Elemental Composition and Concentration of Atmospheric Particulate Matter in Kuwait

Corresponding author: Jasem M. Al-Awadhi Department of Earth and Environmental Sciences, Faculty of Science, Kuwait University, P.O. Box 5969, 13060 Safat, Kuwait Email: jawadhi1@live.com Abstract: Hourly PM10 and PM2.5 samples were simultaneously collected at 10 and 3 urban sites, respectively in Kuwait from March 2014 to February 2015, to study the air quality and heavy metals content and Total Petroleum Hydrocarbons (TPHs) in PM10 samples. The annual average concentrations of PM10 and PM2.5 were 152 and 97.3 μg/m 3 , respectively, with an average PM2.5/PM10 ratio of 70%. The contamination level of heavy metals in PM10 samples was assessed in terms of Enrichment Factor (EF) using mathematical models. The elements associated with an anthropogenic activity in the PM10 samples; i.e., Pb, Cu, Co, Cd and Zn, were 2.4, 12.9, 14.6, 42.6 and 156.2 times higher than the corresponding background values in Kuwait soil. TPH was the dominant pollutant in the PM10 samples. In general, most daily average Air Quality Indices (AQI) of PM10 concentrations fell under the categories of “good” and “moderate”.


Introduction
Atmospheric particulate Matter (PM) consists of a mixture of solid particles and liquid droplets from a variety of sources and can be divided according to particulate size into dust fall (with an aerodynamic diameter of 100-1000 µm), Total Suspended Particulate (TSP, with an aerodynamic diameter of less than 100 µm), inhalable particulate matter (PM 10 , with an aerodynamic diameter of less than 10 µm), coarse particulate (PM 2.5-10 , with an aerodynamic diameter of 2.5-10 µm) and fine particulate (PM 2.5 , with an aerodynamic diameter of less than 2.5 µm) (SEP, 2003). In general PM 10 originates from mineral dust, while PM 2.5 and finer PM, which have more serious health effects compared to coarser PM, have various sources, normally dominated by combustion.
PM is composed of various heavy metal elements, most of them having toxic effects that have been extensively studied in urban areas. The determination of heavy metal element concentrations in the ambient suspended particulate matter of major urban areas is an important component of pollution studies (Goyer, 1996;Donaldson and MacNee, 1998;Singh, 2001). Windblown dust generally contributes to the release of heavy metals in the ambient atmosphere (USEPA, 1999a;AMAP, 1997). Hazardous suspended PM pollutants are released by various sources like factories, refineries, automobiles, power plants, sewage plants, etc. (Dannecker et al., 1990). Pollution of this nature is a worldwide problem, including in Kuwait's urban areas, since these metals persist in the environment and most of them are toxic to living organisms. Although considered essential for various human biological functions in trace quantities, several of these heavy metals pose serious toxicological risks at high levels (Addo et al., 2012).
Emissions of PM in Kuwait have been increasing remarkably in the past two decades; thus, it has become a problem of significant concern (Al-Awadhi and Al-Awadhi, 2006;KEPA, 2014). The PM levels in Kuwait's cities have been on the rise, attributable to a significant increase in vehicular traffic-the number of vehicles in 2004 was 1.042 million, whereas this number reached to 1.916 million in 2014 (KEPA, 2014). Traffic remains the main source of air pollution in Kuwait's cities, while upstream/downstream industrial sources such as power plants, oil fields and various other industries contribute to a much lesser degree (Al-Salem and Khan, 2008). Table 1 shows the total PM 10 and PM 2.5 emission rates from various sources in Kuwait city.
Although real-time monitoring of suspended PM is a vital means for predicting pollution levels, it does not describe the composition of the pollution. Accordingly, this study aims to investigate the air quality at ten sites in Kuwait, in terms of concentrations of suspended PM (PM 10 and PM 2.5 ) and the elemental composition of PM 10 in terms of heavy metals. The measured PM 10 levels are compared with the Kuwait Environment Public Authority (K-EPA) guideline limits.

Prototype Data Collection
The PM 10 and PM 2.5 data were collected from ten and three Air Quality Monitoring (AQM) stations, respectively, set up by K-EPA, using Met-One (USA) Model Beta Attenuation Mass (BAM)-1022 monitors. Continuous measurements of PM 10 and PM 2.5 , corresponding to a period between March 1st, 2014 and February 28th, 2015 (i.e., 12 months), were collected. For correlation purpose, the major meteorological parameters, including wind speed m/s) and direction (degrees), relative humidity (%) and ambient temperature (°C), were also obtained from the AQM stations (Gill Instruments, UK) over the same monitoring period. The monitoring stations for PM 10 are: Mutla (MU), Al-Jahra (J), Saad Al-Abdullah (SA), Shuwaikh (SH), Mansuria (MA), Al-Salam (S), Rumaithiya (R), Al-Ahmadi (A), Fahaheel (F) and Ali Al-Salem (AS) (Fig. 1), while the monitoring stations for PM 2.5 are: Al-Jahra (J), Saad Al-Abdullah (SA) and Al-Ahmadi (A). Each of these stations is located in a residential area which differs in its surroundings, geographical characteristics and sources of air pollution.
Each station is provided with a continuous gas monitor. The monitors are operated automatically and measurements are normally recorded every five minutes continuously, 24 h a day, throughout the year. PM concentrations are measured by the beta-gauge method. A span calibration is used to check the validity of the data, where a sample of known concentration of pollutants is tested by the machine. This is done every three to four days to check for the behavior and accuracy of the monitors. Also, full system checks are routinely carried out once every three months by specialists.
Ambient PM measurements were retrieved from a central online data acquisition system managed and controlled by EnviDAS software. The data was filtered manually by discarding NULL values and the total percentage of filtered data recorded was about 96.5%: i.e., 3.5% of the data values were cleaned), which is considered an acceptable percentage of data recovery for air quality assessment (Khan and Al-Salem, 2007

Methodology
Inductively Coupled Plasma Optical Emission Spectrometry (ICPOES) was used for determining element presence in 15 PM 10 samples. The sample containers (PTFE flasks) were soaked overnight with 10% HNO 3 , followed by rinsing thrice with the same acid and thrice with de-ionized water and finally dried in an oven at 50°C. For chemical analysis, the extraction and analysis of heavy metals was carried out as per the US EPA method IO-3.2 (USEPA, 1999b). As per the method, PM 10 samples collected on glass fiber, along with their respective blank filter papers (1 blank per sample), was digested by Mars 6 Microwave Digestion system using aquaregia (3HCl + 1HNO3) for 23 min at about 180°C. After digestion the solutions were neutralized with 2% boric acid and gauged. The advantage of using hydrochloric acid is the dissolution of the quartz filter, which avoids the need for filtration. Field blanks and certified urban dust (NIST 1648) were digested and analyzed along with the samples. For quality assurance, only samples with a recovery value >95% were considered for analysis, ensuring the precision and reproducibility of the measurements.
Total Petroleum Hydrocarbons (TPHs) in the PM 10 samples were measured using the US-EPA 8015C method (GC analysis) and US-EPA 3540C (Extraction) procedure. The filter samples containing particulates matters were extracted on Soxhlet Extraction unit (7890A GC system with flame Ionization Detector-Agilent) for 4 h with 60 mL dichloromethane, filtered with hexane, concentrated to 1 mL and analyzed on GC system equipped with fused silica capillary column, operated in split mode using carrier gas, helium. Then, the samples were heated at 50°C and gradually increased through 3 ramps up to 330°C. The GC system was calibrated using C8-C40 standards from 175 to 1750 µg mL −1 and the samples were quantitated against the curve generated through calibration.
The Enrichment Factor (EF) equation (Sutherland, 2000) was employed for calculating the level of PM contamination by heavy elements: Where: The persistency of pollutant exceedances; hourly, daily and monthly averages; pollutant ratios (PM 2.5 /PM 10 ) and correlation coefficients between various locations and PMs were investigated using the SPSS software package (Version 19).
Air quality in terms of PM 10 was also assessed through the use of the Air Quality Index (AQI) developed by Al-Shayji et al. (2008), for Kuwait, based on the guidelines proposed by the US Environmental Protection Agency (USEPA, 2006). Table 2 shows the AQI category codes, suggested for the Kuwait EPA and their ranges for PM 10 , based on health effects.

Results
Basic Analysis for PM 10 Table 3 describes the statistic of average all measured concentration of PM 10 . The results indicate that the variation in the PM 10 concentrations ranges between 5 and 9.6% and the mean values are greater than the median values having positive skewness values, i.e., values approaching to zero from the positive direction. Thus under these circumstances, PM 10 pollutants can be considered as a stable condition.
The range of the two percentile points, defining the upper and lower tails of data, indicates a clear difference between the maximum and minimum measured values. Such difference is also shown in the inter quartile (difference between the 75th and 25th percentile) and the skewness number indicates that the data deviates from symmetry around the mean, while positive kurtosis value indicates a shape flatter of data than normal.

The Arithmetic Mean Concentration
The monthly average concentrations of PM 10 at each station are shown in Fig. 2. It is noticed that SA station has the maximum monthly average concentration among all stations, with a concentration of 387.5 µg/m 3 , which is 1.1 to 2.5 times higher than at other stations. The monthly average concentration at the 10 stations varied from 23.1 µg/m 3 (in November) to 387.5 µg/m 3 (in February), with an average of 152 µg/m 3 (Fig. 3). The highest concentration observed in February deviates from the normal trend associated with dusty days, which peak annually in June. The concentrations by season varied from 107 µg/m 3 (autumn) to 173-174 µg/m 3 (spring and summer). High PM 10 concentrations in summer can be attributed to the effects of northwesterly winds, which carry dust from the Iraqi Desert into Kuwait. High winter averages for PM 2.5 and PM 10 occur due to various factors that account for elevated amounts of PM 10 in that season, such as the noticeable increase in the traffic volume due to the influx of workers and students during winter season; moreover, the normal lower mixing heights with stable boundary layers (inversion layer) during the winter season, results in trapping more PM and limiting its dispersion in the atmosphere. The annual average PM 10 concentration for Kuwait's cities reached 152.2 µg/m 3 , with minimum and maximum concentrations of 112.0 µg/m 3 (J) and 190.5 µg/m 3 (AS), respectively.

Exceedance of PM 10 Permissible Limits
A comparison between the daily mean concentrations and applicable air quality standards promulgated by K-EPA (350 µg/m 3 ) for residential areas and the proposed standard (150 µg/m 3 ) reveals that the data in all monitoring stations exceeded the permissible limits. With respect to effective K-EPA rules and regulations (350 µg/m 3 ), the percentage of data exceeding the limit at the 10 stations varied from 3.3 to 10.1%, with an average of 6.7%, while on the proposed value by K-EPA (150 µg/m 3 ), the percentage of data exceeding the limit at the 10 stations varied from 15.3 to 34.0%, with an average of 26.2% (Fig. 4). On average, 24 and 96 exceedances were recorded per year during the period between March 1st, 2014 and February 28th, 2015 applying PM 10 standard values of 350 and 150 µg/m 3 , respectively. Considering the proposed PM 10 permissible limit of 150 µg/m 3 , the total average percentage of exceedances during spring, summer, fall and winter were 28.4, 37.8, 13.5 and 24.9%, respectively. Note that highly elevated levels of PM 10 are known to occur in Kuwait (mainly in summer) resulting in a reduction of visibility (Al-Hajraf et al., 2005). The annual limit value of 90 µg/m 3 , specified by K-EPA, was exceeded at all monitoring stations over the 12 months of monitoring. The annual mean concentration averaged over all 10 stations was 152.2 µg/m 3 .  175.0 The range of the two percentile points, defining the upper and lower tails of data, indicates a clear difference between the maximum and minimum measured values. Such difference is also shown in the inter quartile (difference between the 75th and 25th percentile) and the skewness number indicates that the data deviates from symmetry around the mean, while positive kurtosis value indicates a shape flatter of data than normal  Annual mean PM 10 concentration roses were constructed with 16 wind directions in order to investigate the relative contributions of winds blowing PM 10 from each direction (Fig. 5). The plots revealed that winds blowing PM 10 came predominantly from between the northwest and south-west corridors at the monitoring stations located near the edge of the open desert (i.e., MU, SA and AS). This suggests that dust potentially contributes to the ambient PM 10 load in these open areas. Rising/suspended dust and dust storms in Kuwait are generated by the prevailing northwesterly winds, representing 60% of wind-direction measurements taken in Kuwait; moreover, these winds are dominant in the summer season (Al-Basri, 1993). On the other hand, the stations that are located in the inner cities (i.e., R, SH, F and J), were subjected to PM 10 blowing in from all directions with generally elevated levels from the southwest side. In Ahmedi station (A), located to the east of the Burgan oil field, winds blowing PM 10 came predominantly from the northeast and southeast corridors, suggesting that PM 10 from the oil field contributes significantly to the ambient load in this area. The concentrations of PM 10 at all stations showed a high correlation, ranging from 0.91 to 0.03 (r = 0.75) ( Table 4), implying that PM 10 may have common sources, such as traffic, at all stations. The highest correlation occurred between Ahmadi (A) and Ali Al-Salem (AS); these two areas are located near the boundary of the Shuaiba petro-industrial area and the Burgan oil field. Shuwaikh (SH), which is located in the seaport zone near Kuwait city, at a distance of approximately 100 km from these two areas, showed a lower correlation with A and AS, i.e., 0.8 and 0.4, respectively. The prevailing northwesterly winds are likely to influence the diffusion of PM 10 concentrations generated from the petro-industrial area away from the other sites: • Correlation is significant at the 0.05 level (twotailed) • Correlation is significant at the 0.01 level (twotailed)

Variation of PM 10 Levels during the Day
The evolution of hourly pollutant concentrations measured during the day is presented in Fig. 6a and 6b. Figure 6a indicates that an elevation in concentrations occurs during noontime and around midnight. Similarly, Fig. 6b confirms that this finding is applicable during all months. The noontime elevation may be a result of a substantial secondary component beside the exhaust component, such as suspended dust. The midnight elevation may be associated with the inversion layer behavior during the midnight hours (cooling time) resulting in limited dispersion of pollutants and increasing the potentiality of trapping them the air atmosphere; this is due to the relatively calmer conditions from around midnight until the early morning hours favoring the accumulation of pollutants and causing them to reach the ground. Figure 7 shows an inverse correlation between the hourly average variation in relative humidity and PM 10 concentration. This indicates that as the humidity increases, the potentiality of PM 10 emissions from various sources reduces. Figure 8 shows a positive correlation between the hourly average variation in temperature and PM 10 concentration, suggesting that high temperatures during the day impact PM 10 pollution levels in the air. These high temperatures play a major role in rising convective motions, turbulent flows and atmospheric diffusion capacity in the lower part of the atmosphere and as a consequence higher emissions of fine particles is achieved.

Ambient Concentrations of PM 2.5
The variation in PM 2.5 concentrations during the data collecting period is presented in Table 5; these range from 47.1 to 177.1 µg/m 3 at three monitoring stations (A, J and SA), with an average concentration of 97.3 µg/m 3 . These measurements are 4.7 and 17.7 times more than the annual US NAAQS standard of PM 2.5 (10 µg/m 3 ), respectively (Fig. 9). The results obviously indicate that fine PM should be seriously considered as a health impact factor in Kuwait's urban areas. Hourly averages of PM 10 and PM 2.5 pollutants show that both pollutants follow a relatively similar trend of variations during a day (Fig. 10). The calculated ratios of PM 2.5 /PM 10 varied spatially, ranging from 0.31 to 1.4 with an average of 0.7, indicating that fine PM at urban sites is also a concerning problem.     According to Duan et al. (2006), high ratios (larger than the average value) are generally associated with the secondary particulate formation of species and organics, while low ratios are associated with the primary particulate formation of fugitive dust or sand dust blowing from far sources; i.e., long-distance transport. Table 6 presents the hourly analysis of correlations of the PM 10 and PM 2.5 pollutants. The correlation analysis showed that PM 10 and PM 2.5 are strongly correlated (r>0.4 and reaches 0.8), indicating common sources (traffic emissions or dust). The simple linear model method was employed to estimate the mathematical relationship between PM 10 and PM 2.5 . A positive correlation between the PM 10 and the PM 2.5 is recognized through the following relationship (Fig. 11 The value of R 2 for this relationship was 0.86 with a significant correlation coefficient of r = 0.8: • Correlation is significant at the 0.05 level (twotailed) • Correlation is significant at the 0.01 level (twotailed)

Elemental Concentration in PM 10
The elements were categorized into two groups; namely, anthropogenic activity and typical crustal elements. Table 7 shows the concentration of 15 identified elements in the PM 10 samples. Al-Awadhi and Al-Shuaibi (2013) determined the elemental composition of Kuwait top sediments through the analysis of 184 top surface sediment samples collected from various existing aeolian sediments found across the Kuwaiti desert. The calculated ratios of the average crustal elements in the PM 10 sample to crustal elements in the background surface sediment of Kuwait for Ca, Al, Fe and Mg ranged from 0.2 to 0.4, while for Na and K the ratios were found to be 16.3 and 6.6, respectively. This implies that the main source of fine particulate pollution at the urban sites could be from traffic and industrial sources with partial contributions from dust fallout. Average concentrations of the indicator elements associated with an anthropogenic activity that come primarily from industrial sources; i.e., Pb, Cu, Co, Cd and Zn, were 2.4, 12.9, 14.6, 42.6 and 156.2 times higher than corresponding sediment background values (measured from local aeolian sediments), respectively. The average concentrations of Cr, Ni and V were found to be 80, 70 and 60% less than their corresponding sediment background values, respectively. This may lead to the fact that most of the pollutants in the PM 10 samples are produced from anthropogenic sources.

Enrichment Factor Analysis
A cluster analysis using average linkage clustering was performed on the standardized elemental content of 15 PM 10 data with the help of a Statistics Toolbox function (Fig. 12). Figure 12 displays four clusters at a level around 1.5: (1) Co-Cr-Be-Cd-V-Pb-Ni-Mn-Cu; (2) Zn-Mg-K; (3) Fe-Ca-Al; and (4) Na. The analysis shows that clusters 1 and 2 are linked at a higher level (~2), potentially pointing to a common source, while the large separation of Na in cluster 4 suggests that Na might not share the same source as the other elements. Accordingly, this study uses Na as a reference element. To calculate the Enrichment Factors (EFs) for the various elements in the PM 10 samples, the average concentration of Na in the Kuwait sediment is used as the reference element baseline (B ref ), while the average content of each element in the samples is selected as that element's baseline (B n ). Table 8 lists the EFs for 10 elements identified in the PM 10 samples. Table 8 indicates that the mean EFs increases in the following order: Cr, Ni, V, Fe, Mn, Pb, Cd, Cu, Co and Zn., while only Cu, Co and Zn, have mean EFs higher or equal than 2.
The EF helps to identify which elements may have originated from human activities as opposed to natural processes and to assess the degree of this anthropogenic influence. Based on specific EF range, Table 9 presents five contamination categories (Sutherland, 2000;Loska and Wiechuya, 2003). Usually, an EF of 10 and above (high level of enrichment) indicates an anthropogenic origin (Lee et al., 1994;Liu et al., 2003;Balasubramanian and Qian, 2004;Meza-Figueroa et al., 2007). Thus, Co, Cu and Zn in the PM 10 samples, can be attributed mainly to anthropogenic activity.  The PM 10 samples are moderately contaminated by Pb and Cd, since their maximum EF values range between 2 and 5. However, the PM 10 samples are highly contaminated by Zn and Cu, since their maximum EF values fell between 20 and 40, while Co had a maximum EF of more than 40, thus indicating extremely high contamination. Cu and Pb can be emitted from fossil fuel combustion, metal processing, tires abrasion, brake linings, exhaust catalysts, road pavement and corrosion of galvanized protection barriers (Al-Awadhi and Al-Awadhi, 2013). The maximum EFs of other elements were low, indicating that these elements are insignificant contaminants.

Mineralogical and SEM Analysis
Mineralogical analysis of three PM 10 samples showed that all samples consisted of calcite (29.9% on average), dolomite (19.9% on average) and quartz (13.9% on average) minerals, with an appreciable amount of illite, dolomite and albite and rare percentages of orthoclase (Table 10). Khalaf (1989) and Al-Awadhi and Al-Shuaibi (2013) found appreciable percentages of calcite and quartz within fine size fractions of surface sediments and dust samples collected in Kuwait. Accordingly, the genesis and composition of the local dust and aeolian sand should have a direct influence on the mineralogical composition of PM 10 .
The grain size and morphological characteristics of one of the 20 collected PM 10 samples, as observed by the SEM, is illustrated in Fig. 13. The figure distinctly shows different morphological structures and sizes of the grains. Most of the samples generally contain quartz, calcite, dolomite and clay particles. A few samples contain gypsum and feldspars. Fibrous cyan bacteria occur in most samples as well as some pollen grains. Clay particles are commonly less than 0.5 µm in size, while the other minerals (i.e., quartz, calcite, dolomite, gypsum and feldspars) occur as grains or clustered grains of more than 4 µm in size. No significant difference in the sample composition was recognized; however, samples from Ahmadi (A) and Mutla (M) stations, near north and south oil fields, respectively, are relatively rich in carbonates.

Total Petroleum Hydrocarbon (TPH) Analysis
An attempt was made to investigate the toxicity of the persistent organic contaminants in the PM 10 via measuring the TPH at 8 monitoring stations. The qualitative concentrations of TPHs in the PM 10 samples are presented in Table 11. The areas located near oil field/industrial area exhibits higher PM 10 contaminations of TPH than in other areas. Higher mean TPHs concentrations are recorded in AS (296 µg/filter), F and A stations, while lower mean concentration is recorded in J station (189 µg/filter). The elevated mean concentration of TPH in PM 10 samples at R station may be associated with high emission rate due to vehicle movements in this area. Such finding may indicate that while the traffic remains a main source of PM 10 pollution in the residential areas in Kuwait, petrochemical industries and oil fields/refineries are also possible sources in the vicinity of these areas. Figure 14 illustrates the results of AQI values for the daily average of PM 10 concentrations for the period from March 2014 to February 2015 at the AQM stations. On average, 39.6 and 53.64% of the daily calculated AQI values for PM 10 concentrations are categorized as "good" and "moderate", respectively. The moderate values were more abundant in areas with heavy traffic, such as SH, MA and S, as well as in AS, which is located downwind of the Shuaiba industrial area. In the winter season, 43% of the daily average data was categorized as "moderate", while in the summer season, this percentage increased to 80% (Fig. 15). The percentage of elevated values in the "moderate" category in summer, in some cases, may be directly associated with regular dust phenomenon during this season; roadside dust may be another possible source. The overall monthly assessment of the PM 10 in Kuwait (Fig. 16), indicates that about 7.3% of AQI values are categorized as "unhealthy" and "very unhealthy", while 1.3 and 3.8% of AQI values are categorized as "hazardous 1" and "hazardous 2", respectively. This indicates that PM 10 is a worrying pollutant in the cities of Kuwait.

Conclusion
In this study, one-year PM 10 and PM 2.5 data set (March 2014 to February 2015) was evaluated and analyzed for different urban areas of Kuwait. In some areas, 68.4 % of daily mean values of PM 10 exceeded the Kuwait EPA permissible limit (350 µgm 3 ), while the average concentration of PM 2.5 is found to be 9.7 times more than the annual US NAAQS standard (10 µg/m 3 ). Beside the traffic emissions, the prevailing northwesterly winds are likely to influence the diffusion of PM 10 /PM 2.5 concentrations generated from the oil fields and industrial areas, towards the urban areas. The high percentages of calcite and the appreciable amount of quartz found in the PM 10 samples may indicate that dust fallout and the roadside dust are other possible sources. A number of toxic metal were noticeably enriched in the PM 10 samples due to anthropogenic activities. Enrichment factor analysis revealed that PM 10 being contaminated by the following metals with an increase in the order of Cr, Ni, V, Fe, Mn, Pb, Cd, Cu, Co and Zn. The TPH results also show relatively higher values in PM 10 samples collected near oil industrial areas than in other areas.
Seasonal variability of PM levels was clearly noticed. In winter season, lower concentrations in PM levels is attributed to low wind velocity that likely results in lower concentrations of re-suspended roadside dust particles, while higher concentrations in PM levels during summer season are accounted to the effects of frequent dust storms. However, in some summer days, under certain circumstances when there is no dust storm, the PM levels were likely lower due to either perfect atmospheric mixing or reduced number of the vehicles on the road. The analysis of hourly variation in the PM 10 concentrations during the day, also, showed elevation and reduction in the level with respect to the temperature and humidity, respectively.
Although site-to-site differences in PM 10 and PM 2.5 were found, a strong relationship between them was observed, this may indicate similar source origination from either local traffic or industrial sources. In general, the result of AQI shows that PM 10 levels can be characterized as ''moderate''. With such unappreciated levels of PM 10 as well PM 2.5 pollutants in Kuwait's urban areas, mitigations should be implemented to minimize emission rate and health and environmental impact assessment study is recommended to investigate long-term impacts. To decrease PM emission rates, concerned parties (Kuwait EPA) must regulate car emissions and force PM 10 and PM 2.5 regulations governing emissions from industries such as refineries, cement, ceramic and stone factories.