Presentation: Farmer-led climate adaptation - Project launch and overview by ...
Low Cost Sensors to Measure Air Quality
1. MONITORING URBAN AIR QUALITY USING DISTRIBUTED
NETWORKS OF LOW-COST SENSORS
DAVID H. HAGAN, JESSE H. KROLL
SUMMER 2017
2. WHO AM I?
…a design engineer and atmospheric chemist with experience
across academia and industry.
3. OBJECTIVE
...to develop a more complete understanding of urban
atmospheric chemistry in megacities using dense networks of
low-cost atmospheric measurement devices.
...to inform results-driven public policy that will have lasting
change.
4. OUTLINE
What is air pollution and why do we care?
Current research direction at MIT
MIT AQ Monitoring Networks
5. Values from Hallquist et al, 2009
GASES / VAPORS
The carbon life-cycle is complicated
Particles, SO2, NOx,
CO, VOCs
EMISSIONS
( 1350 TgC yr -1 )
PARTICLES FORMED
FROM GASES
( 60-240 TgC yr -1 )OXIDATION
PARTICLE AND VAPOR DEPOSITION
( ~1000 TgC yr -1 )
OXIDATION TO CO2
( 300 - 500 TgC yr -1 )
6. Large fraction of PM1 in Kanpur is secondary
NR-PM1 Chemical Composition (AMS) AMS-PMF Factors for OA
Kumar et al, 2016
7. Common/Regulated Pollutants
POLLUTANT INDIA CPCB, 24H MEAN US EPA, 24H MEAN
SO2 30.5 ppb 75 ppbc
NO - -
NO2 42.5 ppb 100 ppbc
O3 50 ppba 70 ppba
CO 1.75 ppma 9 ppm
PM1 - -
PM2.5 60 μgm-3 35 μgm-3
PM10 100 μgm-3 150 μgm-3
VOLATILE ORGANIC COMPOUNDS
Benzene 1.57 ppbb -
Ammonia 575 ppb -
**There are other gas and particle phase pollutants that are not of interest to us at this time (metals, etc)**
a 8-Hour Mean
b Annual Mean
c 1-Hour Mean
9. Air pollution impedes quality of life
3.2M attributable deaths from
ambient PM2.5 pollution - (GBD, 2010)
adapted from Lim, et al. , Lancet (2012)
10. Air pollution decreases life expectancy
• 3.2M premature mortalities
world-wide1
• PM2.5 and O3 reduce life
expectancy by 6.3 years in
Delhi and 3.4 years across
IN2
• $640B decrease in GDP (in
IN) due to mortalities2
[1] Apte et al (2015)
[2] Ghude et al
11. Particle diameter is important for health effects
Nasal/Pharyngeal
• peak at ~1 nm, 1 um
Oberdorster, et al. (2005)
Nasal/Pharyngeal
• peak at ~5 nm
Alveolar
• peak at ~20 nm
12. Monitoring is sparse
Area = 1484 km2 Pop. (2011) = 16.3M
DPCC, 2015
Data from OpenAQ through DPCC
COV MIN MAX
SO2 13 % 0 ppb 115 ppb
CO 65 % 0 ppm 33 ppm
NO2 66 % 0 ppb 182 ppb
O3 65 % 0 ppb 461 ppb
PM2.5 65 % 1 μgm-3 985 μgm-3
DELHI DPCC RESULTS (2016)
14. “Data of poor or unknown quality is less
useful than no data since it can lead to
wrong decisions”
Snyder, et al. “The Changing Paradigm of Air Pollution Monitoring”, ES&T 2013.
18. Research objectives include…
1) Can nonparametric regression techniques be used to “get-
around” current modeling capabilities for electrochemical
sensors?
2) How do perturbations in ambient aerosol distributions affect
low-cost particle counters?
3) Can we use an array of broadband VOC sensors to obtain
real-time information about the distribution of volatiles by
chemical class?
4) Can we obtain information about particle formation and
growth using a multi-pollutant air quality sensor?
20. State of electrochemical sensors
MOTIVATION
• current algorithms have proven ineffective
• mostly LR
• RF methods can’t extrapolate
(Zimmerman et al)
• Cross-sensitive species and environmental
artifacts are very important
• most methods are not real-time (Mead et al)
• Long-term sensitivity decay is unknown
APPROACH
• Develop a method that combines the pros and
cons of parametric (LR) and nonparametric
(kNN) algorithms into one.
• Validate using laboratory experiments and
long-term co-location.
21. Temperature effects on EC sensors
At low concentrations,
T is very important
Hagan et al, 2017
22. k-Nearest Neighbors Hybrid Regression
• use LR at high
concentrations
• use kNN at low
concentrations
• combine using a linear
combination of the two
algorithms
Hagan et al, 2017
23. We find no significant decay over 18 weeks
Hagan et al, 2017
25. Optical particle counters have limitations
MOTIVATION
• widespread adoption in industry
and through community-led
initiatives
• potential for rich dataset on
PM/PN
• optical methods know nothing
about optical properties
• most OPCs cannot see most
particles
APPROACH
Model the response of OPCs to
various aerosol distributions to
probe the uncertaintly
according to the underlying
aerosol physics.
26. OPCs come in many shapes and sizes
Sharp GP2Y1010AUOF
Dmin : ~ 0.5 μm
Dmax : unknown
Nbins: 1
Cost: ~$10-20
Output: Analog signal ∝ Conc.
Dylos DC1100 Pro
Dmin: 0.5 μm
Dmax: 10 μm
Nbins: 2
Cost: ~$200
Output: #/ft3
Alphasense OPC-N2
Dmin: 0.38 μm
Dmax: 17.5 μm
Nbins: 16
Cost: ~$400
Output: #/cm3
27. REPRESENTATIVE AEROSOL DISTRIBUTION SIMULATED OPC
• the sum of n lognormal modes, each described by:
• Ni: total number of particles
• Dpi: median diameter
• σi: standard deviation
• described by parameters:
• Number of Bins
• Minimum cutoff diameter (Dmin)
• Maximum cutoff diameter (Dmax)
• Counting efficiency
f(RI, ρ, κ, composition, morphology)
30. Metrics of evaluation for simulated OPCs
NUMBER-VOLUME CORRELATION
• Assume number concentration is proportional
to mass/volume (Wang et al (2015), DaCunto
et al (2015))
• Ex. Sharp, Shinyei, Samyoung, etc
• Limitations: Nm/Va changes as the underlying
aerosol distribution changes
INTEGRATED VOLUME
• Sum the inferred volumes on a bin-by-bin
basis
• Ex. Alphasense OPC-N2, Dylos (sort of…)
• Limitations: all volume below Dmin is lost
31. Nbins and Dmin are important
Adding bins for a single distribution Changing distribution (GM) for an OPC
Hagan et al, 2017 (in prep)
32. Initial conclusions and next steps
• multi-bin resolution is needed to capture nuance of changing
particle size distribution
• sensitivity and accuracy of both methods improves with lower
Dmin
• lowering Dmin is more important than more bins (for design)
Is there a way we can get around these limitations without
needed an OPC that can see down to <100 nm?
36. HI-VOG Network, Hawai’i USA
• 12 SO2 sensors spread across
elementary/middle schools and DOH
sites
• Launched (for good) Jan. 2017
• ~40 more sensors will be added in
2017-2018 along with CO and PM
measurements
• 6 sensors are permanently co-located
with DOH reference stations for
validation
• Initial results are published in AMTD
Hagan et al, 2017
37. Dorchester Air Quality Sensor Study,
Boston USA
• 5 multi-pollutant sensors spread
across East Boston
• 2 at MA DEP sites for co-location
• Operational since 2015
• Measuring:
• PM2.5, PM10
• O3, CO, NO, NO2
• In collaboration with Aerodyne
Research and the Harvard School
of Public Health
Cross et al, 2017
38. Multi-Pollutant Air Quality Sensor
(MPAQS), Delhi IN
• 24 sensors (12 co-located, 12 at
private company)
• Engineered specifically for Delhi (high
particle loadings in winter)
• Running since 2015 (multiple
versions)
• co-located instruments include E-BAM
(PM2.5), ACSM (PM1 composition),
Aetholometer (BC), SMPS (size
distribution), 2BTech 202 (O3)
• work with Josh Apte (UT Austin)
