Tracking Nonpoint Source Nitrogen Pollution in Human-Impacted Watersheds

Synergistic impacts of land-use change and soil property variation on non-point source nitrogen pollution in a freeze–thaw area

In recent years, with the growth of the population and the continuous expansion of agricultural land, non-point source (NPS) pollution has gradually become the primary cause of deteriorating water quality in the aquatic environment. Compared to point source pollution, NPS pollution is more diffuse, complex in its mechanisms, and challenging to pinpoint its sources. This study utilized the SWAT-Land Use Update Tool (SWAT-LUT) to dynamically update multi-year land use and land cover (LULC) data into the SWAT model to investigate the differences in nitrogen pollution sources in Jincheng City under different LULC scenarios. Two models were constructed in this study: SWAT-UNI, which utilized static 1997 LULC data, and SWAT-MULTI, which incorporated dynamic LULC data from 1997 to 2022. During the calibration period, SWAT achieved R2 and NSE values exceeding 0.82 for the daily streamflow simulation results, and these values remained above 0.76 during the validation period. Additionally, the Patch-generating Land Use Simulation (PLUS) model was employed to forecast the land use evolution in Jincheng City from 2022 to 2032 to explore the future response of NPS nitrogen pollution. From 1997 to 2022, significant changes were observed in agricultural land, forested land, and grassland areas within Jincheng City. Agricultural land and forested land increased by 3.29% and 4.71% of the total area of Jincheng City, respectively, while grassland decreased by 10.4%. In the prediction of land use evolution from 2022 to 2032, the evolutionary trends remained similar to previous patterns, albeit with a slightly decelerated pace. Simulation results indicated that the top three sources of nitrogen pollution in Jincheng City's water bodies in 1997 were atmospheric deposition (39.8%), nitrogen fertilizer application (29.8%), and soil nitrogen reservoirs (21.4%). With the continuous expansion of agricultural land, nitrogen pollution from nitrogen fertilizer application accounted for 35.6% of the TN (Total Nitrogen) load in water bodies in 2022, surpassing atmospheric deposition to become the dominant factor. The contribution of soil nitrogen reservoirs to nitrogen pollution in water bodies within Jincheng City showed a continuous upward trend over the twenty-five years, resulting in a total nitrogen load of 565.1 tons in 2022, ranking second and becoming a crucial aspect in pollution control efforts. Regarding seasonal distribution, the crop growing season (March to September) was identified as the critical period for controlling nitrogen pollution from nitrogen fertilizer application, while the autumn and winter seasons were crucial for controlling nitrogen pollution from atmospheric deposition and soil nitrogen reservoirs. The predictive results for future NPS nitrogen pollution indicate a continual increase in annual TN inflow into the river from nitrogen fertilizer application and soil nitrogen reservoirs, reaching 1841.6 tons in 2032, accounting for 65.2% of the total inflow. This research contributes to supporting decision-making for NPS pollution control measures in Jincheng City.

Urban ecosystems are often sources of nonpoint source (NPS) nitrogen (N) pollution to aquatic ecosystems. However, N export from urban watersheds is highly variable. Examples of densely urbanized watersheds are not well studied, and these may have comparatively low export rates. Commonly used metrics of landscape heterogeneity may obscure our ability to discern relationships among landscape characteristics that can explain these lower export rates. We expected that differences not often captured by these metrics in the relative cover of vegetation, structures, and impervious surfaces would better explain observed variation in N export. We examined these relationships during storms in residential watersheds. Contrary to expectations, land cover did not directly predict variation in N or water export. Instead, N export was strongly linked to drainage infrastructure density. Our research highlights the role of fine‐scaled landscape attributes, mainly infrastructure, in explaining patterns of N export from densely urbanized watersheds. Changes to hydrologic flow paths by infrastructure explained more variation in N export than land cover. Our findings support further development of landscape ecological models of urban N export that focus on hydrologic modification by infrastructure rather than traditional landscape measures such as land use, as indicators for evaluating patterns of NPS nitrogen pollution in densely urbanized watersheds.

Management optimization of nonpoint source pollution considering the risk of exceeding criteria under uncertainty

Efforts to reduce land-based non-point source (NPS) pollutions from watersheds to coastal waters are ongoing all around the world. In this study, annual yield of NPS nitrogen (NPS-N) pollution in Jiaodong Peninsula, China from 1979 to 2008 was estimated. The results showed that: from 1979 to 2008, NPS-N yields exhibited significant inter-annual variations and an increasing trend on decadal scale. High NPS-N yield was mainly found in east and south parts, as well as the urbanized coastal regions in Jiaodong Peninsula. Among the 32 river basins, the three largest basins yielded more than 41.16% of the NPS-N. However, some small coastal watersheds along the South Yellow Sea and Jiaozhou Bay had higher per unit area yield. Most of the small watersheds characterized by seasonal runoff had coastal waters pertain to mild and moderate pollution levels. The ratio of watershed area to shoreline length and the up-stream land use had significant impacts on NPS-N flux through the shoreline. Among the four adjacent coastal areas of Jiaodong Peninsula, Jiaozhou Bay was the most noteworthy one not only because of high levels of land-based NPS-N pollution but also because of its nearly enclosed structure. The combination between integrated coastal zone management and integrated river basin management, land use planning and landscape designing in Jiaodong Peninsula is recommended. Copyright © 2013 John Wiley & Sons, Ltd.

Based on the relevant statistics on the agricultural non-point source pollution in Binzhou City, we studied the spatio-temporal variation in agricultural non-point source nitrogen pollution and phosphorus pollution in Binzhou City during 2008—2015. The results showed that the agricultural non-point source nitrogen pollution and phosphorus pollution in Binzhou City changed from 114400 t and 28800 t in 2008 to 110600 t and 24400 t in 2015, respectively; for the non-point source nitrogen pollution sources, the proportion of chemical fertilizer, manure and domestic sewage changed from 80.41%, 10.47% and 9.13% in 2008 to 82.94%, 4.77% and 12.29% in 2015, respectively; for the non-point source phosphorus pollution sources, the proportion of chemical fertilizer, manure and domestic sewage changed from 89.29%, 8.32% and 2.39% in 2008 to 91.6%, 4.78% and 3.62% in 2015, respectively; in 2015, the non-point source nitrogen pollution was most serious in Wudi County and Huimin County and least serious in Zhanhua County; the non-point source phosphorus pollution was most serious in Zouping County and least serious in Wudi County.

Analysis of non-point source nitrogen pollution in watersheds based on SWAT model

In the Sanjiang Plain, agricultural drainage ditches effectively alleviate agricultural non-point source nitrogen pollution. However, limited information is known about the characteristics of bidirectional trans-ports of nitrogen between sediment and overlying and pore water in different patterns of ditches undergoing seasonal freezing-thawing cycles. It is vital to better understand nitrogen interception and purification by ecological ditches. In order to clarify the interception of ecological ditches on internal and external nitrogen, overlying water and sediment samples of two typical agricultural ditches were collected and analyzed in Sanjiang Plain during the growing seasons of 2015–2017. The results indicated that the N-NO3− in overlying water, which was higher than N-NH4+, was the dominant inorganic nitrogen, whilst, in the sediment, N-NH4+ was much higher than N-NO3−, which should be attributed to the soil’s adsorption of N-NH4+. In contrast to the dryland ditch, the paddy ditch had a more significant amount of inorganic nitrogen both in overlying water and sediment, which means that the non-point source nitrogen pollution caused by paddy fields was more severe than that of drylands. Compared with dryland ditches, N-NH4+ in the sediment of pad ditches seemed to be much easier to migrate to a deeper layer, which may cause a greater risk of nitrogen pollution to groundwater. Both in the overlying water and the sediment of ditches, nitrogen content fluctuated during different periods, and inter-annual variation was noticeable, which results means that estimation or prediction of the non-point source pollution output needs to extend the monitoring period and increase sampling frequency to reduce the great uncertainty. The findings may provide a foundation for forecasting agricultural nitrogen pollution and guide best management practices (BMPs) of non-point source nitrogen pollution control in seasonally frozen areas.

ABSTRACT: Water quality and nonpoint source (NPS) pollution are important issues in many areas of the world, including the Inner Bluegrass Region of Kentucky where urban development is changing formerly rural watersheds into urban and mixed use watersheds. In watersheds where land use is mixed, the relative contributions of NPS pollution from rural and urban land uses can be difficult to separate. To better understand NPS pollution sources in mixed use watersheds, surface water samples were taken at three sites that varied in land use to examine the effect of land use on water quality. Within the group of three watersheds, one was predominately agriculture (Agricultural), one was predominately urban (Urban), and a third had relatively equal representation of both types of land uses (Mixed). Nitrogen (N), phosphorus (P), total suspended solids (TSS), turbidity, pH, temperature, and streamflow were measured for one year. Comparisons are made among watersheds for concentration and fluxes of water quality parameters. Nitrate and orthophosphate concentrations were found to be significantly higher in the Agricultural watershed. Total suspended solids, turbidity, temperature, and pH, were found to be generally higher in the Urban and Mixed watersheds. No differences were found for streamflow (per unit area), total phosphorus, and ammonium concentrations among watersheds. Fluxes of orthophosphate were greater in the Agricultural watershed that in the Urban watershed while fluxes of TSS were greater in the Mixed watershed when compared to the Agricultural watershed. Fluxes of nitrate, ammonium, and total phosphorus did not vary among watersheds. It is apparent from the data that Agricultural land uses are generally a greater source of nutrients than the Urban land uses while Urban land uses are generally a greater source of suspended sediment.

Rapid industrialization and population growth have elevated the concerns over water quality. Excessive nitrates and phosphates in the water system have an adverse effect on the aquatic ecosystem. In recent years, machine learning (ML) algorithms have been extensively employed to estimate water quality over traditional methods. In this study, the performance of nine different ML algorithms is evaluated to predict nitrate and phosphorus concentration for five different watersheds with different land-use practices. The land-use distribution affects the model performance for all methods. In urban watersheds, the regular and predictable nature of nitrate concentration from wastewater treatment plants results in more accurate estimates. For the nitrate prediction, ANN outperforms other ML models for the urban and agricultural watersheds, while RT-BO performs well for the forested Grand watershed. For the total phosphorus prediction, ensemble-BO and M-SVM outperform other ML models for the agricultural and forested watershed, while the ANN performs better than other ML models for the urban Cuyahoga watershed. In predicting phosphorus concentration, the model predictability is better for agricultural and forested watersheds. Regarding consistency, Bayesian optimized RT, ensemble, and GPR consistently yielded good performance for all watersheds. The methodology and results outlined in this study will assist policymakers in accurately predicting nitrate and phosphorus concentration which will be instrumental in drafting a proper plan to deal with the problem of water pollution.

Quantitative identification of nitrate pollution sources and uncertainty analysis based on dual isotope approach in an agricultural watershed

An agro-hydrological process-based export coefficient model for estimating monthly non-point source loads in a semiarid agricultural area

The purpose of this research was to thoroughly analyze the influences of environmental factors on denitrification processes in urban riparian soils. Besides, the study was also carried out to identify whether the denitrification processes in urban riparian soils could control nonpoint source nitrogen pollution in urban areas. The denitrification rates (DR) over 1 year were measured using an acetylene inhibition technique during the incubation of intact soil cores from six urban riparian sites, which could be divided into three types according to their vegetation. The soil samples were analyzed to determine the soil organic carbon (SOC), soil total nitrogen (STN), C/N ratio, extractable NO3 (-)-N and NH4 (+)-N, pH value, soil water content (SWC), and the soil nitrification potential to evaluate which of these factors determined the final outcome of denitrification. A nitrate amendment experiment further indicated that the riparian DR was responsive to added nitrate. Although the DRs were very low (0.099 ~ 33.23 ng N2O-N g(-1) h(-1)) due to the small amount of nitrogen moving into the urban riparian zone, the spatial and temporal patterns of denitrification differed significantly. The extractable NO3 (-)-N proved to be the dominant factor influencing the spatial distribution of denitrification, whereas the soil temperature was a determinant of the seasonal DR variation. The six riparian sites could also be divided into two types (a nitrate-abundant and a nitrate-stressed riparian system) according to the soil NO3 (-)-N concentration. The DR in nitrate-abundant riparian systems was significantly higher than that in the nitrate-stressed riparian systems. The DR in riparian zones that were covered with bushes and had adjacent cropland was higher than in grass-covered riparian sites. Furthermore, the riparian DR decreased with soil depth, which was mainly attributed to the concentrated nitrate in surface soils. The DR was not associated with the SOC, STN, C/N ratio, and pH. Nitrate supply and temperature finally decided the spatiotemporal distribution patterns of urban riparian denitrification. Considering both the low DR of existing riparian soils and the significance of nonpoint source nitrogen pollution, the substantial denitrification potential of urban riparian soils should be utilized to reduce nitrogen pollution using proper engineering measures that would collect the polluted urban rainfall runoff and make it flow through the riparian zones.

Although the effects of anthropogenic nitrogen (N) inputs on the dynamics of inorganic N in watersheds have been studied extensively, “the influence of N enrichment on organic N loss” is not as well understood. We compiled and synthesized data on surface water N concentrations from 348 forested and human-dominated watersheds with a range of N loads (from less than 100 to 7,100 kg N km−2 y−1) to evaluate the effects of N loading via atmospheric deposition, fertilization, and wastewater on dissolved organic N (DON) concentrations. Our results indicate that, on average, DON accounts for half of the total dissolved N (TDN) concentrations from forested watersheds, but it accounts for a smaller fraction of TDN in runoff from urban and agricultural watersheds with higher N loading. A significant but weak correlation (r 2 = 0.06) suggests that N loading has little influence on DON concentrations in forested watersheds. This result contrasts with observations from some plot-scale N fertilization studies and suggests that variability in watershed characteristics and climate among forested watersheds may be a more important control on DON losses than N loading from atmospheric sources. Mean DON concentrations were positively correlated, however, with N load across the entire land-use gradient (r 2 = 0.37, P < 0.01), with the highest concentrations found in agricultural and urban watersheds. We hypothesize that both direct contributions of DON from wastewater and agricultural amendments and indirect transformations of inorganic N to organic N represent important sources of DON to surface waters in human-dominated watersheds. We conclude that DON is an important component of N loss in surface waters draining forested and human-dominated watersheds and suggest several research priorities that may be useful in elucidating the role of N enrichment in watershed DON dynamics.

The role of precise management in mitigating non-point source nitrogen pollution of a reservoir region: Environmental profits and economic benefits