Multi-scale trend analysis of water quality using error propagation of generalized additive models

Modeling dissolved organic carbon export from water supply catchments in the northeastern United States

Natural organic matter (NOM) in rivers is an important energy source to sustain aquatic ecosystem health. However, in surface water supply systems where chlorination is often used for disinfection, NOM is also a precursor for the carcinogenic and mutagenic disinfection byproducts such as trihalomethanes and haloacetic acids. Effective management of NOM in rivers to maintain both aquatic ecosystem functions and high-quality water supply requires better understanding of the NOM transport patterns. NOM is often operationally measured by dissolved organic carbon (DOC). Challenges of using DOC data for analysis on a catchment scale largely relate to the spatial and temporal variations in DOC, and low sampling frequency which fails to capture the multi-scale transport patterns. To help improve the understanding of DOC sources and transport, we analyzed its long-term patterns in six water supply catchments in the New York City Water Supply System using monitoring data and models. We tested six empirical models for DOC prediction including linear, nonlinear and time-series based model formulations. We found that generalized additive models (GAMs) produced the most robust results across catchments. Then, we applied the calibrated GAM to predict daily DOC concentrations to estimate fluxes and analyze for trends. Finally, we compared the relationships between temporal patterns in DOC and catchment features to investigate the regional differences, focusing on the catchment mechanistic processes associated with DOC by parsing out the hydrological signals. The results showed that hydrology plays a larger role on DOC temporal patterns in three catchments where the top 5 % streamflow corresponded to nearly 50 % of the annual DOC export, whereas nutrient associated production processes were more important in others. The study presents a robust approach for DOC prediction in streams and can inform targeted monitoring strategies for DOC management in water supply source waters.

Drivers analysis and future scenario-based predictions of nutrient loads in key lakes and reservoirs of the Yangtze River Catchment

The excessive nutrient loading in lakes and reservoirs poses significant threats to water quality and ecological health, especially under the influence of global climate change and intensified human activities. This study focuses on the long-term trends in nutrient content and ratios, as well as their driving factors in six major lakes and reservoirs (Chaohu Lake, Danjiangkou Reservoir, Dianchi Lake, Dongtinghu Lake, Poyanghu Lake, and Taihu Lake) within the Yangtze River Catchment from 2002 to 2021. Utilizing Redundancy Analysis, Random Forest and Generalized Additive Model, we identify the shifts in natural and socio-economic factors influencing nutrient concentrations and predict future trends under various scenarios. The main driving factors of nutrient content and their ratios have undergone significant changes at different historical stages, with livestock poultry breeding (LPB) and hydraulic retention time (Res_time) being consistently influential. Our findings highlight the dominant role of livestock and poultry breeding and hydraulic retention time in shaping TN content, whereas TP levels are significantly affected by both natural factors (Temperature and Rainfall) and socio-economic activities. Scenario analyses reveal that despite improvements in water management, nutrient loads remain high, posing ongoing risks of eutrophication. Future lakes nutrient content can meet existing water quality standards under socio-economic development scenarios that significantly reduce hydraulic retention time and the contributions of livestock poultry breeding and other socio-economic drivers.

Assessing groundwater quality dynamics in Madhya Pradesh: Chemical contaminants and their temporal patterns

Groundwater is essential for maintaining ecosystem health and overall well-being as a pivotal resource for plants and animals. The increasing public consciousness of the deterioration of groundwater quality has emphasized the significance of undertaking extended evaluations of groundwater water quality, particularly in regions undergoing substantial hydrological alterations. This study primarily aims to investigate the spatio-temporal variations in groundwater quality and evaluate its suitability for potable purposes in the region of Madhya Pradesh. The study combines the Mann-Kendall (MK) test and Sen's Slope (SS) to analyze the changes in groundwater quality of all 51 districts of Madhya Pradesh, India, utilizing 12 water quality indices using MATLAB. Data was sourced from the Central Ground Water Board (CGWB) in India from the year 2001-2021. The data was then tested for homogeneity at all 1154 sampling stations using the software XLSTAT. Piper plot clustering characterized the state's groundwater as bicarbonate-calcium-magnesium (HCO3--Ca2+-Mg2+) type. The study found that the groundwater in the area is heavily impacted by high levels of nitrate and hardness, which is caused by an increase in multivalent cations. The water was classified as ranging from hard to extremely hard, and approximately 25.49% of the state's groundwater has nitrate levels that exceed the acceptable limits. The MK test showed a significant increasing correlation in trends for parameters such as nitrate, sulfate, fluoride, chloride, bicarbonate, total hardness, and electrical conductivity. It also showed a significant decreasing correlation for calcium, magnesium, potassium, and sodium. These results were observed at a confidence level of 95%. The analysis of trends has shown that human-related factors have a considerable effect on the characteristics of groundwater quality. It is therefore recommended that such human-related factors be taken into consideration when developing policies for managing groundwater resources. Consequently, these policies should emphasize the strict enforcement of rules and standards that limit the overuse of fertilizers, ensure the appropriate disposal of municipal solid and liquid wastes, and regulate industrial pollutants.

Tutorial review of error evaluation in experimental water research at the example of membrane filtration

Experimental water research lacks clear methodology to estimate experimental error. Especially when natural waters are involved, the characterization tools bear method-specific artifacts while the varying environmental conditions prevent regular repeats. This tutorial review identifies common mistakes, and proposes a practical procedure to determine experimental errors at the example of membrane filtration. Statistical analysis is often applied to an insufficient number of repeated measurements, while not all error sources and contributions are considered. This results in an underestimation of the experimental error. Variations in relevant experimental parameters need to be investigated systematically, and the related errors are quantified as a half of the variation between the max and min values when standard deviation is not applicable. Error of calculated parameters (e.g. flux, pollutant removal and mass loss) is estimated by applying error propagation, where weighing contributions of the experimental parameters are considered. Appropriate judgment and five-time repetition of a selected experiment under identical conditions are proposed to validate the propagated experimental error. For validation, the five repeated data points should lie within the estimated error range of the error bar. The proposed error evaluation procedure is adaptable in experimental water research and intended for researchers to identify the contributing factors of an experimental error and carry out appropriate error quantification and validation. The most important aim is to raise awareness of the necessity to question error methodology and reproducibility of experimental data, to produce and publish high quality research.

Estuarine environmental flow assessment based on the flow-ecological health index relation model: a case study in Yangtze River Estuary, China

Environmental flow (e-flow) is the water demand of one given ecosystem, which can become the flow regulation target for protection and restoration of river or estuarine ecosystems. In this study, an e-flow assessment based on the flow-ecological health index (EHI) relation model was conducted to improve ecosystem health of the Yangtze River Estuary (YRE). Monitoring data of hydrology, biology, and water environment in the last decades were used for the model establishment. For the description of the YRE ecosystem, an EHI system was developed by cumulative frequency distribution curves and adaption of national standards. After preprocessing original flow values into proportional flow values, the generalized additive model and Monte Carlo random sampling were used for the establishment of the flow-EHI relation model. From the model calculation, the e-flow assessment results were that, in proportional flow values, the suitable flow range was 1.05-1.35, and the optimum flow range was 1.15-1.25 (flows in Yangtze River Datong Station). For flow regulation in two crucial periods, flows of 42,630-65,545 m3/s or over 14,675 m3/s are needed for the suitable flow of YRE in summer (June-August) or January, respectively. An adaptive management framework of ecological health-based estuarine e-flow assessment for YRE was contrived due to the limitation of current established model when facing the extreme drought in summer, 2022. The methodology and framework in this study are expected to provide valuable management and data support for the sustainable development of estuarine ecosystems and to bring inspiration for further studies at even continental or global levels.

Long-term oscillations in the normalized biomass-size spectrum reveal the impact of oligotrophication on zooplankton trophic structure in the North Atlantic Subtropical Gyre

Ocean warming of the North Atlantic Subtropical Gyre (NASG) induced oligotrophication and a decrease in integrated net primary production during the 2010s, potentially affecting higher trophic levels. We analyzed long-term records (1994-2019) of daytime and nighttime zooplankton biomass in five size classes from the NASG. Daytime biomass decreased in the three largest size classes during the 2010s, while decrease in nighttime biomass was less evident due to the relative stability in diel vertical migrator biomass. We used the normalized biomass size spectrum (NBSS) to estimate the relative transfer efficiency between trophic levels. The steepness of the NBSS slope at the end of the time series increased by 14% (daytime) and 24% (nighttime) from the maximum observed annual average values (2011 and 2009, respectively). This suggests oligotrophication during the 2010s led to a significant reduction in the transfer of biomass across trophic levels, with negative impacts on the NASG planktonic food web.

Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed the considerable impact of COVID-19 lockdowns on surface WQ. In response, this research aimed to assess the impact of COVID-19 lockdowns on surface water quality in Ireland using an advanced WQ model. To achieve this goal, six years of water quality monitoring data from 2017 to 2022 were collected for nine water quality indicators in Cork Harbour, Ireland, before, during, and after the lockdowns. These indicators include pH, water temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved oxygen (DOX), transparency (TRAN), and three nutrient enrichment indicators-dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), and total oxidized nitrogen (TON). The results showed that the lockdown had a significant impact on various WQ indicators, particularly pH, TEMP, TON, and BOD5. Over the study period, most indicators were within the permissible limit except for MRP, with the exception of during COVID-19. During the pandemic, TON and DIN decreased, while water transparency significantly improved. In contrast, after COVID-19, WQ at 7% of monitoring sites significantly deteriorated. Overall, WQ in Cork Harbour was categorized as "good," "fair," and "marginal" classes over the study period. Compared to temporal variation, WQ improved at 17% of monitoring sites during the lockdown period in Cork Harbour. However, no significant trend in WQ was observed. Furthermore, the study analyzed the advanced model's performance in assessing the impact of COVID-19 on WQ. The results indicate that the advanced WQ model could be an effective tool for monitoring and evaluating lockdowns' impact on surface water quality. The model can provide valuable information for decision-making and planning to protect aquatic ecosystems.

Long-term water quality monitoring in agricultural catchments in Sweden: Impact of climatic drivers on diffuse nutrient loads

Water quality related to non-point source pollution continues to pose challenges in agricultural landscapes, despite two completed cycles of Water Framework Directive actions by farmers and landowners. Future climate projections will cause new challenges in landscape hydrology and subsequently, the potential responses in water quality. Investigating the nutrient trends in surface waters and studying the efficiency of mitigation measures revealed that loads and measures are highly variable both spatially and temporally in catchments with different agro-climatic and environmental conditions. In Sweden, nitrogen and phosphorus loads in eight agricultural catchments (470-3300 ha) have been intensively monitored for >20 years. This study investigated the relationship between precipitation, air temperature, and discharge patterns in relation to nitrogen (N) and phosphorus (P) loads at catchment outlets. The time series data analysis was carried out by integrating Mann-Kendall test, Pettitt break-points, and Generalized Additive Model. The results showed that the nutrient loads highly depend on water discharge, which had large variation in annual average (158-441 mm yr^-1). The annual average loads were also considerably different among the catchments with total N (TN) loads ranging from 6.76 to 35.73 kg ha^-1, and total P (TP) loads ranging from 0.11 to 1.04 kg ha^-1. The climatic drivers were highly significant indicators of nutrient loads but with varying degree of significance. Precipitation (28-962 mm yr^-1) was a significant indicator of TN loads in five catchments (loamy sand/sandy loam) while annual average temperature (6.5-8.7 °C yr^-1) was a significant driver of TN loads in six out of eight catchments. TP loads were associated with precipitation in two catchments and significantly correlated to water discharge in six catchments. Considering the more frequent occurrence of extreme weather events, it is necessary to tailor N and P mitigation measures to future climate-change features of precipitation, temperature, and discharge.