Abstract
Abstract. In this study, we present a novel approach for assessing nearshore seepage atmospheric emissions through modeling of air quality station data, specifically a Gaussian plume inversion model. A total of 3 decades of air quality station meteorology and total hydrocarbon concentration, THC, data were analyzed to study emissions from the Coal Oil Point marine seep field offshore California. THC in the seep field directions was significantly elevated and Gaussian with respect to wind direction, θ. An inversion model of the seep field, θ-resolved anomaly, THC′(θ)-derived atmospheric emissions is given. The model inversion is for the far field, which was satisfied by gridding the sonar seepage and treating each grid cell as a separate Gaussian plume. This assumption was validated by offshore in situ data that showed major seep area plumes were Gaussian. Plume total carbon, TC (TC = THC + carbon dioxide, CO2, + carbon monoxide), 18 % was CO2 and 82 % was THC; 85 % of THC was CH4. These compositions were similar to the seabed composition, demonstrating efficient vertical plume transport of dissolved seep gases. Air samples also measured atmospheric alkane plume composition. The inversion model used observed winds and derived the 3-decade-average (1990–2021) field-wide atmospheric emissions of 83 400 ± 12 000 m3 THC d−1 (27 Gg THC yr−1 based on 19.6 g mol−1 for THC). Based on a 50 : 50 air-to-seawater partitioning, this implies seabed emissions of 167 000 m3 THC d−1. Based on atmospheric plume composition, C1–C6 alkane emissions were 19, 1.3, 2.5, 2.2, 1.1, and 0.15 Gg yr−1, respectively. The spatially averaged CH4 emissions over the ∼ 6.3 km2 of 25 × 25 m2 bins with sonar values above noise were 5.7 µM m−2 s−1. The approach can be extended to derive emissions from other dispersed sources such as landfills, industrial sites, or terrestrial seepage if source locations are constrained spatially.
Highlights
1.1 Seepage and methaneOn decadal timescales, the important greenhouse gas methane, CH4, affects atmospheric radiative balance far more strongly than carbon dioxide, CO2 (IPCC, 2007, Fig. 2.21), yet CH4 has large uncertainties for many sources (IPCC, 2013) and is very sensitive to hydroxyl (OH) concentration, the primary CH4 loss mechanism (Zhao et al, 2020)
Further impetus for a CH4 focus is a recent estimate that 40 % of CH4 emissions reductions are feasible at no net cost for the oil and gas, O&G, industry (IEA, 2020), a major anthropogenic CH4 source (IPCC, 2014)
We present a novel approach for assessing nearshore seepage atmospheric emissions – air quality station data modeling, using a Gaussian plume inversion model
Summary
1.1 Seepage and methaneOn decadal timescales, the important greenhouse gas methane, CH4, affects atmospheric radiative balance far more strongly than carbon dioxide, CO2 (IPCC, 2007, Fig. 2.21), yet CH4 has large uncertainties for many sources (IPCC, 2013) and is very sensitive to hydroxyl (OH) concentration, the primary CH4 loss mechanism (Zhao et al, 2020). The significantly shorter lifetime of CH4 than CO2 argues for CH4 regulatory priority as emission reductions (and changes to the radiative balance) manifest more quickly as atmospheric concentrations decrease (Shindell et al, 2005). Further impetus for a CH4 focus is a recent estimate that 40 % of CH4 emissions reductions are feasible at no net cost for the oil and gas, O&G, industry (IEA, 2020), a major anthropogenic CH4 source (IPCC, 2014). This is salient given a recent estimate that half of recent CH4 increases are from the O&G industry (Jackson et al, 2020).
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