# Presentation: 949166

## PM2.5 Forecasting Method Development and Operations for Salt Lake City, Utah

**Presented by**
**Timothy S. Dye    Dianne S. Miller**
**Craig B. Anderson  Clinton P. MacDonald**
**Charley A. Knoderer Beverly S. Thompson**
**Sonoma Technology, Inc.**
**Petaluma, CA**
**(707) 665-9900**
**www.sonomatech.com**
**tim@sonomatech.com**
**Presented at EPA’s National Air Quality Conference:**
**Mapping and Forecasting**
**San Francisco, CA**
**February 4-6, 2002**

- 901491-2146

**Notes:**
PM Forecasting pilot project

Part of a large project to develop an PM2.5 forecasting guidance document

SLC

Thank co-authors

## Objective and Outline

- Objective:  Develop PM2.5 forecasting methods and forecast PM2.5 for Salt Lake City area during winter 2002
- Outline:
- Data analysis
- Forecasting method (tool) development
- Operational forecasting
- Initial results
- Data issues
- Future plans

**Notes:**
Objective is ....

Goal is to understand some of the issues in prediction PM2.5

SLC selected:
	-Complex met
	-Wintertime AQ problem
	-Good historical record of PM fine data
	-Continuous PM2.5 monitors

To develop program
	-Conducted DA
	-Operations
	-Results
	-Data issues
		-Filer vs. continuous measurements

## Data Analysis

- Description of data sources
- PM2.5 and PM10 data (AIRS database)
- Winter months (Oct. – Mar.)
- 5 years of PM10 data (1996 – 2001)
- 2 to 4 years of PM2.5 data (1998 – 2001)
- 14 sites with PM10 data, 16 sites with PM2.5 data
- Used only the 24-hr filter data
- Continuous PM2.5 and PM10 data from three TEOM monitors (1999 – 2001)
- Meteorological data (NOAA)
- 5 years of hourly surface observations and upper-air soundings from Salt Lake City airport
- Daily Weather Maps
- Data processing
- Performed quality control checks and time/units standardization
- Used an MS Access database to store raw and computed data values

**Notes:**
PM Data
	-Two types PM10 and PM2.5
	-Two measuremnet methods:  FRM-Integrated 24 hr average filter sampler and continuously (hourly) instrument (TEOM)
	-Used only filer data for developing forecasting tools and methods
	-Also, have 3 years of PM2.5 data from TEOMS

Met data
	-Typical
	-Surface parameters (key ws, wd, t, rh, and wx)
	-Synoptic typing

## Data Analysis

**Notes:**
Lots of historical filter sites
	-Used to conduct historical analyses

Few continuous sites
	-3 sites
	-1 in each air basin

## Data Analysis

- Developed a climatology:  Examined the frequency and characteristics of PM episodes in Salt Lake City
- Examined the following based on an AQI computed from PM2.5:
- Yearly trend
- Monthly frequency
- Day of week frequency
- Overview of results
- Higher PM2.5 tends to occur
- on Thursday – Sunday
- in December and January

**Average monthly distribution of AQI categories (based on PM****2.5****) for Salt Lake City**

**Notes:**
To begin understanding the air quality, we...

Develop an AQ climatology
-Understand the characteristics of PM
-Yearly, monthly, DOW frequency

General results
-Higher PM2.5 on Thurs-Sunday
-Wintertime (Dec-Jan)

Frequency of days in each Month of different AQI levels
	-See in February
		-7days moderate
		-1-2 days USG
	-Help forecasters understand the issue

## Data Analysis

- Day-of-week frequency for AQI categories (based on PM2.5) in the Salt Lake City region (1998-2001)

**Notes:**
Looked at number days wth AQI categories segmented into day of week

Explain plot

-Thurs-Sunday has higher PM2.5 concentration
-Given same weather pattern, this period will produce a higher concentrations

## Forecasting Method Development

- Analyzed PM2.5 episodes for general relationships in weather conditions and identified several important variables
- 500-mb pattern (ridge or trough overhead)
- Strength and duration of temperature inversion
- Surface wind speed and direction
- Developed forecast guidelines based on these variables (subjective)
- Developed regression equations and Classification and Regression Tree (objective)

**Notes:**
Reviewed weather data and maps for major episode from 1999-2001.

Found several key relationships, not surprising

-500 mb pattern
-Inversion strenght
-Surface winds
-Fog was important

-Develop both subjective and objective forecasting tools

## Forecast Guidelines

- Forecast PM2.5 ranges and associated meteorological conditions

| 0-15 ug/m3 | 15-25 ug/m3 | 25-40 ug/m3 | >40 ug/m3  |
| --- | --- | --- | --- |
| Strong trough  | Weak ridge or trough  | Moderate ridge  | Strong ridge  |
| No inversion | Weak inversion | Moderate inversion | Strong inversion |
|  | Light to moderate winds | Light winds | Light, decoupled winds |
|  |  | Moderate surface high  | Strong surface high |
|  |  | Weak daytime heating | Weak daytime heating |
|  |  |  | Morning haze or fog |
| If holiday, add 0 ug/m3 to forecast | If holiday, add 10 ug/m3 to forecast | If holiday, add 20 ug/m3 to forecast | If holiday, add 20 ug/m3 to forecast |

**Notes:**
Forecasting matrix
How to use

PM2.5 concentrations across top
Typical wx conditions in matrix
Ask questions of forecast data:
	-500 mb pattern
	-Inversion characteristics
	-Winds
	-Other

Provides a general indication of PM2.5 concentration ranges

## Forecasting Method Development

- Objective Tools
- Produced scatter plots of PM2.5 and meteorological data
- Ran CART and linear regression to develop equations
- Resulting regression equation:
- Next Day PM2.5 = 53.4 + 3.4*Holiday – 0.2*Precip – 0.5*WSday + 1.0*(700T12Z – Tmin) + 0.8*(700T00Z – Tmax) + 0.2*700Td00Z – 0.3*850WS00Z – 0.3*Tmax
- Checked physical relationship between each variable and PM2.5
- Tested both techniques operationally during two weeks of forecasting

**Notes:**
Developed several objective tools

CART and regression

Regression equation:
-500 mb variables
-Inversion variables
-Temperature variables
-Precip/Clouds variables

Plug in forecasted values, provides forecast PM2.5 concentration

## Data Acquisition and Dissemination

**- Data collection**
**- Formatting**
**- Quality control**
**- 24-hr average calcs.**
**- Conversion factor**
**- AQI calculations**
**- Forecast creation**

- STI Weather Center

- FTP

- Phone

**E-mail forecast to Utah DEQ**

**STI SmogWatch web page for Salt Lake City **

**Notes:**
Setup operational process

Timeline here, started forecasting development in December, started operations in mid January

Operationally it consists of:
-Data set to up by UDEQ
-QC, store in database, convert to AQI
-Use AQ data and WX data/forecast to predict PM
-Disseminate

## Daily Forecasting

_**0900 - 1000 MST**_
- Verify that current data is being received
_**1100 MST**_
- Review previous day’s observations and forecast
- Review current day’s AQ data, weather forecast data, and NWS discussions
- Run regression equation
- Run CART
- Create and review current- and next-day forecasts
_**1200 MST**_
- E-mail forecast to UDEQ
_**Afternoon**_
- Monitor conditions for any dynamic changes

**Notes:**
Timeline

Takes generally less than 1 hr to do forecasting, but monitor throght the afternoon

Process of generating the forecast.

Email....

## Forecast Dissemination

- Daily PM2.5 Forecast for Salt Lake City, UT
- Today's Date:  January 25, 2002
- Yesterday's regional maximum AQI:            49 (15 ug/m3) - Good
- Today's forecasted regional maximum AQI:     60 (20 ug/m3) - Moderate
- Tomorrow's forecasted regional maximum AQI:  60 (20 ug/m3) - Moderate
- Discussion:
- The upper-level low pressure system that was expected to move towards Utah today has slowed some resulting in a stronger inversion than originally anticipated.  The models keep the inversion intact today despite increased winds aloft.  This will result in Moderate PM2.5 values for today.  The upper-level low pressure system moves towards Utah tomorrow increasing winds aloft; however, the inversion is forecasted to remain intact through Sunday resulting in Moderate PM2.5 levels.
- Forecaster:  Charley Knoderer
- Sonoma Technology, Inc.
- (707) 665-9900

- Sample Daily E-mail

**Notes:**
Forecast for Today and Tomorrow

Discussion, highlighting weather and air quality features

## Forecast Dissemination

- Salt Lake City SmogWatch Web Page

**Notes:**
Also post on SmogWatch private web page

-It’s an on-line database of both real-time and historical data and forecast information

-Allows users to query past data on the web.

## Initial Results

- Slight tendency to overpredict the next-day forecast

**Notes:**
Explain plot

Regression over predicts – due to using filter dataset

Picks up general trend.

## Data Issues

- Continuous PM2.5 values from the TEOMs can be significantly less than the FRM filter values.  Difference varies, based on several meteorological parameters.
- Continuous PM2.5 monitoring network is limited - only one site in each air basin.
- How to communicate hourly AQI when “standard” is based on a 24-hour average?

**Notes:**
A major issue with using the continuous data has surfaced

Difference between FRM (historical) and TEOM (continuous) data
Continuous data underestimates the FRM data, key issue because in real-time under estimate health effect

Differences due to sampling method in the TEOM which heats the air before sampling, this dries the air and volatizes some of the PM, hence the tendency to under estimate the FRM data

We can adjust data

FRM data obtained from AIRS and used to develop the program

But operationally we used the TEOM data to monitor AQ and forecast

Other issues:
	-limited data
	-Convert to 1-hr AQI

## Data Issues:  TEOM vs. FRM

**Moderate**

**USG**

**Unhealthy**

- R2 = 0.63

- Collocated TEOM (continuous) and FRM (filter)
- TEOM PM2.5 underestimates the FRM

- PM2.5 Continuous vs. Filter Intercomparison for the Lindon site (24-hr average data from December to March of 1999, 2000, and 2001)

**Notes:**
Here’s a scatter plot of FRM and TEOM

Perfect fit 1:1

Above line = Continuous data underestates FRM
Below line = Over estimates

Several points ...
AQI category underestimation

## Data Issues:  TEOM vs. FRM

- Adjusted PM2.5 Continuous vs. Filter Intercomparison for the Lindon site (24-hr average data from December to March of 1999, 2000, and 2001)

- Adjusted TEOM data using a site specific regression equation that incorporated temperature and humidity
- Reduces underestimation by TEOM

- Tc = –1.98 + 1.08 TEOM – 0.67 Avg. Temp. + 0.08 Avg. RH

**Notes:**
Correct for temp and RH

Much better cont. estimate FRM!

## Next Steps

- Implement relative humidity and temperature conversion factor to adjust continuous TEOM data to better match FRM filter data.
- Continue forecasting until March 1
- Modify and adjust forecasting tools as “new truths” are learned
- Continue to evaluate forecast performance
- Acknowledgments
- Utah Department of Environmental Quality
- U.S. Environmental Protection Agency