Determining the connectivity of tribal communities to wastewater treatment facilities for use in environmental contamination and exposure assessments by wastewater-based surveillance.

BACKGROUND: Limited information is available on the connectivity of Tribal communities to wastewater treatment facilities (WWTFs). This is important for understanding current sanitation infrastructure which drives public health and community construction, knowledge of potential routes of exposure through lack of infrastructure and/or discharging facilities, and opportunities to assess community health through wastewater-based surveillance (WBS). OBJECTIVES: The objective of this work was to assess current wastewater infrastructure for 574 Federally Recognized Indian Tribes (FRITs) in the United States (US) to determine the number and location of facilities on or adjacent to Tribal reservations and Off-Reservation Trust Lands, with the goal of determining the feasibility of employing wastewater-based surveillance within these communities and to identify areas with inadequate sanitation infrastructure. METHODS: Here, we identified available National Pollutant Discharge Elimination System (NPDES) wastewater discharge permits in the Environmental Protection Agency's Environmental Compliance History Online database to assess proximity to and within spatial boundaries of Tribal lands. These data were coupled to race data and tribal spatial boundary information from the US Census Bureau. RESULTS: 94 FRITs have registered NPDES permits within Tribal boundaries including a total of 522 facilities. 210 of these are American Indian (AI)-serving (>50% AI) with the ability to reach 135,000 AI-people through the wastewater network to provide community health assessments via WBS. Of the remaining facilities, 153 predominantly serve non-Tribal populations raising concerns about infrastructure placement and indigenous sovereignty. 523 FRITs were identified as without permitted discharging WWTFs, which may suggest inadequate or alternative infrastructure. IMPACT STATEMENT: Here, multiple data sources including permit information from the Environmental Protection Agency's National Pollution Discharge Elimination System and US Census Bureau data were used to determine the number of wastewater treatment facilities on or adjacent to Tribal lands and how many community members were connected to those municipal systems. This information was used to assess which Tribal communities may be a viable option for wastewater public health surveillance techniques and were used to answer supplemental questions related to basic sanitation and environmental justice concerns.

Development of a point-source model to improve simulations of manure lagoon interactions with the environment.

In recent decades animal agriculture in the U.S. has moved from small, distributed operations to larger, concentrated animal feeding operations (CAFOs). CAFOs are defined by federal regulations based on animal numbers and confinement criteria. Because of the size of these operations, the excessive amount of manure generated is typically stored in lagoons, pits, or barns prior to field application or transport to other farms. Water and air quality near CAFOs can be impaired through the overflow of lagoons, stormwater runoff, lagoon seepage or emissions, motivating the following research question: what manure lagoon parameters impact pollutant fate and transport across multiple mechanisms? To address this question, a CAFO lagoon module was developed to assess lagoon overflow risk, groundwater quality, and ammonia emissions of a dairy lagoon. The results from 10,000 Monte Carlo simulations indicated that lagoon overflow is a rare occurrence for simulated environmental conditions in Washington (3.17%). Second, we developed a groundwater module to complete a groundwater quality assessment of a Dairy Lagoon in Washington using analytical and semi-analytical solutions for groundwater solute transport. The long-term effects of this lagoon on water quality were explored as well as the effectiveness of improving the lagoon lining to reduce seepage. Doubling the lagoon liner thickness attenuated peak groundwater ammonia-N concentrations by 2.8. Lastly, we constructed an air quality module and found that ammonia emissions were not sensitive to changes in lagoon liner construction, but small reductions in lagoon pH significantly reduced yearly average ammonia emissions. The combined model can be used to improve understanding of the impacts of CAFO lagoon overflow and seepage and develop sustainable management practices at the field scale for these key components of the agricultural landscape.

Using remote sensing to identify liquid manure applications in eastern North Carolina.

Nutrient pollution from farm fertilizers and manure is a global concern. Excess nitrogen and phosphorous has been linked to algal blooms and a host of other water quality issues. In the U.S., most animal production occurs in concentrated animal feeding operations (CAFOs) housing a significant number of animals in a confined space. CAFOs tend to cluster in space and thus generate large quantities of manures within a small area. Liquid manure from CAFOs is often stored in open-air lagoons and then applied via irrigation to crops on nearby 'sprayfields'. The full scope and extent of CAFO impacts remain unclear because of the paucity of public information regarding animal numbers, barn and lagoon locations, and manure management practices. Where and when manure is applied on the landscape is key missing data that is needed to better understand and mitigate consequences of CAFO management practices. The aim of this study was to detect land applications of liquid manure using a remote sensing approach. We used random forest models incorporating C-Band synthetic-aperture radar, multispectral imagery, and other predictors to examine soil moisture conditions indicating probable liquid manure applications across known sprayfields in eastern North Carolina. Our models successfully distinguished saturated and unsaturated soils within corn, soybean, grassland, and 'other' crops, with 93-98% accuracy against validation for clear weather periods during the dormant, early, and late growing seasons. A Kruskal-Wallis test revealed that the mean soil saturation frequency was significantly higher on sprayfields than non-sprayfields of the same crop type (p < 2.2e-16). We also found that manure applications were concentrated within ∼1 km from the point of generation. This is the first application of satellite-based radar for identifying the location and timing of manure applications over broad areas. Future work can build on these methods to further understand manure management at CAFOs, as well as to improve pollution source tracking and modeling.

Spatiotemporal land use change and environmental degradation surrounding CAFOs in Michigan and North Carolina.

Concentrated Animal Feeding Operations (CAFOs) have arisen and expanded in the U.S. and globally to address efficiencies in livestock production. CAFOs tend to cluster in space for logistical purposes. Efficient distribution of concentrated manures produced by these operations is often not economically feasible, which may lead to accumulation on land near CAFOs, potentially resulting in local environmental changes. Moreover, as CAFOs are established or expand, they may need more lands to apply their manures, likely driving land use changes even after their establishment. Studies have yet to investigate these spatiotemporal impacts of CAFOs. We investigated whether the presence of regulated liquid waste CAFOs is associated with land use change over time and space as well as degraded environmental conditions surrounding those facilities. We used MODIS (Moderate Resolution Imaging Spectrometer) images from 2000 to 2018 to examine these questions in Michigan and North Carolina- states with varied CAFO establishment histories. We found that cropland extent increased while savanna and forest decreased near CAFOs. Similar observations did not occur outside of areas influenced by CAFOs. We also found evidence of environmental degradation, including decreased evapotranspiration and increased day and nighttime land surface temperatures in North Carolina. This study advances our understanding of environmental impacts surrounding CAFOs. Our findings can support policy changes and highlight the need to better understand these globally increasing entities.

COVID-19 Reveals Vulnerabilities of the Food-Energy-Water Nexus to Viral Pandemics.

Food, energy, and water (FEW) sectors are inextricably linked, making one sector vulnerable to disruptions in another. Interactions between FEW systems, viral pandemics, and human health have not been widely studied. We mined scientific and news/media articles for causal relations among FEW and COVID-19 variables and qualitatively characterized system dynamics. Food systems promoted the emergence and spread of COVID-19, leading to illness and death. Major supply-side breakdowns were avoided (likely due to low morbidity/mortality among working-age people). However, COVID-19 and physical distancing disrupted labor and capital inputs and stressed supply chains, while creating economic insecurity among the already vulnerable poor. This led to demand-side FEW insecurities, in turn increasing susceptibility to COVID-19 among people with many comorbidities. COVID-19 revealed trade-offs such as allocation of water to hygiene versus to food production and disease burden avoided by physical distancing versus disease burden from increased FEW insecurities. News/media articles suggest great public interest in FEW insecurities triggered by COVID-19 interventions among individuals with low COVID-19 case-fatality rates. There is virtually no quantitative analysis of any of these trade-offs or feedbacks. Enhanced quantitative FEW and health models are urgently needed as future pandemics are likely and may have greater morbidity and mortality than COVID-19.

Agricultural practices drive biological loads, seasonal patterns and potential pathogens in the aerobiome of a mixed-land-use dryland.

Air carries a diverse load of particulate microscopic biological matter in suspension, either aerosolized or aggregated with dust particles, the aerobiome, which is dispersed by winds from sources to sinks. The aerobiome is known to contain microbes, including pathogens, as well as debris or small-sized propagules from plants and animals, but its variability and composition has not been studied comprehensibly. To gain a dynamic insight into the aerobiome existing over a mixed-use dryland setting, we conducted a biologically comprehensive, year-long survey of its composition and dynamics for particles less than 10 µm in diameter based on quantitative analyses of DNA content coupled to genomic sequencing. Airborne biological loads were more dependent on seasonal events than on meteorological conditions and only weakly correlated with dust loads. Core aerobiome species could be understood as a mixture of high elevation (e.g. Microbacteriaceae, Micrococcaceae, Deinococci), and local plant and soil sources (e.g. Sphingomonas, Streptomyces, Acinetobacter). Despite the mixed used of the land surrounding the sampling site, taxa that contributed to high load events were largely traceable to proximal agricultural practices like cotton and livestock farming. This included not only the predominance of specific crop plant signals over those of native vegetation, but also that of their pathogens (bacterial, viral and eukaryotic). Faecal bacterial loads were also seasonally important, possibly sourced in intensive animal husbandry or manure fertilization activity, and this microbial load was enriched in tetracycline resistance genes. The presence of the native opportunistic pathogen, Coccidioides spp., by contrast, was detected only with highly sensitive techniques, and only rarely. We conclude that agricultural activity exerts a much stronger influence that the native vegetation as a mass loss factor to the land system and as an input to dryland aerobiomes, including in the dispersal of plant, animal and human pathogens and their genetic resistance characteristics.

Distribution of environmental justice metrics for exposure to CAFOs in North Carolina, USA.

BACKGROUND: Several studies have reported environmental disparities regarding exposure to concentrated animal feeding operations (CAFOs). Public health implications of environmental justice from the intensive livestock industry are of great concern in North Carolina (NC), USA, a state with a large number and extensive history of CAFOs. OBJECTIVES: We examined disparities by exposure to CAFOs using several environmental justice metrics and considering potentially vulnerable subpopulations. METHODS: We obtained data on permitted animal facilities from NC Department of Environmental Quality (DEQ). Using ZIP code level variables from the 2010 Census, we evaluated environmental disparities by eight environmental justice metrics (i.e., percentage of Non-Hispanic White, Non-Hispanic Black, or Hispanic; percentage living below the poverty level; median household income; percentage with education less than high school diploma; racial residential isolation (RI) for Non-Hispanic Black; and educational residential isolation (ERI) for population without college degree). We applied two approaches to assign CAFOs exposure for each ZIP code: (1) a count method based on the number of CAFOs within ZIP code; and (2) a buffer method based on the area-weighted number of CAFOs using a 15 km buffer. RESULTS: Spatial distributions of CAFOs exposure generally showed similar patterns between the two exposure methods. However, some ZIP codes had different estimated CAFOs exposure for the different approaches, with higher exposure when using the buffer method. Our findings indicate that CAFOs are located disproportionately in communities with higher percentage of minorities and in low-income communities. Distributions of environmental justice metrics generally showed similar patterns for both exposure methods, however starker disparities were observed using a buffer method. CONCLUSIONS: Our findings of the disproportionate location of CAFOs provide evidence of environmental disparities with respect to race and socioeconomic status in NC and have implications for future studies of environmental and health impacts of CAFOs.

Source contribution to phosphorus loads from the Maumee River watershed to Lake Erie.

Coastal eutrophication is a leading cause of degraded water quality around the world. Identifying the sources and their relative contributions to impaired downstream water quality is an important step in developing management plans to address water quality concerns. Recent mass-balance studies of Total Phosphorus (TP) loads of the Maumee River watershed highlight the considerable phosphorus contributions of non-point sources, including agricultural sources, degrading regional downstream water quality. This analysis builds upon these mass-balance studies by using the Soil and Water Assessment Tool to simulate the movement of phosphorus from manure, inorganic fertilizer, point sources, and soil sources, and respective loads of TP and Dissolved Reactive Phosphorus (DRP). This yields a more explicit estimation of source contribution from the watershed. Model simulations indicate that inorganic fertilizers contribute a greater proportion of TP (45% compared to 8%) and DRP (58% compared to 12%) discharged from the watershed than manure sources in the March-July period, the season driving harmful algal blooms. Although inorganic fertilizers contributed a greater mass of TP and DRP than manure sources, the two sources had similar average delivery fractions of TP (2.7% for inorganic fertilizers vs. 3.0% for manure sources) as well as DRP (0.7% for inorganic fertilizers vs. 1.2% for manure sources). Point sources contributed similar proportions of TP (5%) and DRP (12%) discharged in March-July as manure sources. Soil sources of phosphorus contributed over 40% of the March-July TP load and 20% of the March-July DRP load from the watershed to Lake Erie. Reductions of manures and inorganic fertilizers corresponded to a greater proportion of phosphorus delivered from soil sources of phosphorus, indicating that legacy phosphorus in soils may need to be a focus of management efforts to reach nutrient load reduction goals. In agricultural watersheds aground the world, including the Maumee River watershed, upstream nutrient management should not focus solely on an individual nutrient source; rather a comprehensive approach involving numerous sources should be undertaken.

Quantifying uncertainty cascading from climate, watershed, and lake models in harmful algal bloom predictions.

In response to increased harmful algal blooms (HABs), hypoxia, and nearshore algae growth in Lake Erie, the United States and Canada agreed to phosphorus load reduction targets. While the load targets were guided by an ensemble of models, none of them considered the effects of climate change. Some watershed models developed to guide load reduction strategies have simulated climate effects, but without extending the resulting loads or their uncertainties to HAB projections. In this study, we integrated an ensemble of four climate models, three watershed models, and four HAB models. Nutrient loads and HAB predictions were generated for historical (1985-1999), current (2002-2017), and mid-21st-century (2051-2065) periods. For the current and historical periods, modeled loads and HABs are comparable to observations but exhibit less interannual variability. Our results show that climate impacts on watershed processes are likely to lead to reductions in future loading, assuming land use and watershed management practices are unchanged. This reduction in load should help reduce the magnitude of future HABs, although increases in lake temperature could mitigate that decrease. Using Monte-Carlo analysis to attribute sources of uncertainty from this cascade of models, we show that the uncertainty associated with each model is significant, and that improvements in all three are needed to build confidence in future projections.

Bias correction of climate model outputs influences watershed model nutrient load predictions.

Waterbodies around the world experience problems associated with elevated phosphorus (P) and nitrogen (N) loads. While vital for ecosystem functioning, when present in excess amounts these nutrients can impair water quality and create symptoms of eutrophication, including harmful algal blooms. Under a changing climate, nutrient loads are likely to change. While climate models can serve as inputs to watershed models, the climate models often do not adequately represent the distribution of observed data, generating uncertainties that can be addressed to some degree with bias correction. However, the impacts of bias correction on nutrient models are not well understood. This study compares 4 univariate and 3 multivariate bias correction methods, which correct precipitation and temperature variables from 4 climate models in the historical (1980-1999) and mid-century future (2046-2065) time periods. These variables served as inputs to a calibrated Soil and Water Assessment Tool (SWAT) model of Lake Erie's Maumee River watershed. We compared the performance of SWAT outputs driven with climate model outputs that were bias-corrected (BC) and not bias-corrected (no-BC) for dissolved reactive P, total P, and total N. Results based on graphical comparisons and goodness of fit metrics showed that the choice of BC method impacts both the direction of change and magnitude of nutrient loads and hydrological processes. While the Delta method performed best, it should be used with caution since it considers historical variable relationships as the basis for predictions, which may not hold true under future climate. Quantile Delta Mapping (QDM) and Multivariate Bias Correction N-dimensional probability density function transform (MBCn) BC methods also performed well and work well for non-stationary climate scenarios. Furthermore, results suggest that February-July cumulative load in the Maumee basin is likely to decrease in the mid-century as runoff and snowfall decrease, and evapotranspiration increases with warming temperatures.

Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models.

Reducing harmful algal blooms in Lake Erie, situated between the United States and Canada, requires implementing best management practices to decrease nutrient loading from upstream sources. Bi-national water quality targets have been set for total and dissolved phosphorus loads, with the ultimate goal of reaching these targets in 9-out-of-10 years. Row crop agriculture dominates the land use in the Western Lake Erie Basin thus requiring efforts to mitigate nutrient loads from agricultural systems. To determine the types and extent of agricultural management practices needed to reach the water quality goals, we used five independently developed Soil and Water Assessment Tool models to evaluate the effects of 18 management scenarios over a 10-year period on nutrient export. Guidance from a stakeholder group was provided throughout the project, and resulted in improved data, development of realistic scenarios, and expanded outreach. Subsurface placement of phosphorus fertilizers, cover crops, riparian buffers, and wetlands were among the most effective management options. But, only in one realistic scenario did a majority (3/5) of the models predict that the total phosphorus loading target would be met in 9-out-of-10 years. Further, the dissolved phosphorus loading target was predicted to meet the 9-out-of-10-year goal by only one model and only in three scenarios. In all scenarios evaluated, the 9-out-of-10-year goal was not met based on the average of model predictions. Ensemble modeling revealed general agreement about the effects of several practices although some scenarios resulted in a wide range of uncertainty. Overall, our results demonstrate that there are multiple pathways to approach the established water quality goals, but greater adoption rates of practices than those tested here will likely be needed to attain the management targets.

Repeated Hurricanes Reveal Risks and Opportunities for Social-Ecological Resilience to Flooding and Water Quality Problems.

Hurricanes that damage lives and property can also impact pollutant sources and trigger poor water quality. Yet, these water quality impacts that affect both human and natural communities are difficult to quantify. We developed an operational remote sensing-based hurricane flood extent mapping method, examined potential water quality implications of two "500-year" hurricanes in 2016 and 2018, and identified options to increase social-ecological resilience in North Carolina. Flooding detected with synthetic aperture radar (>91% accuracy) extended beyond state-mapped hazard zones. Furthermore, the legal floodplain underestimated impacts for communities with higher proportions of older adults, disabilities, unemployment, and mobile homes, as well as for headwater streams with restricted elevation gradients. Pollution sources were repeatedly affected, including ∼55% of wastewater treatment plant capacity and swine operations that generate ∼500 M tons/y manure. We identified ∼4.8 million km2 for possible forest and wetland conservation and ∼1.7 million km2 for restoration or altered management opportunities. The results suggest that current hazard mapping is inadequate for resilience planning; increased storm frequency and intensity necessitate modification of design standards, land-use policies, and infrastructure operation. Implementation of interventions can be guided by a greater understanding of social-ecological vulnerabilities within hazard and exposure areas.