Impacts of meteorology and vegetation on surface dust concentrations in Middle Eastern countries

Science of the Total Environment 712 (2020) 136597

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Impacts of meteorology and vegetation on surface dust concentrations in Middle Eastern countries Jing Li a, Eric Garshick b,c, Ali Al-Hemoud d, Shaodan Huang a,⁎, Petros Koutrakis a a

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston 02115, USA Pulmonary, Allergy, Sleep, and Critical Care Medicine Section, Medical Service, VA Boston Healthcare System, Boston, MA 02132, USA Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA d Crisis Decision Support Program, Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Safat 13109, Kuwait b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• We investigated the characteristics of dust levels in five Middle Eastern countries. • The dust levels in Kuwait were higher than the other countries. • Dust levels showed a distinct seasonal pattern. • Dust levels were affected by regional emissions, wind speed and vegetation. • We explored the associations between influencing factors and dust levels.

a r t i c l e

i n f o

Article history: Received 15 October 2019 Received in revised form 29 December 2019 Accepted 7 January 2020 Available online 8 January 2020 Editor: Jianmin Chen Keywords: Dust NDVI Meteorology Arid region The Middle East

a b s t r a c t Severe dust events have occurred frequently in arid regions, which greatly impacted air quality, climate, and public health. The Middle East is one of the areas in the world impacted by intense dust storms. We investigated the characteristics of airborne dust levels in five Middle Eastern countries (Kuwait, Iraq, Iran, Saudi Arabia, and Syria) from 2001 to 2017. Surface level dust concentrations were determined using the Modern-Era Retrospective analysis for Research and Applications version 2. Kuwait was selected as an example to assess sources and other factors influencing dust levels in arid regions. We performed backward trajectory analysis to identify the dust transport pathways. We quantitatively assessed the impacts of meteorological parameters along with the Normalized Difference Vegetation Index (NDVI). Dust levels in Kuwait were higher than the other four countries, and had a distinct seasonal pattern, with the highest in summer and the lowest in winter. Our results showed that dust levels in Kuwait in January were influenced largely by local emissions, whereas in June they were affected more by emissions attributable to long-distance transport. There were significant positive associations between wind speed in the five countries, particularly Iraq, with dust levels in Kuwait, indicating the impact of nearby desert areas. Significant negative associations were observed between NDVI in Kuwait, Iraq, and Saudi Arabia with dust levels in Kuwait. Our result highlights that climatic variations and vegetation conditions are associated with changes in dust levels in arid regions. © 2020 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail address: [email protected] (S. Huang).

https://doi.org/10.1016/j.scitotenv.2020.136597 0048-9697/© 2020 Elsevier B.V. All rights reserved.

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1. Introduction Dust emissions affect many countries across the world (Goudie, 2014; Lee and Sohn, 2011; Neff et al., 2008; Zhang et al., 2016; Zhao et al., 2010). High levels of dust particles can impact air quality, climate, and public health (Ashrafi et al., 2017; Crooks et al., 2016; Giannadaki et al., 2014; Goudie, 2014; Khaniabadi et al., 2017; Thalib and Al-Taiar, 2012). Dust may influence atmospheric temperature because of its ability to absorb and scatter solar radiation (Alpert et al., 1998), resulting also in a decrease in visibility (Yassin et al., 2018). Dust events were found to be associated with hospital admissions for respiratory disease, such as asthma (Trianti et al., 2017), pneumonia (Kang et al., 2012), COPD exacerbations (Vodonos et al., 2014), increases in mortality from non-accidental causes (Crooks et al., 2016), respiratory mortality (Perez et al., 2008), and cardiovascular mortality (Neophytou et al., 2013). Specifically in Kuwait, Achilleos et al. (2019) reported that between 2000 through 2016 that low visibility days and dust storm days were both associated with increased total daily mortality. Dust storms in Kuwait have also been associated with emergency hospital admissions for asthma and respiratory causes (Thalib and Al-Taiar, 2012). In Ahvaz, Iran between 2014 and 2017, daily mortality attributable to particulate matter was greatest during dust days (Shahsavani et al., 2020). Because of climate change and desertification, dust levels have increased in many arid or semi-arid regions (Ashrafi et al., 2017; Chudnovsky et al., 2017; Namdari et al., 2018; Parolari et al., 2016; Saeed and Al-Dashti, 2010; Sofue et al., 2018; Sternberg and Edwards, 2017). The Middle East is one of the areas in the world impacted by intense dust storms (Goudie, 2014). Because the Middle East has been involved in wars since early 2000s the land surfaces have been affected by heavy trucks and other military activities (Yassin et al., 2018). Kuwait, a typical arid Middle Eastern nation, is largely covered by desert and impacted by intense and frequent dust storms, as are other countries in the region (Al-Hemoud et al., 2018). This study addresses dust issues in Kuwait in greater detail because it reasonably characterizes features of dust in most of the arid regions of the Middle East. Efforts using different approaches have investigated the origin, intensity, and frequency of dust events in Kuwait. Brown et al. (2012); Draxler et al. (2001) analyzed PM10 ambient samples during dust storms. Alolayan et al. (2013) found that in Kuwait dust contributed to N50% of fine particles (PM2.5) mass using positive matrix factorization (PMF). Al-Hemoud et al. (2019) studied the frequency of dust storms in Kuwait based on visibility, wind direction, and wind speed data. Yassin et al. (2018) investigated the origins of dust storms in Kuwait using backward trajectory simulations. Persson (2013); Saeed and Al-Dashti (2010) evaluated dust levels in Kuwait using the aerosol optical depth (AOD) data from the satellite Moderate Resolution Imaging Spectroradiometer (MODIS). Traditional methods, which are based only on ground observation data such as PM10, PM2.5, and visibility, are not very effective because of their limited spatial and temporal coverage. By contrast, remote sensing has acquired extensive data resulting in excellent spatial and temporal resolution. The dust levels are closely related to meteorological and surface conditions (An et al., 2018). It needs to analysis the impacts of meteorological and surface conditions on dust levels in Kuwait to determine the main controls for dust occurrences. Yassin et al. (2018) found that Shamal winds from Iraq are the main contributor to dust levels in Kuwait. Al-Dabbas et al. (2012) identified that the northeastern desert of Saudi Arabia is a major dust source affecting Kuwait. Furthermore, Adamo et al. (2018) suggested that climate change influences dust generation in the Middle East and, consequently loss of cultivable land to due to desertification. However, most previous studies are qualitative and lack of quantitative assessments. In this study, we investigate the spatial and temporal patterns of surface dust levels in five Middle Eastern countries between 2001 and 2017 using an index of surface dust concentration provided by the ModernEra Retrospective Analysis for Research and Applications version 2

(MERRA-2). This source provides excellent spatial and temporal resolution of dust levels for an extended period of time. The potential dust source regions for Kuwait were identified by the HYbrid SingleParticle Lagrangian Integrated Trajectory (HYSPLIT) model. Using statistical methods, we quantitatively assessed the impacts of meteorology and vegetation in the surrounding region on surface dust concentrations in Kuwait. 2. Materials and methods 2.1. Study region The geographical location of Kuwait is between longitudes 46° E and 49° E, and latitudes 28° N and 31° N, in the north-east region of the Arabian Peninsula. Kuwait has an arid climate which is characterized by dry climatic conditions with extreme hot weather, rare precipitation, high levels of surface dust and low humidity. Vegetation is very scarce, and due to the fragile dry sand sheets, winds can remove silt and claysized particles (b63 μm) from the surface in the form of dust storms. The summer in Kuwait is very long and extends for almost 6 months. It is extremely hot and dry, and officially starts from 21 May to 4 November. The winter is very short and lasts for about 3 months, from 6 December to 15 February, with great decrease in temperature, sparse rain and very cold northwesterly wind. To take into account regional effects, we assessed a larger study region that shared climate characteristics, with a geographic location between 16.5°N and 37.5°N latitude, and 34°E and 56°E longitude, that includes Kuwait, Saudi Arabia, Iran, Iraq and Syria (Fig. 1). 2.2. Data collection Surface dust concentrations from 2001 to 2017 were provided by MERRA-2 (https://disc.gsfc.nasa.gov/datasets/M2TUNXAER_5.12.4/ summary). MERRA-2 is a U.S. National Aeronautics and Space Administration (NASA) atmospheric reanalysis platform using the Goddard Earth Observing System Model, Version 5 (GEOS-5) with its Atmospheric Data Assimilation System (ADAS), version 5.12.4. MERRA-2 makes extensive use of satellite data from both research and operational instruments to estimate ambient dust concentration (Rienecker et al., 2011; Veselovskii et al., 2018). It has been favorably compared with the results of simulation and those of satellite observations as well as ground-based measurements (Colarco et al., 2010; Ginoux et al., 2001; Rienecker et al., 2011). It was widely used for studying global climate change and regional environment with high data quality (He et al., 2019; Randles et al., 2017). MERRA-2 includes aerosols, meteorology, and stratospheric ozone at a spatial resolution of a 0.625°(longitude) × 0.5°(latitude) grid. We used the Aerosol Diagnostics data set of MERRA-2 for the variable “Dust Surface Mass Concentration” to determine dust levels of the study region. Meteorological data were also obtained from the MERRA-2 dataset. The parameters used in this research included total column precipitation, relative humidity (RH), surface temperature at 2 m, and wind speed at 10 m. The meteorological data have the same temporal and spatial resolution as the surface dust concentrations. In addition, we also identified the dust storm days for 2001–2017 in Kuwait from the Kuwait official meteorological station (https://www.met.gov.kw/). Surface vegetation may greatly impact dust levels (Kim et al., 2017). The Normalized Difference Vegetation Index (NDVI) was used to evaluate variation in vegetation (Zhang et al., 2017). The NDVI data used in this study was obtained from MODIS. The MODIS NDVI product, which is considered to be superior to other NDVI dataset (Fensholt and Proud, 2012), has been a primary data source for the research on vegetation dynamics (Gillespie et al., 2018). The product used in this research was Terra MOD13A3. The dataset was acquired from the Land Processes Distributed Active Archive Center of NASA (https://lpdaac.

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Fig. 1. The location of the study region (Black Square) and Kuwait (red line).

usgs.gov/data-access) with a 1000 m spatial resolution and monthly temporal resolution. 2.3. Backward trajectory To determine the possible sources dust in Kuwait, we calculated the backward trajectories of air masses using version 4 of HYSPLIT model developed by the U.S. National Oceanic and Air Administration (Stein et al., 2015; Yassin et al., 2018). The 72-h backward trajectories were calculated at a height of 1000 m at local times of 00:00, 06:00, 12:00, and 18:00 h at a receptor site (29.37°N, 47.98°E) in Kuwait City. Clustering concentration weighted trajectory (CWT), and potential source contribution function (PSCF) analysis were performed to examine the calculated trajectories for each month in 2017 with the TrajStat software (Wang et al., 2009). In order to conduct the CWT and PSCF analyses, the entire region covered by the backward trajectories was divided into i × j grid cells, each covering an area of 0.5°(latitude) × 0.5°(longitude). We used clustering analysis to identify the possible sources of surface dust in Kuwait and the paths of the dust plumes in the region (Li et al., 2017). The CWT analysis was used to determine the source strength of a grid cell to the receptor site based on the trajectories' weighted concentrations (Jeong et al., 2011). High CWT values indicate high source contributions to the dust level at the receptor site. The PSCF analysis is a widely used method to produce a probability map of the potential sources of air pollutants (Byčenkienė et al., 2014). The values generated by PSCF represent the probability that the concentration of surface dust is higher than a given threshold criterion value for passage of air through the i,j cell. The cells that have higher PSCF values are indicative of higher potential contributions for dust pollutant. The PSCF value for the grid cell was calculated using following equation: PSCF i; j ¼

mi; j ni; j

ð1Þ

where i,j are the indices of the grid cell; ni, j is the total number of air masses falling into the i,j cell during the study period; and mi, j is the number of segment trajectory endpoints in the i,j cell on the days where the source contribution was greater than the threshold criterion value. This study used the threshold surface dust concentration of 0.02 mg/m3.

2.4. Statistical analysis The impacts of meteorological parameters and vegetation index for Kuwait and the surrounding countries on dust levels in Kuwait were evaluated using the following linear regression model. We used separate models for each of the four surrounding countries (Iraq, Iran, Saudi Arabia, and Syria). logðKuwait Dust i Þ ¼ ε þ β0 þ β1i  month þ β2  Kuwait NDVIi þ β3  Kuwait Windi þ β4  CountryNDVI i þ β5  CountryWindi

ð2Þ

where ε is the random error; β0 is the regression intercept; β1 is regression coefficient for categorical variable month; i stands for each month; Kuwait_Dusti, Kuwait_NDVIi, and Kuwait_windi are the monthly average dust surface concentration; the monthly average NDVI, and the monthly average wind speed in Kuwait, respectively; Country_NDVIi, and Country_windi are the monthly average NDVI and wind speed in the surrounding countries, respectively. We use the natural log of the surface dust concentration in Kuwait to meet the assumptions of a linear model Yang et al., 2018. 3. Results and discussion 3.1. Distribution of dust surface concentrations Fig. 2a shows the spatial distributions of the mean surface dust concentrations in the study region from 2001 to 2017. The annual mean surface dust concentrations of each grid over the observed period were regressed on year to assess the time trends. The trend results are presented in Fig. 2b. Table 1 and Table S1 displays the descriptive statistics of the surface dust concentration of each country. According to Table 1, the mean surface dust concentration of the region during the period 2001 to 2017 was 0.20 mg/m3. High concentrations of surface dust were observed in Kuwait, mid-west and southern Iraq, and eastern Saudi Arabia, with Kuwait being the highest, 0.53 mg/m3, and Iraq and Saudi Arabia being both over 0.20 mg/m3. By contrast, the mean surface dust concentrations in Syria and Iran were both b0.20 mg/m3. The maximum monthly surface dust concentration in Kuwait was 1.14 mg/m3. The maximum levels for Iraq, Saudi, Iran, and Syria were 0.41, 0.40, 0.33 and 0.22 mg/m3, respectively. The highest monthly values for all the countries occurred in June 2008

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Fig. 2. Annual mean (a) and year trend (b) of surface dust concentrations (mg/m3) in the study region from 2001 to 2017.

except for Syria, which was in June 2007. The highest annual values for all countries were in 2008. A yearly increase of 0.0005 mg/m3 in the regional annual mean of surface dust concentration was observed for the period 2001 to 2017. Kuwait had the largest annual increase, 0.0014 mg/m3. The annual increase of dust levels in Saudi Arabia, and Iran were 0.0008 and 0.0006 mg/m3, respectively. On the contrary, annual decreases of 0.0005 and 0.0003 mg/m3 were observed for Iraq and Syria, respectively. Together these results indicate that Kuwait is the country which is most impacted by surface dust. Fig. 3 displays the annual and monthly surface dust concentrations in Kuwait during 2001–2017. The highest annual surface dust concentration occurred in 2008 (0.59 mg/m3) and the lowest in 2002 (0.47 mg/m3). The results indicated that the dust levels in Kuwait exhibit a seasonal pattern, with the highest dust levels observed in summer (June and July) and the lowest levels in the winter. The temporal variations of the surface dust concentrations are consistent with AERONET coarse AOD (World Meteorological Organization, 2013) and numbers of dust storm day (Al-Hemoud et al., 2017). It is possible that the extreme drought of 2008 contributed to the high dust level in 2008. The year 2008 had the third lowest annual total rainfall (39.1 mm) over the last 60 years, after 1964 (32.2 mm) and 1973 (36.8 mm). The strong summer Shamal winds and dry surface conditions would have produced high summer dust levels in Kuwait (Parolari et al., 2016).

3.2. Distribution of meteorological data and NDVI Fig. 4 displays the spatial distributions of the annual mean and trend of wind speed, temperature, RH, precipitation, and NDVI from 2001 to 2017. The annual changes of these variables by country are shown in Fig. S1 and Table S2. The seasonal patterns of the meteorological data are shown in Fig. S2. Table 1 Descriptive statistics for the surface dust concentration (mg/m3) during 2001–2017 for the region and by country. Country

Mean

SD

Maximum

Minimum

Trenda

Kuwait Iraq Saudi Iran Syria Region

0.53 0.25 0.23 0.16 0.13 0.20

0.16 0.06 0.05 0.05 0.03 0.05

1.14 0.41 0.40 0.33 0.22 0.34

0.25 0.13 0.14 0.07 0.05 0.13

0.0014 −0.0005 0.0008 0.0006 −0.0003 0.0005

a

Slope of linear trend on annual scale.

The mean wind speed in the study region was 2.18 m/s, which increased by 0.0012 m/s per year. The highest wind speed observed in summer. The mean wind speed values for Kuwait, Iraq, Saudi Arabia, Iran, and Syria were 2.58, 1.79, 2.22, 1.96 and 2.09 m/s, respectively. Kuwait had the largest mean wind speed among the five countries with an annual increase considerably higher than those observed in the other countries. Fig. 4 shows that the wind speed increased significantly around the joint area of Kuwait and Iran. The mean temperature in the study region during 2001–2017 was 23.4 °C with an annual increase of 0.0151 °C. The highest temperature observed in summer and the lowest temperature in winter. An increase in average temperature was observed in almost the entire study region. The range of temperature increases for the five countries was from 0.0054 (Iran) to 0.0331 (Syria) °C per year. The mean precipitation in the study region during 2001–2017 was 3.55 mg/m2 s. The highest precipitation observed in summer. The mean precipitation values in Kuwait, Iraq, Saudi Arabia, Iraq, Iran, and Syria were 1.46, 4.37, 1.73, 6.91, and 7.00 mg/m2 s, respectively. The annual precipitation during 2001–2017 decreased in Kuwait and Syria, and increased in the other countries. Kuwait had the lowest precipitation in the region with the largest decrease of 0.0692 mg/m2 s per year. The mean RH in the region was 34%, suggesting a very dry climate. The mean RH values in Kuwait, Iraq, Saudi Arabia, Iran, and Syria were 27%, 34%, 31%, 36%, and 46%, respectively. As with precipitation, the lowest RH was observed in Kuwait, along with the largest decrease in the region. The mean NDVI in the region during 2001–2017 was 0.12, indicating a very low coverage of vegetation. Kuwait is largely covered by desert, with a mean NDVI of 0.08 during 2001–2017. Saudi Arabia has the largest desert area and its mean NDVI was 0.09. Iran, Iraq, and Syria had larger vegetation coverage than Kuwait and Saudi Arabia with mean values of 0.14, 0.15, and 0.17, respectively. The areas with higher NDVI were located at the joint area of Iran, Iraq and northern Syria. The annual trends of the NDVI in Kuwait and Syria were negative with a yearly decrease by 0.00023 and 0.00055, respectively. In contrast, positive annual mean trends of NDVI were observed in Saudi Arabia, Iraq, and Iran, indicating that vegetation coverage has improved and dust emissions have been suppressed in these areas. The spatial distribution of mean NDVI in the region was highly correlated with those of RH and precipitation. As expected, the areas with higher NDVI where those with more precipitation and higher RH, and vice versa. Kuwait had the highest dust level, temperature, wind speed, and the lowest NDVI, precipitation and RH among the study countries. The loss of vegetation in Kuwait in recent years might be a result of the extreme drought conditions. Fig. S3a displays numbers of dust storm days in Kuwait for 2001 to 2017. A total of 131 dust storm days occurred in Kuwait from 2001 to

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Fig. 3. Annual and monthly surface dust concentrations in Kuwait during 2001–2017.

2017 with the highest number of dust storm days recorded in 2008, which was consistent with the temporal distribution of surface dust concentrations in Kuwait. Significant negative correlation was found between the number of dust storm days for each year and the yearly NDVI in Kuwait (Fig. S3b). Kuwait has the highest dust level, lowest NDVI, precipitation, and RH among those countries. The effect of desertification expansion in Kuwait in recent years might due to extreme drought conditions. 3.3. Impacts of meteorology and NDVI on dust levels in Kuwait The mean surface dust concentration in Kuwait was significantly higher than that in the other countries in the region. In addition, Kuwait showed the largest increasing dust level trend among the five countries. Because of this we selected Kuwait to extensively assess the influencing factors on dust levels. 3.3.1. Pollution pathways and source distribution Fig. 5 depicts the trajectory clusters, CWT values, and PSCF values for Kuwait City in January and June 2017. The trajectory clusters for the other months are shown in Fig. S4. Four major directions of trajectories in January to May and October to December were determined i.e. north, northwest, west, and south (Fig. 5a). During these months, some trajectories traveled across Iraq before reaching Kuwait, and some trajectories originated from central and southeast Saudi Arabia and traveled to north Saudi Arabia before reaching Kuwait. In addition, during these months there were many trajectories that originated from northern

Africa and traveled over northern Saudi Arabia before reaching Kuwait. However, during June–September, most of the trajectories traveled across Syria and Iraq before reaching Kuwait (Fig. 5b). The path of the trajectories in summer might be influenced by the summer monsoon anticyclone, where the air flows in a clockwise manner (Lelieveld et al., 2018). The period from June to September had the highest dust surface concentrations in Kuwait (Fig. 3). The higher dust levels during summer in Kuwait appear to be influenced by the air mass passing through Syria and Iraq. Cluster analysis shows the pathways of air mass in Kuwait, while the CWT and PSCF analysis make it possible to identify the potential dust sources and their contributions to the receptor sites based on the calculated trajectories and dust concentration data. The CWT map in January (Fig. 5c) shows that the dust sources outside Kuwait were mainly localized around Kuwait, including eastern Saudi Arabia extending across the Qasim region and the Adibdibah and As-Summan Plateau regions (Alharbi et al., 2013), and south of Iraq between Al-Muthana, Qar and Al-Qadsiya borderlines (Al-Dabbas et al., 2010), with dust contributions of above 0.01 mg/m3. In June (Fig. 5d), these sources were mainly in Iraq with dust contributions of above 0.01 mg/m3, as well as the east part of Syria. The CWT maps for January and June indicate that the dust concentrations in Kuwait City in January are more influenced by local emissions within Kuwait, and the dust concentrations in June are more affected by long-distance transport of emissions. The PSCF map (Fig. 5e) indicates that the areas of Saudi Arabia bordering southern Kuwait were the main source region of high surface

Fig. 4. Annual mean (first row) and trend (second row) of wind speed (m/s), temperature (°C), RH, precipitation (mg/m2 s), and NDVI during 2001–2017.

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Fig. 5. 72-Hour HYSPLIT backward trajectories clustering, CWT (μg/m3), and PSCF analysis for Kuwait in January and June 2017.

dust concentration in January (WPSCF N 0.3). In June (Fig. 5f) most of Iraq was the main potential source region of high dust levels in Kuwait (WPSCF N 0.6). The eastern part of Syria had contributions to high dust levels in Kuwait in June as well. The areas with potential contributions to high dust concentrations in June were larger than that in January. Fig. 6 displays the correlations between the monthly surface dust concentrations in Kuwait and in each of the other grids in the region during 2001–2017. The monthly surface dust concentrations in most grids showed significant correlations with those in Kuwait (p b 0.05). The monthly surface dust concentrations in central and southern Iraq and northeastern Saudi Arabia had the strongest correlations with the dust levels in Kuwait. The result is consistent with that of the backward trajectory cluster analysis.

3.3.2. Relationships between dust levels and meteorological parameters and as well as NDVI The impacts of local and regional parameters on dust levels in Kuwait were quantitatively analyzed using multiple linear regression models. Since the surface dust concentrations in Kuwait exhibited seasonal and yearly patterns (Fig. S5), the model was adjusted for month. Significant relationships were observed between surface dust concentration in Kuwait and the monthly values for both NDVI and wind speed. Since the dust levels in Kuwait were affected by local and regional sources we used models to examine the combined impacts of wind speed and NDVI in Kuwait, along with the wind speed and NDVI in the countries surrounding Kuwait. Table 2 shows the regression results of models for each of the 4 surrounding countries, as well as the changes of dust level in Kuwait per interquartile range (IQR) value of

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observed. No significant associations between the NDVI in Syria or Iran with the dust levels in Kuwait were found. The increase of dust levels in Kuwait per IQR of NDVI in Saudi Arabia and Iraq were sharper than that related to an IQR of NDVI in Kuwait. Among all the variables in Iraq, Iran, Saudi Arabia, and Syria, the wind speed in Iraq had the highest influence on the dust levels in Kuwait.

4. Conclusions

Fig. 6. Correlations between monthly surface dust concentrations in Kuwait and in each of the other grids of the study region during 2001–2017.

each variable. These impacts were used to estimate the change of the dependent variable per IQR in the surface dust concentration in Kuwait. We found a highly significant positive relationship (P-value b 0.001) between the wind speed in Kuwait and surface dust concentration in Kuwait in all four models. The strong relationship between Kuwait surface dust concentration and local wind indicates the impact of Kuwait of nearby deserts. Significant positive relationships were found between wind speeds in the four countries surrounding Kuwait and surface dust concentration in Kuwait. The wind speed in Iraq has the highest influence. The dust level increase per IQR of wind speed in Kuwait was smaller than that estimated per IQR of wind speed in one of the surrounding countries, i.e., Iraq (model 1); however, it was higher than the increase with wind speed in other surrounding countries (models 2, 3, 4). Significantly negative relationships between NDVI in Kuwait, Iraq, and Saudi Arabia and surface dust concentration in Kuwait were

Table 2 Linear regression model results and changes of dust level in Kuwait with per IQR value of each variable. Model ID

Country

Variable

IQR

Coefficient

Changea

P-value

1

Iraq

Kuwait_Wind Iraq_Wind Kuwait_NDVI Iraq_NDVI Kuwait_Wind Saudi _Wind Kuwait_NDVI Saudi _NDVI Kuwait_Wind Iran _Wind Kuwait_NDVI Iran _NDVI Kuwait_Wind Syria _Wind Kuwait_NDVI Syria _NDVI

2.35 1.37 0.01 0.03 2.35 0.57 0.01 0.01 2.35 0.90 0.01 0.03 2.35 1.70 0.01 0.04

0.07 0.18 −3.80 −2.66 0.10 0.14 0.69 −22.85 0.09 0.15 −5.91 0.44 0.09 0.07 −5.81 0.04

0.16 0.25 −0.02 −0.09 0.23 0.08 0.00 −0.11 0.20 0.14 −0.03 0.01 0.22 0.00 −0.03 0.12

b0.001⁎⁎⁎ b0.001⁎⁎⁎ 0.029⁎ 0.002⁎⁎ b0.001⁎⁎⁎ 0.023⁎

2

Saudi

3

Iran

4

Syria

a

0.759 b0.001⁎⁎⁎ b0.001⁎⁎⁎ b0.001⁎⁎⁎ b0.001⁎⁎⁎ 0.393 b0.001⁎⁎⁎ b0.001⁎⁎⁎ b0.001⁎⁎⁎ 0.900

The change is calculated as the regression coefficient multiplied by the IQR. ⁎ P b 0.05. ⁎⁎ P b 0.01. ⁎⁎⁎ P b 0.001.

A comprehensive analysis was performed to investigate factors influencing dust levels in 5 Middle Eastern countries. The analysis was based on surface dust concentration data, meteorological data, and the vegetation index during the period 2001 to 2017. Dust levels in Kuwait were higher than the other countries, with an average surface dust concentration of 0.53 mg/m3 and an annual increasing trend of 0.0014 mg/(m3 year) from 2001 to 2017. Kuwait was chosen to assess the source distribution, and factors influencing dust levels in arid regions. The dust concentrations in Kuwait City in January appeared to be primarily influenced by local emissions, while the dust concentrations in June appeared to be primarily influenced by long-distance transport of emissions. We found a highly significant positive relationship between wind speed and surface dust concentration in Kuwait, indicating that dust levels were impacted by deserts in Kuwait and surrounding areas. Significant negative relationships between NDVI in Kuwait, Iraq and Saudi Arabia and surface dust concentration in Kuwait were observed. Furthermore, our results suggest that in Kuwait during the study period, the wind speed and temperature increased while RH, precipitation and NDVI decreased. By promoting greater dust concentrations, these climate-related factors most likely impact public health in Kuwait since greater mortality (Achilleos et al., 2019) and emergency hospital admissions for asthma and respiratory (Thalib and Al-Taiar, 2012) have been observed during dust storm days. We also found some additional factors, such as regional emissions, wind speed and vegetation conditions that were associated with dust levels. This increased understanding of the factors influencing dust levels in desert regions may be helpful for dust control planning in these regions.

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the VA Cooperative Studies Program #595: Respiratory Health and Deployment to Iraq and Afghanistan, from the U.S. Department of Veterans Affairs, Office of Research and Development, Clinical Science Research and Development, Cooperative Studies Program. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government. This publication was also made possible by U.S. Environmental Protection Agency (EPA) grant RD-835872. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the EPA or the U.S. Government. The authors appreciate Dr. Jack M. Wolfson for proofreading the article. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2020.136597.

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