A parameterization scheme of aerosol vertical distribution for surface-level visibility retrieval from satellite remote sensing

He, Qianshan; Li, Chengcai; Geng, Feng; Zhou, Guangqiang; Gao, Wei; Yu, Wei; Li, Zhenkun; Du, Ming

doi:10.1016/j.rse.2016.03.016

Cited by 29 publications

(16 citation statements)

References 31 publications

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“…The aerosol vertical profile plays a non-negligible role in aerosol radiative forcing. Here, the aerosol vertical profile model retrieved by He et al (2016) was applied in each grid to take the place of the default in the radiative model. The retrieval can be briefly described as follows.…”

Section: Data 95mentioning

confidence: 99%

Retrieval of Gridded Aerosol Direct Radiative Forcing Based on Multiplatform Datasets

Wang¹,

Lyu²,

Xie³

et al. 2019

Preprint

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Abstract. Atmospheric aerosols play a crucial role in regional radiative budgets. Previous studies on clear-sky aerosol direct radiative forcing (ADRF) have mainly been limited to site-scale observations or model simulations for short-term cases, and long-term distributions of ADRF in China has not been portrayed yet. In this study, an accurate fine-resolution ADRF estimate at the surface was proposed. Multiplatform datasets, including satellite (Terra and Aqua) and reanalysis datasets, served as inputs to the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model for ADRF simulation with consideration of the aerosol vertical profile over East China during 2000–2016. Specifically, single scattering albedo (SSA) from the Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) was validated with sunphotometers over East China. The gridded asymmetry parameter (ASY) was then simulated by matching the calculated top-of-atmosphere (TOA) radiative fluxes from the radiative transfer model with satellite observations (Clouds and the Earth's Radiant Energy System (CERES)). The high correlation and small discrepancy (6–8 W m-2) between simulated and observed radiative fluxes at three sites (Baoshan, Fuzhou, and Yong'an) indicated that ADRF retrieval is feasible and has high accuracy over East China. Then this method was applied in each grid of East China and the overall picture of ADRF distributions over East China during 2000–2016 was displayed. ADRF ranges from −220 to −20 W m-2, and annual mean ADRF is −100.21 W m-2, implying that aerosols have strong cooling effect at the surface during past 16 years. Finally, uncertainty analysis was also evaluated. Our method provides the long-term ADRF distribution over East China for the first time, with highlighting the importance of aerosol radiative impact under the climate change.

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Section: Data 95mentioning

confidence: 99%

Retrieval of Gridded Aerosol Direct Radiative Forcing Based on Multiplatform Datasets

Wang¹,

Lyu²,

Xie³

et al. 2019

Preprint

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“…Vertical and humidity corrections are needed to calculate PM 2.5 concentrations in urban forests from AOD data. To do this, the Height of Planetary Boundary Layer (HPBL) was incorporated into the vertical correction, while the effects of hygroscopic growth were used to determine the humidity correction [25,26]. In this study, AOD data were converted to a near-surface "dry" aerosol extinction coefficient and their correlation relationships with ground PM 2.5 concentrations [27,28].…”

Section: Calculation Of Pm 25 From Aod Datamentioning

confidence: 99%

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

et al. 2018

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Abstract:Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM 2.5 ). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM 2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM 2.5 ), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM 2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM 2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM 2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM 2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM 2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.

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“…In addition, the RH correction based on the aerosol hygroscopic growth factor is widely adopted in retrieving satellite‐observed aerosol optical depth to determine surface PM 2.5 concentrations (Q. He et al, ; J. Guo et al, ; Kong et al, ; J. Lin & Li, ; Ma et al, ; C. Zheng, Zhao, et al, ). Moreover, the visibility may display either an exponential or power law relationship with the PM 2.5 concentrations at the same location, depending on the conditions of wind speed, wind direction, and precipitation intensity (J. Wang et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the RH correction based on the aerosol hygroscopic growth factor is widely adopted in retrieving satellite-observed aerosol optical depth to determine surface PM 2.5 concentrations (Q. He et al, 2016;J. Guo et al, 2009;Kong et al, 2017;J.…”

Section: Introductionmentioning

confidence: 99%

The Effects of PM_2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing

2019

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Atmospheric visibility is often used as a proxy for ambient air quality. However, in addition to the concentrations of particulate matter, visibility is also affected by meteorological conditions. The relative contributions of PM2.5 concentrations and meteorological conditions to visibility are not yet clear. In this study, the individual contributions of PM2.5 concentrations and the relative humidity (RH) to the visibility are measured based on observations at 12 stations in Beijing. We find that visibility decreases quickly as PM2.5 concentrations increase at clean condition and decreases slowly when PM2.5 concentrations exceed 100 μg/m3. The visibility decreases linearly as PM2.5 concentrations increase when PM2.5 concentrations lower than 50 μg/m3; however, there tends to be an exponential relationship as PM2.5 concentrations increase. The PM2.5 concentrations can explain 50% of the variance in visibility in clean and high‐humidity environments, whereas this fraction decreases to 10–15% when the PM2.5 concentrations exceed 200 μg/m3. In contrast, RH has little effect on visibility under dry conditions. When the RH exceeds 40%, atmospheric visibility tends to display an inversely proportional and exponential relationship with RH under polluted and clean conditions, respectively. Under high‐humidity and highly polluted conditions, up to 40% of the variance in visibility is associated with RH. The PM2.5 concentrations dominate the variations in visibility under dry or low PM2.5 concentrations, and the contributions of RH become increasingly important as PM2.5 concentrations and humidity increase. The difference of aerosol types and weather conditions increase the uncertainties of correlation coefficients between visibility and PM2.5/RH. This study emphasizes the atmospheric conditions when employing visibility as a proxy for air pollution.

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A parameterization scheme of aerosol vertical distribution for surface-level visibility retrieval from satellite remote sensing

Cited by 29 publications

References 31 publications

Retrieval of Gridded Aerosol Direct Radiative Forcing Based on Multiplatform Datasets

Retrieval of Gridded Aerosol Direct Radiative Forcing Based on Multiplatform Datasets

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

The Effects of PM_2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing

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A parameterization scheme of aerosol vertical distribution for surface-level visibility retrieval from satellite remote sensing

Cited by 29 publications

References 31 publications

Retrieval of Gridded Aerosol Direct Radiative Forcing Based on Multiplatform Datasets

Retrieval of Gridded Aerosol Direct Radiative Forcing Based on Multiplatform Datasets

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

The Effects of PM2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing

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The Effects of PM_2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing