Air Mass Characterization of Air Quality and Health Impacts under Current and Future Climate Scenarios

Adel Hanna1, Aijun Xiu1, Karin Yeatts1, Richard Smith,1 Zhengyuan Zhu1, Neil Davis1, Kevin Talgo1, Zac Adelman1 Sarav Arunachalam1, Gurmeet

Arora1,Qingyu Meng2, Scott Sheridan3, and Joseph Pinto2

1The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599

2U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711

3Kent State University, Kent, Ohio 44242

Outline

Motivation and Objectives Data and Models Concept of “Air Mass/Weather Type”

Weather Classification Meteorological characteristics of Air Masses Air Quality and Air Mass

Statistical Modeling Approach Climate Scenarios and Trends in Air Mass

Variability

Summary and Conclusions

Objectives

Define more precisely the interrelationships among changes in climate and meteorological conditions, air pollution, and heat- and cold-related morbidity severe enough to warrant clinical

contact. .

Examine future climate scenarios in terms of potential impacts on air quality and Human health

Data and ModelsNine Years of data (1996 -2004)

Meteorological Data The National Climatic Data Center archives of surface and upper-air

data over the U.S. Air Quality Data

AQS measurements of ambient concentrations of ozone, Health Data

Morbidity measures include asthma and MI hospital admissions.

Models (Years 2001-2003, 2018- 2020, 2048-2050) CCSM, WRF, CMAQ SMOKE (IPCC)

The Concept of Air Mass

What is an air mass? How is it related to basic meteorological parameters

(temperature, pressure, winds, etc.)? How is it different from analysis of basic meteoro-

logical parameters? Source Duration Spatial coverage

Spatial Synoptic Classification

Sheridan Spatial Synoptic Classification system (2001) (sheridan.geog.kent.edu/ssc.html)

Classification (air mass) types: DM: Dry Moderate (mild and dry) DP: Dry Polar (very cold temperatures – advection from Canada) DT: Dry Tropical (hottest and driest conditions at any location) MM: Moist Moderate (warmer and more humid than MP) MP: Moist Polar (cloudy, humid, and cool) MT: Moist Tropical (warm and very humid) Tr: Transition (one air mass giving way to another) MT+: Moist Tropical+ (upper limits of the MT)

Monthly frequency of seven air mass types based on daily meteorological analyses during 1996-2004

Monthly frequency of occurence of different air masses in North Carolinabased on daily weather analysis during the period 1996 to 2005

Month

Per

cent

age

0

10

20

30

40

50

60

Jan

Feb Mar Apr

May Ju

n Jul

Aug Sep OctNov Dec

DM

Jan

Feb Mar Apr

May Ju

n Jul

Aug Sep OctNov Dec

DP

DT

0

10

20

30

40

50

60MM

0

10

20

30

40

50

60MP MT

0

10

20

30

40

50

60TR

Ashev illeCharlotteGreensboroRaleighWilmington

Characteristics of the air mass types

Air Mass Ozone Characteristics

Probability (expressed as a percentage) of finding O3 concentrations above a threshold concentration for a given air mass (P(O3|AM))

Probability (expressed as a percentage) of having a particular air mass present when O3 concentrations are

above a threshold concentration (P(AM|O3))

DM DP DT

MT+

Trajectory clusters of 72-hour backward trajectories for Charlotte

MM MP

MT++ TR

Dry Tropical (DT), Dry Moderate (DM), and the Moist Tropical (MT), are always among the top three circulation patterns associated with the high Ozone concentrations. DT shows highest ozone concentrations.

DT has westerly to southwesterly flow (72 hours back trajectory

MT shows air traveling over the Atlantic and the Gulf of Mexico

DM shows air traveling along Northwest and Northeast

Air Mass/ Air Quality

Statistical Analysis-General Linear Models

Evaluated association of ozone with asthma and MI hospitalizations for different air masses

Modeling strategy: Joint modeling of ozone and air mass

Assumed a Poisson distribution of the outcomes, Checked for overdispersion

Used B-spline function with 24 knots to adjust for nonlinear seasonal effect and long term trend.

Adjusted for differences in dew point and day of the week.

Health Data

Hospitalizations and ER from all of North Carolina (North Carolina Center for State Health Statistics)

Asthma (ICD9 493.x) Myocardial infarction (ICD 410)

Percent Change/10 ppb O3 and 95% CL for Charlotte, Raleigh and Greensboro by air mass

-20 -10 0 10 20Percent change (or 95% CI) in Asthma admissions per 10ppb rise in Ozone (Charlotte, Greensboro, Raleigh)

Controlled for Dew point, Air mass, Ozone, and Splines (df=24, degree=4)

Ozone@DM

Ozone@DP

Ozone@DT

Ozone@MM

Ozone@MP

Ozone@MT

Ozone@TR

Ozone@MT+/++

currentlag1lag2lag3lag4lag5

Asthma

-20 -10 0 10 20Percent change (or 95% CI) in ER asthma (visits) per 10ppb rise in Ozone (Charlotte, Greensboro, Raleigh)

Controlled for Dew point, Air mass, Ozone, and Splines (df=24, degree=4)

Ozone@DM

Ozone@DP

Ozone@DT

Ozone@MM

Ozone@MP

Ozone@MT

Ozone@TR

Ozone@MT+/++

currentlag1lag2lag3lag4lag5

Asthma ER)

Percent Change/10 ppb O3 and 95% CL for Charlotte, Raleigh and Greensboro by air mass

-20 -10 0 10 20Percent change (or 95% CI) in MI admissions per 10ppb rise in Ozone (Charlotte, Greensboro, Raleigh)

Controlled for Dew point, Air mass, Ozone, and Splines (df=24, degree=4)

Ozone@DM

Ozone@DP

Ozone@DT

Ozone@MM

Ozone@MP

Ozone@MT

Ozone@TR

Ozone@MT+/++

currentlag1lag2lag3lag4lag5

MI

Health Associations

Asthma Hospitalization and ER

Ozone-dry tropical– Current day and all lags show increase in asthma hospitalizations

Ozone -Transitional and Moist Tropical (MT+/MT++) – higher lags

MI Hospitalization

Ozone-Moist Tropical (MT+ and MT++)– 5 day lag

Future Climate Scenarios

Examine Seasonal and Inter-annual Variability

How to use our results as a Forecasting Tool to provide longer term anticipation of local air quality conditions (Ozone Code Red and Code Orange days)

Projection of future climate patterns Year (2018-2020 and 2048-2050) CCSM/WRF/CMAQ model simulations

Research Question: Does Future Climate ‘Air Mass” Type Frequency stay the same as current Climate?

WRF model domains

Current and Future Climate Modeling Configurations

May, June, July, and August of the years 2001, 2002, and 2003, representing current climate conditions; and the years 2018, 2019, 2020, 2048, 2049, and 2050, representing future climate conditions.

Dynamical downscaling of the CCSM meteorological outputs to provide initial and boundary conditions for WRF at the 36-km grid resolution,

SRES A1B driven CCSM results used for IPCC AR4 on a T85 Gaussian grid. Constant anthropogenic emissions within each period and to develop hourly

biogenic emissions using simulated meteorology data. For Period 1 we used the 2002 National Emission Inventory version 3 (NEI2002v3)

from EPA for the United States, the 1999 National Emission Inventory Phase III for Mexico (MNEI99p3), and the 2000 National Pollutant Release Inventory (NPRI2000) for Canada to represent the anthropogenic emissions for each year during the period.

For Period 2, we used the NEI2002v3-based 2020 NEI (NEI2002v3_2020) from EPA for the United States, the 2018 NEI for Mexico (MNEI2018), and the 2020 NPRI (NPRI2020) for Canada to represent the anthropogenic emissions

For Period 3, we used the year 2050 inventories developed at GaTech for studying how future climate change will impact regional air quality (Woo et al., 2008).

CCSM

WRF 3.0 August 2002, Surface Temperature

WRF 3.0 August 2048, Surface Temperature

WRF

Isoprene Emissions

Grid Resolution

Future Frequency of air masses for 108 km (dO1), 36 km (dO2), 12 km (dO3)June-July-August

Conclusions Specific air masses (DT,DM,MT) are associated with episodes of

high ozone concentrations in North Carolina. Highest levels are associated with the DT air mass.

Each air mass shows a distinctive meteorological and air quality characteristics including upwind source regions.

The DT circulation pattern, in conjunction with ambient ozone, was The DT circulation pattern, in conjunction with ambient ozone, was most strongly associated with increased asthma hospitalizations most strongly associated with increased asthma hospitalizations while MT+ while MT+

Future Climate simulations show that classification of air masses is Future Climate simulations show that classification of air masses is sensitive to model resolutionsensitive to model resolution

The Frequency of the DT air mass tend to increase in future decades The Frequency of the DT air mass tend to increase in future decades 2020 and 20502020 and 2050

The concept of air mass could be useful in public health planning by projecting pollution episodes and associated health impacts

Acknowledgments

EPA- STAR program (Bryan Bloomer and Barbara Glenn), Dr. Ted Russell and Dr. Praveen Amar

R832751010

North Carolina Population Map

Five Cities Most cities are within counties in Nonattainment areas (8-hour

Ozone) and some (PM2.5)

DM and DT Air Mass

MT and MT+ Air Mass

Percent change – Hospital Admissions (NC)

Asthma MI