Measuring Greenhouse Gas Emissions from Point Sources with Mobile Systems

Abstract

The traditional least squares method for the retrieval of CO2 emissions from CO2 emission sources is affected by the nonlinear characteristics of the Gaussian plume model, which leads to the optimal estimation of CO2 emissions easily falling into local minima. In this study, ACA–IPFM (ant colony algorithm and interior point penalty function) is proposed to remedy the shortcomings of the traditional least squares method, which makes full use of the global search property of the ant colony algorithm and the local exact search capability of the interior point penalty function to make the optimal estimation of CO2 emissions closer to the global optimum. We evaluate the errors of several parameters that are most likely to affect the accuracy of the CO2 emission retrieval and analyze these errors jointly. These parameters include wind speed measurement error, wind direction measurement error, CO2 concentration measurement error, and the number of CO2 concentration measurements. When the wind speed error is less than 20%, the inverse error of CO2 concentration emission is less than 1% and the uncertainty is less than 3%, when the wind direction error is less than 55 degrees, the inverse error is less than 1% and the uncertainty is less than 3%, when the CO2 concentration measurement error is less than 10%, the inverse error is less than 1% and the uncertainty is less than 3.3%, and when the measurement quantity is higher than 60, the inverse error is less than 1% and the uncertainty is less than 3%. In addition, we simulate the concentration observations on different paths under the same conditions, and invert the CO2 emissions based on these simulated values. Through the retrieval results, we evaluate the errors caused by different paths of measurements, and have demonstrated that different paths are affected by different emission sources to different degrees, resulting in different inversion accuracies for different paths under the same conditions in the end, which can provide some reference for the actual measurement route planning of the mobile system. Combined with the characteristics of the agility of the mobile system, ACA–IPFM can extend the monitoring of CO2 emissions to a wider area.