Toward on-demand measurements of greenhouse gas emissions using an uncrewed aircraft AirCore system

This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH4) and carbon dioxide (CO2), and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations in the San Joaquin Valley of California where CH4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11 m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s−1 when the wind magnitude is less than 3.5 m s−1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH4 and CO2 occurring over periods of 10 s or longer. Results from the UAAS and dispersion modeling use case further demonstrate that UAASs have great potential as low-cost tools for detecting and quantifying CH4 emissions in near real time.</p

Ground solar absorption observations of total column CO, CO₂, CH₄, and aerosol optical depth from California's Sequoia Lightning Complex Fire: emission factors and modified combustion efficiency at regional scales

With global wildfires becoming more widespread and severe, tracking their emissions of greenhouse gases and air pollutants is becoming increasingly important. Wildfire emissions have primarily been characterized by in situ laboratory and field observations at fine scales. While this approach captures the mechanisms relating emissions to combustion phase and fuel properties, their evaluation on regional-scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols during the 2020 wildfire season from smoke plumes in the Sierra Nevada of California with an EM27/SUN solar Fourier transform infrared (FTIR) spectrometer. We derive total column aerosol optical depth (AOD), emission factors (EFs) and modified combustion efficiency (MCE) for these fires and evaluate relationships between them, based on combustion phase at regional scales. We demonstrate that the EM27/SUN effectively detects changes in CO, CO2, and CH4 in the atmospheric column at ∼10 km horizontal scales that are attributed to wildfire emissions. These observations are used to derive total column EFCO of 120.5±12.2 and EFCH4 of 4.3±0.8 for a regional smoke plume event in mixed combustion phases. These values are consistent with in situ relationships measured in similar temperate coniferous forest wildfires. FTIR-derived AOD was compared to a nearby AERONET (AErosol RObotic NETwork) station and observed ratios of XCO to AOD were consistent with those previously observed from satellites. We also show that co-located XCO observations from the TROPOspheric Monitoring Instrument (TROPOMI) satellite-based instrument are 9.7±1.3 % higher than our EM27/SUN observations during the wildfire period. Finally, we put wildfire CH4 emissions in context of the California state CH4 budget and estimate that 213.7±49.8 Gg CH4 were emitted by large wildfires in California during 2020, about 13.7 % of the total state CH4 emissions in 2020. Our work demonstrates a novel application of the ground-based EM27/SUN solar spectrometers in wildfire monitoring by integrating regional-scale measurements of trace gases and aerosols from smoke plumes.</p

Vista-LA: Mapping methane-emitting infrastructure in the Los Angeles megacity

Methane (CH4) is a potent greenhouse gas (GHG) and a critical target of climate mitigation efforts. However, actionable emission reduction efforts are complicated by large uncertainties in the methane budget on relevant scales. Here, we present Vista, a Geographic Information System (GIS)-based approach to map potential methane emissions sources in the South Coast Air Basin (SoCAB) that encompasses Los Angeles, an area with a dense, complex mixture of methane sources. The goal of this work is to provide a database that, together with atmospheric observations, improves methane emissions estimates in urban areas with complex infrastructure. We aggregated methane source location information into three sectors (energy, agriculture, and waste) following the frameworks used by the State of California GHG Inventory and the Intergovernmental Panel on Climate Change (IPCC) Guidelines for GHG Reporting. Geospatial modeling was applied to publicly available datasets to precisely geolocate facilities and infrastructure comprising major anthropogenic methane source sectors. The final database, Vista-Los Angeles (Vista-LA), is presented as maps of infrastructure known or expected to emit CH4. Vista-LA contains over 33 000 features concentrated on  &lt;  1 % of land area in the region. Currently, Vista-LA is used as a planning and analysis tool for atmospheric measurement surveys of methane sources, particularly for airborne remote sensing, and methane hotspot detection using regional observations. This study represents a first step towards developing an accurate, spatially resolved methane flux estimate for point sources in SoCAB, with the potential to address discrepancies between bottom–up and top–down methane emissions accounting in this region. The Vista-LA datasets and associated metadata are available from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC; https://doi.org/10.3334/ORNLDAAC/1525)

Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground-Based Remote Sensing Observations in the Summer and Winter

The dairy industry in the San Joaquin Valley (SJV) is one of California’s largest methane (CH4) sources. Reducing dairy emissions is a priority for the state’s climate change plans. Observations of current dairy CH4 emissions are key to monitoring actions taken toward this goal. To help support this, we present new ground-based measurements of atmospheric column-averaged CH4 mixing ratio (XCH4) gradients across a group of 600 dairies in the central SJV using EM27/SUN solar spectrometers. We used measurements from the 2019 summer and 2020 winter seasons for a top-down emission inversion based on the WRF-STILT model. Our top-down estimates of the region’s dairy emissions range from 90% to 183% of the current CALGEM inventory’s emissions of 277&nbsp;Gg/yr. In contrast to the strong temperature dependence found by earlier dairy CH4 emission studies, we also find that our top-down emissions during the winter measurement days are comparable to the summer measurement days, possibly due to seasonal changes in dairy management practices and meteorological conditions. Furthermore, we find significant interday variability in our measurements and find that our emission estimates overlap with earlier top-down studies and bottom-up inventories in this region. Our study demonstrates how analysis of ground-based remotely sensed CH4 gradient observations can help improve our understanding of CH4 sources at scales relevant to mitigation policy. It also reflects the need for long-term monitoring of CH4 emissions in the region and at individual facilities to better understand their emissions