{"title":"Air Quality Study of Three Non-attainment Zones of West Bengal: A Modelling Approach","authors":"Sanchari Sarkar, Sanghamitra Sanyal, Moitreyee Chakrabarty","doi":"10.1007/s41810-025-00321-4","DOIUrl":null,"url":null,"abstract":"<div><p>West Bengal has seven of 132 air pollution non-attainment zones identified by Central Pollution Control Board (CPCB) in 2019. This study examines Paschim Bardhaman, comprising three of these industrial areas. Data was collected on Particulate Matter (PM<sub>10</sub>), Nitrogen Dioxide (NO<sub>2</sub>), and Sulphur Dioxide (SO<sub>2</sub>) from West Bengal Pollution Control Board (WBPCB) for Asansol, Raniganj, Benachity, Angadpur, and PCBL More. Time series forecasting was performed using Seasonal ARIMA (SARIMA), Exponential Smoothing (ETS), Artificial Neural Networks (ANN), Trigonometric Seasonality, Box-Cox Transformation, ARMA Errors, and Trend and Seasonal Components (TBATS). Best-fit model was determined through Time Series Cross-Validation (TSCV) and Hold-out validation methods, ANN was identified as the optimal model for all study sites. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) comparisons validated the accuracy of ANN. Normalized Mean Squared Error (NMSE) values for PM<sub>10</sub> ranged from 0.153 to 0.489, for NO<sub>2</sub> from 0.118 to 0.347, and for SO<sub>2</sub> from 0.009 to 0.318 across the sites. Results highlight superiority of ANN due to its minimal overprediction and underprediction. Raniganj exhibited the highest PM<sub>10</sub> levels, followed by Benachity. Projections indicate Benachity will have the highest NO<sub>2</sub> levels, followed by Asansol and Raniganj. SO<sub>2</sub> concentrations were within acceptable limits. PM<sub>10</sub> is the primary pollutant at all sites, with significant variations in concentrations across the cities, none of which meet prescribed levels of CPCB. This highlights the immediate need for effective mitigation strategies.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 4","pages":"639 - 652"},"PeriodicalIF":2.3000,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aerosol Science and Engineering","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s41810-025-00321-4","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
West Bengal has seven of 132 air pollution non-attainment zones identified by Central Pollution Control Board (CPCB) in 2019. This study examines Paschim Bardhaman, comprising three of these industrial areas. Data was collected on Particulate Matter (PM10), Nitrogen Dioxide (NO2), and Sulphur Dioxide (SO2) from West Bengal Pollution Control Board (WBPCB) for Asansol, Raniganj, Benachity, Angadpur, and PCBL More. Time series forecasting was performed using Seasonal ARIMA (SARIMA), Exponential Smoothing (ETS), Artificial Neural Networks (ANN), Trigonometric Seasonality, Box-Cox Transformation, ARMA Errors, and Trend and Seasonal Components (TBATS). Best-fit model was determined through Time Series Cross-Validation (TSCV) and Hold-out validation methods, ANN was identified as the optimal model for all study sites. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) comparisons validated the accuracy of ANN. Normalized Mean Squared Error (NMSE) values for PM10 ranged from 0.153 to 0.489, for NO2 from 0.118 to 0.347, and for SO2 from 0.009 to 0.318 across the sites. Results highlight superiority of ANN due to its minimal overprediction and underprediction. Raniganj exhibited the highest PM10 levels, followed by Benachity. Projections indicate Benachity will have the highest NO2 levels, followed by Asansol and Raniganj. SO2 concentrations were within acceptable limits. PM10 is the primary pollutant at all sites, with significant variations in concentrations across the cities, none of which meet prescribed levels of CPCB. This highlights the immediate need for effective mitigation strategies.
期刊介绍:
ASE is an international journal that publishes high-quality papers, communications, and discussion that advance aerosol science and engineering. Acceptable article forms include original research papers, review articles, letters, commentaries, news and views, research highlights, editorials, correspondence, and new-direction columns. ASE emphasizes the application of aerosol technology to both environmental and technical issues, and it provides a platform not only for basic research but also for industrial interests. We encourage scientists and researchers to submit papers that will advance our knowledge of aerosols and highlight new approaches for aerosol studies and new technologies for pollution control. ASE promotes cutting-edge studies of aerosol science and state-of-art instrumentation, but it is not limited to academic topics and instead aims to bridge the gap between basic science and industrial applications. ASE accepts papers covering a broad range of aerosol-related topics, including aerosol physical and chemical properties, composition, formation, transport and deposition, numerical simulation of air pollution incidents, chemical processes in the atmosphere, aerosol control technologies and industrial applications. In addition, ASE welcomes papers involving new and advanced methods and technologies that focus on aerosol pollution, sampling and analysis, including the invention and development of instrumentation, nanoparticle formation, nano technology, indoor and outdoor air quality monitoring, air pollution control, and air pollution remediation and feasibility assessments.