In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geo...In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geochemical modeling.Cluster analysis identified three main water types based on the major ion contents,where mineralization increased from group 1 to group 3.These groups were confirmed by FA/PCA,which demonstrated that groundwater quality is influenced by geochemical processes(water-rock interaction)and human practice(irrigation).The exponential semivariogram model WQI.Groundwater chemistry has a strong spatial structure for Mg,Na,Cl,and NO3,and a moderate spatial structure for EC,Ca,K,HCO3,and SO4.Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results.All water groups are supersaturated with respect to carbonate minerals,and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area.展开更多
CO_(2)+O_(2) in-situ leaching(ISL)of sandstonetype uranium ore represents the third generation of solution mining in China.In this study,reactive transport modeling of the interaction between hydrodynamic and geochemi...CO_(2)+O_(2) in-situ leaching(ISL)of sandstonetype uranium ore represents the third generation of solution mining in China.In this study,reactive transport modeling of the interaction between hydrodynamic and geochemical reactions is performed to enable better prediction and regulation of the CO_(2)+O_(2) in-situ leaching process of uranium.Geochemical reactions between mining solutions and rock,and the kinetic uranium dissolution controlled by O_(2)(aq)and bicarbonate(HCO_(3)-)are considered in the CO_(2)+O_(2) ISL reactive transport model of a typical sandstone-hosted uranium ore deposit in northern China.The reactive leaching of uranium is most sensitive to the spatial distribution of the mineralogical properties of the uranium deposit.Stochastic geostatistical models are used to represent the uncertainty on the spatial distribution of mineral grades.A Monte Carlo analysis was also performed to simulate the uranium production variability over an entire set of geostatistical realizations.The ISL stochastic simulation performed with the selected geostatistical realizations approximates the uranium production variability well.The simulation results of the ISL reactive transport model show that the extent of the uranium plume is highly dependent on mineralogical heterogeneity.The uncertainty analysis suggests the effect of uranium grade heterogeneity was found to be important to improve the accurate capture of the uncertainty.This study provides guidance for the accurate simulation and dynamic regulation of the CO_(2)+O_(2) leaching process of uranium at the scale of large mining areas.展开更多
Background Opisthorchiasis infected by Opisthorchis felineus has represented a significant but understudied public health issue for the population residing in Western Siberia and the Ural Region of the Russian Federat...Background Opisthorchiasis infected by Opisthorchis felineus has represented a significant but understudied public health issue for the population residing in Western Siberia and the Ural Region of the Russian Federation.This study aimed to produce high-resolution spatial–temporal disease risk maps for guiding prevention strategy in the above region.Methods Data on prevalence and surveillance data reflecting reported annual incidence rate of O.felineus infection in the study region were collected through systematic review and the annual reports of the Ministry of Health of the Russian Federation.Environmental,socioeconomic and demographic data were downloaded from different open-access data sources.An advanced multivariate Bayesian geostatistical modeling approach was developed to estimate the O.felineus infection risk at high-resolution spatial–temporal by joint analysis of survey and surveillance data,incorporating potential influencing factors and spatial–temporal random effects.The annual spatial–temporal risk maps of O.felineus infection at a resolution of 5×5 km^(2) were produced.Results The final dataset included 76 locations of survey data and 303 locations of surveillance data on O.felineus infection.The infection risk was high(>25%)in most part of central and eastern regions,and relatively low(<25%)in most part of western region,while temporal variations were observed across the sub-regions in recent decades.Particularly,in the densely populated eastern region,there was an increased trend of infection risk from 30.46%(95%Bayesian credible intervals,BCI 10.78–53.45%)in 1980 to 53.39%(95%BCI 13.77–91.93%)in 2019 and gradually transformed into high-risk.In the study region(excluding the western region due to data sparsity),the population-adjusted estimated prevalence was 46.61%(95%BCI 15.09–76.50%)in 2019,corresponding to approximately 7.91 million(95%BCI 2.56–12.98 million)people infected.Conclusions The high-resolution risk maps of O.felineus in Western Siberia and the Ural Region of the Russian Federation have effectively captured the risk profiles,suggesting the infection risk remains high in recent years and providing substantial evidence for spatial-target control and preventive strategies.展开更多
Background Clonorchiasis is caused by the ingestion of raw freshwater fish containing infective metacercariae ofClonorchis sinensis.This study aimed to fully evaluate disease burden in terms of disability-adjusted lif...Background Clonorchiasis is caused by the ingestion of raw freshwater fish containing infective metacercariae ofClonorchis sinensis.This study aimed to fully evaluate disease burden in terms of disability-adjusted life years(DALYs)for clonorchiasis in China.Methods Following our previous study which established the fine-scale prevalence distribution ofC.sinensis infection in China,we further adopted Bayesian geostatistical models to estimate the infection intensity in terms of eggs per gram of feces(EPG)in infected individuals based on the national surveillance data of clonorchiasis between 2016 and 2021.Disability weight was then captured through its quantitative association with EPG,and used to estimate years of life living with a disability(YLDs).Incidence of cholangiocarcinoma attributed toC.sinensis infection was employed to calculate years of life lost(YLLs).DALYs was then estimated at 5×5 km2 resolution,and aggregated by areas and populations.Results In 2020,431,009[95%Bayesian credible interval(BCI):370,427 to 500,553]DALYs were exerted due to clonorchiasis in China,of which 372,918(95%BCI:318,775-435,727)was due to YLDs and 57,998(95%BCI:50,816-66,069)due to YLLs.The DALYs,YLDs and YLLs per 1000 were 0.31(95%BCI:0.26-0.35),0.26(95%BCI:0.23-0.31),and 0.04(95%BCI:0.04-0.05),respectively.The DALYs predominantly distributed in southern areas including Guangxi(201,029,95%BCI:157,589-248,287)and Guangdong(161,958,95%BCI:128,326-211,358).The DALYs was over doubled in male(302,678,95%BCI:262,028-348,300)than in female(127,970,95%BCI:106,834-151,699),and high in middle aged population.Conclusions Clonorchiasis causes significant disease burden in China especially in southern areas including Guangxi and Guangdong.Urgent control is needed for clonorchiasis in the endemic areas with high burden,and adult males need to be prioritized.展开更多
National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies a...National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.展开更多
文摘In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geochemical modeling.Cluster analysis identified three main water types based on the major ion contents,where mineralization increased from group 1 to group 3.These groups were confirmed by FA/PCA,which demonstrated that groundwater quality is influenced by geochemical processes(water-rock interaction)and human practice(irrigation).The exponential semivariogram model WQI.Groundwater chemistry has a strong spatial structure for Mg,Na,Cl,and NO3,and a moderate spatial structure for EC,Ca,K,HCO3,and SO4.Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results.All water groups are supersaturated with respect to carbonate minerals,and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area.
基金jointly supported by the National Key Research and Development Program of China(No.2019YFC1804304)the National Natural Science Foundation of China(Nos.2167212,41772254)。
文摘CO_(2)+O_(2) in-situ leaching(ISL)of sandstonetype uranium ore represents the third generation of solution mining in China.In this study,reactive transport modeling of the interaction between hydrodynamic and geochemical reactions is performed to enable better prediction and regulation of the CO_(2)+O_(2) in-situ leaching process of uranium.Geochemical reactions between mining solutions and rock,and the kinetic uranium dissolution controlled by O_(2)(aq)and bicarbonate(HCO_(3)-)are considered in the CO_(2)+O_(2) ISL reactive transport model of a typical sandstone-hosted uranium ore deposit in northern China.The reactive leaching of uranium is most sensitive to the spatial distribution of the mineralogical properties of the uranium deposit.Stochastic geostatistical models are used to represent the uncertainty on the spatial distribution of mineral grades.A Monte Carlo analysis was also performed to simulate the uranium production variability over an entire set of geostatistical realizations.The ISL stochastic simulation performed with the selected geostatistical realizations approximates the uranium production variability well.The simulation results of the ISL reactive transport model show that the extent of the uranium plume is highly dependent on mineralogical heterogeneity.The uncertainty analysis suggests the effect of uranium grade heterogeneity was found to be important to improve the accurate capture of the uncertainty.This study provides guidance for the accurate simulation and dynamic regulation of the CO_(2)+O_(2) leaching process of uranium at the scale of large mining areas.
基金Natural Science Foundation of Guangdong Province(project no.2025A1515011200)The National Natural Science Foundation of China(project no.82073665)CMB Open Competition program(Grant 17-274).
文摘Background Opisthorchiasis infected by Opisthorchis felineus has represented a significant but understudied public health issue for the population residing in Western Siberia and the Ural Region of the Russian Federation.This study aimed to produce high-resolution spatial–temporal disease risk maps for guiding prevention strategy in the above region.Methods Data on prevalence and surveillance data reflecting reported annual incidence rate of O.felineus infection in the study region were collected through systematic review and the annual reports of the Ministry of Health of the Russian Federation.Environmental,socioeconomic and demographic data were downloaded from different open-access data sources.An advanced multivariate Bayesian geostatistical modeling approach was developed to estimate the O.felineus infection risk at high-resolution spatial–temporal by joint analysis of survey and surveillance data,incorporating potential influencing factors and spatial–temporal random effects.The annual spatial–temporal risk maps of O.felineus infection at a resolution of 5×5 km^(2) were produced.Results The final dataset included 76 locations of survey data and 303 locations of surveillance data on O.felineus infection.The infection risk was high(>25%)in most part of central and eastern regions,and relatively low(<25%)in most part of western region,while temporal variations were observed across the sub-regions in recent decades.Particularly,in the densely populated eastern region,there was an increased trend of infection risk from 30.46%(95%Bayesian credible intervals,BCI 10.78–53.45%)in 1980 to 53.39%(95%BCI 13.77–91.93%)in 2019 and gradually transformed into high-risk.In the study region(excluding the western region due to data sparsity),the population-adjusted estimated prevalence was 46.61%(95%BCI 15.09–76.50%)in 2019,corresponding to approximately 7.91 million(95%BCI 2.56–12.98 million)people infected.Conclusions The high-resolution risk maps of O.felineus in Western Siberia and the Ural Region of the Russian Federation have effectively captured the risk profiles,suggesting the infection risk remains high in recent years and providing substantial evidence for spatial-target control and preventive strategies.
基金National Natural Science Foundation of China(Grant No.82373645)Shanghai Talent Program,National Key Research and Development Program of China(Grant Nos.2021YFC2300800 and 2021YFC2300804)National Natural Science Foundation of China(Grant No.82073665)。
文摘Background Clonorchiasis is caused by the ingestion of raw freshwater fish containing infective metacercariae ofClonorchis sinensis.This study aimed to fully evaluate disease burden in terms of disability-adjusted life years(DALYs)for clonorchiasis in China.Methods Following our previous study which established the fine-scale prevalence distribution ofC.sinensis infection in China,we further adopted Bayesian geostatistical models to estimate the infection intensity in terms of eggs per gram of feces(EPG)in infected individuals based on the national surveillance data of clonorchiasis between 2016 and 2021.Disability weight was then captured through its quantitative association with EPG,and used to estimate years of life living with a disability(YLDs).Incidence of cholangiocarcinoma attributed toC.sinensis infection was employed to calculate years of life lost(YLLs).DALYs was then estimated at 5×5 km2 resolution,and aggregated by areas and populations.Results In 2020,431,009[95%Bayesian credible interval(BCI):370,427 to 500,553]DALYs were exerted due to clonorchiasis in China,of which 372,918(95%BCI:318,775-435,727)was due to YLDs and 57,998(95%BCI:50,816-66,069)due to YLLs.The DALYs,YLDs and YLLs per 1000 were 0.31(95%BCI:0.26-0.35),0.26(95%BCI:0.23-0.31),and 0.04(95%BCI:0.04-0.05),respectively.The DALYs predominantly distributed in southern areas including Guangxi(201,029,95%BCI:157,589-248,287)and Guangdong(161,958,95%BCI:128,326-211,358).The DALYs was over doubled in male(302,678,95%BCI:262,028-348,300)than in female(127,970,95%BCI:106,834-151,699),and high in middle aged population.Conclusions Clonorchiasis causes significant disease burden in China especially in southern areas including Guangxi and Guangdong.Urgent control is needed for clonorchiasis in the endemic areas with high burden,and adult males need to be prioritized.
基金funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101036458.
文摘National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.
