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$\text{DMRadio-}{\mathrm{m}}^3$is an experimental search for dark matter axions. It uses a solenoidal dc magnetic field to convert an axion dark-matter signal to an ac electromagnetic response in a coaxial copper pickup. The current induced by this axion signal is measured by dc SQUIDs. $\text{DMRadio-}{\mathrm{m}}^3$is designed to be sensitive to Kim-Shifman-Vainshtein-Zakharov (KSVZ) and Dine-Fischler-Srednicki-Zhitnisky (DFSZ) QCD axion models in the 10–200 MHz ( $41\mathrm{neV}/c^2–0.83\mathrm{\mu }\mathrm{eV}/c^2$) range, and to axions with $g_{a\gamma \gamma }=g_{a\gamma \gamma ,\mathrm{DFSZ}}\left(30\mathrm{MHz}\right)=1.87\times {10}^{-17}{\mathrm{GeV}}^{-1}$over 5–30 MHz as an extended goal. In this work, we present the electromagnetic modeling of the response of the experiment to an axion signal over the full frequency range of $\text{DMRadio-}{\mathrm{m}}^3$, which extends from the low-frequency, lumped-element limit to a regime where the axion Compton wavelength is only a factor of 2 larger than the detector size. With these results, we determine the live time and sensitivity of the experiment. The primary science goal of sensitivity to DFSZ axions across 30–200 MHz can be achieved with a $3\sigma$live scan time of 2.9 years. 

Abstract We synthesized N₂O emissions over North America using 17 bottom‐up (BU) estimates from 1980–2016 and five top‐down (TD) estimates from 1998 to 2016. The BU‐based total emission shows a slight increase owing to U.S. agriculture, while no consistent trend is shown in TD estimates. During 2007–2016, North American N₂O emissions are estimated at 1.7 (1.0–3.0) Tg N yr⁻¹(BU) and 1.3 (0.9–1.5) Tg N yr⁻¹(TD). Anthropogenic emissions were twice as large as natural fluxes from soil and water. Direct agricultural and industrial activities accounted for 68% of total anthropogenic emissions, 71% of which was contributed by the U.S. Our estimates of U.S. agricultural emissions are comparable to the EPA greenhouse gas (GHG) inventory, which includes estimates from IPCC tier 1 (emission factor) and tier 3 (process‐based modeling) approaches. Conversely, our estimated agricultural emissions for Canada and Mexico are twice as large as the respective national GHG inventories.