Development of a two-dimensional eutrophication model in an urban lake (China) and the application of uncertainty analysis

Perfluoroalkyl acids (PFAAs) in the aquatic food web of a temperate urban lake in East China: Bioaccumulation, biomagnification, and probabilistic human health risk

The bioaccumulation and biomagnification of perfluoroalkyl acids (PFAAs) in temperate urban lacustrine ecosystems is poorly understood. We investigated the occurrence and trophic transfer of and probabilistic health risk from 15 PFAAs in the food web of Luoma Lake, a temperate urban lake in East China. The target PFAAs were widely distributed in the water (∑PFAA: 77.09 ± 9.07 ng/L), suspended particulate matter (SPM) (∑PFAA: 284.07 ± 118.05 ng/g dw), and sediment samples (∑PFAA: 67.77 ± 17.96 ng/g dw) and occurred in all biotic samples (∑PFAA: 443.27 ± 124.89 ng/g dw for aquatic plants; 294.99 ± 90.82 for aquatic animals). PFBA was predominant in water and SPM, with 40.11% and 21.35% of the total PFAAs, respectively, while PFOS was the most abundant in sediments (14.11% of the total PFAAs) and organisms (14.33% of the total PFAAs). Sediment exposure may be the major route of biological uptake of PFAAs. The PFAA accumulation capacity was the highest in submerged plants, followed by emergent plants > bivalves > crustaceans > fish > floating plants. Long-chain PFAAs were biomagnified, and short-chain PFAAs were biodiluted across the entire lacustrine food web. PFOS exhibited the greatest bioaccumulation and biomagnification potential among the target PFAAs. However, biomagnification of short-chain PFAAs was also observed within the low trophic-level part of the food web. Human health risk assessment indicated that perfluorooctanesulfonate (PFOS) and perfluorooctanoic acid (PFOA) posed health risks to all age groups, while the other PFAAs were unlikely to cause immediate harm to consumers in the region. This study fills a gap in the knowledge of the transfer of PFAAs in the food webs of temperate urban lakes.

A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins.

Organic P transformations and release from riparian soils responding to water level fluctuation

To manage eutrophication of reservoirs, it is important to consider the potential for unexpected releases of organic phosphorus (OP) from areas around the reservoir where the water level fluctuates. In this study, we investigated the absorption and release of OP from a riparian soil/sediment from the Miyun Reservoir under fluctuating water levels using laboratory simulations. The total organic phosphorus (TOP) content in the soils/sediments ranged from 250.76 to 298.62 mg/kg, which accounted for between 5.6 and 38.5% of the total phosphorus (TP) content. We measured three OP fractions and found that the concentration of moderately labile OP (MLOP) was the highest, followed by labile OP (LOP), and the concentration of non-labile OP (NLOP) was the lowest. As the soils and sediments dried, they adsorbed phosphorus (P). The inorganic phosphorus (IP) contents were significantly and negatively correlated with the LOP and MLOP contents, indicating exchange between IP with these two fractions when the concentrations of bioavailable phosphorus in the soil are low. During flooding, the physicochemical properties varied at the sediment-water interface, inducing the release of Fe/Al-P. Some of the LOP and MLOP in the sediments were mineralized to IP. Our results suggest that when there are external P inputs, P may be released when sediments around a reservoir are subjected to wetting and drying as water levels fluctuate, which may cause P enrichment in reservoirs, especially in areas with poor water exchange.

A combination method for multicriteria uncertainty analysis and parameter estimation: a case study of Chaohu Lake in Eastern China

Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling.

Zero valent iron supported biological denitrification for farmland drainage treatments with low organic carbon: Performance and potential mechanisms

In this work, the feasibility and performance of zero valent iron (ZVI) coupled anaerobic microorganisms in nitrogen removal under low organic carbon condition were investigated, through the comparison of mono-ZVI system and mono-cell system. Coupled system showed the highest total nitrogen (TN) removal efficiency of 67.85% with the addition of 15 g L^-1 iron shavings at pH 7.0, which was higher than 29.62% in the mono-ZVI system and 43.86% in the mono-cell system. Besides, the activities of nitrate reductase (NAR), nitrite reductase (NIR), nitric oxide reductase (NOR) and nitrous oxide reductase (N₂OR) were significantly improved at ZVI dosage of 15 g L^-1 and pH 7.0, which contributed to the higher TN removal efficiency in coupled system. The extent of sludge granulation was greater in the coupled system than mono-cell system, which benefited to the high operational performance and stability of coupled system. The promoted generation of extracellular polymeric substances (EPS) and formation of iron oxides in the coupled system also took advantages on nitrogen removal through adsorption. In addition, ZVI could largely enrich the functional species related to nitrogen removal in the system at phyla and genera level, which could be reasoned for the enhanced nitrogen removal efficiency. In conclusion, this study will improve the understandings of nitrogen removal in the coupled system and be useful to ensure the application of ZVI-supported biological process in the remediation of farmland drainage.

Enhanced lake-eutrophication model combined with a fish sub-model using a microcosm experiment

Eutrophication models are effective tools for assessing aquatic environments. The lake ecosystem consists of at least three trophic levels: phytoplankton, zooplankton, and fish. However, only a few studies have included fish sub-models in existing eutrophication models. In addition, no specific value or range is available for certain parameters of the fish sub-model. In the present study, a lake microcosm experimental system was established to determine the range of fish sub-model parameters. A three-trophic-level eutrophication model was established by combining the fish sub-model and eutrophication model. The Bayesian Markov Chain Monte Carlo and genetic algorithm method was used to calibrate the parameters of the eutrophication model. The results show that the maximum relative errors were due to phosphate (5.31%), the minimum relative error was due to nitrate (1.94%), and the relative error of dissolved oxygen, ammonia N, zooplankton, and chlorophyll ranged from 3 to 4%. Compared with the two-trophic-level eutrophication model, the relative errors of ammonia nitrogen (4.17%), phosphate (- 5.31%), and nitrate (1.94%) in the three-trophic-level eutrophication model were lower than those in the two-trophic-level eutrophication model, indicating that the three-trophic-level eutrophication model can obtain highly accurate simulation results and provide a better understanding of eutrophication models for future use.

Assessing the effects of climate change on water quality of plateau deep-water lake - A study case of Hongfeng Lake

Climate change-related temperature increases and sea level rise have a significant impact on the atmosphere, hydrosphere, biosphere, and anthroposphere. Impacts on ecosystems will mostly occur over the long term and short-term effects may consequently attract comparatively less attention from researchers and decision-makers. In this study, we investigate eight meteorological factors and eleven water quality indicators of deep-water lakes in the Yunnan-Guizhou Plateau in southwestern China. A robust proxy model based on a seven-year dataset (2010-2016) was established to predict the effects of climate change on water quality in Hongfeng Lake over the coming years. Perturbation analysis revealed that global warming has a more significant effect on chlorophyll a levels than on total phosphorus or total nitrogen in the lake area, and that external nutrient loading is a key factor aggravating eutrophication. Non-point source pollution induced by heavy precipitation will likely lead to an increase in total nitrogen and the lake may become more phosphorus-restricted. Reducing external inputs and controlling endogenous releases will help alleviate eutrophication.

Contrasting Eutrophication Risks and Countermeasures in Different Water Bodies: Assessments to Support Targeted Watershed Management

Eutrophication is a major problem in China. To combat this issue, the country needs to establish water quality targets, monitoring systems, and intelligent watershed management. This study explores a new watershed management method. Water quality is first assessed using a single factor index method. Then, changes in total nitrogen/total phosphorus (TN/TP) are analyzed to determine the limiting factor. Next, the study compares the eutrophication status of two water function districts, using a comprehensive nutritional state index method and geographic information system (GIS) visualization. Finally, nutrient sources are qualitatively analyzed. Two functional water areas in Tianjin, China were selected and analyzed: Qilihai National Wetland Nature Reserve and Yuqiao Reservoir. The reservoir is a drinking water source. Results indicate that total nitrogen (TN) and total phosphorus (TP) pollution are the main factors driving eutrophication in the Qilihai Wetland and Yuqiao Reservoir. Phosphorus was the limiting factor in the Yuqiao Reservoir; nitrogen was the limiting factor in the Qilihai Wetland. Pollution in Qilihai Wetland is more serious than in Yuqiao Reservoir. The study found that external sources are the main source of pollution. These two functional water areas are vital for Tianjin; as such, the study proposes targeted management measures.