Influencing factor analysis of phosphorus loads from non-point source: a case study in central China

Contribution of non-point source pollution that migrated with underground runoff process based on the SWAT model and a digital filter algorithm

Non-point source (NPS) pollution has always been the focus of research worldwide, and understanding the migration process is the basis for effective control of NPS pollution. In this study, the SWAT model and digital filtering algorithm were combined to explore the contribution of NPS pollution that migrated with underground runoff (UR) process to the Xiangxi River watershed. The results showed that the surface runoff (SR) was the main migration process of NPS pollution, while the contribution of NPS pollution that migrated with the UR process only accounted for 30.9%. With the decrease in annual precipitation among the three selected hydrological years, the proportion of NPS pollution that migrated with the UR process for TN decreased, whereas the proportion for TP increased. The contribution of NPS pollution migrated with UR process varied remarkably during different months. Although the maximum total load and the load of NPS pollution that migrated with the UR process for TN and TP all appeared in the wet season, due to the hysteresis effect, the load of NPS pollution that migrated with the UR process for TP appeared 1 month later than the total load of NPS pollution. With an increase in precipitation from the dry season to the wet season, the proportion of NPS pollution that migrated with the UR process for TN and TP decreased gradually, and the degree of decrease in NPS pollution that migrated with the UR process for TP was more evident than that for TN. Besides, being affected by topography, land use, and other factors, the proportion of NPS pollution that migrated with the UR process for TN decreased from 80% in upstream areas to 9% in downstream areas, while that for TP reached a maximum of 20% in downstream areas. Based on the research results, the contribution of soil and groundwater cumulative nitrogen and phosphorus should be considered, and different managements and control measures for different migration routes should be adopted in controlling pollution.

Spatial-temporal characteristics of corrected total phosphorus pollution loads from agricultural non-point sources in Tuojiang River watershed, Sichuan Province of southwestern China

Traditional method of estimating pollution loads may neglect the internal spatial heterogeneity of socio-economic driving factors, which can result in overestimate and underestimate of pollution loads. In this study, the corrected approach to estimating total phosphorus (TP) pollution load was proposed to explore its future variation to develop effective phosphorus pollution control strategies for water environment management. As the first-class tributary of the Yangtze River, the TP out of limits in the Tuojiang River is serious. Thus, based on the presently related basic datasets related to TP pollution load estimation, we firstly adopted the GM (1,1) model to predict their varied trends from 2021 to 2025. We then used the pollution emission coefficient method to calculate the TP pollution load. Moreover, considering the temporal and spatial heterogeneity of the pollutant generation coefficient, we further introduced population and GDP factors to further modify the pollutant generation coefficient to correct TP pollution load. Finally, we employed the exploratory spatial data analysis (ESDA) method to explore spatial distribution characteristics and spatial autocorrelation of TP pollution load from diverse pollution sources in 2025. The results showed that the total TP pollution load from diverse pollution sources will increase from 12,194.92 t in 2021 to 12,461.26 t in 2025, an increase of 2.18%. More concretely, the TP pollution load from rural domestic sewage, rural domestic waste and livestock, and poultry pollution sources will separately decrease by 94.24 t, 77.9 t, and 86.52 t. However, the TP pollution load from agricultural runoff and agricultural solid wastes pollution sources will increase by 74.52 t and 451.49 t, respectively. The contribution of TP pollution load from diverse pollution sources to total TP pollution load will be as follows: livestock and poultry (63.49%) > agricultural solid wastes (16.72%) > agricultural runoff (12.26%) > rural domestic sewage (4.12%) > rural domestic waste (3.41%). The difference in the spatial distribution of TP pollution load from diverse pollution sources in 2025 will be prominent. TP pollution from rural domestic sewage and rural domestic waste pollution sources is more serious in the Xindu and Longquanyi districts, and that from agricultural runoff and agricultural solid wastes pollution sources is more prominent in the midstream and downstream. TP pollution load from livestock and poultry pollution source is higher in the Renshou, Anyue, Rongxian, Luxian counties, and Jiangyang district. Additionally, TP pollution load from rural domestic sewage, rural domestic waste, agricultural runoff, and agricultural solid wastes pollution sources in 2025 will show a clear spatial correlation, but the spatial correlation of TP pollution load from livestock and poultry pollution source will be weak. The study is effective to eliminate the influence of temporal and spatial variation of pollutants generates coefficients on TP pollution load estimation. The method can reflect the actual condition of pollution loads in watersheds more objectively, which can be applied to estimate other pollution loads of similar watersheds with intensive socio-economic activities. The findings in this study can provide a critical reference for the stakeholders to balance water environment conservation and socio-economic development.

Effect of agricultural activities on surface water quality from páramo ecosystems

Páramos are high mountain ecosystems strategic for water provision in South America. Currently, páramos are under threat due to agricultural intensification that impairs surface water sources. This research analyzed the effect of agriculture (spring onion-Allium fistulosum, potato-Solanum tuberosum, and livestock farming) on water quality in páramo ecosystems. A Hydrographic Unit upstream of the Jordan river catchment (Colombia) was selected and monitored in two different rainfall regimes, following the paired catchments and upstream-downstream approaches to compare water quality from natural and anthropic areas. Twenty-two parameters related to agricultural activities were analyzed (nutrients, salts, organic matter, sediments, and pathogens). The studied agricultural activities increased loads of surface water in quality in nitrates (0.02 to 2.56 mg N-NO₃/L), potassium (0.13 to 1.24 mg K/L), and Escherichia coli (63 to 2718 FCU/100 mL), generating risks on the human health and promoting eutrophication. Total nitrogen and organic matter in the rainy season were higher than dry. BOD₅, COD, turbidity, and E. coli were above international standards for direct human consumption. However, water could be used for irrigation, livestock watering, and aquatic life ambient freshwater. The results show that a small land-use change of almost 15% from natural páramo vegetation to agricultural uses in these ecosystems impairs water quality, limiting its uses, and the need to harmonize small-scale livelihoods in the páramo with the sustainability of ecosystem service provision.

Combined impact of land cover, precipitation, and catchment area on discharge and phosphorus in the Mississippi basin's subcatchments

Phosphorus (P) supplies (concentrations and fluxes) are essential drivers for biological activities in rivers and should be controlled to prevent eutrophication that usually results from urbanization and agricultural expansion. In this study, data from 26 subcatchments in the Mississippi basin were compiled from 2013 to 2017 to identify how catchment area, precipitation, and land cover affect discharge and total P (TP) and how TP yield diverges from a generalized local response mode. Results revealed that area-weighted discharge (Q_area ) is controlled by precipitation and land cover (i.e., increases with precipitation and with both urban and forestland covers and decreases with both shrub/scrub and pasture/grassland covers). Total P concentration increases with agricultural land cover and decreases with both forest and water/wetland covers. Total P yield (Q_area × concentration) is governed mainly by Q_area because the latter changes by a higher order of magnitude compared with concentration in the current study. Hence, TP yield follows the same trends that Q_area exhibits with precipitation and land cover. In all catchments, TP yield varied significantly (p < .05) and positively with instantaneous discharge. However, the rate of yield variations with discharge exhibited a significant (p < .0001) strong negative (r²  = -.74) correlation with catchment area. This study provided a robust model that can predict the TP concentration and yield across different catchment scales in the Mississippi basin by means of discharge readings.