Source localization and emission strength quantification is an ongoing challenge for distributed pollution sources. Here we outline a wireless sensor approach to localize all potential emission sources on an oil and gas well pad under well controlled experimental conditions. Using backtracking algorithms and time synchronized methane and wind measurements, sources are attributed to equipment on the well pad. After localizing the sources, we estimate source magnitude and uncertainty using a Bayesian inference method. The approach outlined in this work can identify and quantify leaks in the close proximity of the sources under dynamic plume dispersion taking into account the site layout, potential source locations and the characteristics of the sensor network. Localization of the system is within a meter from the emission location and the Bayesian approach yields rates that are within a factor 3 of the actual rate. Further, the actual rates are generally within the 95% confidence intervals for the prediction.

We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of times-tamps. Given an expected frequency ΔT-1, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t1, t2,..., tN. In practice, the method proves useful to e.g. identify time intervals of missing data or to locate isolated events. Moreover, we define measures to quantify a series of events by varying ΔT to e.g. determine the quality of an Internet of Things service.

The Minimum Airflow Particle Counter (MAPC) is a portable, low-power, low-cost, wireless optical counter which has been specifically designed for ultra-low-maintenance operation in heavily polluted environments. When exposed continuously to air with high particulate matter concentrations, the primary mode of failure for particle counters is a build-up of dust within the instrument. The MAPC circumvents this failure mode by severely restricting airflow through the system, enabling an estimated 5-year maintenance cycle. Such a long operational lifetime makes this instrument particularly suitable for IOT applications such as environmental air quality monitoring and pollutant source attribution using spatially distributed wireless sensor networks. Here, we present the theory of operation, instrument design, and collected data from a two-month field deployment in Beijing. We find that the MAPC performs comparably to other low-cost optical counters, but with a significantly enhanced maintenance-free operational lifetime.

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.

Solar car race competitions offer realistic conditions to test and demonstrate the state-of-the-art technologies in multidisciplinary fields. In such races the solar panels mounted on the car produce the energy required to power the vehicle. A simulator runs during the race determines the optimal race speed based on the predicted availability of solar energy and other parameters as well as road conditions. The accuracy of the forecasts, especially the solar irradiance forecasts, has a significant impact on the race strategy. Here we report on the experience of providing irradiance forecasts for two races run by the University of Michigan Solar Car Team at the Bridgestone World Solar Challenge 2015 in Australia and at the American Solar Challenge 2016 from Ohio to South Dakota. The probabilistic forecasts of hourly solar irradiance generated from machine learning algorithms were deployed to optimally decide on the race strategy. This work showcases an example of real time decision making based on insights derived from machine learning utilizing big geospatial data — weather models and measurement data from weather station networks.