A simple semi-empirical model for predicting missing carbon monoxide concentrations

Carbon monoxide monitoring using continuous samplers is carried out in most major urban centres in the world and generally forms the basis for air quality assessments. Such assessments become less reliable as the proportion of data missing due to equipment failure and periods of calibration increases. This paper presents a semi-empirical model for the prediction of atmospheric carbon monoxide concentrations near roads for the purpose of interpolating missing data without the need for any traffic or emissions information. The model produces reliable predictions while remaining computationally simple by being site-specifically optimized. The model was developed for, and evaluated at, both a suburban site and an inner city site in Hamilton, New Zealand. Model performance statistics were found to be significantly better than other simple methods of interpolation with little additional computational complexity.