Exposure and measurement contributions to estimates of acute air pollution effects

Is model-estimated PM2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis

Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.

Maternal, infant, and environmental risk factors for sudden unexpected infant deaths: results from a large, administrative cohort

OBJECTIVES

Many studies of sudden unexpected infant death (SUID) have focused on individual domains of risk factors (maternal, infant, and environmental), resulting in limited capture of this multifactorial outcome. The objective of this study was to examine the geographic distribution of SUID in San Diego County, and assess maternal, infant, and environmental risk factors from a large, administrative research platform.

STUDY DESIGN

Births in California between 2005 and 2017 were linked to hospital discharge summaries and death files. From this retrospective birth cohort, cases of SUID were identified from infant death files in San Diego County. We estimated adjusted hazard ratios (aHRs) for infant, maternal, and environmental factors and SUID in multivariable Cox regression analysis. Models were adjusted for maternal sociodemographic characteristics and prenatal nicotine exposure.

RESULTS

There were 211 (44/100,000 live births; absolute risk 0.04%) infants with a SUID among 484,905 live births. There was heterogeneity in geographic distribution of cases. Multiparity (0.05%; aHR 1.4, 95% confidence interval (CI) 1.1, 1.9), maternal depression (0.11%; aHR 1.8, 95% CI 1.0, 3.4), substance-related diagnoses (0.27%; aHR 2.3, 95% CI 1.3, 3.8), cannabis-related diagnosis (0.35%; aHR 2.7, 95% CI 1.5, 5.0), prenatal nicotine use (0.23%; aHR 2.5, 95% CI 1.5, 4.2), preexisting hypertension (0.11%; aHR 2.3, 95% CI 1.2, 4.3), preterm delivery (0.09%; aHR 2.1, 95% CI 1.5, 3.0), infant with a major malformation (0.09%; aHR 2.0, 95% CI 1.1, 3.6), respiratory distress syndrome (0.12%; aHR 2.6, 95% CI 1.5, 4.6), and select environmental factors were all associated with SUID.

CONCLUSIONS

Multiple risk factors were confirmed and expanded upon, and the geographic distribution for SUID in San Diego County was identified. Through this approach, prevention efforts can be targeted to geographies that would benefit the most.

Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA

A number of studies have found differing associations of disease outcomes with PM_2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO₂, NO_x, O₃, and SO₂), total PM_2.5 mass, and speciated PM_2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM_2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM_2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

Influence of exposure measurement errors on results from epidemiologic studies of different designs

In epidemiologic studies of health effects of air pollution, measurements or models are used to estimate exposure. Exposure estimates have errors that propagate to effect estimates in exposure-response models. We critically evaluate how types of exposure measurement error influenced bias and precision of effect estimates to understand conditions affecting interpretation of exposure-response models for epidemiologic studies of exposure to PM_2.5, NO₂, and SO₂. We reviewed available literature on exposure measurement error for time-series and long-term exposure epidemiology studies. For time-series studies, time-activity error (daily exposure concentration did not account for variation in exposure due to time-activity during a day) and nonambient (indoor) sources negatively biased the effect estimates and increased standard error, so uncertainty grew with increasing bias while underestimating the true health effect in these studies. Spatial error (deviation between true exposure concentration at an individual's location and concentration at a receptor) was ascribed to negatively biased effect estimates in most cases. Positive bias occurred for spatially variable pollutants when the variance of error correlated with the exposure estimate. For long-term exposure studies, most spatial errors did not bias the effect estimate. For both time-series and long-term exposure studies reviewed, large uncertainties were observed when exposure concentration was modeled with low spatial and temporal resolution for a spatially variable pollutant.

Estimation of personal exposure to fine particles (PM2.5) of ambient origin for healthy adults in Hong Kong

Personal exposure and ambient fine particles (PM_2.5) measurements for 13 adult subjects (ages 19-57) were conducted in Hong Kong between April 2014 and June 2015. Six to 21 personal samples (mean = 19) per subject were obtained throughout the study period. Samples were analyzed for mass by gravimetric analysis, and 19 elements (from Na to Pb) were analyzed using X-Ray Fluorescence. Higher subject-specific correlations between personal and ambient sulfur (r_s = 0.92; p < 0.001) were found as compared to PM_2.5 mass (r_s = 0.79; p < 0.001) and other elements (0.06 < r_s < 0.86). Personal vs. ambient sulfur regression yielded an average exposure factor (F_pex) of 0.73 ± 0.02, supporting the use of sulfur as a surrogate to estimate personal exposure to PM_2.5 of ambient origin (E_a). E_a accounted for 41-82% and 57-73% of total personal PM_2.5 exposures (P) by season and by subject, respectively. The importance of both E_a and non-ambient exposures (E_na, 11.2 ± 5.6 μg/m³; 32.5 ± 10.9%) are noted. Mixed-effects models were applied to estimate the relationships between ambient PM_2.5 concentrations and their corresponding exposure variables (E_a, P). Higher correlations for E_a (0.90; p < 0.001) than for P (0.58; p < 0.01) were found. A calibration coefficient < 1 suggests an attenuation of 22% (ranging 16-28%) of the true effect estimates when using average ambient concentrations at central monitoring stations as surrogates for E_a. Stationary ambient data can be used to assess population exposure only if PM exposure is dominated by E_a.

Individual-level modifiers of the acute effects of air pollution on mortality in Wuhan, China

Background

Ambient air pollution has posed negative effects on human health. Individual-level factors may modify this effect, but previous studies have controversial conclusions, and evidence is lacking especially in developing countries. This study aims to examine the modifying effects of sex, age, and education level of individuals on the associated between daily mortality and air pollutants, including particulate matter < 10 μm in aerodynamic diameter (PM₁₀), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂).

Methods

Time-series analysis was conducted to investigate the acute effects of the air pollution on daily mortality from January 2002 to December 2010 in Wuhan, China. Generalized Additive Models (GAM) were used to examine the association stratified by sex for non-accidental, cardiovascular, and respiratory mortality. For non-accidental mortality, stratified analysis was also conducted by age and educational level.

Results

Outdoor air pollution was associated with daily non-accidental and cardiovascular mortality. An increase of 10 μg/m³ in a 2-day average concentration of PM₁₀, SO₂, and NO₂ was corresponding to the increase in non-accidental mortality of 0.29% (95%CI: 0.06–0.53%), 1.22% (95%CI: 0.77–1.67%) and 1.60% (95%CI: 1.00–2.19%), respectively. The effects of air pollution were faster in females than males. The magnitude of the estimates was higher for females with low education, aged 65–75 years for PM₁₀ and < 65 years for SO₂. To be more specific, we observed that per 10 μg/m³ increase in SO₂ was association with increases in non-accidental mortality of 2.03% (95%CI: 1.38–2.67) for all females and 3.10% (95%CI: 2.05–4.16) for females with low education.

Conclusion

Females and people with low-education are more susceptible to the effect of air pollution, which would provide a sound scientific basis for determination of air pollution standards.

The effect of meteorological conditions and air pollution on the occurrence of type A and B acute aortic dissections

To explore the association of weather conditions and air pollutants with incidence risk of acute aortic dissection (AAD), we included patients who consecutively admitted to the emergency units of our hospital for AAD between Dec. 1, 2013, and Apr. 30, 2017. Their medical records were reviewed. The meteorological data (daily precipitation, minimal and maximal temperatures, mean atmospheric pressure, relative humidity) and air pollutants values [air daily index (AQI), aerodynamic diameter of 2.5 mm or less (PM2.5), aerodynamic diameter of 10 mm or less (PM10), ozone, nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3_8h)] over the same period were provided by the Chengdu Meteorological Bureau. Finally, a total of 345 patients were admitted with AAD. The results showed that the incidence of AAD was higher in winter than in summer (p < 0.001). Statistical analysis highlighted lower the atmospheric temperature, higher the incidence of AAD (p < 0.001). A significant correlation was found between air pollutants and AAD onset. AQI, PM2.5, SO2, and NO2 were independent predictors of incidence of AAD (OR = 1.006, p = 0.007; OR = 1.020, p < 0.001; OR = 1.037, p < 0.001; and OR = 0.925, p < 0.001; respectively). While, PM10, CO, and O3_8H had a neutral effect on risk of AAD onset. In conclusions, cold atmospheric temperature and larger daily temperature change were correlated with a higher incidence of AAD. AQI, PM2.5, and SO2 played important roles in triggering acute aortic events.

Short-term effects of ambient air pollution on emergency room admissions due to cardiovascular causes in Beijing, China

Ambient air pollution has been a major global public health issue. A number of studies have shown various adverse effects of ambient air pollution on cardiovascular diseases. In the current study, we investigated the short-term effects of ambient air pollution on emergency room (ER) admissions due to cardiovascular causes in Beijing from 2009 to 2012 using a time-series analysis. A total of 82430 ER cardiovascular admissions were recorded. Different gender (male and female) and age groups (15yrs ≤ age <65 yrs and age ≥ 65 yrs) were also examined by single model and multiple-pollutant model. Three major pollutants (SO₂, NO₂ and PM₁₀) had lag effects of 0-2 days on cardiovascular ER admissions. The relative risks (95% CI) of per 10 μg/m³ increase in PM₁₀, SO₂ and NO₂ were 1.008 (0.997-1.020), 1.008(0.999-1.018) and 1.014(1.003-1.024), respectively. The effect was more pronounced in age ≥65 and males in Beijing. We also found the stronger acute effects on the elderly and females at lag 0 than on the younger people and males at lag 2.

Survival analysis with functions of mismeasured covariate histories: the case of chronic air pollution exposure in relation to mortality in the nurses' health study

Environmental epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories, such as the 12-month moving average, in relation to chronic disease incidence or mortality. The individual exposure measurements that comprise such an exposure history are usually mis-measured, at least moderately, and, often, more substantially. To obtain unbiased estimates of Cox model hazard ratios for these complex mis-measured exposure functions, an extended risk set regression calibration method for Cox models is developed and applied to a study of long-term exposure to the fine particulate matter (PM_2.5) component of air pollution in relation to all-cause mortality in the Nurses' Health Study. Simulation studies under several realistic assumptions about the measurement error model and about the correlation structure of the repeated exposure measurements were conducted to assess the finite sample properties of this new method, and found that the method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage.

Spatial measurement errors in the field of spatial epidemiology

Background

Spatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological analysis of spatial data.

Methods

Google Scholar, Medline, and Scopus databases were searched using a broad set of terms for papers indexed by a term indicating location (space or geography or location or position) and measurement error (measurement error or measurement inaccuracy or misclassification or uncertainty): we reviewed all papers appearing before December 20, 2014. These papers and their citations were reviewed to identify the relevance to our review.

Results

We were able to define and classify spatial measurement errors into four groups: (1) pure spatial location measurement errors, including both non-instrumental errors (multiple addresses, geocoding errors, outcome aggregations, and covariate aggregation) and instrumental errors; (2) location-based outcome measurement error (purely outcome measurement errors and missing outcome measurements); (3) location-based covariate measurement errors (address proxies); and (4) Covariate-Outcome spatial misaligned measurement errors. We propose how these four classes of errors can be unified within an integrated theoretical model and possible solutions were discussed.

Conclusion

Spatial measurement errors are ubiquitous threat to the validity of spatial epidemiological studies. We propose a systematic framework for understanding the various mechanisms which generate spatial measurement errors and present practical examples of such errors.

Association between Ambient Air Pollution and Emergency Room Visits for Respiratory Diseases in Spring Dust Storm Season in Lanzhou, China

Background: Air pollution has become a major global public health problem. A number of studies have confirmed the association between air pollutants and emergency room (ER) visits for respiratory diseases in developed countries and some Asian countries, but little evidence has been seen in Western China. This study aims to concentrate on this region. Methods: A time-series analysis was used to examine the specific effects of major air pollutants (PM₁₀, SO₂ and NO₂) on ER visits for respiratory diseases from 2007 to 2011 in the severely polluted city of Lanzhou. We examined the effects of air pollutants for stratified groups by age and gender, accounting for the modifying effect of dust storms in spring to test the possible interaction. Results: Significant associations were found between outdoor air pollution concentrations and respiratory diseases, as expressed by daily ER visits in Lanzhou in the spring dust season. The association between air pollution and ER visits appeared to be more evident on dust days than non-dust days. Relative risks (RRs) and 95% CIs per 10 µg/m³ increase in 3-day PM₁₀ (L3), 5-day SO₂ (L5), and the average of current and previous 2-day NO₂ (L01) were 1.140 (1.071–1.214), 1.080 (0.967–1.205), and 1.298 (1.158–1.454), respectively, on dust days. More significant associations between PM₁₀, SO₂ and NO₂ and ER visits were found on dust days for elderly females, elderly males and adult males, respectively. Conclusions: This study strengthens the evidence of dust-exacerbated ER visits for respiratory diseases in Lanzhou.

Ambient air pollution and congenital heart defects in Lanzhou, China

Congenital heart defects are the most prevalent type of birth defects. The association of air pollution with congenital heart defects is not well understood. We investigated a cohort of 8,969 singleton live births in Lanzhou, China during 2010-2012. Using inverse distance weighting, maternal exposures to particulate matter with diameter ≤10μm (PM₁₀), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) were estimated as a combination of monitoring station levels for the time spent at home and the work location. We used logistic regression to estimate the associations, adjusting for maternal age, education, income, BMI, disease, folic acid intake and therapeutic drug use, and smoking; season of conception; fuels for cooking; and temperature. We found significant positive associations of Patent Ductus Arteriosus (PDA) with PM₁₀ during the 1^st trimester, 2^nd trimester and the entire pregnancy (OR _{1st trimester}=3.96, 95% Confidence Interval (CI): 1.36, 11.53; OR _{2nd trimester}=3.59, 95% Confidence Interval (CI): 1.57, 8.22; OR _{entire pregnancy}=2.09, 95% CI: 1.21, 3.62, per interquartile range (IQR) increment for PM₁₀ (IQR=71.2, 61.6, and 27.4 μg/m³ respectively)), and associations with NO₂ during 2^nd trimester and entire pregnancy (OR _{2nd trimester}= 1.92, 95% CI: 1.11, 3.34; OR _{entire pregnancy}=2.32, 95% Cl: 1.14, 4.71, per IQR increment for NO₂ (IQR=13.4 and 10.9 μg/m³ respectively)). The associations for congenital malformations of the great arteries and pooled cases showed consistent patterns. We also found positive associations for congenital malformations of cardiac septa with PM₁₀ exposures in the 2^nd trimester and the entire pregnancy, and SO₂ exposures in the entire pregnancy. Results indicate a health burden from maternal exposures to air pollution, with increased risk of congenital heart defects.

Modification of the effect of ambient air pollution on pediatric asthma emergency visits: susceptible subpopulations

BACKGROUND

Children may have differing susceptibility to ambient air pollution concentrations depending on various background characteristics of the children.

METHODS

Using emergency department (ED) data linked with birth records from Atlanta, Georgia, we identified ED visits for asthma or wheeze among children 2 to 16 years of age from 1 January 2002 through 30 June 2010 (n = 109,758). We stratified by preterm delivery, term low birth weight, maternal race, Medicaid status, maternal education, maternal smoking, delivery method, and history of a bronchiolitis ED visit. Population-weighted daily average concentrations were calculated for 1-hour maximum carbon monoxide and nitrogen dioxide; 8-hour maximum ozone; and 24-hour average particulate matter less than 10 microns in diameter, particulate matter less than 2.5 microns in diameter (PM2.5), and the PM2.5 components sulfate, nitrate, ammonium, elemental carbon, and organic carbon, using measurements from stationary monitors. Poisson time-series models were used to estimate rate ratios for associations between 3-day moving average pollutant concentrations and daily ED visit counts and to investigate effect-measure modification by the stratification factors.

RESULTS

Associations between pollutant concentrations and asthma exacerbations were larger among children born preterm and among children born to African American mothers. Stratification by race and preterm status together suggested that both factors affected susceptibility. The largest estimated effect size (for an interquartile range increase in pollution) was observed for ozone among preterm births to African American mothers: rate ratio = 1.138 (95% confidence interval = 1.077-1.203). In contrast, the rate ratio for the ozone association among full-term births to mothers of other races was 1.025 (0.970-1.083).

CONCLUSIONS

Results support the hypothesis that children vary in their susceptibility to ambient air pollutants.

Effects of ambient air pollution measurement error on health effect estimates in time-series studies: a simulation-based analysis

In this study, we investigated bias caused by spatial variability and spatial heterogeneity in outdoor air-pollutant concentrations, instrument imprecision, and choice of daily pollutant metric on risk ratio (RR) estimates obtained from a Poisson time-series analysis. Daily concentrations for 12 pollutants were simulated for Atlanta, Georgia, at 5 km resolution during a 6-year period. Viewing these as being representative of the true concentrations, a population-level pollutant health effect (RR) was specified, and daily counts of health events were simulated. Error representative of instrument imprecision was added to the simulated concentrations at the locations of fixed site monitors in Atlanta, and these mismeasured values were combined to create three different city-wide daily metrics (central monitor, unweighted average, and population-weighted average). Given our assumptions, the median bias in the RR per unit increase in concentration was found to be lowest for the population-weighted average metric. Although the Berkson component of error caused bias away from the null in the log-linear models, the net bias due to measurement error tended to be towards the null. The relative differences in bias among the metrics were lessened, although not eliminated, by scaling results to interquartile range increases in concentration.

Validity of geographically modeled environmental exposure estimates

Geographic modeling is increasingly being used to estimate long-term environmental exposures in epidemiologic studies of chronic disease outcomes. However, without validation against measured environmental concentrations, personal exposure levels, or biologic doses, these models cannot be assumed a priori to be accurate. This article discusses three examples of epidemiologic associations involving exposures estimated using geographic modeling, and identifies important issues that affect geographically modeled exposure assessment in these areas. In air pollution epidemiology, geographic models of fine particulate matter levels have frequently been validated against measured environmental levels, but comparisons between ambient and personal exposure levels have shown only moderate correlations. Estimating exposure to magnetic fields by using geographically modeled distances is problematic because the error is larger at short distances, where field levels can vary substantially. Geographic models of environmental exposure to pesticides, including paraquat, have seldom been validated against environmental or personal levels, and validation studies have yielded inconsistent and typically modest results. In general, the exposure misclassification resulting from geographic models of environmental exposures can be differential and can result in bias away from the null even if non-differential. Therefore, geographic exposure models must be rigorously constructed and validated if they are to be relied upon to produce credible scientific results to inform epidemiologic research. To our knowledge, such models have not yet successfully predicted an association between an environmental exposure and a chronic disease outcome that has eventually been established as causal, and may not be capable of doing so in the absence of thorough validation.

Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies

BACKGROUND

Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures.

METHODS

Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects' homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models.

RESULTS

When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5.

CONCLUSIONS

Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.

Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies

BACKGROUND

Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures.

METHODS

Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects' homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models.

RESULTS

When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5.

CONCLUSIONS

Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.

Measurement error in time-series analysis: a simulation study comparing modelled and monitored data

Background

Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data.

Methods

Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003–2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban log_e(daily 1-hour maximum NO₂).

Results

When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background log_e(NO₂) and 38% for rural log_e(NO₂). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural log_e(NO₂) but more marked for urban log_e(NO₂).

Conclusion

Even if correlations between model and monitor data appear reasonably strong, additive classical measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful.

Particulate air pollution, ambulatory heart rate variability, and cardiac arrhythmia in retirement community residents with coronary artery disease

BACKGROUND

Decreased heart rate variability (HRV) has been associated with future cardiac morbidity and mortality and is often used as a marker of altered cardiac autonomic balance in studies of health effects of airborne particulate matter. Fewer studies have evaluated associations between air pollutants and cardiac arrhythmia.

OBJECTIVES

We examined relationships between cardiac arrhythmias, HRV, and exposures to airborne particulate matter.

METHODS

We measured HRV and arrhythmia with ambulatory electrocardiograms in a cohort panel study for up to 235 hr per participant among 50 nonsmokers with coronary artery disease who were ≥ 71 years of age and living in four retirement communities in the Los Angeles, California, Air Basin. Exposures included hourly outdoor gases, hourly traffic-related and secondary organic aerosol markers, and daily size-fractionated particle mass. We used repeated measures analyses, adjusting for actigraph-derived physical activity and heart rate, temperature, day of week, season, and community location.

RESULTS

Ventricular tachycardia was significantly increased in association with increases in markers of traffic-related particles, secondary organic carbon, and ozone. Few consistent associations were observed for supraventricular tachycardia. Particulates were significantly associated with decreased ambulatory HRV only in the 20 participants using ACE (angiotensin I-converting enzyme) inhibitors.

CONCLUSIONS

Although these data support the hypothesis that particulate exposures may increase the risk of ventricular tachycardia for elderly people with coronary artery disease, HRV was not associated with exposure in most of our participants. These results are consistent with previous findings in this cohort for systemic inflammation, blood pressure, and ST segment depression.

Characterization of Ambient Air Pollution Measurement Error in a Time-Series Health Study using a Geostatistical Simulation Approach

In recent years, geostatistical modeling has been used to inform air pollution health studies. In this study, distributions of daily ambient concentrations were modeled over space and time for 12 air pollutants. Simulated pollutant fields were produced for a 6-year time period over the 20-county metropolitan Atlanta area using the Stanford Geostatistical Modeling Software (SGeMS). These simulations incorporate the temporal and spatial autocorrelation structure of ambient pollutants, as well as season and day-of-week temporal and spatial trends; these fields were considered to be the true ambient pollutant fields for the purposes of the simulations that followed. Simulated monitor data at the locations of actual monitors were then generated that contain error representative of instrument imprecision. From the simulated monitor data, four exposure metrics were calculated: central monitor and unweighted, population-weighted, and area-weighted averages. For each metric, the amount and type of error relative to the simulated pollutant fields are characterized and the impact of error on an epidemiologic time-series analysis is predicted. The amount of error, as indicated by a lack of spatial autocorrelation, is greater for primary pollutants than for secondary pollutants and is only moderately reduced by averaging across monitors; more error will result in less statistical power in the epidemiologic analysis. The type of error, as indicated by the correlations of error with the monitor data and with the true ambient concentration, varies with exposure metric, with error in the central monitor metric more of the classical type (i.e., independent of the monitor data) and error in the spatial average metrics more of the Berkson type (i.e., independent of the true ambient concentration). Error type will affect the bias in the health risk estimate, with bias toward the null and away from the null predicted depending on the exposure metric; population-weighting yielded the least bias.

Confounding and exposure measurement error in air pollution epidemiology

Studies in air pollution epidemiology may suffer from some specific forms of confounding and exposure measurement error. This contribution discusses these, mostly in the framework of cohort studies. Evaluation of potential confounding is critical in studies of the health effects of air pollution. The association between long-term exposure to ambient air pollution and mortality has been investigated using cohort studies in which subjects are followed over time with respect to their vital status. In such studies, control for individual-level confounders such as smoking is important, as is control for area-level confounders such as neighborhood socio-economic status. In addition, there may be spatial dependencies in the survival data that need to be addressed. These issues are illustrated using the American Cancer Society Cancer Prevention II cohort. Exposure measurement error is a challenge in epidemiology because inference about health effects can be incorrect when the measured or predicted exposure used in the analysis is different from the underlying true exposure. Air pollution epidemiology rarely if ever uses personal measurements of exposure for reasons of cost and feasibility. Exposure measurement error in air pollution epidemiology comes in various dominant forms, which are different for time-series and cohort studies. The challenges are reviewed and a number of suggested solutions are discussed for both study domains.

Measurement error in environmental epidemiology and the shape of exposure-response curves

Both classical and Berkson exposure measurement errors as encountered in environmental epidemiology data can result in biases in fitted exposure-response relationships that are large enough to affect the interpretation and use of the apparent exposure-response shapes in risk assessment applications. A variety of sources of potential measurement error exist in the process of estimating individual exposures to environmental contaminants, and the authors review the evaluation in the literature of the magnitudes and patterns of exposure measurement errors that prevail in actual practice. It is well known among statisticians that random errors in the values of independent variables (such as exposure in exposure-response curves) may tend to bias regression results. For increasing curves, this effect tends to flatten and apparently linearize what is in truth a steeper and perhaps more curvilinear or even threshold-bearing relationship. The degree of bias is tied to the magnitude of the measurement error in the independent variables. It has been shown that the degree of bias known to apply to actual studies is sufficient to produce a false linear result, and that although nonparametric smoothing and other error-mitigating techniques may assist in identifying a threshold, they do not guarantee detection of a threshold. The consequences of this could be great, as it could lead to a misallocation of resources towards regulations that do not offer any benefit to public health.

A retrospective cohort study of stroke onset: implications for characterizing short term effects from ambient air pollution

BACKGROUND

Case-crossover studies used to investigate associations between an environmental exposure and an acute health response, such as stroke, will often use the day an individual presents to an emergency department (ED) or is admitted to hospital to infer when the stroke occurred. Similarly, they will use patient's place of residence to assign exposure. The validity of using these two data elements, typically extracted from administrative databases or patient charts, to define the time of stroke onset and to assign exposure are critical in this field of research as air pollutant concentrations are temporally and spatially variable. Our a priori hypotheses were that date of presentation differs from the date of stroke onset for a substantial number of patients, and that assigning exposure to ambient pollution using place of residence introduces an important source of exposure measurement error. The objective of this study was to improve our understanding on how these sources of errors influence risk estimates derived using a case-crossover study design.

METHODS

We sought to collect survey data from stroke patients presenting to hospital EDs in Edmonton, Canada on the date, time, location and nature of activities at onset of stroke symptoms. The daily mean ambient concentrations of NO₂ and PM(2.5) on the self-reported day of stroke onset was estimated from continuous fixed-site monitoring stations.

RESULTS

Of the 336 participating patients, 241 were able to recall when their stroke started and 72.6% (95% confidence interval [CI]: 66.9-78.3%) experienced stroke onset the same day they presented to the ED. For subjects whose day of stroke onset differed from the day of presentation to the ED, this difference ranged from 1 to 12 days (mean = 1.8; median = 1). In these subjects, there were no systematic differences in assigned pollution levels for either NO₂ or PM(2.5) when day of presentation rather than day of stroke onset was used. At the time of stroke onset, 89.9% (95% CI: 86.6-93.1%) reported that they were inside, while 84.5% (95% CI: 80.6 - 88.4%) reported that for most of the day they were within a 15 minute drive from home. We estimated that due to the mis-specification of the day of stroke onset, the risk of hospitalization for stroke would be understated by 15% and 20%, for NO₂ and PM(2.5), respectively.

CONCLUSIONS

Our data suggest that day of presentation and residential location data obtained from administrative records reasonably captures the time and location of stroke onset for most patients. Under these conditions, any associated errors are unlikely to be an important source of bias when estimating air pollution risks in this population.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

BACKGROUND

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.

METHODS

Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.

RESULTS

Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.

CONCLUSIONS

For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Ambient carbon monoxide and fine particulate matter in relation to preeclampsia and preterm delivery in western Washington State

BACKGROUND

Preterm delivery and preeclampsia are common adverse pregnancy outcomes that have been inconsistently associated with ambient air pollutant exposures.

OBJECTIVES

We aimed to prospectively examine relations between exposures to ambient carbon monoxide (CO) and fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)] and risks of preeclampsia and preterm delivery.

METHODS

We used data from 3,509 western Washington women who delivered infants between 1996 and 2006. We predicted ambient CO and PM2.5 exposures using regression models based on regional air pollutant monitoring data. Models contained predictor terms for year, month, weather, and land use characteristics. We evaluated several exposure windows, including prepregnancy, early pregnancy, the first two trimesters, the last month, and the last 3 months of pregnancy. Outcomes were identified using abstracted maternal medical record data. Covariate information was obtained from maternal interviews.

RESULTS

Predicted periconceptional CO exposure was significantly associated with preeclampsia after adjustment for maternal characteristics and season of conception [adjusted odds ratio (OR) per 0.1 ppm=1.07; 95% confidence interval (CI), 1.02-1.13]. However, further adjustment for year of conception essentially nullified the association (adjusted OR=0.98; 95% CI, 0.91-1.06). Associations between PM2.5 and preeclampsia were nonsignificant and weaker than associations estimated for CO, and neither air pollutant was strongly associated with preterm delivery. Patterns were similar across all exposure windows.

CONCLUSIONS

Because both CO concentrations and preeclampsia incidence declined during the study period, secular changes in another preeclampsia risk factor may explain the association observed here. We saw little evidence of other associations with preeclampsia or preterm delivery in this setting.

Implications of different approaches for characterizing ambient air pollutant concentrations within the urban airshed for time-series studies and health benefits analyses

BACKGROUND

In time-series studies of the health effects of urban air pollutants, decisions must be made about how to characterize pollutant levels within the airshed.

METHODS

Emergency department visits for pediatric asthma exacerbations were collected from Atlanta hospitals. Concentrations of carbon monoxide, nitrogen dioxide, ozone, sulfur dioxide, particulate matter less than 10 microns in diameter (PM10), particulate matter less than 2.5 microns in diameter (PM2.5), and the PM2.5 components elemental carbon, organic carbon, and sulfate were obtained from networks of ambient air quality monitors. For each pollutant we created three different daily metrics. For one metric we used the measurements from a centrally-located monitor; for the second we averaged measurements across the network of monitors; and for the third we estimated the population-weighted average concentration using an isotropic spatial model. Rate ratios for each of the metrics were estimated from time-series models.

RESULTS

For pollutants with relatively homogeneous spatial distributions we observed only small differences in the rate ratio across the three metrics. Conversely, for spatially heterogeneous pollutants we observed larger differences in the rate ratios. For a given pollutant, the strength of evidence for an association (i.e., chi-square statistics) tended to be similar across metrics.

CONCLUSIONS

Given that the chi-square statistics were similar across the metrics, the differences in the rate ratios for the spatially heterogeneous pollutants may seem like a relatively small issue. However, these differences are important for health benefits analyses, where results from epidemiological studies on the health effects of pollutants (per unit change in concentration) are used to predict the health impacts of a reduction in pollutant concentrations. We discuss the relative merits of the different metrics as they pertain to time-series studies and health benefits analyses.

Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error

In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse particulate matter (PM) with aerodynamic diameter between 2.5 and 10 μm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In this paper, we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved population average exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 US counties between the period 1999 and 2005, we find a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.

The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates

Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ~16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2 h spent at least 10 km away, 0.66 for ≥ 10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates.

Ambient air pollutant measurement error: characterization and impacts in a time-series epidemiologic study in Atlanta

In time-series studies of ambient air pollution and health in large urban areas, measurement errors associated with instrument precision and spatial variability vary widely across pollutants. In this paper, we characterize these errors for selected air pollutants and estimate their impacts on epidemiologic results from an ongoing study of air pollution and emergency department visits in Atlanta. Error was modeled for daily measures of 12 air pollutants using collocated monitor data to characterize instrument precision and data from multiple study area monitors to estimate population-weighted spatial variance. Time-series simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. Reductions in risk ratio due to instrument precision error were less than 6%. Error due to spatial variability resulted in average risk ratio reductions of less than 16% for secondary pollutants (O(3), PM(2.5) sulfate, nitrate and ammonium) and between 43% and 68% for primary pollutants (NO(x), NO(2), SO(2), CO, PM(2.5) elemental carbon); pollutants of mixed origin (PM(10), PM(2.5), PM(2.5) organic carbon) had intermediate impacts. Quantifying impacts of measurement error on health effect estimates improves interpretation across ambient pollutants.

An examination of exposure measurement error from air pollutant spatial variability in time-series studies

Relatively few studies have evaluated the effects of heterogeneous spatiotemporal pollutant distributions on health risk estimates in time-series analyses that use data from a central monitor to assign exposures. We present a method for examining the effects of exposure measurement error relating to spatiotemporal variability in ambient air pollutant concentrations on air pollution health risk estimates in a daily time-series analysis of emergency department visits in Atlanta, Georgia. We used Poisson generalized linear models to estimate associations between current-day pollutant concentrations and circulatory emergency department visits for the 1998-2004 time period. Data from monitoring sites located in different geographical regions of the study area and at different distances from several urban geographical subpopulations served as alternative measures of exposure. We observed associations for spatially heterogeneous pollutants (CO and NO(2)) using data from several different urban monitoring sites. These associations were not observed when using data from the most rural site, located 38 miles from the city center. In contrast, associations for spatially homogeneous pollutants (O(3) and PM(2.5)) were similar, regardless of the monitoring site location. We found that monitoring site location and the distance of a monitoring site to a population of interest did not meaningfully affect estimated associations for any pollutant when using data from urban sites located within 20 miles from the population center under study. However, for CO and NO(2), these factors were important when using data from rural sites located > or = 30 miles from the population center, most likely owing to exposure measurement error. Overall, our findings lend support to the use of pollutant data from urban central sites to assess population exposures within geographically dispersed study populations in Atlanta and similar cities.

The relationship between averaged sulfate exposures and concentrations: results from exposure assessment panel studies in four U.S. cities

This analysis examines differences between measured ambient indoor, and personal sulfate concentrations across cities, seasons, and individuals to elucidate how these differences may impact PM2.5 exposure measurement error. Data were analyzed from four panel studies conducted in Atlanta, Baltimore, Boston, and Steubenville (OH). Among the study locations, 1912 person-days of personal sulfate data were collected over 396 days involving 245 individual sampling sessions. Long-term differences in ambient and personal levels averaged over time are examined. Differences between averaged ambient and personal sulfate among and within cities were observed, driven by between subject and city differences in sulfate infiltration, F(inf), from outdoors to indoors. Neglecting this source of variability in associations may introduce bias in studies examining long-term exposures and chronic health. Indoor sulfate was highly correlated with and similar in magnitude to personal sulfate, suggesting indoor PM monitoring may be another means of characterizing true exposure variability.

A conditional expectation approach for associating ambient air pollutant exposures with health outcomes

Our research focuses on the association between exposure to an airborne pollutant and counts of emergency department visits attributed to a specific chronic illness. The motivating example for this analysis of measurement error in time series studies of air pollution and acute health outcomes was a study of emergency department visits from a 20-county Atlanta metropolitan statistical area from 1993-1999. The research presented illustrates the impact of using various surrogates for unobserved measurements of ambient concentrations at the zip code level. Simulation results indicate that the impact of measurement error on the association between pollutant exposure and a health outcome can be substantial. The proposed conditional expectation approach provided reliable estimates of the association and exhibited good confidence interval coverage for a variety of magnitudes of association. Use of a single-centrally located monitor, the arithmetic average, the nearest-neighbor monitor, and the inverse-distance weighted average surrogates resulted in biased estimates and poor coverage rates, especially for larger magnitudes of the association. A focus on obtaining reasonable exposure measurements within clearly defined subregions is important when the pollutant exposure of interest exhibits strong spatial variability.

Effect modification by community characteristics on the short-term effects of ozone exposure and mortality in 98 US communities

Previous research provided evidence of an association between short-term exposure to ozone and mortality risk and of heterogeneity in the risk across communities. The authors investigated whether this heterogeneity can be explained by community-specific characteristics: race, income, education, urbanization, transportation use, particulate matter and ozone levels, number of ozone monitors, weather, and use of air conditioning. Their study included data on 98 US urban communities for 1987 to 2000 from the National Morbidity, Mortality, and Air Pollution Study; US Census; and American Housing Survey. On average across the communities, a 10-ppb increase in the previous week's ozone level was associated with a 0.52% (95% posterior interval: 0.28, 0.77) increase in mortality. The authors found that community-level characteristics modify the relation between ozone and mortality. Higher effect estimates were associated with higher unemployment, fraction of the Black/African-American population, and public transportation use and with lower temperatures or prevalence of central air conditioning. These differences may relate to underlying health status, differences in exposure, or other factors. Results show that some segments of the population may face higher health burdens of ozone pollution.

Personal and ambient air pollution exposures and lung function decrements in children with asthma

BACKGROUND

Epidemiologic studies have shown associations between asthma outcomes and outdoor air pollutants such as nitrogen dioxide and particulate matter mass < 2.5 microm in diameter (PM(2.5)). Independent effects of specific pollutants have been difficult to detect because most studies have relied on highly correlated central-site measurements.

OBJECTIVES

This study was designed to evaluate the relationship of daily changes in percent-predicted forced expiratory volume in 1 sec (FEV(1)) with personal and ambient air pollutant exposures.

METHODS

For 10 days each, we followed 53 subjects with asthma who were 9-18 years of age and living in the Los Angeles, California, air basin. Subjects self-administered home spirometry in themorning, afternoon, and evening. We measured personal hourly PM(2.5) mass, 24-hr PM(2.5) elemental and organic carbon (EC-OC), and 24-hr NO(2), and the same 24-hr average outdoor central-site(ambient) exposures. We analyzed data with transitional mixed models controlling for personal temperature and humidity, and as-needed beta(2)-agonist inhaler use.

RESULTS

FEV(1) decrements were significantly associated with increasing hourly peak and daily average personal PM(2.5), but not ambient PM(2.5). Personal NO(2) was also inversely associated with FEV(1). Ambient NO(2) was more weakly associated. We found stronger associations among 37 subjects not taking controller bronchodilators as follows: Personal EC-OC was inversely associated with morning FEV(1); for an interquartile increase of 71 microg/m(3) 1-hr maximum personal PM(2.5), overall percent-predicted FEV(1) decreased by 1.32% [95% confidence interval (CI), -2.00 to -0.65%]; and for an interquartile increase of 16.8 ppb 2-day average personal NO(2), overall percent-predicted FEV(1) decreased by 2.45% (95% CI, -3.57 to -1.33%). Associations of both personal PM(2.5) and NO(2) with FEV(1) remained when co-regressed, and both confounded ambient NO(2).

CONCLUSIONS

Independent pollutant associations with lung function might be missed using ambient data alone. Different sets of causal components are suggested by independence of FEV(1) associations with personal PM(2.5) mass from associations with personal NO(2).

Panel discussion review: session 1--exposure assessment and related errors in air pollution epidemiologic studies

Examining the validity of exposure metrics used in air pollution epidemiologic models has been a key focus of recent exposure assessment studies. The objective of this work has been, largely, to determine what a given exposure metric represents and to quantify and reduce any potential errors resulting from using these metrics in lieu of true exposure measurements. The current manuscript summarizes the presentations of the co-authors from a recent EPA workshop, held in December 2006, dealing with the role and contributions of exposure assessment in addressing these issues. Results are presented from US and Canadian exposure and pollutant measurement studies as well as theoretical simulations to investigate what both particulate and gaseous pollutant concentrations represent and the potential errors resulting from their use in air pollution epidemiologic studies. Quantifying the association between ambient pollutant concentrations and corresponding personal exposures has led to the concept of defining attenuation factors, or alpha. Specifically, characterizing pollutant-specific estimates for alpha was shown to be useful in developing regression calibration methods involving PM epidemiologic risk estimates. For some gaseous pollutants such as NO2 and SO2, the associations between ambient concentrations and personal exposures were shown to be complex and still poorly understood. Results from recent panel studies suggest that ambient NO2 measurements may, in some locations, be serving as surrogates to traffic pollutants, including traffic-related PM2.5, hopanes, steranes, and oxidized nitrogen compounds (rather than NO2).

Personal and ambient air pollution is associated with increased exhaled nitric oxide in children with asthma

BACKGROUND

Research has shown associations between pediatric asthma outcomes and airborne particulate matter (PM). The importance of particle components remains to be determined.

METHODS

We followed a panel of 45 schoolchildren with persistent asthma living in Southern California. Subjects were monitored over 10 days with offline fractional exhaled nitric oxide (FeNO), a biomarker of airway inflammation. Personal active sampler exposures included continuous particulate matter < 2.5 microm in aerodynamic diameter (PM2.5), 24-hr PM2.5 elemental and organic carbon (EC, OC), and 24-hr nitrogen dioxide. Ambient exposures included PM2.5, PM2.5 EC and OC, and NO2. Data were analyzed with mixed models controlling for personal temperature, humidity and 10-day period.

RESULTS

The strongest positive associations were between FeNO and 2-day average pollutant concentrations. Per interquartile range pollutant increase, these were: for 24 microg/m3 personal PM2.5, 1.1 ppb FeNO [95% confidence interval (CI), 0.1-1.9]; for 0.6 microg/m3 personal EC, 0.7 ppb FeNO (95% CI, 0.3-1.1); for 17 ppb personal NO2, 1.6 ppb FeNO (95% CI, 0.4-2.8). Larger associations were found for ambient EC and smaller associations for ambient NO2. Ambient PM2.5 and personal and ambient OC were significant only in subjects taking inhaled corticosteroids (ICS) alone. Subjects taking both ICS and antileukotrienes showed no significant associations. Distributed lag models showed personal PM2.5 in the preceding 5 hr was associated with FeNO. In two-pollutant models, the most robust associations were for personal and ambient EC and NO2, and for personal but not ambient PM2.5.

CONCLUSION

PM associations with airway inflammation in asthmatics may be missed using ambient particle mass, which may not sufficiently represent causal pollutant components from fossil fuel combustion.

Comparison of oxidative properties, light absorbance, total and elemental mass concentration of ambient PM2.5 collected at 20 European sites

OBJECTIVE

It has been proposed that the redox activity of particles may represent a major determinant of their toxicity. We measured the in vitro ability of ambient fine particles [particulate matter with aerodynamic diameters<or=2.5 microm (PM2.5)] to form hydroxyl radicals (.OH) in an oxidant environment, as well as to deplete physiologic antioxidants (ascorbic acid, glutathione) in the naturally reducing environment of the respiratory tract lining fluid (RTLF). The objective was to examine how these toxicologically relevant measures were related to other PM characteristics, such as total and elemental mass concentration and light absorbance.

DESIGN

Gravimetric PM2.5 samples (n=716) collected over 1 year from 20 centers participating in the European Community Respiratory Health Survey were available. Light absorbance of these filters was measured with reflectometry. PM suspensions were recovered from filters by vortexing and sonication before dilution to a standard concentration. The oxidative activity of these particle suspensions was then assessed by measuring their ability to generate .OH in the presence of hydrogen peroxide, using electron spin resonance and 5,5-dimethyl-1-pyrroline-N-oxide as spin trap, or by establishing their capacity to deplete antioxidants from a synthetic model of the RTLF.

RESULTS AND CONCLUSION

PM oxidative activity varied significantly among European sampling sites. Correlations between oxidative activity and all other characteristics of PM were low, both within centers (temporal correlation) and across communities (annual mean). Thus, no single surrogate measure of PM redox activity could be identified. Because these novel measures are suggested to reflect crucial biologic mechanisms of PM, their use may be pertinent in epidemiologic studies. Therefore, it is important to define the appropriate methods to determine oxidative activity of PM.