VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model

Wu, J.; Yao, Fei; Li, Weifeng; Si, Menglin

doi:10.1016/j.rse.2016.07.015

Cited by 114 publications

(58 citation statements)

References 32 publications

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“…Satellite-based PM monitoring has the potential to provide information on air quality over vast areas at high spatial resolution. Many studies have examined the use of satellitebased products to estimate surface PM concentrations (Liu et al, 2005;Gupta and Christopher, 2009a, b;Van Donkelaar et al, 2010Chudnovsky et al, 2014;Li et al, 2015;Xu et al, 2015a;You et al, 2015;Wu et al, 2016). AOD is the most widely used parameter that can be derived from satellite remote sensing to estimate ground-level PM concentrations.…”

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confidence: 99%

“…In addition, recent studies have used PBLH, RH, wind speed, and other meteorological variables and land use information because these factors are related to PM concentrations and thus can be used to improve estimation models (Gupta and Christopher, 2009a;Liu et al, 2009;Wu et al, 2012Wu et al, , 2016Chudnovsky et al, 2014;You et al, 2015;Li et al, 2017b;Yeganeh et al, 2017). In this study, we adopted the machine learning approach, RF, to develop models estimating ground-level PM 10 and PM 2.5 concentrations using satellite-derived products, numerical and emission model output, and ancillary spatial data over South Korea.…”

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confidence: 99%

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Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Park

Shin

et al. 2019

Atmos. Chem. Phys.

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Abstract. Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

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confidence: 99%

mentioning

confidence: 99%

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Park

Shin

et al. 2019

Atmos. Chem. Phys.

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“…Data groups with invalid or missing variables values were filtered. Because a minimum of four data groups is required in the GWR model for model fitting and cross-validation [29], we discarded the days with less than four data groups and screened days with four or more data groups. After filtering and screening, a total of 1659 valid data groups were retained for model fitting.…”

Section: Data Integrationmentioning

confidence: 99%

Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China

et al. 2019

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Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km × 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R2), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, −0.378 µg/m3, and 12.746 µg/m3, respectively, and the model cross-validation results were 0.703, 1.451 µg/m3, and 15.731 µg/m3, respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM2.5 concentrations in the Guanzhong Basin, and PM2.5 concentrations vary with the seasons. This method can provide reliable PM2.5 predictions to reduce the bias of exposure assessment in air pollution and health research.

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“…Many studies have shown that, at small spatial scales, elevation affects particulate concentrations by influencing air flow, pressure, temperature, and precipitation [38,39]. There are several possible explanations for this finding: (1) Particulates are more buoyant in the air at low elevation (over time, PM 2.5 at high elevations will sink down to low elevations, adding to the concentration of PM 2.5 at the lower elevation); (2) Each 100 m increase in elevation coincides with a 0.6 • C drop in temperature, which affects particulate concentrations [40]. At higher elevations, the ground absorbs more radiation from the sun, which warms air near the ground and causes it to rise, creating convection currents in the upper atmosphere.…”

Section: Individual Functionsmentioning

confidence: 99%

“…These particulates, known as PM 2.5 (aerodynamic diameters < 2.5 µm [1]), originate from vehicle exhaust, coal-fired power plants, building construction (dust), and domestic heating (coal). Fine particulates are not only detrimental to human health (respiratory problems, lung disease, etc.…”

Section: Introductionmentioning

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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VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model

Cited by 114 publications

References 32 publications

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China

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

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