Satellite-based ground PM2.5 estimation using timely structure adaptive modeling

Fang, Xin; Zou, Bin; Liu, Xiaoping; Sternberg, Troy; Zhai, Liang

doi:10.1016/j.rse.2016.08.027

Cited by 178 publications

(108 citation statements)

References 39 publications

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“…According to previous remote sensing-based research findings [9,26,27], the data that could be used as potential covariates to predict PM2.5 concentrations in this study are AOD, PM2.5 emissionsrelated data, such as pollution sources, road networks, land use/cover, population, as well as dispersion condition data, including meteorological parameters and terrain data. .…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

“…To reduce the negative influence of cloud on the quality of MODIS images and AODs in the study domain, we fitted a linear regression to define the relationship between the Terra MODIS AOD (MOD04) and the Aqua MODIS AOD (MYD04). We used this regression to predict the missing AOD value (i.e., predict MOD04 with the available MYD04, and vice versa), then MOD04 and MYD04 were averaged as the daily AOD if both of them were available [27,32]. Finally, all the remediated AODs were transformed to seasonal and annual average to improve spatial coverage.…”

Section: Satellite-retrieved Aod Datamentioning

confidence: 99%

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Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling

et al. 2016

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Satellite-based PM 2.5 concentration estimation is growing as a popular solution to map the PM 2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM 2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM 2.5 concentration mapping. In this process, the linear and non-linear relationships between PM 2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R 2 and lower RMSEs. This is confirmed by the cross-validation-based adjusted R 2 with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the "straight line" relations, the "smoothed curves" from GAM-based apportionment analysis reveals that factors contributing to PM 2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM 2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM 2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM 2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM 2.5 .

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Section: Data Collection and Preprocessingmentioning

confidence: 99%

Section: Satellite-retrieved Aod Datamentioning

confidence: 99%

Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling

et al. 2016

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“…Particulate matter (PM) has been a growing public concern due to its severe health impacts and significant visibility reduction (Dockery and Pope, 1994;Dominici et al, 2014;Fang et al, 2016). PM is usually divided and nominated by its aerodynamic diameter, and the most widely monitored particles are PM 10 and PM 2.5 , which refer to particles with aerodynamic diameter less than or equal to 10 or 2.5 micrometers, respectively (Nel, 2005).…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variability of the PM2.5/PM10 Ratio in Wuhan, Central China

Jiao

Zhang

et al. 2017

Aerosol Air Qual. Res.

168

105

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Fine particles (PM 2.5 ) and coarse particles (PM 2.5-10 ) are generally produced by different sources, so the PM 2.5 /PM 10 ratio reveals characteristics of particle pollution. The ratio can be used to characterize the underlying atmospheric processes and evaluate historical PM 2.5 pollution in absence of direct measurements. However, application of the ratio needs its varying pattern because PM concentrations change significantly at time and space. Hourly PM 2.5 and PM 10 observations at nine monitoring sites in urban area (Urban-sites) and one remote Background-site in Wuhan in 2013-2015 were collected to investigate both long-term, short-term temporal variation and spatial distribution, spatial disparity of the ratio at a city scale. The results show that annual average PM 2.5 /PM 10 ratio is 0.62 at Urban-sites and 0.68 at Background-site with apparent seasonal, monthly and daily variations. The ratio reaches the maximum in winter because of stable atmospheric conditions. There are apparent night-day differences of daily variation of the ratio, which increases at night in all seasons in consequence of temperature inversion and declines in the daytime with a moderate rise in the afternoon. We find obvious spatial gradients of the ratio that gradually increases from urban core to urban fringe and to suburban. This study provides further insights to the spatio-temporal variability of PM 2.5 /PM 10 ratio. The evidence indicates that the variability of PM 2.5 /PM 10 should be noticed in its applications.

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“…Therefore, the linear models had been widely used in PM 2.5 concentration mapping. To evaluate the regional correlation, we estimate the spatial PM 2.5 concentration through the timely structure adaptive modeling (TSAM) method [50]. It retrieves the spatial PM 2.5 concentration with the aid of satellite-based AOD, and geographic factors of emission (i.e., industrial smoke and dust, vehicle exhaust, surface dust, and land use type) and dispersion (i.e., wind speed, relative humidity, elevation).…”

Section: Estimation Of Spatial Pm 25 Concentrationmentioning

confidence: 99%

“…Therefore, it can not only represent the day-to-day variation of the contributing strength of model predictors establishing AOD-PM 2.5 relationships, but can also reflect the spatial heterogeneity of contributing predictors. The method had been already applied in China, with an accuracy of R 2 = 0.80 and a root mean square error (RMSE) is 22.75 µg/m 3 [50]. The formulation of this method is as follow: …”

Section: Estimation Of Spatial Pm 25 Concentrationmentioning

confidence: 99%

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

2017

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Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM 2.5 ) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon's diversity index (SHDI). Then, the correlation between PM 2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM 2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM 2.5 at a relatively large scale. Furthermore, regional PM 2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM 2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM 2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM 2.5 pollution for each urban agglomeration.

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Satellite-based ground PM2.5 estimation using timely structure adaptive modeling

Cited by 178 publications

References 39 publications

Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling

Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling

Spatial and Temporal Variability of the PM2.5/PM10 Ratio in Wuhan, Central China

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

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