Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children

Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile

Biomass burning is common in much of the world, and in some areas, residential wood-burning has increased. However, air pollution resulting from biomass burning is an important public health problem. A sampling campaign was carried out between May 2017 and July 2018 in over 64 sites in four sessions, to develop a spatio-temporal land use regression (LUR) model for fine particulate matter (PM) and wood-burning tracers levoglucosan and soluble potassium (Ksol) in a city heavily impacted by wood-burning. The mean (sd) was 46.5 (37.4) μg m-3 for PM2.5, 0.607 (0.538) μg m-3 for levoglucosan, and 0.635 (0.489) μg m-3 for Ksol. LUR models for PM2.5, levoglucosan, and Ksol had a satisfactory performance (LOSOCV R2), explaining 88.8%, 87.4%, and 87.3% of the total variance, respectively. All models included sociodemographic predictors consistent with the pattern of use of wood-burning in homes. The models were applied to predict concentrations surfaces and to estimate exposures for an epidemiological study.

Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO2 Concentrations Using Measurements Sampled with Google Street View Cars

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO₂ on every street in Amsterdam (n = 46.664) and Copenhagen (n = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers (n = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated r_s (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated r_s = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher r_s = 0.65 with the deterministic model predictions compared to the data-only (r_s = 0.50) and LUR model (r_s = 0.61). In Copenhagen, mixed model estimates correlated r_s = 0.51 with external model predictions compared to r_s = 0.45 and r_s = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations (r_s = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.

An Investigation into Which Methods Best Explain Children’s Exposure to Traffic-Related Air Pollution

There have been several methods employed to quantify individual-level exposure to ambient traffic-related air pollutants (TRAP). These include an individual’s residential proximity to roads, measurement of individual pollutants as surrogates or markers, as well as dispersion and land use regression (LUR) models. Hopanes are organic compounds still commonly found on ambient particulate matter and are specific markers of combustion engine primary emissions, but they have not been previously used in personal exposure studies. In this paper, children’s personal exposures to TRAP were evaluated using hopanes determined from weekly integrated filters collected as part of a personal exposure study in Windsor, Canada. These hopane measurements were used to evaluate how well other commonly used proxies of exposure to TRAP performed. Several of the LUR exposure estimates for a range of air pollutants were associated with the children’s summer personal hopane exposures (r = 0.41–0.74). However, all personal hopane exposures in summer were more strongly associated with the length of major roadways within 500 m of their homes. In contrast, metrics of major roadways and LUR estimates were poorly correlated with any winter personal hopanes. Our findings suggest that available TRAP exposure indicators have the potential for exposure misclassification in winter vs. summer and more so for LUR than for metrics of major road density. As such, limitations are evident when using traditional proxy methods for assigning traffic exposures and these may be especially important when attempting to assign exposures for children’s key growth and developmental windows. If long-term chronic exposures are being estimated, our data suggest that measures of major road lengths in proximity to homes are a more-specific approach for assigning personal TRAP exposures.

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.

Critical Time Windows for Air Pollution Exposure and Birth Weight in a Multicity Canadian Pregnancy Cohort

BACKGROUND

Maternal prenatal exposure to air pollution has been associated with adverse birth outcomes. However, previous studies focused on a priori time intervals such as trimesters reported inconsistent associations.

OBJECTIVES

We investigated time-varying vulnerability of birth weight to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) using flexible time intervals.

METHODS

We analyzed 1,300 live, full-term births from Maternal-Infant Research on Environmental Chemicals, a Canadian prospective pregnancy cohort spanning 10 cities (2008-2011). Daily PM2.5 and NO2 concentrations were estimated from ground-level monitoring, satellite models, and land-use regression, and assigned to participants from pre-pregnancy through delivery. We developed a flexible two-stage modeling method-using a Bayesian Metropolis-Hastings algorithm and empirical density threshold-to identify time-dependent vulnerability to air pollution without specifying exposure periods a priori. This approach identified critical windows with varying lengths (2-363 days) and critical windows that fell within, or straddled, predetermined time periods (i.e., trimesters). We adjusted the models for detailed infant and maternal covariates.

RESULTS

Critical windows associated with reduced birth weight were identified during mid- to late-pregnancy for both PM2.5 and NO2: -6 g (95% credible interval: -11, -1 g) and -5 g (-10, -0.1 g) per µg/m3 PM2.5 during gestational days 91-139 and 249-272, respectively; and -3 g (-5, -1 g) per ppb NO2 during days 55-145.

DISCUSSION

We used a novel, flexible selection method to identify critical windows when maternal exposures to air pollution were associated with decrements in birth weight. Our results suggest that air pollution impacts on fetal development may not be adequately captured by trimester-based analyses.

Selection of metric for indoor-outdoor source apportionment of metals in PM2.5 : mg/kg versus ng/m3

Trends in the elemental composition of fine particulate matter (PM_2.5 ) collected from indoor, outdoor, and personal microenvironments were investigated using two metrics: ng/m³ and mg/kg. Pearson correlations that were positive using one metric commonly disappeared or flipped to become negative when the other metric was applied to the same dataset. For example, the correlation between Mo and S in the outdoor microenvironment was positive using ng/m³ (p < 0.05) but negative using mg/kg (p < 0.05). In general, elemental concentrations (mg/kg) within PM_2.5 decreased significantly (p < 0.05) as PM_2.5 concentrations (µg/m³ ) increased-a dilution effect that was observed in all microenvironments and seasons. An exception was S: in the outdoor microenvironment, the correlation between wt% S and PM_2.5  flipped from negative in the winter (p < 0.01) to positive (p < 0.01) in the summer, whereas in the indoor microenvironment, this correlation was negative year-round (p < 0.05). Correlation analyses using mg/kg indicated that elemental associations may arise from Fe-Mn oxyhydroxide sorption processes that occur as particles age, with or without the presence of a common anthropogenic source. Application of mass-normalized concentration metrics (mg/kg or wt%), enabled by careful gravimetric analysis, revealed new evidence of the importance of indoor sources of elements in PM_2.5 .

Ambient air pollution and inflammatory effects in a Canadian pregnancy cohort

Supplemental Digital Content is available in the text.

Background:

Epidemiologic studies have consistently reported associations between air pollution and pregnancy outcomes including preeclampsia and gestational diabetes. However, the biologic mechanisms underlying these relationships remain unclear as few studies have collected relevant biomarker data. We examined relationships between ambient PM_2.5 and NO₂ with markers of inflammation during pregnancy in a prospective cohort of Canadian women.

Methods:

We analyzed data from 1170 women enrolled in the Maternal-Infant Research on Environmental Chemicals study. Daily residential PM_2.5 and NO₂ exposures during pregnancy were estimated using satellite-based and land-use regression models and used to create 14-day and 30-day exposure windows before blood-draw. Inflammatory markers C-reactive protein, interleukin-6, interleukin-8, and tumor necrosis factor-α were measured in third trimester plasma samples. Multivariable linear regression was used to estimate associations for an interquartile range (IQR) increase in PM_2.5 and NO₂ and markers of inflammation, while adjusting for individual-level confounders.

Results:

Fourteen-day (IQR: 6.85 µg/m³) and 30-day (IQR: 6.15 µg/m³) average PM_2.5 exposures before blood-draw were positively associated with C-reactive protein after adjustment for covariates (24.6% [95% CI = 9.4, 41.9] and 17.4% [95% CI = 1.0, 35.0] increases, respectively). This association was found to be robust in several sensitivity analyses. Neither PM_2.5 nor NO₂ exposures were associated with interleukin-6, interleukin-8, or tumor necrosis factor-α.

Conclusion:

Exposure to ambient PM_2.5 is positively associated with maternal inflammatory pathways in late pregnancy. This may contribute to positive associations between ambient PM_2.5 and risk of adverse pregnancy outcomes.

Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements

A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO₂) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO₂ concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R² = 0.71 for prediction of NO₂ data in 2002 and 2004) uses daily average NO₂ concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO₂ levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO₂ patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.

Long-term exposure to PM2.5 and Children's lung function: a dose-based association analysis

Background

The current literature is still not consist regarding the effect of long-term exposure to PM2.5 and children’s lung function, partly due to inadequate or inaccurate exposure assessment. In this study, we aim to investigate the associations between long-term exposure to PM2.5, estimated as average daily dose (ADD), and lung function in school-age children.

Methods

We recruited 684 participants of 7–12 years old from the city of Lanzhou located in northwestern China. Participants underwent spirometric tests for lung function and responded to a questionnaire survey. Detailed information about individual air exposure and personal information were collected, including length of school hours, home address, age, gender, etc. Combining the spatial distribution of PM2.5 concentrations in the past 5 years and individual time-activity data, we estimated annual ADD for 5 years preceding the lung function tests and 5-year average ADD, respectively. We used multiple linear regression models to examine the associations between ADD values and lung function, controlling for a range of individual-level covariates.

Results

The 5-year average ADD among all the participants was 50.5 µg/kg-d, with higher values estimated for children living in the urban area than the suburban area, for boys than girls, and for children whose parents received a lower education attainment. We found that a 1 μg/kg-d increment in ADD of PM2.5 was associated with a 10.49 mL (95% CI: −20.47, −0.50) decrease in forced vital capacity (FVC) and a 7.68 mL (95% CI: −15.80, −0.44) decrease in forced exploratory volume in 1 second (FEV₁). Among the annual ADDs estimated for the preceding 5 years, the immediate past year prior to lung function measurement had the greatest effect on lung function. The effect was greater in girls than in boys. We found no associations between annual exposure of PM2.5 (instead of ADD) and lung function when defined concentration was used as an exposure variable.

Conclusions

Long-term PM2.5 exposure, when estimated as exposure dose averaged over a year or longer, was associated with statistically significant reductions in FVC and FEV1 in children of elementary-school age. Future studies may consider the use of individual-level dose estimates (as opposed to exposure concentrations) to improve the dose-response assessment.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

Ambient air pollution and incidence of early-onset paediatric type 1 diabetes: A retrospective population-based cohort study

BACKGROUND

Studies have reported increasing incidence rates of paediatric diabetes, especially among those aged 0-5 years. Epidemiological evidence linking ambient air pollution to paediatric diabetes remains mixed.

OBJECTIVE

This study investigated the association between maternal and early-life exposures to common air pollutants (NO₂, PM_2.5, O₃, and oxidant capacity [Ox; the redox-weighted average of O₃ and NO₂]) and the incidence of paediatric diabetes in children up to 6 years of age.

METHODS

All registered singleton births in Ontario, Ca nada occurring between April 1st^, 2006 and March 31st^, 2012 were included through linkage from health administrative data. Monthly exposures to NO₂, PM_2.5, O₃, and O_x were estimated across trimesters, the entire pregnancy period and during childhood. Random effects Cox proportional hazards models were used to assess the relationships with paediatric diabetes incidence while controlling for important covariates. We also modelled the shape of concentration-response (CR) relationships.

RESULTS

There were 1094 children out of a cohort of 754,698 diagnosed with diabetes before the age of six. O₃ exposures during the first trimester of pregnancy were associated with paediatric diabetes incidence (hazard ratio (HR) per interquartile (IQR) increase = 2.00, 95% CI: 1.04-3.86). The CR relationship between O₃ during the first trimester and paediatric diabetes incidence appeared to have a risk threshold, in which there was little-to-no risk below 25 ppb of O₃, while above this level risk increased sigmoidally. No other associations were observed.

CONCLUSION

O₃ exposures during a critical period of development were associated with an increased risk of paediatric diabetes incidence.

Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging

NO₂ is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO₂ levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO₂ model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R² of 0.788 overall, a spatial R² of 0.844, and a temporal R² of 0.729. The relationship between daily monitored and predicted NO₂ is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO₂ levels. This NO₂ estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO₂ in unmonitored areas. We found the highest NO₂ levels along highways and in cities. We also observed that nationwide NO₂ levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.

Predicting Fine-Scale Daily NO2 for 2005-2016 Incorporating OMI Satellite Data Across Switzerland

Nitrogen dioxide (NO₂) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO₂ concentrations in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining ∼58% (R² range, 0.56-0.64) of the variation in measured NO₂ concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% (R² range, 0.70-0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO₂ across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO₂. The predicted NO₂ concentrations will be made available to facilitate health research in Switzerland.

Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters

The Mexico City Metropolitan Area (MCMA) is one of the largest and most populated urban environments in the world and experiences high air pollution levels. To develop models that estimate pollutant concentrations at fine spatiotemporal scales and provide improved air pollution exposure assessments for health studies in Mexico City. We developed finer spatiotemporal land use regression (LUR) models for PM2.5, PM10, O3, NO2, CO and SO2 using mixed effect models with the Least Absolute Shrinkage and Selection Operator (LASSO). Hourly traffic density was included as a temporal variable besides meteorological and holiday variables. Models of hourly, daily, monthly, 6-monthly and annual averages were developed and evaluated using traditional and novel indices. The developed spatiotemporal LUR models yielded predicted concentrations with good spatial and temporal agreements with measured pollutant levels except for the hourly PM2.5, PM10 and SO2. Most of the LUR models met performance goals based on the standardized indices. LUR models with temporal scales greater than one hour were successfully developed using mixed effect models with LASSO and showed superior model performance compared to earlier LUR models, especially for time scales of a day or longer. The newly developed LUR models will be further refined with ongoing Mexico City air pollution sampling campaigns to improve personal exposure assessments.

The value of using seasonality and meteorological variables to model intra-urban PM2.5 variation

A yearlong air monitoring campaign was conducted to assess the impact of local temperature, relative humidity, and wind speed on the temporal and spatial variability of PM_2.5 in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM_2.5 variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM_2.5 events that contributed to elevated seasonal PM_2.5 levels. Similarly, in spring, high PM_2.5 events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM_2.5 fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM_2.5 and meteorological variables either changed from positive to negative or dissolved depending on the season. Overall, the results suggest that mixed effects analysis with season and site as fixed factors and meteorological variables as covariates could increase the explanatory value of LUR models for PM_2.5.

Air pollution and the incidence of ischaemic and haemorrhagic stroke in the South London Stroke Register: a case-cross-over analysis

Background

Few European studies investigating associations between short-term exposure to air pollution and incident stroke have considered stroke subtypes. Using information from the South London Stroke Register for 2005–2012, we investigated associations between daily concentrations of gaseous and particulate air pollutants and incident stroke subtypes in an ethnically diverse area of London, UK.

Methods

Modelled daily pollutant concentrations based on a combination of measurements and dispersion modelling were linked at postcode level to incident stroke events stratified by haemorrhagic and ischaemic subtypes. The data were analysed using a time-stratified case–cross-over approach. Conditional logistic regression models included natural cubic splines for daily mean temperature and daily mean relative humidity, a binary term for public holidays and a sine–cosine annual cycle. Of primary interest were same day mean concentrations of particulate matter <2.5 and <10 µm in diameter (PM_2.5, PM₁₀), ozone (O₃), nitrogen dioxide (NO₂) and NO₂+nitrogen oxide (NO_X).

Results

Our analysis was based on 1758 incident strokes (1311 were ischaemic and 256 were haemorrhagic). We found no evidence of an association between all stroke or ischaemic stroke and same day exposure to PM_2.5, PM₁₀, O₃, NO₂ or NO_X. For haemorrhagic stroke, we found a negative association with PM₁₀ suggestive of a 14.6% (95% CI 0.7% to 26.5%) fall in risk per 10 µg/m³ increase in pollutant.

Conclusions

Using data from the South London Stroke Register, we found no evidence of a positive association between outdoor air pollution and incident stroke or its subtypes. These results, though in contrast to recent meta-analyses, are not inconsistent with the mixed findings of other UK studies.

Combining land use regression models and fixed site monitoring to reconstruct spatiotemporal variability of NO2 concentrations over a wide geographical area

The epidemiological research benefits from an accurate characterization of both spatial and temporal variability of exposure to air pollution. This work aims at proposing a method to combine the high spatial resolution of Land Use Regression (LUR) models with the high temporal resolution of fixed site monitoring data, to model spatiotemporal variability of NO₂ over a wide geographical area in Northern Italy. We developed seasonal LUR models to reconstruct the spatial distribution of a scaling factor that relates local concentrations to those measured at two reference central sites, one for the northern flat area and one for the southern mountain area. We calculated the daily average concentrations at 19 locations spread over the study areas as the product of the local scaling factor and the reference central site concentrations. We evaluated model performance comparing modeled and measured NO₂ data. LUR model's R² ranges from 0.76 to 0.92. The main predictors refers substantially to traffic, industrial land use, buildings volume and altitude a.s.l. The model's performance in reproducing measured concentrations was satisfactory. The temporal variability of concentrations was well captured: Spearman correlation between model and measures was >0.7 for almost all sites. Model's average absolute errors were in the order of 10μgm^-3. The model for the southern area tends to overestimate measured concentrations. Our modeling framework was able to reproduce spatiotemporal differences in NO₂ concentrations. This kind of model is less data-intensive than usual regional atmospheric models and it may be very helpful to assess population exposure within studies in which individual relevant exposure occurs along periods of days or months.

Association of short-term exposure to fine particulate matter and nitrogen dioxide with acute cardiovascular effects

This study evaluated whether exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) is associated with cardiovascular effects by examining a panel of 89 healthy subjects in Taipei, Taiwan. The subjects received two health examinations approximately 8months apart in 2013. Brachial-ankle pulse wave velocity (baPWV), a physiological indicator of arterial stiffness, and high-sensitivity C-reactive protein (hsCRP), a biomarker of vascular inflammations, were measured during each examination. Two exposure assessment methods were used for estimating the subjects' exposure to PM2.5 and NO2. The first method involved constructing daily land use regression (LUR) models according to measurements collected at ambient air quality monitoring stations. The second method required combining the LUR estimates with indoor monitoring data at the workplace of the subjects. Linear mixed models were used to examine the association between the exposure estimates and health outcomes. The results showed that a 10-μg/m(3) increase in PM2.5 concentration at a 1-day lag was associated with 2.1% (95% confidence interval: 0.7%-3.6%) and 2.4% (0.8%-4.0%) increases in baPWV based on the two exposure assessment methods, whereas no significant association was observed for NO2. The significant effects of PM2.5 remained in the two-pollutant models. By contrast, NO2, but not PM2.5, was significantly associated with increased hsCRP levels (16.0%-37.3% in single-pollutant models and 26.4%-44.6% in two-pollutant models, per 10-ppb increase in NO2). In conclusion, arterial stiffness might be more sensitive to short-term PM2.5 exposure than is inflammation.

Air Pollution During Pregnancy and Cord Blood Immune System Biomarkers

OBJECTIVES

We aimed to determine whether average and trimester-specific exposures to ambient measures of nitrogen dioxide (NO2) and particular matter (PM2.5) were associated with elevated cord blood concentrations of immunoglobulin E (IgE) and two epithelial cell produced cytokines: interleukin-33 (IL-33) and thymic stromal lymphopoietin (TSLP).

METHODS

This study utilized data and biospecimens from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study. There were 2001 pregnant women recruited between 2008 and 2011 from 10 Canadian cities. Maternal exposure to NO2 and PM2.5 was estimated using land use regression and satellite-derived models.

RESULTS

We observed statistically significant associations between maternal NO2 exposure and elevated cord blood concentrations of both IL-33 and TSLP among girls but not boys.

CONCLUSIONS

Maternal NO2 exposure may impact the development of the newborn immune system as measured by cord blood concentrations of two cytokines.

Ambient air pollution and adverse birth outcomes: Differences by maternal comorbidities

BACKGROUND

Prenatal exposure to ambient air pollution has been associated with adverse birth outcomes, but the potential modifying effect of maternal comorbidities remains understudied. Our objective was to investigate whether associations between prenatal air pollution exposures and birth outcomes differ by maternal comorbidities.

METHODS

A total of 818,400 singleton live births were identified in the province of Ontario, Canada from 2005 to 2012. We assigned exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) to maternal residences during pregnancy. We evaluated potential effect modification by maternal comorbidities (i.e. asthma, hypertension, pre-existing diabetes mellitus, heart disease, gestational diabetes and preeclampsia) on the associations between prenatal air pollution and preterm birth, term low birth weight and small for gestational age.

RESULTS

Interquartile range (IQR) increases in PM2.5 (2μg/m(3)), NO2 (9ppb) and O3 (5ppb) over the entire pregnancy were associated with a 4% (95% CI: 2.4-5.6%), 8.4% (95% CI: 5.5-10.3%) and 2% (95% CI: 0.5-4.1%) increase in the odds of preterm birth, respectively. Increases of 10.6% (95% CI: 0.2-2.1%) and 23.8% (95% CI: 5.5-44.8%) in the odds of preterm birth were observed among women with pre-existing diabetes while the increases were of 3.8% (95% CI: 2.2-5.4%) and 6.5% (95% CI: 3.7-8.4%) among women without this condition for pregnancy exposure to PM2.5 and NO2, respectively (Pint<0.01). The increase in the odds of preterm birth for exposure to PM2.5 during pregnancy was higher among women with preeclampsia (8.3%, 95% CI: 0.8-16.4%) than among women without (3.6%, 95% CI: 1.8-5.3%) (Pint=0.04). A stronger increase in the odds of preterm birth was found for exposure to O3 during pregnancy among asthmatic women (12.0%, 95% CI: 3.5-21.1%) compared to non-asthmatic women (2.0%, 95% CI: 0.1-3.5%) (Pint<0.01). We did not find statistically significant effect modification for the other outcomes investigated.

CONCLUSIONS

Findings of this study suggest that associations of ambient air pollution with preterm birth are stronger among women with pre-existing diabetes, asthma, and preeclampsia.

Spatio-temporal ozone variation in a case-crossover analysis of childhood asthma hospital visits in New York City

BACKGROUND

Childhood asthma morbidity has been associated with short-term air pollution exposure. To date, most investigations have used time-series models, and it is not well understood how exposure misclassification arising from unmeasured spatial variation may impact epidemiological effect estimates. Here, we develop case-crossover models integrating temporal and spatial individual-level exposure information, toward reducing exposure misclassification in estimating associations between air pollution and child asthma exacerbations in New York City (NYC).

METHODS

Air pollution data included: (a) highly spatially-resolved intra-urban concentration surfaces for ozone and co-pollutants (nitrogen dioxide and fine particulate matter) from the New York City Community Air Survey (NYCCAS), and (b) daily regulatory monitoring data. Case data included citywide hospital records for years 2005-2011 warm-season (June-August) asthma hospitalizations (n=2353) and Emergency Department (ED) visits (n=11,719) among children aged 5-17 years. Case residential locations were geocoded using a multi-step process to maximize positional accuracy and precision in near-residence exposure estimates. We used conditional logistic regression to model associations between ozone and child asthma exacerbations for lag days 0-6, adjusting for co-pollutant and temperature exposures. To evaluate the effect of increased exposure specificity through spatial air pollution information, we sequentially incorporated spatial variation into daily exposure estimates for ozone, temperature, and co-pollutants.

RESULTS

Percent excess risk per 10ppb ozone exposure in spatio-temporal models were significant on lag days 1 through 5, ranging from 6.5 (95% CI: 0.2-13.1) to 13.0 (6.0-20.6) for inpatient hospitalizations, and from 2.9 (95% CI: 0.1-5.7) to 9.4 (6.3-12.7) for ED visits, with strongest associations consistently observed on lag day 2. Spatio-temporal excess risk estimates were consistently but not statistically significantly higher than temporal-only estimates on lag days 0-3.

CONCLUSION

Incorporating case-level spatial exposure variation produced small, non-significant increases in excess risk estimates. Our modeling approach enables a refined understanding of potential measurement error in temporal-only versus spatio-temporal air pollution exposure assessments. As ozone generally varies over much larger spatial scales than that observed within NYC, further work is necessary to evaluate potential reductions in exposure misclassification for populations spanning wider geographic areas, and for other pollutants.

Air Pollution Exposure During Pregnancy and Fetal Markers of Metabolic function: The MIREC Study

Previous evidence suggests that exposure to outdoor air pollution during pregnancy could alter fetal metabolic function, which could increase the risk of obesity in childhood. However, to our knowledge, no epidemiologic study has investigated the association between prenatal exposure to air pollution and indicators of fetal metabolic function. We investigated the association between maternal exposure to nitrogen dioxide and fine particulate matter (aerodynamic diameter ≤2.5 µm) and umbilical cord blood leptin and adiponectin levels with mixed-effects linear regression models among 1,257 mother-infant pairs from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, conducted in Canada (2008-2011). We observed that an interquartile-range increase in average exposure to fine particulate matter (3.2 µg/m(3)) during pregnancy was associated with an 11% (95% confidence interval: 4, 17) increase in adiponectin levels. We also observed 13% (95% confidence interval: 6, 20) higher adiponectin levels per interquartile-range increase in average exposure to nitrogen dioxide (13.6 parts per billion) during pregnancy. Significant associations were seen between air pollution markers and cord blood leptin levels in models that adjusted for birth weight z score but not in models that did not adjust for birth weight z score. The roles of prenatal exposure to air pollution and fetal metabolic function in the potential development of childhood obesity should be further explored.

Air pollution is associated with primary health care visits for asthma in Sweden: A case-crossover design with a distributed lag non-linear model

BACKGROUND

Air pollution can increase the symptoms of asthma and has an acute effect on the number of emergency room visits and hospital admissions because of asthma, but little is known about the effect of air pollution on the number of primary health care (PHC) visits for asthma.

OBJECTIVE

To investigate the association between air pollution and the number of PHC visits for asthma in Scania, southern Sweden.

METHODS

Data on daily PHC visits for asthma were obtained from a regional healthcare database in Scania, which covers approximately half a million people. Air pollution data from 2005 to 2010 were obtained from six urban background stations. We used a case-crossover study design and a distributed lag non-linear model in the analysis.

RESULTS

The air pollution levels were generally within the EU air quality guidelines. The mean number of daily PHC visits for asthma was 34. The number of PHC visits increased by 5% (95% confidence interval (CI): 3.91-6.25%) with every 10µg m(-3) increase in daily mean NO2 lag (0-15), suggesting that daily air pollution levels are associated with PHC visits for asthma.

CONCLUSION

Even though the air quality in Scania between 2005 and 2010 was within EU's guidelines, the number of PHC visits for asthma increased with increasing levels of air pollution. This suggests that as well as increasing hospital and emergency room visits, air pollution increases the burden on PHC due to milder symptoms of asthma.

Neighborhood-Scale Spatial Models of Diesel Exhaust Concentration Profile Using 1-Nitropyrene and Other Nitroarenes

With emerging evidence that diesel exhaust exposure poses distinct risks to human health, the need for fine-scale models of diesel exhaust pollutants is growing. We modeled the spatial distribution of several nitrated polycyclic aromatic hydrocarbons (NPAHs) to identify fine-scale gradients in diesel exhaust pollution in two Seattle, WA neighborhoods. Our modeling approach fused land-use regression, meteorological dispersion modeling, and pollutant monitoring from both fixed and mobile platforms. We applied these modeling techniques to concentrations of 1-nitropyrene (1-NP), a highly specific diesel exhaust marker, at the neighborhood scale. We developed models of two additional nitroarenes present in secondary organic aerosol: 2-nitropyrene and 2-nitrofluoranthene. Summer predictors of 1-NP, including distance to railroad, truck emissions, and mobile black carbon measurements, showed a greater specificity to diesel sources than predictors of other NPAHs. Winter sampling results did not yield stable models, likely due to regional mixing of pollutants in turbulent weather conditions. The model of summer 1-NP had an R(2) of 0.87 and cross-validated R(2) of 0.73. The synthesis of high-density sampling and hybrid modeling was successful in predicting diesel exhaust pollution at a very fine scale and identifying clear gradients in NPAH concentrations within urban neighborhoods.

Assessing traffic and industrial contributions to ambient nitrogen dioxide and volatile organic compounds in a low pollution urban environment

Land use regression (LUR) modeling is an effective method for estimating fine-scale distributions of ambient air pollutants. The objectives of this study are to advance the methodology for use in urban environments with relatively low levels of industrial activity and provide exposure assessments for research on health effects of air pollution. Intraurban distributions of nitrogen dioxide (NO2) and the volatile organic compounds (VOCs) benzene, toluene and m- and p-xylene were characterized based on spatial monitoring and LUR modeling in Ottawa, Ontario, Canada. Passive samplers were deployed at 50 locations throughout Ottawa for two consecutive weeks in October 2008 and May 2009. Land use variables representing point, area and line sources were tested as predictors of pooled pollutant distributions. LUR models explained 96% of the spatial variability in NO2 and 75-79% of the variability in the VOC species. Proximity to highways, green space, industrial and residential land uses were significant in the final models. More notably, proximity to industrial point sources and road network intersections were significant predictors for all pollutants. The strong contribution of industrial point sources to VOC distributions in Ottawa suggests that facility emission data should be considered whenever possible. The study also suggests that proximity to road network intersections may be an effective proxy in areas where reliable traffic data are not available.

Effect of Personal Exposure to PM2.5 on Respiratory Health in a Mexican Panel of Patients with COPD

Background: Air pollution is a problem, especially in developing countries. We examined the association between personal exposure to particulate matter with an aerodynamic diameter less than 2.5 µm (PM_2.5) on respiratory health in a group of adults with chronic obstructive pulmonary disease (COPD). Methods: All participants resided in Mexico City and during follow-up, personal exposure to PM_2.5, respiratory symptoms, medications, and daily activity were registered daily. Peak expiratory flow (PEF) was measured twice daily, from February through December, 2000, in 29 adults with moderate, severe, and very severe COPD. PEF changes were estimated for each 10 µg/m³ increment of PM_2.5, adjustment for severity of COPD, minimum temperature, and day of the sampling. Results: For a 10-µg/m³ increase in the daily average of a two-day personal exposure to PM_2.5, there was a significant 33% increase in cough (95% CI, range, 5‒69%), and 23% in phlegm (95% CI, range, 2‒54%), a reduction of the PEF average in the morning of −1.4 L/min. (95% CI , range, −2.8 to −0.04), and at night of −3.0 L/min (95% CI, range, −5.7 to −0.3), respectively. Conclusions: Exposure to PM_2.5 was associated with reductions in PEF and increased respiratory symptoms in adults with COPD. The PEF reduction was observed both at morning and at night.

Fine particulate air pollution, nitrogen dioxide, and systemic autoimmune rheumatic disease in Calgary, Alberta

OBJECTIVE

To estimate the association between fine particulate (PM2.5) and nitrogen dioxide (NO2) pollution and systemic autoimmune rheumatic diseases (SARDs).

METHODS

Associations between ambient air pollution (PM2.5 and NO2) and SARDs were assessed using land-use regression models for Calgary, Alberta and administrative health data (1993-2007). SARD case definitions were based on ≥2 physician claims, or ≥1 rheumatology billing code; or ≥1 hospitalization code (for systemic lupus, Sjogren's Syndrome, scleroderma, polymyositis, dermatomyositis, or undifferentiated connective tissue disease). Bayesian hierarchical latent class regression models estimated the probability that each resident was a SARD case, based on these case definitions. The sum of individual level probabilities provided the estimated number of cases in each area. The latent class model included terms for age, sex, and an interaction term between age and sex. Bayesian logistic regression models were used to generate adjusted odds ratios (OR) for NO2 and PM2.5. pollutant models, adjusting for neighbourhood income, age, sex, and an interaction between age and sex. We also examined models stratified for First-Nations (FN) and non-FN subgroups.

RESULTS

Residents that were female and/or aged >45 had a greater probability of being a SARD case, with the highest OR estimates for older females. Independently, the odds of being a SARDs case increased with PM2.5 levels, but the results were inconclusive for NO2. The results stratified by FN and non-FN groups were not distinctly different.

CONCLUSION

In this urban Canadian sample, adjusting for demographics, exposure to PM2.5 was associated with an increased risk of SARDs. The results for NO2 were inconclusive.

The effects of outdoor air pollution on the respiratory health of Canadian children: A systematic review of epidemiological studies

BACKGROUND

Outdoor air pollution is a global problem with serious effects on human health, and children are considered to be highly susceptible to the effects of air pollution.

OBJECTIVE

To conduct a comprehensive and updated systematic review of the literature reporting the effects of outdoor air pollution on the respiratory health of children in Canada.

METHODS

Searches of four electronic databases between January 2004 and November 2014 were conducted to identify epidemiological studies evaluating the effect of exposure to outdoor air pollutants on respiratory symptoms, lung function measurements and the use of health services due to respiratory conditions in Canadian children. The selection process and quality assessment, using the Newcastle-Ottawa Scale, were conducted independently by two reviewers.

RESULTS

Twenty-seven studies that were heterogeneous with regard to study design, population, respiratory outcome and air pollution exposure were identified. Overall, the included studies reported adverse effects of outdoor air pollution at concentrations that were below Canadian and United States standards. Heterogeneous effects of air pollutants were reported according to city, sex, socioeconomic status and seasonality. The present review also describes trends in research related to the effect of air pollution on Canadian children over the past 25 years.

CONCLUSION

The present study reconfirms the adverse effects of outdoor air pollution on the respiratory health of children in Canada. It will help researchers, clinicians and environmental health authorities identify the available evidence of the adverse effect of outdoor air pollution, research gaps and the limitations for further research.

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.

Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia

Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM(2.5) and its components (elemental carbon (EC), SO(4)), O(3), carbon monoxide (CO), and nitrogen oxides (NOx). Metrics were applied in a study investigating the respiratory health effects of these pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related pollutants (CO, NO(x), and EC), but little spatial variability among ZIP code centroids for regional pollutants (PM(2.5), SO(4), and O(3)); (ii) for all pollutants except NO(x), temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong (r>0.82) for all pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.

Spatial and temporal estimation of air pollutants in New York City: exposure assignment for use in a birth outcomes study

BACKGROUND

Recent epidemiological studies have examined the associations between air pollution and birth outcomes. Regulatory air quality monitors often used in these studies, however, were spatially sparse and unable to capture relevant within-city variation in exposure during pregnancy.

METHODS

This study developed two-week average exposure estimates for fine particles (PM2.5) and nitrogen dioxide (NO2) during pregnancy for 274,996 New York City births in 2008-2010. The two-week average exposures were constructed by first developing land use regression (LUR) models of spatial variation in annual average PM2.5 and NO2 data from 150 locations in the New York City Community Air Survey and emissions source data near monitors. The annual average concentrations from the spatial models were adjusted to account for city-wide temporal trends using time series derived from regulatory monitors. Models were developed using Year 1 data and validated using Year 2 data. Two-week average exposures were then estimated for three buffers of maternal address and were averaged into the last six weeks, the trimesters, and the entire period of gestation. We characterized temporal variation of exposure estimates, correlation between PM2.5 and NO2, and correlation of exposures across trimesters.

RESULTS

The LUR models of average annual concentrations explained a substantial amount of the spatial variation (R2 = 0.79 for PM2.5 and 0.80 for NO2). In the validation, predictions of Year 2 two-week average concentrations showed strong agreement with measured concentrations (R2 = 0.83 for PM2.5 and 0.79 for NO2). PM2.5 exhibited greater temporal variation than NO2. The relative contribution of temporal vs. spatial variation in the estimated exposures varied by time window. The differing seasonal cycle of these pollutants (bi-annual for PM2.5 and annual for NO2) resulted in different patterns of correlations in the estimated exposures across trimesters. The three levels of spatial buffer did not make a substantive difference in estimated exposures.

CONCLUSIONS

The combination of spatially resolved monitoring data, LUR models and temporal adjustment using regulatory monitoring data yielded exposure estimates for PM2.5 and NO2 that performed well in validation tests. The interaction between seasonality of air pollution and exposure intervals during pregnancy needs to be considered in future studies.