Air-Bot Networks: Development of a sensor network to aid in community air investigations (Greg) Gregory M. Zarus, ATSDR Atmospheric Scientist DCHI Brown-Bag Discussion October 2017 The findings and conclusions in this report are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention/the Agency for Toxic Substances and Disease Registry
Agency for Toxic Substances and Disease Registry Division of Community Health Investigations
Outline • • • • • • • •
Purpose of Air-Bots and Network Our conceptual Air-Bot design Our conceptual design of Network Difference between ours and theirs The science behind the science Evaluating reliability of Air-Bots Web interface What’s next?
reliable low cost
Data Gap
easy
safe
Tweak
Test Build/ Program
Why Did We Build Air-Bots? •
• •
Communities exposed to short-bursts of chemical released into the air EPA’s network cannot capture short events ATSDR’s manual efforts are expensive
4
Our Vision • Build Robotic Air Samplers to: • “sniff”, text, and turn collect a sample • take the place of ATSDR staff • reduce number of expensive samples
reliable low cost
AirBot
easy
safe
• Innovative Solution: • Save time and money: sensors are cheap and portable • Reliable: some vetted by EPA and universities • Responsive: provides some quick answers to public
5
OUR CONCEPTUAL DESIGN (HARDWARE) Data to CDC/ATSDR Build and Program a team of Air-Bots that can use the web to talk
6
OUR CONCEPTUAL DESIGN (SOFTWARE) Data to CDC/ATSDR
Programming a team of Air-Bots
1. Sniff (Monitor) for chemicals
Decisions data to CDC/ATSDR
2. Decide which RAS is in a good position
Levels over 50 here
3. That RAS turns on a sampler
Levels >100 here
>150 here I’ll get a sample
Hey ATSDR come to 123 Main St to get sample to Lab
4. That RAS texts me to get the sample to the lab
Literature Review: Student Intern Tables Particulate Matter Sensors (Choosing Air-Bot Parts) Sensor
Cost Category
Reliability
Detection Limits (µg/m3)
Interferences
Dylos 1700
Low
0.8
1.9 – 10.6
N/A
2 – 10
Temperature and relative humidity had significant effects
Shinyei PPD42NS
$350 Ultra-Low 0.82 $24
Steinle Austin
Looking at some parts
Can you guess which sensor doesn’t belong? 3.0
3.0
PPD60PV F
PPD60PV A
Analog Output Voltage (V)
2.5
2.0
1.5
-7
1.0
-14 2
y=1.01+5.54 10 x+4.8 10 x 2 R =0.64 Cellulose Ammonium Sulfate Sodium Chloride
0.5
0.0
2.0
1.5 -7
Cellulose Ammonium Sulfate Sodium Chloride
0.5
0
0
6
1x10
6
2x10
6
3x10
6
6
4x10
5x10
-14 2
y=1.03+5.74 10 x-5.32 10 x 2 R =0.61
1.0
0.0 6
6
1x10
6
2x10
6
3x10
6
6
4x10
6
5x10
6x10
-1
6x10
HHPC Concentration (pcs cf )
-1
HHPC Concentration (pcs cf ) 3.0 2.8 2.6
Cellulose Ammonium Sulfate Sodium Chloride
PPD60PV E
2.4
Analog Output Voltage (V)
Analog Output Voltage (V)
2.5
2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0.0
6
2.0x10
6
4.0x10
6
6
6.0x10
8.0x10 -1
HHPC Concentration (pcs cf )
7
1.0x10
Refraction of light
~890 nm source
Water (H2O) =1 g/cm3
Some Properties
Salt (NaCl) =2.16 g/cm3 Ammonium Sulfate (NH42SO4) = 1.77 g/cm3 Cellulose (C6H10O5)n =1.5 g/cm3
Testing of Particles
Transforming Data Water (H2O) = 1 g/cm3 Salt (NaCl) = 2.16 g/cm3
Cellulose (C6H10O5)n = 1.5 g/cm3 Ammonium Sulfate (NH42SO4) = 1.77 g/cm3
Particle Distribution
Pause for Clarification About Particle Distributions This represent average distribution
Distributions change over time
Photomicrograph of PM in Air Si-Mn (slag) Soil Si-Mn (particle) Flyash Coal burning SiO2 CaCO3 Lime --Ca(OH)2
Soot Pollen
Photomicrograph of PM in Lab Cellulose
NaCl
Ammonium Sulfate
What WHO says on PM and LS …the fractions of the airborne particles inhaled and deposited in the various regions depend on many factors. However, for sampling purposes conventions have been agreed in terms of aerodynamic diameter…” “…the amount of light scattered is not directly dependent on mass, it is necessary to calibrate such instruments, and even then, a change in size distribution or particle composition can change the relation between light scattered and mass concentration. “
(WHO 2014)
Partners: Roles for the Dust-Bot • GT improves communication between Bots and motor skills • UCin evaluates variability and uncertainty of sensor
• ATSDR and NIOSH mentor universities, coordinate fieldwork
Dust-Bot
20
Testing PM Sensors in Labs
PM Sensor Results (pcs/ft3) Shinyei 60PV $150
Shinyei 42NS
$24
Related Source: O Zervaki et al. 2016. Calibration of low cost dust sensors. Air and Waste Management Association. Annual meeting and exhibition. Pittsburg June 6, 2016
Apples to Apples: Comparing Same Sensor Type 3.0
3.0
PPD60PV F
PPD60PV A 2.5
Analog Output Voltage (V)
Analog Output Voltage (V)
2.5
2.0
1.5
-7
1.0
-14 2
y=1.01+5.54 10 x+4.8 10 x 2 R =0.64 Cellulose Ammonium Sulfate Sodium Chloride
0.5
0.0
2.0
1.5 -7
-14 2
y=1.03+5.74 10 x-5.32 10 x 2 R =0.61
1.0
Cellulose Ammonium Sulfate Sodium Chloride
0.5
0.0 0
6
1x10
6
2x10
6
3x10
6
6
4x10
5x10 -1
HHPC Concentration (pcs cf )
6
6x10
0
6
1x10
6
2x10
6
3x10
6
6
4x10
5x10 -1
HHPC Concentration (pcs cf )
6
6x10
Apples and Oranges: Same Sensor Different Particles
10 min = 60.6 µg/m3
10 min = 54.4 µg/m3
Apple to Apple: Cellulose Distributions and Two Sensors
30 min = 188 µg/m3
30 min = 99.8 µg/m3
PM Sensor Results (µg/m3) Sharp GP2 $6-15
Nova SDS011 $24
Related Source: O Zervaki et al. 2016. Calibration of low cost dust sensors. Air and Waste Management Association. Annual meeting and exhibition. Pittsburg June 6, 2016
Nova Sensor Results (µg/m3) NaCl
(NH4)2SO4
cellulose
Putting the Parts Together (PM) Sa
Basic board with Wi-Fi ($4-$20)
Or
Extra board ($10$30) to control sampling pump
Related Sources: G Zarus et al. Development of a network of portable atmospheric pollutant sentinels to conduct remote air sampling. Feb 2016. DOI: 10.13140/RG.2.1.1878.9527 G Zarus, D Jackson, B Gurbaxani, 2016. Developing a Team of Robotic Dust Sensors to Collect Worst Case Samples. Georgia Tech. Senior Capstone Proposal. February 2016. Sims, Ilunga, Mahbub, Kela, Jacob Smith. 2016. ECE 4012 Capstone Team. CDC Arduino sensor array project. Dec 6, 2016.
GT 2016 (PM) Capstone Team
Source: Sims, Ilunga, Mahbub, Kela, Morello, Smith. 2016. ECE 4012 Capstone Team. CDC Arduino sensor array project. Dec 6, 2016.
Sims, Ilunga, Mahbub, Kela, Morello, Smith. 2016. ECE 4012 Capstone Team. CDC Arduino sensor array project. Dec 6, 2016.
Partners: Roles for the Gas-Bot • GT improves communication between Bots and motor skills • UTA evaluates variability and uncertainty of sensor
• ATSDR and NIOSH mentor universities, coordinate fieldwork
Gas-Bot
31
Testing Gas Sensors in Labs
Source: M Azizi, P Dasgupta, CP Shelor, G Zarus. 2017. Laboratory evaluation of gas sensors. University of Texas Arlington. March 26, 2017. Calibration of low cost dust sensors.
H2S Gas Sensor Results (mg/m3) MQ-136 and H2S ($30-35)
Response times of MQ Sensors (tests examples)
To Gas
To Air
Source: M Azizi, P Dasgupta, CP Shelor, G Zarus. 2017. Laboratory evaluation of gas sensors. University of Texas Arlington. March 26, 2017. Calibration of low cost dust sensors.
NH3 Gas Sensor Results (mg/m3) MQ-135 and NH3 ($1-$7)
MQ-137 and NH3 ($30)
Source: M Azizi, P Dasgupta, CP Shelor, G Zarus. 2017. Laboratory evaluation of gas sensors. University of Texas Arlington. March 26, 2017. Calibration of low cost dust sensors.
Gas Sensor Results (mg/m3) MQ-135 and NH3 and Humidity ($1-$7)
Source: M Azizi, P Dasgupta, CP Shelor, G Zarus. 2017. Laboratory evaluation of gas sensors. University of Texas Arlington. March 26, 2017. Calibration of low cost dust sensors.
Putting the Parts Together (Gas) Mini board ($2) with Wi-Fi (20)
Related Sources: G Zarus et al. Development of a network of portable atmospheric pollutant sentinels to conduct remote air sampling. Feb 2016. DOI: 10.13140/RG.2.1.1878.9527 G Zarus, D Jackson, B Gurbaxani, 2016. Developing a Team of Robotic Hazardous Sulfide Sensors to Collect Worst Case Samples. Georgia Tech. Senior Capstone Proposal. February 2016. Sisk, Lawrence, Yoder, Molander, Torres, Hunt. 2017. ECE 4011 Capstone Team. CDC Gas Aware sensor array project. April 26 2017
GT 2017 (Gas) Capstone Team
Web Site Integrating Google Maps https://gasaware.herokuapp.com/
Sisk, Lawrence, Yoder, Molander, Torres, Hunt. 2017. ECE 4011 Capstone Team. CDC Gas Aware sensor array project. April 26 2017
How this could support Citizen Science?
How this could support Citizen Science?
How this could support Citizen Science?
How this could support Citizen Science?
How this could support Citizen Science?
How this could support Citizen Science?
How this could support Citizen Science?
45
What’s next • Evaluate data • Field test • Integrate best features of DB and Web design • STEM class to teach HS students to build PM sentinels • Encourage Cit Sci
Source: B Johnson. Development of a school-based dust sensor (draft). GCU intern capstone, June 2017.
Contact Information
Greg Zarus
[email protected] 770-488-0778 Diane Jackson,
[email protected] 770.488.0759 Custodio Muianga,
[email protected] 770.488.3890
Agency for Toxic Substances and Disease Registry Division of Community Health Investigations The findings and conclusions in this report are those of the author and do not necessarily represent the official position of the Centers for
