Comparative effectiveness of carbon nanoparticles and biochar in alleviating copper stress in corn (Zea mays L.).

The application of carbon nanoparticles (CNPs) and biochar in agriculture for improving plant health and soil quality and alleviating metal stress offers alternative approaches to meet the ever-increasing demand for food. However, poor understanding of their roles in improving crop production under Cu stress represents a significant obstacle to their wide application in agriculture. To clarify how CNPs and biochar affect corn (Zea mays L.) seed germination, seedling growth, plant health, and nutrient uptake under different Cu stress levels, soil-less Petri-dish and greenhouse soil-based bioassays were conducted. The results revealed that CNPs and biochar stimulated corn seed germination and seedling growth. Besides, they were effective in immobilizing Cu2+ sorption in sandy soil and alleviating Cu stress for plant growth, as shown by the increased plant height and dry biomass. The plant nutrient uptake efficiency (NUE) was significantly increased by CNPs, with a maximum increase of 63.1% for N and 63.3% for K at the highest Cu2+ stress level (400 mg Cu2+ L-1). In contrast, non-significant effects on NUE were observed with biochar treatments regardless of Cu stress levels. Interestingly, CNPs significantly increased plant uptake of Cu in the Petri dish test, while biochar inhibited plant uptake of Cu under both experimental conditions. Principle component analysis (PCA) and Pearson correlation analysis indicated that CNPs mitigated Cu stress mainly by elevating antioxidant enzyme activities, enhancing plant photochemical efficiency, and increasing plant uptake of N and K, while biochar was more likely to reduce bioavailability and uptake of Cu in the plant. These findings have great implications for the application of CNPs and biochar as plant growth stimulators and de-toxicity agents in agriculture.

[Classification and Identification of Non-point Source Nitrogen Pollution in Surface Flow of the Shangwu River Watershed in the Qiandao Lake Region].

Accurate quantification of non-point source pollution is an important step for non-point source pollution control and management at the watershed scale. Considering the non-point source pollution from baseflow, an improved export coefficient model (IECM) on a weekly scale was established based on the traditional export coefficient model (ECM), which was then used to estimate the surface flow non-point source total nitrogen (TN) loads contributed by different land use types of the Shangwu River watershed in the Qiandao Lake Region. The results showed that IECM performed well for the predictions of TN loads in the studied watershed, with the Nash-Sutcliffe efficiency coefficient (NSE) and R2values of 0.82 and 0.77 (P<0.01) for the calibration period and 0.87 and 0.84 (P<0.01) for the validation period, respectively. The IECM estimated TN exports through surface flow and baseflow were 5.74 kg·(hm2·a)-1and 9.85 kg·(hm2·a)-1 from the Shangwu River watershed in the period of Nov. 2020 to Oct. 2021, which accounted for 36.80% and 63.20% of the corresponding streamflow TN load, respectively. Without consideration of the baseflow non-point source TN pollution, the ECM-estimated surface flow TN loading was 54.21% higher than that estimated by IECM. Obviously, attributing baseflow non-point source pollution to surface flow directly would lead to a serious load overestimation of surface flow. According to IECM, the estimated TN export intensity through surface flow from paddy fields, grasslands, woodlands, rainfed croplands, and residential lands was 10.95, 5.42, 5.20, 12.34, and 2.77 kg·(hm2·a)-1, respectively, which accounted for 5.80%, 4.00%, 26.55%, 0.38%, and 0.03% of the corresponding total streamflow TN loads. Therefore, the future management of non-point source nitrogen pollution in the studied watershed should focus mainly on the prevention and management of groundwater non-point source pollution and control of load export from surface flow on cultivated land (paddy fields and rainfed croplands).

Baseflow estimation based on a self-adaptive non-linear reservoir algorithm in a rainy watershed of eastern China.

Accurate baseflow estimation is critical for water resources evaluation and management, and non-point source pollution quantification. Nonlinear reservoir algorithm (NRA) has been increasingly applied to baseflow separation because of its good approximation to the real groundwater discharge (commonly dominated by the unconfined aquifer) in most watersheds. However, in the rainy regions, large uncertainties may remain in the traditional NRA-separated baseflow sequences due to its empirical transition function for the rising limb of discharge process, and the evident variations of baseflow recession in the initial period of the falling limb caused by the disturbance from surface flow or rainfall events. To improve the reliability of baseflow separation, a self-adaptive non-linear reservoir algorithm (SA-NRA) was developed in this study based on the NRA, a self-adaptive groundwater discharge modified parameter, and the Particle Swarm Optimization algorithm (PSO). The validation of SA-NRA in a rainy watershed of eastern China showed that SA-NRA could be the approach to provide a goodness-of-fit for baseflow recession behaviors in the rainy regions. The traditional NRA and Eckhardt's two-parameter recursive digital filter (ERDF), calibrated (or validated) only with the pure baseflow recession data, can hardly provide reliable baseflow predictions for the non-pure baseflow recession periods (including the rising limb and the falling limb with surface flow or rainfall disturbance) due to the apparent variations of baseflow recession behavior. Therefore, more attentions should be paid to the uncertainties of baseflow separation for the non-pure baseflow recession periods in the rainy regions.

[Characteristics and Drivers of Dissolved Carbon Dioxide and Methane Concentrations in the Nantiaoxi River System in the Upper Reaches of the Taihu Lake Basin During Summer-Autumn].

Inland waters are vital sinks for active carbon (C) and potential sources of greenhouse gas emissions. In this study, the characteristics of dissolved carbon dioxide (CO2) and methane (CH4) concentrations in the Nantiaoxi River system in the upper reaches of the Taihu Lake basin were observed between Jul. 2019 and Nov. 2019 (summer and autumn) using headspace equilibration-gas chromatography. Simultaneously, physical and chemical parameters were also determined to understand the factors influencing dissolved CO2 and CH4 concentrations. The results showed that the mean dissolved CO2 concentrations and saturation levels in water were (505.47±16.99) µg·L-1 and (256.31±8.32)%, respectively, and the corresponding values for CH4 were (1.88±0.09) µg·L-1 and (5218.74±264.30)%, respectively. The saturation levels of dissolved CO2 and CH4 at all observation points were greater than 100%, indicating that the Nantiaoxi River system is a potential source of CO2 and CH4. The highest mean dissolved CO2 concentrations in water were found in agricultural areas followed by residential and forest areas, and there were significant differences among the three land-use types. The mean dissolved CH4 concentrations in the water in residential areas were significantly higher than those in agricultural area forest areas. The dissolved CO2 concentrations, saturation levels of CO2, dissolved CH4 concentrations, and saturation levels of CH4 in water were all negatively correlated with oxidation reduction potential (ORP) (P<0.01) and positively correlated with electrical conductivity (EC) (P<0.01). The discrepancies in chlorophyll (Chl-a), nitrate (NO3--N), total nitrogen (TN), and EC were the main reasons for differences in dissolved CO2 concentrations among the different land use types. Phytoplankton growth could be promoted by the higher input of nitrogen pollutants into rivers in agricultural and residential areas, and respiration could be also enhanced, resulting in higher dissolved CO2 concentrations. The higher concentrations of dissolved organic carbon (DOC) and ammonium nitrogen (NH4+-N) in the water, and the water temperature in residential areas, were probably the main causes of the higher dissolved CH4 concentrations. Rainfall also had some influence on dissolved CO2 and CH4 concentrations in the water associated with the different land use types. Specifically, higher concentrations of nitrogen pollutants and the enhancement of DOC were the main drivers of high dissolved CO2 concentrations in agricultural areas as well as the higher dissolved CH4 concentrations in residential areas following rainfall events.

Dissolved phosphorus export through baseflow in an intensively cultivated agricultural watershed of eastern China.

Inputs of phosphorus (P) during baseflow period usually come from groundwater, bed sediments, and some other sources. Baseflow P can have critical effects on nutrient enrichment of surface waters in some intensively cultivated agricultural watersheds. This study was conducted to estimate the baseflow dissolved phosphorus (DP) export in a typical rainy agricultural watershed of eastern China using a recursive tracing source algorithm (RTSA) and reveal the rules and trends of baseflow DP loads and concentrations. Results indicated that RTSA provided a satisfactory prediction for baseflow DP load (Nash-Sutcliffe efficiency (NSE) = 0.72, R2 = 0.74). From 2003 to 2012, the annual baseflow DP loads ranged from 0.159 (2004) to 0.771 (2012) kg/ha which contributed about 64.3% of the mean total annual DP export in stream (0.597 kg/ha). The annual flow-weighted DP concentrations in streamflow (0.076-0.125 mg/L) and baseflow (0.076-0.137 mg/L) far exceeded the eutrophication threshold of DP (0.01 mg/L). Significantly increasing trends were obtained in the streamflow and baseflow DP loads and the flow-weighted concentrations (Mann-Kendall test, Zs > 2.56, p < 0.01) because of the changes of hydro-meteorological conditions. This indicates that, in the context of global climate change, baseflow DP export would be one of key issues for nonpoint source pollution control in the intensive agricultural watershed.