Diurnal patterns in ambient PM2.5 exposure over India using MERRA-2 reanalysis data

被引:25
|
作者
Bali, Kunal [1 ]
Dey, Sagnik [1 ,2 ]
Ganguly, Dilip [1 ]
机构
[1] Indian Inst Technol Delhi, Ctr Atmospher Sci, New Delhi, India
[2] Indian Inst Technol Delhi, Ctr Excellence Res Clean Air, New Delhi, India
关键词
Ambient PM2.5; India; MERRA-2; Diurnal pattern; Dry-season;
D O I
10.1016/j.atmosenv.2020.118180
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Modeling of ambient PM2.5 exposure has improved in recent years with the application of satellite data, but in India, where the ground-based measurements are still inadequate, satellite-based assessment cannot provide temporal continuity. In this work, we analyze MERRA-2 (Modern-Era Retrospective analysis for Research and Applications) reanalysis aerosol products to estimate PM2.5 at an hourly scale over India to fill the temporal sampling gap. MERRA-2 PM2.5 calibrated using reference-grade monitors from the Central Pollution Control Board (CPCB) network show a statistically significant (at 95% CI) correlation (r = 0.9) against coincident in-situ measurements. Analyzing 18-years (2000-2017) data, we report the first detailed account of multi-year ambient PM2.5 diurnal patterns in India. Diurnal amplitude is found to be quite large (>30 mu gm(-3)) in the Indo-Gangetic Basin (IGB) and western arid regions. We find a decrease in ambient PM2.5 over the western dust source region and an increase over the parts of IGB and central India, primarily in the morning and evening hours. The annual trend is strongly governed by the trend during the dry season (October-February), attributed to a combination of the changing emission and meteorology. Our results suggest that the satellite-based exposure estimates that typically represent late morning to early afternoon hours are usually lower than the 24-h average exposure in most parts of India. Therefore, we call for the integration of reanalysis data that provide better temporal resolution with satellite data that provide better spatial resolution to further improve exposure estimates in data void regions like India for air quality studies.
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页数:8
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