**1. Introduction**

One of the most important emerging environmental issues in Asian cities is air pollution. Air pollution is an atmospheric condition in which the concentration and duration of certain substances present in the air produce injurious and destructive effects on both man and the surrounding environment [1]. The most common pollutants in air are sulfur oxide, nitrogen dioxide, carbon monoxide and dioxide, and particulate matter.

Geographical Information Systems (GISs) are computer-based applications used for mapping and analyzing the earth and related spatially distributed phenomena. GIS applications integrate unique visualizations with common databases, which make it possible to capture, model, manipulate, retrieve, analyze, and present the geographically referenced data. Com‐ pared to other information systems, GIS systems have advantages, including the high power of analyzing spatial data and handling large spatial databases.

GIS applications can be used in air quality management and for controlling pollution, for handling and managing large amount of data. GIS systems manage spatial and statistical data, which facilitates depiction of the association between the frequency of human activities leading to bad environmental health and poor air quality. GIS modeling and statistical analysis also enables to examine and predict the impact of climatic variables on air pollution. In this way, GIS systems help in monitoring air pollution and emissions of pollutants from different sources.

Air pollution mapping is a helpful method for determining the concentration of pollutants. As the result of air pollution mapping, overviews of pollution in cities can be created and their sources of pollution emission can be identified, which help in controlling emissions. Different studies have been executed on air pollution in conjunction with GIS [2-11]. Consequently, GIS

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applications in air monitoring are necessary to determine air quality to reduce pollution to such a level at which harmful impacts on human health and the environment is reduced.

With the help of GIS applications, an output report of pollutants in Air Quality Management Systems (AQMSs) can be achieved in the form of three-dimensional (spatial) records. In AQMS emission time, concentration and place of air pollutants are regulated in order to achieve the predefined air quality standards of ambient air. It encompasses the estimation of the pollutants' emission schedule in a way to determine the consequences to air quality and the design of alternative programs for emission control in order to meet air quality standards, which are subject to some limitations, for example, technological viability and lowest charges. For environmental modeling with GIS applications, AQMSs are considered to locate monitoring stations, for development of geospatial model for air quality, and for spatial decision-support systems. However, the most significant step in an AQMS is data mining. The data mining method is a skill, which is used to analyze the data, uncover hidden patterns, and find interesting information from large amounts of data or huge databases. The most commonly used technique in data mining is artificial neural networks [12].

The human brain consists of a large number of neurons connected to each other by synapses to make networks, and these networks of neurons are called neural networks, or natural neural networks. Similarly, the artificial neural network (ANN) is basically a mathematical model of a natural neural network. The ANN uses a mathematical or computational model based on connectionist approach for solving the given problem. The concept of ANN is derived from biological neural network systems. The key applications of neural networks are control systems, classification systems, and prediction and vision systems.

Three basic components are important in order to make functional model, like: synapses of neuron; an added that sum all input in form of weights; and activation function. In Figure 1, synapses are shown by weights. Basically, a strong connection between input and neuron is noted by synapses or value of weight. Negative values reflect inhibitory connections, whereas excitatory connections are shown by positive values. Activation functions regulate the output of neurons within an acceptable range from -1 to 1.

#### **1.1. Sources**

Air pollution takes place due to natural and anthropogenic activities. But air pollution as the result of man-made activities like fossil fuel combustion, construction, mining, agriculture, and warfare are the most significant and cause problems in the atmosphere [13].

Basically, two types of pollution sources have been categorized, i.e., Stationary and Mobile. The stationary source is a type of source that is fixed or is a preset pollutant emitter, for example, fossil fuel burning power plants and refineries. The mobile source is a nonstationary type of pollutant emitter, for example, vehicles. The most emerging and leading cause of air pollution is the motor vehicle [14]. Pollutants that are emitted directly from the source into the air are known as primary pollutants, for example, carbon dioxide, carbon monoxide, sulfur dioxide, etc. When these primary pollutants react in atmosphere with each other to form another type of pollutants, they are called secondary pollutants, which are not directly emitted

**Figure 1.** Model of a neuron

applications in air monitoring are necessary to determine air quality to reduce pollution to such a level at which harmful impacts on human health and the environment is reduced.

With the help of GIS applications, an output report of pollutants in Air Quality Management Systems (AQMSs) can be achieved in the form of three-dimensional (spatial) records. In AQMS emission time, concentration and place of air pollutants are regulated in order to achieve the predefined air quality standards of ambient air. It encompasses the estimation of the pollutants' emission schedule in a way to determine the consequences to air quality and the design of alternative programs for emission control in order to meet air quality standards, which are subject to some limitations, for example, technological viability and lowest charges. For environmental modeling with GIS applications, AQMSs are considered to locate monitoring stations, for development of geospatial model for air quality, and for spatial decision-support systems. However, the most significant step in an AQMS is data mining. The data mining method is a skill, which is used to analyze the data, uncover hidden patterns, and find interesting information from large amounts of data or huge databases. The most commonly

The human brain consists of a large number of neurons connected to each other by synapses to make networks, and these networks of neurons are called neural networks, or natural neural networks. Similarly, the artificial neural network (ANN) is basically a mathematical model of a natural neural network. The ANN uses a mathematical or computational model based on connectionist approach for solving the given problem. The concept of ANN is derived from biological neural network systems. The key applications of neural networks are control

Three basic components are important in order to make functional model, like: synapses of neuron; an added that sum all input in form of weights; and activation function. In Figure 1, synapses are shown by weights. Basically, a strong connection between input and neuron is noted by synapses or value of weight. Negative values reflect inhibitory connections, whereas excitatory connections are shown by positive values. Activation functions regulate the output

Air pollution takes place due to natural and anthropogenic activities. But air pollution as the result of man-made activities like fossil fuel combustion, construction, mining, agriculture,

Basically, two types of pollution sources have been categorized, i.e., Stationary and Mobile. The stationary source is a type of source that is fixed or is a preset pollutant emitter, for example, fossil fuel burning power plants and refineries. The mobile source is a nonstationary type of pollutant emitter, for example, vehicles. The most emerging and leading cause of air pollution is the motor vehicle [14]. Pollutants that are emitted directly from the source into the air are known as primary pollutants, for example, carbon dioxide, carbon monoxide, sulfur dioxide, etc. When these primary pollutants react in atmosphere with each other to form another type of pollutants, they are called secondary pollutants, which are not directly emitted

and warfare are the most significant and cause problems in the atmosphere [13].

used technique in data mining is artificial neural networks [12].

systems, classification systems, and prediction and vision systems.

of neurons within an acceptable range from -1 to 1.

**1.1. Sources**

88 Current Air Quality Issues

but formed as a result of primary pollutants' reaction in the atmosphere. For example, ozone forms when nitrogen oxides react with hydrocarbons in the presence of sunlight, and the resulting nitrogen dioxide reacts further with oxygen and forms ozone as pollutant.

#### **1.2. Health effects**

Air pollution and its effects in rural and urban areas are directly related to the ongoing activities. For example, in cities, pollution is related to the products of combustion in industries and vehicles. Many large cities all over the world exhibit excessive levels of air pollutants. Among all dangerous pollutants, nitrogen dioxide (NO2) is important due to its capacity of causing dangerous effects on humans and the environment, which results in photochemical oxidation and acid rain.

The effects of air pollution cannot be ignored even within homes. Many air pollutants can cause cancer and other diseases among inhabitants. In 1985, it was reported that indoor toxic chemicals are three times more potent in causing cancer than outdoor air pollutants [15]. In America, health issues caused by buildings are called "sick building syndrome"[16].

#### **1.3. Case study**

In Pakistan, air pollution is emerging as a serious problem in its mega cities, which needs to be monitored and addressed at the root level in order to reduce the lethal impacts of pollutants on man and environmental health. The present study of Pakistan focuses on the most important twin cities of Pakistan, which are Rawalpindi and Islamabad. Both cities are commonly viewed as one unit and are 15 km apart. The study area with 135 sampling locations is shown in Figure 2. The climatic condition of Rawalpindi and Islamabad is sub-humid to tropical, with hot and long summers (May to August) accompanied by a monsoon season (July to August) followed by short and mild winters (October to March). The average low temperature is 12.05 °C in January and average high temperature is 31.13 °C in July.

**Figure 2.** Base map

For the monitoring campaign, the maximum area (135 sampling sites) was covered in order to represent different traffic intensity and congestion levels in the urban area of Rawalpindi and Islamabad, for sampling. These sites included dual carriageways, major, linking, and small roads, healthcare centers, educational institutes, commercial areas, old residential areas, modern residential areas, recreational spots and semi-rural areas.

Research was carried out in order to monitor the NO2 concentration in the ambient air of Rawalpindi city. Passive samplers were used within the city from January to December in 2008. The average concentration found was 27.46±0.32 ppb. The highest concentration was recorded near the main roads and in the vicinity of schools and colleges due to the large number of transport vehicles, which exceeded the set limit concentration value given by the World Health Organization.
