Data Files Accompanying: Acidity Across the Interface: From the Ocean Surface to Sea Spray Aerosol
Authors:  Angle, K. J.; Crocker, D. R.; Simpson, R. M. C.; Mayer, K. J.; Garofalo, L. A.; Moore, A. N.; Mora Garcia, S. L.; Or, V. W.; Srinivasan, S.; Farhan, M.; Sauer, J. S.; Lee, C.; Pothier, M. A.; Farmer, D. K.; Martz, T. R.; Bertram, T. H.; Cappa, C. D.; Prather, K. A.; Grassian, V. H.
Journal:

Contact: 
Vicki H. Grassian. vhgrassian@ucsd.edu. Department of Chemistry and Biochemistry, and Scripps Institution of Oceanography, and Department of Nanoengineering, University of California, San Diego, La Jolla, CA 92037.

Cite as:
Angle, K. J.; Crocker, D. R.; Simpson, R. M. C.; Mayer, K. J.; Garofalo, L. A.; Moore, A. N.; Mora Garcia, S. L.; Or, V. W.; Srinivasan, S.; Farhan, M.; Sauer, J. S.; Lee, C.; Pothier, M. A.; Farmer, D. K.; Martz, T. R.; Bertram, T. H.; Cappa, C. D.; Prather, K. A.; Grassian, V. H. (2020). Data from: Acidity Across the Interface: From the Ocean Surface to Sea Spray Aerosol. In Center for Aerosol Impacts on Chemistry of the Environment (CAICE) Collection. UC San Diego Library Digital Collections.
DOI: 10.6075/J028065J


This data package contains the data and analysis code associated with the manuscript cited above. The files have been placed in directories and the contents are described below.
SeaSCAPE_compiled_data
This directory contains raw and processed data from the SeaSCAPE sampling intensive. MOUDI_pH_compiled, TSP_pH_compiled, and SeaSCAPE_SSML_pH_compiled_R1 contain the raw data for submicron nSSA pH, TSP pH, and SSML pH throughout the intensive, respectively. SeaSCAPE_APS_N_Bloom3_R1 and SeaSCAPE_SMPS_N_Bloom3_R1 contain the number size distribution data collected by the APS (Aerodynamic Particle Sizer) and SMPS (Scanning Mobility Particle Sizer), respectively, and is the source data for Fig. S4. SeaSCAPE_HRAMS_CSU_Fill3_R2 contains the High-Resolution Aerosol Mass Spectrometer data, which is the source data for E-AIM calculations. SeaSCAPE2019_CO2 contains the full data set of minute-resolved data (Pacific Time) for temperature, salinity, pCO2, pH, Dissolved Inorganic Carbon, and Alkalinity. The file eaim_ouputs contains the results from E-AIM Model II calculations, corresponding to the abbreviated results reported in the SI. Finally, pH_vs_collection_time contains the data used to demonstrate the absence of correlation between these variables that is quoted in the SI.
MATLAB_codes_and_data
This directory contains MATLAB R2019b files used for data analysis. Acidity_MATLAB_Figs contains code which can be used to generate several of the figures in the manuscript and SI. For figures with small numbers of data points, the data is explicitly written in MATLAB arrays. For larger data sets, the figures were saved separately. These include glass_submicron_nebulizer, MOUDI_RH, and supermicron_nebulizer. For Figure 3 in the main text, the large quantity of data made it most practical to save the workspace variables together, and this is named timeSeriesGraphFinal. To generate this figure, one must make sure this file is in the MATLAB path. Once it is, the Figure 2: Time SeriesÓ section of Acidity_MATLAB_Figs can be run to create the figure. Finally, pH_paper_imageJ_analysis contains the code used to convert imageJ RGB triplets to pH values by generating a calibration curve for that image such as shown in Fig. S1b. Note that this code does not correct for salt content, which must be done with a calibration curve such as shown in Fig. S1d. The code was applied to the images of pH strips collected throughout the intensive, which have been deposited on the CAICE Google Drive where they may be freely accessed: https://drive.google.com/drive/u/1/folders/1G9RrsURJ4xsQnt3o-TfCwueG6uPEwYpT. This code contains comments on usage, and it is important that the RGB values are imported in the order specified in the first section. Note that different pH papers respond to different salts differently, so for a given analysis, the best results may be obtained by using pH 3-5.5 paper in a more acidic range. The script submicron_vs_supermicron_mass calculates the percentage contribution of supermicron SSA to a TSP collection, and requires the variable massDistributionMerged, which contains the full SMPS and APS number distribution data sets converted with a density of 1.8 g/cm^3.