High-dimensional data(a dataset with many features)were collected from 64 sampling sites to analyze the water quality in estuaries along the coast of the Bohai Sea,North China.The twenty-five water quality parameters ...High-dimensional data(a dataset with many features)were collected from 64 sampling sites to analyze the water quality in estuaries along the coast of the Bohai Sea,North China.The twenty-five water quality parameters analyzed were collected monthly from January 2021 to December 2021.Multivariate statistical techniques,such as the absolute principal component score-multiple linear regression model(APCS-MLR),correlation analysis,and analysis of variance were used to identify and quantify the potential sources or factors affecting water quality and to analyze the spatial-temporal variation in water quality.The water quality indices(WQIs),ranging from 67.96 to 70.67,showed that the water quality was at an intermediate level in the estuaries during both the flood and nonflood seasons.The concentrations of total phosphorus(TP),ammonia N(AN),and organic pollutants were greater in the Haihe River Basin than in the Liaohe River and Huanghe-Huaihe River Basins.The concentration of total nitrogen(TN)in the Haihe River Basin was lower than that in the Liaohe River and Huanghe-Huaihe River Basins.Heavy metal concentrations in the Liaohe River Basin were greater than those in the Haihe River and Huanghe-Huaihe River Basins.The annual mean concentrations of AN in the estuaries of the Haihe,Liaohe,and Huanghe(Yellow)rivers exhibited significant decreasing trends from 2013 to 2022,but no significant decreasing trends were found for permanganate index(COD_(Mn))or the TP.The concentrations of TN and AN were lower in the flood season than in the nonflood season,and the TP concentration was greater in the flood season than in the nonflood season.However,the concentrations of organic pollutants did not exhibit significant differences.Domestic sewage and industrial wastewater,substance exchange between air and water,nonpoint sources from rural and urban areas,and aquaculture wastewater were the major sources or factors responsible for water pollution in the estuaries.展开更多
The abandoned smelters present a substantial pollution threat to the nearby soil and groundwater.In this study,63 surface soil samples were collected from a zinc smelter to quantitatively describe the pollution charac...The abandoned smelters present a substantial pollution threat to the nearby soil and groundwater.In this study,63 surface soil samples were collected from a zinc smelter to quantitatively describe the pollution characteristics,ecological risks,and source apportionment of heavy metal(loid)s(HMs).The results revealed that the average contents of Zn,Cd,Pb,As,and Hg were 0.4,12.2,3.3,5.3,and 12.7 times higher than the risk screening values of the construction sites,respectively.Notably,the smelter was accumulated heavily with Cd and Hg,and the contribution of Cd(0.38)and Hg(0.53)to ecological risk was 91.58%.ZZ3 and ZZ7 were the most polluted workshops,accounting for 25.7%and 35.0%of the pollution load and ecological risk,respectively.The influence of soil parent materials on pollution was minor compared to various workshops within the smelter.Combined with PMF,APCS-MLR and GIS analysis,four sources of HMs were identified:P1(25.5%)and A3(18.4%)were atmospheric deposition from the electric defogging workshop and surface runoff from the smelter;P2(32.7%)and A2(20.9%)were surface runoff of As-Pb foul acid;P3(14.5%)and A4(49.8%)were atmospheric deposition from the leach slag drying workshop;P4(27.3%)and A1(10.8%)were the smelting process of zinc products.This paper described the distribution characteristics and specific sources of HMs in different process workshops,providing a new perspective for the precise remediation of the smelter by determining the priority control factors.展开更多
Estimating heavy metal(HM) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to det...Estimating heavy metal(HM) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to detect the concentrations of eight HMs(As, Hg, Cu, Cr, Ni, Zn, Pb, and Cd) in the herb growing area of Luanping County, northeastern Hebei Province, China. An absolute principal component score-multiple linear regression(APCS-MLR) model was used to quantify pollution source contributions to soil HMs. Furthermore, the source contribution rates and environmental data of each sampling point were simultaneously incorporated into a stepwise linear regression model to identify the crucial indicators for predicting soil HM spatial distributions. Results showed that 88% of Cu, 72% of Cr, and 72% of Ni came from natural sources;50% of Zn, 49% of Pb, and 59% of Cd were mainly caused by agricultural activities;and 44% of As and 56% of Hg originated from industrial activities. When three-type(natural, agricultural, and industrial) source contribution rates and environmental data were simultaneously incorporated into the stepwise linear regression model, the fitting accuracy was significantly improved and the model could explain 31%–86% of the total variance in soil HM concentrations. This study introduced three-type source contributions of each sampling point based on APCS-MLR analysis as new covariates to improve soil HM estimation precision, thus providing a new approach for predicting the spatial distribution of HMs using small sample sizes at the county scale.展开更多
基金Supported by the National Natural Science Foundation of China(No.41571479)。
文摘High-dimensional data(a dataset with many features)were collected from 64 sampling sites to analyze the water quality in estuaries along the coast of the Bohai Sea,North China.The twenty-five water quality parameters analyzed were collected monthly from January 2021 to December 2021.Multivariate statistical techniques,such as the absolute principal component score-multiple linear regression model(APCS-MLR),correlation analysis,and analysis of variance were used to identify and quantify the potential sources or factors affecting water quality and to analyze the spatial-temporal variation in water quality.The water quality indices(WQIs),ranging from 67.96 to 70.67,showed that the water quality was at an intermediate level in the estuaries during both the flood and nonflood seasons.The concentrations of total phosphorus(TP),ammonia N(AN),and organic pollutants were greater in the Haihe River Basin than in the Liaohe River and Huanghe-Huaihe River Basins.The concentration of total nitrogen(TN)in the Haihe River Basin was lower than that in the Liaohe River and Huanghe-Huaihe River Basins.Heavy metal concentrations in the Liaohe River Basin were greater than those in the Haihe River and Huanghe-Huaihe River Basins.The annual mean concentrations of AN in the estuaries of the Haihe,Liaohe,and Huanghe(Yellow)rivers exhibited significant decreasing trends from 2013 to 2022,but no significant decreasing trends were found for permanganate index(COD_(Mn))or the TP.The concentrations of TN and AN were lower in the flood season than in the nonflood season,and the TP concentration was greater in the flood season than in the nonflood season.However,the concentrations of organic pollutants did not exhibit significant differences.Domestic sewage and industrial wastewater,substance exchange between air and water,nonpoint sources from rural and urban areas,and aquaculture wastewater were the major sources or factors responsible for water pollution in the estuaries.
基金This work was supported by the National Key Research and Development Program of China(No.2019YFC1803603).
文摘The abandoned smelters present a substantial pollution threat to the nearby soil and groundwater.In this study,63 surface soil samples were collected from a zinc smelter to quantitatively describe the pollution characteristics,ecological risks,and source apportionment of heavy metal(loid)s(HMs).The results revealed that the average contents of Zn,Cd,Pb,As,and Hg were 0.4,12.2,3.3,5.3,and 12.7 times higher than the risk screening values of the construction sites,respectively.Notably,the smelter was accumulated heavily with Cd and Hg,and the contribution of Cd(0.38)and Hg(0.53)to ecological risk was 91.58%.ZZ3 and ZZ7 were the most polluted workshops,accounting for 25.7%and 35.0%of the pollution load and ecological risk,respectively.The influence of soil parent materials on pollution was minor compared to various workshops within the smelter.Combined with PMF,APCS-MLR and GIS analysis,four sources of HMs were identified:P1(25.5%)and A3(18.4%)were atmospheric deposition from the electric defogging workshop and surface runoff from the smelter;P2(32.7%)and A2(20.9%)were surface runoff of As-Pb foul acid;P3(14.5%)and A4(49.8%)were atmospheric deposition from the leach slag drying workshop;P4(27.3%)and A1(10.8%)were the smelting process of zinc products.This paper described the distribution characteristics and specific sources of HMs in different process workshops,providing a new perspective for the precise remediation of the smelter by determining the priority control factors.
基金supported by the special project of the National Key Research and Development Program of China(Nos.2021YFC1809104 and 2018YFC1800104)。
文摘Estimating heavy metal(HM) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to detect the concentrations of eight HMs(As, Hg, Cu, Cr, Ni, Zn, Pb, and Cd) in the herb growing area of Luanping County, northeastern Hebei Province, China. An absolute principal component score-multiple linear regression(APCS-MLR) model was used to quantify pollution source contributions to soil HMs. Furthermore, the source contribution rates and environmental data of each sampling point were simultaneously incorporated into a stepwise linear regression model to identify the crucial indicators for predicting soil HM spatial distributions. Results showed that 88% of Cu, 72% of Cr, and 72% of Ni came from natural sources;50% of Zn, 49% of Pb, and 59% of Cd were mainly caused by agricultural activities;and 44% of As and 56% of Hg originated from industrial activities. When three-type(natural, agricultural, and industrial) source contribution rates and environmental data were simultaneously incorporated into the stepwise linear regression model, the fitting accuracy was significantly improved and the model could explain 31%–86% of the total variance in soil HM concentrations. This study introduced three-type source contributions of each sampling point based on APCS-MLR analysis as new covariates to improve soil HM estimation precision, thus providing a new approach for predicting the spatial distribution of HMs using small sample sizes at the county scale.
