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

Socioeconomic inequalities and ambient air pollution exposure in school-aged children living in an affluent society: an analysis on individual and aggregated data in Belgium

BACKGROUND

Individuals with lower socioeconomic status (SES) are at a higher risk of being exposed to adverse environmental factors. Children are more vulnerable to the harmful effects of air pollutants. Therefore, this study examined socioeconomic inequalities in air pollution exposure among children in Flanders, Belgium.

METHODS

Data were used from 298 children (age range: 9-12 years), and from their parents who participated in the COGNition and Air pollution in Children study. Socioeconomic status was measured using highest parental education at the individual level and median income at the neighborhood (aggregated) level. Annual average outdoor concentrations of particulate matter with diameters <2.5 μm (PM2.5) and <10.0 μm (PM10), nitrogen dioxide (NO2), and black carbon (BC) in μg/m3 were estimated at the residential address. Mixed regression models were applied to examine the associations.

RESULTS

Children from parents with a low education level were exposed to significantly higher levels of PM2.5, PM10, and BC compared to children from parents with a high education level. However, the associations were not significant when tested using regression models. Children who lived in areas with a lower median neighborhood income were exposed to significantly higher levels of air pollution; an interquartile range (IQR; €4505.00) decrease in income was associated with an increase in exposure to PM2.5 of 0.198 μg/m3, PM10 of 0.406 μg/m3, NO2 of 0.740 μg/m3, and BC of 0.063 μg/m3. Children of parents with a low/high education level had a higher exposure to PM2.5, PM10, NO2, and BC when living in a low income neighborhood. Exposure to all air pollutants was the highest for low parental education level and low neighborhood income.

CONCLUSIONS

Low neighborhood income was significantly associated with higher levels of air pollution, while parental education level was not significantly associated. Children from parents with a low education and low income were exposed to the highest levels of air pollution.

Associations between long-term air pollution exposure and mortality and cardiovascular morbidity: A comparison of mobility-integrated and residential-only exposure assessment

Epidemiological studies investigating the health effects of long-term air pollution exposure typically only consider the participants' residential addresses when determining exposure. Neglecting mobility may introduce measurement error, potentially leading to bias or reduced precision of exposure-health relationships in epidemiological studies. In this study we compared the exposure-health associations between residential-only and mobility-integrated air pollution exposures. We evaluated two major pollutants, NO2 and PM2.5, and four health outcomes, natural and cause-specific mortality and coronary and cerebrovascular events. Agent-based modeling (ABM) was used to simulate the mobility patterns of the participants in the EPIC-NL cohort in the Netherlands and the Swiss National Cohort (SNC) in Switzerland, based on travel survey information. To obtain mobility-integrated exposures, hourly air pollution surfaces were developed and overlaid with the time-dependent location data from the ABM. We used Cox proportional hazards models within each cohort separately to evaluate the association between residential-only and mobility-integrated exposure and mortality and cardiovascular events, adjusting for major individual and area-level covariates. The mobility-integrated exposure and the residential exposure showed very high correlations for both pollutants and cohorts (R2 > 0.97). The mean exposure was 1-2 % and the exposure contrast 10-20 % lower for the mobility-integrated exposure. For all health outcomes, both pollutants and both cohorts, there were only small differences between residential-only and mobility-integrated exposure effect estimates. For the SNC, Hazard ratios (HRs) for natural mortality were 1.04 (1.03 - 1.04) and 1.03 (1.03 - 1.04) per interquartile range (IQR) increase in NO2 for residential and mobility-integrated exposure, respectively. For PM2.5 the corresponding estimates were 1.01 (1.01 - 1.02) per IQR increase for both approaches. Our findings support the growing evidence that assessment of long-term air pollution exposure at the residential address only in epidemiological studies may not lead to substantial bias and loss of precision in health effects estimates.

A review of geospatial exposure models and approaches for health data integration

BACKGROUND

Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.

OBJECTIVE

Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.

METHODS

We conduct a literature review and synthesis.

RESULTS

First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows.

Variability in mobility-based air pollution exposure assessment: Effects of GPS tracking duration and temporal resolution of air pollution maps

Mobility-based exposure assessment of air pollution has been proposed as a potentially more valid approach than home-based assessments. However, methodological uncertainties in operationalizing mobility-based assessment may still increase inaccuracies in estimating exposures. It remains unclear whether using short-term mobility data and yearly average air pollution concentrations is reliable for estimating personal air pollution exposure. This study aimed to assess variability in exposure estimates modeled by short- and long-term global positioning system (GPS) data and air pollution maps with yearly and monthly temporal resolutions. We tracked 428 participants for a short period (14 days) with a GPS device and for a long period (several months) with a smartphone application. Exposure estimates of nitrogen dioxide, ozone, and fine particulate matter (PM10 and PM2.5) were computed based on GPS data, air pollution maps, and temporal and indoor/outdoor adjustments. The concordance correlation coefficient (CCC) indicated excellent agreement (0.85-0.99) between exposure estimates based on short- and long-term GPS data from smartphones but ranged from moderate to excellent (0.57-0.99) when comparing exposure estimates based on data from different devices. Agreement between yearly and monthly map-based estimates was poor to moderate without temporal adjustment (CCC: 0-0.63) but excellent after temporal adjustment (CCC: 0.92-1.0). The findings suggest that using short-term (i.e., 7 or 14 days) GPS data and yearly average air pollution concentrations in mobility-based assessments can well represent long-term mobility and yearly averages for determining long-term exposures. However, GPS data collected via dedicated devices and smartphones may identify distinct indoor/outdoor patterns, affecting the indoor/outdoor adjustments of exposure estimates. Additionally, careful selection of using yearly or monthly maps is advised for assessing exposures within specific short periods.

Personalised estimation of exposure to ambient air pollution and application in a longitudinal cohort analysis of cognitive function in London-dwelling older adults

BACKGROUND

Accurate estimates of personal exposure to ambient air pollution are difficult to obtain and epidemiological studies generally rely on residence-based estimates, averaged spatially and temporally, derived from monitoring networks or models. Few epidemiological studies have compared the associated health effects of personal exposure and residence-based estimates.

OBJECTIVE

To evaluate the association between exposure to air pollution and cognitive function using exposure estimates taking mobility and location into account.

METHODS

Residence-based dispersion model estimates of ambient NO2, PM10 and PM2.5 were assigned to 768 London-dwelling participants of the English Longitudinal Study of Ageing. The London Hybrid Exposure Model was implemented to adjust estimates per pollutant to reflect the estimated time-activity patterns of each participant based on age and residential location. Single pollutant linear mixed-effects models were fit for both exposure assessment methods to investigate the associations between assigned pollutant concentrations and cognitive function over a follow-up period of up to 15 years.

RESULTS

Increased long-term exposures to residence-based ambient NO2 (IQR: 11.10 µg/m3), PM10 (2.35 µg/m3), and PM2.5 (2.50 µg/m3) were associated with decreases of -0.10 [95% CI: -0.20, 0.00], -0.07 [-0.11, -0.02] and -0.14 [-0.21, -0.06], respectively, in composite memory score. Similar decreases were observed for executive function scores (-0.38 [-0.58, -0.18], -0.11 [-0.20, -0.02] and -0.14 [-0.29, 0.01], respectively). When applying personalised exposure estimates, which were substantially lower, similar decreases were observed for composite memory score per IQR, but a consistent pattern of slightly more adverse effects with executive function score was evident.

IMPACT STATEMENT

The present study constructed a framework through which time-activity information derived from a representative sample could be applied to estimates of ambient air pollution concentrations assigned to individuals in epidemiological cohort studies, with the intention of adjusting commonly used residence-based estimates to reflect population mobility and time spent in various microenvironments. Estimates of exposure were markedly lower when incorporating time-activity, likely because people in European populations spend a large proportion of their time indoors, where their exposure to ambient air pollution may be reduced through infiltration, which is not taken into account in residence-based ambient estimates. Further work into such methods could provide insights into the efficacy of personalising exposure estimates.

Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona

BACKGROUND

Air pollution is the leading environmental risk factor for health. Assessing outdoor air pollution exposure with detailed spatial and temporal variability in urban areas is crucial for evaluating its health effects.

AIM

We developed and compared Land Use Regression (LUR), dispersion (DM), and hybrid (HM) models to estimate outdoor concentrations for NO2, PM2.5, black carbon (BC), and PM2.5-constituents (Fe, Cu, Zn) in Barcelona.

METHODS

Two monitoring campaigns were conducted. In the first, NO2 concentrations were measured twice at 984 home addresses and in the second, NO2, PM2.5, and BC were measured four times at 34 points across Barcelona. LUR and DM were constructed using conventional techniques, while HM was developed using Random Forest (RF). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and 10-fold cross-validation (10-CV) for LUR and HM, and by comparing DM and LUR estimates with routine monitoring stations. NO2 levels estimated by all models were externally validated using the home monitoring campaign. Agreement between models was assessed using Spearman correlation (rs) and Bland-Altman (BA) plots.

RESULTS

Models showed moderate to good performance. LUR exhibited R2LOOCV of 0.62 (NO2), 0.45 (PM2.5), 0.83 (BC), and 0.85 to 0.89 (PM2.5-constituents). DM model comparison showed R2 values of 0.39 (NO2), 0.26 (PM2.5), and 0.65 (BC). HM models had higher R210-CV 0.64 (NO2), 0.66 (PM2.5), 0.86 (BC), and 0.44 to 0.70 (PM2.5-constituents). Validation for NO2 showed R2 values of 0.56 (LUR), 0.44 (DM), and 0.64 (HM). Correlations between models varied from -0.38 to 0.92 for long-term exposure, and - 0.23 to 0.94 for short-term exposure. BA plots showed good agreement between models, especially for NO2 and BC.

CONCLUSIONS

Our models varied substantially, with some models performing better in validation samples (NO2 and BC). Future health studies should use the most accurate methods to minimize bias from exposure measurement error.

Does residential address-based exposure assessment for outdoor air pollution lead to bias in epidemiological studies?

BACKGROUND

Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address.

MAIN BODY

We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups.

CONCLUSIONS

Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Childhood exposure to outdoor air pollution in different microenvironments and cognitive and fine motor function in children from six European cohorts

BACKGROUND

Exposure to air pollution during childhood has been linked with adverse effects on cognitive development and motor function. However, limited research has been done on the associations of air pollution exposure in different microenvironments such as home, school, or while commuting with these outcomes.

OBJECTIVE

To analyze the association between childhood air pollution exposure in different microenvironments and cognitive and fine motor function from six European birth cohorts.

METHODS

We included 1301 children from six European birth cohorts aged 6-11 years from the HELIX project. Average outdoor air pollutants concentrations (NO2, PM2.5) were estimated using land use regression models for different microenvironments (home, school, and commute), for 1-year before the outcome assessment. Attentional function, cognitive flexibility, non-verbal intelligence, and fine motor function were assessed using the Attention Network Test, Trail Making Test A and B, Raven Colored Progressive Matrices test, and the Finger Tapping test, respectively. Adjusted linear regressions models were run to determine the association between each air pollutant from each microenvironment on each outcome.

RESULTS

In pooled analysis we observed high correlation (rs = 0.9) between air pollution exposures levels at home and school. However, the cohort-by-cohort analysis revealed correlations ranging from low to moderate. Air pollution exposure levels while commuting were higher than at home or school. Exposure to air pollution in the different microenvironments was not associated with working memory, attentional function, non-verbal intelligence, and fine motor function. Results remained consistently null in random-effects meta-analysis.

CONCLUSIONS

No association was observed between outdoor air pollution exposure in different microenvironments (home, school, commute) and cognitive and fine motor function in children from six European birth cohorts. Future research should include a more detailed exposure assessment, considering personal measurements and time spent in different microenvironments.

Measuring environmental exposures in people's activity space: The need to account for travel modes and exposure decay

BACKGROUND

Accurately quantifying people's out-of-home environmental exposure is important for identifying disease risk factors. Several activity space-based exposure assessments exist, possibly leading to different exposure estimates, and have neither considered individual travel modes nor exposure-related distance decay effects.

OBJECTIVE

We aimed (1) to develop an activity space-based exposure assessment approach that included travel modes and exposure-related distance decay effects and (2) to compare the size of such spaces and the exposure estimates derived from them across typically used activity space operationalizations.

METHODS

We used 7-day-long global positioning system (GPS)-enabled smartphone-based tracking data of 269 Dutch adults. People's GPS trajectory points were classified into passive and active travel modes. Exposure-related distance decay effects were modeled through linear, exponential, and Gaussian decay functions. We performed cross-comparisons on these three functional decay models and an unweighted model in conjunction with four activity space models (i.e., home-based buffers, minimum convex polygons, two standard deviational ellipses, and time-weighted GPS-based buffers). We applied non-parametric Kruskal-Wallis tests, pair-wise Wilcoxon signed-rank tests, and Spearman correlations to assess mean differences in the extent of the activity spaces and correlations across exposures to particulate matter (PM2.5), noise, green space, and blue space.

RESULTS

Participants spent, on average, 42% of their daily life out-of-home. We observed that including travel modes into activity space delineation resulted in significantly more compact activity spaces. Exposure estimates for PM2.5 and blue space were significantly (p < 0.05) different between exposure estimates that did or did not account for travel modes, unlike noise and green space, for which differences did not reach significance. While the inclusion of distance decay effects significantly affected noise and green space exposure assessments, the decay functions applied appear not to have had any impact on the results. We found that residential exposure estimates appear appropriate for use as proxy values for the overall amount of PM2.5 exposure in people's daily lives, while GPS-based assessments are suitable for noise, green space, and blue space.

SIGNIFICANCE

For some exposures, the tested activity space definitions, although significantly correlated, exhibited differing exposure estimate results based on inclusion or exclusion of travel modes or distance decay effect. Results only supported using home-based buffer values as proxies for individuals' daily short-term PM2.5 exposure.

The impact of long-term exposure to PM10, SO2, O3, NO2, and CO on incident dysglycemia: a population-based cohort study

To examine the associations between long-term exposure to five major air pollutants including SO₂, PM₁₀, O₃, NO₂, and CO_, and incident dysglycemia, impaired fasting glucose (IFG), and diabetes, separately. A total of 4254 (1720 men) normoglycemic individuals aged 20-69 years at baseline were followed from 2001 to 2018 every 3 years. To measure the long-term hazards of air pollutants for incident dysglycemia, the Weibull proportional hazards models for every 10-unit increment adjusted for diabetes risk factors were fitted. The air pollutants were put in the models in the form of averages of 1-, 2-, and 3-year periods. During a median follow-up of 12.2 years, we observed 1780 dysglycemia events. In contrast to NO₂, the increase in SO₂, O₃, and PM₁₀ levels were significantly associated with a higher risk of dysglycemia and IFG in all time spans excluding PM₁₀ at 2 years. The largest hazard ratios for incident dysglycemia and IFG were attributable to PM₁₀ in 3 years (2.20 (95% CI 1.67, 2.89) and 2.08 (1.55, 2.80), respectively). Moreover, exposure to all the pollutants except NO₂ in 1 year (0.89 (0.80, 0.98)) had no significant associations with incident diabetes. There was a signal that younger (< 45 years) and never-smoker individuals were more predispose to dysglycemic effects of air pollution (all P for interactions > 0.03). Our findings suggested that long-term exposure to air pollution increased incident dysglycemia risk, the effect which was mainly attributable to IFG status.