Effluent Nitrogen Concentration Research Articles

Investigation of Nitrogen Removal in Flue Gas Desulfurization and Denitrification Wastewater Utilizing Halophilic Activated Sludge.

In the process of flue gas desulfurization and denitrification, the generation of high-sulfate wastewater containing nitrogen is a significant challenge for biological wastewater treatment. In this study, halophilic activated sludge was inoculated in a Sequencing Batch Reactor to remove nitrogen from wastewater with a high sulfate concentration (60 g/L). With the influent concentration of 180 mg/L, the removal rate of total nitrogen was more than 96.7%. The effluent ammonium nitrogen concentration was lower than 1.94 mg/L, and the effluent nitrate nitrogen and nitrite nitrogen concentrations were even lower than 0.77 mg/L. The salt tolerance of activated sludge is mainly related to the increase in the content of ectoine in microbial cells. The Specific Nitrite Oxidation Rate is quite low, while the Specific Nitrite Reduction Rate and Specific Nitrate Reduction Rate are relatively strong. In the system, there are various nitrogen metabolic processes, including aerobic nitrification, anaerobic denitrification, and simultaneous nitrification-denitrification processes. By analyzing the nitrogen metabolic mechanisms and microbial community structure of the reaction system, dominate bacteria can be identified, such as Azoarcus, Thauera, and Halomonas, which have significant nitrogen removal capabilities.

Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation

Optimizing nitrogen removal is crucial for ensuring the efficient operation of wastewater treatment plants (WWTPs), but it is susceptible to variations in influent conditions and operational parameter constraints, and conflicts with the energy-saving and carbon emission reduction goals. To address these issues, this study proposes a hybrid framework integrating process simulation, machine learning, and multi-objective genetic algorithms for nitrogen removal diagnosis and optimization, aiming to predict the total nitrogen in effluent, diagnose nitrogen over-limit risks, and optimize the control strategies. Taking a full-scale WWTP as a case study, a process time-lag simulation-enhanced machine learning model (PTLS-ML) was developed, achieving R2 values of 0.94 and 0.79 for the training and testing sets, respectively. The proposed model successfully identified the potential reasons of nitrogen over-limit risks under different influent conditions and operational parameters, and accordingly provided optimization suggestions. In addition, the multi-objective optimization (MOO) algorithms analysis further demonstrated that maintaining 4–6 mg/L total nitrogen concentration in effluent by adjusting process operational parameters can effectively balance multiple objectives (i.e., effluent water quality, operating costs, and greenhouse gas emissions), achieving coordinated optimization. This framework can serve as a reference for stable operation, energy-saving, and emission reduction in the nitrogen removal of WWTPs.

A Continuous Plug-Flow Anaerobic-Multistage Anoxic/Aerobic Process Treating Low-C/N Domestic Sewage: Nutrient Removal, Greenhouse Gas Emissions, and Microbial Community Analysis

The anaerobic-multistage anoxic/aerobic (A-MAO) process has shown good potential for advanced nitrogen removal in recent years, but its greenhouse gas emissions still need to be fully explored. The effects of the influent distribution and external carbon source sodium acetate on nutrient removal, greenhouse gas emissions, and the microbial community structure in a continuous plug-flow A-MAO reactor fed with real low C/N ratio domestic sewage were investigated. The results showed that altering the allocation of carbon source resulted in average chemical oxygen demand (COD) and total nitrogen (TN) concentration in effluent reduced to 26.10 ± 4.86 and 6.65 ± 1.73 mg/L, respectively. Both operations reduced the emission rate of greenhouse gas. While the addition of external car-bon sources leaded to lower N2O emission rates and higher CO2 and CH4 emission rates. The addition of sodium acetate facilitated nitrification and denitrification processes, thereby leading to a reduction in N2O production. Meanwhile, it spurred the growth of methanogenic bacteria and heterotrophic microorganisms, thus boosting the production of CO2 and CH4. Influent distribution promoted the increase of Bacteroidota, Chloroflexi and Acidobacteriota of the reactor. The enrichment of typical hydrolytic bacteria and glycogen accumulating organisms (GAOs) increased the utilization efficiency of carbon sources in the system after the addition of sodium acetate. The significant increase of typical denitrifying bacteria (DNBs) Azospira reduced the N2O emission during heterotrophic denitrification process, which was considered to be an important functional genus for increasing nitrogen loss in this system. The rational utilization of carbon source makes the difference in metabolism function. The study provides a valuable strategy for comprehensively evaluating the pollutant removal and greenhouse gas emission reduction from the A-MAO process.

Study on the enhancement of low carbon-to-nitrogen ratio urban wastewater pollutant removal efficiency by adding sulfur electron acceptors.

The effective elimination of nitrogen and phosphorus in urban sewage treatment was always hindered by the deficiency of organic carbon in the low C/N ratio wastewater. To overcome this organic-dependent barrier and investigate community changes after sulfur electron addition. In this study, we conducted a simulated urban wastewater treatment plant (WWTP) bioreactor by using sodium sulfate as an electron acceptor to explore the removal efficiency of characteristic pollutants before and after the addition of sulfur electron acceptor. In the actual operation of 90 days, the removal rate of sulfur electrons' chemical oxygen demand (COD), ammonia nitrogen, and total phosphorus (TP) with sulfur electrons increased to 94.0%, 92.1% and 74%, respectively, compared with before the addition of sulfur electron acceptor. Compared with no added sulfur(phase I), the reactor after adding sulfur electron acceptor(phase II) was demonstrated more robust in nitrogen removal in the case of low C/N influent. the effluent ammonia nitrogen concentration of the aerobic reactor in Pahse II was kept lower than 1.844 mg N / L after day 40 and the overall concentration of total phosphorus in phase II (0.35 mg P/L) was lower than that of phase I(0.76 mg P/L). The microbial community analysis indicates that Rhodanobacter, Bacteroidetes, and Thiobacillus, which were the predominant bacteria in the reactor, may play a crucial role in inorganic nitrogen removal, complex organic degradation, and autotrophic denitrification under the stress of low carbon and nitrogen ratios. This leads to the formation of a distinctive microbial community structure influenced by the sulfur electron receptor and its composition. This study contributes to further development of urban low-carbon-nitrogen ratio wastewater efficient and low-cost wastewater treatment technology.

A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant

Wastewater treatment plant (WWTP) operation is usually intricate due to large variations in influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTP effluent water quality can provide valuable decision-making support to facilitate their operations and management. In this study, we developed a novel hybrid deep learning model by combining the temporal convolutional network (TCN) model with the long short-term memory (LSTM) network model to improve the simulation of hourly total nitrogen (TN) concentration in WWTP effluent. The developed model was tested in a WWTP in Jiangsu Province, China, where the prediction results of the hybrid TCN-LSTM model were compared with those of single deep learning models (TCN and LSTM) and traditional machine learning model (feedforward neural network, FFNN). The hybrid TCN-LSTM model could achieve 33.1 % higher accuracy as compared to the single TCN or LSTM model, and its performance could improve by 63.6 % comparing to the traditional FFNN model. The developed hybrid model also exhibited a higher power prediction of WWTP effluent TN for the next multiple time steps within eight hours, as compared to the standalone TCN, LSTM, and FFNN models. Finally, employing model interpretation approach of Shapley additive explanation to identify the key parameters influencing the behavior of WWTP effluent water quality, it was found that removing variables that did not contribute to the model output could further improve modeling efficiency while optimizing monitoring and management strategies.

A Comparative Study to Forecast the Total Nitrogen Effluent Concentration in a Wastewater Treatment Plant Using Machine Learning Techniques

A Comparative Study to Forecast the Total Nitrogen Effluent Concentration in a Wastewater Treatment Plant Using Machine Learning Techniques

Adding Corbicula fluminea altered the effect of plant species diversity on greenhouse gas emissions and nitrogen removal from constructed wetlands in the low-temperature season

Plant species diversity is crucial in greenhouse gas emissions and nitrogen removal from constructed wetlands (CWs). However, previous studies have overlooked the impact of benthos on cumulative greenhouse gas emissions during the low-temperature season in CWs. In this study, we established 66 vertical flow CWs with three levels of species richness (1, 2, and 4 species) and eleven species compositions. The Corbicula fluminea was added or not added at each diversity level and monitored greenhouse gas emissions and effluent nitrogen concentration. Our findings indicated that (1) in microcosms without C. fluminea, high species richness significantly increased effluent nitrogen concentrations (NO3−-N, NH4+-N, and TIN), but plant species richness did not affect cumulative CH4, N2O, and CO2 emissions. The presence of Hemerocallis fulva significantly increased cumulative CO2 emissions, while the presence of Iris tectorum significantly increased effluent nitrogen (NO3−-N and TIN) concentrations and cumulative N2O emissions; (2) in microcosms with C. fluminea, the lowest cumulative CH4 emissions occurred when there were two species, but plant species richness did not affect cumulative CO2 and N2O emissions. The presence of H. fulva significantly increased cumulative CH4 emissions, while the presence of Reineckea carnea significantly increased effluent nitrogen (NO3−-N, NH4+- N, TIN) concentrations; (3) at the same diversity level, the addition of C. fluminea significantly increased cumulative CH4 and N2O emissions, as well as effluent nitrogen concentrations. These results demonstrate that C. fluminea alters the effect of plant species diversity on cumulative greenhouse gas emissions and nitrogen removal from CWs during the low-temperature season. We recommend using a two-species mixture to reduce greenhouse gas emissions. However, we caution against using plant compositions with H. fulva or I. tectorum for effective wastewater treatment and greenhouse gas reduction in CWs.

Application of alkali-heated corncobs enhanced nitrogen removal and microbial diversity in constructed wetlands for treating low C/N ratio wastewater.

Lack of carbon source is the main limiting factor in the denitrification of low C/N ratio wastewater in the constructed wetlands (CWs). Agricultural waste has been considered as a supplementary carbon source but research is still limited. To solve this problem, ferric carbon (Fe-C) + zeolite, Fe-C + gravel, and gravel were used as substrates to build CWs in this experiment, aiming to investigate the effects of different carbon sources (rice straw, corncobs, alkali-heated corncobs) on nitrogen removal performance and microbial community structure in CWs for low C/N wastewater. The results demonstrated that the microbial community and effluent nitrogen concentration of CWs were mainly influenced by the carbon source rather than the substrate. Alkali-heated corncobs significantly enhanced the removal of NO2--N, NH4+-N, NO3-N, and TN. Carbon sources addition increased microbial diversity. Alkali-heated corncobs addition significantly increased the abundance of heterotrophic denitrifying bacteria (Proteobacteria and Bacteroidota). Furthermore, alkali-heated corncobs addition increased the copy number of nirS, nosZ, and nirK genes while greenhouse gas fluxes were lower than common corncobs. In summary, alkali-heated corncobs can be considered as an effective carbon source.

Modeling the effects of plant uptake dynamics on nitrogen removal of a bioretention system

Bioretention systems are widely used to remove nutrients from urban runoff. Plants play a key role in the nitrogen removal performance of bioretention systems; however, few model studies have focused on the effects of plant uptake dynamics on the performance. In this study, we propose an eco-hydrological model of bioretention systems by coupling plant, hydrological, and nitrogen modules. The eco-hydrological model was verified using observed data on biomass, plant nitrogen uptake, hydrological performance, and effluent nitrogen concentrations of a bioretention system planted with Canna indica L. in Shenzhen, China. The validated model was used to evaluate the effects of seasonal or interannual variation of plant nitrogen uptake on nitrogen removal performance of the bioretention system. The results indicated that (i) The model is able to explicitly describe plant dynamics and nitrogen transformation processes of the system, and the Nash Sutcliffe Efficiency coefficients of biomass, monthly plant nitrogen uptake, and effluent nitrogen concentrations were all greater than 0.6; (ii) The plant nitrogen uptake is sensitive to most parameters in the plant module; The effluent nitrogen concentrations are sensitive to decomposition constant of plant residues (kres) during most months of the year and also sensitive to optimal nitrogen content at the germination (bn1) and optimal nitrogen content at the maturity (bn2) during the months with low influent nitrogen concentrations; and (iii) The seasonal and interannual variations in plant nitrogen uptake significantly affect nitrogen removal efficiencies, especially during rainy seasons. Therefore, the model can consider the influence of plant nitrogen uptake on the long-term nitrogen removal performance of bioretention systems.

Model-based assessment of mainstream nitrate/nitrite-dependent anaerobic methane oxidation and Anammox process in granular sludge at low temperature

The process of nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) coupled with anaerobic ammonium oxidation (Anammox) is one of groundbreaking discoveries for nitrogen removal and methane emission reduction from wastewater simultaneously. Yet its treatment of mainstream wastewater at low temperature is still a major challenge. In this work, a one-dimensional granular sludge model incorporating Arrhenius conversion for temperature effects was constructed to depict the relationships among n-DAMO microorganisms and Anammox. The model framework was successfully evaluated with 380 days measurement data from a membrane granular sludge reactor (MGSR) operated at temperature of 20–10 °C and fed with ammonium and nitrite. The model could satisfactorily predict the kinetics of nitrogen removal rates, effluent nitrogen concentrations and biomass fractions in MGSR at varying temperatures. Despite the decrease in microbial activity of functional microorganisms, the coupled n-DAMO and Anammox process based on granule system in mainstream wastewater treatment achieved a TN removal efficiency of about 98 % and a stable nitrogen removal rate of 0.55 g L−1 d−1. The model developed is expected to facilitate fundamentally understanding the underlying mechanisms of the coupled process and provide proposals for its practical engineering application in wastewater treatment plants.

Nitrogen removal performance in roadside stormwater bioretention cells amended with drinking water treatment residuals.

Bioretention cells, a type of green stormwater infrastructure, have been shown to reduce runoff volumes and remove a variety of pollutants. The ability of bioretention cells to remove nitrogen and phosphorus, however, is variable, and bioretention soil media can act as a net exporter of nutrients. This is concerning as excess loading of nitrogen and phosphorus can lead to eutrophication of surface waters, which green stormwater infrastructure is intended to ameliorate. Drinking water treatment residuals (DWTR), metal (hydr)oxide-rich by-products of the drinking water treatment process, have been studied as an amendment to bioretention soil media due to their high phosphorus sorption capacity. However, very few studies have specifically addressed the effects that DWTRs may have on nitrogen removal performance within bioretention cells. Here, we investigated the effects of DWTR amendment on nitrogen removal in bioretention cells treating stormwater in a roadside setting. We tested the capacity of three different DWTRs to either retain or leach dissolved inorganic nitrogen in the laboratory and also conducted a full-scale field experiment where DWTR-amended bioretention cells and experimental controls were monitored for influent and effluent nitrogen concentrations over two field seasons. We found that DWTRs alone exhibit some capacity to leach nitrate and ammonium, but when integrated into sand- and compost-based bioretention soil media, DWTRs have little to no effect on the removal of nitrogen in bioretention cells. These results suggest that DWTRs can be used in bioretention media for enhanced phosphorus retention without the risk of contributing to nitrogen export in bioretention effluent.

Effects of Regional Climate and BMP Type on Stormwater Nutrient Concentrations in BMPs: A Meta-Analysis.

Nutrient treatment performance of stormwater best management practices (BMPs) is highly variable. Improved nutrient management with BMPs requires a better understanding of factors that influence stormwater BMP treatment processes. We conducted a meta-analysis of vegetated BMPs in the International Stormwater BMP Database and compared influent and effluent nitrogen and phosphorus concentrations to quantify the BMP effect on nutrient management across climates. BMP effect on nutrient concentration change was compared between vegetated BMPs in wet and dry climates. We examined paired dissolved inorganic nitrogen (DIN), total nitrogen (TN), dissolved inorganic phosphorus (DIP), total phosphorus (TP), and combinations of these analytes as dissolved inorganic ratios and N:P ratios. Meta-analysis with subgroup analysis was used to determine differences between wet and dry climates and among vegetated BMP types. We found that across both wet and dry climates, BMPs leach DIP and TP, increase the fraction of dissolved inorganic P (DIP:TP), and decrease dissolved N:P ratios. Dry-climate BMPs leach DIP and TP more consistently and at a higher magnitude than wet-climate BMPs, and bioretention leaches more DIP than grass strips and swales. These findings generally align with biogeochemical cycling, differences in influent chemistry, and BMP design types and goals.

Evolving Deep Delay Echo State Network for Effluent NH4-N Prediction in Wastewater Treatment Plants

In wastewater treatment plants (WWTPs), the prediction of effluent ammonia nitrogen (NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -N) concentration is vital, which is a major cause of lake eutrophication. To solve this problem, the evolving deep delay echo state network (EDDESN) is proposed. Firstly, the EDDESN is decomposed into several serially connected sub-reservoirs, which are inserted delay units to learn the temporal relationships within sequence data. Secondly, the input and reservoir internal weights are generated by singular value decomposition-based matrix design strategy, which can reduce searching dimensions and guarantee the echo state property (ESP). Moreover, the architecture hyperparameters and weights of EDDESN are simultaneously optimized by competitive swarm optimizer (CSO) based two-stages optimization approach. Finally, the experimental results on practical NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -N dataset and simulated Mackey-Glass time series demonstrate the superiority of EDDESN as compared with other time series prediction approaches.

A shallow constructed wetland combining porous filter material and Rotala rotundifolia for advanced treatment of municipal sewage at low HRT.

Water scarcity is a worldwide problem. Recycled municipal wastewater is considered a useful alternative to the conventional types of water resources. In this study, a shallow constructed wetland (SCW) with porous filter material and Rotala rotundifolia was used for advanced municipal sewage treatment. The wetland without plant was set as the control (SCW-C). The pollutant removal performance of the system at different hydraulic retention times (HRTs) was investigated. The diversity of the microbial community was analyzed, and the fate of nutrients, mainly N and P, in the system was discussed. Results showed that SCW was efficient in pollutant removal. Effluent concentrations of chemical oxygen demand (COD), total phosphorus (TP), and ammonium nitrogen (NH4+-N) were 15.0-23.6, 0.19-0.28, and 0.83-1.16mg/L, separately, with average removal efficiencies of 61.2%, 46.3%, and 88.1% at HRT 18h, which met the requirements of type [Formula: see text] water set by the environmental quality standards for surface water in China. The richness and evenness of the bacterial community were significantly higher in the plant-rooted SCW. They increased along with the system. The dominant genera in the system were phosphate-solubilizing bacteria, nitrifying bacteria, and denitrifying bacteria. The P in the influent mainly flowed to the substrate and plant. At the same time, most N was removed by nitrification and denitrification. These findings suggested that the SCW could remove pollutants from the municipal sewage effluent and meet the standard requirement at low HRT.

Metagenomic and Metatranscriptomic Analysis of Nitrogen Removal Functional Microbial Community of Petrochemical Wastewater Biological Treatment Systems

Petrochemicals are one of the pillar industries of China. Despite this, the treatment of petrochemical wastewater has long been seen as a massive challenge in the field of water pollution control, hindering the high-quality and sustainable development of the petrochemical industry. The majority of petrochemical enterprises and zones are located near rivers or seas, so their wastewater discharges can easily cause watershed or regional water ecological risks. Specifically, nitrogen pollution in petrochemical wastewater poses a significant threat to water ecological safety and human health. Sludge samples were collected from a petrochemical wastewater A/O nitrogen removal process line in a chemical industry zone in Shanghai. Metagenomic and metatranscriptomic methods were used to analyze the community structure of microorganisms, the functional characteristics of nitrogen removal bacteria, and the key nitrogen metabolism pathways in different sludges during the period when effluent water quality was stable and fluctuating. During the study, it was found that the nitrite and nitrate removal was relatively stable in this process, but ammonia oxidation fluctuated easily. In the study of microbial communities, it was found to be a nitrification-denitrification pathway that primarily removed nitrogen from the A/O process, and no genes related to ANAMMOX were detected. Approximately 90% of the functional genes responsible for removing nitrogen were responsible for denitrification, whereas only 0.17% of them were involved in the conversion of ammonia nitrogen in the nitrification process. Moreover, the abundance of ammonia-oxidizing bacteria in the process was extremely low, and the main genus was Nitrosomonas. It is likely that this is the main cause of fluctuations in ammonia nitrogen concentration in effluent due to water quality shocks in the process line.

Investigation of an intermittently-aerated moving bed biofilm reactor in rural wastewater treatment under low dissolved oxygen and C/N condition

An intermittently-aerated moving bed biofilm reactor (MBBR) was proposed for nitrogen and carbon removal from low C/N synthetic rural wastewater. In purposes of low energy consumption and costs, the intermittent aeration modes were changed and the dissolved oxygen was reduced gradually during the operation. The results showed that effluent concentrations of ammonia nitrogen and chemical oxygen demand were lower than 15 and 50 mg/L, respectively, even under microaerobic condition (0.1–1.0 mg/L). Meanwhile, the simultaneous nitrification–denitrification was achieved by intermittent aeration. The activity of functional bacteria was still high and the proportion of autotrophic biomass increased significantly under intermittent micro-aeration mode, which improved the nitrification performance. Aerobic denitrifier Hydrogenophaga, anoxic denitrifier Thiothrix, and heterotrophic nitrifier such as Rhodobacter were enriched in the intermittently micro-aerated MBBR, which will provide an applicable solution for rural wastewater treatment under low C/N and costs.

Membrane Photobioreactor Applied for Municipal Wastewater Treatment at a High Solids Retention Time: Effects of Microalgae Decay on Treatment Performance and Biomass Properties.

Membrane photobioreactor (MPBR) technology is a microalgae-based system that can simultaneously realize nutrient recovery and microalgae cultivation in a single step. Current research is mainly focused on the operation of MPBR at a medium SRT. The operation of MPBR at a high SRT is rarely reported in MPBR studies. Therefore, this study conducted a submerged MPBR to treat synthetic municipal wastewater at a long solids retention time of 50 d. It was found that serious microalgae decay occurred on day 23. A series of characterizations, including the biomass concentration, chlorophyll-a content, nutrients removal, and physical-chemical properties of the microalgae, were conducted to evaluate how microalgae decay affects the treatment performance and biomass properties. The results showed that the biomass concentration and chlorophyll-a/MLSS dropped rapidly from 3.48 to 1.94 g/L and 34.56 to 10.71 mg/g, respectively, after the occurrence of decay. The effluent quality significantly deteriorated, corresponding to the total effluent nitrogen and total phosphorus concentration sharply rising and exceeding that of the feed. In addition, the particle became larger, the content of the extracellular polymeric substances (EPSs) decreased, and the soluble microbial products (SMPs) increased instantaneously. However, the filtration resistance had no significant increase because of the comprehensive interactions of the floc size, EPSs, and SMPs. The above results suggest that the MPBR system cannot maintain long-term operation under a high SRT for municipal wastewater treatment. In addition, the biological treatment performance of the MPBR deteriorated while the antifouling performance of the microalgae flocs improved after the occurrence of decay. The occurrence of microalgae decay was attributed to the double stresses from the light shading and intraspecific competition under high biomass concentration. Therefore, to avoid microalgae decay, periodic biomass removal is required to control the environmental stress within the tolerance range of the microalgae. Further studies are required to explore the underlying mechanism of the occurrence of decay.

Species identity but not richness affects effluent nitrogen, phosphorus, and potassium concentrations and the ratios in floating-constructed wetlands.

The nutrient ratio in wastewater discharge has a variety of ecological impacts on aquatic ecosystems. Plant species richness and identity (the presence of certain species in the community) affected the nitrogen (N), phosphorus (P), and potassium (K) removal efficiencies in constructed wetlands (CWs). However, the effects on the ratios of N/P/K are still unknown. This study conducted microcosms simulating floating CWs to explore the effects on these nutrient removal efficiencies and ratios. Results showed that (1) the presence of Canna indica decreased but the presence of Arundo donax increased effluent P and K concentrations, plant richness had no effect on effluent nutrients concentrations; (2) plant species richness only decreased the effluent P:K ratio but no effect on other effluent nutrient ratios; (3) the presence of C. indica increased but the presence of A. donax decreased effluent N:P and N:K ratios; (4) the presence of C. indica increased plant N, P, K pools through increasing plant biomass but the presence of A. donax decreased plant N, P, K pools through decreasing plant biomass. Overall, species identity surpassed species richness in affecting effluent nutrient concentrations and ratios. Assembling proper species composition could decrease effluent P and K concentrations and regulate effluent N/P/K ratios.

The impact of bioretention column internal water storage underdrain height on denitrification under continuous and transient flow

Internal water storage (IWS), a below-grade saturated layer, is a bioretention design component created by adjusting the underdrain outlet elevation. Anaerobic conditions and the presence of a carbon source in IWS facilitates denitrification. Yet it remains unclear how underdrain height within the IWS impacts nitrate (NO3−) removal. This study applied synthetic stormwater with NO3− to three laboratory columns with underdrains located at the bottom, middle, or top of a 32 cm thick gravel-woodchip IWS. Under steady state conditions, underdrain nitrogen removal demonstrated a positive linear relationship with increasing hydraulic residence time (HRT). For a 1 cm/h hydraulic loading rate (HLR), nitrogen removal efficiency increased from 52 to 99% as underdrain height moved from the top to the bottom. Despite identical IWS thickness across columns, immobilize zones below the middle and top underdrains limited the steady state nitrogen removal. Dual isotopes in NO3− also indicated denitrification occurred in mobile zones and showed little or no denitrification in immobile zones due to limited mass transport. Transient flow conditions were applied, to mimic storms, followed by dry conditions. Lower effluent nitrogen concentrations and mass fluxes were observed from the bottom underdrain across the range of HLRs tested (1 to 5 cm/h) but performance of all three underdrains converged after the application of one pore volume. The top underdrain enhanced mixing between new incoming low-DOC stormwater and old IWS water with high-DOC which minimized effluent DOC concentrations. NO3− isotope enrichment factors indicated denitrification during transient flow for all three underdrain heights and enrichment increased for the 5 cm/h HLR. For sites with narrow IWS geometries (width to depth ratio < 1), optimal underdrain height is likely located between the bottom and top of the IWS to promote mixing with old IWS water high in DOC and sustain denitrification during storms.

Effluent ammonia nitrogen prediction using a phase space reconstruction method combining pipelined recurrent wavelet neural network

In wastewater treatment processes (WWTPs), the effluent ammonia nitrogen (NH4-N) concentration is a significant index to measure effluent quality. Recently, the soft computing methods have been widely used to measure effluent NH4-N concentration. However, the performance of soft computing method is closely related with its input variables, which is difficult to choose. As an alternative, the time series prediction method PSR-PRWNN is proposed, which combines phase space reconstruction (PSR) technique and pipelined recurrent wavelet neural network (PRWNN). Different from soft computing methods, the time series prediction method is a method which predicts the effluent NH4-N concentration by using its history data rather than other variables data. Firstly, the chaotic characteristics of effluent NH4-N time series is proved by using the correlation dimension method. Based on chaotic characteristics, the phase space of effluent NH4-N concentration is reconstructed by PSR technique. Then, the relationship model between inputs and output of the reconstructed phase space is established by PRWNN. Thirdly, the parameters of PRWNN are trained by an online gradient algorithm with adaptive learning rates. Finally, the experimental results indicate that the PSR-PRWNN can obtain better training results and prediction accuracy than other algorithms.