Socal Is in Fire!!!! Again 6 Fires Total Some 0 Contained Because of the Santa Ana Winds

• Periodical List
• Geohealth
• v.4(1); 2020 Jan
• PMC7007151

Geohealth. 2020 Jan; 4(one): e2019GH000225.

Santa Ana Winds of Southern California Impact PM_2.5 With and Without Fume From Wildfires

Rosana Aguilera

ⁱ Scripps Establishment of Oceanography, University of California San Diego, La Jolla CA, USA,

Alexander Gershunov

ⁱ Scripps Institution of Oceanography, University of California San Diego, La Jolla CA, USA,

Sindana D. Ilango

² Department of Family Medicine and Public Health, Academy of California San Diego, La Jolla CA, USA,

^three Schoolhouse of Public Wellness, San Diego State University, San Diego CA, USA,

Janin Guzman‐Morales

^ane Scripps Establishment of Oceanography, Academy of California San Diego, La Jolla CA, U.s.a.,

Tarik Benmarhnia

ⁱ Scripps Institution of Oceanography, University of California San Diego, La Jolla CA, USA,

^two Section of Family Medicine and Public Wellness, University of California San Diego, La Jolla CA, USA,

Received 2019 Sep xviii; Revised 2019 Nov 26; Accepted 2019 Dec 3.

Key Points

• Santa Ana winds have a predominant ventilation effect as background PM_2.5 is transported offshore from highly polluted areas

• A polluting issue occurs when SAWs spread fume PM_two.five from wildfires inland toward the coastal region

• Statistical approaches that relate surface wind and PM_2.five over infinite and fourth dimension can assistance in identifying wildfire PM_ii.5

Keywords: PM_2.five, air quality, Santa Ana winds, wildfire smoke, Southern California

Abstract

Fine particulate matter (PM_2.five) raises man health concerns since it can deeply penetrate the respiratory organisation and enter the bloodstream, thus potentially impacting vital organs. Strong winds ship and disperse PM_two.5, which can travel over long distances. Fume from wildfires is a major episodic and seasonal hazard in Southern California (SoCal), where the onset of Santa Ana winds (SAWs) in early on fall before the showtime rains of wintertime is associated with the region's most damaging wildfires. Withal, SAWs also tend to better visibility as they sweep haze particles from highly polluted areas far out to sea. Previous studies characterizing PM_2.5 in the region are limited in time span and spatial extent, and have either addressed only a single event in time or short time series at a limited set up of sites. Here we study the space‐fourth dimension relationship betwixt daily levels of PM_2.five in SoCal and SAWs spanning 1999–2012 and also further identify the impact of wildfire smoke on this relationship. Nosotros used a rolling correlation approach to characterize the spatial‐temporal variability of daily SAW and PM_two.5. SAWs tend to lower PM_two.5 levels, especially along the coast and in urban areas, in the absence of wildfires upwind. On the other paw, SAWs markedly increase PM_2.5 in zip codes downwind of wildfires. These empirical relationships tin exist used to identify windows of vulnerability for public health and orient preventive measures.

i. Introduction

Fine particulate thing with aerodynamic diameter <2.v μm (PM_2.v) can be inhaled into the deepest recesses of the lungs and crusade both curt‐ and long‐term effects on human health, particularly for respiratory and cardiovascular diseases (Franklin et al., 2015; Liu et al., 2015; Pope & Dockery, 2006). Sources of primary ambient PM_two.5 include fuel combustion of motor vehicles and industrial facilities, likewise as ability generation and residential heating. In addition, secondary PM_2.5 aerosols can be formed in the temper from gases such equally sulfur and nitrogen oxides and volatile organic compounds (Chen et al., 2010; Wilson & Suh, 1997). Secondary aerosols tin account for more than than 50% of the total PM_2.5 mass, though it varies greatly amid regions and seasons (Finn et al., 2008). PM_2.5 from anthropogenic sources in the United States, including Southern California (SoCal), has decreased in the past decades due to policy implementation (Lurmann et al., 2015; McClure & Jaffe, 2018).

PM_2.5 as well results from biomass burning and is the master component of wildfire fume with the biggest touch on public health related to short‐term exposure (Gan et al., 2017; Gupta et al., 2018; Liu et al., 2015; McClure & Jaffe, 2018). In the United States, McClure and Jaffe (2018) observed a downward trend in PM_2.5 during the final three decades, except in regions that were prone to wildfires. Furthermore, contempo studies accept predicted that wildfire‐specific PM_2.five and the associated wellness burden will increment with a changing climate (Ford et al., 2018; Liu et al., 2016), contrasting with reduced PM_2.v emissions from other sources.

Stiff winds have a major role in the transport and dispersion of PM_ii.5, since fine particles tin remain airborne for weeks and travel distances of hundreds to thousands of kilometers (WHO, 2006; Tai et al., 2010; Wilson & Suh, 1997). In SoCal, Santa Ana winds (SAWs) are episodic pulses of northeasterly, downslope, offshore flow associated with very dry air accelerating and adiabatically warming and drying over the lee (southwestward sloping) slopes of the coastal topography (Guzman‐Morales et al., 2016; Hughes & Hall, 2010; Raphael, 2003). The onset of SAW, after the dry and long warm flavour and earlier the first rains of wintertime, is associated with the traditional wildfire season in the littoral foothills (Westerling et al., 2004) and downwind pulses of PM_two.5 impacting populated areas on the coast (Phuleria et al., 2005; Wu et al., 2006; Kochi et al., 2016). Anecdotally, SAWs, which typically occur without a surface thermal inversion, under clear skies and without wildfire, have also been related to the all-time seasonal visibility as they sweep pollution offshore and far out to sea (Corbett, 1996). Quantifying how SAW influence the spatial and temporal variability of PM_ii.five, with and without wildfire smoke, can provide insight on which areas are the most impacted in terms of exposure to air pollution.

Studies characterizing spatial and temporal patterns of PM_2.5 in SoCal (Choi et al., 2013; Kim et al., 2000a, 2000b) have observed that PM_two.5 is abundant nether fall stagnation atmospheric condition, whereas fibroid particles of crustal components typically prevail during high SAW conditions in late autumn and winter (Guazzotti, 2001; Qin et al., 2012). In addition, SAW events in the fall have been related to biomass burning PM_2.5 (Qin et al., 2012), emanating from the coastward sloping wildland where vegetation is nearly abundant and the SAWs are the strongest, and causing substantive health impacts downwind (Delfino et al., 2009). The existing body of work characterizing PM_two.v and referencing SAW is limited in time span and spatial extent and has either addressed but a single event or brusk time serial at a limited set up of sites. To our knowledge, no report comprehensively and explicitly assessed the variability of PM_2.v as influenced by SAW in the absence and presence of wildfires burning upwind.

We expect SAW to benefit air quality along the coast in the absence of wildfires upwind and to accept a detrimental effect when wildfires occur. Furthermore, this potential dichotomous relationship between SAW and PM_2.5 could help identifying the timing and extent of exposure to wildfire PM_two.5. Here we study the infinite‐time relationship between daily levels of PM_2.v in Southern California and Santa Ana winds spanning 1999–2012 and aim to place the impact of wildfire on this human relationship. This work resolves hundreds of SAW and dozens of wildfire events at a fine spatial resolution over SoCal for an unprecedented time span of xiv years. Nosotros prefer a rolling correlation arroyo equally a tool for the identification of wildfire‐related spikes of PM_2.v by means of the observed relationship with surface wind. We include a instance study of the October 2007 firestorm, composed of over ii dozen wildfires in SoCal, to further illustrate the effect of SAW on PM_two.5.

2. Information and Methods

two.1. SAW and SAWRI

The SAW season extends from Oct to Apr, peaking in frequency in December and Jan, when it is not uncommon for upwards to half of the days to experience Santa Ana atmospheric condition (Guzman‐Morales et al., 2016). SAW episodes can as well occur every other September and May, on average (Guzman‐Morales et al., 2016), and we therefore too consider these months in our analysis.

The original gridded hourly nearly‐surface winds from the California Regional dynamical downscaling of a global reanalysis product to x × 10 km (CaRD10; Kanamitsu & Kanamaru, 2007) were previously analyzed to derive SAW indices and validate them confronting the available in situ hourly wind observations by Guzman‐Morales et al. (2016). The daily version of the original hourly SAW regional index (SAWRI), which represents the mean air current speed (thousand/southward) evaluated over the Santa Ana wind domain (Guzman‐Morales et al., 2016), is used here (Figure S1 in the supporting information). SAWRI thus provides an observationally validated regional daily summary of the dynamically downscaled SAWs.

2.two. PM_2.v Information and Exposure Estimates

The report area comprised 578 zip lawmaking polygons whose centroids brutal inside the SAW domain (Figure S2 in the supporting information). Daily‐, zip code‐specific concentrations of PM_2.v were estimated from 1999 to 2012 using 24‐hr daily ways sampled and analyzed by the United states EPA Air Quality Arrangement (https://world wide web.epa.gov/aqs). We used daily PM_2.5 measured with the Federal Reference Method from ground monitoring stations within a 20 km radius of each population‐weighted naught code centroid. Values were interpolated using an inverse distance weighting approach, where the measured concentration is weighted by the inverse distance squared to each betoken of involvement; this gives greater importance to values reported by monitoring stations closer to the point of interest than monitoring stations further away in altitude.

Interpolated values at each population‐weighted centroid were then assigned to each zip code for daily‐, nix lawmaking‐specific concentrations of PM_2.v. To validate interpolated estimates, each data indicate was removed and then predicted at that location using remaining information points. Correlations between predicted and actual values at the location of the omitted point were used to assess validity of modeled estimates (r = 0.55; Figure S3). ArcMap10.three (ESRI, 2015) was used to assign buffers, interpolate, and validate. Information management was conducted in SAS 9.three (SAS, 2012).

ii.3. PM_2.5 Anomalies Weighted past SAWRI: Rolling Correlation Betwixt Air Quality and SAW

We calculated daily anomalies (i.e., deviation from the mean) for PM_two.five by obtaining the hateful within a centered 31‐day window and for each of the 578 zip codes previously selected for being located inside the SAW domain, and then subtracting this evolving mean from the original daily PM_2.5 value. Lastly, we weighted the anomalies according to the strength of SAW, past multiplying by SAWRI and thus reflecting the contribution of strong SAWs in the absence and presence of wildfire events. Nosotros also thus obtained values of PM_2.5 weighted anomalies for the days when SAWs were active in the region. We correlated the weighted anomalies of PM_ii.5 with SAWRI for centered rolling windows of 31 days, moving frontward at 1 day increments, for each zip code. The rolling window approach allowed us to visualize and quantify the change in correlation over time and with an emphasis on PM_two.5 during strong SAWs, which tin can signal events such as wildfire smoke. Significant positive and negative Pearson correlations were identified, based on the t test statistic with 95% conviction. We too tested 15‐ and 60‐solar day windows and observed similar predominant patterns in terms of positive and negative correlations. Results presented in the following sections correspond to the 31‐day sliding window. All the above analyses were conducted in R version 3.5.i. (R Core Team, 2018).

2.4. Case Study Setting: October 2007 Firestorm

More than than 2‐dozen fires broke out betwixt 20 and 23 October, driven by stiff SAWs and burning over 972,000 acres across SoCal. Adding to the size and extent of these fires was the historic 2006–2007 drought that contributed to high dead fuel loads (Keeley et al., 2009). Approximately 3,200 structures were destroyed and several were damaged, causing 161 injuries (specially among firefighters) and 7 fatalities (Keeley et al., 2009). The property amercement amounted to $1.8 billion (Karter, 2008) and were concentrated in San Diego Canton (83%) followed by San Bernardino County (14%; Keeley et al., 2009).

In San Diego County, Hutchinson et al. (2018) found that respiratory diagnoses, especially asthma, were elevated for vulnerable populations such as children and adults with low income. In the aforementioned region, Thelen et al. (2013) observed that at elevation fire PM (both coarse and fine) concentrations the odds of a person seeking emergency care increased by ~l% with respect to nonfire conditions. Overall, the 2007 SoCal wildfires imposed an guess of $3.4 million in healthcare costs for hospital and emergency section visits alone (Kochi et al., 2016).

3. Results

3.1. Descriptive Statistics

For the menses 1999–2012, there were one,083 days with SAWs (i.east., SAWRI > 0). The year 2008 had the most SAW days (due north = 125), followed by 2007 (northward = 100). The to the lowest degree number of SAW days was observed in 2010 (n = 30) and 2,000 (northward = 37). SAWRI reflects a liberal definition of SAW days whereby weak SAW conditions expressed over a pocket-sized office of the domain brand for a SAW twenty-four hours (SAWRI > 0), although SAWRI is very small for such days and large for stiff and extensive SAWs. Full monthly regional SAW activeness (i.e., the sum of SAWRI over a month, reflecting both intensity and frequency of SAW; Figureone) peaks between the months of Nov and January. Nosotros summarized daily SAWRI values per calendar month and per calendar year (Tables S1 and S2). The overall maximum regional air current speeds for the strongest SAW events on record were observed in Oct 2007. These regional average values, ranging from 12.7 to thirteen.4 m/s tin can be described as extreme (>x k/due south), being in the 10% of the strongest SAWRI values during a 65‐year record (Guzman‐Morales et al., 2016). The same summary but for daily mean PM_2.five values by month (September to May, the SAW season) showed maximum and average concentrations peaking in fall and winter months, with an exceptional maximum value for the 24‐60 minutes levels in October 2003 (237.three μg/mⁱⁱⁱ), the month/year with the highest wildfire activeness (i.due east., acres burned) in SoCal and moderate‐intensity (albeit long‐duration) SAWs (i.e., SAWRI ranging betwixt 5 and ten k/south; Guzman‐Morales et al., 2016). This supports the notion that even moderately strong SAWs tin cause extreme burn down spread.

Mean PM_2.5 and total SAWRI activeness, summarized by month (a) and by calendar yr (b). The annual values for PM_2.5 consider the months of September to May of each agenda year.

iii.2. Dichotomous Effects of SAW on Daily PM_2.5

SAWs predominantly reduce daily PM_2.v as evidenced by the negative correlations during typical SAW seasons (blue bars in Figure2, showing a coastal zippo lawmaking equally an example). They do so past transporting pollution particles offshore. However, intermittently, SAWs have the opposite—polluting—effect when significant positive correlations with PM_ii.v (red confined in Figure2) are observed. Beneath, we show that this occurs mostly on days with wildfires upwind, with SAWs supporting wildfire growth and advecting fume toward coastal areas. This dichotomous outcome of SAW thus allowed usa to identify zip codes afflicted by episodic, wildfire‐related PM_2.five.

Significant positive (red) and negative (blue) correlations in a San Diego littoral zip code (location shown in Effigy S2 in the supporting information). Dates for positive correlations associated with wildfires upwind are highlighted in red; in addition, the Viejas Fire burned eleven,000 acres in January 2001. Other wildfire names are besides included to highlight the contribution of fume to item positive correlations.

Examining the frequency of counts of pregnant correlations between SAWRI and PM_two.5 for all SAW days and zip codes resolved in this study, nosotros found that the highest number of negative correlations occurred during the months of November, December, January, and February and during years devoid of large wildfires, for example, 2004, 2005, and 2011 (Effigy3). This effect reflects the ventilation effect of SAW during the winter peak of the SAW season, when SAWs are nearly frequent and typically non associated with wildfires after the start of the rainy flavour. Positive correlations were near frequent in October and November and in years 2003, 2007, and 2008 (Effigyiiib), when autumn wildfires were numerous and widespread across Southern California (Tables S1 and S2).

Count of significant positive and negative correlations per day/zip code, summarized per (a) month and (b) calendar year. Months and years among the largest burned areas (Tables S1 and S2) are highlighted in carmine. Annotation the different scales for the positive and negative correlation counts.

Spatially, inland zip codes (in Los Angeles, Riverside, San Bernardino, and north San Diego counties) and those in the northern half of Orangish County had the highest number of negative correlations (Figure4a), followed by areas surrounding big cities (i.eastward., Los Angeles and San Diego), though these were also the areas with the largest number of air quality observations (Figure S2). On the other hand, the polluting affect of SAW and wildfires was mainly observed in coastal naught codes (Figure4b) where SAWs spread PM_2.5 in smoke plumes, thus causing damage to the health of a large and diverse population in terms of socioeconomic and demographic conditions (Hutchinson et al., 2018; Thelen et al., 2013).

Total counts of negative (a) and positive (b) significant correlations per naught code. Fire perimeters obtained from the Fire and Resource Cess Program of the California Department of Forestry and Burn Protection (http://frap.fire.ca.gov/) brandish the total area burned, and the appointment reflects the start of the fire.

iii.three. Case Written report: Santa Ana‐Driven Wildfire Outbreak of October 2007

Figure5 shows an instance of the relationship between extreme SAW and PM_2.v during a serial of wildfires across SoCal starting 20 Oct 2007. Figure5a displays positive correlations between the two variables in littoral nil codes. Together with the satellite paradigm in Effigyfivea, these results illustrate the detrimental effect of winds on PM_2.5 levels where smoke from wildland fires was transported toward the nigh populated coastal regions (Figure S3). This was the strongest SAW event on our 14‐year record, although not unprecedented in a longer SAW tape (Guzman‐Morales et al., 2016). The negative correlations observed in inland zip codes account for the initial ventilation effect at the onset of SAW effectually 21 Oct and before the fires marked much of SoCal by 22 October.

Case study highlighting significant correlation between PM_ii.5 and wildfires. Daily gridded SAW vectors shown were obtained from Guzman‐Morales et al., 2016. (a) The Moderate Resolution Imaging Spectroradiometer Rapid Response System (https://lance.modaps.eosdis.nasa.gov/cgi-bin/imagery/gallery.cgi) satellite image shows the smoke plumes for fires burning on 22 October, and current of air vectors represent wind velocity for that aforementioned twenty-four hours. High positive correlations are constitute in coastal zip codes, which remained with poor air quality weather after (b) 2 weeks from the onset of the kickoff wildfire. Burn perimeters display the full surface area burned and the date reflects the offset of the burn down.

2 weeks after wildfire onset (Figurevb), more inland goose egg codes started to show a pregnant negative relationship (i.e., a ventilation effect of the second—Figure6b—SAW consequence of the period), while the positive correlations remained strong and widespread in the coastal zone. Effigyhalf-dozena shows levels of PM_two.5 in 2 littoral naught codes peaking four–5 days after the onset of the showtime wildfires upwind. This is when the strong initial SAW consequence was subsiding and the accumulating fume lingered at the declension.

(a) PM_2.five concentrations for coastal goose egg codes in Long Beach (in Los Angeles County) and San Diego (in San Diego County). (b) SAWRI for the 2‐week period of agile wildfires during Oct to November 2007.

4. Give-and-take and Conclusions

Our study shows that the heterogeneity in the inland‐coastal spatial patterns of PM_2.5 in SoCal is enhanced past SAW, which has been previously acknowledged though not explicitly examined (e.g., Choi et al., 2013). SAWs have a predominant negative relationship with PM_2.5, which translates into a beneficial (ventilation) effect as background pollutants are transported offshore and away from inland and littoral areas. This ventilation effect of SAW might be more visible in inland areas since they are typically among the most polluted given pollution sources, topography, and prevailing westerly winds. Though SAWs have been anecdotally linked to improved air quality atmospheric condition in terms of visibility and reduction of haze (Corbett, 1996), our report provides prove of the ventilation consequence on PM_2.v.

The reverse (polluting) upshot is observed mainly when wildfires occur; namely, SAWs contribute to air pollution by transporting fume PM_two.5 from inland areas, where wildfires are spread by SAW, specially to the densely populated coastal zone. Fine particulate affair levels exceeding the 24‐hr national standard of 35 μg/m³ for PM_ii.v, have been associated with specific wildfire events in the region (Phuleria et al., 2005; Wu et al., 2006; Kochi et al., 2016) with detrimental impacts on homo wellness (e.g., Delfino et al., 2009; Hutchinson et al., 2018).

Limitations such every bit differing measurement frequency and time catamenia covered in observational PM_2.v information might have affected our analysis in certain areas with fewer observations. For instance, virtually monitoring stations found in Ventura and Santa Barbara counties take recorded concentrations at three‐twenty-four hours intervals, with a few of these changing to daily frequency starting 2010. This, in particular, could take precluded detecting the bear on of SAWs on PM_2.v in the presence of large wildfires (i.e., Ventura County, Effigy4b) in 2003 and 2007. Monitors located in and in the proximity of highly urbanized areas in Los Angeles, Orange and San Diego counties tend to include daily measurements of PM_ii.five and for the longest time periods within our study framework (1999–2012). In addition, SAWRI is a regional index which does non reflect spatial particularities, for case, in areas such as western Santa Barbara County where SAWs are typically not specially strong.

Unproblematic statistical approaches quantifying relationships between wind and PM_ii.5 in the presence and absence of pollution sources (east.g., wildfire), could exist specially useful to aid in forecasting and identifying wildfire‐generated PM_two.5. The same approach used here could be extended to other downslope wind systems in California and elsewhere, which also spread wildfires, for example, Diablo winds of Northern California (Mass & Ovens, 2019; Smith et al., 2018). Specific air current systems, and wind data in full general, could also serve as the link between wildfire smoke PM_two.5 and wildfire risk in climatic change scenarios (Westerling, 2018).

Future work in relation to SAW and PM_2.5 pollution events during and following wildfires will assess health risks from current of air‐blown smoke associated with historical wildfires. Wildfire action is already on the rise in California due to anthropogenic causes including global climate change (Williams et al., 2019). These regional trends are consistent with a global increase in wildfire activity (Jolly et al., 2015). Contempo work suggests that wildfire severity and take chances specifically in SoCal will probable intensify in the warming time to come, while gradually shifting from fall to winter (Williams et al., 2019). This seasonal shift is associated with a projected weakening of SAW activity, particularly in the autumn and jump (that is coincident with a projected decrease in fall precipitation; Pierce et al., 2013; Swain et al., 2018; and thus more likelihood of dry fuels persisting into winter) and a sharper seasonal SAW meridian in Dec (Guzman‐Morales & Gershunov, 2019).

The Thomas Burn down that burned through well-nigh of Dec 2017 into Jan 2018, briefly condign the largest wildfire (~ 283,000 acres) in California history, is a recent instance of a late flavor wildfire that was made possible past a late onset of winter rains and back‐to‐back SAW events that are mutual at the peak of the SAW season in Dec. In addition, since wildfire ignitions in SoCal are mostly man‐caused, population growth and expansion of evolution into wildland areas increase the risk of wildfires and associated impacts (Syphard et al., 2018). California communities are attempting to mitigate these risks by, for example, considering wildfire risk in blessing new housing development in the wildlands (San Diego Tribune, 2019) and implementing preventive power shutoffs past energy utilities during high fire take a chance atmospheric condition (New York Times, 2019). These are recent developments and more proactive measures will, no dubiety, exist needed.

Exam of projected health risks due to future wildfire risk and changes in SAW is warranted. Confounding the smoke‐health impacts associated with SAW and wildfires, SAW also drive coastal estrus waves that incur their own health risks (Kalkstein et al., 2018), which will alter in a warmer futurity equally SAW‐driven heat waves become less frequent (Guzman‐Morales & Gershunov, 2019) but hotter (Hughes et al., 2011). The ventilation effect of SAW without wildfires, meanwhile, should be benign to respiratory wellness—a hypothesized outcome to exist examined and quantified in future research. Results presented here provide more impetus for a holistic written report of the health impacts from SAWs on a dense and diverse coastal population, with and without wildfires, in our changing climate. Recent widespread California wildfires occurring by the information record available to us here, including the fires called-for in northern and southern California at the time of this writing in the autumn of 2019 and their associated air quality impacts (Los Angeles Times, 2019), provide ongoing motivation for extending this study to quantify downwind health impacts, resolving disparities, and inform more than effective mitigation strategies for the future.

Conflict of Interest

The authors declare no conflicts of interest relevant to this written report.

Supporting information

Supporting Information S1

Acknowledgments

This piece of work was funded by the University of California Function of the President via Multicampus Research Programs and Initiatives (MRPI; Climate and Health Interdisciplinary Inquiry Program, MRP‐17‐446315) and the National Oceanic and Atmospheric Administration's Regional Integrated Sciences and Assessments (RISA) California–Nevada Climate Applications Program Award NA17OAR4310284. This work was supported in part by the Alzheimer's Disease Resource Center for advancing Minority Aging Enquiry at the Academy of California San Diego (P30AG059299 National Institute on Aging). This work also contributes to the Department of the Interior'southward Southwest Climate Adaptation Scientific discipline Center. The observational PM_2.5 data set is freely and publicly bachelor from the United states of america EPA Air Quality Arrangement (AQS; https://world wide web.epa.gov/aqs).

Notes

Aguilera, R. , Gershunov, A. , Ilango, South. D. , Guzman‐Morales, J. , & Benmarhnia, T. (2020). Santa Ana winds of Southern California bear upon PM_ii.5 with and without smoke from wildfires. GeoHealth, 4, e2019GH000225 10.1029/2019GH000225 [CrossRef] [Google Scholar]

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007151/

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