Remote Direct-Reading Monitoring of
Residential Air Quality: Initial Experience
Dorr G. Dearborn, PhD, MD
Dept Environmental Health Sciences
Case Western Reserve University
Cleveland, Ohio
Remote Monitoring of Residential Air Quality
• FINANCIAL DISCLOSURES (Dr. Dearborn)
– NONE
• Funding all from federal agencies (NIH, HUD) and
• Intwine Connect (self funding some of the
development; no support to CWRU staff)
Remote Monitoring of Residential Air Quality
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Overview
Calibration and QA
Household Activities
Status of Current Projects
Current Challenges
HUD-COPD Study
Remote Monitoring of Residential Air Quality
• FUNDING– NIH National Children’s Study, formative research project
– HUD Office of Healthy Homes & Lead hazard Control, HH
Technical grant
• RESEARCH TEAM– CWRU• Dept EHS: Dorr Dearborn, Ellen Wells, Jeno Mozes,
Ying Huang, Mary Ellen Scott, NCS staff
• Dept EECS: Ken Loparo, Don Moore
– Environmental Health Watch- Stu Greenberg, Matt Berges
– Battelle MI- Marcia Nishioka, Thomas Kelly
– Intwine Connect, Inc- Dave Martin, Joe Logan
Remote Monitoring of Residential Air Quality
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Overview
Calibration and QA
Household Activities
Status of Current Projects
Current Challenges
HUD-COPD Study
Remote Monitoring of Residential Air Quality
• NCS Goal: To develop a low cost multi-sensor
technology for real-time, remote monitoring of
residential air quality in NCS homes .
NCS Formative Research Project: Develop
and investigate the feasibility, acceptability and
cost of this technology.
• HUD Goal: To monitor air quality in homes
retrofitted to Energy Star and Deep Energy.
Remote Monitoring of Residential Air Quality
• Approach:
– real-time sensor array with telemetry over cellular
networks enabling data transfer to central server for
translation, analysis, and storage
– Home indoor air quality parameters•
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temperature,
humidity,
particulates (PM>2.5 and PM>0.5),
total volatile organic compounds (VOCs),
nitrogen dioxide (NO2)
carbon monoxide (CO)
carbon dioxide (CO2)
Air Quality Monitoring Units
Intwine air quality
monitor (AQM)
DYLOS particulates unit
Data Transmisson
AQM
Residential Air Quality
Temperature
/Humidity
PM
2.5-10 µm
0.5-2.5 µm
CO, CO2,
NO2, & tVOC
Q min
Cellular
Router
CWRU Server
SENSOR CHARACTERISTICS*
Sensor
Model
(type)
Typical Detection Detection Limit
Range
Temp
Microchip TC1047A
(bipolar diode)
-40 C to 125 C
-40 C
Relative
Humidity
Honeywell HIH-5031
(thermoset polymer
capacitive sensor)
0 to 100% RH
0% RH
CO
Figaro TGS 5042
(fuel cell type
electrochemical sensor)
1 to 10,000 ppm
5 ppm
Total VOCs
Figaro TGS 2602
(MOS sensor)
1 to 10 ppm
“Depends on target gases”
NOx
Synkera 706
(MOS sensor)
1 to 20 ppm
0.5 ppm
Particulate
Matter
CO2
Dylos DC1100 Pro
(Laser particle counter)
≥0.5, ≥2.5 micron bins 0.5 micron particles
350 to 10,000 ppm
Figaro TGS 4161
(solid electrolyte sensor)
* MANUFACTURER’S DATA
350ppm
SENSOR CHARACTERISTICS*
Sensor
Model
(type)
Typical Detection Detection Limit
Range
Temp
Microchip TC1047A
(bipolar diode)
-40 C to 125 C
-40 C
Relative
Humidity
Honeywell HIH-5031
(thermoset polymer
capacitive sensor)
0 to 100% RH
0% RH
CO
Figaro TGS 5042
(fuel cell type
electrochemical sensor)
1 to 10,000 ppm
5 ppm
Total VOCs
Figaro TGS 2602
(MOS sensor)
1 to 10 ppm
“Depends on target gases”
NOx
Synkera 706
(MOS sensor)
1 to 20 ppm
0.5 ppm
Particulate
Matter
CO2
Dylos DC1100 Pro
(Laser particle counter)
≥0.5, ≥2.5 micron bins 0.5 micron particles
350 to 10,000 ppm
Figaro TGS 4161
(solid electrolyte sensor)
* MANUFACTURER’S DATA
350ppm
LOW COST - <$1000 PER UNIT
Initial Sensor Challenges
• Sensitivity and Range
• Temperature (humidity) dependent
Phase 1 air quality testing with
PORTABLE monitors
Parameter
INDOOR
(8-9 houses)
OUTDOOR
(5-6 houses)
Sensors*
Average
Range
Average
Range
detect range
Lower
limit*
76.6
68.6-92.0
75.9
62.4-88.4
-40 - 125 °C
-40
Humidity, rel%
56.3
38.9-70.9
60.8
45.2-78.7
0 - 100%
0%
CO, ppm
1.4
0.3-4.2
1.0
0.1-2.0
1-10,000
5
CO2, ppm
609
342-3952
359
331-543
350-10,000
350
NO2, ppm
0.08
0.0-0.6
0.06
0.0-0.4
1-20
0.5
Total VOCs, ppb
342
0 -812
34
0 -204
1000- 107
#
PM >2.5, ct/ft3
5410
1000-20700
145497
1900-604700
2000
PM >0.5, ct/ft3
248609
17800-648100
303626
7500-598600
8000
Temperature,
°F
*manufactures’ values, # depends on target gas
Phase 1 air quality testing with
PORTABLE monitors
Parameter
INDOOR
(8-9 houses)
OUTDOOR
(5-6 houses)
Sensors*
Average
Range
Average
Range
detect range
76.6
68.6-92.0
75.9
62.4-88.4
-40 - 125 °C
Tested
Lower
limita
-40
Humidity, rel%
56.3
38.9-70.9
60.8
45.2-78.7
0 - 100%
0%
CO, ppm
1.4
0.3-4.2
1.0
0.1-2.0
1-10,000
0.2a
CO2, ppm
609
342-3952
359
331-543
350-10,000
331a
NO2, ppm
0.08
0.0-0.6
0.06
0.0-0.4
1-20
Total VOCs, ppb
342
0 -812
34
0 -204
1000- 107
0.005a
0.8a
PM >2.5, ct/ft3
5410
1000-20700
145497
1900-604700
2000
PM >0.5, ct/ft3
248609
17800-648100
303626
7500-598600
8000
Temperature,
°F
*manufactures’ values, # depends on target gas
aSensors
have dynamic range to reach lower levels; however, lowest level tested,
which is listed here as the lower limit, was the background level in ambient air.
Initial Sensor Challenges
• Sensitivity and Range
• Temperature (humidity) dependent
• Individual sensor calibration needed
– Metal oxide sensors are highly variable
– Sensors cross talk
• Each sensor array unit has to be calibrated
• Opportunity to increase sensitivity
• 70 point gas mixture matrix in an
environmental chamber
Remote Monitoring of Residential Air Quality
•
•
•
•
•
•
Overview
Calibration and QA
Household Activities
Status of Current Projects
Current Challenges
HUD-COPD Study
Calibration of Gas Sensors
• Gas sensor platforms (AQMs) are placed in an environmental
chamber with external data logging.
• Concentrations of NO2, CO, CO2 and VOCs(alpha-pinene) are
simultaneously controlled per pre-defined profiles at several
different temperatures and relative humidity.
• Parallel ‘Gold Standard’ and raw AQM data are collected for
a 70 point matrix.
• Calibration equations are derived using a multi-dimensional
linear regression taking the log of the Gold Std
measurements and the raw AQM data.
• Enables a linear relationship between the raw data and
physical concentrations.
Calibration of Gas Sensors
SENSOR DRIFT
Plots of CO and CO2 data using calibration equations which include CO2
(blue lines; note the drift upward) and exclude CO2 (red lines; drift
removed)
AIR QUALITY INSTRUMENTS
Measurement
Temperature
Relative
humidity
Characteristic Gold Standard
Portable Monitors
Model
HOBO Model H14-002/STSI VelociCalc 9555, probe 982
THA-M002
Sensor type
ND
NDb
Typical rangea
-40° to 75°C (-40° to
167°F)
-10 to 60°C
Precisiona
±0.7° @ 20°C
±0.3°C
Model
HOBO Model H14-002/STSI VelociCalc 9555, probe 982
THA-M002
Sensor type
ND
Typical rangea
0% to100% RH (between
0 to 95% RH
0 and 50°C)
Precisiona
±3% RH over range
NDb
±3% RH
a. Based on manufacturer specifications unless otherwise noted.
b. ND = Data not provided
AIR QUALITY INSTRUMENTS
Measurement
Carbon dioxide
Characteristic
Gold Standard
Portable Monitors
Model
LiCOR Model LI-820
Infrared (NDIR) gas
analyzer
0-20,000 ppm
<3% of reading
Thermo Environmental
Model 48C
Gas filter correlation
spectroscopy
0-10,000 ppm
± 1 ppm
TSI VelociCalc 9555, probe 982
Sensor type
Typical rangea
Precisiona
Model
Carbon
monoxide
Sensor type
Typical rangea
Precisiona
NDb
0 to 5000 ppm
±3% or ±50 ppm
TSI VelociCalc 9555, probe 982
NDb
0 to 500 ppm
± 3% or ±3 ppm
a. Based on manufacturer specifications unless otherwise noted.
b. ND = Data not provided
AIR QUALITY INSTRUMENTS
Measurement
Characteristic
Model
Oxides of
nitrogen
Total volatile
organic
compounds
Particulate
matterc
Sensor type
Typical rangea
Precisiona
Model
Sensor type
Typical rangea
Precisiona
Model
Sensor type
Typical rangea
Precisiona
Gold Standard
Thermo Environmental
Model 42C
Chemiluminescence
analyzer
0-100 ppm
±0.4 ppb
VIG Model 20
Flame ionization
0-10,000 ppm
± 1%
Climet CI-500
Laser diode particle
counter
0.3, 0.5, 1.0, 3.0, 5.0,
10.0 μm
ND
Portable Monitors
RAE Systems QRAE Plus
Electrochemical
0-250 ppm (NO);’ 0-20.0 ppm
NDb
RAE Systems, ppbRAE 3000
Photoionization
1 to 10,000 ppb
NDb
Dylos DC1100 Pro
Laser particle counter
≥0.5, ≥2.5 μm bins
+28%c
a. Based on manufacturer specifications unless otherwise noted.
b. DN = data not provided.
c. . Based on the authors’ testing versus the Climet CI-500.
VALIDATION: Application of calibration equations to actual
environmental chamber readings
Field Quality Assurance
Comparison of particulate matter information from remote monitors
versus portable monitors for one volunteer home.
Quality Assurance Testing with Portable Field Instruments
[Have had
difficulties with NO2
readings with the
portable field
instruments]
Remote Monitoring of Residential Air Quality
•
•
•
•
•
•
Overview
Calibration and QA
Household Activities
Status of Current Projects
Current Challenges
HUD-COPD Study
Household Activities
• Smoking
• Screen shots of air quality perturbations
PM >0.5 Counts/0.01ft3
SMOKER vs NON-SMOKER
At pm ≥0.5 um
CO (ppm)
CO
CO2 (ppm)
CO2
VOC (ppm)
VOC
Tea Pot
Particles
Particles
pm≥0.5um
pm≥2.5um
NO2(ppb)
NO2
Coffee Pot
Kitchen Activities: electric coffee percolator vs heating water on gas stove
CO (ppm)
CO2 (ppm)
CO
VOC (ppm)
VOC
VOC
CO2
CO
Particles
Particles
pm≥0.5um
NO2(ppb)
pm≥2.5um
CO
BREAKFAST: GAS STOVE BACON AND EGGS THEN TOASTER
NO2
CO
pm≥0.5um
CO2
VOC
pm≥2.5um
NO2
COOKING DINNER: GAS STOVE TOP + OVEN
CO (ppm)
CO2 (ppm)
CO
VOC (ppm)
CO2
pm≥2.5um
pm≥0.5um
LYSOL SPRAY
VOC
VOC
NO2
CO (ppm)
CO
Particles
pm≥0.5um
CO2 (ppm)
VOC (ppm)
CO2
Particles
pm≥2.5um
VOC
NO2(ppb)
ATTACHED GARAGE: CAR RUNNING
NO2
CO (ppm)
CO2 (ppm)
CO
Particles
pm≥0.5um
Particles
VOC (ppm)
CO2
pm≥2.5um
ATTACHED GARAGE: CAR RUNNING
VOC
NO2(ppb)
NO2
Dry wall sanding clean-up: Particle
counter in upstairs bedroom
Impact of Room Air Cleaner
Air Quality Perturbations: Household Activities
SYSTEMATIC STUDIES: EXAMPLES
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Mothballs
Plug-in air freshener
Incense burning
Lysol spray
Cigarette smoking
Room air cleaner (vs purifier)
Stove (hood alternatives)
– Stove (Bunsen burner) on
– Fry onions (hotplate)
CHARACTERISTIC PATTERNS?
Remote Monitoring of Residential Air Quality
•
•
•
•
•
•
Overview
Calibration and QA
Household Activities
Status of Current Projects
Current Challenges
HUD-COPD Study
National Children’s Study:
Project Status
• FEASIBILITY
– Technology --> Generation 4 system
• ACCEPTIBILITY
– Just completed study with 9 NCS participants
• COST
– AQM unit (2/house)
– Calibration (2/house)
– Transmission, analysis
~$1500
~$500
?
Deep Green and Healthy Homes
• Retrofit of 12 single-family, inner city homes:
6 Energy Star & 6 Deep Energy
• Monitored energy use & indoor air quality
• Challenges:
– Establishing & maintaining connectivity
• within house locations and cellular 3G
– Developing less expensive means of calibration
• Currently assessing the validity of averaging the
calibration equations across multiple NCS units.
– Managing & analyzing a large amount of data (ongoing)
• Initial Results:
– Large amount of short-term variability
Remote Monitoring of Residential Air Quality
•
•
•
•
•
•
Overview
Calibration and QA
Household Activities
Status of Current Projects
Current Challenges
HUD-COPD Study
Data Management
• Each house:
– 2 AQM units/house --> 8 x 2 = 16 parameters
– Data collected every 60 secs
– --> 288 kilobytes/house/day
• Data transmission (cellular):
– Intelligent sampling (threshold approach)
• If all sensors stable --> Q 5 min
• Any change in any sensors --> back to Q60 sec
– ‘Store-Forward’ memory device
• Only transmit Q12h to Q24h
Data Analysis
• Data cleaning– e.g. removing non-reporting times
• Exposure analysis– Algorithms for accumulative exposures
Peak:
- height
- area
- frequency
– Pattern recognition for activities
Practical Questions for Field Study
• Frequency of data points (30-60 sec?)
– How much ‘smoothing’ for routine analysis?
• Stability? (calibrate before install & at end)
• Memory at unit
– Need to cover power outages?
– Need to cover intermittent cellular coverage?
• Acceptability to subjects
– Activity portal
Sensor Challenges
• Sensitivity and Range
• Temperature (humidity) dependent
• Individual sensor array calibration needed
• Long term stability
– Can we go for 12 months?
CURRENT DESIGN CHALLENGES
• Data as absolute or trend values
– Impact on system cost
• Level of resolution?
– Projection of importance for health studies
– Relates to both sensor sensitivity and specificity
• Allowable error?
• Exact calibration conditions/parameters
– Oxygen concentrations
– Barometric pressure
GENERATION 4 SYSTEM
• Sensor array platform
– Optimal sensor selection, e.g. new CO2 sensor
• Considering additional sensors: HCHO, O3, O2,
barometric pressure
– Optimize sensor placement and interface circuits
– Upgrade AQM mechanical / industrial design
• Local server class gateway
– Local calculations, storage, batch transmission
– Remote selection of data window viewing
• Cellular network
– Data storage and analysis site
Health Effects of NO2
• Know to be an inducer of airway inflammation
• Prior exposure associated with more severe virusinduced asthma exacerbations.
[Chauhan et al., Lancet, 361:1939, 2003]
• Indoor NO2 associated with increased asthma
symptoms in inner city children.
[Hansel et al., Envir Hlth Persp, 116:1428, 2008]
• Increased ambient NO2 associated with increased
prevalence of allergic sensitization.
[Weir e al., Resp Med, 107:1763, 2013]
Pollutant Standard and Guideline Concentrations
Logue et al., http://dx.doi.org/10.1289/ehp.1306673
Natural Gas Cooking Burners
Logue et al., http://dx.doi.org/10.1289/ehp.1306673
-simulation modeling in Southern California homes (without
ventilating hoods; winter)
A -exposures exceeding health-based standards &
guidelines.
B –increased sample median of highest simulated 1-hr
indoor concentrations.
NO2
CO
HCHO
_A_
62%
9%
53%
__B__
100 ppb
3000 ppb
20 ppb
• Suggest that 55-70% homes using NGCB are in “non-attainment”
Remote Monitoring of Residential Air Quality
•
•
•
•
•
•
Overview
Calibration and QA
Household Activities
Status of Current Projects
Current Challenges
HUD-COPD Study
PLANNED HUD-COPD STUDY
• Home environment largely ignored in COPD
medical management
• Many of same environmental triggers for asthma
the same with COPD
• PLAN: pilot study of highly selected COPD pts
– Monitor home air quality in parallel with daily
telemedicine monitoring of pulse Ox, peak flow, etc
– Look for any correlation with clinical exacerbations
– Consider interventions to improve air quality and
look for changes in Air Quality and Clinical Outcome
Residential Air Quality
Temperature
/Humidity
PM
2.5-10 µm
0.5-25 µm
CO, CO2,
NO2, & tVOC
Q min
Ventilation,
Appliances
Cellular
Router
[Q 24h]
CWRU Server
Residential Air Quality: COPD Study
Air Quality System: bedroom & kitchen
Temperature
/Humidity
PM
2.5-10 µm
0.5-25 µm
CO, CO2, NO2,
& tVOC
Telemedicine
pO2, peakF, P,
BP, wt, survey
Q min
Ventilation,
Appliances,
(Air cleaner)
[1-6x per day]
Cellular
Router
VA telemedicine
system
[Q 24h]
CWRU
Server
Additional
Clinical Data
Data Analysis
COPD Project: Basic Design
• Patients (pilot study- before & after):
– 15 COPD patients from VA hospital;
– Highly selected- no longer smoke, no significant
co-morbidities
– Telemedicine and clinical parameters
• Housing:
– Single family house without structural problems
• Study:
|__12 mo observation__|^|__12 mo F/U_____|
^ Major house cleaning; bedroom air cleaner
Remote Direct-Reading Monitoring of
Residential Air Quality: Initial Experience
National Children’s Study- Additional
parameters
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NOISE (SOUND)
LIGHT
Remote Direct-Reading Monitoring of
Residential Air Quality: Initial Experience
QUESTIONS?