Analysis of the Long-Term Variability of Poor Visibility Events in the UAE and the Link with Climate Dynamics

atmosphere

Article Analysis of the Long-Term Variability of Poor Visibility Events in the UAE and the Link with Climate Dynamics

Amal Aldababseh * and Marouane Temimi

Chemical and Environmental Engineering Department, Masdar Institute of Science and Technology, Khalifa University of Science and Technology, Masdar City, P.O. Box 54224, Abu Dhabi, UAE; [email protected] * Correspondence: [email protected]

Received: 22 October 2017; Accepted: 28 November 2017; Published: 4 December 2017

Abstract: The goal of this study is to investigate the variability of poor visibility events occurring hourly in the UAE and their relationship to climate dynamics. Hourly visibility observation data spanning more than three decades from ten stations across the country were used. Four intervals of low visibility, between 0.10 km and 5.0 km, were considered. Poor visibility records were analyzed under wet and dry weather conditions. The Mann–Kendall test was used to assess the inferred trends of low visibility records. The relationships between poor visibility measurements and associated meteorological variables and climate oscillations were also investigated. Results show that Fujairah city has the highest average visibility values under wet weather conditions, while Abu Dhabi city has the lowest average visibility values under both wet and dry conditions. Wet weather conditions had a greater impact than dry weather conditions on visibility deterioration in seven out of the ten stations. Results confirm that fog and dust contribute significantly to the deterioration of visibility in the UAE and that Abu Dhabi has been more impacted by those events than Dubai. Furthermore, the numbers of fog and dust events show steep increasing trends for both cities. A change point in poor visibility records triggered by fog and dust events was detected around the year 1999 at Abu Dhabi and Dubai stations after the application of the cumulative sum method. Increasing shifts in the means and the variances were noticed in the total annual fog events when Student’s t-test and Levene’s test were applied. In Abu Dhabi, the mean annual number of dust events was approximately 112.5 before 1999, increasing to 337 dust events after 1999. In Dubai, the number of dust events increased from around 85.5 to 315.6 events. The inferred fog and dust trends were compared to four climate indices. Results showed a significant correlation (positive and negative) between four climate indices and the occurrence of fog and dust events in the UAE.

Keywords: visibility; fog; dust events; climate indices; climate oscillations; UAE

1. Introduction Visibility is commonly known as ‘the longest distance at which an object can be recognized against the horizon in the background’ [1–3]. Visibility is also known in quantitative terminology as the visual range [4]. Good visibility, the ability to see and recognize objects situated on the ground at more than 5.0 km distance, is essential for the safety of all transport sectors including ground, air, and maritime [5–12]. Moreover, visibility can reflect changes in meteorological and air quality conditions and their impact on human health [13–15]. Several studies [2,3,8,13,14,16,17] have analyzed poor visibility records across the world to understand their variability in time and space and the specific triggers that may lead to such degradation [3,13,18–20] through an assessment of long-term visibility data from different geographic locations [3,14]. Overall, a significant decline in visibility across the

Atmosphere 2017, 8, 242; doi:10.3390/atmos8120242 www.mdpi.com/journal/atmosphere Atmosphere 2017, 8, 242 2 of 22 world has been reported [2,3,13,21]. A study about the northern Arabian Gulf, [22] reported a regional decline of 0.8 km in visibility values based on observations from the northeastern part of Saudi Arabia, Kuwait, and Bahrain. The analysis of long-term records of visibility and meteorological records led to the determination of the major factors that may cause visibility degradation [13,14,16]. The impact of meteorological factors on visibility has been extensively evaluated and examined in different countries including the USA [19,23], the UK [17,24], China [2,14,18], and Iran [8]. Several studies reported a negative correlation between visibility and temperature, relative humidity (RH), and wind speed [13,14,16,25]. Overall, previous findings indicate that visibility degradation can occur during wet or dry atmospheric conditions. When dry conditions prevail, natural and anthropogenic sources of trace gases and aerosols, primary or secondary, can cause degradation in visibility as a result of absorption and scattering of light [8,14]. In their research, Jaswal et al. [16] reported significant downward temporal and spatial trends in visibility observations in India during the period from 1961–2008. They stated that visibility degradation is noticeable through increasing morning and decreasing afternoon visibility, which are spatially correlated with decreasing wind speed and increasing RH. Chen et al. [3] analyzed the long-term visibility trend in Chengdu and Chongqing in China, and suggested that the decrease is the result of a complex synoptic mechanism. In another study, Sabetghadam et al. [8] attributed the substantial decline in visibility in Tehran, Iran to air pollution. In their research, Abdi Vishkaee, Flamant et al. [26] concluded that visibility effects associated with the Shamal wind that lifts and transports dust in northern Iran was very low. However, Al Senafi and Anis [22] found a moderate positive relationship (Pearson r = 0.42) between the number of Shamal days and the number of dust storms per year. They concluded that dust is the controlling factor of visibility in the Arabian Gulf with 38% of all dust storms formed during Shamal wind events. A study by Xiao et al. [14] focused on analyzing the relationship between aerosols and visibility in Baoji, China, and showed that the extinction coefficient is proportionally related to the concentration of particulate matter but has an inverse relationship with visibility [27]. Organic matter was found to be the largest contributor to visibility degradation in Baoji [14]. On the other hand, Tiwari et al. [21] demonstrated a relationship between visibility degradation and anthropogenic urban aerosol in Delhi which is experiencing severe foggy conditions yearly during the windy period. The results of their linear regression model show that an increase in pollution levels, mainly aerosol concentration, and a lower depression temperature encourages fog formation. Poor visibility days in the UAE and the region could be associated with dry or wet weather conditions. First, the aridity of the climate in the region increases the likelihood of dust emission especially when wind speed is high over the dry dust sources in the region. One should note that an increase of soil moisture should decrease the emission of dust. However, precipitation records showed low amounts in the region. The presence of aerosols in the air reduces visibility due to light scattering which depends on the morphology and the composition of particles. This implies the importance of soil characterization in arid regions through the determination of its composition and its spatial distribution through variables like soil emissivity especially over dust sources [28,29]. Second, despite the aridity of the region, the proximity to the Gulf along with the high temperature records lead to a significant evaporation over the sea and therefore increase in inland relative humidity with sea land breeze [30]. Hence, under wet conditions, the region is subjected to frequent mist and fog formation which lead to visibility degradation [31]. Moreover, high relative humidity lead to the growth of aerosol particles in the air through coalescence–collision which broaden their size distribution and therefore impact visibility by increasing scattering [32]. During the process of condensation, numerous small particles of atmospheric aerosols combine into more effective mid-visible light scattering particles that range in size and increase the amount of practical scattering in the atmosphere [27]. The subsequent increase in the scattering coefficient leads to reducing visibility [12]. In another study, Chen and Xie [3] confirmed that if relative humidity increases, hygroscopic particles progressively absorb more water, Atmosphere 2017, 8, 242 3 of 22 leading to increased scattering cross sections and proportionately reduced visibility. They have also highlighted the importance of the meteorological factors, relative humidity, wind speed, and air temperature on visibility reductions in the cities of Chengdu and Chongqing in China, and have confirmed the essential role of air pollution in degrading visibility. In Iran, Sabetghadam et al. [8] studied the correlation between visibility, relative humidity, and wind speed. The results show that wind speed has the highest correlation with visibility, while relative humidity has a high negative correlation with visibility. In the UAE, there is no record of previous studies that address visibility trends and analyze their relationship with meteorological or air quality parameters, despite the rapid development of major cities and airports in the country which makes them more exposed to events leading to a reduction in visibility, especially those triggered by anthropogenic factors. This study is, to our knowledge, the first effort to thoroughly examine the variability of poor visibility records across the UAE. Trends of poor visibility records were inferred to identify areas where visibility is degrading or improving, and to understand the long-term variability of visibility and their links with climate oscillations and climate indices. The study has three main sections that focus on (a) studying the variability of poor visibility records and temporal trends for ten airport stations; (b) studying the spatial variations of visibility observations and the link with the prevailing meteorological conditions; and (c) analyzing the occurrence of fog and dust events as possible triggers of visibility degradation, and their link with climate indices.

2. Data and Method

2.1. Study Domain The study domain encompasses the northern and central regions of the UAE which include ten airport stations in the country (Figure1a). The rest of the UAE, the western region of the country, is a desert stretching to the Empty Quarter Desert, which was excluded in this study because of the absence of visibility observation records and major urban development. The Arabian Gulf on the western side and the Sea of Oman on the eastern side limit the study domain. The eastern side of the study domain includes the Al Hajar mountain chain stretching along the eastern coast of the Arabian Peninsula. The mountain chain blocks moisture ﬂuxes that are generated over the Sea of Oman. Wind velocity is stronger on the eastern side of the UAE with a stronger shear along the mountain valleys. Stronger winds are less favorable for fog formation [33]. The eastern coastal zones of the UAE dominated by mountainous terrains yield fewer dust sources. The western coastal side of the UAE, on the other hand, exhibits a terrain of gentle topography, which fosters a deeper inland propagation of moisture generated by the strong evaporation over the Gulf (see Figure1b). The strong nocturnal temperature inversion, common in arid regions, creates condensation and therefore, fog, haze, and mist formation, which all negatively affect visibility. The UAE has an average minimum daily air temperature of 14 ◦C in winter and an average maximum daily air temperature of 45 ◦C in summer. However, temperatures in the desert region may exceed 50 ◦C during the summer, and fall to around 4 ◦C in winter [34]. Relative humidity can reach 90% in summer, with prevailing North West winds. The evaporation rate is high, reaching an average of up to 2–3 m per year [34]. The northeastern mountains receive the highest amount of rainfall (160 mm), while the southern desert receives less than 40 mm per year. In addition, all cities in the UAE, mainly the coastal ones (Figure1c), have undergone rapid economic growth and urban expansion during the last few decades [35]. The total population grew to an estimated 9.39 million people as of March 2017, with an average population growth of about 5.37% during the last three decades [36]. This implies a signiﬁcant change in anthropogenic emissions which leads to increase in aerosols concentration, mainly secondary ones, which contributes to visibility degradation. Atmosphere 2017, 8, 242 4 of 22 Atmosphere 2017, 8, 242 4 of 22

FigureFigure 1. (a )1. Study (a) Study domain; domain; (b )(b locations) locations of of the the tenten stations and and elevation elevation map; map; and and (c) land (c) land use map use map and locationand location of major of major cities cities in thein the UAE. UAE.

2.2. Datasets2.2. Datasets HourlyHourly visibility visibility data data provided provided by by the the National National Oceanic Oceanic and and Atmospheric Atmospheric Administration Administration—— National Climatic Data Center/National Centers for Environmental Information (NOAA— National Climatic Data Center/National Centers for Environmental Information (NOAA—NCDC/ NCDC/NCEI) (https://www.ncdc.noaa.gov/) [37] for ten airport stations in the UAE were used. NCEI)All (https://www.ncdc.noaa.gov/ selected stations are inland (Figure)[37 1]b). for All ten stations airport are stations located inbetween the UAE 5.0 werem and used. 265 m All above selected stationsmean are sea inland level (Figure(See Table1b). 1). All In stations addition are to locatedvisibility, between the analysis 5.0 m included and 265 hourly m above meteorological mean sea level (See Tableobservations1). In addition of relative to humidity visibility, which the analysiswere used included to separate hourly between meteorological dry and wet observationsatmospheric of relativeconditions humidity when which poor werevisibility used events to separate were recorded. between dry and wet atmospheric conditions when poor visibility events were recorded. Table 1. Stations used in this study along with the length of the observation records, coordinates, Tableelevation 1. Stations as well used as average in this visibility study along record with for each the lengthstation and of the its standard observation deviation. records, coordinates, elevation as well as average visibility recordAnnual for Average each station Standard and its standardLatitude deviation.Longitude Elevation Station Name Length of Records Visibility (m) Deviation (m) (N) (E) (m) Abu Dhabi IA Length09/1982 of –03/2016Annual Average9399 Standard3156 24.433Latitude 54.651Longitude 26.8 Elevation Station Name AL Maktoum IA Records05/2010–03/2016 Visibility8355 (m) Deviation2508 (m) 24.886(N) 55.172 (E) 18.9 (m) Al Ain IA 03/1994–03/2016 8926 2717 24.262 55.609 264.9 Abu DhabiAlBateen IA A. 09/1982–03/201601/1983–03/2016 93999797 2594 3156 24.428 24.433 54.458 54.651 4.9 26.8 AL MaktoumAl Dhafra IA A. 05/2010–03/201604/1986–03/2015 83559725 2374 2508 24.25 24.886 54.55 55.172 23 18.9 Al AinAsh IA Shariqa IA.03/1994–03/2016 01/1949–12/1982 892615,327 10,829 2717 25.35 24.262 55.383 55.609 5 264.9 AlBateenDubai A. IA. 01/1983–03/201601/1983–03/2016 97979659 3039 2594 25.255 24.428 55.364 54.458 10.4 4.9 Al DhafraFujairah A. IA. 04/1986–03/201511/1988–03/2016 97258723 2607 2374 25.112 24.25 56.324 54.55 46.3 23 Ash ShariqaRas AlKhaimah IA. 01/1949–12/1982IA. 01/1983–03/2016 15,3279389 10,8291806 25.613 25.35 55.939 55.383 31.1 5 Dubai IA.Sharjah IA. 01/1983–03/201603/1944–03/2016 965910,589 5722 3039 25.8 25.255 55.933 55.364 27 10.4 Fujairah IA. 11/1988–03/2016 8723 2607 25.112 56.324 46.3 Ras AlKhaimah IA. 01/1983–03/2016 9389 1806 25.613 55.939 31.1 SharjahRecords IA. of 03/1944–03/2016aerosols optical depth (10,589AOD) which corresponds 5722 to a measure 25.8 of the 55.933 extinction 27of solar radiation by atmospheric particles, were also compared to low visibility observations under dry weather conditions. AOD data were used as an indication of the amount of aerosol in the total vertical Recordscolumn of of the aerosols atmosphere optical whereas depth visibility (AOD) data which are surface corresponds observations. to a measureAOD values of for the the extinction period of solarof radiation 2000–2015 by were atmospheric extracted particles, from the wereModerate also Resolution compared Imaging to low visibility Spectroradiometer observations (MODIS) under- dry weatherAOD conditions. database that AOD is available data were at www.ladsweb.nascom.nasa.gov used as an indication of the amount/. of aerosol in the total vertical column of the atmosphere whereas visibility data are surface observations. AOD values for the period of 2000–2015 were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS)-AOD database that is available at www.ladsweb.nascom.nasa.gov/. Climate indices were used to investigate the relationship between changes in visibility values throughout the years and climate variability. The cumulative values of the following indices were Atmosphere 2017, 8, 242 5 of 22 used: southern oscillation index (SOI), Paciﬁc decadal oscillation (POD), east Atlantic oscillation (EA) and Atlantic multidecadal oscillation [38]. The indices’ values are available at www.ncdc.noaa.gov/ teleconnections/.

2.3. Methods According to Lambert’s law of extinction, the change in the intensity of radiation emerging from the end of the path is proportional to the incoming intensity of the beam. This proportionality coefficient (known also as the atmospheric coefficient (bext) is a function of the scattering and absorption of particles and gases and varies with the wavelength of light and pollutants’ concentration. According to [5] the atmospheric coefficient can be determined as follows:

= + + + + bext bsp dry bsw bsg bap bag; (1) where bsp dry, bsw, bsg, bap, and bag denote the scattering coefficient for aerosols without liquid water, the scattering coefficient for liquid phase water associated with hygroscopic aerosol constituent, the Rayleigh scattering coefficient, the absorption coefficient for particles and the absorption coefficient for gases, respectively. Visual range (V) is explained and discussed in details by Malm [6], and is defined according to the Koschmieder equation, Equation (2), as:

V = 3.912/bext, (2)

In line with the above definition of visibility,in this study,the analysis of visibility records distinguishes between poor visibility occurring during wet and dry weather conditions. Dominant meteorological factors that trigger low visibility events are different depending on whether wet or dry weather conditions prevail. Hence, the variability of visibility records should differ for each group of weather conditions. Relating poor visibility events to weather conditions necessitates an operational definition of weather conditions, however, we could not find a standard and agreed upon definition of wet and dry weather conditions in the literature. Thus, we defined dry weather conditions as the atmospheric conditions characterized by a lack of water molecules in the atmosphere with relative humidity ≤50% that could be associated with dust events. Consequently, wet weather conditions are the atmospheric conditions characterized by available water molecules in the atmosphere with RH > 50%, and with fog, or mist, or haze associated events. These definition were used to break down the analysis. First, the raw data were screened for different meteorological events using the METAR code (fog (FG), haze (HZ) and mist (BR) for wet weather conditions, and dust (DU) for dry weather conditions). Then, remaining visibility measurements were screened and classified depending on relative humidity values. Previous studies that analyzed visibility records have focused on observations of visibility when the relative humidity was >90% [8,17] which provides a coarse meteorological screening. In this study, we selected a class of 50% to differentiate between wet and dry conditions as suggested by [39]. Monthly and yearly visibility averages for each station were computed from hourly values to obtain a series of the long-term means over the period of the study. Annual averages from monthly mean values were analyzed for each station under wet and dry weather conditions. Then, annual variability of the records at each station was analyzed. As the focus of this study is on examining poor visibility observations of less than 5 km, the analysis was carried out using four classes for poor visibility within the following ranges; 0–0.1 km, 0.1–1.0 km, 1.0–2.5 km, and 2.5–5.0 km. The classes should reflect the magnitude of the degradation of visibility values. The first 5 km threshold used in this study corresponds to the threshold used in the METAR reports in aviation meteorology to indicate a first degradation of the visibility conditions from the white to the green color state [40]. Linear trend analyses were performed on the obtained time series. Trends were tested, for each visibility classes under wet and dry weather conditions, for significance at 95% level of confidence using the Mann–Kendall test. Mann–Kendall is a non-parametric test that is largely applied to time Atmosphere 2017, 8, 242 6 of 22 series for trend detection and assessment [41]. A positive value of the coefficient indicates an increasing trend,Atmosphere while the2017 negative, 8, 242 value indicates a decreasing trend. The null hypothesis can be rejected6 of 22 at a significanceseries for level trend of the detectionp-value. and assessment [41]. A positive value of the coefficient indicates an Focusincreasing was trend, also directedwhile the towards negativedetermining value indicates potential a decreasing change trend. points The innull the hypothesis obtained can time be series usingrejected the cumulative at a significance sum method level of the (CUSUM) p-value. which is a graphical approach that is often used for the detectionFocus of changes was also in timedirected series. towards Substantial determining negative potential or positive change points slopes in indicate the obtained sequences time series of values belowusing or above the cumulative the mean sum value. method The (CUSUM) positions which at the is intersectiona graphical approach of change that of is slope often indicateused for the change points.detection The significance of changes of in the time change series. in Substantial the mean negative and in the or variance positive slopes is evaluated indicate with sequences the Student’s of t-testvalues and the below Levene’s or above test, the respectively.mean value. The A positions detailed at description the intersection of the of usedchange statistical of slope indicate methods is change points. The significance of the change in the mean and in the variance is evaluated with the provided in AppendixA. Student’s t-test and the Levene’s test, respectively. A detailed description of the used statistical 3. Resultsmethods and is Discussionprovided in Appendix A.

3.1. Analysis3. Results of and Raw Discussion VisibilityData Examples3.1. Analysis of of dailyRaw V visibilityisibility Data data for different stations between the 1980s and 2016 are shown in Figure2 toExamples illustrate of the daily fluctuation visibility ofdata visibility for different observations stations between on a daily the basis.1980s and One 2016 can observeare shown frequent in dropsFigure below 2 the to illustrate 5 km threshold the fluctuation selected of to visibility analyze observations poor visibility on recordsa daily basis. in this One study. can For observe example, at Abufrequent Dhabi drops Airport below Station, the 5 km on average,threshold visibilityselected to values analyze between poor visibility 5.0–2.5 records km, 2.5–1.0 in this km, study. 1.0–0.1 For km, and belowexample, 0.1 at km Abu are Dhabi reported Airport during Station, 71days, on average, 50 days, visibility 23 days, values and between 17 days 5.0 per–2.5 year, km, respectively. 2.5–1.0 km, The analysis1.0–0.1 of rawkm, visibilityand below records 0.1 km are at allreported airports during shows 71 significantdays, 50 days, fluctuations 23 days, and of visibility17 days per throughout year, the year.respectively. The absence The analysis of a particular of raw visibility seasonal records pattern at inall the airports visibility shows records significant implies fluctuations that the of factors visibility throughout the year. The absence of a particular seasonal pattern in the visibility records triggering visibility degradation vary throughout the year and that they may be associated with wet or implies that the factors triggering visibility degradation vary throughout the year and that they may dry atmospheric conditions which prevail in different seasons. Such low values could be observed be associated with wet or dry atmospheric conditions which prevail in different seasons. Such low duringvalues all seasonscould be with observed variability during inall theseasons magnitude with variability of the degradation in the magnitude below of 5the km degradation that can reach valuesbelow lower 5 km than that 0.1 can km. reach Trends values analyses lower than showed 0.1 km. significant Trends analyses downward showed temporal significant trends downward in visibility observationstemporal attrends all stations. in visibility See observations Table1 for the at all annual stations. average See Table visibility 1 for the values annual at average all stations. visibility values at all stations.

FigureFigure 2. An 2. illustrativeAn illustrative example example of of long-term long-term averaged daily daily visibility visibility observations observations for two for stations, two stations, Abu DhabiAbu Dhabi and and Dubai, Dubai, during during the the period period of of 1982–2016. 1982–2016. The The dashed dashed line line shows shows the the5 km 5 kmthreshold. threshold.

Visibility trends at Abu Dhabi airport during months with records below the annual average Visibility(low visibility trends months) at Abu and Dhabiabove the airport annual during average months (high visibility with records months) below are shown the annualin Figure average 3. (lowA visibility decreasing months) trend is and noticeable above in the both annual cases. average A trends (high significance visibility assessment months) at arethe shown5% confidence in Figure 3. A decreasinglevel shows trend thatis both noticeable trends are in significant both cases.. Moreover, A trends a significance steeper decrease assessment was noticed at the in the 5% case confidence of levelhigh shows visibility that both values trends at a rate are of significant. 0.076 km per Moreover, year compared a steeper to 0.043 decrease km per wasyear noticedin the case in of the low case of highvisibility ranges. values This at a led rate to of a 0.076reduction km perin the year difference compared between to 0.043 low km and per high year visibility in the casevalues of low which reflects an overall deterioration of visibility conditions. This could lead to a higher frequency visibility ranges. This led to a reduction in the difference between low and high visibility values which of poor visibility days and/or the occurrence of frequent events with extreme degradation of visibility, reflects an overall deterioration of visibility conditions. This could lead to a higher frequency of poor which could be investigated further by analyzing the frequency and the magnitude of visibility visibilitydegradation days and/or across thethe country occurrence and ofits frequentlink with eventsprevailing with meteorological extreme degradation parameters. of visibility, which could be investigated further by analyzing the frequency and the magnitude of visibility degradation across the country and its link with prevailing meteorological parameters.

Atmosphere 2017, 8, 242 7 of 22 Atmosphere 2017, 8, 242 7 of 22

Figure 3. Trends of visibilityvisibility mean values during months with records below and above the annual visibility averagesaverages in Abu Dhabi station.

3.2. Long Long-Term-Term Visibility VariabilityVariability andand TrendTrend AnalysisAnalysis The long-termlong-term mean visibility values and number of low visibility events under wet and dry weather conditionsconditions analyzed analyzed under under four four visibility visibility classes classes are are summarized summarized in Table in Table2 and 2 Figure and Figure4. Three 4. mainThree ﬁndingsmain findings can be can highlighted. be highlighted First,. F underirst, under the ﬁrst the visibility first visibility class class (5.0–2.5 (5.0 km),–2.5 km), it is observedit is observed that drythat weather dry weather conditions conditions have a greater have a impact greater than impact wet weather than wet conditions weather on conditionsvisibility deterioration on visibility in sixdeterioration stations out in ofsix ten, stations except out for of Abu ten, Dhabi,except for Al Maktoum,Abu Dhabi, Al Al Ain, Maktoum, and Dubai Al Ain stations,, and Dubai while stations, wet and drywhile weather wet and conditions dry weather have the conditions same impact have on the visibility same impact deterioration on visibility in Dubai deterioration station. Wet in weather Dubai conditionsstation. Wet have weather more conditions impact on visibilityhave more deterioration impact on visibility under classes deterioration 2 (2.5–1.0 under km) and classes 3 (1.0–0.1 2 (2.5 km).–1.0 Second,km) and based 3 (1.0 on– the0.1 frequencykm). Second, of poor based visibility on the events, frequency visibility of observations poor visibility of less events, than 0.1 visibility km are mainlyobservations associated of less with than wet 0.1 weather km are conditionsmainly associated in all stations, with wet except weather for the conditions Fujairah station,in all stations, where dryexcept weather for the conditions Fujairah station, dominate where visibility dry weather in the sameconditi range.ons dominate Third, Fujairah visibility has in the the best same visibility range. withThird, the Fujairah long-term has averagedthe best visibility poor visibility with the under long- wetterm weather averaged conditions poor visibility very close under to wet those weather under dryconditions conditions, very which close averageto those between under dry 0.00456–0.00430 conditions, which km, respectively. average between However, 0.00456 Abu Dhabi–0.00430 has km, the lowestrespectively. long-term However, visibility Abu average Dhabi has among the alllowest stations long for-term classes visibility 1 and average 2. Furthermore, among all Al stations Maktoum for andclasses Al Dhafra 1 and airports2. Furthermore, showed the Al lowest Maktoum long-term and Al average Dhafra visibility airports under showed the third the lowest class (1.0–0.1 long-term km) withaverage 0.48 visibility km. under the third class (1.0–0.1 km) with 0.48 km. With respect to the geographic location of the station (Figure 11b),b), thethe analysisanalysis ofof thethe recordsrecords inin Table2 2 reveals reveals aa reductionreduction in horizontal surface surface visibility visibility at at all all stations stations in in the the western western coastal coastal side side of ofthe the UAE. UAE. Visibility Visibility is isdecreasing decreasing mainly mainly in in the the central central and and s someome northeastern northeastern regions regions (over Dubai and Sharjah). The geographic coverage of poor visibility under wet weather conditions extends to include Abu Abu Dhabi Dhabi in in addition addition to to the the other other coastal coastal Emirates Emirates on on the the Arabian Arabian Gulf, Gulf, mainly mainly over over Ras Ras Al AlKhaimah Khaimah station. station. Abu Abu Dhabi Dhabi station’s station’s location location near near the the shoreline shoreline might might have have more pronounced negative impacts when it comes to fog onset and its impact on visibilityvisibility [[2121,,3311].]. A monthly comparison of poor visibility events (per class, per station) was conducted. Figure 55 shows the the monthly monthly mean mean number number of occurrences of occurrences of poor of poor hourly hourly visibility visibility events events for all forstations all stations within withineach class each to classlink the tolink frequency the frequency of the degradation of the degradation to its magnitude. to its magnitude. Comparisons Comparisons show monthly show monthlyvariations variations in visibility in visibilityobservations observations in different in differentcities, and cities, for all and classes for all within classes each within location. eachlocation. Results Resultsshow that show poor that visibility poor visibility events occur events more occur frequently more frequently during duringsummer summer (June–August) (June–August) and in winter and in winter(December (December–March).–March). These These periods periods have have the highest the highest number number of of poor poor hourly hourly visibility visibility events. events. Furthermore, Abu Dhabi, A Al-Maktoum,l-Maktoum, Al Al Ain, and Al Dafra Stations have a high number of poor visibility values (less than 0.10 km), while far fewer events were recorded in Ash Ash-Shariqa,-Shariqa, Dubai, Fujairah, Ras AlKhaima, and Shariqa Stations. This This suggests suggests that that cities cities in in the the eastern papartrt of the country encounter fewer poor visibility events than thethe remainingremaining citiescities inin thethe studystudy area.area.

Atmosphere 2017, 8, 242 8 of 22

Table 2. Long-term visibility observations averages and average number of hourly events under wet and dry conditions for four classes for ten stations. Bold numbers represent the highest value per category.

Visibility Class 1 5.0–2.5 km Visibility Class 2 2.5–1 km Visibility Class 3 1–0.1 km Visibility Class 4 <0.1 km Weather Annual Average Annual Average Annual Average Annual Average Station Name Average Average Average Average Conditions Number of Number of Number of Number of Visibility Visibility Visibility Visibility Hourly Events Hourly Events Hourly Events Hourly Events Dry 4.08 173 1.77 49 0.55 5 0.05 1 Abu Dhabi IA. Wet 4.07 221 1.69 75 0.74 51 0.08 38 Dry 4.30 363 1.95 87 0.74 29 0.10 3 AL Maktoum IA. Wet 4.27 271 1.93 52 0.48 74 0.95 21 Dry 4.34 332 2.10 44 0.76 3 0.02 2 Al Ain IA. Wet 4.30 233 2.00 50 0.52 24 0.09 12 Dry 4.22 114 2.05 27 0.92 7 0.07 1 AlBateen A. Wet 4.35 98 2.01 20 0.50 12 0.09 6 Dry 4.20 83 1.85 17 0.79 5 0.06 2 Al Dhafra A. Wet 4.40 148 1.95 23 0.48 17 0.07 12 Dry 4.10 23 1.90 9 0.72 3 0.05 1 Ash Shariqa IA. Wet 4.12 58 1.84 21 0.57 15 0.07 9 Dry 4.20 146 1.96 41 0.79 6 0 2 Dubai IA. Wet 4.20 207 1.99 24 0.56 30 0.09 13 Dry 4.30 123 2.06 29 0.83 5 0.08 3 Fujairah IA. Wet 4.56 487 2.14 11 0.63 4 0.04 2 Dry 4.23 106 1.89 27 0.79 6 0.08 1 Ras AlKhaimah AI Wet 4.34 171 1.85 45 0.61 23 0.09 9 Dry 4.12 58 1.91 18 0.79 2 0.04 1 Sharjah IA Wet 4.18 113 1.89 35 0.54 27 0.08 10 AtmosphereAtmosphere2017 2017,, 8, 2428, 242 9 of9 22 of 22 Atmosphere 2017, 8, 242 9 of 22

Figure 4. Low visibility caused by (A) wet and (B) dry weather conditions per visibility class, per FigureFigurestation 4. Low(%).4. Low visibility visibility caused caused by ( Aby) wet(A) andwet ( Band) dry (B weather) dry weather conditions conditions per visibility per visibility class, per class, station per (%). station (%).

FigureFigure 5. The5. The distribution distribution per per month month of theof the number number of poor of poor visibility visibility days days per class,per class, for the for entire the entire record, perFigurerecord, station: 5.per The (a station:) Abudistribution Dhaib; (a) Abu (perb Dhaib,) Almonth Maktoom; (b )of Al the Maktoom, number (c) Al Ain; of(c )poor Al (d )Ain, AlBateen;visibility (d) AlBateen, days (e) AlDafra;per ( eclass,) AlDafra, ( ffor) Ash-Shariqa; the (f )entire Ash- (g)record,Shariqa, Dubai; per ((hg)) Fujirah;station:Dubai, ((h (ai)) Fujirah, RasAbu AlKhaima;Dhaib, (i) Ras (b AlKhaima,) Al and Maktoom, (j) Shariqa. and (jc)) ShariqAl Ain,a. (d) AlBateen, (e) AlDafra, (f) Ash- Shariqa, (g) Dubai, (h) Fujirah, (i) Ras AlKhaima, and (j) Shariqa.

Atmosphere 2017, 8, 242 10 of 22

The noticeable drop in visibility in summer is likely due to the presence of micro to mesoscale forcing mechanisms such as the Haboob wind (Arabic name of this wind) [42]. Haboobs occur mainly during spring and summer months when strong insolation combines with the influences of monsoonal moisture. At the synoptic scale, the westerly Shamal winds mobilize dust and sand, and transport it east and northeastward through the UAE, resulting in greater visibility impairment in summer [42]. Shamal winds are mostly strongest over the winter and summer seasons, so are the Shamal events (northwesterly winds blowing from the sea over the land) that are associated with low pressure, very high temperature, and relative humidity, which explains the low visibility observations during the summer. Furthermore, the UAE is located in an arid region with low-lying sandy deserts, gravelly plains, alluvial plains, and salts flats in the coastal areas [43]. Around 75% of the country is covered by Torripsamments soil (loamy fine sand, or coarser) which is swept away by wind in dry conditions. It can be inferred that deterioration of visibility in the UAE due to soil-derived aerosols under dry weather conditions is highly probable. In addition, the region is exposed to significant transboundary pollution with frequent summer dust storms leading to high background aerosol concentrations as the region [26]. A study on dust sources by the National Center of Meteorology and Seismology indicated that the horizontal wind speed class for the start of the movement of dust or sand, ranging in diameter from 80–100 microns, ranges from 4.9 m/s to 13.4 m/s [44]. High speed northern and northwesterly winds enhance regional dust, aerosols, and air pollution transport, leading to visibility deterioration [22]. The long-term average maximum wind speed in Abu Dhabi of 15.0 m/s and 17.42 m/s in Dubai, and a northwesterly direction, may explain visibility deterioration under dry weather conditions. The presence of more synoptic systems in spring in the UAE along with the northwesterly wind contribute to transporting air pollutants and improving the visual range [8]. This is in line with the findings of [8] on improved visibility in Tehran city during spring where the highest long-term average values of wind speed over 50 years was 10.3 m/s, that is associated with high visibility [8], most likely due to improved dilution of air pollutants [13]. In contrast, visibility decreases when RH increases most likely because higher RH promotes the growth of hygroscopic particles into larger sized particles that scatter more light, as suggested by [12,13,38]. Decrease in overall wind speed, increase in RH, and decrease in temperature depression in winter periods could foster the occurrence of such events, as illustrated in Figure6a–d. Derived from the National Center for Environmental Prediction Reanalysis data [45], it shows the long-term composite anomalies of RH and vector wind speed at surface level in summer and winter. One can observe that anomalies in surface vector wind, over the UAE, in summer (Figure6a) are higher than those in winter (Figure6b) and that RH anomalies in summer (Figure6c) are lower than those in winter (Figure6d), changing from almost zero to four. In addition, air surface temperature anomalies in summer (Figure6e) are higher than those in winter (Figure6f). Coastal cities in the UAE are subject to perennial sea–land breezes (SLBs), with diurnal and seasonal variations [30,46]. The majority of variations occur during summer (June through September). Two types of SLBs are distinguished in the Arabian Gulf Region, the bay breeze and the ocean breeze [31]. These thermal circulations occur as a result of a weak synoptic force associated with the southwest monsoon [46]. In winter, the strong nocturnal temperature inversion and surface cooling effects, associated with low wind shear and low-level jet formation, weaken and ultimately stop turbulence above ground. As the process of radiative cooling continues, excess water vapor begins to condense, which leads to the formation of the radiation fog [31]. The wind coming from the Arabian Gulf triggers the transportation of the water vapor inland through sea–land breeze processes. However, during the night, the south–southeasterly winds dominate, moving from the desert and cooling down over the sea, thus forming the land–sea breeze. Figure7 shows the visibility time series of yearly averaged values, as well as linear regression lines, fit under wet and dry conditions, for four different visibility class levels in Abu Dhabi and Dubai stations, respectively (only two representative stations are included here). Results show that under dry weather conditions, the overall visibility values are always higher than those under wet weather Atmosphere 2017, 8, 242 11 of 22

Atmosphere 2017, 8, 242 11 of 22 conditionsAtmosphere except 2017, 8, for242 the third class (1.0–0.1 km) at both stations. The trends always increase11 of in 22 both stationsin both under stations dry under weather dry conditionsweather conditions except forexcept the for second the second class (2.5–1.0class (2.5 km)–1.0 km) in Abu in Abu Dhabi Dhabi station. Resultsstation.in both also Results stations show a thatlsounder show under dry that weather wet under weather conditionswet weather conditions except conditions thefor the trend the second trend decreases class decreases (2.5 under–1.0 under km) class class in 2 Abu in 2 Abuin Dhabi Abu Dhabi whichDhabistation. could which Results be could explained also be show explained by that the under increasingby the wet increasing weather inﬂuence influenceconditions of factors of the factors trend that that decreases may may trigger trigger under fog, fog, class mist, mist, 2 in and andAbu haze Dhabi which could be explained by the increasing influence of factors that may trigger fog, mist, and eventshaze in events the region. in the region. haze events in the region.

FigureFigure 6. Thirty6. Thirty three three years years of of windwind speed, relative relative humidity, humidity, and and air air temperature temperature averaged averaged anomalies anomalies Figure 6. Thirty three years of wind speed, relative humidity, and air temperature averaged anomalies at surfaceat surface level, level, (a )(a vector) vector wind wind in in summer;summer, ( (bb)) vector vector wind wind in in winter, winter; (c) (relativec) relative humidity humidity in summer in summer at surface level, (a) vector wind in summer, (b) vector wind in winter, (c) relative humidity in summer (d)( relatived) relative humidity humidity in in winter; winter, ( e(e)) airair temperaturetemperature in in summer, summer; and and (f)( airf) air temperature temperature in winter in winter over over (d) relative humidity in winter, (e) air temperature in summer, and (f) air temperature in winter over thethe Middle Middle East, East, Latitude Latitude 12 12◦°–45–45◦° N,N, and Longitude 25 25°–◦75–75° E,◦ E,with with the the study study area area located located in the in the middlethe Middle of the East, map. Latitude 12°–45° N, and Longitude 25°–75° E, with the study area located in the middlemiddle of the of the map. map.

Figure 7. Abu Dhabi and Dubai long-term average visibility values under wet and dry weather Figure 7. Abu Dhabi and Dubai long-term average visibility values under wet and dry weather Figureconditions 7. Abu for Dhabi different and visibility Dubai classes: long-term (a) 5.0 average–2.5 km, visibility (b) 2.5–1.0 values km, (c) under 1.0–0.10 wet km, and and dry (d) <0.1 weather conditions for different visibility classes: (a) 5.0–2.5 km, (b) 2.5–1.0 km, (c) 1.0–0.10 km, and (d) <0.1 conditionskm. Only for significant different trends visibility are classes:presented. (a) 5.0–2.5 km; (b) 2.5–1.0 km; (c) 1.0–0.10 km; and (d) <0.1 km. km. Only significant trends are presented. Only signiﬁcant trends are presented.

Atmosphere 2017, 8, 242 12 of 22

In Abu Dhabi, the general trend of poor visibility records under 5 km between 1982 and 2016 shows a small increase, with an average rate of 2.5 m and 5.8 m per year under dry and wet conditions, respectively (Figure7). This means that dry weather conditions have more impact on visibility deterioration than wet weather conditions for the first class. However, this trend shows a decline under wet weather conditions, for visibility values under class 2, with an average decline of −2.5 m per year, and −0.22 m per year under dry weather conditions. This suggests that wet weather conditions have a more significant negative impact on visibility in Abu Dhabi under class 2, as visibility values are lower than those under dry weather conditions. Furthermore, wet weather conditions have an increasing impact on visibility deterioration, mainly for visibility ranges of 1.0–2.5 km, while dry weather conditions have a decreasing impact on deterioration in the same range. This is more pronounced for visibility values under 1.0 km. In this category, the general trend of poor visibility shows an average increase of 1.5 m per year under wet weather condition, as opposed to an average decline of −3.95 m per year under dry weather conditions. It is also observed that while dry weather conditions have no impact on visibility observations under class 4, wet weather conditions have an impact on visibility observations, with a trend showing a negligible increase rate of 0.24 m per year. In Dubai, the general trend of poor visibility during the entire period of 1983–2016 under all classes is increasing, except for class 3 under wet weather conditions, which reflects a general enhancement in visibility conditions. Under wet weather conditions, the average rates vary between −1 m per year under class 4, to 8.8 m per year under class 1 (Figure7). This increase in the annual average long-term trends suggests that visibility is improving under wet and dry weather conditions in Dubai. However, the average rate of improvement under wet conditions is higher for visibility values under classes 1 and 2. Results of trends-significance assessment at the 5% level using the Mann–Kendall test of Abu Dhabi and Dubai stations data for the different visibility classes show that two trends are significant in Abu Dhabi and four trends are significant in Dubai. No significant trends mean that the observations obtained are not serially correlated over time (independent) and are identically distributed. Results also show decreasing trends under dry weather conditions in Al Ain and Al Dhafra stations in which reflected aerosol emissions might play an important role in decreasing in occurrences of poor visibility. The formulation of a thermal low depression, resulting from high insolation over the Empty Quarter, and moving towards the UAE eventually reaching Al Hajar Mountain in Oman (to the northeast of the UAE—See Figure1b), deepens the thermal low and accelerates the southerly wind over the mountain, carrying dust and sand towards the UAE [47]. Furthermore, as confirmed by Naizghi, and Ouarda [48] Al Ain city has the highest wind speed, which triggers more significant dust emission and transport.

3.3. Frequency Analysis of Wet- and Dry-Related Poor Visibility Events Figure8 shows the total number of poor visibility events per city for the ten cities in relation to the four visibility classes. Under wet weather conditions (fog, mist and haze), fog events dominated reports. The impact of wet weather conditions is more pronounced as it inﬂuences visibility values at all classes with most readings less than or equal to 5.0–2.5 km in all stations. Under dry conditions, poor visibility is mainly attributed to dust, which is common in a hyper-arid environment like the UAE. The greater frequency of fog events in fall and winter can be attributed to dry air aloft in subsiding conditions, which promotes surface cooling while suppressing surface wind. Light wind associated with a clear sky, and in the presence of dry air above the moist boundary layer, allows much more heat to escape from the earth surface, leading to fog formulation [49]. AtmosphereAtmosphere 2017 2017, 8, ,8 24, 242 2 1313 of of 22 22 Atmosphere 2017, 8, 242 13 of 22

FigureFigure 8. 8. FrequencyFrequency Frequency analysis analysisanalysis of ofof wet wetwet and and dry dry visibility visibility events events in in all all stations: stations: ( (aa(a)) )Abu AbuAbu Dhaib, Dhaib; Dhaib, ( (b(b)) )Al Al Maktoum,Maktoum;Maktoum, ( (c(c)c) )Al AlAl Ain, Ain;Ain, ( d (d) )Al Al Bateen, Bateen, Bateen; ( e( (e)e )AL) AL AL Dafrah, Dafrah, Dafrah; ( f()f ()Ashf )Ash Ash-Shariqa;-Shariqa,-Shariqa, ( g(g) ()Dubai,g Dubai,) Dubai; ( h(h) ()Fujairah,h Fujairah,) Fujairah; ( i()i )Ras (Rasi) RasAl Al- - Khaimah,Al-Khaimah;Khaimah, and and and ( j()j ) (Sharja jSharja) Sharjah.hh. .With With With visibility visibility visibility caused caused caused by by by wet wet wet conditions conditions conditions in in Back, Back, and andand visibility visibilityvisibility caused causedcaused by byby drydry weather weather conditions conditions in in Red. Red. ForFor For four fourfour visibility visibilityvisibility classes: classes:classes: 1 11 (5 (5–2.5(5––2.52.5 km), km),km), 2 22 (2.5 (2.5–1.0(2.5––1.01.0 km), km),km), 3 33 (1.0 (1.0–0.1(1.0––0.10.1 km), km),km), andand 4(<0.1 4 4(<0.1 (<0.1 km) km).km). .

FurtherFurther analysis analysis is is conducted conducted on onon fog fog and and dust dust frequency frequency to to understand understand their theirtheir role rolerole in in visibility visibility deteriorationdeterioration in in Abu Abu Dhabi Dhabi and and Dubai Dubai a airport airportirport s stations tationsstations since since both both have have a a high high pronounced pronounced impact impact on on visibility.visibility. Figure Figure 99 9 showsshows shows thethe the totaltotal total annualannual annual totaltotal total numbernumber number ofof of fogfog fog eventsevents events inin in AbuAbu Abu DhabiDhabi Dhabi andand and DubaiDubai Dubai fromfrom from thethe yea years yearsrs 1982 1982–2016, 1982––2016,2016, and and 1984 1984–2016, 1984––2016,2016, respectively. respectively. The The analysis analysis of of the the total total number number of of fog fog events events over over thethe last last three three decades decades at at both both stations stations reveals reveals a a significant signiﬁcant significant variability. variability. Results Results show show that that Abu Abu Dhabi Dhabi stationstation isis is moremore moreimpacted impacted impacted by by by fog fog fog events, events, events, with with with an an averagean average average annual annual annual total total total of 75 of hof 75 compared75 h h compared compared to 34 hto to in 34 34 Dubai. h h in in Dubai.However,Dubai. However, However, the total the the number total total number number of fog eventsof of fog fog showsevents events steepshows shows increasing steep steep increasing increasing trends fortrends trends both for for cities. both both Fogcities. cities. events Fog Fog eventsoccurevents about occur occur 64about about times, 64 64 ontimes, times, average, on on average, average, per year per per in year Abuyear in Dhabi,in Abu Abu Dhabi, whereasDhabi, whereas whereas the average the the average average number number number of fog eventsof of fog fog eventsperevents year per per in year Dubaiyear in in isDubai Dubai signiﬁcantly is is significantly significantly lower at lower aroundlower at at 28around around days per28 28 days yeardays per forper theyear year past for for threethe the past past decades. three three decades. Thedecades. year The2000The year alsoyear 2000 showed2000 also also theshowed showed highest the the increase highest highest inincrease increase the number in in the the of number number fog events of of fog fog in events both events cities. in in both both cities. cities.

FigureFigure 9. 9.9. Total Total annual annual hourly hourly hourly fog fog fog eve eve eventsntsnts of of Abu ofAbu Abu Dhabi Dhabi Dhabi and and andDubai Dubai Dubai from from fromthe the y years theears years1982 1982––2016, 1982–2016,2016, and and 1984 1984 and–– 2016,1984–2016,2016, respectively respectively respectively.. .

Figure 10 shows the total annual hours of dust events of Abu Dhabi and Dubai for the same Figure 10 shows the total annual hours of dust events of Abu Dhabi and Dubai for the same duration as for fog analysis. The analysis of the number of hourly dust events at both stations reveals duration as for fog analysis. The The analysis analysis of of the the number number of of hourly hourly dust dust events events at at bo bothth stations stations reveals reveals also a significant variation of the total number of hourly dust events. Both stations are impacted by also a significant signiﬁcant variation of the total number of hourly dust events. Both stations are are impacted by by dust events, however, Abu Dhabi station is more impacted as it encountered a higher number of dust dust events, however, Abu Dhabi station is more impacted as it encountered a higher number of dust events. In addition, the total number of hourly dust events shows steep increasing trends in both events. In addition,addition, thethe total total number number of of hourly hourly dust dust events events shows shows steep steep increasing increasing trends trends in both in cities.both cities.cities.

Atmosphere 2017,, 8,, 242242 14 of 22 Atmosphere 2017, 8, 242 14 of 22

Figure 10. Total annual hourly dust events of Abu Dhabi (1982–2016), and Dubai (1984–2016). Figure 10.10. Total annualannual hourlyhourly dustdust eventsevents ofof AbuAbu DhabiDhabi (1982–2016),(1982–2016), andand DubaiDubai (1984–2016).(1984–2016). The annual variability of fog events could be related to the fact that Abu Dhabi airport station is locatedTheThe very annualannual close variabilityvariability to the shoreline, ofof fogfog eventsevents the warm couldcould Gulf, bebe relatedrelated and the toto desert, thethe factfact which thatthat Abu Abumakes DhabiDhabi it prone airportairport to more stationstation fog isis eventslocatedlocated [ very31very,49 close]close, especially toto thethe shoreline, shoreline,the radiation thethe warmwarmtype of Gulf,Gulf, fog andandevents. thethe desert,desert,Abu Dhabi whichwhich airport makesmakes is itit also proneprone located toto moremore at fogthefog limitseventsevents of [31[ 31the,49,49 city],], especially especially which makes the the radiation radiationit closer type to type cooler of fogof nocturnalfog events. events. Abu surface Abu Dhabi Dhabi temperatures airport airport is also isin located alsothe desert.located at the Unlike limitsat the Abuoflimits the Dhabi cityof the which Airport,city which makes Dubai makes it closer Airport it closer to cooler is to located cooler nocturnal innocturnal the surface heart surface temperatures of the temperatures city and in the surrounded in desert. the desert. Unlike by Unlike large Abu industrialDhabiAbu Dhabi Airport, and Airport, residential Dubai Airport Dubai districts Airport is located which is in locatedimplies the heart a in significant of the the heart city differenceand of the surrounded city between and by surrounded land large use industrial conditions by large and aroundresidentialindustrial both a districtsnd airports. residential which The districts implies particular which a significant land implies use difference a conditions significant between around difference land Dubai between use airportconditions land led use around to conditions different both radiativeairports.around both Thetransfer particular airports. processes, The land which particular use conditions may land not foster around use conditions strong Dubai temperature airport around led Dubai inversion, to different airport as radiative is led the to case different transfer in the processes,desertradiative around transfer which Abu may processes, Dhabi not fosterAirport. which strong may temperature not foster strong inversion, temperature as is the caseinversion, in the desertas is the around case in Abu the DhabidesertTo Airport.around understand Abu Dhabi the overall Airport. trend in the frequency of events, the CUSUM method was used. It confirmedToTo understand understand the presence the the overall overall of a change trend trend point in in the the in frequency frequencythe mean value of of events, events, of the the number CUSUM of fog method and dust was events used. forItIt confirmedconfirmed both cities the the that presence presence occurred of of a arounda change change the point point year in in the1999. the mean meanFigure value value 11 ofillustrates of the the number number the ofidentified fogof fog and and dustchange dust events pointsevents for andbothfor bothpresents cities cities that the that occurred mean occurred and around trends around the for yearthe the year 1999.various 1999. Figure segme Figure 11nts illustrates 11 for illustrates the number the identified the of identified fog events change change per points year points and for presentsAbuand presents Dhabi the and mean the Dubaimean and trendsand airport trends for stations. the for various the Results various segments show segme for thatnts the for the number theannual number of time fog of events series fog events peris split year per into foryear Abu two for subgroupsDhabiAbu Dhabi and Dubaiat and the Dubaichangeairport airport stations.point year stations. Results of 1999 Resultsshow and thatthat show thethere annual that is a the shift time annual in series the time mean is split series of into the is twonumber split subgroups into of twofog eventsatsubgroups thechange for the at pointthetwo change stations. year ofpoint In 1999 Abu year and Dhabi, of that 1999 therethe and mean is that ashift value there in isis the abouta shift mean 25 in offog the the events mean number offor the the of number fog period events 1982of fog for– 1998.theevents two For for stations. the the period two In stations. Abuof 1999 Dhabi, –In2016, Abu the the Dhabi, mean mean valuethe increases mean is about value to 99 25 is fog fog about events. events 25 fog A for previous events the period for study the 1982–1998. period reported 1982 For 38– foggythe1998. period Fornights the of a period 1999–2016,year over of 1999 the the p–2016,eriod mean the1982 increases mean–2003 increases [ to49 99]. The fog toaverage events. 99 fog v Aevents.alue previous of Afog previous events study increas reported study edreported 38fourfold foggy 38 innightsfoggy Abu nights aDhabi year a overafter year the 1999,over period theand p fivefolderiod 1982–2003 1982 in –Dubai. [200349]. [ The49 This]. averageThe is inaverage line value with value of the fog of findings fog events events increasedof [ increas50] who fourfolded analyzed fourfold in Abuthein Abu increase Dhabi Dhabi after in afterthe 1999, frequency 1999, and and fivefold of fivefold foggy in Dubai. daysin Dubai. in This India This is over in is line in the line with period with the theof findings 1976 findings–2010. of [ 50of They] [ who50] indicatedwho analyzed analyzed that the theincreasethe largestincrease in shift the in frequency thein the frequency mean of and foggy of variancefoggy days days in of India fogin India frequency over over the periodthe was period observed of 1976–2010. of 1976 in 1998,–2010. They due They indicated to a indicated combination that that the oflargestthe two largest major shift shift in factors: the in the mean changes mean and and variancein variancelarge-scale of of fog circulation,fog frequency frequency and was was the observed observed effects of in in local 1998, 1998, factors due due to to like a a combinationcombination changes in theofof twotwo RH major(gradual factors: increasing changes changes trend), in in large large-scale land-scale use, circulation, circulation, and the role and and of the theaerosols. effects effects ofIn of local Dubai, local factors factors the numberlike like changes changes of fog in eventsinthe the RH RHincreased (gradual (gradual fromincreasing increasing about ninetrend), trend), events land land before use, use, and and1999 the the to role44 role events of of aerosols. aerosols. after 1999. In In Dubai, The Levene’s the number test results ofof fogfog alsoeventsevents indicate increasedincreased a change fromfrom aboutaboutin the nine ninevariance eventsevents of before beforethe total 19991999 number toto 4444 eventsevents of fog after afterevents 1999.1999. for The TheAbu Levene’sLevene’s Dhabi and testtest Dubai. resultsresults Thisalsoalso indicatemeansindicate tha aat change changeone variance inin thethe is variancevariance different of from thethe totalthetotal other numbernumber as the of computed fog events p - forvalue Abu is < Dhabi0.0001 andand Dubai.0.0004 ThisforThis Abu meansmeans Dhabi thattha andt oneone Dubai, variancevariance respectively, isis differentdifferent frombothfrom lower thethe otherother than asas the thethe significance computedcomputedp p -valuelevel-value alpha isis <0.0001<0.0001 = 0.05. andand The 0.00040.0004 risk for Abu Dhabi and Dubai, respectively, both lower than the significance level alpha = 0.05. The risk of offor rejecting Abu Dhabi the nulland Dubai,hypothesis respectively, (H0: The variancesboth lower are than identical) the significance, while it islevel true alpha is lower = 0.05. than The 0.01% risk andrejectingof rejecting 0.04%, the respectively. the null null hypothesis hypothesis (H0 :(H The0: T varianceshe variances are are identical), identical) while, while it is it true is true is lower is lower than than 0.01% 0.01% and 0.04%,and 0.04%, respectively. respectively.

200 200 a) Abu Dhabi b) Dubai 175200 175200 b) Dubai 150175 a) Abu Dhabi 150175

125150 125150 99.3 100125 100125 99.3 10075 75100 46.5 5075 5075

Number of Fog Events Fog of Number 24.93 46.5 Events Fog of Number 2550 8.93 2550

Number of Fog Events Fog of Number 24.93 Events Fog of Number 250 8.93 025 1985 1990 1995 2000 2005 2010 2015 1985 1990 1995 2000 2005 2010 2015 0 0 1985 1990 1995 2000 2005 2010 2015Year 1985 1990 1995 2000 2005 2010 2015 Year Figure 11 11.. A homogeneity analysis for number of fog events in ( a) Abu Dhabi and (b) Dubai cities. Figure 11. A homogeneity analysis for number of fog events in (a) Abu Dhabi and (b) Dubai cities.

Atmosphere 2017, 8, 242 15 of 22 Atmosphere 2017, 8, 242 15 of 22

Figure 12 shows the identified change points and presents the mean and trends for the various Figure 12 shows the identified change points and presents the mean and trends for the various segments for the number of dust events per year for both cities. The trend analyses show a significant segments for the number of dust events per year for both cities. The trend analyses show a significant upward increase in both cities. The mean and variance of the number of dust events for the two upward increase in both cities. The mean and variance of the number of dust events for the two stations, before and after 1999 are significantly different. In Abu Dhabi, the mean value increased stations, before and after 1999 are significantly different. In Abu Dhabi, the mean value increased from about 112.5 to 337 dust events. In Dubai, the number of dust events increases from about 85.5 from about 112.5 to 337 dust events. In Dubai, the number of dust events increases from about 85.5 to to 315.6 events after 1999. The Levene’s test results also indicate a change in the variances of the total 315.6 events after 1999. The Levene’s test results also indicate a change in the variances of the total number of dust events for Abu Dhabi and Dubai. number of dust events for Abu Dhabi and Dubai.

1000 a) Abu Dhabi b) Dubai 800

600

400 337 315.6

85.5 200 112.5

Number of Dust ofEvents Dust Number

0 1985 1990 1995 2000 2005 2010 2015 1985 1990 1995 2000 2005 2010 2015 Year

FigureFigure 12 12.. AA homogeneity homogeneity analysis analysis for for number number of of dust dust events events in in ( (aa)) Abu Abu Dhabi Dhabi and and ( (b)) Dubai Dubai cities cities..

3.4.3.4. Links with Climate DynamicsDynamics

3.4.1.3.4.1. Fog and ClimateClimate OscillationsOscillations TheThe noticeable change between between the the mean mean number number of of fog fog events events before before and and after after 1999 1999 could could be beassociated associated with with an anENSO ENSO event, event, as confirmed as confirmed by byother other studies studies that that tackled tackled climatic climatic variables variables in the in theUAE UAE like like precipitation precipitation and and wind wind [48, [4851,51]. ]. In In their their research, research, [5 [151] ]explained explained the the role role of large-scale large-scale atmosphericatmospheric teleconnectionteleconnection patterns patterns in in altering altering the the surface surface energy energy balance balance and howand theyhow operatethey operate when thewhen strong the westerly strong westerly jet streams jet persistent streams persistentin the upper in atmosphere the upper are atmosphere favorable forare the favorable propagation for the of Rossbypropagation waves of (Section Rossby 3.2 waves, Figure (Se6ction). In a3.2, similar Figure case 6). in In Japan, a similar Zhang case et in al. Japan, [ 52] analyzed Zhang et how al. [ the52] Pacific-Japananalyzed how teleconnection the Pacific-Japan pattern teleconnection influences July pattern sea fog. influences They stated July that sea thefog. covariance They stated indicated that the a closecovariance correlation, indicated with a a close coefficient correlation, of 0.62 with exceeding a coefficient the 0.99 of confidence0.62 exceeding level. the They 0.99 also confidence discussed level. the meridionalThey also discussed sea surface the temperature meridional gradient sea surface and thetemperature geopotential gradient heights and roles the in geopotential creating a favorable heights conditionroles in creating in high a Pacificfavorable Japan condition index years,in high for Pacific the warm Japan and index humid years, air, for when the warm crossing and the humid surface air, seawhen temperature crossing the front, surface to fog sea formation. temperature In front, their study,to fog Caoformation. et al. [53 In] their found study, that thereCao et were al. [5 more3] found fog eventsthat there over were the Northmore fog China events Plain over in the summersNorth China that Plain followed in the La summers Niña events. that followed They also La found Niña thatevents. there They is aalso strong found relationship that there is between a strong strong relationship El Niño between and La strong Niña El events Niño andand theLa Niñ circulationa events anomalyand the circulation [54]. These anomaly regional and[54]. global These studies regional have and shown global the studies connections have shown between the ENSO connections events andbetween some ENSO climate events variables. and some However, climate understanding variables. H howowever, teleconnection understanding mechanisms how teleconnection like ENSO changemechanisms the weather like ENSO patterns change at a the regional weather scale patterns over the at UAEa regio isna stilll scale needed, over with the UAE controls is still for needed, fog and dustwith stormcontrols formulation. for fog and dust storm formulation. ToTo understand understand the the reasons reasons for for this this change change of phase of phase before before and after and 1999, after the 1999, relationship the relationship between fogbetween variability fog variability and climate and oscillations climate oscillations was investigated was investigated (Figure 13 (Figure) which 13 indicates) which thatindicates there that are changesthere are of changes phase forof aphase few climatefor a few indices climate around indices the around year 1999. the year This 1999. could This explain could the explain change the in thechange mean in andthe variancemean and of variance the number of the of fognumber events of beforefog events and afterbefore that and year. after Absolute that year. correlation Absolute valuescorrelation between values those between climate those indices climate and indices the number and the of number fog events of fog at Abuevents Dhabi at Abu station Dhabi are station high. Onlyare high. PDO Only shows PDO a negativeshows a negative correlation correlation with the with number the number of fog events of fog ( revents= −0.568). (r = −0.568). The SOI, The EAO, SOI, andEAO, AMO and AMO show ashow positive a positive correlation correlation with rwith= 0.467, r = 0.467, 0.513, 0.513 and, 0.509,and 0.509, respectively. respectively. The positiveThe positive and negativeand negative correlations correlations between between the climate the clim oscillationsate oscillations (SOI, PDO, (SOI, EAO, PDO, and EAO AMO), and and AMO) the number and the of fognumber events of havefog events shown have that shown climate that oscillations climate oscillations play a role inplay the a occurrencerole in the occurrence of fog in the of UAE, fog in thus the affectingUAE, thus visibility. affecting visibility.

AtmosphereAtmosphere 20172017,, 88,, 242242 1616 ofof 2222 Atmosphere 2017, 8, 242 16 of 22

Figure 13. Relationship between climate Indices and fog frequency occurrence in Abu Dhabi City, Figure 13.13. Relationship between between climate climate Indices Indices and and fog fog frequency frequency occurrence occurrence in in Abu Abu Dhabi Dhabi City City,, (a) Pacific Decadal Oscillation Index, (b) Southern Oscillation Index, (c) East Atlantic Oscillation (a) Paciﬁc Pacific Decadal Decadal Oscillation Oscillation Index; Index, (b ) ( Southernb) Southern Oscillation Oscillation Index; Index, (c) East (c) Atlantic East Atlantic Oscillation Oscillation Index; Index, and (d) Atlantic Multi-Decadal Oscillation Index. andIndex, (d )and Atlantic (d) Atlantic Multi-Decadal Multi-Decadal Oscillation Oscillation Index. Index.

3.4.2. Dust and Climate Oscillations 3.4.2. D Dustust and C Climatelimate O Oscillationsscillations Figure 14 illustrates the relationship between dust events and climate oscillations. It indicates Figure 14 illustratesillustrates thethe relationshiprelationship betweenbetween dustdust eventsevents andand climateclimate oscillations.oscillations. It indicates that there are changes of phase for these climate indices around 1999, such as fog. This may explain that there are changes of phase for thesethese climate indices around 1999, such as fog. This may explain the change in the mean and variance of the number of dust events before and after the year 1999. the change in the mean and vari varianceance of the number of of dust events before before and and after after the the year year 1999. 1999. Correlation values between those climate indices and the number of dust events at Abu Dhabi station Correlation values between those climate indices and the number of dust events at Abu Dhabi station are high. Only PDO shows a negative correlation with the number of dust events (r = −0.571). are high. high. Only Only PDO PDO shows shows a negative a negative correlation correlation with withthe number the number of dust of events dust (r events= −0.571). (r = The −0.571 SOI). The SOI and EAO show positive but moderate correlation with r = 0.236 and 0.301, respectively. andThe EAOSOI and show EAO positive show butpositive moderate but moderate correlation correlation with r = 0.236with andr = 0.236 0.301, and respectively. 0.301, respectively. However, However, AMO shows a positive correlation with r = 0.561. The positive and negative correlations AMOHowever, shows AMO a positive shows correlationa positive withcorrelationr = 0.561. with The r = positive 0.561. The and positive negative and correlations negative betweencorrelations the between the climate indices (SOI, PDO, EAO, and AMO) and the number of dust events have shown climatebetween indices the climate (SOI, indices PDO, EAO, (SOI, andPDO, AMO) EAO, and and the AMO) number and ofthe dust number events of havedust events shown have that climateshown that climate oscillations play a role in the occurrence of dust storms in the UAE, thus causing visibility oscillationsthat climateplay oscillations a role in play the occurrencea role in the of occurrence dust storms of in dust the storms UAE, thus in the causing UAE, visibilitythus causing deterioration. visibility deterioration. In their research, [22] stated that dust is a controlling factor of visibility in the Arabian Indeterioration. their research, In their [22] statedresearch, that [2 dust2] stated is a controllingthat dust is factora controlling of visibility factor in of the visibility Arabian in Gulfthe Arabian as dust Gulf as dust events are significantly correlated to the visibility data. eventsGulf as aredust signiﬁcantly events are significant correlatedly to correlated the visibility to data.the visibility data.

FigureFigure 14.14. Relationship between climate Indices and dust frequency occurrence in Abu Dhabi City City,, Figure 14. Relationship between climate Indices and dust frequency occurrence in Abu Dhabi City, ((aa)) PaciﬁcPacific decadaldecadal oscillationoscillation index;index, ((b)) SouthernSouthern oscillation index;index, (c) EastEast Atlantic oscillation index;index, (a) Pacific decadal oscillation index, (b) Southern oscillation index, (c) East Atlantic oscillation index, andand ((dd)) AtlanticAtlantic multi-decadalmulti-decadal oscillationoscillation index.index. and (d) Atlantic multi-decadal oscillation index.

To understand the role of climate oscillations and synoptic conditions in dust movement, further To understand the role of climate oscillations and synoptic conditions in dust movement, further analyses were carried out on two ENSO events (linked to the SOI index used earlier). The atmospheric analyses were carried out on two ENSO events (linked to the SOI index used earlier). The atmospheric

Atmosphere 2017, 8, 242 17 of 22

Atmosphere 2017, 8, 242 17 of 22 To understand the role of climate oscillations and synoptic conditions in dust movement, further pressureanalyses weremaps carried derived out from on twothe National ENSO events Center (linked for Environmental to the SOI index Prediction used earlier). Reanalysis The atmospheric data [55] atpressure earth surface maps derivedlevel during from six the ENSO National events; Center the for strong Environmental positive even Predictionts in 1991/1992, Reanalysis 2002/2003 data [55, and] at 2015/2016earth surface (El level Niño during-warm six phase) ENSO and events; the negative the strong events positive in 1998/1999, events in 1991/1992, 2000/2001, 2002/2003,and 2010/2011 and (La2015/2016 Niña-cold (El Niño-warmphase) are presented phase) and in theFigure negative 15 [55 events]. In summer, in 1998/1999, the changes 2000/2001, associated and 2010/2011 with the summer(La Niña-cold monsoon phase) low are-pressure presented system in Figureover northwestern 15[ 55]. In summer, India affect the the changes Arabian associated Gulf region with and the generatesummer monsoonShamal winds. low-pressure These changes system overalso reduce northwestern the atmospheric India affect pressure the Arabian and increase Gulf region the temperatureand generate during Shamal Shamal winds. events. These changesAs a result also of reduce the weakening the atmospheric of the summer pressure monsoon and increase during the ENSOtemperature positive during phase Shamal events events. (Figure As 15a a result,b), the of thetropical weakening convection of the summershifts toward monsoon the duringeast cause ENSO a weakerpositive low phase pressure events monsoon (Figure 15 overa,b), India. the tropical This leads convection to a disturbance shifts toward to the the Jet east Stream cause [22 a] weaker which guideslow pressure the planetary monsoon Rossby over India. waves This in leads the to upper a disturbance troposphere to the by Jet alteri Streamng [22 them] which based guides on the the anomalousplanetary Rossby warming waves and in cooling the upper of ENSO troposphere events [ by51] altering. The divergence them based of air on theraised anomalous at upper warminglevels of theand atmosphere cooling of ENSO during events the ENSO [51]. The positive divergence phase and of air associated raised at uppervorticity levels changes of the in atmosphere the upper troposphereduring the ENSO drive positivethe atmospheric phase and Rossby associated wav vorticityes that affect changes global in theatmospheric upper troposphere circulation. drive These the changesatmospheric in planetary Rossby waves affect that affect the global global atmospheric atmospheric circulations circulation. by These altering changes the surface in planetary energy balancewaves affect in extra the-tropics, global atmospheric mainly due circulationsto surface wind by altering speed anomalies the surface which energy affect balance latent in extra-tropics,and sensible heatmainly fluxes due [51 to,56 surface]. It is windnoticeable speed from anomalies Figure which15 that affect there latent is a clear and sensiblechange of heat phase ﬂuxes of surface [51,56]. atmosphericIt is noticeable pressure from Figure associated 15 that with there the is a clear ENSO change warm of and phase cold of surface events. atmospheric This teleconnection pressure mechanismassociated with shows the that ENSO ENSO warm has and an impact cold events. on the This formulation teleconnection of dust mechanism storms in the shows region that during ENSO summerhas an impact periods, on and the thus formulation an effect of on dust visibility. storms In in their the regionresearch, during Naizghi, summer and Ouarda periods, [48 and] prove thus thatan effect climate on oscillations visibility. Inplay their a role research, in dust Naizghi,transportation and Ouarda in the UAE, [48] provehence affecting that climate visibility. oscillations High temperatureplay a role in and dust wind transportation speed improve in the UAE, visibility hence by affecting enhancing visibility. the dispersive High temperature capability and of wind the atmospherespeed improve via visibilitythermal and by enhancingmechanical the turbulence dispersive respectively capability and of the reducing atmosphere aerosol via concentration thermal and levelsmechanical [13]. turbulence respectively and reducing aerosol concentration levels [13].

Figure 15. The seasonal mean surface atmospheric pressure anomalies obtained from NCEP Figure 15. The seasonal mean surface atmospheric pressure anomalies obtained from NCEP Reanalysis-2 for (a) La Nina years 1998/1999, 2000/2001, 2010/2011; and (b) El Nino years 1991/1992, Reanalysis-2 for (a) La Nina years 1998/1999, 2000/2001, 2010/2011, and (b) El Nino years 1991/1992, 2002/2003, 2015/2016. 2002/2003, 2015/2016.

AlthoughAlthough this this work work generates generates important important findings ﬁndings in in relation relation to to visibility, visibility, temporal and spatial variability and and its its associated associated factors factors in in the the UAE, UAE, it itis isworth worth mentioning mentioning that that deeper deeper analysis analysis could could be donebe done using using satellite satellite data data to toinfer infer visibility visibility value, value, or or factors factors that that may may impact impact visibility, visibility, capture capture its spatialspatial distribution, distribution, and and analyze analyze the the synoptic synoptic conditions conditions that that contribute contribute to to inter inter-annual-annual and and seasonal seasonal visibility variability. To further investigate the predominant factors affecting visibility and better predict visibility degradation, future work should focus on the microphysics and additional variables

Atmosphere 2017, 8, 242 18 of 22 visibility variability. To further investigate the predominant factors affecting visibility and better predict visibility degradation, future work should focus on the microphysics and additional variables like liquid water path and droplet size distribution should be analyzed. To understand and assess the impact of Shamal-related dust events and Haboob wind on visibility, additional studies with reliable regional models are needed. Dust particles’ elemental and morphological characterization is also needed to determine its radiative and scattering effects, respectively.

4. Conclusions This study presents the results of analyzing historically recorded data in ten airport stations over a 30-year period to reveal visibility spatial and temporal variations. The selected stations were mainly located in or close to major cities in the eastern and central parts of the UAE. The western region and the deep desert are not covered by existing stations. Observations from ten stations are taken onward for further analysis based on the continuity and reliability of available records. Analyzing the hourly visibility observations at four visual ranges (<0.1 km, 0.1–1.0 km, 1.0–2.5 km, 2.5–5.0 km) for ten airport stations in the UAE reveals that there are different temporal trends (decreasing/increasing), as well as spatial variations. Analyzing the correlation with fog, mist, and relative humidity, as well as aerosols, reveals that visibility varies temporally and spatially under wet and dry weather conditions. In general, wet and dry weather conditions have more pronounced negative impacts on visibility in Abu Dhabi than in Dubai, due to the increased impact of fog and dust frequency resulting from synoptic and mesoscale climatic conditions. On average 62 fog events have occurred in Abu Dhabi City in comparison with 27 fog events in Dubai. However, 225 dust events have occurred in Abu Dhabi during the last three decades, in comparison with 200 events in Dubai over the same time period. Recent massive land use changes have led to changes in energy balance and net radiation. Highly reflective roofs and surfaces, in addition to the dominant light and dry soil, have a high albedo; this leads to a lower net of solar radiation. Net radiation available to be transformed into latent, sensible, and soil heat fluxes generate a warming effect and lead to moisture loss due to dew deposition, which would trigger fog formulation. Furthermore, the turbulent mixing in the boundary layer during stable conditions and the presence of the Al Hajar Mountains on the eastern side of the study domain promotes the initiation of local convection. Therefore, recent land use changes, heat and moisture transport in soil, and topographic effects are important factors in the formation of fog, leading to visibility impairment in the UAE. This study confirms that climate teleconnections play a crucial role in altering the synoptic conditions responsible for fog formulation and dissipation as well as dust movements and transportation in the region, and hence influencing visibility. A change point of the number of fog events was detected around the year 1999, which is significantly correlated with climate indices namely ENSO, IOD, EAO, and PDO. The same trend and change point are detected around the same year for the number of dust events. A brief discussion on the impact of Shamal-related dust events and impacts of Haboob wind on visibility was done. Correlation between the mesoscale and synoptic conditions and visibility observations explained their roles in visibility deteriorations under wet and dry weather conditions. Results not only highlight the importance of meteorological factors on visibility reductions in the study domain but also confirm the essential role of aerosols in degrading visibility in the UAE.

Acknowledgments: Authors thank Jun Zhao for his technical assistance in data processing. This study was conducted with the support from Masdar Institute of Science and Technology Student Support Grant (SSG)-SSG2015-000066. Author Contributions: Amal Aldababseh conceived, designed and applied the methodology, analyzed the data, developed the results and wrote the paper. Marouane Temimi, in his capacity as advisor, oversaw the application of the approach and development of the obtained results. Conﬂicts of Interest: The authors declare no conﬂict of interest. Atmosphere 2017, 8, 242 19 of 22

Appendix A. Description of Statistical Methods Used The Mann–Kendall statistic, denoted as S, for a given data set of size n, is deﬁned as follows:

= n−1 n ( − ) S ∑i=1 ∑j=i+1 sgn Xj Xi , (A1) where Xi is the data value for the period i and Xj are the data value for period j. The sgn(Xj − Xi) is the sign function given by:   1 i f Xj − Xi > 0  sgn Xj − Xi = 0 i f Xj − Xi = 0 (A2)   −1 i f Xj − Xi < 0.

For a given time series y1, y2, ... , yn, the cumulative sum of deviations [57], for any time t, is given by: t Cs = ∑ Yi − Y (A3) i=1 The Student’s t-test is a statistical hypothesis test, used to determine if two sets of data are signiﬁcantly different from each other. The test statistic follows a student’s t-distribution under the null hypothesis, H0 [58]. For a sample of size n = x1, x2, ... , xn, s is the ratio of sample standard deviation overpopulation standard deviation, σ is the population standard deviation of the data, X is the mean for the sample size n, and µ is the population mean. The t-test is calculated as follows: X − √σ √ Z √ µ / n t = p = p (A4) s s The Levene’s test is an inferential statistic used to test the homogeneity of variances, calculated for two or more groups [59]. It is one of the preferable tests as it attempts to test the assumption of having the same variances of the populations from which different samples were drawn [60,61]. It is usually conducted before a comparison of the two groups’ means, using Student’s t-test. The Levene’s test statistic, W, is deﬁned as follows:

2 (N − k) ∑k N (Z − Z ) W = i=1 i i .. (A5) (k − 1) k Ni 2 ∑i=1 ∑j=1 Zij − Zi. where W is the result of the test, k is the number of the groups. In our case, we have two groups; N is the number of cases in all groups, Ni is the number of cases in the ith group. The significance of W is tested against F(α, k − 1, N − k), where F is a quantile of the F-test distribution, with k − 1 and N − k its degrees of freedom, and α is the chosen level of significance (usually 0.05 or 0.01). Z.. and Zi. are defined as follows [58,60]:

1 Ni = k Z.. ∑i=1 ∑ Zij is the mean of all Zij, (A6) N j=1

1 = Ni Zi. ∑j=1 Zij is the mean of the Zij for group i. (A7) Ni and Zij can be calculated as followed: ( Yij − Yi. , Yi. is a mean of the i − th group, = Zij (A8) Yij − Yei. , Yei. is a median of the i − th group. Atmosphere 2017, 8, 242 20 of 22

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