Predicting Effects of Point Source Controls on Escambia Bay using CMAQ and Watershed Models by Jo Ellen Brandmeyer, Steve Beaulieu, Randy Dodd, and Michele Cutrofello RTI International, Research Triangle Park, NC, USA Justin T. Walters and John J. Jansen Southern Company, Birmingham, AL, USA Krish Vijayaraghavan Atmospheric & Environmental Research, Inc., San Ramon, CA, USA Presented at the 6th Annual CMAS Conference Chapel Hill, NC, October 1-3, 2007

Overview  Background of the study  Description of modeling domains  Description of models  Challenges of multimedia modeling studies  Results and conclusions

Background of the Study  Crist Power Plant located on Escambia River, which flows into Escambia Bay  Bay has ecological problems, including eutrophication  Additional emission controls are planned for power plant  Expects to reduce emissions by 8,600 tpy NO x and 37,000 tpy SO 2  What is potential reduction in nitrogen load to Escambia Bay from these additional controls?

Multimedia Modeling Required  Need to calculate a change in nitrogen loading to Escambia Bay due to a change in emissions from one facility  Emissions are in air  Effects of interest are in surface water  Nitrogen chemistry is nonlinear in both air and surface water MM5 CMAQ ???

Air Quality Modeling Domain  Subdomain of the 12×12-km Visibility Improvement State and Tribal Association of the Southeast (VISTAS) modeling domain for the southeast US centered on Alabama and Georgia (i.e., the ALGA domain)  Calendar year 2002

Watershed Modeling Domain  Based on 8-digit hydrologic units  Eight HUCs in modeling domain  Five completely in Escambia Bay drainage area  Two completely in non- Escambia Bay drainage area  One split between drainage areas  Each HUC simulated independently  Water exiting one HUC routed through downstream HUCs to Bay

Air Quality Model Configurations  CMAQ-VISTAS  Based on CMAQ v4.5.1 with VISTAS-developed modifications to the secondary aerosol module.  Does not include coarse sea-salt/HNO 3 chemistry  CMAQ-MADRID  Based on CMAQ v4.5.1 with EPRI-sponsored improvements including an improved aerosol module, organic chemistry, and sea-salt/HNO 3 chemistry  CMAQ-MADRID-APT  Based on CMAQ-MADRID including advanced plume treatment (plume-in-grid).  Included about 40 large power plants, including Crist, as PinG sources.

Additional Presentations  Session 5, Tuesday 11:30 A.M. Modeling of Atmospheric Nitrogen Deposition to the Escambia Bay and Watershed in the Southeastern United States  Krish Vijayaraghavan, Rochelle T. Balmori, Shu-Yun Chen, Prakash Karamchandani and Christian Seigneur – Atmospheric & Environmental Research, Inc.  Justin T. Walters and John J. Jansen – Southern Company  Eladio M. Knipping – Electric Power Research Institute  Session 8, Wednesday 2:30 P.M. Comparative Model Performance Evaluation of CMAQ-VISTAS, CMAQ- MADRID, and CMAQ-MADRID-APT for a Nitrogen Deposition Assessment of the Escambia Bay, Florida Watershed  Jay L. Haney, Sharon G. Douglas, Tom C. Myers – ICF International  Justin T. Walters, John J. Jansen – Southern Company  Krish Vijayaraghavan – Atmospheric & Environmental Research, Inc.

Watershed Model Configurations  Parameters based on land use  Screening-level model  Export Coefficient Method (ECM) based on EPA’s Pollutant Loading Model (PLOAD)  Annual time step  Intermediate-level model  Regional Nutrient Management (ReNuMa) from Cornell University  Modified for daily deposition  Requires calibration

Watershed Model Calibration  Adjusts model parameters to fit model output to monitoring data  Fine tune model to study area  Ensure that the model reacts as expected to watershed characteristics  Fit hydrology (flow) and nitrogen output  2002 Flow from National Water Information System (NWIS) and local precipitation data (MM5 output was inadequate)  Adjust flow-related parameters in the model (e.g., recession coefficient, seepage coefficient, land use curve numbers)  Water quality data from STORET (EPA and FL-DEP)  FLUX model from U.S. Army Corps of Engineers for in-stream flux from sample data and flow records

Other Input Data  Land use from National Land Cover Dataset (NLCD) grouped from 29 categories to 13 general categories  Export coefficients by land use category from literature review  Many more parameters including:  Septic systems  Point sources (direct discharge to surface water) from National Pollutant Discharge Elimination System (NPDES) permits  Soil characteristics by land use and watershed  Rate of nitrogen from fertilizer application  Erosivity coefficients  Recession coefficient (proportion of groundwater added to stream within a watershed)

Challenges of Multimedia Modeling Studies  Spatial representation  Grid cells vs. land use and watershed combination  Created spatial factors  Precipitation data for watershed model calibration  Temporal scale  Hourly vs. daily or annual  GMT vs. local time  Chemical species  Chemical mechanism vs. total nitrogen  Communicating results

Sample of Study Results Total Nitrogen CMAQ-VISTAS ReNuMa watershed model with additional controls

Contributions of Source Types by Watershed

Overall Loadings to Escambia Bay

Nitrogen Deposited, Retained, and Transferred Within the System

Sample of Study Results CMAQ-VISTAS ReNuMa watershed model Difference due to additional controls

Results of Implementing Controls  ReNuMa predicts a smaller decrease in load due to controls compared to the ECM  Reductions for the Escambia Bay WS and Non-Escambia Bay WS similar between the models  Higher ‘transfer rate’ for Non-Escambia Bay system (~18%) compared to Escambia Bay system (~13%) * Change in “Mass to Bay” is due only to change in Atmospheric Deposition load to the bay for the ECM but due to both Atmospheric Deposition and Non-Point Source load changes for ReNuMa

Transfer Rates Due to Controls

Conclusions and Recommendations  This study demonstrated how cross-media modeling can simulate impacts from air emission changes on water quality.  By using the one-atmosphere approach of CMAQ combined with watershed models and routing between watersheds, fate and transport across media can be examined.  The methods developed here are transferable to other locations, but the results are not directly applicable.  The study was constrained by using a single year of meteorology. Abnormal precipitation (e.g., tropical systems) can have significant effects on watershed modeling results.  All efforts such as this project will benefit greatly from additional, site-specific data, both atmospheric and watershed.