial Area (3)** 32±1.23 46±6.09 63±1.00 71±3.57 56±7.02 48±8.41 **Bus Stops (11)** 32±9.11 53±20.30 76±20.07 87±32.40 69±31.34 54±19.54 **Recreational Spots (10)** 37±18.55 52±25.23 71±37.63 84±39.83 57±29.71 46±24.78 **Semi-Rural Areas (7)** 31±9.47 41±7.44 53±6.51 62±6.21 44±7.50 36±6.99

**(b)**

In Table 5 most of the sampling sites of study area showed nearly similar average concentration from month of November 2009 to March 2011. Maximum concentration of NO2 shown on dual

The possible cause of such elevated levels of NO2 concentration is extensive increase in number of vehicles, increase in population, busy roads, fuel inefficient vehicles, driving ways, and traffic jams. Gilbert reported that NO2 is considerably related to both the distance from the

The rest of the categories showed nearly the same average concentration. Major roads and subroads showed average NO2 concentration levels of 53.56 ppb and 51.78 ppb, respectively. Subroads, bus stops, recreational spots, and educational institutions showed similar concentration

nearest highway and the traffic count on the nearest highway [20].

**Table 4.** (a): Seasonal mean values of NO2 from November 2009 to July 2010 (b): Seasonal mean values of NO2 from

**Spring (Mar)**

**Mild Summer (April)**

**Summer (Pre-Monsoon) (May to June)**

**Monsoon (July to August)**

> Educational institutions and recreational spots, being present close to the dual carriage ways, also experience elevated concentration levels. Old residential areas (48.97 ppb) showed slightly higher NO2 concentration levels as compared to modern residential areas (47.59 ppb).

> Narrow road, enclosing architecture, and congestion among the old residential areas result in traffic emission being trapped and buildup leading to higher NO2 concentration levels, whereas in modern residential areas increased vehicular number is the major cause of elevated NO2 levels. The minimum NO2 concentration levels were indicated in semi-rural areas, that is 37.65 ppb. A study in Vilnius commented the same phenomena; NO2 average rates depend upon traffic and are highest in cross roads and lowest at the background suburban areas [21].

> For annual average concentration level of nitrogen dioxide, a spatial interpolation map has been developed by using inverse distance weighted (IDW). IDW in Figure 7 is clearly depicted as the areas of higher and lower concentration level of NO2 in Rawalpindi and Islamabad.

> Higher concentration levels are represented by darker shades while the lower concentration levels are shown with lighter shades. The maximum NO2 values were found at the center of the city, where they reached the concentration of 83–110 ppb. Values were low on the outskirts of the city, with the lowest concentration in north (31–44 ppb).

> A study in Vilnius commented the same phenomena; NO2 average rates depend upon traffic and are highest in cross roads and lowest at the background suburban areas. Dual carriage ways, sub roads, major roads, commercial areas, old residential areas, and areas where schools and colleges are existing have higher concentration levels of NO2. Intense traffic flow and congestion were the major reasons for these elevated levels of nitrogen dioxide concentration in those areas as vehicular emission is the predominant source of NO2.

Vehicle growth rate in twin cities is extensively high. Load of traffic is continuously increasing with growing population rate and demand of motor producing industry. Due to this, traffic congestion is also increasing day by day with growing vehicle population, resulting in highest emission rates per vehicle.

The higher emission rate of NO2 can also be attributed to the type of fuel and quality of fuel [22]. In Figure 7 Rawalpindi showed more concentration levels than Islamabad due their building patterns.

**Figure 7.** Spatial distribution of NO2 concentration

#### **3.1. Neural network data analysis**

Based on the design of neural network, with the neural architecture and properties discussed, the data space is searched by using heuristic search method with 500 iterations and fitness criteria is set to Inverse Test error. The best top 5 networks explored from the space by the heuristic search are graphically shown (Figure 8).

Heuristic search is a problem-solving method that analytically searches a space of problem states. The best network is obtained when the absolute error gets minimum in the initial iterations so the best network out of the 5 best networks is shown (Figure 9).

Results for all data sets produced after training and testing data. Real vs. target graph repre‐ sented a line graph of real- and network-predicted target values for record displayed in Table 6. X-axis shows the selected input column values and Y-axis represents network-predicted output values. Table 6 presents the summary of the real vs. output table after training.

**Figure 8.** The top five networks explored by heuristic search approach

Vehicle growth rate in twin cities is extensively high. Load of traffic is continuously increasing with growing population rate and demand of motor producing industry. Due to this, traffic congestion is also increasing day by day with growing vehicle population, resulting in highest

The higher emission rate of NO2 can also be attributed to the type of fuel and quality of fuel [22]. In Figure 7 Rawalpindi showed more concentration levels than Islamabad due their

Based on the design of neural network, with the neural architecture and properties discussed, the data space is searched by using heuristic search method with 500 iterations and fitness criteria is set to Inverse Test error. The best top 5 networks explored from the space by the

Heuristic search is a problem-solving method that analytically searches a space of problem states. The best network is obtained when the absolute error gets minimum in the initial

Results for all data sets produced after training and testing data. Real vs. target graph repre‐ sented a line graph of real- and network-predicted target values for record displayed in Table 6. X-axis shows the selected input column values and Y-axis represents network-predicted output values. Table 6 presents the summary of the real vs. output table after training.

iterations so the best network out of the 5 best networks is shown (Figure 9).

emission rates per vehicle.

**Figure 7.** Spatial distribution of NO2 concentration

heuristic search are graphically shown (Figure 8).

**3.1. Neural network data analysis**

building patterns.

100 Current Air Quality Issues

**Figure 9.** Network explored by heuristic search


**Table 6.** Summary of real vs. target

The visualization for real vs. output with row number on x-axis and target/output (area\_id) on y-axis is shown (Figure 10).

**Figure 10.** Real vs. network output

Figure 11 shows a scatter plot of the real and forecasted output values. X- axis presents the real values and Y-axis shows predicted network values.

Graph in Figure 12 shows the Network Error Dependence on values, which are numerically input in columns of data sheet. Through graph of Error Dependence, the ranges of the selected input column that can produce network error can be identified.

The last phase after the neural network is trained and tested is to query the network. The concentration is the output value for the neural network. So the input queries are subjected area\_id, season\_id, temperature, relative humidity, and rainfall (Figure 13).

**Figure 11.** Scattered plot of real and output network values

**Target Output AE ARE**

The visualization for real vs. output with row number on x-axis and target/output (area\_id)

Figure 11 shows a scatter plot of the real and forecasted output values. X- axis presents the real

Graph in Figure 12 shows the Network Error Dependence on values, which are numerically input in columns of data sheet. Through graph of Error Dependence, the ranges of the selected

The last phase after the neural network is trained and tested is to query the network. The concentration is the output value for the neural network. So the input queries are subjected

Mean: 45.237265 45.09091 11.341221 0.250292 StdDev: 20.98552 13.97879 11.112997 0.180871 Min: 11.3 20.16986 0.004569 0.000171 Max: 132.72 63.353673 73.986765 1.096446

Correlation, 0.653989; R-squared, -0.290243

**Table 6.** Summary of real vs. target

102 Current Air Quality Issues

on y-axis is shown (Figure 10).

**Figure 10.** Real vs. network output

values and Y-axis shows predicted network values.

input column that can produce network error can be identified.

area\_id, season\_id, temperature, relative humidity, and rainfall (Figure 13).

**Figure 12.** Graph of error dependence

The input Excel sheets are prepared for the GIS mapping. Sheets include area\_id, their latitude, longitude, and their concentrations. With the help of interpolation, maps are created for the service.


**Figure 13.** Excel sheet presenting manual query

Temporal variation can be explained through meteorological recorded conditions. However, most of the variations on a local scale are due to the impact of air pollutants.

**Figure 14.** Relationship of rainfall, temperature, and humidity with NO2 concentration (November 2009–March 2011)

Figure 14 indicates the positive association of NO2 concentration level with humidity (RH in %) and negative association with the temperature. Figure 15 shows the concentration of NO2 during summer when recorded temperature, rainfall, humidity are 310 C, 67, and 17mm, respectively.

Figure 16 shows the concentration of NO2 during the winter season at 11 0 C, 68% humidity, and 9mm rainfall.

**Figure 15.** NO2 concentration in summer

The input Excel sheets are prepared for the GIS mapping. Sheets include area\_id, their latitude, longitude, and their concentrations. With the help of interpolation, maps are created for the

Temporal variation can be explained through meteorological recorded conditions. However,

**Figure 14.** Relationship of rainfall, temperature, and humidity with NO2 concentration (November 2009–March 2011)

Figure 14 indicates the positive association of NO2 concentration level with humidity (RH in %) and negative association with the temperature. Figure 15 shows the concentration of NO2

C, 67, and 17mm,

C, 68% humidity,

during summer when recorded temperature, rainfall, humidity are 310

Figure 16 shows the concentration of NO2 during the winter season at 11 0

most of the variations on a local scale are due to the impact of air pollutants.

service.

104 Current Air Quality Issues

respectively.

and 9mm rainfall.

**Figure 13.** Excel sheet presenting manual query

**Figure 16.** NO2 concentration in winter

Concentration of NO2 during the spring season, shown in Figure 17, when recorded temper‐ ature is 35°C, humidity is 58%, and rainfall is 60 mm.

**Figure 17.** NO2 concentration in spring

Figure 18 shows predicted concentration of NO2in autumn season when recorded temperature, humidity, and rainfall are 29 °C, 69, and 22 mm, respectively.

**Figure 18.** NO2 concentration in autumn

**Figure 19.** Seasonal variation in NO2 concentration levels (November 2009 – March 2011)

Figure 19 shows that concentration of NO2 varies in different seasons. The months from May to August were months in which the minimum value of NO2 was recorded, and the maximum concentration was measured in the winter season from December to January.

#### **4. Conclusion**

Concentration of NO2 during the spring season, shown in Figure 17, when recorded temper‐

Figure 18 shows predicted concentration of NO2in autumn season when recorded temperature,

humidity, and rainfall are 29 °C, 69, and 22 mm, respectively.

ature is 35°C, humidity is 58%, and rainfall is 60 mm.

106 Current Air Quality Issues

**Figure 17.** NO2 concentration in spring

**Figure 18.** NO2 concentration in autumn

NO2 concentration levels were recorded on hourly and weekly basis in Rawalpindi and Islamabad city by using diffusion tubes. Artificial neural networks were trained to generalize the process of air pollutant spread over three dimensions. Prediction capabilities of ANN were analyzed through generalization by using hold-out evaluation method of classification. Results showed the advantage of using rtNEAT-like architecture of ANN where a neural network can modify its architecture to reduce the error up to the maximum possible limit. Results showed that annual average concentration of NO2 concentration was 44 ± 6 ppb. However, the highest concentration was recorded in winter season near the dual carriage ways, schools, and colleges because of the higher number of transport vehicles on the road. This endorsed the fact that the reduced photolysis leads to the accumulation of NO2 during winter due to less solar radiation. This is again attributed by the results of correlation, which reveal the negative correlation of nitrogen dioxide concentration levels with rainfall and temperature and the positive correlation with humidity. Moreover, the results of correlation reveal that the measured NO2 concentration levels at different sampling areas exceeded the set limit of concentration value of the World Health Organization and Pak-EPA standard policy. This type of investigative study of artificial neural networks in the area of air pollution modeling shows promising applications for advanced machine learning algorithms in the emerging area of research called eco-informatics.
