1. CMAS special session Oct 13, 2010 Air pollution exposure estimation: 1.what’s been done? 2.what’s wrong with that? 3.what can be done? 4.how and what to evaluate?

2. what’s been done? 1.use existing monitoring network(s) 2.estimate “exposure” average monitors nearest monitor inverse-distance weighting spatial statistics (eg, kriging) 3.plug estimate into health model

3. what’s wrong? 1.basically, exposure measurement error the dogma: if non-differential, bias of health effect estimate to the null 2.what else? features of air monitoring network data intensity - spatial, temporal, components coverage - spatial, geographic covariates 3.unknown impacts on health effect estimates

4. improvements 1.to monitoring to reflect sources study subject residences/transit/indoors/personal 2.enhance value of monitoring land-use regression

5. early example Hoek et al. Lancet 2002; 360: 1203-9 1.local scale - distance to roads (BS & NO 2 ) 2.urban scale - BS/NO 2 regressed on population density (land use regression) 3.regional scale - inverse-distance weighting using population-oriented monitors

6. use of deterministic air quality models in health studies 1.attempt to address many deficiencies in using monitoring data concentrations at unmonitored locations and times unmeasured pollutants & sources 2.two (at least) ways to use them 1.have monitoring data and estimates of exposure and supplement with AQM predictions 2.start with AQM and supplement with data (“data assimilation”)

7. evaluating value added 1.performance in predicting “exposure” validation 2.impact on health effect estimates variance, coverage, bias, RMSE 3.role of simulation

8. problems with using AQMs 1.for pollutants that are relatively homogeneous spatially, we have monitoring data 2.most models smooth and therefore reduce variability addition of covariates with less variability than that of study subjects 3.for pollutants that are not spatially homogeneous, spatial scale often too large

9. additional points 1.time of exposure for cohort studies

10. = MESA Air region Chestnut Hill, MA Fall River, MA Jacksonville, FL New Brunswick, NJ Greensboro, NC Des Moines, IA Davenport, IA Phoenix, AZ La Jolla, CA Evanston, IL Epidemiology: WHI-OS and MESA Air cardiovascular cohorts

11. NPACT 2-week sampling protocols in MESA communities (in addition to CSN data) type of monitoring measurements (sampler) (1) fixed PM 2.5 (2) home outdoor PM 2.5 (3) home indoor PM 2.5 (4) personal PM 2.5 (5) traffic gradient snapshots (6) PM 2.5 /PM 10 snapshots mass, BC, elements/metals (Teflon filter) EC, OC, DTT (quartz filter) EC, OC only NO x, NO 2, O 3, SO 2 (Ogawa sampler) no O 3 no O 3 NO x & NO 2 (1) STN (collocated), roadside, household (3-7/city [26 total]) (2) rotating every 2 weeks > 50 homes/city in 2 seasons (3) rotating ~ 50/city in 2 seasons (4) rotating ~15/city in 2 seasons (5) ~100 sites/city (roadside, near road, population) in 3 seasons (6) ~ 40 sites/city (Chicago, Minneapolis, Winston-Salem) in 2 seasons

12. NPACT fixed monitoring (red) and CSN (blue) site locations in the 6 MESA cities

13. Silicon, nickel and EC concentrations at New York STN and MESA Air fixed and home outdoor sites As expected, the spatial distribution of concentrations (quintiles) is different for each species SiNiEC

14. monitoring data structure is complex

15. Approach to estimating household concentrations: estimate time trend from temporally rich CSN/STN sites ( and less rich fixed sites ) to estimate time trend ( which can vary over space, esp. for some PM 2.5 components ) at home outdoor sites then, remove time trends from spatially rich ( but temporally sparse ) home outdoor measurements to allow spatial information to be used use spatio-temporal modeling methods, incorporating geographic information

16. Long-term EC concentrations at individual monitoring sites adjusted for temporal trend in LA solid black line = temporal trend dotted black line = mean of trend black dot = observed value at monitoring site red circle = temporal adjustment for observed value red line = adjusted mean at monitoring site

17. Measurements Questionnaires Predictions the NPACT study individual exposure estimation