Multi-scaled response of groundwater nitrate contamination to integrated anthropogenic activities in a rapidly urbanizing agricultural catchment

Distribution, sources and main controlling factors of nitrate in a typical intensive agricultural region, northwestern China: Vertical profile perspectives

Nitrate (NO3-) pollution of groundwater is a global concern in agricultural areas. To gain a comprehensive understanding of the sources and destiny of nitrate in soil and groundwater within intensive agricultural areas, this study employed a combination of chemical indicators, dual isotopes of nitrate (δ15N-NO3- and δ18O-NO3-), random forest model, and Bayesian stable isotope mixing model (MixSIAR). These approaches were utilized to examine the spatial distribution of NO3- in soil profiles and groundwater, identify key variables influencing groundwater nitrate concentration, and quantify the sources contribution at various depths of the vadose zone and groundwater with different nitrate concentrations. The results showed that the nitrate accumulation in the cropland and kiwifruit orchard at depths of 0-400 cm increased, leading to subsequent leaching of nitrate into deeper vadose zones and ultimately groundwater. The mean concentration of nitrate in groundwater was 91.89 mg/L, and 52.94% of the samples exceeded the recommended grade III value (88.57 mg/L) according to national standards. The results of the random forest model suggested that the main variables affecting the nitrate concentration in groundwater were well depth (16.6%), dissolved oxygen (11.6%), and soil nitrate (10.4%). The MixSIAR results revealed that nitrate sources vary at different soil depths, which was caused by the biogeochemical process of nitrate. In addition, the highest contribution of nitrate in groundwater, both with high and low concentrations, was found to be soil nitrogen (SN), accounting for 56.0% and 63.0%, respectively, followed by chemical fertilizer (CF) and manure and sewage (M&S). Through the identification of NO3- pollution sources, this study can take targeted measures to ensure the safety of groundwater in intensive agricultural areas.

Geostatistical estimates of groundwater nitrate-nitrogen concentrations with spatial auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability

Groundwater nitrate-nitrogen contamination typically involves several natural and anthropogenic factors, including those related to hydrology, hydrogeology, topography, and land use (LU). DRASTIC-LU-based aquifer contamination vulnerability could be used to characterize the pollution potentials of groundwater nitrate-nitrogen and to determine groundwater protection zones. This study used regression kriging (RK) with environmental auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability to investigate groundwater nitrate-nitrogen pollution in the Pingtung Plain of Taiwan. First, the relationship between groundwater nitrate-nitrogen pollution and assessments of aquifer contamination vulnerability was determined using stepwise multivariate linear regression (MLR). Subsequently, the residuals between the nitrate-nitrogen observations and MLR predictions were estimated by kriging techniques. Finally, the groundwater nitrate-nitrogen distributions were spatially analyzed using RK, ordinary kriging (OK), and MLR. The findings indicated that the land used for orchards and the medium- and coarse-sand fractions of vadose zones were associated with groundwater nitrate-nitrogen concentrations. The fertilizer used for orchards was identified as the primary source of groundwater nitrate-nitrogen pollution. The RK estimates could be used to analyze the characteristics of the pollution source for land used for orchards and exhibited high spatial variability and accuracy after residual correction. Moreover, RK had an excellent estimate ability for extreme data compared to MLR and OK. Correctly determining groundwater nitrate-nitrogen distributions using RK was useful for administering environmental resources and preventing public health hazards.

Incorporating Landscape Scaling Relations into Catchment Classification for Optimizing Ecological Management

The landscape scaling relation challenges catchment ecological management; however, how the scaling relations change among naturally and anthropogenically differentiated catchments is still unknown. In this study, approximately 1500 soil samples were determined; more than 800 households were surveyed; and the landscape pattern was investigated in 120 sub-catchments of a subtropical Chinese urbanizing agricultural catchment. A scalogram and a coefficient of variation of the commonly used landscape metrics were estimated among various grain sizes, to quantify the Strength of Landscape Scale Effects (SSE) among sub-catchments. Natural and anthropogenic determinants for the SSE were determined. Then, the determinants incorporating landscape scaling relation were applied to classify the sub-catchments through the k-means clustering analysis. The SSE presented different spatial heterogeneity across the 120 sub-catchments and was not expectedly related to the scaling relation over the entire catchment, especially for the Contagion index and Shannon’s Evenness Index. The SSE were significantly related to natural and anthropogenic factors including the soil sand content, the population density, the relief ratio, and the ratio of arable land to woodland. The four factors combing with landscape scaling relations contributed to the four gratifying convergent categories for the 120 sub-catchments. Category I with a large relief and less anthropogenic disturbance had higher spatially non-stationary relationship, while categories II, III, and IV, with varying degrees of relatively small relief and strong intensities of anthropogenic disturbance, had a lower spatial heterogeneity of the landscape scaling relation. The results implied that category I was required to strengthen environmental protection of spatial differences, and categories II, III, and IV could ignore the landscape scale effects and even upscaling management to save management resources when carrying out ecological management within. Our findings could minimize uncertainty in ecological planning and provide opportunities for the application of multiple-scale management.

Evaluating the nitrogen-contaminated groundwater treatment by a denitrifying granular sludge bioreactor: effect of organic matter loading

A sequential bed granular bioreactor was adapted to treat nitrate-polluted synthetic groundwater under anaerobic conditions and agitation with denitrification gas, achieving very efficient performance in total nitrogen removal at influent organic carbon concentrations of 1 g L^-1 (80-90%) and 0.5 g L^-1 (70-80%) sodium acetate, but concentrations below 0.5 g L^-1 caused accumulation of nitrite and nitrate and led to system failure (30-40% removal). Biomass size and settling velocity were higher above 0.5 g L^-1 sodium acetate. Trichosporonaceae dominated the fungal populations at all times, while a dominance of terrestrial group Thaumarchaeota and Acidovorax at 1 and 0.5 g L^-1 passed to a domination of Methanobrevibacter and an unclassified Comamonadaceae clone for NaAc lower than 0.5 g L^-1. The results obtained pointed out that the denitrifying granular sludge technology is a feasible solution for the treatment of nitrogen-contaminated groundwater, and that influent organic matter plays an important role on the conformation of microbial communities within it and, therefore, on the overall efficiency of the system.