The Effect of G-C3N4 on Bacterial Community in Sediment of Xiang River Under Tetracycline Pressure

As photocatalysts applied more and more often to treat pollutants by photocatalytic reactions, they may enter the environment via water spreading. Although there are some investigations about their influence on different organisms, little is known about its impact on the ecological microenvironment. To understand how photocatalysts effect sediment ecological microenvironment in the process of pollution remediation, the impact of typical photocatalyst g-C3N4 (Graphitic carbon nitride) on rivered sediment community polluted by typical antibiotic tetracycline (TC) was investigated. The sediment samples were exposed to different concentrations of TC, g-C3N4 or TC/g-C3N4 (exposed to 60, 120, 180 mg/L TC, or 25, 75, 125 mg/kg g-C3N4, or 25, 75, 125 mg/kg g-C3N4 plus 60, 120, 180 mg/L TC, respectively), and sediment bacterial community were analyzed by Illumina sequencing. The results indicated that the dominant bacterial phyla in the samples were Acidobacteriota, Proteobacteria, Actinobacteriota, Chloroflexi. The diversity and richness of microorganisms in riverbed sediment were increased a little bit by g-C3N4 with different concentrations, which reached the highest value when exposed to 75 mg/kg g-C3N4. g-C3N4 lightly increased the percentage of relative abundance of Cyanobacteria. The bacterial communities’ structure of the samples treated with TC, g-C3N4 or TC/g-C3N4 were distinguishable. g-C3N4 alone had little effect on microbial structure, while TC/g-C3N4 had medium influence and TC had great impact on it. Under TC stress, g-C3N4 slowed down the growth of Cyanobacteria to some extent and restored the changes of bacterial community structure caused by TC, and reduced the residual TC in water body, thus eliminating the side effects of TC. The result shown that g-C3N4 could significantly reduce the residue of TC in riverbed sediment, without affecting the microbial ecology in the environment.

The characteristics of ammonia nitrogen in the Xiang River in Changsha, China

Changsha is a highly industrialized city in Hunan Province, China, where the water quality is of great importance to the development of economy and environment in this area. We have analyzed the characteristics of ammonia nitrogen in the Xiang River in Changsha from 2016 to 2019. The results showed that in the main stem, concentrations of ammonia nitrogen were very low and reached the third water quality level. In the six tributaries, concentrations of ammonia nitrogen have increased, especially in Longwanggang and Liuyang River, where the latter of which has a large number of industries and domestic sewage. Correlations between monthly precipitation and ammonia nitrogen concentrations were negative, besides two sites Jinjiang and Juzizhou, indicating that in most rivers, ammonia nitrogen contents had been diluted by rainfall. In general, concentrations and fluxes of ammonia nitrogen have decreased significantly during this time period, suggesting that water environment has improved greatly under the series of the clean motions by the local government.

Drought Monitoring Utility using Satellite-Based Precipitation Products over the Xiang River Basin in China

Drought is a natural hazard disaster that can deeply affect environments, economies, and societies around the world. Therefore, accurate monitoring of patterns in drought is important. Precipitation is the key variable to define the drought index. However, the spare and uneven distribution of rain gauges limit the access of long-term and reliable in situ observations. Remote sensing techniques enrich the precipitation data at different temporal–spatial resolutions. In this study, the climate prediction center morphing (CMORPH) technique (CMORPH-CRT), the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TRMM 3B42V7), and the integrated multi-satellite retrievals for global precipitation measurement (IMERG V05) were evaluated and compared with in situ observations for the drought monitoring in the Xiang River Basin, a humid region in China. A widely-used drought index, the standardized precipitation index (SPI), was chosen to evaluate the drought monitoring utility. The atmospheric water deficit (AWD) was used for comparison of the drought estimation with SPI. The results were as follows: (1) IMERG V05 precipitation products showed the highest accuracy against grid-based precipitation, followed by CMORPH-CRT, which performed better than TRMM 3B42V7; (2) IMERG V05 showed the best performance in SPI-1 (one-month SPI) estimations compared with CMORPH-CRT and TRMM 3B42V7; (3) SPI-1 was more suitable for drought monitoring than AWD in the Xiang River Basin, because its high R-values and low root mean square error (RMSE) compared with the corresponding index based on in situ observations; (4) drought conditions in 2015 were apparently more severe than that in 2016 and 2017, with the driest area mainly distributed in the southwest part of the Xiang River Basin.

Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

Agricultural drought can have long-lasting and harmful impacts on both the ecosystem and economy. Therefore, it is important to monitor and predict agricultural drought accurately. Soil moisture is the key variable to define the agricultural drought index. However, in situ soil moisture observations are inaccessible in many areas of the world. Remote sensing techniques enrich the surface soil moisture observations at different tempo-spatial resolutions. In this study, the Level 2 L-band radiometer soil moisture dataset was used to estimate the Soil Water Deficit Index (SWDI). The Soil Moisture Active Passive (SMAP) dataset was evaluated with the soil moisture dataset obtained from the China Land Soil Moisture Data Assimilation System (CLSMDAS). The SMAP-derived SWDI (SMAP_SWDI) was compared with the atmospheric water deficit (AWD) calculated with precipitation and evapotranspiration from meteorological stations. Drought monitoring and comparison were accomplished at a weekly scale for the growing season (April to November) from 2015 to 2017. The results were as follows: (1) in terms of Pearson correlation coefficients (R-value) between SMAP and CLSMDAS, around 70% performed well and only 10% performed poorly at the grid scale, and the R-value was 0.62 for the whole basin; (2) severe droughts mainly occurred from mid-June to the end of September from 2015 to 2017; (3) severe droughts were detected in the southern and northeastern Xiang River Basin in mid-May of 2015, and in the northern basin in early August of 2016 and end of November 2017; (4) the values of percentage of drought weeks gradually decreased from 2015 to 2017, and increased from the northeast to the southwest of the basin in 2015 and 2016; and (5) the average value of R and probability of detection between SMAP_SWDI and AWD were 0.6 and 0.79, respectively. These results show SMAP has acceptable accuracy and good performance for drought monitoring in the Xiang River Basin.

Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China

Satellite-based and reanalysis precipitation products provide a practical way to overcome the shortage of gauge precipitation data because of their high spatial and temporal resolution. This study compared two reanalysis precipitation datasets (the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), the National Centers for Environment Prediction Climate Forecast System Reanalysis (NCEP-CFSR)) and two satellite-based datasets (the Tropical Rainfall Measuring Mission 3B42 Version 7 (3B42V7) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR)) with observed precipitation in the Xiang River basin in China at two spatial (grids and the whole basin) and two temporal (daily and monthly) scales. These datasets were then used as inputs to a SWAT model to evaluate their usefulness in hydrological prediction. Bayesian model averaging was used to discriminate dataset performance. The results show that: (1) for daily timesteps, correlations between reanalysis datasets and gauge observations are >0.55, better than satellite-based datasets; The bias values of satellite-based datasets are <10% at most evaluated grid locations and for the whole baseline. PERSIANN-CDR cannot detect the spatial distribution of rainfall events; the probability of detection (POD) of PERSIANN-CDR at most evaluated grids is <0.50; (2) CMADS and 3B42V7 are better than PERSIANN-CDR and NCEP-CFSR in most situations in terms of correlation with gauge observations; satellite-based datasets are better than reanalysis datasets in terms of bias; and (3) CMADS and 3B42V7 simulate streamflow well for both daily (The Nash-Sutcliffe coefficient (NS) > 0.70) and monthly (NS > 0.80) timesteps; NCEP-CFSR is worst because it substantially overestimates streamflow; PERSIANN-CDR is not good because of its low NS (0.40) during the validation period.