Document Type
Article
Journal/Book Title/Conference
Air
Author ORCID Identifier
John R. Lawson https://orcid.org/0000-0003-0305-4090
Seth N. Lyman https://orcid.org/0000-0001-8493-9522
Volume
2
Issue
3
Publisher
MDPI AG
Publication Date
9-18-2024
Journal Article Version
Version of Record
First Page
337
Last Page
361
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
High concentrations of ozone in the Uinta Basin, Utah, can occur after sufficient snowfall and a strong atmospheric anticyclone creates a persistent cold pool that traps emissions from oil and gas operations, where sustained photolysis of the precursors builds ozone to unhealthy concentrations. The basin's winter-ozone system is well understood by domain experts and supported by archives of atmospheric observations. Rules of the system can be formulated in natural language ("sufficient snowfall and high pressure leads to high ozone"), lending itself to analysis with a fuzzy-logic inference system. This method encodes human expertise as machine intelligence in a more prescribed manner than more complex, black-box inference methods such as neural networks, increasing user trustworthiness of our model prototype before further optimization. Herein, we develop an ozone forecasting system, CLYFAR, informed by an archive of meteorological and air-chemistry measurements. This prototype successfully demonstrates proof-of-concept despite rudimentary tuning. We describe our framework for predicting future ozone concentrations if input values are drawn from numerical weather prediction forecasts rather than observations as CLYFAR initial conditions. We evaluate inferred values for one winter, finding our prototype demonstrates mixed performance but promise after optimization to deliver useful forecast guidance for decision-makers when forecast data are used as input. This early version model is the basis of ongoing optimization through machine learning.
Recommended Citation
Lawson, J.R; Lyman, S.N. A Preliminary Fuzzy Inference System for Predicting Atmospheric Ozone in an Intermountain Basin. Air 2024, 2, 337-361. https://doi.org/10.3390/air2030020
