Kuwait is a small country covering an area of 17,818 km², located in the Middle East, bordering Iraq to the north and Saudi Arabia to the south and the Persian Gulf to the east. Kuwait’s topography is gravelly and sandy desert with low to moderate relief, with more than 50 percent of Kuwait desert is covered by aeolian sand [17] [36]. The climate is characterized as hot, dry summers (May-September) with temperatures reaching up to 45˚C and mild winters (October-April), dropping to 8˚C [17]. It has a mean annual rainfall of 119 mm, while mean annual potential evapo-transpiration exceeds 2270 mm [18]. Sand and dust storms are frequent during the summer between March and August, they extend in a NW-SE direction, with dominant northwesterly winds forming 60% of the total wind directions and from the southeast at a lesser extent and shorter duration [36] [37]. The average monthly wind speed ranges in January and December are from 10 to 12.8 mph in June and July, with 12% of the year having a calm wind [4].

There are five major dust sources areas: the southwestern desert of Iraq; Mesopotamian flood plain in Iraq; north eastern desert of Saudi Arabia; drained marshes (Ahwar) area in southern Iraq; and Sabkhas, dry marshes and abandoned farms in Iran at the northern coastal area of the Arabian Gulf [19] [37]. Dust events that occur can affect air temperature through the transportation of aerosols and desert dust modifying radiation fields balance by cooling or heating surface temperatures and between atmospheric layers that can last for several hours depending on several factors like the surface albedo and dust layer elevation [9] [38]. Measurements were taken at 4 radial sampling areas (2, 6, 10, 14 km) from Kuwait’s City center from 10 sampling points that were selected at (248, 238, 226, 216, 206, 196, 184, 174, 165, 154) degrees at each radial area, sampling 40 locations in total (Figure 1). Sampling occurred from the 26^th January to the 8^th February 2019. A wind rose indicating wind speed and direction for the sampling duration (Figure 2).

Magnetic measurements were conducted at the laboratory of the Departments of Bioscience Engineering of the University of Antwerp, Belgium. Dried leaf samples and dust samplers were first wrapped in A4 plastic cling wrap and placed in a 6.7 cm³ plastic cube container. Initially the magnetic susceptibility of the samples was measured at room temperature using a Barrington MS2B magnetic susceptibility magnetometer (Barrington Ltd., U.K.). Samples were first tested for low frequency (χ_LF) and then high frequency (χ_H) enabling frequency dependent susceptibility χ_FD (%) to be calculated. However, χ_FD (%) could not be calculated for dust sampler as readings were too low for χ_LF.

Samples were then measured for natural remnant magnetization (NRM). Then individually magnetized in a direct current (DC) field of 1T and measured for saturated isothermal magnetization (SIRM). Followed by individually magnetized in a direct current (DC) field of 1T and measured for saturated isothermal magnetization and then magnetized at reverse fields −20 mT and −300 mT to measure SOFT (IRM_−20mT), HIRM (IRM_−300mT) (Equations (1)-(3)). All magnetizations were carried out using a Molespin pulse magnetizer (Molspin Ltd., UK) and followed by measurement using an AGICO magnetometer. SOFT and HIRM magnetic parameters were also converted to percentages (Equations (4) & (5)). The s-ratio was calculated to determine the relative concentration of antiferromagnetic material. Each sample was measured thrice to avoid measurement errors and values were normalized to a sampling pot volume of (10 cm³) and by average specific leaf and sampler area (cm²) to obtain a normalized value final SIRM value expressed as μA (A = A m²/m²).

SOFT = ( SIRM − IRM − 20 ) / 2 (1)

HIRM = ( SIRM − IRM − 300 ) / 2 (2)

SoftIRM% = SOFT / SIRM × 100 (3)

HardIRM% = HIRM / SIRM × 100 (4)

s -ratio = IRM − 300 / SIRM (5)

Statistical analysis was performed using Statistical Package for Social Science (SPSS, (21.0)). Descriptive statistics were calculated for NRM, SIRM, s-ratio, HIRM, SOFT, SOFT% and HIRM% for radial distance, sampling degree and azithumal direction. ANOVAs were performed to identify the possible effects of radial distance, sampling degree and azithumal direction on magnetic values NRM, SIRM, HIRM, SOFT, HIRM%, SOFT%. Pearson’s correlation coefficients were performed to test the relationship.

A factorial analysis was carried out to identify whether there are any correlations between the various magnetic parameters. An exploratory factorial analysis was applied, because there is no a previous structure of factors to be tested. Extraction method of factors was conducted through unweighted least squares (ULS), because other approaches (maximum likelihood ML) did not reach convergence. The choice of the number of factors is based on the Kaiser’s criteria (eigen values higher than 1). Varimax rotation was conducted in order to simplify relationships between factors and variables. The assigning of parameters to factors to carry out interpretations consists of saturation retentions > 0.5.

The spatial variation of magnetic parameters and difference between azithumal directions for leaf and dust samplers were illustrated using geostatistical interpolation using Kriging, with a power of 2 to indicate the variance of magnetic concentration and grain size based on the distance on the direction and distance of two sampling locations, using ArcGIS (10.6.1) (Environmental System Research Institute (ESRI), Redlands, Canada). It models the trend as a linear function with independent external variables it derives the local mean (trend) of the dependent magnetic variables enabling the assessment of values at unobserved locations [39]. Kriging produces unbiased predictions with minimal variance based on the principle that variables are regionalized and utilizes the spatial structure of the data [40]. The overall mean of dust sampler of each magnetic parameter was inputted to compare passive biomonitoring techniques to active biomonitoring.

P. dactylifera samples for (W) values were lower than (NW) values for NRM, SIRM and s-ratio. There was correlation between NRM and radius for P. dactylifera (p = 0.001). Overall radial distances there was a difference between mean s-ratio, SIRM and NRM for P. dactylifera (W) and P. dactylifera (NW) was 0.1, 22.58 µA and 2.24 µA. A multiple regression model was adjusted by radius, this exhibited that for two sample points in the same direction and radius, there were significant differences for NRM values between (W) and (NW) P. dactylifera samples (p = 0.009). SIRM at the 2 km radial distance at every sampling point, was determined to be close to statistical significance for P. dactylifera (W) and (NW) (p = 0.064). s-ratio at the 2 km radial distance at every sampling point, was determined to be close to statistical significance for P. dactylifera (W) and (NW) (p = 0.039). The correlation between radial distance, sampling degree and HIRM was observed at the 2 km radial distance, between (W) and (NW) (p = 0.039).

For P. dactylifera (W) a contrary association was observed for HIRM (%) and radius (p = 0.017), with the beta coefficient being negative (−0.374). Radial distance affected NRM and HIRM, with NRM values dropping linearly as radial distance increased away from the city center, indicating a 90% difference between radial distances of 2 and 14 km, (p = 0.001). The contrary was perceived for HIRM values as they rose with distance by 34%, 52% and 9% at 6, 10, 14 km, respectively. While for dust samplers NRM values increased with increasing radial distance (p = 0.002). Figure 3 indicates that distance has the same impact on NRM and is independent of azithumal direction. The south azithumal direction is the lowest of the group (Figure 3), whereas, SIRM was the lowest at 10 km at 85.54 µA > 6 km at 124.96 µA > 2 km at 183.95 µA > 14 km at 190.57 µA. The average s-ratio for dust samplers for overall radial distances is 0.96, with s-ratio increasing as distance increases (p = 0.005). Overall NRM for dust samplers

for all sampling directions ranged from 0.63 to 5.20 ± 1.47 µA. All directions exhibited similar NRM values with the exception of 236˚ indicating lower NRM values than 248˚ (ref) with a weak tendency, (p = 0.066). Dust sampler SIRM ranged from 34.43 to 190.57 µA. At 184˚ significantly higher SIRM values were determined than 248˚ (ref) (p < 0.001), as well as a significantly higher SOFT value (p < 0.01). A significant difference in Soft IRM% was displayed for 236˚ (p = 0.005) and 165˚ (p = 0.004) in comparison to 248˚ (ref). No sampling degree indicated a significant difference for s-ratio, although 165˚ displayed a suggestive trend (p = 0.060).

NRM for dust samplers were; North ranged from 0.96 - 6.42 ± 1.27 µA, East from 0.29 - 6.33 ± 1.41 µA, South ranged from 0.63 - 11.01 ± 1.82 µA and West ranged from 0.37 - 6.05 ± 1.41 µA. Significant differences were determined between dust samplers depending on azimuthal direction, the south azimuths exhibited lower NRM values than the reference direction east (p = 0.022). For dust samplers SIRM values were highest from the North (190.57 µA) > E (106.71) >S (183.95) > W (95.46). Dust samplers west azithumal direction showed higher s-ratio than the east (p = 0.013). There was a strong tendency regarding the west azithumal direction as it exhibited higher Hard IRM% than east (ref) (p = 0.080) (Figure 3). North wind direction showed lower soft IRM% than East (ref) (p = 0.066) (Figure 4).

For dust sampler radial distance, sampling degree and azithumal direction accounted a significant amount of variation of NRM (p = 0.011; R² = 0.184), SIRM (p = 0.001; R² = 0.223) and s-ratio (p = 0.044; R² = 0.166), Soft IRM (p = 0.006; R² = 0.197) and Soft IRM% (p = 0.001; R² = 0.226). The percentage of total variation by factors is 78.7%, there are five factors that justify the 78.7% of common variance between the 14 magnetic parameters, and this demonstrates a moderately high strength of correlation between parameters (Table 1). The first factor accounted for 22.6% of the variance relative to the total variance in all the variables. The second factor 17.6%, the third factor 15.6%, the fourth factor 14.9% and the fifth factor 7.9% (Table 1).

Finally, the rotated factor matrix provided partial correlations between magnetic parameters and sampling material that are necessary to interpret the factors (Table 2). The 1^st dimension positively correlates hard IRM and hard IRM (%) and both negatively with s-ratio, all these parameters are measured for P. dactylifera. The 2^nd dimension positively correlates levels of SIRM, hard and soft IRM for dust samplers. The 3^rd dimension negatively correlates levels of s-ratio and hard IRM (%), both for dust samplers. The 4^th dimension positively correlates levels of SIRM and SOFT, both for P. dactylifera. The 5^th dimension corresponds

solely to the SOFT% for P. dactylifera. It should be noted that each different dimension only included parameters of the same sampling material: 1^st P. dactylifera, 2^nd dust sampler, 3^rd dust samplers, 4^th P. dactylifera and 5^th P. dactylifera.

The predicted surfaces spatial distribution of magnetic parameters and inter-parametric ratios indicating the concentration, mineralogy, and grain size of magnetic minerals present in Kuwait for the duration of the study is illustrated in Figures 9-15. There were some similarities in the spatial distribution patterns

for soft IRM, HIRM, hard IRM% and s-ratio between active and passive biomonitoring methods and the overall mean of dust samplers.

Mean (NW) P. dactylifera values at the 2 km were higher than (NW) values for NRM, SIRM, soft IRM and s-ratio by 56%, 16.5%, 15.11% and 3% respectively. While, HIRM, soft IRM%, and HIRM% of (NW) P. dactylifera were lower by −35.6%, −1.3% and −68.8%, respectively. SIRM at the 2 km radial distance at every sampling point, was determined to be close to statistical significance for P. dactylifera (W) and (NW) (p = 0.064). The efficiency of washing or cleaning leaf surfaces in the removal of particulate matter fractures is dependent on the cleaning or washing procedure, some studies rinse samples for 30 minutes using deionized water [41] [42] [43]. Regardless, the standardization of (W) P. dactylifera in comparison to dust sampler will vary as some concentrations of elements in plant tissues can be technically affected by; the soil or substrate they are growing on, interspecies differences, vegetation effects, altitude and proximity to the source [41]. The washing procedure offers one great advantage which is the elimination of soil-relation effect [42]. Aboal et al. (2011) found evidence that by washing moss, Pseudoscleropodium purum (Hedw.) M for 30 seconds does not completely remove all of the particles deposited on the moss surface [44]. This is further supported by Bustamante et al. (2015) results that suggest that there is a potential link between the sources of the PAHs found in particles attached to mosses and of those bioconcentrated in their tissues as concentration of PAHs in both washed and non-washed were affected in relation to the emission source [43]. However, a study on the use of washed and unwashed P. dactylifera leaves as a biomonitor for Fe, Pb, Zn, Cu, Ni, and Cr concentrations in Ma'an city, Jordan determined that the amount of metals in the investigated area removed from the leaves by washing differed greatly depending on the contaminant level at each studied area, ex. 24% - 60% of the lead was removed by the washing process [44].

Magnetic susceptibility was negligible in both biomonitoring techniques, this can be contributed to magnetic susceptibility being more suited to higher magnetic materials (e.g. soil and rock) and that diamagnetic minerals quartz, calcium carbonate, feldspar and calcite enrich in Kuwait’s dust [45] [46] [47] [48]. Kuwait’s soils are classed “silt loam” and “silt” with a size fraction of (2 - 62.5 µm), containing less than 10% clay [49]. According to Al-Awadhi & AlShuaibi (2013), grain size percentages vary between the city and the open desert, open desert dust consists of 63% fine particles and 37% coarser particles of the total dust samples, while Kuwait city’s dust consist 39% fine and 56% coarse particles [37]. Similar mineralogy’s are displayed across soils across various sites in Kuwait they are made-up of (27% and 54%) quartz, (~21%) plagioclase feldspar, (~10% - 20%) calcite, smaller amounts of clay minerals (palygorskite, illite-montmorillonite, and smaller amounts of kaolinite and little, if any, dolomite [46] [48]. Montmorillonite and palygorskite have elements Al, Fe, Ti and Mn that occur together or attached to them [49]. Small induced magnetizations are acquired by diamagnetic materials, which align opposite to the applied field direction, diamagnetic substances include; water (X ~ –0.9 × 10⁻⁸ m³∙kg⁻¹), quartz (X ~ –0.6 × 10⁻⁸ m³∙kg⁻¹), and calcium carbonate (X ~ –0.5 ×10⁻⁸ m³∙kg⁻¹) [46]. These low susceptibility values of <2.0 are an indication of virtually no or <10% SP grain in samples as a result of dust probably having substantial paramagnetic and diamagnetic contributions. Whereas, ferrimagnetic fractions with higher paramagnetic/diamagnetic contributions are considered as lithogenic if anthropogenically produced magnetic fractions do not significantly dominate those contributions [47]. Re-suspended dust samples from the Middle East have a Fe/Al ratio range of (Fe/Al = 0.87 - 1.38) PM₁₀ and (Fe/Al = 0.74 - 1.10) for PM 2.5 [49]. Fe and Al are predominantly in chemical forms of oxide in species and residual forms probably from the oxides and silica of the land, therefore a lithogenic contribution can be assumed [49] [50]. However, because of the low contribution of iron oxide from the parent rock, the χ lf value of all studied sample were considerably low leading to the conclusion that magnetic minerals all studied samples are predominantly contributed by different anthropogenic sources and not lithogenic or pedogenic ones [51] [52].

For passive biomonitoring P. dactylifera had an overall maximum SIRM average of 155.08 ± 35.74 μA. Similar findings were observed by Van Wittenberghe et al. (2013) for Phoenix canariensis, which was determined to have a maximum SIRM average of (189 ± 49) × 10⁻⁶ A [53]. While active biomonitoring, dust samplers values were lower accounting for a 36% difference in mean SIRM values between (W) P. dactylifera and dust samplers and a 54% difference for (NW) P. dactylifera, suggesting passive biomonitoring through the use of P. dactylifera accumulates a higher number of ferrimagnetic material than active biomonitoring using dust samplers. The difference in SIRM values is due to P. dactylifera leaves undergoing longer exposure duration time, in comparison to dust samplers period of 2 weeks as well as wax encapsulation. These observations coincide by Declercq et al. (2020) that interpolated active (strawberry) and passive (grass) biomagnetic monitoring methods revealed enhanced SIRM values distinctive for industrial areas in comparison to (passive) plastic coated cardboards (PCCs) biomagnetic monitoring that did not allow to accurately delineate a clear spatial pollution trend because of the smooth sampling surface resulting in the majority of SIRM values not to exceeded 50 μA [54]. These observations are further supported by Barima et al. (2016), three ornamental species (Jatropha interrigima, Ficus benjamina and Barleria prionitis) in Abidjan (Ivory Coast) were evaluated for PM retention and it had been determined that leaf SIRM values were higher on mature leaves suggesting the accumulation of particles in leaves over time especially in waxy species (Ficus benjamina) as PM is encapsulated in the wax structure over the growing season [30].

Both (NW) P. dactylifera and dust sampler have considerably higher SOFT values than HIRM values, with mean HIRM at 2.81 μA, and 3.01 μA, where as SOFT values were 176% and 163% higher, respectively. Both (NW) P. dactylifera and dust samplers have considerably higher values of Soft% than hard IRM%, with soft% making up 58.7% of SIRM for (NW) P. dactylifera and 67.8% for dust samplers. Xia et al. (2012) determined that there is a linear correlation between SIRM and SOFT for surface soil samples which suggests that they are dominated by ferrimagnetic minerals, as IRM of surface samples were around 98% of the SIRM at magnetic field of 300 mT, implying that low coercivity magnetite and maghemite dominant the remanent magnetization of the samples [55]. This signifies that magnetic minerals in Kuwait are dominated by ferrimagnetic mineral of anthropogenic origin that exhibit soft magnetic behavior, with low coercivity, that are in the multidomain (MD) range, with a grain size of (>10 - 15 µm) and at the superparamagnetic/stable single domain (SP/SD) border (80 nm - ~10 - 15 µm) [56] [57]. As magnetic iron oxide is associated to large number of magnetic particles that are released from traffic emission, industrial activities and fuel combustions they act by a preferential adsorption with heavy metals Pb, Zn, Cu, Cr, Cd on the surface of iron (III) oxyhydroxides or present in the form of substituted spinels of Fe_3-xM_xO₄, this enables magnetic measurements to order to monitor environmental pollution [58]. Magnetic iron oxides are associated to a large number of magnetic particles that are released from traffic emission, industrial activities and fuel combustions they act by a preferential adsorption with heavy metals Pb, Zn, Cu, Cr, Cd on the surface of iron (III) oxyhydroxides or present in the form of substituted spinels of Fe_3-xM_xO₄.

Low HIRM% values for (NW) P. dactylifera and dust samplers average at 3.77% and 6.57%, an indication of fairly low concentration of hematite/goethite. This data is comparable to modern African soil samples from Niger, Morocco and Tunisia sampled as potential dust sources that displayed a HIRM_100% between 8% and 32% [46] [59] [60]. This implies that low coercivity multidomain (MD) ferromagnetic minerals (magnetite) dominate the magnetic properties of PM, while incomplete anti-ferromagnetic minerals make up a low proportion [61]. Natural sources are reflected through hard IRM%, this is possibly due to the formation of hematite favoring drier climates with low water content and high temperatures owing to the dehydration ferrihydrates [62] [63] [64]. The presence of ferrimagnetic mineral components dominating the samples is further confirmed as s-ratios were close to 1 reaching almost to unity over all samples, as low s-ratio is identified as haematite, high s-ratio is greigite [65].

A relationship between radial distance and NRM for P. dactylifera was observed, with NRM values dropping as radial distance increased, moving away from the highly urbanized city center and the coast towards more suburban, industrial areas. The relationship between magnetic intensity and land use corresponds with Kardel et al. (2012) and Mitchell and Maher (2009) findings that noted that SIRM is lower in less urbanized land classes in comparison to central city areas that experience a higher intensity of traffic [22] [45]. The opposite was found for HIRM% as values declined with radial distance this may signify that there are different sources away from the city center and/or mixing with other sediment magnetic components [46]. Radial distance significantly correlated with s-ratio, Hard IRM% and soft IRM% and xFD(%) for dust samplers. All these parameters increased as distance increased in a linear pattern. Only for dust samplers were there some specific correlations in regards to sampling degrees. For example, with direction 9 suggesting higher values of NRM, HF and LF in comparison to reference (direction 1) over radial distances of 2, 10, 14 km. Indicating a longitudinal pattern from the city center outwards that may be affected by the airport that within the vicinity.

Correlations between SIRM, HIRM and SOFT magnetic concentration for dust sampler in this study were R² values are above 0.7. It is clear that high values of χ_lf, SIRM, SOFT are identified as magnetite concentration-related parameters and are linked to high element concentrations that arise from high temperature fossil-fuel combustion processes, vehicles, power plant and cement plant operations, building and road surface materials (e.g., metallic fragments, slag and building materials) [57] [59] [63]. These results coincide with findings on the correlation of magnetic properties and heavy metals content in urban soils of Hangzhou City, China (Lu & Bai, 2006), that identified that there is a significant correlation that exists between Cu, Zn, Cd and Pb and χ, ARM, SOFT and SIRM, demonstrating the presence of heavy metals and magnetic particles MD grains that are of anthropogenic origin [57]. These results are also consistent with findings from Wang et al. (2012) that χ_lf, SIRM, SOFT, and HIRM, as well as S-300 are linked to relatively greater hematite contributions along with As, Ba, Mn, Cu, Pb, Fe and Ti [59]. However it must be noted, that the factorial analysis showed that there was no relationship between magnetic parameters obtained from dust samplers and those from P. dactylifera. It may be assumed that the difference in values may be a result of exposure time, surface morphology, as well as the fact that leaf contents sum up from depositions, root uptake and also by seasonal differences in emissions influencing PM accumulation rate [25] [66].

The effect of azithumal direction was tested using the dust samplers to test the effect of azithumal sampling degree at each sampling location impacted magnetic parametric readings. It was observed that the prevailing wind direction (NW) at 8 - 10 m/s coincide with the direction in which exhibited the highest SIRM values for dust samplers, SIRM were highest from the North (190.57) > E (106.71) > S (183.95) > W (95.46). s-ratio and HIRM% were also highest in the west azithumal direction indicating that the azimuthal position of where a sample collected causes a NRM, SIRM, s-ratio soft IRM% variations. Mass concentration of total suspended particles (TSP), PM10, PM 10 - 2.5 fractions are affected by meteorological parameters; wind speed, wind direction, rainfall and temperature [67]. Dust events in the Middle East are determined to have relatively weak wind speeds that can reach 10 ms causing dust-in-suspension to predominantly to occur [68]. These findings concede with Hofman et al. (2013) results in the assessment of the spatial distribution of particulate matter of deposited particles on tree crowns in an urban street canyon that determined that air circulation caused azimuthal effects and that it is directly influenced by street architecture, as individual tree crowns indicated an azimuthal leaf SIRM variation (p = 0.0446) depending on their position in a street canyon [69]. Hofman et al. (2013) found that all edge trees that were exposed to intense air circulation were determined to have the lowest SIRM values at the windward side of the tree crown (NW-SW), whilst the highest leaf SIRM values were at the leeward sides of the tree crown (NE, SE) as changing airflows increase of impaction and interception of particles causing azimuthal SIRM variations [69]. This is further confirmed by a study conducted by Zhang et al. (2008) on Chinese willow (Salix matsudana) tree ring cores, that suggests that magnetic particles enter into the xylem during the growing season as tree ring cores facing the pollution source displayed higher SIRM than other sides of the tree [70]. It must be noted further research needs to conduct on the comparison of azithumal effects between P. dactylifera (active) and dust sampler (passive) biomonitoring effects over a long duration of time to see if the amount of deposited magnetic particles differed at similar azithumal degrees or whether they reach equilibrium.

Interpolation of data has enabled more insight into the spatial distribution and behaviors of pollutants in the area as it included all input points including the outliers. However the magnetic maps may not be true spatial representatives of the local concentrations as previous studies that have used kriging to map PM have observed that by mapping large geographical areas with large distances between monitoring sites to have reduced reliability in the predicted values [71]. As seen on the maps there were some similarities in the spatial distribution’s trends of soft IRM, HIRM, hard IRM% and s-ratio between active and passive biomonitoring methods and the overall mean of dust samplers. Passive biomonitoring offers more information on long term deposition rates and pinpoints hot spots and PM sources in comparison to active biomonitoring that offers more short-term PM deposition accumulation rate and behaviors in relation to meteorological conditions during the sampling time [54].

The azithumal direction in which the dust samplers were placed gave distinct results this may be a result of varying infrastructure across the area as well as building height and wind direction (Figures 11-13). The magnetic outcomes for dust samplers follow a similar pattern to the wind trajectories. The highest concentration appeared in Kuwait City as well near the vicinity of industrial areas, ports and power plants and desalination plants and their surrounding area. As industrial hotspots and dense residential areas with high vehicular traffic tend to be exposed to higher PM 2.5 concentrations [72]. The prevailing wind direction during the sampling was northwest, the decrease in SIRM and NRM values and spread in maps can be attributed to the prevailing wind direction especially when comparing dust samplers to P. dactylifera (Figure 9 & Figure 10). The transportation, dispersal and dilution of air pollution are actively impacted by local winds, high amounts of air pollutants particularly from pointary or linear sources that produce high proportions emissions, to receptors or samplers at distinct wind directions [67]. Al-Nassar et al. (2005) determined that wind power density (WPD), that is defined as the frequency distribution of wind speed and the dependence of wind power on air density and the cube of the wind speed, expressed in Watt per square meter (W/m²), in Kuwait is lower at the bay and increases outwards into open flat desert areas in the northern, northwestern and southern parts of the country, such as Al-Wafra, Al-Taweel and Umm Omara, indicating that wind direction has a significant effect on daily magnetic mineralogizes and grain size [73]. Prevailing wind direction is seen to impact, magnetite concentration to a range of 0.24% - 0.89% at distance of 500 m away from cement plants, while decreasing to a concentration of 0.02% to 0.16% at a distance of 3 km or more [74]. As well as parallel MS profiles measured perpendicular to road surfaces at a distance of 20 m on either side of a road have been determined to be a reflection of clear symmetry to prevailing winds [75]. A study on pollution utilizing roadside plant leaves in an Indo-Burma hot spot region found that plants showed higher magnetic concentrations in sites with poor roads that experience heavy traffic, street dust load and tall buildings that tend to concentrate pollutants as there is lower rate of PM dispersal [76]. As wind flow patterns can be skewed in cities with individual or groups of buildings, governing the interaction between the flow and buildings in the vicinity and local air pollutant dispersion [77]. Further research can address these limitations through the application of 3D spatial visualization that incorporates seasonal and meteorological analytics that can better help in the examination of local air circulation and its role in the dispersion and transport of PM throughout urban infrastructure.

The highest magnetic concentration values of NRM and SIRM for P. dactylifera occur near Kuwait bay, with the majority of urban infrastructure and PM sources located at the bay, the proximity of Kuwait Bay mandates the effects land and sea breeze impacting air flow and PM dispersion. In a previous study conducted by Engelbrecht et al. (2009) on magnetic measurements of dust samples collected on Nuclepore filters it was determined that coastal Kuwait samples are 3 to 5 times more magnetic, in comparison to other locations [78]. Higher SIRM values are distinct for dust sampler although its more distinct in Kuwait City in comparison to P. dactylifera which has high SIRM values generating from the Kuwait bay area inwards (Figure 10). The soft diamagnetic behavior was found to be similar to North of Kuwait, while the central and southern dust samples were distinctive by their ‘hard’, haematite-like behavior [78]. This could be as a result of an association between lithogenic origins of soil, organic matter content and certain magnetic properties of urban street dust that may vary due to lithogenic origins depending on varying locations within an area and roads [79] [80].

The maps clearly demonstrate that dust samplers although time consuming are suitable method in the determination of short-term PM pollution distribution patterns that are heavily influenced by prevailing wind directions, while P. dactylifera offers corresponding information towards determining long term PM pollution patterns, hot spots and sources. The reliability of these predictions would thoroughly be improved in further research through the addition of meteorological influences to the predicted values to better discriminate magnetic particles dispersal patterns [72].