Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers

Enhanced iodinated disinfection byproducts formation in iodide/iodate-containing water undergoing UV-chloramine sequential disinfection: Machine learning-aided identification of reaction mechanisms

Restricted to the complex nature of dissolved organic matter (DOM) in various aquatic environments, the mechanisms of enhanced iodinated disinfection byproducts (I-DBPs) formation in water containing both I- and IO3- (designated as I-/IO3- in this study) during the ultraviolet (UV)-chloramine sequential disinfection process remains unclear. In this study, four machine learning (ML) models were established to predict I-DBP formation by using DOM and disinfection features as input variables. Extreme gradient boosting (XGB) algorithm outperformed the others in model development using synthetic waters and in cross-dataset generalization of surface waters. Shapley additive explanation (SHAP) analysis, partial dependence plots (PDPs), and individual conditional expectation (ICE) analysis were then employed to explain the models' workings and feature interactions, aiding in identification and quantification of underlying mechanisms. A type of DOM component (namely DC_b) was found as the greatest contributor and identified as reduced quinones associated with broken-down lignin within higher plant-derived fulvic substance, serving as precursors and electron shuttles for I-DBP formation. Based on the interactional effects acquired from explanation results, the ejection of e-aq from excited DOM and pre-existing I- in the I-/IO3- system were identified responsible for the enhanced generation of I-DBPs compared to that in the I- or IO3- alone systems; extra DOM scavenged reactive iodine species (RIS), contributing to a limited enhancement. These findings and the methodology developed here together enhance our understanding of the mechanisms how DOM limitedly promotes I-DBP formation during UV-chloramine sequential disinfection of I-/IO3--containing water and facilitate effective online monitoring in the future.

Solar-Light-Mediated Phototransformation of Herbicide Tribenuron-Methyl Initiated by Its Coexisting Nitrate Ion in Sunlit Agricultural Drainages

Understanding the environmental fate of chemical herbicides is crucial to sustainable agriculture. Due to their joint-use with nitrogen fertilizers, their residues often coexist with NO3- in agricultural drainages. In this study, tribenuron-methyl was used as a model to evaluate the role of NO3- in the phototransformation of chemical herbicides, which was characterized by a two-stage process. Initially, a slow hydrolysis occurs (kobs = 2.573 × 10-4 min-1), producing two hydrolysis products: methyl-2-(aminosulfonyl)-benzoate (MSB) and 2-methyl-4-methylamino-6-methoxy-1, 3, 5-triazine (MMT), which can be significantly accelerated by solar irradiation (kobs = 2.152 × 10-2 min-1). Subsequently, MSB undergoes a rapid NO3--initiated photodegradation process (kobs = 2.251 × 10-2 min-1). MMT was identified as the refractory unit and undergoes a slow NO3--initiated photodegradation process (kobs = 4.494 × 10-4 min-1). The underlying mechanisms were elucidated through electron paramagnetic resonance spectroscopy and reactive species quenching experiments. This study fills a knowledge gap on the interaction between NO3- and chemical herbicides, highlighting the pivotal role of NO3- in the phototransformation of chemical herbicides.

The impacts of socioeconomic development and climate change on long-term nutrient dynamics: A case study in Poyang Lake

The anthropogenic activities associated with rapid socioeconomic development affect global climate change and the water quality of lake ecosystems. However, the impacts of socioeconomic and climate changes on lake nutrient dynamics require additional study. In this study, we used a long-term dataset (1987-2021) of Poyang Lake to identify the nutrient dynamics and assess the impacts of social and climatic factors on nutrient concentrations. The filtering trajectory method (FTM) suggested that in Poyang Lake, nutrients first increased and then decreased, with TP reaching its highest value of 157 μg/L in 2015. The study employs a combination of structural equation modeling (SEM) and FTM to identify the complex interactions between socio-economic and climatic factors affecting nutrient concentrations in Poyang Lake. The SEM results revealed that socioeconomic factors rather than climate change determined the long-term changes in TN and TP. Additionally, FTM results verified that GDP, urbanization (Ur) and P-fertilizer (Pfer) were the key drivers of TN; Ur, population (P), and sewerage treatment rate (STR) were the primary factors of TP. Through generalized additive models (GAMs), we observed that GDP accounted for 86 % of the temporal variability in TN and 45.7 % of that in TP, exhibiting inverted U-shaped relationships with both TN and TP. Air temperature (AT), a climatic factor accounted for only 44.6 % and 14.8 % of the variation in TN and TP, respectively. In addition, Pfer explained 66.0 % of the variation in TN, and STR explained 50.4 % of the variation in TP with a peak TP at the STR threshold of approximately 80 %. Our findings highlight the importance of Pfer and STR as critical indicators for watershed nutrient management. The identification of key temporal drivers and nutrient trajectories provides a scientific basis for developing management strategies. The results highlight coordinated control strategies for water pollution and carbon reduction as essential measures for mitigating climate change.

Endogenous phosphorus release from plateau lakes responds significantly to temperature variability over the last 50 years

The ecological environment of plateau lakes is very sensitive to temperature changes. Higher temperatures accelerate the cycling processes between lake sediments and water nutrients. Quantitatively investigating the influence mechanism of regional climate change and sediment phosphorus release over a long time series is difficult in revealing the causes of eutrophication in plateau lakes. This paper quantitatively reveals the long-term response mechanism of endogenous phosphorus release to temperature change in Dianchi, the largest plateau eutrophic lake in China, based on nearly 50 years of temperature and sediment phosphorus data from 1964 to 2013, and taking advantage of the Random Forest machine learning algorithm for deep processing of long time series and nonlinear relation. The results showed that: (1) Over the past 50 years, endogenous phosphorus release and temperature showed no trend for 22 years, followed by a consistent, significant increase in both after 1986. (2) Random Forest analysis showed that before the increase of temperature, the contribution to the phosphorus release was weak, while after the mutation, the contribution reached 52.6%, and typically was concentrated from March to August each year. (3) The response relationship between temperature and endogenous phosphorus release had non-linear variation with a threshold interval of 18.3 °C-19.2 °C. This research aims to explore the theoretical scientific knowledge of endogenous phosphorus release processes and complex mechanisms in plateau lakes under changing environments, and further explores the effects of long-term temperature variability on endogenous phosphorus loading in plateau lakes. That is, long-term temperature mutations can alter the internal cycling processes of sedimentary phosphorus by stimulating algal growth, which have a more drastic effect than short-term temperature variations.

Increasing phosphorus loss despite widespread concentration decline in US rivers

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980-2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.

How much nutrient reaches a stream: Insights from a hybrid model and implications for watershed nitrogen export and removal

Excess nitrogen (N) discharged into streams and rivers degrades freshwater quality and threatens ecosystems worldwide. Land use patterns may influence riverine N export, yet the effect of location on N export and removal is not fully understood. We proposed a hybrid model to analyze N export and removal within the watersheds. The proposed model is satisfied for the riverine N modelling. The KGE and R2 are 0.75 and 0.72 in the calibration period which are 0.76 and 0.61 in the validation period. Human-impacted land use may modify the N yield in the watershed, and the net N export from built-up to the in-stream system was highest in the urbanized sub-watersheds (0.81), followed by the agricultural sub-watersheds (0.88), and forested sub-watersheds (0.96). Agricultural activities make a large contribution to the N exports in the watersheds, and the mean N input from the agricultural land use to in-stream were 2069-4353 kg km-2 yr-1. Besides, the excess inputs of N by overapplication of fertilizer and manure during the agricultural activities may increase legacy N in soil and groundwater. Biological processes for the riverine N removal may be controlled by the available substrate in the freshwater system, and temperature sensitivity of denitrification is highest in the flood seasons, especially for the human-impacted sub-watersheds. The riverine biological processes may be limited by other competitions. Our model results provide evidence that quantity and location of specific land use may control biogeochemistry within watersheds. We demonstrate the need to understand nutrient export and removal within watersheds by improving the representation of spatial patterns in existing watershed models, and we consider this study to be a new effort for the spatially explicit modeling to support land-use based N management in watersheds.

Citizen science shows that small agricultural streams in Germany are in a poor ecological status

Agricultural pesticides, nutrients, and habitat degradation are major causes of insect declines in lowland streams. To effectively conserve and restore stream habitats, standardized stream monitoring data and societal support for freshwater protection are needed. Here, we sampled 137 small stream monitoring sites across Germany, 83 % of which were located in agricultural catchments, with >900 citizen scientists in 96 monitoring groups. Sampling was carried out according to Water Framework Directive standards as part of the citizen science freshwater monitoring program FLOW in spring and summer 2021, 2022 and 2023. The biological indicator SPEARpesticides was used to assess pesticide exposure and effects based on macroinvertebrate community composition. Overall, 58 % of the agricultural monitoring sites failed to achieve a good ecological status in terms of macroinvertebrate community composition and indicated high pesticide exposure (SPEARpesticides status class: 29 % "moderate", 19 % "poor", 11 % "bad"). The indicated pesticide pressure in streams was related to the proportion of arable land in the catchment areas (R2 = 0.23, p < 0.001). Also with regards to hydromorphology, monitoring results revealed that 65 % of the agricultural monitoring sites failed to reach a good status. The database produced by citizen science groups was characterized by a high degree of accuracy, as results obtained by citizen scientists and professionals were highly correlated for SPEARpesticides index (R2 = 0.79, p < 0.001) and hydromorphology index values (R2 = 0.72, p < 0.001). Such citizen-driven monitoring of the status of watercourses could play a crucial role in monitoring and implementing the objectives of the European Water Framework Directive, thus contributing to restoring and protecting freshwater ecosystems.

Deep learning for water quality

Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.

The recovery of river chemistry from acid rain in the Mississippi River basin amid intensifying anthropogenic activities and climate change

Acid rain has degraded the environmental health of many regions worldwide since the Industrial Revolution. Signatures of river chemistry recovery from acid rain since the Clean Air Act and similar legislation have been reported extensively in small streams but are often subdued or masked in large rivers by complex, co-occurring drivers. Here we assess the recovery of river chemistry from acid rain deposition in the Mississippi River Basin (MRB), the largest river basin in North America. We combine analysis of temporal trends of acid rain indicator solutes with Bayesian statistical models to assess the large-scale recovery from acid rain and characterize effects of anthropogenic activities. We found evidence of river chemistry recovery from acid rain; however, the effects of other anthropogenic activities, including fertilizer application and road salting, and changing climate, are likely intensifying. Trends of pH, alkalinity and SO4 export suggest acid rain recovery at large in the MRB, with stronger evidence of recovery in the historically afflicted eastern region of the basin. The concentrations of acid rain indicators generally correlate positively to NO3 and Cl, indicating that N-fertilizer application may have significantly increased weathering, and possibly acidification, and road salt application likely increased cation loss from catchments and contributed to SO4 export. Temperature correlates positively with solute concentrations, possibly through respiration-driven weathering or evaporation. The concentrations of acid rain indicators correlate negatively and most strongly to discharge, indicating discharge as a predominant driver and that lower discharge during droughts can elevate concentrations of riverine solutes in a changing climate. Using long-term data, this study represents a rare, comprehensive assessment of the recovery from acid rain in a large river basin, taking into consideration the entangled effects of multiple human activities and climate change. Our results highlight the ever-present need for adaptive environmental management in a constantly changing world.

An integrated risk assessment framework using information theory-based coupling methods for basin-scale water quality management: A case study in the Danjiangkou Reservoir Basin, China

As the second largest reservoir in China, the Danjiangkou Reservoir (DJKR) serves as the water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the currently longest (1273 km) inter-basin water diversion project in the world, for more than eight years. The water quality status of the DJKR basin has been receiving worldwide attention because it is related to the health and safety of >100 million people and the integrity of an ecosystem covering >92,500 km2. In this study, basin-scale water quality sampling campaigns were conducted monthly at 47 monitoring sites in river systems of the DJKRB from the year 2020 to 2022, covering nine water quality indicators, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and fluoride (F-). The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. An integrated risk assessment framework simultaneously considered intra and inter-regional factors using information theory-based and the SPA (Set-Pair Analysis) methods were proposed for basin-scale water quality management. The results showed that the water quality of the DJKR and its tributaries stably maintained a "good" status, with all the average WQIs >60 of river systems during the monitoring period. The spatial variations of all WQIs in the basin showed significantly different (Kruskal-Wallis tests, P < 0.01), while no seasonal differences were found. The increase in built-up land use and agricultural water consumption revealed the highest contributions (Mantel's r > 0.5, P < 0.05) to the rise of nutrient loadings of all river systems, showing the intensive anthropogenic activities can eclipse the power of natural processes on water quality variations to some extent. The risks of specific sub-basins that may cause water quality degradation on the MRSNWDPC were effectively quantified and identified into five classifications based on transfer entropy and the SPA methods. This study provides an informative risk assessment framework that was relatively easy to be applied by professionals and non-experts for basin-scale water quality management, thus providing a valuable and reliable reference for the administrative department to conduct effective pollution control in the future.

Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China

Accurate understanding of spatial distribution and variability of soil total nitrogen (TN) is critical for the site-specific nitrogen management. Based on 4337 newly obtained soil observations and 33 covariates, this study applied the random forest (RF) algorithm and modified regression kriging (RF combined with residual kriging: RFK, hereafter) model to spatially predict and map topsoil TN content in agricultural areas of Henan Province, central China. According to the RFK prediction, topsoil TN content ranged from 0.52 to 1.81 g kg^-1, and the farmland with the topsoil TN contents of 1.00-1.23 g kg^-1 and 0.80-1.23 g kg^-1 accounted for 48.2% and 81.2% of the total farmland area, respectively. Spatially, the topsoil TN in the study area was generally higher in the west and lower in the east. By using the Boruta variable selection algorithm, soil organic matter (SOM) and available potassium contents in topsoil, nitrogen deposition, average annual precipitation, livestock discharges, and topsoil pH were identified as the main factors driving the spatial distribution and variation of soil TN in the study area. The RF and RFK models used showed the expected performance and achieved acceptable TN prediction accuracy. In comparison, RFK performed slightly better than the RF model. The R² and RMSE achieved by the RFK model were improved by 4.5% and 4.5%, respectively, compared with that by the RF model. However, the results suggest that RFK was inferior to the RF model in quantifying prediction uncertainty and thus may have a slight disadvantage in model reliability.