Model based analysis of trace metal dosing strategies to improve methane yield in anaerobic digestion systems

Favourable effects of trace metals (TMs) on regulating anaerobic digestion (AD) performance are extensively utilised to improve methane yield. This study discusses a model-based approach to find out the best TM dosing strategies. The model has been applied to compare continuous, preloading, pulse dosing and in-situ loading. Simulations were also carried out to comprehend appropriate dosing form, dosing time and quantity of metals to be dosed. Model results show that the best way to dose TMs is repeated pulse dosing at low concentration levels in the optimum range with high frequency. Best dosing strategy for the system in this study was found to be 5 µM pulse loading at 5 days intervals as it gave maximum methane production and low effluent metal loss. Preferable dosing form depends on reactor configuration and this has been verified after model calibration with experimental data. Easily dissociable metal chlorides are ideal for continuous reactors.

Parameterisation of a bluetongue virus mathematical model using a systematic literature review

Bluetongue virus (BT) is a vector-borne virus that causes a disease, called bluetongue, which results in significant economic loss and morbidity in sheep, cattle, goats and wild ungulates across all continents of the world except Antarctica. Despite the geographical breadth of its impact, most BT epidemiological models are informed by parameters derived from the 2006-2009 BTV-8 European outbreak. The aim of this study was to develop a highly adaptable model for BT which could be used elsewhere in the world, as well as to identify the parameters which most influence outbreak dynamics, so that policy makers can be properly informed with the most current information to aid in disease planning. To provide a framework for future outbreak modelling and an updated parameterisation that reflects natural variation in infections, a newly developed and parameterised two-host, two-vector species ordinary differential equation model was formulated and analysed. The model was designed to be adaptable to be implemented in any region of the world and able to model both epidemic and endemic scenarios. It was parameterised using a systematic literature review of host-to-vector and vector-to-host transmission rates, host latent periods, host infectious periods, and vaccine protection factors. The model was demonstrated using the updated parameters, with South Africa as a setting based on the Western Cape's known cattle and sheep populations, local environmental parameters, and Culicoides spp. presence data. The sensitivity analysis identified that the duration of the infectious period for sheep and cows had the greatest impact on the outbreak length and number of animals infected at the peak of the outbreak. Transmission rates from cows and sheep to C. imicola midges greatly influenced the day on which the peak of the outbreak occurred, along with the duration of incubation period, and infectious period for cows. Finally, the protection factor of the vaccine had the greatest influence on the total number of animals infected. This knowledge could aid in the development of control measures. Due to gradual climate and anthropological change resulting in alterations in vector habitat suitability, BT outbreaks are likely to continue to increase in range and frequency. Therefore, this research provides an updated BT modelling framework for future outbreaks around the world to explore transmission, outbreak dynamics and control measures.

Attractors for Hopfield lattice model in weighted spaces

The investigation into the dynamic behavior of infinite lattice systems holds paramount significance in the realm of physical phenomena, particularly in mechanics. This intricate domain has captivated the attention of both mathematicians and physicists. In acknowledgment of the inherent noise prevalent in real-world environments, our study embraces this aspect by introducing a random term into our model. This deliberate inclusion of stochasticity engenders a novel perspective, giving rise to a stochastic lattice differential equation. This model proves to be a versatile tool for accurately characterizing spatial structures characterized by discrete components and the associated uncertainties that pervade them. This research elucidates the intricate interplay between lattice dynamics and environmental noise, shedding light on the complex behavior of such systems in a realistic context. Our result generalizes many results in three directions: extending the connections between the terms to non-linear, extending the connection neighborhood from 3 (as in most cases) to arbitrary value n, and extending the results that are in ℓ2 to ℓρ2.

External validation of a clinical mathematical model estimating post-operative urine output following cardiac surgery in children

BACKGROUND

This study aims to externally validate a clinical mathematical model designed to predict urine output (UOP) during the initial post-operative period in pediatric patients who underwent cardiac surgery with cardiopulmonary bypass (CPB).

METHODS

Children aged 0-18 years admitted to the pediatric cardiac intensive care unit at Cleveland Clinic Children's from April 2018 to April 2023, who underwent cardiac surgery with CPB were included. Patients were excluded if they had pre-operative kidney failure requiring kidney replacement therapy (KRT), re-operation or extracorporeal membrane oxygenation or KRT requirement within the first 32 post-operative hours or had indwelling urinary catheter for fewer than the initial 32 post-operative hours, or had vasoactive-inotrope score of 0, or those with missing data in the electronic health records.

RESULTS

A total of 213 encounters were analyzed; median age (days): 172 (IQR 25-75th%: 51-1655), weight (kg): 6.1 (IQR 25-75th%: 3.8-15.5), median UOP ml/kg/hr in the first 32 post-operative hours: 2.59 (IQR 25-75th%: 1.93-3.26) and post-operative 30-day mortality: 1, (0.4%). The mathematical model achieved the following metrics in the entire dataset: mean absolute error (95th% Confidence Interval (CI)): 0.70 (0.67-0.73), median absolute error (95th% CI): 0.54 (0.52-0.56), mean squared error (95th% CI): 0.97 (0.89-1.05), root mean squared error (95th% CI): 0.99 (0.95-1.03) and R2 Score (95th% CI): 0.29 (0.24-0.34).

CONCLUSIONS

This study provides encouraging external validation results of a mathematical model predicting post-operative UOP in pediatric cardiac surgery patients. Further multicenter studies must explore its broader applicability.

Prediction and optimization of wastewater treatment process effluent chemical oxygen demand and energy consumption based on typical ensemble learning models

Pollution integration and carbon reduction has become a primary focus in wastewater treatment processes. In this study, water quality and control indicators were used as input features and the dataset was extended using the moving average method. Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine algorithms were used to predict the effluent chemical oxygen demand (COD) and total energy consumption (TEC). The results indicated that the model prediction performance could be effectively improved when the data were amplified by two times and that the XGBoost model exhibited the best prediction performance for effluent COD and TEC. The Non-dominated Sorting Genetic Algorithm II model was employed for the multi-objective optimization of effluent COD and TEC, resulting in reductions of 15% and 18%, respectively. The ensemble learning model proposed in this study to achieve synergy between water quality improvement and energy saving is practical.

Habitat degradation changes and disturbance factors in the Tibetan plateau in the 21st century

Land use changes driven by human activities significantly impact biodiversity in plateau regions. However, current research is largely confined to identifying correlations between various factors and both habitat quality and degradation, overlooking the nonlinear relationships between them. To address this gap, we applied the PLUS-INVEST model to investigate the spatial effects of land-use changes on habitat quality and degradation patterns across the Tibetan Plateau during the 21st century. By employing a geographic detector, we determined the contribution rates of disturbance factors to habitat quality and degradation, and established constraint lines and threshold ranges between these factors. The findings reveal that: (1) The PLUS model demonstrates an exceptional performance in land-use simulation, with an overall accuracy of 0.8465. (2) The high-quality habitat area exhibits a declining trend, while the habitat degradation index steadily rises from 2000 to 2100, indicating a significant loss of biodiversity within the region. Habitat quality displays a spatial distribution pattern characterized by higher values in the south and lower values in the north, with areas in proximity to road threat sources experiencing more pronounced habitat degradation. (3) NDVI emerges as the most influential factor in promoting habitat quality, while the interaction of NDVI_Temperature exerts the greatest influence on spatial heterogeneity. The distance to resident emerges as the primary disturbance factor contributing to habitat degradation, with the interaction strength of GI_Resident being the most significant contributor. (4) Threshold intervals for ANPP, NDVI, precipitation, temperature, and distance to resident of optimal habitat quality and most severe degradation. This provides a novel scientific approach for designating areas for targeted conservation and intensive management restoration.

Distilling mathematical reasoning capabilities into Small Language Models

This work addresses the challenge of democratizing advanced Large Language Models (LLMs) by compressing their mathematical reasoning capabilities into sub-billion parameter Small Language Models (SLMs) without compromising performance. We introduce Equation-of-Thought Distillation (EoTD), a novel technique that encapsulates the reasoning process into equation-based representations to construct an EoTD dataset for fine-tuning SLMs. Additionally, we propose the Ensemble Thoughts Distillation (ETD) framework to enhance the reasoning performance of SLMs. This involves creating a reasoning dataset with multiple thought processes, including Chain-of-Thought (CoT), Program-of-Thought (PoT), and Equation-of-Thought (EoT), and using it for fine-tuning. Our experimental performance demonstrates that EoTD significantly boosts the reasoning abilities of SLMs, while ETD enables these models to achieve state-of-the-art reasoning performance.

An interaction-based mixing model for predicting porosity and tensile strength of directly compressed ternary blends of pharmaceutical powders

Predicting the mechanical properties of powder mixtures without extensive experimentation is important for model driven design in solid dosage form manufacture. Here, a new binary interaction-based model is proposed for predicting the compressibility and compactability of directly compressed pharmaceutical powder mixtures based on the mixture composition. The model is validated using blends of MCC, lactose and paracetamol or ibuprofen. Both compressibility and compactability profiles are predicted well for a variety of blend compositions of ternary mixtures for the two formulations. The model performs well over a wide range of compositions for both blends and better than either an ideal mixing model or a ternary interaction model. A design of experiments which reduces the amount of API required for fitting the model parameters for a new formulation is proposed to reduce amount of API required. The design requires only three blends containing API. The model gives similar performance to the well-known Reynolds et al. model (2017) when trained using the same data sets. The binary interaction model approach is generalizable to other powder mixture properties. The model presented in this work is limited to curve-fitting of empirical compaction models for mixtures of common pharmaceutical powders and is not intended to provide guidance on the practical operating space (or design space) limits.

A two-stage mechanistic reduced-order model of pharmaceutical tablet dissolution: Population balance modeling and tablet wetting functions

We propose a two-stage reduced-order model (ROM) of pharmaceutical tablet dissolution that is comprised of (i) a mechanistic dissolution function of the active pharmaceutical ingredient (API) and (ii) a tablet wetting function. The former is derived from a population balance model, using a high-resolution finite volume algorithm for a given API crystal size distribution and dissolution rate coefficient. The latter is obtained from the mechanistic understanding of water penetration inside a porous tablet, and it estimates the rate at which the API is exposed to the buffer solution for a given formulation and the dimensions of the tablet, contact angle, and surface tension between the solid and liquid phases, liquid viscosity, and mean effective capillary radius of the pore solid structure. In turn, the two-stage model is mechanistic in nature and one-way coupled by means of convolution in time to capture the start time of the API dissolution process as water uptake, swelling, and disintegration take place. The two-stage model correlates dissolution profiles with critical process parameters (CPPs), critical material attributes (CMAs), and other crucial critical quality attributes (CQAs). We demonstrate the model's versatility and effectiveness in predicting the dissolution profiles of diverse pharmaceutical formulations. Specifically, we formulate and fabricate acetaminophen and lomustine solid tablets using different API content and size distributions, characterize their dissolution behavior, and estimate capillary radius as a function of tablet porosity. The estimations generated by the proposed models consistently match the experimental data across all cases investigated in this study.

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here, we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-importantly-the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Modelling enhancement of Ecosystem Services provision through integrated agri-environment and forestry measures

Agri-environment and forest schemes can support landowners to conserve and enhance agricultural and forest ecosystems. The effectiveness of these schemes is often debated due to discrepancies that occur between the application of such measures and the delivery of Ecosystem Services (ES). We simulated the application of a suite of farmland and forest measures within a range of biophysical contexts in known High Nature Value landscapes across the Republic of Ireland. Three high resolution geospatial scenarios simulated the anticipated effects of the measures: i) a Baseline Scenario of current conditions, ii) an Enhanced Scenario simulated the application of measures, and iii) using the new 'Restoration Planner' freeware, an Enhanced + Connectivity Scenario simulated the application of additional targeted measures for ecosystem connectivity. Across all scenarios, we modelled and compared the responses of a range of ES including: habitat quality, carbon storage, production income and ecosystem connectivity. Multivariate analyses were used to ordinate and determine eight bundles of measures and their associated effect on ES and connectivity. These bundles were subsequently contextualised by examining unique landscape characteristics in which they occurred. The results show that measures applied under the Enhanced Scenario resulted in weak gains to carbon storage (2 %), strong gains to habitat quality (28 %), and weak losses to production income (-7 %) and ecosystem connectivity (-2 %). Similarities were observed under the Enhanced + Connectivity Scenario, though with comparably stronger gains to ecosystem connectivity (15 %). This study is the first to demonstrate the potential synergies and trade-offs to ES that can result from the integrated and targeted application of both farmland and forest measures within a variety of landscape characteristics.

Large-scale flood modeling and forecasting with FloodCast

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptive flood modeling and forecasting framework that can perform at large scales, namely FloodCast. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the multi-satellite observation module, a real-time unsupervised change detection method and a rainfall processing and analysis tool are proposed to harness the full potential of multi-satellite observations in large-scale flood prediction. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced, benefiting from the absence of a requirement for training data in physics-informed neural networks (PINNs) and featuring a fast, accurate, and resolution-invariant architecture with Fourier neural operators. To adapt to complex river geometries, we reformulate PINNs in a geometry-adaptive space. GeoPINS demonstrates impressive performance on popular partial differential equations across regular and irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. This model employs sequence-to-sequence learning and hard-encoding of boundary conditions. Next, we establish a benchmark dataset in the 2022 Pakistan flood using a widely accepted finite difference numerical solution to assess various flood simulation methods. Finally, we validate the model in three dimensions - flood inundation range, depth, and transferability of spatiotemporal downscaling - utilizing SAR-based flood data, traditional hydrodynamic benchmarks, and concurrent optical remote sensing images. Traditional hydrodynamics and sequence-to-sequence GeoPINS exhibit exceptional agreement during high water levels, while comparative assessments with SAR-based flood depth data show that sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with smaller simulation errors. The experimental results for the 2022 Pakistan flood demonstrate that the proposed method enables high-precision, large-scale flood modeling with an average MAPE of 14.93 % and an average Mean Absolute Error (MAE) of 0.0610 m for 14-day water depth simulations while facilitating real-time flood hazard forecasting using reliable precipitation data.

The response of non-point source pollution to climate change in an orchard-dominant coastal watershed

China is the largest global orchard distribution area, where high fertilization rates, complex terrain, and uncertainties associated with future climate change present challenges in managing non-point source pollution (NPSP) in orchard-dominant growing areas (ODGA). Given the complex processes of climate, hydrology, and soil nutrient loss, this study utilized an enhanced Soil and Water Assessment Tool model (SWAT-CO2) to investigate the impact of future climate on NPSP in ODGA in a coastal basin of North China. Our investigation focused on climate-induced variations in hydrology, nitrogen (N), and phosphorus (P) losses in soil, considering three Coupled Model Intercomparison Project phase 6 (CMIP6) climate scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Research results indicated that continuous changes in CO2 levels significantly influenced evapotranspiration (ET) and water yield in ODGA. Influenced by sandy soils, nitrate leaching through percolation was the principal pathway for N loss in the ODGA. Surface runoff was identified as the primary pathway for P loss. Compared to the reference period (1971-2000), under three future climate scenarios, the increase in precipitation of ODGA ranged from 15% to 28%, while the growth rates of P loss and surface runoff were the most significant, both exceeding 120%. Orchards in the northwest basin proved susceptible to nitrate leaching, while others were more sensitive to N and P losses via surface runoff. Implementing targeted strategies, such as augmenting organic fertilizer usage and constructing terraced fields, based on ODGA's response characteristics to future climate, could effectively improve the basin's environment.

Modeling the settling and resuspension of microplastics in rivers: Effect of particle properties and flow conditions

Microplastics have numerous different shapes, affecting the fate and transport of these particles in the environment. However, theoretical models generally assume microplastics to be spherical. This study aims to develop a modeling approach that incorporates the shapes of microplastics to investigate the vertical transport of microplastics in rivers and simulate the effect of particle and flow characteristics on settling and resuspension. To achieve these aims, a mechanistic model was developed utilizing the mass-balance and hydrodynamic equations. Scenario analysis was implemented assigning different values to model parameters, such as bed shear stress, shape factor and particle size to simulate the effect of flow patterns and particle properties. The model outcomes revealed that the residence time of microplastics in the water column was longest in medium bed shear stress, whilst it was shortest in low bed shear stress. This suggests that the influence of turbulence is not unidirectional; it can both increase and decrease microplastic concentrations and residence time in the water column. According to the scenario analysis, the settling flux of microplastics was the highest for near-spherical particles and increased with the size of the particles, as well as with increasing bed shear stress. However, the resuspension of particles was primarily influenced by increasing bed shear stress, but the ranking of resuspension flux values for different shaped and sized microplastics exhibited alterations with changing flow patterns. Turbulent conditions predominantly influenced the resuspension of near-spheres and large microplastics. On the contrary, the settling of fibers and small microplastics were significantly influenced by changing flow patterns, whereas near-spheres and largest particles were least affected. The model results were sensitive to changes in shape factor developed for this model, therefore this parameter should be improved in future studies.

Online state estimation in water distribution systems via Extended Kalman Filtering

Operators of water distribution systems (WDSs) need continuous and timely information on pressures and flows to ensure smooth operation and respond quickly to unexpected events. While hydraulic models provide reasonable estimates of pressures and flows in WDSs, updating model predictions with real-time sensor data provides clearer insights into true system behavior and enables more effective real-time response. Despite the growing prevalence of distributed sensing within WDSs, standard hydraulic modeling software like EPANET do not support synchronous data assimilation. This study presents a new method for state estimation in WDSs that combines a fully physically-based model of WDS hydraulics with an Extended Kalman Filter (EKF) to estimate system flows and heads based on sparse sensor measurements. To perform state estimation via EKF, a state-space model of the hydraulic system is first formulated based on the 1-D Saint-Venant equations of conservation of mass and momentum. Results demonstrate that the proposed model closely matches steady-state extended-period models simulated using EPANET. Next, through a holdout analysis it is found that fusing sensor data with EKF produces flow and head estimates that closely match ground truth flows and heads at unmonitored locations, indicating that state estimation successfully infers internal hydraulic states from sparse sensor measurements. These findings pave the way towards real-time operational models of WDSs that will enable online detection and mitigation of hazards like pipe leaks, main bursts, and hydraulic transients.

Collaborative management of environmental pollution and carbon emissions drives local green growth: An analysis based on spatial effects

Collaborative management of environmental pollution and carbon emissions (CMPC) has been a major policy instrument to promote Sustainable Development Goals (SDG) in recent years. However, the relationship between the benefits and drawbacks of this environmental management practice for green growth in and around a local area remains to be clarified. Using 30 provinces in China during 2001-2019 as the object of analysis, we assessed the efficiency of local CMPC practices using the nonradial directional distance function (NDDF) model, predicted local green growth using the frontier green complexity index (GCI), and empirically examined the spatial effects, locational heterogeneity, and threshold characteristics of the relationship using the spatial Durbin model and the panel threshold model. Our study finds that although efficient CMPC does drive local green growth, the promotion effect is nonlinear with decreasing marginal effect. This effect is particularly obvious in economically developed regions with higher CMPCs, which will absorb resources from neighboring regions and create a "siphoning" effect. It was found that local financial support and foreign direct investment (FDI) can radiate green growth to neighboring regions; therefore, CMPC practice needs to pay more attention to the effect of joint governance, supplemented by financial and foreign investment policy tools, to better promote the green transformation of local economy.

Oceanic realistic application of a microplastic biofouling model to the river discharge case

Marine biofouling is considered one of the major biophysical processes influencing the vertical dynamics of plastic debris in seawater. We numerically implement, for the first time, this mechanism within a fine-resolution, regional model of the Tyrrhenian Sea, in order to simulate the dispersion of microplastics (MPs) released at the mouth of a highly polluting river. Four polymers and three particle sizes are used to quantify algal concentration influence on the trajectories, fates, and accumulation spots of the tracked MPs, by comparing 2002 winter and summer runs encompassing or not biofouling. Besides a marked seasonality for most of the MP types and radii tested, biofouling effects are prominently observed for only 2 polymers and particles bigger than 1μm. Thus, further realistic applications of the biofouling mechanism in oceanic circulation models are required to achieve a thorough assessment of its impact on plastic density within distinctive basins of the world seas.

Chemical and isotopic tracers combined with mixing models for tracking nitrate contamination in the Pampa de Pocho aquifer, Argentina

In recent years, it has become evident that human activities have significantly disrupted the nitrogen cycle surpassing acceptable environmental thresholds. In this study, chemical and isotopic tracers were combined with a mathematical mass balance model (EMMA), PHREEQC inverse mixing model, and statistical analyses to evaluate groundwater quality, across an area experiencing substantial human activities, with a specific focus on tracing the origin of nitrate (NO3-) with potential water mixing processes. This multi-technique approach was applied to an unconfined aquifer underlying an agricultural area setting in an inter-mountain depression (i.e., the "Pampa de Pocho Plain" in Argentina). Here, the primary identified geochemical processes occurring in the investigated groundwater system include the dissolution of carbonate salts, cation exchange, and hydrolysis of alumino-silicates along with incorporating ions from precipitation. It was observed that the chemistry of groundwater, predominantly of sodium bicarbonate with sulfate water types, is controlled by the area's geology, recharge from precipitation, and stream water infiltration originating from the surrounding hills. Chemical results reveal that 60% of groundwater samples have NO3- concentrations exceeding the regional natural background level, confirming the impact of human activities on groundwater quality. The dual plot of δ15NNO3 versus δ18ONO3 values indicates that groundwater is affected by NO3- sources overlapping manure/sewage with organic-rich soil. The mathematical EMMA model and PHREEQC inverse modeling, suggest organic-rich soil as an important source of nitrogen in the aquifer. Here, 64 % of samples exhibit a main mixture of organic-rich soil with manure, whereas 36 % of samples are affected mainly by a mixture of manure and fertilizer. This study demonstrates the utility of combining isotope tracers with mathematical modeling and statistical analyses for a better understanding of groundwater quality deterioration in situations where isotopic signatures of contamination sources overlap.

Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning

Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollution sources. Specifically, anthropogenic activities' contribution to the pollution have been underestimated in previous research. Here, we coupled a hydrodynamic-based water quality (HWQ) model with a machine learning (ML) model, namely attention-based Gated Recurrent Unit, to decipher the daily pollution loads (i.e., chemical oxygen demand, COD; total phosphorus, TP) and their sources in the Middle-Lower Yangtze River from 2014 to 2018. The coupled HWQ-ML model outperformed the standalone ML model with KGE values ranging 0.77-0.91 for COD and 0.47-0.64 for TP, while also reducing parameter uncertainty. When examining the relative contributions at the Middle Yangtze River Hankou cross-section, we observed that the main stream and tributaries, lateral anthropogenic discharges, and parameter uncertainty contributed 15, 66, and 19% to COD, and 58, 35, and 7% to TP, respectively. For the Lower Yangtze River Datong cross-section, the contributions were 6, 69, and 25% for COD and 41, 42, and 17% for TP. According to the attention weights of the coupled model, the primary drivers of lateral anthropogenic pollution sources, in descending order of importance, were temperature, date, and precipitation, reflecting seasonal pollution discharge, industrial effluent, and first flush effect and combined sewer overflows, respectively. This study emphasizes the synergy between physical modeling and machine learning, offering new insights into pollution load dynamics in the Yangtze River.

Host-feeding preferences and temperature shape the dynamics of West Nile virus: A mathematical model to predict the impacts of vector-host interactions and vector management on R0

West Nile virus (WNV) is prevalent across the United States, but its transmission patterns and spatio-temporal intensity vary significantly, particularly in the Eastern United States. For instance, Chicago has long been a hotspot for WNV cases due to its high cumulative incidence of infection, with the number of cases varying considerably from year to year. The abilities of host species to maintain and disseminate WNV, along with eco-epidemiological factors that influence vector-host contact rates underlie WNV transmission potential. There is growing evidence that several vectors exhibit strong feeding preferences towards different host communities. In our research study, we construct a process based weather driven ordinary differential equation (ODE) model to understand the impact of one vector species (Culex pipiens), its preferred avian and non-preferred human hosts on the basic reproduction number (R0). In developing this WNV transmission model, we account for the feeding index, which is defined as the relative preference of the vectors for taking blood meals from a competent avian host versus a non-competent mammalian host. We also include continuous introduction of infected agents into the model during the simulations as the introduction of WNV is not a single event phenomenon. We derive an analytic form of R0 to predict the conditions under which there will be an outbreak of WNV and the relationship between the feeding index and the efficacy of adulticide is highly nonlinear. In our mechanistic model, we also demonstrate that adulticide treatments produced significant reductions in the Culex pipiens population. Sensitivity analysis demonstrates that feeding index and rate of introduction of infected agents are two important factors beside the efficacy of adulticide. We validate our model by comparing simulations to surveillance data collected for the Culex pipiens complex in Cook County, Illinois, USA. Our results reveal that the interaction between the feeding index and mosquito abatement strategy is intricate, especially considering the fluctuating temperature conditions. This induces heterogeneous transmission patterns that need to be incorporated when modelling multi-host, multi-vector transmission models.

Safety assessment for autonomous vehicles: A reference driver model for highway merging scenarios

Driver models are crucial for the safety assessment of autonomous vehicles (AVs) because of their role as reference models. Specifically, an AV is expected to achieve at least the same level of safety performance as a careful and competent driver model. To make this comparison possible, quantitative modeling of careful and competent driver models is essential. Thus, the UNECE Regulation No. 157 proposes two driver models as benchmarks for AVs, enabling safety assessment of AV longitudinal behaviors. However, these two driver models are unable to be applied in non-car-following scenarios, limiting their applications in scenarios such as highway merging. To this end, we propose a careful and competent driver model for highway merging (CCDM2) scenarios using interpretable reinforcement learning-based decision-making and safety constraint control. We compare our model's safe driving capabilities with human drivers in challenging merging scenarios and demonstrate the "careful" and "competent" characteristics of our model while ensuring its interpretability. The results indicate the model's capability to handle merging scenarios with even better safety performance than human drivers. This model is of great value for AV safety assessment in merging scenarios and contributes to future reference driver models to be included in AV safety regulations.

Supporting data-enhanced hybrid ordinary differential equation model for phosphate dynamics in municipal wastewater treatment

A parallel hybrid ordinary differential equation (ODE) integrating the Activated Sludge Model No. 2d (ASM2d) and an artificial neural network (ANN) was developed to simulate biological phosphorus removal (BPR) with high accuracy and interpretability. Two novelties were introduced; first, the involved supporting data (i.e., phosphate-release activity) were incorporated as an input in the ANN. Second, the outputs of the ANN were selective. Three models were implemented using different ANN outputs, and all three outperformed ASM2d in phosphate estimation for anaerobic/aerobic sequencing batch reactor operation. In particular, the incorporation of four variables responsible for BPR into the ANN enabled the highest performance (R2 = 0.93) owing to the capture of increasing phosphate-accumulating organisms (PAOs). The ANN with the supporting data worked satisfactorily to compensate for ASM2d by adding proper PAOs, resulting in improvement in phosphate estimation. The novel parallel hybrid ODE can simulate BPR while maintaining physical meaning.

Large-eddy simulation of aerosol concentrations in a realistic urban environment: Model validation and transport mechanism

Air pollution in urban environments exhibits large spatial and temporal variations due to high heterogeneous air flow and emissions. To address the complexity of local air pollutant dynamics, a comprehensive large-eddy simulation using the PALM model system v6.0 was conducted. The distribution of flow and vehicle emitted aerosol particles in a realistic urban environment in Malmö, Sweden, was studied and evaluated against on-site measurements made using portable instrumentation on a spring morning in 2021. The canyon transport mechanisms were investigated, and the convective and turbulent mass-transport rates compared to clarify their role in aerosol transport. The horizontal distribution of aerosols showed acceptable evaluation metrics for both mass and number. Flow and pollutant concentrations were more complex than those in idealized street canyon networks. Vertical turbulent mass-transport rate was found to dominate the mass transport process compared with the convective transport rate, contributing more than 70% of the pollutant transport process. Our findings highlight the necessity of examining various aerosol metric due their distinct dispersion behaviour. This study introduces a comprehensive high-resolution modelling framework that accounts for dynamic meteorological and aerosol background boundary conditions, real-time traffic emission, and detailed building features, offering a robust toll for local urban air quality assessment.

Comparative analysis of the dispersion modeling and dose projection results performed under BARCO international project

Radiological assessments on zones to take protective actions in case of a nuclear or radiological emergency involve a series of real-time forecasts of radiological impact on the public at various distances from the release point, using actual weather or forecast data, information on the source term or facility status, and primary radiation monitoring data. This practice is implemented during the operation of emergency centers around the world in order to promptly report the occurrence and possible consequences of radiological accidents in the country and abroad in the event of a possible transboundary impact. Since the Chornobyl disaster, a lot of emergency exercises, research programs and projects, in particular, benchmarking, have served as international platforms for improving modeling capacity in atmospheric dispersion. This activity is carried out both on the basis of past severe accidents with significant atmospheric releases and corresponding radiological consequences, and on the basis of specific conditional (hypothetical) events that are developed in accordance with the purpose of the study. The paper is focused on the comparison results performed under the international project "Benchmarking on Assessment of Radiological COnsequences" (BARCO) conducted in 2020-2021 between five technical support organisations - members of the European Technical Safety Organisations Network (ETSON). The work contains a short overview of relevant international activity conducted in the past, a description of the BARCO project and its objectives, a list of participants, project tasks, initial data (source term, meteorology, list of benchmarking quantities, approach to data exchange, codes used). The study presents some of comparative analysis results obtained via two techniques such as code-to-code analysis (CTCA) and matched-pair analysis (MPA). The results discussion concentrates on the overall recommendations for code users. Conclusions provide the main outputs of the project.

Causal diagnostic model and governance strategies to reduce pollution from ship accidents in Chinese waters

Ship transportation is a primary mode for global trade and cargo transport, yet even minor discrepancies can lead to ship accidents, causing severe secondary environmental pollution. Maritime accidents involve complex and numerous factors. Formal Concept Analysis (FCA) can identify the key contributing factors and their impact levels by eliminating homogenization factors in maritime accidents. This study constructs an innovative FCA model of ship accidents in Chinese waters, utilizing 172 ship accident reports released by the China Maritime Safety Administration. The analysis reveals seven reduced sets and 23 diagnostic rules of ship accidents. Results show that failed ship registration/security inspection, deficient nautical data and instruments, and management issues are the most critical factors. Three accident chains are identified and corresponding mitigation strategies are proposed to reduce potential pollution from ship accidents. These strategies offer significant reference value for preventing ship accidents and reducing their environmental impact in China and globally.