Escherichia coli concentration, multiscale monitoring over the decade 2011–2021 in the Mekong basin, Lao PDR

Bacterial pathogens in surface waters may threaten human health, especially in developing countries, where untreated surface water is often used for domestic needs. The objective of the long-term multiscale monitoring of Escherichia coli concentration in stream water, and that of associated variables (temperature, electrical conductance, dissolved oxygen concentration and saturation, pH, oxidation-reduction potential, turbidity, and total suspended sediment concentration), was to identify the drivers of bacterial dissemination across tropical catchments. This data description paper presents three datasets (see section Data availability) collected at 31 sampling stations located within the Mekong river and its tributaries in Lao PDR (0.6–25,946 km2) from 2011 to 2021. The 1,602 records have been used to describe the hydrological processes driving in-stream Escherichia coli concentration during flood events, to understand land-use impact on bacterial dissemination on small and large catchment scales, to relate stream water quality and diarrhea outbreaks, and to build numerical models. The database may be further used e.g. to interpret new variables measured in the monitored catchments, or to map the health risk posed by fecal pathogens.

Dispersed urban-stormwater control improved stream water quality in a catchment-scale experiment

Traditional approaches to urban drainage degrade receiving waters. Alternative approaches have potential to protect downstream waters and provide other benefits to cities, including greater water security. Their widespread adoption requires robust demonstration of their feasibility and effectiveness. We conducted a catchment-scale, before-after-control-reference-impact experiment to assess the effect of dispersed stormwater control on stream ecosystems. We used a variant of effective imperviousness (EI), integrating catchment-scale stormwater runoff impact and stormwater-control-measure (SCM) performance, as the measure of experimental effect. We assessed the response of water quality variables in 6 sites on 2 streams, following SCM implementation in their catchments. We compared changes in those streams over 7 years, as SCM implementation increased, to the 12 preceding years, and over the 19 years in 3 reference and 2 control streams. SCMs reduced phosphorus and nitrogen concentrations and temperature, and increased electrical conductivity; with effect size negatively correlated with antecedent rain. SCM-induced reductions in phosphorus and temperature were of a similar magnitude to increases from urban development, when assessed as a function of change in EI. Nitrogen reductions were observed, even though concentrations among sites were not correlated with EI, being more influenced by septic tank seepage. SCMs had no effect on suspended solids concentrations, which were lower in urban streams than in reference streams. This experiment strengthens the inference that urban stormwater drainage increases contaminant concentrations in urban streams, and demonstrates that such impacts are reversible and likely preventable. SCMs reduce contaminant concentrations by reducing the frequency and magnitude of uncontrolled drainage flows and augmenting reduced baseflows. Increased EC and reduced temperature are likely a result of increased contribution of groundwater to baseflows. The stormwater control achieved by the experiment did not fully return phosphorus or nitrogen concentrations to reference levels, but their responses indicate such an outcome is possible in dominant conditions (up to ~20 mm of 24-h antecedent rain). This would require nearly all impervious surfaces draining to SCMs with large retention capacity, thus requiring more downslope space and water demand. EI predicts stream water quality responses to SCMs, allowing better catchment prioritization and SCM design standards for stream protection.

Intercomparison of evaluation of low-flow characteristics of streams using statistical modelling approach

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Inputs from wastewater treatment plant effluent influence the temporal variability of nutrient uptake in an intermittent stream

Effluents from wastewater treatment plants (WWTP) affect water chemistry and in-stream nutrient uptake capacity from receiving freshwaters, thus altering the amount and fate of nutrients exported. In Mediterranean regions, the dilution capacity of receiving streams to buffer the WWTP biogeochemical fingerprint can vary seasonally due to changes in hydrologic conditions. We assessed the temporal patterns and controls on nutrient uptake in an intermittent Mediterranean stream receiving WWTP effluent inputs. We compiled data on longitudinal profiles of ambient concentrations of dissolved inorganic nitrogen and phosphorus along a 800 m reach on 47 sampling dates between 2001 and 2017 that cover a wide range of hydrological conditions. Data were used to estimate net nutrient uptake in the receiving stream. Ammonium concentration decreased along the reach in 72% of dates, and these decreases were coupled with increases of either nitrite or nitrate. This phenomenon suggests that the stream acted as a hot spot of nitrification. Conversely, concentration of phosphorus did not show any longitudinal pattern in 75% of dates, suggesting that uptake and release processes for this element were commonly counterbalanced. Finally, ammonium net uptake decreased when the stream had a low dilution capacity, suggesting that excess of available nutrients associated with WWTP inputs control de temporal variation of the bioreactive capacity of the receiving streams. Overall, this study suggests that water management should consider the biogeochemical interplay between WWTP operation and the functioning of receiving streams as a strategy to improve stream water quality in urban landscapes.

Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA

The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided.

Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. Thus, quantitative analysis of the relationship between different land cover types and river water quality contributes to a better understanding of the effects of land cover on water quality, the landscape planning of water quality protection, and integrated water resources management. Based on water quality data of 2006–2018 at 18 typical water quality stations in the Yangtze River basin, this study analyzed the spatial and temporal variation characteristics of water quality by using the single-factor water quality identification index through statistical analysis. Furthermore, the Spearman correlation analysis method was adopted to quantify the spatial-scale and temporal-scale effects of various land uses, including agricultural land (AL), forest land (FL), grassland (GL), water area (WA), and construction land (CL), on the stream water quality of dissolved oxygen (DO), chemical oxygen demand (CODMn), and ammonia (NH3-N). The results showed that (1) in terms of temporal variation, the water quality of the river has improved significantly and the tributaries have improved more than the main rivers; (2) in the spatial variation respect, the water quality pollutants in the tributaries are significantly higher than those in the main stream, and the concentration of pollutants increases with the decrease of the distance from the estuary; and (3) the correlation between DO and land use is low, while that between NH3-N, CODMn, and land use is high. CL and AL have a negative effect on water quality, while FL and GL have a purifying effect on water quality. In particular, AL and CL have a significant positive correlation with pollutants in water. Compared with NH3-N, CODMn has a higher correlation with land use at a larger scale. The results highlight the spatial scale and seasonal dependence of land use on water quality, which can provide a scientific basis for land management and seasonal pollution control.