Taking Space

The HabitatMap & AirCasting Blog

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

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

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

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: menu button > “Settings” > “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 . . . ” > press “Yes” when prompted to connect. 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.