N treatment performance in stormwater biofilters: Relationships between sequestered pollutants, environmental conditions, and N cycling soil bacteria.

Stormwater runoff imposes large hydrologic and nutrient imbalances in urban areas through the delivery of large pollutant loads to surface waters. To mitigate this pollution, green stormwater infrastructure (GSI) approaches are increasingly used. Nitrogen (N) removal in GSI varies, depending on soil characteristics and plant species. However, GSI may often leach nitrate and export N. Removal of N is mediated by nitrifying and denitrifying bacteria; whether these bacteria are affected by retained soil contaminants, such as trace metals, is unknown. If accumulated soil metals reach levels that inhibit N cycling microorganisms, N treatment may be reduced. Further N treatment uncertainty arises due to limitations from prior studies, which have been mostly performed under steady-state conditions, in controlled lab environments, or have insufficiently considered transient flow conditions. A detailed understanding of the timing and magnitude of N processes across transient storms is needed to examine the role of biofilters as sources or sinks of aqueous and gaseous N, and to determine how biofilters should be managed to mitigate N export.This doctoral research aims to address these knowledge gaps by 1) evaluating total and bioavailable metal concentrations in soils of field-scale GSI, and how accumulated metals may be predicted from drainage area characteristics, 2) assessing how soil properties, as well as total and bioavailable metal concentrations, influence nitrifying and denitrifying bacteria across representative GSI, and 3) comprehensively assessing N fates and N transformation processes within and in between storms in a field-scale GSI receiving high-flow storm events. Results show that metals can accumulate in GSI soils, and that total metals are significantly correlated to the ratio of impervious drainage area to GSI area. Thus, monitoring efforts may prioritize soils with highest impervious ratios. Results from representative GSI show that linear regression models including soil properties and metal concentrations provide good estimates of nitrifying and denitrifying gene abundances in soils. Bioavailable fractions of Cd and Pb seem to reduce gene abundances of denitrifying microorganisms (nirS, nosZ), with implications for N2O release. In contrast, total Cu, Ni and V appear to exert a positive influence on functional gene abundances, suggesting metal limitation in soils. Results reinforce including bioavailable metal fractions in metal risk assessments. In the final study, chemical, bacterial, and stable isotope data show that denitrification is limited even for high-frequency, large storms, and that GSI systems perform poorly, in terms of N removal, when challenged with a large transient storm, behaving as persistent N sources in subsequent storms. I propose an alternate design consisting of a treatment train of a real-time control stormwater capture system, sequentially followed by a fast-draining cell, and a slow-draining cell.This dissertation has advanced the understanding of N processing in GSI; the potential interactions between soil nitrifying and denitrifying bacteria and accumulated soil metals has also been evaluated. Recommendations were provided to prioritize metal risk assessments, improve N treatment in GSI, and minimize N export and undesirable environmental consequences

Evaluating the relationships between specific drainage area characteristics and soil metal concentrations in long-established bioswales receiving suburban stormwater runoff.

Bioswales are used to attenuate stormwater pollution, but their long-term sustainability regarding sequestered metals is relatively unknown, and a clear rationale for prioritizing soil management is lacking. Impervious areas draining into four 14-year-old suburban bioswales were delineated, for which surface soils (top 10 cm; 72 samples) were sampled; soils from 4 adjacent reference sites were also sampled. Total and water soluble metals (Cd, Cu, Pb, Zn) were quantified, and the relationships between metal concentrations and drainage area characteristics evaluated. Annual metal loads were estimated using regional runoff data to simulate current and future metal concentrations; risks to soil biota were assessed by comparing metal concentrations to ecological screening levels. The drainage areas' percent imperviousness (37-71%) and ratios of impervious drainage area to bioswale area (2.0-5.7) varied, owing to differing proportions of rooftops, paved surfaces, lawns, and natural soils. Total Cu and Zn ranged from 10.0 to 43.2 mg/kg dry soil, and 15.6 to 129.5 mg/kg dry soil, respectively. Across all bioswales, total Zn was positively correlated to percent impervious area (r = 0.32, p = 0.0073), the ratio of connected impervious drainage area to infiltration area (r = 0.32, p = 0.0073), and percent drainage area as paved surfaces (r = 0.46, p = 5.6 E-05), but negatively correlated to percent drainage area as lawns (r = -0.48; p = 2.4 E-05). Water soluble metal concentrations were orders of magnitude lower than total metals. Given annual metal loads (0.2-0.4 mg Cu/kg dry soil; 1.5-3.1 mg Zn/kg dry soil), replacing bioswale soils to constrain metal concentrations would be unnecessary for decades. Taken together, this study proposes a transferable approach of estimating, then verifying via sampling and analysis, bioswale soil metal concentrations, such that soil management decisions can be benchmarked to ecological screening levels

Spatial Models of Sewer Pipe Leakage Predict the Occurrence of Wastewater Indicators in Shallow Urban Groundwater

Twentieth century municipal wastewater infrastructure greatly improved U.S. urban public health and water quality. However, sewer pipes deteriorate, and their accumulated structural defects may release untreated wastewater to the environment via acute breaks or insidious exfiltration. Exfiltrated wastewater constitutes a loss of potentially reusable water and delivers a complex and variable mix of contaminants to urban shallow groundwater. Yet, predicting where deteriorated sewers impinge on shallow groundwater has been challenging. Here we develop and test a spatially explicit model of exfiltration probability based on pipe attributes and groundwater elevation without prior knowledge of exfiltrating defect locations. We find that models of exfiltration probability can predict the probable occurrence in underlying shallow groundwater of established wastewater indicators including the artificial sweetener acesulfame, tryptophan-like fluorescent dissolved organic matter, nitrate, and a stable isotope of water (δ¹⁸O). The strength of the association between exfiltration probability and indicators of wastewater increased when multiple pipe attributes, distance weighting, and groundwater flow direction were considered in the model. The results prove that available sanitary sewer databases and groundwater digital elevation data can be analyzed to predict where pipes are likely leaking and contaminating groundwater. Such understanding could direct sewer infrastructure reinvestment toward water resource protection

Limited Bacterial Removal in Full-Scale Stormwater Biofilters as Evidenced by Community Sequencing Analysis.

In urban areas, untreated stormwater runoff can pollute downstream surface waters. To intercept and treat runoff, low-impact or "green infrastructure" approaches such as using biofilters are adopted. Yet, actual biofilter pollutant removal is poorly understood; removal is often studied in laboratory columns, with variable removal of viable and culturable microbial cell numbers including pathogens. Here, to assess bacterial pollutant removal in full-scale planted biofilters, stormwater was applied, unspiked or spiked with untreated sewage, in simulated storm events under transient flow conditions, during which biofilter influents versus effluents were compared. Based on microbial biomass, sequences of bacterial community genes encoding 16S rRNA, and gene copies of the human fecal marker HF183 and of the Enterococcus spp. marker Entero1A, removal of bacterial pollutants in biofilters was limited. Dominant bacterial taxa were similar for influent versus effluent aqueous samples within each inflow treatment of either spiked or unspiked stormwater. Bacterial pollutants in soil were gradually washed out, albeit incompletely, during simulated storm flushing events. In post-storm biofilter soil cores, retained influent bacteria were concentrated in the top layers (0-10 cm), indicating that the removal of bacterial pollutants was spatially limited to surface soils. To the extent that plant-associated processes are responsible for this spatial pattern, treatment performance might be enhanced by biofilter designs that maximize influent contact with the rhizosphere

Citizen Science as an Approach for Overcoming Insufficient Monitoring and Inadequate Stakeholder Buy-in in Adaptive Management: Criteria and Evidence

Adaptive management is broadly recognized as critical for managing natural resources, yet in practice it often fails to achieve intended results for two main reasons: insufficient monitoring and inadequate stakeholder buy-in. Citizen science is gaining momentum as an approach that can inform natural resource management and has some promise for solving the problems faced by adaptive management. Based on adaptive management literature, we developed a set of criteria for successfully addressing monitoring and stakeholder related failures in adaptive management and then used these criteria to evaluate 83 citizen science case studies from peer-reviewed literature. The results suggest that citizen science can be a cost-effective method to collect essential monitoring information and can also produce the high levels of citizen engagement that are vital to the adaptive management learning process. The analysis also provides a set of recommendations for citizen science program design that addresses spatial and temporal scale, data quality, costs, and effective incentives to facilitate participation and integration of findings into adaptive management

Citizen Science as an Approach for Overcoming Insufficient Monitoring and Inadequate Stakeholder Buy-in in Adaptive Management: Criteria and Evidence

Adaptive management is broadly recognized as critical for managing natural resources, yet in practice it often fails to achieve intended results for two main reasons: insufficient monitoring and inadequate stakeholder buy-in. Citizen science is gaining momentum as an approach that can inform natural resource management and has some promise for solving the problems faced by adaptive management. Based on adaptive management literature, we developed a set of criteria for successfully addressing monitoring and stakeholder related failures in adaptive management and then used these criteria to evaluate 83 citizen science case studies from peer-reviewed literature. The results suggest that citizen science can be a cost-effective method to collect essential monitoring information and can also produce the high levels of citizen engagement that are vital to the adaptive management learning process. The analysis also provides a set of recommendations for citizen science program design that addresses spatial and temporal scale, data quality, costs, and effective incentives to facilitate participation and integration of findings into adaptive management