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Advanced treated municipal wastewater is an important alternative water source for agricultural irrigation. However, the possible persistence of chemical and microbiological contaminants in these waters raise potential safety concerns with regard to reusing treated wastewater for food crop irrigation. Two low-cost and environmentally-friendly filter media, biochar (BC) and zero-valent iron (ZVI), have attracted great interest in terms of treating reused water. Here, we evaluated the efficacy of BC-, nanosilver-amended biochar- (Ag-BC) and ZVI-sand filters, in reducing contaminants of emerging concern (CECs),Escherichia coli (E. coli)and total bacterial diversity from wastewater effluent. Six experiments were conducted with control quartz sand and sand columns containing BC, Ag-BC, ZVI, BC with ZVI, or Ag-BC with ZVI. After filtration, Ag-BC, ZVI, BC with ZVI and Ag-BC with ZVI demonstrated more than 90% (> 1 log) removal ofE. colifrom wastewater samples, while BC, Ag-BC, BC with ZVI and Ag-BC with ZVI also demonstrated efficient removal of tested CECs. Lower bacterial diversity was also observed after filtration; however, differences were marginally significant. In addition, significantly (p < 0.05) higher bacterial diversity was observed in wastewater samples collected during warmer versus colder months. Leaching of silver ions occurred from Ag-BC columns; however, this was prevented through the addition of ZVI. In conclusion, our data suggest that the BC with ZVI and Ag-BC with ZVI sand filters, which demonstrated more than 99% removal of both CECs andE. coliwithout silver ion release, may be effective, low-cost options for decentralized treatment of reused wastewater.

Graphical Abstract

 

Climate change is impacting global crop productivity, and agricultural land suitability is predicted to significantly shift in the future. Responses to changing conditions and increasing yield variability can range from altered management strategies to outright land use conversions that may have significant environmental and socioeconomic ramifications. However, the extent to which agricultural land use changes in response to variations in climate is unclear at larger scales. Improved understanding of these dynamics is important since land use changes will have consequences not only for food security but also for ecosystem health, biodiversity, carbon storage, and regional and global climate. In this study, we combine land use products derived from the Moderate Resolution Imaging Spectroradiometer with climate reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 to analyze correspondence between changes in cropland and changes in temperature and water availability from 2001 to 2018. While climate trends explained little of the variability in land cover changes, increasing temperature, extreme heat days, potential evaporation, and drought severity were associated with higher levels of cropland loss. These patterns were strongest in regions with more cropland change, and generally reflected underlying climate suitability—they were amplified in hotter and drier regions, and reversed direction in cooler and wetter regions. At national scales, climate response patterns varied significantly, reflecting the importance of socioeconomic, political, and geographic factors, as well as differences in adaptation strategies. This global-scale analysis does not attempt to explain local mechanisms of change but identifies climate-cropland patterns that exist in aggregate and may be hard to perceive at local scales. It is intended to supplement regional studies, providing further context for locally-observed phenomena and highlighting patterns that require further analysis.

 

Infections with nontyphoidalSalmonellacause an estimated 19,336 hospitalizations each year in the United States. Sources of infection can vary by state and include animal and plant-based foods, as well as environmental reservoirs. Several studies have recognized the importance of increased ambient temperature and precipitation in the spread and persistence ofSalmonellain soil and food. However, the impact of extreme weather events onSalmonellainfection rates among the most prevalent serovars, has not been fully evaluated across distinct U.S. regions.

To address this knowledge gap, we obtainedSalmonellacase data forS.Enteriditis,S.Typhimurium,S.Newport, andS.Javiana (2004-2014; n = 32,951) from the Foodborne Diseases Active Surveillance Network (FoodNet), and weather data from the National Climatic Data Center (1960-2014). Extreme heat and precipitation events for the study period (2004-2014) were identified using location and calendar day specific 95^thpercentile thresholds derived using a 30-year baseline (1960-1989). Negative binomial generalized estimating equations were used to evaluate the association between exposure to extreme events and salmonellosis rates.

We observed that extreme heat exposure was associated with increased rates of infection withS.Newport in Maryland (Incidence Rate Ratio (IRR): 1.07, 95% Confidence Interval (CI): 1.01, 1.14), and Tennessee (IRR: 1.06, 95% CI: 1.04, 1.09), both FoodNet sites with high densities of animal feeding operations (e.g., broiler chickens and cattle). Extreme precipitation events were also associated with increased rates ofS.Javiana infections, by 22% in Connecticut (IRR: 1.22, 95% CI: 1.10, 1.35) and by 5% in Georgia (IRR: 1.05, 95% CI: 1.01, 1.08), respectively. In addition, there was an 11% (IRR: 1.11, 95% CI: 1.04-1.18) increased rate ofS. Newport infections in Maryland associated with extreme precipitation events.

Overall, our study suggests a stronger association between extreme precipitation events, compared to extreme heat, and salmonellosis across multiple U.S. regions. In addition, the rates of infection withSalmonellaserovars that persist in environmental or plant-based reservoirs, such asS.Javiana andS.Newport, appear to be of particular significance regarding increased heat and rainfall events.

 

Nontraditional irrigation water sources (e.g., recycled water, brackish water) may harbor human pathogens, includingVibriospp., that could be present in a viable-but-nonculturable (VBNC) state, stymieing current culture-based detection methods. To overcome this challenge, we coupled 5-bromo-2′-deoxyuridine (BrdU) labeling, enrichment techniques, and 16S rRNA sequencing to identify metabolically-activeVibriospp.in nontraditional irrigation water (recycled water, pond water, non-tidal freshwater, and tidal brackish water). Our coupled BrdU-labeling and sequencing approach revealed the presence of metabolically-activeVibriospp. at all sampling sites. Whereas, the culture-based method only detected vibrios at three of the four sites. We observed the presence ofV. cholerae,V. vulnificus, andV. parahaemolyticususing both methods, whileV. aesturianusandV. shiloniiwere detected only through our labeling/sequencing approach. Multiple other pathogens of concern to human health were also identified through our labeling/sequencing approach includingP. shigelloides,B. cereusandE. cloacae. Most importantly, 16S rRNA sequencing of BrdU-labeled samples resulted inVibriospp. detection even when our culture-based methods resulted in negative detection. This suggests that our novel approach can effectively detect metabolically-activeVibriospp. that may have been present in a VBNC state, refining our understanding of the prevalence of vibrios in nontraditional irrigation waters.

 

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.