Revealing the drivers and genesis of NO3-N pollution classification in shallow groundwater of the Shaying River Basin by explainable machine learning and pathway analysis method.

Nitrate (NO3-N), as one of the ubiquitous contaminants in groundwater worldwide, has posed a serious threat to public health and the ecological environment. Despite extensive research on its genesis, little is known about the differences in the genesis of NO3-N pollution across different concentrations. Herein, a study of NO3-N pollution concentration classification was conducted using the Shaying River Basin as a typical area, followed by examining the genesis differences across different pollution classifications. Results demonstrated that three classifications (0-9.98 mg/L, 10.14-27.44 mg/L, and 28.34-136.30 mg/L) were effectively identified for NO3-N pollution using Jenks natural breaks method. Random forest exhibited superior performance in describing NO3-N pollution and was thereby affirmed as the optimal explanatory method. With this method coupling SEMs, the genesis of different NO3-N pollution classifications was proven to be significantly different. Specifically, strongly reducing conditions represented by Mn2+, Eh, and NO2-N played a dominant role in causing residual NO3-N at low levels. Manure and sewage (represented by Cl-) leaching into groundwater through precipitation is mainly responsible for NO3-N in the 10-30 mg/L classification, with a cumulative contribution rate exceeding 80 %. NO3-N concentrations >30 mg/L are primarily caused by the anthropogenic loads stemming from manure, sewage, and agricultural fertilization (represented by Cl- and K+) infiltrating under precipitation in vulnerable hydrogeological conditions. Pathway analysis based on standardized effect and significance further confirmed the rationality and reliability of the above results. The findings will provide more accurate information for policymakers in groundwater resource management to implement effective strategies to mitigate NO3-N pollution.

Revealing discrepancies and drivers in the impact of lomefloxacin on groundwater denitrification throughout microbial community growth and succession.

The coexistence of antibiotics and nitrates has raised great concern about antibiotic's impact on denitrification. However, conflicting results in these studies are very puzzling, possibly due to differences in microbial succession stages. This study investigated the effects of the high-priority urgent antibiotic, lomefloxacin (LOM), on groundwater denitrification throughout microbial growth and succession. The results demonstrated that LOM's impact on denitrification varied significantly across three successional stages, with the most pronounced effects exhibited in the initial stage (53.8% promotion at 100 ng/L-LOM, 84.6% inhibition at 100 µg/L-LOM), followed by the decline stage (13.3-18.2% inhibition), while no effect in the stable stage. Hence, a distinct pattern encompassing susceptibility, insusceptibility, and sub-susceptibility in LOM's impact on denitrification was discovered. Microbial metabolism and environment variation drove the pattern, with bacterial numbers and antibiotic resistance as primary influencers (22.5% and 15.3%, p < 0.01), followed by carbon metabolism and microbial community (5.0% and 3.68%, p < 0.01). The structural equation model confirmed results reliability. Bacterial numbers and resistance influenced susceptibility by regulating compensation and bacteriostasis, while carbon metabolism and microbial community impacted energy, electron transfer, and gene composition. These findings provide valuable insights into the complex interplay between antibiotics and denitrification patterns in groundwater.

Effect and genesis of soil nitrogen loading and hydrogeological conditions on the distribution of shallow groundwater nitrogen pollution in the North China Plain.

The North China Plain (NCP) has experienced increasingly severe groundwater nitrogen (TN) pollution. However, the factors influencing TN distribution are still poorly understood. Previous studies have identified surface soil nitrogen (TSN) loading and intrinsic groundwater vulnerability (Inv) as the main factors controlling groundwater TN pollution. However, in this study, based on 3245 shallow groundwater samples in the NCP, the multiple regression analysis results(R2=0.105, p<0.001) revealed that the TN was not mainly controlled by TSN and Inv. The lower prediction accuracy indicated the large data dispersion of TN, which might be affected by nitrogen attenuation or accumulation. Thus, the NCP was divided into balance, attenuation, and accumulation zones according to the regression equation. The attenuation zone was mainly distributed in the inter-fan and fan edge parts of the pre-mountain alluvial floodplain, as well as the west and south of the runoff area, while the accumulation zone was mainly distributed in the top part of the pre-mountain alluvial floodplain and the east of discharge area. Multi-indicators comparative analysis showed that compared to the balance (Eh= 76.2 mV) and accumulation (Eh=126.7 mV) zones, the attenuation zone has a stronger reducing environment (Eh=30.8 mV) favorable to denitrification, which can reduce the TN pollution (0.49 mg/L) caused by surface nitrogen input and consume more electron donors. Conversely, the stronger oxidizing environment in the accumulation zone limited denitrification, resulting in higher TN concentrations (19.14 mg/L) in the aquifers under the same TSN and Inv conditions as the other two zones. The standardized effects and significance on each path of the structural equation model (SEMs) fully confirmed the reliability of the above zonal analysis. Importantly, the feature importance (23.6%) of random forest and standardized effects (0.455, p<0.001) of SEMs showed that the Eh had the strongest influence on TN. Thus, the redox conditions of the aquifer, in addition to TSN and Inv, played a crucial role in controlling the TN pollution in the groundwater of a large region. The zoning work and the analysis of influencing factors are important to guide scientific prevention and control of groundwater nitrogen pollution.