Environmental DNA metabarcoding reveals the effect of environmental selection on phytoplankton community structure along a subtropical river.

The Dongjiang River, a major tributary of the Pearl River system that supplies water to more than 40 million people in Guangdong Province and neighboring regions of China, harbors rich biodiversity, including many endemic and endangered species. However, human activities such as urbanization, agriculture, and industrialization have posed serious threats to its water quality and biodiversity. To assess the status and drivers of phytoplankton diversity, which is a key indicator of aquatic ecosystem health, this study used Environmental DNA (eDNA) metabarcoding combined with machine learning methods to explore spatial variations in the composition and structure of phytoplankton communities along the Dongjiang River, including its estuary. The results showed that phytoplankton diversity exhibited spatial distribution patterns, with higher community structure similarity and lower network complexity in the upstream than in the downstream regions. Environmental selection was the main mechanism shaping phytoplankton community composition, with natural factors driving the dominance of Pyrrophyta, Ochrophyta, and Cryptophyta in the upstream regions and estuaries. In contrast, the downstream regions was influenced by high concentrations of pollutants, resulting in increased abundance of Cryptophyta. The random forest model identified temperature, dissolved oxygen, chlorophyll a, NO2-, and NH4+ as the main factors influencing the primary phytoplankton communities and could be used to predict changes during wet periods. This study provides valuable insights into the factors influencing phytoplankton diversity and community composition in the Dongjiang River, and demonstrates the application value of eDNA metabarcoding technique in large-scale, long-distance river biodiversity monitoring.

Characteristics and fractionations of sediment oxygen demand in a complex tidal river network area.

Tidal river networks are affected by the tide and influenced by complex factors related to sediment oxygen demand (SOD). In this study, we used chemical inhibition to measure the oxygen consumption of different types of SOD to explore the specific oxygen consumption mechanism of sediments. Then, we evaluated the diffusion fluxes of the sediment-water interface and factors affecting SOD using diffusive gradients in thin films. Total SOD in the tidal river network area of the Pearl River basin was ∼0.5928 g/m2/day, which was 8.47% higher than that in the non-tidal river network area but lower than that in black and odorous water reported previously. In the tidal river network area, biological SOD was 15.6% higher in summer than in winter, and the difference in total SOD was greatly influenced by human activity. We observed a significant effect of sediment on SOD in winter, whereas there were no significant correlations between sediment and SOD in summer. Different particle-size distributions lead to different organic matter contents, resulting in different biological SOD ratios between seasons. Our study found that seasonal tidal changes can affect ion exchange at the sediment water interface, leading to changes in SOD.These findings will be of great significance for the study of phenomena associated with low dissolved oxygen in tidal river networks and provide directions for future sediment pollution control.

Multivariate optimization of characteristic parameters of continuous-flow system with a front buffer tank for industrial reverse osmosis concentrate treatment.

Industrial reverse osmosis concentrate (ROC) was electrochemically oxidized using a continuous-flow system (CFS) with a front buffer tank. Multivariate optimization including Plackett-Burman (PBD) and central composite design based on response surface method (CCD-RSM) was implemented to investigate the effects of characteristic (e.g., recirculation ratio (R value), ratio of buffer tank and electrolytic zone (RV value)) and routine (e.g., current density (i), inflow linear velocity (v) and electrode spacing (d)) parameters. R, v values and current density significantly influenced chemical oxygen demand (COD) and NH4+-N removal and effluent active chlorine species (ACS) level, while electrode spacing and RV value had negligible effects. High chloride content of industrial ROC facilitated the generation of ACS and subsequent mass transfer, low hydraulic retention time (HRT) of electrolytic cell improved the mass transfer efficiency, and high HRT of buffer tank prolonged the reaction between the pollutants and oxidants. The significance levels of COD removal, energy efficiency, effluent ACS level and toxic byproduct level CCD-RSM models were validated by statistical test results, including higher F value than critical effect value, lower P value than 0.05, low deviation between predicted and observed values, and normal distribution of calculated residuals. The highest pollutant removal was achieved at a high R value, a high current density and a low v value; the highest energy efficiency was achieved at a high R, a low current density and a high v value; the lowest effluent ACS and toxic byproduct levels were achieved at a low R value, a low current density and a high v value. Following the multivariate optimization, the optimum parameters were decided to be v = 1.2 cm h-1, i ≥ 8 mA cm-2, d ≥ 4, RV = 10-20 and R = 1 to achieve better effluent quality (i.e., lower effluent pollutant, ACS and toxic byproduct levels).

Concentration estimation of dissolved oxygen in Pearl River Basin using input variable selection and machine learning techniques.

Dissolved oxygen (DO) concentration is an essential index for water environment assessment. Here, we present a modeling approach to estimate DO concentrations using input variable selection and data-driven models. Specifically, the input variable selection technique, the maximal information coefficient (MIC), was used to identify and screen the primary environmental factors driving variation in DO. The data-driven model, support vector regression (SVR), was then used to construct a robust model to estimate DO concentration. The approach was illustrated through a case study of the Pearl River Basin in China. We show that the MIC technique can effectively screen major local environmental factors affecting DO concentrations. MIC value tended to stabilize when the sample size >3000 and EC had the highest score with an MIC >0.3 at both of the stations. The variable-reduced datasets improved the performance of the SVR model by a reduction of 28.65% in RMSE, and increase of 22.16%, 56.27% in R2, NSE, respectively, relative to complete candidate sets. The MIC-SVR model constructed at the tidal river network performed better than nontidal river network by a reduction of approximately 63.01% in RMSE, an increase of 62.36% in NSE, and R2 >0.9. Overall, the proposed technique was able to handle nonlinearity among environmental factors and accurately estimate DO concentrations in tidal river network regions.

Assessment of nutrient and heavy metal contamination in surface sediments of the Xiashan stream, eastern Guangdong Province, China.

In this study, nutrient and heavy metal contamination in surface sediments of the Xiashan stream were investigated. Determining pollution degree of nutrient and heavy metal were the main objectives of this investigation. For this reason, the concentrations of total nitrogen (TN), total phosphorus (TP), seven heavy metals (Cu, Zn, Pb, Cd, Cr, Ni, Hg), and metalloid (As) were analyzed at 55 sampling sites. The mean TN concentration in surface sediments was 5.007 g/kg, while the mean TP concentration was 0.385 g/kg. Based on the sediment quality guideline (SQGs) and background values of Chinese soil and sediment, the majority of the mean TN concentrations in surface sediments were higher than their background values, while the TP concentrations were different from those observed for TN. For heavy metal concentrations in surface sediments, most of sampling stations were higher than their background values. The mean geo-accumulation (Igeo) indices for heavy metals were ranked as follows: Cd > Hg > Cu > Zn > Pb > Ni > Cr > As. The potential ecological risk index (RI) indicated heavy metal contamination level in Xiashan stream was very high ecological risk. According to Igeo and RI values, heavy metals especially Cd and Hg are influenced significantly by anthropogenic activities. Cd and Hg are not only as pollutant but also considerable contributor to ecological risk. Multivariate statistical investigations showed that there is a significant and positive correlation between Pb, As, and Cd. Cu, Ni, and Cr have similar characteristic and therefore probably originated from the same sources. Suggested by the results, it is necessary to control nitrogen and heavy metal contamination caused by human activities in the study area.

[Effects of Continuous Extreme Rainfall on Water Quality of the Dongjiang River Basin].

In order to research the impact of continuous extreme rainfall on the water quality of the Dongjiang River, which is a drinking water source, the characteristics of extreme rainfall events in the basin were analyzed for last 38 years. The impacts of these events on water quality are discussed by considering both hydrological data and water quality data. Using SWAT2012 software, a high-precision basin model was established for the flux of major pollutants during extreme rainfall and water quality. The results indicate that there were 173 extreme rainfall events in the Dongjiang River basin over the past 38 years. The annual frequency of extreme rainfall events in high-flow years was higher than in other years. During the year, rainfall was mainly affected by climate, particularly from March to September (80%), with the peak rainfall usually occurring in June. Spatially, the Zengcheng-Bolo-Huizhou-Longmen area had the highest frequency of extreme events. During the study period, rainfall was significantly positively correlated with the concentrations of cyanide, Pb, Fe, Mn, TP, and with turbidity, and the correlation coefficients for the concentrations of TP and turbidity with rainfall were relatively high. Rainfall was significantly negatively correlated with pH, conductivity, the concentration of Zn, as well as some other indicators. These observations show that water quality is affected by rainfall to some extent. Turbidity, TN, ammonia nitrogen, and TP concentrations all showed increasing trends, to different degrees, during rainstorm runoff periods. Turbidity and TP concentrations showed a significant and consistent relationship with flow rate, peaking earlier than the flow rate peak (by approximately 1 d), showing a significant initial flushing effect. The pH curve showed an opposite trend to the flow rate, forming a "V" shape, which may be affected by the rainfall, soil acidity, and confluence conditions in the upstream mountains. Ammonia nitrogen was subject to initial flushing in the early stages of extreme rainfall but was diluted by the clean rainwater; initially, ammonia nitrogen showed high values that declined during the middle and late stages. The variations in pollutant loads were consistent with that of runoff flux, and the peaks in TN, ammonia nitrogen, and TP flux appeared later than the flow peak (by approximately 1 d), thus differing from the pollutant concentration peak. The pollutant load mainly showed a significant increase during storm runoff periods. The proportion of pollutant COD, ammonia nitrogen, and TP transported by 59.48% of the runoff reached 68.42%, 54.68%, and 70.20%, respectively, demonstrating the characteristics of rapid and high-impact pollutant loads. These characteristics have a great influence on the quality of Dongjiang River drinking water and it is suggested that initial rainwater treatment should be strengthened to reduce the negative impact of rainstorm runoff periods on water quality.

[Analysis on nitrogen and phosphorus loading of non-point sources in Shiqiao river watershed based on L-THIA model].

Based on the Long-term Hydrological Impact Assessment (L-THIA) model, the effect of land use and rainfall change on nitrogen and phosphorus loading of non-point sources in Shiqiao river watershed was analyzed. The parameters in L-THIA model were revised according to the data recorded in the scene of runoff plots, which were set up in the watershed. The results showed that the distribution of areas with high pollution load was mainly concentrated in agricultural land and urban land. Agricultural land was the biggest contributor to nitrogen and phosphorus load. From 1995 to 2010, the load of major pollutants, namely TN and TP, showed an obviously increasing trend with increase rates of 17.91% and 25.30%, respectively. With the urbanization in the watershed, urban land increased rapidly and its area proportion reached 43.94%. The contribution of urban land to nitrogen and phosphorus load was over 40% in 2010. This was the main reason why pollution load still increased obviously while the agricultural land decreased greatly in the past 15 years. The rainfall occurred in the watershed was mainly concentrated in the flood season, so the nitrogen and phosphorus load of the flood season was far higher than that of the non-flood season and the proportion accounting for the whole year was over 85%. Pearson regression analysis between pollution load and the frequency of different patterns of rainfall demonstrated that rainfall exceeding 20 mm in a day was the main rainfall type causing non-point source pollution.