Evaluating an Air Quality Health Index (AQHI) amendment for communities impacted by residential woodsmoke in British Columbia,Canada

ABSTRACT

Smoke from burning biomass is an important source of fine particulate matter (PM_2.5), but the health risks may not be fully captured by the Canadian Air Quality Health Index (AQHI). In May 2018, the province of British Columbia launched an evidence-based amendment (AQHI-Plus) to improve AQHI performance for wildfire smoke, but the AQHI-Plus was not developed or tested on data from the residential woodsmoke season. This study assesses how the AQHI and AQHI-Plus are associated with acute health outcomes during the cooler seasons of 2010–2017 in British Columbia, Canada. Monthly and daily patterns of temperature and PM_2.5 concentrations were used to identify Local Health Areas (LHAs) that were impacted by residential woodsmoke. The effects of the AQHI and AQHI-Plus on five acute health outcomes (including non-accidental mortality, outpatient physician visits, and medical dispensations for cardiopulmonary conditions) were estimated using generalized linear mixed effect models with Poisson distributions adjusted for long- and short-term temperature trends. Values of the Akaike information criterion (AIC) were compared to evaluate whether the AQHI or AQHI-Plus was better fitted to each health outcome. Eleven LHAs were categorized as woodsmoke-impacted. In these LHAs, the AQHI and AQHI-Plus associations with acute health outcomes were sensitive to temperature adjustments. After temperature adjustments, the most consistent associations were observed for the two asthma-specific outcomes where the AQHI-Plus was better fitted than the AQHI. The improved performance of the AQHI-Plus for susceptible populations with asthma is consistent between communities impacted by residential woodsmoke and wildfire smoke.

Implications: Canada’s Air Quality Health Index (AQHI) is a three pollutant index used to communicate the short term health impact of degraded air quality. As fine particulate matter (PM_2.5) is the lowest weighted pollutant in the AQHI, the index is poorly reflective of woodsmoke impacts. The present analysis found that an AQHI amendment developed for improved sensitivity to PM_2.5 during wildfire seasons (AQHI-Plus) is also more predictive of acute asthma-related health outcomes in communities impacted by residential woodsmoke. The BC Ministry of Environment and Climate Change Strategy has piloted the AQHI-Plus year-round. Other jurisdictions should consider whether their air quality indices are reflective of the risks posed by woodsmoke.     

Wildfire and prescribed burning impacts on air quality in the United States

ABSTRACT

Air quality impacts from wildfires have been dramatic in recent years, with millions of people exposed to elevated and sometimes hazardous fine particulate matter (PM _2.5 ) concentrations for extended periods. Fires emit particulate matter (PM) and gaseous compounds that can negatively impact human health and reduce visibility. While the overall trend in U.S. air quality has been improving for decades, largely due to implementation of the Clean Air Act, seasonal wildfires threaten to undo this in some regions of the United States. Our understanding of the health effects of smoke is growing with regard to respiratory and cardiovascular consequences and mortality. The costs of these health outcomes can exceed the billions already spent on wildfire suppression. In this critical review, we examine each of the processes that influence wildland fires and the effects of fires, including the natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry, and human health impacts. We highlight key data gaps and examine the complexity and scope and scale of fire occurrence, estimated emissions, and resulting effects on regional air quality across the United States. The goal is to clarify which areas are well understood and which need more study. We conclude with a set of recommendations for future research.     

The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5 over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions

Abstract

Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 µm (PM_2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM_2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005–2006) match-up dataset of MODIS AOD and hourly PM_2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM_2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each region are used to estimate the PM_2.5 from AOD retrievals, and it is shown that the estimation using this method is more accurate than that using a fixed ratio between PM_2.5 and AOD. Two versions of AOD from Terra (v4.0.1 and v5.2.6) are also compared in terms of the inversion methods and screening algorithms. The v5.2.6 AOD retrievals demonstrate better correlation with PM_2.5 than v4.0.1 retrievals, but they have much less coverage because of the differences in the cloud-screening algorithm.     

A reanalysis of fine particulate matter air pollution versus life expectancy in the United States

A reduction in population exposure to fine particulate matter air pollution (PM2.5) has been associated with improvements in life expectancy. This article presents a reanalysis of this relationship and comments on the results from a study on the reduction of ambient air PM_2.5 concentrations versus life expectancy in metropolitan areas of the United States. The results of the reanalysis show that the statistical significance of the correlation is lost after removing one of the metropolitan areas from the regression analysis, suggesting that the results may not be suitable for a meaningful and reliable inference.

Implications: The observed loss of statistical significance in the correlation between the reduction of ambient air PM_2.5 concentrations and life expectancy in metropolitan areas of the United States, after removing one of the metropolitan areas from the regression analysis, may raise concern for the policymakers in decisions regarding further reductions in permitted levels of air pollution emissions.     

A combined model–observation approach to estimate historic gridded fields of PM2.5 mass and species concentrations

This paper introduces a methodology for estimating gridded fields of total and speciated fine particulate matter (PM_2.5) concentrations for time periods and regions not covered by observational data. The methodology is based on performing long-term regional scale meteorological and air quality simulations and then integrating these simulations with available observational data. To illustrate this methodology, we present an application in which year-round simulations with a meteorological model (the National Center for Atmospheric Research/Penn State Mesoscale Model, hereafter referred to as MM5) and a photochemical air quality model (the Community Multiscale Air Quality Model, hereafter referred to as CMAQ) have been performed over the northeastern United States for 1988–2005. Model evaluation results for total PM_2.5 mass and individual species for the time period from 2000 to 2005 show that model performance varies by species, season, and location. Therefore, an approach is developed to adjust CMAQ output with factors based on these three variables. The adjusted model values for total PM_2.5 mass for 2000–2005 are compared against independent measurements not utilized for the adjustment approach. This comparison reveals that the adjusted model values have a lower root mean square error (RMSE) and higher correlation coefficients than the original model values. Furthermore, the PM_2.5 estimates from these adjusted model values are compared against an alternate method for estimating historic PM_2.5 values that is based on PM_2.5/PM₁₀ ratios calculated at co-located monitors. Results reveal that both methods yield estimates of historic PM_2.5 mass that are broadly consistent; however, the adjusted CMAQ values provide greater spatial coverage and information for PM_2.5 species in addition to total PM_2.5 mass. Finally, strengths and limitations of the proposed approach are discussed in the context of potential uses of this method.     

Physicochemical and toxicological characteristics of urban aerosols during a recent Indonesian biomass burning episode

Air particulate matter (PM) samples were collected in Singapore from 21 to 29 October 2010. During this time period, a severe regional smoke haze episode lasted for a few days (21–23 October). Physicochemical and toxicological characteristics of both haze and non-haze aerosols were evaluated. The average mass concentration of PM_2.5 (PM with aerodynamic diameter of ≤2.5 μm) increased by a factor of 4 during the smoke haze period (107.2 μg/m³) as compared to that during the non-smoke haze period (27.0 μg/m³). The PM_2.5 samples were analyzed for 16 priority polycyclic aromatic hydrocarbons (PAHs) listed by the United States Environmental Protection Agency and 10 transition metals. Out of the seven PAHs known as potential or suspected carcinogens, five were found in significantly higher levels in smoke haze aerosols as compared to those in the background air. Metal concentrations were also found to be higher in haze aerosols. Additionally, the toxicological profile of the PM_2.5 samples was evaluated using a human epithelial lung cell line (A549). Cell viability and death counts were measured after a direct exposure of PM_2.5 samples to A459 cells for a period of 48 h. The percentage of metabolically active cells decreased significantly following a direct exposure to PM samples collected during the haze period. To provide further insights into the toxicological characteristics of the aerosol particles, glutathione levels, as an indirect measure of oxidative stress and caspase-3/7 levels as a measure of apoptotic death, were also evaluated.     

The impact of wood smoke on ambient PM2.5 in northern Rocky Mountain valley communities

During the winters of 2006/2007 and 2007/2008, PM_2.5 source apportionment programs were carried out within five western Montana valley communities. Filter samples were analyzed for mass and chemical composition. Information was utilized in a Chemical Mass Balance (CMB) computer model to apportion the sources of PM_2.5. Results showed that wood smoke (likely residential woodstoves) was the major source of PM_2.5 in each of the communities, contributing from 56% to 77% of the measured wintertime PM_2.5. Results of ¹⁴C analyses showed that between 44% and 76% of the measured PM_2.5 came from a new carbon (wood smoke) source, confirming the results of the CMB modeling. In summary, the CMB model results, coupled with the ¹⁴C results, support that wood smoke is the major contributor to the overall PM_2.5 mass in these rural, northern Rocky Mountain airsheds throughout the winter months.     

Laboratory and field evaluation of real-time and near real-time PM2.5 smoke monitors

ABSTRACT

Increases in large wildfire frequency and intensity and a longer fire season in the western United States are resulting in a significant increase in air pollution, including concentrations of PM_2.5 (particulate matter <2.5 µm in aerodynamic diameter) that pose significant health risks to nearby communities. During wildfires, government agencies monitor PM_2.5 mass concentrations providing information and actions needed to protect affected communities; this requires continuously measuring instruments. This study assessed the performance of seven candidate instruments: (1) Met One Environmental beta attenuation monitor (EBAM), (2) Met One ES model 642 (ES642), (3) Grimm Environmental Dust Monitor 164 (EDM), (4) Thermo ADR 1500 (ADR), (5) TSI DRX model 8543 (DRX), (6) Dylos 1700 (Dylos), and (7) Purple Air II (PA-II) in comparison with a BAM 1020 (BAM) reference instrument. With the exception of the EBAM, all candidates use light scattering to determine PM_2.5 mass concentrations. Our comparison study included environmental chamber and field components, with two of each candidate instrument operating next to the reference instrument. The chamber component involved 6 days of comparisons for biomass combustion emissions. The field component involved operating all instruments in an air monitoring station for 39.5 days with hourly average relative humidity (RH) ranging from 19% to 98%. Goals were to assess instrument precision and accuracy and effects of RH, elemental carbon (EC), and organic carbon (OC) concentrations. All replicate candidate instruments showed high hourly correlations (R² ≥ 0.80) and higher daily average correlations (R² ≥ 0.90), where all instruments correlated well (R² ≥ 0.80) with the reference. The DRX and Purple Air overestimated PM_2.5 mass concentrations by a factor of ~two. Differences between candidates and reference were more pronounced at higher PM_2.5 concentrations. All optical instruments were affected by high RH and by the EC/OC ratio. Equations to convert candidate instruments data to FEM BAM type data are provided to enhance the usability of data from candidate instruments.

Implications: This study tested the performance of seven candidate PM_2.5 mass concentration measuring instruments in two settings - environmental chamber and field. The instruments were tested to determine their suitability for use during biomass combustion events and the effects of RH, PM mass concentrations, and concentrations of EC and OC on their performance. The accuracy and precision of each monitor and effect of RH, PM concentration, EC and OC concentrations are varied. The data show that most of these candidate instruments are suitable for measuring PM_2.5 concentration during biomass combustions with a proper correction factor for each instrument type.     

Spatial and temporal variations in traffic-related particulate matter at New York City high schools

Relatively little is known about exposures to traffic-related particulate matter at schools located in dense urban areas. The purpose of this study was to examine the influences of diesel traffic proximity and intensity on ambient concentrations of fine particulate matter (PM_2.5) and black carbon (BC), an indicator of diesel exhaust particles, at New York City (NYC) high schools. Outdoor PM_2.5 and BC were monitored continuously for 4–6 weeks at each of 3 NYC schools and 1 suburban school located 40 km upwind of the city. Traffic count data were obtained using an automated traffic counter or video camera. BC concentrations were 2–3 fold higher at urban schools compared with the suburban school, and among the 3 urban schools, BC concentrations were higher at schools located adjacent to highways. PM_2.5 concentrations were significantly higher at urban schools than at the suburban school, but concentrations did not vary significantly among urban schools. Both hourly average counts of trucks and buses and meteorological factors such as wind direction, wind speed, and humidity were significantly associated with hourly average ambient BC and PM_2.5 concentrations in multivariate regression models. An increase of 443 trucks/buses per hour was associated with a 0.62 μg/m³ increase in hourly average BC at an NYC school located adjacent to a major interstate highway. Car traffic counts were not associated with BC. The results suggest that local diesel vehicle traffic may be important sources of airborne fine particles in dense urban areas and consequently may contribute to local variations in PM_2.5 concentrations. In urban areas with higher levels of diesel traffic, local, neighborhood-scale monitoring of pollutants such as BC, which compared to PM_2.5, is a more specific indicator of diesel exhaust particles, may more accurately represent population exposures.     

Application of positive matrix factorization in characterization of PM(10) and PM(2.5) emission sources at urban roadside

The 24-h average coarse (PM₁₀) and fine (PM_2.5) fraction of airborne particulate matter (PM) samples were collected for winter, summer and monsoon seasons during November 2008-April 2009 at an busy roadside in Chennai city, India. Results showed that the 24-h average ambient PM₁₀ and PM_2.5 concentrations were significantly higher in winter and monsoon seasons than in summer season. The 24-h average PM₁₀ concentration of weekdays was significantly higher (12-30%) than weekends of winter and monsoon seasons. On weekends, the PM_2.5 concentration was found to slightly higher (4-15%) in monsoon and summer seasons. The chemical composition of PM₁₀ and PM_2.5 masses showed a high concentration in winter followed by monsoon and summer seasons.The U.S.EPA-PMF (positive matrix factorization) version 3 was applied to identify the source contribution of ambient PM₁₀ and PM_2.5 concentrations at the study area. Results indicated that marine aerosol (40.4% in PM₁₀ and 21.5% in PM_2.5) and secondary PM (22.9% in PM₁₀ and 42.1% in PM_2.5) were found to be the major source contributors at the study site followed by the motor vehicles (16% in PM₁₀ and 6% in PM_2.5), biomass burning (0.7% in PM₁₀ and 14% in PM_2.5), tire and brake wear (4.1% in PM₁₀ and 5.4% in PM_2.5), soil (3.4% in PM₁₀ and 4.3% in PM_2.5) and other sources (12.7% in PM₁₀ and 6.8% in PM_2.5).     

Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II—particulate matter

This paper is Part II in a pair of papers that examines the results of the Community Multiscale Air Quality (CMAQ) model version 4.5 (v4.5) and discusses the potential explanations for the model performance characteristics seen. The focus of this paper is on fine particulate matter (PM_2.5) and its chemical composition. Improvements made to the dry deposition velocity and cloud treatment in CMAQ v4.5 addressing compensating errors in 36-km simulations improved particulate sulfate (SO₄²⁻) predictions. Large overpredictions of particulate nitrate (NO₃⁻) and ammonium (NH₄⁺) in the fall are likely due to a gross overestimation of seasonal ammonia (NH₃) emissions. Carbonaceous aerosol concentrations are substantially underpredicted during the late spring and summer months, most likely due, in part, to a lack of some secondary organic aerosol (SOA) formation pathways in the model. Comparisons of CMAQ PM_2.5 predictions with observed PM_2.5 mass show mixed seasonal performance. Spring and summer show the best overall performance, while performance in the winter and fall is relatively poor, with significant overpredictions of total PM_2.5 mass in those seasons. The model biases in PM_2.5 mass cannot be explained by summing the model biases for the major inorganic ions plus carbon. Errors in the prediction of other unspeciated PM_2.5 (PM_Other) are largely to blame for the errors in total PM_2.5 mass predictions, and efforts are underway to identify the cause of these errors.     

Seasonal ambient particulate matter and population health outcomes among communities impacted by road dust in British Columbia,Canada

In recent years, many air quality monitoring programs have favored measurement of particles less than 2.5 µm (PM_2.5) over particles less than 10 µm (PM₁₀) in light of evidence that health impacts are mostly from the fine fraction. However, the coarse fraction (PM_10-2.5) may have independent health impacts that support continued measurement of PM₁₀ in some areas, such as those affected by road dust. The objective of this study was to evaluate the associations between different measures of daily PM exposure and two daily indicators of population health in seven communities in British Columbia, Canada, where road dust is an ongoing concern. The measures of exposure were PM₁₀, PM_2.5, PM_10-2.5, PM_2.5 adjusted for PM_10-2.5, and PM_10-2.5 adjusted for PM_2.5. The indicators of population health were dispensations of the respiratory reliever medication salbutamol sulfate and nonaccidental mortality. This study followed a time-series design using Poisson regression over a 2003–2015 study period, with analyses stratified by three seasons: residential woodsmoke in winter; road dust in spring; and wildfire smoke in summer. A random-effects meta-analysis was conducted to establish a pooled estimate. Overall, an interquartile range increase in daily PM_10-2.5 was associated with a 3.6% [1.6, 5.6] increase in nonaccidental mortality during the road dust season, which was reduced to 3.1% [0.8, 5.4] after adjustment for PM_2.5. The adjusted coarse fraction had no effect on salbutamol dispensations in any season. However, an interquartile range increase in PM_2.5 was associated with a 2.7% [2.0, 3.4] increase in dispensations during the wildfire season. These analyses suggest different impacts of different PM fractions by season, with a robust association between the coarse fraction and nonaccidental mortality in communities and periods affected by road dust. We recommend that PM₁₀ monitoring networks be maintained in these communities to provide feedback for future dust mitigation programs.

Implications: There was a significant association between daily concentrations of the coarse fraction and nonaccidental mortality during the road dust season, even after adjustment for the fine fraction. The acute and chronic health effects associated with exposure to the coarse fraction remain unclear, which supports the maintenance of PM₁₀ monitoring networks to allow for further research in communities affected by sources such as road dust.     

Sensitivity analysis of source regions to PM2.5 concentration at Fukue Island,Japan

The authors analyze the sensitivities of source regions in East Asia to PM_2.5 (particulate matter with an aerodynamic diameter of ≤2.5 µm) concentration at Fukue Island located in the western part of Japan by using a regional chemical transport model with emission sensitivity simulations for the year 2010. The temporal variations in PM_2.5 concentration are generally reproduced, but the absolute concentration is underestimated by the model. Chemical composition of PM_2.5 in the model is compared with filter sampling data in spring; simulated sulfate, ammonium, and elemental carbon are consistent with observations, but mass concentration of particulate organic matters is underestimated. The relative contribution from each source region shows the seasonal variation, especially in summer. The contribution from central north China (105°E–124°E, 34°N–42°N) accounts for 50–60% of PM_2.5 at Fukue Island except in summer; it significantly decreases in summer (18%). Central south China (105°E–123°E, 26°N–34°N) has the relative contribution of 15–30%. The contribution from the Korean Peninsula is estimated at about 10% except in summer. The domestic contribution accounts for about 7% in spring and autumn and increases to 19% in summer. We also estimate the relative contribution to daily average concentration in high PM_2.5 days (>35 μg m^?3). Central north China has a significant contribution of 60–70% except in summer. The relative contribution from central south China is estimated at 46% in summer and about 30% in the other seasons. The contributions from central north and south China on high PM_2.5 days are generally larger than those of their seasonal mean contributions. The domestic contribution is smaller than the seasonal mean value in every season; it is less than 10% even in summer. These model results suggest that foreign anthropogenic sources have a substantial impact on attainment of the atmospheric environmental standard of Japan at Fukue Island.

Implications: The contribution from several source regions in East Asia to PM_2.5 concentration at Fukue Island, a remote island located in the western part of Japan and close to the Asian continent, is estimated using a three-dimensional chemical transport model. The model results suggest that PM_2.5 that is attributed to foreign anthropogenic sources have a larger contribution than that of domestic pollution and have a substantial impact on attainment of the atmospheric environmental standard of Japan at Fukue Island.     

Analysis of PM10 Trends in the United States from 1988 through 1995

ABSTRACT

Because the U. S. Environmental Protection Agency (EPA) has changed the National Ambient Air Quality Standards (NAAQS) for ambient particulate matter (PM), there is a great deal of interest in determining recent PM trends. This paper examines trends in PM₁₀ (i.e., particulate matter less than 10 micrometers in diameter) for areas of the United States based on their attainment status—for PM₁₀ and ozone nonattainment and attainment areas. The analysis also focuses on urban, suburban, and rural areas, and eastern and western areas. The time period of evaluation is from 1988 through 1995. To shed further light on the ambient PM₁₀ trends, trends in ambient SO₂, NO₂, and volatile organic compounds (VOCs) are also analyzed. Finally, trends in emission inventories of SO₂, NO_x, VOCs, and PM₁₀ are evaluated. Results of the analysis show that widespread and similar reductions in PM₁₀ levels have occurred over the last seven years. Annual reductions range from 3.0% to 3.8%, with the greatest reductions coming in PM₁₀ nonattainment areas, but with very significant reductions also in PM₁₀ attainment areas, ozone attainment areas, and rural areas. The widespread reductions appear to be due to a set of controls or common factors that are having a fairly uniform effect in all of the areas. The consistency of the reductions in different areas suggests that the reductions may also be primarily in the fine particles (i.e., those less than 2.5 micrometers in diameter, or PM_2.5), which are more readily transported than coarse particles.     

The impact of airborne particulate matter on pediatric hospital admissions for pneumonia among children in Jinan,China: A case-crossover study

This study aims to examine the effect of short-term changes in the concentration of particulate matter of diameter ≤2.5 µm (PM_2.5) and ≤10 µm (PM₁₀) on pediatric hospital admissions for pneumonia in Jinan, China. It explores confoundings factors of weather, season, and chemical pollutants. Information on pediatric hospital admissions for pneumonia in 2014 was extracted from the database of Jinan Qilu Hospital. The relative risk of pediatric hospital admissions for pneumonia was assessed using a case-crossover approach, controlling weather variables, day of the week, and seasonality. The single-pollutant model demonstrated that increased risk of pediatric hospital admissions for pneumonia was significantly associated with elevated PM_2.5 concentrations the day before hospital admission and elevated PM₁₀ concentrations 2 days before hospital admission. An increment of 10 μg/m³ in PM_2.5 and PM₁₀ was correlated with a 6% (95% CI 1.02–-1.10) and 4% (95% CI 1.00–1.08) rise in number of admissions for pneumonia, respectively. In two pollutant models, PM_2.5 and PM₁₀ remained significant after inclusion of sulfur dioxide or nitrogen dioxide but not carbon monoxide. This study demonstrated that short-term exposure to atmospheric particulate matter (PM_2.5/PM₁₀) may be an important determinant of pediatric hospital admissions for pneumonia in Jinan, China.

Implications: This study demonstrated that short-term exposure to atmospheric particulate matter (PM_2.5/PM₁₀) may be an important determinant of pediatric hospital admissions for pneumonia in Jinan, China, and suggested the relevance of pollutant exposure levels and their effects. As a specific group, children are sensitive to airborne particulate matter. This study estimated the short-term effects attribute to other air pollutants to provide references for relevant studies.     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

Marginal PM25: Nonlinear Aerosol Mass Response to Sulfate Reductions in the Eastern United States

ABSTRACT

Reductions in airborne sulfate concentration may cause inorganic fine particulate matter (PM₂₅) to respond nonlinearly, as nitric acid gas may transfer to the aerosol phase. Where this occurs, reductions in sulfur dioxide (SO₂) emissions will be much less effective than expected at reducing PM_2.5. As a measure of the efficacy of reductions in sulfate concentration on PM , we define marginal PM_2.5 as the local change in PM_2.5 resulting from a small change in sulfate concentration. Using seasonal-average conditions and assuming thermodynamic equilibrium, we find that the conditions for PM_2.5 to respond nonlinearly to sulfate reductions are common in the eastern United States in winter, occurring at half of the sites considered, and uncommon in summer, due primarily to the influence of temperature. Accounting for diurnal and intraseasonal variability, we find that seasonal-average conditions provide a reasonable indicator of the time-averaged PM_2.5 response. These results indicate that reductions in sulfate concentration may be up to 50% less effective at reducing the annual-average PM_2.5 than if the role of nitric acid is neglected. Further, large reductions in sulfate will also cause an increase in aerosol nitrate in many regions that are the most acidic.     

Particulate matter speciation profiles for light-duty gasoline vehicles in the United States

Representative profiles for particulate matter particles less than or equal to 2.5 µm (PM_2.5) are developed from the Kansas City Light-Duty Vehicle Emissions Study for use in the U.S. Environmental Protection Agency (EPA) vehicle emission model, the Motor Vehicle Emission Simulator (MOVES), and for inclusion in the EPA SPECIATE database for speciation profiles. The profiles are compatible with the inputs of current photochemical air quality models, including the Community Multiscale Air Quality Aerosol Module Version 6 (AE6). The composition of light-duty gasoline PM_2.5 emissions differs significantly between cold start and hot stabilized running emissions, and between older and newer vehicles, reflecting both impacts of aging/deterioration and changes in vehicle technology. Fleet-average PM_2.5 profiles are estimated for cold start and hot stabilized running emission processes. Fleet-average profiles are calculated to include emissions from deteriorated high-emitting vehicles that are expected to continue to contribute disproportionately to the fleet-wide PM_2.5 emissions into the future. The profiles are calculated using a weighted average of the PM_2.5 composition according to the contribution of PM_2.5 emissions from each class of vehicles in the on-road gasoline fleet in the Kansas City Metropolitan Statistical Area. The paper introduces methods to exclude insignificant measurements, correct for organic carbon positive artifact, and control for contamination from the testing infrastructure in developing speciation profiles. The uncertainty of the PM_2.5 species fraction in each profile is quantified using sampling survey analysis methods. The primary use of the profiles is to develop PM_2.5 emissions inventories for the United States, but the profiles may also be used in source apportionment, atmospheric modeling, and exposure assessment, and as a basis for light-duty gasoline emission profiles for countries with limited data.

Implications: PM_2.5 speciation profiles were developed from a large sample of light-duty gasoline vehicles tested in the Kansas City area. Separate PM_2.5 profiles represent cold start and hot stabilized running emission processes to distinguish important differences in chemical composition. Statistical analysis was used to construct profiles that represent PM_2.5 emissions from the U.S. vehicle fleet based on vehicles tested from the 2005 calendar year Kansas City metropolitan area. The profiles have been incorporated into the EPA MOVES emissions model, as well as the EPA SPECIATE database, to improve emission inventories and provide the PM_2.5 chemical characterization needed by CMAQv5.0 for atmospheric chemistry modeling.     

Investigation and modeling of the residential infiltration of fine particulate matter in Beijing,China

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM_2.5) and to develop models to predict PM_2.5 Finf in Beijing. Eighty-eight paired indoor–outdoor PM_2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM_2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM_2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM_2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM_2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM_2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification.

Implications: Failure to consider the differences between indoor and outdoor PM_2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM_2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM_2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM_2.5 of outdoor origin in epidemiological studies. Localized predictive models for PM_2.5 infiltration factor were also developed.     

Aerosol transport model evaluation of an extreme smoke episode in Southeast Asia

Biomass burning is one of many sources of particulate pollution in Southeast Asia, but its irregular spatial and temporal patterns mean that large episodes can cause acute air quality problems in urban areas. Fires in Sumatra and Borneo during September and October 2006 contributed to 24-h mean PM₁₀ concentrations above 150 μg m^?3 at multiple locations in Singapore and Malaysia over several days. We use the FLAMBE model of biomass burning emissions and the NAAPS model of aerosol transport and evolution to simulate these events, and compare our simulation results to 24-h average PM₁₀ measurements from 54 stations in Singapore and Malaysia. The model simulation, including the FLAMBE smoke source as well as dust, sulfate, and sea salt aerosol species, was able to explain 50% or more of the variance in 24-h PM₁₀ observations at 29 of 54 sites. Simulation results indicated that biomass burning smoke contributed to nearly all of the extreme PM₁₀ observations during September–November 2006, but the exact contribution of smoke was unclear because the model severely underestimated total smoke emissions. Using regression analysis at each site, the bias in the smoke aerosol flux was determined to be a factor of between 2.5 and 10, and an overall factor of 3.5 was estimated. After application of this factor, the simulated smoke aerosol concentration averaged 20% of observed PM₁₀, and 40% of PM₁₀ for days with 24-h average concentrations above 150 μg m^?3. These results suggest that aerosol transport models can aid analysis of severe pollution events in Southeast Asia, but that improvements are needed in models of biomass burning smoke emissions.