Abstract  Ambient air pollution has been associated with increased mortality and morbidity; however, few studies have examined the short-term effect of air pollution specifically on chronic obstructive pulmonary disease (COPD), which is an important cause of mortality and morbidity world wide. In this analysis, we examined the associations between daily air pollution levels [particulate matter less than 10 microns in aerodynamic diameter (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2)] and COPD mortality in four Chinese cities. We used Poisson regression models with natural spline smoothing functions to adjust for long-term and seasonal trends of COPD mortality, as well as other time-varying covariates. We did a meta-analysis to obtain the 4-city average estimates. Air pollution (PM10, SO2, and NO2) was found to be associated with increased risk of COPD mortality in these four cities. Using the random-effects model, an increase of 10 mu g m(-3) of 2-day moving average concentrations of PM10, SO2 and NO2 corresponded to a 0.78% (95% CI, 0.13-1.42), 1.30% (95% CI, 0.61-1.99), and 1.78% (95% CI, 1.10-2.46) increase of COPD mortality, respectively. The concentration response curves indicated linear associations without threshold. Only NO2 remained significant in the multi-pollutant models. To our knowledge, this is the first multi-city study in Asian developing region to report the short-term effect of air pollution on COPD mortality. Our results contribute to very limited data on the effects of air pollution on COPD mortality for high exposure settings typical in developing countries. (C) 2013 Elsevier Ltd. All rights reserved.

Abstract  This paper tests factors thought to be important in explaining the choices people make in where they spend time. Three aggregate locations are analyzed: outdoors, indoors, and in-vehicles for two different sample groups: a year-long (longitudinal) sample of one individual and a cross-sectional sample of 169 individuals from the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD). The cross-sectional sample consists of persons similar to the longitudinal subject in terms of age, work status, education, and residential type. The sample groups are remarkably similar in the time spent per day in the tested locations, although there are differences in participation rates: the percentage of days frequenting a particular location. Time spent outdoors exhibits the most relative variability of any location tested, with in-vehicle time being the next. The factors found to be most important in explaining daily time usage in both sample groups are: season of the year, season/temperature combinations, precipitation levels, and day-type (work/nonwork is the most distinct, but weekday/weekend is also significant). Season, season/temperature, and day-type are also important for explaining time spent indoors. None of the variables tested are consistent in explaining in-vehicle time in either the cross-sectional or longitudinal samples. Given these findings, we recommend that exposure modelers subdivide their population activity data into at least season/temperature, precipitation, and day-type "cohorts" as these factors are important discriminating variables affecting where people spend their time.

Abstract  In this work we discuss the uncertainty in estimating the human health risk due to exposure to air pollution, including personal and population average exposure error, epidemiological designs and methods of analysis. Different epidemiological models may lead to very different conclusions for the same set of data. Thus, evaluation of the assumptions made and sensitivity analysis are necessary.Short-term health impact indicators may be calculated using concentration-response (C-R) functions. We discuss different methods to combine C-R function estimates from a given locale and time period with the larger body of evidence from other locales and periods and with the literature. A shrunken method is recommended to combine C-R function estimates from multiple-locales. This shrunken estimate includes information from the overall and the local estimates, and thus it characterizes the estimated excess of risk due to heterogeneity between the different locations.

Abstract  In this work, we studied the reactions of alkyl peroxyl radicals with (NO2)-N-. and (NO)-N-. using the pulse radiolysis technique. The rate constants for the reaction of (NO2)-N-. with (CH3)(2)C(OH)CH2OO., CH3OO. and c-C5H9OO. vary between 7 x 10(8) and 1.5 x 10(9) M-1 s(-1). The reaction produces relatively long-lived alkyl peroxynitrates, which are in equilibrium with the parent radicals and have no appreciable absorption above 270 nm. It is also shown that (NO)-N-. adds rapidly to (CH3)(2)C(OH)CH2OO. and CH3OO. to form alkyl peroxynitrites. The rate constants for these reactions were determined to be 2.8 x 10(9) and 3.5 x 10(9) M-1 s(-1), respectively. However, in contrast to alkyl peroxynitrates, alkyl peroxynitrites do not accumulate. Rather, they decompose rapidly via homolysis along the relatively weak O-O bond, initially forming a geminate pair. Most of this pair collapses in the cage to form an alkyl nitrate, RONO2, and about 14% diffuses out as free alkoxyl and (NO2)-N-. radicals. A thermokinetic analysis predicts the half-life of CH3OONO in water to be less than 1 mus, an estimate that agrees well with previous experimental findings of ours for other alkyl peroxynitrites. A comparison of aqueous and gaseous thermochemistry of alkyl peroxynitrates reveals that alkyl peroxyl radicals and the corresponding alkyl peroxynitrates are similarly solvated by water.

Abstract  Nitrogen dioxide at 100 ppm in air greatly accelerated the oxidation of methyl esters of linoleic and linolenic acid in both non-aqueous and aqueous systems. Nitrogen dioxide stimulated oxygen uptake, conjugated diene formation and increase in the peroxide value of methyl linoleate and methyl linolenate. The products formed from methyl linoleate were identified as four isomeric hydroperoxides; the ratio of the amounts of the isomers was similar to that of the hydroperoxides formed by autoxidation. No nitrogen-containing products were formed. The products formed from methyl linolenate were identified as eight isomeric hydroperoxides that were also formed by autoxidation.

Abstract  BACKGROUND: Elevated fractional excretion of exhaled nitric oxide (FENO) reflects airway inflammation, but few studies have established its normal values. This study aims to establish the normal values and thresholds for the clinical interpretation of FENO in the US general population.

METHODS: Thirteen thousand two hundred seventy-five subjects aged 6 to 80 years sampled for the National Health and Nutrition Examination Survey (NHANES) 2007-2010 underwent interviews, physical examination, and FENO analysis at 50 mL/s using an online chemiluminescence device according to American Thoracic Society/European Respiratory Society guidelines. After excluding subjects with self-reported asthma and subjects with wheeze in the prior 12 months, prediction equations for the natural logarithm (ln) of FENO were constructed using age, sex, ethnicity, height, BMI, active/passive smoke exposure, and hay fever episodes as covariates.

RESULTS: The fifth to 95th percentile values of FENO were 3.5 to 36.5 parts per billion (ppb) for children < 12 years of age and 3.5 to 39 ppb for subjects 12 to 80 years of age. Using multiple linear regression, prediction equations explained only 10.3% to 15.7% of the variation in the general population. In the general population, 39% to 45% had ln(FENO) levels > 2 SD of the predicted means. When applied to the general population inclusive of subjects who reported asthma but who did not have attacks within the past year, nearly identical results were obtained.

CONCLUSIONS: Assuming 95% of the healthy US general population had no clinically significant airway inflammation as assessed by FENO, values exceeding the 95th percentiles indicated abnormality and a high risk of airway inflammation. A large variation of normal FENO values existed in the general population, which was poorly predicted by multiple linear regression models.

Abstract  Land use regression (LUR) models are often used in epidemiologic studies to predict the air pollution exposure of health study participants. Such models are often based on a small to moderate number of air pollution measurement sites across the study area, and on a set of variables characterizing factors such as traffic patterns and surrounding land uses that are used as potential predictors. We used resampling techniques on a set of 148 measurement sites of NO2 in the urban area of Girona (Spain) to investigate the effect of the number of measurement sites on the LUR model performance, in particular on predictive ability and on the variables being chosen in the final model. In addition, we investigated the effect of the number of potential predictors and the variable selection algorithm used, and the consequences of the use of LUR predictions in regression models for a health outcome. Our results showed that, especially in small samples, both the adjusted within-sample R-2 and the leave-one-out cross-validation R-2 tended to give highly inflated values when compared to their prediction ability in a validation dataset. When the number of potential predictors was high, LUR models developed with a small number of measurement sites tended to give higher within-sample and cross-validated R-2 than those developed with more sites. Validation dataset R-2 showed a poor performance of models developed with a small number of sites that improved as the number of sites increased. Models developed with a small number of sites tended to select a different set of variables every time, were very sensitive to the number of potential predictors offered and resulted in stronger attenuation of coefficients when air pollution predictions were used in a health model. Our results suggest that LUR models aimed at characterizing local air pollution levels in complex urban settings should be based on a large number of measurement sites (>80 in our setting) and that the set of potential predictors should be restricted.

Abstract  Remote sensing may be a useful tool for exploring spatial variability of air pollution exposure within an urban area. To evaluate the extent to which satellite data from the Ozone Monitoring Instrument (OMI) can resolve urban-scale gradients in ground-level nitrogen dioxide (NO2) within a large urban area, we compared estimates of surface NO2 concentrations derived from OMI measurements and US EPA ambient monitoring stations. OMI, aboard NASA's Aura satellite, provides daily afternoon (similar to 13:30 local time) measurements of NO2 tropospheric column abundance. We used scaling factors (surface-to-column ratios) to relate satellite column measurements to ground-level concentrations. We compared 4138 sets of paired data for 25 monitoring stations in the South Coast Air Basin of California for all of 2005. OMI measurements include more data gaps than the ground monitors (60% versus 5% of available data, respectively), owing to cloud contamination and imposed limits on pixel size. The spatial correlation between OMI columns and corrected in situ measurements is strong (r=0.93 for annual average data), indicating that the within-urban spatial signature of surface NO2 is well resolved by the satellite sensor. Satellite-based surface estimates employing scaling factors from an urban model provide a reliable measure (annual mean bias: -13%; seasonal mean bias: <1% [spring] to -22% [fall]) of fine-scale surface NO2. We also find that OMI provides good spatial density in the study region (average area [km(2)] per measurement: 730 for the satellite sensor vs. 1100 for the monitors). Our findings indicate that satellite observations of NO2 from the OMI sensor provide a reliable measure of spatial variability in ground-level NO2 exposure for a large urban area. Published by Elsevier Ltd.

Abstract  In this paper, a shifted power-law model, based on the wind profile model, had been supposed to simulate concentration gradient of nitrogen dioxide (NO2) with distance from a highway. Field experiments were performed for NO2 gradients from a highway in Shanghai by using passive samplers. The shifted power-law model was fitted well with experimental results of field experiments both in this study and in the literature. The results not only verified the validity of shifted power-law relationship between NO2 concentration and the distance from a highway, but also partially demonstrated that there were some significant similarities between wind profile and air pollutants concentration profile near highway. With known concentration of chosen reference point and appropriate value of the parameter k, the model could be practically applied for predicting the NO2 distributions near a highway. The methods of determining the parameter k were also discussed for further detailed studies. (c) 2006 Elsevier Ltd. All rights reserved.

Abstract  We have measured emission factors for 19 trace gas species and particulate matter (PM2.5) from 14 prescribed fires in chaparral and oak savanna in the southwestern US, as well as conifer forest understory in the southeastern US and Sierra Nevada mountains of California. These are likely the most extensive emission factor field measurements for temperate biomass burning to date and the only published emission factors for temperate oak savanna fuels. This study helps to close the gap in emissions data available for temperate zone fires relative to tropical biomass burning. We present the first field measurements of the biomass burning emissions of glycolaldehyde, a possible precursor for aqueous phase secondary organic aerosol formation. We also measured the emissions of phenol, another aqueous phase secondary organic aerosol precursor. Our data confirm previous observations that urban deposition can impact the NOx emission factors and thus subsequent plume chemistry. For two fires, we measured both the emissions in the convective smoke plume from our airborne platform and the unlofted residual smoldering combustion emissions with our ground-based platform. The smoke from residual smoldering combustion was characterized by emission factors for hydrocarbon and oxygenated organic species that were up to ten times higher than in the lofted plume, including high 1,3-butadiene and isoprene concentrations which were not observed in the lofted plume. This should be considered in modeling the air quality impacts for smoke that disperses at ground level. We also show that the often ignored unlofted emissions can significantly impact estimates of total emissions. Preliminary evidence suggests large emissions of monoterpenes in the residual smoldering smoke. These data should lead to an improved capacity to model the impacts of biomass burning in similar temperate ecosystems.

Abstract  BACKGROUND: Steel production is a major industry worldwide yet there is relatively little information on the pulmonary effects of air quality near steel manufacturing plants. OBJECTIVES: The aim of this study was to examine how lung function changes acutely when healthy subjects are situated near a steel plant which is adjacent to a residential area. METHODS: Sixty-one subjects were randomly assigned to spend 5 consecutive, 8-hour days in a residential neighborhood approximately 0.9km from a steel plant, or approximately 4.5km away at a college campus. Subjects crossed-over between sites after a nine-day washout period. Lung function was measured daily at both sites along with air pollutants including SO2, NO2, O3, PM2.5, and ultrafine particles. Diffusion capacity and pulse oximetry were also examined. RESULTS: Compared with the college site, the forced expiratory volume in 1-second/forced vital capacity, forced expiratory flow between 25% and 75% of the FVC, total lung capacity, functional residual capacity, and residual volume were lower near the steel plant by 0.67% (95% CI: 0.28, 1.06),1.62% (95% CI: 0.50, 2.75), 1.54% (95% CI: 0.68, 2.39), 3.54% (95% CI: 1.95, 5.13) and 11.3% (95% CI: 4.92, 17.75), respectively. Diffusion capacity, forced expiratory volume in 1s, and pulse oximetry were also lower near the plant but these effects were not statistically significant. Sulfur dioxide, ultrafine particulates, and oxides of nitrogen were greater near the steel plant site compared to the college site. CONCLUSIONS: Spending short periods of time near a steel plant is associated with a decrease in lung function.

Abstract  The diffusing capacity, DL, is a critical physiological parameter of the lung used to assess gas exchange clinically. Most models developed to analyze experimental data from a single breath maneuver have assumed a well-mixed or uniform alveolar region, including the clinically accepted Jones-Meade method. In addition, all previous models have assumed a constant DL, which is independent of alveolar volume, VA. In contrast, experimental data provide evidence for a non-uniform alveolar region coupled with sequential filling of the lung. In addition, although the DL for carbon monoxide is a weak function of VA, the DL of nitric oxide depends strongly on VA. We have developed a new mathematical model of the single breath maneuver that considers both a variable degree of sequential filling and a variable DL. Our model predicts that the Jones-Meade method overestimates DL when the exhaled gas sample is collected late in the exhalation, but underestimates DL if the exhaled gas sample is collected early in the exhalation phase due to the effect of sequential filling. Utilizing a prolonged constant exhalation method, or a three-equation method, will also produce erroneous predictions of DL. We conclude that current methods may introduce significant error in the estimation of DL by ignoring the sequential filling of the lung, and the dependence of DL on VA.

Abstract  The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates relationships between long-term exposure to outdoor air pollution and health using cohort studies across Europe. This paper analyses the spatial variation of PM2.5, PM2.5 absorbance, PM10 and PMcoarse concentrations between and within 20 study areas across Europe. We measured NO2, NOx, PM2.5, PM2.5 absorbance and PM10 between October 2008 and April 2011 using standardized methods. PMcoarse was determined as the difference between PM10 and PM2.5. In each of the twenty study areas, we selected twenty PM monitoring sites to represent the variability in important air quality predictors, including population density, traffic intensity and altitude. Each site was monitored over three 14-day periods spread over a year, using Harvard impactors. Results for each site were averaged after correcting for temporal variation using data obtained from a reference site, which was operated year-round. Substantial concentration differences were observed between and within study areas. Concentrations for all components were higher in Southern Europe than in Western and Northern Europe, but the pattern differed per component with the highest average PM2.5 concentrations found in Turin and the highest PMcoarse in Heraklion. Street/urban background concentration ratios for PMcoarse (mean ratio 1.42) were as large as for PM2.5 absorbance (mean ratio 1.38) and higher than those for PM2.5 (1.14) and PM10 (1.23), documenting the importance of non-tailpipe emissions. Correlations between components varied between areas, but were generally high between NO2 and PM2.5 absorbance (average R2 = 0.80). Correlations between PM2.5 and PMcoarse were lower (average R2 = 0.39). Despite high correlations, concentration ratios between components varied, e.g. the NO2/PM2.5 ratio varied between 0.67 and 3.06. In conclusion, substantial variability was found in spatial patterns of PM2.5, PM2.5 absorbance, PM10 and PMcoarse. The highly standardized measurement of particle concentrations across Europe will contribute to a consistent assessment of health effects across Europe.

Abstract  Aqueous triethanolamine (TEA) solutions are widely used as sorption medium for passive sampling of ambient NO(2), with NO(2) trapped and accumulated as nitrite ion. The results of test measurements of ambient NO(2) concentrations using passive sampling method showed that the simple approach commonly used to describe passive sampling process might lead to substantial systematic errors. Presented in the article is a new physicochemical model of the process of passive sampling of gaseous NO(2), with aqueous TEA solution used as a trapping medium. The model is based on the available results of experimental studies of interaction of gaseous NO(2) with TEA/water solutions. The key principles underlying the model are: (1) when absorbed by a trapping solution, NO(2) forms nitrite ion only on the condition that TEA is hydrated; (2) coefficient of conversion of NO(2) to NO (2) (-) is equal to one when reacting with hydrated TEA; and (3) the fraction of hydrated TEA molecules depends on air humidity at the moment of measurement. Validation of the model was made using the data of the field measurements carried out in the Middle Urals in 2007-2009. The new model was used to calculate average NO(2) concentrations. Concentrations calculated agreed well with the results obtained by reference methods. The difference between the datasets was statistically insignificant.

Abstract  The Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) was designed to examine the relationship between near-roadway exposures to air pollutants and respiratory outcomes in a cohort of asthmatic children who live close to major roadways in Detroit, Michigan USA. From September 2010 to December 2012 a total of 139 children with asthma, ages 6-14, were enrolled in the study on the basis of the proximity of their home to major roadways that carried different amounts of diesel traffic. The goal of the study was to investigate the effects of traffic-associated exposures on adverse respiratory outcomes, biomolecular markers of inflammatory and oxidative stress, and how these exposures affect the frequency and severity of respiratory viral infections in a cohort of children with asthma. An integrated measurement and modeling approach was used to quantitatively estimate the contribution of traffic sources to near-roadway air pollution and evaluate predictive models for assessing the impact of near-roadway pollution on children's exposures. Two intensive field campaigns were conducted in Fall 2010 and Spring 2011 to measure a suite of air pollutants including PM2.5 mass and composition, oxides of nitrogen (NO and NO2), carbon monoxide, and black carbon indoors and outdoors of 25 participants' homes, at two area schools, and along a spatial transect adjacent to I-96, a major highway in Detroit. These data were used to evaluate and refine models to estimate air quality and exposures for each child on a daily basis for the health analyses. The study design and methods are described, and selected measurement results from the Fall 2010 field intensive are presented to illustrate the design and successful implementation of the study. These data provide evidence of roadway impacts and exposure variability between study participants that will be further explored for associations with the health measures.

Abstract  BACKGROUND: The prevalence of Autistic Disorder (AD), a serious developmental condition, has risen dramatically over the past two decades but high-quality population-based research addressing etiology is limited. OBJECTIVES: We studied the influence of exposures to traffic-related air pollution during pregnancy on the development of autism using data from air monitoring stations and a land use regression (LUR) model to estimate exposures. METHODS: Children of mothers who gave birth in Los Angeles who were diagnosed with a primary AD diagnosis at ages 3-5 years during 1998-2009 were identified through the California Department of Developmental Services and linked to 1995-2006 California birth certificates. For 7,603 children with autism and 10 controls per case matched by sex, birth year, and minimum gestational age, birth addresses were mapped and linked to the nearest air monitoring station and a LUR model. We used conditional logistic regression, adjusting for maternal and perinatal characteristics including indicators of SES. RESULTS: Per interquartile range (IQR) increase, we estimated a 12-15% relative increase in odds of autism for O3 (OR = 1.12, 95% CI: 1.06, 1.19; per 11.54 ppb increase) and PM2.5 (OR = 1.15, 95% CI: 1.06, 1.24; per 4.68 μg/m3 increase) when mutually adjusting for both pollutants. Furthermore, we estimated 3-9% relative increases in odds per IQR increase for LUR-based NO and NO2 exposure estimates. LUR-based associations were strongest for children of mothers with less than a high school education. CONCLUSION: Measured and estimated exposures from ambient pollutant monitors and LUR model suggest associations between autism and prenatal air pollution exposure, mostly related to traffic sources.

Abstract  BACKGROUND: Prenatal and early life periods may be critical windows for harmful effects of air pollution on infant health. OBJECTIVES: We studied the association of air pollution exposure during pregnancy and the first year of life with respiratory illnesses, ear infections, and eczema during the first 12-18 months of age in a Spanish birth cohort of 2,199 infants. METHODS: We obtained parentally-reported information on doctor-diagnosed lower respiratory tract infections (LRTI), and parental reports of wheezing, eczema, and ear infections. We estimated individual exposures to nitrogen dioxide (NO2) and benzene with temporally-adjusted land use regression models. We used log-binomial regression models and a combined random-effects meta-analysis to estimate the effects of air pollution exposure on health outcomes across the four study locations. RESULTS: A 10-µg/m3 increase in average NO2 during pregnancy was associated with LRTI (RR = 1.05; 95% CI: 0.98, 1.12) and ear infections (RR = 1.18; 95% CI: 0.98, 1.41). The RRs for an interquartile range (IQR) increase in NO2 were 1.08 (95% CI: 0.97, 1.21) for LRTI and 1.31 (95% CI: 0.97, 1.76) for ear infections. Compared to NO2, the association for an IQR increase in average benzene exposure was similar for LRTI (RR = 1.06; 95% CI: 0.94, 1.19) and slightly lower for ear infections (RR = 1.17; 95% CI: 0.93, 1.46). Associations were slightly stronger among infants whose mothers spent more time at home during pregnancy. Air pollution exposure during the first year was highly correlated with prenatal exposure, thus we were unable to discern the relative importance of each exposure period. CONCLUSIONS: Our findings support the hypothesis that early life exposure to ambient air pollution may increase the risk of upper and lower respiratory tract infections in infants.

Abstract  To show how remote-sensing products can be used to classify the entire CONUS domain into 'geographical regions' and 'chemical regimes', we analyzed the results of simulation from the Community Multiscale Air Quality (CMAQ) model version 4.7.1 over the Conterminous United States (CONUS) for August 2009. In addition, we observe how these classifications capture the weekly cycles of ground-level nitrogen oxide (NOx) and ozone (O-3) at US EPA Air Quality System (AQS) sites. We use the Advanced Very High Resolution Radiometer (AVHRR) land use dominant categories and the Global Ozone Monitoring Experiment-2 (GOME-2) HCHO/NO2 column density ratios to allocate geographical regions (i.e., "urban", "forest", and "other" regions) and chemical regimes (i.e., "NOx-saturated", "NOx-sensitive", and "mixed" regimes). We also show that CMAQ simulations using GOME-2 satellite-adjusted NOx emissions mitigate the discrepancy between the weekly cycles of NOx from AQS observations and that from CMAQ simulation results. We found geographical regions and chemical regimes do not show a one-to-one correspondence: the averaged HCHO/NO2 ratios for AVHRR "urban" and "forest" regions are 2.1 and 4.0, which correspond to GOME-2 'mixed' and "NOx-sensitive" regimes, respectively. Both AQS-observed and CMAQ-simulated weekly cycles of NOx show high concentrations on weekdays and low concentrations on weekends, but with one- or two-day shifts of weekly high peaks in the simulated results, which eventually introduces the shifts in simulated weekly-low O-3 concentration. In addition, whereas the high weekend O-3 anomaly is clearly observable at sites over the GOME-2 NOx-saturated regime in both AQS and CMAQ, the weekend effect is not captured at sites over the AVHRR urban region because of the chemical characteristics of the urban sites (approximate to GOME-2 mixed regime). In addition, the weekend effect from AQS is more clearly discernible at sites above the GOME-2 NOx-saturated regime than at other sites above the CMAQ NOx-saturated regime, suggesting that the GOME-2-based chemical regime classification is more accurate than CMAQ-based chemical classification. Furthermore, the CMAQ simulations using the GOME-2-derived NOx emissions adjustment (decreasing from 462 Gg N to 426 Gg N over the US for August 2009) show large reductions of simulated NOx concentrations (particularly over the urban, or NOx-saturated, regime), and mitigates the large discrepancies between the absolute amount and the weekly pattern of NOx concentrations of the EPA AQS and those of the baseline CMAQ.

Abstract  ABSTRACT While evidence suggests associations between maternal exposure to air pollution and adverse birth outcomes, pregnant women's exposure to household air pollution in developing countries is understudied. Personal exposures of pregnant women (n = 100) in Trujillo, Peru to air pollutants and their indoor concentrations were measured. The effects of stove-use related characteristics and ambient air pollution on exposure were determined using mixed-effects models. Significant differences in 48-hr kitchen concentrations of particulate matter (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2) concentrations were observed across fuel-types (p < 0.05). Geometric mean PM2.5 concentrations were 112 μg/m (3) (CLs: 52, 242 μg/m(3)) and 42 μg/m(3) (21, 82 μg/m(3)) in homes were wood and gas were used respectively. PM2.5 exposure was at levels which recent exposure-response analyses suggest may not result in substantial reduction in health risks even in homes where cleaner burning gas stoves were used.

Abstract  BACKGROUND: The association of long-term air pollution and lung function has not been studied across adult European multi-national populations before. The aim of this study was to determine the association between long-term urban background air pollution and lung function levels, as well as change in lung function among European adults. METHODS:Forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and the ratio thereof (FEV1/FVC) were assessed at baseline and after 9 years of follow-up in adults from 21 European centres (followed-up sample 5610). Fine particles (PM(2.5)) were measured in 2000/2001 using central monitors. RESULTS: Despite sufficient statistical power no significant associations were found between city-specific annual mean PM(2.5) and average lung function levels. The findings also do not support an effect on change in lung function, albeit statistical power was insufficient to significantly detect such an association. CONCLUSIONS: The inability to refuse the null hypothesis may reflect (i) no effect of urban air pollution on lung function or (ii) inherent biases due to the study design. Examples of the latter are lack of individual-level air quality assignment, not quantified within-city contrasts in traffic-related pollution, or the heterogeneity of the studied populations and their urban environments. Future studies on long-term effects of air pollution on lung function could increase statistical power and reduce potential misclassification and confounding by characterizing exposure on the level of individuals, capturing contrasts due to local sources, in particular traffic.

Abstract  Multipollutant models are frequently used to differentiate roles of multiple pollutants in epidemiologic studies of ambient air pollution. In the presence of differing levels of measurement error across pollutants under consideration, however, they can be biased and as misleading as single-pollutant models. Their appropriate interpretation depends on the relationships among the pollutant measurements and the outcomes in question. In situations where two or more pollutant variables may be acting as surrogates for the etiologic agent(s), multipollutant models can help identify the best surrogate, but the risk estimates may be influenced by inclusion of a second variable that is not itself an independent risk factor for the outcome in question. In this paper, these issues will be illustrated in the context of an ongoing study of emergency visits in Atlanta. Emergency department visits from 41 of 42 hospitals serving the twenty-county Atlanta metropolitan area for the period 1993-2004 (n=10,206,389 visits) were studied in relation to ambient pollutant levels, including speciated particle measurements from an intensive monitoring campaign at a downtown station starting in 1998. Relative to our earlier publications, reporting results through 2000, the period for which the speciated data are now available is now tripled (six years in length). Poisson generalized linear models were used to examine outcome counts in relation to three-day moving average concentrations of pollutants of a priori interest (ozone, nitrogen dioxide, carbon monoxide, sulfur dioxide, oxygenated hydrocarbons, PM10, coarse PM, PM2.5, and the following components of PM2.5: elemental carbon, organic carbon, sulfate, water-soluble transition metals.) In the present analysis, we report results for two outcome groups: a respiratory outcomes group and a cardiovascular outcomes group. For cardiovascular visits, associations were observed with CO, 3 NO2, and PM2.5 elemental carbon and organic carbon. In multipollutant models, CO was the strongest predictor. For respiratory visits, associations were observed with ozone, PM10, CO and NO2 in single-pollutant models. In multipollutant models, PM10 and ozone persisted as predictors, with ozone the stronger predictor. Caveats and considerations in interpreting the multipollutant model results are discussed.

Abstract  This article describes the governing equations, computational algorithms, and other components entering into the Community Multiscale Air Quality (CMAQ) modeling system. This system has been designed to approach air quality as a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including tropospheric ozone, fine particles, acid deposition, and visibility degradation. CMAQ was also designed to have multiscale capabilities so that separate models were not needed for urban and regional scale air quality modeling. By making CMAQ a modeling system that addresses multiple pollutants and different spatial scales, it has a "one-atmosphere" perspective that combines the efforts of the scientific community. To implement multiscale capabilities in CMAQ, several issues (such as scalable atmospheric dynamics and generalized coordinates), which depend on the desired model resolution, are addressed. A set of governing equations for compressible nonhydrostatic atmospheres is available to better resolve atmospheric dynamics at smaller scales. Because CMAQ is designed to handle scale-dependent meteorological formulations and a large amount of flexibility, its governing equations are expressed in a generalized coordinate system. This approach ensures consistency between CMAQ and the meteorological modeling system. The generalized coordinate system determines the necessary grid and coordinate transformations, and it can accommodate various vertical coordinates and map projections. The CMAQ modeling system simulates various chemical and physical processes that are thought to be important for understanding atmospheric trace gas transformations and distributions. The modeling system contains three types of modeling components (Models-3): a meteorological modeling system for the description of atmospheric states and motions, emission models for man-made and natural emissions that are injected into the atmosphere, and a chemistry-transport modeling system for simulation of the chemical transformation and fate. The chemical transport model includes the following process modules: horizontal advection, vertical advection, mass conservation adjustments for advection processes, horizontal diffusion, vertical diffusion, gas-phase chemical reactions and solvers, photolytic rate computation, aqueous-phase reactions and cloud mixing, aerosol dynamics, size distributions and chemistry, plume chemistry effects, and gas and aerosol deposition velocity estimation. This paper describes the Models-3 CMAQ system, its governing equations, important science algorithms, and a few application examples.

Abstract  Aims: To investigate the chronic effects of air pollution caused mainly by automobiles in healthy adult females. Methods: Respiratory symptoms were investigated in 5682 adult females who had lived in the Tokyo metropolitan area for three years or more in 1987; 733 of them were subjected to pulmonary function tests over eight years from 1987 to 1994. The subjects were divided into three groups by the level of air pollution they were exposed to during the study period. The concentrations of nitrogen dioxide and suspended particulate matter were the highest in group 1, and the lowest in group 3. Results: The prevalence rates of respiratory symptoms in group 1 were higher than those in groups 2 and 3, except for wheezing. Multiple logistic regression analysis showed significant differences in persistent phlegm and breathlessness. The subjects selected for the analysis of pulmonary function were 94, 210, and 102 females in groups 1, 2, and 3, respectively. The annual mean change of FEV1 in group 1 was the largest (-0.020 l/y), followed by that in group 2 (-0.015 l/y), and that in group 3 (-0.009 l/y). Testing for trends showed a significant larger decrease of FEV1 with the increase in the level of air pollution. Conclusions: The subjects living in areas with high levels of air pollution showed higher prevalence rates of respiratory symptoms and a larger decrease of FEV1 compared with those living in areas with low levels of air pollution. Since the traffic density is larger in areas with high air pollution, the differences among the groups may reflect the effect of air pollution attributable to particulate matter found in automobile exhaust.