Design and Characterization of Electrospun Polyamide Nanofiber Media for Air Filtration Applications

Electrospun polyamide 6 (PA 6) and polyamide 6/6 (PA 6/6) nanofibers were produced in order to investigate their experimental characteristics with the goal of obtaining filtration relevant fiber media. The experimental design model of each PA nanofibers contained the following variables: polymer concentration, ratio of solvents, nanofiber media collection time, tip-to-collector distance, and the deposition voltage. The average diameter of the fibers, their morphology, basis weight, thickness, and resulting media solidity were investigated. Effects of each variable on the essential characteristics of PA 6/6 and PA 6 nanofiber media were studied. The comparative analysis of the obtained PA 6/6 and PA 6 nanofiber characteristics revealed that PA 6/6 had higher potential to be used in filtration applications. Based on the experimental results, the graphical representation—response surfaces—for obtaining nanofiber media with the desirable fiber diameter and basis weight characteristics were derived. Based on the modelling results the nanofiber filter media (mats) were fabricated. Filtration results revealed that nanofiber filter media electrospun from PA6/6 8% (w/vol) solutions with the smallest fiber diameters (62–66 nm) had the highest filtration efficiency (PA6/6_30 = 84.9–90.9%) and the highest quality factor (PA6/6_10 = 0.0486–0.0749 Pa−1)

The air quality impacts of road closures associated with the 2004 Democratic National Convention in Boston

BACKGROUND: The Democratic National Convention (DNC) in Boston, Massachusetts in 2004 provided an opportunity to evaluate the impacts of a localized and short-term but potentially significant change in traffic patterns on air quality, and to determine the optimal monitoring approach to address events of this nature. It was anticipated that the road closures associated with the DNC would both influence the overall air pollution level and the distribution of concentrations across the city, through shifts in traffic patterns. METHODS: To capture these effects, we placed passive nitrogen dioxide badges at 40 sites around metropolitan Boston before, during, and after the DNC, with the goal of capturing the array of hypothesized impacts. In addition, we continuously measured elemental carbon at three sites, and gathered continuous air pollution data from US EPA fixed-site monitors and traffic count data from the Massachusetts Highway Department. RESULTS: There were significant reductions in traffic volume on the highway with closures north of Boston, with relatively little change along other highways, indicating a more isolated traffic reduction rather than an across-the-board decrease. For our nitrogen dioxide samples, while there was a relatively small change in mean concentrations, there was significant heterogeneity across sites, which corresponded with our a priori classifications of road segments. The median ratio of nitrogen dioxide concentrations during the DNC relative to non-DNC sampling periods was 0.58 at sites with hypothesized traffic reductions, versus 0.88 for sites with no changes hypothesized and 1.15 for sites with hypothesized traffic increases. Continuous monitors measured slightly lower concentrations of elemental carbon and nitrogen dioxide during road closure periods at monitors proximate to closed highway segments, but not for PM(2.5 )or further from major highways. CONCLUSION: We conclude that there was a small but measurable influence of DNC-related road closures on air quality patterns in the Boston area, and that a low-cost monitoring study combining passive badges for spatial heterogeneity and continuous monitors for temporal heterogeneity can provide useful insight for community air quality assessments

Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants

Background: There is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques. Methods: We measured fine particulate matter (PM2.5), nitrogen dioxide (NO2), and elemental carbon (EC) outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations. Results: PM2.5 was strongly associated with the central site monitor (R2 = 0.68). Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76). EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52). NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction), and with higher concentrations during summer (R2 = 0.56). Conclusion: Each pollutant examined displayed somewhat different spatial patterns within urban neighborhoods, and were differently related to local traffic and meteorology. Our results indicate a need for multi-pollutant exposure modeling to disentangle causal agents in epidemiological studies, and further investigation of site-specific and meteorological modification of the traffic-concentration relationship in urban neighborhoods

Exploring Variation and Predictors of Residential Fine Particulate Matter Infiltration

Although individuals spend the majority of their time indoors, most epidemiological studies estimate personal air pollution exposures based on outdoor levels. This almost certainly results in exposure misclassification as pollutant infiltration varies between homes. However, it is often not possible to collect detailed measures of infiltration for individual homes in large-scale epidemiological studies and thus there is currently a need to develop models that can be used to predict these values. To address this need, we examined infiltration of fine particulate matter (PM2.5) and identified determinants of infiltration for 46 residential homes in Toronto, Canada. Infiltration was estimated using the indoor/outdoor sulphur ratio and information on hypothesized predictors of infiltration were collected using questionnaires and publicly available databases. Multiple linear regression was used to develop the models. Mean infiltration was 0.52 ± 0.21 with no significant difference across heating and non-heating seasons. Predictors of infiltration were air exchange, presence of central air conditioning, and forced air heating. These variables accounted for 38% of the variability in infiltration. Without air exchange, the model accounted for 26% of the variability. Effective modelling of infiltration in individual homes remains difficult, although key variables such as use of central air conditioning show potential as an easily attainable indicator of infiltration

Intensive short term measurements of the ambient aerosol in the Greater Cincinnati airshed

As part of a larger study undertaken in the Greater Cincinnati area to determine if diesel truck emissions are adjuvant to naturally occurring bioaerosols in the initiation of allergies in children, a more detailed intensive measurement campaign was undertaken to elucidate the characteristics of the ambient aerosol and compare to the regular, integrated measurements being conducted. The mass concentration, total number concentration, size distributions, and morphologies were established at several locations including a residential area far from major traffic (Mernic), a suburban area on both sides of a major highway (I-275, Blue Ash), a site in the city center very close to the highway (I-75, Findlay), and an enclosed oval track at a Truck Driving School. Differences between real-time tapered element oscillating microbalance (TEOM) average mass concentrations and integrated Harvard impactor (HI) measurements were observed, with the magnitude of the difference being dependent on location and the organic compounds (OC) concentrations in the sample. Qualitative variation of the peaks in real-time PM 2.5 concentrations were observed with variation in truck traffic at the Findlay site; and no peaks in real-time PM 2.5 levels were observed at Mernic. Minimal variation in PM 2.5 was observed with distance from the highway at the Blue Ash site (fewer trucks). The site at Mernic had a smaller fraction of aggregated particles in comparison to the other sites. The two-dimensional fractal dimensions measured at the Findlay, Blue Ash, and Truck Driving School sites were statistically identical (1.58–1.61) but were higher than that measured at the Mernic site (1.41). Implications of the intensive measurement campaign vis-à-vis the epidemiological study are discussed briefly

UNMIX modeling of ambient PM2.5 near an interstate highway in Cincinnati, OH, USA

The “Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS)” is underway to determine if infants who are exposed to diesel engine exhaust particles are at an increased risk for atopy and atopic respiratory disorders, and to determine if this effect is magnified in a genetically at risk population. In support of this study, a methodology has been developed to allocate local traffic source contributions to ambient PM(2.5) in the Cincinnati airshed. As a first step towards this allocation, UNMIX was used to generate factors for ambient PM(2.5) at two sites near at interstate highway. Procedures adopted to collect, analyze and prepare the data sets to run UNMIX are described. The factors attributed to traffic sources were similar for the two sites. These factors were also similar to locally measured truck engine-exhaust enriched ambient profiles. The temporal variation of the factors was analyzed with clear differences observed between factors attributed to traffic sources and combustion-related regional secondary sources