Accuracy of different sensors for the estimation of pollutant concentrations (total suspended solids, total and dissolved chemical oxygen demand) in wastewater and stormwater.

Many field investigations have used continuous sensors (turbidimeters and/or ultraviolet (UV)-visible spectrophotometers) to estimate with a short time step pollutant concentrations in sewer systems. Few, if any, publications compare the performance of various sensors for the same set of samples. Different surrogate sensors (turbidity sensors, UV-visible spectrophotometer, pH meter, conductivity meter and microwave sensor) were tested to link concentrations of total suspended solids (TSS), total and dissolved chemical oxygen demand (COD), and sensors' outputs. In the combined sewer at the inlet of a wastewater treatment plant, 94 samples were collected during dry weather, 44 samples were collected during wet weather, and 165 samples were collected under both dry and wet weather conditions. From these samples, triplicate standard laboratory analyses were performed and corresponding sensors outputs were recorded. Two outlier detection methods were developed, based, respectively, on the Mahalanobis and Euclidean distances. Several hundred regression models were tested, and the best ones (according to the root mean square error criterion) are presented in order of decreasing performance. No sensor appears as the best one for all three investigated pollutants.

Classifying pollutant flush signals in stormwater using functional data analysis on TSS MV curves.

Pollution levels in stormwater vary significantly during rain events, with pollutant flushes carrying a major fraction of an event pollutant load in a short period. Understanding these flushes is thus essential for stormwater management. However, current studies mainly focus on describing the first flush or are limited by predetermined flush categories. This study provides a new perspective on the topic by applying data-driven approaches to categorise Mass Volume (MV) curves for TSS into distinct classes of flush tailored to specific monitoring location. Functional Data Analysis (FDA) was used to investigate the dynamics of MV curves in two large data sets, consisting of 343 measured events and 915 modelled events, respectively. Potential links between classes of MV curves and combinations of rain characteristics were explored through a priori clustering. This yielded correct class assignments for 23-63% of the events using different combinations of MV curve clustering and rainfall characteristics. This suggests that while global rainfall characteristics influence flush, they are not sufficient as sole explanatory variables of different flush phenomena, and additional explanatory variables are needed to assign MV curves into classes with a predictive power that is suitable for e.g. design of stormwater control measures. Our results highlight the great potential of the FDA methodology as a new approach for classifying, describing, and understanding pollutant flush signals in stormwater.

Combination of Lagrangian Discrete Phase Model and sediment physico-chemical characteristics for the prediction of the distribution of trace metal contamination in a stormwater detention basin.

Elevated trace metal concentrations in sediments pose a major problem for the management of stormwater detention basins. These basins provide a nature-based solution to remove particulate pollutants through settling, but the resuspension of these contaminated deposits may impact the quality of both surface and groundwater. A better understanding of trace metal distribution will help to improve basin design and sediment management. This study aims to predict the distribution of trace metal contamination in a stormwater detention basin through (i) investigation of the correlation between metal content in sediments and their settling velocity, and (ii) the coupling of such correlation with a Lagrangian Discrete Phase Model (LDPM). The correlation between Fe, Cr, Cu, Ni, Pb contents and the settling velocity is firstly investigated, based on the sediments collected from 6 sites (inlet and 5 traps at the bottom of a detention basin situated in Chassieu, France) during 5 campaigns in 2017. Results show that Fe is strongly correlated to settling velocity and can be considered as a good indicator of trace metal contents. The derived correlation is then combined with a LDPM for the prediction of trace metal distribution, producing results consistent with in situ measurements. The proposed methodology can be applied for other stormwater basins (dry or wet). As described in this article, the interactions between hydrodynamics and sediment physico-chemical characteristics is crucial for the design and management of stormwater detention basins, allowing managers to target the highest contaminated sediments.

Priority substances in accumulated sediments in a stormwater detention basin from an industrial area.

One of the most adopted solutions in developed countries to manage stormwater is detention/retention basins which generate large quantities of sediments that have to be removed regularly. In order to manage them properly, accurate data are needed about their physical and chemical characteristics, particularly on micropollutant concentrations and their associated risk. This work consisted in a two-year sampling of dry sediments from a detention-settling basin. Priority substances, including pesticides, polybrominated diphenyl ethers (PBDE), alkylphenols and bisphenol A (BPA), were monitored. Different sites in the basin bottom were sampled in order to investigate spatial distribution of the contamination. Results show that the increase of the sediment thickness in the basin was heterogeneous with a maximum of 15 cm after two years. Pesticides and PBDE were, if detected, mainly found in low concentrations from 2 ng/g to 286 ng/g. Conversely, alkylphenols and bisphenol A were always quantified at concentrations varying from 6 ng/g to 3400 ng/g. These high levels suggest that these sediments should be managed with precautions. Spatial heterogeneity of alkylphenol ethoxylates and BPA concentrations was observed, with higher contamination of alkylphenol ethoxylates in anaerobic zones and BPA levels correlated with total organic carbon and in a lesser extent to fine particles.

Incidence of hydrological, chemical, and physical constraints on bacterial pathogens, Nocardia cells, and fecal indicator bacteria trapped in an urban stormwater detention basin in Chassieu, France.

The nature and fate of urban contaminants washed by stormwater events and accumulating in a detention basin (DB) were investigated. Relations between bacterial and chemical contaminants of trapped urban sediments, and field parameters were analyzed. Fecal indicators and some pathogens known to be environmentally transmitted (Nocardia, Pseudomonas aeruginosa, and Aeromonas caviae) were tracked, and their persistence investigated. Six sampling campaigns were carried out over 3 years, using five sites including a settling chamber (SC). Aerosolized bacteria at these sites were also monitored. Deposits in the basin were made of fine particles and their content in chemical pollutants was found highly variable. High polycyclic aromatic hydrocarbon (PAH) contents were measured but only three pesticides, over 22, were detected. Deposits were significantly contaminated by fecal indicator bacteria (FIB), P. aeruginosa, A. caviae, and by Nocardia. Only A. caviae showed significant numbers in aerosolized particles recovered over the detention basin. Nocardia spp. cells heavily contaminated the SC. The efficacy of the detention basin at reducing bacterial counts per rain event and over time were estimated. A slight drop in the counts was monitored for fecal indicators but not for the other bacterial groups. Hydrodynamic parameters had a strong impact on the distribution and features of the deposits. Multiple factors impacted the fate of FIB, P. aeruginosa, A. caviae, and Nocardia cells, but in a group dependent manner. Nocardia counts were found positively correlated with volatile organic matter. FIB appeared highly efficient colonizers of the DB.

Bacteriome genetic structures of urban deposits are indicative of their origin and impacted by chemical pollutants.

Urban activities generate surface deposits over impervious surfaces that can represent ecological and health hazards. Bacteriome genetic structures of deposits washed off during rainfall events, over an urban industrial watershed, were inferred from 16 S rRNA gene (rrs) sequences generated by high throughput sequencing. Deposits were sampled over a 4 year-period from a detention basin (DB). Major shifts, matching key management practices, in the structure of these urban bacteriomes, were recorded. Correlation analyses of rrs similarities between samples and their respective concentrations in chemical pollutants, markers of human fecal contaminations (HF183) and antimicrobial resistances (integrons), were performed. Harsher environmental constraints building up in the older deposits led to an increase number of rrs reads from extremophiles such as Acidibacter and Haliangium. Deposits accumulating in the decantation pit of the DB showed an increase in rrs reads from warm blooded intestinal tract bacteria such as Bacteroides and Prevotella. This enrichment matched higher concentrations of Bacteroides HF183 genotypes normally restricted to humans. Bacteriomes of urban deposits appeared good indicators of human-driven environmental changes. Their composition was found representative of their origin. Soil particles and rain appeared to be major contributors of the inferred bacterial taxa recovered from recent deposits.

Human-Driven Microbiological Contamination of Benthic and Hyporheic Sediments of an Intermittent Peri-Urban River Assessed from MST and 16S rRNA Genetic Structure Analyses.

Rivers are often challenged by fecal contaminations. The barrier effect of sediments against fecal bacteria was investigated through the use of a microbial source tracking (MST) toolbox, and by Next Generation Sequencing (NGS) of V5-V6 16S rRNA gene (rrs) sequences. Non-metric multi-dimensional scaling analysis of V5-V6 16S rRNA gene sequences differentiated bacteriomes according to their compartment of origin i.e., surface water against benthic and hyporheic sediments. Classification of these reads showed the most prevalent operating taxonomic units (OTU) to be allocated to Flavobacterium and Aquabacterium. Relative numbers of Gaiella, Haliangium, and Thermoleophilum OTU matched the observed differentiation of bacteriomes according to river compartments. OTU patterns were found impacted by combined sewer overflows (CSO) through an observed increase in diversity from the sewer to the hyporheic sediments. These changes appeared driven by direct transfers of bacterial contaminants from wastewaters but also by organic inputs favoring previously undetectable bacterial groups among sediments. These NGS datasets appeared more sensitive at tracking community changes than MST markers. The human-specific MST marker HF183 was strictly detected among CSO-impacted surface waters and not river bed sediments. The ruminant-specific DNA marker was more broadly distributed but intense bovine pollution was required to detect transfers from surface water to benthic and hyporheic sediments. Some OTU showed distribution patterns in line with these MST datasets such as those allocated to the Aeromonas, Acinetobacter, and Pseudomonas. Fecal indicators (Escherichia coli and total thermotolerant coliforms) were detected all over the river course but their concentrations were not correlated with MST ones. Overall, MST and NGS datasets suggested a poor colonization of river sediments by bovine and sewer bacterial contaminants. No environmental outbreak of these bacterial contaminants was detected.

Calibration of UV/Vis spectrophotometers: A review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers.

UV/Vis spectrophotometers have been used for one decade to monitor water quality in various locations: sewers, rivers, wastewater treatment plants (WWTPs), tap water networks, etc. Resulting equivalent concentrations of interest can be estimated by three ways: i) by manufacturer global calibration; ii) by local calibration based on the provided global calibration and grab sampling; iii) by advanced calibration looking for relations between UV/Vis spectra and corresponding concentrations from grab sampling. However, no study has compared the applied methods so far. This collaborative work presents a comparison between five different methods. A Linear Regression (LR), Support Vector Machine (SVM), EVOlutionary algorithm method (EVO) and Partial Least Squares (PLS) have been applied on various data sets (sewers, rivers, WWTPs under dry, wet and all weather conditions) and for three water quality parameters: TSS, COD total and dissolved. Two criteria (r(2) and Root Mean Square Error RMSE) have been calculated - on calibration and verification data subsets - to evaluate accuracy and robustness of the applied methods. Values of criteria have then been statistically analysed for all and separated data sets. Non-consistent outcomes come through this study. According to the Kruskal-Wallis test and RMSEs, PLS and SVM seem to be the best methods. According to uncertainties in laboratory analysis and ranking of methods, LR and EVO appear more robust and sustainable for concentration estimations. Conclusions are mostly independent of water matrices, weather conditions or concentrations investigated.

Long-term stormwater quantity and quality analysis using continuous measurements in a French urban catchment.

The assessment of urban stormwater quantity and quality is important for evaluating and controlling the impact of the stormwater to natural water and environment. This study mainly addresses long-term evolution of stormwater quantity and quality in a French urban catchment using continuous measured data from 2004 to 2011. Storm event-based data series are obtained (716 rainfall events and 521 runoff events are available) from measured continuous time series. The Mann-Kendall test is applied to these event-based data series for trend detection. A lack of trend is found in rainfall and an increasing trend in runoff is detected. As a result, an increasing trend is present in the runoff coefficient, likely due to growing imperviousness of the catchment caused by urbanization. The event mean concentration of the total suspended solid (TSS) in stormwater does not present a trend, whereas the event load of TSS has an increasing tendency, which is attributed to the increasing event runoff volume. Uncertainty analysis suggests that the major uncertainty in trend detection results lies in uncertainty due to available data. A lack of events due to missing data leads to dramatically increased uncertainty in trend detection results. In contrast, measurement uncertainty in time series data plays a trivial role. The intra-event distribution of TSS is studied based on both M(V) curves and pollutant concentrations of absolute runoff volumes. The trend detection test reveals no significant change in intra-event distributions of TSS in the studied catchment.