The famous statistician George Box said “All models are wrong, but some are useful”—for calculating air quality indices and emissions estimates this is certainly true. One way to increase the utility of models is to use up-to-the-minute, local Planetary Boundary Layer (PBL) measurements as an input when generating top-down emissions estimates.
Understanding the local PBL should be a primary concern for any municipality or surveyor who is responsible for understanding local air quality or measuring emission plume inversions. As urbanization continues to spread, pollution is increasing and the quality (or cleanliness) of air being breathed by the local populace is steadily declining. Municipalities recognize the negative impact on quality of life for its citizens, but many are not yet convinced that investing in remote sensing technology is justified. This opinion is the combined result of existing models not approximating reality well enough to be put in the useful “Box” (pun intended), and the importance of the PBL measurement to plume inversions not yet being fully appreciated.
I believe there are economic objectives and health reasons that justify improving the accuracy of PBL measurements, which in turn will improve the reliability of models that provide warning of adverse weather and pollution events. When a municipality can accurately predict what time the haze is going to lift that day, or a surveyor can quantify a natural gas pipeline emission plume inversion with greater accuracy, the technology will have clearly proven its value.
The PBL is the area of the lower troposphere we often think of as a capping layer of the atmosphere. It is the highest point above the surface of the Earth where a range of aerosols, from pollen to traffic particulates to industrial emissions, collect, capped by the temperature inversion layer. The height and thickness of the PBL is influenced by surface topography, temperature and winds. The PBL can be 1‒2 km deep and impacts climate, weather and air quality. It changes throughout the day as the ground heats up and cools down, and is affected by surface turbulence, atmospheric moisture, winds, mountains, tall buildings, traffic patterns and many other human factors.
When weather researchers rely on estimated PBL information, their model’s usefulness declines. Due to the dynamic local atmosphere, and the transport of particles in and out of any area, the actual PBL may be drastically different than an arbitrarily selected estimate. From an air quality perspective, if the real PBL is lower than the estimate used in the model, particles in the air are closer to the surface than predicted and are more likely to impact the population. A higher PBL is generally better because there is more area to mix and disperse pollutants; however, a high PBL allows air currents to carry unexpected particles into the area from other places.
Atmospheric conditions are constantly changing. Using traditional technology, such as radiosondes carried by balloons, atmospheric readings are taken only twice a day at specific geographic locations according to set international standards. The measurements of temperature, pressure, and moisture are very accurate; however, the accuracy starts to decay as soon as the radiosonde comes down.
To produce an accurate emission flux calculation, up-to-the-minute local measurements are needed. Continuous monitoring with aerosol-detecting remote sensors, such as Micro Pulse LiDAR (MPL) and Mini Micro Pulse LiDAR (MiniMPL) systems, provides valuable data that complement the traditional collection methods. In addition, the MiniMPL is easily transportable, capable of running 24/7/365 without human attention, and is reusable, unlike radiosondes which are restricted to designated launch sites and are typically single use. The MPL technology detects and measures the size and type of particles across a broad vertical range, rather than only near the surface. These data further refine the PBL calculations.
By integrating data from multiple instruments, more accurate PBL measurements are achievable. The U.S. Department of Energy has been experimenting with an algorithm that combines data from radiosondes, an MPL, and an atmospheric emitted radiance interferometer (AERI).