Characterizing Spatial Patterns of Air Pollution

Transcription

1 Characterizing Spatial Patterns of Air Pollution Ryan Allen Assistant Professor Faculty of Health Sciences Simon Fraser University Workshop on Visualization and Communication of Climate Change Risk April 14, 2009

2 Presentation Overview Why characterize spatial patterns of air pollution? Air pollution epidemiology Two examples Traffic-generated air pollution Residential wood stove emissions

3 Why Characterize Spatial Patterns? Exposure assessment for epidemiology Risk assessment Explore links with health and determine concentration-response relationships Chronic effects studies Identify individual source effects on health (e.g. traffic) Determine (sensitive) populations exposed, exposure levels, etc. Risk management Make decisions about how to best eliminate or minimize health risks

4 Air Pollution Epidemiology Chronic Exposure Studies Harvard 6 Cities Study: Prospective cohort study of ~8,000 adults years followup Assume one exposure level per city Elevated mortality risks associated with PM2.5 concentration Dockery et al., 1993

5 Air Pollution Epidemiology Chronic Exposure Studies We observed [PM2.5] effects nearly 3 times greater than in [studies] relying on comparisons between communities.

6 Air Quality Monitoring Stations Metro Vancouver, 2007 Lower Fraser Valley Air Quality Report

7 Traffic-Generated Air Pollution Zhu et al., 2004

8 Land Use Regression (LUR) Slide courtesy of Michael Brauer, UBC

9 Steps in LUR Model Development Edmonton, AB and Winnipeg, MB 1. Select monitoring sites 2. Measure indicator pollutant 3. Adjust for temporal trends 4. Calculate relevant predictor variables 5. Develop LUR models 6. Estimate exposure for study participants -Birth cohort study of asthma and allergy

10 Selection of Sampling Locations Location Allocation Approach Create demand surface Semivariance of assumed NO2 concentration Weighted by population density Algorithm to determine locations of 50 monitors that attend to greatest total demand Result: samplers in areas of concentration gradients and relatively high population density, spread throughout the study area Kanaroglou, Atmos Environ, 2005 Edmonton, AB

11 14-Day Sampling

12 Edmonton Weekly Avg NO (ppb) 2 Adjusting for Temporal Trends Annual Avg ppb Session E ppb Adjust for temporal trends: e.g., multiply all Edmonton round 1 NO2 measurements by 0.68 (or 26.1 / 17.8) 2. Average the two detrended measurements from each of the 50 locations Jul-07 Winnipeg Weekly Avg NO2 (ppb) Session E ppb Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul Session W ppb Annual Avg. 9.8 ppb Session W ppb Session W3 6.5 ppb Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08

13 Calculating Predictor Variables m m 0 5 Sampling Location Slide courtesy of Michael Brauer, UBC

14 Potential Predictors 75 Variables Screened in LUR Model Development Category Units Buffer Radii (m) Subcategory Land Use Hectares in circular buffer 300, 400, 500, 750, 1000 Water Commercial Residential Government Industrial Open Road Length KM in circular buffer 50, 100, 200, 300, 500, 750, 1000, 1500 All Roads Major Roads Highways Pop. Density Persons per hectare 750, 1000, 1250, 1500, 2000, 2500 Latitude Longitude Location Abbreviation # of Variables WTR COM RES GOV IND OPN 30 RD MJR HWY 24 DENS 6 Y X 2 Distance to City Centre Kilometers DCC 1 Elevation Meters ELEV 1 Proximity to Major Road KM to nearest log (KM to nearest) MJRdist logmjrdist 2 Proximity to Highway KM to nearest log (KM to nearest) HWYdist loghwydist 2 Point Sources Number in circular buffer PS 6 Proximity to Point Source M to nearest PSdist , 2000, 2500, 3000, 4000, 5000

15 City-Specific Models City Pollutant N Model Model R2 LOO R Edmonton NO (DCC) 0.09(WTR.1000) (IND.1000) (MJR.50) (HWY.500) (RD.200) Winnipeg NO (Y) (IND.400) (MJR.100) 1.23(logHWYdist) LOO = leave one out

16 Predicted NO2 Surfaces Winnipeg Edmonton

17 Residential Wood Burning

18 Woodstove Exchange Study Series of studies evaluating environmental & health impacts of a woodstove exchange program in the Bulkley Valley & Lakes district of BC One goal to identify air pollution hot spots to target during intervention sub-study

19 Mobile Monitoring Woodsmoke tracers exist, but require expensive sampling equipment & analysis Not feasible to measure at many locations GPS Receiver Air Inlet Mobile measurements of non-specific pollutant (fine particles) during times when woodsmoke peaks (evening and Larson et al., 2007

20 Smithers, BC Drive a predetermined route on cold, clear nights Adjust for temporal trends using a fixed monitor Repeat over many nights to identify spatial patterns Slide courtesy of Gail Millar, UNBC

21 Summary Useful to characterize air pollution spatial patterns for epidemiology, risk assessment, risk management Substantial variability in concentrations over 10s 100s of meters for some pollutants, sources Routine monitoring networks not adequately dense Modeling and mobile sampling approaches useful for capturing smallscale variations

22 Funding