Wood pellet storage safety is an important aspect for implementing woody biomass as a renewable energy source. When wood pellets are stored indoors in large quantities (tons) in poorly ventilated spaces in buildings, such as in basements, off-gassing of volatile organic compounds (VOCs) can significantly affect indoor air quality. To determine the emission rates and potential impact of VOC emissions, a series of laboratory and field measurements were conducted using softwood, hardwood, and blended wood pellets manufactured in New York. Evacuated canisters were used to collect air samples from the headspace of drums containing pellets and then in basements and pellet storage areas of homes and small businesses. Multiple peaks were identified during GC/MS and GC/FID analysis, and four primary VOCs were characterized and quantified: methanol, pentane, pentanal, and hexanal. Laboratory results show that total VOCs (TVOCs) concentrations for softwood (SW) were statistically (p < 0.02) higher than blended or hardwood (HW) (SW: 412 ± 25; blended: 203 ± 4; HW: 99 ± 8, ppb). The emission rate from HW was the fastest, followed by blended and SW, respectively. Emissions rates were found to range from 10−1 to 10−5 units, depending upon environmental factors. Field measurements resulted in airborne concentrations ranging from 67 ± 8 to 5000 ± 3000 ppb of TVOCs and 12 to 1500 ppb of aldehydes, with higher concentrations found in a basement with a large fabric bag storage unit after fresh pellet delivery and lower concentrations for aged pellets. These results suggest that large fabric bag storage units resulted in a substantial release of VOCs into the building air. Occupants of the buildings tested discussed concerns about odor and sensory irritation when new pellets were delivered. The sensory response was likely due to the aldehydes.
Although insertion sequence (IS) elements are generally considered genomic parasites, they can mediate adaptive genetic changes in bacterial genomes. We discovered that among 12 laboratory-evolved Escherichia coli populations, three had experienced at least six different IS1-mediated deletions of flagellar genes. These deletions all involved the master flagellar regulator flhDC, and as such completely incapacitate motility. Two lines of evidence strongly suggest that these deletions were adaptive in our evolution experiment: (1) parallel evolution in three independent populations is highly unlikely just by chance, and (2) one of these deletion mutations swept to fixation within ~1000 generations, which is over two million times faster than expected if this deletion was instead selectively neutral and thus evolving by genetic drift. Because flagella are energetically expensive to synthesize and operate, we suspect that debilitating their construction conferred a fitness advantage in our well-stirred evolution experiment. These findings underscore the important role that IS elements can play in mediating adaptive loss-of-function mutations in bacteria.
Objectives This study: (i) assessed the relationship between noise exposure and injury risk, comprehensively adjusting for individual factors, psychosocial stressors, and organizational influences; (ii) determined the relative importance of noise on injuries; (iii) estimated the lowest observed adverse effect level (LOAEL) of noise on injury risk to determine the threshold of noise considered hazardous to injuries; and (iv) quantified the fraction of injuries that could be attributed to hazardous noise exposure. Methods In this cross-sectional study at 10 US surface mine sites, traditional mixed effects, Poisson regression, and boosted regression tree (BRT) models were run on the number of reported work-related injuries in the last year. The LOAEL of noise on injuries was identified by estimating the percent increase in work-related injuries at different thresholds of noise exposure using a counterfactual estimator through the BRT model. A population attributable fraction (PAF) was quantified with this counterfactual estimator to predict reductions in injuries at the LOAEL. Results Among 18 predictors of work-related injuries, mine site, perceived job safety, age, and sleepiness were the most important predictors. Occupational noise exposure was the seventh most important predictor. The LOAEL of noise for work-related injuries was a full-shift exposure of 88 dBA. Exposure ≥88 dBA was attributed to 20.3% (95% CI: 11.2%, 29.3%) of reported work-related injuries in the last year among the participants. Conclusions This study further supports hypotheses of a dose–response relationship between occupational noise exposure and work-related injuries, and suggests that exposures ≥88 dBA may increase injury risk in mining.
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