Guangzhi Sun

The salinization of freshwater lakes by agricultural activities poses a threat to many lake ecosystems around the world. Quantitative, medium- to long-term studies are needed to understand how some common agricultural practices, such as the discharge of crop irrigation in the vicinities of large lakes, may affect lake salinization. In this study, hydrological, hydrodynamics, water quality and meteorological datasets were used to analyze the long-term spatial-temporal variations of water salinities of a major lake, the Chagan Lake, in Northeast China. An integrated hydrodynamics-salinity model was used to simulate lake water salinity changes taking place at different times and locations, including (i) salt accumulations during a non-frozen period, and (ii) the time when water salinity may reach a significant threshold (1 psu) that jeopardizes a major environmental and economic value of this lake (i.e., the cultivation of local fish species). The results confirmed that Chagan Lake was indeed undergoing salinization in the ten year period between 2008 and 2018. The spatial-temporal patterns of the salinization processes were identified. For instance, (i) the mean salinity of the lake water was found to be 0.55 psu in the summer season of the region and 0.53 psu in the winter, and (ii) between May to October the salinity was up to 0.62 psu in the western region of the lake. The rate of salt accumulation was found to be 97 ton per annum during the non-frozen period. The simulation predicted that by 2024 the lake water will become sub-saline (salinity > 1.07 psu) which is toxic to fish species, if the current practice of irrigation discharge into the lake continues. In the scenario that the amount of irrigation discharges into the lake doubles, the western region of the lake will become sub-saline within one year, and then the whole lake within three years. Overall, this study has produced results that are useful to authorities around the world, for balancing the risks and benefits of developing crop irrigation fields in areas surrounding large freshwater lakes.

Herein, the production of biohydrogen by dark fermentation was optimized using a novel hybrid approach that combines ANNs (artificial neural networks) with RSM (response surface methodology). Using the limited numbers of data (15 runs) as training data set together with one cross‐out method for validation, the complete 29 runs of well‐established data matrix were created from ANNs for RSM statistical analysis in order to correlated the critical process parameters with hydrogen production performance. This methodology was found to be robust, cost‐effective, reliable, and can be extensively analyzed the critical operational parameters, that is, carbon sources (obtained from potato peel wastes and starchy wastes), metal cofactor Fe⁰, pH, and dose levels of microbes on the hydrogen production, along with concentrations of other metabolites, such as acetic acid, propionic acid, butyric acid, valeric acid, and ethanol. The established ANNs‐RSM model using Box–Behnken design indicates the significant changes caused by the variations of a few critical operation parameters. The resultant model shows an exceptionally good result in terms of nonlinear noisy processes. Both single and multiple objective optimizations for dark hydrogen fermentation can achieve by using the established hybrid ANN‐RSM system. The optimal operating conditions (starch 6.2 kg/m³, pH 6.7, Fe⁰ 11.7 g/m³, sludge 24.6 g/m³) could lead to the generation of hydrogen with a yield of 106.2 (cm³/g) and metabolites, that is, propionic acid (2.8 kg/m³), butyric acid (2E−2 kg/m³), valeric acid (4E−4 kg/m³) acetic acid (1.9 kg/m³), and ethanol (0.1 kg/m³) simultaneously.

Water quality safety is the key factor to maintain the ecosystem service functions of lakes. Field investigations and statistical analyses were carried out to study the water quality of a large, agriculture-stressed lakes (e.g., Chagan Lake) in Northeast China. The hydro-chemical properties of the Chagan Lake are HCO₃·CO₃-Na. Nutrient (N and P) and non-nutrient (pH and F⁻) were found to be the major factors that threaten water quality safety of the lake. The concentration of total nitrogen (TN) and total phosphorus (TP) was found to vary seasonally and at different locations. The overall lake water had mean TN and TP values of 2.19 mg/L and 0.49 mg/L, respectively, in summer. TN was the major factor for water quality deterioration in the western region of the lake, while TP was the principal factor in the other regions, as determined by a principal component analysis (PCA). Fluoride (F⁻) concentration in the lake water were related to the values of total dissolved solid (TDS), pH, and electrical conductivity (EC). In addition, eutrophication is a fundamental index that has been affecting the ecological evaluation of water quality. The results showed that trophic level index (TLI), trophic state index (TSI), and eutrophication index (EI) were evaluated to quantify the risk of eutrophication. However, TLI and TSI can better describe the purification effect of the wetland. These indices showed that the lake water was hyper-eutrophic in summer, with TLI, TSI, and EI values of 60.1, 63.0, and 66.6, respectively. Disparities in water quality were observed among whole areas of the lake. Overall, this study revealed that controlling agriculture drainage is crucial for lake water quality management. The study generated critical data for making water quality management plans to control the risk.

Bio-hydrogen production (BHP) produced from renewable bio-resources is an attractive route for green energy production, due to its compelling advantages of relative high efficiency, cost-effectiveness, and lower ecological impact. This study reviewed different BHP pathways, and the most important enzymes involved in these pathways, to identify technological gaps and effective approaches for process intensification in industrial applications. Among the various approaches reviewed in this study, a particular focus was set on the latest methods of chemicals/metal addition for improving hydrogen generation during dark fermentation (DF) processes; the up-to-date findings of different chemicals/metal addition methods have been quantitatively evaluated and thoroughly compared in this paper. A new efficiency evaluation criterion is also proposed, allowing different BHP processes to be compared with greater simplicity and validity.

An experimental study was carried out using in pilot-scale constructed wetland systems, operated in parallel to treat raw sewage. Each system consisted of a vertical flow (VF) unit that was filled with biochar as the main media, followed by a horizontal flow (HF) unit filled with crushed cement mortar. Hydraulic loading (HL) ranged 340–680 mm/day was applied on the VF wetland units, where high total nitrogen (TN) mass removal rate (20–23 g N/m² d) was obtained, demonstrating that biochar media had a beneficial effect on the degradation of nitrogenous pollutants. Total phosphorus (TP) removal percentage (concentration based) was ≥ 86% in HF wetlands packed with mortar materials. In one system, the flow direction of the sewage was directed by the deployment of downflow pipes and vertical baffles, aiming to facilitate the formation of aerobic and anaerobic zones in the wetland matrices. The effects of such arrangement were analyzed by comparing pollutant removal efficiencies in the two systems. On average, 99, 96, 93, and 86 percentage removals were obtained for ammonia (NH₄-N), TN, biochemical oxygen demand (BOD), and TP, respectively, during the experiments. Biochar and crushed mortar proved to be a highly effective combination as media in subsurface flow constructed wetlands for wastewater treatment.