Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models.

The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem. The classification model performance for CH4 detection was evaluated using accuracy, F1 score, Matthew's Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.

Characterizing the detectability of emission signals from a North Korean nuclear detonation.

The detectability of emission sources, defined by a low-level of mixing with other sources, was estimated for various locations surrounding the Sea of Japan, including a site within North Korea. A high-resolution meteorological model coupled to a dispersion model was used to simulate plume dynamics for four periods, and two metrics of airborne plume mixing were calculated for each source. While emissions from several known sources in this area tended to blend with others while dispersing downwind, the North Korean plume often remained relatively distinct, thereby making it potentially easier to unambiguously 'backtrack' it to its source.