Project mission
Safecast Air is the second phase of the Safecast project. Its focus is to map and monitor air quality from across the planet, and return all of the information on an open online database.

Project overview
  1. Motivation
  2. Science
  3. Hardware
  4. Software
  5. Other data sources

The goal of the Safecast Air project is to collect a variety of metrics for air quality using a set of fixed and internet-connected sensors. Additionally, we seek to accomplish this distributed monitoring effort using commonly available equipment and then report the data in real-time to an open online database.
While air quality information is currently collected at a number of fixed sites it is often not collected on a fine geographic scale, especially in urban environments. The Safecast Air project will hopefully fill this need by aggregating air quality data, such as ozone and carbon dioxide concentrations, on a fine-grained scale in space and time.

  1. Atmospheric particulate matter
    1. Particulate matter concentration is often characterized using both the density and size of the particles. Density is recorded as a number of particles per cubic meter and particle size is record as the diameter of the particles in microns. Particle size is then often referred to as pm1, pm2.5, or pm10 where 'pm' stands for 'particulate matter diameter of' and the number gives the micron diameter.
  2. Gas concentrations
    1. Gas concentration is generally recorded in ppm (parts per million) and ppb (parts per billion) for a number of pollutants. Safecast air will monitor such gases as ozone, carbon dioxide, carbon monoxide, and nitrogen dioxide using a standard set of resistive sensors. These sensors contain an element which will change its resistivity in response to particular trace compounds and output a voltage corresponding to the gas concentration. Concentrations can also me monitored using Non-Dispersive Infrared absorption (NDIR) sensing, where the intensity of absorption lines in a given part of the infrared spectrum are used to infer the concentration of various gas types.
  3. Gas types
    1. Ozone
    2. Nitrogen dioxide
    3. Carbon dioxide
    4. Carbon monoxide
    5. Hydrocarbon gases (methane, propane, etc)
  4. Other data captures
    1. GPS
      1. The data captured by our GPS receiver, the GT720-F, is set in GLL mode. The raw return we have is a csv string which captures latitude, longitude, time, and status.
    2. Temperature
      1. The data captured for temperature comes from the Sensirion SHT15 temperature and humidity sensor: Data sheet
    3. Humidity
      1. The data captured for humidity comes from the Sensirion SHT15 temperature and humidity sensor: Data sheet

  1. The Arduino Uno
    1. The main brain of the Safecast Air module.
  2. Arduino Ethernet Shield
    1. This shield allows for a fixed position Safecast Air module to relay its data online on a continuous basis.
  3. GT-720F GPS receiver
    1. This sensor gives a time and location stamp to the collected data for later geographical visualization and analysis.
  4. Shinyei PPD42NS Particle Sensor
    1. This sensor records the concentration of particulates in the air with a diameter greater than one micron. The concentration is determined every thirty seconds.
    2. Specification sheet
    3. Getting your Shinyei PPD42NS to talk to your Arduino and give results on the concentration of pm1
    4. Getting your Shinyei PPD42NS installed and how to keep it running in the long term
  5. MQ-6 LPG gas sensor
    1. This sensor measures the concentration of natural gas in ppm. For future iterations of Safecast Air this sensor will likely be replaced by higher precision gas sensors. This sensor is low-cost and, given the easy access of natural gas, it is useful for testing purposes.
    2. Data sheet
    3. Resistance to ppm calibration curve
    4. Temperature and humidity calibration curve
  6. Sensirion SHT15 temperature and humidity sensor
    1. For gas sensors which determine gas concentrations through a change in sensor resistance it is necessary to factor in atmospheric temperature and humidity in order to more accurately determine the true gas concentration. This data is collected for the purposes of calibrating gas sensors.
    2. Data sheet
  7. AT Tiny 85
    1. This Microcontroller Unit (MCU) forms the brain of a sensor module. The module takes in analog data from two sensors on a module board, then relays that information to the main Arduino board so that it can be digitized and calibrated before being sent off to the internet.
    2. Data sheet

Other data source
  1. MyAirBase
  1. GitHub link for the Safecast Air project
  2. Diagram for how the slave microcontroller unit (MCU) communicates with the master Arduino board: SafecastModuleI2C.png
  3. Softwarefor converting image files from specification sheets into spreadsheet data for the creation of best-fit calibration curves.
    1. The image files of calibration curve graphs are imported from manufacturer's specifications and then converted into a set of coordinate points in order to make a best-fit calibration curve function. This is useful for a number of gas sensors with no calibration curve function listed for direct use in the code to convert sensor output voltage into a concentration.
  4. The Safecast API for organizing the data online.