Spatial, temporal, and interspecies patterns in fine particulate matter in Texas

The Big Bend Regional Aerosol and Visibility Observational (BRAVO) field study was conducted from July to October 1999 and was followed by several years of modeling and data analyses to examine the causes of haze at Big Bend National Park TX (BBNP). During BRAVO, daily speciated fine (diameter <2.5 microm) particulate concentrations were measured at 37 sites throughout Texas. At the primary receptor site, K-Bar Ranch, there were many additional measurements including a "high-sensitivity" version of the 24-hr fine particulate elemental data. The spatial, temporal, and interspecies patterns in these data are examined here to qualitatively investigate source regions and source types influencing the fine particulate concentrations in Texas with an emphasis on sources of sulfates, the largest contributor to fine mass and light extinction. Peak values of particulate sulfur (S) varied spatially and seasonally. Maximum S was in Northeast Texas during the summer, whereas peak S at BBNP was in the fall. Sulfate acidity at BBNP also varied by month. Sources of Se were evident in Northeast Texas and from the Carbón I and II plants. High S episodes at BBNP during BRAVO had several different trace element characteristics. Carbon concentrations at BBNP during BRAVO were probably mostly urban-related, with arrival from the Houston area likely. The Houston artificial tracer released during the second half of BRAVO was highly correlated with some carbon fractions. There was evidence of the influence of African dust at sites throughout Texas during the summer. Patterns in several trace elements were also examined. Vanadium was associated with air masses from Mexico. Lead concentrations in southern Texas have dropped dramatically over the past several years.     

Estimated hourly personal exposures to ambient and nonambient particulate matter among sensitive populations in Seattle, Washington

Epidemiological studies of particulate matter (PM) routinely use concentrations measured with stationary outdoor monitors as surrogates for personal exposure. Despite the frequently reported poor correlations between ambient concentrations and total personal exposure, the epidemiologic associations between ambient concentrations and health effects depend on the correlation between ambient concentrations and personal exposure to ambient-generated PM. This paper separates personal PM exposure into ambient and nonambient components and estimates the outdoor contribution to personal PM exposures with continuous light scattering data collected from 38 subjects in Seattle, WA. Across all subjects, the average exposure encountered indoors at home was lower than in all other microenvironments. Cooking and being at school were associated with elevated levels of exposure. Previously published estimates of particle infiltration (Finf) were combined with time-location data to estimate an ambient contribution fraction (alpha, mean = 0.66+/-0.21) for each subject. The mean alpha was significantly lower for subjects monitored during the heating season (0.55+/-0.16) than for those monitored during the nonheating season (0.80+/-0.17). Our modeled alpha estimates agreed well with those estimated with the sulfur-tracer method (slope = 1.08; R2 = 0.67). We modeled exposure to ambient and nonambient PM with both continuous light scattering and 24-hr gravimetric data and found good agreement between the two methods. On average, ambient particles accounted for 48% of total personal exposure (range = 21-80%). The personal activity exposure was highly influenced by time spent away from monitored microenvironments. The median hourly longitudinal correlation between central site concentrations and personal exposures was 0.30. Although both alpha and the nonambient sources influence the personal-central relationship, the latter seems to dominate. Thus, total personal exposure may be poorly predicted by stationary outdoor monitors, particularly among persons whose PM exposure is dominated by nonambient exposures, for example, those living in tightly sealed homes, those who cook, and children.     

Spatial and temporal variability in urban fine particulate matter concentrations

Identification of hot spots for urban fine particulate matter (PM(2.5)) concentrations is complicated by the significant contributions from regional atmospheric transport and the dependence of spatial and temporal variability on averaging time. We focus on PM(2.5) patterns in New York City, which includes significant local sources, street canyons, and upwind contributions to concentrations. A literature synthesis demonstrates that long-term (e.g., one-year) average PM(2.5) concentrations at a small number of widely-distributed monitoring sites would not show substantial variability, whereas short-term (e.g., 1-h) average measurements with high spatial density would show significant variability. Statistical analyses of ambient monitoring data as a function of wind speed and direction reinforce the significance of regional transport but show evidence of local contributions. We conclude that current monitor siting may not adequately capture PM(2.5) variability in an urban area, especially in a mega-city, reinforcing the necessity of dispersion modeling and methods for analyzing high-resolution monitoring observations.     

Sources of fine urban particulate matter in Detroit, MI

Data from the speciation trends network (STN) was used to evaluate the amount and temporal patterns of particulate matter originating from local industrial sources and long-range transport at two sites in Detroit, MI: Allen Park, MI, southwest of both Detroit and the areas of heavy industrial activity; Dearborn, MI, located on the south side of Detroit near the most heavily industrialized region. Using positive matrix factorization (PMF) and comparing source contributions at Allen Park to those in Dearborn, contributions made by local industrial sources (power plants, coke refineries, iron smelting, waste incineration), local area sources (automobile and diesel truck) and long range sources of PM(2.5) can be distinguished in greater Detroit. Overall, the mean mass concentration measured at Dearborn was 19% higher than that measured at Allen Park. The mass at Allen Park was apportioned as: secondary sulfate 31%, secondary nitrate 28%, soil 8%, mixed aged sea and road salts 4%, gasoline 15%, diesel 4%, and biomass burning 3%. At Dearborn the mass was apportioned as: secondary sulfate 25%, secondary nitrate 20%, soil 12%, mixed aged sea and road salts 4%, gasoline 20%, diesel 8%, iron and steel, 5%, and mixed industrial 7%. The impact of the iron and steel, soil, and mixed aged sea and road salt was much higher at the Dearborn site than at the Allen Park site, suggesting that close proximity to a local industrial complex has a direct negative impact on local air quality.     

Transport of atmospheric fine particulate matter: part 2--findings from recent field programs on the intraurban variability in fine particulate matter

Air quality field data, collected as part of the fine particulate matter Supersites Program and other field measurements programs, have been used to assess the degree of intraurban variability for various physical and chemical properties of ambient fine particulate matter. Spatial patterns vary from nearly homogeneous to quite heterogeneous, depending on the city, parameter of interest, and the approach or method used to define spatial variability. Secondary formation, which is often regional in nature, drives fine particulate matter mass and the relevant chemical components toward high intraurban spatial homogeneity. Those particulate matter components that are dominated by primary emissions within the urban area, such as black carbon and several trace elements, tend to exhibit greater spatial heterogeneity. A variety of study designs and data analysis approaches have been used to characterize intraurban variability. High temporal correlation does not imply spatial homogeneity. For example, there can be high temporal correlation but with spatial heterogeneity manifested as smooth spatial gradients, often emanating from areas of high emissions such as the urban core or industrial zones.     

Determination of levoglucosan in atmospheric fine particulate matter

A microanalytical method suitable for the quantitative determination of the sugar anhydride levoglucosan in low-volume samples of atmospheric fine particulate matter (PM) has been developed and validated. The method incorporates two sugar anhydrides as quality control standards. The recovery standard sedoheptulosan (2,7-anhydro-beta-D-altro-heptulopyranose) in 20 microL solvent is added onto samples of the atmospheric fine PM and aged for 1 hr before ultrasonic extraction with ethylacetate/ triethylamine. The extract is reduced in volume, an internal standard is added (1,5-anhydro-D-mannitol), and a portion of the extract is derivatized with 10% by volume N-trimethylsilylimidazole. The derivatized extract is analyzed by gas chromatography/mass spectrometry (GC/MS). The recovery of levoglucosan using this procedure was 69 +/- 6% from five filters amended with 2 microg levoglucosan, and the reproducibility of the assay is 9%. The limit of detection is approximately 0.1 microg/mL, which is equivalent to approximately 3.5 ng/m3 for a 10 L/min sampler or approximately 8.7 ng/m3 for a 4 L/min personal sampler (assuming 24-hr integrated samples). We demonstrated that levoglucosan concentrations in collocated samples (expressed as ng/m3) were identical irrespective of whether samples were collected by PM with aerodynamic diameter < or = 2.5 microm or PM with aerodynamic diameter < or = 10 microm impactors. It was also demonstrated that X-ray fluorescence analysis of samples of atmospheric PM, before levoglucosan determinations, did not alter the levels of levoglucosan.     

Mesoscale modeling of wintertime particulate matter episodes in eastern Washington, USA

The Spokane, Washington area is classified as a non-attainment area for the 24-h PM₁₀ standard due to a history of high particulate matter concentrations. A Eulerian regional air quality model (CALMET/CALGRID) has been used to characterize the emission, transport and dispersion of PM₁₀ and PM_2.5 in Spokane. Observations from a residential site (Rockwood, RW) and an industrial site (Crown Zellerbach, CZ), spanning July 1994–August 1996 were used to evaluate the current emission inventory. Two major tasks were devised to conduct the objectives of this investigation. First, a simple and efficient urban dispersion model (WYNDValley) was used to simulate important episodes characterized by the highest PM₁₀ and PM_2.5 concentrations. The selected episodes included four days with wet conditions for which no roads would have been emitting and seven days with dry conditions for which roads would emit. In the second step, a single road-emitting event was selected from the previous predicted results for further analysis using the Eulerian regional air quality model to examine the emission inventory. The urban and regional models predicted the observed concentration distributions reasonably well for the source emissions inventoried in Spokane. The mass concentrations of PM₁₀ were well predicted for the roads emitting case examined by both models indicating that the emission inventory based primarily upon area sources including roads is reasonably well characterized, at least at the RW site. The area sources around CZ are less well characterized, so that the PM₁₀ concentrations are underpredicted at CZ. The models appear unable to reach an equilibrium mass balance status at the beginning of the simulation, and the urban model seems unable to properly resolve the nocturnal boundary layer.     

Molecular composition of organic fine particulate matter in Houston,TX

Organic fine particulate matter collected in Houston, TX between March 1997 and March 1998 was analyzed to determine the concentration of individual organic compounds. Samples from four sites were analyzed including two industrial locations (Houston Regional Monitoring Corporation (HRM-3) site in Channelview and Clinton Drive site near the Ship Channel Turning Basin), one suburban location (Bingle Drive site in Northwest Houston) and one background site (Galveston Island). At the three urban locations, samples were divided into three seasonal sample aggregates (spring, summer and winter), while at the background site a single annual average sample pool was used. Between 10 and 16 individual samples were pooled to get aggregate samples with enough organic carbon mass for analysis. Overall, 82 individual organic compounds were quantified. These include molecular markers which are compounds unique to specific fine particle sources and can be used to track the relative contribution of source emissions to ambient fine particle levels. The differences both spatially and temporally in these tracers can be used to evaluate the variability in emission source strengths.     

Trends in speciated fine particulate matter and visibility across monitoring networks in the Southeastern United States

Trends in fine particulate matter <2.5 microm in diameter (PM2.5) and visibility in the Southeastern United States were evaluated for sites in the Interagency Monitoring of Protected Visual Environments, Speciated Trends Network, and Southeastern Aerosol Research and Characterization Study networks. These analyses are part of the technical assessment by Visibility Improvement-State and Tribal Association of the Southeast (VISTAS), the regional planning organization for the southeastern states, in support of State Implementation Plans for the regional haze rule. At all of the VISTAS IMPROVE sites, ammonium sulfate and organic carbon (OC) are the largest and second largest contributors, respectively, to light extinction on both the 20% haziest and 20% clearest days. Ammonium nitrate, elemental carbon (EC), soils, and coarse particles make comparatively small contributions to PM2.5 mass and light extinction on most days at the Class I areas. At Southern Appalachian sites, the 20% haziest days occur primarily in the late spring to fall, whereas at coastal sites, the 20% haziest days can occur through out the year. Levels of ammonium sulfate in Class I areas are similar to those in nearby urban areas and are generally higher at the interior sites than the coastal sites. Concentrations of OC, ammonium nitrate, and, sometimes, EC, tend to be higher in the urban areas than in nearby Class I areas, although differences in measurement methods complicate comparisons between networks. Results support regional controls of sulfur dioxide for both regional haze and PM2.5 implementation and suggest that controls of local sources of OC, EC, or nitrogen oxides might also be considered for urban areas that are not attaining the annual National Ambient Air Quality Standard for PM2.5.     

The Steubenville comprehensive air monitoring program (SCAMP): associations among fine particulate matter, co-pollutants, and meteorological conditions

We determined 24-hr average ambient concentrations of PM2.5 and its ionic and carbonaceous components in Steubenville, OH, between May 2000 and May 2002. We also determined daily average gaseous co-pollutant concentrations, meteorological conditions, and pollen and mold spore counts. Data were analyzed graphically and by linear regression and time series models. Multiple-day episodes of elevated fine particulate matter (PM2.5) concentrations often occurred during periods of locally high temperature (especially during summer), high pressure, or low wind speed (especially during winter) and generally ended with the passage of a frontal system. After removing autocorrelation, we observed statistically significant positive associations between concentrations of PM2.5 and concentrations of CO, NOx, and SO2. Associations with NOx and CO exhibited significant seasonal dependencies, with the strongest correlations during fall and winter. NOx, CO, SO2, O3, temperature, relative humidity, and wind speed were all significant predictors of PM2.5 concentration in a time-series model with external regressors, which successfully accounted for 79% of the variance in log-transformed daily PM2.5 concentrations. Coefficient estimates for NOx and temperature varied significantly by season. The results provide insight that may be useful in the development of future PM2.5 reduction strategies for Steubenville. Additionally, they demonstrate the need for PM epidemiology studies in Steubenville (and elsewhere) to carefully consider the potential confounding effects of gaseous co-pollutants, such as CO and NOx, and their seasonally dependent associations with PM2.5.     

Measurement, analysis, and modeling of fine particulate matter in eastern North Carolina

An analysis of fine particulate data in eastern North Carolina was conducted to investigate the impact of the hog industry and its emissions of ammonia into the atmosphere. The fine particulate data are simulated using ISORROPIA, an equilibrium thermodynamic model that simulates the gas and aerosol equilibrium of inorganic atmospheric species. The observational data analyses show that the major constituents of fine particulate matter (PM2.5) are organic carbon, elemental carbon, sulfate, nitrate, and ammonium. The observed PM2.5 concentration is positively correlated with temperature but anticorrelated with wind speed. The correlation between PM2.5 and wind direction at some locations suggests an impact of ammonia emissions from hog facilities on PM2.5 formation. The modeled results are in good agreement with observations, with slightly better agreement at urban sites than at rural sites. The predicted total inorganic particulate matter (PM) concentrations are within 5% of the observed values under conditions with median initial total PM species concentrations, median relative humidity (RH), and median temperature. Ambient conditions with high PM precursor concentrations, low temperature, and high RH appear to favor the formation of secondary PM.     

Nonlinear regression adjustments of multiple continuous monitoring methods produce effective characterization of short-term fine particulate matter

This study comprehensively characterizes hourly fine particulate matter (PM(2.5)) concentrations measured via a tapered element oscillating microbalance (TEOM), beta-gauge, and nephelometer from four different monitoring sites in U.S. Environment Protection Agency (EPA) Region 5 (in U.S. states Illinois, Michigan, and Wisconsin) and compares them to the Federal Reference Method (FRM). Hourly characterization uses time series and autocorrelation. Hourly data are compared with FRM by averaging across 24-hr sampling periods and modeling against respective daily FRM concentrations. Modeling uses traditional two-variable linear least-squares regression as well as innovative nonlinear regression involving additional meteorological variables such as temperature and humidity. The TEOM shows a relationship with season and temperature, linear correlation as low as 0.7924 and nonlinear model correlation as high as 0.9370 when modeled with temperature. The beta-gauge shows no relationship with season or meteorological variables. It exhibits a linear correlation as low as 0.8505 with the FRM and a nonlinear model correlation as high as 0.9339 when modeled with humidity. The nephelometer shows no relationship with season or temperature but a strong relationship with humidity is observed. A linear correlation as low as 0.3050 and a nonlinear model correlation as high as 0.9508 is observed when modeled with humidity. Nonlinear models have higher correlation than linear models applied to the same dataset. This correlation difference is not always substantial, which may introduce a tradeoff between simplicity of model and degree of statistical association. This project shows that continuous monitor technology produces valid PM(2.5) characterization, with at least partial accounting for variations in concentration from gravimetric reference monitors once appropriate nonlinear adjustments are applied. Although only one regression technically meets new EPA National Ambient Air Quality Standards (NAAQS) Federal Equivalent Method (FEM) correlation coefficient criteria, several others are extremely close, showing optimistic potential for use of this nonlinear adjustment model in garnering EPA NAAQS FEM approval for continuous PM(2.5) sampling methods.     

Source contributions to fine particulate matter in an urban atmosphere

This paper proposes a practical method for estimating source attribution by using a three-step methodology. The main objective of this study is to explore the use of the three-step methodology for quantifying the source impacts of 24-h PM2.5 particles at an urban site in Seoul, Korea. 12-h PM2.5 samples were collected and analyzed for their elemental composition by ICP-AES/ICP-MS/AAS to generate the source composition profiles. In order to assess the daily average PM2.5 source impacts, 24-h PM2.5 and polycyclic aromatic hydrocarbons (PAH) ambient samples were simultaneously collected at the same site. The PM2.5 particle samples were then analyzed for trace elements. Ionic and carbonaceous species concentrations were measured by ICP-AES/ICP-MS/AAS, IC, and a selective thermal MnO2 oxidation method. The 12-h PM2.5 chemical data was used to estimate possible source signatures using the principal component analysis (PCA) and the absolute principal component scores method followed by the multiple linear regression analysis. The 24-h PM2.5 source categories were extracted with a combination of PM2.5 and some PAH chemical data using the PCA, and their quantitative source contributions were estimated by chemical mass balance (CMB) receptor model using the estimated source profiles and those in the literature. The results of PM2.5 source apportionment using the 12-h derived source composition profiles show that the CMB performance indices; chi2, R2, and percent of mass accounted for are 2.3%, 0.97%, and 100.7%, which are within the target range specified. According to the average PM2.5 source contribution estimate results, motor vehicle exhaust was the major contributor at the sampling site, contributing 26% on average of measured PM2.5 mass (41.8 microg m-3), followed by secondary sulfate (23%) and nitrate (16%), refuse incineration (15%), soil dust (13%), field burning (4%), oil combustion (2.7%), and marine aerosol (1.3%). It can be concluded that quantitative source attribution to PM2.5 in an urban area where source profiles have not been developed can be estimated using the proposed three-step methodology approach.     

Source apportionment of fine particulate matter in Phoenix, AZ, using positive matrix factorization

Speciated particulate matter (PM)2.5 data collected as part of the Interagency Monitoring of Protected Visual Environments (IMPROVE) program in Phoenix, AZ, from April 2001 through October 2003 were analyzed using the multivariate receptor model, positive matrix factorization (PMF). Over 250 samples and 24 species were used, including the organic carbon and elemental carbon analytical temperature fractions from the thermal optical reflectance method. A two-step approach was used. First, the species excluding the carbon fractions were used, and initially eight factors were identified; non-soil potassium was calculated and included to better refine the burning factor. Next, the mass associated with the burning factor was removed, and the data set rerun with the carbon fractions. Results were very similar (i.e., within a few percent), but this step enabled a separation of the mobile factor into gasoline and diesel vehicle emissions. The identified factors were burning (on average 2% of the mass), secondary transport (7%), regional power generation (13%), dust (25%), nitrate (9%), industrial As/Pb/Se (2%), Cu/Ni/V (7%), diesel (9%), and general mobile (26%). The overall contribution from mobile sources also increased, as some mass (OC and nitrate) from the nitrate and regional power generation factors were apportioned with the mobile factors. This approach allowed better apportionment of carbon as well as total mass. Additionally, the use of multiple supporting analyses, including air mass trajectories, activity trends, and emission inventory information, helped increase confidence in factor identification.     

Temporal variations of fine and coarse particulate matter sources in Jeddah,Saudi Arabia

This study provides the first comprehensive analysis of the seasonal variations and weekday/weekend differences in fine (aerodynamic diameter <2.5 μm; PM_2.5) and coarse (aerodynamic diameter 2.5–10 μm; PM_2.5–10) particulate matter mass concentrations, elemental constituents, and potential source origins in Jeddah, Saudi Arabia. Air quality samples were collected over 1 yr, from June 2011 to May 2012 at a frequency of three times per week, and analyzed. The average mass concentrations of PM_2.5 (21.9 μg/m³) and PM₁₀ (107.8 μg/m³) during the sampling period exceeded the recommended annual average levels by the World Health Organization (WHO) for PM_2.5 (10 μg/m³) and PM₁₀ (20 μg/m³), respectively. Similar to other Middle Eastern locales, PM_2.5–10 is the prevailing mass component of atmospheric particulate matter at Jeddah, accounting for approximately 80% of the PM₁₀ mass. Considerations of enrichment factors, absolute principal component analysis (APCA), concentration roses, and backward trajectories identified the following source categories for both PM_2.5 and PM_2.5–10: (1) soil/road dust, (2) incineration, and (3) traffic; and for PM_2.5 only, (4) residual oil burning. Soil/road dust accounted for a major portion of both the PM_2.5 (27%) and PM_2.5–10 (77%) mass, and the largest source contributor for PM_2.5 was from residual oil burning (63%). Temporal variations of PM_2.5–10 and PM_2.5 were observed, with the elevated concentration levels observed for mass during the spring (due to increased dust storm frequency) and on weekdays (due to increased traffic). The predominant role of windblown soil and road dust in both the PM_2.5 and PM_2.5–10 masses in this city may have implications regarding the toxicity of these particles versus those in the Western world where most PM health assessments have been made in the past. These results support the need for region-specific epidemiological investigations to be conducted and considered in future PM standard setting.

Implications: Temporal variations of fine and coarse PM mass, elemental constituents, and sources were examined in Jeddah, Saudi Arabia, for the first time. The main source of PM_2.5–10 is natural windblown soil and road dust, whereas the predominant source of PM_2.5 is residual oil burning, generated from the port and oil refinery located west of the air sampler, suggesting that targeted emission controls could significantly improve the air quality in the city. The compositional differences point to a need for health effect studies to be conducted in this region, so as to directly assess the applicability of the existing guidelines to the Middle East air pollution.     

Characterization of visibility impacts related to fine particulate matter in Canada

Canada has recently established standards for the management of particulate matter (PM) air quality. National networks currently measure PM mass concentrations and chemical speciation. Methods used in the U.S. IMPROVE network are applied to the 1994--2000 Canadian fine PM data to obtain a regional reconstruction of the visibility based on particle composition. Nationally, the greatest light extinction occurs in the Windsor-Quebec City corridor. Variations in the dominant chemical species responsible for the reduction in visibility are presented for regions across the country. In most regions, sulfate and nitrate contribute most greatly to reduced visibility. The visibility implications of achieving the Canada-Wide Standard (CWS) across the country are evaluated, with the greatest improvement in visibility associated with achieving the CWS in southern Ontario. Elsewhere in the country, achieving the CWS will actually result in deteriorating air quality. Improving current estimates of visibility requires higher spatially and temporally resolved measurements of organic and elemental carbon fractions and particulate nitrate.     

Source apportionment of airborne fine particulate matter in an underground mine

The chemical mass balance source apportionment technique was applied to an underground gold mine to assess the contribution of diesel exhaust, rock dust, oil mists, and cigarette smoke to airborne fine (<2.5 microm) particulate matter (PM). Apportionments were conducted in two locations in the mine, one near the mining operations and one near the exit of the mine where the ventilated mine air was exhausted. Results showed that diesel exhaust contributed 78-98% of the fine particulate mass and greater than 90% of the fine particle carbon, with rock dust making up the remainder. Oil mists and cigarette smoke contributions were below detection limits for this study. The diesel exhaust fraction of the total fine PM was higher than the recently implemented mine air quality standards based on total carbon at both sample locations in the mine.     

Ambient fine particulate matter in China: Its negative impacts and possible countermeasures

In recent decades, China has experienced rapid economic development accompanied by increasing concentrations of ambient PM_2.5, particulate matter of less than 2.5 μm in diameter. PM_2.5 is now believed to be a carcinogen, causing higher lung cancer risks and generating losses to the economy and society. This meta-analysis evaluates the losses generated by ambient PM_2.5 in Suzhou from 2014 to 2016 and predicts losses at different concentrations. Estimations of total losses in Beijing, Shanghai, Hangzhou, Guangzhou, Dalian, and Xiamen are also presented, with a total national loss in 2015. The authors then demonstrate that lowering ambient PM_2.5 concentrations would be a realistic way for China to reduce the evaluated social losses in the short term. Possible legal measures are listed for lowering ambient PM_2.5 concentrations.

Implications: The present findings quantify the economic effects of ambient PM_2.5 due to the increased incidence rate and mortality rate of lung cancer. Lowering ambient PM_2.5 concentrations would be the most realistic way for China to reduce tghe evaluated social losses in the short term. Possible legal measures for lowering ambient PM_2.5 concentrations to reduce the total losses are identified.     

Source apportionment of fine particulate matter in the southeastern United States

Particulate matter (PM) less than 2.5 microm in size (PM2.5) source apportionment by chemical mass balance receptor modeling was performed to enhance regional characterization of source impacts in the southeastern United States. Secondary particles, such as NH4HSO4, (NH4)2SO4, NH4NO3, and secondary organic carbon (OC) (SOC), formed by atmospheric photochemical reactions, contribute the majority (>50%) of ambient PM2.5 with strong seasonality. Source apportionment results indicate that motor vehicle and biomass burning are the two main primary sources in the southeast, showing relatively more motor vehicle source impacts rather than biomass burning source impacts in populated urban areas and vice versa in less urbanized areas. Spatial distributions of primary source impacts show that each primary source has distinctively different spatial source impacts. Results also find impacts from shipping activities along the coast. Spatiotemporal correlations indicate that secondary particles are more regionally distributed, as are biomass burning and dust, whereas impacts of other primary sources are more local.     

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.