TY - JOUR
T1 - Discordant future climate-driven changes in winter PM2.5pollution across India under a warming climate
AU - Zhang, Xiaorui
AU - Xiao, Xiang
AU - Wang, Fan
AU - Yang, Yang
AU - Liao, Hong
AU - Wang, Shixin
AU - Gao, Meng
N1 - Funding information:
This study was supported by grants from Research Grants Council of the Hong Kong Special Administrative Region, China (project numbers HKBU22201820 and HKBU12202021), National Natural Science Foundation of China (No. 42005084), and National Key Research and Development Programs of China (No. 2022YFC3700103).
Publisher Copyright:
© 2023 The Author(s).
PY - 2023/7/7
Y1 - 2023/7/7
N2 - India s megacities have been suffering from frequent winter particulate matter (PM2.5) pollution episodes, and how impacts of meteorology on air quality will evolve with time under a warming climate remains a concern. In this study, we identified conducive meteorological weather conditions in 5 megacities across India and found that quantile regression models can better describe the meteorological impacts under high pollution level and capture more observed high PM2.5events than linear regression.The future climate-driven changes in winter PM2.5pollution in India were offered with quantile regression models using Coupled Model Intercomparison Project 6 simulations under the SSP585 and SSP245 scenarios. Under SSP585 scenario, northern Indian megacities are likely to suffer from a stagnant weather condition in the near future, and higher boundary layer height and more atmospheric dispersion conditions during the second half of 21st century. Compared with the mean levels over 1990-2019, New Delhi and Kolkata would experience 6.1 and 5.7 more PM2.5exceedances per season over 2030-2059 and 4.1 and 2.5 fewer exceedances per season during 2070-2099, respectively. Owing to increasing surface humidity and boundary layer height, air quality is projected to improve in Mumbai and Hyderabad with more than 6.1 and 1.2 fewer exceedances per season over 2050-2099. However, more than 6 exceedances will occur in Chennai due to enhanced lower-Tropospheric stability.The negative impact of future meteorology on PM2.5exceedances would become weak under SSP245. Our results can provide references for the Indian government to optimize their emission control plans to minimize adverse impacts of air quality on health, ecosystem, and climate.
AB - India s megacities have been suffering from frequent winter particulate matter (PM2.5) pollution episodes, and how impacts of meteorology on air quality will evolve with time under a warming climate remains a concern. In this study, we identified conducive meteorological weather conditions in 5 megacities across India and found that quantile regression models can better describe the meteorological impacts under high pollution level and capture more observed high PM2.5events than linear regression.The future climate-driven changes in winter PM2.5pollution in India were offered with quantile regression models using Coupled Model Intercomparison Project 6 simulations under the SSP585 and SSP245 scenarios. Under SSP585 scenario, northern Indian megacities are likely to suffer from a stagnant weather condition in the near future, and higher boundary layer height and more atmospheric dispersion conditions during the second half of 21st century. Compared with the mean levels over 1990-2019, New Delhi and Kolkata would experience 6.1 and 5.7 more PM2.5exceedances per season over 2030-2059 and 4.1 and 2.5 fewer exceedances per season during 2070-2099, respectively. Owing to increasing surface humidity and boundary layer height, air quality is projected to improve in Mumbai and Hyderabad with more than 6.1 and 1.2 fewer exceedances per season over 2050-2099. However, more than 6 exceedances will occur in Chennai due to enhanced lower-Tropospheric stability.The negative impact of future meteorology on PM2.5exceedances would become weak under SSP245. Our results can provide references for the Indian government to optimize their emission control plans to minimize adverse impacts of air quality on health, ecosystem, and climate.
KW - Climate change
KW - Meteorological condition
KW - Particulate matters
KW - Quantile regression
UR - http://www.scopus.com/inward/record.url?scp=85166565537&partnerID=8YFLogxK
U2 - 10.1525/elementa.2022.00149
DO - 10.1525/elementa.2022.00149
M3 - Journal article
SN - 2325-1026
VL - 11
JO - Elementa
JF - Elementa
IS - 1
M1 - elementa.2022.00149
ER -