The interaction of dissolved organic nitrogen removal and microbial abundance in iron-filings based green environmental media for stormwater treatment

Simultaneous removal of nutrients and biological pollutants via specialty absorbents in a water filtration system for watershed remediation

To investigate watershed remediation within a Total Maximum Daily Load program, this study examined the field-scale filtration performance of two specialty absorbents. The goal was to simultaneously remove nutrients and biological pollutants along Canal 23 (C-23) in the St. Lucie River Basin, Florida. The filtration system installed in the C-23 river corridor was equipped with either clay-perlite with sand sorption media (CPS) or zero-valent iron and perlite green environmental media (ZIPGEM). Both media were formulated with varying combinations of sand, clay, perlite, and/or recycled iron based on distinct recipes. In comparison with CPS, ZIPGEM exhibited higher average removal percentages for nutrients. Findings indicated that ZIPGEM could remove total nitrogen up to 49.3%, total Kjeldahl nitrogen up to 67.1%, dissolved organic nitrogen (DON) up to 72.9%, total phosphorus up to 79.6%, and orthophosphate up to 73.2%. Both ZIPGEM and CPS demonstrated similar efficiency in eliminating biological pollutants, such as E. coli (both media exhibiting an 80% removal percentage) and chlorophyll a (both media achieving approximately 95% removal). Seasonality effects were also evident in nutrient removal efficiencies, particularly in the case of ammonia nitrogen; the negative removal efficiency of ammonia nitrogen from the fifth sampling event could be attributed to processes such as photochemical ammonification, microbial transformation, and mineralization of DON in wet seasons. Overall, ZIPGEM demonstrated a more stable nutrient removal efficiency than CPS in the phase of seasonal changes.

Insights into the transport and bio-degradation of dissolved inorganic nitrogen in the biochar-pyrite amended stormwater biofilter using dynamic modeling

The stormwater biofilter is a prevailing green infrastructure for urban stormwater management, but it is less effective in dissolved nitrogen removal, especially for nitrate. The mechanism that governs the nitrate leaching and performance stability of stormwater biofilters is poorly understood. In this study, a water quality model was developed to predict the ammonium and nitrate dynamics in a biochar-pyrite amended stormwater biofilter. The transport of dissolved nitrogen species was described by advection-dispersion models. The kinetics of adsorption and pyrite-based autotrophic denitrification are included in the model and simulated with a steady-state saturated flow. The model was calibrated and validated using eleven storm events. The modeling results reveal that the contribution of pyrite-based autotrophic denitrification to nitrate leaching alleviation improves with the increased drying duration. The nitrate removal efficiency was affected by a series of design parameters. Pyrite filling rate has a minor effect on nitrate removal promotion. Service area ratio and submerged zone depth are the key parameters to prevent nitrate leaching, as they influence the emergence and discharge time of nitrate breakthrough. The high inflow volume (high service area ratio) and small submerged zone can lead to earlier and increased discharge of peak nitrate otherwise the peak nitrate could be retained in the submerged zone and denitrified during the drying period. The developed mechanistic model provides a useful tool to evaluate the treatment ability of stormwater biofilters under varying conditions and offers a guideline for biofilter design optimization.

Optimization of a compact on-site stormwater runoff treatment system: Process performance and reactor design

Stormwater runoff has become a major anthropogenic urban pollution source that threatens water quality. In this study, coagulation-sedimentation, and ammonium ion exchange and regeneration (AIR) modules were coupled as a CAIR system to efficiently treat stormwater runoff. In the coagulation module, 99.3%, 91.7%, and 97.0% of turbidity, total phosphorus, and chemical oxygen demand could be removed at an optimized poly-aluminum ferric chloride dosage of 30 mg/L, and the continuous experiment confirmed that the full load mode was more suitable for its rapid start-up. In the AIR module, dynamic ammonium removal indicated that the breakthrough time decreased with the rising initial concentration and superficial velocity. The Modified Dose Response (MDR) model described the ammonium exchange behavior better than the Thomas and the Bohart-Adams models. Then, a design flow of the ion exchange reactor was constructed by correlating constants in the MDR model with engineering parameters, and the ion exchange reactor was designed for continuous operation of the CAIR system. The average concentrations of chemical oxygen demand, total phosphorus, ammonium nitrogen, and total nitrogen in the effluent of the CAIR system were 7.22 ± 2.26, 0.17 ± 0.05, 1.49 ± 0.01, and 1.62 ± 0.02 mg/L, respectively. The almost unchanged exchange capacity and physicochemical properties after the multicycle operation confirmed the durability of zeolite for ion exchange. Techno-economic analysis suggested that the CAIR system is practically promising for stormwater management with efficient pollutants removal, small footprint, and acceptable operating cost.

Integration of machine learning classifiers and higher order tensors for screening the optimal recipe of filter media in stormwater treatment

Filter media have oftentimes been used in fixed-bed column tests to examine their removal efficiencies for various pollutants, such as nutrients in stormwater runoff. With limited data sets from column studies, a response surface method (RSM), such as the Box-Behnken Design (BBD), and machine learning methods, can be used to transition from discrete mode assessment to continuous mode optimization, from which the key ingredients of filter media can be better synergized. In this study, similarly to drug discovery via chemometrics, RSM is used to generate meta-models and identify the optimum ratio between clay and iron-filings contents in Iron-filings-based Green Environmental Media (IFGEM) for nutrient removal in stormwater treatment. To achieve the continuous mode optimization, artificial neural network (ANN), deep belief network (DBN), and extreme learning machine (ELM) were selected as machine learning models to compare with BBD to explore the limited column data sets and improve the data science. While separate RSM can help realize the removal efficiencies of total nitrogen (TN), total phosphorus (TP), and ammonia based on varying ratios of clay and iron-filings contents in IFGEM, heterogeneous and inconsistent response surfaces generated from the four learners or classifiers (ANN, ELM, DBN, and BBD) complicate the selection of the final optimal recipe. The power of higher order singular value decomposition (HOSVD) helps synergize the optimal clay and iron filings matrixes of IFGEM in the context of continuous mode optimization via ANN, ELM, DBN, and BBD. With the aid of HOSVD, the optimal recipe for a holistic nutrient removal of TN, TP, and ammonia was determined to be 5% clay, 10% iron filings, 10% tire crumb, and 75% sand.

Nitrate removal uncertainty in stormwater control measures: Is the design or climate a culprit?

Eutrophication is caused by excess nitrate and other nutrient exported via stormwater runoff to surface waters, which is projected to increase as a result of climate change. Despite recent increases in the implementation of stormwater control measures (SCM), nutrient export has not abated, indicating poor or inconsistent removal capacities of SCM for nitrate. However, the cause of the variability is unclear. We show that both design and local climate can explain nitrate removal variability by critically analyzing data reported on the international BMP database for nitrate removal by four common types of SCM: bioretention cells, grass swales, media filters, and retention ponds. The relative importance of climate or design on nitrate removal depends on the SCM type. Nitrate removal in grass swales and bioretention systems is more sensitive to local climate than design specifications, whereas nitrate removal in the retention ponds is less sensitive to climate and more sensitive to design features such as vegetation and pond volume. Media filters without amendment have the least capacity compared to other SCM types surveyed, and their removal capacity was independent of the local climate. Adding amendments made up of carbon biomass, iron-based media, or a mixture of these amendments can significantly improve nitrate removal. The type of carbon biomass is also a factor since biochar does not appear to affect nitrate removal. This analysis can help inform the selection of SCM and modification of their design based on local and projected climate to maximize nitrate removal and minimize eutrophication.