Prevalence, Serotypes, and Antimicrobial Resistance of Salmonella from Mink Feces and Feed in the United States.

Nontyphoidal Salmonella is a leading cause of foodborne illnesses, and concentrated animal production such as commercial mink farming can be a reservoir. The objective of this study was to determine the prevalence, serotypes, antimicrobial resistance, virulence genes and mobile genetic elements, and phylogenetic characterization of Salmonella from mink fecal (n = 42) and feed (n = 8) samples at a commercial mink farm in the United States. Salmonella was detected from 11 (26.2%) fecal and 1 (12.5%) feed samples. Twelve fecal isolates obtained from the positive samples were identified into four serotypes: Salmonella Uganda (eight isolates; two isolates were obtained from one sample), Salmonella Reading (two isolates), Salmonella Heidelberg (one isolate), and Salmonella Orion (one isolate). Two isolates from the positive feed sample were identified as Salmonella Cerro. Twelve isolates, one isolate per positive sample, were whole genome sequenced for further characterization. The two Salmonella Reading isolates were multidrug resistant (MDR) with the classical ASSuT (ampicillin, streptomycin, sulfisoxazole, and tetracycline) phenotype. Genotypically, the isolates were correspondingly positive, both by polymerase chain reaction and by whole-genome sequencing (WGS), for blaTEM-1, aph(3″)-Ib and aph(6)-Id, sul2, and tet(A). WGS additionally identified trimethoprim resistance gene dfrA8 in Salmonella Reading, and fosfomycin resistance gene fosA7 in Salmonella Heidelberg. All isolates carried Salmonella-specific multidrug resistance (mdsA/B), gold resistance, and intimin-like virulence genes. In addition, Salmonella Orion carried copper and silver resistance gene clusters. The two Salmonella Reading isolates also carried a cytolethal distension toxin (cdtB) gene. Salmonella Reading isolates belonged to ST412, a predominant sequence type among turkey and human isolates obtained in relation to recent salmonellosis outbreaks in North America. Phylogenetically, Salmonella Uganda, Salmonella Heidelberg, and Salmonella Reading were mostly associated with historic human/clinical, and poultry and swine source isolates. This study indicates that mink production can be a reservoir for foodborne pathogens such as Salmonella with MDR serovars commonly associated with human illnesses.

Detection of Extended-Spectrum Beta-Lactamase-Producing and Carbapenem-Resistant Bacteria from Mink Feces and Feed in the United States.

Antibiotic-resistant infections caused by extended-spectrum ß-lactamases (ESBLs) and carbapenemases are increasing worldwide. Bacteria resistant to extended-spectrum cephalosporins and last resort carbapenems have been reported from food animals and their environments. Other concentrated nonfood-producing animals such as mink farming can be a reservoir of bacteria resistant to these critically important antibiotics. The objective of this study was to determine the prevalence of ESBL-producing bacteria and carbapenem-resistant (CR) bacteria from mink fecal (n = 42) and feed (n = 8) samples obtained from a commercial mink farm in the United States. The most prevalent ESBL-producing bacteria identified from the fecal samples were Escherichia coli (93%), Klebsiella pneumoniae (76%), and Proteus species (88%). E. coli (100%) and K. pneumoniae (75%) were also the most prevalent ESBL-producing bacteria identified from feed samples. All ESBL E. coli isolates were resistant to penicillin and most cephem beta-lactam antibiotics. Among the ESBL E. coli isolates, co-resistance was observed to ciprofloxacin (33%) and gentamicin (28%) indicating multidrug resistance. ESBL E. coli isolates predominantly carried blaCTX-M-14 and blaCTX-M-15 genes. Although all feed K. pneumoniae isolates carried blaCTX-M-9, all fecal K. pneumoniae isolates carried blaSHV. CR Pseudomonas species (7%), Hafnia alvei (24%), and Myroides odoratimimus (9.5%) were detected from fecal samples. H. alvei (37.5%) was the only CR bacteria detected from the feed samples. All CR isolates were polymerase chain reaction negative for the tested carbapenemases that are commonly reported, which may indicate intrinsic rather than acquired resistance. This study indicates that mink production can be a reservoir for bacteria resistant to the highest priority critically important antibiotics for human health.

Third-Generation Cephalosporin- and Tetracycline-Resistant Escherichia coli and Antimicrobial Resistance Genes from Metagenomes of Mink Feces and Feed.

American mink (Neovison vison) is a significant source of global fur production. Except for a few studies from Denmark and Canada reporting antimicrobial resistance in bacteria isolated from clinical cases, studies from the general mink population are scarce and absent in the United States. Mink feces (n = 42) and feed (n = 8) samples obtained from a mink farm were cultured for the enumeration and detection of tetracycline-resistant (TETr)- and third-generation cephalosporin-resistant (TGCr)-Escherichia coli. Isolates were characterized phenotypically for their resistance to other antibiotics and genotypically for resistance genes. TETrE. coli were detected from 98% of feces samples (mean concentration = 6 log10) and from 100% of feed samples (mean concentration = 3.2 logs). Among TETrE. coli isolates, 44% (n = 41) of fecal- and 50% (n = 8) of feed isolates were multidrug resistant (MDR; resistance to ≥3 antimicrobial classes), and 96% (n = 49) of TETr isolates were positive for tet(A) and/or tet(B). TGCrE. coli were detected from 95% of feces and 75% of feed samples with 78% (n = 40) of fecal isolates, and all six of the feed isolates were MDR. Nearly two-thirds (65%) of the TGCrE. coli isolates (n = 46) were positive for blaCMY-2; the remaining 35% were positive for blaCTX-M, with the blaCTX-M-14 being the predominant (75%, n = 16) variant detected. Metagenomic DNA was extracted directly from feces and feed samples, and it was tested for 84 antimicrobial resistance genes by using quantitative polymerase chain reaction (PCR) array; selected genes were also quantified by droplet digital PCR. The genes detected from the fecal samples belonged mainly to five antimicrobial classes: macrolide-lincosamide-streptogramin B (MLSB; 100% prevalence), TETs (88.1%), ß-lactams (71.4%), aminoglycosides (66.7%), and fluoroquinolones (47.6%). ß-Lactam, MLSB, and TET resistance genes were also detected from feed samples. Our study serves as a baseline for further studies and to streamline antimicrobial use in mink production in accordance with current regulations as in food animals.

Coupling between Chemical and Meteorological Processes under Persistent Cold-Air Pool Conditions: Evolution of Wintertime PM2.5 Pollution Events and N2O5 Observations in Utah's Salt Lake Valley.

The Salt Lake Valley experiences severe fine particulate matter pollution episodes in winter during persistent cold-air pools (PCAPs). We employ measurements throughout an entire winter from different elevations to examine the chemical and dynamical processes driving these episodes. Whereas primary pollutants such as NOx and CO were enhanced twofold during PCAPs, O3 concentrations were approximately threefold lower. Atmospheric composition varies strongly with altitude within a PCAP at night with lower NOx and higher oxidants (O3) and oxidized reactive nitrogen (N2O5) aloft. We present observations of N2O5 during PCAPs that provide evidence for its role in cold-pool nitrate formation. Our observations suggest that nighttime and early morning chemistry in the upper levels of a PCAP plays an important role in aerosol nitrate formation. Subsequent daytime mixing enhances surface PM2.5 by dispersing the aerosol throughout the PCAP. As pollutants accumulate and deplete oxidants, nitrate chemistry becomes less active during the later stages of the pollution episodes. This leads to distinct stages of PM2.5 pollution episodes, starting with a period of PM2.5 buildup and followed by a period with plateauing concentrations. We discuss the implications of these findings for mitigation strategies.

Particulate emissions calculations from fall tillage operations using point and remote sensors.

Soil preparation for agricultural crops produces aerosols that may significantly contribute to seasonal atmospheric particulate matter (PM). Efforts to reduce PM emissions from tillage through a variety of conservation management practices (CMPs) have been made, but the reductions from many of these practices have not been measured in the field. A study was conducted in California's San Joaquin Valley to quantify emissions reductions from fall tillage CMP. Emissions were measured from conventional tillage methods and from a "combined operations" CMP, which combines several implements to reduce tractor passes. Measurements were made of soil moisture, bulk density, meteorological profiles, filter-based total suspended PM (TSP), concentrations of PM with an equivalent aerodynamic diameter ≤10 µm (PM) and PM with an equivalent aerodynamic diameter ≤2.5 µm (PM), and aerosol size distribution. A mass-calibrated, scanning, three-wavelength light detection and ranging (LIDAR) procedure estimated PM through a series of algorithms. Emissions were calculated via inverse modeling with mass concentration measurements and applying a mass balance to LIDAR data. Inverse modeling emission estimates were higher, often with statistically significant differences. Derived PM emissions for conventional operations generally agree with literature values. Sampling irregularities with a few filter-based samples prevented calculation of a complete set of emissions through inverse modeling; however, the LIDAR-based emissions dataset was complete. The CMP control effectiveness was calculated based on LIDAR-derived emissions to be 29 ± 2%, 60 ± 1%, and 25 ± 1% for PM, PM, and TSP size fractions, respectively. Implementation of this CMP provides an effective method for the reduction of PM emissions.

Tetracycline- and Macrolide-Resistant Enterococcus Species Isolated from a Mink Farm in the United States.

Enterococcus species are a normal flora of animals and humans. However, life-threatening opportunistic infections can be caused by antimicrobial resistant strains. Fecal (n = 42) and feed (n = 8) samples were obtained from a mink farm and cultured for the enumeration and detection of erythromycin-resistant (a macrolide; ERYr)- and tetracycline-resistant (TETr) enterococci. ERYr and TETr enterococci were detected from all fecal (mean concentrations = 6 and 7 logs, respectively) and feed (mean concentrations = 5 and 4 logs, respectively) samples. While Enterococcus faecalis and Enterococcus faecium were detected at equal proportions among the fecal TETr isolates, E. faecium predominated among ERYr fecal isolates. All ERYr and 90% of the TETr isolates (n = 50) were multidrug resistant (resistant to three or more antimicrobial classes). Among ERYr isolates, while 83% of E. faecalis (n = 12) were positive for erm(B), 58% of E. faecium (n = 38) isolates were positive for msr(C). Among ERYr isolates, tet(M) was detected from 92% of E. faecalis (n = 12) and 97% of E. faecium (n = 38) isolates. Conversely, however, erm(B) was detected in 18% of E. faecalis (n = 22) and 33% of E. faecium (n = 27) TETr isolates. Our study provides a baseline for future efforts to reduce antimicrobial resistance and improve antimicrobial stewardship in commercial mink production facilities.

Coupled Air Quality and Boundary-Layer Meteorology in Western U.S. Basins during Winter: Design and Rationale for a Comprehensive Study.

Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical-meteorological interactions that drive high pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in Western U.S. basins. Approximately 120 people participated, representing 50 institutions and 5 countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary-layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological-chemical linkages outlined here, nor to validate complex processes within coupled atmosphere-chemistry models.

On-the-road testing of the effects of driver's experience, gender, speed, and road grade on car emissions.

On-road vehicles have become a dominant source of air pollution and energy consumption in many parts of the world. As a result, estimating the amount of pollution from these vehicles and analyzing the factors affecting their emission is necessary to understand and manage ambient air quality. Traditionally, automobile emissions have been measured with dynamometer tests using representative driving cycles. A review of the related literature shows that there is a lack of real life, on-the-road testing of automobile emissions. Moreover, a few previous studies have directly discussed the impact of driver variability on emissions from the vehicles. This research analyzes the impacts of driver experience, gender, speed, and road grade on vehicle emissions through on-the-road testing experiment in Logan, Utah, USA during summer of 2016. The methodology of the research starts by selecting a representative car to perform the tests on. The next step was to choose test drivers representing four groups: young males, young females, experienced males, and experienced females. After that, the drivers were assigned a specified route that has different speed limits and grades. Emissions from the car exhaust (specifically carbon monoxide-CO, hydrocarbons-HC, and nitrogen oxides-NOx) in addition to the engines rotational speed (rpm), car speed, and exhaust temperature, were measured every second while driving on the specified route. Statistical analysis of the results shows that contrary to the common stereotypes, experienced drivers emitted 52% more HC and 49% more NOx than young drivers and female drivers, and male drivers emitted 14% more HC and 44% more NOx than female drivers. It also shows that CO emission is not significantly dependent on age, gender, nor driving conditions. Finally, driving through low-speed segments emits significantly higher HC (79%), while driving through uphill segments emits significantly higher (98%) NOx than driving through downhill segment. Implications: This study showed that there are significant differences in vehicular emissions among drivers from different genders and age. These differences should be taking into consideration in future emission modeling studies and regulatory scenarios.

Spatial PM2.5, NO2, O3 and BC models for Western Europe - Evaluation of spatiotemporal stability.

BACKGROUND: In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe. METHODS: We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013). RESULTS: The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale. CONCLUSIONS: We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.