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ISPRS Journal of Photogrammetry and Remote Sensing ; 184:31-44, 2022.
Article in English | ScienceDirect | ID: covidwho-1568786


In spite of a variety of PM2.5 modeling schemes, general guidance for full-coverage PM2.5 concentration mapping from satellite observations is still lacking. The current technical gap is tied to how to integrate multiscale data from multiple sources to generate a spatially contiguous map that can better recognize PM2.5 distribution patterns without compromising modeling accuracy. In this study, ten different PM2.5 concentration data sets were generated using distinct mapping strategies and compared to one another, aiming to facilitate full-coverage PM2.5 concentration mapping with a generalized approach. The inter-comparison results indicated that different mapping strategies could yield comparable modeling accuracy albeit distinct PM2.5 distributions over space. Although the inclusion of PM2.5 autocorrelation terms as predictors can markedly improve the modeling accuracy, spatial patterns of PM2.5 estimations could be apparently distorted under different parameter configurations. In an attempt to balance the conflicting objectives, the optimal PM2.5 mapping scheme was proposed for broadened applications. A daily full-coverage PM2.5 concentration data set with 5-km resolution in China between 2015 and 2020 was generated for a demonstration to infer an apparent decreasing trend of PM2.5 across China over the past five years. Besides, the examination of COVID-19 pandemic impacts on regional air quality variations reveals a pattern of marked PM2.5 concentration decrease that cannot be easily realized by site-based air quality measurements. It is indicative that the proposed approach in this study can offer an optimal framework in support of various full-coverage PM2.5 mapping practices.

Atmos Environ (1994) ; 268: 118848, 2022 Jan 01.
Article in English | MEDLINE | ID: covidwho-1509577


The role of meteorological conditions has long been recognized in modulating regional air quality. The impact of near-surface turbulence, nevertheless, remains poorly understood. To curb the spread of COVID-19, a variety of lockdown measures were implemented, providing us an unprecedented opportunity to examine the joint impact of emission control and meteorology on regional air quality. Here we examined the variations of planetary boundary layer (PBL) height, PM2.5 concentrations, turbulence kinetic energy (TKE), vertical wind shear, and their associations in Chengdu, Sichuan province in Southwest China between January 13 and February 24, 2020, by synergistically using micro pulse lidar, ground-level meteorological and PM2.5 measurements, as well as ultrasonic anemometer observations. During the study period, Sichuan basin was primarily regulated by the straight west wind, with an averaged wind speed of 2-3 m/s at 850 hPa, indicative of a relatively stable atmospheric dispersion condition. TKE was positively correlated with PBL height but negatively correlated with PM2.5. The PM2.5 concentration varied dramatically during pre- and post-lockdown periods but remained near constant at a relatively low level during the lockdown period. Meanwhile, the negative correlation between TKE and PM2.5 was much stronger during the lockdown and post-lockdown periods, when aerosol emissions were significantly reduced. Moreover, the correlation between TKE and PM2.5 exhibited large diurnal variability, with the strongest correlation observed during the daytime when solar radiation and turbulent mixing generally reached their peaks. Overall, the observational results in Chengdu underscore the non-negligible impact of turbulence on regional PM2.5 concentrations, which could help better understand the variation of regional air pollution events.

Chemosphere ; 278: 130406, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1169125


During the 2019 novel coronavirus (COVID-19) pandemic, many countries took strong lockdown policy to reduce disease spreading, resulting in mitigating the ambient air pollution due to less traffic and industrial emissions. However, limited studies focused on the household air pollution especially in rural area, the potential risk induced by indoor air pollution exposure was unknown during this period. This field study continuously measured real-time PM2.5 levels in kitchen, living room, and outdoor in the normal days (Period-1) and the days of COVID-19 lockdown overlapping the Chinese Spring Festival (Period-2) in rural homes in China. The average daily PM2.5 concentrations increased by 17.4 and 5.1 µg/m3 in kitchen and living room during Period-2, respectively, which may be due to more fuel consumption for cooking and heating caused by larger family sizes than those during the normal days. The ambient PM2.5 concentration in rural areas in Period-2 decreased by 6.7 µg/m3 compared to the Period-1, less than the drop in urban areas (26.8 µg/m3). An increase of mass fraction of very fine particles in ambient air was observed during lockdown overlapping annual festival days, which could be explained by the residential solid fuel burning. Due to higher indoor air pollution level and longer time spent in indoor environments, daily personal exposure to PM2.5 was 134 ± 40 µg/m3 in Period-2, which was significantly higher than that during in Period-1 (126 ± 27 µg/m3, p < 0.05). The increase of personal PM2.5 exposure during Period-2 could potentially have negative impact on human health, indicating further investigations should be performed to estimate the health impact of global COVID-19 lockdown on community, especially in rural homes using solid fuels as the routine fuels.

Air Pollutants , Air Pollution, Indoor , COVID-19 , Air Pollutants/analysis , Air Pollution, Indoor/analysis , China , Communicable Disease Control , Cooking , Environmental Monitoring , Family Characteristics , Holidays , Humans , Particulate Matter/analysis , Rural Population , SARS-CoV-2