Leaching of antibiotic resistance genes and microbial assemblages following poultry litter applications in karst and non-karst landscapes.

Antibiotic resistance is increasingly recognized as a critical challenge affecting human, animal, and environmental health. Yet, environmental dynamics and transport of antibiotic resistance genes (ARGs) and microbial communities in karst and non-karst leachate following poultry litter land applications are not well understood. This study investigates impacts of broiler poultry litter application on the proliferation of ARGs (tetW, qnrS, ermB, sulI, and blaCTX-M-32), class 1 integron (intI1 i), and alterations in microbial communities (16S rRNA) within karst derived soils, which are crucial and under-researched systems in the global hydrological cycle, and non-karst landscapes. Using large, intact soil columns (45 cm diam. × 100 cm depth) from karst and non-karst landscapes, the role of preferential flow and ARG transport in leachate was enumerated following surface application of poultry litter and simulated rain events. This research demonstrated that in poultry litter amended karst soils, ARG (i.e., ermB and tetW) abundance in leachate increased 1.5 times compared to non-karst systems (p < 0.05), highlighting the influence of geological factors on ARG proliferation. Notably, microbial communities in karst soil leachate exhibited increased diversity and abundance, suggesting a potential linkage between microbial composition and ARG presence. Further, our correlation and network analyses identified relationships between leachate ARGs, microbial taxa, and physicochemical properties, underscoring the complex interplay in these environmentally sensitive areas. These findings illuminate the critical role of karst systems in shaping ARG abundance and pollutant dispersal and microbial community dynamics, thus emphasizing the need for landscape-specific approaches in managing ARG dissemination to the environment. This study provides a deeper understanding of hydrogeological ARG dynamics but also lays the groundwork for future research and strategies to mitigate ARG dissemination through targeted manure applications across agricultural landscapes.

Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors.

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R2), concordance correlation coefficient (ρc), and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5 % quantiles provided by HGB. The model explained up to 50 % of the variance in the data with RMSE ranging between 0.16 (mg kg-1) for Cu and 23.4 (mg kg-1) for Zn, respectively. Likewise, ρc ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide.