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Existing source apportionment methods for soil heavy metals fail to identify the actual landscapes related to pollutant sources and quantify their contributions to the accumulation of soil heavy metals. In this work, we propose a new source identification and apportionment approach for soil heavy metal accumulation by integrating pollution landscapes, pathways, and receptors. Datasets for soil lead (Pb) concentrations in Daye city, China, which was sampled in 2018, were used. First, based on the spatial distribution of Pb, the source landscapes were identified using GeoDetector and spatial analysis methods. Second, a source landscape apportionment model (SLAM) was developed considering both atmospheric deposition and surface runoff as diffusion pathways. Third, considering soil properties and topography as receptor attributes, ordinary least squares (OLS) and geographically weighted regression (GWR) models were employed to further adjust the soil Pb accumulation at receptor locations. The results showed that SLAM followed by the GWR model (SLAM-GWR) had the highest fitting accuracy. Then, the spatial distributions and ranges of contributions of each identified source landscape to Pb accumulation through different pathways were obtained. Finally, the advantages and disadvantages of the proposed approach were discussed.
Xue Yang; Yong Yang; Yongyong Wan; Ruojing Wu; Dekun Feng; Ke Li. Source identification and comprehensive apportionment of the accumulation of soil heavy metals by integrating pollution landscapes, pathways, and receptors. Science of The Total Environment 2021, 786, 147436 .
AMA StyleXue Yang, Yong Yang, Yongyong Wan, Ruojing Wu, Dekun Feng, Ke Li. Source identification and comprehensive apportionment of the accumulation of soil heavy metals by integrating pollution landscapes, pathways, and receptors. Science of The Total Environment. 2021; 786 ():147436.
Chicago/Turabian StyleXue Yang; Yong Yang; Yongyong Wan; Ruojing Wu; Dekun Feng; Ke Li. 2021. "Source identification and comprehensive apportionment of the accumulation of soil heavy metals by integrating pollution landscapes, pathways, and receptors." Science of The Total Environment 786, no. : 147436.
Due to human activities and industrial production, heavy metals accumulate continuously in soils, resulting in environmental ecological risks. Thus, it is critical to reveal the spatial patterns of the increments in soil heavy metals and their influencing factors to prevent the continuous deterioration of soil due to heavy metal pollution. In this study, based on soil samples collected in 2016 and 2019 at the same sites in the southern part of Daye city, the spatial distributions of increments in soil heavy metals were obtained using spatial interpolation and overlap methods. Then, the geographically weighted regression (GWR) model was used to analyze the influence of various environmental factors in three categories (location characteristics, topographical factors, and soil properties) on the increments in soil heavy metals. The results showed the following: (1) The soils in the study region were severely polluted with Cd, Cu, Pb, and Zn. Throughout almost the whole study region, the concentrations of these four heavy metals in soil exceeded local background values. (2) The concentrations of Cd, Cu, Pb, and Zn increased from 2016 to 2019 in 77.38%, 59.71%, 68.42%, and 49.21% of the study region, respectively. According to the spatial distribution of comprehensive change index values, soil heavy metal pollution continued to deteriorate in 74.4% of the study region from 2016 to 2019. (3) The GWR model revealed spatially varying relationships between the increases in soil heavy meals and environmental factors, and the results indicated that location characteristics and topographical factors had the largest and smallest influences, respectively, on the spatiotemporal increments in soil heavy metals. The influences of soil properties on the increments in soil heavy metals were similar to the influences on their concentrations. The GWR model had a higher R2 and lower AICc than the ordinary least square regression model, indicating that GWR had a stronger ability to explain the relationships between the increments in soil heavy metals and environmental factors.
Hao Li; Peihong Fu; Yong Yang; Xue Yang; Hongjie Gao; Ke Li. Exploring spatial distributions of increments in soil heavy metals and their relationships with environmental factors using GWR. Stochastic Environmental Research and Risk Assessment 2021, 1 -14.
AMA StyleHao Li, Peihong Fu, Yong Yang, Xue Yang, Hongjie Gao, Ke Li. Exploring spatial distributions of increments in soil heavy metals and their relationships with environmental factors using GWR. Stochastic Environmental Research and Risk Assessment. 2021; ():1-14.
Chicago/Turabian StyleHao Li; Peihong Fu; Yong Yang; Xue Yang; Hongjie Gao; Ke Li. 2021. "Exploring spatial distributions of increments in soil heavy metals and their relationships with environmental factors using GWR." Stochastic Environmental Research and Risk Assessment , no. : 1-14.
In this study, 1-km gridded land use maps from 1980, 1990, 1995, 2000, 2005, 2010, and 2015 were used to analyze transitions in the spatial distribution and land use/cover in the Haihe River Basin (HRB) of China. The patterns of changes in land use/cover were characterized by an increase in rural–urban industrial lands and decreases in cropland, forestland, grassland, water, and unused land. Meanwhile, the land use/cover in 93% of the area in the HRB remained unchanged from 1980 to 2015. The results of a multi-temporal analysis of transition pathways from and to different land use/cover classes clearly revealed the transitional process of each class. Further analysis of the dynamic mechanisms underlying the five most common transitions showed that croplands in the areas with better locations (proximal to a city), traffic (near roads), topography (low altitude and flat terrain), and hydrology (close to a river) and rapid economic and population growth were likely to be changed into construction lands. Grasslands in areas at low altitude, over flat terrain, near a city, and with decreasing rainfall were easily changed to croplands, and croplands and forestlands in areas with unfavorable topographic (high altitude and uneven terrain) and hydrological (far from a river) conditions were likely to be converted into grasslands.
Yong Yang; Xue Yang; Enguang Li; Wei Huang. Transitions in land use and cover and their dynamic mechanisms in the Haihe River Basin, China. Environmental Earth Sciences 2021, 80, 1 -14.
AMA StyleYong Yang, Xue Yang, Enguang Li, Wei Huang. Transitions in land use and cover and their dynamic mechanisms in the Haihe River Basin, China. Environmental Earth Sciences. 2021; 80 (2):1-14.
Chicago/Turabian StyleYong Yang; Xue Yang; Enguang Li; Wei Huang. 2021. "Transitions in land use and cover and their dynamic mechanisms in the Haihe River Basin, China." Environmental Earth Sciences 80, no. 2: 1-14.
Quantifying the impacts of associated factors on soil heavy metal (HM) accumulation and mapping the accumulation risks can provide valuable information for the soil remediation and protection. This study investigates the risk of soil HM accumulation and its relationships with human activities, which were expressed by weighted industry distance (WID), weighted road density (WRD), and population density (LnPD). Three models, namely logistic regression (LR), geographically weighted logistic regression (GWLR), and kriging with external drift (KED), were used and compared. The results, which were based on the soil Pb contents in Wuhan city as an example, showed that the coefficients of LnPD, WRD and WID in LR model were all positive, meaning the increase of LnPD, WRD and WID will generally elevate the risk of Pb accumulation. Whereas in GWLR model, the coefficients were spatially varying, thus distinct dominant factors can be identified at every location by comparing the GWLR coefficients. Furthermore, GWLR gave significant higher model accuracy than LR, and had approximate but more straightforward explanatory power compared to KED. The results suggest that GWLR is a promising method in analyzing and mapping the spatial nonstationary relationships between the risk of soil HM accumulation and human activities.
Chutian Zhang; Yong Yang. Modeling the spatial variations in anthropogenic factors of soil heavy metal accumulation by geographically weighted logistic regression. Science of The Total Environment 2020, 717, 137096 .
AMA StyleChutian Zhang, Yong Yang. Modeling the spatial variations in anthropogenic factors of soil heavy metal accumulation by geographically weighted logistic regression. Science of The Total Environment. 2020; 717 ():137096.
Chicago/Turabian StyleChutian Zhang; Yong Yang. 2020. "Modeling the spatial variations in anthropogenic factors of soil heavy metal accumulation by geographically weighted logistic regression." Science of The Total Environment 717, no. : 137096.
Understanding the spatiotemporal (ST) characterization of precipitation is one key component in evaluating the climatic environment, which provides executable and effective suggestions for the implementers of climatic strategy. In this paper, the ST ordinary kriging method was used to obtain the ST distribution of annual precipitation (AP) based on the data collected from the 961 meteorological monitoring stations of the Huanghuaihai basin in China during 1956–2016. Then, stochastic site indicators were applied to quantitatively assess the ST uncertainties, and the results indicated the probability that higher precipitation (beyond 1000 mm) occurred simultaneously at any two regions was extremely low. In terms of the AP intensity analysis, the annual rainfall in 50% of the regions exceeded 500 mm, and the largest fragmentation of AP was calculated from this threshold. Furthermore, the ST characterization and the trend of AP were analyzed in different climatic environments, indicating that (1) the AP increased from west to east, exhibiting a significant gradient distribution; (2) overall, the temperature of the study area increased by approximately 0.027 °C per year, while the AP decreased by 0.2 mm per year; (3) the temperature in three subregions [high-altitude region (HAR), mid-altitude region (MAR), low-altitude region (LAR)] exhibited an increasing trend, and the significant increasing trend of temperature occurred primarily in the LAR; (4) the precipitation in the MAR and LAR exhibited a nonsignificant decreasing trend, while the precipitation in the HAR showed a significant increasing trend.
Xue Yang; Yong Yang; Ke Li; Ruojing Wu. Estimation and characterization of annual precipitation based on spatiotemporal kriging in the Huanghuaihai basin of China during 1956–2016. Stochastic Environmental Research and Risk Assessment 2019, 34, 1407 -1420.
AMA StyleXue Yang, Yong Yang, Ke Li, Ruojing Wu. Estimation and characterization of annual precipitation based on spatiotemporal kriging in the Huanghuaihai basin of China during 1956–2016. Stochastic Environmental Research and Risk Assessment. 2019; 34 (9):1407-1420.
Chicago/Turabian StyleXue Yang; Yong Yang; Ke Li; Ruojing Wu. 2019. "Estimation and characterization of annual precipitation based on spatiotemporal kriging in the Huanghuaihai basin of China during 1956–2016." Stochastic Environmental Research and Risk Assessment 34, no. 9: 1407-1420.
In this work, we propose a method that is not limited in the identification of the type of pollution source but it also suggests the land covers that emit heavy metals into the surrounding soils by introducing a three-stage procedure, as follows: (a) the Principal Component Analysis/Absolute Principal Component Scores technique is applied to the spatial distribution of soil heavy metal accumulations to identify the type of source that is responsible for soil heavy metal accumulation, (b) based on the spatial distribution of the principal component scores and on four selected driving factors (land cover, distance to mine or smelter, distance to road, and topographic elevation), the Geographical Detector model was used to identify the effect intensity of the driving factors on soil heavy metal accumulation and obtain the landscape type of pollutant sources, and (c) GIS analysis (buffer and overlap analysis) was performed on the principal component scores around the suspected land covers linked to the landscape type of pollutant sources to determine the land covers that, in fact, emit heavy metals into the surrounding soils. Based on the proposed approach, four mining and metallurgy land or land groups were determined to be the actual sources of soil heavy metal pollution in Daye city, Hubei Province, China. Lastly, a Multiple Linear Regression model with decay function was proposed to quantify the contributions of previously identified pollution sources to soil heavy metals accumulation. It was found that the HuangJin mountain quarry, the Tonglu mountain cooper mine (together with some related mineral processing and smelting enterprises), the Lion mountain mining and mineral processing base, and the large Oujia mountain mine are the four sources that contributed 3.2%, 34.3%, 8.3%, and 44% of the total soil heavy metal accumulations in the study area.
Yong Yang; Xue Yang; Mingjun He; George Christakos. Beyond mere pollution source identification: Determination of land covers emitting soil heavy metals by combining PCA/APCS, GeoDetector and GIS analysis. CATENA 2019, 185, 104297 .
AMA StyleYong Yang, Xue Yang, Mingjun He, George Christakos. Beyond mere pollution source identification: Determination of land covers emitting soil heavy metals by combining PCA/APCS, GeoDetector and GIS analysis. CATENA. 2019; 185 ():104297.
Chicago/Turabian StyleYong Yang; Xue Yang; Mingjun He; George Christakos. 2019. "Beyond mere pollution source identification: Determination of land covers emitting soil heavy metals by combining PCA/APCS, GeoDetector and GIS analysis." CATENA 185, no. : 104297.
A better understanding of the spatial pattern of soil organic matter (SOM) is important for scientific soil management. As multisource secondary data become increasingly cheap and readily available, numerous methods have been established to incorporate secondary information; however, these methods exhibit limitations under certain conditions due to their relatively strict requirements on secondary data. In this study, we tried to integrate sampled soil data and secondary data more effectively within the framework of Bayesian maximum entropy (BME). Specifically, multiple linear regression (MLR) and geographically weighted regression (GWR) were run 100 times based on environmental covariates such as terrain indices, vegetation indices and categorical variables obtained from soil maps. Then, the 95% confidence interval was derived from the multiple prediction values at each of the soft data points. For comparison, some conventional techniques, including ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK), were also applied. The results showed that BME exhibited a prediction accuracy comparable to that of OK and maintained the prediction uncertainty at a low level, while other studied methods (MLR, GWR, RK and GWRK) were all significantly inferior to BME and OK. The proposed methodology in this study represents a promising scenario for the digital soil mapping, especially when the relationships between the target soil attributes and various secondary information are not strong or residuals of trend models show insignificant spatial autocorrelation.
Chu-Tian Zhang; Yong Yang. Can the spatial prediction of soil organic matter be improved by incorporating multiple regression confidence intervals as soft data into BME method? CATENA 2019, 178, 322 -334.
AMA StyleChu-Tian Zhang, Yong Yang. Can the spatial prediction of soil organic matter be improved by incorporating multiple regression confidence intervals as soft data into BME method? CATENA. 2019; 178 ():322-334.
Chicago/Turabian StyleChu-Tian Zhang; Yong Yang. 2019. "Can the spatial prediction of soil organic matter be improved by incorporating multiple regression confidence intervals as soft data into BME method?" CATENA 178, no. : 322-334.
Understanding the spatial patterns of heavy metals is important for the protection and remediation of urban soil. Considering that the conventional Geostatistical methods, such as ordinary kriging (OK), are sensitive to dataset outliers, this study converted the identified outliers into a discrete probability density function (PDF). Then, the PDF was used as soft data in the Bayesian maximum entropy (BME) framework to perform a spatial prediction of soil Zn contents in Wuhan City, Central China. By using OK as the reference method, the BME framework was found to produce an overall further accurate prediction, and the PDF of BME predictions was further informative and close to the observed Zn concentrations. An improved BME performance can be expected if soft data with high quality are provided. The BME is a promising method in environmental science, where the so-called outliers that probably carry important information are common.
Chu-Tian Zhang; Yong Yang. Improving the spatial prediction of soil Zn by converting outliers into soft data for BME method. Stochastic Environmental Research and Risk Assessment 2019, 33, 855 -864.
AMA StyleChu-Tian Zhang, Yong Yang. Improving the spatial prediction of soil Zn by converting outliers into soft data for BME method. Stochastic Environmental Research and Risk Assessment. 2019; 33 (3):855-864.
Chicago/Turabian StyleChu-Tian Zhang; Yong Yang. 2019. "Improving the spatial prediction of soil Zn by converting outliers into soft data for BME method." Stochastic Environmental Research and Risk Assessment 33, no. 3: 855-864.
Improving the understanding and characterization of spatial soil heavy metal distribution is becoming an important component of risk assessment and environmental policy. In this work, 213 soil samples collected from Daye (Hubei Province, China) were used as the empirical dataset. First, maps of soil heavy metal distributions, including Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn, were obtained using the ordinary Kriging method. Then, the pollution index (PI) and integrated pollution index (IPI) were calculated based on the ordinary Kriging maps to obtain a comprehensive quantitative pollution characterization of the eight heavy metals in the Daye soil. The results showed that 46.1%, 32.1%, and 0.5% of the soil in the study region are moderately, highly and extremely polluted, respectively. Finally, the one- and two-point stochastic site indicators of IPI were used to assess quantitatively the uncertainties and risks associated with soil heavy metal distributions in the polluted regions. These results showed that the IPI values exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative area of excess pollution decreased steadily with increasing threshold. Among the site pairs considered in the study region, about 70% and 26% of them simultaneously experienced moderate and high pollution risk, respectively.
Junyu He; Yong Yang; George Christakos; Yajun Liu; Xue Yang. Assessment of soil heavy metal pollution using stochastic site indicators. Geoderma 2018, 337, 359 -367.
AMA StyleJunyu He, Yong Yang, George Christakos, Yajun Liu, Xue Yang. Assessment of soil heavy metal pollution using stochastic site indicators. Geoderma. 2018; 337 ():359-367.
Chicago/Turabian StyleJunyu He; Yong Yang; George Christakos; Yajun Liu; Xue Yang. 2018. "Assessment of soil heavy metal pollution using stochastic site indicators." Geoderma 337, no. : 359-367.
Daye is a city in China known for its rich mineral resources, with a history of metal mining and smelting that dates back more than 3000 years. To analyze the spatial distribution patterns, ecological risk, and sources of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn) in soils, 213 topsoil samples were collected in the main urban area of Daye in September 2016. The mean concentrations of Cd, Cu, Pb, and Zn were higher than the corresponding background values, with the mean concentration of Cd being almost seven times its background value. Spatially, the high concentrations of Cd, Mn, Pb, and Zn were mainly concentrated in the southeastern part of the region due to nonferrous metal mining and smelting. However, the high concentrations of Co and Cu were concentrated in the central part of the study area, resulted from copper mining and smelting. The data of the geoaccumulation index showed that the contamination levels ranged from no pollution (Co, Cr, Mn, and Ni) to heavy contamination (Cd, Cu, and Pb). Ecological risk assessment showed that Cd posed a high, serious, and even severe ecological risk in 53.78% of the area of Daye. According to the results of the principal component analysis, mineral exploitation and smelting involving a variety of minerals (ES_M), mining exploitation, and smelting of copper ore (ES_C), and natural sources are the three main sources of heavy metals in these soils. Furthermore, the absolute principal component scores showed that 69.21% and 23.17% of the heavy metal concentrations were ascribed to ES_M and ES_C, respectively.
Li Hua; Xue Yang; Yajun Liu; Xiuli Tan; Yong Yang. Spatial Distributions, Pollution Assessment, and Qualified Source Apportionment of Soil Heavy Metals in a Typical Mineral Mining City in China. Sustainability 2018, 10, 3115 .
AMA StyleLi Hua, Xue Yang, Yajun Liu, Xiuli Tan, Yong Yang. Spatial Distributions, Pollution Assessment, and Qualified Source Apportionment of Soil Heavy Metals in a Typical Mineral Mining City in China. Sustainability. 2018; 10 (9):3115.
Chicago/Turabian StyleLi Hua; Xue Yang; Yajun Liu; Xiuli Tan; Yong Yang. 2018. "Spatial Distributions, Pollution Assessment, and Qualified Source Apportionment of Soil Heavy Metals in a Typical Mineral Mining City in China." Sustainability 10, no. 9: 3115.
The present work uses a new space-time projection (STP) technique to study the distribution of PM2.5 concentrations in one of the most populous, highly developed and highly polluted regions in China, the Shandong Province, during the period Jan 1–31, 2014. The theoretical and interpretational features of the STP technique are pointed out. A key feature of the technique is that it transfers the study of pollutant distribution from the original space-time domain R2 × T onto a traveling domain of lower-dimensionality R2 that moves in the pollutant spread direction; analysis and computations are much easier in the R2 domain, avoiding the complexities of the R2 × T domain; and the results are back-transformed to the original R2 × T to generate space-time PM2.5 concentration maps over the entire region of interest. The Shandong study shows that the proposed STP technique has certain noteworthy analytical and computational advantages over mainstream mapping techniques of higher dimensionality (like space-time ordinary kriging, STOK): it avoids serious difficulties associated with space-time metric determination and variogram estimation in the original space-time domain, it allows the selection of more appropriate variogram models representing the PM2.5 variation, it generates more accurate PM2.5 maps, and it is also a computationally more efficient technique than the STOK technique.
George Christakos; Yong Yang; Jiaping Wu; Chutian Zhang; Yang Mei; Junyu He. Improved space-time mapping of PM2.5 distribution using a domain transformation method. Ecological Indicators 2018, 85, 1273 -1279.
AMA StyleGeorge Christakos, Yong Yang, Jiaping Wu, Chutian Zhang, Yang Mei, Junyu He. Improved space-time mapping of PM2.5 distribution using a domain transformation method. Ecological Indicators. 2018; 85 ():1273-1279.
Chicago/Turabian StyleGeorge Christakos; Yong Yang; Jiaping Wu; Chutian Zhang; Yang Mei; Junyu He. 2018. "Improved space-time mapping of PM2.5 distribution using a domain transformation method." Ecological Indicators 85, no. : 1273-1279.
Soil heavy metals exhibit significant spatiotemporal variability and are strongly correlated with other soil heavy metals. Thus, other heavy metals can be used to improve the accuracy of predictions when performing spatiotemporal predictions of soil heavy metals within a given area. In this study, we propose the spatiotemporal cokriging (STCK) method to enable the use of historical sampling points and co-variables in the spatial prediction of soil heavy metals. Moreover, experimental spatiotemporal (ST) semivariogram and ST cross-semivariogram computational methods, a fitting strategy to the ST semivariogram and ST cross-semivariogram models based on the Bilonick model, and the STCK interpolation algorithm are introduced; these methods are based on spatiotemporal kriging (STK) and cokriging (CK). The data used in this study consist of measurements of soil heavy metals from 2010 to 2014 in Wuhan City, China. The results show that the behavior of predictions of the concentrations of heavy metals in soils is physically more realistic, and the prediction uncertainties are slightly smaller, when STCK is used with greater numbers of co-variables and neighboring points.
Bei Zhang; Yong Yang. Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging. Scientific Reports 2017, 7, 16750 .
AMA StyleBei Zhang, Yong Yang. Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging. Scientific Reports. 2017; 7 (1):16750.
Chicago/Turabian StyleBei Zhang; Yong Yang. 2017. "Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging." Scientific Reports 7, no. 1: 16750.
Because of the rapid economic growth in China, many regions are subjected to severe particulate matter pollution. Thus, improving the methods of determining the spatiotemporal distribution and uncertainty of air pollution can provide considerable benefits when developing risk assessments and environmental policies. The uncertainty assessment methods currently in use include the sequential indicator simulation (SIS) and indicator kriging techniques. However, these methods cannot be employed to assess multi-temporal data. In this work, a spatiotemporal sequential indicator simulation (STSIS) based on a non-separable spatiotemporal semivariogram model was used to assimilate multi-temporal data in the mapping and uncertainty assessment of PM2.5 distributions in a contaminated atmosphere. PM2.5 concentrations recorded throughout 2014 in Shandong Province, China were used as the experimental dataset. Based on the number of STSIS procedures, we assessed various types of mapping uncertainties, including single-location uncertainties over one day and multiple days and multi-location uncertainties over one day and multiple days. A comparison of the STSIS technique with the SIS technique indicate that a better performance was obtained with the STSIS method.
Yong Yang; George Christakos; Wei Huang; Chengda Lin; Peihong Fu; Yang Mei. Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data. Scientific Reports 2016, 6, 24335 .
AMA StyleYong Yang, George Christakos, Wei Huang, Chengda Lin, Peihong Fu, Yang Mei. Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data. Scientific Reports. 2016; 6 (1):24335.
Chicago/Turabian StyleYong Yang; George Christakos; Wei Huang; Chengda Lin; Peihong Fu; Yang Mei. 2016. "Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data." Scientific Reports 6, no. 1: 24335.
Surface soil samples from 467 sample sites were collected in urban area of Wuhan City in 2013, and total concentrations of five trace metals (Pb, Zn, Cu, Cr, and Cd) were measured. Multivariate and geostatistical analyses showed that concentrations of Pb, Zn, and Cu are higher along Yangtze River in the northern area of Wuhan, gradually decrease from city center to suburbs, and are mainly controlled by anthropogenic activities, while those of Cr and Cd are relatively spatially homogenous and mainly controlled by soil parent materials. Pb, Zn, Cu, and Cd have generally higher concentrations in roadsides, industrial areas, and residential areas than in school areas, greenbelts, and agricultural areas. Areas with higher road and population densities and longer urban construction history usually have higher trace metal concentrations. According to estimated results of the potential ecological risk index and Nemero synthesis pollution index, almost the whole urban area of Wuhan is facing considerable potential ecological risk caused by soil trace metals. These results reveal obvious trends of trace metal pollution, and an important impact of anthropogenic activities on the accumulation of trace metals in soil in Wuhan. Vehicular emission, industrial activities, and household wastes may be the three main sources for trace metal accumulation. Increasing vegetation cover may reduce this threat. It should be pointed out that Cd, which is strongly accumulated in soil, could be the largest soil pollution factor in Wuhan. Effective measures should be taken as soon as possible to deal with Cd enrichment, and other trace metals in soil should also be reduced, so as to protect human health in this important large city.
Chutian Zhang; Yong Yang; Weidong Li; Chuanrong Zhang; Ruoxi Zhang; Yang Mei; Xiangsen Liao; Yingying Liu. Spatial distribution and ecological risk assessment of trace metals in urban soils in Wuhan, central China. Environmental Monitoring and Assessment 2015, 187, 1 -16.
AMA StyleChutian Zhang, Yong Yang, Weidong Li, Chuanrong Zhang, Ruoxi Zhang, Yang Mei, Xiangsen Liao, Yingying Liu. Spatial distribution and ecological risk assessment of trace metals in urban soils in Wuhan, central China. Environmental Monitoring and Assessment. 2015; 187 (9):1-16.
Chicago/Turabian StyleChutian Zhang; Yong Yang; Weidong Li; Chuanrong Zhang; Ruoxi Zhang; Yang Mei; Xiangsen Liao; Yingying Liu. 2015. "Spatial distribution and ecological risk assessment of trace metals in urban soils in Wuhan, central China." Environmental Monitoring and Assessment 187, no. 9: 1-16.