Evaluating the impacts of inter-basin water transfer projects on ecosystem services in the Fenhe River Basin using the SWAT model.

Inter-basin water transfer (IBWT) projects have been widely constructed to alleviate the pressure on water resources in water shortage basins. However, the ecological effects of IBWT projects have often been ignored. Based on the Soil and Water Assessment Tool (SWAT) model and a constructed total ecosystem services (TES) index, the impacts of IBWT projects on recipient basin ecosystem services were analyzed in this study. The results showed that the TES index was relatively stable from 2010 to 2020, but in the wet season it was 1.36 times that of the other months with high water yield and nutrient loads. Spatially, areas with high index values were mainly distributed in the sub-basins around the reservoirs. The IBWT projects had positive impacts on ecosystem services, and the TES index with IBWT projects was 5.98% higher than that without projects. Water yield and total nitrogen were the two most affected indexes, with increased of 5.65% and 5.41%, respectively, under the impacts of IBWT projects. Seasonally, the change rates of the TES index were less than 3% while the change rates of water yield and nitrogen load peaked at 8.23% and 53.42%, respectively, in March, owing to the large amount of water released from the reservoirs. Areas affected by the three evaluated IBWT projects accounted for 61%, 18%, and 11% of the watershed, respectively. Under the impact of each project, the TES index generally increased, whereas the impact decreased as the distance from the inflow location increased. Intense changes in ecosystem services occurred in sub-basin 23, the sub-basin closest to an IBWT project, with water yield, water flow, and local climate regulation increasing the largest.

Significance of using dynamic land-use data and its threshold in hydrology and water quality simulation models.

Land-use changes have a significant impact on the hydrological cycle and non-point source (NPS) pollution discharge and transport. Thus, using dynamic land-use inputs in the simulation models is important. However, there is currently no clear standard for which situation the land-use data should be updated in the models. In this study, we quantified the impacts of land-use change on hydrological and NPS pollution simulation outputs, and analyzed the thresholds for land-use change level and time nodes. The results indicated that the error caused by land-use change had a linear relationship with the land-use change level. The total nitrogen (TN) output error was the most sensitive to land-use change, with a gradient of 0.73. The impact of land-use change on the model outputs was different at different temporal scales. Flow and TN had the highest output errors at a daily scale, while sediment had the highest output error at an annual scale. The threshold analysis results revealed that the land-use change thresholds for the flow, sediment, and TN simulations were 40%, 30%, and 10%, respectively. When the land-use change level exceeded the threshold, the model simulation error increased dramatically. The land-use change time node would also affect the simulation performance, especially for TN. This study initially explored the quantified standard for land-use data updates in the SWAT model. The results could be useful for improving the simulation accuracy of the SWAT model and may provide ideas for follow-up studies.

Multi-scenario simulation of ecological risk assessment based on ecosystem service values in the Beijing-Tianjin-Hebei region.

In this study, a framework for ecological risk assessment based on ecosystem service values and risk probability was established. Remote sensing was used to estimate the value of ecosystem services at the regional scale. Considering the natural and anthropogenic factors and using the entropy weight method to assign weights, probability index was constructed. In addition, multiple scenarios based on the ordered weighted averaging (OWA) method were simulated to reduce subjective uncertainty in the assessment. The results showed that the ecosystem service values generated by the gas regulation value accounted for the largest proportion, with a ratio of 46% in the Beijing-Tianjin-Hebei region. From 2005 to 2015, the value of ecosystem services decreased, falling by 2.5 × 107 Yuan. The level of ecological risk was relatively high, with a corresponding area ratio of 32.89%. Spatially, the areas with high risk were concentrated in the southeastern areas, and areas with relatively low risk were distributed in the western and northern areas. This high risk was probably caused by urbanization which was characterized by reduction of farmland and increase in impervious surface. Multi-scenario simulation showed that the areas of unstable ecological risk zones covered 30% and were mainly concentrated in the surroundings of developing cities. In areas of unstable risk distribution, the relationship between development and protection should be considered. This framework increases the reliability and practicability of ecological risk assessment results and has potential application value for regional risk control in the context of urbanization.

A Declining Trend in China's Future Cropland-N2O Emissions Due to Reduced Cropland Area.

Croplands are the largest anthropogenic source of nitrous oxide (N2O), a powerful greenhouse gas that contributes to the growing atmospheric N2O burden. However, few studies provide a comprehensive depiction of future cropland-N2O emissions on a national scale due to a lack of accurate cropland prediction data. Herein, we present a newly developed distributed land-use change prediction model for the high-precision prediction of national-scale land-use change. The high-precision land-use data provide an opportunity to elucidate how the changes in cropland area will affect the magnitude and spatial distribution of N2O emissions from China's croplands during 2020-2070. The results showed a declining trend in China's total cropland-N2O emissions from 0.44 ± 0.03 Tg N/year in 2020 to 0.39 ± 0.07 Tg N/year in 2070, consistent with a cropland area reduction from (1.78 ± 0.02) × 108 ha to (1.40 ± 0.15) × 108 ha. However, approximately 31% of all calculated cities in China would emit more than the present level. Furthermore, different land use and climate change scenarios would have important impacts on cropland-N2O emissions. The Grain for Green Plan implemented in China would effectively control emissions by approximately 12%.

Risk Threshold and Assessment of Chloramphenicol Antibiotics in Sediment in the Fenhe River Basin, China.

Chloramphenicol antibiotics (CAs) are broad-spectrum antibiotics which are widely used in the prevention and treatment of infectious diseases in livestock and poultry breeding. However, overused CAs can enter the watershed and eventually enter the sediment. Antibiotics in sediment can cause secondary pollution through disturbance and suspension. In this study, taking the Fenhe River Basin as the research area, the risk of CAs in sediment were assessed by collecting sediment samples. The results showed that CAs were detected in all sediment samples of the Fenhe River Basin. The mean concentration of CAs was 79.1 µg/kg, and the concentration of thiamphenicol (THI) was dominant, which was up to 58.3 µg/kg. Temporally, there are great differences in different seasons; the concentration of CAs was higher in winter than that in summer, up to 4.79-174 times. Spatially, the mean concentration of CAs in midstream was 83.5 µg/kg, which was higher than that in the upstream and downstream. The concentration of CAs in tributaries were generally higher than that in the main stream, and the mean concentration of tributaries was 1.1 times that of the main stream. CAs in S2 (Lanhe River) was the most prominent among all sample sites; the concentration of CAs was 190.8 µg/kg. The risk threshold of CAs in the sediment was calculated using the Equilibrium Partitioning approach (EqP), based on the distribution coefficient (Kp) and the predicted no-effect concentration (PNEC) in the water, and the values were 0.091-1.44 mg/kg. Based on the risk threshold, the ecological risk of the CAs in sediment was assessed using risk quotients (RQ). The results showed that the Chloramphenicol (CHL) was the most prominent in the Fenhe River Basin, and the proportion of medium-risk areas reached 21.7%, while all the other areas showed low risk. Secondly, the proportion of medium-risk areas was 17.4% for THI, and all the other areas showed low risk. The risk for Florfenicol (FF) was least among all CAs, and the proportion of low-risk areas was only 8.7%, while all the other areas were of insignificant risk.

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.

Reliable and Representative Estimation of Extrapolation Model Application in Deriving Water Quality Criteria for Antibiotics.

Deriving water quality benchmarks based on the species sensitivity distribution (SSD) is crucial for assessing the ecological risks of antibiotics. The application of extrapolation methods such as interspecies correlation estimation (ICE) and acute-to-chronic ratios (ACRs) can effectively supplement insufficient toxicity data for these emerging contaminants. Acute-to-chronic ratios can predict chronic toxicity from acute toxicity, and ICE can extrapolate an acute toxicity value from one species to another species. The present study explored the impact of two extrapolation methods on the reliability of SSDs by analyzing different scenarios. The results show that, compared with the normal and Weibull distributions, the logistic model was the best-fitting model. For most antibiotics, SSDs derived by extrapolation have high reliability, with 82.9% of R2 values being higher than 0.9, and combining ICE and ACR methods can bring a maximum increase of 10% in R2 . Based on the results of Monte Carlo simulation, the statistical uncertainty brought by ICE in SSD is 10-40 times larger than that brought by ACR, and combining the two methods could reduce uncertainty. In addition, the sensitivity test showed that whether the toxicity data came from extrapolation or actual measurement, the lower the value of toxicity endpoints was, the greater the bias caused by the corresponding species in every scenario. Combining the two aforementioned extrapolation methods could effectively increase the stability of SSD, with their bias nearly equal to 1. Environ Toxicol Chem 2023;42:191-204. © 2022 SETAC.

Spatio-temporal distribution and source identification of antibiotics in suspended matter in the Fen River Basin.

In this study, 26 typical antibiotics in the suspended matter of the Fen River basin were analyzed during the wet and dry seasons, and the main sources of antibiotic contamination were further identified. The results showed that the concentrations of antibiotics in the suspended matter varied seasonally. Sixteen antibiotics were detected in the suspended matter during the wet season with an average concentration of 463.56 ng/L. However, a total of 21 antibiotics were detected in the dry season, with an average concentration of 106.00 ng/L. The concentration of chloramphenicol antibiotics was outstanding in the wet season and dry season. The spatial distribution of the antibiotics in suspended matter showed little spatial discrepancy during the wet season. During the dry season, nevertheless, the concentration was higher upstream than midstream and downstream. The main sources of antibiotics in the Fen River Basin were livestock and poultry breeding, wastewater from wastewater treatment plants (WWTPs), agricultural drainage, domestic sewage, and pharmaceutical wastewater. Wastewater from WWTPs and domestic sewage were identified as two primary sources in the suspended matter during the wet season, with wastewater from WWTPs contributing the most accounting for 37%. While the most significant source of antibiotics in the suspended matter in the dry season was pharmaceutical wastewater, accounting for 36%. In addition, the contribution proportion of sources for antibiotics exhibited discrepant spatial distribution characteristics. In the wet season, wastewater from WWTPs dominated in the upstream and midstream, and livestock and poultry breeding was prominent in the midstream and downstream. Pharmaceutical wastewater was the main source in the midstream and downstream regions during the dry season.

Impact of particle sizes on health risks and source-specific health risks for heavy metals in road dust.

To analyze the impact of particle sizes on sources and related health risks for heavy metals, road dust samples in Beijing were collected and sifted into five particle sizes. The positive matrix factorization (PMF), human health risk assessment model (HHRA), and Monte Carlo simulation were used in the health risk assessment and source apportionment. Results showed that mass of particles < 74 µm occupied about 50% of the total particles, while only 8.48% of the particles were > 500 µm. Mass distribution and concentrations of heavy metals in each particle size changed in temporal. Over 85.00% of carcinogenic risks (CR) were from particles <74 µm, whereas CR from particles >250 µm were ignorable. Sources for health risks in each particle size were traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Proportions of sources to CR differed among particle sizes. Traffic exhaust and fuel combustion contributed over 90% to CR in particles <74 µm, whereas construction contributed the highest (31.68-54.14%) among all sources in particles 74-250 µm. Furthermore, the difference between health risks based on sifted road dust and that based on unsifted road dust was quantitatively analyzed. Source-specific health risk apportionment based on unsifted road dust was not presentative to all particle sizes, and true value of health risks could be over 2.5 times of the estimated value based on unsifted road dust, emphasized the importance of sifting of road dust.

Source-specific risk apportionment and critical risk source identification of antibiotic resistance in Fenhe River basin, China.

A comprehensive understanding of the sources and distribution of antibiotic resistance risk is essential for controlling antibiotic pollution and resistance. Based on surface water samples collected from the Fenhe River basin in the flood season, using the positive matrix factorization (PMF) model, the risk quotient (RQ) method and the multiple attribute decision making (MADM) method, the resistance risk and source-specific resistance risk of antibiotics were analyzed in this study. The results showed that sulfonamides (SAs) were the dominant antibiotics with a mean concentration of 118.30 ng/L, whereas tetracyclines (TCs) and macrolides (MLs) had the highest detection frequencies (100%). The significant resistance risk rate of antibiotics in the entire river basin was 48%, but no high risk occurred. The significant resistance risk rate of quinolones (QNs) was the highest (100%), followed by that of MLs and TCs. Owing to human activities, the most serious resistance risk occurred in the midstream of the river basin. The resistance risk was the lowest upstream. The antibiotics were mainly contributed by six sources. Pharmaceutical wastewater was the main source, accounting for 30%, followed by livestock discharge (22%). The resistance risk from the six sources showed clear differences, but none of the sources caused a high risk of antibiotic resistance. Pharmaceutical wastewater poses the greatest risk of antibiotic resistance in the Fenhe River basin and is widely distributed. The second greatest source was livestock discharge, which was mainly concentrated in the upstream and midstream areas. The critical sources upstream, midstream, and downstream were all pharmaceutical wastewater, whereas the sequences of other sources were different because different areas were affected by different human activities. The proposed method might provide an important reference for the identification the key source of antibiotics and management of antibiotic pollution, as well as help for the management of antibiotics in Fenhe and Shanxi Province.

A four-way model (FEST) for source apportionment: Development, verification, and application.

In studying the spatial, temporal, and particle size variations heavy metal sources, a source apportionment model for a four-way array of data is required. In this study, referencing two-way and three-way models, a four-way (particle fractions, elements, sites, and time) source apportionment model (FEST) was developed. Errors in the three-way models solving four-way problems verified the necessity of developing the FEST model. The results showed that the FEST model had a higher accuracy than the existing models, which was probably because of more constraints and input data in the FEST model. Based on the sampled data in Beijing, sources were apportioned for the four-way array of data using the FEST model, and the spatial, temporal, and particle size variations of sources were evaluated. The main sources of heavy metals were similar to those in our prior studies, whereas the contributions of sources to specific heavy metals differed. Traffic exhaust and fuel combustion contributed more to fine particles than coarse particles, indicating that the two should be controlled preferentially among all sources. The management of traffic exhaust should be focused on the central and northern areas in each season, and the control of fuel combustion should be strengthened in the southern area in winter.

Identification and uncertainty analysis of high-risk areas of heavy metals in sediments of the Yangtze River estuary, China.

In this study, ordinary kriging (OK) and indicator kriging (IK) were used to analyze the uncertainty associated with high-risk areas of seven heavy metals (As, Cd, Cr, Cu, Hg, Pb, and Zn) in sediments of the Yangtze River estuary during four seasons. The OK results showed that the high-risk areas of Cd, Cr, Cu, Hg, and Pb had a high proportion, with the highest corresponding to Cr pollution (up to 60%). Predictions based on IK revealed that the proportion of high-risk areas of Cr, Cd, and Hg pollution were high, especially that of Cr was higher than 90%. However, there were uncertainties between the OK and IK results. The uncertainty results revealed that the uncertainty areas of Cr pollution were relatively large, accounting for about 30%, while those of Cd, Cu, and Hg pollution were lower than 10%.

Study of uncertainty of satellite and reanalysis precipitation products and their impact on hydrological simulation.

Satellite and reanalysis precipitation products are potential alternatives in hydrological studies, and it is very important to evaluate their accuracy and potential use for reliable simulations. In this study, three precipitation products (Tropical Rainfall Measuring Mission 3B43 Version 7 (TRMM 3B43), spatial interpolation grid data based on 2472 national meteorological observation stations in China (GRID_0.5), and National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)) were evaluated against gauge observations in the Xiangxi River watershed of Hubei Province. The performance results indicated that the results of the three precipitation products were correlated with those of the rain gauges; however, there were differences among the three products. TRMM 3B43 tended to overestimate precipitation with the highest correlation coefficient, while NCEP-CFSR tended to underestimate precipitation with the least satisfactory performance, and the performance of GRID_0.5 ranked between them. However, the annual and monthly mean errors differed, as the errors of most of the results driven by NCEP-CFSR were lowest. The errors varied at different time scales. During years with high precipitation, the results were often underestimated, while the results are often overestimated during years with low precipitation. According to the average monthly results, the GRID_0.5 results were closest to the gauge observations for most months. During the wet season, TRMM 3B43 performed better, while NCEP-CFSR precipitation performed better during the dry season. The errors from precipitation to streamflow, NPS pollution, and water environmental capacity (WEC) driven by the three precipitation products increased gradually, ranging from 10% for precipitation to over 20% for NPS pollution and almost 100% for WEC. The error increase for NCEP-CFSR was lower than that of the other two products. Although the simulation error from precipitation to the WEC results driven by the three precipitation products gradually increased, the degree of overestimation and underestimation became smaller.

Spatial-temporal characteristics, source-specific variation and uncertainty analysis of health risks associated with heavy metals in road dust in Beijing, China.

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively).. Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.