Health risk assessment of heavy metals based on source analysis and Monte Carlo in the downstream basin of the Zishui.

In this study, stone coal mines in the lower reaches of the Zijiang River were adopted as the research object. To analyze the spatial distribution, sources, and health risks of heavy metals in the surrounding soil of stone coal mines, 82 topsoil samples were collected in the study area, and the contents of 8 heavy metals including Cd, Hg, As, Cr, Pb, Cu, Ni, and Zn were determined. The spatial distribution of heavy metals was analyzed using ArcGIS, and the pollution sources of heavy metals were identified using Positive matrix factorization (PMF). Then, Monte Carlo and health risk assessment models were used to evaluate the health risks of different populations. The results showed that the average content of heavy metals followed the order of Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, and the contents of all heavy metals were higher than the soil background values of Hunan Province. The high-value areas of heavy metals content were mostly concentrated in the central region close to areas with a notable concentration of stone coal mines. PMF identified four pollution sources, namely, mining activities (26.9%), atmospheric deposition (18.8%), natural sources (32.8%) and agricultural sources (21.5%). The carcinogenic and non-carcinogenic risks for children were higher than those for adults, with As and Cd posing higher carcinogenic risks to children. Based on the source of health risks, it was determined that the health risks could be primarily attributed to agricultural sources, and As was the main heavy metal causing health risks. This study provides theoretical support for treating heavy metal pollution in mining basins.

Integrating source apportionment and landscape patterns to capture nutrient variability across a typical urbanized watershed.

Effective integrated watershed management requires models that can characterize the sources and transport processes of pollutants at the watershed with multiple landscape patterns. However, few studies have investigated the influence of landscape spatial configuration on pollutant transport processes. In this study, the SPARROW_TN and SPARROW_TP models were constructed by combining direct pollution source data and landscape pattern data to investigate the source composition and nutrient transport processes and to reveal the influence of landscape patterns on nutrient transport in the urbanized Beiyun River Watershed. The introduction of landscape metrics significantly improved the simulation results of both models, with R2 increasing from 0.89 to 0.85 to 0.93 and 0.91, respectively. Spatial variations existed in TN and TP loads and yields, as well as the source compositions. Pollution hotspots were effectively identified. Source apportionment showed that for the entire watershed, TN came from atmospheric nitrogen deposition (35.25%), untreated sewage (28.23%), agricultural sources (22.60%), and treated sewage (13.92%). In comparison, TP came from untreated sewage (44.94%), agricultural sources (40.22%), and treated sewage (11.51%). In addition, the largest patch index of grassland correlated positively with both TN and TP, whereas the largest shape index of buildup land and interspersion and juxtaposition index of forest were negatively correlated with TN and TP, respectively. The results of this study will provide insight into effective nutrient control measures that consider spatially varying nutrient sources and associated nutrient transport processes.

Long-term variations in external phosphorus inputs and riverine phosphorus export in a typical arid and semiarid irrigation watershed.

Excessive phosphorus (P) along with drained water from farmland in the arid and semiarid watersheds when entering into water bodies brings about serious environmental problems in the aquatic ecosystem. It is critical to explore variations in watershed P balance and the relationship between anthropogenic P input and riverine total phosphorus (TP) export in a typical irrigation watersheds. In this study, long-term anthropogenic P variations in Ulansuhai Nur watershed (UNW), a typical irrigation watershed in Yellow River basin, was investigated using a quantitative Net Anthropogenic Phosphorus Input (NAPI) budget model. The results showed that annual NAPI exhibited a significant upward trend with a multi-year average of 2541.6 kg P km-2 yr-1 in the UNW. Hotspots for watershed NAPI were discovered in Linhe and Hangjin Houqi counties. Chemical P fertilizers and livestock breeding were two dominated sources of NAPI. Annual riverine TP export showed a significantly declined trend with a net decrease of 80.6%. The export ratio of watershed NAPI was 0.6%, lower than those reported for other watersheds worldwide. There was a significant positive linear correlation between NAPI and riverine TP export from 2005 to 2009. However, after 2009, riverine TP export exhibited a decreased trend with increasing watershed NAPI, which was attributed to environmental treatment measures. By reconstructing riverine TP export without the impact of pollution treatment measures, annual average reduction amount of riverine TP export from 2009 to 2019 was estimated to be 237.2 ton, 47.2% and 52.8% of which were attributed to the point and nonpoint sources measures. This study not only widens the application scope of NAPI budget method, but also provides useful information of nutrient management and control in the arid and semiarid irrigation watershed.

Spatio-temporal variation of net anthropogenic nitrogen inputs (NANI) from 1991 to 2019 and its impacts analysis from parameters in Northwest China.

At present, excessive nutrient inputs caused by human activities have resulted in environmental problems such as agricultural non-point source pollution and water eutrophication. The Net Anthropogenic Nitrogen Inputs (NANI) model can be used to estimate the nitrogen (N) inputs to a region that are related to human activities. To explore the net nitrogen input of human activities in the main grain-producing areas of Northwestern China, the county-level statistical data for the Ningxia province and NANI model parameters were collected, the spatio-temporal distribution characteristics of NANI were analyzed and the uncertainty and sensitivity of the parameters for each component of NANI were quantitatively studied. The results showed that: (1) The average value of NANI in Ningxia from 1991 to 2019 was 7752 kg N km-2 yr-1. Over the study period, the inputs first showed an overall increase, followed by a decrease, and then tended to stabilize. Fertilizer N application was the main contributing factor, accounting for 55.6%. The high value of NANI in Ningxia was mainly concentrated in the Yellow River Diversion Irrigation Area. (2) The 95% confidence interval of NANI obtained by the Monte Carlo approach was compared with the results from common parameters in existing literature. The simulation results varied from -6.4% to 27.4% under the influence of the changing parameters. Net food and animal feed imports were the most uncertain input components affected by parameters, the variation range was -20.7%-77%. (3) The parameters of inputs that accounted for higher proportions of the NANI were more sensitive than the inputs with lower contributions. The sensitivity indexes of the parameters contained in the fertilizer N applications were higher than those of net food and animal feed imports and agricultural N-fixation. This study quantified the uncertainty and sensitivity of parameters in the process of NANI simulation and provides a reference for global peers in the application and selection of parameters to obtain more accurate simulation results.

Managing landscape patterns at the riparian zone and sub-basin scale is equally important for water quality protection.

Widespread attention has been given to understanding the effect of the landscape pattern on river water quality. However, which spatial scale (riparian zone versus sub-basin) has the greater impact on water quality has long been controversial, since the key metrics that affect water quality varied with spatial scale. Thus, quantifying the spatial scale effects of key landscape metrics on water quality is critical to clarifying which scale of landscape pattern is more conducive to water quality conservation. Here, we adopted variation partitioning analysis (VPA) and random forest models to quantify the landscape pattern impact on water quality at northern Erhai Lake during the 2019 rainy season (early, mid, and late), and comprehensively analyze the key landscape metrics on different scales. The results revealed that the riparian zone and sub-basin scale landscape patterns explained similar water quality variations (difference only 0.9%) in the mid (August) and late rainy season (October), but exhibited a large difference (24.1%) during the early rainy season (June). Furthermore, rivers were primarily stressed by nitrogen pollution. Maintaining the Grassland_ED > 27.99 m/ha, Grassland_LPI > 4.19%, Farmland_LSI < 3.2 in the riparian zone, and Construction_ED < 1.69 m/ha, Construction_LSI < 2.46, Farmland_PLADJ < 89.0% at the sub-basin scale could significantly reduce the TN concentration in the stream. Meanwhile, managing of these metrics can effectively prevent rapid increases of TN in rivers. Moreover, due to the low phosphorus concentration in the rivers, none of the landscape metrics significantly explained the variation in TP. This study explored the spatial scale effect of landscape patterns on water quality and revealed the driving factors of nutrient variation. This study will provide a scientific basis for aquatic environmental management in plateau watersheds.

Sources of nitrate­nitrogen in urban runoff over and during rainfall events with different grades.

Nitrogen discharged from urban areas greatly deteriorates the water quality of downstream surface water. In this study, sub-hourly high-frequency samples of urban runoff during six rainfall events were collected at the outfall of the stormwater network in an urban watershed in Beijing to explore nitrate export and transportation. Isotopic values of local road dust, soil, and network sediment were measured and used as the sources of nitrate to better elucidate the sources of the urban watershed. The contributions of various sources over and during three rainfall events with different grades were quantified and compared. The results showed that the contribution of sources changed dramatically over and during rainfall events. Along with the increase in the total rainfall amount and the going on of rainfall events, the wash-off effect in the atmosphere and on land surfaces played a more important role in nitrate output. Atmospheric deposition was the dominant contributor of nitrate in heavy and storm events (mean 59.3% and 64.8%, respectively). Network sediment contributed large proportions of nitrate in moderate and heavy events (mean 35.6% and 15.9%, respectively). The contribution of soil increased substantially in the storm event (mean 26.1%). Road dust and network sediment contributed greatly in the early stage of the heavy event. The contribution of fertilizer in heavy events was mainly because of the wash-off of road dust. The changing pattern of sources from atmospheric deposition to inorganic N fertilizer existed during the process of the storm event. The contribution of NO3- fertilizer from soil surfaces increased substantially in the later stage of the storm event. These results provide valuable references for urban nutrient management and mitigation measures implementation.

Sources, transformations of suspended particulate organic matter and their linkage with landscape patterns in the urbanized Beiyun river Watershed of Beijing, China.

This study explored the sources, transformations of suspended particulate organic matter (POM), and the influence of landscape patterns on POM within the Beiyun River Watershed by applying the stable carbon and nitrogen isotope technique combined with multiple statistical analyses. The POM variables showed great spatial fluctuations under different urban development gradients. Analysis of multiple isotopes revealed that assimilation of phytoplankton might exist in the rainy season, while nitrification occurs in the dry season. SIAR modeling results indicated that the sewage debris and phytoplankton were the main sources of POM in both seasons, accounting for 52.58% and 38.39% in the rainy season, 33.17% and 31.95% in the dry season, respectively. Spatiotemporal variations of POM sources existed in the study watershed, probably due to urbanization and human disturbance. The multiple linear stepwise regression and redundant analysis results indicated that landscape metrics reflecting contagion and fragmentation at the class level correlated well with the POM variables over seasons. Interspersion and juxtaposition indices of grassland and water were negatively related to POM variables in the rainy season, whereas the landscape division index of buildup land showed negative correlations with POM parameters in the dry season. Increasing the adjacency of grassland and water to other land uses, while reducing the aggregation of buildup lands would be an efficient way for urban river water quality improvement.

Source identification and impact of landscape pattern on riverine nitrogen pollution in a typical urbanized watershed, Beijing, China.

This study explored the sources of nitrate and the impact of landscape pattern on nitrogen pollution in the highly urbanized Beiyun River Watershed, China during 2016 by applying a dual stable isotope approach (δ15N-NO3-and δ18O-NO3-) combined with multiple statistical analyses. The sources of riverine nitrate principally originated from manure and sewage, nitrification of soil nitrogen, fertilizer nitrification, and atmospheric deposition. A Bayesian model was used to estimate the source contributions and results showed that manure and sewage were the major contributors to river nitrate with combined proportions of 77.59% and 89.57% in the rainy season and the dry season, respectively. Results from multiple stepwise regression indicated that the typical artificial land use types and landscape configuration metrics reflecting landscape fragmentation related well with riverine nitrogen variables for different seasons (R2>0.6). Industrial land, unused land and patch density of built-up land and road were positively correlated with riverine nitrogen over seasons, whereas the interspersion and juxtaposition index of forest land was negatively related with nitrate. Regulating built-up land and unused land, connecting forest land with other land use types and diminishing discharges of industrial and domestic wastewater would be effective ways to improve urban river water quality.

Predicting fecal coliform using the interval-to-interval approach and SWAT in the Miyun watershed, China.

Pathogens in manure can cause waterborne-disease outbreaks, serious illness, and even death in humans. Therefore, information about the transformation and transport of bacteria is crucial for determining their source. In this study, the Soil and Water Assessment Tool (SWAT) was applied to simulate fecal coliform bacteria load in the Miyun Reservoir watershed, China. The data for the fecal coliform were obtained at three sampling sites, Chenying (CY), Gubeikou (GBK), and Xiahui (XH). The calibration processes of the fecal coliform were conducted using the CY and GBK sites, and validation was conducted at the XH site. An interval-to-interval approach was designed and incorporated into the processes of fecal coliform calibration and validation. The 95% confidence interval of the predicted values and the 95% confidence interval of measured values were considered during calibration and validation in the interval-to-interval approach. Compared with the traditional point-to-point comparison, this method can improve simulation accuracy. The results indicated that the simulation of fecal coliform using the interval-to-interval approach was reasonable for the watershed. This method could provide a new research direction for future model calibration and validation studies.