Harvard ACE Project 2:

Air Pollutant Mixtures in Eastern Massachusetts: SpatialMulti-resolution Analysis of Trends, Effects of Modifiable

Factors, Climate, and Particle-induced Mortality

Brent Coull, Petros Koutrakis, Joel Schwartz, Itai Kloog, AntonellaZanobetti, Joseph Antonelli, Ander Wilson, Jeremiah Zhu,

Weeberb Requia, Choong-Min Kang, Marianthi-AnnaKioumourtzoglou, Ce Li, Glen McGee

May 30, 2018

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Objectives

1 Decompose high-resolution PM2.5 mass and ground air temperature datainto regional, sub-regional, and local spatial scales.

2 Conduct a spatiotemporal analysis of sub-regional and local variation inPM2.5 mass and ground air temperature, and local PM2.5 emissions.

3 Conduct spatial multi-resolution analysis of PM2.5 mixtures.

4 Conduct air pollution mortality and birth outcome studies inMassachusetts using multi-resolution PM2.5 mass and species data.

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Spatio-temporal Models for Black Carbon

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Prediction Models for Black Carbon:Support Vector Machines

Abu Awad et al., Environmental International 2017.

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Spatio-temporal Models for Black Carbon

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Spatio-temporal Modeling of PM2.5 Elements from XRF

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Spatio-temporal Modeling of PM2.5 Elements

Name Type Sites Time Samples DurationHSPH Outdoor 1 03/2006− 12/2010 1755 DailyMAD EPA Outdoor 6 03/2006− 12/2010 796 DailyMPO PPG Outdoor 53 03/2006− 10/2008 444 Daily

MPI PPG Indoor 68 10/2006− 07/2010 341 WeeklyMNI NAS Indoor 270 07/2010− 12/2010 321 Weekly

Elements Modeled:

K, CA, Fe, Zn, Cu, Ti, Al, Pb, V, Ni

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Spatio-temporal Modeling of PM2.5 Elements

Outcomes considered1 Element Concentration2 Element Concentration / PM2.5

3 PCA Component Scores

Modeling frameworks1 Spatio-temporal Generalized Additive Models (GAMs)2 Random forests3 Support Vector Machines4 GAMs + Gradient Boosting

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Spatio-temporal GAMs + Gradient Boosting

Validation R2

ElementData I-95 loop K Fe Cu

MAD out 0.42 0.58 0.47in 0.48 0.60 0.50

overall 0.42 0.58 0.46

MNI out 0.46 0.52 0.44in 0.47 0.54 0.49

overall 0.45 0.53 0.44

MPI out 0.46 0.61 0.49in 0.49 0.63 0.51

overall 0.46 0.61 0.49

MPO out 0.44 0.62 0.42in 0.47 0.64 0.47

overall 0.44 0.62 0.42

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Temporal Distribution of XRF Data, 2006-2012

(a) MAD (b) MPI

(c) MPO (d) MNI

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Spatio-temporal Modeling of PM2.5 Elements from XRF

Metal concentrations (Red: < 3∗Uncertainty)

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Spatio-temporal Modeling of PM2.5 Elements: Next Steps

MADEP data to be added:1 7 new locations2 2002-20093 6500 samples (will triple existing data)

Ensemble (averaging) of different models

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Model Ensembling: Accounting for Model Uncertainty

Spatial distribution of PM2.5 model CV errors from two exposure models

(a) IK (b) QD

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Model Ensembling: Accounting for Model Uncertainty

Estimated Effect Heterogeneity Using Three Exposure Models

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AVIKQD

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Bayesian Spatially-Varying Model Ensembles

y(x): Pollution Spatial Process

{yk}Kk=1: Prediction from K base models

We seek model ensemble estimates of the form

yens(x) =∑K

k=1 wk(x)yk(x)

where spatially varying weight functions are modeled as

wk =exp(w ′

k)∑Kk=1 exp(w ′

k)where

w ′k(x) ∼ GP

[0, kw ,k(x, x′|l)

]We obtain posterior predictive distribution of y that incorporates both

model-specific prediction errormodel-to-model uncertainty

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Bayesian Spatially-Varying Model Ensembles: Simulation

Simulation: Residual process from individual base models

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Bayesian Spatially-Varying Model Ensembles: Simulation

Ensemble weight flexibility v.s. Cross-validated RMSE

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Critical Windows of Exposure for Children’s Health

Evidence supports associations between maternal exposure to air pollutionduring pregnancy and children’s health outcomes.

Recent interest focuses on critical windows of vulnerability.

We have estimated daily exposures from multiple pollutants:

NO2,OC,EC,Sulfate,O3,NH4

Recent work has shown distributed lag modeling (DLM) can outperformmodels based on trimester-averaged exposures (TAE).

No corresponding methods for air pollution mixtures.

Goal: Develop DLM methods for air pollution mixtures.

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Distributed Lag Model for Single Exposure

Exposure zi (t) is time-specific (week of pregnancy)

Yi = α + γ

∫zi (t)w(t)dt + xTi β + εi

w(t) identifies critical windows of vulnerability

γ is the within-window effect

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Bayesian Kernel Machine Regression for Mixtures

1 Yi is a health endpoint, xi contains potential confounders.

2 zi = (zi1, . . . , ziM)T are M (univariate) pollutant concentrations.

Yi = h (zi1, . . . , ziM) + xTi β + εi , i = 1, . . . , n

h (·) is an unknown function

β: effects of the confounders; εiiid∼ N

(0, σ2

)

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Bayesian Kernel Machine Regression - DLM

Ultimately interest focuses on zmi (t), m = 1, . . . ,M

Yi = h(E1i , . . . ,EMi ) + xTi β + εi .

Emi =

∫zmi (t)wm(t)dt.

Model fitting estimates the critical windows wm(t) and the mixture effecth() simultaneously.

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ACCESS Prospective Birth Cohort

traditional LUR predictors to yieldresidence-specific estimates of daily PM2.5

as detailed previously (22). The model wasrun using day-specific calibrations of AODdata using ground PM2.5 measurementsfrom 78 monitoring stations coveringNew England and LUR and meteorologicvariables (temperature, wind speed,visibility, elevation, distance to major roads,percent open space, point emissions,and area emissions). This approachincorporates highly resolved spatialinformation from the LUR data andimportant spatiotemporal data from theremote sensing satellite data.

The AOD-PM2.5 relationship wascalibrated for each day using data fromgrid cells with both monitor and AODvalues using mixed models with randomslopes for day, nested within region. Fordays without AOD data (because of cloudcoverage, snow, and so forth), the modelwas fit with a smooth function of latitudeand longitude and a random intercept foreach cell (similar to universal Kriging).The “out of sample” 10-fold crossvalidation R2 for daily values was 0.83 and0.81 for days with and without availableAOD data, respectively. For use in thehealth effect models, to reduce potentialnoise caused by day-to-day PM2.5 variation,daily levels were averaged into weeklyexposure profiles. Predicted overall prenatalPM2.5 levels at participant’s residence inrelation to the 103 10 km grids for whichAOD data were available are shown inFigure 1. Although levels were higheraround major roadways as anticipated,there was reasonable heterogeneity.

AsthmaMaternal-reported clinician-diagnosedasthma was ascertained from birth up toage 6 years through telephone and face-to-face interviews at approximately 3-monthintervals for the first 24 months thenannually thereafter. Mothers were asked,“Has a doctor or nurse ever said that yourchild had asthma?” Most of these childrenwere given a diagnosis of asthma after theage of 3 years (78.6%) (see Figure E1 inthe online supplement).

CovariatesMaternal age, race, education, andprepregnancy height and weight, and child’ssex were ascertained by questionnaire; dateof birth, gestational age, and birth weightwere obtained by medical record review.

A validation analysis on a subset of 121ACCESS women showed no difference inthe level of agreement/disagreement forheight and weight when comparing valuesmeasured early in pregnancy (,10 wk)with self-report (34). Women were askedabout smoking at enrollment and in thethird trimester and classified as prenatalsmokers if smoking at either visit. Mothersreported postnatal smoking and whetherothers smoked in the home at eachpostpartum interview. Household crowdingwas calculated by dividing the number ofpersons living in the home by the numberof rooms based on maternal report inpregnancy. Maternal atopy was defined byself-reported doctor-diagnosed asthma,eczema, and/or hay fever. Body mass indexwas calculated by dividing weight by heightsquared (kg/m2); obesity was defined asbody mass index greater than or equal to30 kg/m2 (35).

Because prenatal stress may covarywith pollution and has been associated withasthma (36), this was also considered as

a confounder. We measured stress usingthe Crisis in Family Systems-Revised surveyadministered prenatally within 2 weeks ofenrollment (37, 38). This survey assesseslife events experienced across 11 domains(e.g., financial, relationships, violence,housing, discrimination/prejudice).Mothers endorsed events experienced inthe past 6 months and rated each aspositive, negative, or neutral. The numberof domains with one or more negative eventwas summed to create a continuousnegative life events (NLEs) domain score,with higher scores indicating greater stress.Because birth weight and gestationalage may be on the pathway betweenprenatal PM and asthma risk, birthweight for gestational age z score (39)was considered in sensitivity analyses.

Statistical AnalysisAnalyses included 736 singleton full-term(gestational age >37 wk) children withtwo or more postnatal interviews followedup to age 6 years and air pollution exposure

N

8.46–9.98

PM2.5 estimates over pregnancy (µg/m3)

9.99–10.9610.97–11.8411.85–13.66Major road ways

0 10 20Kilometers

10×10km prediction grid

Figure 1. Predicted daily particulate matter with a diameter less than or equal to 2.5 mm (PM2.5)levels for Asthma Coalition on Community, Environment and Social Stress participants averaged overpregnancy. This figure demonstrates predicted daily PM2.5 levels for study participants based onresidence and averaged throughout the gestation period. The 103 10 km aerosol optical depth gridused to predict daily PM2.5 levels is also depicted.

ORIGINAL ARTICLE

1054 American Journal of Respiratory and Critical Care Medicine Volume 192 Number 9 | November 1 2015

Study participants (i):191 Boston-area birthsbetween 8/2002 and 1/2007

Exposure (Zmit): NO2,OC,EC,S,O3,NH4 at maternal residence for eachweek (t) of pregnancy

Outcomes (Yi ): FEV1 at age 8

Baseline covariates (Xi ): child sex, maternal pre-pregnancy BMI, age,education, race/ethnicity, atopy, self reported smoking during pregnancy, stressindex, neighborhood disadvantage index

[figure source: Hsu et al. Am. J. Respir. Crit. Care Med. 2015]

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BKMR using pregnancy-average exposure

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BKMR-DLM: Exposure-response of each exposure at low/high levels ofanother

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BDLM analyses of NH4, by low/high NO2, EC, Sulfate, O3

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Delay of Onset and Exposure Error in Case-CrossoverDesigns

Time-stratified case-crossover studies often used to estimate effect of airpollution on acute events

For each event, create set of reference times based on same year, month,DOW, and hour

Delayed recording of event onset yields exposure error.

Can yield severe attenuation of effect estimates (Lokken et al. 2009).

Goal: Develop a method that corrects for this bias based on a validationsample of delay times.

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(A) Distribution of delay times and (B) Resulting Exposure Error in24-hour PM2.5 in Boston Stroke Study

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Subtle problem: Matched sets change with error.

None of the typical measurement error assumptions hold.

We developed the following error corrections:

Marginal likelihood estimatorRegression calibration estimatorConditional score estimatorEach of these + second-stage parametric bootstrap

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Comparative analysis of Boston stoke study using true and delayed eventtimes and measurement error corrections.

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ndelays=300

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Spatial multi-resolution analysis: Conceptual Framework

Goal: Decompose daily pollution surfaces into different scales, which arerepresentative of different sources of pollution

Examine health effects, predictors, and trends at different scales

Figure taken from HSPH class EH521 Notes (Annette Peters)

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Applied to Satellite PM2.5 Predictions

Two-dimensional wavelet decomposition:

All panels are averaged over days in 2006

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Multi-resolution Work - Integration within Larger Center

Currently using wavelet decompositions for:

mortality and scale-specific PM2.5 in New England, 2000-2015source-receptor mapping of power-plant emissions (Project 4)emissions modeling (Project 1)

Reproducible Software

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Project 2 Posters

1 Abu Awad et al. A spatio-temporal prediction model for Black Carbonbased on ensemble machine learning.

2 Liu et al. Spatio-temporal modeling of ambient PM2.5 elementalconcentrations in eastern Massachusetts.

3 Liu et al. Adaptive Bayesian spatio-temporal ensemble of air pollutionpredictive models.

4 Wilson et al. Distributed lag models for assessing critical windows ofexposure to air pollution mixtures.

5 Coull et al. Corrections for measurement error due to delayed onset ofillness for case-crossover designs.

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Harvard ACE SAC Review 2017

“...we encourage the Center to also consider the potential for confoundingand/or effect modification by the broader pollutant mixture...”

BC, XRF modelingBKMR-DLM

“Each project should try to quantify the uncertainties, how theypropagate, and identify which are the major uncertainties of concern.”

Model ensembling

Consider birth cohorts in addition to administrative records

ACCESS, VIVA

Multi-resolution analysis of PM2.5-mortality relationship

Data and computational infrastructure completeResults being generated now

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