Evaluating the Impact of Neighborhood Characteristics on Differences

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Ecotoxicology and Human Environmental Health

Evaluating the Impact of Neighborhood Characteristics on Differences between Residential and Mobility-based Exposures to Outdoor Air Pollution Masoud Fallah Shorshani, Marianne Hatzopoulou, Nancy Ross, Zachary Patterson, and Scott Weichenthal Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b02260 • Publication Date (Web): 17 Aug 2018 Downloaded from http://pubs.acs.org on August 20, 2018

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Environmental Science & Technology

Evaluating the Impact of Neighborhood Characteristics on Differences between Residential and Mobility-based Exposures to Outdoor Air Pollution Masoud Fallah-Shorshani1, Marianne Hatzopoulou2, Nancy A. Ross3, Zachary Patterson4, Scott Weichenthal*1 1

McGill University, Department of Epidemiology, Biostatistics and Occupational Health,

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Montreal, Quebec H3A 1A2, Canada

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2

University of Toronto, Department of Civil Engineering, Toronto, Ontario, M5S 1A4, Canada

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4

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Quebec, HG3 1M8

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McGill University, Department of Geography, Montreal, Quebec H3A 2K6, Canada Concordia University, Department of Geography, Planning and Environment, Montreal,

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*Corresponding Author

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Scott Weichenthal Faculty of Medicine Department of Epidemiology, Biostatistics, and Occupational Health McGill University 1020 Pins Ave. West Montreal, QC H3A 1A2, Canada Email: [email protected] Tel: (514) 398-1584

Abstract

1 ACS Paragon Plus Environment


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Epidemiological studies often assign outdoor air pollution concentrations to

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residential locations without accounting for mobility patterns. In this study, we examined

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how neighborhood characteristics may influence differences in exposure assessments

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between outdoor residential concentrations and mobility-based exposures. To do this,

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we linked residential location and mobility data to exposure surfaces for NO2, PM2.5, and

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ultrafine particles in Montreal, Canada for 5452 people in 2016. Mobility data were

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collected using the MTL Trajet smartphone application (mean: 16 days/subject).

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Generalized additive models were used to identify important neighborhood predictors of

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differences between residential and mobility-based exposures and included residential

49

distances to highways, traffic counts within 500 meters of the residence, neighborhood

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walkability, median income, and unemployment rate. Final models including these

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parameters provided unbiased estimates of differences between residential and

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mobility-based exposures with small root mean square error values in 10-fold cross

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validation samples. In general, our findings suggest that differences between

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residential and mobility-based exposures are not evenly distributed across cities and

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are greater for pollutants with higher spatial variability like NO2. It may be possible to

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use neighborhood characteristics to predict the magnitude and direction of this error to

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better understand its likely impact on risk estimates in epidemiological analyses.

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1.

Introduction


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Large population-based studies of the long-term health effects of outdoor air

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pollution generally rely on exposure estimates assigned to residential locations.1-2 This

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is a reasonable approach given other options are not readily available, and people often

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spend a large portion of their time in and around their home. This approach, however,

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has clear limitations as it does not account for individual-level mobility patterns, which

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may act to increase or decrease long-term exposures relative to outdoor residential

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concentrations. Moreover, it is not clear how urban environments (e.g. local traffic

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density, access to transit, walkability) may impact differences between residential and

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mobility-based-exposure measures, or how these patterns may differ between

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pollutants. These are important considerations as the validity of residential outdoor

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concentrations as surrogate measures of long-term personal exposures to outdoor air

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pollution has a direct impact on the validity of health risk estimates derived from these

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exposures.

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Previous studies have reported important differences between personal

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exposures to air pollution and those estimated using mobility information, home/work

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locations, or commute distance.3-5 In addition, studies based on mobility surveys6

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suggest that the health risks of air pollution may be underestimated when mobility is not

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considered in the exposure assessment process and that this bias may be stronger

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when the spatial variability of pollution concentrations is greater. Other studies have

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used activity space questionnaires to analyze the impact of non-residential

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neighborhood exposures on self-reported health7,8 and Chaix et al.9 proposed an

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integrated approach combining GPS tracking, accelerometers, and an electronic web-

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based mobility survey to improve measures of exposure by accounting daily mobility



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patterns. Smartphone applications have also been developed to track time-activity

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patterns for the purpose of exposure assessment10,11 but to our knowledge studies have

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yet to evaluate important neighborhood-level predictors of differences (i.e. potential

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bias) between mobility-based exposures and residential exposure estimates. Here we address this question by comparing residential PM2.5, NO2, and ultrafine

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particle (UFPs, 55 years) and were predominantly male (61%). On average, residential and mobility-based exposure estimates were similar for

207 208

the population as a whole (Figure 2 and Supplemental Material: Table S1). However,

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overall averages masked important spatial patterns that were apparent when we

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mapped differences between residential and mobility-based exposures across

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dissemination areas in Montreal. Specifically, Figure 3 was generated by first calculating

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individual differences between residential and mobility-based exposures for each

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subject in our study, assigning these differences to residential locations, and then

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averaging these differences for all residential locations within each dissemination area

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(areas with no data are shown as white in Figure 3). In general, residential exposure

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estimates for NO2, PM2.5, and UFPs in the suburban areas on the west portion of the

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island tended to underestimate mobility-based exposures (residential exposure is

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smaller than mobility exposure), whereas residential estimates in the downtown core

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tended to overestimate mobility-based exposures. Scatter plots of residential

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concentrations versus differences between mobility-based and residential estimates are

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shown in Supplemental Figure S1. These plots illustrate a trend whereby individual-level

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mobility patterns tend to increase exposures for people living in low exposure areas and

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decrease exposure for people living in high exposure areas. Simple scatter plots of

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mobility-based versus residential exposure estimates are shown in Supplemental Figure

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S2. Descriptive data for the built environment and land use parameters examined in

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the GAM models are listed in Table 1; variables retained in final models are listed in



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Table 2. Most parameters were not strongly correlated (r < 0.55) with the exception of

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length of bike lanes and length of roads (r=0.95). Length of bike lanes was removed

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from the UFP model as this was the only model that included both of these parameters.

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The relationships between various built-environment/neighborhood characteristics and

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differences between residential and mobility-based exposure are illustrated in Figures 4-

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6. Many of these relationships were non-linear and the magnitudes of differences

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across the various built-environment/neighborhood factors were greater for NO2 and

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UFPs than for PM2.5. For NO2 (Figure 4), residential exposures tended to underestimate mobility-

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based exposures with increasing distance from metro lines (panel a) and as median

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income (panel f) and park space (panel i) increased. On the other hand, residential

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exposures tended to overestimate mobility-based estimates as dissemination area

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unemployment rates increased (panel j) and as the length of highways within 100 m of

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the residence increased (panel h). Residential exposures tended to underestimate

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mobility-based exposures for subjects living in the least walkable areas and

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overestimate exposures in the most walkable areas (panel k). In some cases, u-shaped

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(distance to highways, length of bus lanes) or inverted u-shaped (distance to

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expressway) relationships were observed with the magnitude of over/underestimation

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varying across the distribution of land use factors. For UFPs, residential exposures tended to overestimate mobility-based

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estimates as the length of bus lanes, highways, and rail within 100 m increased around

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residences (panels b, c, and f). An inverted u-shaped relationship was observed for the

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length of streets within 100 m buffers of residences with residential concentrations


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overestimating mobility-based exposures as the length of streets within this buffer

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increased (panel h). As for NO2, residential UFP exposures tended to underestimate

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mobility-based estimates for subjects living in the least walkable areas and overestimate

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exposures in the most walkable areas (panel g).

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For PM2.5, residential exposures tended to underestimate mobility-based

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exposures as distance to metro lines, traffic counts (100 m buffer), and length of bus

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lanes (100 m buffer) increased (panels a, b, and k). Inverted u-shaped relationships

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were observed for distance to expressways, park space (100 m buffer), and length of

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railways (100 m buffer) with residential exposures tending to overestimate mobility-

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based measures at higher values of these parameters (panels e, d, and l). Finally, u-

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shaped relationships were observed for walkability, median income, unemployment

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rate, and length of bike lanes (100-m buffer) (panels j, n, o, and m). As for UFPs and

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NO2, residential PM2.5 exposures tended to underestimate mobility-based estimates for

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subjects in the least walkable areas. The results of model evaluations using 10-fold cross-validation procedures

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(n=545 per iteration) are presented in Table 3. All three models provided unbiased

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estimates of differences between outdoor residential concentrations and mobility-based

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air pollution exposures. R2 values were low but RMSE values were small relative to the

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typical exposures levels (12%, 16%, and 3.0% of residential concentrations for NO2,

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UFPs, and PM2.5, respectively) suggesting that these models may still be useful in

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practice given that they also provide unbiased estimates. Indeed, the low R2 values are

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likely due in part to the narrow range of differences modeled and, in this case, RMSE

273

values likely provides a more useful measure of model utility.8



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4.

Discussion

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evaluate the long-term health effects of these exposures. This is a reasonable

278

approach, but one clear limitation is their inability to capture exposure variations

279

resulting from individual-level mobility patterns. This is an important issue as the validity

280

of risk estimates derived from these studies is directly linked to the extent to which

281

residential concentrations represent long-term personal exposures to air pollution of

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ambient origin.

Residential estimates of outdoor air pollution concentrations are widely used to

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We examined how the urban built environment and various neighborhood

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characteristics may influence differences between residential and mobility-based

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exposures using individual-level mobility data and exposure models for outdoor NO2,

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UFPs, and PM2.5. Our results suggest that there may be substantial within-city spatial

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variations in the validity of outdoor residential concentrations as estimates of long-term

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exposures to outdoor air pollution. The magnitude of the difference between residential

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and mobility-based estimates appears to be greater for pollutants with more spatial

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variability (e.g., NO2 and UFP) and is influenced by a number of built

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environment/neighborhood characteristics including residential proximity to traffic,

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neighborhood walkability, median income, and unemployment rate. Importantly, our

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results suggest that it may be possible to use various built environment/neighborhood-

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level characteristics to adjust residential concentrations to more accurately reflect

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mobility-based exposures. Additionally, future studies could use this approach to

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estimate the magnitude and direction of this error to better understand the likely impact

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on risk estimates in epidemiological analyses. However, the additional effort required to


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implement this analysis may not be equally justifiable for all pollutants. Specifically, our

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findings suggest that differences between residential and mobility-based exposures are

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greatest for pollutants with high spatial variability (i.e. UFPs and NO2) with smaller

301

differences observed for PM2.5. Therefore, it may be more efficient to focus on other

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sources of error (e.g. penetration of outdoor air indoors) for pollutants with lower spatial

303

variations.

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To our knowledge this is the first study to evaluate how various built environment

305

and neighborhood level characteristics may impact differences between residential and

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mobility-based exposures to outdoor air pollution using individual-level mobility data.

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Tonne et al.27 noted that residential exposures consistently overestimated personal

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exposures in London, UK based on simulated routes between origins and destinations

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identified in London’s annual travel demand survey. This team also reported that

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overestimation depended in part on household income and area-level income

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deprivation and our findings for median income and unemployment rate in Montreal are

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consistent with these results. In general, predictors of potential bias in residential

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exposure estimates may be city-specific owing to different spatial patterns of

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populations in relation to various built environment/neighborhood-level factors.

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However, some patterns may be more generalizable across cities including those for

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suburban commuting to downtown (i.e. mobility-bases exposures are likely higher than

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residential concentrations). Our results and those of Tonne et al.27 do, however, point to

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several issues that should be considered in future epidemiological investigations. First,

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the magnitude (and direction) of exposure measurement error is likely not constant for

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populations distributed across cities when residential outdoor air pollution



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concentrations are used as the primary exposure variable. The impact of this error on

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risk estimates will depend on the measurement error structure in any given study and

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could result in bias either toward or away from the null. In the future, more attention

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should be paid to the impact that these various built environment/neighborhood-level

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factors could have on the magnitude of exposure measurement error in a given study in

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order to minimize their impact on study results or at least understand the likely direction

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of potential bias in risk estimates. While this study had a number of methodological strengths including a detailed

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time-series of individual-level mobility data mapped to multiple air pollution exposure

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surfaces, we note several limitations. First, our study population is not representative of

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the overall population of Montreal and in particular our sample included few elderly

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people. As a result, our results may not reflect the magnitudes/directions of potential

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differences between residential and mobility-based exposures for subjects outside the

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age range included in our study. Similarly, participant residential locations were not

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randomized and thus our sample may not be representative of spatial patterns across

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the entire island of Montreal (although we did have participants living in most areas of

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the island). In addition, the spatial resolution of our PM2.5 exposure surface was limited

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to 1 km2 and our ability to identify differences between residential and mobility-based

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exposure estimates was less than for NO2 or UFPs. In practice, however, within-city

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spatial differences in outdoor PM2.5 concentrations vary less than these pollutants and

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our results would not be expected to change dramatically if a higher resolution model

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were available.


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We did not attempt to evaluate personal air pollution exposures in homes or in

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transit environments (e.g., subways, buses). Instead, our comparisons focused on

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residential outdoor concentrations and outdoor concentrations encountered along

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typical outdoor daily mobility paths. Some may view this as a limitation, but transit-

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specific exposures are not typically considered in epidemiological studies of long-term

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exposure to outdoor air pollution as the parameters needed to estimate these values

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are not readily available for application to large population-based cohorts. Likewise, we

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did not consider built environment factors encountered along mobility paths as

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predictors in our models as this information would also not be available in population-

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based studies. Moreover, our database did not support a detailed evaluation of

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individual-level factors that may influence differences between residential and mobility-

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based exposures. Future studies should explore these important questions. Finally,

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while the application used in this study (MTL Trajet) provided an efficient means of

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collecting mobility data, the block-level spatial accuracy of this application may have

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underestimated spatial variations in mobility-based exposures and future studies will

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aim to refine this metric.



In summary, residential estimates of long-term exposures to outdoor air pollution

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may not adequately reflect exposures that account for individual-level mobility patterns.

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This is particularly true for pollutants with high spatial variability like UFPs and NO2 and

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may be less of a concern for pollutants with less spatial variability such as PM2.5. In

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some cases, it may be possible to use various built environment and neighborhood level

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characteristics to estimate differences between residential concentrations and mobility-

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based exposures which in turn may help to evaluate the likely impact of this source of

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measurement error on health risk estimates.

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Supporting Information

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Supplemental Methods: The MTL Trajet Study; Descriptive statistics for residential and

370

mobility-based exposures (Table S1); Scatter plots of differences between mobility and

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residential exposures versus residential estimates for UFPs, PM2.5, and NO2 (Figure

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S1); Scatter plots of mobility-based versus residential exposure estimates for UFPs,

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PM2.5, and NO2 (Figure S2).

374 375 376 377 378 379 380 381 382 16 ACS Paragon Plus Environment

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TABLES

481

Table 1. Descriptive statistics for land use and built environment parameters Variable

Best buffer

Mean

Median

Maximum

Minimum

Length Variables (m) Length of bus lanes

100

353

241

4350

0

Length of the bike lanes

100

539

539

1745

0

Length of the highways

100

22

0

1415

0

Length of the all roads

100

545

542

1745

197

Length of rail lines

100

2

0

590

0

Distance from metro line

1766

737

24,070

1

Distance from expressways

1552

1450

5753

5

Distance from highways

733

564

4826

1

Distance from the shore

1261

1129

4004

25

500

7843

7125

32,662

194

Area of the open area

500

35,190

14,580

660,400

0

Area of the parks

500

55,330

38,030

492,800

0

Area of the governmental

500

66,630

54,590

719,400

0

Area of the industrial

500

118,600

88,200

712,100

0

9

8

29

0

Median Household Income ($)

41290

37264

210,809

17

Population Count

7471

7241

73,982

0

234,414

176,138

27,084,300

36,121

1.61

1.43

13.7

-8.49

Distance Variables (m)

Traffic Counts 2

Land Use Area (m )

Dissemination Area Variables Unemployment rate (%)

Real estate value ($) Walkability index

482 21 ACS Paragon Plus Environment


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483 484 485 486 487 488 489 490 491

Table 2. Final model parameters in GAM models for the difference between residential and mobility-based exposures to NO2, UFPs, and PM2.5 in Montreal, Canada NO2 Distance to metro line Distance to highway Traffic Counts Length of bus lines Walkability index Median income Unemployment rate Distance to expressway Distance to shore Parks Length of highway

2

492 493 494

UFPs Distance to metro line Distance to highway Traffic Counts Length of bus lines Walkability index Median income Unemployment rate Distance to shore Length of highway Length of all roads Length of railways Open area

2

PM2.5 Distance to metro line Distance to highway Traffic Counts Length of bus lines Walkability index Median income Unemployment rate Distance to expressway Length of bike lanes Length of railways Industrial area Open area Population Density Parks Age 2

LOOCV R = 0.30 LOOCV R = 0.14 LOOCV R = 0.13 LOOCV, leave one out cross validation results for model selection procedure

495 496 497 498 499 500 501


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502 503 504 505 506 507 508 509

Table 3. Model evaluation using a 10-fold cross-validation procedure for the difference between residential and mobility-based exposure to NO2, UFPs, and PM2.5 Linear Models for Measured vs. Predicted Values Pollutant NO2 (ppb) UFPs 3 (count/cm )

Mean Slope (95% CI) 1.00 (0.939, 1.06)

Mean R (95% CI) 0.29 (0.26, 0.32)

Mean RMSE (95% CI) 2.91 (2.81, 3.01)

195 (-64, 455)

0.93 (0.78, 1.08)

0.14 (0.11, 0.17)

4030 (3688, 4372)

3

-0.0111 0.96 0.13 0.26 (-0.0277, 0.00542) (0.81, 1.11) (0.089, 0.16) (0.25, 0.28) Results are based on 10 repeated hold out samples with the model developed on a random 90% of the data and tested on the remaining 10% (n=545) PM2.5 (µg/m )

510 511 512

2

Mean Intercept (95% CI) 0.0291 (-0.0495, 0.108)

513 514 515 516 517 518 519 520 521 522 523 524 23 ACS Paragon Plus Environment


525 526 527 528

FIGURES

529

Figure 1. Spatial distribution of study participants by 3-digit postal codes in Montreal,

530

Canada.

531 532 533 534 535 536 537 24 ACS Paragon Plus Environment

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538 539 540 541 542 543 544 545 546 547 548


Figure 2. Distributions of residential and mobility-based estimates of ultrafine particle (UFP) (A), nitrogen dioxide (NO2) (B), and PM2.5 (C) exposures.

549 550 551 25 ACS Paragon Plus Environment


552 553 554 555 556 557 558 559 560 561 562 563

Figure 3. Spatial distributions of differences between residential and mobility-based exposure estimates for NO2 (A), UFPs (B) and PM2.5 (C) in Montreal, Canada. Red areas indicate dissemination areas where on average residential exposure estimates are greater than mobility-based exposures. Green areas indicate dissemination areas where on average residential exposure estimates are lower than mobility-based exposures. Dissemination areas with no subjects are colored white on the map. A

564 565

B


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566 567


C

568 569 570 571 572 573 574 575 576 27 ACS Paragon Plus Environment


577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599

Figure 4. Relationships between built environment factors and differences between residential and mobility-based NO2 (ppb) exposures in Montreal, Canada. The red horizontal line indicates no difference between mobility-based exposures and residential estimates. Portions of the graphs above the red lines indicate when mobility-based exposures exceed residential estimates; portions below the red lines indicate when mobility-based exposures are lower than residential estimates.


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a

600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621

b

c

d

e

f

g

h

i

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Figure 5. Relationships between built environment factors and differences between residential and mobility-based UFP (per cm3) exposures in Montreal, Canada. The red horizontal line indicates no difference between mobility-based exposures and residential estimates. Portions of the graphs above the red lines indicate when mobility-based exposures exceed residential estimates; portions below the red lines indicate when mobility-based exposures are lower than residential estimates.



622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637

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Figure 6. Relationships between built environment factors and differences between residential and mobility-based PM2.5 (µg/m3) exposure in Montreal, Canada. The red horizontal line indicates no difference between mobility-based exposures and residential estimates. Portions of the graphs above the red lines indicate when mobility-based exposures exceed residential estimates; portions below the red lines indicate when mobility-based exposures are lower than residential estimates.


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638 639 640 641 642 643 644 645

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TOC ART


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Evaluating the Impact of Neighborhood Characteristics on Differences

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