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Yan X, Zuo C, Li Z, Chen HW, Jiang Y, Wang Q, Wang G, Jia K, A Y, Chen Z, Chen J. Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:15661-15671. [PMID: 39163486 DOI: 10.1021/acs.est.4c02498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.
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Affiliation(s)
- Xing Yan
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Chen Zuo
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Zhanqing Li
- Department of Atmospheric and Oceanic Science and ESSIC, University of Maryland, College Park, Maryland 20740, United States
| | - Hans W Chen
- Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg 41296, Sweden
| | - Yize Jiang
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Qiao Wang
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Guoqiang Wang
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Kun Jia
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Yinglan A
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Ziyue Chen
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Jiayi Chen
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Cao Z, Luan K, Zhou P, Shen W, Wang Z, Zhu W, Qiu Z, Wang J. Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean. TOXICS 2023; 11:813. [PMID: 37888664 PMCID: PMC10611072 DOI: 10.3390/toxics11100813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 09/22/2023] [Accepted: 09/23/2023] [Indexed: 10/28/2023]
Abstract
The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols' impact and trends.
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Affiliation(s)
- Zhaoxiang Cao
- College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
| | - Kuifeng Luan
- College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
- Estuarine and Oceanographic Mapping Engineering Research Center of Shanghai, Shanghai 200123, China
| | - Peng Zhou
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
- State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
| | - Wei Shen
- College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
- Estuarine and Oceanographic Mapping Engineering Research Center of Shanghai, Shanghai 200123, China
| | - Zhenhua Wang
- College of Information Science, Shanghai Ocean University, Shanghai 201306, China
| | - Weidong Zhu
- College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
- Estuarine and Oceanographic Mapping Engineering Research Center of Shanghai, Shanghai 200123, China
| | - Zhenge Qiu
- College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
- Estuarine and Oceanographic Mapping Engineering Research Center of Shanghai, Shanghai 200123, China
| | - Jie Wang
- College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
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Xia X, Che H, Shi H, Chen H, Zhang X, Wang P, Goloub P, Holben B. Advances in sunphotometer-measured aerosol optical properties and related topics in China: Impetus and perspectives. ATMOSPHERIC RESEARCH 2021; 249:105286. [PMID: 33012934 PMCID: PMC7518977 DOI: 10.1016/j.atmosres.2020.105286] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 09/21/2020] [Accepted: 09/21/2020] [Indexed: 06/02/2023]
Abstract
Aerosol is a critical trace component of the atmosphere. Many processes in the Earth's climate system are intimately related to aerosols via their direct and indirect radiative effects. Aerosol effects are not limited to these climatic aspects, however. They are also closely related to human health, photosynthesis, new energy, etc., which makes aerosol a central focus in many research fields. A fundamental requirement for improving our understanding of the diverse aerosol effects is to accumulate high-quality aerosol data by various measurement techniques. Sunphotometer remote sensing is one of the techniques that has been playing an increasingly important role in characterizing aerosols across the world. Much progress has been made on this aspect in China during the past decade, which is the work reviewed in this paper. Three sunphotometer networks have been established to provide high-quality observations of long-term aerosol optical properties across the country. Using this valuable dataset, our understanding of spatiotemporal variability and long-term trends of aerosol optical properties has been much improved. The radiative effects of aerosols both at the bottom and at the top of the atmosphere are comprehensively assessed. Substantial warming of the atmosphere by aerosol absorption is revealed. The long-range transport of dust from the Taklimakan Desert in Northwest China and anthropogenic aerosols from South Asia to the Tibetan Plateau is characterized based on ground-based and satellite remote sensing as well as model simulations. Effective methods to estimate chemical compositions from sunphotometer aerosol products are developed. Dozens of satellite and model aerosol products are validated, shedding new light on how to improve these products. These advances improve our understanding of the critical role played by aerosols in both the climate and environment. Finally, a perspective on future research is presented.
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Affiliation(s)
- Xiangao Xia
- LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Hongrong Shi
- LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Hongbin Chen
- LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Pucai Wang
- LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Phillipe Goloub
- Univ. Lille, CNRS, UMR 8518 - LOA - Laboratoire d'Optique Atmosphérique, F-59000 Lille, France
| | - Brent Holben
- Biospheric Sciences Branch, Code 923, NASA/Goddard Space Flight Center, Greenbelt, MD, USA
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Spatiotemporal Distribution of Major Aerosol Types over China Based on MODIS Products between 2008 and 2017. ATMOSPHERE 2020. [DOI: 10.3390/atmos11070703] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Knowledge of aerosol-type distribution is critical to the evaluation of aerosol–climate effects. However, research on aerosol-type distribution covering all is limited. This study characterized the spatiotemporal distribution of major aerosol types over China by using MODerate resolution Imaging Spectroradiometer (MODIS) products from 2008 to 2017. Two aerosol-type classification methods were combined to achieve this goal. One was for relatively high aerosol load (AOD ≥ 0.2) using aerosol optical depth (AOD) and aerosol relative optical depth (AROD) and the other was for low aerosol load (AOD < 0.2) using land use and population density information, which assumed that aerosols are closely related to local emissions. Results showed that the dominant aerosol-type distribution has a distinct spatial and temporal pattern. In western China, background aerosols (mainly dust/desert dust and continent aerosol) dominate with a combined occurrence ratio over 70% and they have slight variations on seasonal scale. While in eastern China, the dominant aerosols show strong seasonal variations. Spatially, mixed aerosols dominate most parts of eastern China in spring due to the influence of long-range transported dust from Taklamakan and Gobi desert and urban/industry aerosols take place in summer due to strong photochemical reactions. Temporally, mixed and urban/industry aerosols co-dominate eastern China.
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5
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Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China. REMOTE SENSING 2020. [DOI: 10.3390/rs12060991] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Visible Infrared Imaging Radiometer Suite (VIIRS) has been observing aerosol optical depth (AOD), which is a critical parameter in air pollution and climate change, for more than 7 years since 2012. Due to limited and uneven distribution of the Aerosol Robotic Network (AERONET) station in China, the independent data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) was used to evaluate the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD products in six typical sites and analyze the influence of the aerosol model selection process in five subregions, particularly for dust. Compared with ground-based observations, the performance of all retrievals (except the Shapotou (SPT) site) is similar to other previous studies on a global scale. However, the results illustrate that the AOD retrievals with the dust model showed poor consistency with a regression equation as y = 0.312x + 0.086, while the retrievals obtained from the other models perform much better with a regression equation as y = 0.783x + 0.119. The poor AOD retrieval with the dust model was also verified by a comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product. The results show they have a lower correlation coefficient (R) and a higher mean relative error (MRE) when the aerosol model used in the retrieval is identified as dust. According to the Ultraviolet Aerosol Index (UVAI), the frequency of dust type over southern China is inconsistent with the actual atmospheric condition. In addition, a comparison of ground-based Ångström exponent (α) values yields an unexpected result that the dust model percentage exceed 40% when α < 1.0, and the mean α shows a high value of ~0.75. Meanwhile, the α peak value (~1.1) of the “dust” model determined by a satellite retravel algorithm indicate there is some problem in the dust model selection process. This mismatching of the aerosol model may partly explain the low accuracy at the SPT and the systemic biases in regional and global validations.
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6
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Intercomparison of Multiple Satellite Aerosol Products against AERONET over the North China Plain. ATMOSPHERE 2019. [DOI: 10.3390/atmos10090480] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this study, using Aerosol Robotic Network aerosol optical depth (AOD) products at three stations in the North China Plain (NCP)—a heavily polluted region in China—the AOD products from six satellite-borne radiometers: the Moderate Resolution Imagining Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), Ozone Mapping Imaging (OMI), the Visible Infrared Imaging Radiometer (VIIRS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Polarization and Directionality of the Earth’s Reflectances (POLDER), were thoroughly validated, shedding new light on their advantages and disadvantages. The MODIS Deep Blue (DB) products provide more accurate retrievals than the MODIS Dark Target (DT) and other satellite products at the Beijing site (BJ,a megacity), with higher correlations with AERONET (R > 0.93), lower mean absolute bias (MB < 0.012), and higher percentages (>68%) falling within the expected error (EE). All MODIS DT and DB products perform better than the other satellite products at the Xianghe site (XH, a suburb). The MODIS/Aqua DT products at both 3-km and 10-km resolutions performed better than the other space-borne AOD products at the Xinglong site (XL, a rural area at the top of a mountain). MISR, VIIRS, and SeaWiFS tend to underestimate high AOD values and overestimate AOD values under very low AOD conditions in the NCP. Both OMI and POLDER significantly underestimate the AOD. In terms of data volume, MISR with the limited swath width of 380 km has less data volume than the other satellite sensors. MODIS products have the highest sampling rate, especially the MODIS DT and DB merged products, and can be used for various climate study and air-quality monitoring.
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7
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Zheng Y, Che H, Xia X, Wang Y, Wang H, Wu Y, Tao J, Zhao H, An L, Li L, Gui K, Sun T, Li X, Sheng Z, Liu C, Yang X, Liang Y, Zhang L, Liu C, Kuang X, Luo S, You Y, Zhang X. Five-year observation of aerosol optical properties and its radiative effects to planetary boundary layer during air pollution episodes in North China: Intercomparison of a plain site and a mountainous site in Beijing. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 674:140-158. [PMID: 31004891 DOI: 10.1016/j.scitotenv.2019.03.418] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 03/22/2019] [Accepted: 03/26/2019] [Indexed: 05/16/2023]
Abstract
The aerosol microphysical, optical and radiative properties of the whole column and upper planetary boundary layer (PBL) were investigated during 2013 to 2018 based on long-term sun-photometer observations at a surface site (~106 m a.s.l.) and a mountainous site (~1225 m a.s.l.) in Beijing. Raman-Mie lidar data combined with radiosonde data were used to explore the aerosol radiative effects to PBL during dust and haze episodes. The results showed size distribution exhibited mostly bimodal pattern for the whole column and the upper PBL throughout the year, except in July for the upper PBL, when a trimodal distribution occurred due to the coagulation and hygroscopic growth of fine particles. The seasonal mean values of aerosol optical depth at 440 nm for the upper PBL were 0.31 ± 0.34, 0.30 ± 0.37, 0.17 ± 0.30 and 0.14 ± 0.09 in spring, summer, autumn and winter, respectively. The single-scattering albedo at 440 nm of the upper PBL varied oppositely to that of the whole column, with the monthly mean value between 0.91 and 0.96, indicating weakly to slightly strong absorptive ability at visible spectrum. The monthly mean direct aerosol radiative forcing at the Earth's surface and the top of the atmosphere varied from -40 ± 7 to -105 ± 25 and from -18 ± 4 to -49 ± 17 W m-2, respectively, and the maximum atmospheric heating was found in summer (~66 ± 12 W m-2). From a radiative point of view, during dust episode, the presence of mineral dust heated the lower atmosphere, thus promoting vertical turbulence, causing more air pollutants being transported to the upper air by the increasing PBLH. In contrast, during haze episode, a large quantity of absorbing aerosols (such as black carbon) had a cooling effect on the surface and a heating effect on the upper atmosphere, which favored the stabilization of PBL and occurrence of inversion layer, contributing to the depression of the PBLH.
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Affiliation(s)
- Yu Zheng
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science &Technology, Nanjing 210044, China; State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China.
| | - Xiangao Xia
- Laboratory for Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; School of Geoscience University of Chinese Academy of Science, Beijing 100049, China
| | - Yaqiang Wang
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Hong Wang
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Yunfei Wu
- CAS Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Jun Tao
- South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Hujia Zhao
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Linchang An
- National Meteorological Center, CMA, Beijing 100081, China
| | - Lei Li
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Ke Gui
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Tianze Sun
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Xiaopan Li
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Zhizhong Sheng
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Chao Liu
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China; School of Surveying and Land Information Engineering, Henan Polytechnic University, Henan 454000, China
| | - Xianyi Yang
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Yuanxin Liang
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Lei Zhang
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Chong Liu
- School of Atmospheric Sciences, Nanjing University, Nanjing 210093, China
| | - Xiang Kuang
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science &Technology, Nanjing 210044, China
| | - Shi Luo
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science &Technology, Nanjing 210044, China
| | - Yingchang You
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science &Technology, Nanjing 210044, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
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Stafoggia M, Bellander T, Bucci S, Davoli M, de Hoogh K, De' Donato F, Gariazzo C, Lyapustin A, Michelozzi P, Renzi M, Scortichini M, Shtein A, Viegi G, Kloog I, Schwartz J. Estimation of daily PM 10 and PM 2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model. ENVIRONMENT INTERNATIONAL 2019; 124:170-179. [PMID: 30654325 DOI: 10.1016/j.envint.2019.01.016] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 01/04/2019] [Accepted: 01/06/2019] [Indexed: 05/28/2023]
Abstract
Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM < 10 μm), fine (PM < 2.5 μm, PM2.5) and coarse particles (PM between 2.5 and 10 μm, PM2.5-10) at 1-km2 grid for 2013-2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5-10 concentrations in monitors where only PM10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5-10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects.
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Affiliation(s)
- Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy; Karolinska Institutet, Institute of Environmental Medicine, Stockholm, Sweden.
| | - Tom Bellander
- Karolinska Institutet, Institute of Environmental Medicine, Stockholm, Sweden
| | - Simone Bucci
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Marina Davoli
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Francesca De' Donato
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Claudio Gariazzo
- INAIL, Department of Occupational & Environmental Medicine, Monteporzio Catone, Italy
| | - Alexei Lyapustin
- National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD, USA
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Matteo Renzi
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Matteo Scortichini
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Alexandra Shtein
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Giovanni Viegi
- Institute of Biomedicine and Molecular Immunology "Alberto Monroy", National Research Council, Palermo, Italy
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Joel Schwartz
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Cambridge, MA, USA
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9
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Validation and Comparison of MODIS C6.1 and C6 Aerosol Products over Beijing, China. REMOTE SENSING 2018. [DOI: 10.3390/rs10122021] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Products (APs) have provided long-term and wide-spatial-coverage aerosol optical properties across the globe, such as aerosol optical depth (AOD). However, the performance of the latest Collection 6.1 (C6.1) of MODIS APs is still unclear over urban areas that feature complex surface characteristics and aerosol models. The aim of this study was to validate and compare the performance of the MODIS C6.1 and C6 APs (MxD04, x = O for Terra, x = Y for Aqua) over Beijing, China. The results of the Dark Target (DT) and Deep Blue (DB) algorithms were validated against Aerosol Robotic Network (AERONET) ground-based observations at local sites. The retrieval uncertainties and accuracies were evaluated using the expected error (EE: ±0.05 + 15%) and the root-mean-square error (RMSE). It was found that the MODIS C6.1 DT products performed better than the C6 DT products, with a greater percentage (by about 13%–14%) of the retrievals falling within the EE. However, the DT retrievals collected from two collections were significantly overestimated in the Beijing region, with more than 64% and 48% of the samples falling above the EE for the Terra and Aqua satellites, respectively. The MODIS C6.1 DB products performed similarly to the C6 DB products, with 70%–73% of the retrievals matching within the EE and estimation uncertainties. Moreover, the DB algorithm performed much better than DT algorithm over urban areas, especially in winter where abundant missing pixels were found in DT products. To investigate the effects of factors on AOD retrievals, the variability in the assumed surface reflectance and the main optical properties applied in DT and DB algorithms are also analyzed.
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How Long should the MISR Record Be when Evaluating Aerosol Optical Depth Climatology in Climate Models? REMOTE SENSING 2018. [DOI: 10.3390/rs10091326] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study used the nearly continuous 17-year observation record from the Multi- angle Imaging SpectroRadiometer (MISR) instrument on the National Aeronautics and Space Administration (NASA) Terra Earth Observing System satellite to determine which temporal subsets are long enough to define statistically stable speciated aerosol optical depth (AOD) climatologies (i.e., AOD by particle types) for purposes of climate model evaluation. A random subsampling of seasonally averaged total and speciated AOD retrievals was performed to quantitatively assess the statistical stability in the climatology, represented by the minimum record length required for the standard deviation of the subsampled mean AODs to be less than a certain threshold. Our results indicate that the multi-year mean speciated AOD from MISR is stable on a global scale; however, there is substantial regional variability in the assessed stability. This implies that in some regions, even 17 years may not provide a long enough sample to define regional mean total and speciated AOD climatologies. We further investigated the agreement between the statistical stability of total AOD retrievals from MISR and the Moderate Resolution Imaging Spectroradiometer (MODIS), also on the NASA Terra satellite. The difference in the minimum record lengths between MISR and MODIS climatologies of total AOD is less than three years for most of the globe, with the exception of certain regions. Finally, we compared the seasonal cycles in the MISR total and speciated AODs with those simulated by three global chemistry transport models in the regions of climatologically stable speciated AODs. We found that only one model reproduced the observed seasonal cycles of the total and non-absorbing AODs over East China, but the seasonal cycles in total and dust AODs in all models are similar to those from MISR in Western Africa. This work provides a new method for considering the statistical stability of satellite-derived climatologies and illustrates the value of MISR’s speciated AOD data record for evaluating aerosols in global models.
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Characteristic and Driving Factors of Aerosol Optical Depth over Mainland China during 1980–2017. REMOTE SENSING 2018. [DOI: 10.3390/rs10071064] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin. REMOTE SENSING 2018. [DOI: 10.3390/rs10010117] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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How Do Aerosol Properties Affect the Temporal Variation of MODIS AOD Bias in Eastern China? REMOTE SENSING 2017. [DOI: 10.3390/rs9080800] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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