Dynamic spatiotemporal change of net anthropogenic phosphorus inputs and its response of water quality in the Liao river basin.

The Liao river is one of the seven major rivers in China, and the process of phosphorus (P) cycling and change of water quality in this basin are influenced to a considerable extent human activities. In this work, the traditional net anthropogenic phosphorus inputs (NAPI) model was improved by considering the dynamic change of wastewater treatment capacity and P deposition (PDEP) and reclassifying the sources of phosphorus into human P consumption (PHUM), agriculture P consumption (PAGR), livestock P consumption (PANIM) and PDEP to analyze its dynamic spatio-temporal change in the Liao river basin. The results showed that the annual mean NAPI was 785.53 kg P km-2 yr-1 (2001-2020), the maximum value was 940.49 kg P km-2 yr-1 in 2009, and the minimum value was 586.04 kg P km-2 yr-1 in 2001. The temporal variation of NAPI presented an increasing-fluctuation-increasing trend and was basically in line with that of the water quality throughout the three stages, and the spatial distribution of NAPI gradually increased from upstream to downstream. During the two decades, PANIM was the predominant component of NAPI with a share of 64.32%. PHUM, PAGR, and PDEP accounted for 15.97%, 11.54%, and 8.17%, respectively, and the point source NAPI (NAPIP) contributed to 4.95% of NAPI. Further, the INAPI (Improved NAPI) -MR (Multiple Regression) -SWAT (Soil and Water Assessment Tool) model was developed to predict the spatial distribution of P flux under two scenarios. The results showed that the Liao river basin experienced a reduction in P flux to different degrees due to the improvement of the wastewater treatment system, which was more significant in its downstream area. Long-term water quality monitoring is encouraged to develop refined water quality models in the future.

Accumulation characteristic of nitrogen in reservoirs during the ice-covered period under superimposed influence of ice and sediments: A case study of Biliuhe reservoir.

Reservoirs located in middle and high latitudes freeze for months in winter, where the accumulation characteristics of pollutants are changed by superimposed influence of salt exclusion from ice on the surface and pollution release from sediments at the bottom. Taking total nitrogen (TN) of Biliuhe reservoir in Northeast China as an example, we developed a model to simulate TN accumulation characteristics influenced by ice and sediments during the freezing period (NACISF), and quantified contributions of TN from ice and sediments. Model parameters of ice and sediments were determined by laboratory freeze-up simulation experiment and sediment release flux simulation experiment, and water quality data were obtained from field investigations. Results showed that the annual average amount of TN input during the ice-covered period from 2015 to 2020 was 220.77 t, the output was 400.11 t, and the accumulated amount was 589.52 t. TN excluded from ice and released from sediments contributed 8.12% and 7.17% of the total TN inputs in winter, respectively. Analysis showed that the TN excluded from ice was positively correlated with ice thickness and initial TN concentration. The maximum ice thickness of Biliuhe reservoir had a 13 year cyclic feature, and the proportion of TN excluded from ice to the total TN inputs in different periods ranged from 10.68% to 17.30% (mean 13.18%). Meanwhile, TN accumulated seasonally as summer > autumn > winter > spring. The TN exclusion effect in 2050 would be weakened when considering the combined effects of climate change and human activities, with a reduction of about 40.85% compared to the current. It is concluded that the NACISF model took into account the influences of both ice and sediments, which provided a detailed understanding of the accumulation characteristics of TN during freezing period, and had important reference significance for water quality management in winter.

Response of a Coastal Groundwater System to Natural and Anthropogenic Factors: Case Study on East Coast of Laizhou Bay, China.

With a shifting climate pattern and enhancement of human activities, coastal areas are exposed to threats of groundwater environmental issues. This work takes the eastern coast of Laizhou Bay as a research area to study the response of a coastal groundwater system to natural and human impacts with a combination of statistical, hydrogeochemical, and fuzzy classification methods. First, the groundwater level dynamics from 1980 to 2017 were analyzed. The average annual groundwater level dropped 13.16 m with a descent rate of 0.379 m/a. The main external environmental factors that affected the groundwater level were extracted, including natural factors (rainfall and temperature), as well as human activities (irrigated area, water-saving irrigated area, sown area of high-water-consumption crops, etc.). Back-propagation artificial neural network was used to model the response of groundwater level to the above driving factors, and sensitivity analysis was conducted to measure the extent of impact of these factors on groundwater level. The results verified that human factors including irrigated area and water-saving irrigated area were the most important influencing factors on groundwater level dynamics, followed by annual precipitation. Further, groundwater samples were collected over the study area to analyze the groundwater hydrogeochemical signatures. With the hydrochemical diagrams and ion ratios, the formation of groundwater, the sources of groundwater components, and the main hydrogeochemical processes controlling the groundwater evolution were discussed to understand the natural background of groundwater environment. The fuzzy C-means clustering method was adopted to classify the groundwater samples into four clusters based on their hydrochemical characteristics to reveal the spatial variation of groundwater quality in the research area. Each cluster was spatially continuous, and there were great differences in groundwater hydrochemical and pollution characteristics between different clusters. The natural and human factors resulted in this difference were discussed based on the natural background of the groundwater environment, and the types and intensity of human activity.

Water Level Decline in a Reservoir: Implications for Water Quality Variation and Pollution Source Identification.

Continuous water-level decline makes the changes of water quality in reservoirs more complicated. This paper uses trend analyses, wavelet analysis and principal component analysis-multiple linear regression to explore the changes and pollution sources affecting water quality during a period of continuous reservoir water level decline (from 65.37 m to 54.15 m), taking the Biliuhe reservoir as an example. The results showed that the change of water level of Biliuhe reservoir has a significant 13-year periodicity. The unusual water quality changes during the low water level period were as follows: total nitrogen continued to decrease. And iron was lower than its historical level. pH, total phosphorus, and ammonia nitrogen were higher than historical levels and fluctuated seasonally. Permanganate index increased as water level decreased after initial fluctuations. Dissolved oxygen was characterized by high content in winter and relatively low content in summer. The pollutant sources of non-point source pollution (PC1), sediment and groundwater pollution (PC2), atmospheric and production & domestic sewage (PC3), other sources of pollution (PC4) were identified. The main source of DO, pH, TP, TN, NH4-N, Fe and CODMn were respectively PC3 (42.13%), PC1 (47.67%), PC3 (47.62%), PC1 (29.75%), PC2 (47.01%), PC1 (56.97%) and PC2 (50%). It is concluded that the continuous decline of water level has a significant impact on the changes and pollution sources affecting water quality. Detailed experiments focusing on sediment pollution release flux, and biological action will be explored next.

Seasonal Release Potential of Sediments in Reservoirs and its Impact on Water Quality Assessment.

Reservoir sediments act as important receivers and sources for nutrients in the overlaying water. To determine the sediments adsorption and release characteristics and their impacts on water quality, surface sediments samples collected from Biliuhe reservoir in August 2015 were studied through simulation experiments in a laboratory. The results demonstrated that the equilibrium concentrations (EC0) of ammonia nitrogen (NH4+-N) and total phosphorus (TP) were 8.29 mg/L and 0.025 mg/L, respectively, which were both higher than the average concentrations in the overlying water. Therefore, the sediments of Biliuhe reservoir mainly acted as the pollution sources for NH4+-N and TP, and the release to water showed a seasonal variation. When potential release amounts were considered, the average concentrations of NH4+-N and TP in the overlaying water could reach 6.4 mg/L and 0.21 mg/L, respectively, which significantly exceeded the current contents. Further, water quality exhibited a decreased tendency after taking the release potential of nutrients into account of water quality assessment. Among the samples, 42% and 33% of them in summer and autumn exceeded the third level of the National Surface Water Quality Standards of China (GB3838-2002). The results indicated that sediments release potential had an unignored influence on water quality during various seasons.

Dynamic assessment of water quality based on a variable fuzzy pattern recognition model.

Water quality assessment is an important foundation of water resource protection and is affected by many indicators. The dynamic and fuzzy changes of water quality lead to problems for proper assessment. This paper explores a method which is in accordance with the water quality changes. The proposed method is based on the variable fuzzy pattern recognition (VFPR) model and combines the analytic hierarchy process (AHP) model with the entropy weight (EW) method. The proposed method was applied to dynamically assess the water quality of Biliuhe Reservoir (Dailan, China). The results show that the water quality level is between levels 2 and 3 and worse in August or September, caused by the increasing water temperature and rainfall. Weights and methods are compared and random errors of the values of indicators are analyzed. It is concluded that the proposed method has advantages of dynamism, fuzzification and stability by considering the interval influence of multiple indicators and using the average level characteristic values of four models as results.