Relying on the conceptual DPSIR framework and MODFLOW analysis,this study used a mixed approach to produce groundwater resource management solutions for the Najafabad area in central Iran.According to DPSIR results,ag...Relying on the conceptual DPSIR framework and MODFLOW analysis,this study used a mixed approach to produce groundwater resource management solutions for the Najafabad area in central Iran.According to DPSIR results,agricultural activities put the highest pressure on groundwater resources in this region.The results showed the effectiveness of reducing waterwithdrawal over 30 years in maintaining the aquifer in a state of equilibrium.The best scenario consisted of cutting down extraction by 10%over the said period.Output maps of the water table rise at the Najafabad aquifer clearly showed that the groundwater management scenario involving a 10%reduction ofwater withdrawal was the most effective solution,as itwould raise thewater level by 6.7 m.Regarding other scenarios,reducing cultivated area by 20%was found to raise the water table by 5.03 m on average,while cutting down water withdrawal by 5%increased the water table by 3.6 m,and a 10%reduction of the cultivated area resulted in a 1.85mrise.The combinedmodel proposed here can be used for similar aquifers and can aid decision-makers and managers.展开更多
The United Nations adopted 17 Sustainable Development Goals(SDGs)to address societal,economic and environmental sustainability issues.The efficiency of SDGs monitoring could be improved by essential variables(EVs),whi...The United Nations adopted 17 Sustainable Development Goals(SDGs)to address societal,economic and environmental sustainability issues.The efficiency of SDGs monitoring could be improved by essential variables(EVs),which can help to better deal with massive data,interdisciplinary knowledge and workloads.However,in practice,effectively combining EVs with SDGs monitoring remains challenging.In this paper,we proposed a refining method of essential SDGs variables(ESDGVs)to land degradation.Firstly,we selected northwest China as our experimental region and extracted a group of variables related to land degradation from SDG indicators based on the DPSIR framework.Next,we identify the essential ones using a combined qualitative and quantitative methods with the criteria of feasibility,spatialization,and relevance which considered the issues of data acquisition,monitoring scale,and closeness to the land degradation.Finally,we analysed the monitoring role of ESDGVs.Results show that,compared to conventional observations,ESDGVs facilitate the monitoring and evaluation of regional SDGs with reduced efforts.And both climate and human activities have a facilitating or inhibiting effect on land degradation processes.In the future,we hope to have more mature data sets and consider adding more SDG indicators for ESDGVs’refinement.展开更多
Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,wa...Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.展开更多
文摘Relying on the conceptual DPSIR framework and MODFLOW analysis,this study used a mixed approach to produce groundwater resource management solutions for the Najafabad area in central Iran.According to DPSIR results,agricultural activities put the highest pressure on groundwater resources in this region.The results showed the effectiveness of reducing waterwithdrawal over 30 years in maintaining the aquifer in a state of equilibrium.The best scenario consisted of cutting down extraction by 10%over the said period.Output maps of the water table rise at the Najafabad aquifer clearly showed that the groundwater management scenario involving a 10%reduction ofwater withdrawal was the most effective solution,as itwould raise thewater level by 6.7 m.Regarding other scenarios,reducing cultivated area by 20%was found to raise the water table by 5.03 m on average,while cutting down water withdrawal by 5%increased the water table by 3.6 m,and a 10%reduction of the cultivated area resulted in a 1.85mrise.The combinedmodel proposed here can be used for similar aquifers and can aid decision-makers and managers.
基金supported by the key program of National Natural Science Foundation of China[grant number 41930650].
文摘The United Nations adopted 17 Sustainable Development Goals(SDGs)to address societal,economic and environmental sustainability issues.The efficiency of SDGs monitoring could be improved by essential variables(EVs),which can help to better deal with massive data,interdisciplinary knowledge and workloads.However,in practice,effectively combining EVs with SDGs monitoring remains challenging.In this paper,we proposed a refining method of essential SDGs variables(ESDGVs)to land degradation.Firstly,we selected northwest China as our experimental region and extracted a group of variables related to land degradation from SDG indicators based on the DPSIR framework.Next,we identify the essential ones using a combined qualitative and quantitative methods with the criteria of feasibility,spatialization,and relevance which considered the issues of data acquisition,monitoring scale,and closeness to the land degradation.Finally,we analysed the monitoring role of ESDGVs.Results show that,compared to conventional observations,ESDGVs facilitate the monitoring and evaluation of regional SDGs with reduced efforts.And both climate and human activities have a facilitating or inhibiting effect on land degradation processes.In the future,we hope to have more mature data sets and consider adding more SDG indicators for ESDGVs’refinement.
文摘Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.
