Methods For Air Pollution Exposure Assessment Research Articles

A Review of Ambient Air Pollution Exposure Assessment Methods in Determining Childhood Respiratory Health Effects in Children under Five

Various epidemiological studies have reported on air pollution exposure-related lung function decline and respiratory health effects in children. Children have increased susceptibility to ambient air pollutants as physiological and structural changes of the lung are still occurring within the first five years of life after birth. This review examines applications in air pollution exposure assessment methods when evaluating lung function and respiratory health concentration–response effects in young children, while considering the effects of critical windows of exposure. We identified 13 studies that used various methods of exposure assessment in assessing respiratory health outcomes (presence of lower respiratory tract infections, respiratory symptoms, wheezing and asthma) in children under five. The methods applied included personal monitoring (n = 1), proximity-based methods (n = 3), inverse distance weighting (n = 2), geographic weighted regression (n = 1), dispersion modeling (n = 1), satellite-based methods (n = 2) and land use regression modeling (n = 5). These studies assessed exposure and outcomes at different “windows of susceptibility”: antenatally/specific trimesters (n = 8), infancy (n = 5) and early childhood (n = 6). In most studies, the reported measures of air pollutants were noted to be below the prescribed limits, though for some, a cause–effect association was observed. It was also noted that there was very little variation in estimates between time points or trimesters of exposure, likely attributed to limitations in the selected exposure assessment method. Moderate to high correlations between trimesters were reported for most studies.

Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK.

Land use regression (LUR) and dispersion/chemical transport models (D/CTMs) are frequently applied to predict exposure to air pollution concentrations at a fine scale for use in epidemiological studies. Moreover, the use of satellite aerosol optical depth data has been a key predictor especially for particulate matter pollution and when studying large populations. Within the STEAM project we present a hybrid spatio-temporal modeling framework by (a) incorporating predictions from dispersion modeling of nitrogen dioxide (NO2), ozone (O3) and particulate matter with an aerodynamic diameter equal or less than 10 μm (PM10) and less than 2.5 μm (PM2.5) into a spatio-temporal LUR model; and (b) combining the predictions LUR and dispersion modeling and additionally, only for PM2.5, from an ensemble machine learning approach using a generalized additive model (GAM). We used air pollution measurements from 2009 to 2013 from 62 fixed monitoring sites for O3, 115 for particles and up to 130 for NO2, obtained from the dense network in the Greater London Area, UK. We assessed all models following a 10-fold cross validation (10-fold CV) procedure. The hybrid models performed better compared to separate LUR models. Incorporation of the dispersion estimates in the LUR models as a predictor, improved the LUR model fit: CV-R2 increased to 0.76 from 0.71 for NO2, to 0.79 from 0.57 for PM10, to 0.81 to 0.66 for PM2.5 and to 0.75 from 0.62 for O3. The CV-R2 obtained from the hybrid GAM framework was also increased compared to separate LUR models (CV-R2 = 0.80 for NO2, 0.76 for PM10, 0.79 for PM2.5 and 0.75 for O3). Our study supports the combined use of different air pollution exposure assessment methods in a single modeling framework to improve the accuracy of spatio-temporal predictions for subsequent use in epidemiological studies.

A comparison of associations with childhood lung function between air pollution exposure assessment methods with and without accounting for time-activity patterns

BackgroundTo investigate associations between annual average air pollution exposures and health, most epidemiological studies rely on estimated residential exposures because information on actual time-activity patterns can only be collected for small populations and short periods of time due to costs and logistic constraints. In the current study, we aim to compare exposure assessment methodologies that use data on time-activity patterns of children with residence-based exposure assessment. We compare estimated exposures and associations with lung function for residential exposures and exposures accounting for time activity patterns. MethodsWe compared four annual average air pollution exposure assessment methodologies; two rely on residential exposures only, the other two incorporate estimated time activity patterns. The time-activity patterns were based on assumptions about the activity space and make use of available external data sources for the duration of each activity. Mapping of multiple air pollutants (NO2, NOX, PM2.5, PM2.5absorbance, PM10) at a fine resolution as input to exposure assessment was based on land use regression modelling. First, we assessed the correlations between the exposures from the four exposure methods. Second, we compared estimates of the cross-sectional associations between air pollution exposures and lung function at age 8 within the PIAMA birth cohort study for the four exposure assessment methodologies. ResultsThe exposures derived from the four exposure assessment methodologies were highly correlated (R > 0.95) for all air pollutants. Similar statistically significant decreases in lung function were found for all four methods. For example, for NO2 the decrease in FEV1 was −1.40% (CI; −2.54, −0.24%) per IQR (9.14 μg/m3) for front door exposure, and −1.50% (CI; −2.68, −0.30%) for the methodology which incorporates time activity pattern and actual school addresses. ConclusionsExposure estimates from methods based on the residential location only and methods including time activity patterns were highly correlated and associated with similar decreases in lung function. Our study illustrates that the annual average exposure to air pollution for 8-year-old children in the Netherlands is sufficiently captured by residential exposures.

Air pollution and mortality in the Netherlands

PDS 65: Exposure assessment: implications for epidemiology, Exhibition Hall (PDS), Ground floor, August 27, 2019, 1:30 PM - 3:00 PM Background/aim: To assess associations of long-term exposure to ambient air pollution with natural-cause mortality, air pollution exposures are generally estimated using either land-use regression (LUR) or dispersion models (DM). Within the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) study, we evaluated associations of annual average air pollution concentrations based both on a Europe-wide hybrid LUR model and on a national DM in a Dutch National cohort. Methods: Concentrations of NO2, PM2.5 and Black Carbon (BC) based on European-wide hybrid LUR model and a national DM were linked to the home address of approximately 10.5 million Dutch adults. We used Cox proportional hazard models to investigate associations with natural-cause mortality. Following a harmonised protocol, models were adjusted for individual covariates and several area-level indicators of socio-economic status. Results: We observed 606,527 natural-cause deaths during the 5 year follow-up (2008-2012). We found strong correlations between LUR NO2 and DM NO2 (Pearson R = 0.79), correlations were slightly lower for PM2.5 (Pearson R = 0.69) and BC (Pearson R = 0.72). In fully adjusted models, we found significant associations with both LUR and DM air pollutants, however, hazard rates (HR) differed. For example, we found a HR of 1.03 per 10 µg/m3 (95% CI: 1.02, 1.04) for LUR NO2 and a HR of 1.01 per 10 µg/m3 (95% CI: 1.01, 1.02) for DM NO2. In two pollutants models of LUR NO2 and PM2.5, associations remained significant for NO2 only, while in two pollutants models of DM NO2 and PM2.5, associations remained significant for PM2.5 only. Conclusion: We found significant associations of annual average air pollution with natural-cause mortality based both on a Europe-wide hybrid LUR model and on a national DM. Different air pollution exposure assessment methods may lead to somewhat different effect estimates.

Interactions between environmental pollutants and genetic susceptibility in asthma risk.

Exposure to air pollution is associated with enhanced risk of developing asthma, notably in the presence of genetic risk factors. Interaction analyses have shown that both outdoor and indoor air pollution interact with genetic variability to increase the incidence of asthma. In this review, we summarize recent progress in candidate gene-based studies, as well as genome-wide gene-air pollution interaction studies. Advances in epigenetics have provided evidence for DNA methylation as a mediator in gene-air pollution interactions. Emerging strategies for study design and statistical analyses may improve power in future studies. Improved air pollution exposure assessment methods and asthma endo-typing can also be expected to increase the ability to detect biologically driven gene-air pollution interaction effects.

Assessment of Particle Oxidative Potential as an Air Pollution Exposure Metric: A Systematic Review

Ambient particulate air pollution is linked to numerous acute and chronic health outcomes. The standard approach to measuring particulate exposure has been to estimate particle mass concentration. New measures account for oxidative potential, the ability of particles to cause oxidative stress in the body. There has been no systematic comparison of particulate air pollution exposure assessment methods between those that incorporate oxidative potential and those that do not. The aim of this review is to determine if associations between particulate air pollution exposure and health outcomes are stronger when oxidative potential is used as the exposure metric, in comparison to particle mass concentration. The databases Medline and Embase were searched from inception to 28 March 2018 for studies that reported an association between particulate air pollution exposure and a health outcome in a human population, and in which exposure was measured by both particle mass concentration and oxidative potential. Study quality was assessed using a US National Toxicology Program instrument. We identified 18 publications meeting the selection criteria. The most common oxidative potential assay was antioxidant depletion in synthetic respiratory tract lining fluid, used in 12 articles. The rate of consumption of dithiothreitol was also frequently used as an oxidative potential assay and appeared in 5 studies. The dithiothreitol assay most consistently produced stronger effect estimates, as 10/14 endpoints showed higher point estimates of associations compared to particle mass concentration estimates. Other assays showed mixed results. To date there is no consistent evidence that oxidative potential is more strongly associated with health outcomes than particle mass concentrations; however, these assays do have the advantage of not treating all particles as equally harmful and further work should explore which specific assays are most relevant to evaluating air pollution health risks.

An assessment of air pollutant exposure methods in Mexico City, Mexico

Geostatistical interpolation methods to estimate individual exposure to outdoor air pollutants can be used in pregnancy cohorts where personal exposure data are not collected. Our objectives were to a) develop four assessment methods (citywide average (CWA); nearest monitor (NM); inverse distance weighting (IDW); and ordinary Kriging (OK)), and b) compare daily metrics and cross-validations of interpolation models. We obtained 2008 hourly data from Mexico City’s outdoor air monitoring network for PM10, PM2.5, O3, CO, NO2, and SO2 and constructed daily exposure metrics for 1,000 simulated individual locations across five populated geographic zones. Descriptive statistics from all methods were calculated for dry and wet seasons, and by zone. We also evaluated IDW and OK methods’ ability to predict measured concentrations at monitors using cross validation and a coefficient of variation (COV). All methods were performed using SAS 9.3, except ordinary Kriging which was modeled using R’s gstat package. Overall, mean concentrations and standard deviations were similar among the different methods for each pollutant. Correlations between methods were generally high (r = 0.77 to 0.99). However, ranges of estimated concentrations determined by NM, IDW, and OK were wider than the ranges for CWA. Root mean square errors for OK were consistently equal to or lower than for the IDW method. OK standard errors varied considerably between pollutants and the computed COVs ranged from 0.46 (least error) for SO2 and PM10 to 3.91 (most error) for PM2.5. OK predicted concentrations measured at the monitors better than IDW and NM. Given the similarity in results for the exposure methods, OK is preferred because this method alone provides predicted standard errors which can be incorporated in statistical models. The daily estimated exposures calculated using these different exposure methods provide flexibility to evaluate multiple windows of exposure during pregnancy, not just trimester or pregnancy-long exposures.Implications: Many studies evaluating associations between outdoor air pollution and adverse pregnancy outcomes rely on outdoor air pollution monitoring data linked to information gathered from large birth registries, and often lack residence location information needed to estimate individual exposure. This study simulated 1,000 residential locations to evaluate four air pollution exposure assessment methods, and describes possible exposure misclassification from using spatial averaging versus geostatistical interpolation models. An implication of this work is that policies to reduce air pollution and exposure among pregnant women based on epidemiologic literature should take into account possible error in estimates of effect when spatial averages alone are evaluated.

Personal real-time air pollution exposure assessment methods promoted by information technological advances

This review article discusses the current status of personal real-time air pollution exposure assessment methods promoted by recent development in information technologies. Relevant information technologies are mobile positioning technology, mobile wireless communication technology, Web 2.0 technology, and volunteered geographic information technology. Two groups of personal real-time air pollution exposure assessment methods are reviewed, namely the genuine integrated methods and the pseudo integrated methods. The genuine methods monitor personal exposure directly and are individual-based approaches. The pseudo methods monitor personal exposure indirectly, and they are hybrid approaches of individual-based assessment and place-based assessment. We conclude this article by suggesting promoting public participation and government input for individual-based air pollution exposure measures. More specifically, efforts should be focused on applying new technologies and research methods, developing a cost-effective and quick-responding ‘grassroots volunteer network' for air pollution exposure assessment, and enhancing public awareness and end-user experience.

Residential traffic exposure and pregnancy-related outcomes: a prospective birth cohort study

BackgroundThe effects of ambient air pollution on pregnancy outcomes are under debate. Previous studies have used different air pollution exposure assessment methods. The considerable traffic-related intra-urban spatial variation needs to be considered in exposure assessment. Residential proximity to traffic is a proxy for traffic-related exposures that takes into account within-city contrasts.MethodsWe investigated the association between residential proximity to traffic and various birth and pregnancy outcomes in 7,339 pregnant women and their children participating in a population-based cohort study. Residential proximity to traffic was defined as 1) distance-weighted traffic density in a 150 meter radius, and 2) proximity to a major road. We estimated associations of these exposures with birth weight, and with the risks of preterm birth and small size for gestational age at birth. Additionally, we examined associations with pregnancy-induced hypertension, (pre)eclampsia, and gestational diabetes.ResultsThere was considerable variation in distance-weighted traffic density. Almost fifteen percent of the participants lived within 50 m of a major road. Residential proximity to traffic was not associated with birth and pregnancy outcomes in the main analysis and in various sensitivity analyses.ConclusionsMothers exposed to residential traffic had no higher risk of adverse birth outcomes or pregnancy complications in this study. Future studies may be refined by taking both temporal and spatial variation in air pollution exposure into account.

Air pollution exposure assessment methods utilized in epidemiological studies

The assessment of personal exposure to air pollution is a critical component of epidemiological studies associating air pollution and health effects. This paper critically reviewed 157 studies over 29 years that utilized one of five categories of exposure methods (proximity, air dispersion, hybrid, human inhalation, and biomarkers). Proximity models were found to be a questionable technique as they assume that closer proximity equates to greater exposure. Inhalation models and biomarker estimates were the most effective in assessing personal exposure, but are often cost prohibitive for large study populations. This review suggests that: (i) factors such as uncertainty, validity, data availability, and transferability related to exposure assessment methods should be considered when selecting a model; and (ii) although an entirely discreet new class of approach is not necessary, significant progress could be made through the development of a 'hybrid' model utilizing the strengths of several existing methods. Future work should systematically evaluate the performance of hybrid models compared to other individual exposure assessment methods utilizing geospatial information technologies (e.g. geographic information systems (GIS) and remote sensing (RS)) to more robustly refine estimates of ambient exposure and quantify the linkages and differences between outdoor, indoor and personal exposure estimates.