Rahaman Saidur, Tu Xiang, Ahmad Khalil, Qadeer Abdul
State Key Laboratory of Environmental Criteria and Risk Assessment, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, State Environmental Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Science, Beijing, China; Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; School of Geographic Sciences, East China Normal University, Shanghai 200241, China.
State Key Laboratory of Environmental Criteria and Risk Assessment, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, State Environmental Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Science, Beijing, China.
J Hazard Mater. 2024 Nov 5;479:135711. doi: 10.1016/j.jhazmat.2024.135711. Epub 2024 Sep 7.
China and India are two of the fastest-growing developing economies covering about 35 % of the world's population. Due to the extensive prevalence of air pollution across cities in China and India, contemporary assessment of atmospheric pollution through real-time and remote sensing observations is inadequate. The study aims to determine the spatial distribution and temporal variation of hazardous atmospheric pollutants across cities in China (Shanghai, Nanjing, Jinan, Zhengzhou and Beijing) and India (Kolkata, Asansol, Patna, Kanpur and Delhi). Ground observation data on CO, O, PM, PM, NO and SO along with remote sensing data on AOD, CO, O, BC, NO, SO and dust surface mass concentrations are used to assess atmospheric pollution. This study examines daily, zonal and longitudinal pollutant distributions using Sentinel-5 P data and surface mass concentrations over the vertical column evaluated from NASA satellite data. The Mann-Kendall test and relative change methods have been implemented to assess pollutant trends while Sen's Slope identifies the magnitude of change. The similarity test and data validation methods including NRMSE, PC and MBias have been employed to ensure consistency in analysing annual trends for each air pollutant in the datasets. Additionally, multiple correlation matrix analysis has been used to examine the associations among different pollutants from both datasets based on their annual averages. Remote sensing data reveals that eastern China and north-eastern India have the highest aerosol, BC, CO, NO and SO while western China and southern India lowest. Dust peaks in the west while O levels are highest in the northern part of China and India. Ground observation data indicates that Chinese cities have higher annual mean SO and O concentrations with yearly declines in PM, PM, NO, SO and CO notably SO. Indian cities witnessed overall increases in PM, PM, NO and SO from 2012 to 2019 with a slight decline in 2020 followed by a resurgence in 2023. The findings provide insights for implementing regional policy measures to reduce air pollution based on changes in pollutant behaviour. The study suggests that addressing atmospheric pollutants, particularly NO, CO, PM, PM, and SO requires a comprehensive environmental policy framework involving central and state governments and enforcing stringent environmental protection laws.
中国和印度是发展最快的两个经济体，约占世界人口的35%。由于中国和印度各城市空气污染普遍存在，通过实时和遥感观测对大气污染进行的当代评估并不充分。该研究旨在确定中国（上海、南京、济南、郑州和北京）和印度（加尔各答、阿桑索尔、巴特那、坎普尔和德里）各城市有害大气污染物的空间分布和时间变化。利用一氧化碳（CO）、臭氧（O₃）、细颗粒物（PM₂.₅）、可吸入颗粒物（PM₁₀）、氮氧化物（NOₓ）和二氧化硫（SO₂）的地面观测数据以及气溶胶光学厚度（AOD）、一氧化碳、臭氧、黑碳（BC）、氮氧化物、二氧化硫和沙尘表面质量浓度的遥感数据来评估大气污染。本研究使用哨兵 - 5P数据和从美国国家航空航天局（NASA）卫星数据评估的垂直柱上的表面质量浓度，研究每日、纬向和纵向的污染物分布。采用曼 - 肯德尔检验和相对变化方法来评估污染物趋势，而森斜率则确定变化幅度。采用相似性检验和包括归一化均方根误差（NRMSE）、皮尔逊相关系数（PC）和平均偏差（MBias）在内的数据验证方法，以确保分析数据集中每种空气污染物年度趋势的一致性。此外，基于年度平均值，使用多重相关矩阵分析来研究两个数据集中不同污染物之间的关联。遥感数据显示，中国东部和印度东北部的气溶胶、黑碳、一氧化碳、氮氧化物和二氧化硫含量最高，而中国西部和印度南部最低。沙尘在西部达到峰值，而臭氧水平在中国和印度北部最高。地面观测数据表明，中国城市的年均二氧化硫和臭氧浓度较高，细颗粒物、可吸入颗粒物、氮氧化物、二氧化硫和一氧化碳含量逐年下降，尤其是二氧化硫。印度城市在2012年至2019年期间，细颗粒物、可吸入颗粒物、氮氧化物和二氧化硫总体呈上升趋势，2020年略有下降，随后在2023年又有所回升。这些研究结果为根据污染物行为变化实施区域政策措施以减少空气污染提供了见解。该研究表明，应对大气污染物，特别是氮氧化物、一氧化碳、细颗粒物、可吸入颗粒物和二氧化硫，需要一个涉及中央和邦政府的全面环境政策框架，并执行严格的环境保护法律。
