PM Background Concentrations Used for PM Hot-Spot Analysis

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Establishing Representative Background
Concentrations for Quantitative Hot-Spot Analyses
for Particulate Matter
Adam N. Pasch1, Ashley R. Russell1, Leo Tidd2,
Douglas S. Eisinger1, Daniel M. Alrick1, Hilary
R. Hafner1, and Song Bai1 1Sonoma Technology,
Inc., Petaluma, CA 2The Louis Berger Group, Inc.,
Morristown, NJ for National Cooperative Highway
Research Program AASHTO Standing Committee on the
Environment NCHRP 25-25/Task 89 August 20, 2014
STI-6051
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NCHRP Background PM Study

• Overview
• Project motivation
• Research purpose
• EPA guidance
• NCHRP study (focus of this presentation)
• Ambient data use
• Four-step method
• Phoenix, AZ examples
• CTM use
• Future research needs

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Project Motivation
Overview

• Background concentrations are required for PM
  hot-spot analysis
• Determination of representative background
  concentrations is critical (especially when the
  project increment is small)
• Current guidance is limited on how to assess
  representativeness

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Research Purpose
Overview

• NCHRP 25-25 Task 89
• Support PM hot-spot analyses
• Develop step-by-step methods
• Create illustrative examples and template
• Key technical issues
• Selection of representative monitor(s)
• Identification of exceptional or exceptional-type
  events

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EPA Guidance Two Methods
EPA Guidance

• Estimate background PM concentrations using
  ambient data (three years)
• Single representative monitor
• Interpolation among representative monitors
• Calculate background PM concentrations using
  chemical transport modeling (CTM) outputs (not
  discussed in this talk)
• Interagency consultation is required.

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EPA Guidance Exceptional Events (EEs)
EPA Guidance

• Exceptional events unusual or naturally
  occurring events that affect air quality but are
  not reasonably controllable (NAAQS violation).
• Require a detailed demonstration to be submitted
  and approval by EPA to remove data
• Regulatory impact
• Exceptional-type events (no NAAQS violation or no
  demonstration packet submitted). Handled as
  research only at this time.

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Using Ambient Data Major Steps
NCHRP Study

• Select representative PM monitoring site(s).
• Acquire and process PM concentration data.
• Assess data quality and representativeness.
• Calculate background PM concentrations, following
  EPA requirements.
• Determine data impacted by an exceptional-type or
  air transport event and document and remove these
  data from consideration (research purposes only).

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NCHRP Study
Step 1 Select Representative Monitor Site

• Considerations include
• Distance from project site
• Wind patterns (upwind of project preferred)
• Land use/density/mix of sources
• Monitor height and elevation
• Monitor type and purpose
• Data availability and completeness
• Interagency consultation

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NCHRP Study
Identify Candidate Monitors and Data
Example PM10 monitor sites and data acquisition
from EPA AirData website.
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NCHRP Study
Assess Meteorology and Land Use
Example below Map of land use types based on
USGS data.
Example above wind rose created using the
AirNow-Tech website.
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NCHRP Study
Step 2 Acquire and Process PM Data

• Sources include
• AirData (replaces AirExplorer linked to AQS)
  recommended by EPA guidance
• AirNow-Tech (backfilled with AQS data)
• AQS Data Mart
• AQS Web Application
• Local air quality agency

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NCHRP Study
Example of PM Data Acquisition Methods
Example below data acquisition from the
AirNow-Tech website.
Example above data acquisition from the AirData
website.
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NCHRP Study
Step 3 Assess Quality, Representativeness

• Identify and remove concurred EEs
• Cautionary notes for AirData users
• AirData flags data as Exceptional, but not
  Exceptional and concurred
• Analysts need to manually identify and exclude
  concurred EEs within AirData
• Check data completeness (75 by quarter, over
  three years minimum)
• Identify exceptional-type events (research)

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NCHRP Study
Screen Anomalous PM Data

• Considerations
• Temperature (was residential wood burning
  likely?)
• Visibility
• Wind (i.e., wind speeds greater than 25 mph)
• Smoke or haze reported (or smoke plumes evident
  from satellite observations)
• Transport (i.e., trajectories from a source
  region)

Research only
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NCHRP Study
Phoenix PM10 Data Exceptional Event
Data obtained from AirNow backfilled with AQS
data.
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NCHRP Study
Met. Data Blowing Dust All Quadrants
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NCHRP Study
Visibility Photos August 3, 2011
1200 a.m.
300 a.m.
Source of images Arizona Department of
Environmental Quality (ADEQ) http//www.azdeq.gov/
environ/air/plan/download/eed_080311.pdf
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NCHRP Study
Step 4 Calculate Background PM

• PM10 design value
• 24-hr maximum over three years
• PM2.5 design value
• Annual average average for each quarter, then
  average for each year over three years
• 24-hr
• Tier 1 simpler, more conservative design values
• Tier 2 more complex

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NCHRP Study
Step 4 Calculate Background PM

• Using 20102012 data
• Before 341 µg/m3
• Removing PM10 data
• All exceptional events
• 144 µg/m3
• Exceptional-type events
• 129 µg/m3 (research)
• (24-hr PM10 NAAQS 150 µg/m3)

2010 to 2012 maximum daily PM10 concentrations
for the Central Phoenix Monitor (based on data
obtained from AirData).
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Using CTM Data Considerations
NCHRP Study

• Reviewed CTM information available from EPA
  rulemakings and SIP submissions
• Limited utility of this method because of
• Limited future-year emissions data (estimates
  out of date)
• Documentation of CTM may be inadequate
• May require extensive interagency consultation to
  understand CTM setup and applicability

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Future Research Needs

• EPA-approvable data exclusion methods to handle
  exceptional-type events.
• Help to obtain CTM outputs for use in forecasting
  future background PM concentrations.
• Best practices and lessons learned from
  real-world PM hot-spot analyses.
• Processes to encourage SIP development to support
  background PM estimation.

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Conclusions

• Monitor site selection will be influenced by many
  practical considerations multiple sites may be
  needed for large, spatially complex projects.
• Project analysts should budget analyses to cover
  complex data processing such as exceptional event
  removal and multi-year data assessments.
• Exceptional-type events can substantially impact
  background concentrations.