Total contents of heavy metals （ Pb, Cr, Cd, Hg, As, Cu, Zn, and Ni） in 68 soil samples were determined to evaluate the contamination level of soil from five coal mine areas in Xingren County, Guizhou province. The ...Total contents of heavy metals （ Pb, Cr, Cd, Hg, As, Cu, Zn, and Ni） in 68 soil samples were determined to evaluate the contamination level of soil from five coal mine areas in Xingren County, Guizhou province. The pollution levels of heavy metals were characterized by anthro- pogenic influence multiple, and the pollution of heavy metal in soil was evaluated using geo-accumulation index. Based on the health risk models recommended by the U.S. Environmental Protection Agency （ US EPA）, the health risks of heavy metals in the soils were assessed. The results showed that the abandoned land in the five coal mining areas were contaminated by heavy metals, and the order of pollution levels of eight heavy metals was As 〉 Pb 〉 Cu 〉 Hg 〉 Ni 〉 Cr 〉 Cd 〉 Zn. The soils suffered moderate-heavy pollution from As and Pb, and the contents of As and Pb in the soils were 13.16 and 4.76 times higher than the background values of Guizhou Province, followed by Cu, Hg and Ni , while soil pollution from Cr, Cd and Zn was mild. The order of non-caminogenic risks of the heavy metals in the soils was As 〉 Pb 〉 Cr 〉 Ni 〉 Cu 〉 Hg 〉 Cd 〉 Zn, and the non-carcinogenic dsk value of As was higher than that its threshold value 1, which indicated that As had a high potential health risk to adults and children. The order of carcinogen risks of the four heavy metals was As 〉 Cd 〉 Cr 〉 Ni, and the carcinogenic risk value of As was higher than its threshold value, indicating that As had a high carcinogenic risk to adults and children. The carcinogenic risk values of other three heavy metals （ Cd, Cr and Ni） were lower than the threshold value, so they had no carcinogenic risk to human health. Moreover, children were more sensitively affect- ed by heavy metals from the abandoned land. Therefore, the health of children in such areas should be paid more attention to. The HI value of the eight heavy metals to children was seven times higher than that to adults. The contribution rate of HQAs to HI was about 88%, and the contribution rate of CRA, to TCR was about 98%, so As was the most crucial factor influencing non-carcinogenic and carcinogenic dsk in the abandoned soils.展开更多
Abandoned chemical smelting sites containing toxic substances can seriously threaten and pose a risk to the surrounding ecological environment.Soil samples were collected from different depths(0 to 13 m)and analyzed f...Abandoned chemical smelting sites containing toxic substances can seriously threaten and pose a risk to the surrounding ecological environment.Soil samples were collected from different depths(0 to 13 m)and analyzed for metal(loid)s content and fractionation,as well as microbial activities.The potential ecological risk indices for the different soil depths(ordered from high to low)were:1 m(D-1)>surface(S-0)>5 m(D-5)>13 m(D-13)>9 m(D-9),ranging between 1840.65-13,089.62,and representing extremely high environmental risks,of which Cd(and probably not arsenic)contributed to the highest environmental risk.A modified combined pollution risk index(MCR)combining total content and mobile proportion of metal(loid)s,and relative toxicities,was used to evaluate the degree of contamination and potential environmental risks.For the near-surface samples(S-0 and D-1 layers),the MCR considered that As,Cd,Pb,Sb,and Zn achieved high and alarming degrees of contamination,whereas Fe,Mn,and Ti were negligible or low to moderate pollution degrees.Combined microcalorimetry and enzymatic activity measurements of contaminated soil samples were used to assess the microbial metabolic activity characteristics.Correlation analysis elucidated the relationship between metal(loid)s exchangeable fraction or content and microbial activity characteristics(p<0.05).The microbial metabolic activity in the D-1 layer was low presumably due to heavy metal stress.Enzyme activity indicators and microcalorimetric growth rate(k)measurements were considered sensitive indicators to reflect the soil microbial activities in abandoned chemical smelting sites.展开更多
Mining tailing ponds are large infrastructure objects whose life cycle spans over several decades.They are indispensable for certain types of mines where technological process produces and rejects mud.They also have p...Mining tailing ponds are large infrastructure objects whose life cycle spans over several decades.They are indispensable for certain types of mines where technological process produces and rejects mud.They also have potential to generate risks for human life,property and environment.For that reason,it is essential to adequately manage them throughout all the stages of their life cycle.The phase of their closure and abandonment is less studied and understood.The paper proposes a holistic resilience-based approach for analyzing this phase of their life cycle.The proposed methodology is validated through a case study at an actual surface iron ore mine in Bosnia and Herzegovina.展开更多
Meteorological metrics have been used for weather forecasting and climate prediction. Remote sensing images proved to be a valuable resource to represent the terrain of earth’s surface. Recently, there has been exten...Meteorological metrics have been used for weather forecasting and climate prediction. Remote sensing images proved to be a valuable resource to represent the terrain of earth’s surface. Recently, there has been extensive research to model changes on the earth’s landscape including water bodies using remote sensing images. Meanwhile, meteorological data have been used mainly to model climate changes. This research tries to leverage both resources to achieve enhanced monitoring of the Dead Sea shrinkage: first, an attempt to model the relation between several meteorological variables and Dead Sea shrinkage using machine learning;second, formulating Dead Sea shrinkage in terms of water level and surface area using data extraction from remote sensing images;finally, confronting the two models to derive a novel approach for predicting Dead Sea shrinkage based on spatiotemporal images and meteorological measures. The main machine learning algorithms for modeling the water shrinkage in this empirical research are Decision Table, Linear Regression, and Multi Layer Perceptron Neural Networks. The Mean Absolute Error measure of the best model is 1.743 and 0.015. It is challenging to model the relation between meteorological variables and the water level. However, the obtained results are promising to formulate a model of the water level decline rate, which in its turn will be an essential tool for estimating the consumption limits and inflow needs to save the Dead Sea.展开更多
基金Supported by the National Natural Science Foundation of China(21467005)
文摘Total contents of heavy metals （ Pb, Cr, Cd, Hg, As, Cu, Zn, and Ni） in 68 soil samples were determined to evaluate the contamination level of soil from five coal mine areas in Xingren County, Guizhou province. The pollution levels of heavy metals were characterized by anthro- pogenic influence multiple, and the pollution of heavy metal in soil was evaluated using geo-accumulation index. Based on the health risk models recommended by the U.S. Environmental Protection Agency （ US EPA）, the health risks of heavy metals in the soils were assessed. The results showed that the abandoned land in the five coal mining areas were contaminated by heavy metals, and the order of pollution levels of eight heavy metals was As 〉 Pb 〉 Cu 〉 Hg 〉 Ni 〉 Cr 〉 Cd 〉 Zn. The soils suffered moderate-heavy pollution from As and Pb, and the contents of As and Pb in the soils were 13.16 and 4.76 times higher than the background values of Guizhou Province, followed by Cu, Hg and Ni , while soil pollution from Cr, Cd and Zn was mild. The order of non-caminogenic risks of the heavy metals in the soils was As 〉 Pb 〉 Cr 〉 Ni 〉 Cu 〉 Hg 〉 Cd 〉 Zn, and the non-carcinogenic dsk value of As was higher than that its threshold value 1, which indicated that As had a high potential health risk to adults and children. The order of carcinogen risks of the four heavy metals was As 〉 Cd 〉 Cr 〉 Ni, and the carcinogenic risk value of As was higher than its threshold value, indicating that As had a high carcinogenic risk to adults and children. The carcinogenic risk values of other three heavy metals （ Cd, Cr and Ni） were lower than the threshold value, so they had no carcinogenic risk to human health. Moreover, children were more sensitively affect- ed by heavy metals from the abandoned land. Therefore, the health of children in such areas should be paid more attention to. The HI value of the eight heavy metals to children was seven times higher than that to adults. The contribution rate of HQAs to HI was about 88%, and the contribution rate of CRA, to TCR was about 98%, so As was the most crucial factor influencing non-carcinogenic and carcinogenic dsk in the abandoned soils.
基金supported by the National Key Research and Development Program of China(No.2019YFC1803500)the National Natural Science Foundation of China(No.42230716)+3 种基金the National Natural Science Foundation of China(No.42007289)the 111 Project(No.B21017)the Centre National de la Recherche Scientifique(No.CNRS PRC1416,France)the 1000-Talents Plan project(No.WQ2017110423)。
文摘Abandoned chemical smelting sites containing toxic substances can seriously threaten and pose a risk to the surrounding ecological environment.Soil samples were collected from different depths(0 to 13 m)and analyzed for metal(loid)s content and fractionation,as well as microbial activities.The potential ecological risk indices for the different soil depths(ordered from high to low)were:1 m(D-1)>surface(S-0)>5 m(D-5)>13 m(D-13)>9 m(D-9),ranging between 1840.65-13,089.62,and representing extremely high environmental risks,of which Cd(and probably not arsenic)contributed to the highest environmental risk.A modified combined pollution risk index(MCR)combining total content and mobile proportion of metal(loid)s,and relative toxicities,was used to evaluate the degree of contamination and potential environmental risks.For the near-surface samples(S-0 and D-1 layers),the MCR considered that As,Cd,Pb,Sb,and Zn achieved high and alarming degrees of contamination,whereas Fe,Mn,and Ti were negligible or low to moderate pollution degrees.Combined microcalorimetry and enzymatic activity measurements of contaminated soil samples were used to assess the microbial metabolic activity characteristics.Correlation analysis elucidated the relationship between metal(loid)s exchangeable fraction or content and microbial activity characteristics(p<0.05).The microbial metabolic activity in the D-1 layer was low presumably due to heavy metal stress.Enzyme activity indicators and microcalorimetric growth rate(k)measurements were considered sensitive indicators to reflect the soil microbial activities in abandoned chemical smelting sites.
文摘Mining tailing ponds are large infrastructure objects whose life cycle spans over several decades.They are indispensable for certain types of mines where technological process produces and rejects mud.They also have potential to generate risks for human life,property and environment.For that reason,it is essential to adequately manage them throughout all the stages of their life cycle.The phase of their closure and abandonment is less studied and understood.The paper proposes a holistic resilience-based approach for analyzing this phase of their life cycle.The proposed methodology is validated through a case study at an actual surface iron ore mine in Bosnia and Herzegovina.
文摘Meteorological metrics have been used for weather forecasting and climate prediction. Remote sensing images proved to be a valuable resource to represent the terrain of earth’s surface. Recently, there has been extensive research to model changes on the earth’s landscape including water bodies using remote sensing images. Meanwhile, meteorological data have been used mainly to model climate changes. This research tries to leverage both resources to achieve enhanced monitoring of the Dead Sea shrinkage: first, an attempt to model the relation between several meteorological variables and Dead Sea shrinkage using machine learning;second, formulating Dead Sea shrinkage in terms of water level and surface area using data extraction from remote sensing images;finally, confronting the two models to derive a novel approach for predicting Dead Sea shrinkage based on spatiotemporal images and meteorological measures. The main machine learning algorithms for modeling the water shrinkage in this empirical research are Decision Table, Linear Regression, and Multi Layer Perceptron Neural Networks. The Mean Absolute Error measure of the best model is 1.743 and 0.015. It is challenging to model the relation between meteorological variables and the water level. However, the obtained results are promising to formulate a model of the water level decline rate, which in its turn will be an essential tool for estimating the consumption limits and inflow needs to save the Dead Sea.
