simulation model to capture the future trend of air quality in [email protected] 2research &...

Simulation model to capture the future trend of airquality in urban Colombo

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

1Department of Computational Mathematics,Faculty of Information Technology,University of Moratuwa, Sri Lanka.

[email protected] & 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 measurethe air quality level of cities in urban areas considering indirect measurements.The number of factories, number of power plants, population density, vehicleintensity, green coverage, temperature and wind speed are selected as theindirect measurements of air quality. In this paper, we demonstrate how thisalready developed model can be used to measure the future risk of air qualityin urban zones in Colombo, Sri Lanka.

First, the sensitivity of the model is assessed considering 3D evaluationgraphs. For this purpose different simulations are carried out to demonstratethe relationship between indirect measurements and air quality levels. Next,case study is carried out by selecting Colombo Municipal Council region inSri Lanka. Present situation and future trend of air quality in this area areobtained using the model equation. Finally, the control strategies which wecan use to reduce the future risk of air pollution are discussed. MATLABprogram is used for the simulations.

According to the simulation, most of the zones in Colombo Municipalcouncil region will reach to unhealthy levels in the year 2022. The zonesFort, Bambalapitiya, Dematagoda, Pettah and Bloemendhal will attain tothe Hazardous level in year 2022. If we implement some control strategies,for instance increasing the green coverage level by 2% and reducing the vehicleintensity by 15% we will be able maintain the air quality level as at presentlevel.

AMS Subject Classification: 47S40, 97M99Key Words and Phrases: Air quality, Fuzzy operators, Indirect mea-

surements, Control strategies

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

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1 Introduction

Clean air is an essential need for human health. The composition of the naturalair has shown gradual variations as long as the earth has existed due to rapidurbanization and increasing levels of industrializations. The change in the global,chemical composition of the preindustrial atmosphere due to human influence canbe called as air pollution [2]. There is a growing indication of air pollutants in SriLanka. According to the “BreatheLife” campaign [4] which was led by the WorldHealth Organization (WHO), air pollution level in Sri Lanka is 2.7 times higherthan the safe level and about 7,792 people die from an air pollution related diseaseeach year. Air pollution levels in Colombo city which is the commercial, economicaland 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 airquality levels in cities and develop control strategies in order to keep the air qualityin safe level. Due to the lack of resources, it is not possible to have continuous airpollutant monitoring stations in Sri Lanka. Therefore, people are not aware abouttheir surrounding air quality and its future trends. However, some factors such asnumber of vehicles, number of factories and population density are proportional tothe air pollution. These factors can be identified as the indirect measurements ofair pollution.

The objective of this study is to identify the future risk of air pollution inColombo 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 themodel sensitivity and to identify the future trend. Simulations are done by using aMATLAB program.

2 Simulation Model

The air quality level considering the combined effect of indirect measurements isdescribed, 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; numberof industries in the area, population density, traffic intensity, weather condition andgreen area respectively. Here MH is the modified Hamacher operator which isdefined for the intersection of two fuzzy sets X and Y by

MH(X, Y ; p, p1, p2)(x) =f

1p1X (x)f

1p2Y (x)

p + (1 − p)[f1p1X (x) + f

1p2Y (x) − f

1p1X (x)f

1p2Y (x)]

,

0 ≤ p ≤ 1, p1, p2 > 0.

(2)

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

Table 1: Fuzzy set range of air quality factors.Factors Linguistic Scale Membership ValueNumber of factoriesNumber of powerplants Populationdensity Vehicle densityTemperature

Low 1Moderate [0.5, 1)High (0, 0.5)Very High 0

Wind speedHigh 1Moderate [0.5, 1)Low [0, 0.5)

Green area

Very High 1High [0.5, 1)Moderate (0, 0.5)Low 0

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


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3 Results and Discussion

Model sensitivity with respect to the indirect measurements are evaluated using 3Dgraphs as shown in Figure 1, 2 and 3. For example, Figure 1 shows the relationshipbetween traffic intensity and population for different membership values of factories.Here the membership values of power plants, wind, temperature and green coverageare 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 themembership values of population and traffic between 0 to 1 the air quality valuesare simulated. According to the top two figures of Figure 1, if the membershipvalues of factories less than 0.5 the quality of air is hazardous or unhealthy for allthe membership values of population and traffic. As shown in bottom two figures ofFigure 1, by controlling the factories along with population and traffic, the quality ofair level can be brought up. Table 3, 4 and 5 show the different scenarios developedconsidering 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 valuesof factories.

Scenario

Membership Value

Air QualityPowerPlants

Factories Wind Temperature Population TrafficIntensity

GreenCoverage

1 1 0.2 0.8 0.7 0 - 1 0 - 1 0.8 Very Unhealthy2 1 0.5 0.8 0.7 0 - 1 0 - 1 0.8 Unhealthy3 1 0.8 0.8 0.7 0.4 < 0.8 < 0.8 Moderate4 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 oftraffic intensity.

Scenario

Membership Value



GreenCoverage

1 1 0 - 1 0.8 0.7 0 - 1 0.2 0.8 Very Unhealthy2 1 0 - 1 0.8 0.7 0 - 1 0.5 0.8 Unhealthy3 1 0.9 < 0.8 0.7 0.6 < 0.8 0.8 Moderate4 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): GreenCoverage = 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 valuesof green coverage.

Scenario

Membership Value



GreenCoverage

1 1 0.9 < 0.6 0.5 0.7 0.9 < 0.8Unhealthyfor SensitiveGroup

2 1 0.9 < 0.6 0.5 0.7 0.9 < 0.8 Moderate3 1 0.8 < 0.6 0.5 0.7 0.8 < 0.8 Moderate4 1 0.8 < 0.6 0.5 0.7 1 0.8 < Good

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

Colombo is the financial and administrative capital of Sri Lanka. Therefore it ishome 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 275,000vehicles enter in to Colombo city limit every day. The main electricity thermalpower plant called as “Kelanitissa” and the only oil refinery are also placed in thisarea. It is one of the highest polluted city in Sri Lanka. Therefore it is important toidentify the present and future air quality level in zones in urban Colombo in orderto develop control strategies to keep this city in safe level.

This case study is based on the geographical area governed by the ColomboMunicipal Council i.e. Colombo 1 - Colombo 15. The zones are Fort (C1), Slave Is-land (C2), Kollupitiya (C3), Bambalapitiya (C4), Havelock Town/Kirilapone (C5),Wellawatte/ Pamankada/Narahenpita (C6), Cinnamon Gardens (C7), Borella (C8),Dematagoda (C9), Maradana/ Panchikawatte (C10), Pettah (C11), Hultsdorf (C12),Kotahena/Bloemendhal (C13), Grandpass (C14) and Mutwal/Modera/Mattakk-uliya/Madampitiy (C15). The data of this area is collected from Department ofCensus 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 rateis 1.1%, number of vehicles increasing rate is 8%, number of factories increasingrate is 0.4% and green coverage decreasing rate is 1.1%. Table 6 shows the presentand future air quality levels in CMC area. For the simulation process we assumedthat 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 tothe Hazardous level in year 2022. If we implement some control strategies, forinstance increasing the green coverage level by 2% and reducing the number ofvehicles 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.

Zone 20172022

Without Controls With ControlC1 5 6 6C2 3 4 3C3 3 5 5C4 5 6 5C5 2 2 2C6 2 2 2C7 1 1 1C8 3 4 3C9 5 6 5C10 2 2 2C11 5 6 5C12 2 2 2C13 5 6 6C14 3 5 4C15 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 airpollution. The model sensitivity is assessed considering the different levels of indirectmeasurements. Seven indirect measurements such as number of factories, numberof power plants, population density, vehicle density, green coverage, temperatureand wind speed in the area are selected for this study. A case study is carried outconsidering 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 introducingan attractive public transport system and discourage the use of private vehiclesduring peak periods in the city limit, promote natural gas and renewable energysuch as wind and solar power for transport system, planting trees and introducinggreen belts around the pollutant sources such as oil refinery and power plants whichare situated in urban areas, introducing green roofs by planting trees on roof topsof 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, ComputationalTechniques and Available Databases and Software, EnviroComp Institute andWaste Management Association, USA, Vol.I, 2003.


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[3] I.T.S. Piyatilake, S.S.N. Perera and S.K. Boralugoda. “Mathematical modelto quantify air quality: Indirect measurement approach”. British Journal ofApplied 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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