# Presentation: 379871

## Assessing Traffic-Related Exposures in the California Environmental Health Tracking Program

- Presented at the 2005 National Air Quality Conference,
- San Francisco, CA, February 14, 2005
**Paul English, PhD MPH, Principal Investigator**
**Environmental Health Investigations Branch**
**California Department of Health Services**

## Acknowledgements

- Environmental data analysis
- Svetlana Smorodinsky, MPH
- Zev Ross, MS
- Michael Jerrett, PhD
- Rusty Scalf
- Kaiser Permanente of Northern California
- Stephen K. van den Eeden, PhD
- G. Thomas Ray, MS
- Demonstration Pilot Manager
- Eric Roberts, MD, PhD
- Administration
- Maile Newman

- Research Director
- Geoff Lomax, DrPH
- Project Coordinator
- Mimi Johnson, MPH
- Community Health Education
- Eddie Oh, MPH
- Michelle Wong, MPH
- Information Technology/DBM
- Craig Wolff, MS Eng
- Samuel Valdez
- Makinde Falade, MS-GIS

- Funding provided by the Centers for Disease Control and Prevention
- and the National Cancer Institute

## EPHT Program

***Environmental public health tracking is the ongoing collection, integration, analysis, and interpretation of data about environmental hazards, exposure to environmental hazards and human health effects potentially related to exposures to environmental hazards.***

## The CDC Program Grantees

## CEHTP Demonstration Projects

**Alameda County**
*Funded 2002*
- Adverse birth outcomes
- Asthma
- Motor vehicle traffic

**Central Valley and South**
**Coast Air Basins**
*Funded 2003*
- Adverse birth outcomes
- SIDS
- Idiopathic mental retardation
- Autism spectrum disorders
- Commercial pesticide applications
- Air toxics
- Lead biomonitoring

## The significance of traffic-related pollutants

- Traffic contributes to high proportion of overall air pollution burden
- e.g.: 99% of CO burden and 76% of NO2 burden in London attributable to traffic
- Major source of greenhouse gas emissions (CO2)
- Increasing recognition of association with wide spectrum of health effects

## Health Effects Associated with Motor-Vehicle Emissions

**Motor-Vehicle**
**Emissions**

**Benzene**

**Childhood Leukemia**

**CO, PM**

**Fetal Hypoxia, Growth,**
**Birth weight, Prematurity, **
**Birth defects**

**NO****2****, Ozone,**
** ****PM, DEPs**

**Respiratory Illness,**
** ****Lung Function**

- Lung Cancer

- PM, DEPs

**Cardiovascular **
**Disease**
**Premature death**

**nitration of pollen**
**proteins - allergies**

## Importance of predicting exposure at intra-urban scale

- Assessing exposure to air pollution *within *cities may be associated with larger health effects than between cities
- e.g. Doubling of cardiovascular mortality for Dutch subjects living near busy roads (Hoek et al 2002)
- Fixed air monitors used for regulatory purposes to pick up background levels (i.e. primarily not located near busy roads)

## Simple

- Complex

- Traffic density at census block group

- Limitations:

- Misclassification
- (e.g. wind)

- Distance-weighted traffic volume

- Misclassification
- (e.g. building heights, wind)

- Residence near fixed air monitors

- Assumes homogenous
- Exposure; underestimate
- of exposure

- GIS w/ dispersion modeling
- GIS w/ regression-based models

- More data inputs (e.g.
- meteorology, building
- configurations, emissions,
- etc.)

- More complex modeling
- (e.g. ADMS- Urban)

- Computationally
- complex, cost

- Simple to Complex Measures of Traffic-Related Pollutants

## Orange County, CA

## Traffic density –Evaluation results

| Air Pollutant | CO | 1,3 – Butadiene | Benzene |
| --- | --- | --- | --- |
| Traffic Density (log)
r | 0.65 | 0.56 | 0.67 |

- Source: Hertz et al, 2000

## Distance-Weighted Traffic Flow

- Personal and outdoor measurements of NO2 have been found to be strongly related to distance to nearby roads and degree of traffic density (Rijnders, 2001)

## Slide 13

##   Residence of Case 

**Legend**
-   **Residence of Case **
** **** ****Residence of Control**
** ****ADT Road Segment and associated volume (cars/day)**
**Distance of Residence from ADT Segment**
- -** 550 ft Buffer Boundary**

## Slide 15

## Source:  English et al. 1999

## Land Use Regression(Briggs)

-   Using traffic flow, land use, census data, etc. to predict NO2 concentrations using regression models

## Sampling Locations

**Sampling Locations**

- Palmes Tubes (NO2)
- N=38 Range: 8.1-28.7 ppb;
- Mean=15.0; sd=4.7

- San Diego, CA

## Twelve Removed

**Twelve Removed**
**for Validation**

## Define Buffers

- 500 m

- 1000 m

- 300 m

- Field sampling
- location

- 40 m

**Define Buffers**

## Clip to Buffers

## Final Model

*R-Squared 79%*

## Predictions

*On average predicts *
*to within 2.1 ppb*

- All Validation Samples

## Slide 24

## ADMS-Urban (v. 2)

- Integrated Meteorological Emission Model
- Incorporates boundary layer effects, photochemistry, and dispersion
- Run with 10 modeling domains, each 14.4 sq km
- Inputs: traffic count data; areas sources; point sources to 1 ton/year (inc. height and diameter of stacks); met data

## Modeled total NOx for 2000, San Diego County

## Asthma ER visits by children

**Asthma ER visits by children**
**Kaiser Permanente & Medi-Cal**
**Alameda County, 2001**

- California Environmental Health Tracking Program

## Conclusions

- Several methods exist to assess exposure to traffic-related pollutants – need to be assessed re:
- Amount of exposure misclassification
- Evaluation results from field validation studies
- Model assumptions, data inputs, cost

## Conclusions

- Emphasis should continue on attempts to model intra-urban variability of traffic related pollutants due to potential large health impacts
- Range of endpoints should be explored, including cancer, CVD, and reproductive, in addition to respiratory