Journal Paper Format

International Journal of Science and Technology Vol. 28, No. 12, (2019), pp. 57-67

Deterministic Approach for Temporal Patterns of Particle Pollution Analysis

S.L. Sailaja1, Dr.P. Rajesh2 1Research Scholar, Dept. of CSE, Koneru Lakshmaiah University, Vaddeswaram, AP. 2Associate Professor, Dept. of CSE, Koneru Lakshmaiah University, Vaddeswaram, AP. [email protected] [email protected]

Abstract This paper presents a detailed analysis of air pollutants trend in Vijayawada. Vijayawada, designated as part of the state capital Amaravati, smoke and existing pollution levels in the city has exceeded the standard levels due to increase in population and the constructional activities being taken up in the recent years after the bifurcation of the Telugu states. This has made a profound influence to carry out the study on pollution in Vijayawada using data analytics. Descriptive analysis has been carried out to study the trends of air pollutants like Suspended Particulate Matter (SPM) (PM2.5 and PM10), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Carbon Monoxide (CO), Ozone (O3) based on Air Quality Index (AQI) scale. AQI scale is a standard scale defined by the governments of respective countries. Then the contamination characteristics of particulate matters were analyzed, which further served to determine the characteristics of temporal patterns pollution variations of NO2, SO2, CO, O3.

Most of the air pollution monitoring systems in India are ground-based and are dependent on meteorological data which reflects inaccurate predictions of pollutant concentrations. Geo-Spatial data integrated with Deep Learning techniques facilitates an increased awareness on the geospatial diversity, scalable to different locations. The proposed work intends to model, predict the particulate pollutant levels before they reach abnormal levels, predict the chronic disease patterns caused by particulate matter, thereby creating relevant human-health awareness.

Keywords: Temporal Patterns, Air Pollutants, Suspended Particulate Matter, Spatio- Temporal Patterns.

1. Introduction

Air Pollution is a global phenomenon. Rapid Urbanization and Industrialization have an adverse effect on both outdoor and indoor pollution, a serious risk factor that intensifies severe acute and chronic diseases. Natural and anthropogenic sources of outdoor air pollution include dust storms and forest fires. Human actions are the primary cause of pollution. As the pollution increases, the attendant pollution problems also increase proportionately. These human actions include residential cooking, municipal and agricultural wastes, industrial wastes, heat and power generation from power plants and boilers, fuel combustion from motor vehicles. Most of the adverse health effects have been observed either by the short-term or long-term exposure to air pollution both indoor

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and outdoor. Bronchitis and asthma are likely to increase significantly in coming decades due to air pollution. As per the observations made by the environmental scientists and the pulmonologists, " Though dust and smoke are at moderate levels when compared to Hyderabad and Visakhapatnam, 30% to 40% of residents of Vijayawada are infected by air pollution caused by industrial emissions and vehicular emissions. As per the National Ambient Air Quality Standards, the respirable suspended particulate matter is highly registered (Defined - 60g/m3, Registered- 90-110g/m3).

The major sources of air pollution in Vijayawada are due to vehicle emissions, road dust, biomass burning, industrial emissions and usage of fuel other than LPG for cooking.

1.1 Sources of Pollution 1.1.1 Industrial emissions: One of the major industries in Vijayawada is Vijayawada Thermal Power Station (VTPS) located at Ibrahimpatnam on the west side of Vijayawada. Apart from this, there are two industrial estates around the city - Autonagar industrial estate and another Industrial estate located at Kondapalli.

1.1.2 Vehicular Emissions: The commercial capital city- Vijayawada is well connected to other parts of the country by national highways NH-5, NH-9, NH-221. Besides the floating pollution due to the heavy transportation on these national highways, the city is also interconnected with local public transport as well as the individual transportation [1].

Also, it is observed that Particulate matter such as PM10 are high at Benz circle, Police Control Room and Autonagar during January-July when compared with other locations in the city. Apart from the harmful air pollutants such as CO, NO2, SO2, there is a need for government and public to focus on PM2.5, a mixed air pollutant with aerodynamic diameters of 2.5µm and inhalable particles PM10 with aerodynamic diameters of 10µm [2]. Chronic exposures to higher concentrations of PM2.5 and PM10 leads to increased morbidity and mortality to general public.

To emphasize on the action plan to reduce levels of PM10 as stated by Anumita Roy Chowdhury, Executive Director of Centre for Science and Environment " Vijayawada requires to reduce its PM10 concentration by approximately 42% while Visakhapatnam by 11%"[3] [4].

1.2 Types of Pollutants in Ambient Air and associated health risks involved Air Pollutants with the strongest evidence of public health concern, include concentrations of particulate matter (PM2.5, PM10), Ozone (O3), nitrogen dioxide (NO2) and Sulphur dioxide (SO2), Carbon Monoxide (CO) [5]. Emission of air pollutants is caused by different anthropogenic processes which can be categorized into the source groups motor traffic, industry, power plants, trade, and domestic fuel [6][7]. The associated health risks of the respective air pollutants are listed as below in Table 1 [8] [9].

International Journal of Science and Technology Vol. 28, No. 12, (2019), pp. 57-67

Table 1: Pollutants and their associated health risks

Pollutant Sources Health Risk Particulate Combustion engines, Solid fuel  Penetrates through lungs and matter combustion in domestic and enter the bloodstream. industries. Nitrogen Power generation sources,  Bronchitis Dioxide Industrial and traffic sources.  Asthma (NO2)  Reduced lung function and growth  Premature mortality from cardiovascular and respiratory diseases. Sulphur Burning of fossil fuels, Smelting  Inflammation of the respiratory Dioxide of mineral ores. tract aggravates asthma, (SO2) chronic bronchitis. Carbon Motor vehicle emissions and  Headache monoxide burning of fossil fuels.  Nausea, Dizziness (CO)  Increased risk of Heart diseases  Become Unconscious Ground level Major component of  Asthma Ozone (O3) photochemical smog due to motor  Reduced lung function and vehicle exhausts, industrial respiratory diseases. facilities, chemical solvents

2. Existing System

Air Quality Monitoring helps us to assess the level of pollution of an area in relation to the ambient air quality standards. Now-a-days Air quality controlling and monitoring has become a predominant domain in India. Data generation [10] for air quality controlling and monitoring requires involvement of monitoring agencies, equipment for data sampling, chemical analysis, reporting mechanism round the clock [11].

Applied worldwide, WHO Air Quality standard guideline the Table.2:

Table 2: Air Quality Standard Guidelines as defined by WHO Particle Defined Value Fine Particulate 10 μg/m3 annual mean 3 Matter (PM2.5) 25 μg/m 24-hour mean Coarse Particulate 20 μg/m3 annual mean 3 Matter (PM10) 50 μg/m 24-hour mean 3 Nitrogen Dioxide 40 μg/m annual mean (NO2) 200 μg/m3 1-hour mean Sulphur Dioxide 78 μg/m3 annual mean (SO2) 20 μg/m3 24-hour mean 500 μg/m3 10-minute mean Ground level Ozone 100 μg/m3 8-hour mean

International Journal of Science and Technology Vol. 28, No. 12, (2019), pp. 57-67

To measure AQI values, the fixed and high cost air-quality monitoring stations are required.

2.1 Number of Monitoring Stations in Andhra Pradesh There are 25 monitoring stations in 15 cities/towns in Andhra Pradesh state. Table.3 given below shows state-wise and city-wise number of operating monitoring stations.

Table 3. List of Monitoring stations in AP State Name City Name No. of Monitoring stations Anantapur 1 Chittoor 2 Eluru 1 Guntur 1 Kadapa 1 Kakinada 1 Kurnool 1 Andhra Pradesh Nellore 1 Ongole 1 Rajahmundry 1 Srikakulam 1 Tirupati 1 Vijayawada 3 Vishakhapatnam 8 Vizianagaram 1

Data on ambient air pollution is taken from Andhra Pradesh Pollution Control Board (APPCB) [12] under its National Air Monitoring Program (NAMP). The sampling location chosen is PWD Grounds, Vijayawada.

As stated by Government of India, 523 manual monitoring stations across 215 cities and towns are operated across states, the air quality monitoring is limited in scope. The recorded values are qualitative with an immense time lag in reporting the data. The trends derived from the monitoring stations represent more of an urban trend and the network is not designed to track the nationwide emission trends. Real time actions on the air quality profile does not guarantee the quantitative confirmation on the downward trend of pollution emissions. To setup monitoring stations based on the density of the population, to study the pollutants they are exposed still remains a challenging task due to lack of government commitment, stakeholder participation, limited portion of the budget allocated for air quality management.

3. Proposed System

As the infrastructure grows in a region, so the pollutants do increase. At the same time, the air quality is affected by multi-dimensional factors such as time, geographical location, meteorological factors, etc., To monitor ambient air pollution in real-time it’s data to send out air quality reports. Due to the advancements in big data applications and the availability of environmental sensing networks and vast amounts of sensor data analytics approaches [13] are used to study, evaluate and predict the air quality.

International Journal of Science and Technology Vol. 28, No. 12, (2019), pp. 57-67

For the better health of future generations, the objectives of the present study are set to assess the ambient air quality with respect to PM10, PM2.5, SO2, NO2. Trends of various pollutants are studied over a period of time and space to create awareness among public about environmental pollution. Sulphur dioxide, Nitrogen dioxide and Suspended Particulate Matter (SPM) are regarded as major air pollutants in India. One of the factors for the air to be rendered impure is the trade, traffic, manufacturing processes that give off dust, fumes vapours and gases. Vehicular emission is a major threat to air quality as it contributes to 40 % to 80% of the total air pollution.

In this work we concentrate to predict the spatio temporal patterns [14] [15] [16] of the air quality for the selected region for both short term and long-term periods. Before making the relation among the spatio-temporal patterns of a particular region we analyze the trends of the major air pollutants of the specific region-Vijayawada.

In this work, the experiments are conducted based on the data sources: the meteorological data, the traffic data, the records of the monitoring sites (CPCB, APPCB). From the Air Quality Records, we collect the Air Quality Index of major air pollutants.

PM10, PM2.5, SO2, NO which will be reported by the monitoring stations set in Vijayawada. In this work, the average monthly levels of four key pollutants are taken to measure the ambient air pollution.

Data on ambient air pollution is taken from Andhra Pradesh Pollution Control Board (APPCB) under its National Air Monitoring Program (NAMP) [17] as shown in Figure 1.

Figure 1. Sampling location at PWD grounds, Vijayawada

3.1 Dataset Used

The dataset has been collected from CPCB.A snapshot of the dataset used is as shown in Figure 2. The dataset contains 8 attributes: Month, PM2.5, PM10, NO2, NH3, SO2, CO, Ozone. The 'Month' attribute describes the sampling month and other parameters give the average of the individual concentrations in air for the respective months. The data has been collected for four quarters where Q1 is April'18-Jun'18, Q2 is Jul'18-Sep'18, Q3 is Oct'18-Dec'18, Q4 is Jan'19-Mar'19, for 'PWD grounds, Vijayawada'.

International Journal of Science and Technology Vol. 28, No. 12, (2019), pp. 57-67

Figure 2. Sample Dataset for AQI

The approach starts with collection of the data for four quarters from the CPCB site. The collected data has been pre-processed to remove the missing values, redundant values. Further, descriptive analytics have been carried out for the processed data.

The average monthly analysis of the air pollutants from April-2018 to March-2019 is as shown below in the Figure 3:

Figure 3. Level of Pollutants for period Apr’2018 to Mar’2019

From the above trend analysis carried out for the major air pollutants over a period Apr’18-Mar’19, PM2.5 and PM10 can be identified as the pollutants with maximum level. It has been observed from the trend analysis of the air pollutants, PM2.5 and PM10 are the major pollutants, which causes reduced visibility, premature death in people with lung diseases, irregular heartbeat, decreased lung functions and aggravated asthma, difficulty in breathing, irritation of the respiratory system.

The following Figure.4 shows the box plot analysis for each pollutant:

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Figure 4. Analysis of each pollutant for the period Apr’2018 to Mar’2019

3.2 Observed Trends of Pollutants

Particulate Matter - Particle pollution, a mixture of solid particles and liquid droplets found in the air, are emitted directly from sources such as construction sites, smokestacks, fields, etc., and are associated with fatal effects such as irregular heartbeat, aggravated asthma, premature death in people with lung diseases, reduced visibility, lakes and stream acidification.

The trend of PM2.5 for period July'18 to Mar'19 is as shown below in Figure 5.:

Figure 5. Analysis of PM2.5

From Figure.5 it is observed that the yearly average analysis for PM2.5 varies from a minimum of 36 to a maximum of 80 during the month of November. The AQI average 3 annual mean threshold for PM2.5 is 10 μg/m annual mean. But it has been observed that the AQI has reached a maximum level of 54 μg/m3.

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The trend of PM10 for the period Apr'18 to Mar'19 is as shown below:

Figure 6. Annual Analysis of PM10

From Figure.6 it has been observed that the average monthly mean for PM10 varies from a minimum of 61 in the month of August to a maximum of 78 during the month of 3 December. The AQI average annual mean threshold for PM10 is 20 μg/m . But it has been observed that the AQI has reached a maximum level of 62 μg/m3.

Nitrogen Dioxide - The trend of NO2 for the period Apr'18 to Mar'19 is as shown below:

Figure 7. Annual Analysis of NO2

From Figure.7, it has been observed that the average monthly mean for NO2 varies from a maximum of 23 in the month of April-18 to a minimum of 9 during the month of July-18. 3 The AQI average annual mean threshold for NO2 is 40 μg/m . But it has been observed that the AQI is within the standard limits with a value of 15 μg/m3.

The major source of Sulphur Dioxide is burning of fossil fuels by power plants and industries. SOx can react with other compounds in the atmosphere to form smaller

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particulates. Exposure to these particulates can harm the respiratory system and make breathing difficult.

Figure 8. Annual Analysis of SO2

From Figure.8, it is observed that the average monthly mean for SO2 varies from a maximum of 20 in the month of April'18 to a minimum of 9 during month of March'19. 3 The AQI average annual mean threshold for SO2 is 78 μg/m . But it has been observed that the AQI is within the standard limits with a value of 15 μg/m3.

27% of greenhouse gas emissions are from transportation. Also, GHG emissions from transportation sector has increased more than any other sector. Pollution resulting from carbon monoxide combined with particulate matter contributes to increased morbidity and mortality in the general population. The trend of CO is observed as below from April'18 to Jan'19 as shown in the Figure.9.

Figure 9. Annual Analysis of CO

From the Figure.9, it has been observed that as the levels of particulate matter increases, the level of CO is either less than or equal to the values of SPM. It is clear that PM2.5, PM10 increases, there is a decrease in the levels of CO but these smaller particulates can penetrate through the lower regions of the respiratory tract causing more harm to the public.

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Fig 10. Annual Analysis of O3

The impact of climate change on air pollutants like Ozone, compared to particulate matter, is less certain. Ozone in the atmosphere warms the climate, while different components of particulate matter (PM) can have either warming or cooling effects on the climate. From the observations it is clear that, for the Quarter April-18 to June-18, the PM10 values are 56,54,57 respectively and the trend of Ozone is 35, 30,21 as seen in the Figure.10. For the quarter July-18 to September-18, as the values of PM2.5, PM10 are observed at the general maximum, the values of Ozone are recorded at general minimum. The trend clearly shows that as PM2.5, PM10 values increases, there is a decrease in the Ozone levels.

Once the preliminary analysis has been carried out, based on the real time inputs and to allow forecast for long term and short-term predictions a deep learning-based model [19] can be developed to collect heterogenous data from monitoring stations and evaluate the behavior of air pollutant emissions and variations.

Conclusion

The objective of this study is to use technology to create awareness among the general public as well as the government bodies to adopt proper measures to reduce pollution in Vijayawada city. This study focused on temporal patterns of the air pollutants, to fathom the concentrations of pollutants in Vijayawada.

The future scope of this work involves the air quality predictions using Deep Learning algorithms, taking into consideration both spatial and temporal distribution affected by factors such as human activities, weather deposition, traffic flow, etc., Deep Learning algorithms using multiple layer architectures extract data from lowest to highest level, layer-by-layer, lead to good air quality predictions without having prior knowledge of the air quality features.

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International Journal of Science and Technology Vol. 28, No. 12, (2019), pp. 57-67

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