Assessing the Intensity of Marine Biogenic Influence on the Lower Atmosphere: An Insight into the Distribution of Marine Biogenic Aerosols over the Eastern China Seas

Zhou, Shuyi; Chen, Ying; Wang, Fanghui; Bao, Yang; Ding, Xiaoyi; Xu, Zongjun

doi:10.1021/acs.est.3c04382

Cited by 2 publications

(1 citation statement)

References 67 publications

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“…MSA and non-sea-salt SO 4 2 (nss-SO 4 2 ) are the main products of atmospheric DMS oxidation (Barnes et al, 2006;Read et al, 2008). The E chl can reflect the impact of marine biogenic emissions and atmospheric DMS oxidation on air masses (M. Zhang et al, 2020;Zhou et al, 2023). We plotted the 48 hr backward trajectory for each hour along the sampling path from the start to the end of aerosol sampling, as shown in Figure 6a.…”

Section: Relationship Between the Msa And The E Chlmentioning

confidence: 99%

Factors Controlling DMS Emission and Atmospheric Sulfate Aerosols in the Western Pacific Continental Sea

Yan,

Xu,

Zhang

et al. 2024

JGR Oceans

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The western Pacific continental sea significantly influences the regulation of climate‐active gases budget and the burden of sulfate aerosols. An underway shipboard measurement device was used to determine the dimethyl sulfide (DMS) in the surface seawater and overlying atmosphere in the East China Continental Sea. The average concentration of DMS in atmosphere and seawater was 122.8 ± 86.2pptv and 6.47 ± 3.58 nmol L−1, respectively. The variation trend of surface water DMS in the western Pacific continental sea was influenced by the abundance and composition of phytoplankton under different ocean current systems, with a significant impact from eddies on DMS production in the South China Sea. By eliminating the influence of terrestrial sources and limiting air mass transport within the marine boundary layer, strong correlations were established between atmospheric DMS and air mass exposure to chlorophyll (Echl), as well as between aerosol methanesulfonic acid (MSA) and Echl. During the aerosol sampling period, the atmospheric DMS levels (1,057 ± 371 ng/m3) were significantly higher than MSA levels (46.3 ± 59.8 ng/m3) in the East China Sea, where the conversion of DMS to MSA was not affected by changes in DMS concentration. The sea‐to‐air fluxes of DMS varied over a wide range, from 0.02 to 156.0 μmol m−2 d−1, with an average of 14.35 ± 18.58 μmol m−2 d−1. Marine DMS emissions play a critical role in the formation of sulfur aerosols on the western Pacific continental shelf, accounting for 24.6 ± 7.6% (14.8%–37.8%) of the total sulfate aerosols.

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Section: Relationship Between the Msa And The E Chlmentioning

confidence: 99%

Factors Controlling DMS Emission and Atmospheric Sulfate Aerosols in the Western Pacific Continental Sea

Yan,

Xu,

Zhang

et al. 2024

JGR Oceans

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IPB-MSA&SO₄: a daily 0.25° resolution dataset of in situ-produced biogenic methanesulfonic acid and sulfate over the North Atlantic during 1998–2022 based on machine learning

Mansour,

Decesari,

Ceburnis

et al. 2024

Earth Syst. Sci. Data

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Abstract. Accurate long-term marine-derived biogenic sulfur aerosol concentrations at high spatial and temporal resolutions are critical for a wide range of studies, including climatology, trend analysis, and model evaluation; this information is also imperative for the accurate investigation of the contribution of marine-derived biogenic sulfur aerosol concentrations to the aerosol burden, for the elucidation of their radiative impacts, and to provide boundary conditions for regional models. By applying machine learning algorithms, we constructed the first publicly available daily gridded dataset of in situ-produced biogenic methanesulfonic acid (MSA) and non-sea-salt sulfate (nss-SO4=) concentrations covering the North Atlantic. The dataset is of high spatial resolution (0.25° × 0.25°) and spans 25 years (1998–2022), far exceeding what observations alone could achieve both spatially and temporally. The machine learning models were generated by combining in situ observations of sulfur aerosol data from Mace Head Atmospheric Research Station, located on the west coast of Ireland, and from the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) cruises in the northwestern Atlantic with the constructed sea-to-air dimethylsulfide flux (FDMS) and ECMWF ERA5 reanalysis datasets. To determine the optimal method for regression, we employed five machine learning model types: support vector machines, decision tree, regression ensemble, Gaussian process regression, and artificial neural networks. A comparison of the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) revealed that Gaussian process regression (GPR) was the most effective algorithm, outperforming the other models with respect to simulating the biogenic MSA and nss-SO4= concentrations. For predicting daily MSA (nss-SO4=), GPR displayed the highest R2 value of 0.86 (0.72) and the lowest MAE of 0.014 (0.10) µg m−3. GPR partial dependence analysis suggests that the relationships between predictors and MSA and nss-SO4= concentrations are complex rather than linear. Using the GPR algorithm, we produced a high-resolution daily dataset of in situ-produced biogenic MSA and nss-SO4= sea-level concentrations over the North Atlantic, which we named “In-situ Produced Biogenic Methanesulfonic Acid and Sulfate over the North Atlantic” (IPB-MSA&SO4). The obtained IPB-MSA&SO4 data allowed us to analyze the spatiotemporal patterns of MSA and nss-SO4= as well as the ratio between them (MSA:nss-SO4=). A comparison with the existing Copernicus Atmosphere Monitoring Service ECMWF Atmospheric Composition Reanalysis 4 (CAMS-EAC4) reanalysis suggested that our high-resolution dataset reproduces the spatial and temporal patterns of the biogenic sulfur aerosol concentration with high accuracy and has high consistency with independent measurements in the Atlantic Ocean. IPB-MSA&SO4 is publicly available at https://doi.org/10.17632/j8bzd5dvpx.1 (Mansour et al., 2023b).

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Assessing the Intensity of Marine Biogenic Influence on the Lower Atmosphere: An Insight into the Distribution of Marine Biogenic Aerosols over the Eastern China Seas

Cited by 2 publications

References 67 publications

Factors Controlling DMS Emission and Atmospheric Sulfate Aerosols in the Western Pacific Continental Sea

Factors Controlling DMS Emission and Atmospheric Sulfate Aerosols in the Western Pacific Continental Sea

IPB-MSA&SO₄: a daily 0.25° resolution dataset of in situ-produced biogenic methanesulfonic acid and sulfate over the North Atlantic during 1998–2022 based on machine learning

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Assessing the Intensity of Marine Biogenic Influence on the Lower Atmosphere: An Insight into the Distribution of Marine Biogenic Aerosols over the Eastern China Seas

Cited by 2 publications

References 67 publications

Factors Controlling DMS Emission and Atmospheric Sulfate Aerosols in the Western Pacific Continental Sea

Factors Controlling DMS Emission and Atmospheric Sulfate Aerosols in the Western Pacific Continental Sea

IPB-MSA&SO4: a daily 0.25° resolution dataset of in situ-produced biogenic methanesulfonic acid and sulfate over the North Atlantic during 1998–2022 based on machine learning

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IPB-MSA&SO₄: a daily 0.25° resolution dataset of in situ-produced biogenic methanesulfonic acid and sulfate over the North Atlantic during 1998–2022 based on machine learning