2. IMPROVE Cost Savings in a Flat Funded World Scott Copeland 10/16/14

3. We have done this before June 2006 – Expected FY2007 EPA budget cuts prompted an evaluation of which IMPROVE sites should be removed. June-December 2006 – Marc Pitchford led a process to rank sites in the order to be shut down if necessary. Several analyses done by Schichtel, Poirot and others. Site List produced. Comments solicited from states and FLMs were compiled.

4. Basic Reality An purely objective method for site selection and ranking does not exist! Need to use objective criteria and information to guide a subjective process – Select sites to keep under all circumstances (keepers) – Select sites suitable for decommissioning and rank them Schichtel 6/2006

5. Sites with high fraction errors heavily weighted towards keeping Site with low fraction errors Because haze is dominated in the east by sulfate, which is the most spatially uniform component, more of the eastern sites are redundant Also show parts of AZ & MT as having redundant sites Aerosol Bext Fractional Error Schichtel 6/2006

6. Poirot 6/2006

8. Overview of Comments General comments received from 18 states, 5 RPOs, 4 EPA Regions, numerous FLMs – its premature (with regard to the RHR process) to shut down any of the 110 sites – SIPs not yet complete; need to ensure progress by trends tracking; some sites with only a few complete years of data; don’t know the fate of other protocol sites that would be caretakers – reducing the number of sites effectively diminishes the number of visibility- protected areas since the RHR uses monitoring data to define the pace of progress and document its performance – IMPROVE Steering Committee is not the appropriate body to make decisions since they can’t balance it against other air program needs – other approaches to reduce cost should be considered, instead of shutting down sites – the methodology of using current data to make decisions about redundancy is flawed for a 60-year trends program where emissions will undoubtedly change significantly – concerns that depending on a state or tribal protocol site for RHR tracking is vulnerable to changing priorities of the sponsor – No written comments were received supporting the reduction of IMPROVE monitoring network

9. IMPROVE Response to Comments Issues being considered (brief responses in red) – Should we proceed with the priority listing of sites for decommissioning? Yes, by categorizing sites instead of a single priority ordered list. Are we the appropriate organization to do this? Yes. Is this the best time to do it? If not, then when? Categorization now, final selection after the budget is available. Should we pursue other ways to reduce cost (e.g. 1 day in 6 instead of 1 day in 3 sampling) instead of reducing sites? Not at this time. – Should we modify the current list of sites and if so how? Yes. Do we want to redo a data-based assessment to identify redundancy using other parameters or a different approach? No, except for minor changes. Should we work from the current list making changes based on comments received? Yes, except for minor changes. Should we change the reassignment of class I areas to remaining monitoring sites based on comments received? Yes, in some cases. Should we explicitly indicate our judgment about the degree of representation a site has for the class I areas assigned to it? Yes, this is the thrust of our response. – Should we consider other ways to reduce cost in addition to reducing the number of sites? Rejected at this time to preserve the utility of data at remaining sites for RHR tracking, source attribution, model testing, etc. most sites only operating 4 years out of each 5 most sites only weighing the samples until years end when we choose the extreme mass events to analyze one day in six instead of one day in 3

10. From Assessment of the Potential Data Redundancy between Nonmonitored CIAs and Their Representative Sites, Schichtel, 2007: “These results suggest that at least some of the nonmonitored CIAs have sufficiently different concentrations from the representative CIA that they warrant the addition of IMPROVE monitoring sites within or closer to the CIA.”

11. We have done this before v 2 October 2012 – Flat funding necessitated cost savings. October 2012-February 2013 – I led a series of budget work group calls. Several analyses done by me, Hand, Moore, Poirot and others. Cost savings measures were implemented which avoided shutting down sites. Summary report produced. Those cost savings measures are still in place.

12. Excerpts from Moore, 2012 Data Requirements for Tracking Progress Under the Regional Haze Rule StepResponsible Agency Reference *Comment Choice of Deciview as metricn/a40 CFR 51.308 (d)(1) GTP Sec. 1.6 Identifies deciview as tracking metric, but does not define it mathematically. Identify best/worst daysIMPROVE programGTP Sec. 2.2, Step 8 GTP Sec. 4.2 The "best" and "worst" days are here defined as the cleanest (lowest) 20% and dirtiest (highest) 20% daily deciview values, per complete year (as determined in GTP Step 7). Provides the calculation for determining how to identify the 20% best/worst days. Calculation of the 5-yr deciview metric IMPROVE programGTP Sec. 2.2, Step 9 GTP Sec. 2.2, Step 10 GTP Sec. 4.3 Calculation of annual average deciviews for best/worst days (as determined in GTP Step 8). Calculation of 5-yr average deciviews from the annual best/worst average deciviews for complete years (as determined in GTP Step 9). It is these 5-yr average deciview values for best/worst days which are to be used for setting the baseline and tracking progress. Repeat of above discussion. Also defines 5-yr progress periods as "2005-2009, 2010-2014, etc."

13. Ancillary Uses of IMPROVE Monitoring Data – Potentially Affected by Funding Reductions A. Development and refinement of sampling and analytical methods 1-15 1-15 B. Assessment of sampling, analytical and data processing artifacts, errors and uncertainties 16-31 16-31 C. Comparison, evaluation and synthesis of methods and data across measurement networks 32-39 32-39 D. Characterization of aerosol formation mechanisms, composition and morphology 40-57 40-57 E. Improved understanding of aerosol vs. optical relationships 58-89 58-89 F. Assessment of long-term temporal and spatial patterns and trends 90-107 90-107 G. Regional and historical background for short-term intensive field and/or tracer studies 108-130 108-130 H. Performance evaluation and refinement of regional and global air quality models 131-153 131-153 I. Input data for multivariate mathematical and/or back trajectory receptor models 154-195 154-195 J. Evaluation of “natural” source impacts (smoke, dust, sea salt, etc.) and regional air quality events 196-215 196-215 K. Assessment of sources, composition, optical & radiative properties of carbonaceous aerosols 216-233 216-233 L. Assessment of transboundary and intercontinental aerosol transport influences 234-245 234-245 M. Comparisons to and synthesis with remote sensing, modeling and surface observation data 246-251 246-251 N. Inverse modeling / development, confirmation and refinement of emissions estimates 252-257 252-257 O. Assessment of human health and/or environmental impacts of specific aerosol species 258-267 258-267 P. Assessment of sources of potentially toxic trace elements 268-274 268-274 Q. Evaluation of single source impacts and control strategies 275-278 275-278 Excerpts from Poirot, 2012

14. Jenny Hand 2013

15. Cost Cutting Measures adopted Projected Effective Savings Stop ARS support of newsletter and reduce steering committee meeting support to note taking. $ 50,000 Stop collection and analysis of backup quartz filters at 7 newest sites. Continue collection at original 6. $ 34,000 Reduce number of collocated sites to 3 of each module rather than 6 $ 43,000 Skip one week of sampling network wide at Christmas $ 38,000 Reduce funding for cooperative services agreement $ 100,000 Reducing annual site maintenance efforts $ 122,000 Use NPS dollars from 3 NPS protocol sites (TRCR1, DEVA1, INGA1) and MALO1 site for routine network operations $ 63,000 Total: $ 450,000

16. We have done this before v 3 October 2013 – Flat funding necessitated cost savings. October 2013-January 2014 – I led a series of budget work group calls. Several analyses done by myself and Hand. Final call in January ended with a nearly perfect split in preference for site removal versus reducing sample frequency, leaving consensus in doubt.

17. Why the split? Eliminating n samples per month would be harder to implement for the contractor. Data completeness criteria would need to be redefined. Introduces a discontinuity in data record. It is likely that it would be less efficient in terms of cost savings per sample lost or cost savings per relative error introduced. Will yield errors much larger than the reported iterative means at some sites each year. Eliminating a site is “easy” for the contractors but focuses the pain on individual states, MJOs, EPA regions, and FLMs. Restarting sites could be problematic. It is implied that class I areas that lose a sampler would be reassigned to (RHTS?) site with highest redundancy.

18. “-1” indicates COHU1 or GRSM1 value below ~3*mdl

19. Fractional RMSE in annual haziest 20% dv siteOne per month * siteOne per month * siteOne per month * siteOne per month * siteSite Removal SAWT13%CHIR12%LOST11%COHU11%COHU12% SENE12%CRLA13%LYBR12%DOME12%DOME12% SEQU12%CRMO12%MACA11%CACR11%CACR13% SHEN11%DENA13%MELA11%JARI11%JARI13% SHRO11%DOSO11%MEVE12%SAMA11%SAMA14% SIAN12%EVER12%MING11%SWAN11%SWAN14% SIME11%GAMO13%MOHO12%IKBA11%IKBA15% SIPS11%GICL13%MONT13%MORA11%MORA15% SNPA11%GLAC12%MOOS12%THRO12%THRO15% STAR12%GRCA23%MOZI13%VOYA22%VOYA25% SULA13%GRGU12%NOAB12%AGTI11%AGTI17% SYCA12%GRSA12%NOCA12%ACAD12% THSI12%GRSM11%OKEF11%BADL11% TONT11%GUMO12%OLYM11%BALD12% TRIN13%HALE12%PASA13%BAND12% TUXE12%HAVO13%PEFO12%BIBE11%Cumulative Fractional Error One per month:195% ULBE12%HECA12%PINN11%BLIS12%Cumulative Fractional Error Remove 11 sites:45% UPBU11%HEGL11%PORE11%BOAP12% VIIS11%HOOV13%RAFA11%BOWA12% WEMI12%ISLE12%REDW11%BRCA12% WHIT12%JARB12%ROMA11%BRID12% WHPA12%JOSH11%ROMO12%BRIG11% WHPE12%KAIS12%SACR11%BRIS11% WHRI12%KALM12%SAGA11%CABI12% WICA12%LABE12%SAGO11%CANY12% WIMO11%LAVO12%SAGU11%CAPI12% YELL22%LIGO11%SAPE13%CHAS11% YOSE12% ZICA12% *Mean of 100 Iterations

20. We have done this before v 4 October 2014 – Flat funding necessitates cost savings. October 2014 + It is not clear that more budget work group calls will yield a consensus. My thought is to split the cost savings between removing sites and decreasing sample frequency. Everybody suffers!