Assessment of seasonal and annual patterns in phosphorus content in a monitored catchment through a partitioning approach based on hydrometeorological data.

High amounts of phosphorus (P) in rivers come mainly from two sources: fertilizers washed off from agricultural and urban areas by runoff water (non-point sources) and urban and industrial development which are translated in P discharges from wastewater treatment plants (WWTP). This work analyses the content of P in water for nearly 40 years inquiring into the origin of the sources, based on the hypothesis of runoff generation from the detection of river streamflow increases during the P contribution episode and the previous precipitation. For this purpose, the Guadaira River, which is located in the South of Spain and has a drainage surface of 1524 km2, was selected. In this watershed agricultural land use converges with numerous human activities resulting in high pressures on water quality. We found 40% of the P contribution episodes found seem to come from the runoff generated after the heaviest rainfall events, which normally occur between November and May. The remaining 60% of the P contribution episodes were found to be linked to point sources, which become more relevant from June to September, reaching the highest concentration values (6-17 mg/L). The results highlight that the target phosphate concentration value of 0.34 mg PO4/L imposed by the national legislation for a good state following the Water Framework Directive 2000/60/EC is exceeded by 96% of the measurements during the period from 1981 to 2022. On a monthly basis, PO4 loads showed a linear relationship with river streamflow (R2 = 0.94). However, on field measurements scale, a potential relationship between both variables was found, which changed according to the improvement in the wastewater treatment and facilities for 1982-1994, 1995-2017 and 2018-2022. In these three periods, different significant decreasing trends of the P content were found, mainly marked by the setup of each individual WWTP.

Assessing nonpoint source pollution risk in watersheds using a water-functioning zone approach.

A thorough understanding of the geographical and spatial attributes of nonpoint source pollution risk in watersheds is crucial for conducting nonpoint source pollutant studies and implementing effective scientific administration strategies. The inclusion of a water-related functioning zone was considered during the nonpoint source's pollution risks assessment procedure. Nevertheless, there has not been a thorough investigation into the potential risk of nonpoint sources of pollution to adequately safeguard the quality of water in watersheds having varying capacities to handle contaminants in the water. This research presents an innovative approach for assessing the risk of nonpoint sources contamination. This allows for a quantitative evaluation of the effect of discharges of pollution from a sub-catchment on the quality of water bodies nearby. The nonpoint source losses of nutrients process, as modeled by the Water and Soil Assessment Tool, had been used to assess the hazard of nonpoint source contamination in Le 'a River Watersheds. This assessment happened on both yearly and monthly scales. The findings indicated that the risk of nonpoint source contamination exhibits both seasonal and regional variations and is significantly impacted by the ability of the fluid ecosystem. Elevated nonpoint sources pollutants do not always equate to elevated pollutant dangers. On the other hand, a small amount of nutrients in the nonpoint sources does not indicate an insignificant degree of susceptibility to region risk. Furthermore, by utilizing a risk assessment method that considers the capacity of the water's environment, it is possible to identify variations in risk levels that may be overlooked when solely considering nonpoint sources contaminant losses, and fluid functioning zone. This approach allows for precise regulation of nonpoint sources of pollution administration.

Occurrence and distribution of legacy and novel brominated flame retardants in river and sediments in southwest China: A seasonal investigation.

Brominated flame retardants (BFRs) and their substitutes are prevalent in the environment, especially near industrial point sources. In non-point source pollution areas, it is crucial to investigate the seasonal pollution characteristics to identify the pollution sources. In this study, compositional profiles, seasonal variations, and ecological risks of legacy BFRs and novel BFRs (NBFRs) in the water and sediment from the Tuojiang River located in southwest China were investigated. The results indicated that ΣBFRs ranged from not detected (n.d.) to 42.0 ng/L in water and from 0.13 to 17.6 ng/g in sediment, while ΣNBFRs ranged from n.d. to 15.8 ng/L in water, and from 0.25 to 6.82 ng/g in sediment. A significant seasonal variation was observed in water and sediments with high proportions of legacy BFRs (median percentage of 68.8% and 51.3% in water and sediment) in the dry season, while NBFRs (median percentage of 53.2% and 71.6% in water and sediment) exhibited predominance in the wet season. This highlighted the importance of surface runoff and atmospheric deposition as important sources of NBFRs in aquatic environments. Moreover, there were high ratios of decabromodiphenyl ethane (DBDPE) and BDE-209 (average: 1.38 and 2.76 in dry and wet season) in sediments adjacent to the residual areas, indicating a consumption shift from legacy BFRs to NBFRs in China. It was observed that legacy BFRs showed higher ecological risks compared to NBFRs in both water and sediment environments, with BDE-209 posing low to medium risks to sediment organisms. This study provides better understanding of contamination characteristics and sources of legacy BFRs and NBFRs in non-point source pollution areas.

Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach.

Lake eutrophication caused by nitrogen and phosphorus has led to frequent harmful algal blooms (HABs), especially under the unknown challenges of climate change, which have seriously damaged human life and property. In this study, a coupled SWAT-Bayesian Network (SWAT-BN) model framework was constructed to elucidate the mechanisms between non-point source nitrogen pollution in agricultural lake watersheds and algal activities. A typical agricultural shallow lake basin, the Taihu Basin (TB), China, was chosen in this study, aiming to investigate the effectiveness of best management practices (BMPs) in controlling HABs risks in TB. By modeling total nitrogen concentration of Taihu Lake from 2007 to 2022 with four BMPs (filter strips, grassed waterway, fertilizer application reduction and no-till agriculture), the results indicated that fertilizer application reduction proved to be the most effective BMP with 0.130 of Harmful Algal Blooms Probability Reduction (HABs-PR) when reducing 40% of fertilizer, followed by filter strips with 0.01 of HABs-PR when 4815ha of filter strips were conducted, while grassed waterway and no-till agriculture showed no significant effect on preventing HABs. Furthermore, the combined practice between 40% fertilizer application reduction and 4815ha filter strips construction showed synergistic effects with HABs-PR increasing to 0.171. Precipitation and temperature data were distorted to model scenarios of extreme events. As a result, the combined approach outperformed any single BMP in terms of robustness under extreme climates. This research provides a watershed-level perspective on HABs risks mitigation and highlights the strategies to address HABs under the influence of climate change.

A spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination in sub-watersheds.

Best management practices (BMPs) have been extensively employed in effective watershed management for non-point source pollution. The weights of objective functions and the restrictive conditions of combined BMPs are the vital requirements for BMPs allocation. Therefore, it is more beneficial to explore that a spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination. Here is the novel framework based on Soil and Water Assessment Tool (SWAT) and the Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ), which considers multiple objectives in deriving watershed-scale pollution control practices by considering BMPs cost and combined reduction rates of total nitrogen (TN) and total phosphorus (TP). The framework also integrates combined Entropy Weight method (EWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the weights of TN and TP, and considers the attributes of the sub-basin itself, which is more local suitability. Four categories of BMPs, tillage management, nutrient management, vegetative filter strips, and landscape management, were evaluated in the Jing River Basin (JRB) and resulted in reduction rates of 9.77%, 10.53%, 16.40%, and 14.27% averagely, respectively. BMP allocation schemes, derived from multi-objective optimization, are stratified into three financial scenarios. Low-cost scenario, costing up to 2 billion RMB, primarily targets the grain for green program in 28.81% of sub-basins. Medium-cost scenario, between 2 and 6 billion RMB, predominantly utilizes the grain for green in areas with a slope greater than 15°, accounting for 20.00% of sub-basins. High-cost scenario exceeds 6 billion RMB, mainly due to the implementation of multiple combination measures. The three configuration scenarios can provide decision-makers with a trade-off between measure costs and reduction efficiency. Overall, the innovative framework not only facilitates cost-effective implementation but provides a beneficial methodology for selecting cost-effective conservation practices in other regions.

Spatiotemporal drivers of agricultural non-point source pollution: A case study of the Huang-Huai-Hai Plain, China.

Agricultural non-point source pollution (ANPSP) poses a severe threat to ecological environments, especially in China's major grain-producing regions. Despite the increasing attention, existing studies often overlook the spatial heterogeneity and driving mechanisms of ANPSP within different functional regions. This study addresses this research gap by constructing a bottom-up regional inventory of ANPSP for the Huang-Huai-Hai Plain (HHHP) and applying the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyse the spatio-temporal patterns of ANPSP from 2000 to 2020. Spatial econometric models were further applied to examine the spatial spillover effects of driving factors from the perspective of Major Function-oriented Zoning (MFZ). The results show that while ANPSP emissions in the HHHP have generally increased over the past two decades, a slight decrease has been observed since 2015. Grain yield capacity and cropping intensity were identified as the primary drivers of ANPSP growth, particularly in urbanised zones (UZs) and main agricultural production zones (MAPZs). The study also highlights significant spatial heterogeneity in the impact of driving factors on ANPSP across different MFZs, with marked differences in both the direct and spatial spillover effects of these factors. This underlines the need for differentiated environmental protection policies tailored to the functions and characteristics of each region. By integrating the LMDI decomposition method with spatial econometric models, this study offers a new framework for understanding the ANPSP dynamics within the context of MFZs, providing policymakers with valuable insights for designing effective, regionally coordinated governance strategies.

The use of the self-organizing map (SOM) methodology to study the dissolved organic matter (DOM) in different water body types.

This study investigated the characteristics of dissolved organic matter (DOM) in two distinct water bodies, through the utilization of three-dimensional fluorescence spectroscopy coupled with self-organizing map (SOM) methodology. Specifically, this analysis concentrated on neurons 3, 14, and 17 within the SOM model, identifying notable differences in the DOM compositions of a coal subsidence water body (TX) and the MaChang Reservoir (MC). The humic substance content of DOM TX exceeded that of MC. The origin of DOM in TX was primarily linked to agricultural inputs and rainfall runoff, whereas the DOM in MC was associated with human activities, displaying distinctive autochthonous features and heightened biological activity. Principal component analysis revealed that humic substances dominated the DOM in TX, while the natural DOM in MC was primarily autochthonous. Furthermore, a multiple linear regression model (MLR) determined that external pollution was responsible for 99.11% of variation in the humification index (HIX) of water bodies.

Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.

Spatial distribution of pesticide use based on crop rotation data in La Plata River basin: a case study from an agricultural region of Uruguay.

The intensive global use of pesticides presents an escalating threat to human health, ecosystems, and water quality. To develop national and local environmental management strategies for mitigating pollution caused by pesticides, it is essential to understand the quantities, timing, and location of their application. This study aims to estimate the spatial distribution of pesticide use in an agricultural region of La Plata River basin in Uruguay. Estimates of pesticide use were made by surveying doses applied to each crop. This information was spatialized through identifying agricultural rotations using remote sensing techniques. The study identified the 60 major agricultural rotations in the region and mapped the use and application amount of the nine most significant active ingredients (glyphosate, 2,4-dichlorophenoxyacetic acid, flumioxazin, S-metolachlor, clethodim, flumetsulam, triflumuron, chlorantraniliprole, and fipronil). The results reveal that glyphosate is the most extensively used pesticide (53.5% of the area) and highest amount of use (> 1.44 kg/ha). Moreover, in 19% of the area, at least seven active ingredients are applied in crop rotations. This study marks the initial step in identifying rotations and estimating pesticide applications with high spatial resolution at a regional scale in agricultural regions of La Plata River basin. The results improve the understanding of pesticide spatial distribution based on data obtained from agronomists, technicians, and producers and provide a replicable methodological approach for other geographic and productive contexts. Generating baseline information is key to environmental management and decision making, towards the design of more robust monitoring systems and human exposure assessment.

Spatio-temporal characteristics and multi-scale risk identification of pollution load based on sensitivity analysis in small watersheds located in Tuojiang River Basin, China.

High-quality development of water resources supports high-quality socio-economic development. High-quality development connects high-quality life, and clarifying the key management contents of small watersheds plays an important role in building ecologically clean small watersheds and promoting regional production and life. Previous research on pollution loads has focused on examining the impact of various external drivers on pollution loads but still lacks research on the impact of changes in pollution sources themselves on pollution loads. In this study, sensitivity analysis was used to determine the impact of changes from different sources on the total pollution loads, which can recognize the critical pollution sources. We first employed the pollutant discharge coefficient method to quantify non-point source pollution loads in the small watershed in the upstream Tuojiang River basin from 2010 to 2021. Then, combination sensitivity analysis with Getis-Ord Gi* was used to identify the critical sources and their crucial areas at the global, districts (counties), and towns (streets) scales, respectively. The results indicate: (1) The pollution loads of COD, NH3-N, TN, and TP all show a decreasing trend, reducing by 18.3%, 16.2%, 18.6%, and 28.1% from 2010 to 2021, respectively; (2) Livestock and poultry breeding pollution source is the most critical source for majority areas across watershed; (3) High-risk areas are mainly concentrated in Jingyang district and its subordinate towns (streets). There is a trend of low-pollution risk areas transitioning to high-pollution risk areas, with high-risk areas predominantly concentrated in the southeast and exhibiting a noticeable phenomenon of pollution load spilling around. This study can promote other similar small watersheds, holding significant importance for non-point source pollution control in small watersheds.