Non-point Source Research Articles

Impact of Fine Sediment on Benthic Macroinvertebrates Communities of Freshwater Ecosystem: A Review

The freshwater environment in Ethiopia is exposed to severe human influence because of fast population growth and other factors. Anthropogenic activities in developing countries such as river regulation and pollution have a potentially negative cumulative impact on water quantity and quality of the rivers and wetlands, hence negatively impacting benthic invertebrates and fishes. This review aims to identify the impacts of fine sediment on the community of benthic macroinvertebrates, and freshwater food webs, and to assess the response of benthic invertebrates to fine sediment pollution. Literature review was used as a methodology. Fine segment load on the freshwater ecosystem from both point and nonpoint sources may directly/indirectly alter the macroinvertebrate communities. Organic and inorganic contaminants as well as other sediment variables (sediment grain size, total organic carbon, nutrients, etc.) were affecting the community composition of benthic invertebrates. However, habitat features like the number of supplements or the silt grain measure clarify the variation in macroinvertebrate indices and metrics. The impact of metals and, to a lesser extent, organic contaminants may also be significant. Benthic macroinvertebrates are commonly used indicators of river ecological conditions that can be adversely affected by fine sediment loads. Sedimentation can change the suitability of the substrate for some taxa, increase macroinvertebrate drift, and affect respiration and feeding. To conclude, excessive fine sediment severely alters the structure and function of macroinvertebrates. At the regional and national levels, different governmental and non-governmental organizations, research institutions, and policy makers are recommended to take responsibility for reducing sediment discharge to the freshwater ecosystem by applying different conservation measures of benthic macroinvertebrates.

Research on a Biofilter for a Typical Application Scenario in China: Treatment of Pesticide Residue Wastewater in Orchards

To reduce pesticide pollution and promote sustainable agricultural development in China, we designed a pilot-scale biofilter system to treat residual imidacloprid wastewater in an orchard. The biofilter system demonstrated a high rate of removal of imidacloprid from the biodegradation wastewater, with removal rates from the outlet exceeding 99% at different concentrations of pesticides. Among environmental factors, imidacloprid concentration at the inlet and biomixture significantly affected the activity of imidacloprid-degrading bacteria. The dominant microbial communities during the stable operation of the biofilter system included Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes at the phylum level and Bacillus, Methylobacter, and unclassified_f__Microbacteriaceae at the genus level. In future initiatives to improve biofilter performance and applicability, increasing attention should be paid to the dominant microbial communities, the number of biofilter units, and important environmental factors. Orchard workers in China should improve the existing treatment of residual pesticide wastewater to mitigate agricultural non-point source pollution.

Effects of agriculture and urbanization on the geochemistry of the Scioto River, Ohio

Despite efforts to reduce nonpoint source contamination of water resources, agriculture and urbanization remain primary sources that impair surface water quality. The Scioto River in Ohio, USA has headwaters in land dominated by primarily row-crop agriculture, then flows through metropolitan Columbus, the 14th largest city in the U.S. The river serves as a major drinking water source for the city and is dammed to form two reservoirs. The Scioto River was sampled for major ions, nutrients, and select trace elements weekly to monthly at five locations along changing agricultural and urban land use. Dissolved concentrations of many analytes were highest at the most upstream agricultural location, decreased to downtown Columbus, and increased at the location furthest downstream in agricultural land. The same trend was observed in discharge-weighted yields. Because much of the study area is underlain by carbonate bedrock, correlations with Ca2+ were likely to indicate a primarily geogenic source of the solute. Elements with moderate to strong positive correlations with Ca2+ included SO42−, Ni2+, Rb+, Mo6+, and Ba2+. Inorganic N species, emanating primarily from an agricultural source, had negative relationships with Ca2+. Concentration-discharge relationships revealed chemostatic to weak dilution of analytes positively correlated with Ca2+, suggesting a geogenic source of these elements that was weakly diluted with increasing discharge. Cu2+ showed a positive concentration-discharge slope, suggesting an anthropogenic input, but a defined source was not identified. Dams on the Scioto River that form drinking water reservoirs dampened chemical correlations and concentration-discharge relationships. Both agricultural and urban sources influence the river's chemistry based on hydroclimatic conditions and land use. Higher frequency sampling of the Scioto River during high flows allowed for a more nuanced understanding of sources of solutes to the river compared to previous work. Continued water quality monitoring is imperative to maintain and protect water quality resources, especially as urban populations are predicted to grow significantly in the next century.

Spatial Layout of Vegetation Buffer Zones around Water Bodies to Avoid Non-Point Source Pollution in a Mining Area

The riparian vegetation buffer zone plays an important role in the prevention and control of non-point source pollution, and consideration should be given to selecting one with a lower cost and better effect. The SWAT model was used to simulate the regulation ability of riparian vegetation buffer zone layout on runoff, nitrogen, and phosphorus in the Jiawang Basin, and five different vegetation buffer zone layout scenarios were set up. The results showed that (1) the SWAT model has good applicability in simulating runoff and water quality in the Jiawang Basin; (2) the reduction rates of runoff, total nitrogen, and total phosphorus in continuous forest riverbank buffer zones reached 2.46%, 6.63%, and 9.18%, and their regulatory effects were better than those in grasslands; (3) there is not much difference in the inhibitory effect of forest and grassland on total nitrogen, but the discontinuous forest buffer zone has a better reduction effect on total phosphorus than grassland. Therefore, in the actual arrangement of vegetation buffer zones, it should be tailored to local conditions to achieve ideal non-point source pollution prevention and control effects at a lower cost.

Evaluating the long-term effects of best management practices on pollution reduction and soil quality improvement in sloping farmland of the Three Gorges Reservoir area

Reducing nonpoint-source pollution and improving soil quality by applying best management practices (BMPs) on sloping farmland are essential for ensuring water environmental safety and sustainable agricultural development. However, little is known about the accumulative effect of the long-term application of BMPs on reducing pollution and improving soil quality in major land use types of sloping farmland in watersheds. We conducted a nine-year field experiment for citrus orchard and cropland of sloping farmland in the Three Gorges Reservoir area of China. The treatment settings for the citrus experimental plots were as follows: (1) citrus intercropped with white clover (WC), (2) citrus orchard soil mulched with straw (SM), (3) citrus intercropped with Hemerocallis flava contour hedgerows (HF) and (4) conventional citrus planting pattern (C-CK). The treatment settings for the crop experimental plots were as follows: (1) wheat-peanut rotation intercropped with Toona sinensis contour hedgerows (TS), (2) wheat-peanut rotation intercropped with alfalfa contour hedgerows (AF), (3) ryegrass-sesame rotation (RS) and (4) conventional wheat-peanut rotation (W-CK). We found that compared with C-CK and W-CK, (1) runoff, sediment, total nitrogen and total phosphorus losses were reduced by 18.0 %, 66.1 %, 35.6 % and 40.2 %, respectively, in WC; by 22.4 %, 61.2 %, 43.9 % and 52.4 %, respectively, in SM; by 25.4%, 69.5 %, 52.0 % and 58.1 %, respectively, in HF; by 45.1 %, 73.7 %, 64.6 % and 66.0 %, respectively, in TS; by 41.8 %, 67.4 %, 59.3 % and 60.8 %, respectively, in AF; and by 24.1 %, 42.9 %, 39.3 % and 40.3 %, respectively, in RS; (2) the soil quality index values of the BMP treatments increased; and (3) the application of these BMPs did not significantly affect yield or product value. We suggest the application of these BMPs on sloping farmland to (1) efficiently reduce total runoff, total sediment, and soil total nitrogen and total phosphorus losses; (2) improve soil quality; and (3) have no significant effect on output, of which the BMP HF applied to citrus orchard and the BMP TS applied to cropland are the most effective.

Evaluation of the Best Management Practices for Reducing Phosphorus Load in a Watershed in Terms of Cost and Greenhouse Gas Emissions

Effective management of water quality in watersheds is crucial because it is directly linked to the sustainability of aquatic ecosystems. In conventional watershed management, best management practices (BMPs) have been instrumental in addressing deteriorating water quality issues caused by non-point source pollution. Greenhouse gas (GHG) emissions have emerged as a global concern, necessitating immediate and diverse environmental actions to mitigate their impacts. This study aims to explore BMPs that maximize total phosphorus (TP) load removal efficiencies, while minimizing costs and GHG emissions within watersheds, using the Soil and Water Assessment Tool (SWAT) and non-dominated sorting genetic algorithm III (NSGA-III). The Yeongsan River Watershed between 2012 and 2021 was selected as the study area. Hydrological and BMP data were analyzed. Applying identical BMPs to the watershed showed that the BMPs with high TP removal efficiency may not be effective in terms of cost and GHG emissions. Therefore, the optimal combination of BMPs for the Yeongsan River Watershed was determined using NSGA-III considering TP removal efficiency, cost, and GHG emissions. This study is the first to consider GHG emissions at the watershed level when applying BMPs and is expected to contribute to the development of BMP implementation incorporating GHG emissions.

Salty chemical cocktails as water quality signatures: Longitudinal trends and breakpoints along different U.S. streams

Along urban streams and rivers, various processes, including road salt application, sewage leaks, and weathering of the built environment, contribute to novel chemical cocktails made up of metals, salts, nutrients, and organic matter. In order to track the impacts of urbanization and management strategies on water quality, we conducted longitudinal stream synoptic (LSS) monitoring in nine watersheds in five major metropolitan areas of the U.S. During each LSS monitoring survey, 10–53 sites were sampled along the flowpath of streams as they flowed along rural to urban gradients. Results demonstrated that major ions derived from salts (Ca2+, Mg2+, Na+, and K+) and correlated elements (e.g. Sr2+, N, Cu) formed ‘salty chemical cocktails’ that increased along rural to urban flowpaths. Salty chemical cocktails explained 46.1% of the overall variability in geochemistry among streams and showed distinct typologies, trends, and transitions along flowpaths through metropolitan regions. Multiple linear regression predicted 62.9% of the variance in the salty chemical cocktails using the six following significant drivers (p < 0.05): percent urban land, wastewater treatment plant discharge, mean annual precipitation, percent silicic residual material, percent volcanic material, and percent carbonate residual material. Mean annual precipitation and percent urban area were the most important in the regression, explaining 29.6% and 13.0% of the variance. Different pollution sources (wastewater, road salt, urban runoff) in streams were tracked downstream based on salty chemical cocktails. Streams flowing through stream-floodplain restoration projects and conservation areas with extensive riparian forest buffers did not show longitudinal increases in salty chemical cocktails, suggesting that there could be attenuation via conservation and restoration. Salinization represents a common urban water quality signature and longitudinal patterns of distinct chemical cocktails and ionic mixtures have the potential to track the sources, fate, and transport of different point and nonpoint pollution sources along streams across different regions.

Spatial distribution of trace and rare earth elements of bottom sediments in Lake Ostrowite, Bory Tucholskie National Park, Poland

AbstractLake pollution has attracted concerns worldwide; especially the excessive accumulation of trace elements (TEs) and rare earth elements (REEs) in bottom sediments can pose a serious threat to ecosystem health. However, there is still a knowledge gap on the level of sediment pollution in lakes isolated from the direct impact of pollution sources, their spatial variability, and also on the factors influencing this state. The aim of this study is to investigate the level and spatial variability of TEs and REEs concentrations, as well as to determine their source and the factors determining their distribution in the bottom sediments of Lake Ostrowite. Lake Ostrowite is the largest and deepest water body located in the Bory Tuholskie National Park (in northern Poland), which completely isolates the lake from the direct impact of pollution sources. The study covered analyses of 32 surface samples of bottom sediments. The concentrations of 24 TEs and 14 REEs were determined using inductively coupled plasma mass spectrometry (ICP‐QQQ‐MS). The assessment of the enrichment of bottom sediments in TEs and REEs employed geochemical background values (GBV) that provided the basis for the calculation of relative concentrations and geochemical indices. The determination of their sources and supply routes was based on the cluster analysis and principal component analysis. The obtained results point to the enrichment of the bottom sediments with TEs and REEs. Relative concentration values of TEs and REEs in reference to geochemical background values were in ranges from 0.01 to 7.31, at an average of 0.99, and from 0.03 to 4.29, averaging 1.76, respectively. The enrichment factor values show moderately severe enrichment of sediments at the study sites. This was primarily determined by the concentrations of Ag (from the TEs group) and Lu (from the REEs group). The metal pollution index values showed an approximate spatial distribution of points in terms of the presence of TEs and REEs. The lowest concentrations of TEs and REEs occurred on the eastern shore of the western basin of Lake Ostrowite. TEs and REEs concentrations in sediments are positively correlated with the content of organic matter and depth and negatively correlated with distance of the sampling point from the river outflow from Lake Ostrowite. On the eastern shore of the western basin, TEs and REEs concentrations are additionally shaped by wind, predominantly from the western direction. With water wave action, organic matter is transported to the central part of the western basin, where it is accumulated. Since the lake is isolated from point and nonpoint pollution sources, relevant from a biogeochemical point of view are dry and wet depositions from the atmosphere as well as aquatic vegetation, shoreline vegetation, forest litter, soil, and groundwater.

The effect of growth, deforestation, forest fires, and volcanoes on Indonesian regional air quality

As a rapidly developing country, Indonesia faces a challenge in improving air quality that is made more difficult by frequent forest fires linked to deforestation and volcanic eruptions. This paper analyses the link between development and air pollution in 30 Indonesian cities from 2002 to 2019. Air quality is measured using two novel regional air quality indicators (AQIs) for PM2.5 and PM10 estimated from weather observations and NASA satellite data on Aerosol Optical Depth (AOD). Results from an Environmental Kuznets Curve (EKC) analysis, based on a dynamic panel data model, show that forest fires and volcanic activity have significantly worsened regional air quality in Indonesia. The model also shows that the EKC hypothesis is rejected for Indonesia as cities have followed a monotonically increasing trend in air pollution with respect to economic growth. An additional 1% of economic growth increased long run pollution as measured by AQI PM2.5 and PM10 by 0.35% and 0.46%, and forest fire events by 0.11% and 0.06%. A partial Benefit-Cost Analysis (BCA) of forest fires shows that particulate matter pollution is excessive. Decreasing the frequency of forest fires by 1% would generate a net benefit of between US$17 million to 145 million. The lower estimate of the health benefits exceeds the agricultural benefits of forest fire without accounting for other non-market costs due to forest fires. Our results imply that, as Indonesia grows economically, air quality is likely to continue deteriorating. Further, regions affected by regular volcanic activity and wildfires should apply tighter emission standards to controllable point source emissions from industry and non-point source emissions such as agricultural forest fires.

Evaluating Best Management Practice Efficacy Based on Seasonal Variability and Spatial Scales

Implementing best management practices (BMPs) has proven to be an efficient method for reducing non-point source (NPS) pollutants. Agricultural NPS pollution is considered to be a major contributor to water quality impairment. This study aims to assess the variation in hydrologic and water quality outputs at field and watershed scales when BMPs are implemented using modeling approaches. The Yazoo River Watershed (YRW) is the largest watershed basin in the state of Mississippi with approximately 50% agricultural land. Runoff generated from agricultural areas carries sediments and nutrients. The Merigold watershed (MW) is a sub-basin of the YRW and a field-scale watershed with most of the land use being agriculture. It is essential to quantify the streamflow, sediment, total nitrogen (TN), and total phosphorus (TP) when BMPs are implemented. BMPs such as vegetative filter strips (VFS) and cover crops (CC) were tested in this study. The Soil and Water Assessment Tool (SWAT) model was applied to quantify the watershed’s hydrologic and water quality outputs. SWAT model accuracy assessment was performed by calibration and validation process using the Nash and Sutcliffe Efficiency Index (NSE). Model performance was satisfactory for monthly streamflow, with NSE values in the range of 0.62 to 0.81, and for daily sediments, TN, and TP load estimation, with NSE values of 0.21, 0.20, and 0.47, respectively. CC was planted after harvesting the main crop. Therefore, it is essential to quantify the seasonal reduction in pollutants. Water quality was improved after BMP implementation, and an overall decrease in streamflow, sediment, TN, and TP loads was observed for both MW and YRW during dry and wet seasons. Previous studies regarding seasonal assessments with CC implementation in the MW and YRW were limited. Therefore, the results from this study could be a unique addition to the scientific literature.

Spatial scale effects of landscape patterns on non-point source pollution: A case study of Taizi River Basin in Northeast China

River water quality is influenced by natural processes and human activities. Multi-scale landscape patterns can affect river water quality by altering the generation and transport processes of pollutants at different spatial scales. Taking Taizi River Basin in Northeast China as an example, we analyzed the relationship between landscape patterns and non-point source pollution in rivers based on water quality monitoring data and land use data by using correlation analysis and redundancy analysis methods. We aimed to determine the key spatial scales for the responses of landscape patterns to non-point source pollution and identify the key landscape indices influencing river non-point source pollution. The results showed that water quality of Taizi River Basin had seasonal differences, with better water quality during the flood season than non-flood season. Spatially, total nitrogen (TN) and total phosphorus (TP) were higher at the confluence points of tributaries and downstream areas. The impact of landscape patterns on non-point source pollution was stronger during the non-flood season than the flood season, while the influence on TN was stronger than on TP. At the spatial scale of within 500 m buffer zone during the flood season and at the sub-watershed scale during the non-flood season, landscape patterns showed the highest explanatory power for the variations of TN and TP. At the type level, built-up land, cropland, and bare land were positively correlated with TN and TP, while forest was negatively correlated with TN and TP, which were the key types influencing non-point source pollution. At the landscape level, patch density, percentage of like adjacencies, and contagion index were key indicators affecting watershed water quality. Lower patch density was associated with better connectivity and aggregation of "sink" landscapes, leading to better purification effects on TN, but more pronounced retention effects on TP. Conversely, higher landscape diversity and denser pattern of multiple types would cause the deterioration of water quality. Our results suggested that rational allocation of landscape types within the watershed and riparian buffer zones, appropriately enriching landscape diversity, and optimizing landscape aggregation and connectivity would be effective measures for improving water quality and achieving sustainable ecological management.

Assessment of Agricultural Non-point Source Pollution Loads Applying Improved Export Coefficient Model (IECM) : A Case Study in Taihu Lake Basin

Accurate and comprehensive estimation of agricultural non-point source pollution (ANPSP) is of vital importance in watershed pollution control, while data inaccessibility and complexity of process-based models may be main restrictions in practice. An estimation methodology framework for ANPSP was established based on improved export coefficient model (IECM) in this research. A series of updated, regional coefficients were collected and/or calculated with multiple pollution sources considered (i.e., animal husbandry, farming and forestry, fishery, and rural domestic sewage) to provide a comprehensive estimation of TN and TP pollution loads in Taihu Lake Basin. According to this model, pollution loads of TN and TP in Taihu Lake Basin were 65791.28 and 11400.38 t in 2016, respectively. Animal husbandry was main source of ANPSP, accounting for 31.05% and 66.70% for TN and TP, respectively, which requires more efficient and clean agricultural production modes and strict pollution control measures. IECM-based estimation framework may provide a reference for ANPSP management practice in large scale watershed.

Review Paper: A Quarter of a Century of the European Water Framework Directive — The Slow Path Towards Sustainable Water Management

The European Water Framework Directive (WFD) is one of the most analyzed environmental legislations in the scientific literature, influencing the water management in some non-European countries. The WFD has the strong ambition of achieving a good ecological status of water bodies across all river basins in Europe. However, the advances towards sustainable management are falling far behind the planned schedule. The emphasis of the Directive is focused on water quality rather than on water quantity. The advances during the last quarter of the century since its inception have been strong on urban and industrial point pollution, but not on agricultural non-point pollution that remains high and even increases in major basins. Water quantity aspects have been mostly left aside in the Directive, despite the fact that water scarcity is a serious problem in Southern European countries, and will become more critical with climate change in most basins across Europe. Some policy measures of the WFD need to be reformed, in particular measures for abating agricultural pollution, and new measures for addressing water scarcity. The narrow focus of the WFD on water pricing to solve at the same time issues of financing, water allocation and efficiency, environment, opportunity costs and pollution abatement, should be broadened. The challenge is giving more emphasis to command &amp; control and collective action instruments, and designing combinations of instruments adapted to sectoral and spatial locations in basins. This overhaul of the water policy instruments by the European Commission will be needed to advance in the sustainable management of river basins in Europe.

The potential of carbon markets to accelerate green infrastructure based water quality trading

Green infrastructure solutions can improve in-stream water quality in lieu of building electricity-consuming gray infrastructure. Permitted under the United States Clean Water Act, these programs allow regulated utilities to trade point-source water quality obligations with non-point source mitigation efforts in the watershed. Carbon financing can provide an incentive for water quality trading. Here we combine data on impaired waters, treatment technologies, and life cycle greenhouse gas emissions in the Contiguous United States, and compare traditional treatment technologies to alternative green infrastructure. We find green infrastructure could save $15.6 billion dollars, 21.2 terawatt-hours of electricity, and 29.8 million tonnes of carbon dioxide equivalent emissions per year while sequestering over 4.2 million tonnes CO2e per year over a 40 year time horizon. Green infrastructure solutions may have the potential to generate $679 million annually in carbon credit revenue (at $20 per credit), which represents a unique opportunity to help accelerate water quality trading.

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.

“Period-area-source” hierarchical management for agricultural non-point source pollution in typical watershed with integrated planting and breeding

Agricultural non-point source (NPS) pollution threatens the aquatic environments significantly. Insufficient attention to the migration characteristics of multiple sources of pollution as well as the variability of anthropogenic interference hinders effective pollution control, particularly in watersheds with integrated planting and breeding (IPB). In this study, we proposed a distributed dual-structure empirical export model for IPB (DSEEM-IPB) that optimizes hydrological processes in pollutant transport and refines NPS load estimation from animal breeding sources to simulate agricultural NPS pollution in a typical IPB watershed. Based on the estimated NPS load, a “period-area-source” management approach for NPS pollution is proposed with a co-analysis of critical periods (CPs), critical source areas (CSAs), and critical source types (CSTs). The results show that (1) the CPs of only two months (June and July) contributed 62.9% and 56.2% of the total nitrogen (TN) and total phosphorus (TP) load respectively; (2) the CSAs in the CPs contributed 17.6% of the TN load within 14.0% of the area and 16.2% of the TP load within 13.4% of the area; (3) planting (74.4%) is the primary source of nitrogen loss while breeding (58.3%) is the primary source of phosphorus loss; and (4) optimal control measures should target planting sources in CSAs and sub-CSAs within CPs (yielding 32.4% of annual TN loads) and breeding sources in CSAs within non-CPs (yielding 22.1% of annual TP loads). This study indicates that prioritizing pollution management of critical sources from critical areas or periods can achieve efficient and targeted control of agricultural NPS pollution.

‘Cropaganda’: Mythology of Corn Belt agriculture

Even as scholars have increasingly recognized the role of industrial agricultural practices in contributing to non-point source pollution, drinking water in the Corn Belt remains perilously contaminated with excess nitrates, which pose a significant risk to human health and the environment. A recent lawsuit filed by the city of Des Moines, Iowa against three upstream counties over chronic nitrate pollution sparked heated debate around the roles and responsibilities of agricultural production. Drawing on Barthes’ theory of mythology, this paper explores how three core myths influence agricultural management paradigms and practices that contribute to the water quality crisis in Iowa and shape how key stakeholders have responded (Barthes, R. 1972 [1957]. Mythologies. New York: Hill and Wang). This article relies on ethnographic data to explain agricultural nitrate pollution and the stalled progress on water quality improvement. Together these myths draw on post-enlightenment ideas of nature, security, and modernity to perpetuate productivist behavior, claim resources for conventional commodity agriculture, and impede widespread adoption of alternative agricultural practices. By identifying and interrogating each myth, I aim to reveal and complicate the inherent contradictions in its representation of reality to strip it of ideological function and, in doing so, cultivate opportunities to imagine and create an alternative system that benefits both people and the planet.

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

Evolutionary Trend Analysis of Agricultural Non-Point Source Pollution Load in Chongqing Based on Land Use Simulation

Analysis of the relationship between future land use change and agricultural non-point source pollution (ANPSP) evolution is vital to promoting sustainable regional development. By simulating future land use types, we can identify and analyze the evolution trend of ANPSP. This study takes Chongqing as a case study to establish an integrated solution based on the PLUS model, output coefficient model, and GIS technology. The solution can simulate data, identify trends, and identify key control areas under future development scenarios. The results show that the PLUS model can simulate land use types at the provincial scale with high accuracy, with a Kappa coefficient of around 0.9. The land use type changes show that urban expansion has occupied a large amount of cultivated land. From 2000 to 2020, the proportion of high-load areas with TN pollution load levels was 4.93%, 5.02%, and 4.73%, respectively. Under the two scenarios in 2030–2050, the number of high-load areas decreased, and the average load level decreased from west to east. Sensitivity analysis found that risk changes are more sensitive to the increase in fertilizer application. When the TN and TP output coefficients are increased, the number of towns with increased levels is greater than those with decreased levels when the output coefficients are decreased. Sensitivity analysis can better identify key pollution control areas. The areas sensitive to changes in farmers’ behavior are mainly the Hechuan District, Nanchuan District, Qijiang District, Jiangjin District, and Bishan District. This study provides data and decision-making support for rural green development and water environment improvement.

Roles of Hydrology and Transport Processes in Denitrification at Watershed Scale

AbstractRainfall runoff and leaching are the main driving forces that nitrogen, an important non‐point source (NPS) pollutant, enters streams, lakes, and groundwater. Hydrological and transport processes thus play a pivotal role in NPS nitrogen pollution. Existing hydro‐environmental models for nitrogen pollution often over‐simplify the within‐watershed processes. It is unclear how such simplification affects the pollution assessment regarding the formation and distribution of denitrification hot spots—which is important for the design of land‐based countermeasures. To study this problem, we developed a model, DHSVM‐N, and its variant, DHSVM‐N_alt. DHSVM‐N is developed by integrating nitrogen‐related processes of SWAT into a comprehensive process‐based hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM). DHSVM‐N includes detailed representations of nitrate transport process at a fine spatial resolution with good landscape connectivity to accommodate interactions between hydrological and biogeochemical processes along the flow travel pathways. Because of the lack of spatially distributed observational data for validation, a model‐to‐model comparison study is conducted. Through comparison studies on a representative catchment using SWAT, DHSVM‐N and DHSVM‐N_alt, we quantify the critical roles of hydrological processes and nitrate transport processes in modeling the denitrification process. That is, the capabilities to give reasonable soil moisture estimates and to account for essential processes that take place along flow pathways are keys to simulate denitrification hot spots and their spatial variation. Furthermore, DHSVM‐N results show that terrestrial denitrification from hotspots alone can reach as high as 36% of the annual stream nitrate export of the watershed.