Long-Term Effects of Anthropogenic Factors on Nonpoint Source Pollution in the Upper Reaches of the Yangtze River

Assessing spatiotemporal risks of nonpoint source pollution via soil erosion: a coastal case in the Yellow River Delta, China

Nonpoint source pollution (NPSP) has always been the dominant threat to regional waters. Based on empirical models of the revised universal soil loss equation and the phosphorus index, an NPSP risk assessment model denoted as SL-NPSRI was developed. The surface soil pollutant loss was estimated by simulating the rain-runoff topographic process, and the influence of path attenuation was quantified. A case study in the Yellow River Delta and corresponding field surveys of soil pollutants and water quality showed that the established model can be applied to evaluate the spatial heterogeneity of NPSP. NPSP usually occurs during high-intensity rainfall periods and in larger estuaries. Summer rainfall increased pollutant transport into the sea from late July to mid-August and caused estuarine dilution. Higher NPSP risks often correspond to coastal areas with lower vegetation coverage, higher soil erodibility, and higher soil pollutant concentrations. Agricultural NPSP originating from cropland significantly increase the pollutant fluxes. Therefore, area-specific land use management and vegetation coverage improvement, and temporal-specific strategies can be explored for NPSP control during source-transport hydrological processes. This research provides a novel insight for coastal NPSP simulations by comprehensively analyzing the soil erosion process and its associated pollutant loss effects, which can be useful for targeted spatiotemporal solutions.

A BPNN-based ecologically extended input-output model for virtual water metabolism network management of Kazakhstan

In this study, a back-propagation-neural-network-based ecologically extended input-output model (abbreviated as BPNN-EIOM) is developed for virtual water metabolism network (VWMN) management. BPNN-EIOM can identify key consumption sectors, simulate performance of VWMN, and predict water consumption. BPNN-EIOM is then applied to analyzing VWMN of Kazakhstan, where multiple scenarios under different gross domestic production (GDP) growth rates, sectoral added values, and final demands are designed for determining the optimal management strategies. The major findings are (i) Kazakhstan typically relies on net virtual water import (reaching 1497.9 × 10⁶ m³ in 2015); (ii) agriculture is the major exporter and advanced manufacture is the major importer; (iii) by 2025, Kazakhstan's water consumption would increase to [19322, 22016] × 10⁶ m³ under multiple scenarios; (iv) when Kazakhstan's GDP growth rate, manufacturing's added value, and final demand are scheduled to 5.5%, 8.5%, and 5.8%, its VWMN can reach the optimum. The findings are useful for decision makers to optimize Kazakhstan's industrial structure, mitigate the national water scarcity, and promote its socio-economic sustainable development.

Supercarriers of antibiotic resistome in a world's large river

BACKGROUND

Antibiotic resistome has been found to strongly interact with the core microbiota in the human gut, yet little is known about how antibiotic resistance genes (ARGs) correlate with certain microbes in large rivers that are regarded as "terrestrial gut."

RESULTS

By creating the integral pattern for ARGs and antibiotic-resistant microbes in water and sediment along a 4300-km continuum of the Yangtze River, we found that human pathogen bacteria (HPB) share 13.4% and 5.9% of the ARG hosts in water and sediment but contribute 64% and 46% to the total number of planktonic and sedimentary ARGs, respectively. Moreover, the planktonic HPB harbored 79 ARG combinations that are dominated by "natural" supercarriers (e.g., Rheinheimera texasensis and Noviherbaspirillum sp. Root189) in river basins.

CONCLUSIONS

We confirmed that terrestrial HPB are the major ARG hosts in the river, rather than conventional supercarriers (e.g., Enterococcus spp. and other fecal indicator bacteria) that prevail in the human gut. The discovery of HPB as natural supercarriers in a world's large river not only interprets the inconsistency between the spatial dissimilarities in ARGs and their hosts, but also highlights the top priority of controlling terrestrial HPB in the future ARG-related risk management of riverine ecosystems globally. Video Abstract.

Nitrogen and Phosphorus Retention Risk Assessment in a Drinking Water Source Area under Anthropogenic Activities

Excessive nitrogen (N) and phosphorus (P) input resulting from anthropogenic activities seriously threatens the supply security of drinking water sources. Assessing nutrient input and export as well as retention risks is critical to ensuring the quality and safety of drinking water sources. Conventional balance methods for nutrient estimation rely on statistical data and a huge number of estimation coefficients, which introduces uncertainty into the model results. This study aimed to propose a convenient, reliable, and accurate nutrient prediction model to evaluate the potential nutrient retention risks of drinking water sources and reduce the uncertainty inherent in the traditional balance model. The spatial distribution of pollutants was characterized using time-series satellite images. By embedding human activity indicators, machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were constructed to estimate the input and export of nutrients. We demonstrated the proposed model’s potential using a case study in the Yanghe Reservoir Basin in the North China Plain. The results indicate that the area information concerning pollution source types was effectively established based on a multi-temporal fusion method and the RF classification algorithm, and the overall classification low-end accuracy was 92%. The SVM model was found to be the best in terms of predicting nutrient input and export. The determination coefficient (R2) and Root Mean Square Error (RMSE) of N input, P input, N export, and P export were 0.95, 0.94, 0.91, and 0.93, respectively, and 32.75, 5.18, 1.45, and 0.18, respectively. The low export ratios (2.8–3.0% and 1.1–2.2%) of N and P, the ratio of export to input, further confirmed that more than 97% and 98% of N and P, respectively, were retained in the watershed, which poses a pollution risk to the soil and the quality of drinking water sources. This nutrient prediction model is able to improve the accuracy of non-point source pollution risk assessment and provide useful information for water environment management in drinking water source regions.

Bromine and iodine species in drinking water supply system along the Changjiang River in China: Occurrence and transformation

Bromine (Br) and iodine (I) in source water can form highly toxic brominated or iodinated disinfection byproducts in treatment plants. For the first time, the occurrence of Br and I speciation and their proportion, transformation in the drinking water supply system along the Changjiang River were investigated. 96 water samples were collected from eight drinking water treatment plants under conditions of low, normal, and flood water regimes. Total Br (TBr) and total I (TI) concentrations were quantified by inductively coupled plasma mass spectrometry (ICPMS) and inorganic Br/I forms (bromide, bromate, iodide, and iodate) were determined by high-performance liquid chromatography coupled with ICPMS. Concentrations of organic Br/I were calculated as the difference between total Br/I and inorganic Br/I. Water regimes had different effect on Br and I species, and there were different rules in untreated and treated water samples. Apparent increase of TBr and TI concentrations after water treatment were observed, which indicated the possibility of Br/I introduction by chlorine-containing disinfectant. The occurrence of TBr, TI, bromide, and total organic I in the river were investigated to increase with the direction of flow. In addition, TBr and TI concentrations correlated with the concentrations of artificial sweeteners (e.g., acesulfame and sucralose, a kind of wastewater indicator), suggesting the influence of domestic sewage on Br and I in the river. In untreated water, bromide was the main Br species, and after treatment more than 50% was transformed into organic Br. Iodoorganics were the majority of I species in raw water and were partly transformed into iodate after treatment. Overall, the Br/I species have accumulation potential in the Changjiang River and organic forms occupy high proportion in treated water samples, which should be paid more attention.

Canonical Correlation Study on the Relationship between Shipping Development and Water Environment of the Yangtze River

The sustainable development of the Yangtze River will affect the lives of the people who live along it as well as the development of cities beside it. This study investigated the relationship between shipping development and the water environment of the Yangtze River. Canonical correlation analysis is a multivariate statistical method used to study the correlation between two groups of variables; this study employed it to analyze data relevant to shipping and the water environment of the Yangtze River from 2006 to 2016. Furthermore, the Yangtze River Shipping Prosperity Index and Yangtze River mainline freight volume were used to characterize the development of Yangtze River shipping. The water environment of the Yangtze River is characterized by wastewater discharge, ammonia nitrogen concentration, biochemical oxygen demand, the potassium permanganate index, and petroleum pollution. The results showed that a significant correlation exists between Yangtze River shipping and the river’s water environment. Furthermore, mainline freight volume has a significant impact on the quantity of wastewater discharged and petroleum pollution in the water environment.