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Estimation of spatiotemporal PM1.0 distributions in China by combining PM2.5 observations with satellite aerosol optical depth
摘要： Particulates smaller than 1.0 μm (PM1.0) have strong associations with public health and environment, and considerable exposure data should be obtained to understand the actual environmental burden. This study presented a PM1.0 estimation strategy based on the generalised regression neural network model. The proposed strategy combined ground-based observations of PM2.5 and satellite-derived aerosol optical depth (AOD) to estimate PM1.0 concentrations in China from July 2015 to June 2017. Results indicated that the PM1.0 estimates agreed well with the ground-based measurements with an R2 of 0.74, root mean square error of 19.0 μg/m3 and mean absolute error of 11.4 μg/m3 as calculated with the tenfold cross-validation method. The diurnal estimation performance displayed remarkable single-peak variation with the highest R2 of 0.80 at noon, and the seasonal estimation performance showed that the proposed method could effectively capture high-pollution events of PM1.0 in winter. Spatially, the most polluted areas were clustered in the North China Plain, where the average estimates presented a bimodal distribution during daytime. In addition, the quality of satellite-derived AOD, the robustness of the interpolation algorithm and the proportion of PM1.0 in PM2.5 were confirmed to affect the estimation accuracy of the proposed model.
关键词： Himawari-8,PM1.0,Neural network,Air pollution,Aerosol optical depth
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Assessment of two Satellite-Based Land Surface Shortwave Downward Radiation Datasets Over the Tibetan Plateau
摘要： Land surface shortwave downward radiation (SWDR), as one of major components of the surface radiation budget (SRB), plays an important role in the fields of atmospheric, oceanic, and land processes, and ultimately influences the Earth’s climate as well as the matter and energy cycle of the earth system. Currently, regional or global SWDR can be obtained either from reanalysis products or from satellite observations based on statistical or physical-based retrieval models. Although great efforts have been made to assess the applicability and accuracy of those different SWDR datasets, few studies have been conducted to evaluate the performance of the Clouds and the Earth’s Radiant Energy System Synoptic (CERES-SYN) Edition 3a and Himawari-8 SWDR datasets over the Tibetan Plateau. In this study, the both SWDR datasets are validated against in-situ data at 11 ground sites from the China Meteorological Administration (CMA). It is found that the Himawari-8 SWDR product has a slightly higher accuracy in these two SWDR datasets but with a significantly higher spatial resolution (5km). The mean bias is 1.7 W/m2 for CERES-SYN and -1.6 W/m2 for Himawari-8, respectively, the root mean square errors (RMSE) are 31.3 W/m2 for CERES-SYN and 31.2 W/m2 for Himawari-8, respectively. Mean coefficient of determination (R2) of the two datasets are both over 0.8. It is clearly that CERES-SYN tends to overestimate SWDR somewhat while the Himawari-8 has slight underestimation over the Tibetan Plateau. The findings in this paper can be valuable for hydrological, ecological, agrometeorological and biogeochemical applications and researches.
关键词： validation,the Tibetan Plateau,land surface shortwave downward radiation,CERES-SYN,Himawari-8
更新于2025-09-23 15:21:21
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Validation of Himawari-8 aerosol optical depth retrievals over China
摘要： High temporal resolution (every 10 min) aerosol observations are rarely provided by satellite sensors. The Advanced Himawari Imager (AHI) aboard Himawari-8 can provide aerosol optical depth (AOD) over China with this frequency. The sensor provides great opportunity to retrieve the particle matter near the ground and improve air quality modeling using the aerosol products. However, there is still lack of quality validation about AHI AOD. A comprehensive research was conducted to evaluate the performance of AHI aerosol products based on sixteen sun-photometers stations in AErosol RObotic NETwork (AERONET) and Sun–Sky Radiometer Observation Network (SONET) over China. The overall comparison of AHI AOD and ground AOD shows a high correlation (R2=0.67). However, there is only 55% of AHI AOD falling in the expected error envelops (±0.05±0.2*AOD ground). AOD bias between AHI AOD and ground AOD increases with the AOD magnitude. The accuracy of AHI AOD is also highly depend on seasons and surface land cover types. Best performance of AHI aerosol retrievals is shown in summer and for urban region. The diurnal variability validation shows that AHI AOD catch the diurnal AOD variations well, especially for summer. Large differences between AHI AOD and MODerate-resolution Imaging Spectrometer (MODIS) aerosol products are shown, especially for northwest China. The analysis indicates that the uncertainties of AHI aerosol retrievals are induced by large errors of aerosol models and surface reflectance estimation in the algorithm.
关键词： MODIS,validation,Aerosol optical depth,Himawari-8
更新于2025-09-10 09:29:36