Fixing the Philosophy of Environmental Monitoring
Updated: Mar 15, 2021
We didn't mean to do it. It just happened over the years. What wasn't possible then has simply become status quo now, and everyone kind of "forgot" the compromises that were made along the way.
One of the core principles of science, and philosophy, is the difference between the a-priori and a-posteriori mindset. Can we achieve knowledge primarily through reason with limited amounts of observation (a-priori)? Or do we require lots of observation and evidence in order to achieve knowledge (a-posteriori).
Random latin philosophy terms aside, the differences behind these world-views sits at the heart of many discussions regarding how much air monitoring is "enough" air monitoring, and how "perfect" a measurement system has to be in order to be valuable.
Air quality monitoring hasn't really changed in 50 years. Oh, some of the technologies have advanced, or gotten smaller, but the basic premise of putting expensive equipment in a climate-controlled shelter and then pumping hundreds of thousands of dollars into maintenance and calibration of said equipment in order to get the best possible measurement hasn't changed. Environmental monitoring has always been expensive, and because of that, it's always been limited.
The costs of monitoring is what has led to the "forgetting" that there could be a better way.
With traditional air monitoring, we simply couldn't afford to put all that many monitoring sites in the world. Colorado, for example, has 18 spread throughout the state. Similarly, the pollutants that are measured by at each of these monitoring sites has been defined but the EPA under a basic premise that the scientific community should focus on the "hardest-hitting" pollutants that we know of - the "Criteria Pollutants" - because it would be completely infeasible to measure every possible pollutant and compound in the air. Not only would it be expensive, but the amount of data to be sifted through simply couldn't be managed - at least when these problems were being addressed.
These compromises led to reliance on a belief that we can collect a little bit of data and reason out the true state of the world (a-priori knowledge).
This approach makes sense, but only as long as the underlying premise remains true.
Is it infeasible to monitor multiple types of pollutants in many locations and be able to manage and make sense of the massive amounts of data?
I argue that the world has outrun the premise that it's infeasible to measure everywhere and wrangle massive amounts of data.
Sensor technologies have advanced tremendously in the last 5 years and are only getting better, less expensive, and are measuring more compounds. Similarly, data storage and compute power are unbelievably more advanced than they were even 2 years ago. Yet when you look at the analysis tools most environmental scientists use, they're running applications on Windows XP systems, or using the same R libraries that were created 30 years ago.
Because we've outrun this premise, the environmental monitoring space is primed for a major transition from a-priori (limited measurement, lots of reason) philosophy, to an a-posteriori (more data) philosophy. And, at the root of this transition? Concerned world-citizens who will trust a local measurement more than a scientists (or trade associations) assumption.
Now that we've set a somewhat long-winded frame of reference, let's get back to this philosophy issue.
At the root of this philosophical issue is:
"How much environmental measurement is enough environmental measurement?"
If you've heard me speak, you've probably been introduced to this spectrum.
On one end of the spectrum, we have no knowledge of our air quality. On the other end of the spectrum, we have ideal knowledge - the real concentration of every compound for every second in every cubic meter of space on earth.
Side note: Even with all the improvements in measurement technology and compute power, we still don't have capability to collect that much information. But we can still keep it in mind as our ideal.
There are two things that move us down this spectrum, toward our ideal knowledge.
2) Assumption (which most people fancy up with the term "modeling")
Side note: Both measurement and assumption can have their own bubbles of uncertainty around them, but dealing with that is a topic for another day.
Now, with traditional monitoring approaches, we are severely limited in spatial coverage, time coverage, and pollutant coverage. This severely limited measurement means that we have to make a lot of assumptions to get to enough knowledge to be able to answer important questions.
How do these pollutants impact the population health? How about my kid's health?
Where might these pollutants be coming from? What are the biggest sources?
How are these pollutants impacting our global environment?
What kind of changes can we make to reduce these pollutants?
With a reliance on limited data and extensive reason (an a-priori mindset), we have to stretch our ability to reason pretty far in order to try to answer these questions.
That's not to say we aren't making progress, we are. It's just progress in the same way you can still drive a car with a blindfold on. There'll be some bumps and course corrections, and plenty of people will get scared along the way.
It may be better to figure out how to take off the blindfold.
At Ajax Analytics, our philosophy is that higher resolution data is always going to answer more questions (even if it only raises more questions at first).
The principle behind our purpose as a company is that the world must move further down this spectrum, while shrinking the amount of assumption required to answer these big, important questions.
We all need to shift our mindset regarding environmental monitoring toward observation and evidence. We need to make an a-posteriori philosophy shift.
With every new generation of sensor technology, and every new data center that pops up in the world, we get closer and closer to achieving the ideal environmental knowledge. It may take a few years for government regulators to catch up to the pace of innovation, for corporations to realize the value of measured facts over assumptive rhetoric, and for innovative companies like Ajax Analytics and our sensor technology partners to figure out how to collect and wrangle these massive datasets at scale, but this innovation is happening right now.
Granted, scientists and government regulators aren't known for shifting a mindset quickly, however, philosophical shifts happen much more quickly when a very vocal global public is pushing for better knowledge faster.
We can make this shift. We can prove the value of these technological innovations. We make good use of the growing amounts of environmental data.
In the end, we'll all be in a place full of measured observation, trusted data, constructive conversations between passionate stakeholders, and corporate and government policy adjustments that actually make a difference.