Available Analytical Approaches forEstimating Fire Impacts on Ozone Formation

Hilary HafnerStephen Reid

Clinton MacDonaldSonoma Technology, Inc.

Petaluma, CA

WESTAR Wildfire and Ozone Exceptional Events WorkshopSacramento, CAMarch 6, 2013

910417-5607

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Presentation Outline

• Statistical modeling approaches– Overview– Sample analysis (Northern CA wildfires)– Strengths and weaknesses

• Other tools– MetDat2– AirNow-Tech

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Statistical Modeling Overview (1 of 4)

Regression equations•Represent a statistical method for quantifying the relationship among different variables (e.g., air quality and meteorological parameters)•Have been successfully used to predict daily or sub-daily pollutant concentrations in many areas of the country•Are developed using several years of data to describe the relationship between air quality and meteorology under typical emission patterns

Statistical Modeling Overview (2 of 4)

• Need to understand meteorological conditions leading to high ozone concentrations– Transport patterns, synoptic typing,

meteorological “cut points” (e.g., max. temperature must be > 85°F)

• Typical predictor variables for ozone– Max temperature, AM/PM wind speed and

direction, 850 mb temp, 500 mb geopotential height, % cloud cover

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Statistical Modeling Overview (3 of 4)

Multi-linear regression:

Ozone = c1 V1 + c2 V2 ……. cn Vn + constant

Where:

Ozone = predictand

c = coefficients (weighting factors)

V = meteorological predictor variables

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Statistical Modeling Overview (4 of 4)

Example equation and predictor variables:

1-hr Ozone = exp (13.72 – 0.03*Clouds – 0.04*WindSpeed1 + 0.01*WindSpeed2 + 0.0002*WindDirection – 0.01*Pressure – 0.02*DewPoint + 0.03*AloftTemperature – 0.009*AloftWindSpeed

+ 0.009*TemperatureDifference)

Variable Abbreviations Description

CloudsAverage hourly cloud cover from 6:00 a.m. PST to 6:00 p.m. PST where clear = 0, partly cloudy = 1, mostly cloudy = 2, and overcast = 3

WindSpeed1 Average wind speed from 6:00 a.m. PST to 12:00 p.m. PST in m/s

WindSpeed2 Surface wind speed at 00Z (4:00 p.m. PST on the previous day) in m/s

WindDirection Surface wind direction at 00Z (4:00 p.m. PST on the previous day)

Pressure Surface pressure at 12Z (4:00 a.m. PST) in mb

DewPoint Surface dew point temperature at 12Z (4:00 a.m. PST) in °C

AloftTemperature 925-mb temperature at 00Z (4:00 p.m. PST on the previous day) in °C

AloftWindSpeed 925-mb wind speed at 12Z (4:00 a.m. PST) in m/s

TemperatureDifference Temperature difference from 850 mb to the surface at 12Z (4:00 a.m. PST) in °C

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Sample Analysis (1 of 6)

Sacramento Regional Nonattainment Area (California Air Resources Board):•From June 20-22, 2008, lightning strikes ignited a series of wildfires in Northern CA•Over 1 million acres burned through mid-July, most within 200 miles of Sacramento•The Sacramento region was covered in a thick layer of smoke from June 23 through much of July•Sacramento area monitors recorded very high 1-hr ozone (>160 ppb) and 24-hr PM2.5 concentrations (> 60 µg/m3) during this time period

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Sample Analysis (2 of 6)

Conceptual Model: 8-hr ozone concentrations above 95 ppb generally occur in Sacramento when:• An aloft ridge of high pressure is

located over California; and

• Lower surface pressure (a thermal trough) extends inland to Sacramento.

Above: Plot of 500-mb heights for 8/15/08 indicating a ridge of high pressure over California.

Right: Surface weather map for 8/15/08 indicating a thermal trough extending from the Sacramento region northward. Surface temperatures were warm.

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Sample Analysis (3 of 6)

In 2004, STI developed a regression equation to assist SMAQMD with daily ozone forecasting:

• Obtained and processed 6 years (1997-2003) of ozone season (May-Oct) data for 7 sites in Sacramento County

• Determined proper inputs for the regression algorithm to yield statistically sound equations (stepwise procedure for adding and removing variables from the model)

• Evaluated output variables for physical sense (i.e., higher ozone correlated with lower wind speeds)

• Evaluated the statistical strength of the equation (T-test, P2-tail, standard coefficients of a variable, etc.)

• Tested final equation against observations from data set reserved for testing only (not model development)

Sample Analysis (4 of 6)

Applying the model to exceptional events analysis:• Tested the performance of the regression equation for the 2007

ozone season to evaluate the impacts of ozone precursor reductions since 2003

• Found a positive bias of 8 to 13 ppb (depending on which NCEP Eta model results were used) for 2007

• Made adjustments to account for this bias in the 2008 analyses

• Applied the regression equation to the 2008 ozone season and compared predicted and observed 1-hr ozone concentrations during the fire event

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6/23/2008 6/27/2008 7/10/2008

1-hr

Ozo

ne (p

pb)

Observed

Predicted

8453

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Sample Analysis (5 of 6)

Evaluating uncertainty and error:•Analysis of deviation distributions (observed – predicted) for data from 2007 and portions of 2008 ozone seasons showed that the 95th percentile of the daily differences was 27.6 ppb

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Sample Analysis (6 of 6)

Evaluating uncertainty and error:•Conservative thresholds of maximum predicted ozone concentrations were calculated by adding the 95th percentile value of 27.6 ppb to daily results from the regression model (June 2008 results below)

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Method Strengths

• Provides an objective method that weights relationships that are difficult to quantify

• Leverages existing resources for regions that are currently forecasting ozone

• Provides a quantitative estimate of anticipated ozone concentrations in the absence of fires

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Method Weaknesses

• Requires air quality and meteorological data for a period of several years

• Requires analysis of met conditions leading to high ozone concentrations

• Requires expertise in regression analysis techniques, meteorology, and air quality

• May require updates (or bias testing) as emissions sources and land use changes

• May not be appropriate for ozone exceedances that are close to the standard

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Other Tools (1 of 6)

EPA’s MetDat2 System•Database of meteorological variables for the years 2001-2011•Includes surface and upper-air observational data from ISH and IGRA•Includes gridded (32-km resolution) data from the North American Reanalysis (NARR) model•Could be used to develop regression equations for ozone forecasting

ISH = Integrated Surface Hourly databaseIGRA = Integrated Global Radiosonde Archive

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Other Tools (2 of 6)

AirNow-Tech NavigatorMulti-function, web-based air quality GIS used to:•Run HYSPLIT trajectories•Save multiple map configurations, set a default view•View Hazard Mapping System fire locations and smoke plumes

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Other Tools (6 of 6)

AirNow-Tech Navigator•Upcoming improvements

– Satellite data layers including MODIS AOD and true color imagery

– Connection with AQS to backfill AirNow with regulatory data

– Additional data reporting and graphic output capabilities

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Summary

• Regression equations provide a method for quantifying what ozone concentrations would have been “but for” a fire event

• The method is data intensive and may not be applicable to all areas

• MedDat2 data system may be useful in regression equation development

• Other analytical tools exist, such as AirNow-Tech, to aid analysts in preparing exceptional event packets

Contact Information

Hilary Hafner

hilary@sonomatech.com

(707) 665-9900

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