Trends of the response-relationship between net anthropogenic nitrogen and phosphorus inputs (NANI/NAPI) and TN/TP export fluxes in Raohe basin, China

Effects of the catastrophic 2020 Yangtze River seasonal floods on microcystins and environmental conditions in Three Gorges Reservoir Area, China

Introduction

During July and August 2020, Three Gorges Reservoir Area (TGRA) suffered from catastrophic seasonal floods. Floods changed environmental conditions and caused increase in concentration of microcystins (MCs) which is a common and potent cyanotoxin. However, the effects and seasonal variations of MCs, cyanobacteria, and environmental conditions in TGRA after the 2020 Yangtze River extreme seasonal floods remain largely unclear, and relevant studies are lacking in the literature.

Methods

A total of 12 representative sampling sites were selected to perform concentration measurement of relevant water quality objectives and MCs in the representative area of the TGRA. The sampling period was from July 2020 to October 2021, which included the flood period. Organic membrane filters were used to perform the DNA extraction and analyses of the 16S rRNA microbiome sequencing data.

Results

Results showed the seasonal floods result in significant increases in the mean values of microcystin-RR (MCRR), microcystin-YR (MCYR), and microcystin-LR (MCLR) concentration and some water quality objectives (i.e., turbidity) in the hinterland of TGRA compared with that in non-flood periods (p < 0.05). The mean values of some water quality objectives (i.e., total nitrogen (TN), total phosphorus (TP), total dissolved phosphorus (TDP), and turbidity), MC concentration (i.e., MCRR, MCYR, and MCLR), and cyanobacteria abundance (i.e., Cyanobium_PCC-6307 and Planktothrix_NIVA-CYA_15) displayed clear tendency of increasing in summer and autumn and decreasing in winter and spring in the hinterland of TGRA.

Discussions

The results suggest that seasonal floods lead to changes in MC concentration and environmental conditions in the hinterland of TGRA. Moreover, the increase in temperature leads to changes in water quality objectives, which may cause water eutrophication. In turn, water eutrophication results in the increase in cyanobacteria abundance and MC concentration. In particular, the increased MC concentration may further contribute to adverse effects on human health.

Dynamic spatiotemporal change of net anthropogenic phosphorus inputs and its response of water quality in the Liao river basin

The Liao river is one of the seven major rivers in China, and the process of phosphorus (P) cycling and change of water quality in this basin are influenced to a considerable extent human activities. In this work, the traditional net anthropogenic phosphorus inputs (NAPI) model was improved by considering the dynamic change of wastewater treatment capacity and P deposition (PDEP) and reclassifying the sources of phosphorus into human P consumption (PHUM), agriculture P consumption (PAGR), livestock P consumption (PANIM) and PDEP to analyze its dynamic spatio-temporal change in the Liao river basin. The results showed that the annual mean NAPI was 785.53 kg P km^-2 yr^-1 (2001-2020), the maximum value was 940.49 kg P km^-2 yr^-1 in 2009, and the minimum value was 586.04 kg P km^-2 yr^-1 in 2001. The temporal variation of NAPI presented an increasing-fluctuation-increasing trend and was basically in line with that of the water quality throughout the three stages, and the spatial distribution of NAPI gradually increased from upstream to downstream. During the two decades, PANIM was the predominant component of NAPI with a share of 64.32%. PHUM, PAGR, and PDEP accounted for 15.97%, 11.54%, and 8.17%, respectively, and the point source NAPI (NAPIP) contributed to 4.95% of NAPI. Further, the INAPI (Improved NAPI) -MR (Multiple Regression) -SWAT (Soil and Water Assessment Tool) model was developed to predict the spatial distribution of P flux under two scenarios. The results showed that the Liao river basin experienced a reduction in P flux to different degrees due to the improvement of the wastewater treatment system, which was more significant in its downstream area. Long-term water quality monitoring is encouraged to develop refined water quality models in the future.

Integrating source apportionment and landscape patterns to capture nutrient variability across a typical urbanized watershed

Effective integrated watershed management requires models that can characterize the sources and transport processes of pollutants at the watershed with multiple landscape patterns. However, few studies have investigated the influence of landscape spatial configuration on pollutant transport processes. In this study, the SPARROW_TN and SPARROW_TP models were constructed by combining direct pollution source data and landscape pattern data to investigate the source composition and nutrient transport processes and to reveal the influence of landscape patterns on nutrient transport in the urbanized Beiyun River Watershed. The introduction of landscape metrics significantly improved the simulation results of both models, with R² increasing from 0.89 to 0.85 to 0.93 and 0.91, respectively. Spatial variations existed in TN and TP loads and yields, as well as the source compositions. Pollution hotspots were effectively identified. Source apportionment showed that for the entire watershed, TN came from atmospheric nitrogen deposition (35.25%), untreated sewage (28.23%), agricultural sources (22.60%), and treated sewage (13.92%). In comparison, TP came from untreated sewage (44.94%), agricultural sources (40.22%), and treated sewage (11.51%). In addition, the largest patch index of grassland correlated positively with both TN and TP, whereas the largest shape index of buildup land and interspersion and juxtaposition index of forest were negatively correlated with TN and TP, respectively. The results of this study will provide insight into effective nutrient control measures that consider spatially varying nutrient sources and associated nutrient transport processes.

Spatio-temporal variation of net anthropogenic nitrogen inputs (NANI) from 1991 to 2019 and its impacts analysis from parameters in Northwest China

At present, excessive nutrient inputs caused by human activities have resulted in environmental problems such as agricultural non-point source pollution and water eutrophication. The Net Anthropogenic Nitrogen Inputs (NANI) model can be used to estimate the nitrogen (N) inputs to a region that are related to human activities. To explore the net nitrogen input of human activities in the main grain-producing areas of Northwestern China, the county-level statistical data for the Ningxia province and NANI model parameters were collected, the spatio-temporal distribution characteristics of NANI were analyzed and the uncertainty and sensitivity of the parameters for each component of NANI were quantitatively studied. The results showed that: (1) The average value of NANI in Ningxia from 1991 to 2019 was 7752 kg N km^-2 yr^-1. Over the study period, the inputs first showed an overall increase, followed by a decrease, and then tended to stabilize. Fertilizer N application was the main contributing factor, accounting for 55.6%. The high value of NANI in Ningxia was mainly concentrated in the Yellow River Diversion Irrigation Area. (2) The 95% confidence interval of NANI obtained by the Monte Carlo approach was compared with the results from common parameters in existing literature. The simulation results varied from -6.4% to 27.4% under the influence of the changing parameters. Net food and animal feed imports were the most uncertain input components affected by parameters, the variation range was -20.7%-77%. (3) The parameters of inputs that accounted for higher proportions of the NANI were more sensitive than the inputs with lower contributions. The sensitivity indexes of the parameters contained in the fertilizer N applications were higher than those of net food and animal feed imports and agricultural N-fixation. This study quantified the uncertainty and sensitivity of parameters in the process of NANI simulation and provides a reference for global peers in the application and selection of parameters to obtain more accurate simulation results.