Statistical models for carbon monoxide in El Paso, Texas

Abstract

This study was conducted to develop a statistical model that would enable decision‐makers, planners, and the general public to use existing large quantities of ambient air carbon monoxide (CO) data in establishing an effective air quality management program for El Paso, Texas. Two models, a General Linear Model (GLM) and a 20‐term Quadratic Model (QM) were produced. Both models predict well for periods of up to two years. The models enable the user not only to predict ambient air CO levels for specified time periods, but also provide a mechanism whereby environmental factors such as meteorological variables and area emissions may be evaluated for magnitude of impact on ambient air CO levels. The use of the definition of scenario conditions, the “conditioning” process, also enables the user to assess the influences of non‐scalar variables such as wind direction and geographical location on ambient air CO levels. The basic framework for model development, calibration, and validation, plus the evaluation of ...