The recent wildfires in California, the most intense and extensive in the state’s recorded history, have destroyed an area of more than 674,957 hectares, filling the area with smoke during an interminable week, sending air quality indicators to worrying levels, matching those in the most polluted cities in the world.
The air quality indicators raised concerns for residents: while the owners of Tesla Model S and Model X vehicles tested the Bioweapon Defense Mode on their air filtering systems, ten times bigger than a conventional vehicle and that claims to eliminate “99.97% of particles and gaseous pollutants, as well as bacteria, viruses, pollen and spores” confirming that its capabilities more than matched its claims, sales of devices to monitor pollution, such as Atmotube, Flow, Awair or Aeroqual, among others, rose sharply.
As the evidence of climate change caused mainly by human-generated emissions mounts, measuring air quality is an increasingly important concern for many people. Google, for example, has incorporated sensors in the vehicles it uses to prepare its maps, which will make possible the inclusion of these metrics, although based on specific data. Other companies, such as Breezometer, aim to collect data from public and private pollution sensor networks that can be displayed in a smartphone app or in vehicles, helping make decisions about whether it is appropriate to go out, taking part in sporting activities, drive with the windows open or whether to close them and activate the air conditioning.
But why not collectivize all this information by turning our smartphones into distributed mobile pollution monitoring stations feeding maps with their data? Most modern smartphones and even some vehicles, such as Teslas, process air quality data and transmit it to an app, and even generate recommendations or activate certain protocols. Getting all those distributed sensors to feed an application would create more detailed maps and avoid what happened in Madrid in 2009 under Mayor Ana Botella, when pollution levels in the Spanish capital significantly exceeded EU limits and City Hall simply dismantled measuring stations with the worst readings, relocating others to the outskirts or in parks and gardens, a practice that was later investigated by the European Commission. The cheating mayor’s example highlights the importance of creating unbiased maps based on as many sources as possible, away from the grubby hands of authorities interested unable to confront reality.
Using vehicles to generate data is already relatively common: fleets of autonomous vehicles, for example, use the multiple sensors of all their members to collect valuable data that helps them learn collectively. In China, unsurprisingly, the government monitors the location and recharge data of people’s electric vehicles with the assistance of companies desperate to access an important market and willing to sign anything to do so, and quite possibly, without the owners of the vehicles knowing. When all vehicles are connected to the internet, air quality evaluation data collected from different sources could permanently update maps, allowing us to evaluate emission reduction initiatives that will be required as awareness of this issue increases.
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