# Presentation: 223058

## Air Quality Forecasting Tools

- OVERVIEW
-   Background
-   Acquiring historical data
-   Forecasting tools (examples, strengths/weaknesses)
- Climatology
- Statistical
- Modeling
- Details on developing forecasting methods are provided in the Ozone and PM2.5 Forecasting Guidance Document (U.S. Environmental Protection Agency, 2003).

## Background

- Forecasters use a variety of data products, information, tools, and experience to predict air quality.
- Forecasting tools:
- Subjective
- Objective
- Tools provide information to help guide the forecasting process.
- Forecasting tools are built upon an understanding of the processes that control air quality.
- More forecasting tools = better results.

## Background

- Tool development is a function of:
- Amount and quality of data (AQ and meteorological)
- Resources for development (human, software, computing)
- Resources for operations (human, software, computing)
- Types of tools:
- Persistence
- Climatology*
- Criteria, Thresholds, Rules of thumb
- Regression equations*
- Classification and Regression Trees (CART)*
- Neural networks
- Fuzzy logic
- Numerical modeling*
- Conceptual and experience

-  *Discussed in this course

## Acquiring Historical Data (1 of 2)

- Sources
- Air Quality Data
- EPA’s AIRS database
- AIRNow (regional images)
- Meteorological Data
- See Appendix for list of data sources
- Soundings and surface observations
- Data and weather maps
- Trajectories
- Satellite images

## Acquiring Historical Data (2 of 2)

- Sample size
- Acquire 3 to 5 years of data
- Be aware of changes in emissions (fuel changes, new sources, new monitors)
- Other issues
- Time standard and units
- Air quality data are usually in Local Standard Time
- Meteorological data are usually in UTC
- Concentrations in μg/m3, ppm, ppb
- Data quality
- Review data for quality prior to use
- Data completeness
- 75% of data are needed to compute daily, monthly, and annual averages

## Climatology

- Study of average and extreme weather (or air quality) conditions
- Examines past conditions for
- Maximum and minimum values
- Duration of poor air quality days
- Average number of days in each AQI category
- Diurnal patterns
- Day-of-week distributions
- Weekend/weekday distributions
- Helps guide forecasters

## Climatology – Example (1 of 2)

## Climatology – Example (2 of 2)

## Climatology

- Strengths
- Easy to develop and update
- Helps guide the forecast
- Weaknesses
- Not a stand-alone method
- Does not provide pollutant concentrations

## Statistical – Examples

- Develop statistical relationship between historical air quality and meteorological data
- Several methods
- Regression
- Classification and Regression Trees (CART)

## Statistical – Example (1 of 3)

- PM2.5 Regression Equation
- PM2.5 (μg/m3) = 53.429 + 3.382*Holiday – 0.189*Precip – 0.31*Tmax
-  		– 0.541*SurfaceWS + 1.008*(T@700mb – Tmin)
- 		+ 0.838*(Stability) + 0.183*Td@700mb00Z – 0.292*WS@850mb00Z

| Variable | Description |
| --- | --- |
| Holiday | 1 for Valentine’s Day, Martin Luther King, Jr. Day, Presidents’ Day, Veterans’ Day, and Super Bowl Sunday.
2 for Thanksgiving weekend and Christmas Eve through New Year’s Day.  
1 for weekends immediately preceding or following any of the above holidays. 
0 for all other days. |
| Precip | Forecasted precipitation in inches during the 24-hr forecast period. |
| Tmax | Forecasted daytime maximum temperature (oF) |
| SurfaceWS | Average resultant wind speed from 12Z to 00Z (0500 to 1700 MST) |
| T@700mb | Temperature at 700 mb at 12Z (0500 MST) (oC) |
| Tmin | Forecasted or observed minimum temperature (oC) |
| Stability | Temperature at 700 mb at 00Z (1700 MST) (oC) minus the forecasted daytime maximum temperature (oC) at the surface |
| Td@700mb00Z | Dew-point temperature at 700 mb at 00Z (1700 MST) (oC) |
| WS@850mb00Z | Wind speed at 850 mb at 00Z (1700 MST) (m/s) |

## Statistical – Example (2 of 3)

- Classification and Regression Trees (CART)
- Software develops the decision tree with human guidance
- CART splits data sets into similar and dissimilar groups

**Ozone **
**(Low–High) **

_**Variables:**_
*T850* - 12Z 850 MB temp
*DELTAP* - the pressure difference between the base and top of the inversion
*MI0* - Synoptic weather potential (scale from 1-low to 5-high).
*FAVGTMP* - 24-hour average temperature at La Paz
*FAVGRH* - 24-hour average relative humidity at La Paz.

- CART classification PM10 in Santiago, Chile

-                 **Node x**
**Variable and criteria**
**STD  = Standard deviation **
**Avg = Average PM****10**** (ug/m****3****)**
**N = number of cases in ****	****	****node**

- Cassmassi (1999)

- Is the forecasted temperature at
- 850 mb  10.5°C?

- Yes

- No

- Statistical – Example (3 of 3)

## Statistical

- Strengths
- Easy to operate
- Provides concentration
-     or AQI category estimates

- Salt Lake City, UT
- Next Day PM2.5 Forecast and Observed PM2.5

- Weaknesses
- Requires more expertise to develop
- Must be updated every three years or so
- Tends to underpredict peak concentrations

## Modeling

- Numerically model the processes
- Requirements:
- Gridded emission model
- Meteorological model forecasts
- Photochemical model
- Other supporting data
- Land use
- Boundary conditions (air quality)
- Computer resources
- More sources of uncertainty

## Research and operational modeling is being conducted by a number of organizations:  Environment Canada, MCNC, NOAA, Ohio State, Sonoma Technology, Inc., SUNY-Albany, Washington University

- Modeling – Example (1 of 2)

## Modeling – Example (2 of 2)

**NOAA ARL’s HYSPLIT**

**MCNC’s MAQSIP**

**AIRNow Data**

- Ozone concentration forecasts and observations on August 5, 2002, at 0400 EDT

- Ozone (ppb)

## Modeling

- Strengths
- Based on atmospheric and chemistry physics
- Provides forecasts in areas without monitors
- Helps further explain air quality processes
- Provides better temporal resolution than other methods
- Weaknesses
- High level of expertise and funding needed to develop, operate, and improve*
- Requires substantial computer resources

- *	NOAA is planning to run a model and provide forecast guidance to air quality forecasters.

## Evolution of Forecasting Programs

**1          2         3         4        5        6        7        8        9        10+ **

- Limited data
- Limited experience and understanding
- One forecasting tool/technique
- Modest accuracy

## Summary

- Air Quality Forecasting Tools
- Data availability and quality
- Develop understanding
- Develop tools
- The more tools the better

- Next step - Lunch
- Questions