Event Mean Concentrations Research Articles

Regional heterogeneity and driving factors of road runoff pollution from urban areas in China.

Due to the multiple influences of natural and anthropogenic factors, stormwater runoff from urban roads generally presents heterogeneous pollution among cities. The identification of regional heterogeneity and related driving factors of road runoff pollution is of significance for the optimal management of road runoff pollution according to the local circumstances. In this study, the regional heterogeneity of urban road runoff pollution from fourteen representative cities in China is analyzed for four typical pollutants including total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). The results show wide variations in TSS, COD, TN and TP pollution among cities, with the average event mean concentrations ranging from 77.0 to 1347.9, 31.4 to 488.1, 0.81 to 8.46, 0.139 to 1.930mg/L, respectively. One-way ANOVA analyses demonstrate significant differences in road runoff pollution among cities. The TSS pollution is significantly heavier for northern and northwestern inland cities than that for eastern and southern cities. Pearson correlation analysis and Stepwise linear regression analysis are performed to identify and rank the influence of climate, population, economy, industry structure, traffic and environmental quality. Direct relationships of road runoff pollution are detected with PM2.5, PM10, secondary industry, tertiary industry, annual rainfall, and urban green coverage, among which PM10 and urban green coverage are the most important and common factors exerting positive and negative influences on road runoff pollution, respectively. Based on the findings of this work, improvement of atmospheric particulate pollution and increase in urban greenness are recommended measures to manage the road runoff pollution. Furthermore, the traffic-related emissions accompanying the upgrading of industry structure should be effectively controlled to attenuate the TSS and COD pollution in road runoff.

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.

The effects of antecedent dry days and land use types on urban runoff quality in a semi-Arid region

ABSTRACT This study simulated the effects of land-use changes on runoff pollution in a catchment of Tehran, Iran. The catchment has a semi-arid climate, a relatively long period from dry summer to rainy autumn and highly varying precipitation levels. Urban runoff samples were collected from six stations including five different land-based activities and a mixed land-use, which supported all land-use types. Event Mean Concentration (EMC) was applied to determine the Nonpoint Source (NPS) pollution of urban runoff such as Total Suspended Solid (TSS), Total Nitrogen (TN), Total Phosphorus (TP) and heavy metals, including zinc (Zn), copper (Cu) and lead (Pb), in five land-uses. Sampling was performed during five events with different Antecedent Dry Days (ADDs) in time period 2019-2020. The Storm Water Management Model (SWMM) was employed to simulate the quality of urban runoff base on measured EMCs in each event for the different parameters under investigation and the average of measured EMCs. The results illustrated that that the maximum and minimum amount of EMCs were pertained to the events with 115 and 1 dry days respectively. While, daily simulation of runoff pollution based on measured amount of EMCs in the catchment indicated that the calibration criteria such as Root Mean Square Error (RMSE), Normalized Objective Function (NOF), Nash-Sutcliffe Efficiency (NSE) and R 2 were very good for the minimum amount of EMCs and satisfactory for the average of EMC`s calculated, NSE values was not satisfactory for the maximum amount of EMCs. Uncertainty of ADDs can significantly affect the results of the maximum EMC simulation. This study also found higher TSS and TP concentrations in runoff in open space land-use and significantly higher heavy metals and TN concentrations in runoff in the industrial land-use compared with other land-use types in the catchment.

A decade of monitoring micropollutants in urban wet-weather flows: What did we learn?

Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true" value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.

Assessment of Diffuse Pollution Loads in Peri-Urban Rivers—Analysis of the Accuracy of Estimation Based on Monthly Monitoring Data

Diffuse pollution loads are crucial information for water resource management, and yet field data are often scarce, implying questionable accuracy in load estimates made from low-frequency water quality monitoring. This paper aimed to characterize diffuse pollution in a stream of a mixed-land-cover watershed with a significant portion of urbanized areas through intensive monitoring and to perform a comparative analysis between the loads estimated by pollutant rating curves obtained by regression and the estimates using monthly water quality data, which is the method currently used. Continuous rainfall and flow monitoring was conducted between 2019 and 2021, and samples were collected during flood events and the dry period for water quality analysis. Flood events were found to induce an increase in suspended solids (TSS) and COD concentrations, while inorganic nitrogen (Inorg-N) concentrations were higher in the dry season. Flood characteristics showed a positive correlation with solids and COD event mean concentrations (EMCs) and negative with Inorg-N EMCs, while rainfall characteristics, such as antecedent dry days and intensity, correlate positively with all these pollutants. The rating curves performed well for total load estimation in low discharge events (R2 and NSE > 0.8), except for total phosphorus (TP) loads. Estimated annual unit loads found for the watershed were 2 ton TSS/ha.year, 300 kg COD/ha.year, 5 kg Inorg-N/ha.year, and 0.5 kg TP/ha.year, showing high pollution generated in the watershed. Finally, a comparison with estimates based on monthly monitoring data indicated that this method is sufficient for accurate nutrient loads, but not for TSS and COD loads, which require continuous monitoring to improve the accuracy of estimation.

Bioretention Systems Optimization and Design Characterization Model Using Fuzzy Rough Set Theory

Urban stormwater has become a persistent concern on a global scale due to its adverse environmental implications. It is the prime vector of aquatic contaminants worldwide that causes pollutants when water bodies drain. Bioretention systems are increasingly used to alleviate setbacks associated with stormwater run-off in urban locales. It has played a substantial role in the implementation of low impact development (LID), a concept that addresses urban stormwater problems caused by land changes and development. The use of LID technologies is an innovative approach. However, it is beset with challenges, such as the insufficiency of data on rainfall distribution and difficulty in interpreting data. To address these research gaps, the present study developed a fuzzy rough set data algorithm for bioretention systems. Event mean concentration calculations and fuzzification of rainfall were performed to produce a rough set-based decision rule. Using the Weibull probability distribution, fuzzification of rainfall and parameter data, rule induction, and Preece testing, bioretention design considerations were determined. The bioretention characterizations generated evident pollutants present in the catch basin before and after filtration. In addition, the bioretention characterization conducted in this study was able to reduce the number of tests needed for rainfall identification based on the different attributes.

Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China

Climate change and rapid urbanization have made it difficult to predict the risk of pollution in cities under different types of rainfall. In this study, a data-driven approach to quantify the effects of rainfall characteristics on river pollution was proposed and applied in a case study of Shiyan River, Shenzhen, China. The results indicate that the most important factor affecting river pollution is the dry period followed by average rainfall intensity, maximum rainfall in 10 min, total amount of rainfall, and initial runoff intensity. In addition, an artificial neural network model was developed to predict the event mean concentration (EMC) of COD in the river based on the correlations between rainfall characteristics and EMC. Compared to under light rain (< 10 mm/day), the predicted EMC was five times lower under heavy rain (25–49.9 mm/day) and two times lower under moderate rain (10–24.9 mm/day). By converting the EMC to chemical oxygen demand in the river, the pollution load under non-point-source runoff was estimated to be 497.6 t/year (with an accuracy of 95.98%) in Shiyan River under typical rainfall characteristics. The results of this study can be used to guide urban rainwater utilization and engineering design in Shenzhen. The findings also provide insights for predicting the risk of rainfall-runoff pollution and developing related policies in other cities.

Analysis of Pollution Characteristics and Sources of Rainfall Runoff from Roofs in the Central District of Beijing

The rainwater and rainfall runoff of roofs in the central district of Beijing from June to September in 2019 were sampled and analyzed to study the characteristics of the water quality, the first flush effect, and the main influential factors and sources of pollutants. The results showed that the roof runoff was seriously polluted by total nitrogen, ammonia nitrogen, chemical oxygen demand, and total suspended solids whose event mean concentration (EMC) exceeded the fifth level of environmental quality standards for surface water (GB 3838-2002) (the EMC of suspended solids exceeded the second level of discharge standard of pollutants for municipal wastewater treatment plants (GB 18918-2002)). The rainwater was relatively less polluted than the rainfall runoff, but the EMC of ammonia nitrogen and total nitrogen of the rainwater also exceeded the standard in some rainfall events. The first flush intensity of the rainfall runoffs was between weak and medium. The sequence of strength of the first flush of different pollutants was ammonia nitrogen>total suspended solids>chemical oxygen demand>total nitrogen>mercury>zinc>total phosphorus>lead. The concentration of total suspended solids, chemical oxygen demand, and total phosphorus in roof runoff were significantly positively correlated with the length of rainfall and the dry period and negatively correlated with the rainfall intensity. According to the results of principal component analysis, the main pollutant in rainwater was nitrogen emitted by vehicles, and the main pollutants in roof runoffs were suspended solids, organic matters, and phosphorus pollutants released from the aging of roofing materials and the corrosion of metal down pipes.

Event mean concentration and first flush from residential catchments in different climate zones

Most studies on EMC (Event mean concentration) and first flush are reported as local studies; however variations of EMC and first flush across catchments in different climate zones has not been studied. This research collected continuous flow and discrete water quality data and rainfall measurements from 17 catchments, EMC and rainfall data from 14 catchments, and an additional dataset where only average EMC values are reported (19 catchments). The data are from residential sites across temperate, tropical, dry, and continental climate zones and include water quality parameters in particulate (total suspended solids), mixed (total nitrogen and total phosphorus) and dissolved (orthophosphate and ammonium nitrogen) forms. Our study shows that EMC differs significantly between climate zones. The average EMC is highest in dry followed by continental and temperate, with lowest in the tropical zone. Pearson's correlation analysis revealed that the rainfall depth is negatively correlated with EMC for particulate and mixed form parameters for the tropical, temperate and dry zones, but positively correlated for the continental zone. The discrete time-series data from the 17 catchments were used to evaluate first flush and it was found that catchments in the tropics exhibit stronger first flush than temperate zone catchments, for all the water quality parameters with particulate showing a stronger first flush compared to dissolved forms. Based on the distribution of the data, new limits for very strong, strong, moderate, and very weak to no first flush are suggested for TSS for different climate zones. The new limits were quantified by fitting the function L^=V^bwhere L^and V^are the normalized cumulative runoff load and volume, respectively. For catchments in the tropics, this corresponds to b < 0.47, 0.6 > b > 0.47, 0.76 > b > 0.6 and b > 0.76, respectively. For the temperate zone, b < 0.5, 0.67 > b > 0.5, 0.85 > b > 0.67 and b > 0.85, are appropriate. From a design perspective, the FF20 concept defined as the load corresponding to 20% runoff volume, is often used. The ranges FF20 < 0.27, 0.36 > FF20 > 0.27, 0.45 > FF20 > 0.36 and FF20 > 0.45 and FF20 < 0.24, 0.31 > FF20 > 0.24, 0.31 > FF20 > 0.39 and FF20 > 0.39 are proposed for tropical and temperate catchments, respectively. Other limits for TP, TN, OP and NH4-N are also suggested. This is the first study of its kind and an expended dataset especially for continental and dry regions is needed to further validate the findings.

Nitrogen Treatment by a Dry Detention Basin with Stormwater Wetland Characteristics

Dry detention basins (DB) are commonly used to reduce the rate of runoff in urban areas and may provide open space for recreation between storms. However, most are not effective at nitrogen removal in comparison to other measures, such as constructed wetlands. The study goal was to assess the nitrogen treatment efficiency of a DB that exhibited some wetland characteristics, including saturated soil near the inlet and wetland vegetation that covered 40% of the surface area. Influent and effluent samples were collected during multiple stages of eight storm events for nitrogen concentration analyses. High-frequency water stage, pH, dissolved oxygen (DO), and temperature loggers were deployed at the inlet and outlet prior to anticipated rain. As stormwater passed through the DB, the event mean concentrations (EMCs) and masses of TN declined by 20.7% and 52.3%, respectively, while the DO and pH dropped by 62% and 20.5%, respectively. Load reductions of TN exceeding 93% were observed during two small storms with rain depths of less than 0.16 cm and when the outflow volumes were reduced by greater than 82%. Temperature was significantly correlated (p &lt; 0.001; r = 0.964) with volume reductions (via infiltration and evapotranspiration), and, thus, the treatment was better during warmer periods. The DB was effective at removing inorganic nitrogen, likely via nitrification, denitrification, and immobilization, but frequently exported higher EMCs of organic nitrogen. Overall, the DB exceeded the 10% TN removal expectation for dry basins. The findings from this study suggest that the TN treatment efficiency of DBs may be improved by incorporating wetland characteristics.

The Effect of Land-Use Changes on the Amount of Heavy Metal Pollution in Urban Runoff in Tehran

Background &amp; Aims of the Study: Urban development trends and land-use changes harm the quality of urban runoff. Heavy metals are one of the most important pollutants in urban runoff. This study aimed to investigate the amount of heavy metal pollutants (Zn, Pb, Cu, As, Hg, and Cd) in different land uses in a densely populated urban area of Tehran. Materials and Methods: Six stations were selected for urban runoff sampling in five land uses. Land uses were residential, commercial, industrial, traffic, and outdoor. A station (sixth station) with mixed land use was selected, which included the runoff of all uses. The event mean concentration (EMC) model was used to estimate this study’s heavy metal pollution load. Results: The amount of all heavy metals except Arsenic in fields with industrial use was higher than other land uses (Cu=0.292 mg/l), (Pb=0.6166 mg/l), (Zn=1.36 mg/l), (Cd=0.0114 mg/l), and (Hg=0.1332 mg/l). While the amount of Arsenic metal in the station with outdoor land use (AS=0.111 mg/l) was the highest. Conclusion: The results of this study indicate that the risk of metals on long dry days without precipitation is very high. Uncertainties related to the accumulation of pollution and various human activities can be attributed to the high amount of heavy metals in the mixed land use compared to land use alone

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.

Assessing major drivers of runoff water quality using principal component analysis: a case study from a Colombian and a Brazilian catchments

ABSTRACT Understanding the major drivers of runoff water quality in urban areas is important for sustainable water management. In this study, runoff water quality in a catchment in Colombia and another in Brazil was monitored and related to rain, and meteorological variables through correlation and principal component analysis. High correlations were found between event mean concentration of total suspended solids (TSS) and total phosphorus (TP), as well as with total Kjeldahl nitrogen (TKN) and total organic carbon (TOC). The results indicated that TSS and TP were related to sediment transport in the catchment, while NTK and TOC were derived from point sources (improper wastewater disposal). Rain depth, maximum specific discharge, air temperature, and solar radiation were the main factors explaining variability of water quality. These results contribute to a better understanding of local processes driving runoff water quality, and bring valuable insights for modelling and management of stormwater in urban catchments.

Efficient Urban Runoff Quantity and Quality Modelling Using SWMM Model and Field Data in an Urban Watershed of Tehran Metropolis

This study aims to calibrate and validate the EPA Storm Water Management Model from field measurements of rainfall and runoff, in order to simulate the rainfall-runoff process in an urban watershed of Tehran metropolis, Iran. During and after three significant storm events, the flow rates, total suspended solids (TSS), total phosphorus (TP), and total Kjeldahl nitrogen (TKN) concentrations were measured at the outlet of the catchment, and were used in the model calibration and validation process. The performance of the SWMM model was evaluated based on the statistical criteria, as well as graphical techniques. In this study, a local sensitivity analysis was carried out to identify the key model parameters, show that “the percentage of impervious surface in each subwatershed had the most effect on the model output”. Based on the analysis of the results, SWMM model calibration and validation can be judged as satisfactory, and the goodness-of-fit indices for simulating runoff quality and quantity are placed in acceptable ranges. The adjustment obtained for the variations in the measured and simulated flow rates, pollutograph concentrations, total pollutant load, peak concentration, and the event mean concentration (EMC) confirms the considerable predictive capability of the SWMM model when it is well calibrated by using field measurements.

Hydrology and runoff water quality from three improved pastures compared with virgin brigalow (

The Fitzroy Basin in central Queensland has the largest cattle herd of any natural resource management region in Australia, and legumes have been widely used to boost fertility of rundown soil and improve cattle liveweight gains. However, there is a paucity of information on the effect of leguminous pastures on hydrology and water quality. This study investigated runoff water quality over eight hydrological years from virgin brigalow (Acacia harpophylla) woodland and three improved pastures, namely, buffel grass (Pennisetum ciliare), butterfly pea (Clitoria ternatea) and leucaena (Leucaena leucocephala). Runoff event mean concentrations and loads of total and dissolved nitrogen, phosphorus, and carbon in addition to total suspended solids are reported. Brigalow woodland had the greatest loss of sediment and nitrogen attributed to the inherently fertile Vertosols (clay soil), but the low occurrence and amount of runoff meant that it had a low risk to water quality. Despite a similar number of runoff events from the improved pastures, leucaena pasture had less total runoff and a lower maximum peak runoff rate in addition to lower nitrogen and carbon in runoff. Total suspended solids and carbon in runoff were greater from grass pasture than from the leguminous pastures, whereas nitrogen and phosphorus were greatest from the butterfly pea pasture, especially in the first 2 years post-planting. Greater exports of phosphorus from the improved pastures were concerning, given the potential for downstream impacts.

Integrating Tank Model and adsorption/desorption characteristics of filter media to simulate outflow water quantity and quality of a bioretention basin: A case study of biochar-based bioretention basin

Reliable approaches for accurately assessing the performance of stormwater treatment systems is essential for their effective design, including filter media selection which can be a significant constituent in stormwater treatment systems. This study presents an innovative modelling approach integrating the Tank Model with the adsorption-desorption characteristics of the filter media. The resulting modelling approach was applied to simulate a field-scale bioretention basin where biochar was used as filter media with over ten years of rainfall records. The resulting outflow and overflow volumes were compared with observed data for calibration. The Stormwater Treatment Tank Model (STTM) was validated using the Leave-One-Out-Cross-Validation (LOOCV) method. The simulation outcomes include water outflow and overflow (quantity) from the bioretention basin as well as outflow water quality represented by three heavy metals (Pb, Cu, and Zn). The modelling approach developed was found to be capable of accurately simulating outflow and overflow volumes, with outlet water quantity being significantly influenced by the total rainfall depth. The modeling results also suggested that a sole treatment system would not be adequate, particularly for large rainfall events (>100 mm) and a treatment train would be more effective. Simulating long-term (over ten years) pollutant removal performance in the bioretention basin indicated that heavy metals outflow event mean concentration (EMCs) values calculated using simulated results of 30% biochar application rate generated the best pollutant removal with consistent values (2.7 μg/L, 3.0 μg/L, 17.2 μg/L for Pb, Cu, and Zn, respectively). These results confirm that the modelling approach is reliable for assessing long-term treatment performance, as well as a robust tool able to contribute to more effective treatment system design, particularly filter media selection and evaluation.

Characterization and sources apportionment of overflow pollution in urban separate stormwater systems inappropriately connected with sewage

In recent years, combined sewer overflow (CSO) has been identified as a significant contributor to the deterioration of the urban water environment. It is thought that remolding it to a separate sewer system is a thorough and effective method of controlling the CSO in the appropriate area. However, according to current research, the separate stormwater sewer systems will also have overflow pollution due to functional defects, damaged or inappropriately connected with sewage, which has serious consequences for the separate system's operational efficiencies and the urban water environment. The event mean concentration, first flush effect, source apportionment, and correlation analysis of variables in overflow pollution generated in three residential catchments in Nanning, China, were investigated in this study. The results showed that the event mean concentration values in drainage outlets inappropriately connected with sewage were 2–4 times higher than those in stormwater outlets, especially for NH3–N, TN, and TP. Meanwhile, more than 80% of overflow events at outlets inappropriately connected with sewage had a weak first flush or even a weak dilution effect, with peak pollutant concentrations occurring 40–60 min after the overflow began. Besides, the discharge pollution load was primarily derived from the inside of the sewer. When the rainfall was heavy, the contribution rate of sewer sediment erosion exceeded 60%, which was much higher than the contribution rate of rainfall runoff and sewage. The variability in event mean COD and TSS concentrations was primarily attributed to the antecedent dry period and rainfall intensity. The COD concentration increased from 140.7 to 277.1 mg/L with the increase of antecedent dry period from 3 to 10 days. This study could help guide the implementation of targeted measures to treat overflow pollution in urban residential catchments, as well as the development of strategies to mitigate the effects on receiving water bodies.

New framework for assessing urban stormwater management measures in the context of climate change

Global climate change has necessitated the update of urban stormwater management measures (SMMs), but this task is extremely difficult due to the deficiency of evaluation caused by discreteness and the limitation of selected storm events, the ignorance of antecedent dry day (ADD) and lack of suitable stochastic storm generation method. In this study, a new framework that considers both stochastic precipitation and ADD is introduced to evaluate urban SMMs more adequately. Gamma distribution fitting, the space discarding method, the production of probability density distribution maps and multiple nonlinear regression were combined with a physical-based model to assess the effectiveness of SMMs under changing climates. Taking low impact development practices (LIDs) as an example of SSMs, the case study showed that the proposed framework provided effectiveness probability density distribution map and regression equations with more evaluation details by increasing the number and type of storm events compared with current monitoring. Moreover, it is demonstrated that ADD should be considered as one important factor in the design of LIDs, especially for controlling urban non-point source pollution. The value of ADD will significantly affect the control effect of LIDs on pollutant loads and event mean concentration in runoff, which varied for different pollutants. Through case study, it shows there is a risk that LIDs would be less effective at controlling runoff and non-point source pollution in future climate scenarios, especially for RCP 8.5 which is more extreme. Therefore, adaptation capacity of climate change should be considered in the design of SMMs. The proposed framework will be a useful tool in the assessment, design and planning of urban SMMs considering climate change.

Chemical characterization of urban stormwater: Traditional and emerging contaminants

Increases in urbanization have led to increased stormwater runoff and mobilization of pollutants from urban watersheds. Discharge of these pollutants often leads to contamination of receiving water bodies. Chemical characterization of urban stormwater is necessary to gain deeper insights into the ecological impacts of urban runoff and to evaluate parameters that influence possible treatment technologies. This study assessed stormwater event mean concentrations and particle size fractions from field studies reported in national/international stormwater quality databases (The National Stormwater Quality and The Best Management Practices databases) and peer-reviewed literature. This characterization of urban stormwater includes statistical evaluation of probability distribution, consideration of dissolved and particulate-bound pollutants and focuses on partitioning and speciation behavior. Solids, nutrients, metals, organic pollutants, and bacterial pathogen indicators were evaluated. A significant fraction of stormwater phosphorus, metals and organic pollutants are particle-bound. Results from the speciation of metals demonstrated that metals are predominantly present as either inner-sphere or electrostatic complexes with dissolved organic matter. This study provides a comprehensive overview of the myriad pollutants found in urban stormwater and provides a starting point for addressing ubiquitous and emerging contaminants. Finally, research needs for further detailed stormwater characterization were identified.

Statistical Analysis and Review of Event Mean Concentrations in Stormwater Runoff from Agricultural Nonpoint Source Pollution among Different Land Use Types

Objectives : In this study, the characteristics of stormwater runoff from agricultural nonpoint pollution sources investigated under various experimental conditions were evaluated among different land use types (e.g., paddy, field, field (alpine), and vinyl house), and event mean concentrations (EMCs) for each water quality parameter were statistically analyzed. These results can be used in calculating the contribution of stormwater runoff to water quality of receiving water body by performing quantitative and qualitative analysis. The unit loads calculated were compared with Ministry of Environment TMDL (2019) to secure the reliability of the calculated unit loads.Methods : EMCs and unit loads investigated in various studies were classified in terms of paddy, field, field (alpine), and vinyl house. Among various land use types, EMCs and unit loads were statistically analyzed quantitatively and qualitatively. For EMCs, a null hypothesis is that ‘EMCs of water quality parameters among different land use types are not different at a statistically significant level (α=0.05)’. Based on the results of statistical analysis, heteroscedasticity (p&lt;0.05) and Welch-test method were consequently applied, and post hoc test was performed using the Games-Howell method. Finally, unit loads was compared and reviewed against the TMDL (2019) unit loads of the Ministry of Environment.Results and Discussion : Various EMCs in all water quality parameters were found among different land use types (i.e., paddy, field, field (alpine) and vinyl house). For most water quality parameters, EMCs tended to decrease in the order of field (alpine) &gt; field &gt; vinyl house &gt; paddy. The coefficient of variance (CV) values of all water q uality parameters were 0.5 or greater. Based on these results, EMCs in agricultural nonpoint source pollution are very diverse and deviated due to the combination of natural and artificial factors. Post hoc test results indicated different statistical significance among all water quality parameters. In addition to the land use types, both natural factors (i.e., season, rainfall, antecedent rainfall day, and, rainfall runoff rate) and artificial factors (i.e., cultivator manipulation, emission route, type of crop, and amount of compost) affect the characteristics of stormwater runoff. In particular, in the case of field (alpine) with prominent topographical feature of slope, and EMCs were statistically greater than those from other land use types in all water quality categories (p&lt;0.05).Conclusions : Countermeasures for field (alpine)with greater EMCs than paddy, field and vinyl house, should be performed priority. EMCs were affected by a complex interaction between natural factors (i.e., season, rainfall, antecedent rainfall day, and, rainfall runoff rate) and artificial factors (i.e., cultivator manipulation, emission route, type of crop, and amount of compost), and additional data and research are required for further study to elucidate these complex interactions.