Futureproofing Medical Education Through Scenario Analysis: Instrumental Case Study.

Developments in technology and climate change, as well as other "megatrends" are having lasting impacts in society and healthcare. A scenario analysis was conducted to explore the impact of megatrends on medical education. Three scenarios were developed for the year 2035, showing varying levels of technological integration and environmental focus. Implications for an updated curricula focus on health inequalities, digital health, and globalization effects.

Managing supply chain resilience assessment model-relevant factors and activities using an FCM-FBWM approach.

Supply chain resilience is essential for companies to survive in today's competitive market, as they face environmental and unforeseeable challenges in their supply chain. This paper aims to model and manage the factors and activities that influence supply chain resilience and how they relate to each other. This will help us devise plans for enhancing the resilience of a supply chain. By taking into account the factors and activities and their interrelationships, organizations can use their limited resources more efficiently to improve their supply chain resilience. We use a management matrix to rank the factors based on how they affect and contribute to the supply chain resilience. We conduct an empirical study in a pharmaceutical company to demonstrate the proposed management approach and provide improvement scenarios based on the ranking of the factors. The results show that the most important factors are "the cooperation and trust between supply chain members", "Visibility & Agility", and "Leadership Support and Commitment". The ranking of the factors may vary in different companies. Therefore, other companies can apply the method described in this paper and perform different improvement scenarios according to the ranking of the factors to effectively allocate their limited management efforts.

Applying the "SOTEC" framework of sociotechnical risk analysis to the development of an autonomous robot swarm for a public cloakroom.

The past decade has seen efforts to develop new forms of autonomous systems with varying applications in different domains, from underwater search and rescue to clinical diagnosis. All of these applications require risk analyses, but such analyses often focus on technical sources of risk without acknowledging its wider systemic and organizational dimensions. In this article, we illustrate this deficit and a way of redressing it by offering a more systematic analysis of the sociotechnical sources of risk in an autonomous system. To this end, the article explores the development, deployment, and operation of an autonomous robot swarm for use in a public cloakroom in light of Macrae's structural, organizational, technological, epistemic, and cultural framework of sociotechnical risk. We argue that this framework provides a useful tool for capturing the complex "nontechnical" dimensions of risk in this domain that might otherwise be overlooked in the more conventional risk analyses that inform regulation and policymaking.

Evaluating impacts of climate and management on reservoir water quality using environmental fluid dynamics code.

Climate change and human interference, notably nutrient input, affect the water quality. Nitrogen (N) and phosphorus (P) are pivotal in managing eutrophication. This study investigated the effects of water dynamics and chemical constituents on water quality in Hongfeng Lake, a typical weakly stratified reservoir suffering from algae blooms in Southwest China, using the Environmental Fluid Dynamics Code. Leveraging climate, hydrological, and water quality data, we constructed, calibrated, and validated the temperature-hydrodynamics-water quality-sediment model. Various scenarios were analyzed, including wind speed, air temperature, solar radiation, rainfall, water discharge, N and P external input, and internal release. The findings revealed that no rain and warming increased trophic state index (TSI) and chlorophyll-a (Chl-a) concentration, and no solar radiation initially elevated nitrate concentration, followed by an increase in ammonium concentration. Besides, no solar radiation and changes in rainfall significantly increased total phosphate concentration. The management scenarios of N and P reduction, halving tributary, and mainstream flow scenarios improved water quality and reduced eutrophication. The wind speed under the N and P reduced scenarios showed that a doubling in wind led to increased concentrations of the particulate organic matter, Chl-a, and dissolved oxygen, alongside decreased ammonium and nitrate, while TSI exhibited minimal change. However, 5- and 10-times wind speed scenarios amplified TSI in shallow water, potentially due to a substantial rise in internal nutrient release. The degradation trend observed in drinking water quality amid climate change (warming and flooding) raises concerns regarding health-related risks. These simulations provided the quantified influence of climate change and environmental management strategies on water quality in the weakly stratified reservoir, notably highlighting the looming threat of exacerbated eutrophication due to warming, necessitating more stringent N and P reduction measures compared to current practices.

A comprehensive emission inventory of air pollutants from municipal solid waste incineration in China's megacity, Beijing based on the field measurements.

The rising of municipal solid waste incineration (MSWI), constituting 5 % of NOx emissions in Beijing, poses a significant challenge to improving air quality. This study establishes a comprehensive historical inventory of air pollutants (APs) emitted from MSWI plants between 2004 and 2023. The inventory was developed using both the continuous emissions monitoring systems (CEMS)-based method and the EF (emission factors) -based method, incorporating detailed plant-level activity data and localized EF derived from field measurements. These include data from CEMS and manual monitoring. Analysis of CEMS data reveals high compliance rates with emission limits for MSW in Beijing, with 99.9 %, 99.5 %, 99.8 %, 98.7 %, and 99.5 % of units meeting standards for PM, SO2, NOx, CO and HCl, respectively. This suggests effective implementation of emission standards in Beijing, although further strengthening of policies, particularly for CO emissions, is warranted. Overall, total AP emissions have increased annually largely attributed to measures implemented for DeSOx, DeNOx, and DePM since 1998. Most MSWI facilities are located in suburban areas rather than urban cores. Emissions of SO2, HCl, CO, Hg, Cd + Ti, other metals, dioxins, VOCs, and NH3 exhibit a spatially homogeneous distribution at the district level, while PM and NOx emissions demonstrate heterogeneity. Scenario analysis underscores the importance of continuous improvement and upgrading of advanced air pollution control devices. This study contributes a methodological framework for estimating emissions, reducing uncertainties, and informing policy-making to mitigate APs emissions in megacities. It serves as a valuable reference for similar cities grappling with air quality challenges.

Study on factors influencing carbon dioxide emissions and carbon peak heterogenous pathways in Chinese provinces.

Implementing a Carbon Peak Action Plan at the regional level requires comprehensive consideration of the developmental heterogeneity among different provinces, which is an effective pathway for China to realize the goal of carbon peak by 2030. However, there is currently no clear provincial roadmap for carbon peak, and existing studies on carbon peak pathways inadequately address provincial heterogeneity. Therefore, this paper employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to decompose assess 8 factors influencing carbon emissions of 30 provinces. According to scenario analysis, the paper explores the differentiated pathways for provincial carbon peaks based on policy expectation indicators (including population, economy, and urbanization rate) and comprises policy control indicators (including the energy structure, energy efficiency, industrial structure, transportation structure, and innovation input). The results indicate that population, per capita GDP, urbanization rate, and innovation input are the primary factors for influencing (negatively) the growth of carbon emissions. In contrast, the optimization and upgrading of the industrial structure, energy intensity, energy structure, and transportation structure have mitigating effects on carbon emissions, especially for the first two factors. The forecasting results reveal that robust regulations of the energy and industry can effectively accelerate carbon peak at a reduced magnitude. If developed at BAU, China cannot achieve carbon peak by 2030, continuing an upward trend. However, by maximizing the adjustment strength of energy and industrial transformation within the scope of provincial capabilities, China could achieve carbon peak as early as 2025, with a peak of 12.069 billion tons. In this scenario, 24 provinces could achieve carbon peak before 2030. Overall, this study suggests the feasibility of differentiated pathway to achieve carbon peaks in China, exploring the carbon peak potential and paths of 30 provinces, and identifying provinces where carbon peak is more challenging. It also provides a reference for the design of carbon peak roadmaps at both provincial and national levels and offers targeted recommendations for the implementation of differentiated policy strategies for the government.

Developing a dynamic life cycle assessment framework for buildings through integrating building information modeling and building energy modeling program.

The construction and building sector is one of the largest contributors to the global carbon emissions. Therefore, it is imperative to accurately assess the carbon emissions of buildings throughout the life cycle. Many studies conducted life cycle assessment (LCA) of buildings to evaluate carbon emissions. However, due to the lack of dynamic data, most studies adopted the static LCA methodology, which neglected the dynamic variations during life cycle stages of a building. Unlike previous studies that collected static data from questionnaires and documents, the present study aims to establish a novel dynamic life cycle assessment (D-LCA) framework for buildings by incorporating the building information modeling (BIM) and the building energy modeling program (BEMP) into the static LCA. First, a static LCA is established as the baseline scenario that covers the "cradle-to-grave" life cycle stages. A BIM model is established using Revit to obtain the inventory of building materials. The Designer Simulation Toolkit (DeST) is used as a BEMP to simulate the operating energy consumption of the studied building, taking into account changes in energy mix, climate change, and occupant behavior. At the same time, the DeST results are further used as a data input for dynamic scenarios. The D-LCA framework is applied to a high-rise commercial building in China. This study found that the difference between static and dynamic scenarios was up to 66.7 %, mainly reflected in the dynamic energy consumption during the operation phase, indicating the inaccuracy of traditional static LCA. Therefore, a D-LCA by integrating BIM and BEMP can facilitate dynamic modeling and improve the accuracy and reliability of LCA for buildings.

Regional policy options for carbon peaking in the Yangtze River Delta under uncertainty.

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity.

Historical development, impact mechanism and future trends of nitrogen footprint in Wuxi City, China.

Human activities have changed the biogeochemical cycle of nitrogen, leading to a large amount of reactive nitrogen (Nr) into the environment, aggravating a series of environmental problems, affecting human and ecosystem health. Cities are the core areas driving nitrogen cycling in terrestrial ecosystems, however, there are numerous influencing factors and their contributions are unclear. The nitrogen footprint is an important index to understand the impact of human activities on the environment, however, the calculation of urban nitrogen footprint needs a simplified and accurate system method. Here we use a nitrogen footprint calculation model at the urban system level based on system nitrogen balance, and a multi-factor extended STIRPAT (stochastic impact by regression on population, affluence, and technology) model suitable for analyzing the impact mechanism of nitrogen footprint to estimate nitrogen footprint of Wuxi City during 1990-2050. We find that: (1) from 1990 to 2020, the total nitrogen footprint of Wuxi City was in an increasing trend, but the per capita nitrogen footprint was in a decreasing trend. The per capita nitrogen footprint of 22.36 kg capita-1 in 2020 was at a lower level globally. (2) Nr discharge from fossil fuel combustion and Haber-Bosch nitrogen fixation accounted for the main proportion of nitrogen footprint. (3) Dietary choice (Ad), GDP per capita (Ag), urbanization rate (Au), population (P), and fossil energy productivity (Te) were the key factors contributing to the increase of the nitrogen footprint, which resulted in an annual increase of 1.39 %. While nitrogen footprint productivity (Tn), nitrogen use efficiency in crop farming (Tc), and nitrogen use efficiency in animal breeding (Ta) were the key inhibit factors that inhibit the increase of nitrogen footprint, and these factors slow down the annual growth rate of nitrogen footprint by 0.39 %. (4) The continuous growth of nitrogen footprint in the baseline and population growth scenarios will bring more environmental problems and greater environmental governance pressure to Wuxi City, while the sustainable scenario that includes comprehensive means such as economic adaptation and technological improvement is more in line with the requirements of high-quality development in China. Several mitigation measures are then proposed by considering Wuxi's realities from both key impact factors and potential for nitrogen footprint reduction in different scenarios, which can provide valuable policy insights to other cities, especially lakeside cities to mitigate nitrogen footprint.

Hospital capacities and response to COVID-19 pandemic surges in Iran: A quantitative model-based study.

The coronavirus 2019 (COVID-19) pandemic resulted in serious limitations for healthcare systems, and this study aimed to investigate the impact of COVID-19 surges on in-patient care capacities in Iran employing the Adaptt tool. Using a cross-sectional study design, our study was carried out in the year 2022 using 1-year epidemiologic (polymerase chain reaction-positive COVID-19 cases) and hospital capacity (beds and human resource) data from the official declaration of the pandemic in Iran in February 2020. We populated several scenarios, and in each scenario, a proportion of hospital capacity is assumed to be allocated to the COVID-19 patients. In most of the scenarios, no significant shortage was found in terms of bed and human resources. However, considering the need for treatment of non- COVID-19 cases, in one of the scenarios, it can be observed that during the peak period, the number of required and available specialists is exactly equal, which was a challenge during surge periods and resulted in extra hours of working and workforce burnout in hospitals. The shortage of intensive care unit beds and doctors specializing in internal medicine, infectious diseases, and anesthesiology also requires more attention for planning during the peak days of COVID-19.

How to effectively achieve air pollutant reduction and carbon mitigation in China's industrial sector? A study based on decomposition analysis and scenario simulation.

Reducing air pollutant and carbon emissions in the industrial sector is crucial for the ecological civilization construction in China. In this study, we first employ the generalized Divisia index method to analyze the driving factors of industrial CO2 and SO2 emissions, incorporating fixed asset investment and R&D input. The key sub-sectors that exert significant impact on emissions of the whole industrial sector are identified. And then, scenario analysis and Monte Carlo simulation are utilized to predict future trends and potential for reducing CO2 and SO2 emissions. Furthermore, the carbon peaking time of the industrial sub-sectors is investigated. The results indicate that fixed asset investment and R&D input both have played positive roles in CO2 and SO2 emissions. Emission reduction is mainly driven by investment emission intensity, output emission intensity, and R&D emission intensity. Sub-sectors S09, S10, S11, S12, and S18 present substantial potential for reducing air pollutant and carbon emissions. Although SO2 emissions from the industrial sector are projected to decrease in the future, the peak of CO2 emissions have not been reached. The carbon peak time for the whole industrial sector is predicted in 2025, with the peak of 7892.33 Mt. The five key sub-sectors are anticipated to reach the respective carbon emission peaks at different times. Therefore, to effectively implement industrial air pollutant and carbon reduction, the role of fixed asset investment and R&D input should be fully utilized, and high-energy consumption and high-emission sub-sectors should be prioritized for action.

Effects of wind-driven current and thermal dynamics in a temperate monomictic reservoir: Implications for manganese transport and treatment in water supply systems.

Increasing manganese (Mn) concentrations in source water contribute to aesthetic and health-related concerns in drinking water. The challenges with Mn in drinking water primarily arise from elevated Mn concentrations in the water supply reservoir, with the inefficacy of Mn treatment largely attributed to fluctuating Mn levels in the water source. A three-dimensional Mn cycle model in a temperate monomictic reservoir, Tarago Reservoir, and a decision support system reflecting Mn variations in the local water treatment plant have been established in previous research. This study aimed to examine Mn variations from the reservoir to raw water and treated water under the influence of wind conditions during different stages of thermal structure, and discover valuable recommendations for Mn treatment in the local water supply system. We crafted 12 scenarios to scrutinize the impact of varying intensities of offshore and onshore winds on hydrodynamic processes and Mn transport during strong thermal stratification, weak thermal stratification, and turnover. The scenario analysis revealed that, during the gradual weakening of thermal stratification, offshore wind induced a substantial amount of Mn to the upper layers near the water intake point. Conversely, onshore wind hindered the upward transport of Mn. The simulated Mn in the raw water under the 12 scenarios indicated that the timing of turnover in the Tarago Reservoir is the primary concern for Mn treatment in the water treatment plant. Additionally, close attention should be given to the frequency and intensity of offshore winds during the weakening of thermal stratification.

Change of spermatophyte family diversity in distribution patterns with climate change in China.

The global climate is undergoing extraordinary changes, profoundly influencing a variety of ecological processes. Understanding the distribution patterns and predicting the future of plant diversity is crucial for biodiversity conservation in the context of climate change. However, current studies on predictive geographic patterns of plant diversity often fail to separate the effects of global climate change from other influencing factors. In this study, we developed a spatial simulation model of spermatophyte family diversity (SSMSFD) based on data collected from 200 nature reserves covering approximately 1,500,000 km2, where direct anthropogenic disturbances to plant diversity and the surrounding environment are absent. We predicted the spermatophyte family diversity for all provinces in China in 2020, 2040, and 2080, considering the impacts of global climate change. On average, China currently exhibits 118 plant families per 25 km2, with a decreasing trend from southeast to northwest. When considering only the effects of global climate change, excluding direct anthropogenic disturbances, our results indicate that under the Shared Socioeconomic Path Scenarios (SSPs) 245 and 585, spermatophyte family diversity is projected to slowly increase in most Chinese provinces from 2021 to 2080. Notably, the increase is more pronounced under SSPs585 compared to SSPs245. Global climate change has a positive effect on plant diversity, in contrast to the negative impact of anthropogenic disturbances that often lead to declines in plant diversity. This research highlights the contrasting outcomes of future plant diversity under the sole influence of global climate change and the significant negative effects of anthropogenic disturbances on diversity.

[Life Cycle Carbon Reduction Benefits of Electric Heavy-duty Truck to Replace Diesel Heavy-duty Truck].

To accurately predict the life-cycle carbon reduction benefits of replacing a diesel heavy-duty truck with an electric one, taking a single heavy-duty truck as the object, the variation trend in electric and diesel carbon emission factors from 2023 to 2050 were predicted; coupled with the life spans and life-cycle mileage of the two types of heavy-duty trucks, a dynamic carbon emission model for the heavy-duty trucks was constructed in stages. The carbon footprints of the trucks under the "Net Zero Emissions by 2050 Scenario (NZE)", "Announced Pledges Scenario (APS)", and "Stated Policies Scenario (STEPS)" were analyzed. In addition, the carbon reduction and carbon reduction rate were calculated. The results showed that battery manufacturing and battery recycling were the main factors to impair the improvement of carbon reduction in the production and recycling stages of electric heavy-duty trucks, respectively. For every 1 g·(kW·h)-1 reduction in the electricity carbon emission factor (CO2), an electric heavy-duty truck could reduce 1.74 t of carbon emissions over its life cycle. Under the three scenarios, the carbon emissions during the operation stage of both types of heavy trucks accounted for more than 90% of the total life-cycle carbon emissions. Carbon reduction benefits from the highest to the lowest were NZE, APS, and STEPS, and their corresponding life-cycle carbon emission reductions were 1054.68, 1021.78, and 1007.97 t, with carbon reduction rates of 54.38%, 52.68%, and 51.97%, respectively.

History and future of water footprint in the Yangtze River Delta of China.

Quantifying the drivers of water footprint evolution in the Yangtze River Delta is vital for the optimization of China's total water consumption. The article aims to decompose and predict the water footprint of the Yangtze River Delta and provide policy recommendations for optimizing water use in the Yangtze River Delta. The paper applies the LMDI method to decompose the water footprint of the Yangtze River Delta and its provinces into five major drivers: water footprint structure, water use intensity, R&D scale, R&D efficiency, and population size. Furthermore, this paper combines scenario analysis and Monte Carlo simulation methods to predict the potential evolution trends of water footprint under the basic, general, and enhanced water conservation scenario, respectively. The results show that (1) the expansion of R&D scale is the main factor promoting the growth of water footprint, the improvement of R&D efficiency, and the reduction of water intensity are the main factors inhibiting the increase of water footprint, and the water footprint structure and population size have less influence on water footprint. (2) The evolution trend of water footprint of each province under three scenarios is different. Compared to the basic scenario, the water footprint decreases more in Shanghai, Zhejiang, and Anhui under the general and enhanced water conservation scenario. The increase in water footprint in Jiangsu under the enhanced scenario is smaller than that of the general water conservation scenario.

Scenario analysis of energy consumption and related emissions in the transportation industry-a case study of Shaanxi Province.

In the context of pursuing carbon neutrality and balancing the use of fossil fuels with renewable energy, the transportation industry faces the challenge of accurately predicting energy demand, related emissions, and assessing the effectiveness of energy technologies and policies. This is crucial for formulating energy management plans and reducing carbon dioxide (CO2) and atmospheric pollutant emissions. Currently, research on energy consumption and emission forecasting primarily relies on energy consumption quantities and emission factors, which lack precision. This study employs the low emissions analysis platform (LEAP) model, utilizing a "bottom-up" modeling approach combined with scenario analysis to predict and analyze the energy demand and related emissions in the transportation industry. Compared to previous studies, the methodological framework proposed in this research offers higher precision and can explore energy-saving and emission-reduction pathways for different modes of transport, providing a valuable energy forecasting tool for transport policy formulation in other regions. The forecast results indicate that under the business-as-usual (BAU) scenario, by 2049, the energy consumption and related emissions in Shaanxi Province's transportation industry are expected to increase by 1.15 to 1.85 times compared to the baseline year. In the comprehensive (CP) scenario, the industry is projected to reach a carbon peak around 2033. The study also finds that energy consumption and emissions predominantly originate from private passenger vehicles, highway freight, and civil aviation passenger, which have the greatest potential for emission reduction under the transport structure optimized (TSO) scenario. Therefore, policymakers should consider regional development characteristics, combine different transportation modes, and specifically analyze the emission reduction potential of the transportation industry in various regions, formulating corresponding reduction policies accordingly.

Large reductions in nutrient losses needed to avoid future coastal eutrophication across Europe.

Rapid technological development in agriculture and fast urbanization have increased nutrient losses in Europe. High nutrient export to seas causes coastal eutrophication and harmful algal blooms. This study aims to assess the river exports of nitrogen (N) and phosphorus (P), and identify required reductions to avoid coastal eutrophication in Europe under global change. We modelled nutrient export by 594 rivers in 2050 for a baseline scenario using the new MARINA-Nutrients model for Europe. Nutrient export to European seas is expected to increase by 13-28% under global change. Manure and fertilizers together contribute to river export of N by 35% in 2050. Sewage systems are responsible for 70% of future P export by rivers. By 2050, the top ten polluted rivers for N and P host 42% of the European population. Avoiding future coastal eutrophication requires over 47% less N and up to 77% less P exports by these polluted rivers.

Environmental impact of Norwegian self-selected diets: comparing current intake with national dietary guidelines and EAT-Lancet targets.

OBJECTIVES: Dietary environmental impact in a Norwegian adult population was estimated for six environmental impact categories. Moreover, environmental benefits of scenario diets complying with the Norwegian Food-Based Dietary Guidelines (FBDG) and the EAT-Lancet reference diet were assessed. DESIGN: The current diet of Norwegian adults was estimated according to 24-h dietary recall data from a national dietary surveillance survey (Norkost 3). Scenario diets were modelled to represent the Norwegian FBDG and the EAT-Lancet healthy reference diet. Dietary environmental impact in terms of global warming potential, freshwater and marine eutrophication, terrestrial acidification, water use and transformation and use of land was estimated for the current and scenario diets using environmental impact data representative of the Norwegian market. Significant associations between impact and gender/educational attainment were assessed at P < 0·05. SETTING: Norway. PARTICIPANTS: Adults (n=1787) aged 18-70 years who participated in the Norkost 3 survey (2010-2011). RESULTS: Environmental impact varied significantly by gender and educational attainment. The food groups contributing most to environmental impact of Norwegian diets were meat, dairy, beverages, grains and composite dishes. Compared with the current Norwegian diet, the FBDG scenario reduced impacts from 2 % (freshwater eutrophication) to 32 % (water use), while the EAT-Lancet scenario reduced impacts from 7 % (marine eutrophication) to 61 % (land use). The EAT-Lancet scenario resulted in 3-48 % larger reductions in impact than the FBDG scenario. CONCLUSIONS: The Norwegian FBDG, while not as environmentally friendly as the EAT-Lancet reference diet, can still be an important tool in lessening environmental burden of Norwegian diets.

Simulation of vertical migration behaviors of heavy metals in polluted soils from arid regions in northern China under extreme weather.

Heavy metal migration behaviors and mechanisms in soils are important for pollution control and remediation. However, there are few related studies in arid areas under extreme weather patterns. In this study, we developed a one-dimensional continuous point source unsaturated solute transport model, and utilized Hydrus-1D to simulate the transport of Cu, As and Zn, in the pack gas zones of soils within the impact areas of two typical mining areas in Inner Mongolia. The results show that the soil has a significant interception capacity, with a short heavy metal vertical migration distance of ≤100 cm. Soil texture and heavy metal sorption affinity are two key factors that influence heavy metal transport. In soils with high contents of sands but low contents of clays, heavy metals have large mobility and thus migrate deeper and are more evenly distributed in the soil profile. The migration of different heavy metals in the same soil also varies considerably, with large migration depth for metals having low binding affinities onto soils. Scenario analysis for extreme drought and rainfall shows that, rainfall amount and intensity are positively correlated with heavy metal transport depth and negatively correlated with the peak concentration. Increasing rainfall/intensity results in a more uniform distribution of heavy metals, and lower profile concentrations owing to enhanced horizontal dispersion of surface runoff. When the total amount and intensity of rainfall remain constant, continuous or intermittent rainfall only affects the transport process but has almost no effect on the final pollutant concentration redistribution in the soil. These results provide theoretical data for estimating the degree of heavy metal pollution, and help design control and remediation strategies for polluted soils.

Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model.

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.