# Presentation: 727276

## 8th Conference on Air Quality Modeling

- Presentation by Ken Steinberg on behalf of the American Petroleum Institute
- September 23, 2005

## American Petroleum Institute Supports Sound Models

- Air quality regulations will require use of AERMOD
- AERMOD will be used for a _host_ of regulatory applications (e.g., residual risk) in addition to tall stacks

- API is strongly interested in this revision of the “Guideline on Air Quality Models”

- API’s goal is to ensure the most scientifically sound (i.e., accurate) air quality models are used for regulatory purposes.

## Summary of API’s Performance Concerns with AERMOD

- “AERMOD: Latest Features and Evaluation Results,” EPA-454/R-03-003, June 2003 and
- “Comparison of Regulatory Design Concentrations: AERMOD vs. ISCST3, CTDMPLUS, and ISC-PRIME,” EPA-454/R-03-002, June 2003.

- Based on reviewing the two U.S. EPA reports on the performance of AERMOD with PRIME

## Summary of API’s Performance Concerns with AERMOD

- Urban/rural performance of AERMOD is _not_ consistent with that of ISC, and
- AERMOD appears to appreciably over-predict majority of concentrations throughout modeling domain

- Analysis of _annual _results from EPA’s limited modeling for _area and volume sources_ shows:

## Urban/Rural Performance of AERMOD Not Consistent with ISC

- Two meteorological data sets (i.e., Pittsburgh 1964 & Oklahoma City 1984)
- Various source configurations, and
- Standard receptor arrays

- Examined Flat & Simple Terrain simulations for area and volume sources from EPA-454/R-03-002

**Simple analysis shows AERMOD does not show the traditional enhanced dispersion in urban areas versus rural**

## Area Source Analysis

| Area Source Cases | ISC | AERMOD:ISC | AERMOD |
| --- | --- | --- | --- |
| RAREFO | 2573 | 0.614 | 1580 |
| RAREFP | 3044 | 0.682 | 2076 |
| Average rural concentration | 2808 | 0.651 | 1828 |
|  |  |  |  |
| UAREFO | 1095 | 1.434 | 1570 |
| UAREFP | 1287 | 1.417 | 1824 |
| Average urban concentration | 1191 | 1.425 | 1697 |

**Comparison of Annual Regulatory Design Concentrations**

- R=rural, U=urban, ARE=Area, F=flat terrain, P=Pittsburgh met data, and O=Oklahoma met data

## Area Source Analysis

- In ISC, significant _*increase*_ in dispersion from rural to urban (Ratio for rural: urban for *area* source in ISC = (2808/1191) = 2.36)
- In AERMOD, minimal _*increase*_ in dispersion from rural to urban (Ratio for rural:  urban for *area* source in AERMOD = (1828/1697) = 1.08
- In rural settings, significant _*increase*_ in dispersion from ISC to AERMOD
- In urban settings, significant _*decrease*_ in dispersion from ISC to AERMOD

**Comparison of Annual Regulatory Design Concentrations**

| Volume Source Cases  | ISC | AERMOD:ISC | AERMOD |
| --- | --- | --- | --- |
| R10VolFO  | 2496 | 1.054 | 2630 |
| R10V0lFP | 1656 | 1.039 | 1720 |
| Average Rural concentration | 2076 | 1.048 | 2175 |
|  |  |  |  |
| U10VolFO | 1775 | 1.348 | 2390 |
| U10VolFP | 1106 | 1.492 | 1650 |
| Average urban concentration | 1440 | 1.403 | 2020 |

-       R=rural, U=urban, Vol=volume source, F=flat terrain, P=Pittsburgh met data, and O=Oklahoma met data

**Volume Source Analysis**

**Comparison of Annual Regulatory Design Concentrations**

## Volume Source Analysis

- In ISC, some _*increase*_ in dispersion from rural to urban.  (Ratio for rural: urban for *volume* source in ISC = (2076/1440) = 1.44)
- In AERMOD, minimal _*increase*_ in dispersion from rural to urban.  (Ratio for rural:  urban for *volume* source in AERMOD = (2175/2020) = 1.08)
- In rural settings, slight _*decrease*_ in dispersion from ISC to AERMOD (*note for the area source, the *_*opposite *_* response was observed from ISC to AERMOD)*
- In urban settings, significant _*decrease*_ in dispersion from ISC to AERMOD

**Comparison of Annual Regulatory Design Concentrations**

- Though smaller, it is again not clear if volume source behaviors are supported by data or are unintentional products of AERMOD.

## AERMOD Appears to Appreciably Over-predict Majority of Concentrations Throughout Modeling Domain

- Residual risk assessments require use of regulatory air quality models, too.
- Model predictions throughout the entire concentration domain are very important, not just the high and high-second-high value.

## Slide 11

## AERMOD Appears to Appreciably Over-predict Majority of Concentrations Throughout Modeling Domain

- AERMOD predictions of concentrations are poor throughout most of the concentration domain.
- AERMOD significantly over-predicts lowest observed concentrations.
- Suggests significant over-prediction of annual averages that are used in risk assessment studies.

## Slide 13

## AERMOD Appears to Appreciably Over-predict Majority of Concentrations Throughout Modeling Domain

- AERMOD predictions are better than for the AGA
- Still show a trend towards poorer performance at the lower concentrations
- AERMOD still over-predicts the concentrations by about 50%
- Again, behavior suggests over-prediction of annual averages that are used in risk assessment studies.

## Conclusions & Recommendations

- Urban and rural performance of AERMOD is _not_ consistent with that of ISC,
- Not apparent whether behaviors (some dramatic) are supported by data
- AERMOD appears to appreciably over-predict majority of concentrations throughout modeling domain

- Analysis of _annual_ results from EPA’s limited modeling for _area and volume sources _ shows:

## Conclusions & Recommendations

- Previous evaluations show good agreement for concentrations at the highest end of the distribution, but show poorer agreement over the remainder of the concentration domain
- This performance would cause annual concentrations used in risk assessments to be significantly over-predicted

- EPA should examine the area/volume performance

## Conclusions & Recommendations

- API believes that concerns regarding model performance suggest, at the very minimum, that the grandfather period be extended to allow these concerns to be technically addressed.

**Conclusions & Recommendations**