Comparison of real-time instruments and gravimetric method when measuring particulate matter in a residential building

This study used several real-time and filter-based aerosol instruments to measure PM_2.5 levels in a high-rise residential green building in the Northeastern US and compared performance of those instruments. PM_2.5 24-hr average concentrations were determined using a Personal Modular Impactor (PMI) with 2.5 µm cut (SKC Inc., Eighty Four, PA) and a direct reading pDR-1500 (Thermo Scientific, Franklin, MA) as well as its filter. 1-hr average PM_2.5 concentrations were measured in the same apartments with an Aerotrak Optical Particle Counter (OPC) (model 8220, TSI, Inc., Shoreview, MN) and a DustTrak DRX mass monitor (model 8534, TSI, Inc., Shoreview, MN). OPC and DRX measurements were compared with concurrent 1-hr mass concentration from the pDR-1500. The pDR-1500 direct reading showed approximately 40% higher particle mass concentration compared to its own filter (n = 41), and 25% higher PM_2.5 mass concentration compared to the PMI_2.5 filter. The pDR-1500 direct reading and PMI_2.5 in non-smoking homes (self-reported) were not significantly different (n = 10, R² = 0.937), while the difference between measurements for smoking homes was 44% (n = 31, R² = 0.773). Both OPC and DRX data had substantial and significant systematic and proportional biases compared with pDR-1500 readings. However, these methods were highly correlated: R² = 0.936 for OPC versus pDR-1500 reading and R² = 0.863 for DRX versus pDR-1500 reading. The data suggest that accuracy of aerosol mass concentrations from direct-reading instruments in indoor environments depends on the instrument, and that correction factors can be used to reduce biases of these real-time monitors in residential green buildings with similar aerosol properties.

Implications: This study used several real-time and filter-based aerosol instruments to measure PM_2.5 levels in a high-rise residential green building in the northeastern United States and compared performance of those instruments. The data show that while the use of real-time monitors is convenient for measurement of airborne PM at short time scales, the accuracy of those monitors depends on a particular instrument. Bias correction factors identified in this paper could provide guidance for other studies using direct-reading instruments to measure PM concentrations.     

PM2.5 characterization for time series studies: Pointwise uncertainty estimation and bulk speciation methods applied in Denver

Many studies have identified associations between adverse health effects and short-term exposure to particulate matter less than 2.5 μm in diameter (PM_2.5). These effects, however, are not consistent across geographical regions. This may be due in part to variations in the chemical make-up of PM_2.5 resulting from unique combinations of sources, both primary and secondary, in different regions. The Denver Aerosol Sources and Health (DASH) study is a multi-year time series study designed to characterize the daily chemical composition of PM_2.5 in Denver, identify the major contributing sources, and investigate associations between sources and a broad array of adverse health outcomes.Measurement methodology, field blank correction, pointwise uncertainty estimation and detection limit consideration are discussed in the context of bulk speciation for the DASH study. Results are presented for the first 4.5 years of mass, inorganic ion and bulk carbon speciation. The derived measurement uncertainties were propagated using the root sum of squares method and show good agreement with precision estimates derived from bi-weekly duplicate samples collected on collocated samplers. Gravimetric mass has the most uncertainty of any measurement and reconstructed mass generated from the sum of the individual species shows less uncertainty than measured mass on average. The methods discussed provide a good framework for PM_2.5 speciation measurements and are generalizable to analysis of other environmental measures.     

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.     

Monitoring and Source Apportionment of Particulate Matter near a Large Phosphorus Production Facility

ABSTRACT

A source apportionment study was conducted to identify sources within a large elemental phosphorus plant that contribute to exceedances of the National Ambient Air Quality Standards (NAAQS) for 24-hr PM₁₀. Ambient data were collected at three monitoring sites from October 1996 through July 1999, and included the following: 24-hr PM₁₀ mass, 24-hr PM_2.5 and PM_10–2.5 mass and chemistry, continuous PM₁₀and PM_2.5 mass, continuous meteorological data, and wind-direction-resolved PM_2.5 and PM₁₀ mass and chemistry. Ambient-based receptor modeling and wind-directional analysis were employed to help identify major sources or source locations and source contributions. Fine-fraction phosphate was the dominant species observed during PM₁₀ exceedances, though in general, re-suspended coarse dusts from raw and processed materials at the plant were also needed to create an exceedance. Major sources that were identified included the calciners, the CO flares, process-related dust, and electric-arc furnace operations.     

Fine particulate speciation profile and emission factor of municipal solid waste incinerator established by dilution sampling method

In this study, fine particulate matter (PM_2.5) emitted from a municipal solid waste incinerator (MSWI) was collected using dilution sampling method. Chemical compositions of the collected PM_2.5 samples, including carbon content, metal elements, and water-soluble ions, were analyzed. Traditional in-stack hot sampling was simultaneously conducted to compare the influences of dilution on PM_2.5 emissions and the characteristics of the bonded chemical species. The results, established by a dilution sampling method, show that PM_2.5 and total particulate matter (TPM) emission factors were 61.6 ± 4.52 and 66.1 ± 5.27 g ton-waste^?1, respectively. The average ratio of PM_2.5/TPM is 0.93, indicating that more than 90% of PM emission from the MSWI was fine particulate. The major chemical species in PM_2.5 included organic carbon (OC), Cl^?, NH₄⁺, elemental carbon (EC) and Si, which account for 69.7% of PM_2.5 mass. OC was from the unburned carbon in the exhaust, which adsorbed onto the particulate during the cooling process. High Cl^? emission is primarily attributable to wastes containing plastic bags made of polyvinyl chloride, salt in kitchen refuse and waste biomass, and so on. Minor species that account for 0.01–1% of PM_2.5 mass included SO₄^2-, K⁺, Na, K, NO₃^?, Al, Ca²⁺, Zn, Ca, Cu, Fe, Pb, and Mg. The mean ratio of dilution method/in-stack hot method was 0.454. The contents of water-soluble ions (Cl^?, SO₄^2-, NO₃^?) were significantly enriched in PM_2.5 via gas-to-particle conversion in the dilution process. Results indicate that in-stack hot sampling would underestimate levels of these species in PM_2.5.

Implications: PM_2.5 samples from a municipal solid waste incinerator (MSWI) were collected simultaneously by a dilution sampling technique and a traditional in-stack method. PM_2.5 emission factors and chemical speciation profiles were established. Dilution sampling provides more reliable data than in-stack hot sampling. The results can be applied to estimate the PM_2.5 emission inventories of MSWI, and the source profile can be used for contribution estimate of chemical mass balance modeling.     

U.S. National PM2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

The U.S. Environmental Protection Agency (EPA) initiated the national PM_2.5 Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range below 2.5 μm aerodynamic diameter (PM_2.5; fine particles). The network peaked at more than 260 sites in 2005. In response to the 1999 Regional Haze Rule and the need to better understand the regional transport of PM, EPA also augmented the long-existing Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility monitoring network in 2000, adding nearly 100 additional IMPROVE sites in rural Class 1 Areas across the country. Both networks measure the major chemical components of PM_2.5 using historically accepted filter-based methods. Components measured by both networks include major anions, carbonaceous material, and a series of trace elements. CSN also measures ammonium and other cations directly, whereas IMPROVE estimates ammonium assuming complete neutralization of the measured sulfate and nitrate. IMPROVE also measures chloride and nitrite. In general, the field and laboratory approaches used in the two networks are similar; however, there are numerous, often subtle differences in sampling and chemical analysis methods, shipping, and quality control practices. These could potentially affect merging the two data sets when used to understand better the impact of sources on PM concentrations and the regional nature and long-range transport of PM_2.5. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences in field and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN.

ImplicationsTwo long-term chemical speciation particle monitoring networks have operated simultaneously in the United States since 2001, when the EPA began regular operations of its PM_2.5 Chemical Speciation Monitoring Network (IMPROVE began in 1988). These networks use similar field sampling and analytical methods, but there are numerous, often subtle differences in equipment and methodologies that can affect the results. This paper describes these networks since 2000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researchers and policymakers with background information they may need (e.g., for 2018 PM_2.5 designation and State Implementation Plan process; McCarthy, 2013) to assess results from each network and decide how these data sets can be mutually employed for enhanced analyses. Changes in CSN and IMPROVE that have occurred over the years also are described.     

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.     

Windblown Dust Contributes to High PM25 Concentrations

ABSTRACT

The revised National Ambient Air Quality Standards for PM include fine particulate standards based upon mass measurements of PM₂₅. It is possible in arid and semi-arid regions to observe significant coarse mode intrusion in the PM_2.5 measurement. In this work, continuous PM₁₀, PM_2.5, and PM_1.0 were measured during several windblown dust events in Spokane, WA. PM_{2 5} constituted ~30% of the PM₁₀ during the dust event days, compared with ~48% on the non-dusty days preceding the dust events. Both PM₁₀ and PM_2.5 were enhanced during the dust events. However, PM_1.0 was not enhanced during dust storms that originated within the state of Washington. During a dust storm that originated in Asia and impacted Spokane, PM_1.0 was also enhanced, although the Asian dust reached Washington during a period of stagnation and poor dispersion, so that local sources were also contributing to high particulate levels. The “intermodal” region of PM, defined as particles ranging in aerodynamic size from 1.0 to 2.5 um, was found to represent a significant fraction of PM₂₅ (~51%) during windblown dust events, compared with 28% during the non-dusty days before the dust events.     

Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods

In studies of coarse particulate matter (PM_10-2.5), mass concentrations are often estimated through the subtraction of PM_2.5 from collocated PM₁₀ tapered element oscillating microbalance (TEOM) measurements. Though all field instruments have yet to be updated, the Filter Dynamic Measurement System (FDMS) was introduced to account for the loss of semivolatile material from heated TEOM filters. To assess errors in PM_10-2.5 estimation when using the possible combinations of PM₁₀ and PM_2.5 TEOM units with and without FDMS, data from three monitoring sites of the Colorado Coarse Rural–Urban Sources and Health (CCRUSH) study were used to simulate four possible subtraction methods for estimating PM_10-2.5 mass concentrations. Assuming all mass is accounted for using collocated TEOMs with FDMS, the three other subtraction methods were assessed for biases in absolute mass concentration, temporal variability, spatial correlation, and homogeneity. Results show collocated units without FDMS closely estimate actual PM_10-2.5 mass and spatial characteristics due to the very low semivolatile PM_10-2.5 concentrations in Colorado. Estimation using either a PM_2.5 or PM₁₀ monitor without FDMS introduced absolute biases of 2.4 µg/m³ (25%) to –2.3 µg/m³ (–24%), respectively. Such errors are directly related to the unmeasured semivolatile mass and alter measures of spatiotemporal variability and homogeneity, all of which have implications for the regulatory and epidemiology communities concerned about PM_10-2.5. Two monitoring sites operated by the state of Colorado were considered for inclusion in the CCRUSH acute health effects study, but concentrations were biased due to sampling with an FDMS-equipped PM_2.5 TEOM and PM₁₀ TEOM not corrected for semivolatile mass loss. A regression-based model was developed for removing the error in these measurements by estimating the semivolatile concentration of PM_2.5 from total PM_2.5 concentrations. By estimating nonvolatile PM_2.5 concentrations from this relationship, PM_10-2.5 was calculated as the difference between nonvolatile PM₁₀ and PM_2.5 concentrations.

Implications: Errors in the estimation of PM_10-2.5 concentrations using subtraction methods were shown to be related to the unmeasured semivolatile mass when using certain combinations of TEOM instruments. For the northeastern Colorado region, the absolute bias associated with this error significantly affects mean and 95th percentile values, which would affect assessment of compliance if PM_10-2.5 is regulated in the future. Estimating PM_10-2.5 mass concentrations using nonvolatile mass concentrations from collocated PM₁₀ and PM_2.5 TEOM monitors closely estimates the total PM_10-2.5 mass concentrations. A corrective model that removes the described error was developed and applied to data from two sites in Denver.

Supplemental Materials: Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association.     

Characterization of PM2.5 fugitive metal in the workplaces and the surrounding environment of a secondary aluminum smelter

Fugitive metal in PM_2.5 at the blast furnace (S₁), reverberatory furnace (S₂), and surrounding environment (S₀) of a secondary aluminum smelter (a secondary ALS) was studied. PM_2.5 mass concentration at the blast furnace exceeded that at the reverberatory furnace and this was especially apparent during operation, giving an early indication that the blast furnace is more important as a pollutant source. Further, PM_2.5 mass concentration levels and patterns at S₀ indicated that emissions from the blast furnace and reverberatory furnace were the major source of the observed fine particle pollution in the surrounding environment. Si and K were the main components and hence pollutants by mass in the PM_2.5 at S₁, S₂ and S₀ during both operation and non-operation. Hg was not detected in the PM_2.5 aerosol during smelter operation but was present at all three sampling locations during non-operation. This is due to the falling blast furnace and reverberatory furnace temperatures during non-operation which cause Hg vapor formed during operation to condense to form detectable Hg particles, and hence Hg contributes to the pollutant load during non-operation. Average S₁/S₀ and S₂/S₀ mass concentration ratios of 40.32 and 18.53, respectively, for all measured metals during operation and 7.83 and 5.73 for all measured metals during non-operation indicate that metal particulate pollution at the workplaces of secondary ALSs, particularly at the blast furnace during operation, is a serious issue. S₁/S₀ mass concentration ratios were higher still for Pb (62.22), Ti (113.40) and Ba (248.64), while the S₂/S₀ mass concentration ratio for Mo was 138.20. Principal component analyses produced a PC1 that explained 32.36–48.16% of the total variance during operation of the smelter and 47.86–69.Ten percent during non-operation. Their strong component loadings were mainly related to the fugitive PM_2.5 mass. Compared to atmospheric metal concentrations reported for other regions of the world, the toxic metals that have relatively higher concentrations in the secondary ALS emissions are Cr, Cd, Cu, As, Pb, Se, Al and Zn, especially during smelter operation. Concentrations of these toxic heavy metals are approximately 2–4 orders of magnitude higher than those reported for various industrial regions and metropolises with heavy traffic across the world.     

The Steubenville Comprehensive Air Monitoring Program (SCAMP): Overview and Statistical Considerations

Abstract

Average concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM_2.5) in Steubenville, OH, have decreased by more than 10 μg/m³ since the landmark Harvard Six Cities Study¹ associated the city’s elevated PM_2.5 concentrations with adverse health effects in the 1980s. Given the promulgation of a new National Ambient Air Quality Standard (NAAQS) for PM_2.5 in 1997, a current assessment of PM_2.5 in the Steubenville region is warranted. The Steubenville Comprehensive Air Monitoring Program (SCAMP) was conducted from 2000 through 2002 to provide such an assessment. The program included both an outdoor ambient air monitoring component and an indoor and personal air sampling component. This paper, which is the first in a series of four that will present results from the outdoor portion of SCAMP, provides an overview of the outdoor ambient air monitoring program and addresses statistical issues, most notably autocorrelation, that have been overlooked by many PM_2.5 data analyses. The average PM_2.5 concentration measured in Steubenville during SCAMP (18.4 μg/m³) was 3.4g/m³ above the annual PM_2.5 NAAQS. On average, sulfate and organic material accounted for ～31% and 25%, respectively, of the total PM_2.5 mass. Local sources contributed an estimated 4.6 μg/m³ to Steubenville’s mean PM_2.5 concentration. PM_2.5 and each of its major ionic components were significantly correlated in space across all pairs of monitoring sites in the region, suggesting the influence of meteorology and long-range transport on regional PM_2.5 concentrations. Statistically significant autocorrelation was observed among time series of PM_2.5 and component data collected at daily and 1-in-4-day frequencies during SCAMP. Results of spatial analyses that accounted for autocorrelation were generally consistent with findings from previous studies that did not consider autocorrelation; however, these analyses also indicated that failure to account for autocorrelation can lead to incorrect conclusions about statistical significance.     

Chemical mass balance source apportionment for combined PM2.5 measurements from U.S. non-urban and urban long-term networks

The Minnesota Particulate Matter 2.5 (PM_2.5) Source Apportionment Study was undertaken to explore the utility of PM_2.5 mass, element, ion, and carbon measurements from long-term speciation networks for pollution source attribution. Ambient monitoring data at eight sites across the state were retrieved from the archives of the Interagency Monitoring of Protected Visual Environments (IMPROVE) and the Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]) and analyzed by an Effective Variance – Chemical Mass Balance (EV-CMB) receptor model with region-specific geological source profiles developed in this study. PM_2.5 was apportioned into contributions of fugitive soil dust, calcium-rich dust, taconite (low grade iron ore) dust, road salt, motor vehicle exhaust, biomass burning, coal-fired utility, and secondary aerosol. Secondary sulfate and nitrate contributed strongly (49–71% of PM_2.5) across all sites and was dominant (≥60%) at IMPROVE sites. Vehicle exhausts accounted for 20–70% of the primary PM_2.5 contribution, largely exceeding the proportion in the primary PM_2.5 emission inventory. The diesel exhaust contribution was separable from the gasoline engine exhaust contribution at the STN sites. Higher detection limits for several marker elements in the STN resulted in non-detectable coal-fired boiler contributions which were detected in the IMPROVE data. Despite the different measured variables, analytical methods, and detection limits, EV-CMB results from a nearby IMPROVE-STN non-urban/urban sites showed similar contributions from regional sources – including fugitive dust and secondary aerosol. Seasonal variations of source contributions were examined and extreme PM_2.5 episodes were explained by both local and regional pollution events.     

Characterization of Particulate Matter in California

ABSTRACT

The size, composition, and concentration of particulate matter (PM) vary with location and time. Several monitoring/sampling programs are operated in California to characterize PM less than 2.5 and 10 µm in aerodynamic diameter (PM_2.5 and PM₁₀). This paper presents a broad summary of the spatial and temporal variations observed in ambient PM_2.5 and PM₁₀ concentrations in California. Many areas that have high PM₁₀ concentrations also have relatively high PM_2.5 concentrations, and data indicate that a significant portion of the PM₁₀ air quality problem is caused by PM_2.5. To develop effective plans for attaining the ambient PM standards, improved understanding of these unique problems is needed. Since 1989, pollution control efforts—whether specifically targeted for particulate matter or indirectly via controls on gaseous emissions—have caused annual average PM_2.5 and PM₁₀ concentrations to decline at most sites in California.     

Nitrate Artifacts during PM25 Sampling in the South Coast Air Basin of California

ABSTRACT

In February 1993, the South Coast Air Basin (SCAB) was redesignated as a “serious” nonattainment area for PM10. To improve the understanding and characterization of fine particulate matter in the SCAB, the South Coast Air Quality Management District (SCAQMD) initiated a comprehensive PM₁₀ Technical Enhancement Program (PTEP). Using enhanced PTEP monitors (specially designed multichannel/multifilter samplers), a one-year fine particulate matter (PM) monitoring program was initiated in January 1995. As part of the special monitoring program, nitric acid, ammonia, and speciated PM₁₀ and PM_2.5 concentrations were measured at five locations in the SCAB (downtown Los Angeles, Anaheim, Diamond Bar, Fontana, and Rubidoux) and at one background station (San Nicolas Island). The PM2.5 data are the first spatially resolved speciated data collected in the SCAB on an annual basis. Within the SCAB, where nitrate is a major component of PM_2.5, nitrate losses have been documented. The spatial and temporal variations of the nitrate losses during PM_2.5 sampling and the uncertainties of the nitrate losses are discussed. Significant losses occur at a low mass range, between 10 and 50 ìg/m³. Significant gains occur at an even lower mass range of less than 30 ìg/m³. On an annual average basis, nitrate losses vary between 1.25 and 2.32 ìg/m³ and the SCAB-wide average value of nitrate loss is 1.8 ìg/m³ based on five PTEP stations in the SCAB. The maximum nitrate losses for each station vary from 6.4 ìg/m³ to 22.5 ìg/m ³. Theoretical prediction of the sampling efficiency of the nitrate during PM_2.5 sam - pling was compared with the PTEP data. In general, theoretical prediction was in good agreement with measured values.     

Exploring relationships between outdoor air particulate-associated polycyclic aromatic hydrocarbon and PM2.5: A case study of benzo(a)pyrene in California metropolitan regions

Polycyclic aromatic hydrocarbons (PAHs) and particulate matter (PM) are co-pollutants emitted as by-products of combustion processes. Convincing evidence exists for PAHs as a primary toxic component of fine PM (PM_2.5). Because PM_2.5 is listed by the US EPA as a “Criteria Pollutant”, it is monitored regularly at sites nationwide. In contrast, very limited data is available on measured ambient air concentrations of PAHs. However, between 1999 and 2001, ambient air concentrations of PM_2.5 and benzo(a)pyrene (BaP) are available for California locations. We use multivariate linear regression models (MLRMs) to predict ambient air levels of BaP in four air basins based on reported PM_2.5 concentrations and spatial, temporal and meteorological variables as variates. We obtain an R² ranging from 0.57 to 0.72 among these basins. Significant variables (p<0.05) include the average daily PM_2.5 concentration, wind speed, temperature and relative humidity, and the coastal distance as well as season, and holiday or weekend. Combining the data from all sites and using only these variables to estimate ambient BaP levels, we obtain an R² of 0.55. These R²-values, combined with analysis of the residual error and cross validation using the PRESS-statistic, demonstrate the potential of our method to estimate reported outdoor air PAH exposure levels in metropolitan regions. These MLRMs provide a first step towards relating outdoor ambient PM_2.5 and PAH concentrations for epidemiological studies when PAH measurements are unavailable, or limited in spatial coverage, based on publicly available meteorological and PM_2.5 data.     

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.     

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.     

The Aerosol Research and Inhalation Epidemiology Study (ARIES): PM25 Mass and Aerosol Component Concentrations and Sampler Intercomparisons

ABSTRACT

The Aerosol Research and Inhalation Epidemiology Study (ARIES) was designed to provide high-quality measurements of PM₂₅, its components, and co-varying pollutants for an air pollution epidemiology study in Atlanta, GA.

Air pollution epidemiology studies have typically relied on available data on particle mass often collected using filter-based methods. Filter-based PM_2.5 sampling is susceptible to both positive and negative errors in the measurement of aerosol mass and particle-phase component concentrations in the undisturbed atmosphere. These biases are introduced by collection of gas-phase aerosol components on the filter media or by volatilization of particle phase components from collected particles. As part of the ARIES, we collected daily 24-hr PM_2.5 mass and speciation samples and continuous PM_2.5 data at a mixed residential-light industrial site in Atlanta. These data facilitate analysis of the effects of a wide variety of factors on sampler performance. We assess the relative importance of PM_2.5 components and consider associations and potential mechanistic linkages of PM_2.5 mass concentrations with several PM_2.5 components.

For the 12 months of validated data collected to date (August 1, 1998-July 31, 1999), the monthly average Federal Reference Method (FRM) PM_{2 5} mass always exceeded the proposed annual average standard (12-month average = 20.3 ± 9.5 ug/m³). The particulate SO₄ ^2- fraction (as (NH₄)₂SO₄) was largest in the summer and exceeded 50% of the FRM mass. The contribution of (NH₄)₂SO₄ to FRM PM_2.5 mass dropped to less than 30% in winter. Particu-late NO₃ ^- collected on a denuded nylon filter averaged 1.1 ± 0.9 ug/m³. Particle-phase organic compounds (as organic carbon × 1.4) measured on a denuded quartz filter sampler averaged 6.4 ± 3.1 ug/m³ (32% of FRM PM_{2 5} mass) with less seasonal variability than SO₄ ^2-.     

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.     

Spatial differences in ambient coarse and fine particles in the Monterrey metropolitan area,Mexico: Implications for source contribution

The ambient air of the Monterrey Metropolitan Area (MMA) in Mexico frequently exhibits high levels of PM₁₀ and PM_2.5. However, no information exists on the chemical composition of coarse particles (PM_c = PM₁₀ – PM_2.5). A monitoring campaign was conducted during the summer of 2015, during which 24-hr average PM₁₀ and PM_2.5 samples were collected using high-volume filter-based instruments to chemically characterize the fine and coarse fractions of the PM. The collected samples were analyzed for anions (Cl^–, NO₃^–, SO₄^2–), cations (Na⁺, NH₄⁺, K⁺), organic carbon (OC), elemental carbon (EC), and 35 trace elements (Al to Pb). During the campaign, the average PM_2.5 concentrations did not showed significance differences among sampling sites, whereas the average PM_c concentrations did. In addition, the PM_c accounted for 75% to 90% of the PM₁₀ across the MMA. The average contribution of the main chemical species to the total mass indicated that geological material including Ca, Fe, Si, and Al (45%) and sulfates (11%) were the principal components of PM_c, whereas sulfates (54%) and organic matter (30%) were the principal components of PM_2.5. The OC-to-EC ratio for PM_c ranged from 4.4 to 13, whereas that for PM_2.5 ranged from 3.97 to 6.08. The estimated contribution of Secondary Organic Aerosol (SOA) to the total mass of organic aerosol in PM_2.5 was estimated to be around 70–80%; for PM_c, the contribution was lower (20–50%). The enrichment factors (EF) for most of the trace elements exhibited high values for PM_2.5 (EF: 10–1000) and low values for PM_c (EF: 1–10). Given the high contribution of crustal elements and the high values of EFs, PM_c is heavily influenced by soil resuspension and PM_2.5 by anthropogenic sources. Finally, the airborne particles found in the eastern region of the MMA were chemically distinguishable from those in its western region.

Implications: Concentration and chemical composition patterns of fine and coarse particles can vary significantly across the MMA. Public policy solutions have to be built based on these observations. There is clear evidence that the spatial variations in the MMA’s coarse fractions are influenced by clearly recognizable primary emission sources, while fine particles exhibit a homogeneous concentration field and a clear spatial pattern of increasing secondary contributions. Important reductions in the coarse fraction can come from primary particles’ emission controls; for fine particles, control of gaseous precursors—particularly sulfur-containing species and organic compounds—should be considered.