Health Assessment and Air Pollution

1. Health Assessment and Air Pollution Ilias G. Kavouras, Ph.D. Department of Environmental and Occupational Health University of Arkansas for Medical Sciences College of Public Health

2. Studytasks • evaluate the relative contribution of local and regional sources on PM10, PM2.5 and O3 concentrations • the concentration trends and spatiotemporal variations of O3in relation to wildfires • produce a regional health profile for air quality related chronic and infectious diseases • obtain baseline information on the state of health and risks factors of the region through acquisition and analysis of available indicators: Mortality and hospital admissions for COPD, asthma, heart failure, stroke and infectious diseases (Flu, pneumonia, pertussis and Coccidioidomycosis) • estimate the relative risks for respiratory and cardiovascular diseases in Dona Ana county • Obtain and analyze emergency rooms visits and hospital admissions and air pollution (PM10, PM2.5 and O3)

3. Air mass residence time Local sources Shipping emissions (SO4, NO3) O3 and precursors Shipping emissions (SO4, NO3) O3 and precursors

4. Tracer Mass Balance model Assumption: measured concentrations at a receptor are linearly related to the frequency of airmass transport from a source region to the receptor site Development of the model: Pitchfordand Pitchford, 1985; Gebhartet al. 1993 Application of the model:Gebhartet al. 2001; 2006; Xu et al. 2006; Huang et al. 2010; Kavouras et al. 2013; Chalbot et al. 2013

5. Source regions contributions 19 regions, four adjacent to Las Cruces (500 km) • The four adjacent sectors are important contributors to PM10, PM2.5 and O3 • Southern California, Arizona, Baja California and southeast Texas are also important determinants • Northwest US contributes up to 8 ppbv of O3

6. O3 monitoring locations

7. Approach • The relative (%ΔC/Ref) differences and the coefficient of divergence (COD)of 8-hr maximum monthly concentrations between two sites (ChiricahuaNational Monument was the reference site). • COD values vary from 0 to 1, with COD values close to unity being suggestive of strong spatial variation. • Ordinary least squares regression of deseasonalized monthly 8-hr maximum O3 concentrations (“Census I” method) • Total counts of fire detections/month • Frequency: >25%, 25-50%, 50-75%, >75% • Distance from the site: 0-160, 160-400, 400-800, 800-1600, 1600-3200, 3200-4800 km

8. Annual and spatial trends a significant at p < 0.001; b significant at p<0.01

9. Fires, distance and O3 • Increasing trend is due to the decomposition of peroxyacetylnitrates (PAN) • Decrease of O3 for fires within 400 km may be due to NO titration of O3 • Increase in extreme fire events within 400 km may be due to changes of NO2 and O3 photolysis rates in the smoke plume

10. Fires, distance and O3 a Significant estimates (at p-value < 0.15)

11. Fires, distance and O3

12. Fires and asthma/COPD hospitalizations • β is related to an increase of 4.25% (asthma,all ages), 1.85% (asthma, children), 2.46 (asthma 15-64 years) and 3.29% (COPD, all ages) for an increase of 10 ppbv of 8-hr maximum O3 concentration (Ji et al., 2011), • p is the county population and • b is the county baseline hospitalizations rates

13. Regional Health Profile • State of New Mexico's, Department of Health, Indicator Based Information System for Public Health (NEW MEXICO-IBIS) Mortality Data • State of New Mexico's, Department of Health, Indicator Based Information System for Public Health (NEW MEXICO-IBIS) Hospital Impatient Discharge Data (HIDD). • State of New Mexico's, Department of Health, Indicator Based Information System for Public Health (NEW MEXICO-IBIS) Behavioral Risk Factor Surveillance System (BRFSS) • State of New Mexico's, Department of Health, Indicator Based Information System for Public Health (NEW MEXICO-IBIS) Youth Risk and Resiliency Survey • New Mexico EnviroNewMexicoent Department, Air Quality Bureau • US Census Bureau • US Department of Health and Human Services, Health Resources and Services Administration • US Department of Labor, Bureau of Labor Statistics • US Environmental Protection Agency Air Quality System

14. Total mortality number of people who died in 2010 in relation to the population size (per 100,000) New Mexico: 742 USA: 798

15. Total mortality per race and age • Males higher than females • Race patterns

16. Leading causes of death

17. Heartfailuremortality

18. Heartfailurehospital admissions • primary diagnosis is the condition to be responsible for the admission of the patient to the hospital. • secondary diagnosis includes the condition that coexist at the time of inpatient admission which affect the treatment received and/or length of stay • Data from federal facilities (military and veteran’s affairs hospitals) and Indian health service facilities are not included

19. COPD mortality

20. COPD hospital admissions

21. Asthmamortality

22. Asthmahospital admissions Middle- and high-school students

23. Stroke mortality

24. Stroke hospital admissions

25. Flu and pneumonia

26. Pertussis and Coccidioidomycosis • Mortality • Pertussis: six deaths are reported in New Mexico; one death in Hidalgo County • Coccidioidomycosis: ten deaths were reported in New Mexico, none of them within the border Counties • in 1999-2010 period • Hospitalizations • Pertussis: 254 are reported in New Mexico; 16 in 2010 • 30 in study area (15 in Dona Ana, eight in Otero County, four in Grant County, two in Luna County and one in Sierra County) • 26 children, 24 of them > 1 years old (17 boys and 7 girls) • Coccidioidomycosis: 131 are reported in New Mexico; 13 in 2010 • 47 in study area (33 in Dona Ana, four in Otero County, four in Grant County, seven in Luna County and three in Sierra County) • 35-44 years ols (56% males and 44% females) • in 1999-2010 period

27. Air pollution indicators

28. Health Status • Behavioral Risk Factor Surveillance System (BRFSS) • Youth Risk and Resiliency Survey • 2006-2010

29. Blood pressure and cholesterol

30. Smoking Middle- and high-school students

31. Health facilities and access • Dona Ana County • Memorial Medical Center in Las Cruces with 293 beds • MountainView Regional Medical Center in Las Cruces with 142 beds • Advanced Care Hospital of Southern New Mexico in Las Cruces with 20 beds • Rehabilitation Hospital of Southern New Mexico in Las Cruces with 40 beds • Mesilla Valley, a psychiatric facility, in Las Cruces with 120 beds. • Otero County • General Champion Memorial Hospital In Alamogordo with 90 beds • Mescalero Indian Hospital in Mescalero with 13 beds • Sierra County • Sierra Vista Hospital in Truth or Consequences with 23 beds • Grant County • Gila Regional Medical Center in Silver City, with 68 beds • Luna County • Mimbres Memorial Hospital in Deming with 115 beds

32. Epidemiologicalanalysis • Daily emergency room (ER) visits and hospital admissions from the Memorial Medical Center (MMC), 2007 to 2011 for the adult population (≥ 18 years of age) were retrieved. • 67.5% of hospital beds (293) and 65% (35,939) of emergency room visits in Dona Ana County • PM10, PM2.5 and O­3 measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) based on the completeness of the datasets. • 24-hr PM10 and PM2.5 mass concentrations and the daily 8-hr maximum O3 concentration were used as metrics of ambient exposures • E[Yt] is the expected value Yt indicating the daily visits or admissions count on day t with Var(Yt)=φE[Yt] with φ being the over-dispersion parameter, • temptis the value of mean temperature on day t, • lag16(tempt) is its lagged effect over the previous six days and • (Pollutant)t is the pollutant’s level on day t. • on the previous day (lag1), on the average of the same and previous days (lags01) • S are the natural splines smoothing functions to capture the non-linear relationship between the time-varying covariates and calendar time and daily admissions, with three df for temperature on the day of the admission and with two df for the previous days • controlled for season and long-term trend with a natural cubic regression spline with 1.5 degrees of freedom (df) for each season and year (corresponding to six df per year). • two- pollutant models using the previous day pollutants concentrations

33. ER and hospital admissions

34. Percent increase in ER visits 10 μg/m3 of PM10, PM2.5 and 10 ppbv of O­3

35. Percent increase in hospital admissions 10 μg/m3 of PM10, PM2.5 and 10 ppbv of O­3

36. Seasonaleffect † Without influenza control, (*) Significant at 5%, (·) Significant at 10%

37. Conclusions • Sources within 500 km, southern California, Baja California, southern Arizona, southeast Texas and north Mexico are responsible for most of PM10, PM2.5 and O3 • Wildfires within 400-800 km and more than 1600 km may contribute up to 11 ppbv of O3 and trigger an increase of up to 3.3% in asthma hospitalizations • More than 70% of residents in the border Counties reported at least one behavioral risk factor (smoking, high blood pressure or cholesterol) for chronic diseases. • The levels of particulate matter are among the highest in the Nation, and exceed the threshold concentrations set by US EPA for the protection of human health; For ozone, threshold concentrations were exceeded in the past; current levels are slightly below the threshold value • Cancer and heart diseases are the primary causes of death for adults; chronic respiratory diseases are among the top six causes of death in the border Counties. • Mortality and hospitalization due to COPD and asthma are above the State’s average and they are increasing • Childhood asthma is a concern, with about 50% of high school students being active smokers. • One-third of hospitalized cases of coccidioidomycosis in New Mexico since 1999 were observed in border Counties; however, none of them died • Positive but not significant associations were observed for asthma, COPD and heart disease • An increase of 3-5% (statistically significant) was computed for emergency cardiovascular and respiratory symptoms for a 10 μg/m3increase in PM mass in the summer.

38. Acknowledgements • Paul Dulin, NM DOH • Dave DuBois, Erin Ward and all study partners • Bruce D. San Filippo and MMC staff • Marie-Cecile Chalbot, Sophia Rodopoulou and EviSamoli