A comprehensive particulate matter monitoring system and dosimetry-based ambient particulate matter standards

A numerical particulate matter (PM) measurement model is developed to characterize and evaluate PM sampling methods. Simulations are conducted using the model to evaluate currently widely used PM samplers, including Federal Reference Method (FRM) samplers. The simulations show that current PM samplers are very vulnerable to both changes in measurement target (i.e., natural variability of particle size distribution) and the sampler's design, manufacturing, and operating conditions, potentially resulting in significant errors in the monitoring data. The numerical model is used in conjunction with two types of commercially available PM monitoring devices to form a Comprehensive Particulate Matter Monitoring System (CPMMS). The first type of device can be any mass-based PM monitor with a well-defined sampling efficiency curve. The second type of device is one capable of measuring particle size distribution with a reasonably good relative accuracy between size categories but not necessarily accurate in measuring absolute mass concentrations. This study shows that CPMMS can produce much higher quality PM monitoring data than the current PM samplers under the same conditions. In addition, unlike past and current PM monitoring data such as total suspended particulates, coarse PM (PM10), fine PM (PM2.5), etc., the CPMMS monitoring data will survive changes in PM regulatory definition. A new concept, dosimetry-based PM metrics and standards, is proposed to define ambient PM level based on the deposition fraction of particles in the human respiratory tract. The dosimetry-based PM metrics is more meaningful because it correlates the ambient PM level with the portion that can be deposited in the respiratory tract without an arbitrary cutoff particle diameter. CPMMS makes dosimetry-based PM metrics and standards feasible.     

Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources

Investigations on the monitoring of ambient air levels of atmospheric particulates were developed around a large source of primary anthropogenic particulate emissions: the industrial ceramic area in the province of Castelló (Eastern Spain). Although these primary particulate emissions have a coarse grain-size distribution, the atmospheric transport dominated by the breeze circulation accounts for a grain-size segregation, which results in ambient air particles occurring mainly in the 2.5–10 μm range. The chemical composition of the ceramic particulate emissions is very similar to the crustal end-member but the use of high Al, Ti and Fe as tracer elements as well as a peculiar grain-size distribution in the insoluble major phases allow us to identify the ceramic input in the bulk particulate matter. PM2.5 instead of PM10 monitoring may avoid the interference of crustal particles without a major reduction in the secondary anthropogenic load, with the exception of nitrate. However, a methodology based in PM2.5 measurement alone is not adequate for monitoring the impact of primary particulate emissions (such as ceramic emissions) on air quality, since the major ambient air particles derived from these emissions are mainly in the range of 2.5–10 μm. Consequently, in areas characterised by major secondary particulate emissions, PM2.5 monitoring should detect anthropogenic particulate pollutants without crustal particulate interference, whereas PM10 measurements should be used in areas with major primary anthropogenic particulate emissions.     

The Steubenville comprehensive air monitoring program (SCAMP): overview and statistical considerations

Average concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 microm (PM2.5) in Steubenville, OH, have decreased by more than 10 microg/m3 since the landmark Harvard Six Cities Study associated the city's elevated PM2.5 concentrations with adverse health effects in the 1980s. Given the promulgation of a new National Ambient Air Quality Standard (NAAQS) for PM2.5 in 1997, a current assessment of PM2.5 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 PM2.5 data analyses. The average PM2.5 concentration measured in Steubenville during SCAMP (18.4 microg/m3) was 3.4 microg/m3 above the annual PM2.5 NAAQS. On average, sulfate and organic material accounted for approximately 31% and 25%, respectively, of the total PM2.5 mass. Local sources contributed an estimated 4.6 microg/m3 to Steubenville's mean PM2.5 concentration. PM2.5 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 PM2.5 concentrations. Statistically significant autocorrelation was observed among time series of PM2.5 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.     

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.     

A procedure for use in estimating human exposure to particulate matter of ambient origin

Modern epidemiology has shown that fluctuations of mortality data are statistically significantly correlated with fluctuations of ambient particulate matter (PM) concentration data. This relation cannot be confounded by exposure to PM of indoor origin because the concentrations of ambient PM are not correlated with concentrations of PM of indoor origin. It has been suggested, given the above understanding, that modern PM exposure measurements and analysis should create separate estimates of exposure to all PM of ambient origin and exposure to all PM of nonambient origin (primarily of indoor origin), and not exposure to total PM. This paper reviews the developments of the form of the general microenvironmental mass balance equation that can be utilized for estimating human exposure to PM of ambient origin and for estimating the portion of total PM exposure that is attributable to nonambient origin PM. The equation is perfectly general and can be applied to conditions of time-varying factors that influence exposure, such as rapidly changing air-exchange rates in a home as doors and windows are opened and closed, and turning on and off air cleaners in a home. It is suggested that this procedure be applied in exposure assessment studies and validated using independent techniques of estimating exposure to PM of ambient origin available in the literature.     

Monitoring study of the near-road PM2.5 concentrations in Maryland

The Maryland State Highway Administration (SHA) monitoring program monitored the impact of vehicular emissions on the concentrations of the fine particles smaller than 2.5 microns (PM2.5). PM2.5 concentrations were monitored in close proximity to a highway in order to determine whether traffic conditions on the roadway impact concentrations at this location. The monitoring program attempted to connect monitored concentrations with the roadway traffic exhaust or with the other sources of PM2.5. PM2.5 concentrations were collected near the Capital Beltway (I-495/I-95) in Largo, Maryland. The monitoring program was launched on May 13, 2009 and continued through the end of 2012. Two co-located monitors, one for continuous PM2.5 measurements and the other for speciation measurements, were used in this program. Meteorological and traffic information was also continuously collected at or near the monitoring site. Additionally, data from the two other monitoring locations, one at the Howard University-Beltsville, MD and one at McMillan Reservoir, DC, was used for comparison with the data collected at the SHA monitoring location. The samples collected by the speciation monitor were analyzed at the RTI and DRI Laboratories to determine the composition and the sources of the collected PM2.5 samples. Based on the apportionment analysis, the contribution of roadway sources is about 12 to 17 percent of PM2.5 at the near-road site.

Implications: PM_2.5 monitoring at 150 m (approximately 500 feet) from a major highway in Maryland near Washington, DC, demonstrated that roadway traffic contributes to the total PM_2.5 concentration near the roadway, but the contribution at such distance is small, in the order of 12–17% of the total.     

The 1999 Fresno particulate matter exposure studies: comparison of community, outdoor, and residential PM mass measurements

Two collaborative studies have been conducted by the U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory (NERL) and National Health and Environmental Effects Research Laboratory to determine personal exposures and physiological responses to particulate matter (PM) of elderly persons living in a retirement facility in Fresno, CA. Measurements of PM and other criteria air pollutants were made inside selected individual residences within the retirement facility and at a central outdoor site on the premises. In addition, personal PM exposure monitoring was conducted for a subset of the participants, and ambient PM monitoring data were available for comparison from the NERL PM research monitoring platform in central Fresno. Both a winter (February 1-28, 1999) and a spring (April 19-May 16, 1999) study were completed so that seasonal effects could be evaluated. During the spring study, a more robust personal exposure component was added, as well as a more detailed evaluation of physical factors, such as air-exchange rate, that are known to influence the penetration of particles into the indoor environment. In this paper, comparisons are made among measured personal PM exposures and PM mass concentrations measured at the NERL Fresno Platform site, outside on the premises of the retirement facility, and inside selected residential apartments at the facility during the two 28-day study periods. The arithmetic daily mean personal PM2.5 exposure during the winter study period was 13.3 micrograms/m3, compared with 9.7, 20.5, and 21.7 micrograms/m3 for daily mean overall apartment, outdoor, and ambient (i.e., platform) concentrations, respectively. The daily mean personal PM2.5 exposure during the spring study period was 11.1 micrograms/m3, compared with 8.0, 10.1, and 8.6 micrograms/m3 for the daily mean apartment, outdoor, and ambient concentrations, respectively.     

Air toxics in Canada measured by the National Air Pollution Surveillance (NAPS) program and their relation to ambient air quality guidelines

This study reports ambient concentrations of 63 air toxics that were measured in Canada by the National Air Pollution Surveillance (NAPS) program over the period 2009–2013. Measured concentrations are compared with ambient air quality guidelines from Canadian jurisdictions, and compounds that exceeded guidelines are identified and discussed. Although this study does not assess risk or cumulative effects, air toxics that approached guidelines are also identified so that their potential contribution to ambient air toxics pollution can be considered. Eleven air toxics exceeded at least one guideline, and an additional 16 approached guidelines during the study period. Four compounds were measured using methods whose detection limits exceeded a guideline value, three of which could not be compared with guidelines, since they were not detected in any samples. The assessment of several metal(loid) concentrations is tentative, since they were measured only in fine particulate matter (PM) but compared with guidelines based on coarse or total PM. Improvements to sampling and analysis techniques for the latter compounds as well as for those whose methods are subject to known uncertainties would improve confidence in reported concentrations and their relation to applicable guidelines. Analysis of sampling strategies for all compounds found to exceed or approach guidelines would contribute to ensuring that their spatiotemporal coverage is adequate. Examination of the air toxics not measured by NAPS but having guidelines in Canadian jurisdictions or being included in other programs such as the U.S. National-Scale Air Toxics Assessment (NATA) would contribute to ensuring that the full suite of pollutants relevant to ambient air quality in Canada is subject to adequate study. The results of this study can be applied to evaluating the effectiveness of toxic substances management in Canada.

Implications: Recent measurements of 63 air toxics in Canada by the National Air Pollution Surveillance (NAPS) program showed that 11 compounds exceeded daily or annual ambient air quality guidelines and that an additional 16 compounds approached such guidelines within an order of magnitude. The results of this study can be applied to evaluating the effectiveness of toxic substances management in Canada and to identifying compounds that merit further investigation.     

Comparison of ambient PM risk with risks estimated from PM components of smoking and occupational exposures

Air pollution studies are based on individual-level health response data and group-level exposure data. Therefore, exposure misclassification occurs, and the results may be biased to an unknown magnitude and direction. Testing the validity of such associations requires a study design using individual-level data for both exposure and response. One can test the plausibility of group-level PM risk estimates by comparing them to individual-level estimates of risk from constituents of ambient air. The twofold purpose of this review is to consider the internal consistency of risks estimated from the three major PM cohort studies and to determine individual-level mortality risks associated with ambient concentrations of tobacco smoke and occupational exposures and compare them with risks associated with ambient PM. The paper demonstrates the risks are not consistent within and between the PM cohort studies. Higher ambient concentration risks (ACRs) from the ambient PM cohort studies are not coherent with ACRs derived from individual-level smoking and occupational risks for total, cardiopulmonary, and lung cancer mortality. Individual-level studies suggest increased risk of mortality cannot be measured with reliability at concentrations found in ambient air.     

Assessment of human exposure to ambient particulate matter

Recent epidemiological studies have consistently shown that the acute mortality effects of high concentrations of ambient particulate matter (PM), documented in historic air pollution episodes, may also be occurring at the low to moderate concentrations of ambient PM found in modern urban areas. In London in December 1952, the unexpected deaths due to PM exposure could be identified and counted as integers by the coroners. In modern times, the PM-related deaths cannot be as readily identified, and they can only be inferred as fractional average daily increases in mortality rates using sophisticated statistical filtering and analyses of the air quality and mortality data. The causality of the relationship between exposure to ambient PM and acute mortality at these lower modern PM concentrations has been questioned because of a perception that there is little significant correlation in time between the ambient PM concentrations and measured personal exposure to PM from all sources (ambient PM plus indoor-generated PM). This article shows that the critical factor supporting the plausibility of a linear PM mortality relationship is the expected high correlation in time of people's exposure to PM of ambient origin with measured ambient PM concentrations, as used in the epidemiological time series studies. The presence of indoor and personal sources of PM masks this underlying relationship, leading to confusion in the scientific literature about the strong underlying temporal relationship between personal exposure to PM of ambient origin and ambient PM concentration. The authors show that the sources of PM of non-ambient origin operate independently of the ambient PM concentrations, so that the mortality effect of non-ambient PM, if any, must be independent of the effects of the ambient PM exposures.     

Fine particulate matter source apportionment for the Chemical speciation Trends Network site at Birmingham, Alabama, using Positive Matrix Factorization

The Positive Matrix Factorization (PMF) receptor model version 1.1 was used with data from the fine particulate matter (PM2.5) Chemical Speciation Trends Network (STN) to estimate source contributions to ambient PM2.5 in a highly industrialized urban setting in the southeastern United States. Model results consistently resolved 10 factors that are interpreted as two secondary, five industrial, one motor vehicle, one road dust, and one biomass burning sources. The STN dataset is generally not corrected for field blank levels, which are significant in the case of organic carbon (OC). Estimation of primary OC using the elemental carbon (EC) tracer method applied on a seasonal basis significantly improved the model's performance. Uniform increase of input data uncertainty and exclusion of a few outlier samples (associated with high potassium) further improved the model results. However, it was found that most PMF factors did not cleanly represent single source types and instead are "contaminated" by other sources, a situation that might be improved by controlling rotational ambiguity within the model. Secondary particulate matter formed by atmospheric processes, such as sulfate and secondary OC, contribute the majority of ambient PM2.5 and exhibit strong seasonality (37 +/- 10% winter vs. 55 +/- 16% summer average). Motor vehicle emissions constitute the biggest primary PM2.5 mass contribution with almost 25 +/- 2% long-term average and winter maximum of 29 +/- 11%. PM2.5 contributions from the five identified industrial sources vary little with season and average 14 +/- 1.3%. In summary, this study demonstrates the utility of the EC tracer method to effectively blank-correct the OC concentrations in the STN dataset. In addition, examination of the effect of input uncertainty estimates on model results indicates that the estimated uncertainties currently being provided with the STN data may be somewhat lower than the levels needed for optimum modeling results.     

Daily mortality in the Philadelphia metropolitan area and size-classified particulate matter

Time-series of daily mortality data from May 1992 to September 1995 for various portions of the seven-county Philadelphia, PA, metropolitan area were analyzed in relation to weather and a variety of ambient air quality parameters. The air quality data included measurements of size-classified PM, SO4(2-), and H+ that had been collected by the Harvard School of Public Health, as well as routine air pollution monitoring data. Because the various pollutants of interest were measured at different locations within the metropolitan area, it was necessary to test for spatial sensitivity by comparing results for different combinations of locations. Estimates are presented for single pollutants and for multiple-pollutant models, including gaseous pollutants and mutually exclusive components of PM (PM2.5 and coarse particles, SO4(2-) and non-SO4(2-) portions of total suspended particulate [TSP] and PM10), measured on the day of death and the previous day. We concluded that associations between air quality and mortality were not limited to data collected in the same part of the metropolitan area; that is, mortality for one part may be associated with air quality data from another, not necessarily neighboring, part. Significant associations were found for a wide variety of gaseous and particulate pollutants, especially for peak O3. Using joint regressions on peak O3 with various other pollutants, we found that the combined responses were insensitive to the specific other pollutant selected. We saw no systematic differences according to particle size or chemistry. In general, the associations between daily mortality and air pollution depended on the pollutant or the PM metric, the type of collection filter used, and the location of sampling. Although peak O3 seemed to exhibit the most consistent mortality responses, this finding should be confirmed by analyzing separate seasons and other time periods.     

杭州市空气颗粒物污染特征及变化规律研究

根据2006—2010年杭州市空气颗粒物的监测数据及2002、2006、2008年空气颗粒物来源解析结果,对杭州市空气颗粒物浓度、化学组分与污染来源等特征的变化规律进行分析,以期为空气颗粒物污染控制提供决策依据。结果表明,近年来杭州市PM10浓度有所下降,但一类功能区PM10仍超出《环境空气质量标准》(GB 3095—1996)的要求(≤0.04mg/m3),杭州市空气颗粒物污染以细颗粒物为主,空气颗粒物的二次转化、机动车尾气尘等产生的二次粒子污染相对严重;煤烟尘对杭州市PM10的贡献率下降明显,城市扬尘、二次粒子和机动车尾气尘对PM10的贡献率有所增加,是杭州市PM10的主要来源。     

Comparison of Ambient PM Risk with Risks Estimated from PM Components of Smoking and Occupational Exposures

ABSTRACT

Air pollution studies are based on individual-level health response data and group-level exposure data. Therefore, exposure misclassification occurs, and the results may be biased to an unknown magnitude and direction. Testing the validity of such associations requires a study design using individual-level data for both exposure and response. One can test the plausibility of group-level PM risk estimates by comparing them to individual-level estimates of risk from constituents of ambient air. The twofold purpose of this review is to consider the internal consistency of risks estimated from the three major PM cohort studies and to determine individual-level mortality risks associated with ambient concentrations of tobacco smoke and occupational exposures and compare them with risks associated with ambient PM.

The paper demonstrates the risks are not consistent within and between the PM cohort studies. Higher ambient concentration risks (ACRs) from the ambient PM cohort studies are not coherent with ACRs derived from individual-level smoking and occupational risks for total, cardiopulmonary, and lung cancer mortality. Individual-level studies suggest increased risk of mortality cannot be measured with reliability at concentrations found in ambient air.     

Assessment of Human Exposure to Ambient Particulate Matter

ABSTRACT

Recent epidemiological studies have consistently shown that the acute mortality effects of high concentrations of ambient particulate matter (PM), documented in historic air pollution episodes, may also be occurring at the low to moderate concentrations of ambient PM found in modern urban areas. In London in December 1952, the unexpected deaths due to PM exposure could be identified and counted as integers by the coroners. In modern times, the PM-related deaths cannot be as readily identified, and they can only be inferred as fractional average daily increases in mortality rates using sophisticated statistical filtering and analyses of the air quality and mortality data. The causality of the relationship between exposure to ambient PM and acute mortality at these lower modern PM concentrations has been questioned because of a perception that there is little significant correlation in time between the ambient PM concentrations and measured personal exposure to PM from all sources (ambient PM plus indoor-generated PM).

This article shows that the critical factor supporting the plausibility of a linear PM mortality relationship is the expected high correlation in time of people's exposure to PM of ambient origin with measured ambient PM concentrations, as used in the epidemiological time series studies. The presence of indoor and personal sources of PM masks this underlying relationship, leading to confusion in the scientific literature about the strong underlying temporal relationship between personal exposure to PM of ambient origin and ambient PM concentration. The authors show that the sources of PM of non-ambient origin operate independently of the ambient PM concentrations, so that the mortality effect of non-ambient PM, if any, must be independent of the effects of the ambient PM exposures.     

Estimation of Additive Effects from Multiple Sources

Using meteorological diffusion equations, a computer program has been developed to calculate the distribution of pollutants in an area affected by multiple sources, such as in a typical industrial district. Using rapid printout on a teletype terminal, data processing costs can be as low as two to three dollars for a printout showing distribution of pollutants over a distance of several miles and illustrating additive effects from multiple sources in the area. By integrating this method of data processing into a monitoring network and an emission inventory, the usefulness of monitoring data could be greatly increased to provide a much more realistic evaluation of ambient air quality within the area.     

Air quality at Santiago,Chile: a box modeling approach II. PM2.5, coarse and PM10 particulate matter fractions

Ambient monitored data at Santiago, Chile, are analyzed using box models with the goal of assessing contributions of different economic activities to air pollution levels. The box modeling approach was applied to PM₁₀, PM_2.5 and coarse (PM₁₀–PM_2.5) particulate matter (PM) fractions; the period analyzed is 1989–1999. A linear model for each PM fraction was obtained, having as independent variables CO and SO₂ concentrations, plus a term proportional to (wind speed)⁻¹ that lumps together non-combustion emissions and secondary generation terms; wet scavenging is included as another independent variable. Model identification results show good agreement for the different parameters across monitoring stations. The washout ratios and scavenging coefficients agree with data published in the literature, being higher for the coarse PM fraction. The CO and SO₂ coefficients fitted for 1989–1995 agree with a priori estimates for the same period. Background estimates for the PM fractions are in agreement with measurement campaigns in upwind sites. Results show that transportation sources have become the dominant contributors to ambient PM levels, while stationary sources have decreased their contributions in the last years. The relative importance of mobile sources to PM_2.5 ambient concentrations has doubled in the last 10 years, whereas stationary sources have reduced their relative contributions to half the value in the early 1990s. Model estimates of regional background of PM_2.5 and PM₁₀ have decreased 50% and 22% in the last decade, respectively; coarse background has shown no significant change. The final conclusion is that there is room and need for a more intensive emission reduction strategy for Santiago, focusing on mobile sources. The approach pursued in this work is feasible for cities or regions where comprehensive, transport and chemistry models are not available yet, but estimates of air quality contributions are needed for policy purposes. The methodology requires data on ambient air quality measurements and surface meteorology.     

Characterisation of dust material emitted during harbour operations (HADA Project)

Harbour activities such as loading, unloading and transport of dusty loose materials may be an important source of atmospheric particulate matter (PM). Depending on the materials, the type of operation and the meteorological scenarios, these activities may have an impact on the levels of ambient air PM around harbour areas. Moreover, air quality at harbours may be affected by nearby urban and industrial emissions. The aim of this work is to compile an inventory of the main characteristics (chemical, morphological, mineralogical and grain size parameters) of the bulk cargo materials and of the material emitted during different port operations for possible use as tracers of the fugitive PM emission sources. For all cases, the tracer characteristics determined for each bulk material were also identified in the corresponding PM material emitted. This inventory could assist the harbour authorities to identify the origin of high PM events recorded by air quality monitoring networks in harbour areas, and could also help modellers to predict the impact of harbour activities on ambient PM levels. The harbour of Tarragona (north-east Spain) was selected for this study given the high volume of solids in bulk handled. To this end, 12 handling operations of selected materials (clinker, phosphate, pyrite ash, Mn mineral, fine Si–Mn, coke (coal), bituminous coal, tapioca, soybean, alfalfa, corn, andalusite) were selected for the characterisation of suspended and deposited PM. In spite of the coarse grain size distribution of these bulk cargo materials, with a very low % in the fraction <10 μm, manipulation of these materials during harbour operations may result in high emissions of PM10 with relatively high levels of potential toxic elements. Furthermore, ambient PM10 in the area with the highest traffic density of the harbour was also sampled and characterised.     

A two-step approach for relating tapered element oscillating microbalance and dichotomous air sampler PM2.5 measurements

A method for transforming continuous monitoring (CM) fine particulate matter (aerodynamic diameter <2.5 μm; PM_2.5) data (i.e., by tapered element oscillating microbalance [TEOM]) obtained from the Canadian National Air Pollution Surveillance (NAPS) program to meet the data quality objective (DQO) of R² > 0.8 against the co-located federal reference method (i.e., dichotomous air sampler) is described. By using a two-step linear regression to account for the effect of the ambient temperature, 16 out of the 23 examined sites met the common model adequacy threshold of R² > 0.8. After the transformation, 20 out of the 23 examined sites met the DQO of R² > 0.7, as recommended by the U.S. Environmental Protection Agency (EPA). A combined two-step statistical approach was also examined and revealed similar results. The methods described herein show that the CM data can be successfully transformed to meet DQOs for representative sites across Canada using year-round (both summer and winter) data.

Implications:This study provides a transformation approach to correct ambient TEOM data against the federal reference method without dividing the ambient data according to warm and cold seasons. This transformation approach will significantly improve the correlation coefficient between TEOM and dichotomous air sampler data. It is possible that TEOM data at many Canadian locations can be transformed to meet the EPA data quality objective, thus making this transformation approach useful for comparisons of ambient PM data across jurisdictions.