Download - Detroit Multipollutant Pilot Project
Detroit Multipollutant
Pilot Project
Panel discussion 4: Technical Efforts to Support NAAQS and Air Toxics Programs
March 31, 2008
Karen Wesson
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Detroit Multipollutant Pilot Project:
Background Part of the technical component of the Air Quality
Management Plan (AQMP) Pilot Project to inform pilot project about the technical tools/methods/databases available and demonstrate their application by using Detroit as a testbed.
Address increasing need to provide multipollutant (MP) & multi-resolution air quality information for regulatory/policy development. Determine what source & pollutants to focus on to “maximize
benefits” of control programs & polices.
Improve information to public & stakeholders on multipollutant air quality issues and associated risks to health & environment
Provide framework to others (e.g. state & local agencies) on how to apply technical tools for MP assessments.
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Testing Multipollutant & Multi-Resolution:
Technical Challenges
Multipollutant: Release, control, and chemical formation of pollutants are interrelated NRC report: recommended that “the US transition from a pollutant-by-
pollutant approach to air quality management to a multipollutant, risk-based approach”….
Multi-resolution: Address regional and local-scale impacts of regulations and policies PM2.5 SIPS (e.g. AERMOD - Birmingham, Detroit, Atlanta, Cleveland;
CALPUFF – Allegheny Co, PA; CAMx – St. Louis, IL)
This project provides a challenge for all analytical components: Emissions Inventory: include CAPS & HAPS and support regional and
local scale modeling
Control Information: multi-pollutant for implementation into control strategies and sensitivity analyses
AQ modeling: account for primary & secondary aspects of criteria and toxic pollutants and assess regional and local concentrations and source contribution
Exposure/risk/benefits assessment: provide information on benefit of pollutant reductions at regional and local scales for criteria and toxic pollutants
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Analytical Multipollutant Framework
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View technical work and challenges as
falling into three main categories:
Conceptual Model Development Use 2002 analyses and ambient data to understand technical
and policy implications for multipollutant control strategy development
2002: Model & Tool Implementation and Evaluation Multipollutant framework implementation
Model/Tool Performance Evaluation: CMAQ, AERMOD & Hybrid Approach
Risk & Benefits Assessment Analyses
2020: Implementation of future year and control strategy cases Future year projections
Control strategy selections
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Conceptual
Model
Development
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Developing Conceptual Model
Utilizing MDEQ Reports (e.g. DATI Report, Annual Air Quality Reports, etc.), ambient data, Detroit Pilot Project data, and other data analysis studies determine: What are the important point, mobile, and area source
contributors?
Are emissions dominated by a few source types or more widely distributed throughout the source population?
What are possible sources are co-control?
How does the atmosphere respond to reductions in certain pollutants? When are there dis-benefits?
What controls have the greatest effect on reducing key pollutants?
Use information to develop a Conceptual Model indicating technical and policy implications of the current understanding of air quality
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Develop Conceptual Model specific to
Detroit MP problems VOC-limited regime → suggests focus on VOC controls
for ozone reductions
Important sources of PM2.5 in Detroit: metal processing, commercial cooking, residential wood burning, cement manufacturing → suggests implementing controls on these sectors
Many problem sources are emitting PM2.5 and toxics of concern (e.g. steel mills, cement manufacturing, woodstoves) → suggests potential co-control opportunity
High PM soil (primary) component at Dearborn and Allen Park → suggests focus on controlling local sources
Large mobile source component contribution suggested by receptor modeling → suggests implementing potential controls (could have co-benefits for O3, PM, & toxics (e.g. benzene, formaldehyde))
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2002: Model & Tool
Implementation
and
Evaluation
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2002: Implementing Multipollutant
& Multi-resolution Models & Tools
Emissions Inventory & Modeling 2002 NEI with Integrated CAPS & HAPS
EI improvements
Air Quality Modeling CMAQ v4.6.1
CAMx v4.5
AERMOD
Hybrid approach (2 equations)
Risk & Benefits Assessment BenMAP (O3, PM2.5 & Benzene)
HEM (Toxics)
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2002 NEI: Emissions Improvements
2002 NEI: Integrated HAPs & CAPs
Local-scale EI improvements
Steel Mill Study Data
LADCO Nonroad Study
Solvent Study
Emissions Modeling Improvements
1 km spacial surrogates and other improved land use based
inventory data
Link-based mobile emissions
Criteria & toxic emissions produced using CONCEPT and input
data from SEMCOG network (Generates gridded, hourly, link-
level emissions by vehicle class using highly resolved temporal
profiles for traffic volume and VMT mix)
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Urban-Scale Application – Detroit Network
Source: Alison Pollack of ENVIRON International Corporation
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Air Quality Modeling Photochemical Models
CMAQ v4.6.1 & CAMx 4.5 – “One-atmosphere” models include criteria pollutants and ~ 40 toxics
Modeled at 12 km for Midwest-centered domain
Modeled at fine-scale 4 km & 1km domains
AERMOD Dispersion Model Receptor domain centered on Detroit urban area: 36 x 48 km with
receptors placed every 1 km
Emissions domain extends 36+ km around receptor domain
Hybrid Approach Method Generates local gradients incorporating the advantages of both
CMAQ & AERMOD into one combined model output (via post-processing)
Allows preservation of the granular nature of AERMOD while properly treating chemistry/transport offered by CMAQ.
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4km CMAQ &
CAMx Domain
36x45
1km CMAQ &
CAMx Domain
72x108
AERMOD
Receptor
Domain
36x48
Detroit Domains
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Air Quality Modeling: Hybrid Approach
Method
AERMOD+CMAQCMAQAERMOD
CMAQ
AERMOD
Combined
AERMODAVG
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Benefits and Risk Assessment
The Modeled Attainment Test Software (MATS) tool will be used to provide input data to both BenMAP & HEM Creates a fusion of the ambient and modeled data across
domain
Treats ambient data as “truth” and allows modeled data to provide gradient
BenMAP (Benefits) Will be applied for O3 (12km), PM2.5 (12&1km), and benzene
(1km)
Fine-scale population data included in BenMAP
Local health data input
HEM (Risk) Will be applied for the 11 toxics (12 & 1km)
Includes ability to take gridded modeled input and interpolate to census track
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2002: Model/Tool Evaluation
Model Evaluation (w/ & w/o link-based): Using the AMET tool CMAQ & CAMx
AERMOD
Hybrid method (2 methods)
Risk & Benefits Assessment 12km vs 4km vs 1 km resolution
link vs no-link
Benzene: HEM vs BenMAP
Possible evaluation of HEM, HAPEM, SHEDS (in conjunction with DEARS)
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2020: Implementation of
future year
and
control strategy
cases
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Control Strategy: Sensitivity Analysis Control Database
Use control data available in AirControlNet and special studies.
“Multipollutanize” the control data with help from EPA source-specific engineers for controls in Detroit
Control Strategy 2020 with national rules
Control Strategy 1: “Status Quo”
Use controls for Detroit from illustrative NAAQS 2015 PM2.5 15/35 control scenario as presented in the recent PM2.5 RIA
Use list of controls consistent with those provided in Detroit O3 SIP Strategy Plan
Control Strategy 2: “Multipollutant Based”
Develop a multiple pollutant control strategy based on available “multipollutanized” PM2.5 & O3 control measures and knowledge of AQ issues in the Detroit area
This strategy should achieve PM2.5, O3, and air toxic reductions.
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Exposure/Risk/Benefits
Plans BenMAP (Benefits) - Apply BenMAP for O3 (12km), PM2.5 (12
& 1km), and benzene (1km) using fine-scale population data & local health data
HEM (Risk) – apply for the 11 toxics (12 & 1km)
Considerations How to use risk/benefits results together to quantify “co-benefits”
and make decisions in multipollutant context?
Use this information from Control Strategy 1 for consideration of toxics and criteria pollutant “effects” (i.e., co-benefits and trade-offs) as part of multipollutant control strategy development (CS2)
Apply a tool, such as MIRA, to aid in decision making process?
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Lessons Learned Emissions data used can make a large difference at
local scales (e.g. stack locations, road links) to modeled concentrations and risk & benefits estimates but getting this data can be time consuming.
Running CMAQ/CAMx with 12km grid resolution may not allow local gradients of PM2.5 & toxics to be captured.
Running a dispersion model for a large area with many sources may not be cost effective. Applying a photochemical model at finer grids may be alternative solution.
Multipollutant control information can be hard to find and the emissions inventory may not support it.
And we are still learning ….
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Team Members Louise Camalier
Neal Fann
Tyler Fox
Marc Houyoux
Robin Langdon
Rich Mason
Mark Morris
Sharon Phillips
Tesh Rao
Madeleine Strum
Larry Sorrels
Lee Tooly
Elineth Torres
Darryl Weatherhead
For more info:
http://www.epa.gov/scram001/modelingapps_mp.htm