International Journal of Pure and Applied Mathematics Volume 118 No. 6 2018, 261-269 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu Special Issue ijpam.eu

Simulation model to capture the future trend of air quality in urban

I.T.S. Piyatilake1 and S.S.N. Perera2

1Department of Computational Mathematics, Faculty of Information Technology, University of , . [email protected] 2Research & Development Center for Mathematical Modeling, Faculty of Science, University of Colombo, Colombo 03, Sri Lanka. [email protected]

Abstract The fuzzy operators based mathematical model has been used to measure the air quality level of cities in urban areas considering indirect measurements. The number of factories, number of power plants, population density, vehicle intensity, green coverage, temperature and wind speed are selected as the indirect measurements of air quality. In this paper, we demonstrate how this already developed model can be used to measure the future risk of air quality in urban zones in Colombo, Sri Lanka. First, the sensitivity of the model is assessed considering 3D evaluation graphs. For this purpose different simulations are carried out to demonstrate the relationship between indirect measurements and air quality levels. Next, case study is carried out by selecting Colombo Municipal Council region in Sri Lanka. Present situation and future trend of air quality in this area are obtained using the model equation. Finally, the control strategies which we can use to reduce the future risk of air pollution are discussed. MATLAB program is used for the simulations. According to the simulation, most of the zones in Colombo Municipal council region will reach to unhealthy levels in the year 2022. The zones , , , Pettah and will attain to the Hazardous level in year 2022. If we implement some control strategies, for instance increasing the green coverage level by 2% and reducing the vehicle intensity by 15% we will be able maintain the air quality level as at present level. AMS Subject Classification: 47S40, 97M99 Key Words and Phrases: Air quality, Fuzzy operators, Indirect mea- surements, Control strategies

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1 Introduction

Clean air is an essential need for human health. The composition of the natural air has shown gradual variations as long as the earth has existed due to rapid urbanization and increasing levels of industrializations. The change in the global, chemical composition of the preindustrial atmosphere due to human influence can be called as air pollution [2]. There is a growing indication of air pollutants in Sri Lanka. According to the “BreatheLife” campaign [4] which was led by the World Health Organization (WHO), air pollution level in Sri Lanka is 2.7 times higher than the safe level and about 7,792 people die from an air pollution related disease each year. Air pollution levels in Colombo city which is the commercial, economical and administrative capital of Sri Lanka have reached 3.6 times the WHO safe level. This situation is alarming to relevant authorities in Sri Lanka to determine the air quality levels in cities and develop control strategies in order to keep the air quality in safe level. Due to the lack of resources, it is not possible to have continuous air pollutant monitoring stations in Sri Lanka. Therefore, people are not aware about their surrounding air quality and its future trends. However, some factors such as number of vehicles, number of factories and population density are proportional to the air pollution. These factors can be identified as the indirect measurements of air pollution. The objective of this study is to identify the future risk of air pollution in Colombo Municipal Council region in Sri Lanka using a already developed model [3] which was formed using fuzzy operators. Different scenarios considering the re- lationship between factors and air quality levels are simulated in order to check the model sensitivity and to identify the future trend. Simulations are done by using a MATLAB program.

2 Simulation Model

The air quality level considering the combined effect of indirect measurements is described, by the fuzzy operator based mathematical model [3] of the form,

MH((MH(A3 C2,B, 0.5, 0.1, 0.2) D0.2)0.4E, 0.5, 0.3, 5)0.08, (1) × × where A, B, C, D and E are fuzzy membership values of the factors, namely; number of industries in the area, population density, traffic intensity, weather condition and green area respectively. Here MH is the modified Hamacher operator which is defined for the intersection of two fuzzy sets X and Y by

1 1 p1 p2 fX (x)fY (x) MH(X,Y ; p, p1, p2)(x) = 1 1 1 1 , p + (1 p)[f p1 (x) + f p2 (x) f p1 (x)f p2 (x)] (2) − X Y − X Y 0 p 1, p , p > 0. ≤ ≤ 1 2

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The categories of these seven air quality factors, the upper and lower limits of each category are as shown in Table 1. The categories of output air quality levels according to the study [3] are shown in Table 2.

Table 1: Fuzzy set range of air quality factors. Factors Linguistic Scale Membership Value Number of factories Low 1 Number of power Moderate [0.5, 1) plants Population High (0, 0.5) density Vehicle density Very High 0 Temperature High 1 Wind speed Moderate [0.5, 1) Low [0, 0.5) Very High 1 High [0.5, 1) Green area Moderate (0, 0.5) Low 0

Table 2: Air quality categories. Category Membership Value Good 0.8801 - 1.0000 Moderate 0.5701 - 0.8800 Unhealthy for sensitive group 0.3001 - 0.5700 Unhealthy 0.1503 - 0.3000 Very Unhealthy 0.0031 - 0.1502 Hazardous 0.0000 - 0.0030

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3 Results and Discussion

Model sensitivity with respect to the indirect measurements are evaluated using 3D graphs as shown in Figure 1, 2 and 3. For example, Figure 1 shows the relationship between traffic intensity and population for different membership values of factories. Here the membership values of power plants, wind, temperature and green coverage are kept as constants. The constant membership values of power plants, wind, temperature and green coverage are 1, 0.8, 0.7 and 0.8 respectively. By varying the membership values of population and traffic between 0 to 1 the air quality values are simulated. According to the top two figures of Figure 1, if the membership values of factories less than 0.5 the quality of air is hazardous or unhealthy for all the membership values of population and traffic. As shown in bottom two figures of Figure 1, by controlling the factories along with population and traffic, the quality of air level can be brought up. Table 3, 4 and 5 show the different scenarios developed considering Figure 1, 2 and 3.

Figure 1: Air quality value for population and traffic intensity. Top (Left): Factories = 0.2, Top (Right): Factories = 0.5, Bottom (Left): Factories = 0.8, Bottom (Right): Factories = 1.

Table 3: Scenarios of population and traffic intensity for different membership values of factories. Membership Value

Scenario Power Factories Wind Temperature Population Traffic Green Air Quality Plants Intensity Coverage 1 1 0.2 0.8 0.7 0 - 1 0 - 1 0.8 Very Unhealthy 2 1 0.5 0.8 0.7 0 - 1 0 - 1 0.8 Unhealthy 3 1 0.8 0.8 0.7 0.4 < 0.8 < 0.8 Moderate 4 1 1 0.8 0.7 0.3 < 0.7 < 0.8 Good

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Figure 2: Air quality value for factories and population. Top (Left): Traffic Intensity = 0.2, Top (Right): Traffic Intensity = 0.5, Bottom (Left): Traffic Intensity = 0.8, Bottom (Right): Traffic Intensity = 1.

Table 4: Scenarios of factories and population for different membership values of traffic intensity. Membership Value

Scenario Power Factories Wind Temperature Population Traffic Green Air Quality Plants Intensity Coverage 1 1 0 - 1 0.8 0.7 0 - 1 0.2 0.8 Very Unhealthy 2 1 0 - 1 0.8 0.7 0 - 1 0.5 0.8 Unhealthy 3 1 0.9 < 0.8 0.7 0.6 < 0.8 0.8 Moderate 4 1 0.6 < 0.8 0.7 0.3 < 1 0.8 Good

Figure 3: Air quality value for factories and traffic intensity. Top (Left): Green Coverage = 0.1, Top (Right): Green Coverage = 0.5, Bottom (Left): Green Coverage = 0.8, Bottom (Right): Green Coverage = 1.

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Table 5: Scenarios of factories and traffic intensity for different membership values of green coverage. Membership Value

Scenario Power Factories Wind Temperature Population Traffic Green Air Quality Plants Intensity Coverage Unhealthy 1 1 0.9 < 0.6 0.5 0.7 0.9 < 0.8 for Sensitive Group 2 1 0.9 < 0.6 0.5 0.7 0.9 < 0.8 Moderate 3 1 0.8 < 0.6 0.5 0.7 0.8 < 0.8 Moderate 4 1 0.8 < 0.6 0.5 0.7 1 0.8 < Good

3.1 A Case study of predicting future air quality level in zones of Colombo Municipal Council (CMC) area

Colombo is the financial and administrative capital of Sri Lanka. Therefore it is home to around one million people [1] and nearly 400,000 people visit Colombo Mu- nicipal Council area because main schools, universities, hospitals, industrial zones, shopping complexes, harbor and airport exist in this area. Approximately 275,000 vehicles enter in to Colombo city limit every day. The main electricity thermal power plant called as “Kelanitissa” and the only oil refinery are also placed in this area. It is one of the highest polluted city in Sri Lanka. Therefore it is important to identify the present and future air quality level in zones in urban Colombo in order to develop control strategies to keep this city in safe level. This case study is based on the geographical area governed by the Colombo Municipal Council i.e. Colombo 1 - Colombo 15. The zones are Fort (C1), Slave Is- land (C2), (C3), Bambalapitiya (C4), /Kirilapone (C5), / / (C6), (C7), (C8), Dematagoda (C9), / (C10), Pettah (C11), Hultsdorf (C12), /Bloemendhal (C13), (C14) and Mutwal/Modera/Mattakk- uliya/Madampitiy (C15). The data of this area is collected from Department of Census and Statistics, Department of Meteorology and Department of Motor Traf- fic in Sri Lanka. According to the available records in Sri Lanka annual population growth rate is 1.1%, number of vehicles increasing rate is 8%, number of factories increasing rate is 0.4% and green coverage decreasing rate is 1.1%. Table 6 shows the present and future air quality levels in CMC area. For the simulation process we assumed that the there is no change in the climate factors and the number of power plants. According to the simulation most of the zones will reach to unhealthy level in 2022. The zones Fort, Bambalapitiya, Dematagoda, Pettah and Bloemendhal attains to the Hazardous level in year 2022. If we implement some control strategies, for instance increasing the green coverage level by 2% and reducing the number of vehicles by 15% we can maintain the air quality level at present level.

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Table 6: Present and future air quality level in CMC area. 2022 Zone 2017 Without Controls With Control C1 5 6 6 C2 3 4 3 C3 3 5 5 C4 5 6 5 C5 2 2 2 C6 2 2 2 C7 1 1 1 C8 3 4 3 C9 5 6 5 C10 2 2 2 C11 5 6 5 C12 2 2 2 C13 5 6 6 C14 3 5 4 C15 2 3 3

Note: 1-Good; 2-Moderate; 3-Unhealthy for sensitivity group; 4-Unhealthy; 5-Very unhealthy; 6-Hazardous

4 Conclusion

In this study, fuzzy operator based model is used to identify the future risk of air pollution. The model sensitivity is assessed considering the different levels of indirect measurements. Seven indirect measurements such as number of factories, number of power plants, population density, vehicle density, green coverage, temperature and wind speed in the area are selected for this study. A case study is carried out considering the CMC area and predict the future air quality level in this area. To improve the air quality of unhealthy or hazardous cities we can introducing an attractive public transport system and discourage the use of private vehicles during peak periods in the city limit, promote natural gas and renewable energy such as wind and solar power for transport system, planting trees and introducing green belts around the pollutant sources such as oil refinery and power plants which are situated in urban areas, introducing green roofs by planting trees on roof tops of the buildings and limit the factories in the city limit.

References

[1] Colombo Municipal Council. “City of Colombo”. Available: http://colombo.mc.gov.lk/ [Nov. 15, 2017].

[2] P. Zannetti. Air Quality Modeling, Theories, Methodologies, Computational Techniques and Available Databases and Software, EnviroComp Institute and Waste Management Association, USA, Vol.I, 2003.

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[3] I.T.S. Piyatilake, S.S.N. Perera and S.K. Boralugoda. “Mathematical model to quantify air quality: Indirect measurement approach”. British Journal of Applied Science & Technology, Vol. 11, No. 5, pp. 1-14, 2015.

[4] World Health Organization. “Health and Sustainable Development”. Available: http://www.who.int/sustainable-development/news-events/breath- life/en/, 2017 [July 7, 2017].

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