Air Quality Study of Three Non-attainment Zones of West Bengal: A Modelling Approach

IF 2.3 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Sanchari Sarkar, Sanghamitra Sanyal, Moitreyee Chakrabarty
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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.

西孟加拉邦三个非达标区空气质量研究:模型方法
在中央污染控制委员会(CPCB)于2019年确定的132个空气污染未达标区中,西孟加拉邦有7个。本研究考察了Paschim Bardhaman,包括三个这样的工业区。从西孟加拉邦污染控制委员会(WBPCB)收集了Asansol、Raniganj、Benachity、Angadpur和PCBL More的颗粒物(PM10)、二氧化氮(NO2)和二氧化硫(SO2)数据。采用季节ARIMA (SARIMA)、指数平滑(ETS)、人工神经网络(ANN)、三角季节性、Box-Cox变换、ARMA误差和趋势与季节成分(TBATS)进行时间序列预测。通过时间序列交叉验证(TSCV)和Hold-out验证方法确定最佳拟合模型,ANN被确定为所有研究地点的最佳模型。均方根误差(RMSE)和平均绝对误差(MAE)的比较验证了人工神经网络的准确性。PM10的归一化均方误差(NMSE)在0.153 ~ 0.489之间,NO2在0.118 ~ 0.347之间,SO2在0.009 ~ 0.318之间。结果显示了人工神经网络的优势,因为它具有最小的过预测和欠预测。拉尼甘杰的PM10水平最高,其次是贝纳奇蒂。预测显示,Benachity的二氧化氮水平最高,其次是Asansol和Raniganj。二氧化硫浓度在可接受范围内。PM10是所有监测点的主要污染物,各城市的浓度差异很大,没有一个达到规定的CPCB水平。这突出表明迫切需要有效的缓解战略。
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来源期刊
Aerosol Science and Engineering
Aerosol Science and Engineering Environmental Science-Pollution
CiteScore
3.00
自引率
7.10%
发文量
42
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.
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