TEHRAN UNIVERSITY OF MEDICAL SCIENCES SCHOOL … · (Irva Hertz-Picciotto in: Modern Epidemiology,...

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TEHRAN UNIVERSITY OF MEDICAL SCIENCES

SCHOOL OFPUBLIC HEALTH 1 US Iran Symposium on Air Pollution

in Megacities, 3-5th December


Application of LUR model for chronic exposure estimation to SO2 and

PM10 in Tehran, Iran

Hasan Amini

Seyed Mahmood Taghavi Shahri

Sarah Henderson

Masud yunesian

Institute for Environmental Research and School of Public Health

TUMS, Tehran, Iran

2 US Iran Symposium on Air Pollution



Outline

Introduction Acute and chronic effects of air pollution

Four generations of epidemiological studies

Limitations of existing tools

Conceptual framework of LUR

Methods

Model development

Diagnostics

Results

Limitations

Works in progress and future works

US Iran Symposium on Air Pollution


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Introduction

Health consequences of exposure to air pollution:

Acute: Time series analysis (Ecological studies) studies

Chronic: Cohort and cross sectional studies

The importance of accurate exposure measurement




Introduction Generations of air pollution epidemiology

(Irva Hertz-Picciotto in: Modern Epidemiology, 3rd Edition, 2008)

First generation studies (similar to infectious diseases): within-community comparisons using a before-and-after design







Second generation (comparing communities with higher versus lower pollutant levels): Many standards and Clean Air Act of 1970 in the United States

Individual and region specific confounders (some times >40)









Third generation (comparisons over time within a given area): Good control of individual confounders

Problem of ecological confounders

Inability to evaluate chronic effects









Third generation (comparisons over time within a given area): Good control of individual confounders

Problem of ecological confounders

Inability to evaluate chronic effects

Newer generations (cohorts in which individual-level data are integrated with community-based exposure data): Better exposure measurement (Land use regression models)




Introduction : Limitation of existing tools for exposure

measurement in Tehran

The need to capture within cities variation on a large number of people (for cohort and cross sectional studies)

Insufficient number of monitoring stations to capture all individuals

Inadequacy of existing models for evaluation of personal exposure status in Tehran




Introduction : Limitation of existing tools for exposure measurement

in Tehran-models

1. Proximity Based Assessment and Proxies

2. Geostatistical Interpolation Approaches Kriging, Spline, Inverse Distance Weighted, Theissen Triangulation

3. Dispersion Models Gaussian Plume

Eulerian (grid-cell)

LaGrangian or Puff Models

4. Integrated Meteorological-Emission (IME) Models

5. Hybrid Models




Introduction: Number of papers indexed in Pubmed by year

0

10

20

30

40

50

60

2013?2013201220112010200920082007200620052004

papers

11

US Iran Symposium on Air Pollution in Megacities, 3-5th September


Conceptual framework: How does it work

Construct a model to estimate the averaged level of given pollutant at each monitoring station (Y) using predictor variables (X)

To measure the value of each variable in the final model for any location using digitalized map

Imputing these X values in the model to get the estimation of Y value




Study Area Characteristics

•Largest and the Most Populated City of

Iran

•Resident Population is Roughly 8.7

million

•22 Districts

•Surface Area =612 km2

•Average Elevation =1200 m (Roughly)

•. . . .

13 US Iran Symposium on Air Pollution in Megacities, 3-5th

December


Methods

15

Air Pollution Data sources (Response Variables):

•Air Quality Control Company (AQCC) 16

•Department of Environment (DOE) 7

• We ran the Amelia II program (10 times for each

pollutant)

• 21 out of the 23 monitors were eligible for inclusion in the study

US Iran Symposium on Air Pollution in Megacities, 3-5th

December


Methods

17


December


Methods

18

Response variables: • Annual-mean concentrations for PM10 and

SO2 were averaged from January 01, 2010 to

January 1, 2011 for all the monitors after

imputation for missing data

Predictor Variables: • Geographic attributes that were compiled

within GIS


December


Methods

The mean of the 10 imputation-filled datasets was calculated

Warmer and cooler seasons

April through September

October through March

(Based on WHO guidelines for countries in the Northern hemisphere)




Methods

210 potentially predictive variables (PPVs) in six classes and 73 sub-classes

Traffic Surrogates

Land Use

Distance Variables

Population Density

Product Variables

Geographic Location




Methods

Traffic Surrogates

The vehicular network in buffers with

different radii around the air pollution

monitoring stations




Methods

Land Use Ten land use types within buffers around the stations:

Residential

Green space

Urban facilities

Industrial/workshop

Official/commercial

Transportation

Military

Agriculture

Arid/undeveloped

Other




Methods

Distance Variables

The distance (and natural logarithm of the

distance) from each station to all of the

Traffic Surrogate and Land Use types, and

to a variety of other features

(Due to exponential decay in air pollutant concentrations with increasing distance from pollution sources)




Methods

Population Density

The total population; and the population

excluding unemployed people and children

less than five years of age




Methods

Product Variables

The products of variables in the Traffic

Surrogates class divided by variables in the

Distance Variables class




Methods

Geographic Location

The elevation of each monitoring site,

obtained from a digital elevation model of

Tehran in meters above sea level

A slope variable




Methods

The raw GIS inputs were all in vector format

(Originating from the Japan International Cooperation Agency (JICA) and the Centre for Earthquake and

Environmental Studies of Tehran)

The final predictive variables were all in raster format with a horizontal resolution of 5 meters




Methods: Model development and diagnostics

A step-by-step algorithm considered four key pieces of information:

Consistency with a priori assumptions about the direction of the effect for each variable

A p-value of < 0.1

Increases in R2 for a leave-one-out cross-validation (LOOCV)

A multicollinearity index called the variance inflation factor (VIF)





The algorithm was programmed as a function in the R statistical package

A single variable linear regression model for each of the PPVs in the eligible pool (210 to begin)

Models check for consistency (with a priori assumptions, p-value and the variable with the strongest LOOCV R2 value





All possible second variables were added to the retained single variable model, similarly

In the third step, all possible third variables were added to the two variable model, similarly

Each variable was also removed from the model, and the LOOCV R2 value was calculated





If any of the resulting two variable models had a higher LOOCV R2 value than the model elevated from the second step, the elevated model was replaced and the third step was restarted

If not, the third step model with the highest LOOCV R2 value was elevated to the fourth step

This process was followed until the LOOCV R2

value could no longer be increased by the addition of further variables




Methods: Model development and diagnostics - LOOCV

First we built a model using 20 stations omitting the first one

Then we estimated the level air pollution of omitted station (y) using the model (x)

Then we built the model again, omitting the second station and using the remaining other 20 stations

This process was repeated for all monitoring station

The Pearson correlation coefficient and its square was used as an index




Mehtods: Model development and diagnostics

Multicollinearity:

If VIF greater than 10, that model was considered unacceptable

Sequentially removing each variable from the available pool of variables

stability check of the models:

Minimum, maximum, and coefficient of variation for the set of coefficients for the LOOCV




Methods: Regression mapping

Raster cells outside of the buffer zones as null

All null values for the Distance Variables class were set to zero

All null values for the Product Variables class were set to the maximum values for the layers

The Raster Calculator of the ArcGIS Spatial Analyst extension was used

A quantification limit (QL) for predictions at the low end of the concentration distributions, defined as the lowest measured concentration divided by square root of 2

Very high predictions were set to 120% of the maximum observed concentrations




Results

Of the 210 variables generated, 19 (9%) were significantly predictive in one or more of the LUR models

SO2 Annual: 6

SO2 cooler: 7

SO2 warmer: 7

PM10 annual:4

PM10 cooler:5

PM10 warmer:4 36 US Iran Symposium on Air Pollution



Results

The adjusted R2 ranged from 0.83 to 0.93 for SO2 and ranged from 0.53 to 0.72 for PM10

models respectively

The R2 values for the leave-one-out cross validations ranged from 0.61 to 0.82 for the SO2 models, and from 0.48 to 0.63 for the PM10 models




Results: Model stability

The minimum and maximum of the LOOCV coefficients had the same direction for all variables in all models

For SO2, the maximum coefficients of variation for the LOOCV coefficients in the annual, cooler season, and warmer season models were 12.2%, 10.2%, and 17.9%, respectively

For PM10, the maximum coefficients of variation for the LOOCV coefficients in the annual, cooler season, and warmer season models were 10.8%, 10.4%, and 8.5%, respectively




Results: Estimated annual SO2 and PM10 concentrations from the

final land use regression models




Limitations

Relatively small sample size (usually 20-100 monitoring stations have been used)

Using LOOCR R2 instead of Model adjusted R2 (as the LOOCV tends to be less in models with lower monitoring stations)

Using governmental monitoring stations (did not use location allocation approach)




Works in progress using LUR

Deterioration of Multiple sclerosis

Low birth weight

Childhood Leukemia

Breast cancer

District specific life expectancy (in 22 districts)




Future works

Construction of models for other criteria air pollutants

Construction of models capturing both spatial and temporal variations (real time LUR models)

Using location allocation approach




Aknowledgement

This was part a MS thesis at Theran University of Medical Sciences

We would like to express our appreciation to the organizers of this meeting

Also appreciate kind cooperation of: Department of Environment

Tehran Air quality Control Company

Tehran Municipality

And many other people and organizations who helped us 44 US Iran Symposium on Air Pollution



And Finally

Thank you all for your attention

and

Appreciate any questions, comments or suggestions




US Iran Symposium on Air Pollution


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TEHRAN UNIVERSITY OF MEDICAL SCIENCES SCHOOL … · (Irva Hertz-Picciotto in: Modern Epidemiology,...

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