Testing and Improving a UAV-Based System Designed for Wetland Methane Source Measurements

Wetlands are the single highest emitting methane source category, but the magnitude of wetland fluxes remains difficult to fully characterize due to their large spatial extent and heterogeneity. Fluxes can vary with land surface conditions, vegetation type, and seasonal changes in environmental conditions. Unmanned aerial vehicles (UAVs) are an emerging platform to better characterize spatial variability in these natural ecosystems. While presenting some advantages over traditional techniques like towers and flux chambers, in that they are mobile vertically and horizontally, their use is still challenging, requiring continued improvement in sensor technology and field measurement approaches. In this work, we employ a small, fast response laser spectrometer on a Matrice 600 hexacopter. The system was previously deployed successfully for 40 flights conducted in a four-day period in 2018 near Fairbanks, Alaska. These flights revealed several potential areas for improvement, including: vertical positioning accuracy, the need for sensor health indicators, and approaches to deal with low wind speeds. An additional set of flights was conducted this year near Antioch in California. Flights were conducted several meters above ground up to 15-25 m in a curtain pattern. These curtains were flown both upwind and downwind of a tower site, allowing us to calculate a mass balance methane flux estimate that can be compared to eddy covariance fluxes from the tower. Testing will better characterize the extent to which altitude drifts in-flight and how GPS values compare with measurements from the onboard LIDAR, as well as the agreement between two-dimensional wind speed and direction on the ground versus measured onboard the UAV. Hardware improvements to the sensor and GPS are being considered to help reduce these sources of uncertainty. Results of this testing and how system performance relates to needs for quantifying wetland fluxes, will be presented

Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle: Localization and Quantification of Emission Rate

We describe a set of methods for locating and quantifying natural gas leaks using a small unmanned aerial system equipped with a path-integrated methane sensor. The algorithms are developed as part of a system to enable the continuous monitoring of methane, supported by a series of over 200 methane release trials covering 51 release location and flow rate combinations. The system was found throughout the trials to reliably distinguish between cases with and without a methane release down to 2 standard cubic feet per hour (0.011 g/s). Among several methods evaluated for horizontal localization, the location corresponding to the maximum path-integrated methane reading performed best with a mean absolute error of 1.2 m if the results from several flights are spatially averaged. Additionally, a method of rotating the data around the estimated leak location according to the wind is developed, with the leak magnitude calculated from the average crosswind integrated flux in the region near the source location. The system is initially applied at the well pad scale (100–1000 m2 area). Validation of these methods is presented including tests with unknown leak locations. Sources of error, including GPS uncertainty, meteorological variables, data averaging, and flight pattern coverage, are discussed. The techniques described here are important for surveys of small facilities where the scales for dispersion-based approaches are not readily applicable

Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle: Measurement System Description and Mass Balance Approach

Natural gas is an abundant resource across the United States, of which methane (CH4) is the main component. About 2% of extracted CH4 is lost through leaks. The Remote Methane Leak Detector (RMLD)-Unmanned Aerial Vehicle (UAV) system was developed to investigate natural gas fugitive leaks in this study. The system is composed of three major technologies: miniaturized RMLD (mini-RMLD) based on Backscatter Tunable Diode Laser Absorption Spectroscopy (TDLAS), an autonomous quadrotor UAV and simplified quantification and localization algorithms. With a miniaturized, downward-facing RMLD on a small UAV, the system measures the column-integrated CH4 mixing ratio and can semi-autonomously monitor CH4 leakage from sites associated with natural gas production, providing an advanced capability in detecting leaks at hard-to-access sites compared to traditional manual methods. Automated leak characterization algorithms combined with a wireless data link implement real-time leak quantification and reporting. This study placed particular emphasis on the RMLD-UAV system description and the quantification algorithm development based on a mass balance approach. Early data were gathered to test the prototype system and to evaluate the algorithm performance. The quantification algorithm derived in this study tended to underestimate the gas leak rates and yielded unreliable estimations in detecting leaks under 7 × 10 − 6 m3/s (~1 Standard Cubic Feet per Hour (SCFH)). Zero-leak cases can be ascertained via a skewness indicator, which is unique and promising. The influence of the systematic error was investigated by introducing simulated noises, of which Global Positioning System (GPS) noise presented the greatest impact on leak rate errors. The correlation between estimated leak rates and wind conditions were investigated, and steady winds with higher wind speeds were preferred to get better leak rate estimations, which was accurate to approximately 50% during several field trials. High precision coordinate information from the GPS, accurate wind measurements and preferred wind conditions, appropriate flight strategy and the relative steady survey height of the system are the crucial factors to optimize the leak rate estimations

Ammonia Dry Deposition in an Alpine Ecosystem Traced to Agricultural Emission Hotpots

Elevated reactive nitrogen (Nr) deposition is a concern for alpine ecosystems, and dry NH3 deposition is a key contributor. Understanding how emission hotspots impact downwind ecosystems through dry NH3 deposition provides opportunities for effective mitigation. However, direct NH3 flux measurements with sufficient temporal resolution to quantify such events are rare. Here, we measured NH3 fluxes at Rocky Mountain National Park (RMNP) during two summers and analyzed transport events from upwind agricultural and urban sources in northeastern Colorado. We deployed open-path NH3 sensors on a mobile laboratory and an eddy covariance tower to measure NH3 concentrations and fluxes. Our spatial sampling illustrated an upslope event that transported NH3 emissions from the hotspot to RMNP. Observed NH3 deposition was significantly higher when backtrajectories passed through only the agricultural region (7.9 ng m–2 s–1) versus only the urban area (1.0 ng m–2 s–1) and both urban and agricultural areas (2.7 ng m–2 s–1). Cumulative NH3 fluxes were calculated using observed, bidirectional modeled, and gap-filled fluxes. More than 40% of the total dry NH3 deposition occurred when air masses were traced back to agricultural source regions. More generally, we identified that 10 (25) more national parks in the U.S. are within 100 (200) km of an NH3 hotspot, and more observations are needed to quantify the impacts of these hotspots on dry NH3 deposition in these regions

Vehicle Emissions as an Important Urban Ammonia Source in the United States and China

Ammoniated aerosols are important for urban air quality, but emissions of the key precursor NH₃ are not well quantified. Mobile laboratory observations are used to characterize fleet-integrated NH₃ emissions in six cities in the U.S. and China. Vehicle NH₃:CO₂ emission ratios in the U.S. are similar between cities (0.33–0.40 ppbv/ppmv, 15% uncertainty) despite differences in fleet composition, climate, and fuel composition. While Beijing, China has a comparable emission ratio (0.36 ppbv/ppmv) to the U.S. cities, less developed Chinese cities show higher emission ratios (0.44 and 0.55 ppbv/ppmv). If the vehicle CO₂ inventories are accurate, NH₃ emissions from U.S. vehicles (0.26 ± 0.07 Tg/yr) are more than twice those of the National Emission Inventory (0.12 Tg/yr), while Chinese NH₃ vehicle emissions (0.09 ± 0.02 Tg/yr) are similar to a bottom-up inventory. Vehicle NH₃ emissions are greater than agricultural emissions in counties containing near half of the U.S. population and require reconsideration in urban air quality models due to their colocation with other aerosol precursors and the uncertainties regarding NH₃ losses from upwind agricultural sources. Ammonia emissions in developing cities are especially important because of their high emission ratios and rapid motorizations

Near-Field Characterization of Methane Emission Variability from a Compressor Station Using a Model Aircraft

A model aircraft equipped with a custom laser-based, open-path methane sensor was deployed around a natural gas compressor station to quantify the methane leak rate and its variability at a compressor station in the Barnett Shale. The open-path, laser-based sensor provides fast (10 Hz) and precise (0.1 ppmv) measurements of methane in a compact package while the remote control aircraft provides nimble and safe operation around a local source. Emission rates were measured from 22 flights over a one-week period. Mean emission rates of 14 ± 8 g CH₄ s^–1 (7.4 ± 4.2 g CH₄ s^–1 median) from the station were observed or approximately 0.02% of the station throughput. Significant variability in emission rates (0.3–73 g CH₄ s^–1 range) was observed on time scales of hours to days, and plumes showed high spatial variability in the horizontal and vertical dimensions. Given the high spatiotemporal variability of emissions, individual measurements taken over short durations and from ground-based platforms should be used with caution when examining compressor station emissions. More generally, our results demonstrate the unique advantages and challenges of platforms like small unmanned aerial vehicles for quantifying local emission sources to the atmosphere