critical issues of exposure assessment for human health studies of air pollution

Critical Issues of Exposure Assessment for Human Health Studies of Air PollutionMichelle L. BellYale UniversitySAMSISeptember 15, 2009

OutlineBasic health effects modelMethods of measuring exposureKey challenges in assessing exposureSpatial misalignmentMultiple pollutant exposuresSpecial case of particulate matterCurrent and upcoming approaches to estimating exposureOther challenges

Exposure Assessment for Studies of Air Pollution and HealthBasic health effects modelMethods of measuring exposureKey challenges in assessing exposureSpatial misalignmentMultiple pollutant exposuresSpecial case of particulate matterCurrent and upcoming approaches to estimating exposureOther challenges

Example Air Pollution and Health Effects ModelTime-series model / Acute exposureCommunity-aggregated health dataCommunity-aggregated exposure data

Example Air Pollution and Health Effects ModelEstimated Air Pollution Exposure

Use of ambient monitors+ cost-effective+ can provide large sample sizenot present in all times or locations of interestlocations based on regulatory, not scientific, purposesobscures between-person differences

Personal monitors+ individualized datashort timeframesmall populationlogistical concernsTraditional Approaches to Exposure AssessmentSource: Louisville, KY governmentJiang and Bell EHP 2008

Traditional Approaches to Exposure AssessmentUse of ambient monitors+ cost-effective+ can provide large sample sizenot present in all times or locations of interestlocations based on regulatory, not scientific, purposesobscures between-person differences

Personal monitors+ individualized data+ measure pollutant characteristics of interestshort timeframe and small populationlogistical concerns and expensive

Spatial MisalignmentSpatially heterogeneity in the concentration surface fieldMismatch between data used to estimate exposure and actual subjects locations

Correlation of PM2.5 components by distancePeng and Bell Biostatistics Accepted

Spatial Disconnect in Data

Error ModelPollution data measured at a single or multiple fixed locationsPeng and Bell Biostatistics Accepted

Spatial Misalignment AdjustmentMonitor average ( ) good proxy for true value ( xt ) with good monitor coverage and/or low spatial heterogeneitySpatial misalignment adjustments useful when:Pollutant very spatially heterogeneous (e.g., EC)Poor monitor coverage within area of interest, but monitors elsewhere

Multi-Pollutant ConceptsPhysiologically we respond to a complex mixture of air pollutantsMany studies focus on the effects of a single pollutantAdditional pollutants typically considered with respect to confounding, not complex effectsAir pollution policy set for single pollutantsBased on single pollutant science

Major Air Pollution Emissions Sources Industrial SourcesDomestic SourcesPowerGenerationDieselGasolineSTATIONARYSOURCESVEHICLESOURCES

Particulate MatterWhat is represented by an exposure estimate for PM?Only pollutant regulated without regard to chemical formMay vary in:SizeShapeChemical structureWater contentAcidityAgeEtc.

Different chemical components by PM sizeDifferent sources by PM size

PM as a Pollutant MixturePM2.5 sulfate (2000-2002)Bell et al. EHP 2007

Bell et al. EHP 2007

% Change in Hospital Admissions per 10 mg/m3 PM2.5Respiratory InfectionCOPDHeart FailureHeart RhythmIschemic Heart Disease Peripheral Vascular DiseaseCerebrovascular DiseaseDominici et al. JAMA 2006PM2.5 and Medicare Hospital Admissions

Allow Temporal Variation in Effect Estimates Based on Variation in Exposure

Season Interaction ModelAllows different effect estimates by seasonHarmonic ModelAllows effect estimates to differ throughout the yearBell et al. Am J Epidemiol 2008

Seasonal Variation in PM Total MassHealth Effect EstimatesBell et al. Am J Epidemiol 2008Day of the year

Example: Air Quality Modeling to Estimate ExposureCMAQ / MM5Aug 15 18, 19952,168 cells with 4 km horizontal resolution8 monitors for ozoneBell Environ Int 2006

Bell and Ellis J Air Waste Manage Assoc 2003

Chart1

6064.141

5252.889

5149.654

5153.184

5149.157

4747.141

4148.317

4146.502

3243.277

3140.952

2738.577

2834.934

3231.522

4635.968

6146.305

7055.664

8068.408

8980.43

9987.333

10290.288

10391.152

9891.801

8891.71

7589.205

6784.783

6575.076

6272.638

6169.875

5663.918

4460.251

3621.716

296.573

1414.804

151.694

2023.83

2937.367

3841.058

5042.468

33052.374999999950.087

33052.416666666659.919

6762.557

7063.638

7266.194

7069.214

6571.173

6172.435

6571.504

6069.978

3568.375

5666.49

6260.338

6049.165

5064.446

5963.712

5360.677

4256.361

4452.923

3547.329

2840.469

3932.08

4443.023

6263.815

7282.429

7294.179

81103.699

96110.599

118113.655

131101.288

12596.651

11397.685

9398.788

79101.818

71101.579

Location: Millington, Maryland

Monitor Measurements

Model Estimates

Local Time

O3 (ppb)

Figure4

Figure 4. Comparison of Model-Estimated Ozone Concentrations and Monitor Measurements

Figure4

6064.141

5252.889

5149.654

5153.184

5149.157

4747.141

4148.317

4146.502

3243.277

3140.952

2738.577

2834.934

3231.522

4635.968

6146.305

7055.664

8068.408

8980.43

9987.333

10290.288

10391.152

9891.801

8891.71

7589.205

6784.783

6575.076

6272.638

6169.875

5663.918

4460.251

3621.716

296.573

1414.804

151.694

2023.83

2937.367

3841.058

5042.468

33052.374999999950.087

33052.416666666659.919

6762.557

7063.638

7266.194

7069.214

6571.173

6172.435

6571.504

6069.978

3568.375

5666.49

6260.338

6049.165

5064.446

5963.712

5360.677

4256.361

4452.923

3547.329

2840.469

3932.08

4443.023

6263.815

7282.429

7294.179

81103.699

96110.599

118113.655

131101.288

12596.651

11397.685

9398.788

79101.818

71101.579


Model Estimates

Local Time

O3 [ppb]

Millington, MD

Figure5a

3815.19

5216.981

4628.224

4628.456

4417.655

432.097

400.004

380

330

280

210.381

33051.251.53

2110.788

1034.956

4851.042

8559.422

9865.219

8868.839

8370.157

7969.372

7064.506

4844.508

2539.712

3950.407

3310.093

210.181

200

220

300

273.947

1722.298

1718.679

1910.125

122.08

120.856

142.905

1115.792

2832.009

3743.765

5654.447

7964.591

7669.907

9473.034

9874.987

10477.482

11979.266

10374.606

9166.267

7137.139

420.178

400.055

434.473

310.383

360

380

450

390

240

110.287

33053.24999999990.883

1012.539

3545.704

6973.26

8678.622

9676.689

9177.194

8379.87

8081.913

7784.409

7783.831

7653.609

705.595

577.132


Model Estimates

Local Time

O3 [ppb]

S. 18th and Hayes St., VA

Figure5b

8011.32

710.212

610

620

480

410

490

500

330

280

200.191

201.571

158.387

2119.99

5541.625

7257.881

9369.173

12077.435

13776.216

12972.916

11979.043

11676.916

10567.073

9349.122

8026.21

596.196

550.52

410.108

260.023

160

90

90

170

100

120.451

153.74

3118.112

4936.207

7650.642

9166.044

9578.281

10383.053

10082.427

10585.274

10890.823

11188.624

11590.619

10196.177

8881.63

6852.769

5227.43

4513.693

4314.087

3517.298

270.856

402.866

373.053

330.937

411.982

519.48

6226.353

8143.012

10258.873

11777.252

12280.982

12782.111

11581.325

11878.134

12268.707

12072.73

11979.546

10979.956

10277.214


Model Estimates

Local Time

O3 [ppb]

Lake Clifton, MD

Figure5c

9257.534

8146.163

7841.695

6735.202

5230.264

5429.942

5433.163

5232.455

4431.709

3830.2

3826.195

3822.34

3919.561

4523.061

5842.666

7660.525

9572.344

11085.23

10995.055

10898.801

10795.769

10089.53

9782.586

8972.188

8162.8

7553.78

7046.078

5441.961

4741.113

4140.254

3536.031

2524.497

284.707

250.014

241.057

224.991

3117.251

3839.027

6859.773

8379.168

9495.355

97103.322

106108.933

109111.774

106116.06

132122.841

143124.735

127116.777

117108.021

9099.506

7873.015

6938.762

6326.701

5647.842

5557.154

5557.182

6148.737

6041.342

6134.835

6431.46

7240.424

9055.23

10271.375

11484.471

11483.272

10980.5

10780.649

12384.633

17987.377

12790.737

10994.107

112112.693

81123.287


Model Estimates

Local Time

O3 [ppb]

Aldino, MD

Figures6a_6b

Figure 5a. Maximum Absolute Error for Comparison of Model-Estimated Ozone

Concentrations and Monitor Measurements

Figures6a_6b

52.021Garrison

71.697Ft. Holabird

Lake Clifton70.788

63.49157.37

62.72460.541

39.98964.216

40.62572.538

40.76964.676

33.37554.235

62.7861.476

56.34562.657

50.90360.18

52.99264.422

48.95954.182

54.84291.623

Cub Run Treatment Plant84.943

James S. Long Park97.371

Widewater Elementary School80.576

67.456Phelps Wildlife Management Area

64.40561.986

56.08260.357

5963.927

60.83571.057

65.82148.894

52.06936.678

59.20273.266

79.97435.135

Maryland

Virginia

Delaware

1990

1995

Max |Monitor Measurement - Model Estimate|[O3 ppb]

Figure7a

Figure 5b. Minimum Absolute Error for Comparison of Model-Estimated Ozone


Figure7a

0.183Garrison

0.79Ft. Holabird

Lake Clifton1.01

0.5260.809

0.020.036

0.1420.137

0.291.133

1.0451.302

0.1410.308

0.0450.684

0.0110.981

0.2890.003

0.0570.331

0.870.76

0.4420.503





1.5531.514

0.0510.445

0.2140.577

0.4680.432

0.5890.034

0.1890.286

1.7520.519

0.3640.053

Maryland

Virginia

Delaware

1990

1995

Min |Monitor Measurement - Model Estimate|[O3 ppb]

Figure7b

Figure 5c. Average Absolute Error for Comparison of Model-Estimated Ozone


Figure7b

15.5787123288Garrison

27.1985753425Ft. Holabird

Lake Clifton31.5496164384

16.235569444420.2590555556

16.789808219216.7752739726

13.895589041115.071

15.650071428626.2787

16.013178082219.612369863

10.233605633816.6181232877

19.047136986318.4495205479

13.769847222219.062

16.827902777818.322369863

18.212666666721.0755342466

16.699333333321.0051780822

19.173042857118.5597123288





20.739676056324.6700416667

17.839986111117.7650416667

18.501126760618.8837183099

19.595971428620.446109589

17.287859154920.0214027778

13.705382352913.2268356164

17.625382352915.9515555556

20.333512.5694647887

Maryland

Virginia

Delaware

1990

1995

Avg |Monitor Measurement - Model Estimate|[O3 ppb]

Figures8a_8b

Figure 6a. Comparison of Model-Estimated Ozone Concentrations From

12-km and 4-km Resolution Domains for Greenbelt, Maryland Monitor Location

Figure 6b. Comparison of Model-Estimated Ozone Concentrations From

12-km and 4-km Resolution Domains for Suitland, Maryland Monitor Location

Figures8a_8b

49.73143.92951

45.74338.45752

44.33525.39754

42.15633.05351

38.31436.82243

34.335.61536

28.62529.42933

22.07819.44832

16.3280.86432

11.046025

6.8450.97418

9.2372.44119

19.9579.12525

26.16533.21437

44.4659.13162

68.42274.37584

81.30976.729103

81.26176.49107

81.10777.791103

83.63380.18594

86.39781.70496

86.83383.04583

77.4972.12261

58.17358.7237

40.22959.75623

24.14160.69619

18.49845.2069

10.50417.5544

0.50407

0.0021.5836

0.00237.98311

0.02644.1365

0.14541.9694

0.02322.8774

1.3255.0915

4.69813.31613

17.81926.23827

32.70936.90941

44.73551.58757

58.06462.96878

67.87871.75287

75.19373.139107

78.4273.428120

78.7574.131129

78.89177.214120

81.0182.22145

83.03482.368139

81.42280.897111

58.30866.76391

43.96463.09969

31.70166.12148

22.10156.75935

9.44653.08227

0.2352.04519

0.01840.54922

0.00828.03915

0.0021.02634

0027

1.2030.63819

3.5811.67318

31.2644.61928

51.78218.80440

72.54464.0860

97.7794.07896

101.20899.77198

98.55293.066102

94.25292.44695

95.35996.79190

98.22100.89789

100.575100.67391

101.65895.0885

97.67599.3280

83.85393.58266

Model Estimates - 12 km grid cells



Local Time

O3 [ppb]

Figures9a_9b

62.9739.61265

45.73330.86667

32.60719.74665

25.3826.78662

22.46836.39563

22.85233.93161

20.9227.93558

18.17319.50351

14.22112.01945

7.6449.73937

2.2329.37625

4.93416.8225

16.67531.7238

34.01145.41153

53.6956.98267

68.01970.62279

80.95181.21480

89.90285.04677

96.43387.57583

99.60791.05590

100.16696.41995

99.55497.06593

96.0489.58886

88.08975.38672

72.90971.94463

67.53975.18152

66.09569.77545

56.91553.31644

40.36535.08947

30.69322.52849

26.37121.17443

23.18628.03240

21.13932.55642

15.12932.05338

6.97227.41834

13.6225.90736

27.04932.9137

34.61240.29628

46.38352.31458

63.14568.81182

77.92184.214105

92.91895.041120

104.381102.534124

109.844105.709119

113.003109.184108

113.676112.3105

113.09113.49498

111.656109.54185

99.824100.3266

80.26397.70450

63.01897.3740

59.04988.89438

54.363.135

46.06729.31235

38.36910.26632

32.9160.48329

35.1750.0131

32.597025

15.9050.39124

16.0873.09124

33.01533.96544

50.04448.61361

65.87143.08476

80.95259.578101

94.96390.346123

105.83106.537134

111.433113.097117

111.862111.191112

108.599116.02634895.6249999998

104.698127.72973

99.85125.08775

95.46396.65253

81.24694.13456




Local Time

O3 [ppb]

49.73143.92951

45.74338.45752

44.33525.39754

42.15633.05351

38.31436.82243

34.335.61536

28.62529.42933

22.07819.44832

16.3280.86432

11.046025

6.8450.97418

9.2372.44119

19.9579.12525

26.16533.21437

44.4659.13162

68.42274.37584

81.30976.729103

81.26176.49107

81.10777.791103

83.63380.18594

86.39781.70496

86.83383.04583

77.4972.12261

58.17358.7237

40.22959.75623

24.14160.69619

18.49845.2069

10.50417.5544

0.50407

0.0021.5836

0.00237.98311

0.02644.1365

0.14541.9694

0.02322.8774

1.3255.0915

4.69813.31613

17.81926.23827

32.70936.90941

44.73551.58757

58.06462.96878

67.87871.75287

75.19373.139107

78.4273.428120

78.7574.131129

78.89177.214120

81.0182.22145

83.03482.368139

81.42280.897111

58.30866.76391

43.96463.09969

31.70166.12148

22.10156.75935

9.44653.08227

0.2352.04519

0.01840.54922

0.00828.03915

0.0021.02634

0027

1.2030.63819

3.5811.67318

31.2644.61928

51.78218.80440

72.54464.0860

97.7794.07896

101.20899.77198

98.55293.066102

94.25292.44695

95.35996.79190

98.22100.89789

100.575100.67391

101.65895.0885

97.67599.3280

83.85393.58266




Local Time

O3 [ppb]

Figure 7a. Difference in Model-Estimated Ozone Concentrations Using Baseline Emissions Scenario 1

and Modified Emissions Scenario 2

6.1116.5636.5218.6613.4230.8560.6642.99210.3427.9417.6316.2929.6193.54

2.0315.9942.3424.8432.2572.1331.4071.2543.1964.2255.9058.1732.7930.256

1.5483.171.4650.0022.3476.8841.3881.1830.8560.1433.2912.6252.020.001

1.5631.8864.49901.9724.0821.111.3480.9050.0912.422.0672.1920

1.4322.0042.27401.9041.750.3411.6521.1620.0822.8211.9632.1890

1.0341.7451.79100.9282.451-0.6911.2281.3550.1651.871.8462.080

0.491.412.03200.0012.429-1.8610.5351.1050.2850.2461.6110.9640

-0.290.9722.247002.266-2.445-0.0360.3520.210.030.7150.20

-1.4520.450.198001.737-2.588-0.047-0.018-0.4140.0030.055-0.0040

-1.9010.0020.009000.779-2.71300.012-1.22300-0.0310

-1.3180.0460.4750.0670.0060.599-2.4750.0080.017-1.8530.2040-0.2070.007

-0.3960.2841.7840.820.0362.167-1.4470.0310.059-1.8650.580.104-0.2030.05

0.9793.2915.7885.5521.8573.7390.1470.4010.628-0.5365.7271.8651.8181.915

6.44711.2179.2579.42210.5586.1832.9716.7386.7563.55510.1949.427.6210.323

14.416.9298.3018.89215.995.0768.90715.41114.3629.5019.91715.95713.10316.007

17.7889.9240.671.30916.8211.39712.16620.8586.61914.5134.51510.53112.614.703

11.7833.198-0.863-1.11912.5430.7188.69423.4571.46315.8991.0633.17410.2819.825

7.6710.477-0.748-1.2079.877-0.0327.5523.7321.0559.2820.6310.9478.4677.032

5.515-0.208-0.703-0.83210.279-0.3336.83718.2531.144.9640.780.4817.8585.883

4.7450.082-0.873-0.88310.508-0.1437.55612.082.0143.2740.9880.6847.615.421

2.3350.475-0.911-0.95510.7781.088.2449.7142.4152.281.3921.1846.5185.657

2.6310.902-0.231-0.51511.173.8598.4867.33.3862.5912.0773.7785.1066.318

4.3244.8361.4620.6568.9958.1875.315.1685.5353.323.6914.0367.4898.005

2.1672.27514.9548.8690.26212.41-0.5672.1523.4682.2282.7032.6484.6730.004

1.9962.27412.68311.6670.00415.328-0.4873.132.2330.662.1982.0854.1920.008

2.9282.43713.38712.6160.00212.85-1.2880.0353.8721.6372.4073.5453.0430.02

0.814.345.0744.76606.657-2.1927.910.2581.51612.9360.0010.0010.002

0.5879.8152.1412.29202.4111.0494.8174.4181.1458.125.34400

9.0245.1661.521.5984.4740.6520.2242.1581.6594.3454.6266.29810.2030.54

4.8621.8931.5451.5832.9260.4921.0522.0741.0376.0212.5982.1915.323.501

2.9042.2771.7861.0812.1720.7160.0731.8110.964.8561.7011.6542.7432.019

0.0180.3131.2080.8251.3861.2221.0691.2120.8012.5841.0331.9241.7721.394

0.0570.3630.9350.8060.9141.0274.7660.8550.7541.4541.4511.2580.9920.966

0.5550.6331.161.0791.0770.8671.6561.0410.9930.921.3841.310.9451.05

1.4121.241.4951.3921.5451.1041.6531.4441.161.3041.7011.4151.7271.552

2.3642.7012.2962.2552.4681.8563.7822.0981.7442.2013.1542.772.8632.634

4.7875.7634.0063.6483.7173.3866.0653.3182.5093.6515.9286.5975.9154.281

8.7857.7045.6985.1644.2263.4539.8223.4792.5443.4875.4068.8259.3795.52

10.2976.5145.0485.8825.3542.48113.0883.3292.5362.2763.9786.4910.0417.428

7.7513.442.4424.1455.3761.66414.9843.983.2651.6473.23.947.2297.669

5.7692.6171.3172.2473.9111.48712.6014.1063.9071.9233.0883.2196.6026.248

4.8412.3541.0081.2342.8571.98811.4943.5433.9032.5824.4122.9826.8595.408

5.072.7371.2351.0092.1334.44412.1722.0443.5273.128.653.5797.9753.942

6.195.3451.3471.123.5268.19313.8791.2222.9623.27315.2076.6410.4264.212

7.9179.3392.8141.3285.63715.2814.791.3722.7323.09322.53711.31714.9516.5

6.76213.86110.0514.1566.19918.96812.9281.8692.012.37629.05616.28218.0518.205

5.54521.80613.4458.718.59217.1038.9064.1911.6542.28333.0116.44211.3959.727

5.0261.6516.28711.99310.17817.1457.274.5341.252.25431.1627.2526.9720.063

4.6489.20718.90915.1340.69120.1317.627.4810.8031.60526.6535.0737.5970

4.977.3619.8816.875024.5717.449.1895.3831.30219.4814.9167.8210

5.1555.48617.35813.884025.8166.9070.4316.755.7440.1315.3127.2050

5.1175.4944.8390018.76.11808.7225.0530.0085.7026.9040

4.9435.5774.9360011.6055.81109.2874.0204.7233.3410

5.8214.8562.139009.4365.30201.5033.6304.8830.3130

6.3024.6320.0490.00308.0025.66100.2544.16402.8336.1740

5.9220.3320.0010.00300.0224.37400.0284.81004.9040

4.8920.2090.5750.9840.0221.09-4.3570.0280.8415.430.1770.1654.6750.028

5.4910.3061.4633.1320.1191.989-0.0370.111.7816.3510.6710.361.610.139

5.6711.744.4344.0141.4496.8092.1570.432.0277.4751.9331.1224.5721.579

15.197.90112.57811.4439.96919.6426.5532.7296.0748.55417.5788.61912.718.594

20.39121.02520.06817.51616.48919.11510.06111.17913.36710.00416.23222.76912.8416.783

8.42729.08225.60525.09225.7833.4938.88822.58420.2624.719-0.16222.3513.42725.943

-2.9183.3516.36116.89828.154-1.1517.27437.70829.2143.11-3.0784.7680.80621.348

-4.151-2.85-2.201-0.3825.7-2.6173.88141.9930.993.531-4.253-0.2790.8542.982

-4.074-4.096-3.313-2.9041.426-4.0274.57115.65910.6083.751-4.983-1.5860.1420.269

-4.23-4.317-4.484-3.9540.407-4.9996.7943.2352.732-2.365-5.0630.354-0.443-0.423

-3.857-3.817-5.064-5.051-0.36-4.0019.964.362-1.016-4.159-4.5435.1250.131-1.197

-3.708-2.663-4.149-4.752-1.8120.00310.754.151-1.936-4.518-2.8099.1911.431-2.431

-3.643-2.1280.541-2.3023.0433.3076.131.8122.55-4.0664.7766.8181.7161.632

-1.6641.9434.0053.438.2686.458-1.246-0.406-0.3280.3096.2855.9371.8282.991

-3.2262.947.0446.6086.5377.123-1.6134.454-0.7543.8794.2624.8132.3555.387

Davidsonville

Ft. Meade

Garrison

Padonia

Essex

S. Carroll

S. MD

Edgewood

Aldino

Millington

Rockville

Greenbelt

Suitland

Ft. Holabird

Local Time

Figure 7b. Difference in Model-Estimated Ozone Concentrations Using Baseline Emissions Scenario 1

and Modified Emissions Scenario 3

Model Estimated O3 (w/ adjsuted emissions scenario) - O3 (w/ unadjusted emissions) [ppb]

6.1116.5636.5218.6613.4230.8560.6642.99210.3427.9417.6316.2929.6193.54

2.0315.9942.3424.8432.2572.1331.4071.2543.1964.2255.9058.1732.7930.256

1.5483.171.4650.0022.3476.8841.3881.1830.8560.1433.2912.6252.020.001

1.5631.8864.49901.9724.0821.111.3480.9050.0912.422.0672.1920

1.4322.0042.27401.9041.750.3411.6521.1620.0822.8211.9632.1890

1.0341.7451.79100.9282.451-0.6911.2281.3550.1651.871.8462.080

0.491.412.03200.0012.429-1.8610.5351.1050.2850.2461.6110.9640

-0.290.9722.247002.266-2.445-0.0360.3520.210.030.7150.20

-1.4520.450.198001.737-2.588-0.047-0.018-0.4140.0030.055-0.0040

-1.9010.0020.009000.779-2.71300.012-1.22300-0.0310

-1.3180.0460.4750.0670.0060.599-2.4750.0080.017-1.8530.2040-0.2070.007

-0.3960.2841.7840.820.0362.167-1.4470.0310.059-1.8650.580.104-0.2030.05

0.9793.2915.7885.5521.8573.7390.1470.4010.628-0.5365.7271.8651.8181.915

6.44711.2179.2579.42210.5586.1832.9716.7386.7563.55510.1949.427.6210.323

14.416.9298.3018.89215.995.0768.90715.41114.3629.5019.91715.95713.10316.007

17.7889.9240.671.30916.8211.39712.16620.8586.61914.5134.51510.53112.614.703

11.7833.198-0.863-1.11912.5430.7188.69423.4571.46315.8991.0633.17410.2819.825

7.6710.477-0.748-1.2079.877-0.0327.5523.7321.0559.2820.6310.9478.4677.032

5.515-0.208-0.703-0.83210.279-0.3336.83718.2531.144.9640.780.4817.8585.883

4.7450.082-0.873-0.88310.508-0.1437.55612.082.0143.2740.9880.6847.615.421

2.3350.475-0.911-0.95510.7781.088.2449.7142.4152.281.3921.1846.5185.657

2.6310.902-0.231-0.51511.173.8598.4867.33.3862.5912.0773.7785.1066.318

4.3244.8361.4620.6568.9958.1875.315.1685.5353.323.6914.0367.4898.005

2.1672.27514.9548.8690.26212.41-0.5672.1523.4682.2282.7032.6484.6730.004

1.9962.27412.68311.6670.00415.328-0.4873.132.2330.662.1982.0854.1920.008

2.9282.43713.38712.6160.00212.85-1.2880.0353.8721.6372.4073.5453.0430.02

0.814.345.0744.76606.657-2.1927.910.2581.51612.9360.0010.0010.002

0.5879.8152.1412.29202.4111.0494.8174.4181.1458.125.34400

9.0245.1661.521.5984.4740.6520.2242.1581.6594.3454.6266.29810.2030.54

4.8621.8931.5451.5832.9260.4921.0522.0741.0376.0212.5982.1915.323.501

2.9042.2771.7861.0812.1720.7160.0731.8110.964.8561.7011.6542.7432.019

0.0180.3131.2080.8251.3861.2221.0691.2120.8012.5841.0331.9241.7721.394

0.0570.3630.9350.8060.9141.0274.7660.8550.7541.4541.4511.2580.9920.966

0.5550.6331.161.0791.0770.8671.6561.0410.9930.921.3841.310.9451.05

1.4121.241.4951.3921.5451.1041.6531.4441.161.3041.7011.4151.7271.552

2.3642.7012.2962.2552.4681.8563.7822.0981.7442.2013.1542.772.8632.634

4.7875.7634.0063.6483.7173.3866.0653.3182.5093.6515.9286.5975.9154.281

8.7857.7045.6985.1644.2263.4539.8223.4792.5443.4875.4068.8259.3795.52

10.2976.5145.0485.8825.3542.48113.0883.3292.5362.2763.9786.4910.0417.428

7.7513.442.4424.1455.3761.66414.9843.983.2651.6473.23.947.2297.669

5.7692.6171.3172.2473.9111.48712.6014.1063.9071.9233.0883.2196.6026.248

4.8412.3541.0081.2342.8571.98811.4943.5433.9032.5824.4122.9826.8595.408

5.072.7371.2351.0092.1334.44412.1722.0443.5273.128.653.5797.9753.942

6.195.3451.3471.123.5268.19313.8791.2222.9623.27315.2076.6410.4264.212

7.9179.3392.8141.3285.63715.2814.791.3722.7323.09322.53711.31714.9516.5

6.76213.86110.0514.1566.19918.96812.9281.8692.012.37629.05616.28218.0518.205

5.54521.80613.4458.718.59217.1038.9064.1911.6542.28333.0116.44211.3959.727

5.0261.6516.28711.99310.17817.1457.274.5341.252.25431.1627.2526.9720.063

4.6489.20718.90915.1340.69120.1317.627.4810.8031.60526.6535.0737.5970

4.977.3619.8816.875024.5717.449.1895.3831.30219.4814.9167.8210

5.1555.48617.35813.884025.8166.9070.4316.755.7440.1315.3127.2050

5.1175.4944.8390018.76.11808.7225.0530.0085.7026.9040

4.9435.5774.9360011.6055.81109.2874.0204.7233.3410

5.8214.8562.139009.4365.30201.5033.6304.8830.3130

6.3024.6320.0490.00308.0025.66100.2544.16402.8336.1740

5.9220.3320.0010.00300.0224.37400.0284.81004.9040

4.8920.2090.5750.9840.0221.09-4.3570.0280.8415.430.1770.1654.6750.028

5.4910.3061.4633.1320.1191.989-0.0370.111.7816.3510.6710.361.610.139

5.6711.744.4344.0141.4496.8092.1570.432.0277.4751.9331.1224.5721.579

15.197.90112.57811.4439.96919.6426.5532.7296.0748.55417.5788.61912.718.594

20.39121.02520.06817.51616.48919.11510.06111.17913.36710.00416.23222.76912.8416.783

8.42729.08225.60525.09225.7833.4938.88822.58420.2624.719-0.16222.3513.42725.943

-2.9183.3516.36116.89828.154-1.1517.27437.70829.2143.11-3.0784.7680.80621.348

-4.151-2.85-2.201-0.3825.7-2.6173.88141.9930.993.531-4.253-0.2790.8542.982

-4.074-4.096-3.313-2.9041.426-4.0274.57115.65910.6083.751-4.983-1.5860.1420.269

-4.23-4.317-4.484-3.9540.407-4.9996.7943.2352.732-2.365-5.0630.354-0.443-0.423

-3.857-3.817-5.064-5.051-0.36-4.0019.964.362-1.016-4.159-4.5435.1250.131-1.197

-3.708-2.663-4.149-4.752-1.8120.00310.754.151-1.936-4.518-2.8099.1911.431-2.431

-3.643-2.1280.541-2.3023.0433.3076.131.8122.55-4.0664.7766.8181.7161.632

-1.6641.9434.0053.438.2686.458-1.246-0.406-0.3280.3096.2855.9371.8282.991

-3.2262.947.0446.6086.5377.123-1.6134.454-0.7543.8794.2624.8132.3555.387

Davidsonville

Ft. Meade

Garrison

Padonia

Essex

S. Carroll

S. MD

Edgewood

Aldino

Millington

Rockville

Greenbelt

Suitland

Ft. Holabird

Local Time

Figure 8a. Maximum Absolute Error for Comparison of Model-Estimated Ozone

Concentrations and Monitor Measurements, Using Various Emissions Scenarios

Figure 8b. Average Absolute Error for Comparison of Model-Estimated Ozone

Concentrations and Monitor Measurements, Using Various Emissions Scenarios

Model Estimated O3 (w/ adjsuted emissions scenario) - O3 (w/ unadjusted emissions) [ppb]

59.64660.1563.491

72.41367.09862.724

46.22743.17539.989

48.02248.18840.625

57.36852.76240.769

43.02135.65833.375

49.96156.1462.78

61.52658.31152.021

56.21554.69556.345

64.73468.70671.697

49.4750.21250.903

52.89452.95752.992

46.92446.30348.959

45.89452.23854.842


Model Estimates:

Using Adjusted Emissions Scenario 2


Using Unadjusted Emissions Scenario

Monitor Location

16.235569444416.481763888916.7168472222

16.789808219217.941931506821.2543835616

13.895589041115.423452054815.6892191781

15.650071428617.814971428619.4380285714

16.013178082217.365465753416.8973835616

10.233605633810.818352112713.5702535211

19.047136986319.265479452118.9338493151

15.578712328818.258410958919.6601643836

13.769847222213.379902777814.107375

27.198575342526.698068493225.6354794521

16.827902777818.080972222219.6541527778

18.212666666717.432055555618.2276111111

16.699333333316.960063492117.9296984127

19.173042857118.249142857117.8094


Model Estimates:



Using Unadjusted Emissions Scenario

Monitor Location

Figure 9a. Comparison of 8-Hour Average Model-Estimated Ozone Concentrations

and Monitor Measurements for Millington, Maryland

Figure 9b. Comparison of 8-Hour Average Model-Estimated Ozone Concentrations

and Monitor Measurements for Davidsonville, Maryland

49.2551.373125

45.7548.765125

43.12547.273

40.12545.888375

37.2543.607125

34.87541.40275

34.7540.006125

37.2539.754625

40.87540.899875

46.87544.04125

54.12548.976

63.12555.0705

72.37561.98975

81.2569.4435

87.7576.422625

91.12582.09825

91.7586.290875

90.12588.33775

87.12587.6685

82.585.831625

77.37583.28

71.579.87575

64.7575.932

58.2567.18275

52.556.85375

45.87548.106375

39.62538.933625

34.37532.832625

30.37528.769125

28.12525.911625

28.87523.68875

27.857142857127.235125

27.666666666733.903375

36.539.8725

45.666666666747.6155

54.333333333352.911

61.166666666756.891875

65.666666666760.65625

67.564.402125

67.142857142967.07925

66.2568.336625

62.2569.063875

60.569.420375

59.2568.688375

5866.18225

56.12565.341375

55.87564.251

54.37562.897625

52.12561.1955

53.2559.264

50.62556.868875

46.37554.38525

43.7552.249625

4349.57175

43.37549.584625

45.7552.303625

49.557.030875

54.12563.377875

61.7571.286625

7380.434875

84.589.085875

94.62595.789375

101100.023125

103.625102.068

104.5103.022875

103.25102.757875


Model Estimates

Local Time

8-hr avg O3 [ppb]

Millington, Maryland

72.62535.079875

6830.6825

63.2526.503375

58.2523.353125

53.2521.419375

49.37521.20125

48.2523.40625

48.7528.18475

51.2535.287625

5543.134125

62.551.090875

72.12559.50575

83.12568.257125

92.62576.307875

99.582.05325

104.37584.821

106.87584.53325

107.583.051125

103.7580.404625

97.2575.63175

8968.715875

80.87560.343375

72.7552.406375

65.546.084625

5940.83875

5336.00075

48.37531.502625

45.37528.0835

43.526.277125

43.2527.427875

4330.865375

43.2535.185625

47.540.840625

55.7547.988875

6656.6305

77.87566.661625

9177.19975

102.586.481125

113.2594.365875

121101.014875

123.375105.496

121.25107.793125

116.125108.10625

107.625107.56725

96106.83875

84.25104.932625

70.5100.063625

57.87590.884375

47.2580.803

37.12571.759625

28.37563.262625

21.37553.19775

17.37542.578875

16.62535.599375

20.87531.880625

27.533.425

35.539.297625

45.87547.699375

5857.421125

7268.448

85.579.092625

96.12588.286

103.87598.299125

108.25108.35925

109115.8485

107.875117.47425


Model Estimates

Local Time

8-hr avg O3 [ppb]

Davidsonville, Maryland

Modeling estimatesMonitor in countySpatial interpolation of monitors1-Hour max O3 (ppb)County-level Exposure Estimates Bell Environ Int 2006

1-Hour max O3 (ppb)Nearest monitorIndividual-Level Exposure EstimatesBell Environ Int 2006

Ozone monitors in Georgia 2000 Persons / Sq. Mile

Holford et al. Statistics in Medicine AcceptedExample: Traffic Modeling to Estimate Exposure

Estimated NO2 (traffic) Levels for New Haven County Area (2002)Holford et al. Statistics in Medicine Accepted

Other ChallengesOther factors affecting certainty of monitor valuesDetection limits of monitorsMeasurement error (see co-located monitors)Other factors that affect exposure and variation of exposureMovement through the communityIndoor/outdoor activity patternsBehaviors and activities (e.g., AC, jogging)Differences between exposure and dose

Thank youKey CollaboratorsFrancesca Dominici, Harvard UniversityRoger D. Peng, Johns Hopkins UniversityKeita Ebisu, Yale UniversitySponsorsNational Institute for Environmental Health Sciences (NIEHS)Health Effects InstituteU.S. Environmental Protection Agency-sponsored Johns Hopkins Particulate Matter Research Center

****Time-series model, assessing acute exposuresKey confounders: day of the week, temporal trends, temperatureCould talk more about why weather is important*Time-series model, assessing acute exposuresKey confounders: day of the week, temporal trends, temperatureCould talk more about why weather is important*Ambient monitor measures PM10 and PM2.5 (photo 2)Photo 1 is at a school.

Hybrid approaches (time activity diary, monitors at homes).*Ambient monitors more likely to be in urban locations.

Neither of these are biomarkers or actual dose.**Another issue is time: people move around. Time activity patterns.*Correlations plotted on Fishers z-transform scale.

Based on national U.S. data over a several year period.*Beta and z are vectors representing other confounders (day of the week, etc.)*We are calculating the estimated beta w based on wt (estimated exposure), but we want the beta we would get if we had xt (the true exposure).

What is the problem with E[ut] = 0.To estimate tau^2 (spatial misalignment error variance) var we need multiple mons. This var represents the spatial misalignment error variance for a specific area.This value goes down as the number of monitors increases.*Effect of spatial misalignment is function of spatial variability of pollutant and monitor coverage

******What is the relative toxicity of various PM components?Can the spatial and temporal variation in PM component concentrations explain spatial and temporal differences in PM-health relationships?What sources of PM are most harmful?

****These differences potentially explain spatial and temporal differences in PM health effects estimates. As an example, Roger Peng found higher effect estimates for PM10 and mortality in the Northeast and in Summer, whereas this work led by Francesca Dominici finds strong East/West patterns in the effect estimates for PM2.5 and cause-specific hospital admissions.

204 US Counties, 1999-2002 Cerebrovascular disease: any abnormality of the brain resulting from a pathologic process of the blood vessels Ischemic heart disease: narrowed heart arteriesPeripheral vascular disease: diseases of blood vessels outside the heart and brain. It's often a narrowing of vessels that carry blood to the legs, arms, stomach or kidneysCOPD: slow gradual disease characterized by loss of lung function. Includes chronic bronchitis

Beta and z are vectors representing other confounders (day of the week, etc.)*Iw = 0/1 indicator variable for winter, etc.Seasonal interaction model: also replaced ns(Time) with interaction terms (time x season)*Figure 1. Percent increase in CVD hospital admissions rate per 10 microggm/m3 increase in lag 0 PM2.5Note: Seasonal interaction model results are shown in red and harmonic model results in black. Dashed lines reflect 95% posterior interval.

***Domain 1: 108-km gridcell resolutionDomain 2: 36-km gridcell resolutionDomain 3: 12-km gridcell resolutionDomain 4: 4-km gridcell resolution

Areas of trouble:Nighttime averages too lowDifficulty capturing highest peaksSo what exposure window you consider is critical.

This is not a statistical model. Physical/chemical processes.**Why county? Often health data is available on an aggregated scale often county.

Spatial interpolation is inverse distance weighting. Also applied kriging, but same idea.(correlation between model county estimates and Fig 2 0.61, and Fig 3 0.66)Only 12% of the counties have monitors. Those counties have statistically higher population density and modeled ozone concentrations.

Also note that for Fig. 2 (very common approach), is based on monitors within the county, regardless of where they are.*Case study episode August 15, hour 00 (GMT) to August 18, hour 00 1995 in N. Georgia. Highest hourly value recorded. Eight monitors.

Fig1. Displays strong spatial heterogeneity.Fig2. Nearest monitor. Does include monitors outside the domain, so if an area is closest to a monitor other than these 8, those values are used.

Example: Henry County: modeling 53 ppb, monitor: 12 ppb.Issue with some locations being far away from monitors. Some populations in Bibb county (SE portion) are >90 km from the nearest monitor, and some parts of southern Georgia are even further.

*Linked with urban settings with high population:+ useful for hh studies, but if we had estimates at other locations:Larger sample size (# of people)Exploration of urban vs. rural differences (different pollution mixture)

By 2000, Georgia had 21 ozone monitors in counties averaging the top 12th percentile of population.0,0 point is study subject in center of buffer.This buffer shape assumes an isotropic surface field for pollution levels (unaffected by direction). Could imagine a different shape.

D = specified buffer distance around study subjectd = distance between a node (location on road) and study subjectOpen circles represent nodes dividing roads into segments (C), each of which have a value for traffic volumeS divides each Segment (C) into sub-segments, which each have a distance to study subject and a certain lengthAnnual estimate of traffic volume available for each segment

If we new the dispersion parameters, could use a Gaussian function, but they used a step function, among other approaches, for different distances. Compared to NO2 levels.*Circles are NO2 monitoring locations**We have better estimates of exposure for some pollutants than others.*

critical issues of exposure assessment for human health studies of air pollution

Documents

upcoming approaches

exposure assessmentsource

expensiveexposure assessment

bell ehp

person differencespersonal

large sample sizenot

locations of interestlocations

ecpoor monitor coverage