Spatial variability of nitrate pollution and its sources in a hilly basin of the Yangtze River based on clustering

An indispensable role of overlying water in nitrogen removal in shallow lakes

The nitrogen (N) removal characteristics in water columns and sediments of shallow lakes, influenced by various factors, may exhibit spatial variations in lakes with algal-macrophyte dominance. The N removal rates in water columns and sediments of Lake Taihu were investigated. Our findings indicated that the total N removal rates in Lake Taihu followed the order of algae-dominance > macrophyte-dominance > pelagic lake (without the presence of algae and macrophytes). Correlation analysis revealed that the key environmental factors affecting denitrification and anammox in sediments of algae/macrophyte-type lakes were nitrate nitrogen (NO3--N), nitrite nitrogen (NO2--N), ammonia nitrogen (NH4+-N), and chlorophyll a (Chl-a). The linear regression demonstrated that a significant correlation between the denitrification and the anammox in sediments, with a correlation coefficient of 0.81 (p < 0.01). The contributions to N removal from the water columns and sediments in Lake Taihu were 53 % and 47 %, respectively. Denitrification predominantly drove N removal from sediments, whereas anammox dominated the N removal in water columns. Thus, N removal from the water columns is nonnegligible in shallow eutrophic lakes. This study enhances our understanding of N biogeochemical cycling dynamics in sediment-water and algae/macrophyte ecosystems across various shallow eutrophic lake regions.

Assessing water quality in the Pearl River for the last decade based on clustering: Characteristic, evolution and policy implications

The Pearl River (PR) is China's second-largest river, playing a crucial role in regulating and supplying water in the southeast. However, for the last decade, the PR has been experiencing water quality deterioration due to population growth, rapid economic development, and diverse human activities, particularly in its delta areas. This study analyzed the characteristics and evolution of eight water quality variables, including pH values (pH), water temperature (WT), dissolved oxygen (DO), five-day biochemical oxygen demand (BOD5), permanganate index (PI), total phosphorus (TP), ammonia nitrogen (NH3N), and fluoride (F-), which were monitored monthly at 16 water quality monitoring stations from January 2009 to August 2019. Overall, annual average BOD5 and F- concentrations met Class I water quality standards, while TP and NH3N conformed to lower standards. The cluster results showed noticeable differences for parameter grouping (DO-organic parameters-nutrient and solutes), seasonal variation (wet and dry), and water quality status (contaminated-remediating-fine). The Water Quality Index (WQI) ranged from 8.3 ("very poor") to 91.7 ("excellent") in the entire basin from 2009 to 2019, and NH3N-DO based WQImins were identified using the All-Subsets Linear Regression method. The fitting results of the Generalized Additive Models displayed that the deviance explained by natural factors ranged from 37.2% to 61.3%, while the socioeconomic explanation exceeded 70%. The WQImin component evolution (2003-2019) of Shenzhen River estuary, the most important part of the PR estuary, agreed with key parameter variations in heterogeneous clusters in the entire basin. Moreover, Shenzhen's water quality remediation applications indicated that reasonable-efficient-powerful efforts and support from governments could accelerate recovery. For the management departments, consistent measures should be strictly enforced, and elaborate regionalized management based on clusters could be attempted to maintain excellent water quality.

Nitrate sources and mixing in the Danube watershed: implications for transboundary river basin monitoring and management

Dispersed and unknown pollution sources complicate water management in large transboundary watersheds. We applied stable isotopes of water and nitrate together with contaminants of emerging concern (CECs: carbamazepine, caffeine, sulfamethoxazole, perfluorooctanoic acid and 2,4-dinitrophenol) to evaluate mixing and inputs of water and contaminants from tributaries into the mainstem of the transboundary Danube River. Stable isotope (δ¹⁸O, δ²H) variations from low values (− 13.3 ‰, − 95.1 ‰) in the Upper Danube after the Inn River confluence to high values (− 9.9 ‰, − 69.7 ‰) at the Danube River mouth revealed snowmelt dominated tributary mixing (~ 70%) in the mainstem. Stable isotopes of nitrate (δ¹⁵N-NO₃) in the Danube River varied from lower values (+ 6.7 ‰) in the Upper Danube to higher values after the mixing with Morava River (+ 10.5 ‰) and showed that cold snowmelt can reduce biological activity and controls nitrate biotransformation processes in the mainstem up to 1000 km downstream. Data on emerging contaminants affirmed the low biodegradation potential of organic compounds transferred into the mainstem by tributaries. We found pollutant source tracing in large rivers is complicated by mixing of multiple sources with overlapping isotopic signatures, but additional tracers such as CECs improve the interpretation of hydrological processes (e.g., water transit time) and support tracing of nitrate pollution sources, and biogeochemical processes. Our approach can be applied to other watersheds to improve the understanding of dilution and mixing processes. Moreover, it provides directions for improving national and transboundary water quality monitoring networks.