Climate change effects on denitrification performance of woodchip bioreactors treating agricultural tile drainage.

Denitrifying woodchip bioreactors (WBRs) are a nature-based technology that are increasingly used to control nonpoint source nitrate (NO3-) pollution in agricultural catchments. The treatment effectiveness of WBRs depends on temperature and hydraulic retention time (HRT), both of which are affected by climate change. Warmer temperatures will increase microbial denitrification rates, but the extent to which the resulting benefits to treatment performance may be offset by intensified precipitation and shorter HRTs is not clear. Here, we use three years of monitoring data from a WBR in Central New York State to train an integrated hydrologic-biokinetic model describing links among temperature, precipitation, bioreactor discharge, denitrification kinetics, and NO3- removal efficiencies. Effects of climate warming are assessed by first training a stochastic weather generator with eleven years of weather data from our field site, and then adjusting the distribution of precipitation intensities according to the Clausius-Clapeyron relationship between water vapor and temperature. Modeling results indicate, in our system, faster denitrification rates will outweigh the influence of intensified precipitation and discharge under warming, leading to net improvements in NO3- load reductions. Median cumulative NO3- load reductions at our study site from May - October are projected to increase from 21.7% (interquartile range 17.4%-26.1%) under baseline hydro-climate to 41.0% (interquartile range 32.6-47.1%) with a + 4 °C change in mean air temperature. This improved performance under climate warming is driven by strong nonlinear dependence of NO3- removal rates on temperature. Temperature sensitivity may increase with woodchip age and lead to stronger temperature-response in systems like this one with a highly aged woodchip matrix. While the impacts of hydro-climatic change on WBR performance will depend on site-specific properties, this hydrologic-biokinetic modeling approach provides a framework for assessing climate impacts on the effectiveness of WBRs and other denitrifying nature-based systems.

A predictive computational platform for optimizing the design of bioartificial pancreas devices.

The delivery of encapsulated islets or stem cell-derived insulin-producing cells (i.e., bioartificial pancreas devices) may achieve a functional cure for type 1 diabetes, but their efficacy is limited by mass transport constraints. Modeling such constraints is thus desirable, but previous efforts invoke simplifications which limit the utility of their insights. Herein, we present a computational platform for investigating the therapeutic capacity of generic and user-programmable bioartificial pancreas devices, which accounts for highly influential stochastic properties including the size distribution and random localization of the cells. We first apply the platform in a study which finds that endogenous islet size distribution variance significantly influences device potency. Then we pursue optimizations, determining ideal device structures and estimates of the curative cell dose. Finally, we propose a new, device-specific islet equivalence conversion table, and develop a surrogate machine learning model, hosted on a web application, to rapidly produce these coefficients for user-defined devices.

Reducing adverse impacts of Amazon hydropower expansion.

Proposed hydropower dams at more than 350 sites throughout the Amazon require strategic evaluation of trade-offs between the numerous ecosystem services provided by Earth's largest and most biodiverse river basin. These services are spatially variable, hence collective impacts of newly built dams depend strongly on their configuration. We use multiobjective optimization to identify portfolios of sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals. We find that uncoordinated, dam-by-dam hydropower expansion has resulted in forgone ecosystem service benefits. Minimizing further damage from hydropower development requires considering diverse environmental impacts across the entire basin, as well as cooperation among Amazonian nations. Our findings offer a transferable model for the evaluation of hydropower expansion in transboundary basins.

QSARs to predict adsorption affinity of organic micropollutants for activated carbon and ß-cyclodextrin polymer adsorbents.

The removal of organic micropollutants (MPs) from water by means of adsorption is determined by the physicochemical properties of the adsorbent and the MPs. It is challenging to predict the removal of MPs by specific adsorbents due to the extreme diversity in physicochemical properties among MPs of interest. In this research, we established Quantitative Structure-Activity Relationships (QSARs) between the physicochemical properties of a diverse set of MPs and their distribution coefficients (KD) measured on coconut shell activated carbon (CCAC) and porous ß-cyclodextrin polymer (P-CDP) adsorbents. We conducted batch experiments with a mixture of 200 MPs and used the data to calculate KD values for each MP on each adsorbent under conditions of infinite dilution (i.e., low adsorbate concentrations). We used computational software to calculate 3656 molecular descriptors for each MP. We then developed and applied a model-selection workflow to identify the most significant molecular descriptors for each adsorbent. The functional stability and predictive power of the resulting QSARs were confirmed with internal cross validation and external validation. The applicability domain of the QSARs was defined based on the most significant molecular descriptors selected into each QSAR. The QSARs are predictive tools for evaluating adsorption-based water treatment processes and provide new insights into CCAC and P-CDP adsorption mechanisms.

A water balance based, spatiotemporal evaluation of terrestrial evapotranspiration products across the contiguous United States.

Accurate gridded estimates of evapotranspiration (ET) are essential to the analysis of terrestrial water budgets. In this study, ET estimates from three gridded energy-balance based products (ETEB) with independent model formations and data forcings are evaluated for their ability to capture long term climatology and inter-annual variability in ET derived from a terrestrial water budget (ETWB) for 671 gaged basins across the CONUS. All three ETEB products have low spatial bias and accurately capture inter-annual variability of ETWB in the central US, where ETEB and ancillary estimates of change in total surface water storage (ΔTWS) from the GRACE satellite project appear to close terrestrial water budgets. In humid regions, ETEB products exhibit higher long-term bias, and the covariability of ETEB and ETWB decreases significantly. Several factors related to either failure of ETWB, such as errors in ΔTWS and precipitation, or failure of ETEB, such as treatment of snowfall and horizontal heat advection, explain some of these discrepancies. These results mirror and build on conclusions from other studies: on inter-annual timescales, ΔTWS and error in precipitation estimates are non-negligible uncertainties in ET estimates based on a terrestrial water budget, and this confounds their comparison to energy balance ET models. However, there is also evidence that in at least some regions, climate and landscape features may also influence the accuracy and long-term bias of ET estimates from energy balance models, and these potential errors should be considered when using these gridded products in hydrologic applications.