ABSTRACT Nitrification is limited in horizontal flow (HF) wetlands due to prevailing anaerobic conditions in these systems. This study examined how high interstitial velocity affects nitrification performance in HF systems used for tertiary wastewater treatment. Experiments were conducted using planted and unplanted HF wetlands operated in batch recycle mode, functioning as continuous stirred tank reactors across interstitial velocities of 15, 36, 56, and 72 m/d. From the Reynolds (Re) number, these velocities fall within a transition hydraulic flow range. In planted and unplanted cells, the levels of DO (2.7-3.3 mgO2/L and 1.9-2.4 mgO2/L, respectively) and COD (15-27 mg/L and 21-35 mg/L, respectively) differed significantly at different velocities. However, the rate constants for NH4-N in planted (0.24-0.33 d−1) and unplanted cells (0.18-0.31 d−1) differed insignificantly at varying velocities due to enhanced aeration caused by high velocities. Similarly, NO3-N concentrations did not differ significantly between systems, although each system showed notable changes with velocity. Up to interstitial velocities of 36 m/d and 1<Re<4, mass transfer effects on NO3-N formation were significant, beyond which they were insignificant. This suggests that high interstitial velocities 36 m/d enhance nitrification in HF wetlands for tertiary wastewater treatment. Further research is recommended to improve denitrification in these systems. .

ABSTRACT Evaluation of algal growth potential (AGP) is critical to prevent reservoir eutrophication. However, accurately and promptly monitoring algal species and counts via the microscopic method is challenging due to interference from suspended solids (SS). The study aims to determine the composition and the impact of SS in six major drinking water reservoirs in Taiwan over a 6-month period (November to April). A fluorescent probe was used to quantify algal cells and to determine species diversity. Pearson's correlation was employed to assess the relationship among various indicators, including chlorophyll-a, total phosphate (TP), and SS. The results indicate that TP significantly enhances AGP, showing a moderate correlation (γ = 0.54) with algal suspended solid (ASS). The ASS can serve as an alternative and more robust indicator for assessing AGP in reservoirs. However, algal cell counting may be hindered by inorganic particles, resulting in lower accuracy compared to microscopic enumeration. Consequently, the ‘relative algae count’ emerges as a practical indicator to reach cost-effective species-specific monitoring for reservoir management. Furthermore, the fluorescence probe proved effective in distinguishing algal species fractions (e.g., diatoms, green algae, cyanobacteria, and cryptophyta), even under the impact of SS. It is concluded that fluorescent probing is a rapid-response method for AGP monitoring across diversified drinking reservoirs.

ABSTRACT The safety of drinking water is a global issue, with N-nitrosamines (NAs), a type of nitrogen-containing disinfection by-product (N-DBPs), frequently detected in mixtures worldwide. High concentrations of individual NAs have been shown to possess significant teratogenic, carcinogenic, and genotoxic properties. Nonetheless, the health implications of NAs at environmental concentrations found in drinking water are not well understood. This necessitates research into the potential health risks posed by NA exposure through drinking water. This review focuses on the formation and transformation of NA chlorination DBPs in drinking water, their cytotoxicity, genotoxicity, and carcinogenicity at environmental levels, and the mechanisms behind these toxic effects. The insights gained are intended to guide enhancements in chlorination disinfection processes to minimize DBP formation, ensuring the provision of safer and cleaner drinking water, and to offer scientific evidence on the health effects of low-dose mixed NAs in drinking water.

ABSTRACT The diverse microbial dynamics in polluted ecosystems such as the Iko River estuary offer a resounding opportunity for bioremediation strategies. This research assessed the microbiological, physicochemical, and crude-oil pollution indices in Iko River estuary and the ecosystem sustainability processes. Standard microbiological and analytical methods were employed in the collection and analysis of the samples. From the results obtained, the mean values of microbial counts for the various parameters and microhabitats were 8.0 ± 0.98 (×107), 2.34 ± 0.10 (×107), and 2.36 ± 0.92 (×107) – THB, 1.46 ± 0.18 (×106), 1.56 ± 0.10 (×106), and 1.76 ± 0.2 (×106) – CUB, 1.39 ± 0.18 (×106). The hydrocarbon fractions were total petroleum hydrocarbons (TPH) 36.9–296.1, polycyclic aromatic hydrocarbons (PAH) 21.6–188.4, and benzene, toluene, ethylbenzene, and xylenes (BTEX) 12.9–115.4. From the results, the high level of microbial proliferation in the estuary is mainly due to the constant inflow of petroleum-based contaminants and other industrial polluting agents resulting in an enriched microbial ecosystem with the required capabilities of surviving toxic and stressed environment. A standardized microbiological and physicochemical catalogue in the Iko River estuary has been developed by this study and has revealed that the estuary is polluted by heavy metals and petroleum hydrocarbons due to the anthropogenic activities in the area such as oil exploration and exploitation and other navigational and industrial processes.

ABSTRACT This study evaluates and predicts six water quality indices such as sodium adsorption ratio (SAR), Kelly's ratio (KR), percentage sodium (%Na), permeability index (PI), exchangeable sodium percentage (ESP), and irrigation water quality index (IWQI) using multivariate regression models (MLR, PLSR, PCR, and WLSR) and machine learning (ML) algorithms (ANN, SVM, CART, CRRF, and KNN). The study analyzes data from 360 dug wells in Sundargarh district, India, during 2014–2021 with 70% used for training and 30% for testing. Spatial mapping of SAR, KR, ESP, and PI exhibits higher suitability of groundwater. The Mann–Kendall test of trend analysis shows a monotonic increasing and decreasing trend for SAR, KR, %Na, ESP, PI, and IWQI, respectively, at p > 0.05 during 2014–2021. Principal component analysis and discriminant analysis identify Na+, SAR, KR, %Na, and PI as the most influential WQ variables affecting the groundwater quality for this study area. MLR and WLSR models are superior in predicting SAR and ESP, while ANN is the best-suited ML model for SAR, KR, %Na, PI, and ESP. CRRF predicts IWQI with a relatively higher accuracy. These findings demonstrate the effectiveness of ML models in improving irrigation water quality assessment, providing valuable insights for groundwater-based crop management.

ABSTRACT Water temperature is a determinant variable for the overall health of the river ecosystem and aquatic biota, particularly for cold-water fish. Therefore, the characterization of river temperature is essential for the management of thermal habitats. However, currently, river thermal regime characterization is often achieved by calculating numerous thermal indices that are not often related to cold-water fish physiological requirements and thermal preferences. In this study, we developed a compound thermal index (CTI) based on a methodology used to calculate the water quality index (WQI) in Canada. CTI is composed of specific indicators related to the thermal tolerance thresholds for different life stages for two cold-water species (Atlantic salmon and brook trout), providing a simplified measure of the quality of the thermal habitat for these species. CTI was determined in two salmon/trout rivers in Québec, Canada (Ouelle and Ste-Marguerite). The results showed that (i) CTI allowed the characterization and classification of thermal habitat quality; (ii) the thermal habitat degradation was primarily influenced by climate conditions, particularly during warm and dry years with high temperatures and low precipitations; (iii) the improved thermal habitat quality was associated with air temperature and precipitation values close to seasonal normals; (iv) cold tributaries provided excellent thermal habitats.

ABSTRACT This investigation's overarching goal is to ensure that drinking water quality remains a key public health issue in rural Ethiopia. The study aimed to evaluate the physiochemical and microbial quality of drinking water sources and household storages in rural Ethiopia. A cross-sectional study was conducted in 24 kebeles of the district during the wet season. Escherichia coli presence was analyzed in water samples collected from selected community water points and household storages using membrane filtration, while physiochemical parameters were analyzed following standard water examination protocols. Findings revealed that turbidity (35.7%), pH (78.6%), iron (20%), and free residual chlorine (89.3%) levels in the water samples did not conform with the standard. E. coli contamination detected in 42.9% (n = 56) and 47.9% (n = 96) of samples collected at sources and household storage, respectively. This study revealed that significant water quality contamination was observed at both sources and household storages; hence, public health is at high risk. Additionally, the study emphasizes that improved water sources do not always guarantee safety and highlights the need for comprehensive protective measures. Therefore, proper source protection and treatment and appropriate treatment at the household level should be implemented to provide safe drinking water.

ABSTRACT The critical requirement for treating distillery wastewater is globally recognized due to its significant environmental impact and regulatory requirements. This paper reviews the literature on wastewater treatment, focusing on the application of machine learning (ML) algorithms for analyzing large amounts of data and identifying complex patterns. The study uses the Scopus, ScienceDirect, and Web of Science databases for bibliometric analysis. ML has become increasingly attractive in engineering due to its ability to improve predictions for process output variables. It is used in chemistry and engineering to improve computational chemistry, plan materials synthesis, and model contaminant remediation processes. The research proposes future research directions for distillery wastewater treatment using ML approaches. The aim of this review is to critically evaluate the application of ML models in wastewater treatment, drawing insights from existing studies and exploring their potential application to distillery wastewater. This study provides comparisons and offers recommendations for future research in this field.

ABSTRACT Lake ecosystems provide vital services, but face escalating threats from synthetic microplastic (MP) pollution driven by human activities, necessitating urgent action. This study investigates MP contamination in Dharapadavedu Lake, Tamil Nadu, India, characterizing its presence and distribution. MPs in water and sediment were assessed using NOAA's standardized protocol. The results indicate that mean concentration of MPs in lakeshore sediment and water was 2.46 ± 1.06 particles/kg and 1.26 ± 0.88 particles/L, respectively. Significantly, medium-sized MPs (500–1,000 μm) were most abundant, comprising predominantly white, red, and green colors with fragments and fiber morphotypes. Attenuated total reflection Fourier transform infrared spectroscopy revealed valuable insights into the polymer composition of MPs in the lake, identifying four primary types: nylon (polyamide), high-density polyethylene, low-density polyethylene, and polypropylene. Pollution load index data reveals that MP pollution levels of 2.26 in sediment and 1.46 in water indicate a moderate to high level of risk. These findings reveal that the repercussions of recreational activities, anthropogenic activities, and fishing practices around the lake contributed to the accumulation of MPs in the lake. This study fills a research gap by investigating MP pollution in Dharapadavedu Lake for the first time, establishing a baseline contamination estimate.

ABSTRACT The Newfoundland and Labrador Real-Time Water Quality (RTWQ) monitoring program operates a network of surface water monitoring stations, measuring physical water quality parameters in near-real-time. The network generates vast amounts of data which are too complex for many stakeholders to interpret. While the CCME Water Quality Index (WQI) has been a valuable tool to simplify and summarize ambient quality of water, its use in RTWQ is virtually non-existent. This is largely due to the lack of alignment between the limited number of parameters from real-time networks, and availability of aquatic Water Quality Guidelines. This paper is the first attempt to outline a method of utilizing real-time water quality data to generate site specific water quality guidelines using Background Concentration method. Using these guidelines, we compute weekly WQI scores for select monitoring stations and examine how score is influenced by stage and precipitation. The influence of each parameter on the RTWQ WQI score is also plotted. Comparison is made between the Canadian Environmental Sustainability Indicator (CESI) WQI and RTWQ WQI yearly scores. The paper also discusses the challenges and benefits of using this methodology as well as the sensitivity of WQI scoring at stations with various anthropogenic influences.