There is ample epidemiologic and toxicologic evidence that exposure to fine particulate matter (PM) air pollution [aerodynamic diameter ≤ 2.5 μm (PM2.5)], which derives primarily from combustion processes, can result in increased mortality and morbidity. There is less certainty as to the contribution of coarse PM (PM2.5–10), which derives from crustal materials and from mechanical processes, to mortality and morbidity.
To determine whether coarse PM causes cardiopulmonary effects, we exposed 14 healthy young volunteers to coarse concentrated ambient particles (CAPs) and filtered air. Coarse PM concentration averaged 89.0 μg/m3 (range, 23.7–159.6 μg/m3). Volunteers were exposed to coarse CAPs and filtered air for 2 hr while they underwent intermittent exercise in a single-blind, crossover study. We measured pulmonary, cardiac, and hematologic end points before exposure, immediately after exposure, and again 20 hr after exposure.
Compared with filtered air exposure, coarse CAP exposure produced a small increase in polymorphonuclear neutrophils in the bronchoalveolar lavage fluid 20 hr postexposure, indicating mild pulmonary inflammation. We observed no changes in pulmonary function. Blood tissue plasminogen activator, which is involved in fibrinolysis, was decreased 20 hr after exposure. The standard deviation of normal-to-normal intervals (SDNN), a measure of overall heart rate variability, also decreased 20 hr after exposure to CAPs.
Coarse CAP exposure produces a mild physiologic response in healthy young volunteers approximately 20 hr postexposure. These changes are similar in scope and magnitude to changes we and others have previously reported for volunteers exposed to fine CAPs, suggesting that both size fractions are comparable at inducing cardiopulmonary changes in acute exposure settings.
cardiovascular effects; coarse PM human study
The link between air pollution exposure and adverse birth outcomes is of public health concern due to the relationship between poor pregnancy outcomes and the onset of childhood and adult diseases. As personal exposure measurements are difficult and expensive to obtain, proximate measures of air pollution exposure are traditionally used. We explored how different air pollution exposure metrics affect birthweight regression models. We examined the effect of maternal exposure to ambient levels of particulate matter <10, <2.5 μm in aerodynamic diameter (PM10, PM2.5) on birthweight among infants in North Carolina. We linked maternal residence to the closest monitor during pregnancy for 2000–2002 (n=350,754). County-level averages of air pollution concentrations were estimated for the entire pregnancy and each trimester. For a finer spatially resolved metric, we calculated exposure averages for women living within 20, 10, and 5 km of a monitor. Multiple linear regression was used to determine the association between exposure and birthweight, adjusting for standard covariates. In the county level model, an interquartile increase in PM10 and PM2.5 during the entire gestational period reduced birthweight by 5.3 g (95% CI: 3.3 – 7.4) and 4.6 g (95% CI: 2.3 – 6.8), respectively. This model also showed a reduction in birthweight for PM10 (7.1 g, 95% CI: 1.0–13.2) and PM2.5 (10.4 g, 95% CI: 6.4 – 14.4) during the third trimester. Proximity models for 20, 10, and 5 km distances showed similar results to the county level models. County level models assume that exposure is spatially homogeneous over a larger surface area than proximity models. Sensitivity analysis demonstrated that at varying spatial resolutions, there is still a stable and negative association between air pollution and birthweight, despite North Carolina’s consistent attainment of federal air quality standards.
air pollution; particulate matter; birthweight; birth outcomes; exposure metrics
The extent to which occupational exposure to ozone in ambient air can affect lung function remains unclear. We conducted a panel study in 43 mail carriers by measuring their peak expiratory flow rates (PEFRs) twice daily for 6 weeks in 2001. The daily exposure of each mail carrier to O3, particulate matter < 10 μm in aerodynamic diameter (PM10), and nitrogen dioxide was estimated by one air monitoring station in the center of the mail carrier’s delivery area. Hourly concentrations of air pollutants during their exposure periods were 6–96 ppb for O3, 11–249 μg/m3 for PM10, and 14–92 ppb for NO2. Linear mixed-effects models were used to estimate the association between air pollution exposures and PEFR after adjusting for subject’s sex, age, and disease status and for temperature and humidity. We found that night PEFR and the deviation in night PEFR were significantly decreased in association with 8-hr O3 exposures with a lag 0–2 days and by daily maximum O3 exposures with a lag of 0–1 day in our multipollutant models. By contrast, neither PM10 nor NO2 was associated with a PEFR reduction. Daily 8-hr mean concentrations of O3 had greater reduction effects on PEFR than did daily maximum concentrations. For a 10-ppb increase in the 8-hr average O3 concentration, the night PEFR was decreased by 0.54% for a 0-day lag, 0.69% for a 1-day lag, and 0.52% for a 2-day lag. We found that an acute lung function reduction occurs in mail carriers exposed to O3 concentrations below current ambient air quality standards and occupational exposure limits.
deviation; lung function; mail carrier; ozone exposure; peak expiratory flow rate
Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk, and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter (PM). One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of spatial homogeneity in many of the PM components. Current approaches to estimating short-term health risks via time series modeling do not take into account the spatial properties of the chemical components and therefore could result in biased estimation of those risks. We present a spatial–temporal statistical model for quantifying spatial misalignment error and show how adjusted health risk estimates can be obtained using a regression calibration approach and a 2-stage Bayesian model. We apply our methods to a database containing information on hospital admissions, air pollution, and weather for 20 large urban counties in the United States.
Acute health effects; Cardiovascular disease; Chemical speciation; Measurement error; Particulate matter; Spatial modeling
Objective: To investigate the association between ambient concentrations of air pollutants and respiratory and cardiovascular mortalities in Hong Kong.
Methods: Retrospective ecological study. A Poisson regression of concentrations of daily air pollutants on daily mortalities for respiratory and cardiovascular diseases in Hong Kong from 1995 to the end of 1998 was performed using the air pollution and health: the European approach (APHEA) protocol. The effects of time trend, seasonal variations, temperature, and humidity were adjusted. Autocorrelation and overdispersion were corrected. Daily concentrations of nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), and particulate matter <10 µm in aerodynamic diameter (PM10) were averaged from eight monitoring stations in Hong Kong. Relative risks (RRs) of respiratory and cardiovascular mortalities (per 10 µg/m3 increase in air pollutant concentration) were calculated.
Results: Significant associations were found between mortalities for all respiratory diseases and ischaemic heart diseases (IHD) and the concentrations of all pollutants when analysed singly. The RRs for all respiratory mortalities (for a 10 µg/m3 increase in the concentration of a pollutant) ranged from 1.008 (for PM10) to 1.015 (for SO2) and were higher for chronic obstructive pulmonary diseases (COPD) with all pollutants except SO2, ranging from 1.017 (for PM10) to 1.034 (for O3). RRs for IHD ranged from 1.009 (for O3) to 1.028 (for SO2). In a multipollutant model, O3 and SO2 were significantly associated with all respiratory mortalities, whereas NO2 was associated with mortality from IHD. No interactions were detected between any of the pollutants or with the winter season. A dose-response effect was evident for all air pollutants. Harvesting was not found in the short term.
Conclusions: Mortality risks were detected at current ambient concentrations of air pollutants. The associations with the particulates and some gaseous pollutants when analysed singly were consistent with many reported in temperate countries. PM10 was not associated with respiratory or cardiovascular mortalities in multipollutant analyses.
This paper describes a modeling framework for estimating the acute effects of personal exposure to ambient air pollution in a time series design. First, a spatial hierarchical model is used to relate Census tract-level daily ambient concentrations and simulated exposures for a subset of the study period. The complete exposure time series is then imputed for risk estimation. Modeling exposure via a statistical model reduces the computational burden associated with simulating personal exposures considerably. This allows us to consider personal exposures at a finer spatial resolution to improve exposure assessment and for a longer study period. The proposed approach is applied to an analysis of fine particulate matter of <2.5 μm in aerodynamic diameter (PM2.5) and daily mortality in the New York City metropolitan area during the period 2001–2005. Personal PM2.5 exposures were simulated from the Stochastic Human Exposure and Dose Simulation. Accounting for exposure uncertainty, the authors estimated a 2.32% (95% posterior interval: 0.68, 3.94) increase in mortality per a 10 μg/m3 increase in personal exposure to PM2.5 from outdoor sources on the previous day. The corresponding estimates per a 10 μg/m3 increase in PM2.5 ambient concentration was 1.13% (95% confidence interval: 0.27, 2.00). The risks of mortality associated with PM2.5 were also higher during the summer months.
exposure modeling; particulate matter; time series analysis
A robust methodology is developed to compute population-weighted daily measures of ambient air pollution for use in time-series studies of acute health effects. Ambient data, including criteria pollutants and four fine particulate matter components, from monitors located in the twenty-county metropolitan Atlanta area over the 1999 through 2004 time period were normalized, spatially resolved using inverse distance-square weighting to census tracts, denormalized using descriptive spatial models, and population-weighted. Error associated with applying this procedure with fewer than the maximum number of observations was also calculated. In addition to providing more representative measures of ambient air pollution for the health study population than provided by a central monitor alone and dampening effects of measurement error and local source impacts, results are used to evaluate spatial variability and to identify air pollutants whose ambient concentrations are poorly characterized. The decrease in correlation of daily monitor observations with daily population-weighted average values with increasing distance of the monitor from the urban center is much greater for primary pollutants than for secondary pollutants. Of the criteria pollutant gases, sulfur dioxide observations were least representative due to the failure of ambient networks to capture the spatial variability of this pollutant whose concentrations are dominated by point source impacts. Daily fluctuations in PM10 mass were less well characterized than PM2.5 mass due to a smaller number of PM10 monitors with daily observations. Of the PM2.5 components, the carbon fractions were less well spatially characterized than sulfate and nitrate both due to primary emissions of elemental and organic carbon and due to differences in measurement techniques used to assess these carbon fractions.
We examined the association of infant bronchiolitis with acute exposure to ambient air pollutants.
We employed a time-stratified case–crossover method and based the exposure windows on a priori, biologically based hypotheses.
We evaluated effects in 19,901 infants in the South Coast Air Basin of California in 1995–2000 with a hospital discharge record for bronchiolitis in the first year of life (International Classification of Diseases, 9th Revision, CM466.1).
Study subjects’ ZIP code was linked to ambient air pollution monitors to derive exposures. We estimated the risk of bronchiolitis hospitalization associated with increases in wintertime ambient air pollutants using conditional logistic regression.
We observed no increased risk after acute exposure to particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5), carbon monoxide, or nitrogen dioxide. PM2.5 exposure models suggested a 26–41% increased risk in the most premature infants born at gestational ages between 25 and 29 weeks; however, these findings were based on very small numbers.
We found little support for a link between acute increases in ambient air pollution and infant bronchiolitis except modestly increased risk for PM2.5 exposure among infants born very prematurely. In these infants, the periods of viral acquisition and incubation concurred with the time of increased risk.
Relevance to Professional Practice
We present novel data for the infant period and the key respiratory disease of infancy, bronchiolitis. Incompletely explained trends in rising bronchiolitis hospitalization rates and increasing number of infants born prematurely underscore the importance of evaluating the impact of ambient air pollution in this age group in other populations and studies.
ambient air pollution; bronchiolitis; carbon monoxide; case–crossover; infant; nitrogen dioxide; particulate matter; respiratory disease
During the last week of June 2008, central and northern California experienced thousands of forest and brush fires, giving rise to a week of severe fire-related particulate air pollution throughout the region. California experienced PM10–2.5 (particulate matter with mass median aerodynamic diameter > 2.5 μm to < 10 μm; coarse ) and PM2.5 (particulate matter with mass median aerodynamic diameter < 2.5 μm; fine) concentrations greatly in excess of the air quality standards and among the highest values reported at these stations since data have been collected.
These observations prompt a number of questions about the health impact of exposure to elevated levels of PM10–2.5 and PM2.5 and about the specific toxicity of PM arising from wildfires in this region.
Toxicity of PM10–2.5 and PM2.5 obtained during the time of peak concentrations of smoke in the air was determined with a mouse bioassay and compared with PM samples collected under normal conditions from the region during the month of June 2007.
Concentrations of PM were not only higher during the wildfire episodes, but the PM was much more toxic to the lung on an equal weight basis than was PM collected from normal ambient air in the region. Toxicity was manifested as increased neutrophils and protein in lung lavage and by histologic indicators of increased cell influx and edema in the lung.
We conclude that the wildfire PM contains chemical components toxic to the lung, especially to alveolar macrophages, and they are more toxic to the lung than equal doses of PM collected from ambient air from the same region during a comparable season.
air pollution; alveolar macrophage; lung inflammation; mouse; PM2.5; PM10; source-specific particulate matter
Recent advances in atmospheric remote sensing offer a unique opportunity to compute indirect estimates of air quality, particularly for developing countries that lack adequate spatial–temporal coverage of air pollution monitoring. The present research establishes an empirical relationship between satellite-based aerosol optical depth (AOD) and ambient particulate matter (PM) in Delhi and its environs. The PM data come from two different sources. Firstly, a field campaign was conducted to monitor airborne particles ≤ 2.5 μm and ≤10 μm in aerodynamic diameter (PM2.5 and PM10 respectively) at 113 spatially dispersed sites from July to December 2003 using photometric samplers. Secondly, data on eight hourly PM10 and total suspended particulate (TSP) matter, collected using gravimetric samplers, from 2000 to 2005 were acquired from the Central Pollution Control Board (CPCB). The aerosol optical depths were estimated from MODIS data, acquired from NASA’s Goddard Space Flight Center Earth Sciences Distributed Active Archive Center from 2000 to 2005. Both the PM and AOD data were collocated by time and space: PM mass ± 150 min of AOD time, and ± 2.5 and 5 km radius (separately) of the centroid of the AOD pixel for the 5 and 10 km AOD, respectively. The analysis here shows that PM correlates positively with the 5 km AOD; a 1% change in the AOD explains 0.52% ± 0.20% and 0.39% ± 0.15% changes in PM2.5 within 45 and 150 min intervals (of AOD data) respectively. At a coarser spatial resolution, however, the relationship between AOD and PM is relatively weak. But, the relationship turns significantly stronger when monthly estimates are analysed over a span of six years (2000 to 2005), especially for the winter months, which have relatively stable meteorological conditions.
Exposure of cultured cells to particulate matter air pollution is usually accomplished by collecting particles on a solid matrix, extracting the particles from the matrix, suspending them in liquid, and applying the suspension to cells grown on plastic and submerged in medium. The objective of this work was to develop a more physiologically and environmentally relevant model of air pollutant deposition on cultures of human primary airway epithelial cells. We hypothesize that the toxicology of inhaled particulate matter depends strongly on both the particulate dispersion state and the mode of delivery to cells. Our exposure system employs a combination of unipolar charging and electrostatic force to deposit particles directly from the air onto cells grown at an air-liquid interface in a heated, humidified exposure chamber. Normal human bronchial epithelial cells exposed to concentrated, coarse ambient particulate matter in this system expressed increased levels of inflammatory biomarkers at 1 hour following exposure and relative to controls exposed to particle-free air. More importantly, these effects are seen at particulate loadings that are 1-2 orders of magnitude lower than levels applied using traditional in vitro systems.
Air pollution epidemiologic studies use ambient pollutant concentrations as surrogates of personal exposure. Strong correlations among numerous ambient pollutant concentrations, however, have made it difficult to determine the relative contribution of each pollutant to a given health outcome and have led to criticism that health effect estimates for particulate matter may be biased due to confounding. In the current study we used data collected from a multipollutant exposure study conducted in Baltimore, Maryland, during both the summer and winter to address the potential for confounding further. Twenty-four-hour personal exposures and corresponding ambient concentrations to fine particulate matter (PM(2.5)), ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide were measured for 56 subjects. Results from correlation and regression analyses showed that personal PM(2.5) and gaseous air pollutant exposures were generally not correlated, as only 9 of the 178 individual-specific pairwise correlations were significant. Similarly, ambient concentrations were not associated with their corresponding personal exposures for any of the pollutants, except for PM(2.5), which had significant associations during both seasons (p < 0.0001). Ambient gaseous concentrations were, however, strongly associated with personal PM(2.5) exposures. The strongest associations were shown between ambient O(3) and personal PM(2.5) (p < 0.0001 during both seasons). These results indicate that ambient PM(2.5) concentrations are suitable surrogates for personal PM(2.5) exposures and that ambient gaseous concentrations are surrogates, as opposed to confounders, of PM(2.5). These findings suggest that the use of multiple pollutant models in epidemiologic studies of PM(2.5) may not be suitable and that health effects attributed to the ambient gases may actually be a result of exposures to PM(2.5).
Rationale: Ambient particulate matter concentrations have been positively associated with urinary leukotriene E4 (LTE4) levels and albuterol usage in children with asthma but interactions with environmental tobacco smoke (ETS) exposure have not been demonstrated despite obvious exposure to both pollutants in an urban setting.
Objectives: To assess the health effects of concurrent ETS and ambient particulate matter exposure in children with asthma.
Methods: Albuterol usage and LTE4 levels were monitored in 82 urban schoolchildren with asthma over three consecutive fall to spring school periods. Concentrations of morning maximum ambient particulate matter <2.5 μm in aerodynamic diameter (mmPM2.5) and urine cotinine levels were also measured daily.
Measurements and Main Results: Albuterol usage and LTE4 were related to mmPM2.5 concentrations on days when urine cotinine levels were low (<10 ng/ml/mg creatinine); on these days, mean albuterol usage and LTE4 increased up to 5 or 6% per 10 μg/m3 increase in mmPM2.5. In contrast, no significant relationship was observed when cotinine was high, although mean albuterol usage and LTE4 levels were greater in this case. Model fits for LTE4 levels as a function of mmPM2.5 concentrations were improved when mmPM2.5 concentrations were logged, suggesting a nonlinear dose–response relationship between particulate matter exposure concentrations and airway mediators of asthma, for which the relationship tends to flatten at higher concentrations.
Conclusions: This study suggests that ETS modifies the acute effects of low-level ambient PM2.5 exposure on childhood asthma. This negative interaction, the smaller effect of particulate matter exposure in children exposed to higher ETS, may be related to a nonlinear dose–response relationship between asthma mediators and particulate exposures.
air pollution; leukotriene E4; asthma; interaction; environmental tobacco smoke
Experimental data suggest that asthma exacerbation by ambient air pollutants is enhanced by exposure to endotoxin and allergens; however, there is little supporting epidemiologic evidence.
We evaluated whether the association of exposure to air pollution with annual prevalence of chronic cough, phlegm production, or bronchitis was modified by dog and cat ownership (indicators of allergen and endotoxin exposure). The study population consisted of 475 Southern California children with asthma from a longitudinal cohort of participants in the Children’s Health Study. We estimated average annual ambient exposure to nitrogen dioxide, ozone, particulate matter < 10, 2.5, and 10–2.5 μm in aerodynamic diameter (PM10, PM2.5, and PM10–2.5, respectively), elemental and organic carbon, and acid vapor from monitoring stations in each of the 12 study communities. Multivariate models were used to examine the effect of yearly variation of each pollutant. Effects were scaled to the variability that is common for each pollutant in representative communities in Southern California.
Among children owning a dog, there were strong associations between bronchitic symptoms and all pollutants examined. Odds ratios ranged from 1.30 per 4.2 μg/m3 for PM10–2.5 [95% confidence interval (CI), 0.91–1.87) to 1.91 per 1.2 μg/m3 for organic carbon (95% CI, 1.34–2.71). Effects were somewhat larger among children who owned both a cat and dog. There were no effects or small effects with wide CIs among children without a dog and among children who owned only a cat.
Our results suggest that dog ownership, a source of residential exposure to endotoxin, may worsen the relationship between air pollution and respiratory symptoms in asthmatic children.
air pollution; asthma; cats; child; dogs; endotoxin; epidemiology; indoor allergens; particulate matter
OBJECTIVE: To investigate short term effects of concentrations of pollutants in ambient air on hospital admissions for cardiovascular and respiratory diseases in Hong Kong. METHODS: Retrospective ecological study. A Poisson regression was performed of concentrations of daily air pollutant on daily counts of emergency hospital admissions in 12 major hospitals. The effects of time trend, season, and other cyclical factors, temperature, and humidity were accounted for. Autocorrelation and overdispersion were corrected. Daily concentrations of nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), and particulate matter < 10 microns in aerodynamic diameter (PM10) were obtained from seven air monitoring stations in Hong Kong in 1994 and 1995. Relative risks (RR) of respiratory and cardiovascular disease admissions (for an increase of 10 micrograms/m3 in concentration of air pollutant) were calculated. RESULTS: Significant associations were found between hospital admissions for all respiratory diseases, all cardiovascular diseases, chronic obstructive pulmonary diseases, and heart failure and the concentrations of all four pollutants. Admissions for asthma, pneumonia, and influenza were significantly associated with NO2, O3, and PM10. Relative risk (RR) for admissions for respiratory disease for the four pollutants ranged from 1.013 (for SO2) to 1.022 (for O3), and for admissions for cardiovascular disease, from 1.006 (for PM10) to 1.016 (for SO2). Those aged > or = 65 years were at higher risk. Significant positive interactions were detected between NO2, O3, and PM10, and between O3 and winter months. CONCLUSIONS: Adverse health effects are evident at current ambient concentrations of air pollutants. Further reduction in air pollution is necessary to protect the health of the community, especially that of the high risk group.
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.
Air pollution; Conditional expectation; Environmental epidemiology; Maximum likelihood; Measurement error; Spatial variability
Evidence links exposure to ambient air pollution during pregnancy, particularly gaseous pollutants and particulate matter, to an increased risk of adverse reproductive outcomes but the results for birth defects have been inconsistent.
We compared estimated exposure to ambient air pollutants during early pregnancy among mothers of children with oral cleft defects (cases) to that among mothers of controls, adjusting for available risk factors from birth certificates. We obtained ambient air pollutant data from air monitoring sites in New Jersey for carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), particulate matter less than 10 µm in aerodynamic diameter (PM10) and particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5). We used values from the nearest monitor (within 40 km of the residence at birth) for controls, cleft lip with or without cleft palate (CLP) and cleft palate only (CPO).
Based on logistic regression analyses for each contaminant and all contaminants together, there were no consistent elevated associations between selected air pollutants and cleft malformations. Quartile of CO concentration showed a consistent protective association with CPO (p<.01). For other contaminants, confidence intervals (95%) of the odds ratios for some quartiles excluded one. CLP showed limited evidence of an association with increasing SO2 exposure while CPO showed weak associations with increasing O3 exposure.
There was little consistent evidence associating cleft malformations with maternal exposure to ambient air pollutants. Evaluating particular pollutants or disease subgroups would require more detailed measurement of exposure and classification of cleft defects.
Fine particulate matter (PM2.5) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM2.5 varies across space and time, the association between PM2.5 and mortality could also change with space and season. In this work we develop and implement a statistical multi-stage Bayesian framework that provides a very broad, flexible approach to studying the spatiotemporal associations between mortality and population exposure to daily PM2.5 mass, while accounting for different sources of uncertainty. In stage 1, we map ambient PM2.5 air concentrations using all available monitoring data (IMPROVE and FRM) and an air quality model (CMAQ) at different spatial and temporal scales. In stage 2, we examine the spatial temporal relationships between the health end-points and the exposures to PM2.5 by introducing a spatial-temporal generalized Poisson regression model. We adjust for time-varying confounders, such as seasonal trends. A common seasonal trends model is to use a fixed number of basis functions to account for these confounders, but the results can be sensitive to the number of basis functions. In this study, the number of the basis functions is treated as an unknown parameter in our Bayesian model and we use a space-time stochastic search variable selection approach. We apply our methods to a data set in North Carolina for the year 2001.
air pollution; Bayesian hierarchical models; conditional autoregressive models; computer models; spatial epidemiology
Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and/or factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system (GIS), and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.
As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3–4 day samples of nitrogen dioxide (NO2) and fine particulate matter (PM2.5) in 43 low SES residences across multiple seasons from 2003 – 2005. Elemental carbon concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally-located ambient monitor.
The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO2 with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50m buffer of a home and distance from a truck route as important contributors to indoor levels of NO2 and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population.
indoor air; NO2; PM2.5; EC; geographic information system
Misclassification of exposure usually leads to biased estimates of exposure–response associations. This is particularly an issue in cases with multiple correlated exposures, where the direction of bias is uncertain. It is necessary to address this problem when considering associations with important public health implications such as the one between mortality and air pollution, because biased exposure effects can result in biased risk assessments. The National Morbidity and Mortality Air Pollution Study (NMMAPS) recently reported results from an assessment of multiple pollutants and daily mortality in 90 U.S. cities. That study assessed the independent associations of the selected pollutants with daily mortality in two-pollutant models. Excess mortality was associated with particulate matter of aerodynamic diameter ≤10 μm/m3 (PM10), but not with other pollutants, in these two pollutant models. The extent of bias due to measurement error in these reported results is unclear. Schwartz and Coull recently proposed a method that deals with multiple exposures and, under certain conditions, is resistant to measurement error. We applied this method to reanalyze the data from NMMAPS. For PM10, we found results similar to those reported previously from NMMAPS (0.24% increase in deaths per 10-μg/m3 increase in PM10). In addition, we report an important effect of carbon monoxide that had not been observed previously.
air pollution; carbon monoxide; daily mortality; measurement error; particulate matter
Acute and chronic respiratory diseases, which are causally linked to exposure to indoor air pollution in developing countries, are the leading cause of global morbidity and mortality. Efforts to develop effective intervention strategies and detailed quantification of the exposure-response relationship for indoor particulate matter require accurate estimates of exposure. We used continuous monitoring of indoor air pollution and individual time-activity budget data to construct detailed profiles of exposure for 345 individuals in 55 households in rural Kenya. Data for analysis were from two hundred ten 14-hour days of continuous real-time monitoring of concentrations of particulate matter [less than/equal to] 10 microm in aerodynamic diameter and the location and activities of household members. These data were supplemented by data on the spatial dispersion of pollution and from interviews. Young and adult women had not only the highest absolute exposure to particulate matter (2, 795 and 4,898 microg/m(3) average daily exposure concentrations, respectively) but also the largest exposure relative to that of males in the same age group (2.5 and 4.8 times, respectively). Exposure during brief high-intensity emission episodes accounts for 31-61% of the total exposure of household members who take part in cooking and 0-11% for those who do not. Simple models that neglect the spatial distribution of pollution within the home, intense emission episodes, and activity patterns underestimate exposure by 3-71% for different demographic subgroups, resulting in inaccurate and biased estimations. Health and intervention impact studies should therefore consider in detail the critical role of exposure patterns, including the short periods of intense emission, to avoid spurious assessments of risks and benefits.
New approaches to link health surveillance data with environmental and population exposure information are needed to examine the health benefits of risk management decisions.
We examined the feasibility of conducting a local assessment of the public health impacts of cumulative air pollution reduction activities from federal, state, local, and voluntary actions in the City of New Haven, Connecticut (USA).
Using a hybrid modeling approach that combines regional and local-scale air quality data, we estimated ambient concentrations for multiple air pollutants [e.g., PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), NOx (nitrogen oxides)] for baseline year 2001 and projected emissions for 2010, 2020, and 2030. We assessed the feasibility of detecting health improvements in relation to reductions in air pollution for 26 different pollutant–health outcome linkages using both sample size and exploratory epidemiological simulations to further inform decision-making needs.
Model projections suggested decreases (~ 10–60%) in pollutant concentrations, mainly attributable to decreases in pollutants from local sources between 2001 and 2010. Models indicated considerable spatial variability in the concentrations of most pollutants. Sample size analyses supported the feasibility of identifying linkages between reductions in NOx and improvements in all-cause mortality, prevalence of asthma in children and adults, and cardiovascular and respiratory hospitalizations.
Substantial reductions in air pollution (e.g., ~ 60% for NOx) are needed to detect health impacts of environmental actions using traditional epidemiological study designs in small communities like New Haven. In contrast, exploratory epidemiological simulations suggest that it may be possible to demonstrate the health impacts of PM reductions by predicting intraurban pollution gradients within New Haven using coupled models.
air pollution; feasibility analysis; health effects; nitrogen oxides; particulate matter
Air pollution is associated with respiratory symptoms, lung function decrements, and hospitalizations. However, there is little information about the influence of air pollution on lung injury.
In this study we investigated acute effects of air pollution on pulmonary function and airway oxidative stress and inflammation in asthmatic children.
We studied 182 children with asthma, 9–14 years of age, for 4 weeks. Daily ambient concentrations of sulfur dioxide, nitrogen dioxide, ozone, and particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) were monitored from two stations. Once a week we measured spirometry and fractional exhaled nitric oxide (FeNO), and determined thiobarbituric acid reactive substances (TBARS) and 8-isoprostane—two oxidative stress markers—and interleukin-6 (IL-6) in breath condensate. We tested associations using mixed-effects regression models, adjusting for confounding variables.
Interquartile-range increases in 3-day average SO2 (5.4 ppb), NO2 (6.8 ppb), and PM2.5 (5.4 μg/m3) were associated with decreases in forced expiratory flow between 25% and 75% of forced vital capacity, with changes being −3.1% [95% confidence interval (CI), −5.8 to −0.3], −2.8% (95% CI, −4.8 to −0.8), and −3.0% (95% CI, −4.7 to −1.2), respectively. SO2, NO2, and PM2.5 were associated with increases in TBARS, with changes being 36.2% (95% CI, 15.7 to 57.2), 21.8% (95% CI, 8.2 to 36.0), and 24.8% (95% CI, 10.8 to 39.4), respectively. Risk estimates appear to be larger in children not taking corticosteroids than in children taking corticosteroids. O3 (5.3 ppb) was not associated with health end points. FeNO, 8-isoprostane, and IL-6 were not associated with air pollutants.
Air pollution may increase airway oxidative stress and decrease small airway function of asthmatic children. Inhaled corticosteroids may reduce oxidative stress and improve airway function.
air pollution; asthma; children; exhaled breath condensate; inflammation; oxidative stress; pulmonary function
We extended our previous analyses of term low birth weight (LBW) and preterm birth to 1994–2000, a period of declining air pollution levels in the South Coast Air Basin. We speculated that the effects we observed previously for carbon monoxide, particulate matter < 10 μm in aero-dynamic diameter (PM10), and traffic density were attributable to toxins sorbed to primary exhaust particles. Focusing on CO, PM10, and particulate matter < 2.5 μm in aerodynamic diameter (PM2.5), we examined whether varying residential distances from monitoring stations affected risk estimates, because effect attenuation may result from local pollutant heterogeneity inadequately captured by ambient stations. We geocoded home locations, calculated the distance to the nearest air monitors, estimated exposure levels by pregnancy period, and performed logistic regression analyses for subjects living within 1–4 mi of a station. For women residing within a 1-mi distance, we observed a 27% increase in risk for high (≥ 75th percentile) first-trimester CO exposures and preterm birth and a 36% increase for high third-trimester pregnancy CO exposures and term LBW. For particles, we observed similar size effects during early and late pregnancy for both term LBW and preterm birth. In contrast, smaller or no effects were observed beyond a 1-mi distance of a residence from a station. Associations between CO and PM10 averaged over the whole pregnancy and term LBW were generally smaller than effects for early and late pregnancy. These new results for 1994–2000 generally confirm our previous observations for the period 1989–1993, again linking CO and particle exposures to term LBW and preterm birth. In addition, they confirm our suspicions about having to address local heterogeneity for these pollutants in Los Angeles.
air pollution; epidemiology; low birth weight; preterm birth
Background: Epidemiologic evidence for a causative association between black carbon (BC) and health outcomes is limited.
Objectives: We estimated associations and exposure–response relationships between acute respiratory inflammation in schoolchildren and concentrations of BC and particulate matter with an aerodynamic diameter of ≤ 2.5 μm (PM2.5) in ambient air before and during the air pollution intervention for the 2008 Beijing Olympics.
Methods: We measured exhaled nitric oxide (eNO) as an acute respiratory inflammation biomarker and hourly mean air pollutant concentrations to estimate BC and PM2.5 exposure. We used 1,581 valid observations of 36 subjects over five visits in 2 years to estimate associations of eNO with BC and PM2.5 according to generalized estimating equations with polynomial distributed-lag models, controlling for body mass index, asthma, temperature, and relative humidity. We also assessed the relative importance of BC and PM2.5 with two-pollutant models.
Results: Air pollution concentrations and eNO were clearly lower during the 2008 Olympics. BC and PM2.5 concentrations averaged over 0–24 hr were strongly associated with eNO, which increased by 16.6% [95% confidence interval (CI), 14.1–19.2%] and 18.7% (95% CI, 15.0–22.5%) per interquartile range (IQR) increase in BC (4.0 μg/m3) and PM2.5 (149 μg/m3), respectively. In the two-pollutant model, estimated effects of BC were robust, but associations between PM2.5 and eNO decreased with adjustment for BC. We found that eNO was associated with IQR increases in hourly BC concentrations up to 10 hr after exposure, consistent with effects primarily in the first hours after exposure.
Conclusions: Recent exposure to BC was associated with acute respiratory inflammation in schoolchildren in Beijing. Lower air pollution levels during the 2008 Olympics also were associated with reduced eNO.
air pollution intervention; black carbon; nitric oxide; PM2.5; respiratory inflammation; schoolchildren