Exposure Assessment of Indoor PM Levels During Extreme Dust Episodes

International Journal of Environmental Research and Public Health

Article Exposure Assessment of Indoor PM Levels During Extreme Dust Episodes

Itzhak Katra * and Helena Krasnov Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel; [email protected] * Correspondence: [email protected]

Received: 22 January 2020; Accepted: 29 February 2020; Published: 3 March 2020

Abstract: Millions of people live in areas that are subject to frequent dust events; however gaps remain in our knowledge about the association between dust, air quality and corresponding particulate matter (PM) exposure levels inside buildings. This case study demonstrates how the PM2.5 and PM10 levels in an urban environment respond to strong natural dust episodes. Real-time measurements were recorded simultaneously in indoor and outdoor environments in households in the city of Beer-Sheva, Israel during several strong dust events. A typical strong event was used for a detailed analysis of 3 PM10 and PM2.5. Outdoor daily concentrations were above 1000 µg m− for PM10, the maximum 3 3 hourly value of which was 1320 µg m− . The indoor PM10 peaked at about 700 µg m− and ﬂuctuated in parallel with the outdoor level but with a time lag of about 15 min. Indoor air tended to remain for several hours after the dust event had subsided. Analyses of multiple events revealed that the dependence of indoor PM2.5 and PM10 on natural dust varies but is not directly linked to the level of atmospheric dust concentration. From a health perspective, the exposure risk posed by extreme indoor PM2.5 and PM10 levels generated by natural dust episodes should be considered.

Keywords: PM; dust events; indoor; outdoor

1. Introduction Atmospheric dust from soil sources (dust events) plays an important role in the earth system [1], and as such, the process must be quantitatively understood from a variety of perspectives ranging from climate research (e.g., the source areas of wind-driven soil erosion) to human health. In terms of human health, viable health risk assessments must incorporate information about particulate matter (PM) and its chemical and biological properties [2]. Such exposure can have signiﬁcant impacts on human health in these areas [3–7]. PM exposure data for epidemiological studies are derived from environmental monitoring of outdoor ambient levels that have been scrutinized intensely by regulatory agencies and health assessment panels. In the immediate wake of a strong dust event, atmospheric PM10 concentrations can reach or 3 3 exceed 1000 µg m− [8]. The guideline of WHO (World Health Organization) is a 24 h mean of 50 µg m− 3 for PM10 (and 25 µg m− for PM2.5). The national guideline by the Israeli Ministry of Environmental 3 Protection is 150 µg m− for PM10 only. The Ministry of Environmental Protection provides national alerts before dust storms with recommendations to limit outdoor activities and to stay inside buildings. However, our knowledge about the impact that such strong dust storms have on atmospheric PM10 in urban environments, particularly about the subsequent exposure levels indoors, where people spend most of their time, is clouded with uncertainty. Recent studies indicated that indoor PM levels 3 may possibly be enhanced by even low atmospheric concentrations of less than 100 µg m− [9,10]. In addition, atmospheric dust was shown to be a major controlling factor for indoor PM2.5 and PM10 levels in an arid zone [10]. However, the impact of PM2.5 and PM10 levels generated from strong

Int. J. Environ. Res. Public Health 2020, 17, 1625; doi:10.3390/ijerph17051625 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 1625 2 of 9 events on indoor levels remains unknown, although such events are common in the Mediterranean region [11,12]. Studies in the eastern Mediterranean have shown evidence of increases in the strength and frequency of Saharan dust-raising activity [13]. Moreover, a study by Krasnov et al. [8,14] of atmospheric PM10 levels revealed that the strongest storms in that region have occurred over the last few years. Several studies conducted in the Northern Negev (Israel) have shown the impact of dust- PM10 on public health, including asthma in children [3], cardiovascular morbidity [4], effects on serum glucose levels [15], onset of atrial fibrillation [16], and exacerbation of chronic obstructive pulmonary disease (COPD) [17]. There is growing concern about the potentially detrimental impact that dust pollution has on human health. Drylands (arid and semi-arid zones) that are subject to frequent dust events constitute about 40% of the Earth’s total land surface and contain an estimated 2.1 billion people. The exposure to extreme PM2.5 and PM10 levels due to natural dust episodes may cause or aggravate health effects. Little is known yet on the impact of dust storms on PM levels inside houses. This research paper describes a case where PM2.5 and PM10 levels in an urban environment respond to strong natural dust episodes while hypothesizing that indoor PM2.5 and PM10 concentrations can be very responsive. Real-time analyses enabled us to explore strong dust episodes in an urban environment located along the margin of the global dust belt and to assess their immediate impacts on outdoor and indoor PM2.5 and PM10 levels.

2. Methodology Experimental site. The city of Beer-Sheva is located in the Negev desert region of southern Israel (Figure1), and is suitable to achieve the aim of this research, namely, to assess the changes that occur in urban PM levels in response to strong natural dust episodes from distal and local sources [2,18]. Beer-Sheva frequently experiences dust storms whose durations are typically several hours to a few days [8]. Regional dust storms occur mostly during the winter, and are associated with the passage 1 of cold fronts that are followed by periods with relatively high wind speeds (>6 m s− ). In contrast, the spring and the autumn are characterized by mild storms while the summer period is considered a dust-free season. Environmental data. The distribution of the dust events over time corresponding to the indoor measurements we conducted is presented in Figure1. Atmospheric PM 10 data for 2001–2015 were obtained from a Ministry of Environmental Protection (http://www.sviva.gov.il) monitoring station located in the Negev desert that is part of the Israel National Air Monitoring System. The data were recorded every 5 min by a dichotomous ambient particulate monitor (Thermo Scientific 1405DF; Thermo Fisher Scientific Inc.) that utilizes two tapered element oscillating microbalances (TEOMs) to provide a continuous, direct mass measurement of particle mass. Other data recorded by the station include the levels of major air pollutants (CO, SO2, NO2, and O3) and meteorological variables (air temperature, relative humidity, wind speed and directions, solar radiation). 3 During strong dust events, daily outdoor PM10 concentrations can exceeded 2000 µg m− while 3 hourly concentrations were as high as 5000 µg m− . In this study, real-time measurements were related to strong dust storms (Figure1). PM 10 data were used in order to identify and select strong dust events based on the net contribution of PM10 concentrations to the area’s background levels [8]. 3 3 3 Four storm levels are defined: low = 264 µg m− ; medium = 661 µg m− ; high = 1322 µg m− ; severely 3 high = 1983 µg m− . The strong dust storm of 11 February 2015 was at the focus of this study. Particle characteristics. The chemical and physical properties of the atmospheric dust sample collected during the event of 11 February 2015 was analyzed. The sample was collected on a building roof within the campus of Ben-Gurion University of the Negev using settled dust collectors [2]. The sterilized settling-dust collectors consist of a tray filled with layers of glass/quartz marbles. Atmospheric dust particles that cross the tray aperture are trapped in the marble matrix due to the matrix cohesion and roughness along with reduced air speed near the surface of the tray. At the Int. J. Environ. Res. Public Health 2020, 17, 1625 3 of 9 end of the event the dust sample (~5 g) was moved into sterilized glass vials for size distribution Int. J. Environ. Res. Public Health 2020, 17, x 3 of 10 and elemental analyses. Particle size distributions over the range of 0.08 to 2000 µm were obtained by high-resolutionelemental analyses. laser Particle diffractometer size distributions (ANALYSETTE over the 22range MicroTec of 0.08 to Plus) 2000 [ 19µm,20 were]. Each obtained sample by was dispersedhigh-resolution by sonication laser (atdiffractometer 38 kHz) in (ANALYSETTE a Na-hexametaphosphate 22 MicroTec Plus) solution [19,20]. (0.5%), Each then sample transferred was to a fluiddispersed module by sonication of the instrument (at 38 kHz) (containing in a Na-hexam deionizedetaphosphate water), solution and subjected(0.5%), then to transferred 3 consecutive to 1 a fluid module of the instrument (containing deion1 ized water), and subjected to 3 consecutive 1 min min runs at a medium pump speed of 6 L min− . The data were processed using the Mie scattering −1 modelruns (RI at= a1.56, medium AC = pump0.01) speed with of an 6error L min<.5.0%. The data The were MasControl processedsoftware using the (Fritsch,Mie scattering Idar-Oberstein, model (RI = 1.56, AC = 0.01) with an error < 5.0%. The MasControl software (Fritsch, Idar-Oberstein, Germany) was employed to determine the following relevant parameters: mean size, median, modes in Germany) was employed to determine the following relevant parameters: mean size, median, modes multiplein multiple modal distributions,modal distributions, sorting sorting values, values, and sizeand fractionsize fraction weights. weights. Elemental Elemental analyses analyses were were done usingdone a wavelength using a wavelength dispersive dispersive X-ray fluorescence X-ray fluoresc spectrometer.ence spectrometer. Quantitative Quantitative analysis analysis of the of detected the elementsdetected was doneelements using was Omnian done using software Omnian (Axios, soft Panalyticalware (Axios, Co., Panalytical Malvern, Co., United Malvern, Kingdom). United Real-timeKingdom). PM measurements. Continuous real-time measurements were recorded simultaneously in indoorReal-time and outdoor PM urban measurements. environments Continuous in five households real-time locatedmeasurements in the centerwere ofrecorded Beer-Sheva (Figuresimultaneously1). The selected in indoor households and outdoor were urban apartments environments characterized in five households by common located building in the materialscenter and constructionof Beer-Sheva (concrete-based) (Figure 1). The ofselected Beer Sheva.households The apartmentswere apartments were characterized at the middle by floors common in their buildingbuilding (3–5 stories)materials to and avoid construction possible disturbance (concrete-base becaused) of Beer of proximity Sheva. The to aapartments yard (first were floor) at or the a roof middle floors in their building (3–5 stories) to avoid possible disturbance because of proximity to a (last floor). The indoor measurements at these houses were continued for a duration of about three yard (first floor) or a roof (last floor). The indoor measurements at these houses were continued for a hoursduration during theof about dust three event. hours The during measurements the dust even weret. The obtained measurements with portable were obtained dust monitoring with portable devices 3 DustTrakdust DRXmonitoring 8534 (TSI, devices Shoreview, DustTrak Minnesota,DRX 8534 (TSI, USA); Shoreview, reading Minnesota, resolution USA); of 0.001 reading mg mresolution− , accuracy of 1%)of that 0.001 recorded mg m−3, accuracy both PM of2.5 1%)and that PM recorded10 data andboththat PM2.5 were and PM situated10 data atand each that indoor were situated and outdoor at measurementeach indoor point. and outdoor measurement point.

3 FigureFigure 1. The 1. The study study site site and and average average daily daily concentrationsconcentrations of of outdoor outdoor PM PM10 (µg10 (mµ−g3) mover− ) the over last the 15 last 15 yearsyears (1 January(1 January 2001–15 2001–15 October October 2015)2015) recordedrecorded in in th thee Beer-Sheva Beer-Sheva monitoring monitoring station. station. Dashed Dashed line line 3 −3 representsrepresents strong strong events events (>661 (>661µg µg m− m) based) based on on class class levelslevels by Krasnov Krasnov et et al al. [8]. [8 The]. The locations locations of the of the householdshouseholds (red dots)(red dots) and and the the monitoring monitoring station station (yellow (yellow dot)dot) are presented presented on on the the map. map.

In theIn indoor the indoor environments, environments, the the dust dust monitoring monitoring devices devices were were operatedoperated in the the main main living living area area of each houseof each at house 1 m height at 1 m aboveheight theabove floor the (with floor no(with carpet). no carpet). The PMThe measurementsPM measurements were were performed performed under under conditions of minimal human interference during the dust event (closed windows, air conditions of minimal human interference during the dust event (closed windows, air conditioning conditioning not operating, no cooking). The corresponding outdoor PM2.5 and PM10 were recorded not operating,by another no DustTrak cooking). DRX The 8534 corresponding situated on the outdoorbalcony with PM 2.5overhead,and PM which10 were is at recorded the height by of anotherthe DustTrakapartment DRX 8534floor. situatedThe monitor on was the balconyplaced in withnorth-east overhead, side balconies which isto atavoid the direct height wind of theduring apartment the floor.dust The event monitor (south-west) was placed and keep in north-east measurement side stability. balconies DustTrak to avoid monitors direct were wind factory-calibrated during the dust eventfor (south-west) a respirable and particle keep fraction measurement based onstability. the Arizona DustTrak Test Dust monitors standard were (i.e., factory-calibratedISO 12103-1, A1 test for a respirabledust), particle which is fraction representative based onof thea wide Arizona variety Test of Dustaerosols; standard they generate (i.e., ISO a 12103-1,relatively A1 accurate test dust), whichmeasure is representative of PM2.5 and of aPM wide10 concentrations variety of aerosols; during theya dust generate storm compared a relatively with accurate the TEOMs measure as of demonstrated in Jayaratne et al. [21] and Krasnov et al. [9]. PM2.5 and PM10 concentrations during a dust storm compared with the TEOMs as demonstrated in Jayaratne etThe al. dust [21 ]event and Krasnovwas examined et al. by [9]. satellite images (MODIS Level 1 and VIIRS Level 1). The air mass transport in the region was obtained through Backward Trajectories model (NOAA/ARL The dust event was examined by satellite images (MODIS Level 1 and VIIRS Level 1). The air mass HYSPLIT-4) prior and during the event for three different altitudes (500, 1000, and 1500 m above transportground in thelevel). region This wasrevealed obtained synoptic through condition Backward of a cold Trajectories low-pressure model system (NOAA with the/ARL Cyprus HYSPLIT-4) Low prior andthat is during most prevalent the event in forthe threewinter di seasonfferent [8]. altitudes (500, 1000, and 1500 m above ground level).

Int. J. Environ. Res. Public Health 2020, 17, x 4 of 10

Int. J. Environ.Means Res. Publicand standard Health 2020 deviations, 17, 1625 were calculated with the aim to describe the outdoor and indoor 4 of 9 PM concentrations during the dust storms. Spearman correlation coefficient was used for univariate analysis of indoor and outdoor concentrations. For statistical analysis SPSS (SPSS Inc., Chicago, IL, This revealedUSA) was synopticused. Statistical condition significance of a cold was low-pressuretaken as p ≤ 0.05. system with the Cyprus Low that is most prevalent in the winter season [8]. Means3. Results and standard deviations were calculated with the aim to describe the outdoor and indoor

PM concentrationsFigure 1 shows during the thedaily dust PM10 storms. concentrations Spearman as recorded correlation by the coe environmentalfficient was usedstation for inunivariate Beer- analysisSheva of for indoor the last and 15 outdooryears. Overall, concentrations. there were 679 For natural statistical dust days, analysis i.e., when SPSS the (SPSS average Inc., daily Chicago, PM IL, USA)concentration was used. Statistical exceeded the significance calculated wasthreshold taken value as p (710.05. µg m−3), below which days were classified ≤ as clear, non-dust days (see details in reference 14). In addition, daily PM10 concentrations during 3. Resultseleven storms exceeded 661 µg m−3 (dashed line in Figure 1), which we defined as strong events (which include events with high to severely high PM10 levels). Figure1 shows the daily PM concentrations as recorded by the environmental station in A recent dust event that occurred10 on 11 February 2015, which is a typical strong event of the 15- Beer-Sheva for the last 15 years. Overall, there were 679 natural dust days, i.e., when the average year period studied (but not the strongest one), was explored in detail to represent the phenomenon 3 dailyin PM question. concentration The environmental exceeded the conditions calculated during threshold the event value of 11 (71 Februaryµg m− ),2015 below are whichpresented days in were classifiedFigure as 2. clear, Satellite non-dust images days of the (see study details area inare reference shown together 14). In with addition, air mass daily trajectories PM10 concentrations (Hybrid 3 duringSingle-Particle eleven storms Lagrangian exceeded Integrated 661 µg Trajectory m− (dashed model line [22]) in for Figure three1 different), which altitudes we defined (500, as1000, strong eventsand (which 1500 m include above ground events level). with high to severely high PM10 levels). A recentThe physical dust event and thatchemical occurred properties on 11 of Februarythe dust samples 2015, which during is the a typical11 February strong 2015 event event of the 15-yearwere period analyzed. studied Elemental (but not analysis the strongest showed one),that dust was sample explored compositions in detail to were represent typical theof dust phenomenon from in question.this region The [2,23]. environmental The most commo conditionsn minerals during were Si the from event soil mi ofnerals 11 February and Ca, which 2015 arecan be presented found in mainly in sedimentary environments and in the calcareous soils and rocks characteristic of the arid Figure2. Satellite images of the study area are shown together with air mass trajectories (Hybrid land of the Mediterranean basin (Figure 3a). The dust samples are characterized by a bi-modal Single-Particledistribution Lagrangian of particle sizes. Integrated The cutoff Trajectory is 18 µm model (Figure [ 223b),]) which for three is the di ffsizeerent used altitudes to distinguish (500, 1000, and 1500between m above fine (from ground distal level). source) and coarse (from local source) fractions.

FigureFigure 2. Satellite 2. Satellite images images (MODIS) (MODIS) of of the the studystudy region during during the the dust dust storm storm of 11 of February 11 February 2015, 2015, alongalong with with air mass air mass transport transport at at di differentﬀerent heights heights aboveabove ground ground level level (AGL). (AGL). In the In thelower lower panel: panel: daily daily recordedrecorded averages averages of majorof major meteorological meteorological variables variables and and pollutants pollutants measured measured in the in theenvironmental environmental monitoring station of Beer Sheva during the dust event of 11 February 2015. AT—air temperature;

RH—relative humidity; WS—wind speed; PM—particulate matter. The two photos were taken in the same location, the campus of Ben Gurion University (Beer Sheva), during a non-dust day (left) and during the dust event of February 2015 (right) in the study area. Int. J. Environ. Res. Public Health 2020, 17, 1625 5 of 9

The physical and chemical properties of the dust samples during the 11 February 2015 event were analyzed. Elemental analysis showed that dust sample compositions were typical of dust from this regionInt. [2 J., 23Environ.]. The Res. mostPublic Health common 2020, 17 minerals, x were Si from soil minerals and Ca, which can5 be of found10 mainly in sedimentary environments and in the calcareous soils and rocks characteristic of the arid land monitoring station of Beer Sheva during the dust event of 11 February 2015. AT—air temperature; of the MediterraneanRH—relative humidity; basin (Figure WS—wind3a). Thespeed; dust PM—particula samples arete matter. characterized The two photos by a were bi-modal taken in distribution the of particlesame sizes. location, The cutothe campusﬀ is 18 ofµ Benm (Figure Gurion University3b), which (Beer is theSheva), size during used a tonon-dust distinguish day (left) between and ﬁne (from distalduring source) the dust and event coarse of Februa (fromry local 2015 (right) source) in the fractions. study area.

Figure 3. Chemical and physical characteristics of dust samples collected during the event of 11 Figure 3. Chemical and physical characteristics of dust samples collected during the event of 11 FebruaryFebruary 2015. 2015. Elemental Elemental composition composition by by X-ray X-ray ﬂuorescencefluorescence spectrometer spectrometer on onlogarithmic logarithmic Y-axis Y-axis (a), (a), and particleand particle size distributionsize distributi byon by laser laser di ﬀdiffractometerractometer ( b(b).).

The real-timeThe real-time measurements measurements by by portable portable dustdust monitorsmonitors were were used used to tostudy study the theresponse response of of indoorindoor PM10 PMand10 and PM 2.5PMto2.5 to those those of of the the outdoor outdoor duringduring the the dust dust event event of of February February 2015. 2015. The Theresults, results, shownshown with with a comparison a comparison to to a typicala typical non-dust non-dust dayday in the study study area area (Figure (Figure 4),4 ),represent represent the the afternoonafternoon hours, hours, when when PM PM concentrations concentrations were were typically typically at their their highest highest levels levels during during the thedaily daily dust dust cycle. The outdoor PM10 concentrations measured by the portable device are consistent with the data cycle. The outdoor PM10 concentrations measured by the portable device are consistent with the data recordedrecorded by thethe monitoringmonitoring stat stationion situated situated at at a acentral central loca locationtion in inBeer-Sheva. Beer-Sheva. Outdoor Outdoor PM10 PM −3 −3 10 concentrations were above 1000 µg m3 for PM10 and above 500 µg m 3for PM2.5 (Table 1) compared concentrations were above 1000 µg m− for PM10 and above 500 µg m− for PM2.5 (Table1) compared to a non-dust day, when PM10 levels reached a maximum of 47 µg m−3 and PM2.5 levels were 31 µg µ 3 µ to a non-dustm−3. The day,simultaneously when PM 10measuredlevels reached indoor levels a maximum fluctuated of in 47 parallelg m− withand the PM outdoor2.5 levels levels were but 31 g 3 m− . Thewith simultaneouslya time lag of about measured 15 min for indoor the peak levels of PM ﬂuctuated10 (see arrows in parallelin Figure with 4). The the response outdoor of levels PM2.5 but with awas time less lag consistent of about with 15 minno specific for the time peak lag. of PM10 (see arrows in Figure4). The response of PM 2.5 was less consistent with no speciﬁc time lag. Table 1. Statistical description of particulate matter (PM) concentrations (µg m−3), and the ratio of 3 Tableindoor-to-outdoor 1. Statistical description (I/O) during of particulatethe dust event matter based (PM)on the concentrationsreal-time PM record (µg with m− time), and interval the ratio of of indoor-to-outdoor5 minutes (n = (I30)./O) (SD—standard during the dust deviation). event based on the real-time PM record with time interval of 5 min (n = 30). (SD—standard deviation).Mean Median Maximum Minimum SD I/O Ratio PM10 outdoor 1011.2 821.1 2277.2 323.4 320.1 N/A Indoor 1 Mean 773.2Median 682.3 Maximum 835.3 Minimum 365.2 223.4 SD 0.76 I/O Ratio PM10 outdoorIndoor 2 1011.2 620.1 821.1 560.6 2277.2 790.2 371.8 323.4 185.6 320.1 0.61 N/A Indoor 1Indoor 3 773.2 590.3 682.3 430.3 835.3 683.3 254.1 365.2 175.5 223.4 0.58 0.76 Indoor 2Indoor 4 620.1 433.8 560.6 395.9 790.2 495.6 244.0 371.8 120.5 185.6 0.43 0.61 Indoor 3Indoor 5 590.3 665.3 430.3 480.5 683.3 695.4 349.3 254.1 188.6 175.5 0.66 0.58 IndoorAverage 4 indoor (1–5) 433.8 616.6 395.9 509.9 495.6 699.9 316.9 244.0 178.8 120.5 0.61 0.43 Indoor 5 665.3 480.5 695.4 349.3 188.6 0.66 PM2.5 outdoor 529.5 393.3 744.9 106.8 252.2 N/A Average indoor (1–5) 616.6 509.9 699.9 316.9 178.8 0.61 Indoor 1 501.2 421.3 488.2 212.3 233.5 0.95 PM2.5 outdoorIndoor 2 529.5 410.8 393.3 415.9 744.9 548.0 235.6 106.8 165.8 252.2 0.78 N/A Indoor 1Indoor 3 501.2 403.2 421.3 371.2 488.2 471.2 162.6 212.3 215.2 233.5 0.76 0.95 Indoor 2Indoor 4 410.8 272.5 415.9 261.4 548.0 352.6 120.3 235.6 80.3 165.8 0.51 0.78 Indoor 3 403.2 371.2 471.2 162.6 215.2 0.76 Indoor 5 375.4 351.0 491.1 198.6 133.0 0.71 Indoor 4 272.5 261.4 352.6 120.3 80.3 0.51 Average indoor (1–5) 392.7 364.2 470.2 185.9 163.6 0.74 Indoor 5 375.4 351.0 491.1 198.6 133.0 0.71 Average indoor (1–5) 392.7 364.2 470.2 185.9 163.6 0.74

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The ratio of indoor-to-outdoor concentrations (I/O) may provide insight into the relative contributions of the different PM sources to the indoor concentration levels. The calculated I/O for the houses during the non-dust days is equal to 0.94 ± 0.20 for PM10 and 0.93 ± 0.13 for PM2.5. The I/O during 11 February 2015 was 0.61 for PM10 and 0.74 for PM2.5 (Table 1). The decrease, and a much Int. J. Environ.smaller Res. than Public 1.00 Health ratio2020 of, 17 I/O,, 1625 indicate the absence of indoor PM sources and, therefore, the 6 of 9 overwhelming contribution that the particles from outdoors make on the indoor concentration levels.

Figure 4.Figure(a) The 4. ( recordeda) The recorded PM10 PM10 concentrations concentrations by theby the environmental environmental monitoring monitoring stationstation in in Beer- Beer-Sheva during theSheva dust during storm the in dust 11 February storm in 2015.11 February The average 2015. The PM average10 concentrations PM10 concentrations and storm and intensity storm (AI) intensity (AI) are presented.) (b) Real-time measurements of hourly indoor and outdoor variations in are presented.) (b) Real-time measurements of hourly indoor and outdoor variations in PM10 and PM10 and PM2.5 recorded (n = 30) in the houses in Beer Sheva compared with the concentrations PM2.5 recorded (n = 30) in the houses in Beer Sheva compared with the concentrations recorded in the recorded in the houses during non-dust days. The arrows between the PM10 curves represent the time houses during non-dust days. The arrows between the PM curves represent the time lag between the lag between the indoor and outdoor concentrations. 10 indoor and outdoor concentrations. Because dust storms are common phenomenon not only in the southeastern Mediterranean, but Thealso ratio in many of indoor-to-outdoor other regions around concentrationsthe world, it is essential (I/O) that may we provide improve our insight understanding into the of relative contributionstheir potentially of the di detrimentalfferent PM effect sources on the to indoor the indoorenvironment. concentration We presentlevels. here three The strong calculated dust I/O for the housesevents (12 during December the 2010, non-dust 7 January days 2013, is equal22 March to 0.942013) as0.20 additional for PM cases10 andon the 0.93 impact0.13 of dust for PM2.5. on indoor concentration (Figure 5). The events are presented± with hourly indoor PM10 concentrations± The I/O during 11 February 2015 was 0.61 for PM10 and 0.74 for PM2.5 (Table1). The decrease, together with their average concentrations and event intensity (Ai). The Ai was calculated on an and a much smaller than 1.00 ratio of I/O, indicate the absence of indoor PM sources and, therefore, hourly basis as the area under the storm curve [8,14]. The results clearly show the differences in dust the overwhelmingstorms in terms contribution of hourly PM that levels the particles and event from durations. outdoors Figure make 6 presents on the the indoor correlation concentration between levels. Becauseoutdoor dust and storms indoor arePM common10 concentrations phenomenon for a wide not range only of in dust the southeasternconcentrations from Mediterranean, different but also in manyevents. other The total regions correlation around between the world, indoor and it is outdoor essential measurements that we improve for all storms our understanding is 78%. For of their potentiallystorms with detrimental concentrations effect below on thethe indoorcalculated environment. threshold of strong We present events here(661 µg three m−3), strong the dust events (12correlation December is 50% 2010, while 7 January for higher 2013, concentrations 22 March the 2013) correlation as additional is only 21%. cases on the impact of dust on indoor concentration (Figure5). The events are presented with hourly indoor PM 10 concentrations together with their average concentrations and event intensity (Ai). The Ai was calculated on an hourly basis as the area under the storm curve [8,14]. The results clearly show the differences in dust storms in terms of hourly PM levels and event durations. Figure6 presents the correlation between outdoor and indoor PM10 concentrations for a wide range of dust concentrations from different events. The total correlation between indoor and outdoor measurements for all storms is 78%. For storms with 3 concentrations below the calculated threshold of strong events (661 µg m− ), the correlation is 50% while forInt. higher J. Environ. concentrations Res. Public Health 2020 the, 17 correlation, x is only 21%. 7 of 10

Figure 5. Outdoor PM10 levels and average indoor PM10 concentrations during strong dust storms Figure 5. Outdoor PM10 levels and average indoor PM10 concentrations during strong dust storms recorded in the northern Negev from 2009 to 2014. The parameter Ai represents storm intensity [8]. recorded in the northern Negev from 2009 to 2014. The parameter Ai represents storm intensity [8].

4. Discussion The conditions of the dust event that occurred on 11 February 2015 are shown in Figure 2. The average wind direction (261°) was consistent with the NOAA HYSPLIT Trajectory Model (Boulder, Colorado, USA). The relatively strong winds measured at 10 m height (average speed of 5.3 m s−1) and the low air temperatures were associated with the presence of a low-pressure synoptic system in the eastern Mediterranean and of a cold front to the west. The daily averages of major meteorological variables and daily average levels of major pollutants during the dust event are displayed in Figure 2. With the exception of PM10 and PM2.5, air pollutant levels were not affected by the dust event and remained relatively low. The daily PM10 concentrations, however, were about 20-fold higher than the background value of the area on non-dusty days (71 µg m−3). The possible sources of the finer particles (Figure 3), which can be carried longer distances, are from soils along the North-African–Sinai–Negev path (see mass trajectories in Figure 2). Particles that are smaller than 10 µm constituted only 1.53% of the total suspended dust volume, but the overall result is high atmospheric PM10 concentrations and air pollution. Stormy days in this region engender dramatic atmospheric changes, clearly manifested in the “cloudy” yellowish appearance and corresponding reduced visibility associated with dusty days (Figure 2) The results of the real-time measurements of indoor and outdoor PM10 and PM2.5 during the dust event of February 2015 are presented in Figure 4. The indoor concentrations, which peaked at 700 µg m−3 for PM10 and 470 µg m−3 for PM2.5 at their peaks, remained high as long as the outdoor concentrations were high. During non-dust days, indoor concentrations were, on average, 30 µg m−3 for PM10 and 20 µg m−3 for PM2.5, indicating that these values increased by more than 20 fold during dust events. However, the indoor PM10 levels did not decrease at the same rate as the outdoor levels, meaning that due to dust events, indoor air quality stays dusty for a longer period than the outdoor air due to dust events. Although quantitative information on how long until PM2.5 and PM10 levels returned to baseline levels (>30 µg m−3 for both PM) is not presented in this study, it can be assumed that the baseline will be reached only after several hours considering the settling velocity of dust particles that are < 10 µm [1]. This finding is also supported by the strong correlation for the event of February 11 between the indoor and outdoor concentrations for both PM10 and PM2.5 (r = 0.70). The higher I/O ratios of PM2.5 compared to those of PM10 are related to the more efficient infiltration of the houses by the fine outdoor particles due to their relatively smaller sizes [10]. The weaker correlation coefficient observed for the strong events (Figure 6) can be explained by dust storm behavior over time. As shown in Figure 4, the indoor levels did not decrease at the same rate as the outdoor concentrations mainly because of the dependence of the airborne particles that accumulated indoors on the specific air exchange rates of the building that they infiltrated.

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FigureFigure 6. 6. Correlation between between outdoor outdoor and and indoor indoor PM PM10 concentrations10 concentrations during during the different the different PM levels. PM levels.Strong Strong dust storms dust storms are distinguis are distinguishedhed by their by their PM PMlevels levels that thatexceed exceed the thecalculated calculated threshold threshold of of661 3 661µgµ mg− m3. − . 4. Discussion The main study limitations refer to the number and the representation of the measured households.The conditions In this ofstudy, the dust continuous event that real-time occurred measurements on 11 February were 2015 recorded are shown simultaneously in Figure2 .in Theindoor average and windoutdoor direction urban (261environments◦) was consistent in five withhous theeholds NOAA located HYSPLIT in the center Trajectory of Beer-Sheva. Model (Boulder, Using 1 Colorado,more households USA). The will relatively obviously strong strengthen winds measured the database at 10 and m heightthe signif (averageicance speedof the ofresults. 5.3 m However, s− ) and thesuch low a airstudy temperatures design requires were a associated large set of with PM themoni presencetors, which of a are low-pressure expensive. synoptic Nonetheless, system the in recent the easterntrend of Mediterranean developing and and using of a commercial cold front to off-the-shelf the west. The (COTS) daily sensors averages [24] of may major be a meteorological more plausible variableseconomic and solution daily average for the next levels ge ofneration major pollutantsof air quality during studies. the dust event are displayed in Figure2. With the exception of PM10 and PM2.5, air pollutant levels were not affected by the dust event and remained5. Conclusions relatively low. The daily PM10 concentrations, however, were about 20-fold higher than the 3 backgroundThe main value contribution of the area onof non-dustythis study daysis to (71showµg mhow− ). the The indoor possible air sources quality of is the very finer sensitive particles to (Figure3), which can be carried longer distances, are from soils along the North-African–Sinai–Negev changes in atmospheric PM10 concentrations in an urban environment that is subject to strong dust path (see mass trajectories in Figure2). Particles that are smaller than 10 µm constituted only 1.53% of events. The conclusions revealed from the results of this study are: (1) the indoor PM10 and PM2.5 theconcentrations total suspended increased dust volume, by more but than the 20-fold overall during result isthe high dust atmospheric event. (2) The PM indoor10 concentrations levels peaked and at air pollution. Stormy days in this region engender dramatic atmospheric changes, clearly manifested about 700 µg m−3 (PM10) and 470 µg m−3 (PM2.5) and fluctuated in parallel with the outdoor level, but inwith the “cloudy”a time lag yellowish of about appearance 15 min. (3) and The corresponding I/O ratio emphasizes reduced the visibility outdoor associated effect, while with dusty indoor days PM (Figurelevels 2tend) to remain high for longer periods than the corresponding outdoor levels. (4) The correlationThe results between of the indoor real-time and measurementsoutdoor is higher of in indoor moderate and outdoordust events, PM10 whichand PMare 2.5moreduring frequent. the dust event of February 2015 are presented in Figure4. The indoor concentrations, which peaked at This extreme spike typical in indoor PM2.5 and PM10 levels during dust events characterized by 3 3 700highµg outdoor m− for PM PM levels10 and is 470 assumedµg m− tofor be PMeven2.5 higherat their in peaks,locations remained with poor high housing as long conditions. as the outdoor These 3 concentrationsfindings suggest were that high. considerations During non-dust about days, potential indoor du concentrationsst exposure should were, be on an average, integral 30 partµg of m −the 3 forplanning PM10 and and 20 buildingµg m− forstages. PM2.5 Further, indicating studies that are these needed values to increasedprovide more by more quantitative than 20 fold information during dustabout events. air pollutants However, in the urban indoor environments, PM10 levels did and not their decrease spatial at theand same temporal rate as distributions, the outdoor levels, using meaningadvanced that technology due to dust of small events, sensors indoor in air the quality perspective stays dustyof smart for cities. a longer period than the outdoor air due to dust events. Although quantitative information on how long until PM2.5 and PM10 levels 3 returnedAuthor toContributions: baseline levels Methodology, (>30 µg m− analyses,for both and PM) writing—origin is not presentedal indraft this preparation, study, it can I.K. be assumedand H.K.; thatconceptualization, the baseline will writing—review be reached and only editing, after severalI.K. All authors hours consideringhave read and the agreed settling to the velocity published of version dust particlesof the manuscript. that are < 10 µm [1]. This finding is also supported by the strong correlation for the event ofFunding: February This 11 research between was the funded indoor by and the Environment outdoor concentrations and Health Fund for both(No. RGA1004) PM10 and of PM Israel,2.5 ( rand= 0.70).partial Thecontribution higher I/O from ratios the ofJohn PM R.2.5 Goldsmithcompared Prize. to those of PM10 are related to the more efficient infiltration of the houses by the fine outdoor particles due to their relatively smaller sizes [10]. Conflicts of Interest: The authors declare no conflict of interest.

Int. J. Environ. Res. Public Health 2020, 17, 1625 8 of 9

The weaker correlation coefficient observed for the strong events (Figure6) can be explained by dust storm behavior over time. As shown in Figure4, the indoor levels did not decrease at the same rate as the outdoor concentrations mainly because of the dependence of the airborne particles that accumulated indoors on the specific air exchange rates of the building that they infiltrated. The main study limitations refer to the number and the representation of the measured households. In this study, continuous real-time measurements were recorded simultaneously in indoor and outdoor urban environments in five households located in the center of Beer-Sheva. Using more households will obviously strengthen the database and the significance of the results. However, such a study design requires a large set of PM monitors, which are expensive. Nonetheless, the recent trend of developing and using commercial off-the-shelf (COTS) sensors [24] may be a more plausible economic solution for the next generation of air quality studies.

5. Conclusions The main contribution of this study is to show how the indoor air quality is very sensitive to changes in atmospheric PM10 concentrations in an urban environment that is subject to strong dust events. The conclusions revealed from the results of this study are: (1) the indoor PM10 and PM2.5 concentrations increased by more than 20-fold during the dust event. (2) The indoor levels peaked at 3 3 about 700 µg m− (PM10) and 470 µg m− (PM2.5) and fluctuated in parallel with the outdoor level, but with a time lag of about 15 min. (3) The I/O ratio emphasizes the outdoor effect, while indoor PM levels tend to remain high for longer periods than the corresponding outdoor levels. (4) The correlation between indoor and outdoor is higher in moderate dust events, which are more frequent. This extreme spike typical in indoor PM2.5 and PM10 levels during dust events characterized by high outdoor PM levels is assumed to be even higher in locations with poor housing conditions. These findings suggest that considerations about potential dust exposure should be an integral part of the planning and building stages. Further studies are needed to provide more quantitative information about air pollutants in urban environments, and their spatial and temporal distributions, using advanced technology of small sensors in the perspective of smart cities.

Author Contributions: Methodology, analyses, and writing—original draft preparation, I.K. and H.K.; conceptualization, writing—review and editing, I.K. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Environment and Health Fund (No. RGA1004) of Israel, and partial contribution from the John R. Goldsmith Prize. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Shao, Y.; Wyrwoll, K.; Chappell, A.; Huang, J.; Lin, Z.; McTainsh, G.H.; Mikami, M.; Tanaka, T.Y.; Wang, X.; Yoon, S. Dust cycle: An emerging core theme in earth system science. Aeolian Res. 2011, 2, 181–204. [CrossRef] 2. Katra, I.; Arotsker, L.; Krasnov, H.; Zaritsky, A.; Kushmaro, A.; Ben-Dov, E. Richness and diversity in dust stormborne biomes at the southeast mediterranean. Sci. Rep. 2011, 4, 5265. [CrossRef][PubMed] 3. Yitshak-Sade, M.; Novack, V.; Katra, I.; Gorodischer, R.; Tal, A.; Novack, L. Non-anthropogenic dust exposure and asthma medication purchase in children. Eur. Respir. J. 2015, 45, 652–660. [CrossRef][PubMed] 4. Vodonos, A.; Friger, M.; Katra, I.; Krasnov, H.; Zager, D.; Schwartz, J.; Novack, V. Individual effect modifiers of dust exposure effect on cardiovascular morbidity. PLoS ONE 2015, 10, e0137714. [CrossRef] 5. Gyan, K.; Henry, W.; Lacaille, S.; Laloo, A.; Lamsee-Ebanks, C.; McKay, S.; Antoine, R.M.; Monteil, M.A. African dust clouds are associated with increased paediatric asthma accident and emergency admissions on the caribbean island of trinidad. Int. J. Biometeorol. 2005, 49, 371–376. [CrossRef] 6. Griffin, D.W. Atmospheric movement of microorganisms in clouds of desert dust and implications for human health. Clin. Microbiol. Rev. 2007, 20, 459–477. [CrossRef] 7. Pérez, L.; Tobias, A.; Querol, X.; Künzli, N.; Pey, J.; Alastuey, A.; Viana, M.; Valero, N.; González-Cabré, M.; Sunyer, J. Coarse Particles from Saharan Dust and Daily Mortality. Epidemiology 2008, 19, 800–807. [CrossRef] Int. J. Environ. Res. Public Health 2020, 17, 1625 9 of 9

8. Krasnov, H.; Katra, I.; Koutrakis, P.; Friger, M.D. Contribution of dust storms to PM10 levels in an urban arid environment. J. Air Waste Manag. Assoc. 2014, 64, 89–94. [CrossRef] 9. Kuo, H.; Shen, H. Indoor and outdoor PM 2.5 and PM 10 concentrations in the air during a dust storm. Build. Environ. 2010, 45, 610–614. [CrossRef] 10. Krasnov, H.; Katra, I.; Novack, V.; Vodonos, A.; Friger, M.D. Increased indoor PM concentrations controlled by atmospheric dust events and urban factors. Build. Environ. 2015, 87, 169–176. [CrossRef] 11. Achilleos, S.; Evans, J.S.; Yiallouros, P.K.; Kleanthous, S.; Schwartz, J.; Koutrakis, P. Pm10 concentration levels at an urban and background site in cyprus: The impact of urban sources and dust storms. J. Air Waste Manag. Assoc. 2014, 64, 1352–1360. [CrossRef][PubMed] 12. Kalivitis, N.; Gerasopoulos, E.; Vrekoussis, M.; Kouvarakis, G.; Kubilay, N.; Hatzianastassiou, N. Dust transport over the eastern mediterranean derived from total ozone mapping spectrometer, aerosol robotic network, and surface measurements. J. Geophys. Res. Atmos. 2007, 112, D03202. [CrossRef] 13. Ganor, E.; Osetinsky, I.; Stupp, A.; Alpert, P. Increasing trend of african dust, over 49 years, in the eastern mediterranean. J. Geophys. Res. Atmos. 2010, 115.[CrossRef] 14. Krasnov,H.; Katra, I.; Novack, V.;Vodonos,A.; Friger, M.D. Increase in dust storm related PM10 concentrations: A time series analysis of 2001–2015. Environ. Pollut. 2016, 213, 36–42. [CrossRef][PubMed] 15. Yitshak-Sade, M.; Kloog, I.; Liberty, I.F.; Katra, I.; Novack, L.; Novack, V. Air pollution and serum glucose levels: A population based study. Medicine 2015, 94, e1093. [CrossRef][PubMed] 16. Yitshak-Sade, M.; Vodonos, A.; Novack, V.; Friger, M.; Ami, G.; Katra, I.; Schwartz, J.; Novack, L. Can air pollution trigger an onset of atrial fibrillation: A population based study. Air Qual. Atmos. Health 2014. [CrossRef] 17. Vodonos, A.; Friger, M.; Katra, I.; Avnon, L.; Krasnov, H.; Koutrakis, P.; Schwartz, J.; Lior, O.; Novack, V. The impact of desert dust exposure on hospitalization due to the exacerbation of chronic obstructive pulmonary disease. Air Qual. Atmos. Health 2014.[CrossRef] 18. Tanner, S.; Katra, I.; Haim, A.; Zaady, E. Short-term soil loss by eolian erosion in response to different rain-fed agricultural practices. Soil Tillage Res. 2016, 155, 149–156. [CrossRef] 19. Roskin, J.; Katra, I.; Blumberg, D.G. Particle-size fractionation of eolian sand along the Sinai–Negev erg of Egypt and Israel. Geol. Soc. Am. Bull. 2014, 126, 47–65. [CrossRef] 20. Isenberg, O.; Yizhaq, H.; Tsoar, H.; Wenkar, R.; Karnieli, A.; Kok, J.F.; Katra, I. Megaripple flattening due to strong winds. Geomorphology 2011, 131, 69–84. [CrossRef] 21. Jayaratne, E.; Johnson, G.R.; McGarry, P.; Cheung, H.C.; Morawska, L. Characteristics of airborne ultrafine and coarse particles during the australian dust storm of 23 September 2009. Atmos. Environ. 2011, 45, 3996–4001. [CrossRef] 22. Draxler, R.R.; Rolph, G. Hysplit (Hybrid Single-Particle Lagrangian Integrated Trajectory) Model Access via NOAA ARL Ready; NOAA Air Resources Laboratory: Silver Spring, MD, USA, 2013. Available online: http://www.arl.noaa.gov/ready/hysplit4.html (accessed on 6 January 2020). 23. Kalderon-Asael, B.; Erel, Y.; Sandler, A.; Dayan, U. Mineralogical and chemical characterization of suspended atmospheric particles over the east mediterranean based on synoptic-scale circulation patterns. Atmos. Environ. 2009, 43, 3963–3970. [CrossRef] 24. Nath, V.; Nath, A. Environment Monitoring Using Commercial Off-the-Shelf (COTS) Technologies. In ICT for Promoting Human Development and Protecting the Environment; Mata, F., Pont, A., Eds.; Springer: Basel, Switzerland, 2016; pp. 37–44.

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