Predicting personal exposure of pregnant women to traffic-related air pollutants

Predicting personal PAH exposure using high dimensional questionnaire and wristband data

BACKGROUND

Polycyclic aromatic hydrocarbons (PAHs) are a class of pervasive environmental pollutants with a variety of known health effects. While significant work has been completed to estimate personal exposure to PAHs, less has been done to identify sources of these exposures. Comprehensive characterization of reported sources of personal PAH exposure is a critical step to more easily identify individuals at risk of high levels of exposure and for developing targeted interventions based on source of exposure.

OBJECTIVE

In this study, we leverage data from a New York (NY)-based birth cohort to identify personal characteristics or behaviors associated with personal PAH exposure and develop models for the prediction of PAH exposure.

METHODS

We quantified 61 PAHs measured using silicone wristband samplers in association with 75 questionnaire variables from 177 pregnant individuals. We evaluated univariate associations between each compound and questionnaire variable, conducted regression tree analysis for each PAH compound and completed a principal component analysis of for each participant's entire PAH exposure profile to determine the predictors of PAH levels.

RESULTS

Regression tree analyses of individual compounds and exposure mixture identified income, time spent outdoors, maternal age, country of birth, transportation type, and season as the variables most frequently predictive of exposure.

Maternal exposure to black carbon and nitrogen dioxide during pregnancy and birth weight: Using machine-learning methods to achieve balance in inverse-probability weights

BACKGROUND

Low birth weight is associated with increased risks of health problems in infancy and later life. Among the epidemiological analyses suggesting an association between air pollution and birth weight, few have estimated the effects of black carbon (BC) or together with nitrogen dioxide (NO₂), and even fewer studies have used causal modelling.

METHODS

We examined 1,119,011 birth records between 2001/01/01 and 2015/12/31 from the Massachusetts Birth Registry to investigate causal associations between prenatal exposure to BC and NO₂ and birth weight. We calculated mean residential BC and NO₂ exposures 0-30, and 31-280 days prior to birth from validated spatial-temporal models. We fit generalized propensity score models with gradient boosting tuned by a new algorithm to achieve covariate balance, then fit marginal structural models with stabilized inverse-probability weights.

RESULTS

Throughout pregnancy, the average birth weight would drop by 17.0 g (95% CI: 15.4, 18.6) for an IQR increase of 0.14 μg/m³ in BC and would independently drop by 19.9 g (95% CI: 18.6, 21.3) for an IQR increase of 9.8 ppb in NO₂. Most of the negative effects of BC on birth weight are from 0 to 30 days before the delivery date. The estimated odds ratio of low birth weight for every IQR increase during the entire pregnancy was 1.131 (95% CI: 1.106, 1.156) for BC and 1.082 (95% CI: 1.062, 1.103) for NO₂.

CONCLUSIONS

We found that prenatal exposures to both BC and NO₂ were associated with lower birth weight.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Association between moderated level of air pollution and fetal growth: the potential role of noise exposure

This study aims to analyze, in a population of singletons, the potential confounding or modifying effect of noise on the relationship between fetal growth restriction (FGR) or small for gestational age (SGA) and environmental exposure to air pollution. All women with single pregnancies living in one of two medium-sized cities (Besançon, Dijon) and who delivered at a university hospital between 2005 and 2009 were included. FGR and SGA were obtained from medical records. Outdoor residential exposure to nitrogen dioxide (NO₂) and particulate matter (PM₁₀) was quantified at the mother’s address at delivery over defined pregnancy periods; outdoor noise exposure was considered to be the annual average daily noise levels in the façade of building (L_{Aeq,24 h}). Adjusted odds ratios (OR_a) were estimated by multivariable logistic regressions. Among the 8994 included pregnancies, 587 presented FGR and 918 presented SGA. In the two-exposure models, for SGA, the OR_a for a 10-µg/m³ increase of PM₁₀ during the two last months before delivery was 1.18, 95%CI 1.00–1.41 and for FGR, these OR_a were for the first and the third trimesters, and the two last months before delivery: 0.77 (0.61–0.97), 1.38 (1.12–1.70), and 1.35 (1.11–1.66), respectively. Noise was not associated with SGA or FGR and did not confound the relationship between air pollution and SGA or FGR. These results are in favor of an association between PM₁₀ exposure and fetal growth, independent of noise, particularly towards the end of pregnancy, and of a lack of association between noise and fetal growth.

Comparison of measured residential black carbon levels outdoors and indoors with fixed-site monitoring data and with dispersion modelling

Epidemiologic studies on health effects of air pollution usually rely on time-series of ambient monitoring data or on spatially modelled levels. Little is known how well these estimate residential outdoor and indoor levels. We investigated the agreement of measured residential black carbon (BC) levels outdoors and indoors with fixed-site monitoring data and with levels calculated using a Gaussian dispersion model. One-week residential outdoor and indoor BC measurements were conducted for 15 families living in central Stockholm. Time-series from urban background and street-level monitors were compared to these measurements. The observed weekly concentrations were also standardized to reflect annual averages, using urban background levels, and compared spatially to long-term levels as estimated by dispersion modelling. Weekly average outdoor BC level was 472 ng/m³ (range 261-797 ng/m³). The corresponding fixed-site urban background and street levels were 313 and 1039 ng/m³, respectively. Urban background variation explained 50% of the temporal variation in residential outdoor levels averaged over 24 h. Modelled residential long-term outdoor levels were on average comparable with the standardized measured home outdoor levels, and explained 49% of the spatial variability. The median indoor/outdoor ratio across all addresses was 0.79, with no difference between day and night time. Common exposure estimation approaches in the epidemiology of health effects related to BC displayed high validity for residencies in central Stockholm. Urban background monitored levels explained half of the outdoor day-to-day variability at residential addresses. Long-term dispersion modelling explained half of the spatial differences in outdoor levels. Indoor BC concentrations tended to be somewhat lower than outdoor levels.

Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence

Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM_2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM_2.5 personal exposure were 55.1(82.8) µg/m³ for men and 58.5(58.8) µg/m³ for women; corresponding figures for black carbon were 4.6(7.0) µg/m³ and 6.1(9.6) µg/m³. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R_spearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (R_spearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.

Genomics of Particulate Matter Exposure Associated Cardiopulmonary Disease: A Narrative Review

Particulate matter (PM) exposure is associated with the development of cardiopulmonary disease. Our group has studied the adverse health effects of World Trade Center particulate matter (WTC-PM) exposure on firefighters. To fully understand the complex interplay between exposure, organism, and resultant disease phenotype, it is vital to analyze the underlying role of genomics in mediating this relationship. A PubMed search was performed focused on environmental exposure, genomics, and cardiopulmonary disease. We included original research published within 10 years, on epigenetic modifications and specific genetic or allelic variants. The initial search resulted in 95 studies. We excluded manuscripts that focused on work-related chemicals, heavy metals and tobacco smoke as primary sources of exposure, as well as reviews, prenatal research, and secondary research studies. Seven full-text articles met pre-determined inclusion criteria, and were reviewed. The effects of air pollution were evaluated in terms of methylation (n = 3), oxidative stress (n = 2), and genetic variants (n = 2). There is evidence to suggest that genomics plays a meditating role in the formation of adverse cardiopulmonary symptoms and diseases that surface after exposure events. Genomic modifications and variations affect the association between environmental exposure and cardiopulmonary disease, but additional research is needed to further define this relationship.

Predicting gestational personal exposure to PM2.5 from satellite-driven ambient concentrations in Shanghai

BACKGROUND

It has been widely reported that gestational exposure to fine particulate matters (PM_2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM_2.5 concentrations and personal PM_2.5 exposure would significantly affect the estimation of exposure-response relationship.

OBJECTIVE

Our study aimed to predict gestational personal exposure to PM_2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants.

METHOD

We collected 762 72-h personal exposure samples from a panel of 329 pregnant women in Shanghai, China as well as their time-activity patterns from Feb 2017 to Jun 2018. We established an ambient PM_2.5 model based on MAIAC AOD at 1 km resolution, then used its output as a major predictor to develop a personal exposure model.

RESULTS

Our ambient PM_2.5 model yielded a cross-validation R² of 0.96. Personal PM_2.5 exposure levels were almost identical to the corresponding ambient concentrations. After adjusting for time-activity patterns and meteorological factors, our personal exposure has a CV R² of 0.76.

CONCLUSION

We established a prediction model for gestational personal exposure to PM_2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.

Personal assessment of the external exposome during pregnancy and childhood in Europe

The human exposome affects child development and health later in life, but its personal external levels, variability, and correlations are largely unknown. We characterized the personal external exposome of pregnant women and children in eight European cities. Panel studies included 167 pregnant women and 183 children (aged 6-11 years). A personal exposure monitoring kit composed of smartphone, accelerometer, ultraviolet (UV) dosimeter, and two air pollution monitors were used to monitor physical activity (PA), fine particulate matter (PM_2.5), black carbon, traffic-related noise, UV-B radiation, and natural outdoor environments (NOE). 77% of women performed the adult recommendation of ≥150 min/week of moderate to vigorous PA (MVPA), while only 3% of children achieved the childhood recommendation of ≥60 min/day MVPA. 11% of women and 17% of children were exposed to daily PM_2.5 levels higher than recommended (≥25μg/m³). Mean exposure to noise ranged from Lden 51.1 dB in Kaunas to Lden 65.2 dB in Barcelona. 4% of women and 23% of children exceeded the recommended maximum of 2 Standard-Erythemal-Dose of UV-B at least once a week. 33% of women and 43% of children never reached the minimum NOE contact recommendation of ≥30 min/week. The variations in air and noise pollution exposure were dominated by between-city variability, while most of the variation observed for NOE contact and PA was between-participants. The correlations between all personal exposures ranged from very low to low (Rho < 0.30). The levels of personal external exposures in both pregnant women and children are above the health recommendations, and there is little correlation between the different exposures. The assessment of the personal external exposome is feasible but sampling requires from one day to more than one year depending on exposure due to high variability between and within cities and participants.

Multiple pregnancies and air pollution in moderately polluted cities: Is there an association between air pollution and fetal growth?

BACKGROUND

Multiple pregnancies (where more than one fetus develops simultaneously in the womb) are systematically excluded from studies of the impact of air pollution on pregnancy outcomes. This study aims to analyze, in a population of multiple pregnancies, the relationship between fetal growth restriction (FGR), small for gestational age (SGA) and exposure to air pollution in moderately polluted cities.

METHODS

All women with multiple pregnancies living in the city of Besançon or in the urban area of Dijon and who delivered at a university hospital between 2005 and 2009 were included. FGR and SGA were obtained from medical records. Outdoor residential nitrogen dioxide (NO₂) exposure was assessed using the mother's address, considering a 50 m radius buffer over the following defined pregnancy periods: each trimester, entire pregnancy and two months before delivery. Logistic regression analyses were performed.

RESULTS

This study included 249 multiple pregnancies with 506 newborns. The median of NO₂ concentration considering a 50 m radius buffer during entire pregnancy was 23.1 μg/m³ (minimum at 10.1 μg/m³ and maximum at 46.7 μg/m³). No association was observed between NO₂ and SGA whatever the pregnancy period (the odds ratio (OR) range 0.78 to 0.88). Regarding FGR, the OR associated with an increase of 10 μg/m³ of NO₂ exposure during entire pregnancy was 1.52 (95% Confidence Interval (CI): 1.02-2.26). Similar results were observed for NO₂ exposure during the various pregnancy periods.

CONCLUSIONS

These results are in line with an association between NO₂ and fetal growth in multiple pregnancies for an exposure mostly below the threshold set out in European legislation.

When, Where, and What? Characterizing Personal PM2.5 Exposure in Periurban India by Integrating GPS, Wearable Camera, and Ambient and Personal Monitoring Data

Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day ( when?), location ( where?), and individuals' activities ( what?) to PM_2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions ( n = 227). We integrated data from multiple sources: continuous personal and ambient PM_2.5 concentrations; questionnaire, GPS, and wearable camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m³ (SD 24.6) for men and 39.7 μg/m³ (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM_2.5 was an important predictor of 24-h personal exposure for both genders. Among men, activities predictive of higher hourly average exposure included presence near food preparation, in the kitchen, in the vicinity of smoking, or in industry. For women, predictors of exposure were largely related to cooking.

Spatial variation in nitrogen dioxide concentrations and cardiopulmonary hospital admissions

BACKGROUND

Air pollution episodes are associated with increased cardiopulmonary hospital admissions. Cohort studies showed associations of spatial variation in traffic-related air pollution with respiratory and cardiovascular mortality. Much less is known in particular about associations with cardiovascular morbidity. We explored the relation between spatial variation in nitrogen dioxide (NO₂) concentrations and cardiopulmonary hospital admissions.

METHODS

This ecological study was based on hospital admissions data (2001-2004) from the National Medical Registration and general population data for the West of the Netherlands (population 4.04 million). At the 4-digit postcode area level (n=683) associations between modeled annual average outdoor NO₂ concentrations and hospital admissions for respiratory and cardiovascular causes were evaluated by linear regression with the log of the postcode-specific percentage of subjects that have been admitted at least once during the study period as the dependent variable. All analyses were adjusted for differences in composition of the population of the postcode areas (age, sex, income).

RESULTS

At the postcode level, positive associations were found between outdoor NO₂ concentrations and hospital admission rates for asthma, chronic obstructive pulmonary disease (COPD), all cardiovascular causes, ischemic heart disease and stroke (e.g. adjusted relative risk (95% confidence interval) for the second to fourth quartile relative to the first quartile of exposure were 1.87 (1.46-2.40), 2.34 (1.83-3.01) and 2.81 (2.16-3.65) for asthma; 1.44 (1.19-1.74), 1.50 (1.24-1.82) and 1.60 (1.31-1.96) for COPD). Associations remained after additional (indirect) adjustment for smoking (COPD admission rate) and degree of urbanization.

CONCLUSIONS

Our study suggests an increased risk of hospitalization for respiratory and cardiovascular causes in areas with higher levels of NO₂. Our findings add to the currently limited evidence of a long-term effect of air pollution on hospitalization. The ecological design of our study is a limitation and more studies with individual data are needed to confirm our findings.

Estimation of exposure to atmospheric pollutants during pregnancy integrating space-time activity and indoor air levels: Does it make a difference?

Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3 weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16 h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them.

National Spatiotemporal Exposure Surface for NO2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000-2010

Land-use regression (LUR) is widely used for estimating within-urban variability in air pollution. While LUR has recently been extended to national and continental scales, these models are typically for long-term averages. Here we present NO2 surfaces for the continental United States with excellent spatial resolution (∼100 m) and monthly average concentrations for one decade. We investigate multiple potential data sources (e.g., satellite column and surface estimates, high- and standard-resolution satellite data, and a mechanistic model [WRF-Chem]), approaches to model building (e.g., one model for the whole country versus having separate models for urban and rural areas, monthly LURs versus temporal scaling of a spatial LUR), and spatial interpolation methods for temporal scaling factors (e.g., kriging versus inverse distance weighted). Our core approach uses NO2 measurements from U.S. EPA monitors (2000-2010) to build a spatial LUR and to calculate spatially varying temporal scaling factors. The model captures 82% of the spatial and 76% of the temporal variability (population-weighted average) of monthly mean NO2 concentrations from U.S. EPA monitors with low average bias (21%) and error (2.4 ppb). Model performance in absolute terms is similar near versus far from monitors, and in urban, suburban, and rural locations (mean absolute error 2-3 ppb); since low-density locations generally experience lower concentrations, model performance in relative terms is better near monitors than far from monitors (mean bias 3% versus 40%) and is better for urban and suburban locations (1-6%) than for rural locations (78%, reflecting the relatively clean conditions in many rural areas). During 2000-2010, population-weighted mean NO2 exposure decreased 42% (1.0 ppb [∼5.2%] per year), from 23.2 ppb (year 2000) to 13.5 ppb (year 2010). We apply our approach to all U.S. Census blocks in the contiguous United States to provide 132 months of publicly available, high-resolution NO2 concentration estimates.

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.

Applying land use regression model to estimate spatial variation of PM₂.₅ in Beijing, China

Fine particulate matter (PM2.5) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM2.5 by building separate LUR models in Beijing. Hourly routine PM2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m(3) (SD = 13.7). PM2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R (2) of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m(3). This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework

In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.

Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution

BACKGROUND AND AIMS

In the HEAPS (Health Effects of Air Pollution in Antwerp Schools) study the importance of traffic-related air pollution on the school and home location on children's health was assessed. 130 children (aged 6 to 12) from two schools participated in a biomonitoring study measuring oxidative stress, inflammation and cardiovascular markers.

METHODS

Personal exposure of schoolchildren to black carbon (BC) and nitrogen dioxide (NO2) was assessed using both measured and modeled concentrations. Air quality measurements were done in two seasons at approximately 50 locations, including the schools. The land use regression technique was applied to model concentrations at the children's home address and at the schools.

RESULTS

In this paper the results of the exposure analysis are given. Concentrations measured at school 2h before the medical examination were used for assessing health effects of short term exposure. Over two seasons, this short term BC exposure ranged from 514 ng/m(3) to 6285 ng/m(3), and for NO2 from 11 μg/m(3) to 36 μg/m(3). An integrated exposure was determined until 10 days before the child's examination, taking into account exposures at home and at school and the time spent in each of these microenvironments. Land use regression estimates were therefore recalculated into daily concentrations by using the temporal trend observed at a fixed monitor of the official air quality network. Concentrations at the children's homes were modeled to estimate long term exposure (from 1457 ng/m(3) to 3874 ng/m(3) for BC; and from 19 μg/m(3) to 51 μg/m(3) for NO2).

CONCLUSIONS

The land use regression technique proved to be a fast and accurate means for estimating long term and daily BC and NO2 exposure for children living in the Antwerp area. The spatial and temporal resolution was tailored to the needs of the epidemiologists involved in this study.

PM2.5 exposure and birth outcomes: use of satellite- and monitor-based data

BACKGROUND

Air pollution may be related to adverse birth outcomes. Exposure information from land-based monitoring stations often suffers from limited spatial coverage. Satellite data offer an alternative data source for exposure assessment.

METHODS

We used birth certificate data for births in Connecticut and Massachusetts, United States (2000-2006). Gestational exposure to PM2.5 was estimated from US Environmental Protection Agency monitoring data and from satellite data. Satellite data were processed and modeled by using two methods-denoted satellite (1) and satellite (2)-before exposure assessment. Regression models related PM2.5 exposure to birth outcomes while controlling for several confounders. Birth outcomes were mean birth weight at term birth, low birth weight at term (<2500 g), small for gestational age (SGA, <10th percentile for gestational age and sex), and preterm birth (<37 weeks).

RESULTS

Overall, the exposure assessment method modified the magnitude of the effect estimates of PM2.5 on birth outcomes. Change in birth weight per interquartile range (2.41 μg/m) increase in PM2.5 was -6 g (95% confidence interval = -8 to -5), -16 g (-21 to -11), and -19 g (-23 to -15), using the monitor, satellite (1), and satellite (2) methods, respectively. Adjusted odds ratios, based on the same three exposure methods, for term low birth weight were 1.01 (0.98-1.04), 1.06 (0.97-1.16), and 1.08 (1.01-1.16); for SGA, 1.03 (1.01-1.04), 1.06 (1.03-1.10), and 1.08 (1.04-1.11); and for preterm birth, 1.00 (0.99-1.02), 0.98 (0.94-1.03), and 0.99 (0.95-1.03).

CONCLUSIONS

Under exposure assessment methods, we found associations between PM2.5 exposure and adverse birth outcomes particularly for birth weight among term births and for SGA. These results add to the growing concerns that air pollution adversely affects infant health and suggest that analysis of health consequences based on satellite-based exposure assessment can provide additional useful information.

Agreement of land use regression models with personal exposure measurements of particulate matter and nitrogen oxides air pollution

Land use regression (LUR) models are often used to predict long-term average concentrations of air pollutants. Little is known how well LUR models predict personal exposure. In this study, the agreement of LUR models with measured personal exposure was assessed. The measured components were particulate matter with a diameter smaller than 2.5 μm (PM2.5), soot (reflectance of PM2.5), nitrogen oxides (NOx), and nitrogen dioxide (NO2). In Helsinki, Utrecht, and Barcelona, 15 volunteers (from semiurban, urban background, and traffic sites) followed prescribed time activity patterns. Per participant, six 96 h outdoor, indoor, and personal measurements spread over three seasons were conducted. Soot LUR models were significantly correlated with measured average outdoor and personal soot concentrations. Soot LUR models explained 39%, 44%, and 20% of personal exposure variability (R(2)) in Helsinki, Utrecht, and Barcelona. NO2 LUR models significantly predicted outdoor concentrations and personal exposure in Utrecht and Helsinki, whereas NOx and PM2.5 LUR models did not predict personal exposure. PM2.5, NO2, and NOx models were correlated with personal soot, the component least affected by indoor sources. LUR modeled and measured outdoor, indoor, and personal concentrations were highly correlated for all pollutants when data from the three cities were combined. This study supports the use of intraurban LUR models for especially soot in air pollution epidemiology.

A comparison of population air pollution exposure estimation techniques with personal exposure estimates in a pregnant cohort

There is increasing evidence of the harmful effects for mother and fetus of maternal exposure to air pollutants. Most studies use large retrospective birth outcome datasets and make a best estimate of personal exposure (PE) during pregnancy periods. We compared estimates of personal NOx and NO2 exposure of pregnant women in the North West of England with exposure estimates derived using different modelling techniques. A cohort of 85 pregnant women was recruited from Manchester and Blackpool. Participants completed a time-activity log and questionnaire at 13-22 weeks gestation and were provided with personal Ogawa samplers to measure their NOx/NO2 exposure. PE was compared to monthly averages, the nearest stationary monitor to the participants' home, weighted average of the closest monitor to home and work location, proximity to major roads, as well as to background modelled concentrations (DEFRA), inverse distance weighting (IDW), ordinary kriging (OK), and a land use regression model with and without temporal adjustment. PE was most strongly correlated with monthly adjusted DEFRA (NO2r = 0.61, NOxr = 0.60), OK and IDW (NO2r = 0.60; NOxr = 0.62) concentrations. Correlations were stronger in Blackpool than in Manchester. Where there is evidence for high temporal variability in exposure, methods of exposure estimation which focus solely on spatial methods should be adjusted temporally, with an improvement in estimation expected to be better with increased temporal variability.

The use of ultrasound measurements in environmental epidemiological studies of air pollution and fetal growth

PURPOSE OF REVIEW

Recently, several international research groups have suggested that studies about environmental contaminants and adverse pregnancy outcomes should be designed to elucidate potential underlying biological mechanisms. The purpose of this review is to examine the epidemiological studies addressing maternal exposure to air pollutants and fetal growth during gestation as assessed by ultrasound measurements.

RECENT FINDINGS

The six studies published to date found that exposure to certain ambient air pollutants during pregnancy is negatively associated with the growth rates and average attained size of fetal parameters belonging to the growth profile. Fetal parameters may respond to maternal air pollution exposures uniquely, and this response may vary by pollutant and timing of gestational exposure. Current literature suggests that mean changes in head circumference, abdominal circumference, femur length, and biparietal diameter are negatively associated with early-pregnancy exposures to ambient and vehicle-related air pollution.

SUMMARY

The use of more longitudinal studies, employing ultrasound measures to assess fetal outcomes, may assist with the better understanding of mechanisms responsible for air pollution-related pregnancy outcomes.

Investigating the association between birth weight and complementary air pollution metrics: a cohort study

BACKGROUND

Exposure to air pollution is frequently associated with reductions in birth weight but results of available studies vary widely, possibly in part because of differences in air pollution metrics. Further insight is needed to identify the air pollution metrics most strongly and consistently associated with birth weight.

METHODS

We used a hospital-based obstetric database of more than 70,000 births to study the relationships between air pollution and the risk of low birth weight (LBW, <2,500 g), as well as birth weight as a continuous variable, in term-born infants. Complementary metrics capturing different aspects of air pollution were used (measurements from ambient monitoring stations, predictions from land use regression models and from a Gaussian dispersion model, traffic density, and proximity to roads). Associations between air pollution metrics and birth outcomes were investigated using generalized additive models, adjusting for maternal age, parity, race/ethnicity, insurance status, poverty, gestational age and sex of the infants.

RESULTS

Increased risks of LBW were associated with ambient O(3) concentrations as measured by monitoring stations, as well as traffic density and proximity to major roadways. LBW was not significantly associated with other air pollution metrics, except that a decreased risk was associated with ambient NO(2) concentrations as measured by monitoring stations. When birth weight was analyzed as a continuous variable, small increases in mean birth weight were associated with most air pollution metrics (<40 g per inter-quartile range in air pollution metrics). No such increase was observed for traffic density or proximity to major roadways, and a significant decrease in mean birth weight was associated with ambient O3 concentrations.

CONCLUSIONS

We found contrasting results according to the different air pollution metrics examined. Unmeasured confounders and/or measurement errors might have produced spurious positive associations between birth weight and some air pollution metrics. Despite this, ambient O(3) was associated with a decrement in mean birth weight and significant increases in the risk of LBW were associated with traffic density, proximity to roads and ambient O(3). This suggests that in our study population, these air pollution metrics are more likely related to increased risks of LBW than the other metrics we studied. Further studies are necessary to assess the consistency of such patterns across populations.

Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment

Quantifying human exposure to air pollutants is a challenging task. Ambient concentrations of air pollutants at potentially harmful levels are ubiquitous in urban areas and subject to high spatial and temporal variability. At the same time, every individual has unique activity-patterns. Exposure results from multifaceted relationships and interactions between environmental and human systems, adding complexity to the assessment process. Traditionally, approaches to quantify human exposure have relied on pollutant concentrations from fixed air quality network sites and static population distributions. New developments in sensor technology now enable us to monitor personal exposure to air pollutants directly while people are moving through their activity spaces and varying concentration fields. The literature review on which this paper is based on reflects recent developments in the assessment of human exposure to air pollution. This includes the discussion of methodologies and concepts, and the elaboration of approaches and study designs applied in the field. We identify shortcomings of current approaches and discuss future research needs. We close by proposing a novel conceptual model for the integrated assessment of human exposure to air pollutants taking into account latest technological capabilities and contextual information.

Spatial and temporal variability in urban fine particulate matter concentrations

Identification of hot spots for urban fine particulate matter (PM(2.5)) concentrations is complicated by the significant contributions from regional atmospheric transport and the dependence of spatial and temporal variability on averaging time. We focus on PM(2.5) patterns in New York City, which includes significant local sources, street canyons, and upwind contributions to concentrations. A literature synthesis demonstrates that long-term (e.g., one-year) average PM(2.5) concentrations at a small number of widely-distributed monitoring sites would not show substantial variability, whereas short-term (e.g., 1-h) average measurements with high spatial density would show significant variability. Statistical analyses of ambient monitoring data as a function of wind speed and direction reinforce the significance of regional transport but show evidence of local contributions. We conclude that current monitor siting may not adequately capture PM(2.5) variability in an urban area, especially in a mega-city, reinforcing the necessity of dispersion modeling and methods for analyzing high-resolution monitoring observations.

Social factors associated with nitrogen dioxide (NO2) exposure during pregnancy: the INMA-Valencia project in Spain

Numerous studies have focused on the effects of exposure to air pollution on health; however, certain subsets of the population tend to be more exposed to such pollutants depending on their social or demographic characteristics. In addition, exposure to toxicants during pregnancy may play a deleterious role in fetal development as fetuses are especially vulnerable to external insults. The present study was carried out within the framework of the INMA (Infancia y Medio Ambiente or Childhood and the Environment) multicenter cohort study with the objective of identifying the social, demographic, and life-style factors associated with nitrogen dioxide (NO(2)) exposure in the subjects in the cohort. The study comprised 785 pregnant women who formed part of the INMA cohort in Valencia, Spain. Outdoor levels of NO(2) were measured at 93 sampling sites spread over the study area during four different sampling periods lasting 7 days each. Multiple regression models were used for mapping outdoor NO(2) throughout the area. Individual exposure was assigned as: 1) the estimated outdoor NO(2) levels at home, and 2) the average of estimated outdoor NO(2) levels at home and work, weighted according to the time spent in each environment. The subjects' socio-demographic and life-style information was obtained through a questionnaire. In the multiple linear analyses, the outdoor NO(2) levels assigned to each home were taken to be the dependent variable. Other variables included in the model were: age, country of origin, smoking during pregnancy, parity, season of the year, and social class. These same variables remained in the model when the dependent variable was changed to the NO(2) levels adjusted for the subjects' time-activity patterns. We found that younger women, those coming from Latin American countries, and those belonging to the lower social strata were exposed to higher NO(2) levels, both as measured outside their homes as well as when time-activity patterns were taken into account. These subgroups also have a higher probability of being exposed to NO(2) levels over 40 μg/m(3), which is the annual limit for maximum safe exposure, as established by European Directive 2008/50/EC.

Relationship of personal exposure to volatile organic compounds to home, work and fixed site outdoor concentrations

Personal exposures of 100 adult non-smokers living in the UK, as well as home and workplace microenvironment concentrations of 15 volatile organic compounds were investigated. The strength of the association between personal exposure and indoor home and workplace concentrations as well as with central site ambient air concentrations in medium to low pollution areas was assessed. Home microenvironment concentrations were strongly associated with personal exposures indicating that the home is the driving factor determining personal exposures to VOCs, explaining between 11 and 75% of the total variability. Workplace and central site ambient concentrations were less correlated with the corresponding personal concentrations, explaining up to 11-22% of the variability only at the low exposure end of the concentration range (e.g. benzene concentrations <2.5 μg m(-3)). One of the reasons for the discrepancies between personal exposures and central site data was that the latter does not account for exposure due to personal activities (e.g. commuting, painting). A moderate effect of season on the strength of the association between personal exposure and ambient concentrations was found. This needs to be taken into account when using fixed site measurements to infer exposures.

Estimation of personal NO2 exposure in a cohort of pregnant women

There is a growing concern about the possible adverse effects of exposure to air pollution on health during pregnancy. Therefore, a priority of the INMA (environment and childhood) study was to estimate personal exposure to traffic-related air pollution. In the cohort from Valencia (n=855), ambient levels of NO(2) were measured at 93 sampling sites spread over the study area during four different sampling periods of 7 days each. Multiple regression models were used to map ambient NO(2) over the area. Geographical data and predictions from kriging obtained by the "let one out" procedure were used as predictors. Individual exposure was assigned as 1) the estimated ambient NO(2) level at the home address and 2) the average of estimated ambient NO(2) levels at home and work addresses, weighted by the time spent in each environment. Estimations were temporally customized using the NO(2) levels registered daily by the regional Air Pollution Monitoring Network. The entire pregnancy and each trimester were taken as exposure windows. The model for the mean levels of NO(2) during the sampling periods explained 81% of the variation in NO(2) levels. Relative percent differences between the two models of personal exposure assignment were less than 9% for more than 90% of the participants; however the rest of them showed marked differences. Personal exposure estimates were slightly higher in the second model. In both cases, exposure during the whole pregnancy was strongly correlated with exposure in the second trimester. Considering periods shorter than the entire pregnancy will provide us the opportunity to identify specific windows of susceptibility.

Black carbon exposure, oxidative stress genes, and blood pressure in a repeated-measures study

BACKGROUND

Particulate matter (PM) air pollution has been associated with cardiovascular morbidity and mortality, and elevated blood pressure (BP) is a known risk factor for cardiovascular disease. A small number of studies have investigated the relationship between PM and BP and found mixed results. Evidence suggests that traffic-related air pollution contributes significantly to PM-related cardiovascular effects.

OBJECTIVES

We hypothesized that black carbon (BC), a traffic-related combustion by-product, would be more strongly associated with BP than would fine PM [aerodynamic diameter < or = 2.5 microm (PM(2.5))], a heterogeneous PM mixture, and that these effects would be larger among participants with genetic variants associated with impaired antioxidative defense.

METHODS

We performed a repeated-measures analysis in elderly men to analyze associations between PM(2.5) and BC exposure and BP using mixed-effects models with random intercepts, adjusting for potential confounders. We also examined statistical interaction between BC and genetic variants related to oxidative stress defense: GSTM1, GSTP1, GSTT1, NQO1, catalase, and HMOX-1.

RESULTS

A 1-SD increase in BC concentration was associated with a 1.5-mmHg increase in systolic BP [95% confidence interval (CI), 0.1-2.8] and a 0.9-mmHg increase in diastolic BP (95% CI, 0.2-1.6). We observed no evidence of statistical interaction between BC and any of the genetic variants examined and found no association between PM(2.5) and BP.

CONCLUSIONS

We observed positive associations between BP and BC, but not between BP and PM(2.5), and found no evidence of effect modification of the association between BC and BP by gene variants related to antioxidative defense.

Model development and validation of personal exposure to volatile organic compound concentrations

BACKGROUND

Direct measurement of exposure to volatile organic compounds (VOCs) via personal monitoring is the most accurate exposure assessment method available. However, its wide-scale application to evaluating exposures at the population level is prohibitive in terms of both cost and time. Consequently, indirect measurements via a combination of microenvironment concentrations and personal activity diaries represent a potentially useful alternative.

OBJECTIVE

The aim of this study was to optimize a model of personal exposures (PEs) based on microenvironment concentrations and time/activity diaries and to compare modeled with measured exposures in an independent data set.

MATERIALS

VOC PEs and a range of microenvironment concentrations were collected with active samplers and sorbent tubes. Data were supplemented with information collected through questionnaires. Seven models were tested to predict PE to VOCs in 75% (n = 370) of the measured PE data set, whereas the other 25% (n = 120) was used for validation purposes.

RESULTS

The best model able to predict PE with independence of measurements was based upon stratified microenvironment concentrations, lifestyle factors, and individual-level activities. The proposed model accounts for 40-85% of the variance for individual VOCs and was validated for almost all VOCs, showing normalized mean bias and mean fractional bias below 25% and predicting 60% of the values within a factor of 2.

CONCLUSIONS

The models proposed identify the most important non-weather-related variables for VOC exposures; highlight the effect of personal activities, use of solvents, and exposure to environmental tobacco smoke on PE levels; and may assist in the development of specific models for other locations.

The relationship between air pollution and low birth weight: effects by mother's age, infant sex, co-pollutants, and pre-term births

Previously we identified associations between the mother's air pollution exposure and birth weight for births in Connecticut and Massachusetts from 1999-2002. Other studies also found effects, though results are inconsistent. We explored potential uncertainties in earlier work and further explored associations between air pollution and birth weight for PM₁₀, PM_2.5, CO, NO₂, and SO₂. Specifically we investigated: (1) whether infants of younger (≤24 years) and older (≥40 years) mothers are particularly susceptible to air pollution's effects on birth weight; (2) whether the relationship between air pollution and birth weight differed by infant sex; (3) confounding by co-pollutants and differences in pollutants' measurement frequencies; and (4) whether observed associations were influenced by inclusion of pre-term births. Findings did not indicate higher susceptibility to the relationship between air pollution and birth weight based on the mother's age or the infant's sex. Results were robust to exclusion of pre-term infants and co-pollutant adjustment, although sample size decreased for some pollutant pairs. These findings provide additional evidence for the relationship between air pollution and birth weight, and do not identify susceptible sub-populations based on infant sex or mother's age. We conclude with discussion of key challenges in research on air pollution and pregnancy outcomes.