The Steubenville Comprehensive Air Monitoring Program (SCAMP): analysis of short-term and episodic variations in PM2.5 concentrations using hourly air monitoring data

One-hour average ambient concentrations of particulate matter (PM) with an aerodynamic diameter < 2.5 microm (PM2.5) were determined in Steubenville, OH, between June 2000 and May 2002 with a tapered element oscillating microbalance (TEOM). Hourly average gaseous copollutant [carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxide (NOx), and ozone (O3)] concentrations and meteorological conditions also were measured. Although 75% of the 14,682 hourly PM2.5 concentrations measured during this period were < or = 17 microg/m3, concentrations > 65 microg/m3 were observed 76 times. On average, PM2.5 concentrations at Steubenville exhibited a diurnal pattern of higher early morning concentrations and lower afternoon concentrations, similar to the diurnal profiles of CO and NO(x). This pattern was highly variable; however, PM2.5 concentrations > 65 microg/m3 were never observed during the mid-afternoon between 1:00 p.m. and 5:00 p.m. EST. Twenty-two episodes centered on one or more of these elevated concentrations were identified. Five episodes occurred during the months June through August; the maximum PM2.5 concentration during these episodes was 76.6 microg/m3. Episodes occurring during climatologically cooler months often featured higher peak concentrations (five had maximum concentrations between 95.0 and 139.6 microg/m3), and many exhibited strong covariation between PM2.5 and CO, NO(x), or SO2. Case studies suggested that nocturnal surface-based temperature inversions were influential in driving high nighttime concentrations of these species during several cool season episodes, which typically had dramatically lower afternoon concentrations. These findings provide insights that may be useful in the development of PM2.5 reduction strategies for Steubenville, and suggest that studies assessing possible health effects of PM2.5 should carefully consider exposure issues related to the intraday timing of PM2.5 episodes, as well as the potential for toxicological interactions among PM2.5, and primary gaseous pollutants.     

Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind     

The Steubenville Comprehensive Air Monitoring Program (SCAMP): Analysis of Short-Term and Episodic Variations in PM2.5 Concentrations Using Hourly Air Monitoring Data

Abstract

One-hour average ambient concentrations of particulate matter (PM) with an aerodynamic diameter <2.5 μm (PM_2.5) were determined in Steubenville, OH, between June 2000 and May 2002 with a tapered element oscillating microbalance (TEOM). Hourly average gaseous copollutant [carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen oxide (NO_x), and ozone (O₃)] concentrations and meteorological conditions also were measured. Although 75% of the 14,682 hourly PM_2.5 concentrations measured during this period were ≤17 μg/m³, concentrations >65 μg/m³ were observed 76 times. On average, PM_2.5 concentrations at Steubenville exhibited a diurnal pattern of higher early morning concentrations and lower afternoon concentrations, similar to the diurnal profiles of CO and NO_x. This pattern was highly variable; however, PM_2.5 concentrations >65 μg/m³ were never observed during the mid-afternoon between 1:00 p.m. and 5:00 p.m. EST. Twenty-two episodes centered on one or more of these elevated concentrations were identified. Five episodes occurred during the months June through August; the maximum PM_2.5 concentration during these episodes was 76.6 μg/m³. Episodes occurring during climatologically cooler months often featured higher peak concentrations (five had maximum concentrations between 95.0 and 139.6 μg/m³), and many exhibited strong covariation between PM_2.5 and CO, NO_x, or SO₂. Case studies suggested that nocturnal surface-based temperature inversions were influential in driving high nighttime concentrations of these species during several cool season episodes, which typically had dramatically lower afternoon concentrations. These findings provide insights that may be useful in the development of PM_2.5 reduction strategies for Steubenville, and suggest that studies assessing possible health effects of PM_2.5 should carefully consider exposure issues related to the intraday timing of PM_2.5 episodes, as well as the potential for toxicological interactions among PM_2.5 and primary gaseous pollutants.     

Air quality measurements from the Fresno Supersite

The Fresno Supersite intends to 1) evaluate non-routine monitoring methods, establishing their comparability with existing methods and their applicability to air quality planning, exposure assessment, and health effects studies; 2) provide a better understanding of aerosol characteristics, behavior, and sources to assist regulatory agencies in developing standards and strategies that protect public health; and 3) support studies that evaluate relationships between aerosol properties, co-factors, and observed health end-points. Supersite observables include in-situ, continuous, short-duration measurements of 1) PM2.5, PM10, and coarse (PM10 minus PM2.5) mass; 2) PM2.5 SO4(-2), NO3-, carbon, light absorption, and light extinction; 3) numbers of particles in discrete size bins ranging from 0.01 to approximately 10 microns; 4) criteria pollutant gases (O3, CO, NOx); 5) reactive gases (NO2, NOy, HNO3, peroxyacetyl nitrate [PAN], NH3); and 6) single particle characterization by time-of-flight mass spectrometry. Field sampling and laboratory analysis are applied for gaseous and particulate organic compounds (light hydrocarbons, heavy hydrocarbons, carbonyls, polycyclic aromatic hydrocarbons [PAH], and other semi-volatiles), and PM2.5 mass, elements, ions, and carbon. Observables common to other Supersites are 1) daily PM2.5 24-hr average mass with Federal Reference Method (FRM) samplers; 2) continuous hourly and 5-min average PM2.5 and PM10 mass with beta attenuation monitors (BAM) and tapered element oscillating microbalances (TEOM); 3) PM2.5 chemical speciation with a U.S. Environmental Protection Agency (EPA) speciation monitor and protocol; 4) coarse particle mass by dichotomous sampler and difference between PM10 and PM2.5 BAM and TEOM measurements; 5) coarse particle chemical composition; and 6) high sensitivity and time resolution scalar and vector wind speed, wind direction, temperature, relative humidity, barometric pressure, and solar radiation. The Fresno Supersite is coordinated with health and toxicological studies that will use these data in establishing relationships with asthma, other respiratory disease, and cardiovascular changes in human and animal subjects.     

Effect of meteorological parameters on fine and coarse particulate matter mass concentration in a coal-mining area in Zonguldak, Turkey

In this work, the effect of meteorological parameters and local topography on mass concentrations of fine (PM2.5) and coarse (PM2.5-10) particles and their seasonal behavior was investigated. A total of 236 pairs of samplers were collected using an Anderson Dichotomous sampler between December 2004 and October 2005. The average mass concentrations of PM2.5, PM2.5-10, and particulate matter less than 10 microm in aerodynamic diameter (PM10) were found to be 29.38, 23.85, and 53.23 microg/m3, respectively. The concentrations of PM2.5 and PM10 were found to be higher in heating seasons (December to May) than in summer. The increase of relative humidity, cloudiness, and lower temperature was found to be highly related to the increase of particulate matter (PM) episodic events. During non-rainy days, the episodic events for PM2.5 and PM10 were increased by 30 and 10.7%, respectively. This is a result of the extensive use of fuel during winter for heating purposes and also because of stagnant air masses formed because of low temperature and low wind speed over the study area.     

The Steubenville Comprehensive Air Monitoring Program (SCAMP): concentrations and solubilities of PM(2.5) trace elements and their implications for source apportionment and health research

The elemental compositions of the water-soluble and acid-digestible fractions of 24-hr integrated fine particulate matter (PM(2.5)) samples collected in Steubenville, OH, from 2000 to 2002 were determined using dynamic reaction cell inductively coupled plasma-mass spectrometry. The water-soluble elemental compositions of PM(2.5) samples collected at four satellite monitoring sites in the surrounding region were also determined. Fe was the most abundant but least water soluble of the elements determined at the Steubenville site, having a mean ambient concentration of 272 ng/m3 and a median fractional solubility of 6%. Fe solubility and its correlations with SO4(2-) and temperature varied significantly by season, consistent with the hypothesis that secondary sulfates may help to mobilize soluble Fe under suitable summertime photochemical conditions. Significantly higher ambient concentrations were observed at Steubenville than at each of the four satellite sites for 10 of the 18 elements (Al, As, Ca, Cd, Fe, Mg, Mn, Na, Pb, and Zn) determined in the water-soluble PM(2.5) fraction. Concentrations of Fe, Mn, and Zn at Steubenville were substantially higher than concentrations reported recently for larger U.S. cities. Receptor modeling identified seven sources affecting the Steubenville site. An (NH4)2SO4-dominated source, likely representing secondary PM(2.5) from coal-fired plants to the west and southwest of Steubenville, accounted for 42% of the PM(2.5) mass, and two sources likely dominated by emissions from motor vehicles and from iron and steel facilities in the immediate Steubenville vicinity accounted for 20% and 10%, respectively. Other sources included an NH4NO3 source (15%), a crustal source (6%), a mixed nonferrous metals and industrial source (3%), and a primary coal combustion source (3%). Results suggest the importance of very different regional and local source mechanisms in contributing to PM(2.5) mass at Steubenville and reinforce the need for further research to elucidate whether metals such as Fe, Mn, and Zn play a role in the PM(2.5) health effects observed previously there.     

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.     

Nanoparticle and ultrafine particle events at the Fresno supersite

Continuous measurements of particle size distributions of 3-407 nm were collected from August 2002 to July 2004 at the Fresno Supersite to understand their number concentrations, size distributions, and formation processes. Measurements for fine particulate matter (PM2.5) mass, sulfate (SO4(2-)), nitrate (NO3-), black carbon (BC), particle-bound polycyclic aromatic hydrocarbons (PAHs), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), and meteorological data (wind speed, wind direction, temperature [T], relative humidity [RH], and solar radiation) were used to determine the causes of nanoparticle (3-10 nm) and ultrafine (10-100 nm) particle events. These events were found to be divided into four types: (1) 3- to 10-nm morning nucleation; (2) 10- to 30-nm morning traffic; (3) 10- to 30-nm afternoon photochemical; and (4) 50- to 84-nm evening home heating, including residential wood combustion. Intense examples of the first type (>10(4) number [#]/cm3) were observed on 29 days, nearly always during the summer. The second type of event was observed on more than 73 days and occurred throughout the year. The third type was observed on 36 days, from spring through summer. The fourth type was found on 109 days, all of them during the winter. Although sulfur dioxide (SO2) emissions in Central California are low, the small residual amounts in gasoline and diesel fuel are apparently sufficient to initiate nucleation events. These were measured in the morning, soon after the shallow surface inversion coupled with layers aloft where nucleation probably was initiated. PM2.5 concentrations were poorly correlated with nanoparticle number.     

Assessing the relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore, MD

We conducted a multi-pollutant exposure study in Baltimore, MD, in which 15 non-smoking older adult subjects (> 64 years old) wore a multi-pollutant sampler for 12 days during the summer of 1998 and the winter of 1999. The sampler measured simultaneous 24-hr integrated personal exposures to PM2.5, PM10, SO4(2-), O3, NO2, SO2, and exhaust-related VOCs. Results of this study showed that longitudinal associations between ambient PM2.5 concentrations and corresponding personal exposures tended to be high in the summer (median Spearman's r = 0.74) and low in the winter (median Spearman's r = 0.25). Indoor ventilation was an important determinant of personal PM2.5 exposures and resulting personal-ambient associations. Associations between personal PM2.5 exposures and corresponding ambient concentrations were strongest for well-ventilated indoor environments and decreased with ventilation. This decrease was attributed to the increasing influence of indoor PM2.5 sources. Evidence for this was provided by SO4(2-) measurements, which can be thought of as a tracer for ambient PM2.5. For SO4(2-), personal-ambient associations were strong even in poorly ventilated indoor environments, suggesting that personal exposures to PM2.5 of ambient origin are strongly associated with corresponding ambient concentrations. The results also indicated that the contribution of indoor PM2.5 sources to personal PM2.5 exposures was lowest when individuals spent the majority of their time in well-ventilated indoor environments. Results also indicate that the potential for confounding by PM2.5 co-pollutants is limited, despite significant correlations among ambient pollutant concentrations. In contrast to ambient concentrations, PM2.5 exposures were not significantly correlated with personal exposures to PM2.5-10, PM2.5 of non-ambient origin, O3, NO2, and SO2. Since a confounder must be associated with the exposure of interest, these results provide evidence that the effects observed in the PM2.5 epidemiologic studies are unlikely to be due to confounding by the PM2.5 co-pollutants measured in this study.     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.     

Defining the photochemical contribution to particulate matter in urban areas using time-series analysis

The objective of this project is to demonstrate how the ambient air measurement record can be used to define the relationship between O3 (as a surrogate for photochemistry) and secondary particulate matter (PM) in urban air. The approach used is to develop a time-series transfer-function model describing the daily PM10 (PM with less than 10 microm aerodynamic diameter) concentration as a function of lagged PM and current and lagged O3, NO or NO2, CO, and SO2. Approximately 3 years of daily average PM10, daily maximum 8-hr average O3 and CO, daily 24-hr average SO2 and NO2, and daily 6:00 a.m.-9:00 a.m. average NO from the Aerometric Information Retrieval System (AIRS) air quality subsystem are used for this analysis. Urban areas modeled are Chicago, IL; Los Angeles, CA; Phoenix, AZ; Philadelphia, PA; Sacramento, CA; and Detroit, MI. Time-series analysis identified significant autocorrelation in the O3, PM10, NO, NO2, CO, and SO2 series. Cross correlations between PM10 (dependent variable) and gaseous pollutants (independent variables) show that all of the gases are significantly correlated with PM10 and that O3 is also significantly correlated lagged up to two previous days. Once a transfer-function model of current PM10 is defined for an urban location, the effect of an O3-control strategy on PM concentrations is estimated by calculating daily PM10 concentrations with reduced O3 concentrations. Forecasted summertime PM10 reductions resulting from a 5 percent decrease in ambient O3 range from 1.2 microg/m3 (3.03%) in Chicago to 3.9 microg/m3 (7.65%) in Phoenix.     

A study of the association between daily mortality and ambient air pollutant concentrations in Pittsburgh, Pennsylvania

We have studied the possible association of daily mortality with ambient pollutant concentrations (PM10, CO, O3, SO2, NO2, and fine [PM2.5] and coarse PM) and weather variables (temperature and dew point) in the Pittsburgh, PA, area for two age groups--less than 75, and 75 and over--for the 3-year period of 1989-1991. Correlation functions among pollutant concentrations show important seasonal dependence, and this fact necessitates the use of seasonal models to better identify the link between ambient pollutant concentrations and daily mortality. An analysis of the seasonal model results for the younger-age group reveals significant multicollinearity problems among the highly correlated concentrations of PM10, CO, and NO2 (and O3 in spring and summer), and calls into question the rather consistent results of the single- and multi-pollutant non-seasonal models that show a significant positive association between PM10 and daily mortality. For the older-age group, dew point consistently shows a significant association with daily mortality in all models. Collinearity problems appear in the multi-pollutant seasonal and non-seasonal models such that a significant, positive PM10 coefficient is accompanied by a significant, negative coefficient of another ambient pollutant, and the identity of this other pollutant changes with season. The PM2.5 data set is half that of PM10. Identical-model runs for both data sets reveal instability in the pollutant coefficients, especially for the younger age group. The concern for the instability of the pollutant coefficients due to a small signal-to-noise ratio makes it impossible to ascertain credibly the relative associations of the fine- and coarse-particle modes with daily mortality. In this connection, we call for caution in the interpretation of model results for causal inference when the models use fully or partially estimated PM values to fill large data gaps.     

Particulate air quality model predictions using prognostic vs. diagnostic meteorology in central California

Comparisons were made between three sets of meteorological fields used to support air quality predictions for the California Regional Particulate Air Quality Study (CRPAQS) winter episode from December 15, 2000 to January 6, 2001. The first set of fields was interpolated from observations using an objective analysis method. The second set of fields was generated using the WRF prognostic model without data assimilation. The third set of fields was generated using the WRF prognostic model with the four-dimensional data assimilation (FDDA) technique. The UCD/CIT air quality model was applied with each set of meteorological fields to predict the concentrations of airborne particulate matter and gaseous species in central California. The results show that the WRF model without data assimilation over-predicts surface wind speed by ～30% on average and consequently yields under-predictions for all PM and gaseous species except sulfate (S(VI)) and ozone(O₃). The WRF model with FDDA improves the agreement between predicted and observed wind and temperature values and consequently yields improved predictions for all PM and gaseous species. Overall, diagnostic meteorological fields produced more accurate air quality predictions than either version of the WRF prognostic fields during this episode. Population-weighted average PM_2.5 exposure is 40% higher using diagnostic meteorological fields compared to prognostic meteorological fields created without data assimilation. These results suggest diagnostic meteorological fields based on a dense measurement network are the preferred choice for air quality model studies during stagnant periods in locations with complex topography.     

Particulate matter in California: part 2--Spatial, temporal, and compositional patterns of PM2.5, PM10-2.5, and PM10

Geographic and temporal variations in the concentration and composition of particulate matter (PM) provide important insights into particle sources, atmospheric processes that influence particle formation, and PM management strategies. In the nonurban areas of California, annual-average PM2.5 and PM10 concentrations range from 3 to 10 microg/m3 and from 5 to 18 microg/m3, respectively. In the urban areas of California, annual-averages for PM2.5 range from 7 to 30 microg/m3, with observed 24-hr peaks reaching levels as high as 160 microg/m3. Within each air basin, exceedances are a mixture of isolated events as well as periods of elevated PM2.5 concentrations that are more prolonged and regional in nature. PM2.5 concentrations are generally highest during the winter months. The exception is the South Coast Air Basin, where fairly high values occur throughout the year. Annual-average PM2.5 mass, as well as the concentrations of major components, declined from 1988 to 2000. The declines are especially pronounced for the sulfate (SO4(2-)) and nitrate (NO3-) components of PM2.5 and PM10) and correlate with reductions in ambient levels of oxides of sulfur (SOx) and oxides of nitrogen (NOx). Annual averages for PM10-2.5 and PM10 exhibited similar downwind trends from 1994 to 1999, with a slightly less pronounced decrease in the coarse fraction.     

The Aerosol Research and Inhalation Epidemiology Study (ARIES): PM2.5 mass and aerosol component concentrations and sampler intercomparisons

The Aerosol Research and Inhalation Epidemiology Study (ARIES) was designed to provide high-quality measurements of PM2.5, 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 PM2.5 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 PM2.5 mass and speciation samples and continuous PM2.5 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 PM2.5 components and consider associations and potential mechanistic linkages of PM2.5 mass concentrations with several PM2.5 components. For the 12 months of validated data collected to date (August 1, 1998-July 31, 1999), the monthly average Federal Reference Method (FRM) PM2.5 mass always exceeded the proposed annual average standard (12-month average = 20.3 +/- 9.5 micrograms/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 PM2.5 mass dropped to less than 30% in winter. Particulate NO3- collected on a denuded nylon filter averaged 1.1 +/- 0.9 micrograms/m3. Particle-phase organic compounds (as organic carbon x 1.4) measured on a denuded quartz filter sampler averaged 6.4 +/- 3.1 micrograms/m3 (32% of FRM PM2.5 mass) with less seasonal variability than SO4(2-).     

Associations between particle number and gaseous co-pollutant concentrations in the Los Angeles Basin

Continuous measurements of particle number (PN), particle mass (PM10), and gaseous pollutants [carbon monoxide (CO), nitric oxide (NO), oxides of nitrogen (NOx), and ozone (O3)] were performed at five urban sites in the Los Angeles Basin to support the University of Southern California Children's Health Study in 2002. The degree of correlation between hourly PN and concentrations of CO, NO, and nitrogen dioxide (NO2) at each site over the entire year was generally low to moderate (r values in the range of 0.1-0.5), with a few notable exceptions. In general, associations between PN and O3 were either negative or insignificant. Similar analyses of seasonal data resulted in levels of correlation with large variation, ranging from 0.0 to 0.94 depending on site and season. Summertime data showed a generally higher correlation between the 24-hr average PN concentrations and CO, NO, and NO2 than corresponding hourly concentrations. Hourly correlations between PN and both CO and NO were strengthened during morning rush-hour periods, indicating a common vehicular source. Comparing hourly particle number concentrations between sites also showed low to moderate spatial correlations, with most correlation coefficients below 0.4. Given the low to moderate associations found in this study, gaseous co-pollutants should not be used as surrogates to assess human exposure to airborne particle number concentrations.     

Analysis of PM10, PM2.5, and PM2 5-10 concentrations in Santiago, Chile, from 1989 to 2001

Daily particle samples were collected in Santiago, Chile, at four urban locations from January 1, 1989, through December 31, 2001. Both fine PM with da < 2.5 microm (PM2.5) and coarse PM with 2.5 < da < 10 microm (PM2.5-10) were collected using dichotomous samplers. The inhalable particle fraction, PM10, was determined as the sum of fine and coarse concentrations. Wind speed, temperature and relative humidity (RH) were also measured continuously. Average concentrations of PM2.5 for the 1989-2001 period ranged from 38.5 microg/m3 to 53 microg/m3. For PM2.5-10 levels ranged from 35.8-48.2 microg/m3 and for PM10 results were 74.4-101.2 microg/m3 across the four sites. Both annual and daily PM2.5 and PM10 concentration levels exceeded the U.S. National Ambient Air Quality Standards and the European Union concentration limits. Mean PM2.5 levels during the cold season (April through September) were more than twice as high as those observed in the warm season (October through March); whereas coarse particle levels were similar in both seasons. PM concentration trends were investigated using regression models, controlling for site, weekday, month, wind speed, temperature, and RH. Results showed that PM2.5 concentrations decreased substantially, 52% over the 12-year period (1989-2000), whereas PM2.5-10 concentrations increased by approximately 50% in the first 5 years and then decreased by a similar percentage over the following 7 years. These decreases were evident even after controlling for significant climatic effects. These results suggest that the pollution reduction programs developed and implemented by the Comisión Nacional del Medio Ambiente (CONAMA) have been effective in reducing particle levels in the Santiago Metropolitan region. However, particle levels remain high and it is thus imperative that efforts to improve air quality continue.     

Model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network models

The Borman Expressway is a heavily traveled 16-mi segment of the Interstate 80/94 freeway through Northwestern Indiana. The Lake and Porter counties through which this expressway passes are designated as particulate matter < 2.5 microm (PM2.5) and ozone 8-hr standard nonattainment areas. The Purdue University air quality group has been collecting PM2.5, carbon monoxide (CO), wind speed, wind direction, pressure, and temperature data since September 1999. In this work, regression and neural network models were developed for forecasting hourly PM2.5 and CO concentrations. Time series of PM2.5 and CO concentrations, traffic data, and meteorological parameters were used for developing the neural network and regression models. The models were compared using a number of statistical quality indicators. Both models had reasonable accuracy in predicting hourly PM2.5 concentration with coefficient of determination -0.80, root mean square error (RMSE) <4 microg/m3, and index of agreement (IA) > 0.90. For CO prediction, both models showed moderate forecasting performance with a coefficient of determination -0.55, RMSE < 0.50 ppm, and IA -0.85. These models are computationally less cumbersome and require less number of predictors as compared with the deterministic models. The availability of real time PM2.5 and CO forecasts will help highway managers to identify air pollution episodic events beforehand and to determine mitigation strategies.     

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.