Abstract
Air pollution becomes a priority-based subject to study in India as cardiovascular and respiratory diseases become so frequent day by day. Therefore, the availability of continuous air pollution data and analysis could offer a more feasible future plan, but the lack of adequate monitoring stations in most developing countries like India faces a new set of problems of unavailability of air quality data at very a local level. Land-use regression (LUR) has previously been demonstrated in many studies, to be a viable method of describing the link between land use and air pollution level. A number of 19 station data of PM2.5 and PM10 data and 129 meteorological and land-use predictor variable data have been used to develop the LUR model. According to the study, only three and six variables have explanatory power in the PM2.5 and PM10 models, respectively. Annual relative humidity, build-up area, distance from industry, other roads, water body and open land are the most significant in order to predict PM concentration. Adjusted R2 values for both models are high, with PM2.5 (0.865) and PM10 (0.586). For a better understanding of the spatial distribution of predicted annual PM concentration, a spatial PM concentration surface map has been developed. Low root-mean-square error (RMSE) and a decent correlation made the LUR model feasible for this study area.
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References
Balakrishnan, K., Sambandam, S., Ramaswamy, P., Ghosh, S., Venkatesan, V., Thangavel, G., Mukhopadhyay, K., Johnson, P., Paul, S., Puttaswamy, N., & Dhaliwal, R. S. (2015). Establishing integrated rural–urban cohorts to assess air pollution-related health effects in pregnant women, children and adults in Southern India: An overview of objectives, design and methods in the Tamil Nadu Air Pollution and Health Effects (TAPHE) study. BMJ Open, 5(6), e008090.
Balakrishnan, K., Dey, S., Gupta, T., Dhaliwal, R. S., Brauer, M., Cohen, A. J., Stanaway, J. D., Beig, G., Joshi, T. K., Aggarwal, A. N., & Sabde, Y. (2019). The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The global burden of disease study 2017. The lancet planetary Health, 3(1), e26–e39.
Biswas, T., Pal, S. C., & Saha, A. (2022). Strict lockdown measures reduced PM2. 5 concentrations during the COVID-19 pandemic in Kolkata, India. Sustainable. Water Resources Management, 8(6), 1–5.
Brauer, M., Hoek, G., van Vliet, P., Meliefste, K., Fischer, P., Gehring, U., Heinrich, J., Cyrys, J., Bellander, T., Lewne, M., & Brunekreef, B. (2003). Estimating long-term average particulate air pollution concentrations: Application of traffic indicators and geographic information systems. Epidemiology, 1, 228–239.
Daoud, J. I. (2017). Multicollinearity and regression analysis. Journal of Physics, 949(1), 012009.
Fan, H., Zhao, C., & Yang, Y. (2020). A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018. Atmospheric Environment, 220, 117066.
Franklin, B. A., Brook, R., & Pope, C. A., III. (2015, May 1). Air pollution and cardiovascular disease. Current Problems in Cardiology, 40(5), 207–238.
Kim, K. H., Kabir, E., & Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environment International, 74, 136–143.
Lee, M., Brauer, M., Wong, P., Tang, R., Tsui, T. H., Choi, C., Cheng, W., Lai, P. C., Tian, L., Thach, T. Q., & Allen, R. (2017). Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong. Science of the Total Environment, 592, 306–315.
Miri, M., Ebrahimi Aval, H., Ehrampoush, M. H., Mohammadi, A., Toolabi, A., Nikonahad, A., Derakhshan, Z., & Abdollahnejad, A. (2017). Human health impact assessment of exposure to particulate matter: An AirQ software modeling. Environmental Science and Pollution Research, 24(19), 16513–16519.
Nikoonahad, A., Naserifar, R., Alipour, V., Poursafar, A., Miri, M., Ghafari, H. R., Abdolahnejad, A., Nemati, S., & Mohammadi, A. (2017). Assessment of hospitalization and mortality from exposure to PM10 using AirQ modeling in Ilam, Iran. Environmental Science and Pollution Research, 24(27), 21791–21796.
Ryan, P. H., & LeMasters, G. K. (2007). A review of land-use regression models for characterizing intraurban air pollution exposure. Inhalation Toxicology, 19(sup1), 127–133.
Sharma, A. K., Baliyan, P., & Kumar, P. (2018). Air pollution and public health: The challenges for Delhi, India. Reviews on Environmental Health, 33(1), 77–86.
Shubham, S., Kumar, M., Sarma, D. K., Kumawat, M., Verma, V., Samartha, R. M., & Tiwari, R. R. (2021). Role of air pollution in chronic kidney disease: An update on evidence, mechanisms and mitigation strategies. International Archives of Occupational and Environmental Health, 30, 1–2.
Son, Y., Osornio-Vargas, Á. R., O'Neill, M. S., Hystad, P., Texcalac-Sangrador, J. L., Ohman-Strickland, P., Meng, Q., & Schwander, S. (2018). Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters. Science of the Total Environment, 639, 40–48.
Wong, P. Y., Lee, H. Y., Chen, Y. C., Zeng, Y. T., Chern, Y. R., Chen, N. T., Lung, S. C., Su, H. J., & Wu, C. D. (2021). Using a land use regression model with machine learning to estimate ground level PM2. 5. Environmental Pollution, 277, 116846.
Yadav, I. C., & Devi, N. L. (2018). Biomass burning, regional air quality, and climate change. Earth Systems and Environmental Sciences. Edition: Encyclopedia of Environmental Health. Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.11022-X
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Das, K., Das Chatterjee, N., Bhattacharya, R.K. (2023). Estimating the Variability of Ground-Level Annual PM2.5 and PM10 Using Land-Use Regression Model in Kolkata Municipal Corporation (KMC). In: Sahu, A.S., Das Chatterjee, N. (eds) Environmental Management and Sustainability in India. Springer, Cham. https://doi.org/10.1007/978-3-031-31399-8_17
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