Reconstructing historical changes in phosphorus inputs to rivers from point and nonpoint sources in a rapidly developing watershed in eastern China, 1980-2010

Water quality prediction based on sparse dataset using enhanced machine learning

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for CODMn and 0.57 for NH3N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTM-LOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for CODMn and 21.3% for NH3N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.

Potential synergy of microplastics and nitrogen enrichment on plant holobionts in wetland ecosystems

Wetland ecosystems are global hotspots for environmental contaminants, including microplastics (MPs) and nutrients such as nitrogen (N) and phosphorus (P). While MP and nutrient effects on host plants and their associated microbial communities at the individual level have been studied, their synergistic effects on a plant holobiont (i.e., a plant host plus its microbiota, such as bacteria and fungi) in wetland ecosystems are nearly unknown. As an ecological entity, plant holobionts play pivotal roles in biological nitrogen fixation, promote plant resilience and defense chemistry against pathogens, and enhance biogeochemical processes. We summarize evidence based on recent literature to elaborate on the potential synergy of MPs and nutrient enrichment on plant holobionts in wetland ecosystems. We provide a conceptual framework to explain the interplay of MPs, nutrients, and plant holobionts and discuss major pathways of MPs and nutrients into the wetland milieu. Moreover, we highlight the ecological consequences of loss of plant holobionts in wetland ecosystems and conclude with recommendations for pending questions that warrant urgent research. We found that nutrient enrichment promotes the recruitment of MPs-degraded microorganisms and accelerates microbially mediated degradation of MPs, modifying their distribution and toxicity impacts on plant holobionts in wetland ecosystems. Moreover, a loss of wetland plant holobionts via long-term MP-nutrient interactions may likely exacerbate the disruption of wetland ecosystems' capacity to offer nature-based solutions for climate change mitigation through soil organic C sequestration. In conclusion, MP and nutrient enrichment interactions represent a severe ecological risk that can disorganize plant holobionts and their taxonomic roles, leading to dysbiosis (i.e., the disintegration of a stable plant microbiome) and diminishing wetland ecosystems' integrity and multifunctionality.

Influence of concrete material of runoff collection containers on monitoring of nitrogen and phosphorus pollutants

The accurate monitoring of N and P surface runoff losses from farmland is crucial to control agricultural nonpoint source pollution. A pond constructed with concrete material (CM) is a common collection container used during field experiments in China, but the adsorption characteristics of concrete may cause a considerable underestimation of surface runoff losses from farmland. To characterize any neglected error caused by the collection container material, a laboratory experiment was conducted comparing the N and P contents of runoff samples collected from CM and plastic material (PM) containers. The results indicated that CM containers significantly lowered N and P sample contents compared with PM containers, which was attributed to the adsorption capacity of pollutants by CM containers. This was confirmed by scanning electron microscopy (SEM) images of particles retained in CM containers. In an attempt to alleviate this error, three common water-repellent materials were applied to CM containers that significantly limited the pollutant adsorption of CM containers. Moreover, it was shown that there was no significant difference between the calculated concentration of runoff losses and the total amount of pollutants. To calibrate the observational error from CM containers, stepwise multiple regression models of different forms of N and P pollutants were developed. The results of this study suggest that treating CM containers with water repellent is an effective measure for improving the accuracy of new-built monitor points of agricultural nonpoint source pollutants. In addition, the calibration of observational error from CM containers and delayed sampling is essential to estimate agricultural nonpoint source pollution load via the surface runoff from farmland based on data from monitor points.

Quantifying groundwater phosphorus flux to rivers in a typical agricultural watershed in eastern China

Increasing evidence indicates that groundwater can contain high dissolved phosphorus (P) concentrations, thereby contributing as a potential pollution source for surface waters. However, limited quantitative knowledge is available concerning groundwater P fluxes to rivers. Based on monthly hydrochemical monitoring data for rivers and groundwater in 2017-2020, this study combined baseflow separation methods and a load apportionment model (LAM) to quantify contributions from point sources, surface runoff, and groundwater/subsurface runoff to riverine P pollution in a typical agricultural watershed of eastern China. In the studied Shuanggang River, most total P (TP) and dissolved P (DP) concentrations exceeded targeted water quality standards (i.e., TP ≤ 0.2 mg P L^-1, DP ≤ 0.05 mg P L^-1), with DP (76 ± 20%) being the major riverine P form. Observed DP concentrations in groundwater were generally higher than those of river waters. There was a strong correlation between river and groundwater P concentrations, implying that groundwater might be a considerable P pollution source to rivers. The nonlinear reservoir algorithm estimated that baseflow/groundwater contributed 66-68% of monthly riverine water discharge on average, which was consistent with results estimated by an isotope-based sine-wave fitting method. The LAM incorporating point sources, surface runoff, and groundwater effectively predicted daily riverine TP [calibration: coefficient of determination (R²) = 0.76-0.82, Nash-Sutcliffe Efficiency (NSE) = 0.61-0.77; validation: R² = 0.88-0.98, NSE = 0.54-0.64] and DP loads (calibration: R² = 0.73-0.84, NSE = 0.67-0.72; validation: R² = 0.88-0.97, NSE = 0.56-0.83). The LAM estimated point source, surface runoff, and groundwater contributions to riverine loads were 15-18%, 14-35%, and 46-70% for TP loads and 7-9%, 10-32%, and 59-82% for DP loads, respectively. Groundwater was the dominant riverine P source due to long-term accumulation of P from excess fertilizer and farmyard manure applications. The developed methodology provides an alternative method for quantifying P pollution loads from point sources, surface runoff, and groundwater to rivers. This study highlights the importance of controlling groundwater P pollution from agricultural lands to address riverine water quality objectives and further implies that decreasing fertilizer P application rates and utilizing legacy soil P for crop uptake are required to reduce groundwater P loads to rivers.

Modeling flow-related phosphorus inputs to tropical semiarid reservoirs

Hydrological data and total phosphorus (TP) concentration at reservoirs' outlet were combined in a transient complete-mix model to obtain mean input loads and inlet concentration-flow relationships. This approach was designed to investigate the issue of phosphorus pollution in semiarid regions with intermittent rivers. The methodology was applied for twenty reservoirs in the State of Ceará, Brazilian semiarid. The modeled TP loads correlated well (R² = 0.74) with reference loads estimated from environmental inventories, with only 10% of underestimated results. The average input loads per unit area of the catchments ranged from about 4 to 40 kg km^-2 yr^-1, which were considerably lower than the national average of about 500 kg km^-2 yr^-1. This was attributed to lower precipitation indexes, intermittent river regime and a high-density reservoir network, peculiar of the Brazilian semiarid. Meanwhile, the input load per unit area of a small and highly populated urban catchment, with higher precipitation indexes and deficient sanitation was substantially higher (2626 kg km^-2 yr^-1). Moreover, the fitted TP concentration-flow relationships directly reflected different TP input sources: strong u-shaped behavior marked the curves of highly non-point source dominated catchments, whereas a dilution pattern prevailed in those with significant point source inputs. The model validation with measured riverine TP concentration reached a NSE of 0.63. However, peak values in TP concentration during low flow rates sensitively affected the fitting of the models. In spite of non-point source dominance in the catchments, some relationships presented a slight signal of this use type. The variation range of the fitting parameters in comparison with other studies, as well the expected behavior of the curves in light of land use characteristics, strongly support the methodology applied in this study. The proposed approach will potentially help address the TP issue in tropical semiarid regions. Furthermore, the paper presents a simple way to deal with the challenging lack of monitored data in such environments.

Assessment of non-point source of pollution using chemical mass balance approach: a case study of River Alaknanda, a tributary of River Ganga, India

The low ionic concentration meltwaters of the rivers originating from the Himalayan glaciers play a significant role in diluting the high solute load emanating from Ganga plain catchments. Hence, any change in the qualitative and quantitative characteristics of the Himalayan tributaries of River Ganga under the changing climatic scenario will impact the hydrochemical parameters of River Ganga as well. Hydrochemical investigations have been carried out in the River Alaknanda, a tributary of River Ganga during the period September 2016-May 2018 and revealed that TSS and COD values were observed above the prescribed criteria limit of 10 mg/L for drinking purpose for river as prescribed by CPCB. The anions for all sampling sites and seasons were observed to be in decreasing order of HCO₃^-  > SO₄^2-  > Cl^-  > NO₃^- and cations Ca²⁺  > Mg²⁺  > Na⁺  > K⁺. The weathering of rock forming minerals of drainage basin is responsible for the chemical composition of river water. HCO₃^- being the dominant anion in the study area accounts for its presence due to carbonate and silicate weathering. Ion exchange process controls the major ion chemistry of the river water. The assessment and management of non-point sources (NPS) pollution are difficult by any deterministic method and require a vast amount of data to compensate for their extent of contamination, in the account of their prevailing nature in response to hydrological processes and land use patterns. In the present investigation, the application of a simple chemical mass balance approach based on law of conservation of mass/matter has been applied on River Alaknanda, a tributary of River Ganga for measuring the chemical mass loadings of some selected water quality constituents, viz., major cations (sodium, potassium, calcium, magnesium, and ammonium) and major anions (chloride, sulfate, nitrate, and phosphate) at upstream and downstream of different point source locations for examining the contribution made by non-point sources of pollution to the river. Time series analysis of various ion concentrations at point source sites and upstream/downstream sites inferred that the fluvial variations pertaining to ion concentration and flux are strongly dependent on the seasonal changes. More contribution (> 30-50%) for almost all constituents from uncharacterized sources was observed in the months of November to February, which may be attributed to intensified agricultural activities during the winter months particularly cereals and vegetables.

A Bayesian modeling approach for phosphorus load apportionment in a reservoir with high water transfer disturbance

Phosphorus loading from external and internal sources poses a potential risk to eutrophication of lakes or reservoirs. However, the relative contribution of external and internal sources to eutrophication is still unclear especially for reservoirs with water transfer disturbance. The objective of this paper is to estimate the phosphorus loading from external (water transfer and diffusing emission) and internal sources (sediment release) in Yuqiao Reservoir (YQR) and compare their relative contribution of external and internal sources. In this study, we estimated the phosphorus loading considering both external (water transfer and diffusing source emission) and internal (release from sediment) sources of YQR. The phosphorus loading from water transfer was estimated by total phosphorus (TP) concentration × monthly flow of inflow. The phosphorus loading from nonpoint source emission was estimated using a generalized watershed loading function (GWLF). The phosphorus loading from internal sources was estimated with a Bayesian phosphorus budget model. Our result showed that water transfer TP load is the biggest (45.2%) source of TP load in YQR and internal TP load (20.5%) accounts for a comparable proportion of TP load as nonpoint source (34.3%) in YQR and dominates the total loading in some months. Analysis of seasonal total phosphorus load apportionment indicated that water transfer TP load takes the largest proportion in winter (60.8%), spring (60.2%), and autumn (47.8%). Nonpoint source TP load takes the largest proportion in summer (60.1%), and internal TP load is the second source of YQR in summer (22.4%). Our study indicates that water transfer may be the major driver of eutrophication for some reservoir systems, and sediment release may prevent recovery of many eutrophic lakes and reservoirs. Our analysis suggests that TP pollution control strategies in YQR should be preferentially focused on the improvement of water quality in the upstream reservoir, and nonpoint source TP load reductions should be focused on summer. Compared with conventional nutrient apportionment model applications, this paper provides a new approach to estimate external and internal TP loads simultaneously. Graphical abstract ᅟ.

Source identification and impact of landscape pattern on riverine nitrogen pollution in a typical urbanized watershed, Beijing, China

This study explored the sources of nitrate and the impact of landscape pattern on nitrogen pollution in the highly urbanized Beiyun River Watershed, China during 2016 by applying a dual stable isotope approach (δ¹⁵N-NO₃^-and δ¹⁸O-NO₃^-) combined with multiple statistical analyses. The sources of riverine nitrate principally originated from manure and sewage, nitrification of soil nitrogen, fertilizer nitrification, and atmospheric deposition. A Bayesian model was used to estimate the source contributions and results showed that manure and sewage were the major contributors to river nitrate with combined proportions of 77.59% and 89.57% in the rainy season and the dry season, respectively. Results from multiple stepwise regression indicated that the typical artificial land use types and landscape configuration metrics reflecting landscape fragmentation related well with riverine nitrogen variables for different seasons (R²>0.6). Industrial land, unused land and patch density of built-up land and road were positively correlated with riverine nitrogen over seasons, whereas the interspersion and juxtaposition index of forest land was negatively related with nitrate. Regulating built-up land and unused land, connecting forest land with other land use types and diminishing discharges of industrial and domestic wastewater would be effective ways to improve urban river water quality.

The application of high temporal resolution data in river catchment modelling and management strategies

Modelling changes in river water quality, and by extension developing river management strategies, has historically been reliant on empirical data collected at relatively low temporal resolutions. With access to data collected at higher temporal resolutions, this study investigated how these new dataset types could be employed to assess the precision and accuracy of two phosphorus (P) load apportionment models (LAMs) developed on lower resolution empirical data. Predictions were made of point and diffuse sources of P across ten different sampling scenarios. Sampling resolution ranged from hourly to monthly through the use of 2000 newly created datasets from high frequency P and discharge data collected from a eutrophic river draining a 9.48 km² catchment. Outputs from the two LAMs were found to differ significantly in the P load apportionment (51.4% versus 4.6% from point sources) with reducing precision and increasing bias as sampling frequency decreased. Residual analysis identified a large deviation from observed data at high flows. This deviation affected the apportionment of P from diffuse sources in particular. The study demonstrated the potential problems in developing empirical models such as LAMs based on temporally relatively poorly-resolved data (the level of resolution that is available for the majority of catchments). When these models are applied ad hoc and outside an expert modelling framework using extant datasets of lower resolution, interpretations of their outputs could potentially reduce the effectiveness of management decisions aimed at improving water quality.

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Event-based runoff–pollutant relationships have been the key for water quality management, but the scarcity of measured data results in poor model performance, especially for multiple rainfall events. In this study, a new framework was proposed for event-based non-point source (NPS) prediction and evaluation. The artificial neural network (ANN) was used to extend the runoff–pollutant relationship from complete data events to other data-scarce events. The interpolation method was then used to solve the problem of tail deviation in the simulated pollutographs. In addition, the entropy method was utilized to train the ANN for comprehensive evaluations. A case study was performed in the Three Gorges Reservoir Region, China. Results showed that the ANN performed well in the NPS simulation, especially for light rainfall events, and the phosphorus predictions were always more accurate than the nitrogen predictions under scarce data conditions. In addition, peak pollutant data scarcity had a significant impact on the model performance. Furthermore, these traditional indicators would lead to certain information loss during the model evaluation, but the entropy weighting method could provide a more accurate model evaluation. These results would be valuable for monitoring schemes and the quantitation of event-based NPS pollution, especially in data-poor catchments.

Phototrophic periphyton techniques combine phosphorous removal and recovery for sustainable salt-soil zone

The P (Pi as KH2PO4 and Po as ATP) removal processes by phototrophic periphyton were investigated by determining the removal kinetics, metal content (Ca, Mg, Al, Fe, Cu, and Zn) of the solution and P fractions (Labile-P, Fe/Al-P, Ca-P, and Res-P) within the periphyton. Results showed that the periphyton was able to remove completely both Pi and Po after 48h when periphyton content was greater than 0.2gL(-1) (dry weight). The difference between Pi and Po removal was the conversion of Po into Pi by the periphyton, after that the removal mechanism was similar. The P removal mechanism was mainly due to the adsorption on the surfaces of the periphyton, including two aspects: i) the adsorption of PO4(3-) onto metal salts such as calcium carbonate (~50%) and ii) complexation between PO4(3-) and metal cations such as Ca(2+) (~40%). However, this bio-adsorptional process was significantly influenced by the extracellular polymeric substance (EPS) of periphyton, water hardness, initial P concentration, temperature and light intensity. This study not only deepens the understanding of P biogeochemical process in aquatic ecosystem, but provides a potential biomaterial for combining phosphorous removal and recovery from non-point source wastewaters, especially around salt-soil zone.

Phosphate enhance recovery from wastewater by mechanism analysis and optimization of struvite settleability in fluidized bed reactor

Since phosphorus, a non-renewable and non-substitutable resource, has become the principal contributor and limiting factor to water eutrophication, achieving phosphorus removal and recovery from wastewater is pretty essential. Even though struvite crystallization process has been widely used for phosphate (P) recovery in wastewater treatment, its application is hampered by difficulties controlling small particle size and crystal growth. This study was conducted to control the settleability of struvite by calculating and predicting the struvite-settling percentage (Ps), which is always affected by the initial concentration of P (CP), solution pH (pH), reaction time (t), reaction temperature (T), agitation rate (Ar), and inlet flow velocity (vf) of the fluidized bed reactor. The results showed that the settleability of struvite could be enhanced by increasing T and decreasing pH, Ar, or vf, and would perform worse with overlong t or excessive CP. The dynamic variation process of the solution supersaturated index (SI) combined with the phase equilibrium theory and Ostwald ripening mechanism explained the above results sufficiently. The logistic model was chosen to predict the Ps under multi-factors, but the accuracy needs to be improved.