Enhancing insight into DOM fate in aquatic systems: Introducing the FLOTATION model.

Water quality modeling can help to understand the source, transport, transformation and fate of dissolved organic matter (DOM) in aquatic systems. However, water quality models typically use biological oxygen demand as the state variable for DOM, which poorly represents the bio-refractory fraction of the DOM pool. Furthermore, photodegradation, which has a significant impact on the fate of DOM, is often neglected in water quality models. To fill these gaps, we developed the FLOTATION (FLuorescent dOm Transport And TransformatION) model, which includes three processes: biodegradation, photodegradation, and primary production formation. We applied the model to the Nanfei River to understand the source, spatial distribution, and fate of DOM under low flow conditions. The model was set up and calibrated with the longitudinal measurements of four humic-like components (C1-C4) and one protein-like component (C5) identified by excitation-emission matrix parallel factor analysis (EEM-PARAFAC). The results showed that the simulation reproduced the longitudinal variations of all components well. The photodegradation process removed 18 %, 15 % and 21 % of the total input loadings of the humic-like components C1, C2 and C4, respectively. Algal primary production contributed 18 % of the downstream transport loading, constituting an important autochthonous source. For the protein-like C5, photodegradation and biodegradation together removed 7 % of the input loading. Our newly developed FLOATATION model can facilitate a comprehensive understanding of the fate and transport of DOM in aquatic environments.

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest.

Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is still challenging and expensive. Soft sensors, based on data-driven models, offer an alternative to direct nutrient measurements. However, the high demand for data required for their development poses logistical issues with data handling. To address this, the study aimed to determine the optimal subset of predictors and the sampling frequency for developing nutrient soft sensors using random forest. The study used water quality data at 15-min intervals from 2 automatic stations on the Main River, Germany, and included dissolved oxygen, temperature, conductivity, pH, streamflow, and cyclical time features as predictors. The optimal subset of predictors was identified using forward subset selection, and the models fitted with the optimal predictors produced R2 values above 0.95 for nitrate, orthophosphate, and ammonium for both stations. The study then trained the models on 40 sampling frequencies, ranging from monthly to 15-min intervals. The results showed that as the sampling frequency increased, the model's performance, measured by RMSE, improved. The optimal balance between sampling frequency and model performance was identified using a knee-point determination algorithm. The optimal sampling frequency for nitrate was 3.6 and 2.8 h for the 2 stations, respectively. For orthophosphate, it was 2.4 and 1.8 h. For ammonium, it was 2.2 h for 1 station. The study highlights the utility of surrogate models for monitoring nutrient levels and demonstrates that nutrient soft sensors can function with fewer predictors at lower frequencies without significantly decreasing performance.

Photodegradation-induced biological degradation of treated wastewater effluent organic matter in receiving waters.

Effluent organic matter (EfOM) from wastewater treatment plants (WWTP) constitutes an important source of dissolved organic matter in receiving waters. Photodegradation may alter the properties of WWTP EfOM, thereby impacting its biodegradability and microbial respiration. However, whether and how natural sunlight exposure of EfOM affects its biodegradability and microbial oxygen consumption in the receiving waters are unclear. To address these knowledge gaps, incubation experiments of biodegradation, photodegradation, and bio-photodegradation were conducted with the effluent samples from a tertiary WWTP in Heifei, China. The quantity and quality of the EfOM were examined during the incubations to interpret changes in its lability and composition. The results showed that photodegradation facilitated and accelerated the EfOM biodegradation. After natural sunlight exposure, the EfOM degradation rate was significantly enhanced from 0.004 d-1 to 0.065 d-1 measured by dissolved organic carbon (DOC). Correspondingly, the DOC concentration of EfOM was reduced by 64.2% (26.6% by photodegradation and 37.5% by bio-photodegradation), while the concentration was only reduced by 5.3% in the direct biodegradation. Sunlight exposure of EfOM resulted in lower molecular weight, less aromatic, lower humified, more bleached photoproducts. These substances could be readily metabolized by the native microbial community in the receiving water, stimulating microbial respiration. Correspondingly, the oxygen consumption rate of EfOM increased from almost 0.11 mg L-1 d-1 in the direct biodegradation to 2.17 mg L-1 d-1 in the bio-photodegradation. This study highlights that after EfOM is discharged to the receiving water, its post-processing by sunlight can enhance biodegradability. The existence of the coupled photochemical and biological process is suggested to be considered when determining EfOM fate and managing effluent discharge in receiving waters.

Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine.

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013-2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Limited nitrogen retention in an urban river receiving raw sewage and wastewater treatment plant effluent.

Excessive dissolved inorganic nitrogen (DIN) added to urban river systems by point-source (PS) inputs, including raw sewage and wastewater treatment plant (WWTP) effluent, constitutes a water-quality problem of growing concern worldwide. However, the quantification of their impacts on DIN retention capacity and pathways in receiving water still remains partial. In this study, a spatially intensive water quality monitoring campaign was conducted to support the application of a water quality model to a PS-impacted urban river in Hefei City, China. The DIN retention capacities and pathway of a reference upstream Reach A, a raw-sewage-impacted Reach B and a WWTP-effluent-dominated Reach C were quantified using the model results after a Bayesian approach for parameter estimation and uncertainty analysis. The results showed that the raw sewage discharge elevated the assimilatory uptake rate but lowered its efficiency in Reach B, while the WWTP effluent discharge elevated both the denitrification rate and efficiency and made Reach C a denitrification hotspot with an increased nitrate concentration and hypoxic environment. The effects of the PS inputs on the DIN retention pathways (assimilatory uptake vs. denitrification) were regulated by their impacts on river metabolism. Despite different pathways, the total DIN retention ratios of Reaches A, B and C under low-flow conditions were 30.3% km-1, 14.3% km-1 and 6.5% km-1, respectively, which indicated that the instream DIN retention capacities were significantly impaired by the PS inputs. This result suggests that the DIN discharged from PS inputs to urban rivers will be transported with the potential to create long-term ecological implications not only locally but also more distant downstream.

Identification of sewage markers to indicate sources of contamination: Low cost options for misconnected non-stormwater source tracking in stormwater systems.

There has been increasing research focusing on the detection and occurrence of wastewater contamination in urban water systems. To find suitable markers to indicate industrial and domestic sewage flows inappropriately entering storm drains, this study investigated the occurrence and fate of 52 chemical markers through wastewater treatment facilities of manufacturers of agricultural and sideline products, beverage products, and pharmaceutical products, which are also consumed in our daily life. Of the 52 candidate markers, sodium, chloride, potassium, isomalto-oligosaccharide, acesulfame, theanine, glycerol, and clarithromycin were found to be conservative markers, with an average change in concentrations through the wastewater treatment processes of <30%. These markers are useful in identifying industrial and domestic sewage flow contamination in urban sewers. Specially, sodium, chloride, potassium, isomalto-oligosaccharide, acesulfame, and clarithromycin exhibited higher concentrations in blackwater than in greywater, with detected average concentrations of 43.8 mg/L, 189 mg/L, 37.3 mg/L, 123 µg/L, 37.2 µg/L, and 0.99 µg/L in blackwater, respectively. In contrast, theanine and glycerol were observed with higher concentrations in greywater than in blackwater (average 10.1 µg/L and 19.5 µg/L in greywater, respectively). The benchmark concentrations to discriminate between industrial and domestic sewage were also presented. A study in a storm drainage system of downstream Taihu catchment, China demonstrated the usefulness of the markers as low-cost options to trace and quantify misconnected wastewater entries into storm drains, while denoting priority areas for misconnected entries correction.

The influence of multivalent cations on the flocculation of activated sludge with different sludge retention times.

The mechanism governing the flocculation of activated sludge (AS) with different sludge retention times (SRTs) was studied in this paper. AS samples were cultivated in 8 lab-scale reactors with SRTs of 5 d, 7.5 d, 10 d, 12.5 d, 15 d, 20 d, 30 d, and 40 d. The bulk solution, loosely bound extracellular polymeric substances (LB-EPS), tightly bound EPS (TB-EPS), and pellet were extracted for all 8 AS samples. There was a clear trend that the effluent turbidity decreased as the SRT increased, and we deduced that this is because AS samples with longer SRTs have lower interaction energy barriers and lower LB-EPS content. Furthermore, the concentrations of multivalent cations (especially trivalent cations) in the pellets were found to be closely correlated to the AS flocculability, total interaction energy (Wtot), and LB-EPS content. The multivalent (especially trivalent) cations possess greater binding ability, and this ability to bind tightly to AS in large quantities is responsible for the superior flocculability of AS samples with longer SRTs. Hence, the concentrations of multivalent cations in the pellets are an important indicator of AS flocculability. We deduced that variations in the quantities of multivalent cations that tightly bind with the AS rather than remaining in the influent are the core reason behind observed fluctuations in the AS flocculability with different SRTs.

The influence of additives (Ca2+, Al3+, and Fe3+) on the interaction energy and loosely bound extracellular polymeric substances (EPS) of activated sludge and their flocculation mechanisms.

The activated sludge (AS) flocculability markedly improved after the addition of Al(3+) and Fe(3+) compared to Ca(2+) at a concentration of 2 mEq/L. Though the energy barrier decreased about 30% when Ca(2+) was added, the AS flocculability did not improve substantially. This indicates that extended DLVO theory can explain AS flocculation with Al(3+) and Fe(3+) as additives but is not appropriate for Ca(2+). In addition, no matter which cation was added, the AS flocculability was highly correlated to the loosely bound extracellular polymeric substances (LB-EPS) content. The majority of added Ca(2+) remained in the bulk solution (about 92%), whereas almost all of the Al(3+) and Fe(3+) added was found in the pellet (about 98%). The cation' ability to bind to the AS is closely related to the energy barrier and LB-EPS contents, therefore it is the core reason behind the AS flocculation changes observed upon the addition of multivalent cations.