Temporal variation analysis, impact of COVID-19 on air pollutant concentrations, and forecasting of air pollutants over the cities of Bangalore and Delhi in India

被引:0
作者
Bala Naga Manikanta Meda
Aneesh Mathew
机构
[1] National Institute of Technology,Department of Civil Engineering
关键词
Air Pollution; Temporal variations; COVID 19; Forecasting; Linear model;
D O I
10.1007/s12517-022-09996-2
中图分类号
学科分类号
摘要
Indian cities are highly vulnerable to atmospheric pollution in recent years, due to exponential growth in urbanisation and industrialisation, and the increased pollution has been made to focus on the temporal variation analysis and forecasting of air pollutants over major Indian cities like Delhi and Bangalore. PM2.5 concentrations are nearly 60.5% less than the annual average value during monsoon season while 76.3% more during the winter months. Ozone concentrations increase during the summer months (~ 46.3% more than the annual average) in Delhi, whereas in Bangalore, ozone concentrations are more (~ 75% more than the annual average) during the winter months. Variations of carbon monoxide and nitrogen oxides are significantly less comparatively. COVID-19 lockdown has a substantial positive impact on air pollution. Air pollutant concentrations are reduced during phase I and phase II of the lockdown. Pollutants, especially NOx and PM2.5 concentrations, are drastically reduced compared to the previous years. NOx concentrations are reduced by ~ 20% in Bangalore, whereas ~ 50% in Delhi. PM2.5 concentrations are reduced by ~ 41% in Delhi and ~ 55% in Bangalore. Forecasting of pollutants will be helpful in providing the valuable information for the optimal air pollution control strategies. It has been observed that linear model gives better results compared to ARIMA and Exponential Smoothening models. By forecasting, the concentration of NO2 is 115.288 µg/m3, the ozone is 30.636 µg/m3, SO2 is 11.798 µg/m3, and CO is 2.758 mg/m3 over Delhi in 2021. All the pollutants during forecasting showed a rising trend except sulphur dioxide.
引用
收藏
相关论文
共 181 条
[1]  
Ahammed YN(2006)Seasonal variation of the surface ozone and its precursor gases during 2001–2003, measured at Anantapur (14.62°N), a semi-arid site in India Atmos Res 80 151-164
[2]  
Reddy RR(2004)Modelling the effects of meteorological variables on ozone concentration - A quantile regression approach Atmos Environ 38 4689-4699
[3]  
Gopal KR(2007)Simultaneous measurements of ozone and its precursors on a diurnal scale at a semi urban site in India J Atmos Chem 57 239-253
[4]  
Narasimhulu K(2015)Analysis of surface ozone using a recurrent neural network Sci Total Environ 514 379-387
[5]  
Basha DB(2016)Ambient air pollution exposure estimation for the global burden of disease 2013 Environ Sci Technol 50 79-88
[6]  
Reddy LSS(2019)Visibility graphs of ground-level ozone time series: A multifractal analysis Sci Total Environ 661 138-147
[7]  
Rao TVR(2019)Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison: A case study in hangzhou, China Environ Pollut 252 366-378
[8]  
Baur D(2018)Artificial neural network model for ozone concentration estimation and Monte Carlo analysis Atmos Environ 184 129-139
[9]  
Saisana M(2011)Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico Sci Total Environ 601 128-139
[10]  
Schulze N(2016)Prediction of ground-level ozone concentration in São Paulo, Brazil: Deterministic versus statistic models Atmos Environ 145 365-375