Parameterization of nutrients and sediment build-up/wash-off processes for simulating stormwater quality from specific land uses.

Urbanization changes land cover through the expansion of impermeable surfaces, leading to a significant rise in runoff, sediment, and nutrient loading. The quality of stormwater is related to land use and is highly variable. Currently, stormwater is predominantly described through watershed models that rely minimally, if at all, on field monitoring data. The simple event mean concentration (EMC) wash-off approach by land use is a common method for estimating urban runoff loads. However, a major drawback of the EMC approach is it assumes concentration remains constant across events for a specific land use. Build-up/wash-off equations have been formulated to consider variations in concentration between events. However, several equation parameters are challenging to estimate, making them difficult to use. We conducted a monitoring and modeling study and investigated the impact of land use on stormwater quantity and quality and optimized and investigated the build-up/wash-off parameters for three homogenous urban land uses to estimate nutrients (nitrogen and phosphorus) and sediment loads. Stormwater from commercial, medium-density residential, and transportation land uses was sampled using automatic samplers during storm events, and water quality was characterized for a variety of them for 14 months. Analysis of stormwater samples included assessments for total nitrogen, total phosphorus, and total suspended solids. Results showed that medium-density residential land use had the highest median total nitrogen and total phosphorus event mean concentrations and commercial had the highest median total suspended solids EMCs. Water quality parameters (or build-up/wash-off parameters) exhibited significant variation between land uses, confirming that land use is a key determinant of stormwater quality. The median particle size for each land use was less than 150 µm, indicating that the most common particle size in stormwater was a very fine sand or smaller. This small size should be considered by stakeholders in the design of stormwater treatment systems.

The effect of piping stream channels on dissolved oxygen concentration and ecological health.

Sunlight plays a key role in the nutrient cycle within streams. Streams are often piped to accommodate urban residential or commercial development for buildings, roads, and parking. This results in altered exposure to sunlight, air, and soil, subsequently affecting the growth of aquatic vegetation, reducing reaeration, and thus impairing the water quality and ecological health of streams. While the effects of urbanization on urban streams, including changing flow regimes, stream bank and bed erosion, and degraded water quality, are well understood, the effects of piping streams on dissolved oxygen (DO) concentrations, fish habitats, reaeration, photosynthesis, and respiration rates are not. We addressed this research gap by assessing the effects of stream piping on DO concentrations before and after a 565-m piped section of Stroubles Creek in Blacksburg, VA, for several days during the summer of 2021. Results indicate that the DO level decreased by approximately 18.5% during daylight hours as water flowed through the piped section of the creek. Given the optimum DO level (9.0 mg·L-1) for brook trout (Salvelinus sp.), which are native and present in a portion of Stroubles Creek, the resulting DO deficits were - 0.49 and - 1.24 mg·L-1, for the inlet and outlet, respectively, indicating a possible adverse impact from piping the stream on trout habitat. Photosynthesis and respiration rates were reduced through the piped section, primarily due to the reduced solar radiation and the resultant reduction in oxygen production from aquatic vegetation; however, the reaeration rate increased. This study can inform watershed restoration efforts, particularly decisions regarding stream daylighting with respect to potential water quality and aquatic habitat benefits.

Using Random Forest, a machine learning approach to predict nitrogen, phosphorus, and sediment event mean concentrations in urban runoff.

Estimating pollutant loads from developed watersheds is vitally important to reduce nonpoint source pollution from urban areas, as a key tool in meeting water quality goals is the implementation of Stormwater Control Measures (SCMs). SCMs are selected and sized based on influent pollutant loads. A common method used to estimate pollutant loads in urban runoff is the Event Mean Concentration (EMC) method. In this study, we develop and apply data-driven models using Random Forest (RF), a machine learning approach, to predict Total Nitrogen (TN), Total Phosphorus (TP), Total Suspended Solids (TSS), and Ortho-Phosphorus (Ortho-P) EMCs in urban runoff. The parameters considered in this study were climatological characteristics (i.e., Antecedent Dry Period or ADP, Precipitation Depth or P, Duration or D, and Intensity or I) and catchment characteristics including land use-related parameters including Imperviousness or Imp, Saturated Hydraulic Conductivity or Ksat, and Available Water Capacity or AWC), and site-specific parameters including Slope (S), and Catchment Size (A). Stormwater quality data for this study were obtained from the National Stormwater Quality Database (NSQD), which is the largest repository of stormwater quality data in the U.S. Results demonstrate that land use-related characteristics (i.e., Imp, Ksat, and AWC) were the most effective variables for predicting all EMCs. For TP, TSS, and Ortho-P, site-specific characteristics (S and A) had a greater effect than climatological characteristics (i.e., ADP, P, D, and I). However, for TN, climatological characteristics had a greater effect than site-specific characteristics (S and A). In addition, for TN, TP, and TSS, precipitation characteristics (P, D, and I) were found to be more effective parameters for estimating EMCs than ADP. This study highlights the most influential parameters affecting EMCs which can be used by stakeholders and SCMs designers to improve estimates of nutrients and sediment EMCs. The selection and design of the highest performing SCMs is essential in achieving effective treatment of stormwater, attaining water quality goals, and protecting downstream waterbodies.

What are the relevant sources and factors affecting event mean concentrations (EMCs) of nutrients and sediment in stormwater?

Urbanization increases runoff, sediment, and nutrient loadings downstream, causing flooding, eutrophication, and harmful algal blooms. Stormwater control measures (SCMs) are used to address these concerns and are designed based on inflow loads. Thus, estimating nutrient and sediment loads is important for meeting restoration objectives. Pollutants accumulate on surfaces during dry periods, making Event Mean Concentration (EMC) a function of antecedent dry period (ADP). An EMC results from wash-off of accumulated pollutants from catchment surface during runoff events. However, several studies found little to no correlation between constituent concentrations in stormwater and ADP. The objective of this study is to verify this finding and discover which climatological or catchment characteristics most significantly affect stormwater quality. Stormwater quality data were obtained from the National Stormwater Quality Database (NSQD), which is the largest data repository of stormwater quality data in the U.S. Bayesian Network Structure Learner (BNSL) was used to assess the relationships between catchment characteristics, climatological information, and stormwater quality for selected land uses. Given the optimal BN structure, it was determined which parameters most affect stormwater quality EMCs. The results demonstrate that both catchment and rain characteristics affected stormwater quality EMCs. Among catchment characteristics, land use (LU) was the most important factor and catchment size was the least. Precipitation depth (P) and duration (D) affected Total Phosphorus (TP), Total Nitrogen (TN), and Total Suspended Solids (TSS). This indicated that it is likely that P and D had a greater influence on stormwater quality more than ADP. P, D, and ADP affected the dissolved constituents of TN (i.e. NO2-N/NO3-N) and TP (i.e. Ortho-P). Compared to other factors (i.e. P and D), the effect of ADP on TSS was negligible. Stormwater quality EMCs related to nitrogen were not affected by catchment slope (S). However, TSS and Ortho-P were influenced by S.