Coastal groundwater quality significantly affects the regional sustainable development, due to the water resource shortage in coastal zone. Current studies on coastal groundwater have mainly focused on saline water intrusion and over-extraction. Information on the heavy metal pollution of coastal groundwater with salinization trend is limited. This study investigated heavy metals in groundwater from a typical coastal region with intensive anthropogenic activities and saline water intrusion. The southwestern coastal plains of the study area showed significant groundwater salinization trend. Heavy metals in the coastal groundwater mainly originated from anthropogenic activities and groundwater salinization according to principal component analysis. Relative high concentrations of As, Co, Cr, Cu, Fe, Mn, and Ni occurred in the southwestern coast of the study area with high TDS level, indicating that the concentrations of these heavy metals in shallow aquifer of the study area might be influenced by the groundwater salinization. Average concentrations of heavy metals in groundwater of the study area ranged from 0.03 (Cd) to 686.92 (Fe) μg/L. Fe was the dominant heavy metal in groundwater with the maximal concentration of 2333.76 μg/L and exceeding-standard rate reaching 98.23%. Approximately 13.27% of sampling sites showed moderate or higher heavy metal pollution of groundwater based on heavy metal pollution index. Heavy metals in groundwater exerted low ecological risks. Elements Fe, Ni, and As were the main contributors for ecological risks. Cancer risks of heavy metals for both adults and children were high at all sampling sites. Non-cancer risks for adults and children were unacceptable at 4.42% and 17.70% of sampling sites, respectively. The salinization of the coastal groundwater could also lead to the increase in the ecological and health risks of heavy metals in coastal groundwater. These findings provide initial and important information on heavy metals pollution in coastal aquifer with saline water intrusion.

The occurrence of fluoroquinolone antibiotics in wastewater has drawn great attention. Adsorption of widely used fluoroquinolone antibiotics (enrofloxacin and ofloxacin) in wastewater using bamboo biochar was investigated. More than 99% of fluoroquinolone antibiotics were removed from the synthetic wastewater through adsorption. Adsorption capacities of bamboo biochar slightly changed when pH increased from 3.0 to 10.0. The adsorption capacity of bamboo biochar increased sharply when the initial concentration of enrofloxacin or ofloxacin increased from 1 to 200 mg L⁻¹ and then began to plateau with further increases in initial concentration. The maximum adsorption capacity (45.88 ± 0.90 mg·g⁻¹) was observed when the ratio of bamboo biochar to fluoroquinolone antibiotics was 10. The enrofloxacin adsorption capacity of bamboo biochar decreased from 19.91 ± 0.21 mg·g⁻¹ to 14.30 ± 0.51 mg·g⁻¹ while that of ofloxacin decreased from 19.82 ± 0.22 mg·g⁻¹ to 13.31 ± 0.56 mg·g⁻¹ when the NaCl concentrations increased from 0 to 30 g·L⁻¹. The adsorptions of fluoroquinolone on bamboo biochar have isotherms that obeyed the Freundlich model (r² values were in the range of 0.990–0.991).

Good water quality is important for normal production processes in industrial aquaculture. However, in situ or real-time monitoring is generally not available for many aquacultural systems due to relatively high monitoring costs. Therefore, it is necessary to predict water quality parameters in industrial aquaculture systems to obtain useful information for managing production activities. This study used back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), and least squares support vector machine (LSSVM) to simulate and predict water quality parameters including dissolved oxygen (DO), pH, ammonium-nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), and nitrite-nitrogen (NO₂-N). Published data were used to compare the prediction accuracy of different methods. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting DO were 0.60, 0.99, 0.99, and 0.99, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting pH were 0.56, 0.84, 0.99, and 0.57. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NH₃-N were 0.28, 0.88, 0.99, and 0.25, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NO3-N were 0.96, 0.87, 0.99, and 0.87, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM predicted NO₂-N with correlation coefficients of 0.87, 0.08, 0.99, and 0.75, respectively. SVM obtained the most accurate and stable prediction results, and SVM was used for predicting the water quality parameters of industrial aquaculture systems with groundwater as the source water. The results showed that the SVM achieved the best prediction effect with accuracy of 99% for both published data and measured data from a typical industrial aquaculture system. The SVM model is recommended for simulating and predicting the water quality in industrial aquaculture systems.