These graphs show a sample of what can be learned by comparing air quality data from a sensor network deployed across four cities. We group the network by city in order to learn whether policies or tactics particular to a specific area has an affect on hourly averages, number of exceedence events or hours of the day that may have higher concentrations. Each node in the network measures Carbon Dioxide, Particulate Matter, Carbon Monoxide and Volatile Organic Compounds; select the sensor type to update the graphs with that sensor’s data.
CO2 is a colorless and odorless greenhouse gas, occurring at an average concentration of about 400 parts per million. In urban areas, concentrations are generally higher. Indoors, they can reach 10 times background levels. CO2 is a good indicator of the ventilation rate in tightly enclosed spaces or where occupancy is high.
PM is a mixture of solid and liquid particles made up of a number of components, including acids (such as nitrates and sulfates), organic chemicals, metals, and soil or dust particles. Fine particulate matter (< 2.5 µm) is small enough to pass the blood-air barrier in our lungs and enter into our pulmonary system, potentially triggering heart and lung diseases.
CO is a colorless, odorless gas that can cause harmful health effects by reducing the oxygen delivery to the body’s organs (like the heart and brain) and tissues. At extremely high levels, CO can cause death. Most of the CO in the atmosphere is produced through photochemical reactions in the troposphere. In urban areas, CO concentrates because it is a byproduct of internal combustion.
VOCs include both human-made and naturally occurring chemical compounds. Most scents or odors are made up of VOCs. VOCs evaporate at room temperature and are emitted by a wide array of products used in homes. They cause eye, nose, and throat irritation, headaches, nausea, and can damage the liver, kidney, and central nervous system.
These graphs plot the hourly average across a week’s worth of data of each city (the green line) on top of the entire sensor network’s hourly minimum and maximum readings (the gray area graph). At a glance we can see which city is pushing the network’s maximum with elevated average concentrations. Hover over individual graphs to see the axes and scales.
This graph tallies the number of exceedence events for each type of location for the defined time period. By comparing across location types we can begin to learn whether certain types of spaces have a tendency towards higher concentrations of certain pollutants. Do libraries tend to have a stronger concentration of CO2? Does Tokyo tend towards higher VOCs regardless of space type? Hover over city names to see numerical data. (An “event” is defined as a period of time in which the sensor detected readings that exceed the accepted healthy range of pollutant exposure.)
For each day of the week, we compare the number of hours in that day where the sensor reported concentrations that exceed the accepted healthy range of pollutant exposure. Are there certain days of the week that tend towards higher concentrations? Hover over city names to see numerical data.