Basin-wide tracking of nitrate cycling in Yangtze River through dual isotope and machine learning.

The nitrate (NO3-) input has adversely affected the water quality and ecological function in the whole basin of the Yangtze River. The protection of water sources and implementation of "great protection of Yangtze River" policy require large-scale information on water contamination. In this study, dual isotope and Bayesian mixing model were used to research the transformation and sources of nitrate. Chemical fertilizers contribute 76 % of the nitrate sources in the upstream, while chemical fertilizers were also dominant in the midstream (39 %) and downstream (39 %) of Yangtze River. In addition, nitrification process occurred in the whole basin. Four machine learning models were used to relate nitrate concentrations to explanatory variables describing influence factors to predict nitrate concentrations in the whole basin of Yangtze River. The anthropogenic and natural factors, such as rainfall, GDP and population were chosen to take as predictor variables. The eXtreme Gradient Boosting (XGBoost) model for nitrate has a better predictive performance with an R2 of 0.74. The predictive models of nitrate concentrations will help identify the nitrate distribution and transport in the whole Yangtze River basin. Overall, this study represents the first basin-wide data-driven assessment of the nitrate cycling in the Yangtze River basin.

Occurrence and source analysis of heavy metals and dissolved organic matter in Nanyi Lake, Anhui Province.

Nanyi Lake is a tributary in the lower reaches of the Yangtze River and the largest freshwater lake in southern Anhui. Anthropogenic activities have significantly affected the lake with the rapid development of the surrounding economy in recent years. This study collected a total of 39 × 2 water samples of surface water and overlying water, detected the heavy metal concentrations (As, Cr, Cu, Mn, Ni, Pb, Sb, Zn, Fe, Cd), and analyzed the spectral characteristics of the dissolved organic matter (DOM). The results demonstrated that the concentrations of heavy metals (As, Cr, Cu, Mn, Ni, Pb, Sb, Zn, Fe, Cd) in the surface water were 1.00-7.78, 0.40-5.59, 0.20-4.52, 20.00-269.50, 0.40-5.56, 0.20-5.06, 1.00-7.64, 20.00-252.50, 60.00-590.00, and 0.04-0.60, respectively (unit: µg L-1). The risk assessment showed that the carcinogenic risks of heavy metals (As, Cr, Cd) through drinking water were ranged from 10-7 to 10-4a-1 in Nanyi Lake, and Cr was the main carcinogen which should be the focus of environmental health risk management. The average personal non-carcinogenic risks of heavy metals (Cu, Mn, Ni, Pb, Sb, Zn, Fe) were ranged from 10-9 to 10-13a-1, and considered to be acceptable risk level. The contour map of spatial distribution demonstrated different degrees of heavy metals (except Zn) enrichment near the Langchuan River in the East Lake District. Parallel factor analysis showed that the main components of DOM in Nanyi Lake were tryptophan like, fulvic acid like, and tyrosine like, and the dissolved organic matter was primarily derived from autogenous endogenous sources. The heavy metals Cr, Ni, Pb, Fe, Cd, and HIX in the surface water of West Lake were significantly positively correlated, among which Ni, Fe and C2 were significantly negatively correlated (P < 0.05), Mn and BIX demonstrated extremely significant positive correlations (P < 0.01), while no significant correlation was observed between heavy metals and the DOM indexes in the surface water of the East Lake. The principal component and correlation analysis showed that the heavy metals in Nanyi Lake were primarily derived from the production wastewater discharged by the surrounding industrial and mining enterprises through the rivers input, followed by the non-point source runoff input of the surrounding agricultural production and lake aquaculture.

Distribution, Source Apportionment and Risk Assessment of Polycyclic Aromatic Hydrocarbons (PAHs) in Surface Sediments at the Basin Scale: A Case Study in Taihu Basin, China.

As a systematic research at basin scale, this study investigated the spatial distribution, source apportionment and ecological risks of eighteen polycyclic aromatic hydrocarbons (PAHs) in surface sediments at different functional regions (rivers, lakes and reservoirs) from Taihu basin. Results showed that the mean values of 18 PAHs (defined as ∑18PAHs) in river sediments (1277 ng/g) was much higher than those observed in lake sediments (243 ng/g) and reservoir sediments (134 ng/g). The accumulation of PAHs in river sediments was largely impacted by the local social-economic development and energy consumption. The positive matrix factorization (PMF) and isomer ratios analysis of PAHs suggest that relative contributions to PAHs in sediments were 15% for gasoline and heavy oil combustion, 9% for oil spills, 30% for coal combustion, 23% for traffic source, and 23% for diagenetic source. Ecological risk assessment based upon risk quotient (RQ) method indicated that sediments at Taihu basin have suffered moderate risk of PAHs.