Exposure estimation errors to nitrogen oxides on a population scale due to daytime activity away from home

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.

Social inequalities in green exposure in small- and medium-sized U.S. cities: A mobility-based approach

BACKGROUND

Green space exposure has been considered essential for people's physical and mental health. Researchers have investigated uneven exposure to green space based on individuals' home locations, which may exacerbate health disparities. A mobility-based approach enables a more accurate assessment of green exposure in daily activity patterns. In addition, social inequalities may vary by geographical context and should be examined to address environmental justice concerns.

OBJECTIVE

Study objectives are twofold: to address methodological challenges in exposure assessment studies through mobility-based assessment of green exposure; and to explore whether mobility-based approach can better assess green exposure inequality than home-based measurement.

METHODS

We selected 25 small- and medium-sized U.S. cities as study sites, from which street-view images were collected along 50,823 walk-based commute trajectories. We applied a semantic segmentation technique to street-view images to estimate individual home- and mobility-based green exposure levels.

RESULTS

Results revealed that mobility-based green exposure significantly differs from home-based green exposure. Globally, wealthier individuals and non-minority groups experience significantly greater exposure to green space through both home- and mobility-based approaches compared to their counterparts. Locally, we found more nuanced pictures of green space inequalities when compared at the county level, suggesting locally varying relationships.

SIGNIFICANCE

This study suggests empirical evidence on how mobility-based measurements could help us assess inequality problems in exposure to urban green elements.

IMPACT

Creating urban green corridors that comply with locally varying contexts can contribute to achieving equitable provision of green infrastructure for low-income and racially disadvantaged populations who have undesirable green exposure in their residential locations.

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.

Is residential proximity to polluted sites during pregnancy associated with preterm birth or low birth weight? Results from an integrated exposure database in North Carolina (2003-2015)

BACKGROUND

Preterm birth (PTB) and term low birth weight (LBW) have been associated with pollution and other environmental exposures, but the relationship between these adverse outcomes and specific characteristics of polluted sites is not well studied.

OBJECTIVES

We conducted a retrospective cohort study to examine relationships between residential proximity to polluted sites in North Carolina (NC) and PTB and LBW. We further stratified exposure to polluted sites by route of contaminant emissions and specific contaminants released at each site.

METHODS

We created an integrated exposure geodatabase of polluted sites in NC from 2002 to 2015 including all landfills, Superfund sites, and industrial sites. Using birth certificates, we assembled a cohort of 1,494,651 singleton births in NC from 2003 to 2015. We geocoded the gestational parent residential address on the birth certificate, and defined exposure to polluted sites as residence within one mile of a site. We used log-binomial regression models to estimate adjusted risk ratios (aRR) and 95% confidence intervals (CI). Binomial models were used to estimate adjusted risk differences (aRD) per 10,000 births and 95% CIs for associations between exposure to polluted sites and PTB or LBW.

RESULTS

We observed weak associations between residential proximity to polluted sites and PTB [aRR(95% CI): 1.07(1.06,1.09); aRD(95% CI): 61(48,74)] and LBW [aRR(95% CI): 1.09(1.06,1.12); aRD(95% CI): 24(17,31)]. Secondary analyses showed increased risk of both PTB and LBW among births exposed to sites characterized by water emissions, air emissions, and land impoundment. In analyses of specific contaminants, increased risk of PTB was associated with proximity to sites containing arsenic, benzene, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons. LBW was associated with exposure to arsenic, benzene, cadmium, lead, and mercury.

SIGNIFICANCE

This study provides evidence for potential reproductive health effects of polluted sites, and underscores the importance of accounting for heterogeneity between polluted sites when considering these exposures.

IMPACT STATEMENT

We documented an overall increased risk of both PTB and LBW in births with gestational exposure to polluted sites using a harmonized geodatabase of three site types, and further examined exposures stratified by site characteristics (route of emission, specific contaminants present). We observed increased risk of both PTB and LBW among births exposed to sites with water emissions or air emissions, across site types. Increased risk of PTB was associated with gestational proximity to sites containing arsenic, benzene, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons; increased risk of LBW was associated with exposure to arsenic, benzene, cadmium, lead, and mercury.

The influence of outdoor PM2.5 concentration at workplace on nonaccidental mortality estimates in a Canadian census-based cohort

Background

Associations between mortality and exposure to ambient air pollution are usually explored using concentrations of residential outdoor fine particulate matter (PM_2.5) to estimate individual exposure. Such studies all have an important limitation in that they do not capture data on individual mobility throughout the day to areas where concentrations may be substantially different, leading to possible exposure misclassification. We examine the possible role of outdoor PM_2.5 concentrations at work for a large population-based mortality cohort.

Methods

Using the 2001 Canadian Census Health and Environment Cohort (CanCHEC), we created a time-weighted average that incorporates employment hours worked in the past week and outdoor PM_2.5 concentration at work and home. We used a Cox proportional hazard model with a 15-year follow-up (2001 to 2016) to explore whether inclusion of workplace estimates had an impact on hazard ratios for mortality for this cohort.

Results

Hazard ratios relying on outdoor PM_2.5 concentration at home were not significantly different from those using a time-weighted estimate, for the full cohort, nor for those who commute to a regular workplace. When exploring cohort subgroups according to neighborhood type and commute distance, there was a notable but insignificant change in risk of nonaccidental death for those living in car-oriented neighborhoods, and with commutes greater than 10 km.

Conclusions

Risk analyses performed with large cohorts in low-pollution environments do not seem to be biased if relying solely on outdoor PM_2.5 concentrations at home to estimate exposure.

Associations Between Prenatal Exposure to Air Pollution and Congenital Hypothyroidism

Adequate thyroid hormone availability is required for normal brain development. Studies have found associations between prenatal exposure to air pollutants and thyroid hormones in pregnant women and newborns. We aimed to examine associations of trimester-specific residential exposure to common air pollutants with congenital hypothyroidism (CHT). All term infants born in Israel during 2009-2015 were eligible for inclusion. We used data on CHT from the national neonatal screening lab of Israel, and exposure data from spatiotemporal air pollution models. We used multivariable logistic regression models to estimate associations of exposures with CHT, adjusting for ethnicity, socioeconomic status, geographical area, conception season, conception year, gestational age, birth weight, and child sex. To assess residual confounding, we used postnatal exposures to the same pollutants as negative controls. The study population included 696,461 neonates. We found a positive association between third-trimester nitrogen oxide exposure and CHT (per interquartile-range change, odds ratio = 1.23, 95% confidence interval: 1.08, 1.41) and a similar association for nitrogen dioxide. There was no evidence of residual confounding or bias by correlation among exposure periods for these associations.

Effects of chronic exposure to ambient air pollutants, demographic, and socioeconomic factors on COVID-19 morbidity: The Israeli case study

BACKGROUND

Recent studies conducted in several OECD countries have shown that chronic exposure to elevated levels of air pollutants (especially PM_2.5, PM₁₀ and NOx), might negatively impact COVID-19 morbidity and mortality rates. The aim of this study was to examine the association between chronic exposure to air pollution in Israeli cities and towns, their demographic and socioeconomic status, and COVID-19 morbidity, during the three local morbidity waves.

METHODS

We examined the associations between: (a) annual average concentrations of NOx, CO, PM₁₀, PM_2.5 and SO₂ in 2016-2019, and demographic and socioeconomic parameters, and (b) COVID-19 positive cases in 279 Israeli cities and towns, in the four state-wide morbidity peaks: 1^st wave peak: March 31^st, 2020; 2^nd wave peaks: July 24^th and September 27^th, 2020, and the 3^rd wave peak: January 17^th, 2021, which occurred after the beginning of the nationwide vaccination campaign. These associations were calculated using both Spearman correlations and multivariate linear regressions.

RESULTS

We found statistically significant positive correlations between the concentrations of most pollutants in 2016-19 and COVID-19 morbidity rate at the first three timepoints but not the 4t^h (January 17^th, 2021). Population density and city/town total population were also positively associated with the COVID-19 morbidity rates at these three timepoints, but not the 4^th, in which socioeconomic parameters were more dominant - we found a statistically significant negative correlation between socioeconomic cluster and COVID-19 morbidity. In addition, all multivariate models including PM_2.5 concentrations were statistically significant, and PM_2.5 concentrations were positively associated with the COVID-19 morbidity rates in all models.

CONCLUSIONS

We found a nationwide association between population chronic exposure to five main air pollutants in Israeli cities and towns, and COVID-19 morbidity rates during two of the three morbidity waves experienced in Israel. The widespread morbidity that was related to socioeconomic factors during the 3^rd wave, emphasizes the need for special attention to morbidity prevention in socioeconomically vulnerable populations and especially in large household communities. Nevertheless, this ecological study has several limitations, such as the inability to draw conclusions about causality or mechanisms of action. The growing body of evidence, regarding association between exacerbated COVID-19 morbidity and mortality rates and long-term chronic exposure to elevated concentrations of air pollutants should serve as a wake-up call to policy makers regarding the urgent need to reduce air pollution and its harmful effects.

Assessment of sociodemographic disparities in environmental exposure might be erroneous due to neighborhood effect averaging: Implications for environmental inequality research

The neighborhood effect averaging problem (NEAP) is a major methodological problem that might affect the accuracy of assessments of individual exposure to mobility-dependent environmental factors (e.g., air/noise pollution, green/blue spaces, or healthy food environments). Focusing on outdoor ground-level ozone as a major air pollutant, this paper examines the NEAP in the evaluation of sociodemographic disparities in people's air pollution exposures in Los Angeles using one-day activity-travel diary data of 3790 individuals. It addresses two questions: (1) How does the NEAP affect the evaluation of sociodemographic disparities in people's air pollution exposures? (2) Which social groups with high residence-based exposures do not experience neighborhood effect averaging? The results of our spatial regression models indicate that assessments of sociodemographic disparities in people's outdoor ground-level ozone exposures might be erroneous when people's daily mobility is ignored because of the different manifestations of neighborhood effect averaging for different social/racial groups. The results of our spatial autologistic regression model reveal that non-workers (e.g., the unemployed, homemakers, the retired, and students) do not experience downward averaging: they have significantly lower odds of experiencing downward averaging that could have attenuated their high exposures experienced in their residential neighborhoods while traveling to other neighborhoods (thus, being doubly disadvantaged). Therefore, to avoid erroneous conclusions in environmental inequality research and ineffective public policies, it would be critical to take the NEAP into account in future studies of sociodemographic disparities related to mobility-dependent environmental factors.

Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO₂) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO₂ concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R²) for the LUR models ranged from 0.53-0.87 (PM_2.5), 0.53-0.85 (PM₁₀), and 0.33-0.67 (NO₂). The performance of the daytime/nighttime LUR models for PM_2.5 and PM₁₀ was better than that of the full-day models according to the results of model adjusted R² and validation R². Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO₂ to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM_2.5/PM₁₀ has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM_2.5 and PM₁₀. Nighttime could be a critical exposure period, due to high pollutant concentrations.

Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced?

BACKGROUND

Most studies relying on time-activity diary or traditional air pollution modelling approach are insufficient to suggest the impacts of ignoring individual mobility and air pollution variations on misclassification errors in exposure estimates. Moreover, very few studies have examined whether such impacts differ across socioeconomic groups.

OBJECTIVES

We aim to examine how ignoring individual mobility and PM2.5 variations produces misclassification errors in ambient PM2.5 exposure estimates.

METHODS

We developed a geo-informed backward propagation neural network model to estimate hourly PM2.5 concentrations in terms of remote sensing and geospatial big data. Combining the estimated PM2.5 concentrations and individual trajectories derived from 755,468 mobile phone users on a weekday in Shenzhen, China, we estimated four types of individual total PM2.5 exposures during weekdays at multi-temporal scales. The estimate ignoring individual mobility, PM2.5 variations or both was compared with the hypothetical error-free estimate using paired sample t-test. We then quantified the exposure misclassification error using Pearson correlation analysis. Moreover, we examined whether the misclassification error differs across different socioeconomic groups. Taking findings of ignoring individual mobility as an example, we further investigated whether such findings are robust to the different selections of time.

RESULTS

We found that the estimate ignoring PM2.5 variations, individual mobility or both was statistically different from the hypothetical error-free estimate. Ignoring both factors produced the largest exposure misclassification error. The misclassification error was larger in the estimate ignoring PM2.5 variations than that ignoring individual mobility. People with high economic status suffered from a larger exposure misclassification error. The findings were robust to the different selections of time.

CONCLUSIONS

Ignoring individual mobility, PM2.5 variations or both leads to misclassification errors in ambient PM2.5 exposure estimates. A larger misclassification error occurs in the estimate neglecting PM2.5 variations than that ignoring individual mobility, which is seldom reported before.

Increased risk of gestational diabetes mellitus in women with higher prepregnancy ambient PM2.5 exposure

BACKGROUND

Air pollution is a serious environmental problem in China. This study was designed to investigate whether exposure to particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM2.5) before pregnancy is associated with gestational diabetes mellitus (GDM) and fasting glucose in China.

METHODS

We recruited subjects and collected clinical data from the Nanjing Maternity and Child Health Care Hospital from July 2016 to October 2017. A series of validated land-use regression (LUR) models were built to assess individual exposure to PM2.5 in a 1 × 1 km area at both work and home addresses following a time-weighted pattern. Multiple linear regression and logistic regression analyses were performed to examine the association between PM2.5 exposure and GDM and fasting glucose.

RESULTS

In total, 11,639 of 16,995 women were included in the final analysis. Among the 11,639 women, 2776 (23.85%) had GDM. Individual exposure to PM2.5 within three months before pregnancy ranged from 21.58 to 85.92 μg/m3. Positive associations were observed among the interquartile ranges (IQRs) of exposure to PM2.5 within three months before pregnancy and GDM (OR = 2.61, 95% CI: 1.40-4.93, p < .01) as well as fasting glucose levels (β = 0.57, 95% CI: 0.45-0.68, p < .01). The diabetogenic effects of PM2.5 gradually increased from the first month before pregnancy, peaked in the second month and then gradually decreased until the third month when the week-specific exposure were analyzed to identify the sensitive time window.

CONCLUSION

Our study confirmed that higher exposure to PM2.5 within three months before pregnancy is significantly associated with increased risk of GDM and elevated fasting glucose levels, reflecting the importance of preconceptional environmental exposure in the development of maternal GDM.

Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data

One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, remains under-investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies. How the choices of methods impact results of exposure estimation, especially when detailed mobility information is considered, is still largely unknown. In this study, by using a publicly available large cell phone location dataset containing over 35 million location records collected from 310,989 subjects, we investigated the impact of individual subjects' mobility on their estimated exposures for five chosen ambient pollutants (CO, NO₂, SO₂, O₃ and PM_2.5). We also estimated exposures separately for 10 groups of subjects with different levels of mobility to explore how increased mobility impacted their exposure estimates. Further, we applied and compared two methods to develop concentration fields for exposure estimation, including one based on Community Multiscale Air Quality (CMAQ) model outputs, and the other based on the interpolated observed pollutant concentrations using the inverse distance weighting (IDW) method. Our results suggest that detailed mobility information does not have a significant influence on mean population exposure estimate in our sample population, although impacts can be substantial at the individual level. Additionally, exposure classification error due to the use of home-location data increased for subjects that exhibited higher levels of mobility. Omitting mobility could result in underestimation of exposures to traffic-related pollutants particularly during afternoon rush-hour, and overestimate exposures to ozone especially during mid-afternoon. Between CMAQ and IDW, we found that the IDW method generates smooth concentration fields that were not suitable for exposure estimation with detailed mobility data. Therefore, the method for developing air pollution concentration fields when detailed mobility data were to be applied should be chosen carefully. Our findings have important implications for future air pollution health studies.

Examining Ethnic Exposure through the Perspective of the Neighborhood Effect Averaging Problem: A Case Study of Xining, China

An increasing number of studies have observed that ignoring individual exposures to non-residential environments in people's daily life may result in misleading findings in research on environmental exposure. This issue was recognized as the neighborhood effect averaging problem (NEAP). This study examines ethnic segregation and exposure through the perspective of NEAP. Focusing on Xining, China, it compares the Hui ethnic minorities and the Han majorities. Using 2010 census data and activity diary data collected in 2013, the study found that NEAP exists when examining ethnic exposure. Respondents who live in highly mixed neighborhoods (with high exposures to the other ethnic group) experience lower activity-space exposures because they tend to conduct their daily activities in ethnically less mixed areas outside their home neighborhoods (which are more segregated). By contrast, respondents who live in highly segregated neighborhoods (with low exposures to the other ethnic group) tend to have higher exposures in their activity locations outside their home neighborhoods (which are less segregated). Therefore, taking into account individuals' daily activities in non-residential contexts in the assessment of environmental exposure will likely lead to an overall tendency towards the mean exposure. Using Tobit models, we further found that specific types of activity places, especially workplaces and parks, contribute to NEAP. Ignoring individual exposures in people's activity places will most likely result in misleading findings in the measurement of environmental exposure, including ethnic exposure.

Roof cavity dust as an exposure proxy for extreme air pollution events

Understanding exposure to air pollution during extreme events such as fire emergencies is critical for assessing their potential health impacts. However, air pollution emergencies often affect places without a network of air quality monitoring and characterising exposure retrospectively is methodologically challenging due to the complex behaviour of smoke and other air pollutants. Here we test the potential of roof cavity (attic) dust to act as a robust household-level exposure proxy, using a major air pollution event associated with a coal mine fire in the Latrobe Valley, Australia, as an illustrative study. To assess the relationship between roof cavity dust composition and mine fire exposure, we analysed the elemental and polycyclic aromatic hydrocarbon composition of roof cavity dust (<150μm) from 39 homes along a gradient of exposure to the mine fire plume. These homes were grouped into 12 zones along this exposure gradient: eight zones across Morwell, where mine fire impacts were greatest, and four in other Latrobe Valley towns at increasing distance from the fire. We identified two elements-barium and magnesium-as 'chemical markers' that show a clear and theoretically grounded relationship with the brown coal mine fire plume exposure. This relationship is robust to the influence of plausible confounders and contrasts with other, non-mine fire related elements, which showed distinct and varied distributional patterns. We conclude that targeted components of roof cavity dust can be a useful empirical marker of household exposure to severe air pollution events and their use could support epidemiological studies by providing spatially-resolved exposure estimates post-event.

Estimating exposure to fine particulate matter emissions from vehicle traffic: Exposure misclassification and daily activity patterns in a large, sprawling region

Vehicle traffic is responsible for a significant portion of toxic air pollution in urban areas that has been linked to a wide range of adverse health outcomes. Most vehicle air quality analyses used for transportation planning and health effect studies estimate exposure from the measured or modeled concentration of an air pollutant at a person's home. This study evaluates exposure to fine particulate matter from vehicle traffic and the magnitude and cause of exposure misclassification that result from not accounting for population mobility during the day in a large, sprawling region. We develop a dynamic exposure model by integrating activity-based travel demand, vehicle emission, and air dispersion models to evaluate the magnitude, components and spatial patterns of vehicle exposure misclassification in the Atlanta, Georgia metropolitan area. Overall, we find that population exposure estimates increase by 51% when population mobility is accounted for. Errors are much larger in suburban and rural areas where exposure is underestimated while exposure may be overestimated near high volume roadways and in the urban core. Exposure while at work and traveling account for much of the error. We find much larger errors than prior studies, all of which have focused on more compact urban regions. Since many people spend a large part of their day away from their homes and vehicle emissions are known to create "hotspots" along roadways, home-based exposure is unlikely to be a robust estimator of a person's actual exposure. Accounting for population mobility in vehicle emission exposure studies may reveal more effective mitigation strategies, important differences in exposure between population groups with different travel patterns, and reduce exposure misclassification in health studies.

A hybrid model for evaluating exposure of the general population in Israel to air pollutants

Exposure to air pollution is associated with a wide range of health effects, including increased respiratory symptoms, cancer, reproductive and birth defects, and premature death. Air quality measurements by standardized measuring equipment, although accurate, can only provide an estimate for part of the population, with decreasing accuracy further away from the monitoring sites. Estimating pollution levels over large geographical domains requires the use of air quality models which ideally incorporate air quality measurements. In order to estimate actual exposure of the population to air pollution (population-weighted concentrations of air pollutants), there is a need to combine data from air quality models with population density data. Here we present the results of exposure estimates for the entire population of Israel using a chemical transport model combined with measurements from the national monitoring network. We evaluated the individual exposure levels for the entire population to several air pollutants based on census tract units. Using this hybrid model, we found that the entire population of Israel is exposed to concentrations of PM₁₀ and PM_2.5 that exceed the target values but are below the environmental values according to the Israeli Clean Air Law. In addition, we found and that over 1.5 million residents are exposed to NO_x at concentrations higher than the target values. This data may help decision makers develop targeted interventions to reduce the concentrations of specific pollutants, based on population-weighted exposure.

Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data

The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

Effects of short- and long-term exposures to particulate matter on inflammatory marker levels in the general population

The effect of particulate matter (PM) on health increases with exposure duration but the change from short to longer term is not well studied. We examined the exposure to PM smaller 10 μm (PM₁₀) from short to longer duration and their associations with levels of inflammatory markers in the population-based CoLaus cohort in Lausanne, Switzerland. Baseline and follow-up CoLaus data were used to study the associations between PM₁₀ exposure and inflammatory markers, including the high-sensitivity C-reactive protein (CRP), as well as interleukin 1-beta (IL-1β), interleukin 6 (IL-6), and tumor-necrosis-factor alpha (TNF-α) using mixed models. Exposure was determined for each participant's home address from hourly air quality simulations at a 5-m resolution. Short-term exposure intervals were 1 day, 1 week, and 1 month prior to the hospital visit (blood withdrawal); long-term exposure intervals were 3 and 6 months prior to the visit. In most time windows, IL-6, IL-1β, and TNF-α were positively associated with PM₁₀. No significant associations were identified for CRP. Adjusted associations with long-term exposures were stronger and more significant than those for short-term exposures. In stratified models, gender, age, smoking status, and hypertension only led to small modifications in effect estimates, though a few of the estimates for IL-6 and TNF-α became non-significant. In this general adult cohort exposed to relatively low average PM₁₀ levels, clear associations with markers of systemic inflammation were observed. Longer duration of elevated exposure was associated with an exacerbated inflammatory response. This may partially explain the elevated disease risk observed with chronic PM₁₀ exposure. It also suggests that reducing prolonged episodes of high PM exposure may be a strategy to reduce inflammatory risk.

Beyond Commuting: Ignoring Individuals' Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion

This research examines whether individual exposures to traffic congestion are significantly different between assessments obtained with and without considering individuals’ activity-travel patterns in addition to commuting trips. We used crowdsourced real-time traffic congestion data and the activity-travel data of 250 individuals in Los Angeles to compare these two assessments of individual exposures to traffic congestion. The results revealed that individual exposures to traffic congestion are significantly underestimated when their activity-travel patterns are ignored, which has been postulated as a manifestation of the uncertain geographic context problem (UGCoP). The results also highlighted that the probability distribution function of exposures is heavily skewed but tends to converge to its average when individuals’ activity-travel patterns are considered when compared to one obtained when those patterns are not considered, which indicates the existence of the neighborhood effect averaging problem (NEAP). Lastly, space-time visualizations of individual exposures illustrated that people’s exposures to traffic congestion vary significantly even if they live at the same residential location due to their idiosyncratic activity-travel patterns. The results corroborate the claims in previous studies that using data aggregated over areas (e.g., census tracts) or focusing only on commuting trips (and thus ignoring individuals’ activity-travel patterns) may lead to erroneous assessments of individual exposures to traffic congestion or other environmental influences.

The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect

Ignoring people’s daily mobility and exposures to nonresidential contexts may lead to erroneous results in epidemiological studies of people’s exposures to and the health impact of environmental factors. This paper identifies and describes a phenomenon called neighborhood effect averaging, which may significantly confound the neighborhood effect as a result of such neglect when examining the health impact of mobility-dependent exposures (e.g., air pollution). Several recent studies that provide strong evidence for the neighborhood effect averaging problem (NEAP) are discussed. The paper concludes that, due to the observed attenuation of the neighborhood effect associated with people’s daily mobility, increasing the mobility of those who live in disadvantaged neighborhoods may be helpful for improving their health outcomes.

Seasonal variations of polybrominated flame retardants bound to car dust under Mediterranean climate

Polybrominated diphenyl ethers (PBDEs) are commercial flame retardants that have been commonly used in vehicle interior to reduce fire-related hazards. Due to high temperatures and intense insolation that can be attained inside cars parked in the sun, additive PBDEs are prone to leach out and attach to in-vehicle dust, as well as to photo-debrominate. This study examines seasonal variations of concentrations of three common PBDE congeners (BDE-47, BDE-99 and BDE-209) in car dust in Israel. The overall concentrations of these BDEs ranged from 1 to 29μg/g, and were higher in the summer than in the winter (average of 10.2 and 5.3μg/g, respectively). Congener-specific concentrations showed distinct seasonal pattern, representing the interplay between leaching, evaporation and photodebromination. Photolysis of the three congeners, while adsorbed on glass filters and exposed to solar radiation, revealed first order kinetics with debromination rates on the order of 10^-2/min. Hence, seasonal variations of the meteorological conditions were found to affect the in-vehicle PBDE concentrations, and are therefore expected also to affect the exposure of passengers to PBDEs.

Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong

BACKGROUND

Epidemiological studies typically use subjects' residential address to estimate individuals' air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks.

OBJECTIVES

To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong.

METHODS

A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component.

RESULTS

Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM_2.5, BC, and NO₂ were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses.

CONCLUSIONS

The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than 'true' exposures.

Impacts of Personal Mobility and Diurnal Concentration Variability on Exposure Misclassification to Ambient Pollutants

Appreciating the uncertainty margins of exposure assessment to air pollution requires good understanding of its variability throughout the daily activities. This study describes a modeling framework for estimating exposure to air pollutants for a representative sample of working Israeli adults ( N ∼ 168 000) for which both the residence and workplace addresses were available. Individual daily trajectories were simulated by accounting for five generic daily activities: at home, at work, while in commute from home to work and back, and during out-of-home leisure activities. The integrated daily exposure to nitrogen dioxide (NO₂) was estimated for each individual by tracking the daily trajectory through an NO₂ concentration map, obtained using a dynamic and highly resolved dispersion-like model (temporal resolution, half-hourly; spatial resolution, 500 m). Accounting for the subjects' daily mobility was found to affect their exposure more significantly than accounting solely for the diurnal concentration variability, yet a synergistic effect was noted when accounting for both factors simultaneously. Exposure misclassification varies along the day, with the work microenvironment found to contribute the most to it. In particular, regardless of the high concentrations encountered during the commute, their contribution to the integrated daily exposure is small due to the relatively short time spent in this activity by most people.

Using cell phone location to assess misclassification errors in air pollution exposure estimation

Air pollution epidemiologic and health impact studies often rely on home addresses to estimate individual subject's pollution exposure. In this study, we used detailed cell phone location data, the call detail record (CDR), to account for the impact of spatiotemporal subject mobility on estimates of ambient air pollutant exposure. This approach was applied on a sample with 9886 unique simcard IDs in Shenzhen, China, on one mid-week day in October 2013. Hourly ambient concentrations of six chosen pollutants were simulated by the Community Multi-scale Air Quality model fused with observational data, and matched with detailed location data for these IDs. The results were compared with exposure estimates using home addresses to assess potential exposure misclassification errors. We found the misclassifications errors are likely to be substantial when home location alone is applied. The CDR based approach indicates that the home based approach tends to over-estimate exposures for subjects with higher exposure levels and under-estimate exposures for those with lower exposure levels. Our results show that the cell phone location based approach can be used to assess exposure misclassification error and has the potential for improving exposure estimates in air pollution epidemiology studies.