Traffic Air Pollution and Socio-Economic Status. Gregory C Pratt PhD Kristie Ellickson PhD

Transcription

1 Traffic Air Pollution and Socio-Economic Status Gregory C Pratt PhD Kristie Ellickson PhD

2 Outline Exposure and Risk Metrics Traffic Density MNRiskS Socio-Economic Status Metrics Relationships

3 BACKGROUND: Living near traffic increases exposure to air pollution and is associated with adverse health outcomes. Minorities and populations with lower socio-economic status appear to be disproportionately exposed to traffic and air pollution. They are also more vulnerable and have an increased risk of adverse health outcomes. Minnesota Historical Society, Hennepin & Lyndale, 1937

4 How is traffic exposure quantified? Distance to a major road Traffic counts within specified distances Roadway Kilometers (RK) within specified distances (+/- by type of roadway) Vehicle Kilometers Travelled (VKT) within specified buffer distances RK or VKT within census block boundaries Others We used a GIS-based kernel density algorithm to develop a traffic density surface with a 50 m grid over the state of Minnesota.

5 Minnesota Department of Transportation GIS shapefile of every roadway segment Annual Average Daily Traffic Counts Heavy Commercial AADT Minneapolis-St Paul Metropolitan Area roadways colored by AADT

6 Traffic density traffic exposure Hawth s tools kernel density algorithm (bivariate normal kernel function) Value at each point determined by all traffic on all roads within the distance defined by the kernel function, weighted by distance Minneapolis-St Paul Metropolitan Area

7 Highest Traffic Density in MN Gaussian algorithm with parameters chosen so that 95% of the density impact within 300 m of roadway

8 To compare traffic density with demography the density can be averaged by block group

9 First Environmental Risk Metric = Traffic Density Averaged by Block Group

10 Second Set of Environmental Risk Metrics = MNRiskS 1. Emissions inventory 235 pollutants from point, non-point, and mobile sources 2. Air dispersion & deposition modeling Concentrations & deposition at 60,313 locations in MN 3. EPA multimedia fate methods Water, soil, plants, animals, humans (HHRAP) 4. Dose and risk estimation Inhalation, ingestion, multi-pathway exposure Residents, farmers, fishers, adults, children Toxicity values: MN Health Dept, IRIS, CalEPA, PPRTV We used inhalation risks in this analysis

11 Minnesota Department of Transportation GIS shapefile of every roadway segment Annual Average Daily Traffic Counts Heavy Commercial AADT Minneapolis-St Paul Metropolitan Area roadways colored by AADT

12 High Traffic Corridors Represented as 100 Meter Square Volume Sources in AERMOD air dispersion model (6,372 sources) Emissions were a function of the VKT on each roadway segment

13 All other (non high traffic corridor) mobile source emissions were apportioned to block groups according to VKT

14 MNRiskS Inhalation Noncancer Hazard Index Point Sources Area Sources Onroad Mobile Sources Nonroad Mobile Sources

15 Point Sources Area Sources MNRiskS Inhalation Cancer Risk Onroad Mobile Sources Nonroad Mobile Sources

16 Here: Cancer risk from on-road mobile sources Second Set of Environmental Risk Metrics = MNRiskS results at 60,613 receptors were averaged by block group

17 Census block group demographic variables Can we predict risk using these variables? Variable POV100RATE frx_nonwhite frx_nativeamerican frx_black frx_asian frx_hispanic frx_lesshs pop_density frx_drovealone vehicles_hh HOMEOWNPCT MEDHOMEVAL MEDIANHHI Description Fraction of population below poverty level Fraction of population that identifies as non-white Fraction of population that identifies as Native American Fraction of population that identifies as Black Fraction of population that identifies as Asian Fraction of population that identifies as Hispanic Fraction of population with less than High School Education Population density (number/km2) Fraction of population commuting alone Vehicles per household Percent home ownership Median home value Median household income Age and gender were examined and others will be explored in future

18 Kendall s Tau-b Nonparametric Correlations estimates of pollutant exposure and risk measures of minorities and vulnerable SES measures of higher SES HI_area_sources_MNRiskS Cancer_all_sources_MNRiskS HI_all_sources_MNRiskS MEAN_traffic density Cancer_nonroad_sources_MNRiskS HI_nonroad_sources_MNRiskS Cancer_area_sources_MNRiskS Cancer_point_sources_MNRiskS HI_point_sources_MNRiskS Cancer_onroad_sources_MNRiskS HI_onroad_sources_MNRiskS frx_nativeamerican frx_nonwhite POV100RATE frx_black frx_hispanic frx_asian pop_density frx_lesshs vehicles_hh frx_drovealone MEDIANHHI MEDHOMEVAL HOMEOWNPCT MEAN_traffic density HI_all_sources_MNRiskS.388** Cancer_all_sources_MNRiskS.442**.859** HI_area_sources_MNRiskS.310**.827**.733** Kendall's Tau-B nonparametric correlations block groups in 7-county metro area Cancer_area_sources_MNRiskS.310**.785**.709**.827** **positive p<0.01 HI_nonroad_sources_MNRiskS.333**.773**.748**.793**.685** **positive correlation p<0.01 Cancer_nonroad_sources_MNRiskS.333**.704**.705**.723**.610**.902** **negative correlation p<0.01 HI_onroad_sources_MNRiskS.460**.736**.791**.594**.646**.554**.507** no significant correlation **negative correlation p<0.01 Cancer_onroad_sources_MNRiskS.470**.704**.781**.564**.613**.532**.488**.918** HI_point_sources_MNRiskS.271**.667**.557**.608**.598**.544**.483**.538**.516** Cancer_point_sources_MNRiskS.297**.588**.552**.535**.573**.526**.491**.524**.520**.588** POV100RATE.114**.190**.191**.179**.181**.189**.187**.170**.165**.132**.132** frx_nonwhite.250**.365**.365**.364**.375**.347**.340**.335**.325**.256**.292**.177** frx_nativeamerican.085**.112**.119**.099**.103**.111**.109**.104**.104**.069**.065**.083**.194** frx_black.249**.339**.335**.332**.346**.307**.297**.331**.316**.253**.263**.171**.588**.135** frx_asian.113**.130**.142**.134**.122**.135**.145**.116**.116**.053**.066**.103**.382**.041*.172** frx_hispanic.130**.181**.182**.186**.199**.186**.186**.155**.149**.124**.191**.060**.386**.113**.179**.043** frx_lesshs.132**.215**.220**.215**.223**.214**.211**.201**.190**.127**.147**.142**.397**.156**.318**.131**.248** pop_density.224**.507**.460**.550**.656**.419**.364**.476**.454**.407**.423**.150**.338**.095**.318**.093**.197**.181** frx_drovealone -.119** -.242** -.228** -.225** -.244** -.219** -.202** -.224** -.214** -.216** -.192** -.109** -.161** -.079** -.144** ** -.235** -.170** vehicles_hh -.329** -.480** -.459** -.477** -.507** -.456** -.429** -.425** -.405** -.374** -.362** -.173** -.341** -.115** -.344** -.083** -.176** -.247** -.397**.338** HOMEOWNPCT -.308** -.370** -.364** -.365** -.381** -.366** -.351** -.332** -.314** -.265** -.275** -.191** -.361** -.150** -.358** -.109** -.217** -.284** -.308**.209**.561** MEDHOMEVAL -.180** -.296** -.305** -.308** -.323** -.301** -.293** -.276** -.264** -.179** -.216** -.107** -.342** -.130** -.302** -.100** -.186** -.420** -.266**.123**.320**.247** MEDIANHHI -.278** -.402** -.399** -.400** -.422** -.391** -.374** -.371** -.351** -.273** -.292** -.220** -.399** -.151** -.376** -.097** -.222** -.438** -.329**.261**.570**.560**.521** County Metro Area

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20 Stepwise linear regression (log-log) 7 County Metro estimates of pollutant exposure and risk measures of minorities and vulnerable SES measures of higher SES MEAN_traffic density All block groups 7 county metro Stepwise linear regression coefficients, log transformed data Cancer_all_sources_MNRiskS HI_all_sources_MNRiskS Cancer_area_sources_MNRiskS HI_area_sources_MNRiskS Cancer_nonroad_sources_MNRiskS HI_nonroad_sources_MNRiskS Cancer_onroad_sources_MNRiskS HI_onroad_sources_MNRiskS Cancer_point_sources_MNRiskS HI_point_sources_MNRiskS POV100RATE frx_nonwhite frx_nativeamerican frx_black frx_asian frx_hispanic frx_lesshs pop_density frx_drovealone vehicles_hh HOMEOWNPCT MEDHOMEVAL MEDIANHHI R 2 -adj p<0.01, positive coefficient p<0.01, negative coefficient no shading 0.01 p<0.05

21 Vehicles per Household Is Negatively Related to Traffic Density and to Cancer Risk from On-road Mobile Sources

22 Fraction Identifying as Black Is Positively Related to Traffic Density and to Risk from Onroad Mobile Sources

23 CONCLUSIONS Measures of environmental impairment (traffic exposure and resulting risks from air pollution) were positively related to one another and to low SES and some minorities (e.g., poverty, the fraction nonwhite population, and population density) were negatively related to high SES (e.g., home ownership, median home value, the number of vehicles per household, driving alone, and median household income) People on the lower end of the SES spectrum and some minorities are disproportionately exposed to traffic and air pollution and as a consequence are at higher risk for adverse health outcomes

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25 Independent variables Poisson regression (dependent = asthma exacerbations in Olmsted County) Traffic Density (Kernel density) OR (95% CI) VKT in 250m buffer OR (95% CI) VKT in 500m buffer OR (95% CI) Age 1.00 ( ) 1.00 ( ) 1.00 ( ) Gender (Female vs Male) 1.23** ( ) 1.22** ( ) 1.22** ( ) Poverty (block group income to poverty ratio) 6.31** ( ) 4.66** ( ) 6.51** ( ) Traffic Density 1.07** ( ) **p< ** ( ) 1.07** ( )

26 Car Bicycle Which mode has the highest exposure?

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29 Poverty 150 rate by Block Group

30 Mobile & Non-Point (Area) Source Subcategories Cars On-Road Mobile Sources Buses Trucks (diesel and gasoline) Non-Road Mobile Sources Airports Commercial Marine Vessels Pleasure Craft Rail Yards Rail Operations Agricultural Equipment Recreational Equipment Lawn & Garden Equipment Construction & Mining Equipment Industrial Equipment Commercial Equipment Logging Equipment Area (Nonpoint) Sources Feedlots Industrial, Commercial, and Institutional Boilers Composting Animal Cremation Food & Kindred Products Health Services Miscellaneous Roadway Emissions Miscellaneous Agriculture & Pesticide Emissions Non-industrial Consumer & Commercial Activities Open Burning Petroleum & Products Storage & Transport Residential Fuel Combustion Residential Wood Combustion Outdoor Wood Boilers Residential Wood Combustion Indoor Residential Wood Combustion Outdoor Recreation Surface Coating Agricultural Fires Wildfires & Prescribed Forest Burning

31 At each risk receptor: MNRiskS 2008 will use nested receptor grid (300 m in Metro, 3 km outstate) plus block group centroids plus air concentration maxima for all stacks (~60,000 receptors) Concentrations of every pollutant from every source in every medium Risks from every exposure pathway to every type of individual (Resident/Farmer/ Fisher/Adult/Child)

32 Central Corridor Light Rail Study Mpls St Paul