Analysing Long-Term Trends in Monthly PM2.5 Concentrations Over India Using a Satellite-Derived Dataset

被引:0
|
作者
Athira, T. [1 ]
Agilan, V. [1 ]
机构
[1] Natl Inst Technol, Dept Civil Engn, Calicut 673601, Kerala, India
关键词
Air pollution; India; Mann-Kendall trend test; PM2.5; Trend analysis; SPATIOTEMPORAL TRENDS; PARTICULATE MATTER; CHINA; DELHI;
D O I
10.1007/s41810-024-00260-6
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Particulate matter with a size of 2.5 mu m or smaller (PM2.5) has been a threat to human health and the environment worldwide. Over the years, the pollution patterns in India have changed significantly. However, there are not enough data available to properly assess the temporal variations in PM2.5 concentrations over India. This study aims to quantify the extent of PM2.5 variation across India from 1998 to 2021 using Atmospheric Composition Analysis Group (ACAG) satellite-derived gridded PM2.5 data. For this purpose, the ACAG gridded PM2.5 dataset is validated over India using ground-observed PM2.5 concentrations. Specifically, daily PM2.5 observations from 121 Central Pollution Control Board (CPCB) stations spanning over India are used to validate the ACAG gridded dataset. Four evaluation parameters, namely, the coefficient of determination (R-2), Nash-Sutcliffe model efficiency coefficient (NSE), root mean square error (RMSE), and percentage difference in the peak value (PD), are used. From the results, an acceptable degree of agreement is observed between the ACAG gridded dataset and the CPCB ground observations. Therefore, the ACAG gridded dataset is further used to analyse the long-term trend in the monthly PM2.5 concentrations across India. To examine the long-term trend in the PM2.5 concentration, the Mann-Kendall (MK) trend analysis is conducted on the gridded data at both annual and monthly scales. The results indicate a steady increasing trend in the PM2.5 concentration in both the annual and monthly PM2.5 concentrations. A steep increasing trend in the PM2.5 concentration is observed in the Central and North Indian regions. Major portions of Indian states such as Uttar Pradesh, Haryana, Punjab, Uttarakhand, Delhi, Bihar, and Sikkim exhibited a percentage change of more than 80% in the PM2.5 concentrations during December, January, and February. The results of the trend analysis revealed that a significant percentage of grids over India has a very steep increasing trend (MK tau value >= 0.7) in PM2.5 concentrations during January (20.32%), February (20.22%), and December (20.19%).
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018
    He, Qingqing
    Gao, Kai
    Zhang, Lei
    Song, Yimeng
    Zhang, Ming
    [J]. Environment International, 2021, 156
  • [2] Surface PM2.5 Estimate Using Satellite-Derived Aerosol Optical Depth over India
    Krishna, Rama K.
    Ghude, Sachin D.
    Kumar, Rajesh
    Beig, Gufran
    Kulkarni, Rachana
    Nivdange, Sandip
    Chate, Dilip
    [J]. AEROSOL AND AIR QUALITY RESEARCH, 2019, 19 (01) : 25 - 37
  • [3] Long-term Trends of the Concentrations of Mass and Chemical Composition in PM2.5 over Seoul
    Han, Sang Hee
    Kim, Yong Pyo
    [J]. JOURNAL OF KOREAN SOCIETY FOR ATMOSPHERIC ENVIRONMENT, 2015, 31 (02) : 143 - 156
  • [4] A satellite-derived, ground-measurement-independent monthly PM2.5 mass concentration dataset over China during 2000-2015
    Zhang, Ying
    Li, Zhengqiang
    Wei, Yuanyuan
    Peng, Zongren
    [J]. BIG EARTH DATA, 2022, 6 (04) : 633 - 649
  • [5] Trends of PM2.5 concentrations in China: A long term approach
    Fontes, Tania
    Li, Peilin
    Barros, Nelson
    Zhao, Pengjun
    [J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2017, 196 : 719 - 732
  • [6] Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China
    Xue, Wenhao
    Zhang, Jing
    Zhong, Chao
    Ji, Duoying
    Huang, Wei
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 712
  • [7] Determination of Satellite-Derived PM2.5 for Kampala District, Uganda
    Atuhaire, Christine
    Gidudu, Anthony
    Bainomugisha, Engineer
    Mazimwe, Allan
    [J]. GEOMATICS, 2022, 2 (01): : 125 - 143
  • [8] Long-term characteristics of satellite-based PM2.5 over East China
    He, Qianshan
    Geng, Fuhai
    Li, Chengcai
    Yang, Shiqi
    Wang, Yanyu
    Mu, Haizhen
    Zhou, Guangqiang
    Liu, Xiaobo
    Gao, Wei
    Cheng, Tiantao
    Wu, Zheng
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 612 : 1417 - 1423
  • [9] Spatiotemporal assessment of PM2.5 concentrations and exposure in China from 2013 to 2017 using satellite-derived data
    He, Qingqing
    Zhang, Ming
    Song, Yimeng
    Huang, Bo
    [J]. JOURNAL OF CLEANER PRODUCTION, 2021, 286
  • [10] Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations
    Fang, Xin
    Li, Shenxin
    Xiong, Liwei
    Zou, Bin
    [J]. REMOTE SENSING, 2022, 14 (14)