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Bin Zou
Chinese National Engineering Research Center for Control and Treatment of Heavy Metal Pollution, Central South University, Changsha 410083, China

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Journal article
Published: 06 July 2021 in Remote Sensing
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Visible and near-infrared (VNIR) spectroscopy technology for soil heavy metal (HM) concentration prediction has been widely studied. However, its spectral response characteristics are still uncertain. In this study, a near standard soil Cd samples (NSSCd) spectra enhanced modeling strategy was developed in order to to reveal the soil cadmium (Cd) spectral response characteristics and predict its concentration. NSSCd were produced by adding the quantitative Cd solution into background soil. Then, prior spectral bands (i.e., the bands with higher variable importance in projection (VIP) score in NSSCd spectra) were used for predicting Cd concentration in soil samples collected from the Hengyang mining area and Baoding agriculture area. The partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS) were used for validation. Compared to using entire VNIR spectral ranges, the new modeling strategy performed very well, with the coefficient of determination (R2) and the ratio of prediction to deviation (RPD) showing an improvement from 0.63 and 1.72 to 0.71 and 1.95 in Hengyang and from 0.54 and 1.57 to 0.76 and 2.19 in Baoding. These results suggest that NSS prior spectral bands are critical for soil HM prediction. Our results represent an exciting finding for the future design of remote sensing sensors for soil HM detection.

ACS Style

Yulong Tu; Bin Zou; Huihui Feng; Mo Zhou; Zhihui Yang; Ying Xiong. A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction. Remote Sensing 2021, 13, 2657 .

AMA Style

Yulong Tu, Bin Zou, Huihui Feng, Mo Zhou, Zhihui Yang, Ying Xiong. A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction. Remote Sensing. 2021; 13 (14):2657.

Chicago/Turabian Style

Yulong Tu; Bin Zou; Huihui Feng; Mo Zhou; Zhihui Yang; Ying Xiong. 2021. "A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction." Remote Sensing 13, no. 14: 2657.

Journal article
Published: 10 April 2021 in Environmental Pollution
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Numerous statistical models have established the relationship between ambient fine particulate matter (PM2.5, with an aerodynamic diameter of less than 2.5 μm) and satellite aerosol optical depth (AOD) along with other meteorological/land-related covariates. However, all the models assumed that all covariates affect the PM2.5 concentration at the same scale, and none could provide a posterior uncertainty analysis at each regression point. Therefore, a multiscale geographically and temporally weighted regression (MGTWR) model was proposed by specifying a unique bandwidth for each covariate. However, the lack of a method for predicting values at unsampled points in the MGTWR model greatly restricts its corresponding application. Thus, this study developed a method for inferring unsampled points and used the posterior uncertainty assessment value to improve the model accuracy. With the aid of the high-resolution satellite multi-angle implementation of atmospheric correction (MAIAC) AOD product, daily PM2.5 concentrations with a 1 km x 1 km resolution were generated over the Beijing-Tianjin-Hebei region between 2013 and 2019. The coefficient of determination (R2) and root mean square error (RMSE) of the fitted MGTWR results vary from 0.90 to 0.94 and from 10.66 to 25.11 μg/m3, respectively. The sample-based and site-based cross-validation R2 and RMSE vary from 0.81 to 0.89 and from 14.40 to 34.43 μg/m3 respectively, demonstrating the effectiveness of the proposed inference method at unsampled points. With the uncertainty constraint, the sample-based and site-based validated MGTWR R2 results for all years are further improved by approximately 0.02-0.04, demonstrating the effectiveness of the posterior uncertainty assessment constraint method. These results suggest that the inference method proposed in this study is promising to overcome the defects of the MGTWR model in inferring the prediction values at unsampled points and could consequently enhance the wide applications of MGTWR modeling.

ACS Style

Ning Liu; Bin Zou; Shenxin Li; Honghui Zhang; Kai Qin. Prediction of PM2.5 concentrations at unsampled points using multiscale geographically and temporally weighted regression. Environmental Pollution 2021, 284, 117116 .

AMA Style

Ning Liu, Bin Zou, Shenxin Li, Honghui Zhang, Kai Qin. Prediction of PM2.5 concentrations at unsampled points using multiscale geographically and temporally weighted regression. Environmental Pollution. 2021; 284 ():117116.

Chicago/Turabian Style

Ning Liu; Bin Zou; Shenxin Li; Honghui Zhang; Kai Qin. 2021. "Prediction of PM2.5 concentrations at unsampled points using multiscale geographically and temporally weighted regression." Environmental Pollution 284, no. : 117116.

Journal article
Published: 08 April 2021 in Remote Sensing
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The surface shortwave radiation budget (Rsn) is one of the main drivers of Earth’s ecosystems and varies with atmospheric and surface conditions. Land use and cover change (LUCC) alters radiation through biogeophysical effects. However, due to the complex interactions between atmospheric and surface factors, it is very challenging to quantify the sole impacts of LUCC. Based on satellite data from the Global Land Surface Satellite (GLASS) Product and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, this study introduces an observation-based approach for detecting LUCC influences on the Rsn by examining a humid basin over the Dongting Lake Basin, China from 2001 to 2015. Our results showed that the Rsn of the study area presented a decreasing trend due to the combined effects of LUCC and climate change. Generally, LUCC contributed −0.45 W/m2 to Rsn at the basin scale, which accounted for 2.53% of the total Rsn change. Furthermore, the LUCC contributions reached −0.69 W/m2, 0.21 W/m2, and −0.41 W/m2 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, which accounted for 5.38%, −4.68%, and 2.40% of the total Rsn change, respectively. Physically, LUCC affected surface radiation by altering the surface properties. Specifically, LUCC induced albedo changes of +0.0039 at the basin scale and +0.0061, −0.0020, and +0.0036 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, respectively. Our findings revealed the impact and process of LUCC on the surface radiation budget, which could support the understanding of the physical mechanisms of LUCC’s impact on ecosystems.

ACS Style

Shuchao Ye; Huihui Feng; Bin Zou; Ying Ding; Sijia Zhu; Feng Li; Guotao Dong. Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin. Remote Sensing 2021, 13, 1447 .

AMA Style

Shuchao Ye, Huihui Feng, Bin Zou, Ying Ding, Sijia Zhu, Feng Li, Guotao Dong. Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin. Remote Sensing. 2021; 13 (8):1447.

Chicago/Turabian Style

Shuchao Ye; Huihui Feng; Bin Zou; Ying Ding; Sijia Zhu; Feng Li; Guotao Dong. 2021. "Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin." Remote Sensing 13, no. 8: 1447.

Journal article
Published: 28 February 2021 in Water
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For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP 2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP 2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP 2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP 2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP 2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP 2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

ACS Style

Yun Xue; Lei Zhu; Bin Zou; Yi-Min Wen; Yue-Hong Long; Song-Lin Zhou. Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model. Water 2021, 13, 664 .

AMA Style

Yun Xue, Lei Zhu, Bin Zou, Yi-Min Wen, Yue-Hong Long, Song-Lin Zhou. Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model. Water. 2021; 13 (5):664.

Chicago/Turabian Style

Yun Xue; Lei Zhu; Bin Zou; Yi-Min Wen; Yue-Hong Long; Song-Lin Zhou. 2021. "Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model." Water 13, no. 5: 664.

Journal article
Published: 30 May 2020 in Global and Planetary Change
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The surface radiation budget is of crucial importance to ecosystem evolution but varies with complex atmospheric and surface conditions. Vegetation change alters the surface thermal properties and the subsequent radiation budget; however, the vegetation contribution is difficult to isolate from mixed influences. Based on satellite observations, we apply a novel trajectory-based approach to detect the impact of vegetation change on the global surface radiation variation in recent decades (2001–2016). Satellite data on radiation and vegetation available from the Clouds and the Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are adopted for this investigation. Methodologically, the surface net radiation (Rn) in the nonchanged vegetation trajectory represents the synthetic result of atmospheric influences and serves as a reference for isolating Rn variations due to vegetation change. The results demonstrate that the multiyear mean of global Rn is 71.57 W·m−2 with an increasing trend of 0.053 W·m−2·yr−1. Vegetation change contributes an additional 0.047 W·m−2·yr−1 of radiation in greening regions, accounting for 53.36% of the total increase in Rn. Spatially, the contribution of vegetation presents significant variability, with positive contributions located mainly in western Europe and southern Africa and negative contributions located mainly in parts of Asia and eastern Australia. Physically, the influence of vegetation change on the surface radiation budget originates from its alteration of albedo and emissivity, particularly the former. Specifically, a 1% increase in the normalized difference vegetation index (NDVI) is expected to reduce albedo by −0.003 and increase surface net shortwave radiation by 0.86 W·m−2. It can be concluded that the change in albedo by vegetation change has a nonnegligible influence on the surface radiation budget in different regions. These results help capture the physical mechanism responsible for the evolution of Earth's radiation and support environmental management.

ACS Style

Huihui Feng; Shuchao Ye; Bin Zou. Contribution of vegetation change to the surface radiation budget: A satellite perspective. Global and Planetary Change 2020, 192, 103225 .

AMA Style

Huihui Feng, Shuchao Ye, Bin Zou. Contribution of vegetation change to the surface radiation budget: A satellite perspective. Global and Planetary Change. 2020; 192 ():103225.

Chicago/Turabian Style

Huihui Feng; Shuchao Ye; Bin Zou. 2020. "Contribution of vegetation change to the surface radiation budget: A satellite perspective." Global and Planetary Change 192, no. : 103225.

Journal article
Published: 29 May 2020 in Sustainability
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Chromium is not only an essential trace element for the growth and development of living organisms; it is also a heavy metal pollutant. Excessive chromium in farmland soil will not only cause harm to crops, but could also constitute a serious threat to human health through the cumulative effect of the food chain. The determination of heavy metals in tailings of farmland soil is an essential means of soil environmental protection and sustainable development. Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. However, due to the spectral variation caused by different environmental conditions, the direct application of the indoor spectrum to conduct field surveys is not effective. Soil components are complex, and the effect of linear regression of heavy metal content is not satisfactory. This study builds indoor and outdoor spectral conversion models to eliminate soil spectral differences caused by environmental conditions. Considering the complex effects of soil composition, we introduce a support vector machine model to retrieve chromium content that has advantages in solving problems such as small samples, non-linearity, and a large number of dimensions. Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models of three types of spectra (field spectrum, lab spectrum, and direct standardization (DS) spectrum), two regression methods (stepwise regression and support vector machine regression), and two factors (strong correlation factor and principal component factor). The results show that: (1) As far as the spectral types are concerned, the inversion accuracy of each combination of the field spectrum is generally lower than the accuracy of the corresponding combination of other spectral types, indicating that field environmental interference affects the modeling accuracy. Each combination of DS spectra has higher inversion accuracy than the corresponding combination of field spectra, indicating that DS spectra have a certain effect in eliminating soil spectral differences caused by environmental conditions. (2) The inversion accuracy of each spectrum type of SVR_SC (Support Vector Regression_Strong Correlation) is the highest for the combination of regression method and inversion factor. This indicates the feasibility and superiority of inversion of heavy metals in soil by a support vector machine. However, the inversion accuracy of each spectrum type of SVR_PC (Support Vector Regression_Principal Component) is generally lower than that of other combinations, which indicates that, to obtain superior inversion performance of SVR, the selection of characteristic factors is very important. (3) Through principal component regression analysis, it is found that the pre-processed spectrum is more stable for the inversion of Cr concentration. The regression coefficients of the three types of differential spectra are roughly the same. The five statistically significant characteristic bands are mostly around 384–458 nm, 959–993 nm, 1373–1448 nm, 1970–2014 nm, and 2325–2400 nm. The research results provide a useful reference for the large-scale normalization monitoring of chromium-contaminated soil. They also provide theoretical and technical support for soil environmental protection and sustainable development.

ACS Style

Yun Xue; Bin Zou; Yimin Wen; Yulong Tu; Liwei Xiong. Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra. Sustainability 2020, 12, 4441 .

AMA Style

Yun Xue, Bin Zou, Yimin Wen, Yulong Tu, Liwei Xiong. Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra. Sustainability. 2020; 12 (11):4441.

Chicago/Turabian Style

Yun Xue; Bin Zou; Yimin Wen; Yulong Tu; Liwei Xiong. 2020. "Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra." Sustainability 12, no. 11: 4441.

Journal article
Published: 22 May 2020 in Journal of Cleaner Production
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As a primary air pollutant, fine particulate matter (PM2.5) is increasingly attracting attention. Crowdsourcing observations based methods are thought to be the best solutions for identifying the spatio-temporal distribution of PM2.5 in intra-urban areas. However, inconsistent timing in the collection of crowdsourced data has typically been ignored in previous studies. To address this issue, a temporally calibrated method (TCM) was introduced in this study. By interpolating TCM-estimated observations using the inverse distance weighted (IDW) method, variations of PM2.5 concentrations across the urban areas of Changsha City were captured. The results demonstrate that TCM can efficiently resolve the inconsistent timing defects of raw crowdsourcing observations (R2 was 0.73 and the RMSE was 7.65 μg/m3). Furthermore, PM2.5 distributions developed using TCM-based interpolations are of a finer spatial scale than those developed from raw observations at crowdsourcing locations. With a lack of funds to build sufficient stationary monitoring sites, developing crowdsourcing observation-based technology is the most promising solution for revealing intra-urban PM2.5 variations at a higher spatio-temporal- resolution.

ACS Style

Zhong Zheng; Bin Zou; Yongqian Wang; Shenxin Li; Yanghua Gao; Shiqi Yang. A temporally-calibrated method for crowdsourcing based mapping of intra-urban PM2.5 concentrations. Journal of Cleaner Production 2020, 269, 122347 .

AMA Style

Zhong Zheng, Bin Zou, Yongqian Wang, Shenxin Li, Yanghua Gao, Shiqi Yang. A temporally-calibrated method for crowdsourcing based mapping of intra-urban PM2.5 concentrations. Journal of Cleaner Production. 2020; 269 ():122347.

Chicago/Turabian Style

Zhong Zheng; Bin Zou; Yongqian Wang; Shenxin Li; Yanghua Gao; Shiqi Yang. 2020. "A temporally-calibrated method for crowdsourcing based mapping of intra-urban PM2.5 concentrations." Journal of Cleaner Production 269, no. : 122347.

Articles
Published: 21 April 2020 in International Journal of Remote Sensing
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Both climatic and surface factors affect aerosol change. It is crucial to separate these influences for environment management and air pollution controlling. Though numerous models have been applied to assess contributions of climatic and surface influences, the results were great controversy because of the models’ avoidable uncertainties from initial conditions, model error, and prediction scenarios. Here, we separate the contribution of climatic and surface influences on global aerosol change through satellite observations and statistical methods. Satellite and ground observation data sets of global aerosol, climatic (precipitation, windspeed, temperature, and relative humidity) and surface (land cover and terrain) factors are collected for the investigation. Methodologically, a multilinear regression (MLR) model is first built to simulate the theoretical influence of climatic factors under conditions with a fixed surface influence. Then, the actual surface influence is estimated by measuring the residual trend between observed and MLR-simulated results. Our results show that global aerosol was reduced in the past decade (2001–2016), represented by a temporal trend of −0.00105 a−1 in the aerosol optical depth (AOD). Both climatic and surface factors tend to enhance the reduction of global aerosol. Specifically, the climatic and surface influences are −0.00041 a−1 and 0.00064 a−1, which contribute 39.05% and 60.95% of global aerosol reduction, respectively. Spatially, surface influence is more heterogeneous compared to climatic influence due to the spatial variability of surface properties. Meanwhile, the interaction of climatic and surface factors plays a significant effect on the magnitude and sign of the individual influence, with extreme climate strongly disturbing surface influence. It could be concluded that surface influence acts as the primary contribution to global aerosol change, which fluctuates with the interaction of climate change.

ACS Style

Huihui Feng; Bin Zou. Satellite-based separation of climatic and surface influences on global aerosol change. International Journal of Remote Sensing 2020, 41, 5443 -5456.

AMA Style

Huihui Feng, Bin Zou. Satellite-based separation of climatic and surface influences on global aerosol change. International Journal of Remote Sensing. 2020; 41 (14):5443-5456.

Chicago/Turabian Style

Huihui Feng; Bin Zou. 2020. "Satellite-based separation of climatic and surface influences on global aerosol change." International Journal of Remote Sensing 41, no. 14: 5443-5456.

Journal article
Published: 21 April 2020 in Journal of Cleaner Production
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Fine particulate matter (PM2.5) poses a severe threat to public health in China. Understanding the PM2.5 distributions and relevant key drivers in major Chinese urban agglomerations by considering both spatial and temporal heterogeneities can provide an insight into formulating effective mitigation policies. In this study, we selected 12 urban agglomerations characterized by high levels of urbanization and severe PM2.5 pollution levels as our study area for the period of 1998–2016. Firstly, the indexes of GDP per capita and industrial structure were utilized to divide urban agglomerations into different urbanization stages. Then, advanced economic panel estimations (including the panel-corrected standard error (PCSE) method and feasible generalized least squares (FGLS) method) were employed to quantify the relationships between five anthropogenic factors and PM2.5 concentrations at the entire urbanization scale and the urbanization stage scale. As the results show, the urbanization processes of the 12 urban agglomerations studied can be classified into four different urbanization stages: the Primary Industrial Stage, Middle Industrial Stage, Late Industrial Stage, and Developed Stage. Averaged PM2.5 concentrations first underwent a rapid increase from the Primary Industrial Stage to Middle Industrial Stage and then decreased in the Late Industrial Stage and Developed Stage for most urban agglomerations. At the entire urbanization scale, GDP per capita (GDPP), population density (PD), and the share of secondary industry (IS) are major determinants of PM2.5 concentrations while the foreign direct investment (FDI) and energy intensity (EI) have a comparatively weaker influence. At the urbanization stage scale, GDPP generally made an initially positive and then negative contribution to PM2.5 concentrations throughout the urbanization process while IS and PD became the two main contributing factors as urban agglomerations entered the Late Industrial Stage and Developed Stage. PD significantly elevated PM2.5 levels in populous urban agglomerations throughout the urbanization stages. FDI and EI exhibit quite spatial heterogeneity across the Chinese urban agglomerations. Through a detailed analysis of the spatio-temporal quantification of PM2.5 distributions and of impacts from their determinants, this work contributes to a more thorough understanding of PM2.5 formation in Chinese key urban agglomerations and provides implications for regional joint prevention and control policies.

ACS Style

Xiangping Liu; Bin Zou; Huihui Feng; Ning Liu; Honghui Zhang. Anthropogenic factors of PM2.5 distributions in China’s major urban agglomerations: A spatial-temporal analysis. Journal of Cleaner Production 2020, 264, 121709 .

AMA Style

Xiangping Liu, Bin Zou, Huihui Feng, Ning Liu, Honghui Zhang. Anthropogenic factors of PM2.5 distributions in China’s major urban agglomerations: A spatial-temporal analysis. Journal of Cleaner Production. 2020; 264 ():121709.

Chicago/Turabian Style

Xiangping Liu; Bin Zou; Huihui Feng; Ning Liu; Honghui Zhang. 2020. "Anthropogenic factors of PM2.5 distributions in China’s major urban agglomerations: A spatial-temporal analysis." Journal of Cleaner Production 264, no. : 121709.

Journal article
Published: 30 March 2020 in Remote Sensing
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Current reported spatiotemporal solutions for fusing multisensor aerosol optical depth (AOD) products used to recover gaps either suffer from unacceptable accuracy levels, i.e., fixed rank smooth (FRS), or high time costs, i.e., Bayesian maximum entropy (BME). This problem is generally more serious when dealing with multiple AOD products in a long time series or over large geographic areas. This study proposes a new, effective, and efficient enhanced FRS method (FRS-EE) to fuse satellite AOD products with uncertainty constraints. AOD products used in the fusion experiment include Moderate Resolution Imaging SpectroRadiometer (MODIS) DB/DT_DB_Combined AOD and Multiangle Imaging SpectroRadiometer (MISR) AOD across mainland China from 2016 to 2017. Results show that the average completeness of original, initial FRS fused, and FRS-EE fused AODs with uncertainty constraints are 22.80%, 95.18%, and 65.84%, respectively. Although the correlation coefficient (R = 0.77), root mean square error (RMSE = 0.30), and mean bias (Bias = 0.023) of the initial FRS fused AODs are relatively lower than those of original AODs compared to Aerosol Robotic Network (AERONET) AOD records, the accuracy of FRS-EE fused AODs, which are R = 0.88, RMSE = 0.20, and Bias = 0.022, is obviously improved. More importantly, in regions with fully missing original AODs, the accuracy of FRS-EE fused AODs is close to that of original AODs in regions with valid retrievals. Meanwhile, the time cost of FRS-EE for AOD fusion was only 2.91 h; obviously lower than the 30.46 months taken for BME.

ACS Style

Bin Zou; Ning Liu; Wei Wang; Huihui Feng; Xiangping Liu; Yan Lin. An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products. Remote Sensing 2020, 12, 1102 .

AMA Style

Bin Zou, Ning Liu, Wei Wang, Huihui Feng, Xiangping Liu, Yan Lin. An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products. Remote Sensing. 2020; 12 (7):1102.

Chicago/Turabian Style

Bin Zou; Ning Liu; Wei Wang; Huihui Feng; Xiangping Liu; Yan Lin. 2020. "An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products." Remote Sensing 12, no. 7: 1102.

Journal article
Published: 04 February 2020 in Environment International
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China has made great efforts towards air pollutant concentration control during the past five years, which has led to positive outcomes. However, air pollutant concentration focused efforts were considered separately from human exposure risk. And this might result in a misunderstanding that reducing exposure risk can only rely on the national level measures of air pollutant control. This study integrates the first Chinese survey of human activity patterns and the spatially continuous high-resolution PM2.5 concentration maps to reveal the spatial and temporal variations of China’s air pollution exposure risk from 2013 to 2017. More importantly, the effects on risk reduction from multi-scale and multi-object perspectives (reductions of ambient PM2.5 concentrations by national or provincial measures and changes of individual behavior patterns by personal efforts) are deeply investigated. Results show that the reductions of PM2.5 concentration and associated reductions of exposure risk from 2013 to 2017 were 40% and 35.7%, respectively. They also showed that both the reduction of PM2.5 concentrations and change of personal behavior patterns were effective for risk reduction when China’s total PM2.5 exposure risk was higher than 1.58. However, only individual behavior changes contributed to risk reduction for scenarios with state-level risk value below 1.58. For regional strategies, threshold values for PM2.5 exposure risk control differentiating national measures or personal efforts were spatially and temporally dependent. The role of personal behavior changes on PM2.5 exposure risk reduction was growing in these five years with concentration rapidly decreasing regions. The findings suggest that people-centered air pollution exposure risk prevention not only depends on government management for air pollution control, but also on individual changes of activity patterns. Efforts from the state and individuals are both essential for reducing air pollution exposure risk in China, especially growing individual efforts are needed in regions with the decreasing air pollutant concentrations in the coming future. Moreover, this study mainly discussed the PM2.5 exposure risk from the macroscopic perspective, the research at the microcosmic perspective is also needed in the further study.

ACS Style

Bin Zou; Shenxin Li; Yan Lin; Beibei Wang; Suzhen Cao; Xiuge Zhao; Fen Peng; Ning Qin; Qian Guo; Huihui Feng; Campen J. Matthew; Shunqing Xu; Xiaoli Duan. Efforts in reducing air pollution exposure risk in China: State versus individuals. Environment International 2020, 137, 105504 .

AMA Style

Bin Zou, Shenxin Li, Yan Lin, Beibei Wang, Suzhen Cao, Xiuge Zhao, Fen Peng, Ning Qin, Qian Guo, Huihui Feng, Campen J. Matthew, Shunqing Xu, Xiaoli Duan. Efforts in reducing air pollution exposure risk in China: State versus individuals. Environment International. 2020; 137 ():105504.

Chicago/Turabian Style

Bin Zou; Shenxin Li; Yan Lin; Beibei Wang; Suzhen Cao; Xiuge Zhao; Fen Peng; Ning Qin; Qian Guo; Huihui Feng; Campen J. Matthew; Shunqing Xu; Xiaoli Duan. 2020. "Efforts in reducing air pollution exposure risk in China: State versus individuals." Environment International 137, no. : 105504.

Journal article
Published: 06 January 2020 in Computers, Environment and Urban Systems
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Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.

ACS Style

Bin Zou; Shenxin Li; Zhong Zheng; Benjamin F. Zhan; Zhonglin Yang; Neng Wan. Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk. Computers, Environment and Urban Systems 2020, 80, 101456 .

AMA Style

Bin Zou, Shenxin Li, Zhong Zheng, Benjamin F. Zhan, Zhonglin Yang, Neng Wan. Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk. Computers, Environment and Urban Systems. 2020; 80 ():101456.

Chicago/Turabian Style

Bin Zou; Shenxin Li; Zhong Zheng; Benjamin F. Zhan; Zhonglin Yang; Neng Wan. 2020. "Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk." Computers, Environment and Urban Systems 80, no. : 101456.

Journal article
Published: 14 December 2019 in Science of The Total Environment
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Satellite-based mapping has been proven to be an effective method to reveal the spatiotemporal variations of PM2.5 distributions. However, most satellite AOD (aerosol optical depth) statistical models suffer from unstable accuracy over long time spans. This study thus aims to propose an accurate and stable method for PM2.5 concentration estimations in time series. Specifically, a three-step residual variance constraint method (RVCM) is developed to simulate PM2.5 concentrations from January 2013 to December 2017 with the aid of AODs and other auxiliary data. Results show that the five-year fitting R2 and cross-validation R2 of RVCMs improved from 0.77 to 0.88 and 0.71 to 0.84, respectively, compared to those models without residual variance constraint (WO-RVCM). Additionally, RVCM demonstrated more stable performance on time series simulation of PM2.5 concentrations than WO-RVCM, with the yearly fitting R2 of 0.89, 0.88, 0.85, 0.87 and 0.88, and corresponding cross validation R2 of 0.85, 0.84, 0.80, 0.82 and 0.83, respectively. Furthermore, accuracy verification of removed outliers in residual variance constraint modeling confirmed the credibility of RVCM in outliers' simulation compared to WO-RVCM models. Finally, RVCM-aided estimations of time series PM2.5 concentrations and associated premature deaths in the study area (east and southeast mainland China) revealed their total decrease rates were 35.21% and 21.57%, and excellent air quality days increased from 7% to 35%. These findings suggest that residual variance constraint is effective and could be a reliable solution to providing time series AOD-PM2.5 modeling with stable accuracy over long time spans.

ACS Style

Shenxin Li; Bin Zou; Xin Fang; Yan Lin. Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013–2017. Science of The Total Environment 2019, 710, 135755 .

AMA Style

Shenxin Li, Bin Zou, Xin Fang, Yan Lin. Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013–2017. Science of The Total Environment. 2019; 710 ():135755.

Chicago/Turabian Style

Shenxin Li; Bin Zou; Xin Fang; Yan Lin. 2019. "Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013–2017." Science of The Total Environment 710, no. : 135755.

Journal article
Published: 06 July 2019 in Remote Sensing of Environment
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The aerosol forcing is an essential factor of global climate change, which can be estimated by various models. However, the model results ranging from −2.8 to 2.2 K remain controversial because of unavoidable uncertainty, leaving a great gap for global change prediction. This study aims to evaluate the forcing on the land surface temperature (Ts) using satellite-based observations. Based on the Blackbody radiation and surface radiation budget, first, a semi-physical framework is developed to estimate the Ts. Subsequently, the aerosol forcing is calculated by measuring the Ts difference between the changing aerosol scenario and baseline scenario with a fixed aerosol amount. Results show that the framework simulates Ts with acceptable accuracy (R = 0.62 and RMSE = 1.48 K), which supports the estimation of aerosol forcing. Generally, the change in the aerosol contributes 0.005 ± 0.237 K to the global Ts, which presents significant temporal and spatial variabilities. Temporally, the forcing shows a decreasing trend of −0.0006 K/year (R2 = 0.29, p = 0.031). Spatially, the forcing tends to warm the surface in regions with arid climate, low-cloud fraction, and moderate vapor or in sparsely vegetated and cool regions because of the potential interactions with climatic and environmental factors. The result of this study helps to reduce the uncertainty and validate the model results, which further supports the research on global climatic and environmental change.

ACS Style

Huihui Feng; Bin Zou. Satellite-based estimation of the aerosol forcing contribution to the global land surface temperature in the recent decade. Remote Sensing of Environment 2019, 232, 111299 .

AMA Style

Huihui Feng, Bin Zou. Satellite-based estimation of the aerosol forcing contribution to the global land surface temperature in the recent decade. Remote Sensing of Environment. 2019; 232 ():111299.

Chicago/Turabian Style

Huihui Feng; Bin Zou. 2019. "Satellite-based estimation of the aerosol forcing contribution to the global land surface temperature in the recent decade." Remote Sensing of Environment 232, no. : 111299.

Research article
Published: 26 June 2019 in Atmospheric Chemistry and Physics
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A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.

ACS Style

Ning Liu; Bin Zou; Huihui Feng; Wei Wang; Yuqi Tang; Yu Liang. Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China. Atmospheric Chemistry and Physics 2019, 19, 8243 -8268.

AMA Style

Ning Liu, Bin Zou, Huihui Feng, Wei Wang, Yuqi Tang, Yu Liang. Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China. Atmospheric Chemistry and Physics. 2019; 19 (12):8243-8268.

Chicago/Turabian Style

Ning Liu; Bin Zou; Huihui Feng; Wei Wang; Yuqi Tang; Yu Liang. 2019. "Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China." Atmospheric Chemistry and Physics 19, no. 12: 8243-8268.

Articles
Published: 12 June 2019 in Remote Sensing Letters
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Aerosol forcing has direct, indirect and semidirect effects on radiation and can thus potentially impact recent global temperature changes. However, it is extremely challenging to evaluate the magnitude of aerosol forcing because various factors interact in a complex way to produce temperature change. With the aid of satellite and ground observations, this study, we examine whether aerosols can produce temperature change. Temperature change produced by aerosols occurs when there is a strong aerosol-temperature correlation and a significant aerosol trend. Our results show that aerosols can cause a regional temperature change of 0.027 ± 0.038 Kyear−1. However, the dominant impact occurs over a very small region (1.65%) of the land, resulting in a minor forcing effect (0.0004 ± 0.0006 Kyear−1) on global temperature change. It may be concluded that aerosol forcing only has a significant effect regionally and that the global impact of aerosol forcing appears to have been overestimated in some previous studies.

ACS Style

Huihui Feng; Bin Zou; Ying Ding. Satellite detection of aerosol-produced temperature change. Remote Sensing Letters 2019, 10, 854 -863.

AMA Style

Huihui Feng, Bin Zou, Ying Ding. Satellite detection of aerosol-produced temperature change. Remote Sensing Letters. 2019; 10 (9):854-863.

Chicago/Turabian Style

Huihui Feng; Bin Zou; Ying Ding. 2019. "Satellite detection of aerosol-produced temperature change." Remote Sensing Letters 10, no. 9: 854-863.

Journal article
Published: 28 May 2019 in Atmospheric Measurement Techniques
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Fine particulate matter (PM2.5) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PM2.5 concentrations in unobserved locations due to the sparse number of fixed monitoring stations. Due to an increase in low-cost sensing for air pollution monitoring, crowdsourced monitoring of exposure control has been gradually introduced into cities. However, the optimal mapping method for conventional sparse fixed measurements may not be suitable for this new high-density monitoring approach. This study presents a crowdsourced sampling campaign and strategies of method selection for 100 m scale PM2.5 mapping in an intra-urban area of China. During this process, PM2.5 concentrations were measured by laser air quality monitors through a group of volunteers during two 5 h periods. Three extensively employed modelling methods (ordinary kriging, OK; land use regression, LUR; and regression kriging, RK) were adopted to evaluate the performance. An interesting finding is that PM2.5 concentrations in micro-environments varied in the intra-urban area. These local PM2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PM2.5 concentration mapping should be adjusted according to the changing sampling and pollution circumstances. During this project, OK interpolation performs best in conditions with non-peak traffic situations during a lightly polluted period (holdout validation R2: 0.47–0.82), while the RK modelling can perform better during the heavily polluted period (0.32–0.68) and in conditions with peak traffic and relatively few sampling sites (fewer than ∼100) during the lightly polluted period (0.40–0.69). Additionally, the LUR model demonstrates limited ability in estimating PM2.5 concentrations on very fine spatial and temporal scales in this study (0.04–0.55), which challenges the traditional point about the good performance of the LUR model for air pollution mapping. This method selection strategy provides empirical evidence for the best method selection for PM2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.

ACS Style

Shan Xu; Bin Zou; Yan Lin; Xiuge Zhao; Shenxin Li; Chenxia Hu. Strategies of method selection for fine-scale PM2.5 mapping in an intra-urban area using crowdsourced monitoring. Atmospheric Measurement Techniques 2019, 12, 2933 -2948.

AMA Style

Shan Xu, Bin Zou, Yan Lin, Xiuge Zhao, Shenxin Li, Chenxia Hu. Strategies of method selection for fine-scale PM2.5 mapping in an intra-urban area using crowdsourced monitoring. Atmospheric Measurement Techniques. 2019; 12 (5):2933-2948.

Chicago/Turabian Style

Shan Xu; Bin Zou; Yan Lin; Xiuge Zhao; Shenxin Li; Chenxia Hu. 2019. "Strategies of method selection for fine-scale PM2.5 mapping in an intra-urban area using crowdsourced monitoring." Atmospheric Measurement Techniques 12, no. 5: 2933-2948.

Journal article
Published: 23 May 2019 in Journal of Geophysical Research: Atmospheres
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The Advanced Himawari Imager (AHI), the primary sensor aboard the Japanese Himawari‐8 geostationary satellite, measures regional aerosol observations with high temporal‐spatial resolution. To improve product quality and scientific applications, we performed a comprehensive evaluation of AHI aerosol products (version 1.0). We compared nearly 2 years (July 15, 2015 to June 31, 2017) of AHI aerosol optical depth at 500 nm (AOD500) with AODs from the Aerosol Robotic Network (AERONET) and the Maritime Aerosol Network (MAN). Results showed that, over land, AHI retrievals exhibit a large overall bias of –0.062, with an R of 0.78; over ocean, average bias measured 0.036 (0.051 for MAN), with an R of 0.89 (0.95 for MAN). AHI retrievals collocated with AERONET AODs (τA) showed the following expected AHI AOD errors: (–0.66 × τA + 0.02, –0.34 × τA + 0.16) over land and (–0.24 × τA + 0.03, 0.10 × τA + 0.11) over ocean. AHI retrievals with degraded performance correlated to different regions, angles, aerosol types, and surface types, suggesting that the AHI aerosol algorithm can be improved by changing aerosol optical models, using better cloud filters, and combining multiple methods to estimate ground reflectance. Collocated comparisons of AHI‐MODIS‐AERONET demonstrate that, over land, AHI daytime AODs clearly improve when retrievals with a large viewing zenith angle and small scattering angle are excluded.

ACS Style

Wei Wang; Feiyue Mao; Zengxin Pan; Wei Gong; Mayumi Yoshida; Bin Zou; Huiyun Ma. Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network. Journal of Geophysical Research: Atmospheres 2019, 124, 5516 -5538.

AMA Style

Wei Wang, Feiyue Mao, Zengxin Pan, Wei Gong, Mayumi Yoshida, Bin Zou, Huiyun Ma. Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network. Journal of Geophysical Research: Atmospheres. 2019; 124 (10):5516-5538.

Chicago/Turabian Style

Wei Wang; Feiyue Mao; Zengxin Pan; Wei Gong; Mayumi Yoshida; Bin Zou; Huiyun Ma. 2019. "Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network." Journal of Geophysical Research: Atmospheres 124, no. 10: 5516-5538.

Journal article
Published: 22 April 2019 in Atmosphere
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A novel geostationary satellite, the H8/AHI (Himawari-8/Advanced Himawari Imager), greatly improved the scan times per day covering East Asia, and the operational products have been stably provided for a period of time. Currently, atmospheric aerosol pollution is a major concern in China. H8/AHI aerosol products with a high temporal resolution are helpful for real-time monitoring of subtle aerosol variation. However, the H8/AHI aerosol optical thickness (AOT) product has been updated three times since its launch, and the evaluation of this dataset is currently rare. In order to validate its accuracy, this study compared the H8/AHI Level-3 (L3) hourly AOT products of all versions with measurements obtained from eleven sunphotometer sites located in eastern China from 2015 to 2018. Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOT products from the same period were also used for inter-comparison. Although the H8/AHI AOT retrievals in version 010 show a moderate agreement with ground-based observations (correlation coefficient (R): 0.66–0.85), and the time series analysis shows that it can effectively monitor hourly variation, it suffers from an obvious underestimation of 0.3 compared to ground-based and MODIS observations. After the retrieval algorithm updated the predefined aerosol model, the overall underestimation of AHI AOTs was solved (version 010 slope: 0.43–0.62, version 030 slope: 0.75–1.02), and the AOTs in version 030 show a high agreement with observations from ten sites (R: 0.73–0.91). In addition, the surface reflectance dataset derived from the minimum reflectivity model in version 010 is inaccurate in parts of eastern China, for both “bright” and “dark” land surfaces, which leads to the overestimation of the AOT values under low aerosol loads at the Beijing and Xianghe sites. After the update of the surface dataset in version 030, this phenomenon was alleviated, resulting in no significant difference in scatterplots under different surface conditions. The AOTs of H8/AHI version 030 show a significant improvement compared to the previous two versions, but the spatial distribution of AHI is still different from MODIS AOT products due to the differences in sensors and algorithms. Therefore, although the evaluation in this study demonstrates the effectiveness of H8/AHI AOT products for aerosol monitoring at fine temporal resolutions, the performance of H8/AHI AOT products needs further study by considering more conditions.

ACS Style

Ding Li; Kai Qin; Lixin Wu; Jian Xu; Husi Letu; Bin Zou; Qin He; Yifei Li. Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China. Atmosphere 2019, 10, 215 .

AMA Style

Ding Li, Kai Qin, Lixin Wu, Jian Xu, Husi Letu, Bin Zou, Qin He, Yifei Li. Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China. Atmosphere. 2019; 10 (4):215.

Chicago/Turabian Style

Ding Li; Kai Qin; Lixin Wu; Jian Xu; Husi Letu; Bin Zou; Qin He; Yifei Li. 2019. "Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China." Atmosphere 10, no. 4: 215.

Journal article
Published: 10 April 2019 in Science of The Total Environment
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Widespread and severe PM1.0 (particulate matter ≤1.0 μm) pollution in China has a significant negative influence on human health. However, knowledge of the large-scale distribution and variability of PM1.0 has been hindered by sparsely distributed PM1.0 concentration data. In this study, a two-stage model called linear mixed effect–bagged tree model was developed to estimate hourly PM1.0 pollution levels from July 2015 to June 2017 in China at 0.1° resolution by using Himawari-8 aerosol products and coincident geographic data, meteorology, and site-based PM1.0 concentrations from ground monitoring network. The cross-validation for the developed model displayed R2 and mean absolute error value of 0.80 and 9.3 μg/m3, respectively. Validation demonstrated that the model accurately estimated PM1.0 concentrations with high R2 of 0.63–0.85 and low bias of 8.7–10.1 μg/m3 at the hourly levels. Analysis of the estimated PM1.0 concentrations on daily scale showed peaks with PM1.0 of 36.9 ± 8.4 μg/m3 at rush hours during daytime. Seasonal variations displayed that summer was the cleanest season with an average PM1.0 of 20.9 ± 6.8 μg/m3 and winter was the most polluted season with an average PM1.0 of 45.6 ± 16.8 μg/m3. These results indicated that the proposed satellite-based model can estimate reliable spatial distribution of PM1.0 concentrations at the national scale.

ACS Style

Wei Wang; Feiyue Mao; Bin Zou; Jianping Guo; Lixin Wu; Zengxin Pan; Lin Zang. Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China. Science of The Total Environment 2019, 675, 658 -666.

AMA Style

Wei Wang, Feiyue Mao, Bin Zou, Jianping Guo, Lixin Wu, Zengxin Pan, Lin Zang. Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China. Science of The Total Environment. 2019; 675 ():658-666.

Chicago/Turabian Style

Wei Wang; Feiyue Mao; Bin Zou; Jianping Guo; Lixin Wu; Zengxin Pan; Lin Zang. 2019. "Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China." Science of The Total Environment 675, no. : 658-666.