Developing nitrogen removal models for stormwater bioretention systems.

Bioretention systems have the potential of simultaneous runoff volume reduction and nitrogen removal. Internal water storage (IWS) layers and real-time control (RTC) strategies may further improve performance of bioretention systems. However, optimizing the design of these systems is limited by the lack of effective models to simulate nitrogen transformations under the influences of IWS design and environment conditions including soil moisture and temperature. In this study, nitrogen removal models (NRMs) are developed with two complexity levels of nitrogen cycling: the Single Nitrogen Pool (SP) models and the more complex 3 Nitrogen Pool (3P) models. The 0-order kinetics, 1st order kinetics, and the Michaelis-Menten equations are applied to both SP and 3P models, creating six different NRMs. The Storm Water Management Model (SWMM), in combination with each NRM, is calibrated and validated with a lab dataset. Results show that 0-order kinetics are not suitable in simulating nitrogen removal or transformations in bioretention systems, while 1st order kinetics and Michaelis-Menten equation models have similar performances. The best performing NRM (referred to as 3P-m) can accurately predict nitrogen event mean concentrations in bioretention effluent for 20% more events when compared to SWMM. When only calibrated with soil moisture conditions in bioretention systems without internal storage layers, 3P-m was sufficiently adaptable to predict cumulative nitrogen mass removal rates from systems with IWS or RTC rules with less than ±7% absolute error, while the absolute error from SWMM prediction can reach -23%. In general, 3P models provide higher prediction accuracy and improved time series of biochemical reaction rates, while SP models improve prediction accuracy with less required user input for initial conditions.

Environmental regulations in the United States lead to improvements in untreated stormwater quality over four decades.

Identifying sources of pollutants in watersheds is critical to accurately predicting stormwater quality. Many existing software used to model stormwater quality rely on decades-old data sets which may not represent current runoff quality in the United States. Because of environmental regulations promulgated at the federal level over previous decades, there is a need to understand long-term trends (and potential shifts) in runoff quality to better parameterize models. Pollutant event mean concentrations (EMCs) from the National Stormwater Quality Database (NSQD) were combined with those from recent sources to understand if untreated stormwater quality has changed over the past 40 years. A significant decreasing monotonic trend (i.e., continually decreasing in a nonuniform fashion) was observed for total suspended solids (TSS), total phosphorus (TP), total Kjeldahl nitrogen (TKN), total copper (Cu), total lead (Pb), and total zinc (Zn) in the resultant database, suggesting that runoff quality has become less polluted with time. Median EMCs decreased from 99.2 to 42 mg/L, 0.34 to 0.26 mg/L, 1.27 to 1.03 mg/L, 40 to 6.8 µg/L, 110 to 3.7 µg/L, and 375 to 53.3 µg/L for TSS, TP, TN, Cu, Pb, and Zn, respectively, from the 1980s to the 2010s. These significant reductions often aligned temporally with advancements in clean manufacturing, amendments of the Clean Air Act, and other source control efforts which impact pollutant bioavailability and atmospheric deposition. Results suggest environmental regulations not specifically targeting stormwater management have had a positive impact on stormwater quality and that temporal fluctuations should be considered in modeling.

Building resiliency to climate change uncertainty through bioretention design modifications.

Climate stationarity is a traditional assumption in the design of the urban drainage network, including green infrastructure practices such as bioretention cells. Predicted deviations from historic climate trends associated with global climate change introduce uncertainty in the ability of these systems to maintain service levels in the future. Climate change projections are made using output from coarse-scale general circulation models (GCMs), which can then be downscaled using regional climate models (RCMs) to provide predictions at a finer spatial resolution. However, all models contain sources of error and uncertainty, and predicted changes in future climate can be contradictory between models, requiring an approach that considers multiple projections. The performance of bioretention cells were modeled using USEPA's Storm Water Management Model (SWMM) to determine how design modifications could add resilience to these systems under future climate conditions projected for Knoxville, Tennessee, USA. Ten downscaled climate projections were acquired from the North American Coordinated Regional Downscaling Experiment program, and model bias was corrected using Kernel Density Distribution Mapping (KDDM). Bias-corrected climate projections were used to assess bioretention hydrologic function in future climate conditions. Several scenarios were evaluated using a probabilistic approach to determine the confidence with which design modifications could be implemented to maintain historic performance for both new and existing (retrofitted) bioretention cells. The largest deviations from current design (i.e., concurrently increasing ponding depths, thickness of media layer, media conductivity rates, and bioretention surface areas by 307%, 200%, 200%, and 300%, respectively, beyond current standards) resulted in the greatest improvements on historic performance with respect to annual volumes of infiltration and surface overflow, with all ten future climate scenarios across various soil types yielding increased infiltration and decreased surface overflow compared to historic conditions. However, lower performance was observed for more conservative design modifications; on average, between 13-82% and 77-100% of models fell below historic annual volumes of infiltration and surface overflow, respectively, when ponding zone depth, media layer thickness, and media conductivity were increased alone. Findings demonstrate that increasing bioretention surface area relative to the contributing catchment provides the greatest overall return on historic performance under future climate conditions and should be prioritized in locations with low in situ soil drainage rates. This study highlights the importance of considering local site conditions and management objectives when incorporating resiliency to climate change uncertainty into bioretention designs.

Testing of new stormwater pollution build-up algorithms informed by a genetic programming approach.

Pollution build-up and wash-off processes are often included in urban stormwater quality models. However, these models are often unreliable and have poor performance at large scales and in complicated catchments. This study tried to improve stormwater quality models by adopting the genetic programming (GP) approach to generate new build-up algorithms for three different pollutants (total suspend solids - TSS, total phosphorus - TP and total nitrogen - TN). This was followed by testing of the new models (also traditional build-up and wash-off models as benchmark) using data collected from different catchments in Australia and the USA. The GP approach informed new sets of build-up algorithms with the inclusion of not just the typical antecedent dry weather period (ADWP), but also other less 'traditional' variables - previous rainfall depth for TSS and maximum air temperatures for TP and TN simulation. The traditional models had relatively poor performance (Nash-Sutcliffe coefficient, E < 0.0), except for TP at Gilby Road (GR) (E = 0.21 in calibration and 0.43 in validation). Improved performance was observed using the models with new build-up algorithms informed by GP. Taking TP at GR for example, the best performing model had E of 0.46 in calibration and 0.54 in validation. The best performing models for TSS, TP, and TN are often different, suggesting that specific models shall be used for different pollutants. Insights into further improvements possible for stormwater quality models were given. It is recommended that in addition to the typical build-up and wash-off process, new generations of stormwater quality models should be able to account for the non-conventional pollutant sources (e.g. cross-connections, septic tank leakage, illegal discharges) through stochastic approaches. Emission inventories with information like intensity-frequency-duration (IFD) of pollutant loads from each type of non-conventional source are suggested to be built for stochastic modelling.

Health Impacts of Extreme Weather Events: Exploring Protective Factors with a Capitals Framework.

PURPOSE: Extreme weather events are increasing with climate change. The physical and mental health of people served by social workers may be especially at risk from these hazards. This exploratory study examines if specific types of human, financial, physical, and social capital are associated with health impacts from excessive summer heat and extreme winter weather. METHOD: Data from resident surveys (N = 424) in low- and moderate-income areas of a Southeastern US city are analyzed with descriptive statistics and logistic regression. RESULTS: Key findings are that health status and social cohesion are negatively associated with health impacts of summer heat and winter extremes. CONCLUSION: Further study is needed of how specific types of capital may help people cope with a changing climate. Social capital may be a particularly relevant area for social work to address within the pressing issue of climate, weather, and the health of vulnerable groups.

Heat and Humidity in the City: Neighborhood Heat Index Variability in a Mid-Sized City in the Southeastern United States.

Daily weather conditions for an entire city are usually represented by a single weather station, often located at a nearby airport. This resolution of atmospheric data fails to recognize the microscale climatic variability associated with land use decisions across and within urban neighborhoods. This study uses heat index, a measure of the combined effects of temperature and humidity, to assess the variability of heat exposure from ten weather stations across four urban neighborhoods and two control locations (downtown and in a nearby nature center) in Knoxville, Tennessee, USA. Results suggest that trees may negate a portion of excess urban heat, but are also associated with greater humidity. As a result, the heat index of locations with more trees is significantly higher than downtown and areas with fewer trees. Trees may also reduce heat stress by shading individuals from incoming radiation, though this is not considered in this study. Greater amounts of impervious surfaces correspond with reduced evapotranspiration and greater runoff, in terms of overall mass balance, leading to a higher temperature, but lower relative humidity. Heat index and relative humidity were found to significantly vary between locations with different tree cover and neighborhood characteristics for the full study time period as well as for the top 10% of heat index days. This work demonstrates the need for high-resolution climate data and the use of additional measures beyond temperature to understand urban neighborhood exposure to extreme heat, and expresses the importance of considering vulnerability differences among residents when analyzing neighborhood-scale impacts.