1. 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 2 The 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 Establishing Representative Background Concentrations for Quantitative Hot-Spot Analyses for Particulate Matter STI-6051

2. 2 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

3. 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 3 Overview

4. 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 4 Overview

5. 1.Estimate background PM concentrations using ambient data (three years) –Single representative monitor –Interpolation among representative monitors 2.Calculate background PM concentrations using chemical transport modeling (CTM) outputs (not discussed in this talk) Interagency consultation is required. 5 EPA Guidance: Two Methods EPA Guidance

6. 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. 6 EPA Guidance: Exceptional Events (EEs) EPA Guidance

7. 1.Select representative PM monitoring site(s). 2.Acquire and process PM concentration data. 3.Assess data quality and representativeness. 4.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). 7 NCHRP Study Using Ambient Data: Major Steps

8. 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 8 Step 1: Select Representative Monitor Site NCHRP Study

9. 9 Identify Candidate Monitors and Data Example: PM 10 monitor sites and data acquisition from EPA AirData website. Hypothetical Project Location NCHRP Study

10. 10 Assess Meteorology and Land Use Example above: wind rose created using the AirNow-Tech website. Example below: Map of land use types based on USGS data. NCHRP Study

11. 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 11 Step 2: Acquire and Process PM Data NCHRP Study

12. 12 Example of PM Data Acquisition Methods Example below: data acquisition from the AirNow-Tech website. Example above: data acquisition from the AirData website. NCHRP Study

13. 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) 13 Step 3: Assess Quality, Representativeness NCHRP Study

14. 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) 14 Screen Anomalous PM Data Exceptional-type events Air transport events Research only: NCHRP Study

15. 15 Data obtained from AirNow backfilled with AQS data. Phoenix PM 10 Data: Exceptional Event NCHRP Study

16. 16 Met. Data: Blowing Dust All Quadrants BLDU ALQDS = Blowing Dust All Quadrants Haze NCHRP Study

17. 17 Visibility Photos: August 3, 2011 12:00 a.m.3:00 a.m. Source of images: Arizona Department of Environmental Quality (ADEQ) http://www.azdeq.gov/environ/air/plan/download/eed_080311.pdf NCHRP Study

18. 18 Step 4: Calculate Background PM PM 10 design value –24-hr maximum over three years PM 2.5 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 NCHRP Study

19. Using 2010–2012 data –Before= 341 µg/m 3 Removing PM 10 data –All exceptional events 144 µg/m 3 –Exceptional-type events 129 µg/m 3 (research) (24-hr PM 10 NAAQS = 150 µg/m 3 ) 19 Step 4: Calculate Background PM 2010 to 2012 maximum daily PM 10 concentrations for the Central Phoenix Monitor (based on data obtained from AirData). NCHRP Study

20. Using CTM Data: Considerations 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 20 NCHRP Study

21. 21 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.

22. 22 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.