This paper presents our progress in developing, testing, and implementing a Ubiquitous Sensing Network (USN) for real-time monitoring of methane emissions. This newsensor technology supports environmental management of industrial sites through a decision support system. Upon detection of specific inputs, data is processed before passing it on for appropriate actions (Data→Insight→Actions). The technology integrates wireless methane sensor nodes, weather sensors, edge-based devices and is powered by a self- contained solar-battery powered system. A cloud-based data analytics IoT solution is included for handling continuous sensor monitoring. A sample of results from an in-house simulated well site are presented within the body of this paper. Preliminary predictions seem to correlate well with the true emission rate as indicated by the proximity of the predictions to the forty-five-degree line. Running more tests should allow us to further estimate the error distribution as well as the prediction interval width and the overall emission rate prediction trend. The initial results demonstrate that the developed technology can quantify the emission rate (scfh) within 1% and 45% error, and a localization error within six feet to fifty feet given a test area of 10,000 square feet. This integrated solution is being ruggedized and the analytics are being optimized for continuous monitoring of methane emissions at customer sites for safety, product loss prevention, and regulatory compliance.