Urban stormwater characterization, control, and treatment

A comparative life-cycle assessment and cost analysis of biofilters amended with sludge-based activated carbon and commercial activated carbon for stormwater treatment

Environmental and economic issues resulting from the unsustainable management of sewage sludge from wastewater have necessitated the development of eco-friendly sewage sludge disposal methods, whereas stormwater effluent contains tremendous amounts of pollutants. This study compares the feasibility and environmental impacts associated with incorporating biofilters with sludge-based activated carbon (SBAC) versus commercial activated carbon (CAC) for stormwater treatment. The results demonstrate that the construction and disposal life-cycle stages are the dominant contributors to several environmental impact categories, including resource scarcity, carcinogenic toxicity, terrestrial ecotoxicity, and ozone formation indicators. Across multiple impact categories, the incorporation of biofilters with SBAC can reduce the negative environmental impacts associated with biofilter construction and disposal by 40% over a 50-year analysis period. In contrast, the most significant improvement is on construction-dominant indicators, where the decreased need for biofilter reconstruction results in a higher reduction in environmental impacts. Economically, amending the biofilter with SBAC can increase profits by up to 66% due to extending its lifespan. This study shows that SBAC has similar performance as CAC for lowering the negative environmental impacts resulting from biofilter construction, while increasing the overall net profits of the system. However, converting sewage sludge to an effective sorbent (SBAC) and incorporating SBAC into a biofilter to capture pollutants from stormwater is an economically and environmentally sustainable solution available to practitioners to manage sewage sludge and stormwater effluent. This solution protects the environment in a cost efficient, sustainable manner.

Phosphorus and Heavy Metals Removal from Stormwater Runoff Using Granulated Industrial Waste for Retrofitting Catch Basins

Phosphorus and heavy metals are washed off and transported with stormwater runoff to nearby surface water bodies resulting in environmental and human health risks. Catch basins remain one of the primary gateways through which stormwater runoff and pollutants from urban areas are transported. Retrofitting catch basins to enhance their phosphorus and heavy metal removal can be an effective approach. In this study, aluminum-based water treatment residual (WTR, a non-hazardous byproduct of the water treatment process) was granulated via a green method to serve as a sustainable filter material, called WTR granules, for enhancing the capabilities of catch basins to remove phosphorus and heavy metals. The WTR granules were field tested in a parking lot in Hoboken, New Jersey. Twelve storm events were monitored. The results showed that the WTR granules significantly (p < 0.05) reduced dissolved P, Cu, and Zn, as well as total P, Cu, Pb, and Zn concentrations in stormwater runoff without signs of disintegration. No flooding or water ponding was observed during the implementation. Results suggest the WTR granules are an inexpensive, green filter material that can be used for retrofitting catch basins to remove phosphorus and heavy metals effectively.

Heavy metals from heavy land use? Spatio-temporal patterns of urban runoff metal loads

Urban hydrology is characterized by increased runoff and various pollutant sources. We studied the spatio-temporal patterns of stormwater metal (Al, V, Cr, Mn, Fe, Cu, Zn, and Pb) concentrations and loads in five urbanized and one rural catchment in Southern Finland. The two-year continuous monitoring revealed a non-linear seasonal relationship between catchment urban intensity and metal export. For runoff, seasonal variation decreased with increasing imperviousness. The most urbanized catchments experienced greatest temporal variation in metal concentrations: the annual Cu and Zn loads in most of the studied urbanized catchments were up to 86 times higher compared to the rural site, whereas Fe loads in the urbanized catchments were only circa 29% of the rural load. Total metal levels were highest in the winter, whereas the winter peak of dissolved metal concentrations was less pronounced. The collection of catchment characteristics explained well the total metal concentrations, whereas for the dissolved concentrations the explanatory power was weaker. Our catchment-scale analysis revealed a mosaic of mainly diffuse pollutant sources and calls for catchment-scale management designs. As urban metal export occurred across seasons, solutions that operate also in cold conditions are needed.

Evaluation of the Effectiveness of the SED-BIO System in Reducing the Inflow of Selected Physical, Chemical and Biological Pollutants to a Lake

The aim of this study was to assess the efficiency of the innovative SED-BIO system in limiting the inflow of pollutants to Jelonek Lake. The analyses were conducted in the Gniezno Lake District in Greater Poland (the western part of Poland). Physical and chemical analyses were conducted in the years 2016–2019. The results demonstrate that the system is highly effective in the reduction of such nutrients as nitrogen (NO3−—63%; NH4+—14.9%) and phosphorus (PO43−—19.3%). Although the presence of cyanobacteria was confirmed practically throughout the whole monitoring period of the system (2016), the specimens found in most samples were not toxigenic genotypes with a potential to produce microcystins. Microcystins (3 µg·L−1) were detected only once, immediately after the SED-BIO system had been installed in the river and pond, which demonstrates that this natural toxin was eliminated from the additional pool of contaminants that might be transported to Jelonek Lake.

Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression

Stream nutrient concentrations exhibit marked temporal variation due to hydrology and other factors such as the seasonality of biological processes. Many water quality monitoring programs sample too infrequently (i.e., weekly or monthly) to fully characterize lotic nutrient conditions and to accurately estimate nutrient loadings. A popular solution to this problem is the surrogate-regression approach, a method by which nutrient concentrations are estimated from related parameters (e.g., conductivity or turbidity) that can easily be measured in situ at high frequency using sensors. However, stream water quality data often exhibit skewed distributions, nonlinear relationships, and multicollinearity, all of which can be problematic for linear-regression models. Here, we use a flexible and robust machine learning technique, Random Forests Regression (RFR), to estimate stream nitrogen (N) and phosphorus (P) concentrations from sensor data within a forested, mountainous drainage area in upstate New York. When compared to actual nutrient data from samples tested in the laboratory, this approach explained much of the variation in nitrate (89%), total N (85%), particulate P (76%), and total P (74%). The models were less accurate for total soluble P (47%) and soluble reactive P (32%), though concentrations of these latter parameters were in a relatively low range. Although soil moisture and fluorescent dissolved organic matter are not commonly used as surrogates in nutrient-regression models, they were important predictors in this study. We conclude that RFR shows great promise as a tool for modeling instantaneous stream nutrient concentrations from high-frequency sensor data, and encourage others to evaluate this approach for supplementing traditional (laboratory-determined) nutrient datasets.

Research on the schemes formulation and optimization method of sponge reconstruction in a highway service area

This study proposed a method for constructing a low impact development (LID) plan to improve the utilization rate of rainwater in a highway service area and solve the problem of waterlogging. Firstly, based on the theory of LID, taking the total runoff as the control goal, and combining it with the functional zoning of the highway service area and the characteristics of LID facilities, several LID schemes were proposed. Then, the evaluation system of the LID scheme in service area was established by the analytic hierarchy process (AHP). These preliminary construction schemes were compared from three aspects (runoff control efficiency, economic efficiency and social efficiency) to determine the best LID plan. Finally, taking the Pu'er tunnel service area as an example, the construction scheme of the sponge city service area was optimized.

Urban stormwater characterization, control, and treatment

This review summarizes over 280 studies published in 2019 related to the characterization, control, and management of urban stormwater runoff. A summary of quantity and quality concerns is provided in the first section of the review, serving as the foundation for the following sections which focus on the control and treatment of stormwater runoff. Finally, the impact of stormwater control devices at the watershed scale is discussed. Each section provides a self-contained overview of the 2019 literature, common themes, and future work. Several themes emerged from the 2019 literature including exploration of substrate amendments for improved water quality effluent from stormwater controls, the continued study of the role of vegetation in green infrastructure practices, and a call to action for the development of new models which generate reliable, computationally efficient results under the physical, chemical, biological, and social complexity of stormwater management. PRACTITIONER POINTS: Over 280 studies were published in 2019 related to the characterization, control, and treatment of urban stormwater. Studies on bioretention and general stormwater characteristics represented the two most common subtopics in 2019. Trends in 2019 included novel substrate amendments, studies on the role of vegetation, and advancements in computational models.

Microbial diversity for the improvement of nitrogen removal in stormwater bioretention cells with three aquatic plants

The aquatic plants Iris pseudacorus L., Canna indica L. and Lythrum salicaria L. have been proved to be potential choices for nitrogen removal. However, little is known about microbial diversity for the improvement of nitrogen removal (nitrification and denitrification) in stormwater bioretention cells with the above plants. In this study, batch experiments were conducted to investigate nitrogen removal, substrate layer status, and bacterial community structure to understand microbial diversity and evaluate its effects on performances of nitrogen removal. Ammonia nitrogen removal in the bioretention cell with Lythrum salicaria L. was the highest (88.1%), which was consistent with oxidation reduction potential (ORP) in the bioretention cells. Whilst, removals for both total nitrogen and nitrate were the highest in the bioretention cell with Canna indica L., which was in line with urease activity in the mentioned cells. The used plants had different impact on top 11 dominant microflora at phylum level in the used bioretention cells. Ramlibacter and Nitrosomonadaceaea were both responsible for the difference of nitrogen removal in the bioretention cells with three aquatic plants, suggesting the enhancement of the above dominant microflora could strengthen nitrogen removal in the used bioretention cells.

It Is Not Easy Being Green: Recognizing Unintended Consequences of Green Stormwater Infrastructure

Green infrastructure designed to address urban drainage and water quality issues is often deployed without full knowledge of potential unintended social, ecological, and human health consequences. Though understood in their respective fields of study, these diverse impacts are seldom discussed together in a format understood by a broader audience. This paper takes a first step in addressing that gap by exploring tradeoffs associated with green infrastructure practices that manage urban stormwater including urban trees, stormwater ponds, filtration, infiltration, rain gardens, and green roofs. Each green infrastructure practice type performs best under specific conditions and when targeting specific goals, but regular inspections, maintenance, and monitoring are necessary for any green stormwater infrastructure (GSI) practice to succeed. We review how each of the above practices is intended to function and how they could malfunction in order to improve how green stormwater infrastructure is designed, constructed, monitored, and maintained. Our proposed decision-making framework, using both biophysical (biological and physical) science and social science, could lead to GSI projects that are effective, cost efficient, and just.