A study to access and estimation of air pollution using a multivariate statistical model in Chennai, India
,
Abstract
Introduction: Rapid urbanization and industrial growth are the primary causes of deteriorating urban air quality in developing countries, including India. Vehicular emission is a significant cause of the degradation of air quality. It includes Air Pollution like carbon monoxide, hydrocarbons, oxides of nitrogen, oxides of sulfur, Suspended Particulate Matter (SPM), respiratory Particulate Matters (PM2.5 and PM10), and lead.
Materials and methods: The study has considered land use,land cover, land surface temperature, vegetation, literacy rate, vehicle population, population density, and households, finding the responsible causes of air pollutionfor the analysis. Supervised classification using ArcGIS for extracting land use and land cover, Least Slack Time (LST) algorithms have used to extract land surface temperature, spatial interpolation methods like
Inverse Distance Weighting (IDW) using ArcGIS for identifying the spatial distribution of Literacy rate, vehicle population, population density and households and finally the multivariate statistical model applied to identify the major responsible factor for air pollution using SPSS.
Results: The study reveals that the particulate matter is below the standard value prescribed by the central pollution control board. The Highest air pollution is primarily responsible for vehicle population and industries.
Wednesday and Thursday are the maximum pollution in Chennai, whereas it was recorded as very low on Sunday as compared to other days.
Conclusion: Regression shows that the vehicle population is responsible for air pollution, followed by the population.
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b:poen.0000039950.85422.eb
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Paolini L. Land surface temperature retrieval
from LANDSAT TM 5. Remote Sensing of
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rse.2004.02.003.
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land surface temperature–vegetation abundance
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sensing of Environment. 2004 Feb 29;89(4):467-
83. Available from: http://dx.doi.org/10.1016/j.
rse.2003.11.005.
21. Wang F, Qin Z, Song C, Tu L, Karnieli A,
Zhao S. An improved mono-window algorithm
for land surface temperature retrieval from
Landsat 8 thermal infrared sensor data. Remote
sensing. 2015 Apr 10;7(4):4268-89. Available
from: http://dx.doi.org/10.3390/rs70404268.
22. Stathopoulou M, Cartalis C. Daytime
urban heat islands from Landsat ETM+ and Corine
land cover data: An application to major cities in
Greece. Solar Energy. 2007 Mar 1;81(3):358-
68. Available from: http://dx.doi.org/10.1016/j.
solener.2006.06.014
23. Fredy Alejandro GL, Marco Andres GL,
Nestor Yezid RR. Spatial-temporal assessment
and mapping of the air quality and noise pollution
in a sub-area local environment inside the center
of a Latin American Megacity: Universidad Nacional de Colombia-Bogotá Campus.
Asian Journal of Atmospheric Environment.
2018;12(3):232-43. Available from: http://dx.doi.
org/10.5572/ajae.2018.12.3.232.
24. Zhang X, Gong Z. Spatiotemporal
characteristics of urban air quality in China
and geographic detection of their determinants.
Journal of Geographical Sciences. 2018
May;28:563-78. Available from: http://dx.doi.
org/10.1007/s11442-018-1491-z
25. Chen P. Visualization of real-time
monitoring datagraphic of urban environmental
quality. Eurasip Journal on Image and Video
Processing. 2019 Dec;2019(1):1-9. Available
from: http://dx.doi.org/10.1186/s13640-019-
0443-6.
GJ, Artaxo P, Bond TC, Russell AG, et al.
Tools for improving air quality management:
A review of top-down source apportionment
techniques and their application in developing
countries. Available from: http://hdl.handle.
net/10986/17488
2. Goyal P, Krishna TR. Various methods
of emission estimation of vehicular traffic in
Delhi. Transportation Research Part D: Transport
and Environment. 1998 Sep 1;3(5):309-17.
Available from: http://dx.doi.org/10.1016/s1361-
9209(98)00009-1
3. Cole MA, Neumayer E. Examining the
impact of demographic factors on air pollution.
Population and Environment. 2004 Sep 1:5-
21. Available from: http://dx.doi.org/10.1023/
b:poen.0000039950.85422.eb
4. Gore RW, Deshpande DS. An approach
for classification of health risks based on air
quality levels. In2017 1st International Conference
on Intelligent Systems and Information
Management (ICISIM) 2017 Oct 5 (pp. 58-61).
IEEE. Available from: https://doi.org/10.1016/j.
scitotenv.2021.148605
5. Litchfield I, Dockery D, Ayres J. Health
effects of air pollution. Environmental Medicine
[Internet]. 2010 Jul 30;141–52. Available from:
http://dx.doi.org/10.1201/b13390-16
6. Shaddick G, Thomas ML, Mudu P,
Ruggeri G, Gumy S. Half the world’s population
are exposed to increasing air pollution. NPJ
Climate and Atmospheric Science. 2020 Jun
17;3(1):23. Available from: http://dx.doi.
org/10.1038/s41612-020-0124-2
7. Ali MU, Liu G, Yousaf B, Ullah H, Abbas
Q, Munir MA. A systematic review on global
pollution status of particulate matter-associated
potential toxic elements and health perspectives in
urban environment. Environmental geochemistry
and health. 2019 Jun 15;41:1131-62. Available
from: http://dx.doi.org/10.1007/s10653-018-
0203-z
8. Xu X, Xia J, Gao Y, Zheng W. Additional
focus on particulate matter wash-off events from
leaves is required: A review of studies of urban
plants used to reduce airborne particulate matter
pollution. Urban Forestry & Urban Greening.
2020 Feb 1;48:126559. Available from: http://
dx.doi.org/10.1016/j.ufug.2019.126559
9. Diener A, Mudu P. How can vegetation
protect us from air pollution? A critical review
on green spaces' mitigation abilities for air-borne
particles from a public health perspective-with
implications for urban planning. Science of the
Total Environment. 2021 Nov 20;796:148605.
10. Cole MA, Neumayer E. Examining the
impact of demographic factors on air pollution.
Population and Environment. 2004 Sep 1:5-
21. Available from: http://dx.doi.org/10.1023/
b:poen.0000039950.85422.eb
11. Zou B, Peng F, Wan N, Mamady K,
Wilson GJ. Spatial cluster detection of air
pollution exposure inequities across the United
States. PLoS One. 2014 Mar 19;9(3):e91917.
Available from: http://dx.doi.org/10.1371/
journal.pone.0091917.
12. Ilić I, Vuković M, Štrbac N, Urošević
S. Applying GIS to Control Transportation Air
Pollutants. Polish Journal of Environmental
Studies. 2014 Sep 1;23(5). Available from: https://
doi.org/10.18485/ecologica.2022.29.106.17
13. Syafei AD, Fujiwara A, Zhang J.
A comparative study on NO concentration
interpolation in Sena Baya city. Proceedings of the
Eastern Asia Society for Transportation9. 2013:1-
3. Available from: https://doi.org/10.1016/j.
proenv.2015.07.061
14. Li L, Gong J, Zhou J. Spatial interpolation
of fine particulate matter concentrations using the
shortest wind-field path distance. PloS one. 2014
May 5;9(5):e96111. Available from: https://doi.
org/10.1371/journal.pone.0096111.
15. Halek F, Kavousi-Rahim A. GIS
assessment of the PM10, PM2.5 and PM1.0
concentrations in urban area of Tehran in warm
and cold seasons. International Archives of the
Photogrammetry, Remote Sensing & Spatial
Information Sciences. 2014 Nov 15. Available
from: http://dx.doi.org/10.5194/isprsarchives-xl2-w3-141-2014
16. Barsi JA, Schott JR, Hook SJ, Raqueno
NG, Markham BL, Radocinski RG. Landsat-8
thermal infrared sensor (TIRS) vicarious
radiometric calibration. Remote Sensing. 2014
Nov 21;6(11):11607-26. Available from: http://
dx.doi.org/10.3390/rs61111607.
17. J Jiménez-Muñoz JC, Sobrino JA,
Gillespie A, Sabol D, Gustafson WT. Improved
land surface emissivities over agricultural
areas using ASTER NDVI. Remote Sensing
of Environment. 2006 Aug 30;103(4):474-87.
Available from: http://dx.doi.org/10.1016/j.
rse.2006.04.012.
18. Carlson TN, Ripley DA. On the relation
between NDVI, fractional vegetation cover, and
leaf area index. Remote sensing of Environment.
1997 Dec 1;62(3):241-52. Available from: http://
dx.doi.org/10.1016/s0034-4257(97)00104-1.
19. Sobrino JA, Jiménez-Muñoz JC,
Paolini L. Land surface temperature retrieval
from LANDSAT TM 5. Remote Sensing of
environment. 2004 Apr 30;90(4):434-40.
Available from: http://dx.doi.org/10.1016/j.
rse.2004.02.003.
20. Weng Q, Lu D, Schubring J. Estimation of
land surface temperature–vegetation abundance
relationship for urban heat island studies. Remote
sensing of Environment. 2004 Feb 29;89(4):467-
83. Available from: http://dx.doi.org/10.1016/j.
rse.2003.11.005.
21. Wang F, Qin Z, Song C, Tu L, Karnieli A,
Zhao S. An improved mono-window algorithm
for land surface temperature retrieval from
Landsat 8 thermal infrared sensor data. Remote
sensing. 2015 Apr 10;7(4):4268-89. Available
from: http://dx.doi.org/10.3390/rs70404268.
22. Stathopoulou M, Cartalis C. Daytime
urban heat islands from Landsat ETM+ and Corine
land cover data: An application to major cities in
Greece. Solar Energy. 2007 Mar 1;81(3):358-
68. Available from: http://dx.doi.org/10.1016/j.
solener.2006.06.014
23. Fredy Alejandro GL, Marco Andres GL,
Nestor Yezid RR. Spatial-temporal assessment
and mapping of the air quality and noise pollution
in a sub-area local environment inside the center
of a Latin American Megacity: Universidad Nacional de Colombia-Bogotá Campus.
Asian Journal of Atmospheric Environment.
2018;12(3):232-43. Available from: http://dx.doi.
org/10.5572/ajae.2018.12.3.232.
24. Zhang X, Gong Z. Spatiotemporal
characteristics of urban air quality in China
and geographic detection of their determinants.
Journal of Geographical Sciences. 2018
May;28:563-78. Available from: http://dx.doi.
org/10.1007/s11442-018-1491-z
25. Chen P. Visualization of real-time
monitoring datagraphic of urban environmental
quality. Eurasip Journal on Image and Video
Processing. 2019 Dec;2019(1):1-9. Available
from: http://dx.doi.org/10.1186/s13640-019-
0443-6.
| Files | ||
| Issue | Vol 8 No 1 (2023): Winter 2023 | |
| Section | Original Research | |
| DOI | https://doi.org/10.18502/japh.v8i1.12032 | |
| Keywords | ||
| Air pollution; Particulate matter; Particulate matters less than 2.5 µm (PM2.5); Particulate matters less than 10 µm (PM10); Multivariate statistical model | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Muthulakshmi YR, Mathivanan S, Sindhumol MR. A study to access and estimation of air pollution using a multivariate statistical model in Chennai, India. JAPH. 2023;8(1):87-102.
