Taking Space

The HabitatMap & AirCasting Blog

Raspberry Pi AirBeam Data Logger

Every semester I tell my students at Medgar Evers College that to be a great research chemist, you need to be a bit of everything: a bit plumber, a bit electrician, and especially a bit coder. Environmental research chemists rely on equipment and tools that are essential to their work. The better you can use, maintain, and understand those tools the better and more effective you will be as a researcher. Our research project deploying AirBeam2s provides another excellent example of why this is the case.

The goal of our project is to analyze air quality at various locations around New York City. Students will analyze PM1, PM2.5, and PM10 concentrations around their homes, the buildings here at Medgar Evers College, near airports, in subway stations, and anywhere else of interest. We will then aggregate the air quality data and combine it with data on weather and traffic patterns to see what conclusions can be drawn.

While we believe compelling data will be obtained, our project is focused more on educating students in proper environmental science techniques than groundbreaking results. As such, finding an appropriate air quality sampler was our first concern. The AirBeam2 coupled with the AirCasting platform matched nearly all our criteria: it was inexpensive, durable, and easy to use. Unfortunately, AirBeam2 had one major drawback for our application, it requires a cellular or Wi-Fi connection to publish measurements to the cloud. Given that our project calls for placing AirBeam2s at locations that may not have Wi-Fi or cellular network connectivity and leaving them for days or weeks at a time, this was a problem.

AirBeam2+RaspberryPi

This is where being a little bit of everything comes in handy. Instead of purchasing a significantly more expensive air quality monitoring system that included methods for logging data locally, we developed a simple and inexpensive way to extend local data logging options to the AirBeam2. We plugged the AirBeam2 into a Raspberry Pi single board computer—specifically the Raspberry Pi Zero W which costs $35 for a complete bundle—and used the Raspberry Pi to log and store readings from the AirBeam2. The Raspberry Pi also broadcasts a Wi-Fi network, which will enable our students to retrieve the data and control the data logging system.

We were able to save hundreds if not thousands of dollars in equipment costs by customizing the AirBeam2 to meet our specific needs. Science and technology depend heavily on one another, this experience could apply to literally any device we use. Diversifying your skillset has real, tangible benefits as exemplified here, where a little Python coding knowledge allowed us to stretch our budget further, conduct our research more efficiently, and be more effective educators and scientists.

Dr. Sam Groveman is a research associate and adjunct professor at Medgar Evers College in the Department of Chemistry and Environmental Science where he engages in research with faculty and students focusing on chemistry and the environment. Even so, he still finds time to indulge his interests in computers and hobby electronics.

Dr. Jin Y. Shin is an associate professor in the Department of Chemistry and Environmental Science at Medgar Evers College where he is exploring greenhouse gas emissions associated with the nitrogen and carbon cycle by baselining the emissions of carbon dioxide and nitrous oxide in marsh tidal wetlands to see the effect of sea level change.

Using Citizen Science & Open Source Tools to Promote Community Health

AirBeam2 Technical Specifications, Operation & Performance

AirBeam2 Features and Dimensions

Hardware Specifications
Weight: 5 ounces
Particle Sensor: Plantower PMS7003
Relative Humidity Sensor: Honeywell HIH-5030-001
Temperature Sensor: Microchip MCP9700T-E/TT
Bluetooth: Nova MDCS42, Version 2.1+EDR
WiFi: Espressif ESP8266-ESP-12S, 2.4 GHz
Cellular: SIMCOM SIM808, 2G GSM
Microcontroller: Teensy++

About AirBeam2
AirBeam2 measures fine particulate matter (PM1, PM2.5 & PM10), temperature, and relative humidity. AirBeam2 uses a light scattering method to measure particulate matter. Air is drawn through a sensing chamber wherein light from a laser 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. When recording a mobile session, these measurements are communicated once a second to the AirCasting Android app via Bluetooth. When recording a fixed session, these measurements are communicated once a minute to the AirCasting website via WiFi or cellular. At the end of each mobile 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 PM concentrations are highest and lowest.

Charging
Fully charge your AirBeam2 before powering it on and using it in WiFi or Cellular mode or the AirBeam2 may lose power (even it it’s plugged in).

Power
AirBeam2 has a 2000 mAh 3.7V rechargeable lithium battery that can power the instrument for 10 hours when fully charged. The battery charges via the USB-C Port, which can also be used to power the AirBeam2 directly. The Battery Charging Indicator turns solid green when the AirBeam2 is charging and turns off when the AirBeam2 is either fully charged or unplugged.

Power On/Off
To power on the AirBeam2, press down on the Power Button. The AirBeam2 is on when the Connection Indicator lights up. Push the Power Button a second time to power off the AirBeam2.

Intake & Exhaust
While operating the AirBeam2, keep the Intake and Exhaust free from obstructions

Record Measurements – Mobile (Bluetooth) Session
1) Download the AirCasting app from the Google Play store.
2) Turn on the AirBeam2.
3) Via the Android device “Settings”, pair the AirBeam2 to the Android device.
4) Turn off the AirBeam2.
5) Launch the AirCasting app.
6) Turn on the AirBeam2.
7) When the AirBeam2 “Connection Indicator” turns from red to green > press “Configure AirBeam2″ > press “connect” > press “Mobile”; wait 5 seconds and the AirBeam2 sensor streams will appear on the Dashboard.
8) Press the “record” button in the upper right hand corner, enter a “Title” and “Tags” (neither are required), and press “Start Session”.

When recording a mobile session, measurements are communicated once a second to the AirCasting Android app via Bluetooth. The Android device must stay within 10-20 feet of the AirBeam2 to maintain the Bluetooth connection and receive data from the AirBeam2.

Record Measurements – Fixed (WiFi) Session
1) Download the AirCasting app from the Google Play store.
2) Turn on the AirBeam2.
3) Via the Android device “Settings”, pair the AirBeam2 to the Android device.
4) Turn off the AirBeam2.
5) Launch the AirCasting app.
6) Turn on the AirBeam2.
7) When the AirBeam2 “Connection Indicator” turns from red to green > press “Configure AirBeam2″ > press “connect” > press “Fixed” > press “WiFi” > enter the “WiFi Network Name” and “WiFi Password” and press “Submit” > enter a “Title” and “Tags” (neither are required) and press either “Start Indoor Session” or “Start Outdoor Session > if you selected “Start Outdoor Session”, then set the location. (Note that the AirBeam2 must connect directly to the WiFi router; connecting via a WiFi access point, range extender, or cell hotspot is not possible.)
8) The Dashboard will read “Retrieving streams, please wait”; wait 2-3 minutes and the AirBeam2 sensor streams will appear on the Dashboard.

When recording a fixed WiFi session, the measurements are communicated once a minute to the AirCasting website via WiFi. The Android device no longer needs to be in proximity to the AirBeam2 as it’s retrieving the data from the web via the Android device’s WiFi or cellular connection.

Record Measurements – Fixed (Cellular) Session
1) Unscrew the AirBeam2 enclosure, pull out the circuit board, carefully push down and lift the SIM card slot, insert the SIM card into the SIM card slot, close the SIM card slot, place the circuit board back inside the enclosure, and screw the AirBeam2 together again. (AirBeam2 data and messaging costs are approximately $25 per month when using a Ting SIM card. Note that 2G cellular service is not available in all locations.)
2) Download the AirCasting app from the Google Play store.
3) Turn on the AirBeam2.
4) Via the Android device “Settings”, pair the AirBeam2 to the Android device.
5) Turn off the AirBeam2.
6) Launch the AirCasting app.
7) Turn on the AirBeam2.
8) When the AirBeam2 “Connection Indicator” turns from red to green > press “Configure AirBeam2″ > press “connect” > press “Fixed” > press “Cellular” > enter a “Title” and “Tags” (neither are required) and press either “Start Indoor Session” or “Start Outdoor Session > if you selected “Start Outdoor Session”, then set the location.
9) The Dashboard will read “Retrieving streams, please wait”; wait 2-3 minutes and the AirBeam2 sensor streams will appear on the Dashboard.

When recording a fixed cellular session, the measurements are communicated once a minute to the AirCasting website via the cellular network. The Android device no longer needs to be in proximity to the AirBeam2 as it’s retrieving the data from the web via the Android device’s WiFi or cellular connection.

Connection Indicator
When first powered on, the AirBeam2 Connection Indicator will shine red for ten seconds to indicate the AirBeam2 is performing a system check. During the next fifty seconds, the Connection Indicator will shine green to indicate the AirBeam2 is in configuration mode. Once configuration is begun, the configuration window will remain open until configuration is complete. If configuration is not initiated within the fifty-second window, AirBeam2 will load the last know configuration. To reopen the configuration window, power your AirBeam2 off/on.

When AirBeam2 is configured for a mobile recording session, the AirCasting app will connect to the AirBeam2 via Bluetooth and the Connection Indicator will shine solid white for 2 minutes. If the AirBeam2 is disconnected while in mobile mode, the Connection Indicator will blink red. When AirBeam2 is configured for a fixed recording session, the Connection Indicator will shine blue while acquiring the time and date and then shine white for 2 minutes while AirBeam2 begins taking measurements and sending data to the AirCasting website.

Weather Resistant
AirBeam2 is weather resistant, not weather proof. When used outdoors, hanging the AirBeam2 under an eave or suspending it from the bottom of a stool will prevent its premature demise and deliver better performance.

As the community of AirCasters grows, we look forward to learning more about how AirBeam2 performs under various weather conditions and shelter configurations. Please get in touch and share your experiences AirCasting outdoors so we can update this section with more comprehensive information.

Temperature & Humidity Measurements
AirBeam2 measures the temperature and relative humidity inside the AirBeam2 enclosure. These measurements are not representative of the ambient temperature and relative humidity.

Acquire AirBeam2 Data via Serial Monitor
You can acquire the AirBeam2 data via the USB-C Port using a serial monitor.

Programming
The AirBeam2 board is based on the Teensy++, so you can reprogram your AirBeam using the Arduino IDE.

Open Source
The AirBeam2 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.

Performance Data
When presenting our AirBeam2 performance data below, we include R-squared (R2) values as a way of evaluating intercomparisons between instruments that measure particulate matter. R2 is a statistical measure that indicates how well data fit a statistical model. The R2 value is a number that ranges from 0 to 1 with higher values indicating the regression came more closely to the points. An R2 value of 1 means the predictive power of the model is perfect, that all the data points lie along the line or curve with no scatter.

To begin, we analyzed data provided by South Coast Air Quality Management District (SCAQMD) comparing the performance of PurpleAir-I (PA-I) and PurpleAir-II (PA-II) air quality instruments to a GRIMM EDM180, a Federal Equivalent Method air quality instrument. PA-I integrates a Plantower PMS1003 sensor and PA-II integrates a Plantower PMS5003 sensor. Although these sensors are not identical to the Plantower PMS7003 sensor integrated into the AirBeam2, discussions with SCAQMD staff led us to believe that performance variations between Plantower particulate sensor models were small.

Using five-minute averages, we fit equations for PM1, PM2.5, and PM10 that converted the PurpleAir measurements to the GRIMM measurements, see below plots. After removing significant outliers, our dataset included 17,270 data points. The equations for PM1, R2=0.98, and PM2.5, R2=0.96, showed excellent fit, with a suggestion of an exponential response for the PM1 equation. For the PM10 equation, R2=0.41, the fit was average, with especially imprecise measurements in the lower concentration ranges.

PA vs GRIMM Plots

Next, we updated the AirBeam2 firmware to run the calibration equations we derived from fitting the PurpleAir measurements to the GRIMM measurements. Then we validated our calibration equations by comparing the AirBeam2 measurements to the measurements from a TSI DustTrak DRX Aerosol Monitor 8533. Two AirBeam2s and one TSI were placed inside a concentrated air pollutants (CAPS) chamber located at the NYU School of Medicine Sterling Forest campus. The CAPS chamber is designed for animal exposure tests and concentrates outdoor particulate matter into a small sealed enclosure. Unfortunately, we were unable to obtain particle differentiation for PM10 because CAPS removes large particles before concentrating small particles in the exposure chamber.

NYUSOM CAPS Chamber

The plots below show one-minute averages from our validation experiment. The results indicate high linearity and excellent agreement between the AirBeam2 and the TSI for both PM2.5, R2=0.89, and PM1, R2=0.88, with the red lines representing what would be perfect agreement between the two instruments.

AB2 vs TSI Plots

Our analysis has several limitations. First, to construct the calibration equations we used data that evaluated the performance of earlier model Plantower sensors, which may perform differently than the Plantower sensor integrated into the AirBeam2. Second, we did not have data available for higher concentration ranges, which may potentially ignore non-linearity in responses. Third, we did not adjust for relative humidity or temperature. Prior academic research indicates these variables, especially humidity, can significantly impact the measurements from light scattering sensors. Fourth, we only tested the AirBeam2 against a single aerosol.  Given the geographic and seasonal variation in aerosols around the world and the variable response of light scattering sensors to differing aerosol compositions, our results will be more relevant in some settings than others.

We look forward to learning more about AirBeam2 performance as additional evaluations are performed. Please contact us if you undertake an evaluation of the AirBeam2 and generate data that can be publicly shared as we would be happy to disseminate your findings via the AirCasting network.

AirBeam2 performance data collection, analysis, and findings are the work Chris C. Lim, a doctoral student in the Department of Environmental Medicine at NYU School of Medicine. Michael Heimbinder, Executive Director of HabitatMap, directed the analysis and edited the write-up. Dr. George Thurston, Chris’s academic adviser and professor of Environmental Medicine at NYU School of Medicine, provided guidance and access to some of the materials that made this research possible.

Adding an NO2 sensor to the AirBeam

Some months ago, I took part in the Mobicitair experiment led by ATMO Auvergne Rhône Alpes, an agency that monitors air quality in southeastern France. Mobitcitair uses the AirBeam and the AirCasting platform to enable citizen scientists to take air quality measurements and crowdsource the results.

AirBeam Outdoor Enclosure

I live and work in Grenoble, France, a city surrounded by mountains. Pollution from industrial activity, traffic congestion, and the burning of wood for home heating combined with frequent temperature inversions can lead to long bouts of dirty air. Equipped with an AirBeam and the AirCasting app by ATMO Auvergne Rhône-Alpes, I could observe these phenomena in real time as I went about my daily routine. This helped me understand how dramatically fine particulate matter (PM2.5) can vary over space and time. After several days with the AirBeam, I could intuit what the PM2.5 concentrations would be at certain locales based on previous observations and current weather.

However, I felt I was missing a piece of the puzzle; PM2.5 is just one of many air pollutants I’m exposed to everyday. During my commute to and from work along a highway, the AirBeam frequently indicated that my exposure to PM2.5 was minimal, but the air didn’t seem very clean to me. After discussing this with the scientists and engineers at ATMO, they suggested I add a nitrogen dioxide (NO2) sensor to the AirBeam.  They explained that vehicles are a major source of NO2 and the probable culprit in dirtying the air during my commute.

As I work at INRIA in the Amiqual4Home project, I am fortunate to have the tools and knowledge necessary to add new sensors to the AirBeam. My first step was to dig into the AirBeam schematic and firmware. The AirBeam is built on top of the Arduino Leonardo microcontroller and development environment and is entirely open source. This made modifying the hardware and firmware fairly straightforward. Before modifying it I gathered all needed files and code in an AirBeam repo I created.

The second step was to choose an adequate NO2 sensor. ATMO Auvergne Rhône-Alpes suggested the Cairsens NO2 sensor, which they had previously evaluated and found to have favorable performance versus a reference instrument. I was time limited and wanted to avoid modifying the AirBeam enclosure so I decided to add the NO2 sensor externally and make a few modifications to the circuit board. After some soldering, 3D printing, software implementation, and testing, we ended up with this:

NO2 AirBeam

In total, I made four NO2 AirBeams which were used during an “Explore Air Quality Workshop” that took place in Grenoble over the summer.

Explore Air Quality Workshop

Remi Pincent works as a research and design engineer at INRIA, a French public research agency dedicated to digital science and technology. Currently, Remi is engaged in the Amiqual4Home project where he fabricates IoT prototypes for researchers and private companies.

Can an App Make Our Cities More Breathable?

Leveraging AirBeam Data to Inform Policy Decisions

New York City recently committed to implementing a “zoned” collection system for the commercial waste sector. By dividing the city into zones and having commercial carting companies bid to service each zone, the city’s study found that the number of miles traveled by private collection vehicles will be cut by an astounding 49 to 68 percent!  This is a win for both the private carting companies, which will be able to achieve dramatic efficiencies in operations, and everyday New Yorkers, who will have to contend with less noise and air pollution.

To buttress the City’s decision to implement garbage zoning and guide its implementation, HabitatMap partnered with community-based organizations to measure air quality using AirBeams + AirCasting and count trucks at trucking-intensive intersections in Brooklyn and the Bronx. Together with allies at the New York City Environmental Justice Alliance and ALIGN, we trained young people from UPROSE, THE POINT CDC, El Puente, and Cleanup North Brooklyn to use AirBeams and survey forms to measure air quality and tally trucks in their communities. At intersections in the South Bronx that saw as many as 304 trucks per hour, we measured PM2.5 concentrations up to seven times higher than ambient concentrations, as measured by the nearest NYS Dept. of Environmental Conservation monitoring station. In addition to engaging the community in conducting scientific research and providing tools to advocate for improved air quality, our efforts mobilized a report and a rally/press conference.

Neighborhood Truck Impact Maps

Garbage Zoning, PM2.5 emissions & Health Impacts
On-road vehicles are the second largest source of PM2.5 in NYC. Trucks and buses account for only 6% of all vehicle miles traveled in NYC, yet they are responsible for nearly 40% of the PM2.5 emitted by all on-road vehicles. Cumulation of truck emissions is particularly problematic in the South Bronx and North Brooklyn, which collectively handle over 60% of all waste moving through waste transfer stations in New York City. A leading source of PM2.5 emissions in these communities are the notorious, smoke-belching commercial waste carting vehicles, only 10% of which meet the Environmental Protection Agency’s 2007 emissions standards. Connecting the dots between truck and bus emissions in these communities and their impact on human health, the NYC Dept. of Health and Mental Hygiene found that on-road vehicles in the New York City region contribute to rates of PM2.5 attributable asthma emergency department visits that are 8.3 times higher in very high poverty neighborhoods relative to low poverty neighborhoods.

The overconcentration of solid waste handling facilities in these communities and their concomitant truck traffic is a problem that can only be resolved through the implementation of policy solutions that focus on increasing the efficiency of the freight sector and advancing the city’s “zero waste” goals. One solution that could potentially reduce the burden of dirty diesel trucks borne by these communities is garbage zoning. What is garbage zoning?

Six nights a week, thousands of privately operated waste collection vehicles depart garages located in outer borough neighborhoods to criss-cross the city, following grossly inefficient routes that generate needless air pollution, noise, and safety hazards for communities and workers alike. Whereas the New York City Department of Sanitation can collect a ton of garbage in approximately four miles, the typical private carter, owing to the fact that their business customers are spread over multiple community districts and boroughs, needs to drive three times as far to collect the same load. By dividing the city into zones and having commercial carting companies bid to service each zone, commercial carters will be able to reduce the number of miles they need to drive to fill their trucks.

Garbage Zoning

Garbage zoning will dramatically reduce vehicle miles traveled by eliminating overlapping routes while serving the same business customers. Currently there are blocks in Manhattan serviced by 79 separate carting companies! Graphic excerpted from Don’t Waste LA. Los Angeles has a zoned garbage system.

According to the NYC Dept. of Health and Mental Hygiene, New Yorkers’ exposure to PM2.5 is responsible for more than 3,000 deaths, 2,000 hospital admissions, and 6,000 emergency room visits annually. With the city’s private carter study estimating PM2.5 reductions under a zoned commercial collection system of 34 to 56 percent, there isn’t a child or older adult (those populations most vulnerable to the impacts of air pollution) that doesn’t stand to benefit from reforming NYC’s commercial waste sector.

Carting Workers’ PM2.5 Exposures
On two separate overnight shifts, AirBeams were placed inside the cabs of waste collection vehicles to measure workers’ exposures to PM2.5 and map the circuitous and inefficient routes they travel to service their customers; one route navigates two boroughs, including nearly the entire length of Manhattan. In the cab of their trucks, commercial carting company drivers and helpers were exposed to PM2.5 levels 3 to 7 times higher than ambient levels for the duration of their shifts. The first route shown below, where cab exposures to PM2.5 were on average seven times ambient, was serviced using a truck that did not meet the EPA’s 2007 emissions standards whereas the second route, where cab exposures to PM2.5 were on average three times ambient, was serviced using a truck that did meet the EPA’s 2007 emissions standards.

Carting Workers’ PM2.5 Exposures

Carting Workers’ PM2.5 Exposures

Livestream Your AirBeam

AirBeam Outdoor Enclosure

You can now livestream your AirBeam! To begin, visit the Google Play Store and make sure you are running the most up to date version of the AirCasting app. Then launch the AirCasting app, navigate to the settings screen, check “Streaming”, navigate back to the “AirCasting” screen and press “Start Recording”. It’s as simple as that. Every minute your measurements will be sent to our server where they will be displayed in real time at www.aircasting.org. Look for your AirBeam stream on the “Fixed” tab of the “Maps” page. If you have trouble finding it, filter the sessions using your “Profile name”. To get an idea of how it all works, have a look at the AirBeam livestream from HabitatMap’s Brooklyn headquarters.

The AirBeam is not waterproof, so if you want to livestream your measurements from an outdoor location, we recommend building an enclosure to protect it from the elements. We developed a low-cost, easy to assemble enclosure made from parts available at your local hardware store. The instructions for building it are available on the Instructables website.

The AirBeam enclosure may not be perfect for your particular location or climate, but it’s a good starting point from which to begin making additional modifications. If you develop another design you would like to share with the AirCasting community, please email us at info(at)habitatmap.org and we’ll post a link to it here.

Being device agnostic, the AirCasting app can livestream measurements from any AirCasting compatible instrument, not just the AirBeam. That includes the sound level measurements from your phone microphone or another instrument of your own design.

1,000 AirBeams Worldwide

1,000 AirBeams Worldwide

With over 1,000 AirBeams in use worldwide and more than 100 million data points, the AirCasting platform is now one of the largest open-source databases of community-collected air quality measurements ever created. Community based organizations, educators, academics, regulators, and citizen scientists around the world use the AirBeam to measure, map, stream, and crowdsource PM2.5 measurements. The collective effort of thousands of individual AirCasters made this historic milestone possible. Are you curious about who these folks are and where they come from? To give an idea of how diverse our worldwide community is, we’ve posted some of their organizational affiliations below.

Want to join us and make your community a better place to live, work, and play? Download the free AirCasting Android app and get started. Buy an AirBeam and contribute to a global effort to measure, map, and improve air quality. Visit www.aircasting.org to learn more.

Community-Based Organizations & Non-Profits
Clean Air Carolina, Golden Gate National Parks Conservancy, World Wildlife Fund, Janaagraha Centre for Citizenship and Democracy, Girl Scouts of Southern Nevada, New York Hall of Science, North Brooklyn Boat Club, Realm Charter Schools, Police Athletic League, Mellon Middle School, UPROSE, Central Park Zoo, Choice for All, Clean Air Partners, Rock Environment and Energy Institute, Arts Catalyst, Clean Air Council, WEACT, The Crystal London, National Aquarium, Boston Museum of Science, DIY Girls, Lawrence Hall of Science, Green Kids Now, Neighbors for Clean Air, Napraw Sobie Miasto, Instituto Políticas Alternativas para o Cone Sul, Inside Education, Dubuque Community Schools, Fairfax County Public Schools, Cleanup North Brooklyn, Environmental Justice Australia, Adler Planetarium, Loop Labs, Sunset Spark, Harford County Public Schools, Keystone Area Education Agency, Peer Educators Network, Katherine Delmar Burke School, Maison de la consommation et de l’environnement, Strategic Energy Innovations, Eastern Queens Alliance, Rio Grande Educational Collaborative, Alley Pond Environmental Center, Marymount School of New York, Manylabs

Government
USEPA Office of Research & Development, National Institute for Occupational Safety & Health, South Coast Air Quality Management District, National Parks Service, Puget Sound Clean Air Agency, National Health Research Institutes of Taiwan, Health Canada, Minnesota Pollution Control Agency, New York State Dept. of Health, USEPA Region 10, EPA Tasmania, The City of Calgary, Airparif, Metropolitan Washington Council of Governments, State of Delaware, Georgia Dept. of Natural Resources, Air Rhone-Alpes, Piedmont Triad Regional Council, The City of Dubuque, The City of Edmonton

Academic Institutions
New York University School of Medicine, Columbia University’s Center for Environmental Health, Icahn School of Medicine at Mount Sinai, University of Central Lancashire Media Factory, Hong Kong University of Science & Technology, Carnegie Mellon CREATE Lab, The University of Melbourne, University of Idaho, The University of New Mexico, New York University Wallerstein Collaborative for Urban Environmental Education, Curtin University, Illinois Institute of Technology, Bridgewater State University, Erciyes Universitesi, University of California San Diego, University of Maryland, Clemson University, Pontificia Universidad Católica de Chile, Universidad de La Salle, Institute of Environmental Assessment and Water Research, California State University Fullerton, University of Southern California, Carleton, Rutgers University Robert Wood Johnson Medical School, Seoul National University, University of New Haven, University of Utah, University of California Santa Barbara, Queensland University of Technology, University of Michigan, The University of Sheffield, University of Toronto, University of Dubuque, The University of Sydney, University of Bristol, New York University Tandon School of Engineering, Universita del Salento, The London School of Economics & Political Science, Oklahoma State University, University of Louisville, PEC University of Technology, University of Cincinnati, Cardiff Metropolitan University, The University of Arizona College of Optical Sciences, The University of Chicago, The University of Melbourne, University of British Columbia, University of California Berkeley, Presidio Graduate School, Houston-Tillotson University, Tennessee State University, Monash University, Instituto Tecnológico de Sonora, Stanford University

Corporations
Dyson, Ford, DHL, RTI International, SNC-Lavalin, Macawber Beekay Limited, Saint-Gobain, Sonoma Technology, Aclima, The Boston Consulting Group, GNS GmbH, Vivergy, Multitude, Hayward Lumber, Innovys, Lightwork Design, TechnologyWise, iLenSys, Creative Digital Technology, Edisonweb, FIGmd, Vitals First, ShipConstructor, ArcTech Computers, Nexleaf Analytics, Communique Media, Corus Product design, CSIRO, SkyQuest Technology Consulting, Sol Design Lab, CleanAir, Draxis Environmental, Space Between Design Studio, Wingra Engineering, LabMaker, Enchufate, EME Systems

Reprogram Your AirBeam

The AirBeam – HabitatMap’s low-cost, open-source air quality instrument for measuring PM2.5 – can be reprogrammed to meet the specific needs of your monitoring initiative. Don’t worry; it doesn’t require a PhD in computer engineering. Anyone can do it!  Just follow the step-by-step guide posted below.

There are several reasons why you may opt to reprogram your AirBeam. These include changing the averaging time interval, changing the temperature output to Celsius, outputting the PM2.5 measurement as a particle count, connecting multiple AirBeams to a single Android device, and improving the AirBeam’s accuracy. Directly after the step-by-step guide you’ll find instructions for altering the code to achieve these specific outcomes.

Step-by-Step Guide for Reprogramming Your AirBeam

1. Download the Arduino IDE from the Arduino website and install it on your computer.

2. Download the “DHT-sensor-library-master.zip” file from Dropbox. Unzip the file and place the folder on your desktop. Install the folder in the Arduino “Library” folder: navigate “Sketch” > “Import Library” and select the folder from your desktop.

3. Open a new Arduino file: navigate “File” > “New”. Delete the code that automatically populates the window. Copy the AirBeam firmware code from GitHub and paste it inside the new Arduino file.

4. In the Arduino software, navigate “Tools” > “Board” > “Arduino Leonardo”

5. Connect your computer to the AirBeam using a Micro USB to USB A Cable.

6. Turn on the AirBeam

7. Click the right facing “upload” arrow on the AirBeam program window, wait a few seconds and confirm that the message at the bottom of the window reads “Done uploading”.  You have now successfully reprogrammed the AirBeam!

Now that you’ve mastered the basics, we can introduce some line-by-line code changes that will help you make the most of your AirBeam.

• To change the averaging time interval, update the number of milliseconds on line 15. For example, to change the averaging time interval from one second to one minute, revise line 15 to read “unsigned long sampletime_ms = 60000″.

• To output temperature measurements in Celsius rather than Fahrenheit add double slashes “//” to the beginning of lines 212-216 and remove double slashes from lines 218-222.

• To output PM2.5 measurements in hundreds of particles per cubic foot (hppcf) remove the double slashes from lines 200-204.

• To connect multiple AirBeams to a single Android device you need to assign a unique “Sensor Name” to each sensor stream. (Most Android devices can maintain simultaneous Bluetooth connections with up to six devices). The sensor names for each sensor stream are defined on lines 197, 203, 209, 215, 221, and 227 of the AirBeam firmware. So for example, if you want to connect two AirBeams to a single Android device and record the PM2.5, relative humidity, and Fahrenheit measurements from both AirBeams, you’ll need to reprogram one of your AirBeams accordingly: on line 209 change the text reading “AirBeam-PM” to read “AirBeam-PM-1”, on line 215 change the text reading “AirBeam-F” to read “AirBeam-F-1”, and on line 227 change the text reading “AirBeam-RH” to “AirBeam-RH-1”. Note that when sensors are renamed the measurements no longer appear on the same CrowdMap. That is to say, the data collected from “AirBeam-PM” will appear on a separate CrowdMap than the data collected by “AirBeam-PM-1″.

• In November 2015, we updated the firmware for the AirBeam, implementing an improved calibration function for converting from hundreds of particles per cubic foot (hppcf) to micrograms per cubic meter (ug/m3). Unfortunately, due to a mix-up by our manufacturing partner, there was a 7-month period where we continued shipping AirBeams with outdated firmware. All AirBeams ordered after June 13, 2016 are running the latest firmware containing the updated calibration function. If you ordered an AirBeam prior to June 13, 2016, you can improve its accuracy by reprogramming it with the AirBeam firmware included in the above step-by-step guide.

We updated the calibration function in response to data we received from the South Coast Air Quality Management District (SCAQMD). The data they collected in Los Angeles comparing the AirBeam’s measurements to more expensive and accurate Federal Equivalency Method (FEM) instruments indicated that our initial calibration function for converting from particle count to mass overestimated PM2.5 as concentrations climbed above 30 ug/m3. This can be seen in the below figures.

PM2.5 Count Comparison

PM2.5 Mass Comparison

The R2 value comparing AirBeam measurements to FEM measurements is .89 when comparing particle counts (first figure) and .76 when comparing mass (second figure). (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, i.e. the higher the number the better the agreement between instruments.)  The calibration function included in the November 2015 AirBeam firmware (see line 166) addresses the overestimation problem encountered during SCAQMD’s evaluation, during which the AirBeams were running firmware released in March 2015 that implemented a different calibration function (see line 190).

We want to extend a huge thank you to SCAQMD for including the AirBeam in their evaluation and sharing their data with us. Many thanks!!

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