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Estimating the Value of Data from Non-Governmental Agencies and Citizens
to the AIRNow Program
Timothy S. Dye1, Patrick H. Zahn1, Daniel M. Alrick1, John E. White2, John Birks3, and Jessa Ellenburg4
1Sonoma Technology, Inc.2U.S. Environmental Protection Agency
32B Technologies, Inc.4GO3 Project
Presented at the National Air Quality ConferencesMarch 7-10, 2011
San Diego, CA
4068
2
Outline
• Background
• Pilot project with GO3 Project– Instrument details– Data comparisons– Data merging and fusion– Results
• Challenges and benefits for AIRNow
3
Background – Everything Is (or Will Be) Monitored
4
Background – Monitoring by Citizens
• Citizen science• Measurements made by
– Citizens– Non-government organizations
• Data collected– At fixed locations or moving platforms– Indoors and/or outdoors
• Technology is key– Monitor cost and size– Internet telemetry and reporting
5
Background – Measurement Approaches
1. Miniaturize AQ instruments• BAM EBAM• 2BTech ozone instrument
2. Use low-cost, less accurate sensors• University efforts• Intel Berkeley• Do It Yourself (DIY) community
Stacey KuznetsovCarnegie Mellon University
Allison WoodruffIntel
2B Technologies, Inc.
6
Background – AIRNow
• Data coverage– Increased coverage is costly– Many data gaps still exist
• New mapping systems– Better methods– NASA project data fusion
AIRNow ozone monitoring network coverage
7
Pilot Project with GO3
• The Global Ozone (GO3) Project– Middle and HS students get monitors– Strong focus on education– Student-run ozone monitoring stations
• Locations in– U.S. (mostly Colorado) (40)– International (32)
GO3 Project
GO3 Project
8
Pilot Project with GO3
9
Pilot Project with GO3
20 Sites (CO Dept. of Public Health and Environment)31 Sites (GO3 Project, by June 2011)
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Pilot Project – Data Quality
Location: Rifle, ColoradoMonitors: AIRNow and GO3
(within 0.5 mi)Period: February through August 2010
2BTech vs. AIRNow-Tech Hourly Ozone at Rifle, CO
R2 = 0.8966
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
2BTech ozone conentration (ppb)
AIR
No
w-T
ech
ozo
ne
co
nc
en
tra
tio
n (
pp
b)
GO3 ozone concentration (ppb)
11
Pilot Project – Mapping
• Data– 1-hr ozone data– Focus on Colorado
• Methods to test
70%
100%
90%
85%
Data DataData QC Merged Grid Data QC Weighted Grids Fused Grid
Merged Fused-Weighed
12
Pilot Program – Mapping
AIRNow and GO3 monitors in Colorado
Locations of AIRNow Sites Locations of GO3 Sites
Colorado Colorado
New Mexico New Mexico
Wyoming Wyoming
13
Pilot Program – Mapping Merged
AIRNow-Tech Sites Only AIRNow-Tech and GO3 SitesMean interpolation error: 0.5132 ppb
RMS interpolation error: 7.836 ppb
Mean interpolation error: 0.2214 ppb
RMS interpolation error: 7.108 ppb
Concentration(ppb)
RMS = Root-Mean-Square
1-hour maximum daily ozone concentrations, 07/15/2010
14
Pilot Program – Mapping Merged
Prediction Standard Error (PSE)• Measure uncertainty of AQI estimations in regions without monitors
• PSE was also reduced across the domain
• GO3 data reduced PSE, especially near the added monitors
• Large error (PSE ≥ 13 ppb) reduced by roughly 18,000 km2
The 13-14 ppb PSE contour has been highlighted (in blue) to illustrate a larger area with low PSE resulting from the inclusion of GO3 data (right).
PSE
7 - 7.99
8 - 8.99
9 - 9.99
10 - 10.99
11 - 11.99
12 - 12.99
13 - 13.99
14 - 14.99
15 - 15.99
16 - 16.99
17 - 17.99
18 - 18.99
19 - 19.99Without GO3 sites
Mean PSE = 14.3 ppb
Including GO3 sites (shown in pink)
Mean PSE = 13.8 ppb
15
Pilot Program – Fused-Weighted
AIRNow-Tech Sites Only AIRNow-Tech and GO3 Sites
Bias: -0.12 ppb
Mean Absolute Error: 0.45 ppb
Concentration(ppb)
Bias: 0.0 ppb*
Mean Absolute Error: 0.02 ppb*
*Compares interpolated value to monitor data point
1-hour maximum daily ozone concentrations, 07/15/2010
16
Pilot Program – What’s Next
• Deliver routine GO3 data for 31 sites to AIRNow
• Test data fusion as part of NASA project (satellite, model, observations)
• Post on AIRNow-Tech
• Evaluate improvement/effect on AIRNow
• Present results to AIRNow stakeholders and steering committee
17
Challenges and Benefits for AIRNow
• Data issues– Quality– Reliability– Ownership
• Representativeness
• “Gap filling” in data-sparse areas
• Citizen engagement and involvement
