Taking Space

The HabitatMap & AirCasting Blog

Watch Our Kickstarter Video & Get An AirBeam

The AirBeam

After more than 3 years of research and development and 6 prototypes, we finally cracked the code on bringing a low-cost, portable, accurate, open-source air quality monitor to market. We call it the AirBeam and it’s available starting today on Kickstarter. Watch our video here and get your AirBeam while supplies last!

The AirBeam is a personal, palm-sized air quality monitor that connects to the AirCasting smartphone app via Bluetooth to map, graph, and crowdsource your exposure to fine particulate matter or PM2.5 in real time. Funds raised on Kickstarter will enable HabitatMap to run more programs at schools and in communities and do so cheaper, faster, and with more devices. The good news is the AirBeams have already been designed and many already produced. Now, we just need to scale production, manufacture more AirBeams, and get them into more hands. Meaning our movement grows…. and citizen scientists, schools, makers, and YOU will be participating to help make a difference.

AirBeam Diagram

AirBeam Technical Specifications, Operation & Performance

AirBeam Diagram

Hardware Specifications
Weight: 7 ounces
Particle Sensor: Shinyei PPD60PV
Temperature & Relative Humidity Sensor: MaxDetect RH03
Bluetooth: Nova MDCS42, Version 2.1+EDR
Microcontroller: Atmel ATmega32U4
Bootloader: Arduino Leonardo

About the AirBeam
HabitatMap worked with a community of scientists, educators, engineers, and other non-profits to create the AirBeam. The AirBeam measures fine particulate matter (PM2.5), temperature, and relative humidity. The AirBeam uses a light scattering method to measure PM2.5. Air is drawn through a sensing chamber wherein light from an LED bulb scatters off particles in the airstream. This light scatter is registered by a detector and converted into a measurement that estimates the number of particles in the air. Via Bluetooth, these measurements are communicated approximately once a second to the AirCasting Android app, which maps and graphs the data in real time on your smartphone. At the end of each AirCasting session, the collected data is sent to the AirCasting website, where the data is crowdsourced with data from other AirCasters to generate heat maps indicating where PM2.5 concentrations are highest and lowest. As an open-source platform, modifying our components to take other measurements and or transmit the data to other websites or apps is easy and encouraged. We’ve even included Add-on Sensor Pins on the AirBeam to make adding sensors simple.

The AirBeam has a 2000 mAh 3.7V rechargeable lithium battery. When the battery is fully charged, the AirBeam can operate for 10 hours. The battery charges via the micro-USB port, which can also be used to power the AirBeam directly. The Battery Charging Indicator turns solid green when the AirBeam is charging and turns off when the AirBeam is either fully charged or unplugged.

Power On/Off
To power on the AirBeam, press down on the Power Button. The AirBeam is on when the Bluetooth Connection Indicator blinks red. Push the Power Button a second time to power off the AirBeam.

Intake & Exhaust
While operating the AirBeam, be sure to keep the Intake and Exhaust free from obstructions.

Connect the AirBeam to the AirCasting Android App
Download the AirCasting app from the Google Play Store. Launch the app, then navigate: “External devices” > “Pair with new devices” > “Search for Devices” > pair with the device labeled “AirBeam . . . ” (note that you only need to pair once) > return button > press “AirBeam . . . “. The AirBeam is connected to the AirCasting Android app via Bluetooth when the Bluetooth Connection Indicator is solid red and the AirBeam sensor streams appear on the AirCasting App Sensors Dashboard.

Acquire AirBeam Data via Serial Monitor
You can acquire the AirBeam data via the Micro-USB Port or Bluetooth using a serial monitor.

The AirBeam board is based on the Arduino Leonardo, so you can reprogram your AirBeam using the Arduino IDE.

Add Another Sensor
You can add another sensor to the AirBeam using the Add-on Sensor Port. When the AirBeam is resting on it’s back the five pins, from left to right, are: Ground, 5V, 3.3V, Analog 2, Analog 1. Note that you must insert a tiny screwdriver into the slot above the pin to release the pin.

Open Source
The AirBeam firmware and electronic schematics are available on GitHub. The STL files for 3D printing the AirBeam enclosure can be downloaded from Shapeways.

FCC Compliance Statement
This device complies with part 15 of the FCC Rules. Operating is subject to the following two conditions: (1) This device may not cause harmful interference, and (2) this device must accept any interference received, including interference that may cause undesired operation. Caution: Modifying or tampering with internal components can cause a malfunction and will void FCC authorization to use these products.

This equipment has been tested and found to comply with the limits for a Class B digital device, pursuant to Part 15 of the FCC Rules. These limits are designed to provide reasonable protection against harmful interference in a residential installation. This equipment generates, uses, and can radiate radio frequency energy and, if not installed and used in accordance with the manufacturer’s instructions, may cause interference harmful to radio communications. There is no guarantee, however, that interference will not occur in a particular installation. If this equipment does cause harmful interference to radio or television reception, which can be determined by turning the equipment off and on, the user is encouraged to try to correct the interference by one or more of the following measures: reorient or relocate the receiving antenna; increase the separation between the equipment and receiver; connect the equipment to an outlet on a circuit different from that to which the receiver is connected; and/or consult the dealer or an experienced radio or TV technician for help.

Performance Data
The below claims and disclaimers are based on comparisons between the AirBeam, a Thermo Scientific pDR-1500 with a PM2.5 cut-point inlet, and teflon filter samples subjected to gravimetric analysis. The pDR-1500 is a $5,000, 2.5 lb air quality monitor frequently used by government and academic researchers to evaluate personal exposure to fine particulate matter or PM2.5. Teflon filter samples were taken with a Leland Legacy 10L pump and PM2.5 cut-point inlet and weighed at the NYU School of Medicine’s filter weighing room, which meets EPA guidelines for filter conditioning, storage, and gravimetric measurement of PM2.5 and PM10 filters. Filters subjected to gravimetric analysis are the “gold standard” for measuring PM2.5. Additional research is required to fully characterize the performance of the AirBeam and we look forward to working with the AirCasting community to “fill in the gaps”.

When presenting our performance data on the AirBeam below, we include R2 or R-squared values to indicate how the AirBeam compares with other methods for measuring PM2.5. R2 is a statistical measure that indicates how well data fit a statistical model, in this case, the prediction of the Y-axis (AirBeam) from the X-axis (pDR-1500) using a linear (straight) or nonlinear (curved) line. The R2 value is a fraction that ranges from 0.0 to 1.0 with higher values indicating that the regression came more closely to the points. An R2 value of 1.0 means that the predictive power of the model is perfect, that all the points lie along the line or curve with no scatter.

Below 100 micrograms per cubic meter (µg/m³), samples collected in ambient air in Manhattan (samples were collected on 11 different occasions and averaged over 12 hour periods) and while burning cardboard indoors (samples were collected over a 1 hour period and averaged every minute) both showed a strong linear relationship between the AirBeam and pDR-1500 measurements. As illustrated in Figure 1, the R2 values below 24 µg/m³ for two AirBeams in ambient air in Manhattan were .98 or better.

Lower Manhattan Ambient Air Linear Regression 1

Figure 1

As illustrated in Figure 2, the R2 values below 100 µg/m³ for four AirBeams while burning cardboard indoors were .94 or better. Also shown in Figure 2, “out-of-the-box” variability between AirBeams is more pronounced as the measurements climb above 30 µg/m³. Meaning that measurements recorded by two AirBeams exposed to identical air samples may begin to drift apart as PM2.5 concentrations increase. Out-of-the-box variability can be substantially reduced by using the AirCasting app calibration feature (still in beta) and adjusting the side-facing potentiometer on the Shinyei PPD60PV.

Burning Cardboard Indoors Linear Regression

Figure 2

Because the relationship between the AirBeam and pDR-1500 measurements becomes increasingly non-linear above 100 µg/m³, a nonlinear regression curve was used to determine the relationship between the AirBeam and pDR-1500 measurements at higher concentrations, see Figure 3 (samples were collected over a 1 hour period and averaged every minute). During separate sampling runs, we calculated R2 values for the nonlinear regression curve ranging from 0.60 to 0.80. The decrease in R2 values as compared to the linear regression is likely attributed to higher variability near and above the AirBeam’s maximum limit of detection, which we estimate to be approximately 400 µg/m³.

Cooking Indoors  Nonlinear Regression

Figure 3

Additional research is required to see how the maximum limit of detection is impacted by the reflectivity of the aerosol being sampled. The relative reflectivity of aerosols impacts the AirBeam measurements. Highly reflective aerosols, like wood smoke, bias the AirBeam measurements upwards, whereas less reflective aerosols, like diesel exhaust, bias the AirBeam measurements downwards.

During ambient air sampling in Lower Manhattan during the summer months, measurements from a pDR-1500 and two Airbeams were compared against a teflon filter subjected to gravimetric analysis, see Figure 4. Sampling was done in 12-hour averages each day for 11 days and averaged to compare the real time instruments against the gravimetric filters. When compared against the gravimetric filters, the R2 value of AirBeams was found to be 0.70 compared to 0.76 for the pDR-1500. Time weighted averages of the gravimetric filter data showed consistently higher values as compared to the pDR-1500 at ambient levels. We assume this downward bias is also in effect with the AirBeam, since both are light scattering particle counters. Further, we assume part of this this bias can be attributed to the relative reflectivity of the aerosol being measured. The R2 value of the pDR-1500 measured against the AirBeams during these 12-hour day averages was found to be 0.98.

Lower Manhattan Ambient Air Linear Regression 2

Figure 4

Research conducted by others on light scattering particle counters indicates that high relative humidity (>80%) is likely to have a negative impact on the accuracy of the AirBeam. When relative humidity is high, aerosols take on water becoming more reflective. Additional research is required to better characterize this effect as it applies to the AirBeam.

AirBeam performance data collection, analysis, and findings are the work of Alex Besser and Michael Heimbinder. Alex is a graduate student in Environmental Toxicology at New York University. Michael is the Founder and Executive Director of HabitatMap and AirBeam Lead Developer. Dr. George Thurston, Alex’s academic adviser and professor of Environmental Medicine at New York University School of Medicine, provided the material resources and guidance that made this research possible.

AirCasting Youth

AirCasting Youth is an educational program for high school students that teaches how to build AirCasting air quality instruments, develop air quality monitoring plans, utilize the AirCasting platform to record, map, interpret, and share air quality data, and lead change through civic action. Thanks to support from the Knight Prototype Fund, we’ve been working with Sonoma Technology – an employee-owned firm that provides air quality and meteorological analysis, forecasting, and communications services – to rapid prototype the AirCasting Youth program.

AirCasting in San Francisco with students from the Galileo Academy of Science & Technology

AirCasting in San Francisco with students from the Galileo Academy of Science & Technology

Feedback Loop
Our primary method for gathering feedback and improving the AirCasting Youth program was via workshops conducted with youth in Brooklyn and San Francisco during the Spring of 2014. During the workshops, our team gathered feedback via fly-on-the-wall observation, post-course surveys, and informal group interviews to understand how participants were engaging with our curriculum and technology. The primary questions we sought to answer were:

1) Given the complexities of air quality science, what are the basic principles that students’ need to master?
2) How should the information and activities be sequenced to ensure each builds on what follows?
3) What type of real-time interactive feedback drives sustained participation and enhances participant experience?
4) How could a social experience be integrated into the AirCasting android app?
5) How and where should the AirCasting air quality instrument be worn; can the AirCasting air quality instrument be designed to make proper use intuitive?

The insights we gathered from questions 1 & 2 are reflected in our pilot AirCasting Youth workshop curriculum, available as a free PDF download here. We’ll be refining this curriculum as we teach it and gather additional feedback. Once we have it where it needs to be, we’ll finalize it and make it available as the second educational toolkit in our MapThink series.

Questions 3 & 4 were challenging to answer because we didn’t have the time or funds required to build out and test the interactive and social features suggested by workshop participants and sketched out during post-workshop brainstorming sessions. Many of the ideas centered on facilitating the formation of localized AirCasting community networks by enabling the exchange of real-time location information. Knowing where other AirCasters are located could lead to coordinated efforts to “fill-in the gaps” – taking measurements at times and in places that are missing – and add gaming components to incentivize data collection, facilitate in-person social interaction, and encourage engagement with community-based organizations.

We made tremendous progress on question 5 based on feedback from the AirCasting Youth workshops, which led to a new and improved design for the AirBeam. The AirBeam is an instrument of our own design that utilizes a light scattering sensor to measure fine particulate matter or PM2.5. One of the most important changes we made to our design was to give the AirBeam a face. Participants reported that this not only made the instrument more relatable and memorable; it also made the orientation of the instrument obvious, that is to say “don’t cover the face”. This was extremely important because we located the intake and exhaust ports on the face and if either is blocked the accuracy of the measurements are compromised.

The AirBeam before and after it's Knight Foundation funded makeover.

The AirBeam before and after it's Knight Foundation funded makeover.

During the workshops we had students wear the AirBeams around their necks. The students reported that this was comfortable enough for walking around but wouldn’t work for running or cycling and was far from discreet. After experimenting with and endlessly debating how the AirBeam should be worn we decided to not make a decision, but instead, let the user decide by offering an array of attachment options. The AirBeam can still be worn around the neck with a lanyard but now it can also be attached to a bag strap with a mini-carabiner or clipped to the waist with a belt clip.

Going AirCasting
We were fortunate to have UPROSE as a partner for our Brooklyn workshop. UPROSE is a grass roots, multi-ethnic, intergenerational community-based organization dedicated to environmental and social justice. We spent a full day in Sunset Park with UPROSE Youth Justice members. We taught them about AirCasting and they taught us about the air quality problems in their neighborhood, which is host to multiple power plants and waste transfer stations, dozens of auto shops, and bisected by the Gowanus Expressway.

AirCasting the New York City subway. Note the dark streaks on the wall. Those are particles!

AirCasting the New York City subway. Note the dark streaks coming out of the vent on the wall.

One unexpected finding from our AirCasting sessions were the invisible plumes of particles puffing forth from street level subway vents. We discovered that the characteristic rumble of N/R subway trains passing under 4th Avenue were invariably accompanied by a spike in PM2.5 measurements as particles were ejected from the subway vents that line the roadway median strips.

The red dots on the map and spikes on the graph represent the AirBeam's response to clouds of particulates being ejected from the street level subway vents along 4th Ave. in Brooklyn.

The red dots on the map and spikes on the graph represent the AirBeam's response to the clouds of particulates ejected from street level subway vents along 4th Ave. in Brooklyn. Click map to view on AirCasting.org.

In San Francisco we had the opportunity to work with over 60 seniors from the Galileo Academy of Science & Technology, a public high school that provides students with career pathways in biotechnology, environmental science, health, hospitality and tourism, computer science, and creative media technology.

On the steps of the Palace of Fine Arts Theatre in San Francisco

On the steps of the Palace of Fine Arts Theatre in San Francisco

Due to the large number of students, we spread the workshop out over multiple days which allowed us to compare air quality measurements from one day to the next. We were happy to find that the AirCasting data followed the same pattern observed in the official monitoring data posted to AirNow.gov. On the April 16th the AirCasting CrowdMap is predominantly orange with scattered red and yellow whereas on the 17th it is predominantly yellow with scattered green. The official Air Quality Index from these same days indicated that PM2.5 concentrations in the Bay Area were indeed higher on the 16th, 55/yellow, than the 17th, 45/green.

AirCasting CrowdMap SF 4-16

In agreement with the state run air quality monitoring network, the Galileo AirCasters found that PM2.5 concentrations were higher on 4/16 than 4/17. The CrowdMap displays the average measurement for each area as a colored square: green for low concentration, then yellow, then orange, and red for high concentration.

In agreement with the state run air quality monitoring network, the Galileo AirCasters found that PM2.5 concentrations were higher on 4/16 than 4/17. The CrowdMap displays the average measurement for each area as a colored square: green for low concentration, then yellow, then orange, and red for high concentration. Click maps to view on AirCasting.org.

With support from the Knight Prototype Fund we also made changes to our software to address the number one concern expressed by AirCasters: data quality. More on this in a future post . . .

The AirCasting App Gets a Makeover

We launched the AirCasting app in December 2011. Over the past two years we’ve rolled out a slew of new features but never had a chance to rethink the user interface.  That is until about three months ago when we found the time, and most importantly, the funding.* Since then we’ve been busy translating the constructive feedback we’ve gotten from AirCasters the world over into a new and improved user interface.  If you already have the AirCasting app on your Android phone or tablet, update it now! If not, head to the Google Play Store and check it out. Wondering how it all works? Have a look at the screenshots and “how to” below.

Screenshots from the new AirCasting app UI

The new AirCasting app UI

AirCasting provides three ways to view your sensor data: the Map, the Graph, and the Sensors Dashboard. The default view is the Sensors Dashboard.

On the Sensors Dashboard, tap sensor tiles once to hide/show the peak and average values for your session and tap the tile twice to pause the stream.  To map or graph a sensor stream, long press the tile, then drag and drop it on the “MAP” or “GRAPH” areas at the top of the screen.  Sensor tiles can also be rearranged using the long press, drag and drop method.

The Map displays your current position as a colored dot with a white outline; dots without an outline are past readings. A dot’s color corresponds to the reading’s intensity at a location. Refer to the “Heat Legend ” (the colored bar at the top of the screen) to identify the intensity range for a reading. For example, a yellow dot corresponds to a sound level reading be­tween 61 and 70 decibels. To quickly toggle between sensor streams without returning to the Sensors Dashboard tap the “Avg” “Now” “Peak” circles at the top of the screen.

When viewing the Map, press the CrowdMap button to view the CrowdMap layer. The CrowdMap displays AirCasting data from all contributors. Each square’s color corresponds to the average intensity of all the readings recorded in that area. Refer to the “Heat Leg­end ” to identify the inten­sity range for a square. For example, an orange square corresponds to an average sound level between 71 and 80 decibels. If no colors are displayed, there is no data in that area.

The Graph displays your readings over time. Zoom in and out for more or less de­tail, swipe to pan through the data.

*Funding for this work was provided by The Hive Digital Media Learning Fund in The New York Community Trust.

Air Quality Scientist Goes AirCasting (Part 2): Smokers Beware!

Last week, I wrote about Aircasting with my Car-Neph. This week, I want to tell you about my Backpack-Neph.

The Backpack-Neph

The Backpack-Neph

The below image provides an overhead view of the Puget Sound Clean Air Agency’s office building in downtown Seattle with my AirCasting tracks and graph overlaid. The day was blustery and cool, so there was little background air pollution outside (Green Air Quality Index). I turned on my Backpack-Neph, collected some indoor air, and then headed outside to the bus stop. There weren’t many people there at the time but there were two buses. I brought my Backpack-Neph over to where the buses were and sampled some of that air. The first spike into the yellow on the graph is the bus exhaust. Next, I walked around the city some more before returning to the bus stop. When I showed up again there were several people smoking cigarettes next to two buses. The second spike, which reaches into the red, illustrates the impact the smokers were having on the air I was breathing. Yuck! Smokers beware, I’ll be watching you with  my Backpack-Neph!

Taking the Backpack-Neph for a spin in downtown Seattle

Taking the Backpack-Neph for a spin in downtown Seattle

Part of my job, and one of my passions, is communicating the complexities of air quality science in ways that are accessible to the public. To that end, all my AirCasting sessions are public and have been contributed to the CrowdMap. To view my data, simply visit the AirCasting website and filter the sessions or CrowdMap using my profile name: harpernavy1@gmail.com.

Matt Harper has worked for the Puget Sound Clean Air Agency for 11 years and is currently the Air Monitoring Lead. He has a B.S. in Manufacturing Engineering from Boston University and an M.B.A. from the University of New Haven. He is also a Commander in the United States Navy Reserve, with 7 years of Active and 11 years of Reserve service as a Submarine Warfare Officer. He is interested in using sensing technology to help guide people to healthier daily behaviors.

Air Quality Scientist Goes AirCasting (Part 1)

I’ve worked at the Puget Sound Clean Air Agency (PSCAA) in Seattle for a little over ten years with a team that has monitored air quality here since 1967. We focus on measuring and reporting fine particle pollution (PM 2.5) because of its health risks. Our agency is passionate about communicating air quality information to the public using new technologies.

Our monitoring team collects high quality, quantified PM 2.5 measurements. To do this correctly, we stabilize our measurement system for a long time, collect enough air to get a measurable sample, and always have access to power & data-logging capabilities. In addition, compliance with EPA standards includes complex methods that have many servicing and maintenance requirements that improve the quality of the data we collect. Taking accurate measurements, meeting all the EPA requirements, and responding to cost constraints limits how many air quality monitors we can deploy and usually means anchoring the measurement equipment to a stationary location. The truth is, you can’t have high quality monitors everywhere, all the time, so we rely on sampling in locations that are representative of larger areas and models to generate air quality forecasts for the public.

Less than a year ago I came across AirCasting, a platform capable of ingesting geographically tagged sensor data of any kind and mapping and graphing it in real time on Android devices. I was impressed and thought AirCasting might be useful to our agency – perhaps by enabling us to monitor air quality in a greater number of locations while appropriately managing the trade-off in accuracy this would entail. I talked to my colleagues at PSCAA, and so far, I’ve developed this nephelometer application . . .

I pair a well-established PM2.5 surrogate sensing technology, the nephelometer or “neph”, with the logging and communications function of a modified Aircasting Air Monitor. With the assistance of some of my team members, I took a neph and converted it into two types of mobile monitors: the Car-Neph and the Backpack-Neph. I’ve been testing each of these rigs to see how well they work in the field and how accurate they are.

Inside the Shinyei PPD42NS

Inside and outside the Car Neph.

I AirCasted the Car-Neph from Everett to Seattle on a morning when the air quality was ranked “Moderate” based on the EPA’s system for communicating air quality information to the public. I wanted to compare my Car-Neph measurements to the permanent sites located at Lynnwood, Lake Forest Park, and Olive Street. In the below image, the circles are the fixed monitors and the squares are some of the measurements I took along the route.

Comparing the Car Neph to fixed monitoring sites

Comparing the measurements from the Car Neph (squares) with the fixed monitoring sites (circles).

I know vehicles are a pollution source, so I expected measurements on the road would be slightly higher than the measurements at the permanent monitoring sites. And this is exactly what I observed when comparing the measurements. The units of measurement are inverse megameters. Since light scattering is linear with pollution, this is one way to estimate fine particulate levels. The units are not equivalent to typical PM 2.5 units (micrograms per cubic meter), but my estimates are colored by the equivalent Air Quality Index. Bottom Line: the Car-Neph is “in the ballpark” with the fixed site nephs; the results were very promising.

Visit TakingSpace again next week where I’ll discuss my experiences walking around downtown Seattle with the Backpack-Neph.

Matt Harper has worked for the Puget Sound Clean Air Agency for 11 years and is currently the Air Monitoring Lead. He has a B.S. in Manufacturing Engineering from Boston University and an M.B.A. from the University of New Haven. He is also a Commander in the United States Navy Reserve, with 7 years of Active and 11 years of Reserve service as a Submarine Warfare Officer. He is interested in using sensing technology to help guide people to healthier daily behaviors.

Evaluating Low-Cost Gas Sensors

One of the major challenges facing air quality scientists and citizen scientists is the dearth of information available regarding the performance of low-cost gas sensors. Though manufacturers provide data sheets that describe the performance of their wares, these data sheets are usually incomplete – lacking the information required to evaluate whether the gas sensor is suitable for mobile ambient air quality monitoring. Since the data sheets don’t provide the information we need, we decided to develop our own lab for evaluating the performance of low-cost gas sensors.

In support of New York Hall of Science’s Collect, Construct, Change (C3) youth environmental science program and in partnership with the New York City College of Technology, we evaluated the performance of three gas sensors: the Figaro TGS-2442 and the Alphasense CO-B4 carbon monoxide (CO) sensors and the SGX MiCS-2710 nitrogen dioxide (NO2) sensor. It’s important to note that in order to test the sensors, we first had to develop instruments for acquiring and interpreting their signal outputs. It’s possible that our instruments, the AirCasting Air Monitor and the AirGo, introduced errors that have not been accounted for. Therefore our results should not be considered the final word on the performance of these sensors. It should also be noted that the sensors we tested have warm up times approaching one hour. This compromises their utility for mobile air quality monitoring, where the ability to flip on an instrument and immediately begin recording measurements is an important feature.

Inside the Shinyei PPD42NS

Our lab for evaluating low-cost gas sensors. The gas blender is on the right and our first exposure chamber is on the left. Inside the exposure chamber is one AirGo and two AirCasting Air Monitors.

With an equipment budget of 5k we knew from the outset that our lab set-up would be bare bones. We had just enough money to purchase cylinders of “zero air”, NO2, and CO, a manually adjustable gas blender for mixing different concentrations of our target gases, and materials for constructing a DIY exposure chamber. We didn’t have sufficient resources to purchase reference analyzers to confirm our gas blending targets, scrub our zero air of CO, control for temperature and humidity, or test for response to interfering gases. The complete bill of materials for our lab can be downloaded here, including some preliminary sketches of the set-up authored by Charles Eckman, Lab Manager at the NorLab Division of Norco, Inc, along with some additional photos of our actual set-up.

Comparing the response of the TGS-2442 vs. the CO-B4

Comparing the response of the TGS-2442 vs. the CO-B4.

One thing we learned quickly was that the design for our initial exposure chamber, which was approximately two liters, was too large given the low flow rates that could be achieved by our gas blender. Although the volume of our initial chamber prevented us from being able to establish an equilibrium gas concentration, it could accommodate multiple instruments, making it suitable for running side-by-side comparisons. The above graph shows the results of one of these test runs, where we compared the performance of two TGS-2442s to one CO-B4, while changing the CO gas concentration inside the chamber. Note how the red line representing the CO-B4 response is moving up and down dramatically whereas the blue and green lines representing the two TGS-2442s rise slowly and then stabilize. Right off the bat we realized that the TGS-2442 was unsuitable for ambient air quality monitoring. It was extremely slow to respond to changes in CO concentrations and became saturated, rendering it incapable of responding when CO concentrations dropped.

Running step tests with the CO-B4

Running step tests with the CO-B4.

Upon discovering that our exposure chamber was too big, we crafted a smaller chamber that could fit only one instrument at a time. This made it impossible to run side-by-side comparisons but it did enable greater precision in generating gas concentrations. The above graph illustrates the results from one of our CO-B4 step tests.  We we’re impressed with the CO-B4s performance. It had a fast response time, a suitable lower-detection limit, and when we compared test runs between sensors, manageable out of the box variability. As mentioned earlier, we didn’t have the resources to scrub our “zero air” of CO and this can be seen in our results, with little to no change in the response between “zero air” and 100 ppb. However, we know from tests conducted at the New York State Department of Environmental Conservation’s Queens College air monitoring station that the CO-B4 has a lower detection limit that may match the manufacturers claim of less than 5 ppb. At approximately $90 this electrochemical sensor isn’t cheap, but depending on your budget and intended use it’s favorable performance may be worth the extra expense.

Running step tests with the MiCS-2710

Running step tests with the MiCS-2710.

After seeing the disappointing results from our TGS-2442 tests we were surprised to find that the MiCS-2710, also a metal-oxide sensor, performed as well as it did, see above graph. It’s multi-minute response times weren’t nearly as rapid as the CO-B4, which responded in seconds, but they were adequate and tests conducted by the EPA on the AirCasting Air Monitor indicated a lower detection limit approaching 10 ppb! A fairly good result considering it’s extremely low cost, retailing for approximately $5. However, a word of caution is in order as comparison tests, see below graph, revealed high out of the box variability. With no two sensors responding identically, each unit would need to be individually calibrated. Also, the sensitivity of the MiCS-2710 diminished as the NO2 concentration climbed above 100 ppb.

Comparing the response of one MiCS-2710 sensor to another MiCS-2710 sensor

Comparing the response of one MiCS-2710 sensor to another MiCS-2710 sensor.

Special thanks to Charlie Eckman who helped with our lab setup and protocol; Peter Spellane, Chair of the New York City College of Technology Chemistry Dept., who secured lab space for our experiments and served as an advisor; and Raymond Yap and Leroy Williams who ran all the performance tests. Funding for this project was provided by The Hive Digital Media Learning Fund in The New York Community Trust.

Make Your Own AirCasting Particle Monitor

Join us Tuesday, October 8th for the third installment of Make Magazine’s Urban Sensor Hack. The event takes place on Google Hangout and is free and open to the public. During our hack Michael Heimbinder, Tim Dye, Iem Heng, and Raymond Yap will guide you through a step by step process for making your own AirCasting particle monitor, discuss the challenges involved in achieving accurate air quality measurements, and detail our work with community based organizations and schools to conduct environmental monitoring and advance STEAM education. For those who can’t make the Urban Sensor Hack, we’ve published an illustrated step-by-step guide detailing how to make your own AirCasting particle monitor using the Shinyei PPD42NS optical particle counter.

Inside the Shinyei PPD42NS

Inside the Shinyei PPD42NS

As those who have been following the AirCasting project know, we’ve been conducting R&D on different air quality sensors for nearly two years in pursuit of a low-cost instrument appropriate for personal exposure monitoring. The Shinyei PPD42NS was one of the first sensors we evaluated. Our tests indicate that it responds to particles 2 microns in diameter and larger when air flow and light interference issues are addressed and signal filtering algorithms are in place. As you can see in the below test comparing the performance of the Shinyei PPD42NS with the more expensive Shinyei PPD60PV-T2, the Dylos DC1100 Pro, and the Thermo Scientific pDR-1500; the PPD42NS responds in line with the other instruments but the intensity (dynamic range) of it’s response is off and it’s unfiltered signal is noisy. For these reasons, we’re currently focusing our developments efforts on the PPD60PV-T2 which can detect smaller particles with substantially less noise.

Particulate Matter Instrument Comparison

Particle instrument intercomparison conducted by Tim Dye, Sonoma Technology, Inc. @timsdye. Click to enlarge.

For those looking to dig deeper on the PPD42NS have a look at this thorough breakdown of the product compliments of Tracy Allen at EME Systems. We’d also like to thank Chris Nafis, whose instructions and code for connecting the PPD42NS to an Arduino helped us get started with our own work, and David Holstius, whose blogging also informed our work.

AirCasting: Education Edition

In the spring we had lots of fun teaching students how to make AirCasting compatible air monitors and luminescent accessories.  For details on these educational programs, along with some really great videos, read on.

Making Air Monitors with High School Seniors

In 2012, the Newtown Creek Alliance, in partnership with HabitatMap, received an Environmental Justice Community Impact Grant from the New York State Dept. of Environmental Conservation to develop and teach a course on air quality monitoring at Queens Vocational & Technical High School, located in Sunnyside, Queens, New York City.

The 26 QVT students who participated in the AirCasting program during the spring of 2013:

  • • Built their own air monitors with instruction from a mechanical engineering professor from the New York City College of Technology and an electrical engineering professor from Manhattan College;
  • • Toured the neighborhood around their school with a historian from the Newtown Creek Alliance;
  • • Learned the basics of air quality from the Senior Vice President of Sonoma Technology;
  • • Developed and executed their own air quality monitoring plans using the AirCasting platform; and
  • • Presented their work and findings to their peers, members of the Newtown Creek Alliance, and staff from the New York State Dept. of Environmental Conservation.

To learn more about the program watch the above video and visit the Newtown Creek Alliance website.

Fashion Your Environment

Fashion Your Environment is a youth centered program focused on broadcasting environmental data through fashion and wearable technology. Youth participants were exposed to mentorship and training in design, prototyping, and fabrication of fashion accessories that visualize environmental data using AirCasting Luminescence. AirCasting Luminescence uses a microcontroller connected to the AirCasting app over Bluetooth to illuminate colored LED lights in response to sensor measurements received by the AirCasting app: green for low intensity, then yellow, then orange, and red for high intensity.

To learn more about the program, watch the above video and read our Tumblr blog.

The Fashion Your Environment Program is a collaboration between the New York Hall of Science, Parsons the New School for Design, and HabitatMap. Funding was provided by a grant from the John D. and Catherine T. MacArthur Foundation, administered by Mozilla, and in partnership with Hive Learning Networks.

More AirCasting Air Quality Monitors

It’s been eight months since we released the DIY guide for the first AirCasting compatible air monitor. Since then we’ve been busy evaluating the performance of a variety of different gas sensors and optical particle counters (more on this in later posts) to figure out just how well they work for our particular application: mobile ambient air quality monitoring to assess human exposures to air pollution in real world settings and scenarios. Along the way we were lucky to partner with Michael Taylor and Josh Schapiro from Carnegie Mellon’s CREATE Lab to develop another AirCasting compatible air monitor, the AirGo. This latest addition to the rapidly growing family of AirCasting compatible air monitors measures carbon monoxide (CO) using an Alphsense CO-B4 electrochemical sensor, particulate matter using a Samyoung DSM501A optical particle counter, and temperature & humidity using a Sensirion SHT15.


Michael Taylor & Josh Schapiro's AirGo monitor. Measuring carbon monoxide, particulate matter, temperature & humidity.

The AirGo does two noteworthy things that have improved the quality of the measurements we’re now collecting: 1) Josh Schapiro developed a custom printed circuit board that does a phenomenal job capturing the signal from the CO-B4, so we are now able to measure CO in the low parts per billion range. Of course, the sensor manufacturer also deserves kudos for creating a low-cost sensor appropriate for ambient air quality monitoring, and with it’s rapid response time, also appropriate for mobile monitoring. 2) Michael Taylor developed an algorithm for the Samyoung that reduces the noisiness of the measurements. Because optical particle counters are sampling such a small quantity of air (in this case .01 cubic feet) and the particle composition of air is so heterogeneous (the number of particles in one .01 cubic foot being different from the next) the measurements tend to go up and down dramatically from second to second. By implementing an algorithm that works somewhat like a moving average, he was able to iron out the spikes and valleys making it much easier to visually interpret the data while maintaining the integrity of the measurements. If you’re curious how it all works behind the scenes or want to make your own AirGo, Josh’s schematics and code are available on GitHub and the files for the 3D printed enclosure designed by Michael Taylor are available via Shapeways.

Gadgeteer AirCasting

Thomas Amberg's Gadgeteer AirCasting air monitor. Measuring alcohol, temperature & humidity.

We were also really excited to see this new design for an AirCasting compatible air monitor developed independently by Thomas Amberg using the Gadgeteer platform. You can make your own by following his Instructables guide. The most thrilling thing about Thomas’s design is that it illustrates first hand how flexible the AirCasting platform is. With minimal instruction from our team he was able to develop a novel sensor package on top of an entirely different electronics platform and connect with the AirCasting app to record, display, and share his data. This is the future of AirCasting: thousands of designs, millions of devices, all pushing data to the AirCasting CrowdMap.