RESUMO
The landscape pattern determines water pollution source and sink processes and plays an important role in regulating river water quality. Due to scale effects, studies on the relationship between landscape pattern and river water quality showed variance at different scales. However, there is still a lack of integrated study on the scale effect of landscape pattern and river water quality dynamics. This study collected 4 041 data from results of previous publications to address the characteristics of landscape pattern and river water quality dynamics at different scales and to identify the key temporal and spatial scales as well as landscape pattern indices for regulating river water quality. The results indicated that, compared to precipitation events, base flow periods, and interannual scales, the high-flow period was the key temporal scale for linking landscape pattern on river water quality. Compared to the watershed scale, the landscape pattern of buffer zones had a greater impact on river water quality. The high-flow period-buffer zone scale was the key spatiotemporal coupling scale for linking landscape pattern and river water quality. Compared to croplands, water bodies, grasslands, and the overall landscape of the watershed, the landscape pattern of forests and urban areas had a greater impact on river water quality. Fragmentation degree was the most important landscape pattern factor regulating river water quality. In river water quality management, it is important to focus on the landscape configuration of buffer zones, increase forest area, reduce patch density of forests and water bodies, and decrease the aggregation degree of urban areas.
RESUMO
Accurate source identification/apportionment is essential for optimizing water NO3--N pollution control strategies. This study conducted a meta-analysis based on data from 167 rivers across China from 2000 to 2022 to analyze the spatial and temporal variation patterns of nitrate pollution in seven major river systems and to quantitatively identify the source composition of riverine nitrate. The average ρï¼NO3--Nï¼ in the seven major river systems was ï¼4.54±3.99ï¼ mg·L-1ï¼ with 9.6% of river ρï¼NO3--Nï¼ exceeding 10 mg·L-1. The riverine ρï¼NO3--Nï¼ in eastern China were higher than that in western Chinaï¼ and the highest concentration was observed in the Haihe River system. Additionallyï¼ tributaries experienced more serious NO3--N pollution than that in the main stream. The ρï¼NO3--Nï¼ in most river systems in the dry season was higher than that in the wet seasonï¼ except in the Yellow River system. There was significant nitrification in the Pearl River systemï¼ the middle and lower reaches of the Yellow River systemï¼ the middle reaches of the Liaohe River systemï¼ the Songhua River systemï¼ and the Haihe River systemï¼ whereas there was significant denitrification in the Yangtze River systemï¼ the Huaihe River systemï¼ and the lower reaches of the Pearl River system. Based on the dual stable isotopes-based MixSIAR modelï¼ the major NO3--N source was sewage/manure ï¼ > 50%ï¼ in the Yangtze River systemï¼ Haihe River systemï¼ Liaohe River systemï¼ and Southeast River system. Soil nitrogen was the main NO3--N source in the Songhua River system ï¼56.4%ï¼ï¼ and the contribution of fertilizer nitrogenï¼ soil nitrogenï¼ and sewage/manure to NO3--N pollution in the Pearl River systemï¼ Huai River systemï¼ and Yellow River system was 20%-40%. The contribution rate of sewage/manure to NO3--N in the tributaries was higher than that in the main streamï¼ whereas the contribution rate of soil nitrogen to NO3--N in the main stream was higher than that in the tributaries. The contribution rate of soil nitrogenï¼ fertilizer nitrogenï¼ and atmospheric deposition nitrogen to nitrate nitrogen in the wet season was higher than that in the dry seasonï¼ whereas the contribution rate of sewage/manure to NO3--N pollution in the dry season was higher than that in the wet season. Thereforeï¼ point source pollution such as domestic and production sewage discharge should be controlled in the Haihe River systemï¼ the Yangtze River systemï¼ the Liaohe River systemï¼ the tributaries and the downstream main stream areas of Yellow River systemï¼ and the downstream area of the Pearl River systemï¼ whereas non-point source pollution caused by the loss of fertilizer and soil nitrogen should be controlled in the Huaihe River systemï¼ the Songhua River systemï¼ the middle reaches of the main stream area of the Yellow River systemï¼ and the middle and upper reaches of the Pearl River system. The results can provide a scientific basis for the effective control of nitrate pollution in the river systems in China.
RESUMO
Due to increasing active nitrogen pollution loads, river systems have become an important source of nitrous oxide (N2O) in many areas. Due to the lack of monitoring data in many studies as well as the difficulty in estimating intermediate parameters and expressing temporal-spatial variability in current methods, a high level of uncertainty remains in the estimates of riverine N2O emission quantity. Based on the monthly monitoring efforts conducted for 10 sampling sites across the Yonganxi River system in Zhejiang Province from June 2016 to July 2019, the temporal and spatial dynamics of riverine N2O dissolved concentrations ρ(N2O), N2O fluxes, and their influencing factors were addressed. A multiple regression model was then developed for predicating riverine N2O emission flux to estimate annual N2O emission quantity for the entire river system. The results indicated that observed riverine ρ(N2O) (0.03-2.14 µg·L-1) and the N2O fluxes[1.32-82.79 µg·(m2·h)-1] varied by 1-2 orders of magnitude of temporal-spatial variability. The temporal and spatial variability of ρ(N2O) were mainly influenced by the concentrations of nitrate, ammonia, and dissolved organic carbon, whereas the N2O emission fluxes were mainly affected by river water discharges and ρ(N2O). A multiple regression model that incorporates variables of river water discharge and ρ(N2O) could explain 90% of the variability in riverine N2O emission fluxes and has high accuracy. The model estimated N2O emission quantity from the entire Yonganxi River system of 3.67 t·a-1, with 29% from the main stream and 71% from the tributaries. The IPCC default emission factor method might greatly overestimate and underestimate N2O emission quantities for rivers impacted by low and high pressures of human activities, respectively. This study advances our quantitative understanding of N2O emission for the entire river system and provides a reference method for estimating riverine N2O emission with more accuracy.
