Water Total Phosphorus Research Articles

Assessing the Interaction Impacts of Multi-Scenario Land Use and Landscape Pattern on Water Ecosystem Services in the Greater Bay Area by Multi-Model Coupling

Water ecosystem services (WESs) are intrinsically associated with the livelihood of urban residents and are frequently disrupted by human activities. Land use and landscape patterns are key driving factors of alterations in WESs. However, existing research primarily quantifies single-factor influences and often overlooks the interactions between these factors. This study addresses this gap by employing a multi-model coupling approach, integrating the Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and Geographical Detector (GD) models alongside various indicators to analyse the evolution of land use, landscape patterns and WESs in the Greater Bay Area from 2000 to 2020, and to simulate spatio-temporal change patterns in different scenarios from 2030 to 2050. Additionally, this study examines the multi-factorial interactions between land use, landscape patterns, and WESs. The results indicate that (1) urbanisation steadily increased, leading to intensified landscape fragmentation, and water yield (WY) and total phosphorus (TP) consistently increased, while total nitrogen (TN) in water gradually decreased; (2) urban areas exerted the most significant impact on WY in the Greater Bay Area while Patch density (PD) had a stronger influence on WY, and Shannon’s diversity index (SHDI) had the most pronounced effect on TN and TP; (3) the interaction between any two land-use types or landscape indices exerted a greater impact on WESs compared with the impact of individual factors alone. The interaction between urban areas and cropland substantially influenced WY (q¯ = 0.634) and most strongly affected TN and TP in water (q¯ = 0.74 and 0.73, respectively). SHDI and PD had the most significant impact on WY in the economic development scenario (q¯ = 0.19) and exhibited the greatest influence on the TN and TP levels in the ecological priority scenario (q¯ = 0.12 and 0.15, respectively). Our findings can provide theoretical and technical support for the integrated scientific planning of regional water ecosystems and the development of comprehensive land use policies in the future.

Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

Quantifying the functional genes of C, N, P, and S cycling in a deep lake: Depth patterns and drivers

Microbial functional composition is important for biogeochemical cycles, and is usually constrained by taxonomic species pool and natural environmental gradients. However, the distribution of functional genes in deep lakes and the driving factors remains elusive. Here, we quantified the abundance of 71 functional genes relevant to carbon degradation, carbon fixation, methane metabolism, nitrogen cycling, phosphorus cycling, and sulfur cycling in 38 sediments along the water depth ranging from 0 to 90 m in Lugu Lake, China, using Quantitative Microbial Ecology Chip (QMEC) and then explored their water-depth diversity pattern and abiotic and biotic drivers. Functional gene diversity showed a hump-shaped pattern along the depth and peaked at around 50 m which is the low boundary of thermocline layer. There were specific environmental preferences among functional gene subgroups such as nitrogen and sulfur cycling genes preferring to deeper and shallow waters, respectively. The dissimilarity of total functional genes increased with water depth distance indicating a distance-decay relationship. There was a congruence between functional and taxonomic composition by showing the positive correlations between the compositions of functional genes and bacteria or archaea. This phenomenon is consistently observed for the six functional gene subgroups, and the congruence strength was highest for nitrogen cycling while lowest for sulfur cycling. Compared to abiotic factors, biotic factors were more relevant to the functional gene diversity and composition. Biotic factors explained 28.5 % of the variance of functional gene diversity, while water depth and other environmental factors such as water total phosphorus and sediment carbon explained 4.2 % and 3.8 %, respectively. For functional gene composition, biotic factors accounted for 25.2 % of the variance, whereas water depth and other environmental factors contributed to 0.7 % and 4.4 %, respectively. Among all explanatory variables for function genes, bacterial composition had the highest contribution, which was further supported by showing its direct effects of 1.45 and 0.86 on functional diversity and composition, respectively. This study for the first time quantified the microbial functional genes along water depth in deep lakes and provided a more comprehensive understanding of the functional dynamics in microbial communities within aquatic ecosystems.

Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm.

High-precision evaluations of water environment quality are highly important for improving the accuracy of early warning systems of regional water pollution risk and improving the regional water environment. This paper employs the chimp optimization algorithm (ChOA) to enhance the traditional random forest model, resulting in the chimp optimization algorithm-random forest (ChOA-RF) water quality assessment model for evaluating the Jiansanjiang area in Heilongjiang Province, China. The results show that the overall water environment in Jiansanjiang has the following characteristics: "The water quality of farms in the northwest is poor, and the quality of groundwater is better than that of surface water." Total nitrogen (TN) and total phosphorus (TP) in surface water and ammonium nitrogen (NH3-N), ferrum (Fe), and manganese (Mn) in groundwater are the main pollutants. The TP and TN in surface water and the NH3-N in groundwater exceeded the relevant standards, likely due to the excessive application of chemical fertilizers, especially nitrogen fertilizers. Additionally, Fe and Mn are harmful native substances. According to these findings, targeted improvement strategies, such as reducing nitrogen fertilizer application, plugging well, and increasing the surface water utilization rate, are proposed. Moreover, the ChOA-RF model is compared with the traditional empirical value model and the particle swarm optimization-random forest (PSO-RF) model. The results show that the ChOA-RF model can effectively reduce the root mean square error and mean absolute percentage error and improve the coefficient of determination. The running time and convergence ability are also better than those of the PSO-RF model, which is a more accurate and efficient machine learning model. The model can be used not only for high-precision evaluation of regional water environment quality but also for other machine learning fields.

A self-adaptive model for sensing total phosphorus in natural water bodies using Fourier transform mid-infrared attenuated total reflectance spectroscopy

As the most economically developed area in China, the environmental water quality of Guangdong-Hong Kong-Macao Greater Bay Area has received extensive attention, and the spatial variations of total phosphorus (TP) in the Greater Bay were significant, conventional laboratory analysis is difficult to meet the requirements of TP monitoring due to time and cost consuming, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) technology, combined with the self-adaptive partial least squares (SA-PLS) model, was used to determine the TP concentration. The results showed that P-O vibrations were observed in the wavenumber range of 1200–900 cm−1, and prediction models were established by using this range. For the conventional partial least squares (PLS) model, the R2, RMSE and RPD were 0.817, 0.022 mg L−1 and 2.335, respectively, while for the SA-PLS model the prediction was improved with the values of 0.965, 0.010 mg L−1, 5.296, respectively, and the SA-PLS mode prediction was satisfied when the TP content in water was more than 0.05 mg L−1. It was found that the TP determination was interfered by sulfate, when the sulfate content was < 100 mg L−1, both SA-PLS and the conventional PLS model could be used for quantitative analysis of TP; when sulfate content was > 100 mg L−1, PLS model could not be used while the SA-PLS model still achieved an excellent prediction. Therefore, FTIR-ATR combined with SA-PLS model can rapidly determine TP in water, providing an alternative strategy for monitoring TP in natural water bodies.

Phosphorus accumulation during the ice-on season in macrophyte-dominated eutrophic lakes and its implications

Macrophyte overgrowth in eutrophic lakes can hasten the decline of shallow water bodies, yet the impact of macrophyte deposition on sediment phosphorus (P) accumulation in the ice-on season remains unclear. Comparative analyses of P variations among 13 semi-connected sub-lakes in Wuliangsu Lake in China, a typical MDE lake, considered external flow and macrophyte decomposition as driving forces. Sediment P fractions and water total phosphorus (TP) were analyzed at 35 sampling points across three ice-on season stages, along with macrophyte TP content to assess debris contributions.Our findings reveal that phosphorus accumulation occurs during the ice-on season in the MDE lake, with an average TP content increase of 16 mg/kg. However, we observed a surprisingly small sediment nutrient accumulation ratio (ΔTP/ΔTN=0.006) compared to macrophyte nutrient levels before decomposition. Further analysis of the dominant species, Potamogeton pectinatus, indicates that a significant portion (55%) of macrophyte phosphorus is released before the ice-on season. This highlights the critical importance of timing macrophyte harvesting to precede the phosphorus leaching process, which has implications for lake management and ecosystem restoration efforts.Additionally, our research demonstrates similar transformations among different sediment fractions as previously reported. Macrophyte debris decomposition likely serves as the primary source of Residual P (Res-P) or TP accumulation. In addition, Ca-bound P (Ca–P) generally showed a decrease, which mainly caused by its transformation to Fe/Al-bound P (Fe/Al–P), Exchange-P (Ex-P), and sometimes to Res-P. However, we emphasize the significant impacts of flow dynamics on Ca–P transport and transformations. Its hydrodynamic action increases water dissolved oxygen, which accelerates the transformation of Ca–P to more easily released Fe/Al–P and Ex-P. Furthermore, hydrodynamic transport also leads to upstream Ca–P transport to downstream. This underscores the necessity of considering flow dynamics when estimating phosphorus variations and formulating phosphorus restoration strategies.

TP or Not TP? Successful Comparison of Two Independent Methods Validates Total Phosphorus Inference for Long-Term Eutrophication Studies.

Validating paleo total phosphorus (TP) inference methods over long time scales is essential for understanding historic changes in lake P supply and the processes leading up to the present-day global lake eutrophication crisis. Monitored lake water TP time series have enabled us to identify the drivers of eutrophication over recent decades. However, over longer time scales, the lack of reliable TP inference means our understanding of drivers is speculative. Validation of lake water TP reconstruction, therefore, remains the "ultimate aim" of eutrophication studies. Here, we present the first critical comparison of two fully independent paleo TP inference approaches: the well-established diatom method (DI-TP) and a recently developed sediment geochemical method (SI-TP). Using lake sediment records from a small eutrophic U.K. lake (Crose Mere), we find a statistically significant agreement between the two inferred TP records with greater than 60% shared variance. Both records show identical timings, with a 19th century acceleration in TP concentration and subsequent declines following a peak in 1930. This significant agreement establishes the validity of long-term paleo TP inference for the first time. With this, we can now test assumptions and paradigms that underpin understanding of catchment P sources and pathways over longer time scales.

Summer recreational boating impacts on erosion, turbidity, and phosphorus levels in Canadian freshwater lakes

Increases in boat traffic over time can present a risk to the integrity of aquatic ecosystems. In addition to severe environmental degradation or boat-related disturbances, boat wave’s kinetic energy can induce a cumulative impact on freshwater ecosystems. However, seldom data report the impacts of boat waves on shoreline erosion and physico-chemical properties on freshwater lakes. In this study, we monitored shoreline erosion, turbidity, and total phosphorus levels. Wave-induced shoreline erosion was measured through shoreline rebar pin excavation across five lakes in southern Quebec. Sediment resuspension was measured through turbidity sampling in Lake Massawippi over three years. Total phosphorus was also monitored for potential impacts of sediment resuspension. Our study did not detect significant shoreline erosion. However, water turbidity was positively correlated with boat traffic and wind speed and negatively with the littoral slope angle. Moreover, water total phosphorus levels were positively correlated with turbidity. Thus, despite a lack of detectible impact on the shoreline, our results suggest that boat waves can have an impact on freshwater lakes with an increase in sediment resuspension and phosphorus availability. Management agencies could therefore benefit in the long-term from implementing or reinforcing policies aiming to minimize the impact of boats on sediment resuspension.

Riparian habitat quality as an indicator of land use/land cover effects on riverine water quality

Riparian land use/land cover (LULC) plays a crucial role in maintaining riverine water quality by altering the transport of pollutants and nutrients. Nevertheless, establishing a direct relationship between water quality and LULC is challenging due to the multi-indicator nature of both factors. Water quality encompasses a multitude of physical, chemical, and biological parameters, while LULC represents a diverse array of land use types. Riparian habitat quality (RHQ) serves as an indicator of LULC. Yet, it remains to be seen whether RHQ can act as a proxy of LULC for assessing the impact of LULC on riverine water quality. This study examines the interplay between RHQ, LULC and water quality, and develops a comprehensive indicator to predict water quality. We measured several water quality parameters, including pH (potential of hydrogen), TN (total nitrogen), TP (total phosphorus), Twater (water temperature), DO (dissolved oxygen), and EC (electrical conductivity) of the Yue and Jinshui Rivers draining to the Han River during 2016, 2017 and 2018. The water quality index (WQI) was further calculated. RHQ is assessed by the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model. Our study found noticeable seasonal differences in water quality, with a higher WQI observed in the dry season. The RHQ was strongly correlated with LULC compositions. RHQ positively correlated with WQI, and DO concentration and vegetation land were negatively correlated with Twater, TN, TP, EC, cropland, and construction land. These correlations were stronger in the rainy season. Human-dominated land, such as construction land and cropland, significantly contributed to water quality degradation, whereas vegetation promoted water quality. Regression models showed that the RHQ explained variations in WQI better than LULC types. Our study concludes that RHQ is a new and comprehensive indicator for predicting the dynamics of riverine water quality.

Assessment of the Wanyu River (China) based on a water, sediment and hydrobiont framework.

Due to the striving for the development of economy and agriculture, anthropogenic activities in many countries dramatically alter natural hydrology. These activities are primarily responsible for river deterioration. Thus, we need to assess the river environment and take measures for remediation. According to the survey data, the study identified the critical factors causing water quality deterioration and evaluated the aquatic biodiversity in the Wanyu River. First, based on the monitoring data of water (dissolved oxygen (DO), chemical oxygen demand (COD), total phosphorus (TP), and ammonia nitrogen (NH3-N)), sediment (copper (Cu), zinc (Zn), lead (Pb), arsenic (As), nickel (Ni), mercury (Hg), cadmium (Cd), and chromium (Cr)), and aquatic biodiversity (fish and hydrophyte), the study identified the critical factors causing river quality deterioration. Second, the study provided some recommendations that would consolidate the restoration efforts. Consequently, because of the government's efforts in building the municipal sewage treatment plant, dredging, and other measures, the river environment improved during the 2020-2021 period. The maximum concentrations of COD, NH3-N, and TP in water were reduced by 17.76%, 26.17%, and 20.93%, respectively. The sediment had no risk of heavy metal pollution in the past 2years. And we could utilize sludge as garden soil or compost resource. However, reducing agricultural pollution, internal nutrient loading, and cost-effective restoration and evaluation represent significant challenges in the efforts to recover the river ecosystem.

Fate of high-risk antibiotic resistance genes in large-scale aquaculture sediments: Geographical differentiation and corresponding drivers

Antibiotic resistance genes (ARGs), emerging environmental contaminants, have become challenges of public health security. However, the distribution and drivers of ARGs, especially high-risk ARGs, in large-scale aquaculture sediments remain unknown. Here, we collected sediment samples from 40 crayfish ponds in seven main crayfish culture provinces in China and then investigated the distribution and risk of ARGs based on high-throughput sequencing and quantitative PCR techniques. Our results suggested that aquaculture sediment was potential reservoir of ARGs and the abundance of aadA-02 was the highest. High-risk ARG (floR) was also prevalent in the sediment and was the most abundant in Jiangsu Province, where opportunistic pathogens were also enriched. The abundance of floR was positively correlated with different environmental factors, such as total phosphorus in water and total carbon in sediment. In addition, Mycobacterium sp., opportunistic pathogenic bacteria, might be potential host for floR. Furthermore, the potential propagation pathway of ARGs was from sediment to crayfish gut, and Bacteroidetes and Proteobacteria might be the main bacterial groups responsible for the proliferation of ARGs. Generally, our results illustrate that pond sediment may be an ARG reservoir of aquatic animals. Meanwhile, our study helps develop valuable strategies for accessing risks and managing ARGs.

Pollution source identification of nitrogen and phosphorus in the lower West Main Canal, the Ganfu Plain irrigation district (South China).

The degradation of surface water quality has been a widespread concern around the world. However, irrigation canal water does not attract much attention although it is important to agriculture and population. In this study, a 5-year water quality monitoring of surface water was conducted in the lower West Main Canal of the Ganfu Plain irrigation district to identify the levels and pollution sources of nitrogen and phosphorus.Over 75% of samples had total phosphorus (TP) concentrations of > 0.02mg/L, and all samples had total nitrogen (TN) concentrations of > 0.2mg/L, indicating a risk of eutrophication. The concentrations of NO3--N and NH4+-N averagely occupied 57% and 18% of TN, respectively. PCA analysis showed that phosphorus and nitrogen in canal water were associated with meteorological factors, urban life and surface runoff, agricultural cultivation, livestock-poultry breeding, and water-sediment interaction in the wet season, whereas they were affected by meteorological factors, industrial effluent, urban domestic sewage, and livestock-poultry breeding in the dry season. Absolute principal component score-multiple linear regression (APCS-MLR) model results revealed that (1) agricultural cultivation plus livestock-poultry breeding contributed 43.2% of TP in canal water in the wet season, while livestock-poultry breeding contributed 52.9% in the dry season, and (2) domestic sewage plus surface runoff contributed 29.4% of TN in the wet season, while livestock-poultry breeding contributed 45.9% in the dry season. The unidentified sources had significant contributions of > 20% for almost all variables. So further investigations are required for determining unidentified sources, and anthropogenic pollution control is imperative for canal water quality protection.

Reconstruction and Trends of Total Phosphorus in Shallow Lakes in Eastern China in The Past Century

Lake eutrophication due to excessive nutrient enrichment by human activity is one of the most studied ecosystem regime shifts. The suddenness and irreparability of such eutrophication in shallow lakes cause substantial socio-economic losses, especially in fast-developing areas in eastern China. Although eutrophication has been well documented in many lakes, a regional assessment of the eutrophication process is still missing. Here, we provided a regional assessment of water phosphorus changes since 1900 in eutrophic lakes in eastern China using paleolimnological records and diatom-/chironomid-TP transfer functions. We collated the reconstructed water total phosphorus (TP) of ten lakes and reconstructed the other five records based on identified diatom compositions in sediment cores from previous papers. We found three trend types of decrease, increase and fluctuate in the fifteen TP reconstructions according to cluster analysis of the data correlation results. Increase is the dominated trend, in which TP changes are highly correlated. Among eight lakes with an increasing nutrient, the time-series TP data of six lakes fit step functions better than linear regression models, indicating the main non-linear change in lake nutrient levels over time. Our results show how integrating spatial information on a large scale from paleolimnological records highlights the eutrophication process and further benefits current lake management.

Changes in the spatial distribution of total phosphorus in sediment and water column of a shallow subtropical lake

Julian PJ II, Schafer T, Cohen MJ, Jones P, Osborne TZ. 2023. Changes in the spatial distribution of total phosphorus in sediment and water column of a shallow subtropical lake. Lake Reserv Manage. 39:213–230. In shallow lakes, interactions between bed sediment and the water column strongly influence the availability and transport of nutrients. Lake Okeechobee, in South Florida, is a eutrophic, shallow, polymictic lake that exhibits frequent mixing of the water column and resuspension of unconsolidated sediments. The objective of this study was to evaluate the temporal and spatial patterns of water and sediment characteristics and evaluate the linkage of sediment and water column total phosphorus (TP) concentrations. Spatiotemporal generalized additive models identified key periods during which both surface water total suspended solids (TSS) and TP increased, corresponding to hurricane and tropical storm activity. Our models identified regions with persistently greater water column concentrations than the lake average, indicating potential hot spots in resuspension processes and/or internal loading. Sediment bulk density (BD) and TP were inversely correlated, indicating that light, less dense sediments have greater TP concentrations, potentially contributing to the redistribution of sediments and P. An integrated evaluation of surface water and sediment characteristics using a generalized additive model revealed that sediment TP strongly influences water column TP concentrations, potentially explaining the spatial distribution of surface water TP concentrations. The area of the lake with sediment TP concentration ≤500 mg/kg is increasing while the low bulk density sediments area is decreasing, marking a potential improvement in sediment conditions. If this trend persists, it indicates that the lake is increasingly storing P in sediments that can resist entrainment, with significant implications for assessing the trajectory of lake restoration.

Seasonal sediment phosphorus release across sediment-water interface and its potential role in supporting algal blooms in a large shallow eutrophic Lake (Lake Taihu, China)

Seasonal sediment internal phosphorus (P) release is known to affect annual algal blooms in eutrophic lakes. In this study, a year-long field investigation and laboratory sediment core incubation were conducted to study the relationship between sediment internal P cycling and algal growth in Lake Taihu. The results indicated that the concentrations of water total phosphorus (TP) and chlorophyll-a (Chla) correlated with seasonal temperature and were assumed to be caused by internal P release. From cold winter to warm seasons, sediment internal P (porewater P concentration and P flux) exhibits dynamic changes. Sediment porewater soluble reactive phosphorus (SRP) and its flux in the summer were approximately five times and eight times those during winter, respectively. The release of sediment mobile P in the summer decreases its concentration and can supply SRP for algal blooms. Laboratory core incubation indicated that Chla and phycocyanin concentrations in the overlying water showed similar changes to sediment porewater P and P flux when cores were incubated from low to high temperature. The results of this study indicated that warmer conditions could increase the sediment porewater P concentration and sediment P flux into the bottom waters and consequently enhance sediment P availability to algae. This study provides new insights into the relationship between internal sediment P cycling and algal blooms in Lake Taihu.

Grazing Effects of Xenocypris davidi Bleeker (Cyprinidae, Cypriniformes) on Filamentous Algae and the Consequent Effects on Intestinal Microbiota

Filamentous algae are present in seawater and freshwater ecosystems worldwide. Their growth poses a serious threat to water environments and fishery production, especially Chinese mitten crab and red swamp crayfish culturing. To explore safe and ecological treatment methods, we comparatively studied the grazing effects of Xenocypris davidi Bleeker on Cladophora (one species) and Spirogyra (two species) in indoor glass tanks and examined the intestinal microbiota related to digestion and absorption. The results showed that the fish was more receptive to Spirogyra than to Cladophora, and the intake rates of different species of Spirogyra varied. Fish weight increased slightly after feeding with Spirogyra, and the levels of total nitrogen and total phosphorus in water increased significantly ( p &lt; 0.05 ) relative to the initial values. Intestinal microbiota diversity and richness varied after feeding with three filamentous algal species, and the similarity increased after feeding with two Spirogyra species. The microbial species detected in this study belonged to 40 phyla and 838 genera. The clustering characteristics of different groups and subgroups were obvious based on the microbial phylum composition. Significant differences in the relative abundance rates of eight nutrition-related metabolism functions were found among groups and subgroups ( p &lt; 0.05 ). The correlations between intestinal microbiota and metabolism functions were analyzed, and some meaningful correlations were revealed. The results presented in this study provided a meaningful reference on the control of the overgrowth of filamentous algae in aquaculture waters by the biomanipulation method.

Determination of total phosphorus concentration in water by using visible-near-infrared spectroscopy with machine learning algorithm.

Total phosphorus (TP) content is a crucial evaluation parameter for surface water quality assessment, which is one of the primary causes of eutrophication. High-accuracy, fast-speed approach for the determination of low-concentration TP in water is important. We proposed a rapid, highly sensitive, and pollution-free approach that combines spectroscopy with a machine learning algorithm we improved called synergy interval Extra-Trees regression (siETR) to determine TP concentration in water. Results show that the prediction model based on siETR can get a high coefficient of determination of prediction ([Formula: see text] = 0.9444) and low root mean square error of prediction (RMSEP = 0.0731), which performs well on the prediction of TP concentration. Furthermore, the statistical analysis results further prove that the model based on siETR is superior to other models we studied both in prediction accuracy and robustness. What is more, the prediction model we established with only 140 characteristic wavelengths has the potential for the development of miniature spectral detection instruments, which is expected to achieve in situ determination of TP concentration. These results indicate that Vis-NIR spectroscopy combined with siETR is a promising approach for the determination of TP concentration in water.

Assessing the Effect of a Newly Implemented Basic Wastewater Discharge Standard on the Concentrations of Pharmaceutical and Personal Care Products in the Daqing River Basin, China

Wastewater discharge standards often play a crucial role in water environmental management. However, most of these standards only focus on conventional water pollutants such as chemical oxygen demand (COD), 5-day biochemical oxygen demand (BOD5), ammonia (NH3-N), total nitrogen (TN), and total phosphorus (TP). It is unclear if there is an impact on the removal of pharmaceuticals and personal care products (PPCPs). The Daqing River Basin is an important water system in China. In 2018, a new wastewater discharge standard for the Daqing River Basin (DB 13/2795–2018) was issued, which mainly limits the discharge of conventional water pollutants, including COD (20–40 mg L−1), BOD5 (4–10 mg L−1), NH3-N (1–2 mg L−1), TN (10–15 mg L−1), and TP (0.2–0.4 mg L−1). Herein, we evaluated the performance of the new wastewater discharge standard, especially the effect on the removal of PPCPs. We characterized the occurrence of PPCPs and the resulting ecological impact in the basin, and compared the occurrence of PPCPs before (2017) and after (2021) implementation of the standard. A total of 16 PPCPs were detected, of which diclofenac, carbamazepine, ibuprofen, and bezafibrate pose risks to crustaceans and fish in the basin. A positive impact from the implemented standard, on the removal of some PPCPs, was confirmed, especially for lincomycin and trimethoprim.

Characteristics of plant trait network and its influencing factors in impounded lakes and channel rivers of South-to-North Water Transfer Project, China.

Trait-based approaches have been widely used to evaluate the effects of variable environments on submerged macrophytes communities. However, little research focused on the response of submerged macrophytes to variable environmental factors in impounded lakes and channel rivers of water transfer project, especially from a whole plant trait network (PTN) perspective. Here, we conducted a field survey designed to clarify the characteristic of PTN topology among impounded lakes and channel rivers of the East Route of South-to-North Water Transfer Project (ERSNWTP) and to unravel the effects of determining factors on the PTN topology structure. Overall, our results showed that leaf-related traits and organ mass allocation traits were the hub traits of PTNs in impounded lakes and channel rivers of the ERSNWTP, which traits with high variability were more likely to be the hub traits. Moreover, PTNs showed different structures among impounded lakes and channel rivers, and PTNs topologies were related to the mean functional variation coefficients of lakes and channel rivers. Specially, higher mean functional variation coefficients represented tight PTN, and lower mean functional variation coefficients indicated loose PTN. The PTN structure was significantly affected by water total phosphorus and dissolved oxygen. Edge density increased, while average path length decreased with increasing total phosphorus. Edge density and average clustering coefficient showed significant decreases with increasing dissolved oxygen, while average path length and modularity exhibited significant increases with increasing dissolved oxygen. This study explores the changing patterns and determinants of trait networks along environmental gradients to improve our understanding of ecological rules regulating trait correlations.

Large-scale prediction of stream water quality using an interpretable deep learning approach

Deep learning methods, which have strong capabilities for mapping highly nonlinear relationships with acceptable calculation speed, have been increasingly applied for water quality prediction in recent studies. However, it is argued that the practicality of deep learning methods is limited due to the lack of physical mechanics to explain the prediction results of water quality changes. A knowledge gap exists in rationalizing the deep learning results for water quality predictions. To address this gap, an interpretable deep learning framework was established to predict the spatiotemporal variations of water quality parameters in a large spatial region. Mereological, land-use, and socioeconomic variables were adopted to predict the daily variations of stream water quality parameters across 138 sub-catchments in a total of over 575,250 km2 in southern China. The coefficients of determination of chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) predictions were over 0.80, suggesting a satisfactory prediction performance. The model performance in terms of prediction accuracy could be improved by involving land-use and socioeconomic predictors in addition to hydrological variables. The SHapley Additive exPlanations method used in this study was demonstrated to be effective for interpreting the prediction results by identifying the significant variables and reasoning their influencing directions on the variation of each water quality parameter. The air temperature, proportion of forest area, grain production, population density, and proportion of urban area in each sub-catchment as well as the accumulated rainfall within the previous 3 days were identified as the most significant variables affecting the variations of dissolved oxygen, COD, ammoniacal nitrogen(NH3–N), TN, TP, and turbidity in the stream water in the case area, respectively.