Using Low-Cost PM Sensors for Industrial, Government, and Educational Applications
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1 Using Low-Cost PM Sensors for Industrial, Government, and Educational Applications Tim Dye Sonoma Technology, Inc. Petaluma, CA for New Jersey Clean Air Council Public Hearing Trenton, NJ April 5, 2017 STI-6706
2 2 Path forward Studies Outline PM 10 coal dust for industry PM 10 windblown dust PM 2.5 EJ wood smoke PM 2.5 community wood smoke PM 2.5 education in schools Key challenges
3 3 Path Forward How Good? Evaluations How Useful? Field Projects How Sustainable? Businesses In progress?
4 4 1. Study Coal Dust (PM 10 ) Objectives 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)
5 5 1. Results Coal Dust (PM 10 ) 17 events were identified Short in duration (a few minutes) Concentrations were 2 to 5 times higher than background 37 of 1,392 hours (2.7%) were impacted by windblown dust events Sponsor: Electric Power Research Institute (EPRI)
6 6 1. Results Coal Dust (PM 10 ) Dylos had good correlation with the BAM for events; weak correlation for all data
7 7 2. Study Windblown Dust (PM 10 ) Objectives 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
8 8 2. 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
9 9 3. Study Wood Smoke (PM 2.5 ) Objectives Use low-cost sensors to provide spatial coverage and engage community Assess the contribution of wood burning to air toxics in Sacramento Study Sacramento Metropolitan AQMD project funded by EPA Grant Two existing regulatory monitoring stations, 4 new monitoring sites with FEMs, 12 new sites with low-cost monitors Three-month wintertime study Do no burn rules result in lowered air toxics concentrations? Equipment Reference Instrument Sensors MetOne BAM 1020 (FEM for PM 2.5 ) Aethalometer (BC) AirBeams Sponsor: SMAQMD
10 10 3. Study Wood Smoke (PM 2.5 ) Sponsor: SMAQMD
11 11 3. Study Wood Smoke (PM 2.5 ) Sponsor: SMAQMD
12 12 4. Study Community Sensing (PM 2.5 ) Objectives Explore how the public would use air sensors Generate a Big Data set for analysis and applications Study Petaluma, CA, air study (funded by STI) 1 regulatory monitoring station, 30 air sensors 4-day study Equipment Reference Instrument Sensors MetOne BAM 1020 (FEM for PM 2.5 ) AirBeams Sponsor: STI
13 13 4. Results Community Sensing (PM 2.5 ) Dec Dec Dec Dec Dec Dec
14 4. Results Community Sensing (PM 2.5 ) 14
15 4. Results Community Sensing (PM 2.5 ) 15
16 16 5. Kids Making Sense Kids Making Sense is a complete environmental education and measurement system that teaches youth about air quality, how to measure pollution using small sensors, and how to interpret the data they collect. Learn Measure Discover Interpret
17 17 5. Results Kids Making Sense Training materials Workbook Teacher s guide Labs and experiments Sensors Particulate matter Gases (in progress) Website: kidsmakingsense.org Data exploration Learning more
18 18 New technology Key Challenges Rapid changes; versioning issues with firmware Drift, calibration requirements, and soiling issues Hardware issues Unknown lifetime Data logging Data acquisition systems don t always handle sensors Data formats and time standards Communications Critical for high data availability More challenging and costly
19 19 Key Challenges 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 Scale 3 sensors vs. 10 sensors vs. 100 sensors Scale affects everything (logistics, data management, reliability, costs)
20 20 Path Forward How Good? Evaluations How Useful? Field Projects How Sustainable? Businesses In progress?
21 21 Tim Dye Senior Vice President Chief Business Development
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