Air quality modeling for effective environmental management in Uttarakhand, India: A comparison of logistic regression and naive bayes
,
Abstract
Introduction: Air pollution increases the load of hospitalization cases, especially for those who have respiratory problems. For effective environmental management, this study aims to compare the performance of two classification algorithms in machine learning (logistic regression and naive bayes) and to evaluate the selection of the best algorithm for predicting the air quality class.
Materials and methods: Pollutants data (PM10, SO2 , NO2) have been collected from the Haldwani, Kashipur and Rudrapur regions in Uttarakhand (India). In part I of the study, the Air Quality Index (AQI) is calculated and assigned a class accordingly. In part II, the performance of algorithms is compared, and the air quality class is predicted through the best algorithm. In part III, accuracy is calculated after comparing the predicted class with the actual class. Then, it is compared with the accuracy of our selected algorithm.
Results: The study finds a positive correlation between PM10 and SO2 pollutants. The result shows that the highest accuracy is achieved through logistic regression to predict the air quality class. Further, logistic regression has achieved the same accuracy i.e., 98.70% after comparing predicted values with the actual values.
Conclusion: Logistic regression is the best algorithm to predict the air quality class in the regions of Uttarakhand, where pollutants are being measured in the Government’s hospital. The research also indicates that asthma patients in the Kashipur and Rudrapur regions may experience more health effects dueto moderately polluted air quality; however, the situation is improving during the monsoon season.
1. Zhang K, Batterman S. Air pollution and health
risks due to vehicle traffic. Science of the Total
Environment. 2013;450-451:307-316.
2. Kopnina H. Vehicular air pollution and
asthma: implications for education for health and
environmental sustainability. Local Environment
- The International Journal of Justice &
Sustainability. 2017;22(1):38-48.
3. Delavar MR, Gholami A, Shiran GR, Rashidi
Y, Nakhaeizadeh GR, Fedra K, et al. A novel
method for improving air pollution prediction
based on machine learning approaches: a case
study applied to the capital city of Tehran. ISPRS
International Journal of Geo-Information. 2019
Feb 23;8(2):99.
4. P. Predd. A market for clean air. IEEE Spectrum.
2005;42(6):60.
5. Meyers DG, Neuberger JS, He J. Cardiovascular
effect of bans on smoking in public places.
A systematic review and meta-analysis.
Journal of American College of Cardiology.
2009;54(14):1249-1255.
6. Kim D, Cho S, Tamil L, Song DJ, Seo S.
Predicting asthma attacks: Effects of indoor PM
concentrations on peak expiratory flow rates of
asthmatic children. IEEE Access. 2020;8:8791-
8797.
7. Manisalidis, I., Stavropoulou, E., Stavropoulos,
A., & Bezirtzoglou, E. Environmental and health
impacts of air pollution: A review. Frontiers in
Public Health. 2020;8(14).
8. Bai Y, Li Y, Wang X, Xie J, Li C. Air pollutants
con¬centrations forecasting using back
propagation neural network based on wavelet
decomposition with meteo¬rological conditions.
Atmospheric Pollution Research. 2016;7(3): 557-
66.
9. Douglas MJ, Watkins SJ, Gorman DR, Higgins
M. Erratum: Are cars the new tobacco? Journal of
Public Health (Bangkok). 2011;33(3):472.
10. Ameer S, Shah MA, Khan A, Song H, Maple
C, Asghar MN. Comparative analysis of machine
learning techniques for predicting air quality in
smart cities. IEEE Access. 2017;20.
11. Jamal A, Nabizadeh Nodehi R. Predicting
air quality index based on meteorological data:
A comparison of regression analysis, artificial
neural networks and decision tree. Journal of Air
Pollution & Health. 2017;2(1).
12. Mahesh Babu K, Rene Beulah J. Air quality
prediction based on supervised machine learning
methods. International Journal of Innovative
Technology & Exploring Engineering. 2019;8(9
Special Issue 4):206-212.
13. A. Pant, S. Sharma, M. Bansal, M. Narang.
Comparative analysis of supervised machine
learning techniques for AQI prediction.
IEEE International Conference on Advanced
Computing Technologies and Applications
(ICACTA). March 2022:1-4.
14. India world's largest emitter of sulfur dioxide,
emissions. Greenpeace India. 2019: October.
15. Shaban KB, Kadri A, Rezk E. Urban air
pollution monitoring system with forecasting
models. 2016;16(8):2598-2606.
16. Mahalingam U, Elangovan K, Dobhal H,
Valliappa C. A machine learning model for air
quality prediction for smart cities. International
Conference on Wireless Communications Signal
Processing & Networking. 2019;452-457.
17. Xu C, Zhao W, Zhang M, Cheng B. Pollution
haven or halo? The role of the energy transition in
the impact of FDI on SO2
emissions. The Science
of the Total Environment. April 2021;763.
18. M. Volkodaeva, A. Kiselev. On development
of system for environmental monitoring of
atmospheric air quality. Journal of Mining
Institute. 2017;227:589-596.
19. Irani T, Amiri H, and Deyhim H. Evaluating
visibility range on air pollution using NARX neural
network simulation. Journal of Environmental
Treatment Techniques. 2021;9(2):540-547.
20. Chciałowski A, Agata D, Badyda A, Piotr D.
Ambient air pollution and risk of admission due to
asthma in the three largest urban agglomerations
in Poland : A Time-Stratified, Case-Crossover
Study. International Journal of Environmental
Research and Public Health. 2022;19(10):5988.
21. Gharehchahi E, Mahvi AH, Amini H,
Nabizadeh R, Akhlaghi AA. Health impact
assessment of air pollution in Shiraz, Iran: a
two-part study. Journal of Environmental Health
Sciences and Engineering. 2013:1-8.
22. Nadali, A., Leili, M., Karami, M. et al. The
short-term association between air pollution and
asthma hospitalization: a time-series analysis. Air
Quality, Atmosphere and Health. 2022;15:1153-
1167.
23. Li, S., Song, S., Fei, X. Spatial characteristics
of air pollution in the main city area of Chengdu,
China. 19th International Conference on
Geoinformatics, Shanghai, China. 2011:1-4.
24. Moradi, H., Talaiekhozani, A., Kamyab,
H. et al. Development of equations to predict
the concentration of air pollutants indicators
in Yazd City, Iran. Journal of Inorganic and Organometallic Polymers and Materials. July,
2022.
25. Senthivel S, Chidambaranathan M. Machine
learning approaches used for air quality forecast :
Revue d'Intelligence Artificielle. 2022;36(1):73-
78.
26. S. Yarragunta, M. A. Nabi, J. P., R. S.
Prediction of air pollutants using supervised
machine learning. 5th International Conference
on Intelligent Computing and Control Systems
(ICICCS), 2021:1633-1640.
27. R. W. Gore, D. S. Deshpande. An approach for
classification of health risks based on air quality
levels. 1st International Conference on Intelligent
Systems and Information Management (ICISIM).
2017:58-61.
28. Kumar, K., Pande, B.P. Air pollution prediction
with machine learning: a case study of Indian
cities. International Journal of Environmental
Science & Technology. 2022.
29. Doreswamy, Harishkumar K S1, Yogesh KM,
Ibrahim Gad. Forecasting air pollution particulate
matter (PM2.5) using machine learning models.
Third International Conference on Computing &
Network Communications. Procedia Computer
Science. 2020;171:2057-2066.
30. Yingying Li and Dongxiao Niu. Research
on the relationship between China's economic
growth and SO2
emission. IEEE International
Conference on Test and Measurement. 2009:88-
90.
31. L. Song. Impact analysis of air pollutants
on the air quality index in Jinan Winter. IEEE
International Conference on Computational
Science and Engineering (CSE) & IEEE
International Conference on Embedded and
Ubiquitous Computing (EUC), 2017: 471-474.
32. Chauhan, Avnish, Mayank Pawar, Rajeev
Kumar, P. C. Joshi. Ambient air quality status
in Uttarakhand (India): A Case Study of
Haridwar and Dehradun using Air Quality Index.
2010;6(9):565-574.
33. Madonsela BS, Maphanga T, Silas Chidi B,
Shale K, Zungu V. Assessment of air pollution
in the informal settlements of the Western Cape,
South Africa. JAPH. 2022;7(1):1-14.
34. Marjan Asgari, M., Farnaghi, M. Ghaemi, Z.
Predictive mapping of urban air pollution using
apache spark on a hadoop cluster. ICCBDC
International Conference on Cloud & Big
Data Computing, London: United Kingdom.
September, 2017:89–93.
35. S. Yarragunta, M. A. Nabi, J. P., R. S.
Prediction of air pollutants using supervised
machine learning. 5th International Conference
on Intelligent Computing annd Control Systems
(ICICCS), 2021:1633-1640.
risks due to vehicle traffic. Science of the Total
Environment. 2013;450-451:307-316.
2. Kopnina H. Vehicular air pollution and
asthma: implications for education for health and
environmental sustainability. Local Environment
- The International Journal of Justice &
Sustainability. 2017;22(1):38-48.
3. Delavar MR, Gholami A, Shiran GR, Rashidi
Y, Nakhaeizadeh GR, Fedra K, et al. A novel
method for improving air pollution prediction
based on machine learning approaches: a case
study applied to the capital city of Tehran. ISPRS
International Journal of Geo-Information. 2019
Feb 23;8(2):99.
4. P. Predd. A market for clean air. IEEE Spectrum.
2005;42(6):60.
5. Meyers DG, Neuberger JS, He J. Cardiovascular
effect of bans on smoking in public places.
A systematic review and meta-analysis.
Journal of American College of Cardiology.
2009;54(14):1249-1255.
6. Kim D, Cho S, Tamil L, Song DJ, Seo S.
Predicting asthma attacks: Effects of indoor PM
concentrations on peak expiratory flow rates of
asthmatic children. IEEE Access. 2020;8:8791-
8797.
7. Manisalidis, I., Stavropoulou, E., Stavropoulos,
A., & Bezirtzoglou, E. Environmental and health
impacts of air pollution: A review. Frontiers in
Public Health. 2020;8(14).
8. Bai Y, Li Y, Wang X, Xie J, Li C. Air pollutants
con¬centrations forecasting using back
propagation neural network based on wavelet
decomposition with meteo¬rological conditions.
Atmospheric Pollution Research. 2016;7(3): 557-
66.
9. Douglas MJ, Watkins SJ, Gorman DR, Higgins
M. Erratum: Are cars the new tobacco? Journal of
Public Health (Bangkok). 2011;33(3):472.
10. Ameer S, Shah MA, Khan A, Song H, Maple
C, Asghar MN. Comparative analysis of machine
learning techniques for predicting air quality in
smart cities. IEEE Access. 2017;20.
11. Jamal A, Nabizadeh Nodehi R. Predicting
air quality index based on meteorological data:
A comparison of regression analysis, artificial
neural networks and decision tree. Journal of Air
Pollution & Health. 2017;2(1).
12. Mahesh Babu K, Rene Beulah J. Air quality
prediction based on supervised machine learning
methods. International Journal of Innovative
Technology & Exploring Engineering. 2019;8(9
Special Issue 4):206-212.
13. A. Pant, S. Sharma, M. Bansal, M. Narang.
Comparative analysis of supervised machine
learning techniques for AQI prediction.
IEEE International Conference on Advanced
Computing Technologies and Applications
(ICACTA). March 2022:1-4.
14. India world's largest emitter of sulfur dioxide,
emissions. Greenpeace India. 2019: October.
15. Shaban KB, Kadri A, Rezk E. Urban air
pollution monitoring system with forecasting
models. 2016;16(8):2598-2606.
16. Mahalingam U, Elangovan K, Dobhal H,
Valliappa C. A machine learning model for air
quality prediction for smart cities. International
Conference on Wireless Communications Signal
Processing & Networking. 2019;452-457.
17. Xu C, Zhao W, Zhang M, Cheng B. Pollution
haven or halo? The role of the energy transition in
the impact of FDI on SO2
emissions. The Science
of the Total Environment. April 2021;763.
18. M. Volkodaeva, A. Kiselev. On development
of system for environmental monitoring of
atmospheric air quality. Journal of Mining
Institute. 2017;227:589-596.
19. Irani T, Amiri H, and Deyhim H. Evaluating
visibility range on air pollution using NARX neural
network simulation. Journal of Environmental
Treatment Techniques. 2021;9(2):540-547.
20. Chciałowski A, Agata D, Badyda A, Piotr D.
Ambient air pollution and risk of admission due to
asthma in the three largest urban agglomerations
in Poland : A Time-Stratified, Case-Crossover
Study. International Journal of Environmental
Research and Public Health. 2022;19(10):5988.
21. Gharehchahi E, Mahvi AH, Amini H,
Nabizadeh R, Akhlaghi AA. Health impact
assessment of air pollution in Shiraz, Iran: a
two-part study. Journal of Environmental Health
Sciences and Engineering. 2013:1-8.
22. Nadali, A., Leili, M., Karami, M. et al. The
short-term association between air pollution and
asthma hospitalization: a time-series analysis. Air
Quality, Atmosphere and Health. 2022;15:1153-
1167.
23. Li, S., Song, S., Fei, X. Spatial characteristics
of air pollution in the main city area of Chengdu,
China. 19th International Conference on
Geoinformatics, Shanghai, China. 2011:1-4.
24. Moradi, H., Talaiekhozani, A., Kamyab,
H. et al. Development of equations to predict
the concentration of air pollutants indicators
in Yazd City, Iran. Journal of Inorganic and Organometallic Polymers and Materials. July,
2022.
25. Senthivel S, Chidambaranathan M. Machine
learning approaches used for air quality forecast :
Revue d'Intelligence Artificielle. 2022;36(1):73-
78.
26. S. Yarragunta, M. A. Nabi, J. P., R. S.
Prediction of air pollutants using supervised
machine learning. 5th International Conference
on Intelligent Computing and Control Systems
(ICICCS), 2021:1633-1640.
27. R. W. Gore, D. S. Deshpande. An approach for
classification of health risks based on air quality
levels. 1st International Conference on Intelligent
Systems and Information Management (ICISIM).
2017:58-61.
28. Kumar, K., Pande, B.P. Air pollution prediction
with machine learning: a case study of Indian
cities. International Journal of Environmental
Science & Technology. 2022.
29. Doreswamy, Harishkumar K S1, Yogesh KM,
Ibrahim Gad. Forecasting air pollution particulate
matter (PM2.5) using machine learning models.
Third International Conference on Computing &
Network Communications. Procedia Computer
Science. 2020;171:2057-2066.
30. Yingying Li and Dongxiao Niu. Research
on the relationship between China's economic
growth and SO2
emission. IEEE International
Conference on Test and Measurement. 2009:88-
90.
31. L. Song. Impact analysis of air pollutants
on the air quality index in Jinan Winter. IEEE
International Conference on Computational
Science and Engineering (CSE) & IEEE
International Conference on Embedded and
Ubiquitous Computing (EUC), 2017: 471-474.
32. Chauhan, Avnish, Mayank Pawar, Rajeev
Kumar, P. C. Joshi. Ambient air quality status
in Uttarakhand (India): A Case Study of
Haridwar and Dehradun using Air Quality Index.
2010;6(9):565-574.
33. Madonsela BS, Maphanga T, Silas Chidi B,
Shale K, Zungu V. Assessment of air pollution
in the informal settlements of the Western Cape,
South Africa. JAPH. 2022;7(1):1-14.
34. Marjan Asgari, M., Farnaghi, M. Ghaemi, Z.
Predictive mapping of urban air pollution using
apache spark on a hadoop cluster. ICCBDC
International Conference on Cloud & Big
Data Computing, London: United Kingdom.
September, 2017:89–93.
35. S. Yarragunta, M. A. Nabi, J. P., R. S.
Prediction of air pollutants using supervised
machine learning. 5th International Conference
on Intelligent Computing annd Control Systems
(ICICCS), 2021:1633-1640.
| Files | ||
| Issue | Vol 7 No 3 (2022): Summer 2022 | |
| Section | Original Research | |
| DOI | https://doi.org/10.18502/japh.v7i3.10542 | |
| Keywords | ||
| Pollutants; Air Quality; Logistic regression; Naive bayes; Environmental management | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Pant A, Sharma S, Joshi R. Air quality modeling for effective environmental management in Uttarakhand, India: A comparison of logistic regression and naive bayes. JAPH. 2022;7(3):287-298.
