Air pollution is often monitored by stationary sensors dispersed around a given city many kilometers apart. This model only measures air quality in a few local spots, which provides data concerning only those immediate areas. The underlying problem in this scenario is that five or 10 stationary sensors spread across a city cannot properly measure the air quality throughout all areas of that city. Measuring air pollution in this manner is insufficient and can be misleading when results from one area are used to assume the quality over an extended area. Results have proven that air pollutants can be eight times higher from one end of a block to another.
An emerging method to increase the reach of environmental sensing is to deploy advanced mobile air pollution sensors integrated into connected fleet vehicles. This method collects hyper-local insights into air pollution and air quality. Mobile sensors that travel the roadways of a city can generate more granular air pollution data with vastly increased geographic coverage. This type of clean air initiative can make air quality metrics more visible to policymakers by more accurately mapping air pollution within a city.
Geotab partnered with the Environmental Defense Fund on a study that illustrated a fleet of just 10 or 20 top vehicles (ones with the highest number of passes on all roads within a city) has the ability to cover almost 80 percent of the city's roadways in just six months. In five of the cities evaluated in the study, a fleet of 10 vehicles driving regular routes proved able to map 50 percent or more of the city, and almost 80 percent with just 20 vehicles. An example of this work was conducted in Washington, D.C., where the top 20 public vehicles from DC Water made a minimum of 15 passes on almost 70 percent of the city’s roadways in six months. Much of the areas not covered were either federal- or military-controlled areas.
The city of Houston, Texas, also conducted a pilot project with Geotab to employ regular fleet vehicles fitted with mobile air pollution sensors while driving regular routes, to map hyper-local air quality data. Houston saw 50 percent to 70 percent road coverage in six months utilizing only 10-20 fleet vehicles. Data capture was based on multiple, consistent passes/observations along roadways, at different times of day, on different days, during differing conditions, in order to obtain a critical mass of viable and reliable data. The project was able to isolate potential air pollution “hot spots” that would justify closer attention from City Hall. One hot spot instance identified in the pilot was along a busy intersection with a middle school on one corner.
This illustrates a key reason why this more comprehensive hyper-local version of air pollution mapping is so advantageous. Policymakers need to know where air quality is concerning or more threatening to public health. A “clean air government” needs to know if air quality is poor in one residential neighborhood compared to others, or in sensitive ecological areas, near schools or hospitals, in affluent or less affluent areas and so on. They need to understand why, and then decide what can be done.
Having accurate hyper-local pollution mapping will certainly provoke new thinking and questions that will drive innovation to help solve the problem. Knowing the facts about where air pollution is problematic can bring urgency to policymakers and arm them with the analytics to justify making targeted investments in improving the air we breathe. If cities and municipalities around the world were to fully utilize vehicle-based pollution mapping to combat this major global threat, it would have the potential to help improve the lives of hundreds of millions of people.
To learn more about Geotab's government and smart city insights, email: smartcity@geotab.com or visit www.geotab.com/government.
