Chemical reactivities of ambient air samples in three Southern California communities

The potential adverse health effects of PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm) and vapor samples from three communities that neighbor railyards, Commerce (CM), Long Beach (LB), and San Bernardino (SB), were assessed by determination of chemical reactivities attributed to the induction of oxidative stress by air pollutants. The assays used were dithiothreitol (DTT)- and dihydrobenzoic acid (DHBA)-based procedures for prooxidant content and a glyceraldehyde-3-phosphate dehydrogenase (GAPDH) assay for electrophiles. Prooxidants and electrophiles have been proposed as the reactive chemical species responsible for the induction of oxidative stress by air pollution mixtures. The PM_2.5 samples from CM and LB sites showed seasonal differences in reactivities, with higher levels in the winter, whereas the SB sample differences were reversed. The reactivities in the vapor samples were all very similar, except for the summer SB samples, which contained higher levels of both prooxidants and electrophiles. The results suggest that the observed reactivities reflect general geographical differences rather than direct effects of the railyards. Distributional differences in reactivities were also observed, with PM_2.5 fractions containing most of the prooxidants (74–81%) and the vapor phase most of the electrophiles (82–96%). The high levels of the vapor-phase electrophiles and their potential for adverse biological effects point out the importance of the vapor phase in assessing the potential health effects of ambient air.

Implications:?PM_2.5 and its corresponding vapor phase, containing semivolatile organics, were collected in three communities in the Los Angeles Basin and examined with toxicologically relevant chemical assays. The PM_2.5 phase contained most of the prooxidants and the vapor phase contained most of the electrophiles, whose content was highest in summer samples from a receptor site that reflected greater photochemical processing of the air parcel during its transport. As electrophiles initiate both adverse and adaptive responses to foreign substances by biological systems, their presence in the vapor phase emphasizes the importance of this phase in the overall health effects of ambient air.     

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.     

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

An enhanced PM_2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM_2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM_2.5 air quality is more likely to be critical for human health. The enhanced PM_2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM_2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM_2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.     

Source-to-receptor pathways of anthropogenic PM2.5 in Detroit,Michigan: Comparison of two inhalation exposure studies

Recent studies have attributed toxic effects of ambient fine particulate matter (aerodynamic diameter ≤ 2.5 μm; PM_2.5) to physical and/or chemical properties rather than total mass. However, identifying specific components or sources of a complex mixture of ambient PM_2.5 that are responsible for adverse health effects is still challenging. In order to improve our understanding of source-to-receptor pathways for ambient PM_2.5 (links between sources of ambient PM_2.5 and measures of biologically relevant dose), integrated inhalation toxicology studies using animal models and concentrated air particles (CAPs) were completed in southwest Detroit, a community where the pediatric asthma rate is more than twice the national average. Ambient PM_2.5 was concentrated with a Harvard fine particle concentrator housed in AirCARE1, a mobile air research laboratory which facilitates inhalation exposure studies in real-world settings. Detailed characterizations of ambient PM_2.5 and CAPs, identification of major emission sources of PM_2.5, and quantification of trace elements in the lung tissues of laboratory rats that were exposed to CAPs for two distinct 3-day exposure periods were completed.This paper describes the physical/chemical properties and sources of PM_2.5, pulmonary metal concentrations and meteorology from two different 3-day exposure periods—both conducted at the southwest Detroit location in July 2003—which resulted in disparate biological effects. More specifically, during one of the exposure periods, ambient PM_2.5-derived trace metals were recovered from lung tissues of CAPs-exposed animals, and these metals were linked to local combustion point sources in southwest Detroit via receptor modeling and meteorology; whereas in the other exposure period, no such trace metals were observed. By comparing these two disparate results, this investigation was able to define possible links between PM_2.5 emitted from refineries and incinerators and biologically relevant dose, which in turn may be associated with observed health effects.     

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.     

Characterization of PM25 and PM10 in the South Coast Air Basin of Southern California: Part 1—Spatial Variations

ABSTRACT

In December 1994, the South Coast Air Quality Management District (SCAQMD) initiated a comprehensive program, the PM₁₀ Technical Enhancement Program (PTEP), to characterize fine PM in the South Coast Air Basin (SCAB). A 1-year special particulate monitoring project was conducted from January 1995 to February 1996 as part of the PTEP. Under this enhanced monitoring, HNO₃, NH₃, and speciated PM₁₀ and PM_2.5 concentrations were measured at five stations (Anaheim, downtown Los Angeles, Diamond Bar, Fontana, and Rubidoux) in the SCAB and at one background station at San Nicolas Island. PM_2.5 and PM₁₀ mass and 43 individual species were analyzed for a full chemical speciation of the particle data. The PTEP data indicate that the most abundant chemical components of PM₁₀ and PM₂₅ in the SCAB are NH₄+ (8-9% of PM₁₀ and 14-17% of PM₂₅), NO₃ ^- (23-26% of PM₁₀ and 28-41% of PM₂₅), SO₄= (6-11% of PM₁₀ and 9-18% of PM_{2 5}), organic carbon (OC) (15-19% of PM₁₀ and 18-26% of PM_2.5), and elemental carbon (EC) (5-8% of PM₁₀ and 8-13% of PM₂₅). On an annual average basis, PM₂₅ comprises 52-59% of the SCAB PM₁₀. Annual average PM₁₀ and PM_2.5 concentrations showed strong spatial variations, low at coastal sites and high at inland sites. Annual average PM₁₀ concentrations varied from 40.8 ug/m³ at Anaheim to 76.8 ug/m³ at Rubidoux, while annual average PM_2.5 concentrations varied from 21.7 ug/m³ at Anaheim to 39.8 ug/m³ at Rubidoux. The chemical characterizations of the PM_2.5 and PM₁₀ concentrations, as well as their spatial variations, were examined; the important findings are summarized in this paper, and the temporal variations are discussed in the companion paper.¹     

区域大气环境中PM2.5/PM10空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM_2.5/PM₁₀空间分布的方法,并在2004年12月进行了宁波市全市域PM_2.5/PM₁₀空间分布的研究。数据显示：相同路径所代表的地区PM_2.5和PM₁₀具有很好的相关性,多数路径上PM_2.5与PM₁₀数据的相关系数平方在0.95以上,而不同路径上PM_2.5与PM₁₀的比值不同。文中给出了宁波市PM_2.5/PM₁₀污染的空间分布图,直观地显示出PM_2.5/PM₁₀污染的空间分布情况,突出了污染的重点点位和地区。     

Indoor particulate matter in urban residences of Alexandria,Egypt

Indoor particulate matter samples were collected in 17 homes in an urban area in Alexandria during the summer season. During air measurement in all selected homes, parallel outdoor air samples were taken in the balconies of the domestic residences. It was found that the mean indoor PM_2.5 and PM₁₀ (particulate matter with an aerodynamic diameter ≤2.5 and ≤10 μm, respectively) concentrations were 53.5 ± 15.2 and 77.2 ± 15.1 µg/m³, respectively. The corresponding mean outdoor levels were 66.2 ± 16.5 and 123.8 ± 32.1 µg/m³, respectively. PM_2.5 concentrations accounted, on average, for 68.8 ± 12.8% of the total PM₁₀ concentrations indoors, whereas PM_2.5 contributed to 53.7 ± 4.9% of the total outdoor PM₁₀ concentrations. The median indoor/outdoor mass concentration (I/O) ratios were 0.81 (range: 0.43–1.45) and 0.65 (range: 0.4–1.07) for PM_2.5 and PM₁₀, respectively. Only four homes were found with I/O ratios above 1, indicating significant contribution from indoor sources. Poor correlation was seen between the indoor PM₁₀ and PM_2.5 levels and the corresponding outdoor concentrations. PM₁₀ levels were significantly correlated with PM_2.5 loadings indoors and outdoors and this might be related to PM₁₀ and PM_2.5 originating from similar particulate matter emission sources. Smoking, cooking using gas stoves, and cleaning were the major indoor sources contributed to elevated indoor levels of PM₁₀ and PM_2.5.

Implications: The current study presents results of the first PM_2.5 and PM₁₀ study in homes located in the city of Alexandria, Egypt. Scarce data are available on indoor air quality in Egypt. Poor correlation was seen between the indoor and outdoor particulate matter concentrations. Indoor sources such as smoking, cooking, and cleaning were found to be the major contributors to elevated indoor levels of PM₁₀ and PM_2.5.     

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.     

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

Particulate matter emissions from on-road vehicles in a freeway tunnel study

Particulate matter, including coarse particles (PM_2.5–10, aerodynamic diameter of particle between 2.5 and 10 μm) and fine particles (PM_2.5, aerodynamic diameter of particle lower than 2.5 μm) and their compositions, including elemental carbon, organic carbon, and 11 water-soluble ionic species, and elements, were measured in a tunnel study. A comparison of the six-hour average of light-duty vehicle (LDV) flow of the two sampling periods showed that the peak hours over the weekend were higher than those on weekdays. However, the flow of heavy-duty vehicles (HDVs) on the weekdays was significant higher than that during the weekend in this study. EC and OC content were 49% for PM_2.5–10 and 47% for PM_2.5 in the tunnel center. EC content was higher than OC content in PM_2.5–10, but EC was about 2.3 times OC for PM_2.5. Sulfate, nitrate, ammonium were the main species for PM_2.5–10 and PM_2.5. The element contents of Na, Al, Ca, Fe and K were over 0.8 μg m^?3 in PM_2.5–10 and PM_2.5. In addition, the concentrations of S, Ba, Pb, and Zn were higher than 0.1 μg m^?3 for PM_2.5–10 and PM_2.5. The emission factors of PM_2.5–10 and PM_2.5 were 18 ± 6.5 and 39 ± 11 mg km^?1-vehicle, respectively. The emission factors of EC/OC were 3.6/2.7 mg km^?1-vehicle for PM_2.5–10 and 15/4.7 mg km^?1-vehicle for PM_2.5 Furthermore, the emission factors of water-soluble ions were 0.028(Mg²⁺)–0.81(SO₄^2?) and 0.027(NO₂^?)–0.97(SO₄^2?) mg km^?1-vehicle for PM_2.5–10 and PM_2.5, respectively. Elemental emission factors were 0.003(V)–1.6(Fe) and 0.001(Cd)–1.05(Na) mg km^?1-vehicle for PM_2.5–10 and PM_2.5, respectively.     

Spatial PM2.5 mobile source impacts using a calibrated indicator method

Motor vehicles are major sources of fine particulate matter (PM_2.5), and the PM_2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM_2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NO_x) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NO_x estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM_2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects.

Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM_2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM_2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.     

Chemical characteristics and source apportionment of PM<Subscript>2.5</Subscript> using PCA/APCS,UNMIX, and PMF at an urban site of Delhi,India

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM_2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM_2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM_2.5 was recorded as 121.9 ± 93.2 μg m^?3 (range 25.1–429.8 μg m^?3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ～17% of PM_2.5. Strong seasonal variation was observed in PM_2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM_2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM_2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM_2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM_2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM_2.5 from the north-west India and Pakistan were mostly pertinent.     

PM2.5 characterization for time series studies: Organic molecular marker speciation methods and observations from daily measurements in Denver

Particulate matter less than 2.5 microns in diameter (PM_2.5) has been shown to have a wide range of adverse health effects and consequently is regulated in accordance with the US-EPA's National Ambient Air Quality Standards. PM_2.5 originates from multiple primary sources and is also formed through secondary processes in the atmosphere. It is plausible that some sources form PM_2.5 that is more toxic than PM_2.5 from other sources. Identifying the responsible sources could provide insight into the biological mechanisms causing the observed health effects and provide a more efficient approach to regulation. This is the goal of the Denver Aerosol Sources and Health (DASH) study, a multi-year PM_2.5 source apportionment and health study.The first step in apportioning the PM_2.5 to different sources is to determine the chemical make-up of the PM_2.5. This paper presents the methodology used during the DASH study for organic speciation of PM_2.5. Specifically, methods are covered for solvent extraction of non-polar and semi-polar organic molecular markers using gas chromatography–mass spectrometry (GC–MS). Vast reductions in detection limits were obtained through the use of a programmable temperature vaporization (PTV) inlet along with other method improvements. Results are presented for the first 1.5 years of the DASH study revealing seasonal and source-related patterns in the molecular markers and their long-term correlation structure. Preliminary analysis suggests that point sources are not a significant contributor to the organic molecular markers measured at our receptor site. Several motor vehicle emission markers help identify a gasoline/diesel split in the ambient data. Findings show both similarities and differences when compared with other cities where similar measurements and assessments have been made.     

Influence of sulfur dioxide-related interactions on PM2.5 formation in iron ore sintering

The formation of PM_2.5 (aerosol particulate matter less than 2.5 µm in aerodynamic diameter) in association with SO₂ emission during sintering process has been studied by dividing the whole sintering process into six typical sampling stages. A low-pressure cascade impactor was used to collect PM_2.5 by automatically segregating particulates into six sizes. It was found that strong correlation existed between the emission properties of PM_2.5 and SO₂. Wet mixture layer (overwetted layer and raw mixture layer) had the function to simultaneously capture SO₂ and PM_2.5 during the early sintering stages, and released them back into flue gas mainly in the flue gas temperature-rising period. CaSO₄ crystals constituted the main SO₂-related PM_2.5 during the disappearing process of overwetted layer, which was able to form perfect individual crystals or to form particles with complex chemical compositions. Besides the existence of individual CaSO₄ crystals, mixed crystals of K₂SO₄-CaSO₄ in PM_2.5 were also found during the first half of the temperature-rising period of flue gas. The interaction between fine-grained Ca-based fluxes, potassium vapors, and SO₂ was the potential source of SO₂-related PM_2.5.

Implications: The emission property of PM_2.5 and SO₂ throughout the sintering process exhibited well similarity. This phenomenon tightened the relationship between the formation of PM_2.5 and the emission of SO₂. Through revealing the properties of SO₂-related PM_2.5 during sintering process, the potential interaction between fine-grained Ca-based fluxes, potassium vapors, and SO₂ was found to be the source of SO₂-related PM_2.5. This information can serve as the guidance to develop efficient techniques to control the formation and emission of PM_2.5 in practical sintering plants.     

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.     

Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing

Three years of measurement of PM_2.5 with 5-min time resolution was conducted from 2005 to 2007 in urban and rural environments in Beijing to study the seasonal and diurnal variations in PM_2.5 concentration. Pronounced seasonal variation was observed in the urban area, with the highest concentrations typically observed in the winter and the lowest concentrations generally found in the summer. In the rural area, the maximum in PM_2.5 concentration usually appeared during the spring, followed by a second maximum in the summer, while the minimum generally occurred in the winter. Significant diurnal variations in PM_2.5 concentration were observed in both urban and rural areas. In the urban area, the PM_2.5 concentration displays a bimodal pattern, with peaks between 7:00 and 8:00 a.m. and between 7:00 and 11:00 p.m. The minimum generally appears around noon. The morning peak is attributed to enhanced anthropogenic activity during rush hours. The decreases of boundary layer height and wind speed in the afternoon companying with increased source activity during the afternoon rush hour result in the highest PM_2.5 concentration during evening hours. In the rural area, the PM_2.5 concentration shows a unimodal pattern with a significant peak between 5:00 and 11:00 p.m.The seasonal and diurnal variations in PM_2.5 concentration in the urban area are mostly dominated by the seasonal and diurnal variability of boundary layer and source emissions. The year-to-year variability of rainfall also has an important influence on the seasonal variation of PM_2.5 in the urban area. The seasonal and diurnal wind patterns are more important factors for PM_2.5 variation in the rural area. Southerly winds carry pollutants emitted in southern urban areas northward and significantly enhance the PM_2.5 concentration level in the rural area.     

Determination of Source Contributions to Ambient PM2.5 in Kaohsiung,Taiwan, Using a Receptor Model

ABSTRACT

Ambient particulates of PM_2.5 were sampled at three sites in Kaohsiung, Taiwan, during February and March 1999. In addition, resuspended PM_2.5 collected from traffic tunnels, paved roads, fly ash of a municipal solid waste (MSW) incinerator, and seawater was obtained. All the samples were analyzed for twenty constituents, including water-soluble ions, organic carbon (OC), elemental carbon (EC), and metallic elements. In conjunction with local source profiles and the source profiles in the model library SPECIATE EPA, the receptor model based on chemical mass balance (CMB) was then applied to determine the source contributions to ambient PM_2.5.

The mean concentration of ambient PM_2.5 was 42.6953.68 μj.g/m³ for the sampling period. The abundant species in ambient PM_2.5 in the mass fraction for three sites were OC (12.7-14.2%), SO₄ ^2- (12.8-15.1%), NO₃ ^- (8.110.3%), NH₄+ (6.7-7.5%), and EC (5.3-8.5%). Results of CMB modeling show that major pollution sources for ambient PM_2.5 are traffic exhaust (18-54%), secondary aerosols (30-41% from SO₄ ^2- and NO₃ ^-), and outdoor burning of agriculture wastes (13-17%).     

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.