# Presentation: 549570

## 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

**Notes:**
Make sure to note that this analysis to done for Lew Weinstock in the Monitoring group for the new ozone monitoring rule! Turn to him for any additions or for monitoring questions – especially about the timing, etc. of the upcoming rule. He is the expert and he can help you because he will be a member of the audience.

## 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 SeasonsCurrent 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

## Official Ozone Monitoring SeasonsWhere is the year round monitoring?

- Monitoring
- Season

- Official Ozone Monitoring SeasonsWhere is the year round monitoring?

- Good spatial representation

- Year round monitoring

- Official seasons in AQS

- Monitoring Data in AQS

**Notes:**
Animation on this slide. Note Wisconsin, also note the fact that we have good spatial representation on our year round sites – this is helpful for our analysis!

## 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 Value | Previous 8-Hour
Ozone AQI
(ppm) | New 8-Hour
Ozone AQI
(ppm) | 1-HourOzone 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 |
| Hazardous | 301-400 |  |  | 0.405-0.504 |
|  | 401-500 |  |  | 0.505-0.604 |

_***Changes are in ***__***red***_

*Used in conjunction**with one another*

**Notes:**
Animation is on the slide. Note the new yellow level. We are choosing this as an indicator for public notification of the moderate level.

## Using the new standard (0.075 ppm) to locate areas that may need seasonal modifications 

## Standard scenario: 0.075 ppm

- March

- February

**4 months before common O****3**** 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

**Notes:**
Animation is on this slide.
Make sure to emphasize the difference between the core months and the fringe months (shown). The core months are the months during the summer where every state is required to monitor (“common” months). Also, note the dates that the exceedences are occurring...

## Standard scenario: 0.075 ppm

- Standard scenario: **0.075 ppm**

- January

**4 months after common O****3**** season**

- 1/24-1/31

**Notes:**
Animation is on this slide.

## Using the AQI yellow level (0.060 ppm) to locate areas that may need seasonal modifications 

## Scenario: 0.060 ppm

- February

- March

- Scenario: **0.060 ppm**

- April

- May

**4 months before common O****3**** season**

**Notes:**
Just talk about the fact that the low yellow level causes more exceedences to show up, especially in the fringe months (for many), like March.

## Scenario: 0.060 ppm

- October

- November

- December

- January

**4 months after common O****3**** season**

**Notes:**
You may want to add a note about Florida here... lower humidity (relative to summer) in the non-summer months can cause us to see elevated levels in November.

## Estimating ozone at existing sites when data is not available

- Predicting during the off-season months

## 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**

**Notes:**
Animation is on this slide. Should be self-explanatory...

## 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

**Notes:**
Columbia, South Carolina was an interesting example because of its out of season exceedences. These exceedences are normal behaving. Versus Wyoming.

## 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.

**Notes:**
Technical stuff. Go over as much as you feel comfortable. Please note the reference to the paper – this model is based off of the published model, so for more information, people can see this paper or call me.

## Model Validation

- 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)

**Notes:**
Red dots are following the same pattern as the black dots in the bottom graph. This is a good thing and we can see that our model is fitting well for this city – not overpredicting nor underpredicting...

## Columbia, South Carolina

- exceedances > 75 ppb occur in months outside of current monitoring season (red bars)

- Jan          0
- Feb         0
- Mar      0.6
- April     3.4
- May     4.8
- June    3.9
- July      3.7
- Aug      1.2
- Sep      0.7
- Oct       0.1
- Nov         0
- Dec         0

**Notes:**
Animation on this slide.
Talk about core summer months having a different relationship w/met than other months. This is why we do not include them in the fitting when we are trying to accurately predict exceedences in the “fringe” months.
Second animation is the table of expected exceedences per month, per year. What should we do about monitoring in March when we are predicting 0.6 exceedences of 75 ppb? For one, we can rightly assume that any exceedence would occur near the end of the month.

## Using ambient & predicted data

- Case Example: South Carolina
- Season: 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

**Notes:**
Animation on this slide.
Note that we should use both the data that exists (if there is any) and predicted data to inform our decision. Let’s compare the results between the actual South Carolina data and the predicted SC data. ~10 and ~8 are pretty comparable! Note that we are talking about 60 ppb here (yellow level), not 75 ppb. I wanted to use 60 ppb because we got bigger numbers (tall red bars) – easier than to work with the shorter red bars (for 75).

## 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

**Notes:**
You can say these things and then turn to Lew for any additions. Remember, he is directing this effort!

## Other Questions?

- Contact:
- Louise Camalier
- 	_[Camalier.Louise@epa.gov](mailto:Camalier.Louise@epa.gov)_
- 	(919) 541-0200
- 	EPA,OAR,OAQPS,AQAD, Air Quality Analysis Group (RTP, NC)