Stormwater runoff is a significant contributor of phosphorus (P) loading to waterbodies around the world. Green stormwater infrastructure (GSI) that uses filtration media, such as bioretention, can effectively retain suspended solids and associated particulate P, but is commonly less effective for soluble P retention. The addition of aluminum-based drinking water treatment residuals (DWTRs) may increase P-sorbing capacity of GSI media, though guidance is needed for material selection and to reduce risk of potential contamination. This study examined the P removal capacities of DWTRs (n = 11) from drinking water treatment plants in the New England region (northeastern USA). DWTRs were compared for P-sorption potential using batch isotherm and column experiments and characterized for several material properties as well as arsenic leaching and per and polyfluoroalkyl substances (PFAS) content. Results indicate that P retention capacity of DWTRs is generally high (>1,000 mg P kg(-1)) but varies by approximately one order of magnitude. Lower DWTR bulk density and greater oxalate-extractable Al + Fe were correlated with greater P retention in column experiments. Our findings also indicate that the potential for significant arsenic leaching is low. PFAS were detected in 36% of DWTRs, often at low levels near the method detection limit, with three DWTRs having higher levels of certain PFAS. The addition of DWTRs to GSI is promising for enhanced soluble P removal on a decadal scale (10-90 years), but additional research on As, PFAS, and other contaminants should be pursued prior to use, especially in areas with known or suspected source water contamination. Achieving effective long-term P removal requires selecting DWTRs with favorable material properties (e.g., drier, lower bulk density, greater oxalate-extractable Al + Fe), and mixture with sand at up to 10% DWTR by volume and potentially higher if proven to not impede hydraulic conductivity. Field monitoring of DWTR-enhanced infrastructure at multiple time points postinstallation (e.g., years 1, 5, 10, 20, and 30) is needed to confirm P removal longevity over expected infrastructure lifespans.

Agricultural soils are sources of nitrous oxide (N2O) and, under prolonged saturation, methane (CH4)-two potent greenhouse gases (GHGs). Soil management, field topography, and climate all influence GHG emissions, yet their interactions are not well understood. Over 17 months, we evaluated how three distinct management systems-Conventional, a soil health system (Soil Health), and a flocculated manure solid amendment (Flocculated Solids)-interacted with topographically high and low areas to influence N2O and CH4 emissions in a 21 ha corn (Zea mays L.) silage field in western Vermont, during a period of abnormally high precipitation. At 18 plots (3 treatments × 2 topographic positions × 3 replicates), we measured GHG fluxes year-round alongside soil temperature, moisture, and inorganic nitrogen. Annual N2O emissions were 4.4 times greater in Soil Health-Low plots (74.3 kg N2O-N ha-1 year-1) than in Flocculated Solids plots, which had the lowest emissions (17.0 kg N2O-N ha-1 year-1). Annual CH4 emissions were greatest in low plots across all treatments, with low plots emitting 2.2 times more CH4 than high plots. Boosted regression tree models identified soil moisture, ammonium, CO2 flux, and nitrate as the strongest predictors of daily N2O fluxes. For CH4, inundation duration was the dominant driver, with emissions increasing sharply after >40 days of continuous saturation. Treatment and topography explained <5% of emissions in both models, indicating that their effects are primarily indirect, modifying soil moisture, nitrogen availability, and organic matter inputs that ultimately drive GHG emissions.

Diverting food waste to composting and anaerobic digestion can reduce landfill methane emissions and contribute towards a circular bioeconomy, but trade-offs can be incurred from nutrient loading to aquatic ecosystems and contamination from plastic packaging. Here we used mass balance modelling and life cycle assessment to compare the climate, nutrient loading and plastic pollution impacts of current landfill-dominant US food waste management with hypothetical organics recycling scenarios. We estimate that nationwide implementation of organics recycling could reduce the climate impact of US food waste management by 89-99%, and associated nitrogen and phosphorus loading to downstream waterways by 49-54% and 78-98%, respectively, relative to current landfill-dominant practices. However, land application of food waste-derived composts and digestates would offset <2% of US mineral nitrogen and phosphorus fertilizer consumption annually and, without innovations in food packaging design, could discharge ~20,000 t of plastic to US agricultural soils each year.

Abstract Organics recycling can be hindered by the high water content of food scraps—making transportation costly and greenhouse gas emissions intensive. One potential solution is drying these materials prior to transport, which also reduces volume and odor. However, this approach introduces a new trade-off, as the energy used for drying is also associated with greenhouse gas emissions. We conducted a life cycle assessment (LCA) to explore this trade-off in the context of electric countertop food dehydration technologies, which dehydrate kitchen scraps to reduce the weight of material transported while reducing particle size mechanically. Scenarios analyzed included home composting, centralized composting, landfilling, and countertop dehydration followed by either home or centralized composting. We used sensitivity analysis to evaluate how transportation distance and grid carbon intensity influence the climate impact of each strategy. Use of a countertop food dehydrator prior to composting was estimated to reduce the climate change impact of food waste management (CO2-eq kg–1 food waste) by 42–92% relative to landfilling, depending on the specific scenario. The comparison between composting scenarios with and without prior dehydration was sensitive to transport distance and the carbon intensity of the consumer's electric grid.

Dissolved oxygen (DO) regulates the dominant biogeochemical processes in floodplains and is an important water quality indicator. However, predicting DO dynamics with data driven methods in floodplains is challenging due to data scarcity, limiting our understanding of the efficacy of floodplain restoration for clean water objectives. This study applies domain adaptation transfer learning (TL) to a long short-term memory (LSTM) model to generate floodplain DO predictions. First, a LSTM model was trained on a data-rich river "source domain" and then used to predict floodplain DO. The trained river model was used to initialize a new TL LSTM model which was finetuned to the floodplain "target domain," where the same type of monitoring data were scarcer. A third LSTM model was trained only on the floodplain data, and performance was compared across the three models. The TL model outperformed the river model and performed slightly better than the floodplain model (TL model-root mean squared error (RMSE): 2.79; floodplain model-RMSE: 2.90; river model-RMSE: 4.40). Shapley additive explanation (SHAP) values revealed that while the floodplain model relied more heavily on site-specific attributes, the TL model encoded relationships with dynamic drivers, capturing process-informed behavior from both river and floodplain domains. Our findings suggest that TL produces models that generalize better across sites and are more robust to variable conditions, offering both predictive skill and process insight. Our modeling framework offers a scalable and interpretable solution for data-scarce environments, with broad applicability across water resources and Earth system sciences.

Plastic has become a prominent material type used for numerous purposes since the 1950s and persists in waterbodies, sediments, and terrestrial soils worldwide. Through time, plastics break apart into smaller fragments that become dispersed throughout the environment. Organic waste derived soil amendments, such as compost, may serve as a transport vector for plastics into terrestrial soils. A Vermont-wide survey was conducted to provide a range of plastic abundance, mass, and types found in composts and contextualize future research on plastic effects. Twenty composts were analyzed including composts derived from feedstocks with both high (15% or more by volume) and low/no (5% or less by volume) food waste inclusion. Plastics were isolated in multiple size classes (> 5 mm, 1–5 mm, and 0.5–1 mm) with sieving, 30% hydrogen peroxide digestion, and microscopy. Fourier transform infrared (FTIR) spectrum similarity was also used to identify plastic polymers. Ranges of 0 to 1,201 plastic particles per dry kg of compost and 0 to 0.056% w/w plastic contamination on a dry mass basis were found across the composts. Plastic abundance was not a predictor of plastic mass. No statistical differences were found between the high and low/no food waste compost groups due to variability and relatively low sample sizes, although the five most contaminated composts (> 0.02% w/w) were all in the high food waste group. Methodological challenges and recommendations are also discussed, including emphasis on establishing confidence levels for putative microplastic confirmation under the uncertainty that is inherent to analyzing complex organic matrices using microscopy and FTIR.

Long-term river monitoring of the Kuparuk River (North Slope, Alaska, USA) confirms significant increases in solutes that are indicative of active layer thickening due to thawing permafrost. However, there is no evidence of an increase in total dissolved phosphorus (TDP) or soluble reactive phosphorus (SRP), the nutrient that limits primary production in this and similar rivers in the region. Here, we show that Mehlich-3 extractable iron (Fe) and aluminum (Al) in active layer soils impart high P geochemical sorption capacities across a range of landscape features that we would expect to promote lateral movement of water and solutes to headwater streams in our study watershed. Reanalysis of a recently published pan-Arctic soils database that includes active layer and permafrost soil samples suggests that this high P sorption capacity could be common in other parts of the Arctic region. We conclude that soil minerals enhance P retention on hillslopes and propose pedogenic secondary Fe and Al minerals may continue to retain P in these soils and limit biological productivity in the adjacent river even as active layer thickening increases potential P mobility in the watershed. We suggest that similar interactions may occur in other areas of the Arctic where comparable geochemical conditions prevail. Plain Language Summary This manuscript focuses on interactions of phosphorus with iron and aluminum in tundra soils in one of the most comprehensively studied watersheds in the Arctic, the Kuparuk River on the North Slope, Alaska (USA). We demonstrate that abundant concentrations of iron and aluminum may create a substantial biogeochemical sink for bioavailable phosphorus that sequesters this important nutrient in forms that limit migration from the tundra to adjacent water bodies. We then use two recently published pan-Arctic databases to infer that similar conditions may prevail in many other parts of the Arctic. These findings are important because in our previous research we have found that biological production in most rivers in our study region is limited by exceedingly low concentrations of biologically useful phosphorus-an essential nutrient for primary producers such as algae and bryophytes. We conclude that, in addition to plant uptake, control of bioavailable phosphorus migration to headwater streams by iron and aluminum minerals in the active layer of tundra soils could be an important factor limiting primary production and therefore carbon processing in the headwater streams of the Kuparuk River and perhaps more widely throughout the Arctic, a finding that should be considered in regional and global biogeochemical models.

Analysis of nutrient balance at the watershed scale, including for phosphorus (P), is typically accomplished using aggregate input datasets, resulting in an inability to capture the variability of P status across the study region. This study presents a set of methods to predict and visualize partial P mass balance, soil P saturation ratio (PSR), and soil test P for agricultural parcels across a watershed in the Lake Champlain Basin (Vermont, USA) using granular, field-level data. K-means cluster analyses were used to group agricultural parcels by soil texture, average slope, and crop type. Using a set of parcels accounting for ∼21% of the watershed's agricultural land and having known soil test and nutrient management parameters, predictions of partial P mass balance, PSR, and soil test P for agricultural land across the watershed were made by cluster, incorporating uncertainty. This resulted in an average partial P balance of 5.5 ± 0.2 kg P ha-1 year-1 and an average PSR of 0.0399 ± 0.0002. Furthermore, approximately 30% of agricultural land had predicted soil test P values above optimum levels. Results were used to visualize areas with high P loss potential. Such data and visualizations can inform watershed P modeling and assist practitioners in nutrient management decision making. These techniques can also serve as a framework for bottom-up modeling of nutrient mass balance and soil metrics in other regions.

Reducing the environmental pressures stemming from food production is central to meeting global sustainability targets. Shifting diets represents one lever for improving food system sustainability, and identifying sustainable diet opportunities requires computational models to represent complex systems and allow users to evaluate counterfactual scenarios. Despite an increase in the number of food system sustainability models, there remains a lack of transparency of data inputs and mathematical formulas to facilitate replication by researchers and application by diverse stakeholders. Further, many models lack the ability to model multiple geographic scales. The present study introduces Foodprint 2.0, which fills both gaps. Foodprint 2.0 is an updated biophysical simulation model that estimates the agricultural resource requirements of diet patterns and can be adapted to suit a variety of research purposes. The objectives of this study are to: 1) describe the new features of Foodprint 2.0, and 2) demonstrate model performance by estimating the agricultural resource requirements of food demand in the United States (US) using nationally representative dietary data from the National Health and Nutrition Examination Survey from 2009-2018. New features of the model include embedded functions to integrate individual-level dietary data that allow for variance estimation; new data and calculations to account for the resource requirements of food trade and farmed aquatic food; updated user interface; expanded output data for over 200 foods that include the use of fertilizer nutrients, pesticides, and irrigation water; supplementary files that include input data for all parameters on an annual basis from 1999-2018; sample programming code; and step-by-step instructions for users. This study demonstrates that animal-sourced foods consumed in the US accounted for the greatest share of total land use, fertilizer nutrient use, pesticide use, and irrigation water use, followed by grains, fruits, and vegetables. Greater adherence to the Dietary Guidelines for Americans was associated with lower use of land and fertilizer nutrients, and greater use of pesticides and irrigation water. Foodprint 2.0 is a highly modifiable model that can be a useful resource for informing sustainable diet policy discussions.

Nature-based solutions are of interest in efforts to achieve reductions in phosphorus (P) loads to aquatic ecosystems. One potential solution of this kind is the restoration of riparian wetlands. Many candidate sites for riparian wetland restoration were formerly used for agriculture and therefore may contain legacy soil P from past fertilizer and/or manure applications. Here, we combined 2-year field studies of 3 restored riparian wetlands on formerly farmed land in the Lake Champlain Basin, Vermont, United States, with implementation of a novel wetlandP model to estimate net P retention. In the field, we measured variable inorganic P deposition ranging up to approximately 1 g P m-2 yr-1 and collected data on P stocks and fluxes required for modeling. At 2 sites, observed water quality dynamics during flood events were indicative of internal dissolved inorganic P (DIP) release from soils during low oxygen conditions. We calibrated and verified the wetlandP model using field data and used it to examine numerous scenarios. Our simulations indicated variable net total P (TP) retention, driven by a trade-off between particulate P trapping and DIP release, with most plausible scenarios (95 out of 108) indicating that the study wetlands serve as net TP sinks. Our net TP retention estimates (range = -0.06 to 0.45 g P m-2 yr-1, mean P retention efficiency = 35%) are comparable to prior literature and help clarify key drivers. Our modeling results also show that release of legacy soil P as DIP can be sizable in some cases (range in net DIP retention = -0.11 to 0.02 g P m-2 yr-1 ), especially for wetlands receiving river/stream water with low DIP concentration. We present a conceptual framework to help guide prioritization of riparian wetland restoration by ecological engineers and designers when water quality improvement via P retention is a goal.

The stormwater treatment performance of an increasingly popular horizontal subsurface-flow gravel wetland design in the northeastern United States was characterized by poor phosphorus retention and negligible impacts on chloride transport.

Diverting food waste from landfills to composting or anaerobic digestion can reduce greenhouse gas emissions, enable the recovery of energy in usable forms, and create nutrient-rich soil amendments. However, many food waste streams are mixed with plastic packaging, raising concerns that food waste-derived composts and digestates may inadvertently introduce microplastics into agricultural soils. Research on the occurrence of microplastics in food waste-derived soil amendments is in an early phase and the relative importance of this potential pathway of microplastics to agricultural soils needs further clarification. In this paper, we review what is known and what is not known about the abundance of microplastics in composts, digestates, and food wastes and their effects on agricultural soils. Additionally, we highlight future research needs and suggest ways to harmonize microplastic abundance and ecotoxicity studies with the design of related policies. This review is novel in that it focuses on quantitative measures of microplastics in composts, digestates, and food wastes and discusses limitations of existing methods and implications for policy.

Bioretention cells, a type of green stormwater infrastructure, have been shown to reduce runoff volumes and remove a variety of pollutants. The ability of bioretention cells to remove nitrogen and phosphorus, however, is variable, and bioretention soil media can act as a net exporter of nutrients. This is concerning as excess loading of nitrogen and phosphorus can lead to eutrophication of surface waters, which green stormwater infrastructure is intended to ameliorate. Drinking water treatment residuals (DWTR), metal (hydr)oxide-rich by-products of the drinking water treatment process, have been studied as an amendment to bioretention soil media due to their high phosphorus sorption capacity. However, very few studies have specifically addressed the effects that DWTRs may have on nitrogen removal performance within bioretention cells. Here, we investigated the effects of DWTR amendment on nitrogen removal in bioretention cells treating stormwater in a roadside setting. We tested the capacity of three different DWTRs to either retain or leach dissolved inorganic nitrogen in the laboratory and also conducted a full-scale field experiment where DWTR-amended bioretention cells and experimental controls were monitored for influent and effluent nitrogen concentrations over two field seasons. We found that DWTRs alone exhibit some capacity to leach nitrate and ammonium, but when integrated into sand- and compost-based bioretention soil media, DWTRs have little to no effect on the removal of nitrogen in bioretention cells. These results suggest that DWTRs can be used in bioretention media for enhanced phosphorus retention without the risk of contributing to nitrogen export in bioretention effluent.

Mechanical depackagers separate valuable organics fromresidualfood packaging, creating new opportunities to recover energy (i.e.,biogas) and nutrients (i.e., digestate) via anaerobic digestion (AD).However, the possibility of imperfect separation has raised concernsthat digestate derived from depackaged food waste may contain microplastics(plastic particles <5 mm). To better understand this tradeoff,we evaluated biochemical methane potential (BMP) and other key ADparameters as well as plastic (0.5-1, 1-5, and >5mm)content of two mechanically depackaged food waste streams and a deriveddigestate. The depackaged pre- and post-consumer organics had BMPsof 453 & PLUSMN; 52 and 435 & PLUSMN; 37 NmL CH4 g(-1) VS, respectively, indicating substantial potential for energy recoveryvia AD. However, plastic was found in both depackaged waste streams(0.19 & PLUSMN; 0.13 and 0.062 & PLUSMN; 0.05% w/w, respectively, for pre-and post-consumer) and the derived digestate (0.018 & PLUSMN; 0.019%w/w). While low on a mass basis, plastic contaminationcould limit digestate reuse options, potentially undercutting theenvironmental benefits of AD. Further work is needed to standardizemethods for measuring the plastic content in organic residuals andto evaluate the life cycle costs and benefits of using mechanicaldepackaging to increase food waste diversion to AD. We measured the energy recovery potentialand plastic contentof mechanically depackaged food waste and derived digestate.

Abstract Oxygen (O2) regulates soil reduction‐oxidation processes and therefore modulates biogeochemical cycles. The difficulties associated with accurately characterizing soil O2 variability have prompted the use of soil moisture as a proxy for O2, as O2 diffusion into soil water is much slower than in soil air. The use of soil moisture alone as a proxy measurement for O2 could result in inaccurate O2 estimations. For example, O2 may remain high during cool months when soil respiration rates are low. We analyzed high‐frequency sensor data (e.g., soil moisture, CO2, gas‐phase soil pore O2) with a machine learning technique, the Self‐Organizing Map, to pinpoint suites of soil conditions associated with contrasting O2 regimes. At two riparian sites in northern Vermont, we found that O2 levels varied seasonally, and with soil moisture. For example, 47% of low O2 levels were associated with wet and cool soil conditions, whereas 32% were associated with dry and warm conditions. Contrastingly, the majority (62%) of high O2 conditions occurred under dry and warm conditions. High soil moisture levels did not always lead to low O2, as 38% of high O2 values occurred under wet and cool conditions. Our results highlight challenges with predicting soil O2 solely based on water content, as variable combinations of soil and hydrologic conditions can complicate the relationship between water content and O2. This indicates that process‐based ecosystem and denitrification models that rely solely on soil moisture to estimate O2 may need to incorporate other site and climate‐specific drivers to accurately predict soil O2.

Dataset for "Performance of a compost aeration and heat recovery system at a commercial composting facility"