Ultrafine particle size as a tracer for aircraft turbine emissions.

Ultrafine particle number (UFPN) and size distributions, black carbon, and nitrogen dioxide concentrations were measured downwind of two of the busiest airports in the world, Los Angeles International Airport (LAX) and Hartsfield-Jackson International Airport (ATL - Atlanta, GA) using a mobile monitoring platform. Transects were located between 5 km and 10 km from the ATL and LAX airports. In addition, measurements were taken at 43 additional urban neighborhood locations in each city and on freeways. We found a 3-5 fold increase in UFPN concentrations in transects under the landing approach path to both airports relative to surrounding urban areas with similar ground traffic characteristics. The latter UFPN concentrations measured were distinct in size distributional properties from both freeways and across urban neighborhoods, clearly indicating different sources. Elevated concentrations of Black Carbon (BC) and NO2 were also observed on airport transects, and the corresponding pattern of elevated BC was consistent with the observed excess UFPN concentrations relative to other urban locations.

Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution.

Mobile monitoring has provided a means for broad spatial measurements of air pollutants that are otherwise impractical to measure with multiple fixed site sampling strategies. However, the larger the mobile monitoring route the less temporally dense measurements become, which may limit the usefulness of short-term mobile monitoring for applications that require long-term averages. To investigate the stationarity of short-term mobile monitoring measurements, we calculated long term medians derived from a mobile monitoring campaign that also employed 2-week integrated passive sampler detectors (PSD) for NOx, Ozone, and nine volatile organic compounds at 43 intersections distributed across the entire city of Baltimore, MD. This is one of the largest mobile monitoring campaigns in terms of spatial extent undertaken at this time. The mobile platform made repeat measurements every third day at each intersection for 6-10 minutes at a resolution of 10 s. In two-week periods in both summer and winter seasons, each site was visited 3-4 times, and a temporal adjustment was applied to each dataset. We present the correlations between eight species measured using mobile monitoring and the 2-week PSD data and observe correlations between mobile NOx measurements and PSD NOx measurements in both summer and winter (Pearson's r = 0.84 and 0.48, respectively). The summer season exhibited the strongest correlations between multiple pollutants, whereas the winter had comparatively few statistically significant correlations. In the summer CO was correlated with PSD pentanes (r = 0.81), and PSD NOx was correlated with mobile measurements of black carbon (r = 0.83), two ultrafine particle count measures (r =0.8), and intermodal (1-3 µm) particle counts (r = 0.73). Principal Component Analysis of the combined PSD and mobile monitoring data revealed multipollutant features consistent with light duty vehicle traffic, diesel exhaust and crankcase blow by. These features were more consistent with published source profiles traffic-related air pollutants than features based on the PSD data alone. Short-term mobile monitoring shows promise for capturing long-term spatial patterns of traffic-related air pollution, and is complementary to PSD sampling strategies.

Ambient Ammonia Exposures in an Agricultural Community and Pediatric Asthma Morbidity.

BACKGROUND: Large-scale animal feeding operations compromise regional air quality in the rural US through emission of pollutants, such as ammonia gas. Exposure to airborne pollution from animal feeding operations may cause pediatric asthma exacerbations in surrounding communities. OBJECTIVES: To describe spatial and temporal patterns in ambient ammonia concentrations in an agricultural region, and to investigate associations between short-term fluctuations in ammonia and subsequent changes in respiratory health in children with asthma. METHODS: For 13 months in the Yakima Valley of Washington State, 14 monitors sampled ammonia in outdoor air for 24-hour periods every 6 days. School-age children with asthma (n = 51) were followed for two health outcomes: biweekly reports of asthma symptoms and quick relief medication usage, and daily measurements of forced expiratory volume in 1 second. We assessed associations between each outcome and ammonia using generalized estimating equations. RESULTS: Twenty-four-hour ammonia concentrations varied from 0.2 to 238.1 µg/m during the study period and displayed a strong correlation with proximity to animal feeding operations. The percentage of forced expiratory volume in 1 second was 3.8% lower (95% confidence interval = 0.2, 7.3) per interquartile increase in 1-day lagged ammonia concentration and 3.0% lower (95% confidence interval = 0.5, 5.8) for 2-day lagged concentration. We observed no associations between self-reported asthma symptoms or medication usage and estimated ammonia exposure. CONCLUSIONS: Ammonia concentrations were elevated in this community and strongly predicted by proximity to animal feeding operations. Ammonia's association with acute lung function decrements in children with asthma in the surrounding community may be causal or, alternatively, ammonia may be a marker for other pollutants from animal feeding operations associated with respiratory effects.

Emissions from an international airport increase particle number concentrations 4-fold at 10 km downwind.

We measured the spatial pattern of particle number (PN) concentrations downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle that enabled us to cover larger areas than allowed by traditional stationary measurements. LAX emissions adversely impacted air quality much farther than reported in previous airport studies. We measured at least a 2-fold increase in PN concentrations over unimpacted baseline PN concentrations during most hours of the day in an area of about 60 km(2) that extended to 16 km (10 miles) downwind and a 4- to 5-fold increase to 8-10 km (5-6 miles) downwind. Locations of maximum PN concentrations were aligned to eastern, downwind jet trajectories during prevailing westerly winds and to 8 km downwind concentrations exceeded 75 000 particles/cm(3), more than the average freeway PN concentration in Los Angeles. During infrequent northerly winds, the impact area remained large but shifted to south of the airport. The freeway length that would cause an impact equivalent to that measured in this study (i.e., PN concentration increases weighted by the area impacted) was estimated to be 280-790 km. The total freeway length in Los Angeles is 1500 km. These results suggest that airport emissions are a major source of PN in Los Angeles that are of the same general magnitude as the entire urban freeway network. They also indicate that the air quality impact areas of major airports may have been seriously underestimated.

Multi-pollutant mobile platform measurements of air pollutants adjacent to a major roadway.

A mobile monitoring platform developed at the University of Washington Center for Clean Air Research (CCAR) measured 10 pollutant metrics (10 s measurements at an average speed of 22 km/hr) in two neighborhoods bordering a major interstate in Albuquerque, NM, USA from April 18-24 2012. 5 days of data sharing a common downwind orientation with respect to the roadway were analyzed. The aggregate results show a three-fold increase in black carbon (BC) concentrations within 10 meters of the edge of roadway, in addition to elevated nanoparticle concentration and particulate matter with aerodynamic diameter < 1 µm (PN1) concentrations. A 30% reduction in ozone concentration near the roadway was observed, anti-correlated with an increase in the oxides of nitrogen (NOx). In this study, the pollutants measured have been expanded to include polycyclic aromatic hydrocarbons (PAH), particle size distribution (0.25-32 µm), and ultra-violet absorbing particulate matter (UVPM). The raster sampling scheme combined with spatial and temporal measurement alignment provide a measure of variability in the near roadway concentrations, and allow us to use a principal component analysis to identify multi-pollutant features and analyze their roadway influences.

Cooking behaviors are related to household particulate matter exposure in children with asthma in the urban East Bay Area of Northern California.

BACKGROUND: Asthma is a common childhood disease that leads to many missed days of school and parents' work. There are multiple environmental contributors to asthma symptoms and understanding the potential factors inside children's homes is crucial. METHODS: This is a dual cohort study measuring household particulate matter (PM2.5), behaviors, and factors that influence air quality and asthma symptoms in the urban homes of children (ages 6-10) with asthma; one cohort had cigarette smoke exposure in the home (n = 13) and the other did not (n = 22). Exposure data included measurements every 5 minutes for a month. RESULTS: In the entire study population, a large contributor to elevations in indoor PM2.5 above 35 µg/m3 was not using the stove hood when cooking (8.5% higher, CI 3.1-13.9%, p<0.005). Median PM values during cooking times were 0.88 µg/m3 higher than those during non-cooking times (95% CI 0.33-1.42). Mean monthly household PM2.5 level was significantly related to the presence of a cigarette smoker in the home (10.1 µg/m3 higher, 95% CI 5.2-15.1, p<0.001) when controlling for use of the stove hood and proximity to major roadway. There was a trend toward increased odds of persistent asthma with increases in average monthly PM2.5 (OR 1.1, 95% CI 0.97-1.3, p = 0.16). CONCLUSIONS: Consideration of only outdoor PM2.5 may obscure potentially modifiable risks for asthma symptoms. Specifically, this preliminary study suggests that cooking behaviors may contribute to the burden of PM2.5 in the homes of children with asthma and thus to asthma symptoms.

Use of mobile and passive badge air monitoring data for NOX and ozone air pollution spatial exposure prediction models.

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NOX) and ozone (O3) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NOX and O3, with LOOCV R2s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NOX had LOOCV R2s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O3. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NOX and O3 and are a better source of data for these models than 2-week passive badge data.