Methane emissions from residential natural gas meter set assemblies.

Residential natural gas meter set assemblies (MSAs) emit methane (CH4), but reported emissions factors vary. To test existing emissions factors, we quantified CH4 emissions from 37 residential MSAs in Calgary, Alberta, Canada. A notable difference with previous studies is the targeted measurement of regulator vents in this study, which were measured with a static chamber, while fugitives were measured with a modified hi-flow sampler. Emissions were dominated by pressure regulator vents (emissions factor = 1.18 g CH4/h/MSA), but 7 fugitives were found (emissions factor = 0.018 g CH4/h/MSA). Six regulator vents were emitting at notably higher rates (≥ 1.79 g CH4/h/MSA). The total empirical emissions factor was 1.20 g CH4/h/MSA (95 % CI, 1.03 to 1.37 g/h/MSA). This is ∼7 times higher than the emissions factor for residential MSAs used in the U.S. EPA's Greenhouse Gas Inventory, which may not include emissions from regulator vents. Upscaling to annual CH4 emissions in Calgary indicates 3234.6 t CH4/yr (95 % CI, 2776.4 t to 3692.9 t CH4/yr) could be emitted from MSAs. This is equivalent to 4.1 % (95 % CI, 3.5 % to 4.7 %) of total city-level CH4 emissions as estimated with satellite data. Results suggest residential MSA emissions may be under-estimated and further study isolating root causes of regulator vent emissions is required to guide mitigation and improve emissions modeling.

Global observational coverage of onshore oil and gas methane sources with TROPOMI.

Satellite observations have been used to measure methane (CH4) emissions from the oil and gas (O&G) industry, particularly by revealing previously undocumented, very large emission events and basin-level emission estimates. However, most satellite systems use passive remote sensing to retrieve CH4 mixing ratios, which is sensitive to sunlight, earth surface properties, and atmospheric conditions. Accordingly, the reliability of satellites for routine CH4 emissions monitoring varies across the globe. To better understand the potentials and limitations of routine monitoring of CH4 emissions with satellites, we investigated the global observational coverage of the TROPOMI instrument onboard the Sentinel-5P satellite-the only satellite system currently with daily global coverage. A 0.1° × 0.1° gridded global map that indicates the average number of days with valid observations from TROPOMI for 2019-2021 was generated by following the measurement retrieval quality-assurance threshold (≥ 0.5). We found TROPOMI had promising observational coverage over dryland regions (maximum: 58.6%) but limited coverage over tropical regions and high latitudes (minimum: 0%). Cloud cover and solar zenith angle were the primary factors affecting observational coverage at high latitudes, while aerosol optical thickness was the primary factor over dryland regions. To further assess the country-level reliability of satellites for detecting and quantifying CH4 emissions from the onshore O&G sector, we extracted the average annual TROPOMI observational coverage (TOC) over onshore O&G infrastructure for 160 countries. Seven of the top-10 O&G-producing countries had an average annual TOC < 10% (< 36 days per year), which indicates the limited ability to routinely identify large emissions events, track their duration, and quantify emissions rates using inverse modelling. We further assessed the potential performance of the latter by combining TOC and the uncertainties from the global O&G inventory. Results indicate that the accuracy of emissions quantifications of onshore O&G sources using TROPOMI data and inverse modeling will be higher in countries located in dryland and mid-latitude regions and lower in tropical and high-latitude regions. Therefore, current passive-sensing satellites have low potential for frequent monitoring of large methane emissions from O&G sectors in countries located in tropical and high latitudes (e.g., Canada, Russia, Brazil, Norway, and Venezuela). Alternative methods should be considered for routine emissions monitoring in these regions.

A Comparison of Multiple Odor Source Localization Algorithms.

There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster-Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover's distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow.

Development and validation of a route planning methodology for vehicle-based remote measurements of methane and other emissions from oil and gas wells and facilities.

Multi-sensor vehicle systems have been implemented in large-scale field programs to detect, attribute, and estimate emissions rates of methane (CH4) and other compounds from oil and gas wells and facilities. Most vehicle systems use passive sensing; they must be positioned downwind of sources to detect emissions. A major deployment challenge is predicting the best measurement locations and driving routes to sample infrastructure. Here, we present and validate a methodology that incorporates high-resolution weather forecast and geospatial data to predict measurement locations and optimize driving routes. The methodology estimates the downwind road intersection point (DRIP) of theoretical CH4 plumes emitted from each well or facility. DRIPs serve as waypoints for Dijkstra's shortest path algorithm to determine the optimal driving route. We present a case study to demonstrate the methodology for planning and executing a vehicle-based concentration mapping survey of 50 oil and gas wells near Pecos, Texas. Validation was performed by comparing DRIPs with 174 CH4 plumes measured by vehicle surveys of oil and gas wells and facilities in Alberta, Canada. Results indicate median Manhattan distances of 145.8 m between DRIPs and plume midpoints and 160.3 m between DRIPs and peak plume enhancements. A total of 46 (26%) of the plume segments overlapped DRIPs. Locational errors of DRIPs are related to misattributions of emissions sources and discrepancies between modeled and instantaneous wind direction measured when the vehicle intersects plumes. Although the development of the methodology was motivated by CH4 emissions from oil and gas facilities, it should be applicable to other types of point source air emissions from known facilities.Implications: This paper presents and validates a method that addresses the challenge of measuring industrial emissions from public roads. The method can increase the effectiveness and efficiency of targeted vehicle-based emissions surveys where the locations of potential sources are known. We believe the method has broad application in addition to the upstream oil and gas context it was designed for.

Methane emissions from above-ground natural gas distribution facilities in the urban environment: A fence line methodology and case study in Calgary, Alberta, Canada.

The occurrence and emissions of methane (CH4) from above-ground urban natural gas infrastructure is poorly understood. Compared to below-ground infrastructure, these facilities are relatively easy to monitor and maintain and present an opportunity for cost-effective CH4 reductions. We present a case study and methodology for detecting, attributing, and quantifying CH4 emissions from fence line measurements at above-ground natural gas facilities in the City of Calgary, Alberta, Canada. We produced bounding-box concentration maps by walking around the outer fence of 33 facilities with a backpack-configured trace gas analyzer and a tablet with integrated GPS. Wind measurements were acquired simultaneously from a fixed location on site with a 3D sonic anemometer. We fused geolocation, CH4 concentration, and wind data to determine the likelihood each facility was emitting. We found one definitive leak by carrying out measurements directly alongside an exposed section of pipe. Based on the presence of methyl mercaptan (CH3SH) odor, peak ΔCH4, and the difference between downwind and upwind ΔCH4, we interpret a high plausibility that 22 facilities were emitting CH4, followed by 2 with a medium plausibility, and 8 with a low plausibility. Once verified to plausibly emit, these data were used to estimate emissions flux at six facilities where near-field obstructions were limited. The estimated emissions flux for six facilities was 66.31 mg CH4 s-1, or 2.1 tonnes CH4 yr-1 if this flux remained constant. Overall, this study indicates most above-ground natural gas facilities surveyed in Calgary were emitting CH4. These facilities represent easy mitigation targets for reducing CH4 emissions and improving environmental performance. To our knowledge, this is the first study to integrate qualitative and quantitative information to predict detection plausibility in a complex measurement setting.Implications: The fence line methodology outlined in this study represents an extension of source assessment modes in the US EPA's Other Test Method 33A for human portable systems. This has implications for standardization of emissions measurement in situations where other platforms (e.g., vehicles) are less effective due to access limitations. We believe the methodology presented could become a recognized standard based on performance from controlled testing and added to the regulatory toolkit for emissions verification and compliance.

Weather constraints on global drone flyability.

Small aerial drones are used in a growing number of commercial applications. However, drones cannot fly in all weather, which impacts their reliability for time-sensitive operations. The magnitude and global variability of weather impact is poorly understood. We explore weather-limited drone flyability (the proportion of time drones can fly safely) by comparing historical wind speed, temperature, and precipitation data to manufacturer-reported thresholds of common commercial and weather-resistant drones with a computer simulation. We show that global flyability is highest in warm and dry continental regions and lowest over oceans and at high latitudes. Median global flyability for common drones is low: 5.7 h/day or 2.0 h/day if restricted to daylight hours. Weather-resistant drones have higher flyability (20.4 and 12.3 h/day, respectively). While these estimates do not consider all weather conditions, results suggest that improvements to weather resistance can increase flyability. An inverse analysis for major population centres shows the largest flyability gains for common drones can be achieved by increasing maximum wind speed and precipitation thresholds from 10 to 15 m/s and 0-1 mm/h, respectively.