University of TehranJournal of Environmental Studies1025-862051220250921Risk Analysis of Carcinogenic and Non-Carcinogenic Effects of Heavy Metals in Groundwater of the Eslamshahr RegionRisk Analysis of Carcinogenic and Non-Carcinogenic Effects of Heavy Metals in Groundwater of the Eslamshahr Region14516410375210.22059/jes.2025.385656.1008552FAAmirZahraei SalehiDepartment of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran0009-0000-9567-7249Amir ArsellonPardakhtiDepartment of Environmental Engineering, Aras International Campus, University of Tehran, Aras, Iran0000-0002-7770-137XZeinabGharianpourDepartment of Civil Engineering, Faculty of Technical & Engineering, Imam Khomeini International University, Qazvin, Iran0000-0002-3044-7191Journal Article20241214<strong>Objective:</strong> Heavy metal compounds in drinking water sources are considered a significant public health concern due to their high toxicity, bioaccumulation potential, and environmental persistence. These contaminants can enter groundwater through various human activities such as industrial operations, agricultural runoff, and improper waste disposal, posing health risks, especially to vulnerable groups like children. Assessing the health risks of heavy metals in drinking water is essential for quantifying potential hazards and planning effective mitigation strategies to protect public health.<br /><strong>Method:</strong> This study investigated the carcinogenic and non-carcinogenic health risks posed by selected heavy metals, including cadmium, chromium, lead, nickel, and zinc, in the groundwater of Eslamshahr. Concentrations of these metals were measured based on their annual average values as well as seasonal averages over a one-year period. The health risk assessment considered two exposure pathways, ingestion and dermal contact, and included two distinct age groups: children and adults. The aim was to determine the extent of health risks associated with drinking or skin contact with contaminated water in this region.<br /><strong>Results:</strong> The findings revealed that among the analyzed metals, cadmium presented the highest non-carcinogenic risk for both children and adults, while zinc showed the lowest level of non-carcinogenic hazard. The hazard index (HI), calculated using the average annual concentrations of metals, was found to be 0.58 for children and 0.30 for adults, both of which are below the threshold value of one. These results indicated that no significant non-carcinogenic health risks were posed to either age group under current exposure conditions. However, the carcinogenic risk assessment showed more concerning results. The lifetime cancer risks (ELCR total) for chromium and lead through ingestion and dermal exposure were calculated to be 6.150×10<sup>-5</sup> and 3.131×10<sup>-4</sup>, respectively. Based on these values, it is estimated that approximately 205 individuals in Eslamshahr could develop cancer annually as a result of exposure to these two metals.<br /><strong>Conclusions:</strong> Although the non-carcinogenic risks for both children and adults were found to be within safe limits (HI<1), the potential for carcinogenic effects due to chromium and lead exposure was alarming. These findings highlighted the necessity for implementing advanced groundwater treatment technologies to reduce the concentrations of heavy metals in water intended for drinking or other uses. Additionally, preventive measures, including continuous monitoring of water quality and controlling the entry of pollutants into groundwater, are vital to safeguard the health of the population, particularly that of children, who are more vulnerable to the harmful effects of these contaminants.<strong>Objective:</strong> Heavy metal compounds in drinking water sources are considered a significant public health concern due to their high toxicity, bioaccumulation potential, and environmental persistence. These contaminants can enter groundwater through various human activities such as industrial operations, agricultural runoff, and improper waste disposal, posing health risks, especially to vulnerable groups like children. Assessing the health risks of heavy metals in drinking water is essential for quantifying potential hazards and planning effective mitigation strategies to protect public health.<br /><strong>Method:</strong> This study investigated the carcinogenic and non-carcinogenic health risks posed by selected heavy metals, including cadmium, chromium, lead, nickel, and zinc, in the groundwater of Eslamshahr. Concentrations of these metals were measured based on their annual average values as well as seasonal averages over a one-year period. The health risk assessment considered two exposure pathways, ingestion and dermal contact, and included two distinct age groups: children and adults. The aim was to determine the extent of health risks associated with drinking or skin contact with contaminated water in this region.<br /><strong>Results:</strong> The findings revealed that among the analyzed metals, cadmium presented the highest non-carcinogenic risk for both children and adults, while zinc showed the lowest level of non-carcinogenic hazard. The hazard index (HI), calculated using the average annual concentrations of metals, was found to be 0.58 for children and 0.30 for adults, both of which are below the threshold value of one. These results indicated that no significant non-carcinogenic health risks were posed to either age group under current exposure conditions. However, the carcinogenic risk assessment showed more concerning results. The lifetime cancer risks (ELCR total) for chromium and lead through ingestion and dermal exposure were calculated to be 6.150×10<sup>-5</sup> and 3.131×10<sup>-4</sup>, respectively. Based on these values, it is estimated that approximately 205 individuals in Eslamshahr could develop cancer annually as a result of exposure to these two metals.<br /><strong>Conclusions:</strong> Although the non-carcinogenic risks for both children and adults were found to be within safe limits (HI<1), the potential for carcinogenic effects due to chromium and lead exposure was alarming. These findings highlighted the necessity for implementing advanced groundwater treatment technologies to reduce the concentrations of heavy metals in water intended for drinking or other uses. Additionally, preventive measures, including continuous monitoring of water quality and controlling the entry of pollutants into groundwater, are vital to safeguard the health of the population, particularly that of children, who are more vulnerable to the harmful effects of these contaminants.https://jes.ut.ac.ir/article_103752_361faeffbb0ea9aa3dd88430c8110468.pdfUniversity of TehranJournal of Environmental Studies1025-862051220250921Estimation and Modeling of Emission of Greenhouse Gases and Carbon Monoxide in Urban Landfill (Case Study: Aradkooh Landfill, Tehran)Estimation and Modeling of Emission of Greenhouse Gases and Carbon Monoxide in Urban Landfill (Case Study: Aradkooh Landfill, Tehran)16518810375410.22059/jes.2025.386125.1008554FAMahdiRahimiDepartment of Environmental Engineering, Faculty of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran0009-0000-0353-089XMaryamZare ShahneDepartment of Environmental Engineering, Faculty of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran0000-0003-1393-9466Mohammad AminJavadiDepartment of Environmental Engineering, Faculty of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran0009-0001-8314-2148Journal Article20250426<strong>Objective</strong>: Although sanitary landfilling remains a widely used waste management method in many places, one of its major challenges is the release of greenhouse gases such as methane, which contributes to air pollution, global warming, and safety hazards. Landfills are a major source of potent greenhouse gases, specially in developing countries where the majority of such emissions are produced. This is a critical issue for the case of Tehran, where the lack of research and modeling on gas production from the landfills hinders mitigation and control efforts. Given the current air pollution crisis in Tehran, the extent and proximity of Aradkooh landfill to Tehran have made this landfill's emissions a significant threat that could worsen the region's severe air pollution crisis. This fact has created an urgent need for accurate modeling and monitoring to inform effective recovery projects. Therefore, this study aims to (i) characterize landfill gas composition and formation processes, (ii) review and evaluate gas estimation methods, and (iii) apply the USEPA LANDGEM model to estimate greenhouse gas emissions at the Aradkooh landfill in Tehran.<br /><strong>Method</strong>: Methane generation was modeled using LANDGEM, which applies a first-order decay equation to predict annual landfill gas emissions. Two parameter sets were used: default values provided by the software and theoretical values calibrated to local conditions.<br /><strong>Results</strong>: Biogas generation potential was primarily determined by waste volume and composition. In Tehran, effective biogas potential was calculated as the difference between total waste input and the portion processed annually. Favorable landfill conditions, including adequate burial depth and engineered covers, supported efficient anaerobic degradation and methane recovery.<br /><strong>Conclusions</strong>: Modeling results under the two scenarios highlighted the importance of parameter selection for reliable emission forecasting. The study emphasized the need for accurate modeling and monitoring to support emission management. Future directions include applying advanced kinetic models, evaluating waste diversion impacts, validating results through field measurements, and testing mitigation measures such as geomembrane liners, nanomaterials, and biogas recovery for energy utilization.<strong>Objective</strong>: Although sanitary landfilling remains a widely used waste management method in many places, one of its major challenges is the release of greenhouse gases such as methane, which contributes to air pollution, global warming, and safety hazards. Landfills are a major source of potent greenhouse gases, specially in developing countries where the majority of such emissions are produced. This is a critical issue for the case of Tehran, where the lack of research and modeling on gas production from the landfills hinders mitigation and control efforts. Given the current air pollution crisis in Tehran, the extent and proximity of Aradkooh landfill to Tehran have made this landfill's emissions a significant threat that could worsen the region's severe air pollution crisis. This fact has created an urgent need for accurate modeling and monitoring to inform effective recovery projects. Therefore, this study aims to (i) characterize landfill gas composition and formation processes, (ii) review and evaluate gas estimation methods, and (iii) apply the USEPA LANDGEM model to estimate greenhouse gas emissions at the Aradkooh landfill in Tehran.<br /><strong>Method</strong>: Methane generation was modeled using LANDGEM, which applies a first-order decay equation to predict annual landfill gas emissions. Two parameter sets were used: default values provided by the software and theoretical values calibrated to local conditions.<br /><strong>Results</strong>: Biogas generation potential was primarily determined by waste volume and composition. In Tehran, effective biogas potential was calculated as the difference between total waste input and the portion processed annually. Favorable landfill conditions, including adequate burial depth and engineered covers, supported efficient anaerobic degradation and methane recovery.<br /><strong>Conclusions</strong>: Modeling results under the two scenarios highlighted the importance of parameter selection for reliable emission forecasting. The study emphasized the need for accurate modeling and monitoring to support emission management. Future directions include applying advanced kinetic models, evaluating waste diversion impacts, validating results through field measurements, and testing mitigation measures such as geomembrane liners, nanomaterials, and biogas recovery for energy utilization.https://jes.ut.ac.ir/article_103754_d3e45357bc64e5d43a174e594e2718d8.pdfUniversity of TehranJournal of Environmental Studies1025-862051220250921Optical Monitoring of Coastal-Marine Environments through Colored Dissolved Organic Matter (CDOM) (Southern Pars Region Case Study)Optical Monitoring of Coastal-Marine Environments through Colored Dissolved Organic Matter (CDOM) (Southern Pars Region Case Study)18921010375510.22059/jes.2025.389062.1008574FAMehdiGholamalifardDepartment of Environmental Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran Province, Iran0000-0001-9172-9507BonyadAhmadiDepartment of Environmental Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran Province, Iran0000-0002-4847-9036MaryamNaghdiDepartment of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran Province, Iran0009-0009-4955-9677Seyed MahmoudGhasempouriDepartment of Environmental Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran Province, Iran0000-0003-1129-4406AliSaberDepartment of Environment, Marine environment and wetlands, Tehran, Iran0009-0002-2369-9355SohrabMazloumiDepartment of Environment, Marine environment and wetlands, Tehran, Iran0000-0001-7619-6933Journal Article20250209<strong>Objective:</strong> Colored Dissolved Organic Matter (CDOM) of terrestrial origin plays a crucial role in the biogeochemical cycles of carbon, nitrogen, and phosphorus in coastal regions. This component significantly influences water quality and ecosystem services by affecting light penetration. Despite the importance of CDOM in coastal ecosystems, most global studies have primarily relied on remote sensing techniques due to their efficiency in large-scale observations. However, remote sensing alone cannot fully capture the spatial and temporal variations of CDOM, making in situ field measurements essential for accurate assessments. In Iran, continuous and systematic monitoring of CDOM in coastal waters remains largely unexplored, leaving a significant knowledge gap regarding its seasonal dynamics and anthropogenic influences.<br /><strong>Method:</strong> This study aimed to address this gap by conducting field sampling using a conductivity, temperature, and depth (CTD) device to measure CDOM concentrations in the industrial zone of South Pars and Nayband Marine National Park. To ensure a comprehensive analysis, the study area was divided into four distinct sections: Nayband, the industrial zone, Bonood, and mangrove forests. Each section was selected based on its ecological significance and potential exposure to different sources of CDOM. A total of 144 sampling stations were surveyed across two seasonal periods: winter 2022–2023 and spring 2023. Water samples were collected from various depths to examine vertical distribution patterns of CDOM in addition to spatial variations. The collected data were analyzed to determine the extent of CDOM fluctuations between different regions and seasons.<br /><strong>Results:</strong> The findings revealed that CDOM concentrations varied significantly across different locations and seasons. The highest mean CDOM concentration was observed in the mangrove forests, reaching 6.27 ppb in spring and 3.84 ppb in winter. Similarly, in Nayband, CDOM concentrations were recorded at 1.35 ppb in spring and 7.55 ppb in winter. Additionally, the data indicated that CDOM levels were generally higher in coastal areas and surface water layers, which may be attributed to increased organic matter input from terrestrial sources, such as riverine discharge and coastal vegetation. The contrast between winter and spring concentrations suggests seasonal variations in CDOM production and degradation processes.<br /><strong>Conclusions:</strong> Measuring CDOM concentrations in Bonood, this study also explored potential sources and driving factors behind its variability. The results indicate that anthropogenic activities, particularly industrial and urban wastewater discharge, play a crucial role in shaping CDOM levels in coastal waters. Additionally, submarine groundwater discharge (SGD) was identified as a significant contributor, particularly in regions with high CDOM concentrations, such as Nayband and the mangrove forests. The interaction between natural and human-induced factors highlights the complexity of CDOM dynamics in coastal environments. Given the observed seasonal and spatial variations, continuous monitoring of CDOM is essential for understanding long-term trends and their implications for water quality management.<strong>Objective:</strong> Colored Dissolved Organic Matter (CDOM) of terrestrial origin plays a crucial role in the biogeochemical cycles of carbon, nitrogen, and phosphorus in coastal regions. This component significantly influences water quality and ecosystem services by affecting light penetration. Despite the importance of CDOM in coastal ecosystems, most global studies have primarily relied on remote sensing techniques due to their efficiency in large-scale observations. However, remote sensing alone cannot fully capture the spatial and temporal variations of CDOM, making in situ field measurements essential for accurate assessments. In Iran, continuous and systematic monitoring of CDOM in coastal waters remains largely unexplored, leaving a significant knowledge gap regarding its seasonal dynamics and anthropogenic influences.<br /><strong>Method:</strong> This study aimed to address this gap by conducting field sampling using a conductivity, temperature, and depth (CTD) device to measure CDOM concentrations in the industrial zone of South Pars and Nayband Marine National Park. To ensure a comprehensive analysis, the study area was divided into four distinct sections: Nayband, the industrial zone, Bonood, and mangrove forests. Each section was selected based on its ecological significance and potential exposure to different sources of CDOM. A total of 144 sampling stations were surveyed across two seasonal periods: winter 2022–2023 and spring 2023. Water samples were collected from various depths to examine vertical distribution patterns of CDOM in addition to spatial variations. The collected data were analyzed to determine the extent of CDOM fluctuations between different regions and seasons.<br /><strong>Results:</strong> The findings revealed that CDOM concentrations varied significantly across different locations and seasons. The highest mean CDOM concentration was observed in the mangrove forests, reaching 6.27 ppb in spring and 3.84 ppb in winter. Similarly, in Nayband, CDOM concentrations were recorded at 1.35 ppb in spring and 7.55 ppb in winter. Additionally, the data indicated that CDOM levels were generally higher in coastal areas and surface water layers, which may be attributed to increased organic matter input from terrestrial sources, such as riverine discharge and coastal vegetation. The contrast between winter and spring concentrations suggests seasonal variations in CDOM production and degradation processes.<br /><strong>Conclusions:</strong> Measuring CDOM concentrations in Bonood, this study also explored potential sources and driving factors behind its variability. The results indicate that anthropogenic activities, particularly industrial and urban wastewater discharge, play a crucial role in shaping CDOM levels in coastal waters. Additionally, submarine groundwater discharge (SGD) was identified as a significant contributor, particularly in regions with high CDOM concentrations, such as Nayband and the mangrove forests. The interaction between natural and human-induced factors highlights the complexity of CDOM dynamics in coastal environments. Given the observed seasonal and spatial variations, continuous monitoring of CDOM is essential for understanding long-term trends and their implications for water quality management.https://jes.ut.ac.ir/article_103755_d7ec3c12a5e3fcb285a2304891fa8764.pdfUniversity of TehranJournal of Environmental Studies1025-862051220250921The Social Resilience of Rural Communities in the Western Region of Lake Zaribar in Response to Flooding (Marivan, Kurdistan)The Social Resilience of Rural Communities in the Western Region of Lake Zaribar in Response to Flooding (Marivan, Kurdistan)21123010375610.22059/jes.2025.389122.1008575FAMohammad MehdiHosseinzadehDepartment of Physical Geography, Earth Sciences Faculty, Shahid Beheshti University, Tehran, Iran0000-0002-4961-0657SomayeMoradiDepartment of Physical Geography, Earth Sciences Faculty, Shahid Beheshti University, Tehran, Iran0009-0003-3463-6701Journal Article20250511<strong>Objective</strong>: Natural disasters have historically impacted human life and are viewed as significant barriers to development. The idea of resilience refers to a community's ability to foresee, prepare for, react to, and bounce back from flood incidents, which is vital given the variable nature of climate-associated risks. This study seeks to explore the ability of rural communities in the Khavomirabad district of Marivan, in the Kurdistan Province of Iran, to withstand floods, utilizing both quantitative and field research methods.<br /><strong>Method</strong>: To address flood mitigation, nine indicators were employed (social factors, participation and organization, individual empowerment, knowledge and skills, support, financial and economic factors, security, education and awareness, and infrastructure and transportation), while three indicators (social and cultural, economic, and infrastructure) were utilized to assess resilience. To analyze the data, the initial step involved assessing the condition of the villages concerning each variable through the T-test statistic, focusing on suitable conditions for mitigating flood risk and enhancing flood resilience. Subsequently, Pearson correlation was employed to investigate the relationship among the indicators linked to flood reduction. Then, to identify the influential and significant factors in the relationship between these variables and flood resilience, as well as to evaluate the contribution of each variable in lessening the harmful impacts of floods from the perspective of the villagers, the regression method was applied.<br /><strong>Results</strong>: The correlation analysis results indicated a significant relationship between the region's resilience and factors such as knowledge and skills, finance, and economy, security and infrastructure. Among the evaluated indicators, the economic index reflected the lowest correlation (0.69), whereas the social and cultural index exhibited the highest correlation (0.815). The outcomes of the multivariate regression analysis indicated a notable correlation between the indicators and flood resilience. The multiple correlation coefficient calculated for the indicators and flood resilience was 0.991, while the adjusted R-squared value was found to be 0.981. Given the computed F values alongside a significance level below 0.05, it can be concluded that the combined set of indicators that influence resilience is substantially capable of explaining and predicting the factors involved in mitigating flood risks in the area examined.<br /><strong>Conclusions</strong>: The results indicate that although social factors like recognition of civil rights and community support are quite robust, aspects such as education and infrastructure require enhancement to effectively bolster resilience. Given the susceptibility of rural regions to flooding and the community's limited economic capacity, addressing poverty and enhancing economic prospects in these areas are highlighted as essential within broader flood resilience strategies. In general, this study demonstrated that multiple resilience indicators, such as community engagement, financial assistance, and the improvement of infrastructure, can be combined and utilized together to mitigate flood consequences and enhance community resilience against the adverse effects of flooding.<strong>Objective</strong>: Natural disasters have historically impacted human life and are viewed as significant barriers to development. The idea of resilience refers to a community's ability to foresee, prepare for, react to, and bounce back from flood incidents, which is vital given the variable nature of climate-associated risks. This study seeks to explore the ability of rural communities in the Khavomirabad district of Marivan, in the Kurdistan Province of Iran, to withstand floods, utilizing both quantitative and field research methods.<br /><strong>Method</strong>: To address flood mitigation, nine indicators were employed (social factors, participation and organization, individual empowerment, knowledge and skills, support, financial and economic factors, security, education and awareness, and infrastructure and transportation), while three indicators (social and cultural, economic, and infrastructure) were utilized to assess resilience. To analyze the data, the initial step involved assessing the condition of the villages concerning each variable through the T-test statistic, focusing on suitable conditions for mitigating flood risk and enhancing flood resilience. Subsequently, Pearson correlation was employed to investigate the relationship among the indicators linked to flood reduction. Then, to identify the influential and significant factors in the relationship between these variables and flood resilience, as well as to evaluate the contribution of each variable in lessening the harmful impacts of floods from the perspective of the villagers, the regression method was applied.<br /><strong>Results</strong>: The correlation analysis results indicated a significant relationship between the region's resilience and factors such as knowledge and skills, finance, and economy, security and infrastructure. Among the evaluated indicators, the economic index reflected the lowest correlation (0.69), whereas the social and cultural index exhibited the highest correlation (0.815). The outcomes of the multivariate regression analysis indicated a notable correlation between the indicators and flood resilience. The multiple correlation coefficient calculated for the indicators and flood resilience was 0.991, while the adjusted R-squared value was found to be 0.981. Given the computed F values alongside a significance level below 0.05, it can be concluded that the combined set of indicators that influence resilience is substantially capable of explaining and predicting the factors involved in mitigating flood risks in the area examined.<br /><strong>Conclusions</strong>: The results indicate that although social factors like recognition of civil rights and community support are quite robust, aspects such as education and infrastructure require enhancement to effectively bolster resilience. Given the susceptibility of rural regions to flooding and the community's limited economic capacity, addressing poverty and enhancing economic prospects in these areas are highlighted as essential within broader flood resilience strategies. In general, this study demonstrated that multiple resilience indicators, such as community engagement, financial assistance, and the improvement of infrastructure, can be combined and utilized together to mitigate flood consequences and enhance community resilience against the adverse effects of flooding.https://jes.ut.ac.ir/article_103756_352cc6d853ec8c8c49413e8a41c43332.pdfUniversity of TehranJournal of Environmental Studies1025-862051220250921Optimal Carbon Pricing Considering Human Health Benefits from the Reduction in Air Pollutant Emissions in Urban Energy SystemsOptimal Carbon Pricing Considering Human Health Benefits from the Reduction in Air Pollutant Emissions in Urban Energy Systems23125310375710.22059/jes.2025.395024.1008607FAAliakbarRezazadehEnergy systems engineering group, Department of Energy Engineering, Sharif University of Technology, Tehran, Iran0000-0001-7250-9250AkramAvamiEnergy systems engineering group, Department of Energy Engineering, Sharif University of Technology, Tehran, Iran0000-0002-1137-2278Journal Article20250520<strong>Objective</strong>: Carbon pricing is a critical tool for achieving environmental objectives while maintaining economic equilibrium, yet determining an optimal carbon price remains a complex challenge. Carbon pricing not only influences the optimal conditions and the technological composition of the energy system but also leads to new emission patterns. The energy system has a lot of external costs, including social, health, ecosystem damage, and resource depletion. It is essential to set taxes in a way that minimizes market disruptions while internalizing externalities. Greenhouse gas (GHG) emissions Penalty (GP) cannot only reduce health costs due to global warming but also, through a co-benefit approach, reduce health costs associated with air pollution. Thus, this study estimates the level of GP with the aim of internalizing the human health costs of global warming and air pollution. The structure of the energy system significantly affects carbon emissions and pricing, necessitating an integrated modeling approach.
<strong>Method</strong>: This study developed a novel bi-level integrated assessment model of emission-health-energy, where the lower level optimizes the energy system and the upper level optimizes the carbon price. The Shared Socioeconomic Pathways (SSP) scenarios depict the socioeconomic status, while the Representative Concentration Pathways (RCP) scenarios represent the climatic conditions. Based on the RCP scenarios, General Circulation Models (GCMs) are applied to determine the climate condition. A data-driven Regional Climate Model (RCM) is developed to estimate climate conditions in the study area.
At the upper level, the objective function seeks to minimize the GP annually. In the GP approach, the price should compensate for the health costs stemming from climate change and air pollution. This approach emphasizes that GP has the capacity to offset the damages caused by air pollution, effectively demonstrating a co-benefit between carbon emissions and air pollution in policy-making. In this approach, the discounted GHG emission penalty in each year is equal to discounted health costs.
In the first step, the more complex lower-level model was developed. After running the integrated energy-health-emission model at the lower level, the outputs were compared to verify whether the discounted health costs match the discounted GHG emission penalty. If this condition was not satisfied, the GP would be adjusted; otherwise, the optimal pathway for the energy supply chain and the optimal GP were determined.
<strong>Results</strong>: This model was applied to four scenarios within a case study. The model revealed carbon prices ranging from $3.3 to $7.9 per tCO<sub>2</sub>eq, varying across years and scenarios. The results demonstrated the importance of considering economic benefits in carbon pricing, highlighting significant differences compared to previous studies.
<strong>Conclusions</strong>: The proposed model provides a robust tool for policymakers to align carbon pricing with environmental and climate policy while maintaining economic stability. By linking energy system optimization, health cost internalization, and climate policy, this framework designs efficient carbon policies. Future research could expand its applicability to other regions and externalities, further refining the balance between sustainability and economic growth.<strong>Objective</strong>: Carbon pricing is a critical tool for achieving environmental objectives while maintaining economic equilibrium, yet determining an optimal carbon price remains a complex challenge. Carbon pricing not only influences the optimal conditions and the technological composition of the energy system but also leads to new emission patterns. The energy system has a lot of external costs, including social, health, ecosystem damage, and resource depletion. It is essential to set taxes in a way that minimizes market disruptions while internalizing externalities. Greenhouse gas (GHG) emissions Penalty (GP) cannot only reduce health costs due to global warming but also, through a co-benefit approach, reduce health costs associated with air pollution. Thus, this study estimates the level of GP with the aim of internalizing the human health costs of global warming and air pollution. The structure of the energy system significantly affects carbon emissions and pricing, necessitating an integrated modeling approach.
<strong>Method</strong>: This study developed a novel bi-level integrated assessment model of emission-health-energy, where the lower level optimizes the energy system and the upper level optimizes the carbon price. The Shared Socioeconomic Pathways (SSP) scenarios depict the socioeconomic status, while the Representative Concentration Pathways (RCP) scenarios represent the climatic conditions. Based on the RCP scenarios, General Circulation Models (GCMs) are applied to determine the climate condition. A data-driven Regional Climate Model (RCM) is developed to estimate climate conditions in the study area.
At the upper level, the objective function seeks to minimize the GP annually. In the GP approach, the price should compensate for the health costs stemming from climate change and air pollution. This approach emphasizes that GP has the capacity to offset the damages caused by air pollution, effectively demonstrating a co-benefit between carbon emissions and air pollution in policy-making. In this approach, the discounted GHG emission penalty in each year is equal to discounted health costs.
In the first step, the more complex lower-level model was developed. After running the integrated energy-health-emission model at the lower level, the outputs were compared to verify whether the discounted health costs match the discounted GHG emission penalty. If this condition was not satisfied, the GP would be adjusted; otherwise, the optimal pathway for the energy supply chain and the optimal GP were determined.
<strong>Results</strong>: This model was applied to four scenarios within a case study. The model revealed carbon prices ranging from $3.3 to $7.9 per tCO<sub>2</sub>eq, varying across years and scenarios. The results demonstrated the importance of considering economic benefits in carbon pricing, highlighting significant differences compared to previous studies.
<strong>Conclusions</strong>: The proposed model provides a robust tool for policymakers to align carbon pricing with environmental and climate policy while maintaining economic stability. By linking energy system optimization, health cost internalization, and climate policy, this framework designs efficient carbon policies. Future research could expand its applicability to other regions and externalities, further refining the balance between sustainability and economic growth.https://jes.ut.ac.ir/article_103757_521bfa3f551c20695c52444fa6f98138.pdfUniversity of TehranJournal of Environmental Studies1025-862051220250921Species Distribution Modeling of the Pallas’s Cat (Otocolobus manul) in Iran Using the Maximum Entropy AlgorithmSpecies Distribution Modeling of the Pallas’s Cat (Otocolobus manul) in Iran Using the Maximum Entropy Algorithm25527110375810.22059/jes.2025.398248.1008617FABaharHosseiniDepartment of Environment, Faculty of Natural Resources and Environment, University of Birjand. Iran0009-0002-2288-0814ElhamYousefi RobiatDepartment of Environment, Faculty of Natural Resources and Environment, University of Birjand. Iran0000-0002-5396-6402AliHaghaniDepartment of Environment, Faculty of Fisheries and Environment, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran0000-0001-7437-5639HamidRezaRezaei. Department of Wildlife Management, Faculty of Fisheries and Environment, Gorgan University of Agricultural
Sciences and Natural Resources, Gorgan, Iran0000-0002-9422-5600Journal Article20250720<strong>Objective:</strong> The objective of this study was to model the habitat of the Pallas’s cat (<em>Otocolobus manul</em>) in Iran and to identify the key environmental and climatic variables influencing its spatial distribution, with the aim of providing scientific strategies for the conservation of this species.<br /><strong>Method: </strong>The Maximum Entropy (MaxEnt) algorithm, recognized as a machine learning method with high predictive capacity, was applied in this research. Species occurrence records were obtained from the Department of Environment of Iran, which encompassed the data collected by rangers, specialists, environmental volunteers, and library sources, resulting in a total of 175 occurrence points across the country. The environmental dataset consisted of 18 variables, including climatic, topographic, and land-use factors, which were extracted and prepared from global databases and cartographic maps provided by the National Cartographic Center of Iran. Habitat modeling was performed in MaxEnt using cross-validation procedures, and the predictive accuracy of the model was assessed based on the area under the curve (AUC) criterion.<br /><strong>Results:</strong> According to the results, the AUC value of the model was 0.93, representing a very good predictive performance. Distance from the prey (pika) contributed the most (19.5%) to the habitat suitability for the Pallas’s cat, followed by elevation (18.2%), terrain ruggedness (14.4 %), and mean annual temperature (14.2%). The habitat suitability maps revealed that although the distribution range of the Pallas’s cat in Iran covers a relatively broad geographic extent, it occurred in a patchy pattern within mountainous habitats and areas of moderate to high elevation. The response of the curve analysis demonstrated an increase in the probability of Pallas’s cat occurrence with decreasing distance from the prey (pika) and reaches its maximum in higher elevations and more rugged terrains. Moreover, the species exhibited a preference for areas with moderate mean annual temperatures. These patterns highlighted the combined significance of topographic and climatic variables as key determinants of the species’ spatial distribution.<br /><strong>Conclusions:</strong> According to the findings, the presence of the prey (pika) was a key factor in explaining the distribution pattern of the Pallas’s cat, highlighting the species’ trophic dependence on this prey. In addition, elevation and terrain ruggedness were recognized as the most influential topographic variables, playing a decisive role in the species’ habitat selection. Considering the specific habitat preferences of the Pallas’s cat and its patchy distribution across Iran, conservation of primary prey populations, particularly pika and hare, along with the establishment and expansion of protected areas in regions identified as highly suitable habitats should be regarded as priority measures in conservation programs for this species. <strong>Objective:</strong> The objective of this study was to model the habitat of the Pallas’s cat (<em>Otocolobus manul</em>) in Iran and to identify the key environmental and climatic variables influencing its spatial distribution, with the aim of providing scientific strategies for the conservation of this species.<br /><strong>Method: </strong>The Maximum Entropy (MaxEnt) algorithm, recognized as a machine learning method with high predictive capacity, was applied in this research. Species occurrence records were obtained from the Department of Environment of Iran, which encompassed the data collected by rangers, specialists, environmental volunteers, and library sources, resulting in a total of 175 occurrence points across the country. The environmental dataset consisted of 18 variables, including climatic, topographic, and land-use factors, which were extracted and prepared from global databases and cartographic maps provided by the National Cartographic Center of Iran. Habitat modeling was performed in MaxEnt using cross-validation procedures, and the predictive accuracy of the model was assessed based on the area under the curve (AUC) criterion.<br /><strong>Results:</strong> According to the results, the AUC value of the model was 0.93, representing a very good predictive performance. Distance from the prey (pika) contributed the most (19.5%) to the habitat suitability for the Pallas’s cat, followed by elevation (18.2%), terrain ruggedness (14.4 %), and mean annual temperature (14.2%). The habitat suitability maps revealed that although the distribution range of the Pallas’s cat in Iran covers a relatively broad geographic extent, it occurred in a patchy pattern within mountainous habitats and areas of moderate to high elevation. The response of the curve analysis demonstrated an increase in the probability of Pallas’s cat occurrence with decreasing distance from the prey (pika) and reaches its maximum in higher elevations and more rugged terrains. Moreover, the species exhibited a preference for areas with moderate mean annual temperatures. These patterns highlighted the combined significance of topographic and climatic variables as key determinants of the species’ spatial distribution.<br /><strong>Conclusions:</strong> According to the findings, the presence of the prey (pika) was a key factor in explaining the distribution pattern of the Pallas’s cat, highlighting the species’ trophic dependence on this prey. In addition, elevation and terrain ruggedness were recognized as the most influential topographic variables, playing a decisive role in the species’ habitat selection. Considering the specific habitat preferences of the Pallas’s cat and its patchy distribution across Iran, conservation of primary prey populations, particularly pika and hare, along with the establishment and expansion of protected areas in regions identified as highly suitable habitats should be regarded as priority measures in conservation programs for this species. https://jes.ut.ac.ir/article_103758_f455d066fe901ac19a6d57e006137a00.pdf