Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin

Key factors affecting NH3-N in the Huaihe River Basin due to human activities

Human activity factors have a significant impact on changes in ammonia nitrogen (NH3-N) content in rivers. Existing research mainly focuses on human activity factors as type factors, and lacks research on the key factors affecting river NH3-N among human activity factors. Therefore, this paper aims to study the key factors affecting human activities on NH3-N in the Huaihe River through various statistical analysis methods. The study found that changes in NH3-N content in the Huaihe River are mainly affected by land use patterns in the basin. There are two different ways in which land use affects NH3-N in rivers: direct effects and indirect effects. We also studied the main pathways through which changes in key factors in human activities affect NH3-N in the Huaihe River by constructing a structural equation model. The results showed that crop sowing area and afforestation area have a significant direct effect on NH3-N in the Huaihe River. In addition, crop sowing area and afforestation area can also affect river NH3-N by regulating the amount of nitrogen fertilizer and human excrement. This study is of great significance for understanding how human activities regulate NH3-N content in rivers.

What will the water quality of the Yangtze River be in the future?

The long-term prediction of water quality is important for water pollution control planning and water resource management, but it has received little attention. In this study, the water quality trend in the Yangtze River is found to stabilize at most monitoring stations under environmental protection activities. Based on the physical mechanism and stochastic theory, a novel river water quality prediction model combining pollution source decomposition (including local point, local nonpoint and upstream sources) and time series decomposition (including trend, seasonal and residential components) is developed. The observed water quality data from 76 monitoring stations in the Yangtze River, including permanganate index (COD_Mn) and total phosphorus (TP), are used to drive this model to make long-term water quality predictions. The results show that this model has an acceptable accuracy. In the future, the concentration of COD_Mn will meet the water quality targets at most stations in the Yangtze River, but the concentration of TP will not be able to meet the water quality target at 28.5 % of the stations. Furthermore, the prediction value of COD_Mn is 62.2 % lower than the target on average. However, the prediction value of TP is only 24.4 % lower than the target on average, and it will exceed the water target by >50 % at some stations. This model has the potential to be widely used for long-term water quality prediction in the future.

Impacts of land uses on spatio-temporal variations of seasonal water quality in a regulated river basin, Huai River, China

Land use impacts from agriculture, industrialization, and human population should be considered in surface water quality management. In this study, we utilized an integrated statistical analysis approach mainly including a seasonal Mann-Kendall test, clustering analysis, self-organizing map, Boruta algorithm, and positive matrix factorization to the assessment of the interactions between land use types and water quality in a typical catchment in the Huai River Basin, China, over seven years (2012-2019). Spatially, water quality was clustered into three groups: upstream, midstream, and downstream/mainstream areas. The water quality of upstream sites was better than of mid-, down-, and mainstream. Temporally, water quality did not change significantly during the study period. However, the temporal variation in water quality of up-, down-, and mainstream areas was more stable than in the midstream. The interactions between land use types and water quality parameters at the sub-basin scale varied with seasons. Increasing forest/grassland areas could substantially improve the water quality during the wet season, while nutrients such as phosphorus from cropland and developed land was a driver for water quality deterioration in the dry season. Water area was not a significant factor influencing the variations of ammonia nitrogen (NH₃-N) and total phosphorus (TP) in the wet or dry season, due to the intensive dams and sluices in study area. The parameters TP, and total nitrogen (TN) were principally linked with agricultural sources in the wet and dry seasons. The parameters NH₃-N in the dry season, and chemical oxygen demand (COD_Cr) in the wet season were mainly associated with point source discharges. Agricultural source, and urban point source discharges were the main causes of water quality deterioration in the study area. Collectively, these results highlighted the impacts of land use types on variations of water quality parameters in the regulated basin.

The Rural Fires of 2017 and Their Influences on Water Quality: An Assessment of Causes and Effects

As water is facing increasing pressures from population and economic growth and climate change, it becomes imperative to promote the protection, restoration and management of this resource and its watersheds. Since water quality depends on multiple factors both natural and anthropic, it is not easy to establish their influences. After the October 2017 fires that affected almost 30% of the Mondego hydrographic basin in Central Portugal, 10 catchments were selected for periodic physical-chemical monitoring. These monitoring campaigns started one month after the fires and lasted for two hydrological years, measuring the electric conductivity (EC), pH, dissolved oxygen (DO), turbidity (Turb), alkalinity (Alk), major and minor ions, and trace elements. The obtained data were then statistically analysed alongside the geomorphological characteristics of each catchment coupled with features of land-use and occupation. From the results, it was possible to establish that fire-affected artificial areas, through the atmospheric deposition and surface runoff of combustion products, had the most impact on surface water quality, increasing As, K^-, Ca²⁺, Mg²⁺, NO₃^-, SO₄^2- and Sr, and consequently increasing electrical conductivity. Agricultural land-use seems to play a major influence in raising the water's EC, Cl, K^- and Na²⁺. Regarding natural factors such as catchment geology, it was found that the extent of igneous exposures influences As, and the carbonate sedimentary units are a source of Ca²⁺ and HCO₃^2- concentrations and impose an increase in alkalinity. Rainfall seems, in the short term, to increase the water concentration in Al and NO₃^-, while also raising turbidity due to sediments dragged by surface runoff. While, in the long-term, rainfall reduces the concentrations of elements in surface water and approximates the water's pH to rainfall features.

Effects of watershed land use on coastal marine environments: A multiscale exploratory analysis with multiple biogeochemical indicators in fringing coral reefs of Okinawa Island

The analytical spatial scale and selection of biogeochemical indicators affect interpretations of land-use impacts on coastal marine environments. In this study, nine biogeochemical indicators were sampled from 36 locations of coral reefs fringing a subtropical island, and their relationships with watershed land use were assessed by spatial autoregressive models with spatial weight matrixes based on distance thresholds of a few to 30 km. POM-relevant indicators were associated with agricultural and urban lands of watersheds within relatively small ranges (6-14 km), while the concentrations of inorganic nutrients were associated with watersheds within 20 km or more. The macroalgal δ¹⁵N showed a strong relationship with agricultural lands of watersheds within 7 km and urban/forest lands of watersheds within 24 km. These results demonstrate significant effects of land use on the coral reef ecosystems of the island, and the importance of appropriate combinations of analytical scales and biogeochemical indicators.

Spatiotemporal variations of water quality and their driving forces in the Yangtze River Basin, China, from 2008 to 2020 based on multi-statistical analyses

Water quality deterioration is a prominent issue threatening water security worldwide. As the largest river in China, the Yangtze River Basin is facing severe water pollution due to intense human activities. Analyzing water quality trends and identifying the corresponding driver factors are important components of sustainable water quality management. Thus, spatiotemporal characteristics of the water quality from 2008 to 2020 were analyzed by using a Mann-Kendall test and rescaled range analysis (R/S). In addition, multi-statistical analyses were used to determine the main driving factors of variation in the permanganate index (COD_Mn), ammonia nitrogen (NH₃-N) concentration, and total phosphorus (TP) concentration. The results showed that the mean concentrations of NH₃-N and TP decreased from 0.31 to 0.16 mg/L and 0.16 to 0.07 mg/L, respectively, from 2008 to 2020, indicating that the water quality improved during this period. However, the concentration of COD_Mn did not reduce remarkably. Based on R/S analysis, the NH₃-N concentration was predicted to continue to decrease from 2020 to 2033, whereas the COD_Mn concentration was forecast to increase, highlighting an issue of great concern. In terms of spatial distribution, water quality in the upstream was better than that of the mid-downstream. Multi-statistical analyses revealed that the temporal variation in water quality was predominantly influenced by tertiary industry (TI), the nitrogen fertilizer application rate (N-FAR), the phosphate fertilizer application rate (P-FAR), and the irrigation area of arable land (IAAL), with contribution rates of 15.92%, 14.65%, 3.46%, and 2.84%, respectively. The spatial distribution of COD_Mn was mainly influenced by TI, whereas that of TP was primarily determined by anthropogenic activity factors (e.g., N-FAR, P-FAR). This study provides deep insight into water quality evolution in the Yangtze River Basin that can guide water quality management in this region.

Watershed scale spatiotemporal nitrogen transport and source tracing using dual isotopes among surface water, sediments and groundwater in the Yiluo River Watershed, Middle of China

Nitrogen pollution has been shown to have strong potential threaten to the human drinking water and agriculture. However, identifying the nitrogen and spatial-temporal variation and nitrogen pollution sources among surface water, sediments and groundwater at the watershed scale is still of insufficient understanding. In this study, multi-methods (dual isotopes, hydraulic, hydrogeochemical methods) have been used and 400 samplings (40 sediments, 20 shallow groundwater and 40 surface waters in four periods in dry and wet seasons) were collected from 2018 to 2020. The results showed that the concentration of NO₃^--N, NH₄⁺-N, NO₂^--N and total nitrogen (TN) had variable spatial and temporal changes in whole watershed. The concentration of TN, NO₃^--N, NH₄⁺-N and NO₂^--N in downstream was higher than midstream and upstream both in dry and wet seasons. The concentration of TN, NO₃^--N, NH₄⁺-N and NO₂^--N of the whole watershed in wet season was higher than dry season. The dual isotope values indicate that nitrogen sources were mainly derived from manure and sewage waste input (MSI), agriculture chemical fertilizers (ACFI) and sediments nitrogen input (SNI). Those nitrogen sources have different proportion in downstream, midstream and upstream in dry and wet seasons (the largest proportion: MSI 95.24% in downstream and ACFI 86.26% in upstream both in dry season, SNI 31.75% in midstream in wet season). Water exchange has positive correlation with the nitrogen concentration. High level of nitrogen in river also can be a diver in different location and seasons. Those results can be useful for developing regional management strategies and plans for water pollution control and treatment at watershed-scale.