A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations
https://orcid.org/0000-0002-2362-2940
https://orcid.org/0000-0002-5642-9880
Abstract Despite much effort made in studying human health associated with fine particulate matter (PM2.5), our knowledge about PM2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM2.5 at individual stations (SC-PM2.5). Then we merged SC-PM2.5 with the spatial pattern of GEOS-Chem modeled PM2.5 to obtain a gridded PM2.5 dataset (GC-PM2.5). We validated the GC-PM2.5 data over the North China Plain on a 0.3125° longitude x 0.25° latitude grid in January, April, July and October 2014, using ground-based PM2.5 measurements. The spatial patterns of temporally averaged PM2.5 mass concentrations are consistent between GC-PM2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM2.5 data reproduce the day-to-day variation of observed PM2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 μg/m3 (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM2.5 variations and their health impacts since 1980.
Highlights We integrate visibility data and GEOS-Chem simulations to estimate PM2.5 concentrations in 2014 over North China. Visibility converted PM2.5 are spatiotemporally consistent with PM2.5 measurements. Our method provides a novel, plausible way to retrieve long-term variation of PM2.5.
Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations
https://orcid.org/0000-0002-2362-2940
https://orcid.org/0000-0002-5642-9880
Abstract Despite much effort made in studying human health associated with fine particulate matter (PM2.5), our knowledge about PM2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM2.5 at individual stations (SC-PM2.5). Then we merged SC-PM2.5 with the spatial pattern of GEOS-Chem modeled PM2.5 to obtain a gridded PM2.5 dataset (GC-PM2.5). We validated the GC-PM2.5 data over the North China Plain on a 0.3125° longitude x 0.25° latitude grid in January, April, July and October 2014, using ground-based PM2.5 measurements. The spatial patterns of temporally averaged PM2.5 mass concentrations are consistent between GC-PM2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM2.5 data reproduce the day-to-day variation of observed PM2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 μg/m3 (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM2.5 variations and their health impacts since 1980.
Highlights We integrate visibility data and GEOS-Chem simulations to estimate PM2.5 concentrations in 2014 over North China. Visibility converted PM2.5 are spatiotemporally consistent with PM2.5 measurements. Our method provides a novel, plausible way to retrieve long-term variation of PM2.5.
Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations
https://orcid.org/0000-0002-2362-2940
https://orcid.org/0000-0002-5642-9880
Abstract Despite much effort made in studying human health associated with fine particulate matter (PM2.5), our knowledge about PM2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM2.5 at individual stations (SC-PM2.5). Then we merged SC-PM2.5 with the spatial pattern of GEOS-Chem modeled PM2.5 to obtain a gridded PM2.5 dataset (GC-PM2.5). We validated the GC-PM2.5 data over the North China Plain on a 0.3125° longitude x 0.25° latitude grid in January, April, July and October 2014, using ground-based PM2.5 measurements. The spatial patterns of temporally averaged PM2.5 mass concentrations are consistent between GC-PM2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM2.5 data reproduce the day-to-day variation of observed PM2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 μg/m3 (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM2.5 variations and their health impacts since 1980.
Highlights We integrate visibility data and GEOS-Chem simulations to estimate PM2.5 concentrations in 2014 over North China. Visibility converted PM2.5 are spatiotemporally consistent with PM2.5 measurements. Our method provides a novel, plausible way to retrieve long-term variation of PM2.5.
Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations
Li, Sixuan (author) / Chen, Lulu (author) / Huang, Gang (author) / Lin, Jintai (author) / Yan, Yingying (author) / Ni, Ruijing (author) / Huo, Yanfeng (author) / Wang, Jingxu (author) / Liu, Mengyao (author) / Weng, Hongjian (author)
Atmospheric Environment ; 222
2019-11-06
Article (Journal)
Electronic Resource
English