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XRF and FTIR data from Pismo Beach Air Quality Study from May 2019 - May 2021

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XRF and FTIR data from Pismo Beach Air Quality Study from May 2019 - May 2021

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Lewis, Savannah L.; Russell, Lynn M.; McKinsey, John A.; Harris, William J. (2022). XRF and FTIR data from Pismo Beach Air Quality Study from May 2019 - May 2021. UC San Diego Library Digital Collections.


The Oceano Dunes State Vehicular Recreation Area (ODSVRA) is a large natural source of wind-driven dust emissions that typically include nontoxic particles too large to be inhaled. PM2.5 and PM10 near the Oceano Dunes ODSVRA have been observed to be highest in the afternoons during high wind speeds, and May and October were targeted as the months with the highest average wind speeds (California Air Resource Board, 2022). The sampling site was located downwind of the southern edge of the at the California Department of Fire at Arroyo Grande (hereafter “CDF”) (Oceano Dunes SVRA, 2022). This sampling used limited resources to target different aspects of PM2.5 and PM10, with a focus during the initial sampling from 14 May to 2 June 2019 and 23 September to 5 October 2019 on differences in PM2.5 composition at different times of day. These studies showed that afternoons were consistently the highest concentrations, reducing the need for overnight sampling. Comparisons of offline measured chemical components with online PM measurements indicated the need for gravimetric measurements, which were added for all subsequent studies starting with 27 April to 16 May 2020. PM10 chemical measurements were added for 28 September to 10 October 2020, and they were optimized for high-PM10 mass concentrations for 27 April to 24 May 2021. The local time during these sampling periods at CDF was PDT.

Date Collected
  • 2019-05-14 to 2021-05-24
Date Issued
  • 2022
Principal Investigator

Sample Collection
Aerosol particle sampling used sharp-cut cyclones operated with calibrated flows to collect particles for analysis at ambient diameters with a calibrated cut at 2.5 μm (SCC 2.229 operated at 7.5 L min-1, BGI Inc., Waltham, MA) and a louvered PM10 sampling head (operated at 16.7 L min-1) (Tolocka, Peters, Vanderpool, Chen, & Wiener, 2001).). PM2.5 sampling by SCC has been shown to have comparable 50% cutoff sizes and geometric standard deviations when following manufacturer calibrated flow rates (Du et al., 2020). October 2020 and May 2021 also measured PM10 directly after the PM10 sampling head, and in May 2021 flows were split to improve accuracy at high concentrations (with expected loss of data at low concentrations that were below detection), collecting 2 L min-1 for PM10 and 5 L min-1 for PM2.5. flow rates were calibrated at the beginning and end of each campaign, with recordings every 10 sec to document cyclone flow rates to ensure size cutoff performance.

37 mm Teflon filters (Pall Inc., 1 μm pore size) were used as substrates and have shown negligible adsorption of volatile organic compounds (VOCs) on duplicate back filters collected simultaneously with each sample (Gilardoni et al., 2007; Maria et al., 2003). Blank filters provided a measure of adsorption during sampling and contamination during handling and storage. Samples were quality-controlled with the following criteria: filter and cyclone flow rates were within 5% for the duration of sampling, filter pressure increased by >0.01 psi per m3 air collected, and no anomalous readings in pressure, temperature, and relative humidity (as defined by the instrument specifications).

Mass Concentrations
For evaluation of BAM concentrations relative to federal standards, gravimetric analyses were completed for samples collected in 2020 and 2021 by Chester Labnet (Tigard, OR). These filters were weighed prior to sampling to provide filter-specific tare weights. After sampling, filters were weighed again, and the difference between the sampled weight and the tare was the reported gravimetric mass. The weighing procedure for samples used the PM2.5 federal reference method at 35±5% relative humidity for the 24-hour period (logged every 5 min), making the samples drier during weighing than the ambient conditions at which they were collected.

Organic Functional Groups from FTIR Spectroscopy
Samples were non-destructively analyzed by transmission FTIR spectroscopy. FTIR measurements of absorbance characterized the nonvolatile organic functional groups associated with major carbon bond types, including saturated aliphatic (alkane) groups, alcohol (used here to include phenol and polyol) groups, carboxylic acid groups, non-acidic carbonyl groups, and primary amine groups. The spectra were interpreted using an automated algorithm to perform baselining, peak-fitting, and integration with a revised version of the approach described previously (Maria, Russell, Turpin, & Porcja, 2002; Maria et al., 2003; Maria Steven, Russell Lynn, Gilles Mary, & Myneni Satish, 2004; Russell et al., 2009; Takahama, Johnson, & Russell, 2013), using calibrations revised for the Tensor 27 spectrometer with RT-DLATGS detector (Bruker Optics, Ettlingen, Germany) (Gilardoni et al., 2007). Complete sets of internal standards for organic components of the atmosphere are not available, in part because the ambient particle composition is not fully known. The measured organic functional groups are summed to calculate organic mass (OM). Estimates of the accuracy, errors, and detection limits of this technique for ambient measurements are discussed in Russell (2003). Cosine similarity (dot-product cosine on normalized spectra) was used to quantify spectral similarity of FTIR spectra because it has been shown to be sensitive to small spectral differences in this type of chemical spectra (Frossard et al., 2014; J. Liu et al., 2017; Stein & Scott, 1994; Wan, Vidavsky, & Gross, 2002).

Dust, Sea salt, and Non-Sea Salt Sulfate from XRF Spectroscopy
Sample filters (and associated blank filters) were non-destructively analyzed by X-ray Fluorescence (XRF) measurements conducted by Chester LabNet (Tigard, OR) on the same filters used for gravimetric measurements. XRF analysis provided trace metal concentrations for elements Na and heavier (Maria et al., 2003). The following elements were above the detection limit for 70% or more of the PM2.5 afternoon samples and are used in the results reported here: Na, Mg, Al, Si, S, Cl, K, Ca, Ti, Fe, Zn, Br, and Sr. When Mn was above detection (50% of afternoon PM2.5 samples) and Ba was above detection (35% of the afternoon PM2.5 samples), each was included in the dust calculation and was otherwise assigned to be 0.

Mineral dust was measured above detection if Al and Si were above detection (defined as twice uncertainty), which was true for more than 86% of quality-controlled samples. The mass of dust was calculated from XRF metal concentrations, assuming dust consists of MgCO3, Al2O3 and SiO2 (in the form of Al2SiO5), K2O, CaCO3, TiO2, Fe2O3, MnO, and BaO (Gilardoni et al., 2007; Jun Liu et al., 2018; Usher, Michel, & Grassian, 2003). This calculation increases the mass by an average factor of 2.14 to account for the O and C associated with the measured elements. Because some elements are in both sea salt and mineral dust (K, Ca, Mg), the amount of those elements associated with the Na present was subtracted to avoid double-counting, resulting in ~2% less mass. The mineral dust contribution can also be estimated by calibration to a subset of elements, as discussed in the Supplement (Figure S1) (Frank, 2006; Hains, Chen, Taubman, Doddridge, & Dickerson, 2007; Malm, Sisler, Huffman, Eldred, & Cahill, 1994).

Sea salt was measured above detection when Na and Cl were above detection (defined as twice uncertainty). Atmospheric ambient sea salt concentrations were calculated using measured Cl- and 1.47*Na concentrations to account for the possible depletion of Cl- in the atmosphere, where 1.47 is the ratio of (Na++Mg2++Ca2++K++SO42-+HCO3-)/Na+ in seawater (Frossard et al., 2014; Holland, 1978). This sea-salt calculation represents an upper limit for sea-salt mass because the HCO3- would have been titrated before Cl- was depleted significantly via acid displacement reactions. HCO3- is 0.3% of the mass of sea salt. Excluding HCO3- from the ratio, as a lower limit, the ratio of (Na++Mg2++Ca2++K++SO42-)/Na+  is 1.45, instead of 1.47, making the sea salt mass calculated <2% lower than calculated here.

Non-sea salt sulfate (nss-sulfate) was calculated using measured S concentrations assuming S was present as (NH4)2SO4 and subtracting the amount of SO42- associated with the Na present (Holland, 1978). The semivolatile/unidentified fraction was defined as the difference between filter sample time averaged BAM concentration and the sum of dust, sea salt, nss-sulfate, and organic mass concentrations.


This work was funded by the California Parks and Recreation Contract C18V0003/FS#26915.



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Lynn M. Russell:

Savannah L. Lewis:

Related Resources

    Primary associated publication

    • Lewis, S.L.; L.M. Russell; J.A. McKinsey, W.J. Harris, in review, 2022. Contributions of Dust and Other Chemical Components to PM2.5 and PM10 Concentrations at Oceano Dunes, Atmospheric Environment.


    • California Air Resource Board. (2022). AQMIS2 Meteorological Data. Retrieved from
    • Frank, N. H. (2006). Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in Federal Reference Method Fine Particulate Matter for Six Eastern U.S. Cities. Journal of the Air & Waste Management Association, 56(4), 500-511.
    • Frossard, A. A., Russell, L. M., Burrows, S. M., Elliott, S. M., Bates, T. S., & Quinn, P. K. (2014). Sources and composition of submicron organic mass in marine aerosol particles. Journal of Geophysical Research: Atmospheres, 119(22), 12,977-913,003.
    • Gilardoni, S., et al. (2007). Regional variation of organic functional groups in aerosol particles on four U.S. east coast platforms during the International Consortium for Atmospheric Research on Transport and Transformation 2004 campaign, J. Geophys. Res., 112, D10S27.
    • Hains, J. C., Chen, L. W. A., Taubman, B. F., Doddridge, B. G., & Dickerson, R. R. (2007). A side-by-side comparison of filter-based PM2.5 measurements at a suburban site: A closure study. Atmospheric Environment, 41(29), 6167-6184.
    • Holland, H. D. (1978). The chemistry of the atmosphere and oceans. New York: Wiley.
    • Liu, J., Dedrick, J., Russell, L. M., Senum, G. I., Uin, J., Kuang, C., Springston, S. R., Leaitch, W. R., Aiken, A. C., and Lubin, D. (2018). High summertime aerosol organic functional group concentrations from marine and seabird sources at Ross Island, Antarctica, during AWARE, Atmos. Chem. Phys., 18, 8571–8587.
    • Liu, J., Russell, L. M., Lee, A. K. Y., McKinney, K. A., Surratt, J. D., & Ziemann, P. J. (2017). Observational evidence for pollution-influenced selective uptake contributing to biogenic secondary organic aerosols in the southeastern U.S. Geophysical Research Letters, 44(15), 8056-8064.
    • Malm, W. C., Sisler, J. F., Huffman, D., Eldred, R. A., & Cahill, T. A. (1994). Spatial and seasonal trends in particle concentration and optical extinction in the United States. Journal of Geophysical Research: Atmospheres, 99(D1), 1347-1370.
    • Maria Steven, F., Russell Lynn, M., Gilles Mary, K., & Myneni Satish, C. B. (2004). Organic Aerosol Growth Mechanisms and Their Climate-Forcing Implications. Science, 306(5703), 1921-1924.
    • Maria, S. F., Russell, L. M., Turpin, B. J., & Porcja, R. J. (2002). FTIR measurements of functional groups and organic mass in aerosol samples over the Caribbean. Atmospheric Environment, 36(33), 5185-5196.
    • Maria, S. F., Russell, L. M., Turpin, B. J., Porcja, R. J., Campos, T. L., Weber, R. J., & Huebert, B. J. (2003). Source signatures of carbon monoxide and organic functional groups in Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) submicron aerosol types. Journal of Geophysical Research: Atmospheres, 108(D23).
    • Oceano Dunes SVRA. (2022). Map Area of SVRA. Retrieved from
    • Peng Du, Jianguo Liu, Huaqiao Gui, Jiaoshi Zhang, Tongzhu Yu, Jie Wang, Yin Cheng, Yihuai Lu, Yawei Yao, Qiang Fu, ChihChieh Chen (2020). Development of a static test apparatus for evaluating the performance of three PM2.5 separators commonly used in China. Journal of Environmental Sciences, 87, 238-249.
    • Russell, L. M. (2003). Aerosol Organic-Mass-to-Organic-Carbon Ratio Measurements. Environmental Science & Technology, 37(13), 2982-2987.
    • Russell, L. M., Takahama, S., Liu, S., Hawkins, L. N., Covert, D. S., Quinn, P. K., & Bates, T. S. (2009). Oxygenated fraction and mass of organic aerosol from direct emission and atmospheric processing measured on the R/V Ronald Brown during TEXAQS/GoMACCS 2006. Journal of Geophysical Research: Atmospheres, 114(D7).
    • Stein, S. E., & Scott, D. R. (1994). Optimization and testing of mass spectral library search algorithms for compound identification. Journal of the American Society for Mass Spectrometry, 5(9), 859-866.
    • Takahama, S., Johnson, A., & Russell, L. M. (2013). Quantification of Carboxylic and Carbonyl Functional Groups in Organic Aerosol Infrared Absorbance Spectra. Aerosol Science and Technology, 47(3), 310-325.
    • Tolocka, Michael P.; Peters, Thomas M.; Vanderpool, Robert W.; Chen, Fu-Lin; Wiener, Russell W. (2001). On the Modification of the Low Flow-Rate PM10 Dichotomous Sampler Inlet. Aerosol Science and Technology, Volume 34, Number 5, 1 May 2001, pp. 407-415(9).
    • Usher, C. R., Michel, A. E., & Grassian, V. H. (2003). Reactions on Mineral Dust. Chemical Reviews, 103(12), 4883-4940.
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