# Presentation: 030734

## Overview of the MARAMA PM2.5 Forecasting Project U.S. EPA’s 2003 National Air  Quality ConferenceBill GillespieThe Mid-Atlantic Regional Air Management AssociationFebruary 4, 2003San Antonio, TX

## Presentation Outline

- A Brief Overview of MARAMA
- The Need for PM2.5 Forecasting Support
- The MARAMA PM2.5 Forecasting Project
- A Look at the Future

## The Mid-Atlantic Regional Air Management Association (MARAMA)

- MARAMA
- non-profit association of 10 state and local air pollution control agencies
- organized in 1990 by Mid-Atlantic Governors, Mayors, and County Commissioners
- promotes cooperation and coordination among state and local air quality agencies
- Members include: DE, DC, MD, NJ, NC, PA, VA, WV, Philadelphia, and Allegheny County, PA

## MARAMA’s Mission

- MARAMA's mission is to strengthen the skills and capabilities of member agencies and help agencies work together to prevent and reduce air pollution in the Mid-Atlantic region.

## The Need for PM2.5 Forecasting Support

- Many members have strong ozone forecasting programs, but little or no experience forecasting PM
- There is increasing interest in fine particulate pollution because of its adverse human health impacts.
- MARAMA states are observing relatively high PM 2.5 values in some areas

## 1999-2001 Annual PM 2.5 Design Values in MARAMA Region

- Counties with at least 1 complete site with design value _<_15.0

- Counties with at least 1 complete site with design value >15.0

- Counties without a complete site

**1999-2001 Annual PM 2.5 Design Values in MARAMA Region**

- Data from EPA’s AQS 7/8/02

## MARAMA members are concerned about the resources needed to do year-round air quality forecasts
Human Resources: 
In some state/local agencies, there may not be adequate staff to forecast ozone seasonally and PM year-round
Adequate coverage on weekends and holidays will require more than one meteorologist
Some areas are concerned about needing more continuous monitoring data

- Human Resources:
- In some state/local agencies, there may not be adequate staff to forecast ozone seasonally and PM year-round
- Adequate coverage on weekends and holidays will require more than one meteorologist
- Some areas are concerned about needing more continuous monitoring data

**The Need for PM****2.5**** Forecasting Support**

## Slide 8

## The MARAMA PM2.5 Forecasting Project 

- The purpose of the project is to:
- analyze PM data and trends,
- develop and improve PM2.5 forecasting methods and tools, and
- train and support PM forecasting teams

## The MARAMA PM2.5 Forecasting Project

- MARAMA plans to hire a contractor to:
- develop Classification and Regression Tree (CART) tools for nine cities
- work closely with state and local forecasters to apply, test and evaluate the tools
- conduct a workshop to train forecasters

## The Forecast Cities

- Baltimore, MD
- Bristol, Richmond and Roanoke, VA
- Charlotte, NC
- Newark/Elizabeth, NJ
- Philadelphia, PA
- Washington, DC
- Wilmington, DE

**The Forecast Cities**

## The Project Schedule

## The Selection of the CART Method

- Why CART was chosen for the forecast project:
- CART can be used to identify the different meteorological conditions that lead to different PM levels
- provides a rigorous and objective means for distinguishing among different types of PM episodes
- forecasters in the MARAMA region wanted experience with this tool

## What is CART?

- CART is a statistical tool used to separate historical data into classification “bins”
- bins are associated with a range of values of a classification variable  (e.g., PM2.5 concentration)
- classification in a bin is based on the value of other parameters (e.g., observed meteorological data, regional PM concentration, etc.)
- CART software identifies the parameters with the highest correlation to observed PM concentration

## What is CART?

- CART software uses these parameters to split the dataset into two dissimilar groups or bins
- This is process is repeated until the resulting bins contain days with similar PM concentrations
- In this manner, CART identifies the meteorological conditions (the splits) that are associated with PM concentrations and episode types (the bins)

## An Example of a CART Tree

- CART output takes the form of an up-side-down classification “tree”
- Branches/splits (of the data) determine the binning

- N = 200

- N = 165

- RH> 65%

- RH  65%

- WD=N

- WS  2 m/s

- WD = E, S, W

- WS > 2 m/s

- BIN #1
- CLASS = 3
- 45  PM < 65
- N = 50

- BIN #2
- CLASS = 1
-  PM < 25
- N = 150

- BIN #3
- CLASS = 4
- PM  65
- N = 15

- BIN #4
- CLASS = 2
- 25  PM < 45
- N = 150

- N = 365

## The MARAMA PM2.5 Forecasting Project 

- After the CART tool has been developed, the contractor will:
- conduct a training workshop for MARAMA forecasters next fall
- work with forecasters for four months to evaluate and enhance the CART tool
- The CART tool will serve as another tool in the forecasters “tool kit” for forecasting PM levels
- MARAMA and MARAMA members will “own” the tool, its data and logic so that state staff can modify and improve forecast performance

## Slide 18

## A Look at the Future

- Some challenges lie ahead...
- we do not have a long historical record of continuous data on which to base our PM forecasts and forecast tools
- getting the forecast right may be difficult for some areas and conditions – our credibility is on the line
- many forecasters in the MARAMA region would like  about a year to test and evaluate forecast tools before going public with PM forecasts

## A Look at the Future

- But with the challenges come opportunities...
- we will learn a lot more about air pollution and the atmosphere
- rather than focusing on one pollutant, we will have to take a more holistic view, make a more comprehensive analysis
- new information will improve emission inventories, air quality models, forecasting tools, etc.
- We appreciate the opportunity to help improve air quality forecasting in the MARAMA region

## Notes and Credits

- The MARAMA web site address:
- 		 http://www.marama.org
- Credits:
- Cartoons from: _Einstein Simplified, Cartoons on Science by Sidney Harris_, Rutgers University Press
- CART Tree from Sharon Douglas, ICF/SAI