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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 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.

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 (CH 4) and carbon dioxide (CO 2) , and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH 4 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 CH 4 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 CH 4 and CO 2 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 CH 4 emissions in near real time. [ABSTRACT FROM AUTHOR]

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 large scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols in the 2020 wildfire season of smoke plumes from 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 (EF) and modified combustion efficiency (MCE) for these fires, and evaluate relationships between them based on combustion phase at large scales. We demonstrate that the EM27/SUN effectively detects changes of CO, CO2 and CH4 in the atmospheric column at ~10 km 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 large 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 station and observed ratios of AOD to XCO were consistent with those previously observed from satellites. We also show that co-located XCO observations from the TROPOMI satellite-based instrument are 9.7% 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 13.6% of the total state CH4 emissions in 2019. Our novel application of an EM27/SUN solar spectrometer to quantify wildfire emission ratios at large scales follows predictive relationships that are consistent with in situ studies, offering promise for extensive monitoring from ground networks and satellite remote sensing. [ABSTRACT FROM AUTHOR]