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Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) poses adverse impacts on public health and the environment. It is still a great challenge to estimate high-resolution PM2.5 concentrations at moderate scales. The current study calibrated PM2.5 concentrations at a 1 km resolution scale using ground-level monitoring data, Aerosol Optical Depth (AOD), meteorological data, and auxiliary data via Random Forest (RF) model across China in 2017. The three ten-folded cross-validations (CV) methods including sample-based, time-based, and spatial-based validation combined with Coefficient Square (R2), Root-Mean-Square Error (RMSE), and Mean Predictive Error (MPE) have been used for validation at different temporal scales in terms of daily, monthly, heating seasonal, and non-heating seasonal. Finally, the distribution map of PM2.5 concentrations was illustrated based on the RF model. Some findings were achieved. The RF model performed well, with a relatively high sample-based cross-validation R2 of 0.74, a low RMSE of 16.29 μg × m−3, and a small MPE of −0.282 μg × m−3. Meanwhile, the performance of the RF model in inferring the PM2.5 concentrations was well at urban scales except for Chengyu (CY). North China, the CY urban agglomeration, and the northwest of China exhibited relatively high PM2.5 pollution features, especially in the heating season. The robustness of the RF model in the present study outperformed most statistical regression models for calibrating PM2.5 concentrations. The outcomes can supply an up-to-date scientific dataset for epidemiological and air pollutants exposure risk studies across China.
Bin Guo; Dingming Zhang; Lin Pei; Yi Su; Xiaoxia Wang; Yi Bian; Donghai Zhang; Wanqiang Yao; Zixiang Zhou; Liyu Guo. Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017. Science of The Total Environment 2021, 778, 146288 .
AMA StyleBin Guo, Dingming Zhang, Lin Pei, Yi Su, Xiaoxia Wang, Yi Bian, Donghai Zhang, Wanqiang Yao, Zixiang Zhou, Liyu Guo. Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017. Science of The Total Environment. 2021; 778 ():146288.
Chicago/Turabian StyleBin Guo; Dingming Zhang; Lin Pei; Yi Su; Xiaoxia Wang; Yi Bian; Donghai Zhang; Wanqiang Yao; Zixiang Zhou; Liyu Guo. 2021. "Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017." Science of The Total Environment 778, no. : 146288.
The COVID-19 is still a huge challenge that seriously threatens public health globally. Previous studies focused on the influence of air pollutants and probable meteorological parameters on confirmed COVID-19 infections via epidemiological methods, whereas the findings of relations between possible variables and COVID-19 incidences using geographical perspective were scarce. In the present study, data concerning confirmed COVID-19 cases and possible affecting factors were collected for 325 cities across China up to May 27, 2020. The geographically weighted regression (GWR) model was introduced to explore the impact of probable determinants on confirmed COVID-19 incidences. Some results were obtained. AQI, PM2.5, and PM10 demonstrated significantly positive impacts on COVID-19 during the most study period with the majority lag group (P< 0.05). Nevertheless, the relation of temperature with COVID-19 was significantly negative (P< 0.05). Especially, CO exhibited a negative effect on COVID-19 in most study period with the majority lag group. The impacts of each possible determinant on COVID-19 represented significantly spatial heterogeneity. The obvious influence of the majority of possible factors on COVID-19 was mainly detected during the after lockdown period with the lag 21 group. Although the COVID-19 spreading has been effectively controlled by tough measures taken by the Chinese government, the study findings remind us to address the air pollution issues persistently for protecting human health.
Lin Pei; Xiaoxia Wang; Bin Guo; Hongjun Guo; Yan Yu. Do air pollutants as well as meteorological factors impact Corona Virus Disease 2019 (COVID-19)? Evidence from China based on the geographical perspective. Environmental Science and Pollution Research 2021, 28, 35584 -35596.
AMA StyleLin Pei, Xiaoxia Wang, Bin Guo, Hongjun Guo, Yan Yu. Do air pollutants as well as meteorological factors impact Corona Virus Disease 2019 (COVID-19)? Evidence from China based on the geographical perspective. Environmental Science and Pollution Research. 2021; 28 (27):35584-35596.
Chicago/Turabian StyleLin Pei; Xiaoxia Wang; Bin Guo; Hongjun Guo; Yan Yu. 2021. "Do air pollutants as well as meteorological factors impact Corona Virus Disease 2019 (COVID-19)? Evidence from China based on the geographical perspective." Environmental Science and Pollution Research 28, no. 27: 35584-35596.
Mapping socio-economic indicators with a raster format is still a great challenge. The nighttime light (NTL) datasets have been widely utilized to estimate the socio-economic parameters. However, the precision of the published datasets was too coarse to meet related issues such as flood losses assessment, urban planning, and epidemiological studies. The present study calibrated gross domestic product (GDP), population (POP), electric consumption (EC), and urban build-up area (B-A) at 100 m resolution for 45 cities of China in 2018 using Luojia1-01 NTL datasets via random forest (RF) as well as geographically weighted regression (GWR) model. The linear regression (LR), back propagation neural network (BPNN), and support vector machine (SVM) methods were selected for comparison with GWR and RF models. Besides, the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) was chosen for comparison with Luojia1-01. The ten-folded cross-validation (CV) has been used for evaluating accuracy at county and city scales. Finally, the distribution maps of socio-economic parameters were illustrated and some findings were obtained. First, the validation results revealed that the calibration at the city-scale outperformed the county or district scale. Second, the precision of the Luojia1-01 NTL dataset surpassed the NPP-VIIRS NTL dataset on the same administrative scale except for some specific situations. Third, the precision of the simulation for the gross domestic product (GDP) is the highest than the others, followed by electric consumption (EC), build-up area (B-A), and population (POP). Fourth, the optimum model varied according to the socio-economic parameters. Fifth, the distribution of socio-economic parameters exhibited obvious spatial heterogeneity. This paper can supply scientific support for calibrating socio-economic parameters in other regions.
Bin Guo; Yi Bian; Dingming Zhang; Yi Su; Xiaoxia Wang; Bo Zhang; Yan Wang; Qiuji Chen; Yarui Wu; Pingping Luo. Estimating Socio-Economic Parameters via Machine Learning Methods Using Luojia1-01 Nighttime Light Remotely Sensed Images at Multiple Scales of China in 2018. IEEE Access 2021, 9, 34352 -34365.
AMA StyleBin Guo, Yi Bian, Dingming Zhang, Yi Su, Xiaoxia Wang, Bo Zhang, Yan Wang, Qiuji Chen, Yarui Wu, Pingping Luo. Estimating Socio-Economic Parameters via Machine Learning Methods Using Luojia1-01 Nighttime Light Remotely Sensed Images at Multiple Scales of China in 2018. IEEE Access. 2021; 9 (99):34352-34365.
Chicago/Turabian StyleBin Guo; Yi Bian; Dingming Zhang; Yi Su; Xiaoxia Wang; Bo Zhang; Yan Wang; Qiuji Chen; Yarui Wu; Pingping Luo. 2021. "Estimating Socio-Economic Parameters via Machine Learning Methods Using Luojia1-01 Nighttime Light Remotely Sensed Images at Multiple Scales of China in 2018." IEEE Access 9, no. 99: 34352-34365.
The COVID-19 is still a huge challenge that seriously threatens public health globally. Previous studies focused on the influence of air pollutants and probable meteorological parameters on confirmed COVID-19 infections via epidemiological methods. Whereas, the findings of relations between possible variables and COVID-19 incidences using geographical perspective were scarce. In the present study, data concerning confirmed COVID-19 cases and possible affecting factors were collected for 325 cities across China up to May 27, 2020. The Geographical Weighted Regression (GWR) model was introduced to explore the impact of probable determinants on confirmed COVID-19 incidences. Some results were obtained. AQI, PM2.5, and PM10 demonstrated significantly positive impacts on COVID-19 during the most study period with the majority lag group (P<0.05). Nevertheless, the relation of temperature with COVID-19 was significantly negative (P<0.05). Especially, CO exhibited a negative effect on COVID-19 in most study period with the majority lag group. The impacts of each possible determinant on COVID-19 represented significantly spatial heterogeneity. The obvious influence of the majority of possible factors on COVID-19 was mainly detected during the after lockdown period with the lag 21 group. Although the COVID-19 spreading has been effectively controlled by tough measures taken by the Chinese government, the study findings remind us to address the air pollution issues persistently for protecting human health.
Lin Pei; Xiaoxia Wang; Bin Guo; Hongjun Guo; Yan Yu. Do Air Pollutants as Well as Meteorological Factors Impact Corona Virus Disease 2019 (COVID-19)? Evidence From China Based on the Geographical Perspective. 2021, 1 .
AMA StyleLin Pei, Xiaoxia Wang, Bin Guo, Hongjun Guo, Yan Yu. Do Air Pollutants as Well as Meteorological Factors Impact Corona Virus Disease 2019 (COVID-19)? Evidence From China Based on the Geographical Perspective. . 2021; ():1.
Chicago/Turabian StyleLin Pei; Xiaoxia Wang; Bin Guo; Hongjun Guo; Yan Yu. 2021. "Do Air Pollutants as Well as Meteorological Factors Impact Corona Virus Disease 2019 (COVID-19)? Evidence From China Based on the Geographical Perspective." , no. : 1.
Numerous methods have been implemented to evaluate the relationship between environmental factors and respiratory mortality. However, the previous epidemiological studies seldom considered the spatial and temporal variation of the independent variables. The present study aims to detect the relations between respiratory mortality and related affecting factors across Xi'an during 2014–2016 based on a novel geographically and temporally weighted regression model (GTWR). Meanwhile, the ordinary least square (OLS) and the geographically weighted regression (GWR) model were developed for cross-comparison. Additionally, the spatial autocorrelation and Hot Spot analysis methods were conducted to detect the spatiotemporal dynamic of respiratory mortality. Some important outcomes were obtained. Socioeconomic and environmental determinants represented significant effects on respiratory diseases. The respiratory mortality exhibited an obvious spatial correlation feature, and the respiratory diseases tend to occur in winter and rural areas of the study area. The GTWR model outperformed OLS and GWR for determining the relations between respiratory mortality and socioeconomic as well as environmental determinants. The influence degree of anthropic factors on COPD mortality was higher than natural factors, and the effects of independent variables on COPD varied timely and locally. The results can supply a scientific basis for respiratory disease controlling and health facilities planning.
Bin Guo; Yan Wang; Lin Pei; Yan Yu; Feng Liu; Donghai Zhang; Xiaoxia Wang; Yi Su; Dingming Zhang; Bo Zhang; Hongjun Guo. Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014–2016. Science of The Total Environment 2020, 756, 143869 .
AMA StyleBin Guo, Yan Wang, Lin Pei, Yan Yu, Feng Liu, Donghai Zhang, Xiaoxia Wang, Yi Su, Dingming Zhang, Bo Zhang, Hongjun Guo. Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014–2016. Science of The Total Environment. 2020; 756 ():143869.
Chicago/Turabian StyleBin Guo; Yan Wang; Lin Pei; Yan Yu; Feng Liu; Donghai Zhang; Xiaoxia Wang; Yi Su; Dingming Zhang; Bo Zhang; Hongjun Guo. 2020. "Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014–2016." Science of The Total Environment 756, no. : 143869.
The index of geo-accumulation (Igeo) and the pollution load index (PLI) were used to assess the pollution level and the U.S. Environmental Protection Agency (EPA) health risk model was implemented to determine the health risk for children. Besides, a geographic information system (GIS) was utilized to map the distribution characteristics of risk elements contents. The results showed that: (1) the mean content of Mn was the highest in each park. Besides, the mean content of all risk elements except As exceeded the background value of Shaanxi in each park. (2) The Igeo of Co was obviously higher than other elements in each park and the highest (4.07) content occurred in the Xing Qing Park (XQP). The pollution condition of the Urban Sports Park (USP) was the most serious among the four parks. (3) The children were more susceptible to risk elements pollution through oral intake. (4) The hazard indices (HI) of non-carcinogenic risk (NCR) were within the safe range (HI = 1) and the carcinogenic risks (CR) of Cr, Ni, As and Co for children were within the receivable range in each park.
Bin Guo; Yi Su; Lin Pei; Xiaofeng Wang; Xindong Wei; Bo Zhang; Dingming Zhang; Xiaoxia Wang. Contamination, Distribution and Health Risk Assessment of Risk Elements in Topsoil for Amusement Parks in Xi’an, China. Polish Journal of Environmental Studies 2020, 30, 601 -617.
AMA StyleBin Guo, Yi Su, Lin Pei, Xiaofeng Wang, Xindong Wei, Bo Zhang, Dingming Zhang, Xiaoxia Wang. Contamination, Distribution and Health Risk Assessment of Risk Elements in Topsoil for Amusement Parks in Xi’an, China. Polish Journal of Environmental Studies. 2020; 30 (1):601-617.
Chicago/Turabian StyleBin Guo; Yi Su; Lin Pei; Xiaofeng Wang; Xindong Wei; Bo Zhang; Dingming Zhang; Xiaoxia Wang. 2020. "Contamination, Distribution and Health Risk Assessment of Risk Elements in Topsoil for Amusement Parks in Xi’an, China." Polish Journal of Environmental Studies 30, no. 1: 601-617.
The available statistics on electric consumption (EC) can hardly show the spatial heterogeneity within political boundaries, and the previous studies paid few attention on the relations between EC and urban planning. The present study developed models between nighttime light values and EC using nighttime light images (NLI) from the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) and statistical EC data from local government of Xi’an. A geographic information system (GIS) was utilized to map the distribution of EC on a grid scale, and the spatiotemporal dynamic of EC was obtained. In addition, a slope model and the EC grading threshold were conducted to determine the urban functional districts (UFD). The results were shown as follows: (1) the relation between TNL and EC is significant positive, and the performance of linear regression model (R²=0.76, Root Mean Square Error (RMSE) =9.44×10⁴, Mean Absolute Error (MAE) =6.73×10³, Mean Percent Error (MPE) =-3.71%) is better than logarithmic model (R² =0.68, RMSE =1.09×10⁵, MAE =7.77×10⁴, MPE =-5.44%). (2) In Xi’an, the EC continuously increased during 2013-2017, and the change of EC demonstrated obvious features that increased slowly in the city center, and spread outward to the surrounding region especially to the south of the study area. (3) The UFD have been divided into five sections including tourist district, commercial district, industrial zone, residential community, and ecological zone, respectively. This study will thus provide a reference and scientific basis for urban planning and rational allocation of electric power.
Bin Guo; Dingming Zhang; Donghai Zhang; Yi Su; Xiaoxia Wang; Yi Bian. Detecting Spatiotemporal Dynamic of Regional Electric Consumption Using NPP-VIIRS Nighttime Stable Light Data–A Case Study of Xi’an, China. IEEE Access 2020, 8, 171694 -171702.
AMA StyleBin Guo, Dingming Zhang, Donghai Zhang, Yi Su, Xiaoxia Wang, Yi Bian. Detecting Spatiotemporal Dynamic of Regional Electric Consumption Using NPP-VIIRS Nighttime Stable Light Data–A Case Study of Xi’an, China. IEEE Access. 2020; 8 (99):171694-171702.
Chicago/Turabian StyleBin Guo; Dingming Zhang; Donghai Zhang; Yi Su; Xiaoxia Wang; Yi Bian. 2020. "Detecting Spatiotemporal Dynamic of Regional Electric Consumption Using NPP-VIIRS Nighttime Stable Light Data–A Case Study of Xi’an, China." IEEE Access 8, no. 99: 171694-171702.
Fine particulate matter (PM2.5) is closely related to the air quality and public health. Numerous models have been introduced to simulate the PM2.5 concentrations at large scale based on remote sensing and auxiliary data. However, the data precision provided by these models are inadequate for epidemiology and pollutant exposure studies at medium or small scale. The present study aims to calibrate PM2.5 concentrations at 1 km resolution scale across China during 2015–2018 based on monitoring station data and auxiliary data using a novel geographically and temporally weighted regression model (GTWR). The cross-validation (CV) method and the geographically weighted regression (GWR) model are conducted for validation and cross-comparison. Additionally, the spatial autocorrelation and slope analysis methods are implemented to detect the spatiotemporal dynamic of PM2.5 concentrations. A sample-based CV of the GTWR model demonstrates an acceptable precision with a coefficient of determination equal to 0.67, a root-mean-square error of 10.32 μg/m3, and a mean prediction error of-6.56 μg/m3. This result proves that the GTWR model can simulate PM2.5 concentrations at a higher spatial resolution and accuracy across China than some previous models. Besides, the heterogeneity and spatiotemporal dynamic of PM2.5 concentrations are obvious, that is, the High-High (H-H) agglomeration areas with strong haze pollution were mainly concentrated in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Chengdu-Chongqing (CY), and Guanzhong Plain (GZP). In addition, the PM2.5 concentrations are undergoing a decreasing trend in most of the study area, and the decrease in the BTH is dramatic. The results of the present study are helpful for calibrating and detecting the spatiotemporal dynamic of PM2.5 concentrations and useful for the government to make decisions about decreasing haze pollution in urban agglomeration scale.
Bin Guo; Xiaoxia Wang; Lin Pei; Yi Su; Dingming Zhang; Yan Wang. Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Science of The Total Environment 2020, 751, 141765 .
AMA StyleBin Guo, Xiaoxia Wang, Lin Pei, Yi Su, Dingming Zhang, Yan Wang. Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Science of The Total Environment. 2020; 751 ():141765.
Chicago/Turabian StyleBin Guo; Xiaoxia Wang; Lin Pei; Yi Su; Dingming Zhang; Yan Wang. 2020. "Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018." Science of The Total Environment 751, no. : 141765.
Decreasing of PM2.5 concentration in the heating season was not significant in Xi’an. This article determined a land use regression (LUR) model and researched the distribution characteristics of PM2.5 in heating and non-heating seasons in Xi'an. The results showed that: (1) The R2 of LUR was...
Bin Guo; Xiaoxia Wang; Donghai Zhang; Lin Pei; Dingming Zhang; Xiaofeng Wang. A Land Use Regression Application into Simulating Spatial Distribution Characteristics of Particulate Matter (PM2.5) Concentration in City of Xi’an, China. Polish Journal of Environmental Studies 2020, 29, 4065 -4076.
AMA StyleBin Guo, Xiaoxia Wang, Donghai Zhang, Lin Pei, Dingming Zhang, Xiaofeng Wang. A Land Use Regression Application into Simulating Spatial Distribution Characteristics of Particulate Matter (PM2.5) Concentration in City of Xi’an, China. Polish Journal of Environmental Studies. 2020; 29 (6):4065-4076.
Chicago/Turabian StyleBin Guo; Xiaoxia Wang; Donghai Zhang; Lin Pei; Dingming Zhang; Xiaofeng Wang. 2020. "A Land Use Regression Application into Simulating Spatial Distribution Characteristics of Particulate Matter (PM2.5) Concentration in City of Xi’an, China." Polish Journal of Environmental Studies 29, no. 6: 4065-4076.
Park playgrounds recently are suffering serious heavy metals contamination in China. It is urgent to assess the ecological risk and identify the sources for heavy metals. A total of 111 topsoil samples were collected from four park playgrounds in Xi’an, and the X-ray fluorescence (XRF) instrument was used to measure the concentrations of heavy metals including chromium(Cr), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), manganese (Mn), and cobalt (Co), respectively. Ecological risk (\( {E}_R^i \)) and potential ecological risk index (RI) were introduced to determine the pollution level and ecological risk, and the absolute principal component score-multiple linear regression (APCS-MLR) model was implemented to identify the sources for heavy metals. The main results were as follows. (1) Except As, the mean concentrations of measured heavy metals of four park playgrounds surpassed the soil background values of Shaanxi Province. (2) In each park playground, the \( {E}_R^i \) was below a “low” risk level (\( {E}_R^i \)=10) for Cr, Ni, Zn, As, and Mn; Cu was between a “moderate” and “considerable” risk level; Pb was between a “low” and “moderate” risk level; and \( {E}_R^i \) was between a “considerable” and “high” risk level for Co. Besides, the RI index was on a “high” risk level (120 < RI < 240) with an obvious spatial distinction. (3) The anthropogenic factors were the main sources for heavy metals, and mixed sources and natural sources were considered as the minor sources for metals. (4) The sources contributions for Co had obvious spatial heterogeneity in each park situated in four different urban planning districts.
Bin Guo; Yi Su; Lin Pei; Xiaofeng Wang; Bo Zhang; Dingming Zhang; Xiaoxia Wang. Ecological risk evaluation and source apportionment of heavy metals in park playgrounds: a case study in Xi’an, Shaanxi Province, a northwest city of China. Environmental Science and Pollution Research 2020, 27, 24400 -24412.
AMA StyleBin Guo, Yi Su, Lin Pei, Xiaofeng Wang, Bo Zhang, Dingming Zhang, Xiaoxia Wang. Ecological risk evaluation and source apportionment of heavy metals in park playgrounds: a case study in Xi’an, Shaanxi Province, a northwest city of China. Environmental Science and Pollution Research. 2020; 27 (19):24400-24412.
Chicago/Turabian StyleBin Guo; Yi Su; Lin Pei; Xiaofeng Wang; Bo Zhang; Dingming Zhang; Xiaoxia Wang. 2020. "Ecological risk evaluation and source apportionment of heavy metals in park playgrounds: a case study in Xi’an, Shaanxi Province, a northwest city of China." Environmental Science and Pollution Research 27, no. 19: 24400-24412.