Sources of ambient PM2.5 exposure in 96 global cities.

To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM2.5 exposure caused by within-city emissions varies widely (µ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health.

Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions.

Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005 to 2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single 2-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO2 and NOx concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs and only use mobile data collected within 300 m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R2 changing from 0.40 to 0.82) but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases, additional spatial information from mobile monitoring and personal sampling is potentially cost-efficient inexpensive way of improving exposure predictions at both 2-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign.

Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles.

Mobile monitoring and fixed-site monitoring using passive sampling devices (PSD) are popular air pollutant measurement techniques with complementary strengths and weaknesses. This study investigates the utility of combining data from concurrent 2-week mobile monitoring and fixed-site PSD campaigns in Los Angeles in the summer and early spring to identify sources of traffic-related air pollutants (TRAP) and their spatial distributions. There were strong to moderate correlations between mobile and fixed-site PSD measurements of both NO2 and NO x in the summer and spring (Pearson's r between 0.43 and 0.79), suggesting that the two data sets can be reliably combined for source apportionment. PCA identified the major TRAP sources as light-duty vehicle emissions, diesel exhaust, crankcase vent emissions, and an independent source of combustion-derived ultrafine particle emissions. The component scores of those four sources at each site were significantly correlated across the two seasons (Pearson's r between 0.58 and 0.79). Spatial maps of absolute principal component scores showed all sources to be most prominent near major roadways and the central business district and the ultrafine particle source being, in addition, more prominent near the airport. Mobile monitoring combined with fixed-site PSD sampling can provide high spatial resolution estimates of TRAP and can reveal underlying sources of exposure variability.

Factors influencing the airborne capture of respirable charged particles by surfactants in water sprays.

This research measured the effects of particle diameter, surfactant-containing spray solution, and particle charge on the capture of respirable particles by surfactant-containing water spray droplets. Polystyrene latex particles with diameters of 0.6, 1.0, or 2.1 µm were generated in a wind tunnel. Particles were given either a neutralized, unneutralized, net positive, or net negative charge, and then were captured as they passed through sprays containing anionic, cationic, or nonionic surfactant. The remaining particles were sampled, charge-separated, and counted with the sprays on and off at varying voltage levels to assess collection efficiency. Overall efficiencies were measured for particles with all charge levels, as well as efficiencies for particles with specific charge levels. The overall collection efficiency significantly increased with increasing particle diameter. Collection efficiencies of 21.5% ± 9.0%, 58.8% ± 12.5%, and 86.6% ± 43.5% (Mean ± SD) were observed for particles 0.6, 1.0, and 2.1 µm in diameter, respectively. The combination of surfactant classification and concentration also significantly affected both overall spray collection efficiency and collection efficiency for particles with specific charge levels. Ionic surfactant-containing sprays had the best performance for charged particles with the opposite sign of charge but the worst performance for charged particles with the same sign of charge, while nonionic surfactant-containing spray efficiently removed particles carrying relatively few charges. Particle charge level impacted the spray collection efficiency. Highly charged particles were removed more efficiently than weakly charged particles.

Effects of Spray Surfactant and Particle Charge on Respirable Coal Dust Capture.

BACKGROUND: Surfactant-containing water sprays are commonly used in coal mines to collect dust. This study investigates the dust collection performance of different surfactant types for a range of coal dust particle sizes and charges. METHODS: Bituminous coal dust aerosol was generated in a wind tunnel. The charge of the aerosol was either left unaltered, charge-neutralized with a neutralizer, or positively- or negatively-charged using a diffusion charger after the particles were neutralized. An anionic, cationic, or nonionic surfactant spray or a plain water spray was used to remove the particles from the air flow. Some particles were captured while passing through spray section, whereas remaining particles were charge-separated using an electrostatic classifier. Particle size and concentration of the charge-separated particles were measured using an aerodynamic particle sizer. Measurements were made with the spray on and off to calculate overall collection efficiencies (integrated across all charge levels) and efficiencies of particles with specific charge levels. RESULTS: The diameter of the tested coal dust aerosol was 0.89 µm ± 1.45 [geometric mean ± geometric standard deviations (SD)]. Respirable particle mass was collected with 75.5 ± 5.9% (mean ± SD) efficiency overall. Collection efficiency was correlated with particle size. Surfactant type significantly impacted collection efficiency: charged particle collection by nonionic surfactant sprays was greater than or equal to collection by other sprays, especially for weakly-charged aerosols. Particle charge strength was significantly correlated with collection efficiency. CONCLUSION: Surfactant type affects charged particle spray collection efficiency. Nonionic surfactant sprays performed well in coal dust capture in many of the tested conditions.