Modelling the vertical gradient of nitrogen dioxide in an urban area.

INTRODUCTION: Land use regression models environmental predictors to estimate ground-floor air pollution concentration surfaces of a study area. While many cities are expanding vertically, such models typically ignore the vertical dimension. METHODS: We took integrated measurements of NO2 at up to three different floors on the facades of 25 buildings in the mid-sized European city of Basel, Switzerland. We quantified the decrease in NO2 concentration with increasing height at each facade over two 14-day periods in different seasons. Using predictors of traffic load, population density and street configuration, we built conventional land use regression (LUR) models which predicted ground floor concentrations. We further evaluated which predictors best explained the vertical decay rate. Ultimately, we combined ground floor and decay models to explain the measured concentrations at all heights. RESULTS: We found a clear decrease in mean nitrogen dioxide concentrations between measurements at ground level and those at higher floors for both seasons. The median concentration decrease was 8.1% at 10 m above street level in winter and 10.4% in summer. The decrease with height was sharper at buildings where high concentrations were measured on the ground and in canyon-like street configurations. While the conventional ground floor model was able to explain ground floor concentrations with a model R2 of 0.84 (RMSE 4.1 µg/m3), it predicted measured concentrations at all heights with an R2 of 0.79 (RMSE 4.5 µg/m3), systematically overpredicting concentrations at higher floors. The LUR model considering vertical decay was able to predict ground floor and higher floor concentrations with a model R2 of 0.84 (RMSE 3.8 µg/m3) and without systematic bias. DISCUSSION: Height above the ground is a relevant determinant of outdoor residential exposure, even in medium-sized European cities without much high-rise. It is likely that conventional LUR models overestimate exposure for residences at higher floors near major roads. This overestimation can be minimized by considering decay with height.

Land Use Regression Models for Ultrafine Particles in Six European Areas.

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R2 of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R2 was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.

Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions.

BACKGROUND: Land Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models. METHODS: Air pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution. We developed multi-area LUR models for biannual average concentrations of PM2.5, PM2.5 absorbance, PM10, PMcoarse, PNC and LDSA, as well as alpine, non-alpine and study area specific models for NO2, using predictor variables which were available at a national level. Models were validated using leave-one-out cross-validation, as well as independent external validation with routine monitoring data. RESULTS: Model explained variance (R(2)) was moderate for the various PM mass fractions PM2.5 (0.57), PM10 (0.63) and PMcoarse (0.45), and was high for PM2.5 absorbance (0.81), PNC (0.87) and LDSA (0.91). Study-area specific LUR models for NO2 (R(2) range 0.52-0.89) outperformed combined-area alpine (R (2) = 0.53) and non-alpine (R (2) = 0.65) models in terms of both cross-validation and independent external validation, and were better able to account for between-area variability. Predictor variables related to traffic and national dispersion model estimates were important predictors. CONCLUSIONS: LUR models for all pollutants captured spatial variability of long-term average concentrations, performed adequately in validation, and could be successfully applied to the SAPALDIA cohort. Dispersion model predictions or area indicators served well to capture the between area variance. For NO2, applying study-area specific models was preferable over applying combined-area alpine/non-alpine models. Correlations between pollutants were higher in the model predictions than in the measurements, so it will remain challenging to disentangle their health effects.

Modeling indoor air pollution of outdoor origin in homes of SAPALDIA subjects in Switzerland.

Given the shrinking spatial contrasts in outdoor air pollution in Switzerland and the trends toward tightly insulated buildings, the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) needs to understand to what extent outdoor air pollution remains a determinant for residential indoor exposure. The objectives of this paper are to identify determining factors for indoor air pollution concentrations of particulate matter (PM), ultrafine particles in the size range from 15 to 300nm, black smoke measured as light absorbance of PM (PMabsorbance) and nitrogen dioxide (NO2) and to develop predictive indoor models for SAPALDIA. Multivariable regression models were developed based on indoor and outdoor measurements among homes of selected SAPALDIA participants in three urban (Basel, Geneva, Lugano) and one rural region (Wald ZH) in Switzerland, various home characteristics and reported indoor sources such as cooking. Outdoor levels of air pollutants were important predictors for indoor air pollutants, except for the coarse particle fraction. The fractions of outdoor concentrations infiltrating indoors were between 30% and 66%, the highest one was observed for PMabsorbance. A modifying effect of open windows was found for NO2 and the ultrafine particle number concentration. Cooking was associated with increased particle and NO2 levels. This study shows that outdoor air pollution remains an important determinant of residential indoor air pollution in Switzerland.

Land use regression models for crustal and traffic-related PM2.5 constituents in four areas of the SAPALDIA study.

Many studies have documented adverse health effects of long-term exposure to fine particulate matter (PM2.5), but there is still limited knowledge regarding the causal relationship between specific sources of PM2.5 and such health effects. The spatial variability of PM2.5 constituents and sources, as a exposure assessment strategy for investigating source contributions to health effects, has been little explored so far. Between 2011 and 2012, three measurement campaigns of PM and nitrogen dioxide (NO2) were performed in 80 sites across four areas of the Swiss Study on Air Pollution and Lung and heart Diseases in Adults (SAPALDIA). Reflectance analysis and energy dispersive X-ray fluorescence (XRF) were performed on PM2.5 filter samples to estimate light absorbance and trace element concentrations, respectively. Three air pollution source factors were identified using principal-component factor analysis: vehicular, crustal, and long-range transport. Land use regression (LUR) models were developed for temporally-adjusted scores of each factor, combining the four study areas. Model performance was assessed using two cross-validation methods. Model explained variance was high for the vehicular factor (R(2)=0.76), moderate for the crustal factor (R(2)=0.46), and low for the long-range transport factor (R(2)=0.19). The cross-validation methods suggested that models for the vehicular and crustal factors moderately accounted for both the between and within-area variability, and therefore can be applied to the four study areas to estimate long-term exposures within the SAPALDIA study population. The combination of source apportionment techniques and LUR modelling may help in identifying air pollution sources and disentangling their contribution to observed health effects in epidemiologic studies.

Ambient ultrafine particle levels at residential and reference sites in urban and rural Switzerland.

Although there is evidence that ultrafine particles (UFP) do affect human health there are currently no legal ambient standards. The main reasons are the absence of spatially resolved exposure data to investigate long-term health effects and the challenge of defining representative reference sites for monitoring given the high dependence of UFP on proximity to sources. The objectives of this study were to evaluate the spatial distribution of UFP in four areas of the Swiss Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) and to investigate the representativeness of routine air monitoring stations for residential sites in these areas. Repeated UFP measurements during three seasons have been conducted at a total of 80 residential sites and four area specific reference sites over a median duration of 7 days. Arithmetic mean residential PNC scattered around the median of 10,800 particles/cm(3) (interquartile range [IQR] = 7800 particles/cm(3)). Spatial within area contrasts (90th/10th percentile ratios) were around two; increased contrasts were observed during weekday rush-hours. Temporal UFP patterns were comparable at reference and residential sites in all areas. Our data show that central monitoring sites can represent residential conditions when locations are well chosen with respect to the local sources--namely traffic. For epidemiological research, locally resolved spatial models are needed to estimate individuals' long-term exposures to UFP of outdoor origin at home, during commute and at work.

Differences in indoor versus outdoor concentrations of ultrafine particles, PM2.5, PMabsorbance and NO2 in Swiss homes.

Indoor air quality is a growing concern as we spend the majority of time indoors and as new buildings are increasingly airtight for energy saving purposes. For a better understanding of residential indoor air pollution in Switzerland we conducted repeated 1-2-week-long indoor and outdoor measurements of particle number concentrations (PNC), particulate matter (PM), light absorbance of PM2.5 (PMabsorbance) and nitrogen dioxide (NO2). Residents of all homes were enrolled in the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA). Indoor levels were comparable in urban areas and generally low in rural homes. Average indoor levels were 7800 particles/cm(3) (interquartile range=7200); 8.7 µg/m(3) (6.5) PM2.5 and 10.2 µg/m(3) (11.2) NO2. All pollutants showed large variability of indoor/outdoor ratios between sites. We observed similar diurnal patterns for indoor and outdoor PNC. Nevertheless, the correlation of average indoor and outdoor PNC between sites as well as longitudinal indoor/outdoor correlations within sites were low. Our results show that a careful evaluation of home characteristics is needed when estimating indoor exposure to pollutants with outdoor origin.

Natural-cause mortality and long-term exposure to particle components: an analysis of 19 European cohorts within the multi-center ESCAPE project.

BACKGROUND: Studies have shown associations between mortality and long-term exposure to particulate matter air pollution. Few cohort studies have estimated the effects of the elemental composition of particulate matter on mortality. OBJECTIVES: Our aim was to study the association between natural-cause mortality and long-term exposure to elemental components of particulate matter. METHODS: Mortality and confounder data from 19 European cohort studies were used. Residential exposure to eight a priori-selected components of particulate matter (PM) was characterized following a strictly standardized protocol. Annual average concentrations of copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM size fractions ≤ 2.5 µm (PM2.5) and ≤ 10 µm (PM10) were estimated using land-use regression models. Cohort-specific statistical analyses of the associations between mortality and air pollution were conducted using Cox proportional hazards models using a common protocol followed by meta-analysis. RESULTS: The total study population consisted of 291,816 participants, of whom 25,466 died from a natural cause during follow-up (average time of follow-up, 14.3 years). Hazard ratios were positive for almost all elements and statistically significant for PM2.5 sulfur (1.14; 95% CI: 1.06, 1.23 per 200 ng/m3). In a two-pollutant model, the association with PM2.5 sulfur was robust to adjustment for PM2.5 mass, whereas the association with PM2.5 mass was reduced. CONCLUSIONS: Long-term exposure to PM2.5 sulfur was associated with natural-cause mortality. This association was robust to adjustment for other pollutants and PM2.5.

The relevance of commuter and work/school exposure in an epidemiological study on traffic-related air pollution.

Exposure during transport and at non-residential locations is ignored in most epidemiological studies of traffic-related air pollution. We investigated the impact of separately estimating NO2 long-term outdoor exposures at home, work/school, and while commuting on the association between this marker of exposure and potential health outcomes. We used spatially and temporally resolved commuter route data and model-based NO2 estimates of a population sample in Basel, Switzerland, to assign individual NO2-exposure estimates of increasing complexity, namely (1) home outdoor concentration; (2) time-weighted home and work/school concentrations; and (3) time-weighted concentration incorporating home, work/school and commute. On the basis of their covariance structure, we estimated the expectable relative differences in the regression slopes between a quantitative health outcome and our measures of individual NO2 exposure using a standard measurement error model. The traditional use of home outdoor NO2 alone indicated a 12% (95% CI: 11-14%) underestimation of related health effects as compared with integrating both home and work/school outdoor concentrations. Mean contribution of commuting to total weekly exposure was small (3.2%; range 0.1-13.5%). Thus, ignoring commute in the total population may not significantly underestimate health effects as compared with the model combining home and work/school. For individuals commuting between Basel-City and Basel-Country, ignoring commute may produce, however, a significant attenuation bias of 4% (95% CI: 4-5%). Our results illustrate the importance of including work/school locations in assessments of long-term exposures to traffic-related air pollutants such as NO2. Information on individuals' commuting behavior may further improve exposure estimates, especially for subjects having lengthy commutes along major transportation routes.

Simulation of population-based commuter exposure to NO2 using different air pollution models.

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m(-3), range: 21-61) than with a dispersion model with a lower resolution (39 ± 5 µg m(-3); range: 24-51), and a land use regression model (41 ± 5 µg m(-3); range: 24-54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas.

Performance of multi-city land use regression models for nitrogen dioxide and fine particles.

BACKGROUND: Land use regression (LUR) models have been developed mostly to explain intraurban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. OBJECTIVES: We aimed to develop European and regional LUR models and to examine their transferability to areas not used for model development. METHODS: We evaluated LUR models for nitrogen dioxide (NO2) and particulate matter (PM; PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE (European Study of Cohorts for Air Pollution Effects) study areas across 14 European countries for PM and NO2. Models were evaluated with cross-validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building. RESULTS: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5, and 70% for PM2.5 absorbance. The HV R2s were only slightly lower than the model R2 (NO2, 54%; PM2.5, 80%; PM2.5 absorbance, 70%). The European NO2, PM2.5, and PM2.5 absorbance models explained a median of 59%, 48%, and 70% of within-area variability in individual areas. The transferred models predicted a modest-to-large fraction of variability in areas that were excluded from model building (median R2: NO2, 59%; PM2.5, 42%; PM2.5 absorbance, 67%). CONCLUSIONS: Using a large data set from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted.

Long-term exposure to air pollution and cardiovascular mortality: an analysis of 22 European cohorts.

BACKGROUND: Air pollution has been associated with cardiovascular mortality, but it remains unclear as to whether specific pollutants are related to specific cardiovascular causes of death. Within the multicenter European Study of Cohorts for Air Pollution Effects (ESCAPE), we investigated the associations of long-term exposure to several air pollutants with all cardiovascular disease (CVD) mortality, as well as with specific cardiovascular causes of death. METHODS: Data from 22 European cohort studies were used. Using a standardized protocol, study area-specific air pollution exposure at the residential address was characterized as annual average concentrations of the following: nitrogen oxides (NO2 and NOx); particles with diameters of less than 2.5 µm (PM2.5), less than 10 µm (PM10), and 10 µm to 2.5 µm (PMcoarse); PM2.5 absorbance estimated by land-use regression models; and traffic indicators. We applied cohort-specific Cox proportional hazards models using a standardized protocol. Random-effects meta-analysis was used to obtain pooled effect estimates. RESULTS: The total study population consisted of 367,383 participants, with 9994 deaths from CVD (including 4,992 from ischemic heart disease, 2264 from myocardial infarction, and 2484 from cerebrovascular disease). All hazard ratios were approximately 1.0, except for particle mass and cerebrovascular disease mortality; for PM2.5, the hazard ratio was 1.21 (95% confidence interval = 0.87-1.69) per 5 µg/m and for PM10, 1.22 (0.91-1.63) per 10 µg/m. CONCLUSION: In a joint analysis of data from 22 European cohorts, most hazard ratios for the association of air pollutants with mortality from overall CVD and with specific CVDs were approximately 1.0, with the exception of particulate mass and cerebrovascular disease mortality for which there was suggestive evidence for an association.

Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: results from the ESCAPE and TRANSPHORM projects.

BACKGROUND: Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular (CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents of PM on total CVD mortality have been explored in a single study only. AIMS: The aim of this study was to examine the association of PM composition with cardiovascular mortality. METHODS: We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European Study of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health impacts--Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average exposure to elemental constituents within particle matter smaller than 2.5 and 10 µm (PM2.5 and PM10) was estimated using Land Use Regression models. Eight elements representing major sources were selected a priori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were conducted using Cox proportional hazards models with a standardized protocol. Random-effects meta-analysis was used to calculate combined effect estimates. RESULTS: The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically significant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the pooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was 0.96 (0.84-1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93-1.47), and S in PM2.5 (1.08, 95% CI: 0.95-1.22) and PM10 (1.09, 95% CI: 0.90-1.32). CONCLUSION: In a joint analysis of 19 European cohorts, we found no statistically significant association between long-term exposure to 8 elemental constituents of particles and total cardiovascular mortality.

Air pollution and nonmalignant respiratory mortality in 16 cohorts within the ESCAPE project.

RATIONALE: Prospective cohort studies have shown that chronic exposure to particulate matter and traffic-related air pollution is associated with reduced survival. However, the effects on nonmalignant respiratory mortality are less studied, and the data reported are less consistent. OBJECTIVES: We have investigated the relationship of long-term exposure to air pollution and nonmalignant respiratory mortality in 16 cohorts with individual level data within the multicenter European Study of Cohorts for Air Pollution Effects (ESCAPE). METHODS: Data from 16 ongoing cohort studies from Europe were used. The total number of subjects was 307,553. There were 1,559 respiratory deaths during follow-up. MEASUREMENTS AND MAIN RESULTS: Air pollution exposure was estimated by land use regression models at the baseline residential addresses of study participants and traffic-proximity variables were derived from geographical databases following a standardized procedure within the ESCAPE study. Cohort-specific hazard ratios obtained by Cox proportional hazard models from standardized individual cohort analyses were combined using metaanalyses. We found no significant associations between air pollution exposure and nonmalignant respiratory mortality. Most hazard ratios were slightly below unity, with the exception of the traffic-proximity indicators. CONCLUSIONS: In this study of 16 cohorts, there was no association between air pollution exposure and nonmalignant respiratory mortality.

Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project.

BACKGROUND: Few studies on long-term exposure to air pollution and mortality have been reported from Europe. Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the association between natural-cause mortality and long-term exposure to several air pollutants. METHODS: We used data from 22 European cohort studies, which created a total study population of 367,251 participants. All cohorts were general population samples, although some were restricted to one sex only. With a strictly standardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of particulate matter (PM) with diameters of less than 2.5 µm (PM2.5), less than 10 µm (PM10), and between 10 µm and 2.5 µm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land use regression models. We also investigated two traffic intensity variables-traffic intensity on the nearest road (vehicles per day) and total traffic load on all major roads within a 100 m buffer. We did cohort-specific statistical analyses using confounder models with increasing adjustment for confounder variables, and Cox proportional hazards models with a common protocol. We obtained pooled effect estimates through a random-effects meta-analysis. FINDINGS: The total study population consisted of 367,251 participants who contributed 5,118,039 person-years at risk (average follow-up 13.9 years), of whom 29,076 died from a natural cause during follow-up. A significantly increased hazard ratio (HR) for PM2.5 of 1.07 (95% CI 1.02-1.13) per 5 µg/m(3) was recorded. No heterogeneity was noted between individual cohort effect estimates (I(2) p value=0.95). HRs for PM2.5 remained significantly raised even when we included only participants exposed to pollutant concentrations lower than the European annual mean limit value of 25 µg/m(3) (HR 1.06, 95% CI 1.00-1.12) or below 20 µg/m(3) (1.07, 1.01-1.13). INTERPRETATION: Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even within concentration ranges well below the present European annual mean limit value. FUNDING: European Community's Seventh Framework Program (FP7/2007-2011).

Role of highway traffic on spatial and temporal distributions of air pollutants in a Swiss Alpine valley.

Traffic-related air pollutants show high spatial variability near roads, posing a challenge to adequately assess exposures. Recent modeling approaches (e.g. dispersion models, land-use regression (LUR) models) have addressed this but mostly in urban areas where traffic is abundant. In contrast, our study area was located in a rural Swiss Alpine valley crossed by the main North-south transit highway of Switzerland. We conducted an extensive measurement campaign collecting continuous nitrogen dioxide (NO2), particulate number concentrations (PN), daily respirable particulate matter (PM10), elemental carbon (EC) and organic carbon (OC) at one background, one highway and seven mobile stations from November 2007 to June 2009. Using these measurements, we built a hybrid model to predict daily outdoor NO2 concentrations at residences of children participating in an asthma panel study. With the exception of OC, daily variations of the pollutants followed the temporal trends of heavy-duty traffic counts on the highway. In contrast, variations of weekly/seasonal means were strongly determined by meteorological conditions, e.g., winter inversion episodes. For pollutants related to primary exhaust emissions (i.e. NO2, EC and PN) local spatial variation strongly depended on proximity to the highway. Pollutant concentrations decayed to background levels within 150 to 200 m from the highway. Two separate daily NO2 prediction models were built using LUR approaches with (a) short-term traffic and weather data (model 1) and (b) subsequent addition of daily background NO2 to previous model (model 2). Models 1 and 2 explained 70% and 91% of the variability in outdoor NO2 concentrations, respectively. The biweekly averaged predictions from the final model 2 agreed very well with the independent biweekly integrated passive measurements taken at thirteen homes and nine community sites (validation R(2)=0.74). The excellent spatio-temporal performance of our model provides a very promising basis for the health effect assessment of the panel study.

Examining the representativeness of home outdoor PM(2.5), EC, and OC estimates for daily personal exposures in Southern California.

Recent studies have linked acute respiratory and cardiovascular outcomes to measurements or estimates of traffic-related air pollutants at homes or schools. However, few studies have evaluated these outdoor measurements and estimates against personal exposure measurements. We compared measured and modeled home outdoor concentrations with personal measurements of traffic-related air pollutants in the Los Angeles air basin (Whittier and Riverside). Personal exposure of 63 children with asthma and 15 homes were assessed for particulate matter with an aerodynamic diameter less than 2.5 µm (PM(2.5)), elemental carbon (EC), and organic carbon (OC) during sixteen 10-day monitoring runs. Regression models to predict daily home outdoor PM(2.5), EC, and OC were constructed using home outdoor measurements, geographical and meteorological parameters, as well as CALINE4 estimates at outdoor home sites, which represent the concentrations from local traffic sources. These home outdoor models showed the variance explained (R(2)) was 0.97 and 0.94 for PM(2.5), 0.91 and 0.83 for OC, and 0.76 and 0.87 for EC in Riverside and Whittier, respectively. The PM(2.5) outdoor estimates correlated well with the personal measurements (Riverside R(2) = 0.65 and Whittier R(2) = 0.69). However, excluding potentially inaccurate samples from Riverside, the correlation between personal exposure to carbonaceous species and home outdoor estimates in Whittier was moderate for EC (R(2) = 0.37) and poor for OC (R(2) = 0.08). The CALINE4 estimates alone were not correlated with personal measurements of EC or other pollutants. While home outdoor estimates provide good approximations for daily personal PM(2.5) exposure, they may not be adequate for estimating daily personal exposure to EC and OC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11869-010-0099-y) contains supplementary material, which is available to authorized users.