Soil pollution levels can be quantified via sampling and experimental analysis;however,sampling is performed at discrete points with long distances owing to limited funding and human resources,and is insufficient to c...Soil pollution levels can be quantified via sampling and experimental analysis;however,sampling is performed at discrete points with long distances owing to limited funding and human resources,and is insufficient to characterize the entire study area.Spatial prediction is required to comprehensively investigate potentially contaminated areas.Consequently,machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution.The characteristics,advantages,and applications of machine learning models used to predict soil pollution are reviewed in this study.Satisfactory model performance generally requires the following:1)selection of the most appropriate model with the required structure;2)selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability;3)improvement of model reliability through comprehensive model evaluation;and 4)integration of geostatistics with the machine learning model.With the enrichment of environmental data and development of algorithms,machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.展开更多
The ongoing impact of the novel coronavirus disease 2019(COVID-19)on work and daily life persists as we transition from emergency to normal circumstances.The continuous mutation of viral strains has resulted in a shif...The ongoing impact of the novel coronavirus disease 2019(COVID-19)on work and daily life persists as we transition from emergency to normal circumstances.The continuous mutation of viral strains has resulted in a shift from a single strain to multiple cross-strains,contributing to the spread of the epidemic.Variations in infection rates of the same strain occur because of the implementation of diverse preventive measures at different times.This study investigated the dynamics of the pandemic in the presence of concurrent strains.Building on the classical Susceptible,Exposed,Infected,and Recovered(SEIR)model,a robust piecewise multi-strain cross-epidemic trend prediction model was proposed that employs the Hodges–Lehmann estimator to handle uncertain and contamination-prone epidemic information.A comparative analysis of epidemic spread trend curves across diverse populations using different robust methods revealed the superiority of the Hodges–Lehmann estimator-based model over the traditional method.The accurate prediction results of the model demonstrate its high reliability in tracking the changing trend of the COVID-19 outbreak,thereby supporting its implementation in subsequent epidemic prevention and control measures.展开更多
Multivariate statistical analyses were used to assess the contents and distributions of trace elements in agricultural soils in Xinzhou of Shanxi Province, China, and to identify their sources. Samples with high level...Multivariate statistical analyses were used to assess the contents and distributions of trace elements in agricultural soils in Xinzhou of Shanxi Province, China, and to identify their sources. Samples with high levels of trace elements were concentrated in eastern Xinzhou, with contents declining from the east to west. Principal component and redundancy analyses revealed strong correlations among Co, Cu, Mn, Ni, Se, V, and Zn contents, suggesting that these elements were derived from similar parent materials. There were also strong correlations between the contents of these elements and soil properties. Contents of Cd and Pb were significantly higher in the agricultural soil samples than in the background soil samples(P < 0.05), and were higher in areas with higher levels of gross domestic product but decreased with distance to the nearest road. Therefore, human activities appear to have a strong influence on the Cd and Pb distribution patterns. A novel artificial neural network(ANN) model, using environmental input data, was used to predict the soil Cd and Pb contents of specified test dates. The performances of the ANN model and a traditional multilinear model were compared. The ANN model could successfully predict Cd and Pb content distributions, projecting that soil Cd and Pb contents will increase by 128% and 25%, respectively, by 2020. The results thus indicated that the economic condition of an area has a greater effect on trace element contents and distributions in the soil than the scale of the economy itself.展开更多
Creation of hydroelectric reservoirs in a certain large water system of China has led to a marked rise in mercury content of fish. Correlation analysis and stepwise regression of R (ratio of mercury content in carp fr...Creation of hydroelectric reservoirs in a certain large water system of China has led to a marked rise in mercury content of fish. Correlation analysis and stepwise regression of R (ratio of mercury content in carp from 12 reservoirs and that from rivers of the same water system) and various hydrologic parameters demonstrated that the ratio of catchment area and runoff was the crucial limiting factor. Other limiting factors were the ratio of flooding area of land and reservoir area of dead storage capacity, and the reciprocal of reservoir area. On this basis, the prediction equations of mercury accumulation in carp after reservoir construction, with one and two parameter respectively, were obtained. All these prediction models were proved to be of high precision.展开更多
基金supported by the National Key Research and Development Program of China(No.2018YFC1800100)the National Natural Science Foundation of China(No.42277475).
文摘Soil pollution levels can be quantified via sampling and experimental analysis;however,sampling is performed at discrete points with long distances owing to limited funding and human resources,and is insufficient to characterize the entire study area.Spatial prediction is required to comprehensively investigate potentially contaminated areas.Consequently,machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution.The characteristics,advantages,and applications of machine learning models used to predict soil pollution are reviewed in this study.Satisfactory model performance generally requires the following:1)selection of the most appropriate model with the required structure;2)selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability;3)improvement of model reliability through comprehensive model evaluation;and 4)integration of geostatistics with the machine learning model.With the enrichment of environmental data and development of algorithms,machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.
基金This work was supported by National Science Foundation of China with Grant No.721040202022 Science and Technology Think Tank Young Talent Program with Grant No.20220615zz07110051。
文摘The ongoing impact of the novel coronavirus disease 2019(COVID-19)on work and daily life persists as we transition from emergency to normal circumstances.The continuous mutation of viral strains has resulted in a shift from a single strain to multiple cross-strains,contributing to the spread of the epidemic.Variations in infection rates of the same strain occur because of the implementation of diverse preventive measures at different times.This study investigated the dynamics of the pandemic in the presence of concurrent strains.Building on the classical Susceptible,Exposed,Infected,and Recovered(SEIR)model,a robust piecewise multi-strain cross-epidemic trend prediction model was proposed that employs the Hodges–Lehmann estimator to handle uncertain and contamination-prone epidemic information.A comparative analysis of epidemic spread trend curves across diverse populations using different robust methods revealed the superiority of the Hodges–Lehmann estimator-based model over the traditional method.The accurate prediction results of the model demonstrate its high reliability in tracking the changing trend of the COVID-19 outbreak,thereby supporting its implementation in subsequent epidemic prevention and control measures.
基金supported by the National High Technology Research and Development Program of China (863 Program) (Nos. 2012AA100601 and 2012AA101401)the National Natural Science Foundation of China (Nos. 41271338 and 41301342)
文摘Multivariate statistical analyses were used to assess the contents and distributions of trace elements in agricultural soils in Xinzhou of Shanxi Province, China, and to identify their sources. Samples with high levels of trace elements were concentrated in eastern Xinzhou, with contents declining from the east to west. Principal component and redundancy analyses revealed strong correlations among Co, Cu, Mn, Ni, Se, V, and Zn contents, suggesting that these elements were derived from similar parent materials. There were also strong correlations between the contents of these elements and soil properties. Contents of Cd and Pb were significantly higher in the agricultural soil samples than in the background soil samples(P < 0.05), and were higher in areas with higher levels of gross domestic product but decreased with distance to the nearest road. Therefore, human activities appear to have a strong influence on the Cd and Pb distribution patterns. A novel artificial neural network(ANN) model, using environmental input data, was used to predict the soil Cd and Pb contents of specified test dates. The performances of the ANN model and a traditional multilinear model were compared. The ANN model could successfully predict Cd and Pb content distributions, projecting that soil Cd and Pb contents will increase by 128% and 25%, respectively, by 2020. The results thus indicated that the economic condition of an area has a greater effect on trace element contents and distributions in the soil than the scale of the economy itself.
文摘Creation of hydroelectric reservoirs in a certain large water system of China has led to a marked rise in mercury content of fish. Correlation analysis and stepwise regression of R (ratio of mercury content in carp from 12 reservoirs and that from rivers of the same water system) and various hydrologic parameters demonstrated that the ratio of catchment area and runoff was the crucial limiting factor. Other limiting factors were the ratio of flooding area of land and reservoir area of dead storage capacity, and the reciprocal of reservoir area. On this basis, the prediction equations of mercury accumulation in carp after reservoir construction, with one and two parameter respectively, were obtained. All these prediction models were proved to be of high precision.
