Water management policies in desert agricultural regions critically influence both crop choices and ecosystem dynamics, yet their cascading ecological impacts remain poorly understood. In particular, the complex interactions between water quality, agricultural practices, and wildlife responses require further investigation to inform sustainable management in desert landscapes. Here, we evaluate how water policy, particularly seawater desalination initiatives influencing irrigation and cropping practices, shapes ecological systems in a hyperarid region, the southern Arava Valley of Israel. We integrated community-level questionnaires, agricultural records, animal field observations, and spatially explicit scenario tools into a mixed-methods framework to model social–ecological cascades linking water policy to predator dynamics. Bayesian Belief Networks combined with Generalized Linear Models of predator abundance were used to assess how improved water quality affects cropping patterns and, in turn, regional predator populations. Our findings indicate that desalination is unlikely to alter the predominance of date orchards or the high abundance of range-expanding jackals associated with these systems. However, water quality-driven expansion of field crops corresponds to lower modelled fox abundance and shifts in predicted predator interactions, while jackal populations remain largely influenced by date orchard availability. Under business-as-usual scenarios with lower water quality, farmers are likely to reduce field crop areas, corresponding to further changes in regional predator abundance. These findings suggest that water policy decisions may generate cascading social–ecological responses on both agricultural practices and local desert ecosystems, emphasizing the need for strategies that balance agricultural productivity with ecological sustainability in arid landscapes.

Dechlorination is one of the main processes for the natural degradation of polychlorinated biphenyls (PCBs) in an anaerobic environment. However, PCB dechlorination pathways and products vary with PCB congeners, types of functional dechlorinating bacteria, and environmental conditions. The present study develops a novel model for determining dechlorination pathways and fluxes by tracking redox potential variability, transforming the complex dechlorination process into a stepwise sequence. The redox potential is calculated via the Gibbs free energy of formation, PCB concentrations in reactants and products, and environmental conditions. Thus, the continuous change in the PCB congener composition can be tracked during dechlorination processes. The new model is assessed against four measurements from several published studies on PCB dechlorination. The simulation errors in all four measurements are calculated between 2.67 and 35.1% under minimum (n = 0) and maximum (n = 34) numbers of co-eluters, respectively. The dechlorination fluxes for para-dechlorination pathways dominate PCB dechlorination in all measurements. Furthermore, the model also considers multiple-step dechlorination pathways containing intermediate PCB congeners absent in both the reactants and the products. The present study indicates that redox potential might be an appropriate indicator for predicting PCB dechlorination pathways and fluxes even without prior knowledge of the functional dechlorinating bacteria.

Fire agencies across the United States must make complex resource allocation decisions to manage wildfires using a national network of shared firefighting resources. Firefighters play a critical role in suppressing fires and protecting vulnerable communities. However, they are exposed to health and safety risks associated with fire, smoke inhalation, and infectious disease transmission. The COVID-19 pandemic further complicated these risks, prompting fire agencies to propose resource management adaptations to minimize COVID-19 exposure and transmission. It is unclear if and how the pandemic may have operationally influenced wildland firefighting personnel resource use given compounding wildfire and COVID-19 risks. Therefore, we developed generalized linear mixed models that were fit using multiple integrated datasets to detect changes in personnel resource use for years prior to and during the COVID-19 pandemic, while controlling for historical fire and landscape conditions, societal risks, and management objectives. Analyses of observed and predicted firefighting resource use revealed reductions in the mean personnel resources used per wildfire per day during the pandemic for models developed across the western U.S. and for various western U.S. fire regions. Notably, the Northern California and the Great Basin Coordination Centers showed statistically significant reductions in ground personnel use during the COVID-19 pandemic. Learning from wildland fire management strategies and resource use trends that occurred during the COVID-19 pandemic, fire agencies can better anticipate resource constraints that may arise during the compounding threats of severe wildland fire activity and infectious disease outbreaks to proactively prepare and adapt suppression management strategies.

The Gulf Coast region possesses great potential for CO2 enhanced oil recovery (EOR) and CO2 storage. A geochemically informed leak detection (GILD) model has been applied to CO2 injection sites on the Gulf Coast with considerations of measurement variability. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions were used to create 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO2 leakage concentration. The sources of measurement variability for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. Bayesian belief network (BBN) models were constructed, and measurement variability of different levels were added to compare leak detection probabilities. Increasing measurement variability decreased the power to detect a leak of a given size. For a moderately high CO2 concentration of 0.2 mol/kg, the probability of detecting this leakage effect using pH as the monitoring variable in an aquifer with calcite decreases from 98% (no measurement variability) to 61% (medium variability) to 33% (high variability). The loss in power of the sampling protocol with increasing measurement variability is similar in magnitude when Ca2+ or HCO3- is used as the monitoring parameter, but only for aquifers with calcite.

An in-depth study of the impact of hydraulic fracturing operations on the Marcellus shale formation and overburden is presented by analyzing a unique timelapse crosswell seismic survey suitable for four-component shear wave vector rotations in order to study fracture azimuth and intensity. The borehole source used for the survey generated both oriented compressional and shear wave energy, and the seismic acquisition included perpendicular source orientations at each shot level to allow isolation of fast and slow shear modes using the Alford rotation and linear transform technique. Estimates of fast shear azimuth and slow shear time delay before and after hydraulic fracturing indicate fracture creation throughout the overburden above the Marcellus shale and a possible vertical breach of the bounding Tully limestone formation. The magnitude of the timelapse change is consistent between travel-time and slow shear lag inversion, but the absolute change is small, suggesting that the increase in fracture intensity may not be significant. These results point to operational inefficiencies during hydraulic fracturing operations that could negatively impact production and pose risks to future development. The study demonstrates the value of crosswell seismic surveys for reservoir monitoring and highlights observations that might be missed by a monitoring program that does not include timelapse seismic data and analysis of shear wave changes due to hydraulic fracturing.

Early detection of CO2 leakage through monitoring is important to ensure long-term safety for geologic carbon storage (GCS). A geochemically informed leak detection (GILD) model has been developed for groundwater chemistry monitoring at CO2 injection sites. The GILD model integrates a geochemical model that simulates fluid chemistry changes in CO2 leakage events and a Bayesian belief network (BBN) model that evaluates monitoring observations to identify leakages. The geochemical model is implemented using Geochemists' Workbench to assess fluid chemistry changes as a result of small CO2 leakage in an above-zone monitoring interval (AZMI) formation with varying mineral assemblages and background fluids. Response functions are fitted to the output of the geochemical model and are translated to conditional probabilities in the BBN model. The BBN model gives operational prediction of the leak probability given a set of groundwater monitoring measurements and the probability of detecting a leak at a given magnitude. The detection capabilities of multiple monitoring parameters are compared. For aquifers that contain calcite, it is valuable to incorporate other monitoring parameters with pH to increase the sensitivity of detection. For aquifers with no calcite, pH alone is a sensitive parameter. This research illustrates a method of identifying CO2 leakage into aquifers with both geochemical and statistical tools.

Sustainability challenges, such as solid waste management, are usually scientifically complex and data scarce, which makes them not amenable to science-based analytical forms or data-intensive learning paradigms. Deep integration between data science and sustainability science in highly complementary manners offers new opportunities for tackling these conundrums. This study develops a novel hybrid neural network (HNN) model that imposes the holistic decision-making context of solid waste management systems (SWMS) on a traditional neural network (NN) architecture. Equipped with adaptable hybridization designs of hand-crafted model structure, constrained or predetermined parameters, and a customized loss function, the HNN model is capable of learning various technical, economic, and social aspects of SWMS from a small and heterogeneous data set. In comparison, the versatile HNN model not only outperforms traditional NN models in convergence rates, which leads to a 22% lower mean testing error of 0.20, but also offers superior interpretability. The HNN model is capable of generating insights into the enabling factors, policy interventions, and driving forces of SWMS, laying a solid foundation for data-driven decision making.

Improved seasonal precipitation forecasts can enable more effective water resource management decisions in a number of sectors, including municipal supply, agriculture, hydropower generation, and tourism. This study develops an effective straightforward statistical approach to enhance the quality of seasonal precipitation forecasts through the utilization of El Niño–Southern Oscillation (ENSO) information projected by Coupled General Climate Models (CGCMs). A stochastic weather generation (WG) model is developed to predict seasonal precipitation condition on ENSO condition. The WG model links a nonhomogeneous Markov Chain representing ENSO occurrence model to a bivariate normal distribution for seasonal precipitation conditioned on ENSO phase. Two verification metrics are suggested to measure the degree of predictability of raw, calibrated and climatological seasonal precipitation forecasts over northwest Costa Rica as a case study. Results indicate the potential to narrow the uncertainty of seasonal precipitation forecasts by incorporating CGCMs ENSO cycle information. Precipitation during the late part of the wet season (LS) has more predictability than precipitation in the early part of the wet season (ES). In addition, the degree of predictability decreases with an increase in lead time for a given forecast. A lead time of 1 year maintains a moderate level of predictability likely to support tangible benefits to various decision‐making processes.

Following an exceedance of the lead action level for drinking water in 2016, the Pittsburgh Water and Sewer Authority (PWSA) undertook two sampling programs: the required biannual Lead and Copper Rule (LCR) compliance testing and a home sampling program based on customer requests. The LCR sampling results, at locations expected to be elevated when corrosion is not well controlled, had higher concentrations than customer-requested homes, with 90th percentile values for the LCR sites exceeding the action level through 2019 (except for June 2018). Customer-requested concentrations showed greater variability, with the median lead concentration for customer-requested samples below detection for each year of sampling, suggesting only some homes show elevated lead when corrosion control is not fully effective. Corrosion control adjustments brought the utility back into compliance in 2020 (LCR 90th percentile of 5.1 ppb in June 2020); customer-requested sampling after the addition of orthophosphate indicated below detection levels for 59% of samples. Monte Carlo simulations indicate LCR samples do not all represent high lead risk sites, and the application of corrosion control more significantly affects higher lead concentration sites. Broader water quality sampling provides information about specific homes but is not well suited to assessing the efficacy of corrosion control efforts by utilities.

Increasing municipal solid waste (MSW) generation has become not only a major sustainability challenge and a considerable financial burden for municipalities across the globe, but also an opportunity to promote a circular economy, provided adequate information is made available. Data and information on MSW generation, characterization, and management practices are prerequisites to studying and optimizing solid waste management systems (SWMS). However, such data and information are usually dispersed, unsystematized, and suffering from various availability and quality issues. This study aims to assemble and provide access to the current landscape of MSW data by establishing a comprehensive framework for understanding the interconnectedness of various sub-domains of MSW knowledge. Existing databases and governmental reports were reviewed to compile 1720 records of MSW generation, composition, management practices, and socioeconomic contexts for 219 countries and 410 cities. Multivariate linear regression and additive models were built to relate MSW generation, composition, and recovery rates to demographics, economic development, and climate patterns of cities and regions. These models generate new insights into the complex nature of SWMS and provide an evidence-based decision-making tool to future researchers and policy makers. Specifically, economic development (GDP), density factors (population, population density, and household size), sustainability initiatives, education, and regulation are all identified as positive drivers toward the targets of United Nations Sustainable Development Goal 12.

In a world of finite metallic minerals, demand forecasting is crucial for managing the stocks and flows of these critical resources. Previous studies have projected copper supply and demand at the global level and the regional level of EU and China. However, no comprehensive study exists for the U.S., which has displayed unique copper consumption and dematerialization trends. In this study, we adapted the stock dynamics approach to forecast the U.S. copper in-use stock (IUS), consumption, and end-of-life (EOL) flows from 2016 to 2070 under various U.S.-specific scenarios. Assuming different socio-technological development trajectories, our model results are consistent with a stabilization range of 215-260 kg/person for the IUS. This is projected along with steady growth in the annual copper consumption and EOL copper generation driven mainly by the growing U.S. population. This stabilization trend of per capita IUS indicates that future copper consumption will largely recuperate IUS losses, allowing 34-39% of future demand to be met potentially by recycling 43% of domestic EOL copper. Despite the recent trends of "dematerialization", adaptive policies still need to be designed for enhancing the EOL recovery, especially in light of a potential transitioning to a "green technology" future with increased electrification dictating higher copper demand.

This Zenodo archive includes the TEMOA database files and SRT model used to produce the publication Cotterman et al., "Applying risk tolerance and socio-technical dynamics for more realistic energy transition pathways".

This paper identifies the influence of demographic, local transportation environment, and individual preferences for transportation attributes on multimodal transportation behavior in an urban environment with emergent transportation mode availability. Multimodality is the use of more than one mode of transportation during a given timeframe. Multimodality has been considered a key component of sustainable and efficient transportation systems, as this travel behavior can represent a shift away from personal vehicle use to more sustainable transportation modes, especially in urban environments with diverse transportation systems and emergent shared transportation alternatives (e.g., carsharing, ridehailing, bike sharing). However, it is unclear what factors contribute towards people being more likely to exhibit multimodal transportation behavior in modern urban environments. We assessed commuting behavior based on a survey administered in the San Francisco Bay Area according to whether residents commuted (i) exclusively by vehicle, (ii) by a mix of vehicle and non-vehicle modes, or (iii) exclusively by non-vehicle modes. A classification tree approach identified correlations between commuting classes and demographic variables, preferences for transportation attributes, and location-based information. The characterization of commuting styles could inform regional transportation policy and design that aims to reduce vehicle use by identifying the demographic, preference, and location-based considerations correlated with each commuting style.

A repeat crosswell tomography dataset was recorded in Clearfield County, Pennsylvania to evaluate a hydraulic fracturing completion in a Marcellus reservoir. This analysis focuses on efforts to extract useful information from the shear wave data, as well as the process needed to evaluate the shear wave radiation generated by the downhole shear source. Acquiring perpendicular source settings for each level was necessary to properly orient the source and receiver positions into an inline/xline relationship for final analysis since neither source nor receiver azimuthal orientations could be known initially. This geometric determination enabled the application of the Alford rotation to detect birefringence. Results are shown for the shear travel-time inversion of the direct shear wave arrival and preliminary results from the Alford rotation are discussed. The anisotropy values determined from both measurements are similar, with the Alford rotation being more sensitive. The shear results indicate fracture creation in the overlying formation, suggesting an inefficient process that would have a negative impact on production.

Environmental DNA (eDNA) sampling, the detection of species-specific genetic material in water samples, is an emerging tool for monitoring aquatic invasive species. Optimizing eDNA sampling protocols can be challenging because there is imperfect understanding of how each step of the protocol influences its sensitivity. This paper develops a probabilistic model that characterizes each step of an eDNA sampling protocol to evaluate the protocol's overall detection sensitivity for one sample. The model is then applied to analyse how changes over time made to the eDNA sampling protocol to detect bighead (BH) and silver carp (SC) eDNA have influenced its sensitivity, and hence interpretation of the results. The model shows that changes to the protocol have caused the sensitivity of the protocol to fluctuate. A more efficient extraction method in 2013, new species-specific markers with a qPCR assay in 2014, and a more efficient capture method in 2015 have improved the sensitivity, while switching to a larger elution volume in 2013 and a smaller sample volume in 2015 have reduced the sensitivity. Overall, the sensitivity of the current protocol is higher for BH eDNA detection and SC eDNA detection compared to the original protocol used from 2009 to 2012. The paper shows how this model of eDNA sampling can be used to evaluate the effect of proposed changes in an eDNA sampling and analysis protocol on the sensitivity of that protocol to help researchers optimize their design.