A Study of Seasonal and Temporal Variances in Ambient Air Quality of Highly Polluted Cities in Rajasthan
DOI:
https://doi.org/10.32628/CSEIT24104103Keywords:
Air Pollution, Ambient Air Quality, Rajasthan, Seasonal Variations, Temporal VariationsAbstract
The quality of the urban environment in tropical and subtropical densely populated cities is a complicated subject that has garnered a lot of attention in the current setting. Some of the most polluted cities in Rajasthan are Bhiwadi, Jaipur, Kota, and Udaipur, where the air quality has drastically declined over the previous ten years, according to an IQAir report. In order to determine the seasonal and temporal fluctuations in the concentrations of major air pollutants, such as carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10 and PM2.5), in an urban setting in Rajasthan, this study will examine the ambient air quality in severely polluted cities throughout the state. A comprehensive investigation of the seasonal and temporal variations in ambient air quality throughout Rajasthan's extremely polluted cities was made possible by the application of PCA and the K-Means Clustering Algorithm. We interpreted the intricate patterns of pollution oscillations by means of rigorous time-series analysis, providing insight into the dynamic interactions among meteorological conditions, sources of pollution, and regulatory actions. The results indicate that there were more seasonal variations during the summer, and that levels of particulate matter (PM10 and PM2.5) and nitrogen dioxide (NO2) in places like Jaipur, Bhiwadi, Kota, and Udaipur alarmingly rose above pre-pandemic levels. This highlights the significance of identifying and addressing the various challenges caused by air pollution at different times of the year and in different seasons. Furthermore, identifying the main sources of pollution and assessing the effectiveness of current legislation offer insightful information for focused actions.
Downloads
References
Beloconi, A., Probst-Hensch, N. M., & Vounatsou, P. (2021). Spatio-temporal modelling of changes in air pollution exposure associated to the COVID-19 lockdown measures across Europe. Science of The Total Environment, 787, 147607. DOI: https://doi.org/10.1016/j.scitotenv.2021.147607
Borge, R., Requia, W. J., Yagüe, C., Jhun, I., & Koutrakis, P. (2019). Impact of weather changes on air quality and related mortality in Spain over a 25-year period [1993–2017]. Environment international, 133, 105272. DOI: https://doi.org/10.1016/j.envint.2019.105272
Kaur, J., Parmar, K. S., & Singh, S. (2023). Autoregressive models in environmental forecasting time series: a theoretical and application review. Environmental Science and Pollution Research, 30(8), 19617-19641 DOI: https://doi.org/10.1007/s11356-023-25148-9
Kolehmainen, M., Martikainen, H., & Ruuskanen, J. (2001). Neural networks and periodic components used in air quality forecasting. Atmospheric Environment, 35(5), 815-825. DOI: https://doi.org/10.1016/S1352-2310(00)00385-X
Liu, C. C., Lin, T. C., Yuan, K. Y., & Chiueh, P. T. (2022). Spatio-temporal prediction and factor identification of urban air quality using support vector machine. Urban Climate, 41, 101055. DOI: https://doi.org/10.1016/j.uclim.2021.101055
Majidov, E., Istenes, Z., & Grad-Gyenge, L. Representation Learning Techniques to Conduct Odor Prediction.
Moss, M. A., Hughes, D. D., Crawford, I., Gallagher, M. W., Flynn, M. J., & Topping, D. O. (2023). Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration. Atmosphere, 14(9), 1416. DOI: https://doi.org/10.3390/atmos14091416
Nath, P., Saha, P., Middya, A. I., & Roy, S. (2021). Long-term time-series pollution forecast using statistical and deep learning methods. Neural Computing and Applications, 1-20. DOI: https://doi.org/10.1007/s00521-021-05901-2
Ravindra, K., Singh, T., Pandey, V., & Mor, S. (2020). Air pollution trend in Chandigarh city situated in Indo-Gangetic Plains: Understanding seasonality and impact of mitigation strategies. Science of The Total Environment, 729, 138717 DOI: https://doi.org/10.1016/j.scitotenv.2020.138717
Shelton, S., Liyanage, G., Jayasekara, S., Pushpawela, B., Rathnayake, U., Jayasundara, A., & Jayasooriya, L. D. (2022). Seasonal Variability of Air Pollutants and Their Relationships to Meteorological Parameters in an Urban Environment. Advances in Meteorology, 2022. DOI: https://doi.org/10.1155/2022/5628911
Torres‐Vazquez, A., Pleim, J., Gilliam, R., & Pouliot, G. (2022). Performance Evaluation of the Meteorology and Air Quality Conditions from Multiscale WRF‐CMAQ Simulations for the Long Island Sound Tropospheric Ozone Study (LISTOS). Journal of Geophysical Research: Atmospheres, 127(5), e2021JD035890 DOI: https://doi.org/10.1029/2021JD035890
Verma, A., & Bhatia, D. L. (2023). Analysis of Meteorological Factors Affecting Air Quality in Jaipur Using Time Series Analysis. Available at SSRN 4663529. DOI: https://doi.org/10.2139/ssrn.4663529
Verma, A., & Bhatia, L. Time Series Analysis Using Arima Model for Air Pollution Prediction in Cities of Rajasthan.
Wang, Y., & Kong, T. (2019). Air quality predictive modeling based on an improved decision tree in a weather-smart grid. IEEE Access, 7, 172892-172901. DOI: https://doi.org/10.1109/ACCESS.2019.2956599
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.