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Antonio Donateo Recommended Articles Related Info Link Google Scholar More by Authors Links on DOAJ Wang, X. Gillies, J. A. Kohl, S. Furtak-Cole, E. Tupper, K. A. Cardiel, D. A. on Google Scholar Wang, X. Gillies, J. A. Kohl, S. Furtak-Cole, E. Tupper, K. A. Cardiel, D. A. on PubMed Wang, X. Gillies, J. A. Kohl, S. Furtak-Cole, E. Tupper, K. A. Cardiel, D. A. /ajax/scifeed/subscribe Table of Contents Altmetric share Share announcement Help format_quote Cite thumb_up ... Endorse textsms ... Comment Need Help? Support

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Get Information clear JSmol Viewer clear first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessArticle Quantifying the Source Attribution of PM10 Measured Downwind of the Oceano Dunes State Vehicular Recreation Area by

Xiaoliang Wang 1 ORCID

John A. Gillies 1,* ORCID

Steven Kohl 1,

Eden Furtak-Cole 1 ORCID

Karl A. Tupper 2 and

David A. Cardiel 2 1 Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA 2 San Luis Obispo County Air Pollution Control District, San Luis Obispo, CA 93401, USA * Author to whom correspondence should be addressed. Atmosphere 2023, 14(4), 718; https://doi.org/10.3390/atmos14040718 (registering DOI) Received: 23 March 2023 / Revised: 11 April 2023 / Accepted: 12 April 2023 / Published: 15 April 2023 (This article belongs to the Section Air Quality) Download Download PDF Download XML Browse Figures Review Reports Versions Notes

Abstract: A measurement campaign was undertaken April–October 2021 using PM10 filter samplers to collect 24 h samples downwind of the Oceano Dunes State Vehicular Recreation Area (ODSVRA), an area that allows off-highway driving on its coastal dunes. The PM10 samples were analyzed and these data were used to identify the sources that contributed to the PM10 under varying meteorological conditions. Exposed filters were weighed to calculate mass concentration and analyzed using X-ray fluorescence to quantify elemental composition, ion chromatography to quantify water-soluble ions, and thermal/optical reflectance to quantify organic carbon and elemental carbon in the particulate matter. These speciated data were used to attribute the sources of PM10 for eight days that exceeded the California state 24 h mean PM10 standard and 39 days that were below the standard. The mean attribution of sources for the eight identified exceedance days was mineral dust (43.1%), followed by sea salt (25.0%) and the unidentified category (20.4%). The simultaneous increase in the mineral dust and unidentified categories with increasing levels of PM10 arriving from the direction of the ODSVRA suggests that the unidentified components were unmeasured oxides of minerals and carbonate. This increases the attribution of mineral dust for a mean exceedance day to 63.5%. The source of the mineral dust component of the PM10 is attributable to wind-driven saltation and dust emission processes within the ODSVRA. Keywords: source attribution; dust emissions; off-highway vehicle activity; dust abatement 1. IntroductionThe Oceano Dunes, part of the Callender coastal dune system, in San Luis Obispo County, California (Figure 1), is a known source of fugitive dust emissions [1,2,3,4]. Under conditions of elevated wind speed for westerly winds, exceedances of the US Federal Standard (150 μg m−3) and the State of California Standard (50 μg m−3) for 24 h time-integrated concentrations of particulate matter ≤ 10 μm aerodynamic diameter (PM10) have been observed downwind of the dunes since air-quality monitoring was initiated in 1989. Exceedance of the State of California Standard continues to be observed to the present day (2022), while the Federal Standard has not been exceeded since 2014, according to the San Luis Obispo County Air Pollution Control District (SLOAPCD) records.This California State Park allows off-highway vehicle (OHV) recreation on approximately 338 ha of the beach and dune landscapes (as of December 2022) while prohibiting OHV activity outside this area to protect sensitive areas and critical habitat for identified endangered species (e.g., Charadrius alexandrinus nivosus, Western Snowy Plover and Sterna antillarum browni, California Least Tern). The primary mechanism for emission of dust into the atmosphere from the ODSVRA’s sandy areas is wind-generated rather than OHV recreation actively lofting dust. For winds > 8 m s−1 with dominant westerly components as measured 10 m above ground level (AGL) within the park, the threshold for sand transport is exceeded, and this is accompanied by dust emissions [1,2,3,4]. Gillies et al. [2] reported, however, that OHV activity augments the dust emission potential of the area designated for such activity, producing more PM10 than would occur if the sand areas were not impacted by vehicle travel.A Stipulated Order of Abatement (SOA) approved by the SLOAPCD Hearing Board in April 2018 (Case No. 17-01) required the California Department of Parks and Recreation (Parks) to reduce the PM10 attributable to the Oceano Dunes State Vehicular Recreation Area (ODSVRA, i.e., the ODSVRA is the source area) to achieve the state and federal 24 h mean PM10 standards. It also identified that to work toward achieving compliance, Parks should develop a management strategy that reduces the emissions of PM10 attributable to dust emission processes within the ODSVRA riding area by 50% by the end of 2023. The SOA was amended in November 2019 and again in October 2022. As amended, the SOA requires that by the end of 2025, PM10 emissions from the ODSVRA be reduced to those modeled to approximate the conditions that existed in 1939. This was prior to high levels of OHV activity and assumes a higher degree of vegetation cover than at present [5].Parks implemented a management strategy in 2014 based on using dust-control measures within the ODSVRA to reduce PM10 emissions caused by wind and saltating sand. These measures included increasing the amount of vegetation covering sand dunes and promoting the restoration of a foredune [6,7], which reduced the size of the area from which dust emissions originated as well as modulating the wind energy on and downwind of the control areas [7]. Temporarily installed arrays of sand fences [1] and covering the sand with a layer of straw on designated areas of the dunes have also been emplaced at different times to modify dust emission processes, as they provided immediate suppression of dust emission upon installation. Planted vegetation requires time to reach its full potential to mitigate saltation and dust emission processes as the plants reach maturity and maximize their ability to protect the surface from wind erosion [8].Although wind-generated dust in the PM10 size range within the ODSVRA is the result of dust emissions driven by saltating sand during periods when the wind creates above-threshold conditions, other sources may also be contributing to the observed PM10 concentrations measured east of the ODSVRA at the SLOAPCD monitoring site identified as the California Department of Forestry and Fire Station (hereafter CDF) (Figure 1). This station is downwind of the ODSVRA during periods of westerly wind that are often observed to be associated with high hourly PM10, which, if sustained for a sufficient length of time, leads to exceedance of the State of California Standard 24 h mean concentration of PM10.The attribution of the sources of PM10 measured in the study area (Figure 1) has been a focus of measurement efforts of the SLOAPCD. A one-year filter measurement campaign (2004–2005) in the study area showed that during high-PM10 events at the CDF and Mesa2 sites, (1) high northwesterly wind was observed from the dune area; (2) mass concentrations of coarse particles (PM10-2.5) were higher than those of fine particles (PM2.5); and (3) a large fraction of the PM10 was windblown crustal materials [9]. This evidence suggested that dust emissions from the upwind ODSVRA were a major PM10 source in the study area. This conclusion was supported by a follow-up study in this area [10], which also showed that other sources (e.g., a chemical facility or agricultural fields) were not significant contributors to PM10 during high-PM10 events. The SLOAPCD also found that the contribution of quartz alone to the total PM10 mass approached 12.5% on high-PM10 days when winds were predominately from the west [11]; in addition to quartz, ODSVRA dust has been shown to contain significant feldspar and clay components [12]. However, these studies did not analyze the full chemical composition of the PM10, making source attribution less definitive.A recent study by Lewis et al. [13] argued that the contributions of dust from the ODSVRA to downwind PM2.5 and PM10 are small and dust abatement measures would not improve downwind air quality. Lewis et al. [13] collected filter samples of 6–8 h duration at different times of the day (post-12:00 pm) in 2019–2021. The filters were analyzed for elements with X-ray fluorescence (XRF) and organic functional groups with Fourier-transform infrared (FTIR) spectroscopy. Lewis et al. [13] reported that the mineral dust fraction was 14% (±10%) of the PM10 measured by a Beta Attenuation Monitor (BAM) on high-PM10 days, which were defined as days on which BAM-measured PM10 at the CDF exceeded 140 μg m−3 for one or more reported hours. We note in their study that the PM2.5 and PM10 sampling did not comply with the EPA-designated Federal Reference Method (FRM) or Federal Equivalent Method (FEM), and the gravimetric PM2.5 and PM10 mass concentration had large differences with the FEM BAM concentrations. Accurate attribution of PM10 is needed to inform Parks of the best management practices that will lead to compliance with the SOA. The results presented by Mejia et al. [4] and Gillies et al. [1,2] suggested that the current Parks management strategy to reduce PM10 contributions through dust-control measures is a prudent approach to reach compliance with the SOA, as measurements and modeling have suggested that high PM10 concentrations observed within the ODSVRA contribute substantially to the PM10 measured downwind of the ODSVRA. According to Lewis et al. [13], however, dust-control measures will not be effective, as their results suggested mineral dust is a minor component of PM10 when the hourly mean concentration observed at the CDF is >140 µg m−3. Resolving the relative attribution of PM10 to its sources as measured at the CDF has implications for Parks to effectively manage the PM10 contributions from the ODSVRA to regional PM10 levels to meet the SOA.To aid in resolving the uncertainty of the source attribution of PM10 at the CDF monitoring site, a PM10 measurement campaign was undertaken in 2021. Using Federal Reference Method PM10 filter samplers (Thermo Fisher Scientific, Waltham, MA, USA, Partisol® 2025i Sequential Air Samplers), paired, preweighed 47 mm Teflon-membrane and pretreated 47 mm quartz-fiber filters were used to collect 24 h PM10 samples following the US EPA’s one-in-three days sampling schedule from April to October 2021. This period of the year has the greatest probability for exceedances of the state 24 h mean PM10 standard. The exposed filters were weighed to calculate the 24 h mass concentration and analyzed using XRF to quantify the elemental composition (Na to U), ion chromatography to quantify the water-soluble ions, and thermal/optical reflectance to quantify the organic carbon (OC) and the elemental carbon (EC) in the collected particulate matter. Details on the sampling and analytical methods are provided in the Methods Section. Using these speciated data, analyses were undertaken to provide accounting of the source attribution of PM10, with the attribution for days that exceeded the state 24 h mean PM10 standard being of particular interest.Available data (https://aqs.epa.gov/aqsweb/documents/data_mart_welcome.html (accessed on 3 January 2023) on the temporal record of hourly PM10 and hourly meteorological data at the CDF (i.e., wind speed and wind direction measured at 10 m AGL, 2019–2022) and within the ODSVRA, at a station designated as the S1 tower (Figure 1) (wind speed and wind direction measured at 10 m AGL), were also examined to determine the likelihood of an exceedance of the state 24 h mean PM10 standard when the direction of particle transport was from the ODSVRA toward the CDF. 2. Materials and Methods 2.1. PM10 Sampling and AnalysesPM10 samples were collected on filters over 24 h periods (midnight to midnight) every three days at the CDF monitoring site between April and October 2021 (Figure 1). Collocated FRM samplers were used to collect PM10 on paired filters for gravimetric-mass and chemical analyses (Figure 2). These analyses were carried out by the Environmental Analysis Facility (EAF) of the Desert Research Institute (DRI), Reno, NV. For quality-assurance purposes, additional samples collected on a 1-in-6 day schedule on 47 mm Teflon-membrane filters were submitted for gravimetric analysis to the South Coast Air Quality Management District (SCAQMD), Diamond Bar, CA. To detect possible sampler bias, the samplers were rotated throughout this study so that Teflon, quartz, and QA samples were collected from each of the samplers. Continuous hourly PM10 measurements were made using a BAM, as described below. All sampler and monitor inlets were located on the roof of the CDF monitoring station and were at least 1 m but no more than 4 m from each other.Filter-based PM10 samples were collected in accordance with the requirements of US EPA Designation RFPS-1298-127 for PM10 sample collection [14], following the instrument manual and the California Air Resources Board’s Standard Operating Procedure, AQSB SOP 404 [15]. Briefly, preweighed Teflon-membrane and pretreated quartz-fiber filters in cartridges were obtained from the analytical lab and loaded into the sampling instruments in batches. The instruments were fitted with louvered PM10 inlets, as specified in 40 CFR 50, Appendices J and L, and samples were collected at a calibrated flow rate of 16.7 L min−1 for 24 h. After removal from the sampler, exposed filters were stored and transported to the analytical laboratory at 2° to 4 °C. For sample blanks, preweighed filters were obtained from the analytical lab and then stored along with exposed cassettes at 2 to 4 °C, then returned to the lab for analysis without being placed in a sampler.Continuous hourly PM10 measurements were conducted using a MetOne Instruments BAM 1020 (Grants Pass, Oregon) (Figure 2), which is US EPA-designated FEM EQPM-0798-122 [14]. This instrument was operated in accordance with the US EPA requirements in 40 CFR 58 and its appendices, the SLOAPCD Standard Operating Procedure for the MetOne Instruments BAM 1020 [16], and the instrument manual. For comparison with the gravimetric data, 24 h BAM concentrations were calculated by averaging valid hourly data. For a 24 h mean BAM concentration to be valid, at least 75% of the constituent hourly values were required to be valid. 2.2. Laboratory Chemical AnalysisDetailed laboratory analyses were conducted for each of the PM10 filter samples, including particle mass, elements, ions, carbon fractions, and methanesulfonate, to identify potential source markers and to perform source apportionment [17,18].The teflon-membrane filters, following exposure and shipping, were equilibrated in a clean room with controlled temperature (21.5 ± 1.5 °C) and relative humidity (RH; 35 ± 5%) before gravimetric analysis to minimize particle volatilization and aerosol-liquid-water bias [19,20]. The filters were weighed before and after sampling using an XP6 microbalance (Mettler Toledo Inc., Columbus, OH, USA) at the DRI or a Sartorius MC5 microbalance (Data Weighing Systems, Inc., Wood Dale, IL, USA) at the SCAQMD, each with a sensitivity of ±1 µg. A radioactive source (500 picocuries of Polonium210) and an electrostatic charge neutralizer were used to eliminate static charge on the filters. A total of 51 elements (from Na to U) were quantified on the Teflon-membrane filters using XRF (PANalytical Model Epsilon 5, Almelo, The Netherlands) [21].Half of each quartz-fiber filter was extracted in distilled, deionized water (DDW) and analyzed for eight water-soluble ions, including chloride (Cl−), nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), sodium (Na+), magnesium (Mg2+), potassium (K+), and calcium (Ca2+), via ion chromatography (Dionex ICS 5000+ IC systems, Thermo Scientific, Sunnyvale, CA, USA) [22]. A 0.5 cm2 punch was taken from the other half of each quartz-fiber filter to quantify the OC, the EC, and eight thermal fractions (OC1-OC4, pyrolyzed carbon [OP], and EC1-EC3) following the IMPROVE_A thermal/optical protocol using the DRI Model 2015 Multiwavelength Carbon Analyzer (Magee Scientific, Berkeley, CA) [23,24]. Methanesulfonate (CH3SO3−), a marker species for oceanic biogenic materials, was measured using ion chromatography. 2.3. Data AnalysisThe three independent 24 h PM10 mass-concentration datasets (i.e., the SLOAPCD’s BAM measurements; the gravimetric-mass concentration from the Teflon membranes, determined by the DRI; and the gravimetric-mass concentration from the Teflon membranes, determined by the SCAQMD) were compared via linear regression, both with and without an intercept term; Deming regression, both with and without an intercept term; and the 90th-percentile upper bound of the coefficient of variation (CVUB). While linear regression assumes that the values of the dependent variable are exactly known, Deming regression is an errors-in-variable model that relaxes this assumption. Deming regression is often used to determine the line of best fit when two variables are measured with errors [25]. Deming regression coefficients were calculated in the R software suite [26] using the “Deming” package [27] and assuming a constant coefficient of variation. The CVUB is the statistic used by the US EPA to evaluate the precision of collocated particulate matter samplers. The CVUB is based on the standard deviation of the percentage differences of mass concentrations from collocated samplers and was calculated according to the procedure in 40 CFR 58, Appendix A, Section 4.2. For low-volume PM10 samplers, such as those used in this study, the EPA’s data quality objective is a CVUB of less than 10%.The identification of PM10 sources and the estimation of source contributions used a weight-of-evidence approach [18,28]. First, the detailed chemical data were grouped into major constituent groups representing different sources (e.g., sea salt, mineral dust, traffic emissions, and regional/urban background), and their concentrations and contributions to the PM10 were calculated. Next, the wind speed and direction on days exceeding the state 24 h mean PM10 mass-concentration standard (50 µg m−3) were examined to infer the direction of PM10 transport from the source to the receptor (CDF). The combination of windroses, PM10 roses, and PM10 compositions provided weighted evidence of PM10 sources.Fresh sea salt particles are generated through two main pathways: (1) bubble-bursting when air bubbles entrained by breaking waves rise to the surface and burst to create film and jet drops, and (2) spume drops when the wind shear is sufficiently high to tear water droplets off surface waves [29]. The composition of fresh sea salt is usually considered similar to that of bulk seawater, and the compositions with the highest mass percentages are Cl (55.04%), Na (30.61%), SO42− (7.68%), Mg (3.69%), Ca (1.16%), and K (1.1%) [30]. Once in the air, the spray droplets are transported and dispersed by wind, and chemical reactions with other atmosphere constituents subsequently change their composition. Due to proximity to the ocean, sea salt particles may deposit on beach sands and may be resuspended as fresh or aged sea salt particles along with mineral dust.One of the main chemical reactions during sea salt aging is chloride depletion, often observed in coastal regions, where particulate chloride is displaced as gas-phase hydrogen chloride (HCl) in atmospheric reactions with nitric and sulfuric acids [31]; HNO3 (g) + NaCl (p) → NaNO3 (p) + HCl (g) H2SO4 (g) + 2NaCl (p) → Na2SO4 (p) + 2HCl (g) The degree of chloride depletion can be estimated from the ratios of Cl−/Na+ and (Cl− + NO3−)/Na+.As Na+ is conservative during sea salt aging, we separated the sea salt Na+ (ssNa+) into fresh sea salt Na+ (fsNa+) and aged sea salt Na+ (asNa+). The fresh sea salt (FS) was calculated as the sum of the measured Cl− that had not been displaced, the corresponding fsNa+ had the same Na+/Cl− ratio in the seawater, and the sea salt (ss) contributions of Mg2+, K+, Ca2+, and SO42−. As ssMg2+, ssK+, ssCa2+, and ssSO42− do not change with aging, these ions were estimated using their ratios of total measured ssNa+ in typical fresh seawater [30,32,33]. The equation for estimating FS is FS = fsNa+ + Cl− + ssMg2+ + ssK+ + ssCa2+ + ssSO42− where fsNa+ is estimated as 0.56 × Cl−, ssMg2+ as 0.12 × ssNa+, ssK+ as 0.036 × ssNa+, ssCa2+ as 0.038 × ssNa+, and ssSO42− as 0.252 × ssNa+.The aged sea salt (AS) was estimated by balancing the excess Na+ with NO3− and then with SO42− [34]. The excess Na+ was calculated as the molar equivalent difference between Na+ and Cl− [35]. The equation for estimating AS is AS = asNa+ + asNO3− + asSO42− where asNa+ = ssNa+ − fsNa+, and asNO3− and asSO42− are calculated by balancing asNa+.The measured PM10 species were grouped into seven major compositions, including fresh sea salt (FS); aged sea salt (AS); non-sea-salt sulfate (nssSO42−), which was estimated as the total SO42− minus the sea salt SO42− (ssSO42−); mineral dust (MD); elemental carbon (EC); organic matter (OM = OC × multiplier); and other measured species. The sum of these seven composition groups was defined as the reconstructed mass, and the difference between the gravimetric and reconstructed masses was reported as the “unidentified” mass [36].FS and AS were estimated using Equations (3) and (4), respectively. The MD was estimated as MD = (3.48 × Si) + (1.63 × nssCa) + (2.42 × Fe) + (1.94 × Ti) following the modified IMPROVE formula, where the non-sea-salt Ca (nssCa) is the total Ca minus the sea-salt Ca2+ (ssCa2+) in Equation (3) [36,37].A multiplier of 1.8 was used to convert OC to OM for nonurban aerosols [37,38]. The “Other” category is the sum of other measured ions (e.g., NH4+) and elements (e.g., Br and Ba) without double-counting. The reconstructed mass (RM) was calculated as RM = OM + EC + nssSO42− + FS + AS + MD + Others 3. Results 3.1. Data Quality AssuranceA total of 47 valid 24 h sample pairs were taken between April and October 2021 at the CDF monitoring site (Figure 1 and Figure 2). Of these days (Figure 3), one equaled and eight exceeded the state 24 h mean PM10 mass concentration standard (50 µg m−3) based on the gravimetric measurement of the particle mass and the measured flow volume from the Partisol sampler loaded with Teflon-membrane filters. One sample, from 7 October 2021, was identified as an outlier and removed from further analysis. On that day, the BAM recorded a 24 h average of 25 µg m−3 and the QA sample analyzed by the South Coast AQMD had a mass concentration of 25 µg m−3, while the mass concentration of the sample analyzed by the DRI was 90 µg m−3.The relations between the 24 h PM10 mass concentrations determined by the various measurements (i.e., BAM and gravimetry performed by the DRI and the SCAQMD) were explored for quality-assurance purposes and for comparison with the results of Lewis et al. [13]. Since gravimetric-mass concentration is determined solely from Teflon-membrane filters, the gravimetric analysis for the sample pair may still have been valid even if a paired quartz-fiber filter were invalid or missing. Thus, there were 47 valid 24 h gravimetric-mass concentrations from the DRI analysis, with corresponding valid BAM concentrations, and 26 from the SCAQMD gravimetric analysis. As noted above, linear regression, Deming regression, and the CVUB were used to compare these datasets. For the regression analyses, the BAM concentrations were treated as the dependent variable and the gravimetric concentrations as the independent variable. The results are summarized in Table 1 and Table 2.The comparisons in Table 1 and Table 2 show excellent agreement between the gravimetric and BAM measurements, with R2 values greater than 0.98 and slopes near 1:1, giving confidence that the BAM data provided reasonable measurements of hourly and 24 h mean PM10. In comparison of the DRI gravimetric and SLOAPCD BAM concentrations, the linear-regression models—both with and without an intercept—and the Deming model without an intercept all indicated a statistically significant but small bias of 4% to 5%. Nonetheless, with a CVUB of 6.44%, this pair of samplers was well within the EPA’s data quality objective for collocated monitors. In comparison of the SCAQMD gravimetric and SLOAPCD BAM concentrations, none of the regression models or the CVUB indicated a statistically significant difference between the measurements. 3.2. Fresh and Aged Sea SaltInorganic ions in this costal environment without major local aerosol sources likely come from sea salt, mineral dust, and the regional/urban background. Figure 4 shows that measured cations are highly correlated with anions (R2 = 0.99) with a regression slope of 1.04, indicating that most ions were measured with high quality and the particles were nearly neutral. The slightly higher-than-unity slope (1.04) is dominated by a few data points with high ion concentrations, which was probably caused by the carbonate (CO32−) that is common in mineral dust but was not analyzed in this study.Figure 5 shows that both Mg2+ and K+ are highly correlated (R2 ≥ 0.98) with Na+, and the regression slopes are very close to the expected mass concentration ratios (0.12 for Mg2+:Na+ and 0.036 for K+:Na+) in seawater [30]. Therefore, Na+, Mg2+, and K+ mainly originate from fresh sea salt [35]. In contrast, Figure 6 shows that Ca2+ and SO42− exceed the fresh seawater ratios for most samples and their correlations with Na+ are lower than those in Figure 6. The excess Ca2+ and SO42− indicate additional sources, likely minerals (e.g., CaCO3) for the Ca2+ and the regional/urban background for the SO42−. Water-soluble Ca2+ and SO42− can also form from heterogeneous reactions between sulfuric acid (H2SO4) or sulfur dioxide (SO2) and mineral dust [34,39,40].In the assumption that sea salt was the only source of Na+ and Cl− at the monitoring site, typical fresh sea salt particles have a Cl−/Na+ mass ratio of 1.8 [30]. Figure 7a shows that at the CDF, the average Cl−/Na+ ratio is 1.51: lower than 1.8 for all samples. Therefore, approximately 16% Cl− was displaced by stronger acids (e.g., nitric and/or sulfuric acids). Figure 7b shows that most data points are below the 1:1 line, indicating that both NO3− and SO42− were involved in the Cl− displacement for most samples.Figure 8 shows that the AS/FS ratio decreases with the PM10 concentration when the PM10 concentrations are lower than approximately 40 µg m−3, and the ratio remains < 0.2 at higher PM10 concentrations, indicating that FS dominates SS during high-PM10-concentration events. 3.3. PM10 Major Chemical Composition and Mass ReconstructionThe relation between the mass determined by gravimetric analysis and the reconstructed mass showed a strong correlation (R2 = 0.99), indicating that the gravimetric and chemical measurements were of high accuracy (Figure 9). Since the slope (0.84) of the best-fit linear-regression line was less than unity, it indicated that there were constituents of the PM10 that were not accounted for by those measured in the laboratory analysis or in the mass reconstruction (Equation (6)). The unidentified mass is also shown as the difference between the gravimetric mass (represented by ∗) and the reconstructed mass (represented by the stacked bar height) in Figure 10. The attribution of the unidentified PM10 mass to a source is described later.Figure 10 shows that mineral dust and sea salt had high concentrations during the high-PM10 days at the CDF monitoring station, representing influences from saltation-driven dust emissions and ocean sea spray, while OM was a minor contributor. Additionally, the concentrations of tracers for on-road traffic emissions (represented by EC) and regional pollution (represented by nssSO42−, nssNO3−, and NH4+ (included in the others category)) were low. The concentration of methanesulfonate (CH3SO3−) was 50% of the total daily PM10 associated with the wind direction range of 236–326°. Two of the days in Figure 14, with 50% of exceedance days occurred when winds were from the direction range of 236–326°, with wind speed ≥ 3.6 m s−1 measured at the CDF, which likely corresponds with above threshold wind speed conditions for saltation and dust emissions as measured within the ODSVRA at the S1 tower.Based on the results presented, the mitigative actions taken by California State Parks to reduce dust emissions are wholly justifiable as a management strategy to achieve the requirement of the Stipulated Order of Abatement of lowering PM10 to achieve the stated air-quality objective. Mineral dust was the largest contributor to the PM10 on days that exceeded the state standard for PM10 during the observation period, and controlling dust emission is the only viable strategy, as the other sources, particularly if the significant contribution of sea salt (25.1% ± 14.5%) is predominantly generated by wind and wave actions, cannot be controlled through an intervention strategy. If NaCl is also being derived from saltation and dust emissions, suppression of saltation via the methods being used within the ODSVRA is also the appropriate means to lower this PM10 constituent originating from the park. Author ContributionsConceptualization, K.A.T. and J.A.G.; methodology, K.A.T., D.A.C., X.W., S.K. and J.A.G.; validation, K.A.T., X.W. and S.K.; formal analysis, K.A.T., X.W., E.F.-C. and J.A.G.; writing—original draft preparation, X.W. and J.A.G.; writing—review and editing, K.A.T., X.W., J.A.G. and E.F.-C. All authors have read and agreed to the published version of the manuscript.FundingThis research was funded by the California Department of Parks and Recreation, Contract C1953001 to DRI.Institutional Review Board StatementNot applicable.Informed Consent StatementNot applicable.Data Availability StatementData are available from the DRI following a request to California State Parks, Off-Highway Motor Vehicle Recreation Division, 715 P Street, Sacramento, CA 95814.AcknowledgmentsWe would like to acknowledge the support of C. Gibbons (SLOAPCD), who supported the field measurement campaign, and the intellectual contributions from E. Withycombe (California Air Resources Board, retired). T. Carmona of Parks provided the map. We also gratefully acknowledge the support provided by California State Park Project Managers R. Glick and J. O’Brien to carry out this work. The material is courtesy of California State Parks, 2022.Conflicts of InterestThe authors declare no conflict of interest. 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Figure 1. The location of the APCD’s environmental monitoring station, the CDF, with respect to the ODSVRA. The shaded area demarcates the riding area of the ODSVRA. The solid purple line demarcates the boundary of the ODSVRA. A red circle in the shaded area identifies the location of a 10 m tower designated as S1, where in-park meteorological data are collected, and Mesa2 is another of the APCD’s monitoring stations. Figure 1. The location of the APCD’s environmental monitoring station, the CDF, with respect to the ODSVRA. The shaded area demarcates the riding area of the ODSVRA. The solid purple line demarcates the boundary of the ODSVRA. A red circle in the shaded area identifies the location of a 10 m tower designated as S1, where in-park meteorological data are collected, and Mesa2 is another of the APCD’s monitoring stations. Atmosphere 14 00718 g001

Figure 2. The Partisol samplers and BAM 1020 monitors at the CDF sampling site. All sampler inlets (Partisols and BAMs) were approximately 4.0 m above ground level (Photo credit, David Cardiel). Figure 2. The Partisol samplers and BAM 1020 monitors at the CDF sampling site. All sampler inlets (Partisols and BAMs) were approximately 4.0 m above ground level (Photo credit, David Cardiel). Atmosphere 14 00718 g002

Figure 3. The validated mean 24 h PM10 (µg m−3) concentration for the days sampled between April and October 2021. Concentration of PM10 was determined from gravimetric analysis of the Teflon-membrane filter. The horizontal line represents the state mean 24 h PM10 standard of 50 µg m−3. Figure 3. The validated mean 24 h PM10 (µg m−3) concentration for the days sampled between April and October 2021. Concentration of PM10 was determined from gravimetric analysis of the Teflon-membrane filter. The horizontal line represents the state mean 24 h PM10 standard of 50 µg m−3. Atmosphere 14 00718 g003

Figure 4. Correlation between water-soluble cations and anions. Figure 4. Correlation between water-soluble cations and anions. Atmosphere 14 00718 g004

Figure 5. Correlations between: (a) Mg2+ and Na+, (b) K+ and Na+. The dashed lines indicate ion ratios in fresh seawater. Figure 5. Correlations between: (a) Mg2+ and Na+, (b) K+ and Na+. The dashed lines indicate ion ratios in fresh seawater. Atmosphere 14 00718 g005

Figure 6. Correlations between: (a) Ca2+ and Na+, (b) SO42− and Na+. The dashed lines indicate ion ratios in fresh seawater. Figure 6. Correlations between: (a) Ca2+ and Na+, (b) SO42− and Na+. The dashed lines indicate ion ratios in fresh seawater. Atmosphere 14 00718 g006

Figure 7. Correlations between: (a) Cl− and Na+, (b) NO3− and excess Na+. Dashed line in (a) indicates the typical fresh sea-salt-particles’ Cl−/Na+ mass ratio of 1.8. Dashed line in (b) is the 1:1 line. Figure 7. Correlations between: (a) Cl− and Na+, (b) NO3− and excess Na+. Dashed line in (a) indicates the typical fresh sea-salt-particles’ Cl−/Na+ mass ratio of 1.8. Dashed line in (b) is the 1:1 line. Atmosphere 14 00718 g007

Figure 8. Ratio of aged over fresh sea salt (AS/FS) as a function of PM10 concentration. Figure 8. Ratio of aged over fresh sea salt (AS/FS) as a function of PM10 concentration. Atmosphere 14 00718 g008

Figure 9. Correlation between reconstructed and gravimetric PM10 mass concentrations. Figure 9. Correlation between reconstructed and gravimetric PM10 mass concentrations. Atmosphere 14 00718 g009

Figure 10. Concentration of PM10 chemical constituents (stacked bars) and gravimetric mass (∗) for the days sampled between April and October 2021. The horizontal line represents the state mean 24 h PM10 standard of 50 µg m−3. Figure 10. Concentration of PM10 chemical constituents (stacked bars) and gravimetric mass (∗) for the days sampled between April and October 2021. The horizontal line represents the state mean 24 h PM10 standard of 50 µg m−3. Atmosphere 14 00718 g010

Figure 11. PM10 mass percentages of chemical constituents (stacked bars) and gravimetric mass (∗) for the days sampled between April and October 2021. Figure 11. PM10 mass percentages of chemical constituents (stacked bars) and gravimetric mass (∗) for the days sampled between April and October 2021. Atmosphere 14 00718 g011

Figure 12. The mean source attribution of PM10, representing the eight exceedance days between April and October 2021. Note: OM, organic matter; EC, elemental carbon; FS, fresh sea salt; and AS, aged sea salt. Error bars represent the standard deviation of the mean based on the eight sample days. Figure 12. The mean source attribution of PM10, representing the eight exceedance days between April and October 2021. Note: OM, organic matter; EC, elemental carbon; FS, fresh sea salt; and AS, aged sea salt. Error bars represent the standard deviation of the mean based on the eight sample days. Atmosphere 14 00718 g012

Figure 13. The mean source attribution of PM10, representing the non-exceedance days between April and October 2021. Error bars represent the standard deviations of the means based on 39 sample days. Figure 13. The mean source attribution of PM10, representing the non-exceedance days between April and October 2021. Error bars represent the standard deviations of the means based on 39 sample days. Atmosphere 14 00718 g013

Figure 14. The fractions of the 24 h periods for the wind direction ranges of 236–326° (white portion of bar) and 327–235° (black portion of bar) for the 20 days with the highest 24 h mean PM10 values above 50 µg m−3, 2019–2022. Figure 14. The fractions of the 24 h periods for the wind direction ranges of 236–326° (white portion of bar) and 327–235° (black portion of bar) for the 20 days with the highest 24 h mean PM10 values above 50 µg m−3, 2019–2022. Atmosphere 14 00718 g014

Figure 15. The relation between the MD and unidentified components of the PM10 and the total daily PM10 calculated from the BAM for all the valid sampling days from April to October 2021. Figure 15. The relation between the MD and unidentified components of the PM10 and the total daily PM10 calculated from the BAM for all the valid sampling days from April to October 2021. Atmosphere 14 00718 g015

Figure 16. The relation between the OM and EC components of the PM10 and the total daily PM10 calculated from the BAM for all the valid sampling days from April to October 2021. Figure 16. The relation between the OM and EC components of the PM10 and the total daily PM10 calculated from the BAM for all the valid sampling days from April to October 2021. Atmosphere 14 00718 g016

Table 1. BAM vs. gravimetric PM10 concentration comparisons, linear regressions. Table 1. BAM vs. gravimetric PM10 concentration comparisons, linear regressions. ComparisonSample SizeLinear RegressionLinear Regression through the OriginSlopeInterceptR2SlopeR2(95% CI)(95% CI)(95% CI)SLOAPCD BAM vs. DRI Gravimetric531.047−0.4210.9931.0380.997(1.023–1.071)(−1.323–0.481)(1.024–1.053)SLOAPCD BAM vs. SCAQMD Gravimetric241.004−0.1280.98710.996(0.956–1.051)(−1.675–1.419)(0.973–1.027) Table

Table 2. BAM vs. gravimetric PM10 concentration comparisons—Deming regression and CVUB results. Table 2. BAM vs. gravimetric PM10 concentration comparisons—Deming regression and CVUB results. ComparisonSample SizeDeming RegressionDeming Regression through the OriginCVUBSlopeInterceptSlope(95% CI)(95% CI)(95% CI)SLOAPCD BAM vs. DRI Gravimetric531.0140.3071.0326.44%(0.981–1.046)(−0.129–0.907)(1.010–1.054)SLOAPCD BAM vs. SCAQMD Gravimetric241.003−0.2360.9877.05%(0.947–1.060)(−1.076–0.605)(0.947–1.027) Table

Table 3. Days that exceeded the state 24 h mean PM10, wind and PM10 directional relations, and the attribution of PM10 mass based on the hours of transport from the direction of the ODSVRA to the CDF. Table 3. Days that exceeded the state 24 h mean PM10, wind and PM10 directional relations, and the attribution of PM10 mass based on the hours of transport from the direction of the ODSVRA to the CDF. Date24 h PM10 (µg m−3)Wind RosePM10 RoseSource Attribution% Mass from Direction of ODSVRA4 May 202150 Atmosphere 14 00718 i001

587 May 202170 Atmosphere 14 00718 i004

8419 May 2021103 Atmosphere 14 00718 i007

8912 June 202161 Atmosphere 14 00718 i010

7115 June 202190 Atmosphere 14 00718 i013

9319 September 202178 Atmosphere 14 00718 i016

8528 September 202182 Atmosphere 14 00718 i019

9213 October 202153 Atmosphere 14 00718 i022

63(one hour of missing BAM data) Atmosphere 14 00718 i025

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Wang, X.; Gillies, J.A.; Kohl, S.; Furtak-Cole, E.; Tupper, K.A.; Cardiel, D.A. Quantifying the Source Attribution of PM10 Measured Downwind of the Oceano Dunes State Vehicular Recreation Area. Atmosphere 2023, 14, 718. https://doi.org/10.3390/atmos14040718

AMA Style

Wang X, Gillies JA, Kohl S, Furtak-Cole E, Tupper KA, Cardiel DA. Quantifying the Source Attribution of PM10 Measured Downwind of the Oceano Dunes State Vehicular Recreation Area. Atmosphere. 2023; 14(4):718. https://doi.org/10.3390/atmos14040718

Chicago/Turabian Style

Wang, Xiaoliang, John A. Gillies, Steven Kohl, Eden Furtak-Cole, Karl A. Tupper, and David A. Cardiel. 2023. "Quantifying the Source Attribution of PM10 Measured Downwind of the Oceano Dunes State Vehicular Recreation Area" Atmosphere 14, no. 4: 718. https://doi.org/10.3390/atmos14040718

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