Econometric analysis of the effect of weather on air pollution
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Abstract
Introduction: Air pollution is one of the world’s major global issues. In this research we aimed to calculate the impact of weather factors on air pollution and show the results by using the econometric method of data analysis. After that, we also studied the effect of car exhaust on air pollution in relation to urban congestion and car age.
Materials and methods: Data cleaning methods used in this research include as correcting structural errors, dealing with missing data and sorting data. For calculation, correlation analysis was used to find the relationship of the time series dataset, and then used panel model for the test results, which are estimated by least squares method. In correlation analysis, used air quality and weather’s data of Ulaanbaatar city’s last 3 years.
Results: As a result of the research, we found that the amount of air pollutant depends on weather factors, that is, location and wind speed have the greatest influence on air pollution. Also the decrease in the amount of sulfur dioxide is due to the ban on burning raw coal in the capital. Our findings indicate that the nitrogen dioxide level in the residential area is high even in the warm season, which is due to congestion and age of vehicles.
Conclusion: The most important weather factors affecting air pollution are location and wind direction. In the future, with comprehensive data collection, future research could better identify sources of air pollution and develop effective mitigation strategies.
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33. Ganchimeg G, Image classification techniques in remote sensing. International Online Conference, Information and Media Technology-IMT 2016, Ulaanbaatar, Mongolia, pp 112-117.
2. Health Organization W. Ambient air pollution: a global assessment of exposure and burden of disease. 2016;
3. National Agency for Meteorology and Environment. Weather observation data. Available from: https://weather.gov.mn/observation/weather
4. World's Air Pollution rank: Available from: https://waqi.info/mn/#/c/4.389/7.871/2.3z
5. Improving air quality in Ulaanbaatar. 2022 Nov; ADB Briefs, No 229. Available from: https://www.adb.org/sites/default/files/publication/842651/adb-brief-229-improving-air-quality-ulaanbaatar.pdf
6. World's Air Pollution: Real-time Air Quality Index. Available from: https://www.iqair.com/world-air-quality-ranking,
7. Auditor General of Mongolia. Tsewer agaar san. Available from: https://audit.mn/archive/wp-content/uploads/2018/10/tsewer-agaar-san.pdf
8. MMGG. Plan for calculating the cost of lost opportunities and the negative impact of congestion on the economy, the environment, and human health Mongolia statistics. 1990-Nov, 2024. Available from: https://1212.mn/mn.
9. Khailaast. [Location details on Google Maps]. Available from: https://maps.app.goo.gl/tAqRaqDEfd51QKZ1A
10. Bogd Khaan. [Location details on Google Maps]. Available from: https://maps.app.goo.gl/vfHHGfpoQ4mxV6wM8
11. Ulaanbaatar city Air Pollution: Real-time Air Quality Index. Available from: http://agaar.mn/index?lang=en, http://www.agaar.mn/static/stove-distribution
12. Influence of Temperature and Relative Humidity on PM2.5 Concentration over Delhi. MAPAN - Journal of Metrology Society of India. 2023 Sep;38(3):759-769.
13. Kwiecień J, Szopińska K. Mapping carbon monoxide pollution of residential areas in a Polish city. Remote Sens. 2020;12:2885. doi: 10.3390/rs12182885.
14. Liu Y, Zhou Y, Lu J. Exploring the relationship between air pollution and meteorological conditions in China under environmental governance. Sci Rep. 2020;10:14518.
15. Xu B, Luo L, Lin B. A dynamic analysis of air pollution emissions in China: Evidence from nonparametric additive regression models. Ecol Indic. 2016;63:346-358. doi: 10.1016/j.ecolind.2015.11.012.
16. Du L, Wei C, Cai S. Economic development and carbon dioxide emissions in China: Provincial panel data analysis. China Econ Rev. 2012; 23:371-384. doi: 10.1016/j.chieco. 2012.02.004.
17. Available from: https://aqicn.org/city/ulaanbaatar
18. Kaur G, Gao J, Chiao S, Lu S, Xie G. Air quality prediction: Big data and machine learning approaches. In: Proceedings of the 5th International Conference on Sustainable Environment and Agriculture. 2017 Oct;1.
19. Ridzuan F, Wan Zainon WMN. A review on data cleansing methods for big data. Procedia Comput Sci. 2019; 161:731-738.
20. Obuks AE, Olakekan SO, Majeed O, Oyegoke TB, Nwabueze E, Adegboyega S, et al. Modelling and forecasting temporal PM2.5 concentration using ensemble machine learning methods. Buildings. 2022;12(1):46.
21. Saritha K, Abraham S. Prediction with partitioning: Big data analytics using regression techniques. In: Proceedings of the 2017 International Conference on Network & Advances in Computational Technologies (NetACT). 2017 Oct. ISBN: 978-1-5090-6590-5.
22. Dong M, Yang D, Kuang Y, He D, Erdal S, Kenski D. PM2.5 concentration prediction using hidden semi-Markov model-based time series data mining. Expert Syst Appl. 2009; 36:9046-9055. doi: 10.1016/j.eswa.2008.12.017.
23. Pan L, Yao E, Yang Y. Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information. J Environ Manag. 2016; 183:510-520. doi: 10.1016/j.jenvman.2016.09.010.
24. Number of imported automotives, by country, type, and year 2007-2023. Available from: https://www2.1212.mn/tables.aspx?tbl_id=DT_NSO_1200_013V5&I1200_select_all=1&I1200SingleSelect=&YearY_select_all=1&YearYSingleSelect=&viewtype=table
25. Discussion on “Pollution from car exhaust fumes and environmental safety” by the Investigation Department of the National Crime Agency, Mongolia. 2023; Available from: https://news.mn/r/2630422/
26. Ganchimeg G, Helmut L. Vision-based vehicle monitoring at road intersections. In: Proceedings of the 17th International Conference on Electronics, Information, and Communication (ICEIC 2018); 2018 Jan 24; p. 157–60. doi:10.23919/ELINFOCOM.2018.8330568
27. Ganchimeg G, Leopold H. Survey of Vehicle Detection Methods. In Proceedings of IFOST- 2017. 2017. p. 153-156
28. World Health Organization Mongolia: Health statistics 2023; Available from: http://hdc.gov.mn/media/files /2023%20uzuulelt.pdf
29. Dying to breathe: Mongolia's polluted air | Unreported World. 2018 Apr; Available from: https://www.youtube.com/watch?v=kUNuHxrd7Y0
30. Dhyani R, Sharma N, Maity AK. Prediction of PM2.5 along urban highway corridor under mixed traffic conditions using CALINE4 model. J Environ Manag. 2017; 198:24-32. doi: 10.1016/j.jenvman.2017.04.041.
31. Walsh MP. PM2.5: Global progress in controlling the motor vehicle contribution. Front Environ Sci Eng. 2014; 8:1-17. doi: 10.1007/s11783-014-0634-4.
32. Ganbold G, Chasia S. Comparison between Possibilistic c-Means (PCM) and Artificial Neural Network (ANN) Classification Algorithms in Land use/ Land cover Classification. International Journal of Knowledge Content Development & Technology, Volume 7 Issue 1, 57-78, 2017; doi: 10.5865/IJKCT.2017.7.1.057
33. Ganchimeg G, Image classification techniques in remote sensing. International Online Conference, Information and Media Technology-IMT 2016, Ulaanbaatar, Mongolia, pp 112-117.
| Files | ||
| Issue | Vol 10 No 1 (2025): Winter 2025 | |
| Section | Original Research | |
| Keywords | ||
| Data analysis; Air pollution; Econometric | ||
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How to Cite
1.
Ganchimeg G, Jargal E, Choi J. Econometric analysis of the effect of weather on air pollution. JAPH. 2025;10(1):93-114.
