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Establishing Representative Background Concentrations for Quantitative Hot-Spot Analyses for Particulate Matter. Adam N. Pasch 1 , Ashley R. Russell 1 , Leo Tidd 2 , Douglas S. Eisinger 1 , Daniel M. Alrick 1 , Hilary R. Hafner 1 , and Song Bai 1 1 Sonoma Technology, Inc., Petaluma , CA

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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


National Cooperative Highway Research Program

AASHTO Standing Committee on the Environment

NCHRP 25-25/Task 89

August 20, 2014


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


Project Motivation

  • 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


Research Purpose

  • 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

EPA Guidance

EPA Guidance: Two Methods

  • 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.

EPA Guidance

EPA Guidance: Exceptional Events (EEs)

  • 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.


Using Ambient Data: Major Steps

  • 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-typeor air transport event and document and remove these data from consideration (research purposes only).


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


Identify Candidate Monitors and Data

Hypothetical Project Location

Example: PM10 monitor sites and data acquisition from EPA AirData website.


Assess Meteorology and Land Use

Example below: Map of land usetypes based on USGS data.

Example above: wind rosecreated using the AirNow-Tech website.


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


Example of PM Data Acquisition Methods

Example below: data acquisition from the AirNow-Tech website.

Example above: data acquisition from the AirData website.


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 concurredEEs within AirData

  • Check data completeness (75% by quarter, over three years minimum)

  • Identify exceptional-type events (research)


Screen Anomalous PM Data


  • 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)

Exceptional-type events

Air transport events

Research only:


Phoenix PM10 Data: Exceptional Event

Data obtained from AirNow backfilled with AQS data.


Met. Data: Blowing Dust All Quadrants

BLDU ALQDS = Blowing Dust All Quadrants



Visibility Photos: August 3, 2011

12:00 a.m.

3:00 a.m.

Source of images: Arizona Department of Environmental Quality (ADEQ)


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


Step 4: Calculate Background PM

  • Using 2010–2012 data

    • Before= 341µg/m3

  • Removing PM10 data

    • All exceptional events

      • 144µg/m3

    • Exceptional-type events

      • 129µg/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).

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.


  • 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.

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