In interconnected environmental systems, the innocuous failure of one component can sometimes trigger a subsequent domino-like effect resulting in a cascading collapse of the entire system. Risk analysis in “real world” contexts frequently requires the need to simultaneously contrast numerous uncertain factors and difficult-to-capture dimensions. Monte Carlo simulation modelling has often been employed to integrate uncertain inputs and to construct probability distributions of the resulting outputs. Visual analytics and data visualization can be used to support the processing, analyzing, and communicating of the influence of multi-variable uncertainties on the decision-making process. In this paper, the novel Simulation Decomposition (SimDec) analytical technique is extended into complex assessments of cascading risk analysis and used to quantitatively examine situations involving potentially catastrophic, dominolike collapses of an entire system. SimDec analysis proves to be beneficial due to its ability to reveal interdependencies in complex models, its ease of decision-maker perception, its visualizable analytic capabilities, and its significantly lower computational burdens. The case example visually demonstrates that when a system collapse is a low-probability/high-impact event, more expensive, reactive policies minimize the overall value loss under conditions of system survival, while more proactive policies enable better loss prevention under system survival. However, proactive approaches significantly decrease the likelihoods and magnitudes of losses for scenarios resulting from the collapse of the system. Such findings would not have been revealed without the visualization provided by SimDec.
{"title":"Extending Simulation Decomposition Analysis into Systemic Risk Planning for Domino-Like Cascading Effects in Environmental Systems","authors":"M. Kozlova, J. Yeomans","doi":"10.3808/jeil.202200079","DOIUrl":"https://doi.org/10.3808/jeil.202200079","url":null,"abstract":"In interconnected environmental systems, the innocuous failure of one component can sometimes trigger a subsequent domino-like effect resulting in a cascading collapse of the entire system. Risk analysis in “real world” contexts frequently requires the need to simultaneously contrast numerous uncertain factors and difficult-to-capture dimensions. Monte Carlo simulation modelling has often been employed to integrate uncertain inputs and to construct probability distributions of the resulting outputs. Visual analytics and data visualization can be used to support the processing, analyzing, and communicating of the influence of multi-variable uncertainties on the decision-making process. In this paper, the novel Simulation Decomposition (SimDec) analytical technique is extended into complex assessments of cascading risk analysis and used to quantitatively examine situations involving potentially catastrophic, dominolike collapses of an entire system. SimDec analysis proves to be beneficial due to its ability to reveal interdependencies in complex models, its ease of decision-maker perception, its visualizable analytic capabilities, and its significantly lower computational burdens. The case example visually demonstrates that when a system collapse is a low-probability/high-impact event, more expensive, reactive policies minimize the overall value loss under conditions of system survival, while more proactive policies enable better loss prevention under system survival. However, proactive approaches significantly decrease the likelihoods and magnitudes of losses for scenarios resulting from the collapse of the system. Such findings would not have been revealed without the visualization provided by SimDec.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128077908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
China is a large economy being troubled by excessive energy consumption, serious environmental pollution and carbon emission problems. To reduce energy consumption, pollutant and carbon emission, understanding their trend and their relationships with the socioeconomic development is essential. Among various sectors, transportation sector is energy-intensive and emits a large amount of air toxics and CO2, and therefore deserves primary attention. This study took carbon emission as a proxy of environmental degradation and employed an analytical framework composed of input-output analysis, ecological network analysis and structural decomposition analysis to scrutinize production- and consumption-based energy consumption and carbon emission (ECCE), to analyze effects of final demand elasticity on them, mutualism relationships between transportation sector and other sectors, and pulling/ driving force of trans- portation sector on the ECCE of the whole economy, and to explore the drivers affecting ECCE of transportation sector. Results comprise the increase trend of ECCE of transportation sector, the noticeable relevance of transportation sector to ECCE, the domination of control relationship and the increase of competition relationship between transportation sector and other sectors, the significant effects of final demand structure, per capita final demand, production structure and sectoral carbon emission density on ECCE. According to these results, as for transportation sector, adjusting the energy structure, improving the transportation efficiency and coordinating the rela- tions between the transportation and its relevant sectors are suggested. The analytical framework facilitates ECCE policy devising in transportation sector for China’s target of energy conservation and emission reduction and are instructive for other countries’ ECCE actions.
{"title":"Tracing Energy Conservation and Emission Reduction in China’s Transportation Sector","authors":"R. Hao","doi":"10.3808/jeil.202100069","DOIUrl":"https://doi.org/10.3808/jeil.202100069","url":null,"abstract":"China is a large economy being troubled by excessive energy consumption, serious environmental pollution and carbon emission problems. To reduce energy consumption, pollutant and carbon emission, understanding their trend and their relationships with the socioeconomic development is essential. Among various sectors, transportation sector is energy-intensive and emits a large amount of air toxics and CO2, and therefore deserves primary attention. This study took carbon emission as a proxy of environmental degradation and employed an analytical framework composed of input-output analysis, ecological network analysis and structural decomposition analysis to scrutinize production- and consumption-based energy consumption and carbon emission (ECCE), to analyze effects of final demand elasticity on them, mutualism relationships between transportation sector and other sectors, and pulling/ driving force of trans- portation sector on the ECCE of the whole economy, and to explore the drivers affecting ECCE of transportation sector. Results comprise the increase trend of ECCE of transportation sector, the noticeable relevance of transportation sector to ECCE, the domination of control relationship and the increase of competition relationship between transportation sector and other sectors, the significant effects of final demand structure, per capita final demand, production structure and sectoral carbon emission density on ECCE. According to these results, as for transportation sector, adjusting the energy structure, improving the transportation efficiency and coordinating the rela- tions between the transportation and its relevant sectors are suggested. The analytical framework facilitates ECCE policy devising in transportation sector for China’s target of energy conservation and emission reduction and are instructive for other countries’ ECCE actions.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114207523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the main component of greenhouse gases, excessive carbon emissions are responsible for global climate change and anomalies. Carbon emission metabolism is essential for alleviating climate changes globally. Here, a dynamic input-output model is developed for analyzing carbon emission metabolism and ecological hierarchy. Direct and indirect carbon emissions are evaluated, and mutual relationships between various sectors are explored. The innovation of this research is summarized as follows: firstly, for carbon emission, the competition relationships reveal good interactions in terms of emission reduction while mutualism relationship provides effective pathways to mitigate carbon emissions between pairwise sectors simultaneously. Moreover, analysis of direct and indirect carbon emissions could help to extract embodied sectors with large emissions and identify whether the sectors should adjust their production structure or upgrade the clean combustion technology. In addition, ecological hierarchy analysis would help to identify key sectors lacking driving or pulling force during study period. It provides a new insight into the potential sectors which play an important role in the emission reduction. Results show that carbon emissions mainly concentrated in electricity-generation, manufacturing, and transportation sectors. In Guangdong province, upgrading the clean combustion technology in electric power generation and energy extraction sectors would drive other sectors to cut emissions and adjusting the production structure of the construction sector also contribute to achieve this goal. Tertiary industry is not sufficiently promoting Guangdong’s economic development, and therefore plays a restrictive role in the current economy. Results could help decision makers to develop emission mitigation policies.
{"title":"Dynamic Input Output Analysis: Carbon, Water and Ecological Hierarchy","authors":"J. Z. Li, S. C. Li","doi":"10.3808/jeil.202100065","DOIUrl":"https://doi.org/10.3808/jeil.202100065","url":null,"abstract":"As the main component of greenhouse gases, excessive carbon emissions are responsible for global climate change and anomalies. Carbon emission metabolism is essential for alleviating climate changes globally. Here, a dynamic input-output model is developed for analyzing carbon emission metabolism and ecological hierarchy. Direct and indirect carbon emissions are evaluated, and mutual relationships between various sectors are explored. The innovation of this research is summarized as follows: firstly, for carbon emission, the competition relationships reveal good interactions in terms of emission reduction while mutualism relationship provides effective pathways to mitigate carbon emissions between pairwise sectors simultaneously. Moreover, analysis of direct and indirect carbon emissions could help to extract embodied sectors with large emissions and identify whether the sectors should adjust their production structure or upgrade the clean combustion technology. In addition, ecological hierarchy analysis would help to identify key sectors lacking driving or pulling force during study period. It provides a new insight into the potential sectors which play an important role in the emission reduction. Results show that carbon emissions mainly concentrated in electricity-generation, manufacturing, and transportation sectors. In Guangdong province, upgrading the clean combustion technology in electric power generation and energy extraction sectors would drive other sectors to cut emissions and adjusting the production structure of the construction sector also contribute to achieve this goal. Tertiary industry is not sufficiently promoting Guangdong’s economic development, and therefore plays a restrictive role in the current economy. Results could help decision makers to develop emission mitigation policies.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133495777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work analyzed the spatial and temporal variations of the extreme precipitation over China during the period 1982 to 2019, based on GPCC data. Meanwhile, the spatially clustering characteristic of China is evaluated based on the trends of summer precipitation through K-means. This is the first to clustering based on the trend of summer precipitation. The results indicated that the spatial distribution of MK trends for the summer precipitation indices over China during 1982 ~ 2019 shows increasing and decreasing trends in different regions. The extreme precipitation has been found mainly in Pearl River Basin, Southeastern River Basin, Huaihe River Basin, and the lower reaches of the Yangtze River. The risk of floods in SERB (the Southeastern River Basin), especially those caused by short-term heavy precipitation, may increase in recent decades. Four clusters are identified through K-means, which can be named as stable (59%), extreme wetter (5%), dryer (19%), and wetter (17%) zones. Combining the spatial distribution of the multi-year average of precipitation indicators and four clusters, the ‘wet to wetter and dry to dryer’ is found in China summer precipitation.
{"title":"Extreme Summer Precipitation Events in China and Their Changes during 1982 ~ 2019","authors":"F. Wang, R. Duan, Y. F. Li, C. Tian","doi":"10.3808/jeil.202100066","DOIUrl":"https://doi.org/10.3808/jeil.202100066","url":null,"abstract":"This work analyzed the spatial and temporal variations of the extreme precipitation over China during the period 1982 to 2019, based on GPCC data. Meanwhile, the spatially clustering characteristic of China is evaluated based on the trends of summer precipitation through K-means. This is the first to clustering based on the trend of summer precipitation. The results indicated that the spatial distribution of MK trends for the summer precipitation indices over China during 1982 ~ 2019 shows increasing and decreasing trends in different regions. The extreme precipitation has been found mainly in Pearl River Basin, Southeastern River Basin, Huaihe River Basin, and the lower reaches of the Yangtze River. The risk of floods in SERB (the Southeastern River Basin), especially those caused by short-term heavy precipitation, may increase in recent decades. Four clusters are identified through K-means, which can be named as stable (59%), extreme wetter (5%), dryer (19%), and wetter (17%) zones. Combining the spatial distribution of the multi-year average of precipitation indicators and four clusters, the ‘wet to wetter and dry to dryer’ is found in China summer precipitation.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114401610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seasonal freezing and thawing is a major cause of serious soil salinization in Hetao irrigation area under climate change. In this paper, the Hetao irrigation area has been selected as an example and field monitoring experiments were used to analyze and study the dynamics of soil water, salt, and heat during freezing and thawing periods. The research results show that the unfrozen water presents a “concave” shape change, and as the degree of freezing deepens, the total water content continues to increase; the salt content also shows an increasing trend, with the 0 ~ 100 cm soil layer salt rate being 43.73%, of which 0 ~ 10 cm soil layer is 91.56%, followed by the 10 ~ 20 cm soil layer, which is 41.78%. The surface aggregation effect is obvious. The temperature data obtained by analysis shows that the freezing process is one-way freezing and the melting process is two-way melting. The above analysis results can provide a scientific basis for scientific planting and soil salinization prevention and control in irrigation areas.
{"title":"Characteristics of Seasonal Frozen Soil in Hetao Irrigation District under Climate Change","authors":"R. Duan, F. Wang, Y. F. Li","doi":"10.3808/jeil.202100068","DOIUrl":"https://doi.org/10.3808/jeil.202100068","url":null,"abstract":"Seasonal freezing and thawing is a major cause of serious soil salinization in Hetao irrigation area under climate change. In this paper, the Hetao irrigation area has been selected as an example and field monitoring experiments were used to analyze and study the dynamics of soil water, salt, and heat during freezing and thawing periods. The research results show that the unfrozen water presents a “concave” shape change, and as the degree of freezing deepens, the total water content continues to increase; the salt content also shows an increasing trend, with the 0 ~ 100 cm soil layer salt rate being 43.73%, of which 0 ~ 10 cm soil layer is 91.56%, followed by the 10 ~ 20 cm soil layer, which is 41.78%. The surface aggregation effect is obvious. The temperature data obtained by analysis shows that the freezing process is one-way freezing and the melting process is two-way melting. The above analysis results can provide a scientific basis for scientific planting and soil salinization prevention and control in irrigation areas.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123439116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The change of soil moisture has significant influence on the surface energy distribution and evapotranspiration process and causes the climatic environment changes. It is thus urgent to detect soil moisture facing climate change problems. Based on apparent thermal inertia method, the GLDAS soil moisture of the layer in 0 ~ 10 cm, 10 ~ 40 cm, 40 ~ 100 cm, 100 ~ 200 cm under the ground surface are proceeded for downscaling and the monthly soil moisture are obtained by combination of MODIS data. Statistics of precipitation and temperature of 13 meteorological stations in the Three River Headwater Region (TRHR) are collected to analyze the correlation between soil moisture change and temperature and precipitation. The correlation between soil moisture and evapotranspiration (ET) are analyzed by using the surface energy balance system model to calculate the average evapotranspiration in the study area. The result indicated that (1) Apparent thermal inertia (ATI) is positively correlated with soil depths in the four layers below the surface and has the best correlation with 0 ~ 10 cm soil moisture. Soil moisture increases with soil depth. Soil moisture is lowest under 0 ~ 10 cm depth while highest under 10 ~ 40 cm on average. (2) In terms of spatial distribution of TRHR, soil moisture is higher in the northwest and southeast, but lower in the southwest and northeast than average. The temporal variation of the soil moisture in one year was high in summer and low in winter, and the average annual soil moisture increased with time. (3) Soil moisture is negatively correlated with temperature and positively corre-lated with precipitation. There is a positive correlation between soil moisture and evapotranspiration. The linear regression coefficient of determination R2 is 0.8489.
{"title":"Distribution Characteristics of Soil Moisture in the Three Rivers Headwaters Region, China","authors":"R. Cao, X. Jin","doi":"10.3808/jeil.202100067","DOIUrl":"https://doi.org/10.3808/jeil.202100067","url":null,"abstract":"The change of soil moisture has significant influence on the surface energy distribution and evapotranspiration process and causes the climatic environment changes. It is thus urgent to detect soil moisture facing climate change problems. Based on apparent thermal inertia method, the GLDAS soil moisture of the layer in 0 ~ 10 cm, 10 ~ 40 cm, 40 ~ 100 cm, 100 ~ 200 cm under the ground surface are proceeded for downscaling and the monthly soil moisture are obtained by combination of MODIS data. Statistics of precipitation and temperature of 13 meteorological stations in the Three River Headwater Region (TRHR) are collected to analyze the correlation between soil moisture change and temperature and precipitation. The correlation between soil moisture and evapotranspiration (ET) are analyzed by using the surface energy balance system model to calculate the average evapotranspiration in the study area. The result indicated that (1) Apparent thermal inertia (ATI) is positively correlated with soil depths in the four layers below the surface and has the best correlation with 0 ~ 10 cm soil moisture. Soil moisture increases with soil depth. Soil moisture is lowest under 0 ~ 10 cm depth while highest under 10 ~ 40 cm on average. (2) In terms of spatial distribution of TRHR, soil moisture is higher in the northwest and southeast, but lower in the southwest and northeast than average. The temporal variation of the soil moisture in one year was high in summer and low in winter, and the average annual soil moisture increased with time. (3) Soil moisture is negatively correlated with temperature and positively corre-lated with precipitation. There is a positive correlation between soil moisture and evapotranspiration. The linear regression coefficient of determination R2 is 0.8489.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131395543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The combined cooling, heating, and power (CCHP) system has been explored extensively due to its ability to reduce the carbon dioxide emission and improve the energy-utilization efficiency. However, the existing studies associated with CCHP system rarely concerned for the system reliability, although it was capable of enhancing the stability of operational patterns. In this study, predefined reliability coefficient (i.e., r) was incorporated innovatively into a CCHP system optimization model in order to examine the influence of reliability level on model results. A variety of solutions under different r values were obtained, which effectively reflected the trade-off between system economy and reliability. The CCHP system of a hotel in Shanghai, China, was used as a study case for demonstration. The generated results indicated that the system cost would increase with the increase of reliability level; meanwhile, the user requirements in cooling, heating and electricity were ensured greatly. The successful application of proposed optimization model in real case is expected to be a good example for CCHP system management.
{"title":"A Reliability-Based Optimization Model for Operational Management of CCHP System","authors":"X. Wang, K. Liu, Y. Tang, Y. Xu, W. Li","doi":"10.3808/jeil.202100060","DOIUrl":"https://doi.org/10.3808/jeil.202100060","url":null,"abstract":"The combined cooling, heating, and power (CCHP) system has been explored extensively due to its ability to reduce the carbon dioxide emission and improve the energy-utilization efficiency. However, the existing studies associated with CCHP system rarely concerned for the system reliability, although it was capable of enhancing the stability of operational patterns. In this study, predefined reliability coefficient (i.e., r) was incorporated innovatively into a CCHP system optimization model in order to examine the influence of reliability level on model results. A variety of solutions under different r values were obtained, which effectively reflected the trade-off between system economy and reliability. The CCHP system of a hotel in Shanghai, China, was used as a study case for demonstration. The generated results indicated that the system cost would increase with the increase of reliability level; meanwhile, the user requirements in cooling, heating and electricity were ensured greatly. The successful application of proposed optimization model in real case is expected to be a good example for CCHP system management.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116320126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the typically large sizes of many stormwater ponds, numerically modeling the bacteria fate and transport within these ponds is more practical than in situ monitoring. However, bacteria fate and transport models lack proper verification and rely on numerous assumptions without proper validation of these assumptions. In this paper, a sophisticated hydrodynamic model is developed for estimating bacteria levels in the Inverness stormwater pond, in Calgary, Alberta, Canada, and is verified in two ways. First, the bacteria concentrations predicted by the developed model for several locations within the pond were compared to data collected during two separate field campaigns at the pond. Good agreement was observed and while it was found that contamination increased over time between the two field campaigns, the most contaminated location was consistently in the west wing. Second, fluid flow velocity vectors in numerous locations were measured and compared with the modeled results. The impacts of model assumptions and inputs on the bacteria distribution in the pond were also assessed. The model was run for various particle-attachment rate and sizes, various rain hyetographs and various wind conditions. It was found that synthetic hyetographs can be used for design purposes to find the optimal location for withdrawal. The effect of wind direction was found to be event specific and location specific. In general, wind was found to play a crucial role in the bacteria distribution in the pond.
{"title":"Influence of Environmental Factors in Hydrodynamic Modelling of Bacterial Distribution in Stormwater Ponds","authors":"F. Allafchi, C. Valeo, J. He, N. Neumann","doi":"10.3808/jeil.202100062","DOIUrl":"https://doi.org/10.3808/jeil.202100062","url":null,"abstract":"Due to the typically large sizes of many stormwater ponds, numerically modeling the bacteria fate and transport within these ponds is more practical than in situ monitoring. However, bacteria fate and transport models lack proper verification and rely on numerous assumptions without proper validation of these assumptions. In this paper, a sophisticated hydrodynamic model is developed for estimating bacteria levels in the Inverness stormwater pond, in Calgary, Alberta, Canada, and is verified in two ways. First, the bacteria concentrations predicted by the developed model for several locations within the pond were compared to data collected during two separate field campaigns at the pond. Good agreement was observed and while it was found that contamination increased over time between the two field campaigns, the most contaminated location was consistently in the west wing. Second, fluid flow velocity vectors in numerous locations were measured and compared with the modeled results. The impacts of model assumptions and inputs on the bacteria distribution in the pond were also assessed. The model was run for various particle-attachment rate and sizes, various rain hyetographs and various wind conditions. It was found that synthetic hyetographs can be used for design purposes to find the optimal location for withdrawal. The effect of wind direction was found to be event specific and location specific. In general, wind was found to play a crucial role in the bacteria distribution in the pond.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129888419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Issues of water security are rapidly becoming more widely recognized as impacted. Increased levels of carbon dioxide are clearly evident and long-term temperature increases are clearly evident. These indicators are being used to compile evidence that sea level rise in the future will be between 0.3 and 1.0 m by 2100 and, combined with more severe storms along coastlines, will translate into increasing challenges for coastal cities. The enormous glaciers in Greenland and Antarctica will continue to contribute to sea level rise but fortunately, at modest levels, for thousands of years. On the other hand, land-based glaciers will continue to become depleted and the ramifications to agricultural practices are expected to be profound, with situations of significant percentages of the world’s land-based glaciers being lost by 2100. Further, the disappearance rate of the Arctic Ocean ice cover is already profoundly evident, with losses of ice cover of about 13.1 percent per decade now occurring. Rates of warming in the Arctic are increasing at two to three times the global annual average and warrant further forecasting of the implications. With the reduced ice cover, the water in the Arctic Ocean is now absorbing the energy from the sun, not reflecting the sun’s energy, thereby accelerating further ice cover melting. The result is that the jet stream is weakening and evidence is mounting that there will be increased excursions of the polar vortex causing very cold weather extremes in northern hemisphere areas.
{"title":"Global Climate Change: Assessing the Importance of the Roles of Ice Cover and Glacial Changes","authors":"E. McBean","doi":"10.3808/jeil.202100061","DOIUrl":"https://doi.org/10.3808/jeil.202100061","url":null,"abstract":"Issues of water security are rapidly becoming more widely recognized as impacted. Increased levels of carbon dioxide are clearly evident and long-term temperature increases are clearly evident. These indicators are being used to compile evidence that sea level rise in the future will be between 0.3 and 1.0 m by 2100 and, combined with more severe storms along coastlines, will translate into increasing challenges for coastal cities. The enormous glaciers in Greenland and Antarctica will continue to contribute to sea level rise but fortunately, at modest levels, for thousands of years. On the other hand, land-based glaciers will continue to become depleted and the ramifications to agricultural practices are expected to be profound, with situations of significant percentages of the world’s land-based glaciers being lost by 2100. Further, the disappearance rate of the Arctic Ocean ice cover is already profoundly evident, with losses of ice cover of about 13.1 percent per decade now occurring. Rates of warming in the Arctic are increasing at two to three times the global annual average and warrant further forecasting of the implications. With the reduced ice cover, the water in the Arctic Ocean is now absorbing the energy from the sun, not reflecting the sun’s energy, thereby accelerating further ice cover melting. The result is that the jet stream is weakening and evidence is mounting that there will be increased excursions of the polar vortex causing very cold weather extremes in northern hemisphere areas.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116749715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Research on water environment capacity is an important part of watershed water environment management. Based on the basic theory of water environment capacity, this study obtained and analyzed the dynamic water environment capacity in Fenghuangshan drinking water source area, Three Gorges Reservoir. In this study, eight water indicators were taken as the indicator object, and the dynamic water environment capacity change range from 2012 to 2017 was permanganate index, biochemical oxygen demand, ferrum, ammonia nitrogen, total phosphorus, anionic surfactant, hexavalent chromium and cuprum, in descending order. The the dynamic water environment capacity of anionic surfactant would increase mainly in pre-flood stage and after storage stage and the other indicators would increase mainly in the end of the flood stage and storage stage. Besides, when the difference between inflow and outflow was positive, the dynamic water environment capacity of anionic surfactant would be reduced and other indicators would be increased. Moreover, the results of time-series analysis introduced showed that this method could be used to predict the change trend of dynamic water environment capacity. Overall, the research in this paper could be a new reference for scientists and decision makers in analyzing, predicting and control the change trend of water environment.
{"title":"Study on Dynamic Water Environmental Capacity of Fenghuangshan Drinking Water Source Area in Three Gorges Reservoir","authors":"S. Liu, Q. Zhu, A. Zhai, X. Ding","doi":"10.3808/jeil.202100063","DOIUrl":"https://doi.org/10.3808/jeil.202100063","url":null,"abstract":"Research on water environment capacity is an important part of watershed water environment management. Based on the basic theory of water environment capacity, this study obtained and analyzed the dynamic water environment capacity in Fenghuangshan drinking water source area, Three Gorges Reservoir. In this study, eight water indicators were taken as the indicator object, and the dynamic water environment capacity change range from 2012 to 2017 was permanganate index, biochemical oxygen demand, ferrum, ammonia nitrogen, total phosphorus, anionic surfactant, hexavalent chromium and cuprum, in descending order. The the dynamic water environment capacity of anionic surfactant would increase mainly in pre-flood stage and after storage stage and the other indicators would increase mainly in the end of the flood stage and storage stage. Besides, when the difference between inflow and outflow was positive, the dynamic water environment capacity of anionic surfactant would be reduced and other indicators would be increased. Moreover, the results of time-series analysis introduced showed that this method could be used to predict the change trend of dynamic water environment capacity. Overall, the research in this paper could be a new reference for scientists and decision makers in analyzing, predicting and control the change trend of water environment.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130669095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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