资源环境科技发展态势分析平台

High-time-resolution measurements of in situ aerosol and cloud properties provide the ability to study regional atmospheric processes that occur on timescales of minutes to hours. However, one limitation to this approach is that continuous measurements often include periods when the data collected are not representative of the regional aerosol. Even at remote locations, submicron aerosols are pervasive in the ambient atmosphere with many sources. Therefore, periods dominated by local aerosol should be identified before conducting subsequent analyses to understand aerosol regional processes and aerosol-cloud interactions. Here, we present a novel method to validate the identification of regional baseline aerosol data by applying a mathematical algorithm to the data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility in the eastern North Atlantic (ENA). The ENA central facility (C1) includes an aerosol observing system (AOS) for the measurement of aerosol physical, optical, and chemical properties at time resolutions from seconds to minutes. A second temporary supplementary facility (S1), located similar to 0.75 km from C1, was deployed for similar to 1 year during the Aerosol and Cloud Experiments (ACEENA) campaign in 2017.

First, we investigate the local aerosol at both locations. We associate periods of high submicron number concentration (N-tot) in the fine-mode condensation particle counter (CPC) and size distributions from the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) as a function of wind direction using a meteorology sensor with local sources. Elevated concentrations of Aitken-mode (< 100 nm diameter) particles were observed in correspondence with the wind directions associated with airport operations. At ENA, the Graciosa Airport and its associated activities were found to be the main sources of high-concentration aerosol events at ENA, causing peaks in 1 min N-tot that exceeded 8000 and 10 000 cm(-3) at C1, in summer and winter, respectively, and 5000 cm(-3) at S1 in summer. Periods with high N-tot not associated with these wind directions were also observed. As a result, the diverse local sources at ENA yielded a poor relationship between Ntot measurements collected at C1 and S1 (R-2 = 0.03 with a slope = 0.05 +/- 0.001). As a first approach to mask these events, the time periods when the wind direction was associated with the airport operations (west to northwest and southeast to south at C1 and east to south at S1) were applied. The meteorological masks removed 38.9% of the data at C1 and 43.4% at S1, and they did not significantly improve the relationship between the two sites (R-2 = 0.18 with a slope = 0.06 +/- 0.001).

Due to the complexity of high-N-tot events observed at ENA, we develop and validate a mathematical ENA Aerosol Mask (ENA-AM) to identify high-N-tot events using 1 min resolution data from the AOS CPC at C1 and S1. After its parameterization and application, ENA-AM generated a high correlation between N-tot in the summer at C1 and S1 (R-2 = 0.87 with a slope = 0.84 +/- 0.001). We identified the regional baseline at ENA to be 428 +/- 228 cm(-3) in the summer and 346 +/- 223 cm(-3) in the winter. Lastly, we compared masked measurements from the AOS with the ARM Aerial Facility (AAF) during flights over C1 in the summer to understand submicron aerosol vertical mixing over C1. The high correlation (R-2 = 0.71 with a slope of 1.04 +/- 0.01) observed between C1 and the AAF N-tot collected within an area of 10 km surrounding ENA and at altitudes < 500m indicated that the submicron aerosol at ENA was well mixed within the first 500m of the marine boundary layer during the month of July during ACE-ENA. Our novel method for determining a regional aerosol baseline at ENA can be applied to other time periods and at other locations with validation by a secondary site or additional collocated measurements.