1. 1 Ambient Monitoring Program PM 2.5 Data Lean 6 Sigma Air Director’s Meeting May 2015

2. 2 History PM 2.5 is collected from 23 sites across Georgia –Each site has an FRMs - Integrated filter-based samplers –2 Continuous FEMS for QA and 15 Continuous (non-FEM samplers) Due to weather conditions, personnel and equipment issues - not have enough data (2011-2013) to demonstrate that we were meeting the 2012 standard. Therefore, EPA could not make a determination of the air quality of Georgia -- Alabama and South Carolina were affected as well for some areas As a result, we were asked to submit the 2014 data by February 27, 2015. This is a 33% reduction in time to submit the data Therefore, this project needed to address both quality and timeliness

3. 3 Pilot Project It was decided to use Lean Six Sigma to create a common vocabulary and set of tools along with a team approach to improve this process in a very short period of time Lean reduces waste and bottlenecks to focus on the customer needs Six Sigma reduces variation so that each time, you can be certain of your product

4. 4 Brunswick Data Completeness by month

5. 5 Charter Reduce time needed to provide 2014 Ambient Monitoring Data to the EPA by February 27, 2015 (a reduction from 3 months to 2 months, or 33%). Increase quality at each site to at least 95% per quarter. Soft Benefits of this effort include: –Improved data quality –Better teamwork and cooperation –More satisfied employees and customers

6. 6 Getting Started Created a flow chart of the process Identified five high level steps Further discussion revealed that the impact of the quality is multiplicative and reducing the quality in one area would have detrimental effect on the total Therefore, at least 95% quality is needed to ensure for each of the three groups handling filters (Lab, Field, Data validation)

7. 7 Areas for Focus Looking at longest expected time, first five bars will give you 80% of benefit in improvement Reducing time for Lab export and holding Sample pick up - look at 2x per week pickup Reduce backlog in QA and Data Validation Reduce Second QA Review

8. 8 Teams formed Due to a large number of participants, we were able to form four subteams plus a steering team to manage the Lean Six Sigma process –Quality / data completeness –Lab procedures –Data validation procedures (QA review) –Benchmarking (comparison with other states on how they handle PM 2.5 )

9. 9 Data Completeness Team Focused on the reason for voids each year at each site Compared site voids Recommended Contingencies –Check samplers remotely each day –Supervisor verify operator’s installation, collection, shipping techniques –Hand deliver samples –Install continuous monitor for “problem sites” –Investigate differences between sites –Change shipping measures/procedures at problem sites

10. 10 Lab Team Looked at reasons for high number of voids Identified ways to reduce the time to process filters while maintaining quality to increase customer (Ambient Program) satisfaction Recommendations taken: –Send AMP filter weights 2x per month (reduce process time by 2 weeks) –Weigh filters with very small pinholes (increase valid samples by up to 10 per month) –Standardize comment language to streamline data validation Delineate responsibility for samples that are questionable –What is AMP’s call? What is Lab’s call? –Update SOPs

11. 11 Quality Assurance “Independent Quality Assurance Verification” Required by EPA Involves checking data for at least 20% of PM 2.5 integrated samples –Amount of time taken and errors discovered were metrics Time at Start of Project: –2 weeks to process each month of data –Process involved considerable manual manipulation of data in Excel (non-value- added work) –Some process steps may be redundant

12. 12 Improvement Completed Excel automation macros of current data validation process –Reduces time, likely more consistency and accuracy –Likely less corrections needed by QA Verifier –Easier to train new people Some process redundancies removed –Documentation of what is done at each step QA Verifier only checks summary reports, no longer duplicates data validation process Estimated process time now 5 days (one week) per month of data –Reduction from 10 days to 5 days –Saves ~ 3 months of work for the year

13. 13 Recommendations Reduce transcription errors –Considering electronic field data sheets, new database Consider further analysis / improvement of QA Unit data validation process –Additional redundancies among Lab, Data Reviewer, QA Unit? –Focus on identifying and correcting errors that most impact quality

14. 14 What have we done so far? Using Macros to eliminate manual work Bypassing outdated software that had issues Lab providing weights in two batches each month and much faster reporting of weights Lab no longer voiding filters with very small pinholes Lab no longer voiding filters for field activities Standardized comment language on data entry Some process redundancies removed Implemented contingencies for 2015 winter weather Certified 2014 data on February 3 (24 days early) Developing contingencies for issues resulting in data loss - ongoing

15. 15 Effect of LSS Very small pinholes 2014 storm

16. 16 Benefits of Project Communication!!! Automation of work allows time to focus on issues rather than tedious tasks Streamlined process Focus on preventative measures rather than reactionary measures Staff available for other projects More complete data –Since October 2014 only one site was less than 75% of data completeness for the month –Most sites are meeting the 94% completeness established by the subteam Improvements made in PM 2.5 process have been implemented on other pollutants as well

17. 17 Questions? ? DeAnna Oser Ambient Program Manager Georgia Environmental Protection – Air Protection Branch DeAnna.Oser@dnr.ga.gov (404) 363-7004