an analysis of ozone monitoring seasons in the u.s. louise camalier (not attending) (presented by...
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An Analysis of Ozone Monitoring Seasons in the U.S.
Louise Camalier (not attending)
(Presented by David Mintz)National Air Quality Conference
Portland, Oregon April 8, 2008
Purpose
The ozone NAAQS level is now 0.075 ppm
Are states’ current official monitoring seasons adequate to protect against the adverse health effects protected by the
future primary air quality standard?
Analytical Plan
Use most recently certified 3-yr period 2004-2006
Look at ambient data, examine actual exceedences
Predict ozone to examine potential exceedences (across time) Useful for monitors without year round
data
Official Monitoring SeasonsCurrent CFR vs. AQS Current CFR (mandated season)
Generally, seasons are consistent within a state, excluding Texas and Louisiana (defined by AQCR)
AQS (mandated & modified season) Seasons can be modified on a site-by-site basis,
based on judgment of regional administrator Examples:
California, Nevada, Arizona
May-Sep
Mar-OctMar-Sep
Mar-Nov
Jun-Sep
Apr-Nov
MonitoringSeason
Apr-Oct
Official Ozone Monitoring SeasonsWhere is the year round monitoring?
Good spatial representation
Year round monitoring
Official seasons in AQSMonitoring Data in AQS
Jan-Dec
Apr-SepMay-Oct
Wisconsin is April 15 -
Sept 15
Using Ambient Data What do we see?Ambient, year round data from 531 sites (~45% of total)
Examine number of observed exceedances (8hr daily max) using as much data as possible
Full year Partial year Only within monitoring season
With the data available, are we seeing exceedances occurring outside of a state’s official season?
Exceedances: in or out of season? Common (“core”) monitoring season across all states is
June-Sept Months displayed on the following maps are the “fringe”
months Feb-May (4 months before) Nov-Jan (4 months after)
Are there out of season exceedances when the concentration threshold is lowered?
Scenarios: 0.075 ppm 0.060 ppm*
*indicator for the yellow “AQI” level
Ozone AQI Summary
Category AQI ValuePrevious 8-Hour
Ozone AQI(ppm)
New 8-HourOzone AQI
(ppm)
1-HourOzone AQI
(ppm)
Good 0-50 0.000-0.064 0.000-0.059
Moderate 51-100 0.065-0.084 0.060-0.075
Unhealthy of Sensitive Groups
101-150 0.085-0.104 0.076-0.095 0.125-0.164
Unhealthy 151-200 0.105-0.124 0.096-0.115 0.165-0.204
Very Unhealthy 201-300 0.125-0.374 0.116-0.374 0.205-0.404
Hazardous301-400 0.405-0.504
401-500 0.505-0.604
Changes are in red Used in conjunctionwith one another
Standard scenario: 0.075 ppm
April
MarchFebruary
“in season” exceedances in blue“out of season” exceedances in red
May
4 months before common O3 season
2/1-2/27 3/30-3/31
This situation doesn’t in itself justify expanding the season for the entire month of March
October November
December
Standard scenario: 0.075 ppm“in season” exceedances in blue“out of season” exceedances in red
January
4 months after common O3 season
1/24-1/31
February March
Scenario: 0.060 ppm
April May
“in season” exceedances in blue“out of season” exceedances in red 4 months before common O3 season
Scenario: 0.060 ppm
October November
December January
“in season” exceedances in blue“out of season” exceedances in red 4 months after common O3 season
Estimating ozone at existing sites when data is not available
Predicting during the off-season months
20 40 60 80
2060
100
Modeled Days
Predicted
Obs
erve
d
0 20 40 60 80
020
6010
0
All days with observations
PredictedO
bser
ved
0 5 10 15 20
05
1015
20
Number Exceedance Days(Months with any Observed data)
Predicted
Obs
erve
d
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep Oct
Nov
Dec
Predicted exceedances days above 60
05
1015
Columbia, SC OzoneTarget months in red(Fit months = 3, 4, 5, 9, 10 )
Why Statistically predict ozone? We would like to “fill”
temporal gaps where little or no ozone data are available
Statistical model is used and tailored to accurately predict exceedance rates during off-season months*
Off season month ranges are area specific but typically include months such as February, March, October and November
*Assumes that relationship between ozone and meteorology during other months is similar to data used in fitting (do not use core months)
Example for South Carolina
Red dots are data in the predicted months
Statistical Prediction of Ozone(in non-monitored months)
Case Study: Columbia, South Carolina
South Carolina’s official monitoring season: April - September We want to predict ozone during months outside of the official
season Focusing on predicting for: February, March, and October Core months for ozone season: June, July, and August Ozone and meteorological relationships are different during
“core” months, therefore we only use the surrounding (cooler) months (March, April, May, September, and October) in the model
Using the cooler months is best as this better represents the kind of meteorology and ozone response that occurs during the months which we are trying to predict
About the model Urban area ozone data is combined with meteorological data (1997-
2006) Relationship is developed between maximum 8-hr ozone values and
meteorology- Maximum 8-hr ozone is modeled as a function of daily
meteorological variables (max temperature, humidity, etc.) Best predictions obtained when excluding summer months during the
fitting process (June, July, Aug) Summer relationship is different from spring/fall/winter relationship
Columbia has observed data for months which we are trying to predict (e.g., February, March, October, November) Use these data to validate our model predictions
Results are shown for Columbia For more details:
Camalier, L., Cox, B., and Dolwick, P., 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmospheric Environment 41, 7127-7137.
Example: Columbia, South Carolina
Scatter plot of observed and predicted values for only the data used in the fitting process (March, April, May, Sept, Oct)
Scatter plot of observed and predicted number of exceedances for all month/year combinations (2004-2006) with observed data
Values in red not used in fitting process (February, November)
Model Validation
20 40 60 80
2060
100
Modeled Days
Predicted
Obs
erve
d
0 20 40 60 80
020
6010
0
All days with observations
Predicted
Obs
erve
d
0 5 10 15 20
05
1015
20
Number Exceedance Days(Months with any Observed data)
PredictedO
bser
ved
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep Oct
Nov
Dec
Predicted exceedances days above 60
05
1015
Columbia, SC OzoneTarget months in red(Fit months = 3, 4, 5, 9, 10 )
20 40 60 80
20
60
100
Modeled Days
Predicted
Observ
ed
0 20 40 60 80
020
60
100
All days with observations
Predicted
Observ
ed
0 2 4 6 8
02
46
812
Number Exceedance Days(Months with any Observed data)
Predicted
Observ
ed
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Predicted exceedances days above 75
01
23
4
Columbia, SC OzoneTarget months in red(Fit months = 3, 4, 5, 9, 10 )
exceedances > 75 ppb occur in months outside of current monitoring season (red bars)
Jan 0Feb 0Mar 0.6April 3.4May 4.8June 3.9July 3.7Aug 1.2Sep 0.7Oct 0.1Nov 0Dec 0
Columbia, South Carolina
June, July, and August are not used in the fitting process, however they are behaving the way we expect
Using ambient & predicted dataCase Example: South CarolinaSeason: April-September Used urban area with highest
expected exceedences
Ambient Data (2004-2006) On average, for 60 ppb
~10 exceedences/year between 2/15-3/31
We are predicting ~8 exceedences/year between February and March
20 40 60 80
2060
100
Modeled Days
Predicted
Obs
erve
d
0 20 40 60 80
020
6010
0
All days with observations
Predicted
Obs
erve
d0 5 10 15 20
05
1015
20Number Exceedance Days
(Months with any Observed data)
Predicted
Obs
erve
d
Jan
Feb
Mar
Apr
May Jun
Jul
Aug
Sep Oct
Nov
Dec
Predicted exceedances days above 60
05
1015
Columbia, SC OzoneTarget months in red(Fit months = 3, 4, 5, 9, 10 )
Feb: 1.2March: 6.4
Predicted Exceedences, days above 60 ppb
predicted months
Conclusions
One can use ambient, existing data along with statistically predicted data to guide informed decisions
Any modifications of the official season will be based on monitoring judgment and the results from this analysis
Other Questions?
Contact: Louise Camalier
(919) 541-0200
EPA,OAR,OAQPS,AQAD, Air Quality Analysis Group (RTP, NC)