Using Continuous PM2.5 Monitoring Data to Report an Air Quality Index

Abstract

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM_2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area’s (MSA’s) AQI.¹ Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality.

This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM_2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency’s (EPA’s) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity.² The chosen measure of model adequacy is r², the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greens-boro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r² of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r² of 0.86, based on 214 paired observations.     

Ozone patterns for three metropolitan statistical areas in North Carolina, USA

As part of an effort by the state of North Carolina to develop a State Implementation Plan (SIP) for 1-h peak ozone control, a network of ozone stations was established to monitor surface ozone concentrations across the state. Between 19 and 23 ozone stations made continuous surface measurements between 1993 and 1995 surrounding three major metropolitan statistical areas (MSAs): Raleigh/Durham (RDU), Charlotte/Mecklenburg (CLT), and Greensboro/High Point/Winston-Salem (GSO). Statistical averages of the meteorological and ozone data were performed at each Metropolitan Statistical Area (MSA) to study trends and/or relationships on high ozone days (days in which one of the MSA sites measured an hourly ozone concentration90.0 ppbv). County emission maps of precursor gases, wind roses, total area averages of ozone, total downwind averages of ozone deviations, upwind averages of ozone, and a modified delta ozone analysis were all obtained and analyzed. The results of this study show a reduction in the delta ozone relative to an earlier study at RDU, but no average significant change at CLT (no comparison can be made for GSO). The statistical data analyses in this study are used to quantify the importance of local contributions and regional transport, to ozone air pollution in the MSAs.     

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

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

A Simple Methodology for the Determination of Back Trajectories

Abstract

This study comprehensively characterizes hourly fine particulate matter (PM_2.5) concentrations measured via a tapered element oscillating microbalance (TEOM), β-gauge, and nephelometer from four different monitoring sites in U.S. Environment Protection Agency (EPA) Region 5 (in U.S. states Illinois, Michigan, and Wisconsin) and compares them to the Federal Reference Method (FRM). Hourly characterization uses time series and autocorrelation. Hourly data are compared with FRM by averaging across 24-hr sampling periods and modeling against respective daily FRM concentrations. Modeling uses traditional two-variable linear least-squares regression as well as innovative nonlinear regression involving additional meteorological variables such as temperature and humidity. The TEOM shows a relationship with season and temperature, linear correlation as low as 0.7924 and nonlinear model correlation as high as 0.9370 when modeled with temperature. The β-gauge shows no relationship with season or meteorological variables. It exhibits a linear correlation as low as 0.8505 with the FRM and a nonlinear model correlation as high as 0.9339 when modeled with humidity. The nephelometer shows no relationship with season or temperature but a strong relationship with humidity is observed. A linear correlation as low as 0.3050 and a nonlinear model correlation as high as 0.9508 is observed when modeled with humidity. Nonlinear models have higher correlation than linear models applied to the same dataset. This correlation difference is not always substantial, which may introduce a tradeoff between simplicity of model and degree of statistical association. This project shows that continuous monitor technology produces valid PM_2.5 characterization, with at least partial accounting for variations in concentration from gravimetric reference monitors once appropriate nonlinear adjustments are applied. Although only one regression technically meets new EPA National Ambient Air Quality Standards (NAAQS) Federal Equivalent Method (FEM) correlation coefficient criteria, several others are extremely close, showing optimistic potential for use of this nonlinear adjustment model in garnering EPA NAAQS FEM approval for continuous PM_2.5 sampling methods.     

Evaluation and comparison of continuous fine particulate matter monitors for measurement of ambient aerosols

To provide a scientific basis for the selection and use of continuous monitors for exposure and/or health effects studies, and for compliance and episode measurements at strategic locations in the State of New Jersey, we evaluated the performance of seven continuous fine particulate matter (PM2.5) monitors in the present study. Gravimetric samplers, as reference methods, were collocated with realtime instruments in both laboratory and field tests. The results of intercomparison of real-time monitors showed that the two nephelometers used in this study correlated extremely well (r2 approximately 0.97), and two tapered element oscillating monitors (TEOM 1400 and TEOM filter dynamics measurement system [FDMS]) correlated well (r2 > 0.85), whereas two beta gauges displayed a weaker correlation (r2 < 0.6). During a summertime controlled (laboratory) evaluation, the measurements made with the gravimetric method correlated well with the 24-hr integrated measurements made with the real-time monitors. The SidePak nephelometer overestimated the particle concentration by a factor of approximately 3.4 compared with the gravimetric method. During a summertime field evaluation, the TEOM FDMS monitor reported approximately 30% higher mass concentration than the Federal Reference Method (FRM); and the difference could be explained by the loss of semi-volatile materials from the FRM sampler. Results also demonstrated that 24-hr average PM2.5 mass concentrations measured by beta gauges and TEOM (50 degrees C) in winter correlated well with the integrated gravimetric method. Seasonal differences were observed in the performance of the TEOM (50 degrees C) monitor in measuring the particle mass attributed to the higher semi-volatile material loss in the winter weather. In applying the realtime particulate matter monitoring data into Air Quality Index (AQI) reporting, the Conroy method and the 8-hr end-hour average method were both found to be suitable.     

Techniques for High-Quality Ambient Coarse Particle Mass Measurements

ABSTRACT

Several recent studies have shown associations between ambient concentrations of particle mass (PM) and rates of morbidity and mortality in the general population. These studies have raised the issue of quality of coarse mass (CM, PM between 2.5 and 10 µm) data used for these purposes. CM data may have precision three or more times worse than the associated PM _2.5 or PM₁₀ data, depending on the measurement method, PM _2.5 to PM ₁₀ ratios, and CM concentrations. CM is measured either as the difference between collocated _PM10 and _PM2.5 samplers or more directly with a dichotomous (virtual impactor) sampler. CM precision for the difference method is degraded due to the increased errors inherent with using the difference between two independent measurements, as well as the high PM_2.5 to PM₁₀ ratios (and low CM concentrations) typical of the eastern United States. The dichotomous sampler (dichot) makes a more direct measurement of CM, but there is a potential for significant postexposure loss of particles from unoiled CM dichot filters, as well as uncertainties in the dichot’s CM channel enrichment factor. Compared to the dichot, low-volume inertial impactor samplers such as the Harvard Impactor (HI) or PM2.5 Federal Reference Method (FRM) are simpler to operate and maintain, provide sharper cut points, and do not require oiled filters to prevent loss of CM from the filter during transport. With the recent interest in CM spatial and temporal variability with respect to PM health effects, we have developed modifications to the HI PM method to provide measurements of 24-hour PM with estimated CM precision of better than 5% CV and r² higher than 0.95, primarily by lowering field blank variability and increasing gravimetric analytical precision. These high-precision PM techniques are not limited to the HI sampler; they can also be applied to the _PM2.5 FRM sampler. The measurement methods described here can be applied to future PM studies to avoid the potential problems with exposure assessment caused by CM measurements that have poor precision.     

A comparison of four gravimetric fine particle sampling methods.

A study was conducted to compare four gravimetric methods of measuring fine particle (PM2.5) concentrations in air: the BGI, Inc. PQ200 Federal Reference Method PM2.5 (FRM) sampler; the Harvard-Marple Impactor (HI); the BGI, Inc. GK2.05 KTL Respirable/Thoracic Cyclone (KTL); and the AirMetrics MiniVol (MiniVol). Pairs of FRM, HI, and KTL samplers and one MiniVol sampler were collocated and 24-hr integrated PM2.5 samples were collected on 21 days from January 6 through April 9, 2000. The mean and standard deviation of PM2.5 levels from the FRM samplers were 13.6 and 6.8 microg/m3, respectively. Significant systematic bias was found between mean concentrations from the FRM and the MiniVol (1.14 microg/m3, p = 0.0007), the HI and the MiniVol (0.85 microg/m3, p = 0.0048), and the KTL and the MiniVol (1.23 microg/m3, p = 0.0078) according to paired t test analyses. Linear regression on all pairwise combinations of the sampler types was used to evaluate measurements made by the samplers. None of the regression intercepts was significantly different from 0, and only two of the regression slopes were significantly different from 1, that for the FRM and the MiniVol [beta1 = 0.91, 95% CI (0.83-0.99)] and that for the KTL and the MiniVol [beta1 = 0.88, 95% CI (0.78-0.98)]. Regression R2 terms were 0.96 or greater between all pairs of samplers, and regression root mean square error terms (RMSE) were 1.65 microg/m3 or less. These results suggest that the MiniVol will underestimate measurements made by the FRM, the HI, and the KTL by an amount proportional to PM2.5 concentration. Nonetheless, these results indicate that all of the sampler types are comparable if approximately 10% variation on the mean levels and on individual measurement levels is considered acceptable and the actual concentration is within the range of this study (5-35 microg/m3).     

Adjusting tapered element oscillating microbalance data for comparison with Federal Reference Method PM2.5 measurements in region 5

Continuous monitoring of particulate matter (PM) with a diameter less than 2.5 microm (PM2.5) is quickly gaining acceptance as an alternative means of measuring fine PM in the United States. For this project, data were taken from all monitoring sites within Region 5 that used the tapered element oscillating microbalance (TEOM) for PM2.5 and had a collocated Federal Reference Method (FRM) monitor. Scatter plots of TEOM versus FRM show that for a significant fraction of the observations, an independent factor causes the TEOM to underestimate the FRM value. This underestimation appears to increase as temperature decreases. For this analysis, a linear relationship was fit to the TEOM versus FRM data, allowing a break or knot in the relationship, modeled as a change of slope, at a site-specific temperature. To test whether the models are adequate for adjusting future measurements, models were also developed using the first year of data only, and the remaining observations were used to test the durability of the relationships. For all but one monitor in Minnesota, the models developed for each site had consistently high R2s, were predictive of future measurements, and could be used to derive "FRM-like" results from the TEOM measurements. The temperature knots fitted by the model for individual sites ranged from 12.9 to 20.6 degrees C. Data from all six sites in the state of Michigan were also combined to determine if a single model could be developed for the entire state. While the single model for the state of Michigan worked reasonably well, some of the predicted concentrations at individual sites were systematically underestimating the observed concentrations on more polluted days. The same conclusion was drawn for a Region 5-wide model. This approach was also found to work very well for six individual TEOM monitors in New York State.     

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.     

PM10: Results of a One-Year Monitoring Survey in the Netherlands

A comprehensive field assessment has been made of the measurement performance of PM10 inlets. Both precision and comparability are approximately 4 percent, complying well with the requirements of the proposed Federal Reference Method (FRM). Fluctuations in sampling efficiency play a dominant role. Hence, both comparability and precision can be interpreted in terms of changes in the 50 percent cutoff diameter D50. In this way a D50 performance of about 0.7 μm is deduced, clearly within the proposed FRM requirement of D50 = 10 ± 1 μm. There exists no fixed linear relationship between PM10 and TSP (total suspended particulate matter): different average situations yield different regression coefficients (Western Europe: 0.7 and USA: 0.5). Furthermore, there are different conversion factors, representative of average (0.5-0.7) or episodic situations of high concentration levels (0.8-0.9). Hence, TSP air quality standards should not be replaced by PM10 ones simply by using the regression results from various national studies because this could yield unequal stringent PM10 standards.     

Improving the correlations of ambient tapered element oscillating microbalance PM2.5 data and SHARP 5030 Federal Equivalent Method in Ontario: A multiple linear regression analysis

Tapered element oscillating microbalances equipped with sample equilibration system (TEOM-SES) used by the province of Ontario for the ambient monitoring of PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) in its air quality index (AQI) network were collocated with the Synchronized Hybrid Ambient Real-time Particulate monitor (SHARP 5030) at two monitoring sites for a period spanning approximately 2 years to determine the similarities and differences between the measurement outputs of both instrumental systems. Due mainly to mass loss observed with the TEOM-SES in cooler months, the province has recently switched its PM_2.5 instrumentation at all stations in its monitoring network from the TEOM-SES to the SHARP 5030, which has the U.S. Environmental Protection Agency (EPA) Federal Equivalent Method (FEM) Class III designation. Thus, it has become imperative to develop corrections for historical and future TEOM measurements for the purpose of making them more agreeable to the new FEM method. This work details the authors’ multiple linear regression analyses (MLRAs) of particulate matter data from both instrumental monitors, with the inclusion of operational parameters of physicochemical relevance for both cases of transformations of historical TEOM and TEOM measurements to be made in the future. For historical TEOM data, it was observed that the transformations only benefited winter and fall months. Furthermore, comparisons of the transformed historical TEOM data with PM_2.5 concentrations determined from the Federal Reference Method (FRM) sampler at seven locations within the province showed marked improvements over the observed TEOM-FRM comparisons.

Implications:This work provides a path to correcting the historically observed underreporting of particulate mass in winter and fall in Ontario by making the TEOM-based continuous data resemble the new FEM outputs (in this case, more SHARP-like). It is possible that the transformation of mainly winter TEOM data as detailed in this work may potentially lead to revisions in historical annual composite mean PM_2.5 concentrations and total annual number of days PM_2.5 exceeded the Canada-wide Standard (CWS) metric across the province.     

Demonstrating effectiveness of passive radon-resistant new construction

Fifty percent of homes tested for radon in Rock Island County, IL, have radon levels above the U.S. Environmental Protection Agency (EPA) action guideline of 4 picoCuries per liter (pCi/L) of air. Therefore, the county is classified by the EPA as Zone 1 on the EPA's Map of Radon Potential. Radon-resistant new construction (RRNC) strategies for new homes are recommended by the EPA in Zone 1 areas. One city in the county, East Moline, reduced the cost of building permits for contractors volunteering to build new homes incorporating modified passive RRNC. Forty-six of 124 new homes built with passive RRNC in the city were tested during this study. Only 27 of the homes tested were below 4-pCi/L, justifying the importance of testing the system to ensure levels are below the action guideline. To provide additional support to an argument in favor of changing city building codes to the required RRNC, 23 of the homes were also tested with the systems deactivated. After systems were deactivated, 73% of the homes had radon levels above the action guideline. Four homes were sampled for bioaerosols to evaluate if passive RRNC might impact other indicators of poor indoor air quality (IAQ). The results of the research will be discussed here.     

Analysis of short-term ozone and PM2.5 measurements: Characteristics and relationships for air sensor messaging

Air quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM_2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM_2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.

Implications: Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.     

Collocated comparisons of continuous and filter-based PM2.5 measurements at Fort McMurray,Alberta, Canada

Collocated comparisons for three PM_2.5 monitors were conducted from June 2011 to May 2013 at an air monitoring station in the residential area of Fort McMurray, Alberta, Canada, a city located in the Athabasca Oil Sands Region. Extremely cold winters (down to approximately ?40°C) coupled with low PM_2.5 concentrations present a challenge for continuous measurements. Both the tapered element oscillating microbalance (TEOM), operated at 40°C (i.e., TEOM₄₀), and Synchronized Hybrid Ambient Real-time Particulate (SHARP, a Federal Equivalent Method [FEM]), were compared with a Partisol PM_2.5 U.S. Federal Reference Method (FRM) sampler. While hourly TEOM₄₀ PM_2.5 were consistently ~20–50% lower than that of SHARP, no statistically significant differences were found between the 24-hr averages for FRM and SHARP. Orthogonal regression (OR) equations derived from FRM and TEOM₄₀ were used to adjust the TEOM₄₀ (i.e., TEOM_adj) and improve its agreement with FRM, particularly for the cold season. The 12-year-long hourly TEOM_adj measurements from 1999 to 2011 based on the OR equations between SHARP and TEOM₄₀ were derived from the 2-year (2011–2013) collocated measurements. The trend analysis combining both TEOM_adj and SHARP measurements showed a statistically significant decrease in PM_2.5 concentrations with a seasonal slope of ?0.15 μg m^?3 yr^?1 from 1999 to 2014.Implications: Consistency in PM_2.5 measurements are needed for trend analysis. Collocated comparison among the three PM_2.5 monitors demonstrated the difference between FRM and TEOM, as well as between SHARP and TEOM. The orthogonal regressions equations can be applied to correct historical TEOM data to examine long-term trends within the network.     

Investigation into the use of satellite data in aiding characterization of particulate air quality in the Atlanta, Georgia metropolitan area

Poor air quality episodes occur often in metropolitan Atlanta, GA. The primary focus of this research is to assess the capability of satellites as a tool in characterizing air quality in Atlanta. Results indicate that intracity PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter) concentrations show similar patterns as other U.S. urban areas, with the highest concentrations occurring within the city. PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) have higher values in the summer than spring, yet MODIS AOD doubles in the summer unlike PM2.5. Most (80%) of the Ozone Monitoring Instrument aerosol index (AI) is below 0.5 with little differences between spring and summer. Using this value as a constraint of the carbonaceous aerosol signal in the urban area, aerosol transport events such as wildfire smoke associated with higher positive AI values can be identified. The results indicate that MODIS AOD is well correlated with PM2.5 on a yearly and seasonal basis with correlation coefficients as high as 0.8 for Terra and 0.7 for Aqua. A possible alternative view of the PM2.5 and AOD relationship is seen through the use of AOD thresholds. These probabilistic thresholds provide a means to describe the air quality index (AQI) through the use of multiyear AOD records for a specific area. The National Ambient Air Quality Standards (NAAQS) are used to classify the AOD into different AQI codes and probabilistically determine thresholds of AOD that represent most of a specific AQI category. For example, 80% of cases of moderate AQI days have AOD values between 0.5 and 0.6. The development of AOD thresholds provides a useful tool for evaluating air quality from the use of satellites in regions where there are sparse ground-based measurements of PM2.5.     

Redundant Measurements of Urban Air Monitoring Networks in Air Quality Reporting

Abstract

This paper describes a statistical method to assess site redundancy of urban air monitoring networks in reporting daily Pollutant Standards Index (PSI), average concentrations, and the number of exceedances. Such a statistical method has identified significant redundancy in monitoring sites for one-year measurements of two air monitoring networks in Taiwan. There are five redundant sites out of 15 monitoring sites in the Taipei area and eight redundant sites out of 18 monitoring sites in the Kaohsiung area. By using the statistical method presented in this paper to downsize the monitoring networks, we can determine not only the number of redundant sites but also the priority of site removals. The derived sub-networks can maintain consistency in reporting air quality without significant changes in the spatial variations of air measurements for an existing air monitoring network.     

New York State urban and rural measurements of continuous PM2.5 mass by FDMS, TEOM, and BAM

Field evaluations and comparisons of continuous fine particulate matter (PM2,5) mass measurement technologies at an urban and a rural site in New York state are performed. The continuous measurement technologies include the filter dynamics measurement system (FDMS) tapered element oscillating microbalance (TEOM) monitor, the stand-alone TEOM monitor (without the FDMS), and the beta attenuation monitor (BAM). These continuous measurement methods are also compared with 24-hr integrated filters collected and analyzed under the Federal Reference Method (FRM) protocol. The measurement sites are New York City (the borough of Queens) and Addison, a rural area of southwestern New York state. New York City data comparisons between the FDMS TEOM, BAM, and FRM are examined for bias and seasonality during a 2-yr period. Data comparisons for the FDMS TEOM and FRM from the Addison location are examined for the same 2-yr period. The BAM and FDMS measurements at Queens are highly correlated with each other and the FRM. The BAM and FDMS are very similar to each other in magnitude, and both are approximately 25% higher than the FRM filter measurements at this site. The FDMS at Addison measures approximately 9% more mass than the FRM. Mass reconstructions using the speciation trends network filter data are examined to provide insight as to the contribution of volatile species of PM2.5 in the FDMS mass measurement and the fraction that is likely lost in the FRM mass measurement. The reconstructed mass at Queens is systematically lower than the FDMS by approximately 10%.     

Demonstrating Effectiveness of Passive Radon-Resistant New Construction

ABSTRACT

Fifty percent of homes tested for radon in Rock Island County, IL, have radon levels above the U.S. Environmental Protection Agency (EPA) action guideline of 4 picoCuries per liter (pCi/L) of air. Therefore, the county is classified by the EPA as Zone 1 on the EPA's Map of Radon Potential. Radon-resistant new construction (RRNC) strategies for new homes are recommended by the EPA in Zone 1 areas. One city in the county, East Moline, reduced the cost of building permits for contractors volunteering to build new homes incorporating modified passive RRNC. Forty-six of 124 new homes built with passive RRNC in the city were tested during this study. Only 27 of the homes tested were below 4-pCi/L, justifying the importance of testing the system to ensure levels are below the action guideline. To provide additional support to an argument in favor of changing city building codes to the required RRNC, 23 of the homes were also tested with the systems deactivated. After systems were deactivated, 73% of the homes had radon levels above the action guideline. Four homes were sampled for bioaerosols to evaluate if passive RRNC might impact other indicators of poor indoor air quality (IAQ). The results of the research will be discussed here.     

Retained nitrate, hydrated sulfates, and carbonaceous mass in federal reference method fine particulate matter for six eastern U.S. cities

Material balance of fine particulate matter (PM2.5) measured with the Federal Reference Method (FRM) is developed for one rural and five urban locations in the eastern half of the United States using routine Speciation Trends Network (STN) and FRM chemical measurements and thermodynamic models. The Aerosol Inorganics Model is used to estimate retained particle bound water, and an ammonium nitrate evaporation model is used to estimate nitrate concentrations retained on the Teflon-membrane filter of the FRM. To address large uncertainties in carbonaceous mass calculated from STN carbon measurements, retained carbonaceous mass is derived by material balance between PM2.5 FRM mass and estimates of its non-carbon constituents. The resulting sulfate, adjusted nitrate, derived water, inferred carbonaceous material balance approach (SANDWICH) is compared with reconstructed fine mass (RCFM) using the Interagency Monitoring of Protected Visual Environments monitoring program equation. For this study, the SANDWICH method resulted in approximately 21-27% higher sulfate mass and approximately 24-85% lower nitrate mass. The combined mass associated with sulfates and nitrates, however, are well within +/- 10% of the proportion derived using the more traditional RCFM method. The discrepancies between SANDWICH and measurement-derived carbonaceous mass vary from -21% to +56% on an annual basis and are attributed in part to urban-rural source influences and uncertainties in estimating FRM-retained carbonaceous mass.     

Aerosol Research and Inhalation Epidemiological Study (ARIES): air quality and daily mortality statistical modeling--interim results

The Aerosol Research and Inhalation Epidemiological Study (ARIES) is an EPRI-sponsored project to collect air quality and meteorological data at a single site in northwestern Atlanta, GA. Seventy high-resolution air quality indicators (AQIs) are used to examine statistical relationships between air quality and health outcome end points. Contemporaneous mortality data are collected for Fulton and DeKalb counties in Georgia. Currently, 12 months of air quality and weather data are available for analysis, from August 1998 through July 1999. The interim mortality analysis used Poisson regression in generalized additive models (GAMs). The estimated log-linear association of mortality with various AQIs was adjusted for smoothed functions of time and meteorological data. The analysis considered daily deaths due to all nonaccidental causes, deaths to persons 65 years or older, and deaths in each of the two constituent counties. The fine particle effect associated with the four mortality subgroups, using only today (lag 0), yesterday (lag 1), 2-day average (average of today and yesterday), and first difference (today minus yesterday) measurements of the air quality relative to today's number of deaths was positive for lag 0, lag 1, and 2-day average and positive only for decedents at least 65 years of age using first difference. The t values ranged from 0.81 to 1.15 for lag 0, 1.04 to 1.53 for lag 1, 1.10 to 1.66 for 2-day average, and -0.32 to 0.33 for first difference with 346 or 347 days of data. No statistically significant estimate of the linear coefficient was found for the other 14 air quality variables in our interim analysis for the four mortality subgroups. We discuss diagnostics to support these models. These interim analyses did not include an evaluation of sensitivity to a larger set of lag structures, nonlinear model specifications, multipollutant analyses, alternative weather model and smoothing model specifications, air pollution imputation schemes, or cause-specific mortality indicators, nor did they include a full reporting of model selection or goodness-of-fit indicators. No conclusion can be drawn at this time about whether the findings from subsequent studies have sufficiently greater power to detect effects comparable to those found in other U.S. cities including at least 2 or 3 years of data.