Results and Lessons Learned from Using Low-Cost PM Sensors to Detect Ambient PM 2.5 and PM 10
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1 Results and Lessons Learned from Using Low-Cost PM Sensors to Detect Ambient PM 2.5 and PM 10 Tim Dye, Levi Stanton, Clinton MacDonald, Kevin Smith, Paul Roberts, Ashley Graham, and Max Dillon Sonoma Technology, Inc. Petaluma, CA for National Association of Clean Air Agencies Minneapolis, MN October 17, 2016 STI-6593
2 2 4 studies Outline PM 10 coal dust PM 2.5 winter PM conditions PM 10 windblown dust PM 2.5 wildfire smoke Lessons learned
3 3 1. Study Coal Dust (PM 10 ) Objective Determine whether sensors can detect and quantify fugitive PM 10 from coal piles Identify sensor limitations and technical challenges Study 2-month study in warm climate Weather station Equipment Reference Instrument Sensors MetOne BAM-1020 PM 10 Thermo PDR-1500 Dylos AirBeam Sponsor: Electric Power Research Institute (EPRI)
4 4 1. Results Coal Dust (PM 10 ) 17 events were identified Short in duration (a few minutes) Concentrations were 2 5 times higher than background 37 of 1,392 hours (2.7%) were impacted by windblown dust events Sponsor: Electric Power Research Institute (EPRI)
5 5 1. Results Coal Dust (PM 10 ) Dylos had good correlation with the BAM for events; weak correlation for all data
6 6 2. Study Winter (PM 2.5 ) Objective Examine the use of low-cost PM sensors for answering questions about Tribal air quality Conduct intercomparison study and mobile sampling Study 8-month study in northern Minnesota (Oct-June) Outdoor exposure Equipment Reference Instrument Sensors FRM PM 2.5 (1-in-6 day) AirBeam MicroPEM Sponsor: U.S. EPA and Leech Lake Band of Ojibwe
7 7 2. Results Winter (PM 2.5 ) The MicroPEM and AirBeam B are well correlated during most time periods between calibration/zeroing The MicroPEM was difficult to zero properly and exhibited significant baseline shifts between calibration/zeroing Sponsor: U.S. EPA and Leech Lake Band of Ojibwe
8 8 2. Results Winter (PM 2.5 ) Good correlations (R 2 ) between 24-hr sensor measurements on FRM sample days for AirBeam and bias-corrected MicroPEM FRM 1 FRM 2 MicroPEM AirBeam A AirBeam B FRM FRM MicroPEM 0.01 uc 0.96 bc 0.01 uc 0.89 bc AirBeam A NA NA NA NA - AirBeam B uc 0.95 bc NA 1.00 uc Uncorrected MicroPEM PM 2.5 data bc Bias-corrected MicroPEM PM 2.5 is well correlated with the FRMs Sponsor: U.S. EPA and Leech Lake Band of Ojibwe
9 9 3. Study Windblown Dust (PM 10 ) Objective Can low-cost PM sensors detect dust events? How precise are the sensors? Are they reliable? Can they provide sufficient warning time? Study 3-month springtime study School in eastern Santa Barbara County Equipment Reference Instrument Sensors MetOne BAM 1020 (FEM for PM 10 ) GRIMM 11-R (Particle counts) MetOne E-BAM (PM 10 ) AirBeam (3 units) Alphasense OPC-N2 (3 units) Sponsor: Santa Barbara County Air Pollution Control District
10 10 3. Results Windblown Dust (PM 10 ) Alphasense A vs. BAM Hourly PM 10 measurements R 2 = 0.81 Alphasense A vs. Alphasense B Hourly PM 10 measurements R 2 = 0.81 BAM = 1*x Sponsor: Santa Barbara County Air Pollution Control District
11 11 3. Results Windblown Dust (PM 10 ) Early Detection Alphasense A measures a peak at 21:21, for a lead time of 39 minutes over the FEM instrument. Note: BAM reported at begin hour but not available until after the hour Sponsor: Santa Barbara County Air Pollution Control District
12 12 4. Study Wildfire Smoke (PM 2.5 ) Objective Determine whether low-cost PM sensors can detect wildfire smoke Explore mobile/stationary monitoring Demonstrate real-time communications Study PM sensors, quick-mount, real-time communications, website for viewing Preliminary testing in summer 2016 Planned for fall 2016 Equipment Reference Instrument Sensors None Alphasense OPC-N2 Spec sensors with Intel Briza system Sponsor: U.S. Forest Service
13 Preliminary Results Study Wildfire Smoke (PM 2.5 ) Air Sensors App API Database Display Sponsor: U.S. Forest Service
14 14 3. Results Wildfire Smoke (PM 2.5 ) Street sweeper Smoke from a BBQ restaurant Sponsor: U.S. Forest Service
15 PM Concentration (μg/m 3 ) Results Wildfire Smoke (PM 2.5 ) PM Concentrations Following Street Cleaner Aug 1, 2016 (Petaluma) PM10 - FS PM2.5 - FS :43 12:50 12:57 Time (PDT) 13:04 13:12 13:19 Notes: PM from street cleaner Low PM 2.5 /PM 10 ratios (about 12-20%)
16 PM Concentration (μg/m 3 ) Results Wildfire Smoke (PM 2.5 ) PM Concentrations Drive by Lombardi's (BBQ) Aug 1, 2016 (Petaluma), 15-second averages PM2.5 - FS20002 PM10 - FS :55 13:58 14:00 14:03 14:06 Time (PDT) 14:09 14:12 14:15 14:18 Notes: PM from BBQ smoke High PM 2.5 /PM 10 ratios (about 80-95%)
17 17 Sensors Lessons Learned Rapid changes; versioning issues with firmware Drift, calibration requirements, and soiling issues Hardware issues Data logging Data acquisition systems don t always handle sensors Time standards Communications Critical for high data availability More challenging/costly
18 18 Lessons Learned Data management More challenging than FEM instrument (60 to 3600 times more data and more uncertainty) Cost Projects cost much more than one sensor Operations and data management are more intense
19 19 Path Forward How Good? Evaluations How Useful? Field Projects How Sustainable? Businesses In progress?
20 20 Contact Tim Dye Senior Vice President Chief Business Development
Results and Lessons Learned from Using Low-Cost PM Sensors to Detect Ambient PM 2.5 and PM 10
Results and Lessons Learned from Using Low-Cost PM Sensors to Detect Ambient PM 2.5 and PM 10 Tim Dye, Levi Stanton, Clinton MacDonald, Kevin Smith, Paul Roberts, and Max Dillon Sonoma Technology, Inc.
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