浏览全部资源
扫码关注微信
纸质出版日期： 2022-05-07 ，
扫 描 看 全 文
李珂，白开旭.2022.融合星地多源数据资料的长三角地区高分辨率时空无缝PM2.5浓度数据.遥感学报，26（5）： 1002-1014
Li K and Bai K X. 2022. Synergistic fusion of multisource AOD and air quality measurements for spatially contiguous PM2.5 concentration mapping in the Yangtze River Delta. National Remote Sensing Bulletin， 26（5）：1002-1014
大气污染物浓度全方位动态监测是进行区域大气污染精细化防控的重要前提。为开展长三角地区小时分辨率PM
2.5
浓度无缝制图，本研究通过耦合AOD缺失信息重建与多模数据融合技术，建立了一套能够有效集成卫星遥感、地面观测、数值模拟等多源异构数据资料的近地面PM
2.5
浓度无缝制图方案，并据此生产了2015年—2020年长三角地区小时分辨率无缝PM
2.5
浓度格点数据产品。结果表明：本研究生产的PM
2.5
浓度无缝格点产品与国控站点观测数据的交叉验证相关系数达0.9，平均偏差不超过10 μg·m
-3
。较于空间分布不均且相对稀疏的站点观测PM
2.5
浓度资料，面域无缝PM
2.5
浓度格点数据更能有效揭示长三角地区PM
2.5
污染的时空变化特征；在2015年—2020年研究期内，其平均下降速率超过3 μg·m
-3
·a
-1
。本研究发展的PM
2.5
浓度无缝制图方法和生产的相关数据产品有望为区域灰霾污染防控和PM
2.5
暴露健康风险评估研究提供方法参考和基础数据支撑。
Monitoring concentrations of atmospheric particulate matters is essential to regional haze pollution prevention and control. Satellite-based Aerosol Optical Depth (AOD) data have been frequently used to map regional PM
2.5
concentrations. However
the resultant PM
2.5
concentration maps are always spatially incomplete due to significant data gaps in satellite-based AOD retrievals. This study aims to fill data gaps in AOD imageries to support spatially contiguous PM
2.5
concentration mapping on an hourly basis in the Yangtze River Delta.
An integrated data fusion approach was developed to seamlessly gear up the missing AOD imputation and multimodal data fusion approaches. Specifically
all available Himawari-8 AOD observations during the daytime were fused to maximize hourly AOD coverage in each single snapshot. To further tackle data gaps in fused AOD maps
a virtual AOD monitoring network was constructed by estimating AOD at each state-controlled air quality monitoring station based on ground measured air pollutant concentration. This way enables us to extend the sparsely distributed aerosol monitoring network nationwide
which significantly improves the spatial coverage of AOD. Subsequently
the reconstructed satellite AOD and PM inferred AOD were fused with AOD simulations from MERRA-2 using the optimal interpolation method to generate spatially contiguous yet far more accurate AOD reanalysis. Spatially complete PM
2.5
concentration maps were finally generated on hourly basis over the study region using the random forest method.
Ground validation results indicate that AOD values inferred from air quality measurements agree well with in situ AOD measurements
with R of 0.90 and RMSE of 0.13. The analyzed spatially complete AOD dataset has a correlation of 0.86 and RMSE of 0.16 compared with in situ AOD data
which is much higher than that of raw Himawari-8 AOD. The estimated PM
2.5
concentration data also have a promising accuracy
with R of 0.9 and mean absolute error of 9.87 μg m
-3
compared with in situ PM
2.5
measurements.
Compared with sparsely distributed in situ PM
2.5
measurements
this spatially contiguous PM
2.5
concentration dataset has great advantages in assessing PM
2.5
variations in space and time in the Yangtze River Delta. Statistically significant decreasing trend over the whole study area also highlights the effectiveness of clean air actions in reducing PM
2.5
loadings across China. Overall
the proposed method can be practically used for future PM
2.5
mapping practices and the generated spatially contiguous PM
2.5
concentration dataset is a promising data source for the assessment of the human exposure risk to haze pollution.
遥感PM2.5制图气溶胶光学厚度多源数据融合缺失信息重建空气质量长三角
remote sensingPM2.5 concentration mappingaerosol optical depthmultimodal data fusionmissing value imputationair qualityYangtze River Delta
Bai K X, Li K, Chang N B and Gao W. 2019. Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: a perspective of data mining through in situ PM2.5 measurements. Environmental Pollution, 254: 113047. [DOI: 10.1016/j.envpol.2019.113047http://dx.doi.org/10.1016/j.envpol.2019.113047]
Bai K X, Li K, Guo J P, Yang Y J and Chang N B. 2020a. Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles. Atmospheric Measurement Techniques, 13(3): 1213-1226 [DOI: 10.5194/amt-13-1213-2020http://dx.doi.org/10.5194/amt-13-1213-2020]
Bai K X, Li K, Wu C B, Chang N B and Guo J P. 2020b. A homogenized daily in situ PM2.5 concentration dataset from the national air quality monitoring network in China. Earth System Science Data, 12(4): 3067-3080 [DOI: 10.5194/essd-12-3067-2020http://dx.doi.org/10.5194/essd-12-3067-2020]
Bi J Z, Belle J H, Wang Y J, Lyapustin A I, Wildani A and Liu Y. 2019. Impacts of snow and cloud covers on satellite-derived PM2.5 levels. Remote Sensing of Environment, 221: 665-674 [DOI: 10.1016/j.rse.2018.12.002http://dx.doi.org/10.1016/j.rse.2018.12.002]
Buchard V, Randles C A, da Silva A M, Darmenov A, Colarco P R, Govindaraju R, Ferrare R, Hair J, Beyersdorf A J, Ziemba L D and Yu H. 2017. The MERRA-2 aerosol reanalysis, 1980 onward. Part II: evaluation and case studies. Journal of Climate, 30(17): 6851-6872 [DOI: 10.1175/JCLI-D-16-0613.1http://dx.doi.org/10.1175/JCLI-D-16-0613.1]
Feng F and Wang K C. 2019. Does the modern-era retrospective analysis for research and applications-2 aerosol reanalysis introduce an improvement in the simulation of surface solar radiation over China? International Journal of Climatology, 39(3): 1305-1318 [DOI: 10.1002/joc.5881http://dx.doi.org/10.1002/joc.5881]
Hong J, Mao F Y, Min Q L, Pan Z X, Wang W, Zhang T H and Gong W. 2020. Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations. Environmental Pollution, 263: 114451 [DOI: 10.1016/j.envpol.2020.114451http://dx.doi.org/10.1016/j.envpol.2020.114451]
Jiang T T, Chen B, Chan K K Y and Xu B. 2019. Himawari-8/AHI and MODIS aerosol optical depths in China: evaluation and comparison. Remote Sensing, 11(9): 1011 [DOI: 10.3390/rs11091011http://dx.doi.org/10.3390/rs11091011]
Li D, Qin K, Wu L X, Mei L L, de Leeuw G, Xue Y, Shi Y N and Li Y F. 2020a. Himawari-8-derived aerosol optical depth using an improved time series algorithm over eastern China. Remote Sensing, 12(6): 978 [DOI: 10.3390/rs12060978http://dx.doi.org/10.3390/rs12060978]
Li L F, Franklin M, Girguis M, Lurmann F, Wu J, Pavlovic N, Breton C, Gilliland F and Habre R. 2020b. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling. Remote Sensing of Environment, 237: 111584 [DOI: 10.1016/j.rse.2019.111584http://dx.doi.org/10.1016/j.rse.2019.111584]
Li T W, Shen H F, Yuan Q Q and Zhang L P. 2020c. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 178-188 [DOI: 10.1016/j.isprsjprs.2020.06.019http://dx.doi.org/10.1016/j.isprsjprs.2020.06.019]
Li Z Q, Xu H, Li K T, Li D H, Xie Y S, Li L, Zhang Y, Gu X F, Zhao W, Tian Q J, Deng R R, Su X L, Huang B, Qiao Y L, Cui W Y, Hu Y, Gong C L, Wang Y Q, Wang X F, Wang J P, Du W B, Pan Z Q, Li Z Z and Bu D. 2018. Comprehensive study of optical, physical, chemical, and radiative properties of total columnar atmospheric aerosols over China: an overview of sun–sky radiometer observation network (SONET) measurements. Bulletin of the American Meteorological Society, 99(4): 739-755 [DOI: 10.1175/BAMS-D-17-0133.1http://dx.doi.org/10.1175/BAMS-D-17-0133.1]
Niu X J, Tang J K, Zhang Z L, Cui L L, Xing W P and Song Y. 2019. The method of aerosol retrieval using Himawari-8 satellite data and its application in monitoring haze process. Journal of University of Chinese Academy of Sciences, 36(5): 671-681
牛晓君, 唐家奎, 张自力, 崔林丽, 幸炜鹏, 宋艺. 2019. 基于葵花8号卫星数据的气溶胶反演算法及其在雾霾过程监测中的应用. 中国科学院大学学报, 36(5): 671-681 [DOI: 10.7523/j.issn.2095-6134.2019.05.013http://dx.doi.org/10.7523/j.issn.2095-6134.2019.05.013]
Park Y, Kwon B, Heo J, Hu X F, Liu Y and Moon T. 2020. Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks. Environmental Pollution, 256: 113395 [DOI: 10.1016/j.envpol.2019.113395http://dx.doi.org/10.1016/j.envpol.2019.113395]
Shen F Z, Zhang L, Jiang L, Tang M Q, Gai X Y, Chen M D and Ge X L. 2020. Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China. Environment International, 137: 105556 [DOI: 10.1016/j.envint.2020.105556http://dx.doi.org/10.1016/j.envint.2020.105556]
Shen H F and Li T W. 2019. Progress of remote sensing mapping of atmospheric PM2.5. Acta Geodaetica et Cartographica Sinica, 48(12): 1624-1635
沈焕锋, 李同文. 2019. 大气PM2.5遥感制图研究进展. 测绘学报, 48(12): 1624-1635 [DOI: 10.11947/j.AGCS.2019.20190456http://dx.doi.org/10.11947/j.AGCS.2019.20190456]
Singh M K, Gautam R and Venkatachalam P. 2017a. Bayesian merging of MISR and MODIS aerosol optical depth products using error distributions from AERONET. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12): 5186-5200 [DOI: 10.1109/JSTARS.2017.2734331http://dx.doi.org/10.1109/JSTARS.2017.2734331]
Singh M K, Venkatachalam P and Gautam R. 2017b. Geostatistical methods for filling gaps in level-3 monthly-mean aerosol optical depth data from multi-angle imaging spectroradiometer. Aerosol and Air Quality Research, 17(8): 1963-1974 [DOI: 10.4209/aaqr.2016.02.0084http://dx.doi.org/10.4209/aaqr.2016.02.0084]
Tang Q X, Bo Y C and Zhu Y X. 2016. Spatiotemporal fusion of multiple-satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method. Journal of Geophysical Research, 121(8): 4034-4048 [DOI: 10.1002/2015JD024571http://dx.doi.org/10.1002/2015JD024571]
Tao J H, Zhang M G, Chen L F, Wang Z F, Su L, Ge C, Han X and Zou M M. 2013. A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness. Science China Earth Sciences, 56(8): 1422-1433
陶金花, 张美根, 陈良富, 王子峰, 苏林, 葛萃, 韩霄, 邹铭敏. 2013. 一种基于卫星遥感AOT估算近地面颗粒物的方法. 中国科学: 地球科学, 43(1): 143-154 [DOI: 10.1007/s11430-012-4503-3http://dx.doi.org/10.1007/s11430-012-4503-3]
Wang Y, Yuan Q Q, Li T W, Shen H F, Zheng L and Zhang L P. 2019. Large-scale MODIS AOD products recovery: spatial-temporal hybrid fusion considering aerosol variation mitigation. ISPRS Journal of Photogrammetry and Remote Sensing, 157: 1-12 [DOI: 10.1016/j.isprsjprs.2019.08.017http://dx.doi.org/10.1016/j.isprsjprs.2019.08.017]
Wang Z F, Zeng Q L, Chen L F, Tao J H, Fan M and Zhang Z Y. 2019. Research progress of methodology and applications of PM2.5 estimation using satellite remote sensing. Environmental Monitoring and Forewarning, 11(5): 33-38
王子峰, 曾巧林, 陈良富, 陶金花, 范萌, 张泽宇. 2019. 利用卫星遥感数据估算PM2.5浓度的应用研究进展. 环境监控与预警, 11(5): 33-38 [DOI: 10.3969/j.issn.1674-6732.2019.05.006http://dx.doi.org/10.3969/j.issn.1674-6732.2019.05.006]
Wei J, Li Z X, Pinker R T, Wang J, Sun L, Xue W H, Li R Z and Cribb M. 2021. Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM). Atmospheric Chemistry and Physics, 21(10): 7863-7880 [DOI: 10.5194/acp-21-7863-2021http://dx.doi.org/10.5194/acp-21-7863-2021]
Wei X L, Chang N B, Bai K X and Gao W. 2020. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives. Critical Reviews in Environmental Science and Technology, 50(16): 1640-1725 [DOI: 10.1080/10643389.2019.1665944http://dx.doi.org/10.1080/10643389.2019.1665944]
Werner M, Kryza M, Pagowski M and Guzikowski J. 2019. Assimilation of PM2.5 ground base observations to two chemical schemes in WRF-Chem-The results for the winter and summer period. Atmospheric Environment, 200: 178-189 [DOI: 10.1016/j.atmosenv.2018.12.016http://dx.doi.org/10.1016/j.atmosenv.2018.12.016]
Xie Y, Dai H C, Dong H J, Hanaoka T and Masui T. 2016. Economic impacts from PM2.5 pollution-related health effects in China: a provincial-level analysis. Environmental Science and Technology, 50(9): 4836-4843 [DOI: 10.1021/acs.est.5b05576http://dx.doi.org/10.1021/acs.est.5b05576]
Xu J H and Jiang H. 2015. Estimation of PM2.5 concentration over the Yangtze delta using remote sensing: analysis of spatial and temporal variations. Environmental Science, 36(9): 3119-3127
徐建辉, 江洪. 2015. 长江三角洲PM2.5质量浓度遥感估算与时空分布特征. 环境科学, 36(9): 3119-3127 [DOI: 10.13227/j.hjkx.2015.09.001http://dx.doi.org/10.13227/j.hjkx.2015.09.001]
Yoshida M, Kikuchi M, Nagao T M, Murakami H, Nomaki T and Higurashi A. 2018. Common retrieval of aerosol properties for imaging satellite sensors. Journal of the Meteorological Society of Japan, 96B: 193-209 [DOI: 10.2151/jmsj.2018-039http://dx.doi.org/10.2151/jmsj.2018-039]
Zhang L P and Shen H F. 2016. Progress and future of remote sensing data fusion. Journal of Remote Sensing, 20(5): 1050-1061
张良培, 沈焕锋. 2016. 遥感数据融合的进展与前瞻. 遥感学报, 20(5): 1050-1061 [DOI: 10.11834/jrs.20166243http://dx.doi.org/10.11834/jrs.20166243]
Zhang Z Y, Wu W L, Fan W, Tao W H, Wei J, Jin J, Tan Y H and Wang Q. 2019. Validation of Himawari-8 aerosol optical depth retrievals over China. Atmospheric Environment, 199: 32-44 [DOI: 10.1016/j.atmosenv.2018.11.024http://dx.doi.org/10.1016/j.atmosenv.2018.11.024]
Zou B, Liu N, Wang W, Feng H H, Liu X P and Lin Y. 2020. An effective and efficient enhanced fixed rank smoothing method for the spatiotemporal fusion of multiple-satellite aerosol optical depth products. Remote Sensing, 12(7): 1102 [DOI: 10.3390/rs12071102http://dx.doi.org/10.3390/rs12071102]
相关作者
相关机构
京公网安备11010802024621