Slides - CMAS

Halûk Özkaynak US EPA, Office of Research and Development

National Exposure Research Laboratory, RTP, NC

Presented at the CMAS Special Symposium on Air Quality

October 13, 2010

Evaluating the Uncertainty of Land-Use Regression Models

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Land-Use Regression (LUR) Models

PointPointSourcesSources

LineLineSourcesSources

AreaAreaSourcesSources

PointPointSourcesSources

LineLineSourcesSources

AreaAreaSourcesSources

• Widely-used methodology for estimating individual exposure to ambient air pollution in epidemiologic studies

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• Able to capture smaller-scale variability in community health studies

• Less resource intensive – – Easier to develop and apply compared with

other methods for measuring or estimating subject-specific values (e.g., household measurements, physical modelling)

• Land-use data widely available

LUR Strengths

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• Inputs– Require accurate monitoring data at large number of sites - e.g., in

highly industrialized urban areas with many types of emission sources • Application in health studies

– Not transferable from one urban area to another– Do not address multi-pollutant aspects of air pollution – Lack the fine-scale temporal resolution needed for estimating short-

term exposure to air pollution– Often estimate ambient air pollution only versus indoor and personal

• Lack the ability to connect specific sources of emissions to concentrations for developing pollution mitigation strategies

LUR Limitations

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• Use air pollution predicted by coupled regional (CMAQ) and local (AERMOD) scale air-quality models

• Develop and evaluate land-use regression models for:– Benzene– Nitrogen oxides (NOx)– Particulate matter (PM2.5)

• Examine (in future) the implications of alternate LUR development strategies on model efficacy for multiple pollutants

Analysis Goals: New Haven Case Study*

Source: Johnson, M., Isakov, V., Touma, J.S., Mukerjee, S., and Özkaynak, H. (2010). Evaluation of Land Use Regression Models Used to Predict Air Quality Concentrations in an Urban Area. Atmospheric Environment, Vol. 44, pp: 3660-3668.

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• Air pollution concentrations were predicted at 318 census block group sites in New Haven, Connecticut using a coupled air quality model (Isakov et al., 2009)

Isakov et al. 2009. Journal of the Air and Waste Management Association; 59(4):461-472.

• Predicted daily concentrations for 2-month periods in winter and summer (2001) were used to calculate seasonal average concentrations for benzene, NOx, and PM2.5 at each site– July- August for summer– January- February for winter

• Annual averages were based on 365 daily means for 2001

Air Pollution Data

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Dependent Variables Independent (Predictor) Variables

Pollutant ConcentrationsBenzene, NOx, and PM2.5 Predicted

by Coupled Regional and Local Scale Air Quality Models

=Traffic

Intensity andProximity to Roadways

Proximity to Industrial Sources

Proximity to Ports and Harbors

Population and Housing

Density+ + +




=Traffic





Density+ + +




=Traffic





Density+ + +Pollutant Concentrations

Benzene, NOx, and PM2.5 Predicted by Coupled Regional and Local

Scale Air Quality Models=

Traffic Intensity andProximity to Roadways




Density+ + +

• Traffic intensity near the home (vpd/km2)

• Proximity (1/distance) to major roadways

• Proximity (1/distance) to seaports

• Proximity (1/distance) to harbors

• Population density in census block group

• Housing density in census block group

• Proximity to industrial emitters of:

–Benzene–NOx–PM2.5

• Traffic intensity near the home (vpd/km2)

• Proximity (1/distance) to major roadways

• Proximity (1/distance) to seaports

• Proximity (1/distance) to harbors

• Population density in census block group

• Housing density in census block group

• Proximity to industrial emitters of:

–Benzene–NOx–PM2.5

• Multivariate linear regression models• Initial pool of 60 potential predictors • Eliminated variables based on

– High correlation (R-squared ~1.0) with other selected predictors and/or

– Lack of interpretability

LUR Model Structure and Inputs

19 land-use variables included in model selection

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• Sites– Census block group centroids

• Training Sites– Sites used to fit LUR models– Selected from 318 census block

groups in the study area– Stratified random selection among 4

census regions• Test Sites

– Remaining sites withheld from training set - minimum of 10%

used for independent model evaluation

Site Selection

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• Variable selection– Examined correlation structure for predictive variables

• Model development– All subsets with 3-7 independent predictors– Model selection based on AIC, Mallow’s C(p), adjusted r-squared, and variance

inflation factor• Model evaluation

– Cross-validation within training dataset– Hold-out evaluation within test dataset

• Models for multiple pollutants and training sites– Benzene, NOx, PM2.5– 25, 50, 75, 100, 125, 150, 200, and 285

• Automated, iterative process– Site selection -> model development– Repeated 100x for each pollutant and number of training sites

Model Development and Evaluation

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0

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0 25 50 75 100 125 150 175 200 225 250 275 300

Number of Sites in Training Dataset

Prop

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Var

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(R2)

RSQ Predicted vs Observed Benzene in Test DatasetRSQ LUR Models for Benzene in Training Dataset

Model Performance in Test versus Training Sites: Benzene

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RSQ Predicted vs Observed NOx in Test DatasetRSQ LUR Models for NOx in Training Dataset

Model Performance in Test versus Training Sites: NOx

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0 25 50 75 100 125 150 175 200 225 250 275 300


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RSQ Predicted vs Observed PM2.5 in Test DatasetRSQ LUR Models for PM2.5 in Training Dataset

Model Performance in Test versus Training Sites: PM2.5

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LUR Prediction Errors: NOx

• Prediction error =– Average (+/- SD) of mean predicted minus

observed input values – For 100 iterations - aka

100 LUR models• Analyzed by low, medium,

and high NOx concentration based on total NOx distribution– Low = 0 - 25th percentile– Medium = 25th - 75th – High = 75th - max

)F

Rotterdam Area LUR versus Dispersion Model (Hoek et al., 2010)

Dispersion Model

LUR Model

F ull modelL O O C V

20 locations 40 locationsadj R 2 adj R 2 adj R 2 adj R 2

2005:NO x 0.63 0.61 - 0.66 0.52 - 0.68 0.58 - 0.70NO 2 0.69 0.67 - 0.72 0.60 - 0.78 0.64 - 0.76NO 0.57 0.53 - 0.59 0.45 - 0.61 0.51 - 0.65

2008:NO x 0.62 0.59 - 0.65 0.55 - 0.70 0.65 - 0.67NO 2 0.70 0.68 - 0.72 0.56 - 0.76 0.70 - 0.74NO 0.56 0.51 - 0.59 0.53 - 0.65 0.57 - 0.61

ValidationT raining s ets *

LUR Model Evaluation in Oslo from Hoek et al., 2010Courtesy: Christian Madsen (Oslo)

Comparison of Two LUR Models for Amsterdam(Hoek et al., 2010)

Comparison of Two LUR Models for Amsterdam Denoting Sites Impacted by Traffic or Urban Sources

(Hoek et al., 2010)

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Summary and Conclusions• We used air pollution concentrations predicted by coupled regional and local scale AQ models to

develop and evaluate LUR models in New Haven, CT for benzene, PM2.5, and NOx

• Model performance and robustness improved as number of sites used to build the models increased– R-squares were inflated for models based on pollutant concentrations from 25 trainings sites compared

with models based on 100 -285 training sites– R-squared for LUR model (training dataset) and R-squared predicted versus observed (test dataset)

converged as training sites increased• It is critical to evaluate LUR performance using site-specific independent measurement data sets• Analysis suggests that coupled air quality models could provide a useful tool for improving LUR

estimates of exposure to ambient air pollution in epidemiologic studies• LUR model performance may be considerable poorer than emissions based modeling results for

urban environments with complex sources and landscape characteristics

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• Markey Johnson• Vlad Isakov• Joe Touma• Shaibal Mukerjee• Luther Smith (Alion Incorporated)• Ellen Kinnee (Computer Science Corporation)

Acknowledgements*

*Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy

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Additional Slides

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Mean Contribution of Land-Use Factors in Benzene Models

10%

20%

44%

11%

13% 2%

Models Based on 25 Training Sites

27%

48%

5%

15%5% 0%

Models Based on285 Training Sites

22%

38%

20%

12%8% 0%


Intercept

Traffic Intensity (vpd/km2)

Proximity to Roadways



Population and Housing Density

LEGEND

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15%

24%

38%

21%

1%

1%

32%

48%

5%

15%

0%

0%



28%

38%

17%

16%

1%

0%


Intercept






LEGEND

Mean Contribution of Land-Use Factors in NOx Models

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80%

9%5% 6%

0%

0%

73%

7%

9%8%

2%

1%

83%

11% 1% 5%

0%

0%




Intercept






LEGEND

Mean Contribution of Land-Use Factors in PM2.5 Models

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