Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI)

Zhang, Tianhao; Gong, Wei; Wang, Wei; Ji, Yaping; Zhu, Zhenfeng; Huang, Yusi

doi:10.3390/ijerph13121215

Cited by 40 publications

(23 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Standard deviations of normal distribution for the random-effects terms in the two models. Previous studies on the PM2.5-AOD statistical model mainly used the site-based CV [16] and sample-based 10-fold CV methods [2]. For the site-based CV, one of the PM2.5 monitoring sites was used for validation, and the rest of the sites were used for model fitting; this process was conducted for each round of validation.…”

Section: Verification Of Estimated Pm25mentioning

confidence: 99%

“…Ambient fine particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) are associated with adverse human health effects; thus, they are regarded worldwide as a public health threat [1,2]. Given the finer size of PM2.5 compared with PM10 (aerodynamic diameters less than 10 µm), PM2.5 can be breathed deeply into the lungs and seriously damage human organs [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

Wang

Mao

et al. 2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Abstract:Monitoring fine particulate matter with diameters of less than 2.5 µm (PM2.5) is a critical endeavor in the Beijing-Tianjin-Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R 2 (0.86) and low RMSE (24.5 µg/m 3 ). The average estimated PM2.5 in the BTH region during the study time range was about 55 µg/m 3 . The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 µg/m 3 (1600 local time) to 65.5 ± 54.6 µg/m 3 (1100 local time) at different hours.

show abstract

Section: Verification Of Estimated Pm25mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

Wang

Mao

et al. 2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The spatial heterogeneity of air quality, including autocorrelation and non-stationarity, has been widely studied [48,49], but few studies have included spatial heterogeneity in factor analysis by using the GWR model to address this problem. This study looked at the heterogeneity of the AOD in the BTH, YRD, and PRD, and conducted the factor analysis by GWR rather than by OLS.…”

Section: Discussionmentioning

confidence: 99%

Spatial Factor Analysis for Aerosol Optical Depth in Metropolises in China with Regard to Spatial Heterogeneity

Shi

Zhang

2018

Atmosphere

View full text Add to dashboard Cite

A substantial number of studies have analyzed how driving factors impact aerosols, but they have been little concerned with the spatial heterogeneity of aerosols and the factors that impact aerosols. The spatial distributions of the aerosol optical depth (AOD) retrieved by Moderate Resolution Imaging Spectrometer (MODIS) data at 550-nm and 3-km resolution for three highly developed metropolises, the Beijing-Tianjin-Hebei (BTH) region, the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), in China during 2015 were analyzed. Different degrees of spatial heterogeneity of the AOD were found, which were indexed by Moran's I index giving values of 0.940, 0.715, and 0.680 in BTH, YRD, and PRD, respectively. For the spatial heterogeneity, geographically weighted regression (GWR) was employed to carry out a spatial factor analysis, where terrain, climate condition, urban development, and vegetation coverage were taken as the potential driving factors. The results of the GWR imply varying relationships between the AOD and the factors. The results were generally consistent with existing studies, but the results suggest the following: (1) Elevation increase would more likely lead to a strong negative impact on aerosols (with most of the coefficients ranging from −1.5~0 in the BTH, −1.5~0 in the YRD, and −1~0 in the PRD) in the places with greater elevations where the R-squared values are always larger than 0.5. However, the variation of elevations cannot explain the variation of aerosols in the places with relatively low elevations (with R-squared values approximately 0.1, ranging from 0 to 0.3, and approximately 0.1 in the BTH, YRD, and PRD), such as urban areas in the BTH and YRD. (2) The density of the built-up areas made a strong and positive impact on aerosols in the urban areas of the BTH (R-squared larger than 0.5), while the R-squared dropped to 0.1 in the places far away from the urban areas. (3) The vegetation coverage led to a stronger relief on the AOD in parts of the YRD and PRD (with coefficients less than −0.6 and ranging from −0.4~−0.6, respectively) where there is greater vegetation coverage, and led to a weaker relief on the AOD in the urban area of the PRD with a coefficient of approximately −0.2~−0.4.Atmosphere 2018, 9, 156 2 of 14 images [2,5] and their variations can now be fully studied [6]. Moreover, a factor analysis for aerosols has been widely carried out. The Ordinary Least Square (OLS) based regression is usually used to identify the relationships between aerosols and their impact factors [7][8][9].However, OLS assumes that the relationship between the dependent and independent variables is consistent and cannot handle the spatial data with heterogeneity, which makes it difficult to meet the assumptions and requirements of OLS [10]. Heterogeneity here includes two aspects: spatial autocorrelation and spatially non-stationarity. Spatial autocorrelation means that the value of a variable at a location depends on the value of the same variable at nearby locations [11,12]. Spatial non-stati...

show abstract

“…At present, there have been few studies on the EVI-based downscaling model. Zhang et al [30] used the EVI to estimate PM 2.5 in the GWR model and concluded that the use of EVI could more effectively estimate PM 2.5 than the previous GWR models, mainly because EVI is not easy to saturate even when the chlorophyll content is high. Although studies have shown that the NDVI is prone to supersaturation when the precipitation exceeds a certain threshold, this study used the annual NDVI average, which may have neutralized this saturation effect.…”

Section: Comparison Between the Ndvi-based And Evi-based Gwr Modelsmentioning

confidence: 99%

“…At present, there have been relatively few studies on the use of the EVI in the downscaling model. Zhang et al [30] used the EVI instead of the NDVI to estimate particulate matter less than 2.5 µm (PM 2.5 ) in the GWR model. The results showed that the EVI-based model was more effective than the previous GWR model.…”

Section: Introductionmentioning

confidence: 99%

Spatial Downscaling of Tropical Rainfall Measuring Mission (TRMM) Annual and Monthly Precipitation Data over the Middle and Lower Reaches of the Yangtze River Basin, China

Chen

Zhang

She

et al. 2019

Water

View full text Add to dashboard Cite

Precipitation plays an important role in the global water cycle, in addition to material and energy exchange processes. Therefore, obtaining precipitation data with a high spatial resolution is of great significance. We used a geographically weighted regression (GWR)-based downscaling model to downscale Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data over the middle and lower reaches of the Yangtze River Basin (MLRYRB) from a resolution of 0.25° to 1 km on an annual scale, and the downscaled results were calibrated using the geographical differential analysis (GDA) method. At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable in the downscaling models. However, studies have shown that the relationship between the NDVI and precipitation gradually weakens when precipitation exceeds a certain threshold. In contrast, the enhanced vegetation index (EVI) overcomes the saturation shortcomings of the NDVI. Therefore, this study investigated the performances of EVI-derived and NDVI-derived downscaling models in downscaling TRMM precipitation data. The results showed that the NDVI performed better than the EVI in the annual downscaling model, possibly because this study used the annual average NDVI, which may have neutralized detrimental saturation effects. Moreover, the accuracy of the downscaling model could be effectively improved after correcting for residuals and calibrating the model with the GDA method. Subsequently, the downscaled rainfall was closer to the actual weather station rainfall observations. Furthermore, the downscaled results were decomposed into fractions to obtain monthly precipitation data, showing that the proposed method by utilizing the GDA method could improve not only the spatial resolution of remote sensing precipitation data, but also the accuracy of data.

show abstract

Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI)

Cited by 40 publications

References 48 publications

Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

Spatial Factor Analysis for Aerosol Optical Depth in Metropolises in China with Regard to Spatial Heterogeneity

Spatial Downscaling of Tropical Rainfall Measuring Mission (TRMM) Annual and Monthly Precipitation Data over the Middle and Lower Reaches of the Yangtze River Basin, China

Contact Info

Product

Resources

About