Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Global dynamics of discrete mathematical models of tuberculosis

In this paper, we develop discrete models of Tuberculosis (TB). This includes SEI endogenous and exogenous models without treatment. These models are then extended to a SEIT model with treatment. We develop two types of net reproduction numbers, one is the traditional R 0 which is based on the disease-free equilibrium, and a new net reproduction number R 0 ( E ∗ ) based on the endemic equilibrium. It is shown that the disease-free equilibrium is globally asymptotically stable if R 0 ≤   1 and unstable if R 0 > 1 . Moreover, the endemic equilibrium is locally asymptotically stable if R 0 ( E ∗ ) < 1 < R 0 .

Study of LFG bubble accumulation and discontinuous flow in the highly saturated region of landfill below the leachate level

Landfills in developing countries are typically characterized by high waste water content and elevated leachate levels. Despite the ongoing biodegradation of waste in the highly saturated regions of these landfills, which leads to gas accumulation and bubble formation, the associated gas pressure that poses a risk to landfill stability is often overlooked. This paper introduces a landfill gas (LFG) bubble generation model and a two-fluid model that considers bubble buoyancy and porous medium resistance. The entire process can be divided into two stages based on the force balance and velocity of bubbles: Bubble Development Stage and the Two-Fluid Flow Stage. The models were validated using a one-dimensional analytical solution of hydraulic distribution that considers bubble generation, as well as an experiment involving air injection into a saturated medium. The mechanisms of LFG accumulation and ascent, leachate level rise, and discontinuous leachate-gas flow were then investigated in conjunction with continuous flow in the unsaturated region. The results indicate that the generation of LFG bubbles below the leachate level can cause a rise in the level height of more than 20%. During the Bubble Development Stage, there is a critical height for bubble ascent, above which the buoyancy exceeds the combined forces of gravity and resistance, resulting in less than 10% of bubbles continuously flowing into the unsaturated zone for recovery. The developed model effectively captures the accumulation and flow of LFG bubbles below the leachate level and could be further utilized to study leachate-gas pumping in the future.

Modelling and optimization of an innovative facility for automated sorting of aluminium scraps

The growing demand for aluminium worldwide makes aluminium recycling critical to realising a circular economy and increasing the sustainability of our world. One effective way to improve the impact of aluminium recycling is to develop cost-efficient automated sorting technologies for obtaining pre-defined high-quality aluminium scrap products, thus reducing undesirable downcycling and increasing environmental/economic benefits. In this work, an innovative facility, which includes singulation, sensor scanning, and ejection, is optimised for the automated sorting of aluminium scraps. The sorting facility is computationally studied by a virtual experiment model based on the discrete element method. The model considers particle-scale dynamics of complex-shaped scraps and mimics the automated operation of the facility. Based on virtual experiment modelling, the flow of scrap is optimized by computation, with the feasible operation of the sorting facility being proposed. Accordingly, the sorting facility has been built and model predictions are confirmed in actual operation.

Optimizing the greenhouse gas emissions of waste transfer and transport: An integration of life cycle assessment and vehicle routing problem

This study presents a comprehensive analysis of greenhouse gas (GHG) emissions associated with waste transfer and transport, incorporating derived leachate treatment-a factor often overlooked in existing research. Employing an integration model of life cycle assessment and a vehicle routing problem (VRP) methods, we evaluated the GHG reduction potential of waste transfer and transport system. Two Chinese counties with different topographies and demographics were selected, yielding 80 scenarios that factored in waste source separation as well as vehicle capacity, energy sources, and routes. The functional unit (FU) is transferring and transporting 1 tonne waste and treating derived leachate. The GHG emissions varied from 12 to 39 kg CO2 equivalent per FU. Waste source separation emerged as the most impactful mitigation strategy, not only for the studied system but for an integrated waste management system. Followings are the use of larger capacity vehicles and electrification of the vehicles. These insights are instrumental for policymakers and stakeholders in optimizing waste management systems to reduce GHG emissions.

Modeling and analysis of a multilayer solid tumour with cell physiological age and resource limitations

We study an avascular spherical solid tumour model with cell physiological age and resource constraints in vivo. We divide the tumour cells into three components: proliferating cells, quiescent cells and dead cells in necrotic core. We assume that the division rate of proliferating cells is nonlinear due to the nutritional and spatial constraints. The proportion of newborn tumour cells entering directly into quiescent state is considered, since this proportion can respond to the therapeutic effect of drug. We establish a nonlinear age-structured tumour cell population model. We investigate the existence and uniqueness of the model solution and explore the local and global stabilities of the tumour-free steady state. The existence and local stability of the tumour steady state are studied. Finally, some numerical simulations are performed to verify the theoretical results and to investigate the effects of different parameters on the model.

A novel mathematical template for developing fDOM probe fluorescence signal correction models for freshwaters

The reliable application of field deployable fluorescent dissolved organic matter (fDOM) probes is hindered by several influencing factors which need to be compensated. This manuscript describes the corrections of temperature, pH, turbidity and inner filter effect on fluorescence signal of a commercial fDOM probe (fDOMs). For this, Australian waters with wide ranging qualities were selected, e.g. dissolved organic carbon (DOC) ranging from ∼1 to ∼30 mg/L, specific UV absorbance at 254 nm from ∼1 to ∼6 L/m/mg and turbidity from ∼1 to ∼ 350 FNU. Laboratory-based model calibration experiments (MCEs) were performed. A model template was developed and used for the development of the correction models. For each factor, data generated through MCEs were used to determine model coefficient (α) values by fitting the generated model to the experimental data. Four discrete factor models were generated by determination of a factor-specific α value. The α values derived for each water of the MCEs subset were consistent for each factor model. This indicated generic nature of the four α values across wide-ranging water qualities. High correlation between fDOMs and DOC were achieved after applying the four-factor compensation models to new data (r, 0.96, p < 0.05). Also, average biases (and %) between DOC predicted through fDOMs and actual DOC were decreased by applying the four-factor compensation model (from 3.54 (60.9%) to 1.28 (16.7%) mg/L DOC). These correction models were incorporated into a Microsoft EXCEL-based software termed EXOf-Correct for ready-to-use applications.

Modelling on COVID-19 control with double and booster-dose vaccination

COVID-19 vaccines have been illustrated to lessen the growth of sickness caused by the virus effectively. In any case, inoculation has consistently been controversial, with differing opinions and viewpoints. This has compelled some individuals to decide against receiving the vaccine. These divergent viewpoints have had a trivial impact on the epidemic's dynamics and the disease's development. In response to vaccinated individuals still falling ill, many countries have implemented booster vaccines to protect further. In this specific investigation, a mathematical model composed of seven compartments is employed to examine the effectiveness of a booster dose in preventing and treating the transmission of COVID-19. The principles of mathematics are employed to analyse and investigate the dynamics of the disease. Using a qualitative prototype analysis, we acquired valuable insights into its effectiveness. One essential aspect is the basic reproduction number, a critical determinant of the disease's spread. This calculation is determined by studying the system's equilibrium and evaluating its stability. Furthermore, we examined the balance from a local and global viewpoint, considering the possibility of bifurcation and the model's reproductive number sensitivity index. Through numerical simulations, we have visually illustrated the analytical findings outlined in this research paper and presented a thorough examination of the efficacy of booster shots as a preventive and therapeutic measure in the spread dynamics of COVID-19.

Fugacity model covering abiotic and biotic matrices to investigate the transfer and fate of perfluoroalkyl acids in a large shallow lake of eastern China

Solving the challenges faced during the measurement of the cross-interface transfer of perfluoroalkyl acids (PFAAs) in lakes is crucial for clarifying environmental behaviours of these chemicals and their efficient governance. This study developed a multimedia fugacity model based on the quantitative water-air-sediment interaction (QWASI) covering abiotic/biotic matrices to investigate the cross-interface transfer and fate of PFAAs in Luoma Lake, a typical PFAA-contaminated shallow lake in eastern China. The accuracy and reliability of the established model were confirmed using Percent bias and Monte Carlo simulation, respectively. Using the QWASI model, the multimedia transfer of the PFAAs and their accumulation and persistence in different sub-compartments were described and measured, and the differences among individual PFAAs were explored. The simulation results showed that the sedimentation and resuspension of PFAAs were the most intense cross-interfacial transfers, and the sediments served as a chemical sink in the long term. A significant negative correlation of NC-F (the number of CF bonds) with the relative outflow flux (TW·out-ct) but a positive correlation with the relative net transfer across the interface between water and aquatic plants (Tp-ct) was detected, indicating that the PFAA migration capacity decreased but the bioaccumulation potential increased with the CF bond number. The persistence in water (Pw) of individual PFAAs ranged from 19.65d (PFOA) to 32.22d (PFOS), with an average of 26.15d; their persistence in sediment (Ps) ranged from 432d (PFBA) to 3216d (PFOS), with an average of 1524d, increasing linearly with an increase in NC-F. The water advection flows into and out of the lake (QW·in and QW·out), the PFAA concentration of water inflow (CW·in), and bioconcentration factor of aquatic plants (BCFp) were the primary parameters sensitive to PFAAs in all sub-compartments, which are essential indexes for exploring promising remediation pathways for lacustrine PFAA contamination based on the fugacity model simulation.

Machine learning modeling of thermally assisted biodrying process for municipal sludge

Preparation of activated carbons is an important way to utilize municipal sludge (MS) resources, while drying is a pretreatment method for making activated carbons from MS. In this study, machine learning techniques were used to develop moisture ratio (MR) and composting temperature (CT) prediction models for the thermally assisted biodrying process of MS. First, six machine learning (ML) models were used to construct the MR and CT prediction models, respectively. Then the hyperparameters of the ML models were optimized using the Bayesian optimization algorithm, and the prediction performances of these models after optimization were compared. Finally, the effect of each input feature on the model was also evaluated using SHapley Additive exPlanations (SHAP) analysis and Partial Dependence Plots (PDPs) analysis. The results showed that Gaussian process regression (GPR) was the best model for predicting MR and CT, with R2 of 0.9967 and 0.9958, respectively, and root mean square errors (RMSE) of 0.0059 and 0.354 ℃. In addition, graphical user interface software was developed to facilitate the use of the GPR model for predicting MR and CT by researchers and engineers. This study contributes to the rapid prediction, improvement, and optimization of MR and CT during thermally assisted biodrying of MS, and also provides valuable guidance for the dynamic regulation of the drying process.

Long-term responses of internal environment dynamics in a freshwater lake to variations in external nutrient inputs: A model simulation approach

Lake restoration usually focuses on reducing external nutrient sources. However, when sediments contain nutrients accumulated over multiple years, internal nutrient release can delay restoration progress. In lake restoration and management, it is important to understand the dynamic relationship between nutrient concentrations in a lake and internal and external nutrient sources. In this study, we quantified external nutrient inputs through measurements and compared them with internal sediment release from simulation using the PCLake+ model. Additionally, we evaluated alterations in the internal nutrient release, lake nutrient concentrations, and algae biomass (chlorophyll-a) within the lake following varying degrees of reduction in external nutrient loads. The results demonstrate that the PCLake+ effectively simulated the lake's nutrient concentration and algae biomass. Based on the PCLake+ estimates, internal nutrient loads accounted for 51 % of the total nitrogen (N) and 80 % of the total phosphorus (P) loadings in Lake Erhai in 2019. In 2020, the total contributions were 43 % for TN and 72 % for TP. We simulated four scenarios where external nutrient inputs were reduced to 25 %, 50 %, 75 %, and 99.99 % of their original levels. The 40-year simulation showed that the lake's ecological system initially exhibited a fast internal response but reached equilibrium after eight years. P concentrations took longer to reach equilibrium compared to N concentrations, probably due to the stronger binding characteristics of P. To meet the water quality target in the future, it is necessary to reduce external N and P inputs into Lake Erhai by at least 23 % and 15 %, respectively, under current conditions. Although reducing external nutrient loads can indirectly lower internal nutrient loads, water management should address both external and internal loads simultaneously, as internal release cannot be effectively reduced by external reductions alone. Additionally, the lake's internal release may continue for several years, even with reductions in external inputs.

Assessment of wetland ecosystem health in Rarh Region, India through P-S-R (pressure-state-response) model

The current study attempted to assess wetland ecosystem health (EH) in the Murshidabad district's Rarh tract using the P-S-R (Pressure-State-Response) model and machine learning (ML) algorithms and validated it with a field-based validation approach as well as conventional validation approaches. To assess the ecosystem's health, 27 metrics were used to monitor the wetlands' pressure, state, and response. All of the models found that 46.1 % of wetlands in strong EH zones have transformed to 11.41 % in relatively fragile EH zones during the previous thirty years, demonstrating a progressive loss of EH quality throughout larger wetland areas. All of the applied models were deemed to be acceptable based on the results of the model validation process, however, the Random Forest (RF) model performed exceptionally well. The deterioration of EH in the wetlands happened due to the rapid expansion of settlement areas and agricultural land. So, the findings of the study deepen our knowledge about EH in the Rarh tract's wetlands, assisting decision-makers in creating sustainable wetland management strategies.

Environmental forcing and policy synergy: A multidimensional approach in the governance of air pollution and carbon emission

Policy synergies effectively contribute to the integrated management of air pollution and carbon emissions, which is crucial for safeguarding ecosystem stability and public health. This study uses the causal network model of Gaussian process regression to analyze the combined impacts of dynamic and static carbon emission reduction and air quality policies on carbon emissions and air quality. The causal effects of policy measures and their synergistic effects are also examined. The study results indicate: (1) There is significant geographical heterogeneity in the implementation of environmental policies and regional economic development, with the economically developed eastern coastal regions adopting more stringent carbon emission and air pollution control measures, while the western provinces adopt relatively lax environmental policies. (2) The synergistic effect of carbon emission reduction policies and air quality policies exists, and the two types of static policies are substitutable for managing carbon dioxide emissions and air pollution. (3) Policies' forced effect exists, where the exacerbation of environmental problems leads to the formation and implementation of policies. (4) The value added by the secondary industry is a key motivation for forming carbon emission reduction policies and air quality control policies. Additionally, the value added by the secondary industry directly impacts the incidence of respiratory diseases (e.g., tuberculosis). Finally, dynamic and synergistic policy recommendations are proposed based on the study's findings.

Improved medical waste plasma gasification modelling based on implicit knowledge-guided interpretable machine learning

Ensuring the interpretability of machine learning models in chemical engineering remains challenging due to inherent limitations and data quality issues, hindering their reliable application. In this study, a qualitatively implicit knowledge-guided machine learning framework is proposed to improve plasma gasification modelling. Starting with a pre-trained machine learning model, parameters are further optimized by integrating the heuristic algorithm to minimize the data fitting errors and resolving implicit monotonic inconsistencies. The latter is comprehensively quantified through Monte Carlo simulations. This framework is adaptive to different machine learning techniques, exemplified by artificial neural network (ANN) and support vector machine (SVM) in this study. Validated by a case study on plasma gasification, the results reveal that the improved models achieve better generalizability and scientific interpretability in predicting syngas quality. Specifically, for ANN, the root mean square error (RMSE) and knowledge-based error (KE) reduce by 36.44% and 83.22%, respectively, while SVM displays a decrease of 2.58% in RMSE and a remarkable 100% in KE. Importantly, the improved models successfully capture all desired implicit monotonicity relationships between syngas quality and feedstock characteristics/operating parameters, addressing a limitation that traditional machine learning struggles with.

Assessment of conflicts under human-wildlife interactions: An application of the conservation conflict transformation model in communities adjacent to Nyerere National Park, Tanzania

Conflicts arising from human-wildlife interactions (HWIs) pose a significant challenge in communities neighboring Nyerere National Park. To achieve long-term conservation success, it is critical to understand and resolve complex social conflicts. Currently, most attention is focused on addressing dispute-related conflicts, whereas underlying, and identity-based conflicts are understudied, resulting in inadequate information in literature regarding underlying and identity-based conflicts that drive social conflicts. Through the use of the Conservation Conflict Transformation Model (CCT), this study aimed to identify existing conflicts across three levels of conflict and assess current intervention strategies employed within the study area. Based on data collected from 324 respondents through questionnaire surveys, the study revealed that the dispute level of conflicts was lower than the underlying and identity-based levels of conflicts, emphasizing the limited scope of addressing conflicts solely at the dispute level within the context of conflicts arising from HWIs. To alleviate conflicts at the dispute level, respondents employed both lethal and non-lethal control techniques, with a preference for non-lethal methods. Additionally, socio-demographic factors including age, gender, household size, respondent's attitude towards wildlife, and residence significantly influenced the implementation of intervention strategies (P < 0.05). Furthermore, the findings revealed that respondents faced several challenges, including a lengthy incident reporting process for conflicts arising from HWIs, lack of consolation payment for damages, exclusionary practices, and lack of transparency in seeking assistance from local, wildlife, and government authorities. Overall, the study recommends adopting and implementing a holistic approach aligned with the CCT model to effectively address conflicts under HWIs. Future research should focus on thorough case studies and actual applications of the CCT model to manage conflicts under HWIs.

Extending the SUSI peatland simulator to include dissolved organic carbon formation, transport and biodegradation - Proper water management reduces lateral carbon fluxes and improves carbon balance

Drainage intensity and forest management in peatlands affect carbon dioxide (CO2) emissions to the atmosphere and export of dissolved organic carbon (DOC) to water courses. The peatland carbon (C) balance results from a complex network of ecosystem processes from where lateral C fluxes have typically been ignored. Here, we present a new version of the SUSI Peatland simulator, the first advanced process-based ecosystem model that compiles a full C balance in drained forested peatland including DOC formation, transport and biodegradation. SUSI considers site, stand and terrain characteristics as well as the interactions and feedbacks between ecosystem processes and offers novel ways to evaluate and mitigate adverse environmental impacts with thorough management planning. Here, we extended SUSI by designing and parameterizing a mass-balance based decomposition module (ESOM) based on literature findings and tested the ESOM performance against an independent dataset measured in the laboratory using peat columns collected from Finland, Estonia, Sweden and Ireland. ESOM predicted the CO2 emissions and changes in DOC concentrations with a reasonable accuracy for the peat columns. We applied the new SUSI for drained peatland sites and found that reducing the depth to which ditches are cleaned by 0.3 m decreased the annual DOC export by 34 (17 %), 29 (19 %) and 7 (5 %) kg ha-1 in Finland, Estonia and Sweden, respectively, using typical ditch spacing for these countries. Correspondingly, site annual C sink increased by 305, 409 and 32 kg ha-1 in Finland, Estonia and Sweden, respectively. Our results also indicated that terrain slope can markedly alter the water residence time and consequently DOC biodegradation and export to ditches. We conclude that DOC export can be decreased and site C sink increased by reducing the depth to which ditches are cleaned or by increasing the ditch spacing.

New insights into N2O emission and electron competition under different chemical oxygen demand to nitrogen ratios in a biofilm system

Nitrous oxide (N2O) is a greenhouse gas that could accumulate during the heterotrophic denitrification process. In this study, the effects of different chemical oxygen demand to nitrogen ratio (COD/N) on N2O production and electron competition was investigated. The electron competition was intensified with the decrease of electron supply, and Nos had the best electron competition ability. The model simulation results indicated that the degradation of NOx-Ns was a combination of diffusion and biological degradation. As reaction proceeding, N2O could accumulate inside biofilm. A thinner biofilm and a longer hydraulic retention time (HRT) might be an effective way to control N2O emission. The application of mathematical model is an opportunity to gain deep understanding of substrate degradation and electron competition inside biofilm.

Impact of ionic strength on goethite colloids co-transported with arsenite (As3+) through a saturated sand column under anoxic condition: Experiment and mathematical modeling

The knowledge about co-transport of goethite and As3+ to investigate the effect of goethite colloids on As3+ transport under various degrees of seawater intrusion, particular extremely conditions, in groundwater environment is still limited. The main objective is to investigate the influence of seawater intrusion on the sorption, migration, and reaction of As3+and goethite colloids into sand aquifer media under anoxic conditions by using the bench-scale and reactive geochemical modeling. The research consisted of two parts as follows: 1) column transport experiments consisting of 8 columns, which were packed by using synthesis groundwater at IS of 0.5, 50, 200, and 400 mM referring to the saline of seawater system in the study area, and 2) reactive transport modeling, the mathematical model (HYDRUS-1D) was applied to describe the co-transport of As3+ and goethite. Finally, to explain the interaction of goethite and As3+, the Derjaguin-Landau-Verwey-Overbeek (DLVO) calculation was considered to support the experimental results and HYDRUS-1D model. The results of column experiments showed goethite colloids can significantly inhibit the mobility of As3+ under high IS conditions (>200 mM). The Rf of As3+ bound to goethite grows to higher sizes (47.5 and 65.0 μm for 200 and 400 mM, respectively) of goethite colloid, inhibiting As3+ migration through the sand columns. In contrast, based on Rf value, goethite colloids transport As3+ more rapidly than a solution with a lower IS (0.5 and 50 mM). The knowledge gained from this study would help to better understand the mechanisms of As3+ contamination in urbanized coastal groundwater aquifers and to assess the transport of As3+ in groundwater, which is useful for groundwater management, including the optimum pumping rate and long-term monitoring of groundwater quality.

Improving hydrological modeling to close the gap between elevated CO2 concentration and crop response: Implications for water resources

Rising atmospheric carbon dioxide concentrations ([CO2]) affect crop growth and the associated hydrological cycle through physiological forcing, which is mainly regulated by reducing stomatal conductance (gs) and increasing leaf area index (LAI). However, reduced gs and increased LAI can affect crop water consumption, and the overall effects need to be quantified under elevated [CO2]. Here we develop a SWAT-gs-LAI model by incorporating a nonlinear gs-CO2 equation and a missing LAI-CO2 relationship to investigate the responses of water consumption of grain maize, maize yield, and losses of water and soil to elevated [CO2] in the Upper Mississippi River Basin (UMRB; 492,000 km2). Results exhibited enhanced maize yield with decreased water consumption for increases in [CO2] from 495 ppm to 825 ppm during the historical period (1985-2014). Elevated [CO2] promoted surface runoff but suppressed sediment loss as the predominant impact of LAI-CO2 leading to enhanced surface cover. A comprehensive analysis of future climate change showed increased maize water consumption in comparison to the historical period, driven by the more pronounced effects of overall climate change rather than solely elevated [CO2]. Generally, future climate change promoted maize yield in most regions of the UMRB for three Shared Socioeconomic Pathway (SSP) scenarios. Surface runoff was shown to increase generally in the future with sediment loss increasing by an average of 0.39, 0.42, and 0.66 ton ha-1 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. This was due to negative climatic change effects largely surpassing the positive effect of elevated [CO2], particularly in zones near the middle and lower stream. Our results underscore the crucial role of employing a physically-based model to represent crop physiological processes under elevated [CO2] conditions, improving the reliability of predictions related to crop growth and the hydrological cycle.

Disentangling the contributions of anthropogenic nutrient input and physical forcing to long-term deoxygenation off the Pearl River Estuary, China

Deoxygenation in estuarine and coastal waters worldwide has been largely attributed to the increasing anthropogenic nutrient input, whereas the contribution by long-term (decadal) changes in physical forcing is less investigated. This study aims to disentangle the impacts of three-decade changes in summer river nutrient concentration and physical forcing on the deoxygenation off a large eutrophic estuary, the Pearl River Estuary (PRE) in China. Using a coupled physical-biogeochemical model, we reproduce the observed summer oxygen conditions under the historical (the 1990s) and present (the 2020s) status of river nutrient concentration, freshwater discharge, and wind forcing. We show that the bottom hypoxic (dissolved oxygen < 2 mg/L) area off the PRE in the 2020s has increased by 73 % relative to the 1990s. The expansion is a result of the increased bottom water oxygen consumption outweighing the enhanced vertical oxygen supply, with the former driven by the sharp increase in inorganic nitrogen and phosphorus concentrations (160 %) and the latter caused by the decadal decline in both freshwater discharge (38 %) and wind speed (12.5 %) in summer. Model experiments suggest that if the observed changes in physical forcing had not occurred, the dramatic increase in anthropogenic nutrient concentrations from the 1990s to 2020s could have led to a much greater expansion of hypoxic area (249 %). On the contrary, the decadal decrease in summer freshwater discharge alone (while keeping the nutrient loading the same as in the 1990s) almost eliminates hypoxia off the PRE by weakening water column stratification and limiting the offshore spread of nutrients and organic matter, whereas the declined wind speed increases the hypoxic area by 247 % mainly through enhancing water column stability. Our results reveal that long-term changes in physical forcing are confounding the effects of anthropogenic nutrient input on deoxygenation, underlining the need to consider regional forcing changes in nutrient management to meet water quality goals.

Enhanced autumn phenology model incorporating agricultural drought

The impacts of various drought types on autumn phenology have yet to be extensively explored. We address the influence of pre-season agricultural and meteorological droughts on autumn phenology in the Northern Hemisphere. To this end, enhanced autumn phenology models incorporating drought factors was developed, contributing to a deeper understanding of these complex interactions. The study reveals that there was no significant trend of advancement or delay in the End of Season (EOS) across the Northern Hemisphere based on SIF estimates from 2001 to 2020. The cumulative and delayed impacts of pre-season agricultural drought on EOS were found to be more pronounced than those associated with meteorological drought. The analysis of various evaluation indexes shows that the performance of the Cooling Degree Days (CDD) model incorporating the Standardized Soil Moisture Drought Index (CDDSSMI) in simulating EOS in the Northern Hemisphere is >14 % higher than that of the standard CDD model. Additionally, the performance of the CDD model with the Standardized Precipitation Index (CDDSPI) in simulating EOS in the Northern Hemisphere is improved by >5.6 % compared to the standard CDD model. A comparison of future EOS projections across various models reveals that the CDD model significantly overestimates EOS in different scenarios (SSP245 and SSP585). The CDDSSMI model projects EOS approximately 7 days earlier than the CDD model, and the CDDSPI model projects EOS approximately 5 days earlier than the CDD model. This study highlights the diverse impacts of drought types on plant autumn phenology and underscores the significance of parameterizing drought impacts in autumn phenology models.

A self-sustaining effect induced by iron sulfide generation and reuse in pyrite-woodchip mixotrophic bioretention systems: An experimental and modeling study

Dual electron donor bioretention systems have emerged as a popular strategy to enhance dissolved nitrogen removal from stormwater runoff. Pyrite-woodchip mixotrophic bioretention systems showed a promoted and stabilized removal of dissolved nutrients under complex rainfall conditions, but the sulfate reduction process that can induce iron sulfide generation and reuse was overlooked. In this study, experiments and models were applied to investigate the effects of filler configuration and dissolved organic carbon (DOC) dissolution rate on treatment performance and iron sulfide generation in pyrite-woodchip bioretention systems. Key parameters govern that DOC dissolution and microbe-mediated processes were obtained by experiments. The water quality models that integrate one-dimensional constant flow, sorption and microbial transformation kinetics were used to predict the performance of bioretention systems. Results showed that the mixotrophic bioretention system with woodchip mixed in the vadose zone and pyrite in the saturated zone achieves a better performance in both nitrogen removal efficiency and by-product control. Comparably, woodchip and pyrite mixed in the saturated zone could encounter a high secondary pollution risk. The sensitivity coefficients of oxic/anoxic DOC dissolution rates to total nitrogen removal are 0.36 and -2.43 respectively. Iron sulfide generation was affected by DOC distribution and the competition between heterotrophic denitrifiers, autotrophic denitrifiers, and sulfate-reducing bacteria (SRB). DOC accumulation has an antagonistic effect on iron production and sulfate reduction. Extra DOC accumulation favors sulfate reduction while high DOC concentration inhibits pyrite-based denitrification and reduces Fe(III) production. The recycling of iron sulfide can improve the robustness and sustainability of bioretention systems.

A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model

For identifying the optimal model for real-time conflict prediction, there is a necessity for proposing a quantitative analysis approach that adaptively selects the optimal prediction model from a large pool of task-suited models, while simultaneously considering the computational efficiency and prediction precision. Based on this line, this study developed an innovative approach termed surrogate model-based optimal prediction model selection (SM-OPMS). This approach aims to accelerate the optimal model selection while incorporating prediction precision considerations, under the precondition of comprehensively evaluating task-suited models. An analytical framework was proposed, further illustrated through a detailed case study. In the case study, real vehicle trajectory data from HighD were processed and applied, which can be aggregated to extract both traffic state variables and corresponding conflict data during a specific time interval. As for the conflict detection, Time-to-Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) indicators were utilized to identify risky conditions. Based on the proposed approach, the selection for the optimal prediction model was conducted, and the variable importance in conflict prediction within the optimal models derived from the SM-OPMS was also investigated. Finally, a comparative analysis with the enumeration-based optimal prediction model selection (E-OPMS) approach was conducted to validate the superiority of the proposed approach. Results indicate that SM-OPMS outperforms E-OPMS in optimal model selection, notably enhancing computational efficiency by up to 94.03%, while maintaining prediction precision within a maximum reduction of only 7.91%. The significance of the SM-OPMS approach is revealed by its comprehensive selection of the optimal prediction models for specific traffic scenarios, taking into account both prediction efficiency and precision simultaneously. The proposed approach is expected to contribute to the development of real-time conflict prediction in the future.

The impact and mechanism analysis of Clean Development Mechanism on the synergistic effects of pollution mitigation and carbon reduction

The establishment of the Clean Development Mechanism (CDM) has greatly improved China's carbon emission trading system. However, due to the unbalanced development of CDM in China, the effects and mechanism of CDM on reducing pollution and carbon are still unclear. In order to explore the effects and mechanism of CDM on the synergistic effects of pollution mitigation and carbon reduction, we first set up a theoretical analysis framework. Utilizing panel data from 254 prefecture-level cities across China spanning from 2004 to 2021, we employ a synergy degree model of composite system to evaluate the synergistic effects of pollution mitigation and carbon reduction. By treating CDM as a quasi-natural experimental research subject, we construct a multi-period difference-in-difference model to assess the CDM projects' effects. Our findings indicate a positive association between CDM projects and the synergistic effects of pollution mitigation and carbon reduction. Heterogeneity analysis reveals that CDM projects located in the western region, areas with lower levels of economic development, non-resource cities, non-old industrial bases, and projects with Certified Emission Reductions issued exhibit the most pronounced synergistic effects. Specially, dynamic policy effect analysis shows that only non-resource cities and non-old industrial bases exhibit enhanced policy implementation regarding CDM. Mechanism analysis demonstrates that CDM primarily enhances synergistic effects through improved energy efficiency, technological innovation and energy transition. These findings enrich empirical investigations concerning market-driven emission reduction policy in China, shedding light on pivotal pathways for synergistic control of pollution mitigation and carbon reduction and offering valuable policy insights for comprehensive economic and social green transformation in China.

Optimizing vehicle Front-End structure for e-bike rider Safety: An advanced Multi-Objective approach using injury prediction models

A multi-objective optimization method based on an injury prediction model is proposed to address the increasingly prominent safety issues for e-bike riders in Chinese road traffic. This method aims to enhance the protective effect of vehicle front-end for e-bike riders by encompassing a broader range of test scenarios. Initially, large-scale rider injury response data were collected using automated Madymo simulations. A machine learning model was then trained to accurately predict the risk of rider injury under varied crash conditions. Subsequently, this model was integrated into a multi-objective optimization framework, combined with multi-criteria decision analysis, to effectively evaluate and rank various design alternatives on the Pareto frontier. This process entailed a comparative analysis of the design in a baseline scenario before and after optimization, focusing on both kinematic and injury responses of riders. Through detailed injury mechanism analysis, key design variables such as the height of the hood front and the width of the bumper were identified. This led to the proposal of specific optimization strategies for these structural parameters. The results from this study demonstrate that the proposed optimization method not only guides the design process accurately and efficiently but also balances the injury risks across different body parts. This approach significantly reduces the injury risk for riders in car-to-e-bike collisions and provides actionable insights for vehicle design enhancements.