Atmospheric concentrations of CH4 have tripled since the Industrial Revolution. One culprit of this increase is animal agriculture, contributing 8 to 10% of global greenhouse gas emissions primarily in the form of CH4. According to US Environmental Protection Agency greenhouse gas inventory estimates, the majority of the manure emissions are from manure management on dairy farms (53%). Most of these manure emissions are generated from liquid manure in anaerobic lagoons. Thus, accurate estimates of the emissions from these lagoons are essential for developing management strategies to reduce CH4 emissions. Emissions of methane from two manure lagoons, one in Southern California and the other in Central California, were estimated by fitting results from a state-of-the-art dispersion model to CH4 concentrations measured with a mobile monitor. The sampling was conducted by stationing the mobile monitors at several locations (29-42) around the lagoons for time intervals ranging from 10 to 15 minutes. A sonic anemometer provided micrometeorological measurements used by the dispersion model. Emissions were computed by fitting the time-averaged methane concentrations to model estimates. The 95% confidence intervals for the emissions were computed by bootstrapping pseudo observations created by adding residuals between model estimates and corresponding observations to the best fit model estimates. The coefficient of determination, r2, between model and measurements made at the Southern California dairy was over 0.86 and the geometric standard deviation (sg) was 1.1; the steady westerly wind direction was a major factor for this result. At the Central California dairy, the winds were light and variable resulting in an r2 of about 0.9 and a high sg of 1.4. The sensitivity of the emission estimates to wind direction was determined by running the dispersion model for different wind sectors. We found that the emission estimates were within 1.5 times of each other under all wind conditions. The dispersion model was cross-validated by estimating the emissions using only half the total receptors and then predicting the concentration at other receptors using this emission rate. This technique can be used to improve methane emission estimates in manure management and to assess the effectiveness of the different strategies to reduce emissions.

Since 2007, the global mole fraction of atmospheric methane (CH4) has steadily increased meanwhile the 13C/12C isotopic ratio of CH4 (expressed as δ13C-CH4) has shifted to more negative values. This suggests that CH4 emissions are primarily driven by biogenic sources. However, more in situ isotopic measurements of CH4 are needed at the local scales to identify which biogenic sources dominate CH4 emissions regionally. In California, dairies contribute a substantial amount of CH4 emissions from enteric fermentation and manure management. In this study, we present seasonal atmospheric measurements of δ13C-CH4 from dairy farms in the San Joaquin Valley of California. We used δ13C-CH4 to characterize emissions from enteric fermentation by measuring downwind of cattle housing (e.g., freestall barns, corrals) and from manure management areas (e.g., anaerobic manure lagoons) with a mobile platform equipped with cavity ring-down spectrometers. Across seasons, the δ13C-CH4 from enteric fermentation source areas ranged from -69.7 ± 0.6 per mil (‰) to -51.6 ± 0.1‰ while the δ13C-CH4 from manure lagoons ranged from -49.5 ± 0.1‰ to -40.5 ± 0.2‰. Measurements of δ13C-CH4 of enteric CH4 suggest a greater than 10‰ difference between cattle production groups in accordance with diet. Isotopic signatures of CH4 were used to characterize enteric and manure CH4 from downwind plume sampling of dairies. Our findings show that δ13C-CH4 measurements could improve the attribution of CH4 emissions from dairy sources at scales ranging from individual facilities to regions and help constrain the relative contributions from these different sources of emissions to the CH4 budget.

Fossil fuel CO2 emissions (ffCO2) constitute the majority of greenhouse gas emissions and are the main determinant of global climate change. The COVID-19 pandemic caused wide-scale disruption to human activity and provided an opportunity to evaluate our capability to detect ffCO2 emission reductions. Quantifying changes in ffCO2 levels is especially challenging in cities, where climate mitigation policies are being implemented but local emissions lead to spatially and temporally complex atmospheric mixing ratios. Here, we used direct observations of on-road CO2 mole fractions with analyses of the radiocarbon (14C) content of annual grasses collected by community scientists in Los Angeles and California, USA to assess reductions in ffCO2 emissions during the first two years of the COVID-19 pandemic. With COVID-19 mobility restrictions in place in 2020, we observed a significant reduction in ffCO2 levels across California, especially in urban centers. In Los Angeles, CO2 enhancements on freeways were 60 ± 16% lower and ffCO2 levels were 43-55% lower than in pre-pandemic years. By 2021, California’s ffCO2 levels rebounded to pre-pandemic levels, albeit with substantial spatial heterogeneity related to local and regional pandemic measures. Taken together, our results indicate that a reduction in traffic emissions by ~60% (or 10-24% of Los Angeles’ total ffCO2 emissions) can be robustly detected by plant 14C analysis and pave the way for mobile- and plant-based monitoring of ffCO2 in cities without CO2 monitoring infrastructure such as those in the Global South.