Kenza Khomsi This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Houda Najmi1, Youssef Chelhaoui1, Zineb Souhaili2

Direction de la Météorologie Nationale, Boulevard Mohamed Tayeb Naciri, Hay Hassani, B.P. 8106 Oasis-Casablanca, Morocco
Laboratory of Drugs Science, Biomedical and Biotechnological Research, Faculty of Medicine and Pharmacy, Hassan II University, Ain Chock, Casablanca, P.B. 5696, Morocco


 

Received: October 13, 2019
Revised: March 7, 2020
Accepted: March 11, 2020
Download Citation: ||https://doi.org/10.4209/aaqr.2019.08.0401 

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Cite this article:

Khomsi, K., Najmi, H., Chelhaoui, Y. and Souhaili, Z. (2020). The Contribution of Large-scale Atmospheric Patterns to Pollution With PM10: The New Saharan Oscillation Index. Aerosol Air Qual. Res. 20:1038-1047. https://doi.org/10.4209/aaqr.2019.08.0401


HIGHLIGHTS

  • The Mediterranean Oscillation (MO) plays a major role in particle pollution.
  • A new oscillation may be defined: The Saharan Oscillation (SaO).
  • Particle pollution worldwide may be triggered by the SO and controlled by the MO.
  • A new relevant index may be defined: the Saharan Oscillation Index (SaOI).
  • Computed SaOI is statistically related with PM10 and NAO and MO indexes.
 

ABSTRACT


PM10, an urban air pollutant that originates from both natural and anthropogenic sources (desert dust and industrial and traffic emissions), reduces visibility and threatens human health, particularly in large cities. Casablanca, which exhibits the highest urbanization rate and population density in Morocco, possesses a concentration of industrial units as well as a large vehicle fleet. Marrakech, another of the most populated cities in the country, has also witnessed an increased rate of motorization during the recent years.

The primary objective of this study was to evaluate the relationship between the atmospheric circulation and the PM10 concentrations in Casablanca and Marrakech (based on daily measurements from 2013 to 2016). First, we assessed the correlations between the concentrations and the climate indexes (the North Atlantic Oscillation [NAO] and the Mediterranean Oscillation [MO]). Then, we characterized the contribution of large-scale atmospheric patterns related to extreme PM10 events. Finally, we created the Saharan Oscillation Index (SaOI), a climate index for characterizing the oscillation in the country’s southern desert, between the Saharan depression and the Azores High, and calculated its time series.

Our results elucidate the relationship between the MO and the average PM10 concentrations, demonstrating that particulate pollution in the study area is partly induced by a northeasterly to southwesterly continental flow that is triggered by the Saharan trough and influenced by the high-pressure area in the north. The significant statistical correlations, mainly found in winter, confirm the relationships between the Saharan Oscillation Index, the average PM10 concentrations and the MO and NAO indexes—and thus the applicability of the SaOI—during this season.


Keywords: Particulate Matter (PM10); Correlation; Saharan Depression; Climate Index; Saharan Oscillation Index (SaOI); Air Quality


INTRODUCTION


Nowadays, air pollution and climate change are the greatest atmospheric challenges for societies, and they will continue in future decades. These environmental issues are highly connected, mainly through atmospheric processes and meteorological conditions (von Schneidemesser et al., 2015; Kusumaningtyas et al., 2018; Sfîcă et al., 2018).

Air pollution is derived from local pollution sources but also affected by large-scale movement of air masses that contribute to regional background pollution and air pollution episodes. The World Health Organization (WHO) has estimated that ambient air pollution is responsible for 7,000,000 premature deaths per year in the world (WHO, 2014); 500,000 individuals are affected by premature death every year because of exposure to suspended particulate matter (PM) in ambient air (Alves et al., 2014).

PM is one of the main urban air pollutants (Molina et al., 2017; Hsu and Cheng, 2019), it is a mixture of particles with different chemical composition, shape and size (Khaefi et al., 2017; Cheng and Wang-Li, 2019) and attributed to several natural and artificial sources (Gugamsetty et al., 2012; Guo, 2018). When in high concentrations, the PM has significant impacts on the human health, well-being and environment. It has high potential to be accumulated in the human respiratory system and causes cardiopulmonary diseases and lung cancer (An et al., 2018; Hopke et al., 2018; Kirrane et al., 2019; Li et al., 2019). PM10 is the PM type with aerodynamic diameter less than 10 micrometers, it is inhalable and has the capacity to alter the human respiratory capabilities. Moreover, it can cause the decrease of visibility, smoke, haze, and smog (Agustine et al., 2018).

Weather conditions and meteorological factors play a crucial role in air pollution, mainly by PM10, and many previous studies have described the relationship between that pollutant and weather parameters (Tecer et al., 2008; Czernecki et al., 2016; Sfîcă et al., 2018). Furthermore, some particular synoptic patterns are strictly related to dangerous PM10 incidents that generally coincide with stagnant conditions induced by high relative humidity, thermal inversions, and low wind speeds, that reduce the ventilation and decrease the dispersion of the atmospheric components near to the surface (Pateraki et al., 2012; Hsu and Cheng, 2019; Toro et al., 2019).

In Morocco, air pollution is responsible for more than 13,000 deaths each year. This represents about 7% of all deaths and is thus the 8th largest mortality risk factor. More than 12,000 deaths are attributable to fine particles (IHME, 2018). The country has started to monitor air quality since 1997 in an effort to assess air quality, the risk of its alteration on human, vegetation, and climate and recognize the need to improve air quality and control emissions. Today in Morocco, PM10 is being monitored in 15 cities using automated ground-based instruments.

Casablanca and Marrakech are two large metropolitan cities in Morocco with various sources of pollution by PM10. The few studies already led on particle pollution in these two cities have mainly focused on assessing air data according to standard limits (Croitoru and Sarraf, 2017; Inchaouh and Tahiri, 2017; Inchaouh et al., 2017, 2018).

The goal of this study is to evaluate the concentrations of PM10 and characterize their relationship with large-scale atmospheric circulation through assessing their statistical correlations with the North Atlantic Oscillation (NAO) and the Mediterranean Oscillation (MO) indexes. In fact, for the Mediterranean region to which Morocco belongs, the path that cyclones take usually depends on synoptic-scale atmospheric patterns, connected to NAO and MO climate indexes. Numerous studies have revealed that these connections play a significant role in the determination of moisture transport in the region (Baltacı et al., 2017; Drumond et al., 2017; Şahin et al., 2018). Also it has been found that both indexes impact the climate regime in Morocco (Khomsi et al., 2016). This work is mainly about understanding how does weather contribute to the occurrence of particle pollution, it brings new insights on how the climate may affect trends and levels of PM10 and will serve as a reference to evaluate the current air quality at the country-level scale. Also, it is a first step to identify PM10 sources and distinguish contributions from local emissions and climate-induced fine particles, when necessary data is available. It will be a useful tool in the future to undertake appropriate actions against pollution by PM10 and to develop pollution mitigation strategies.

In this paper, we describe the area of study, the methods used and we elaborate on the data acquisition. In Section 3, we first present the trends in PM10 averages and extremes, we show PM10 seasonal correlations with NAO and MO indexes and we identify and analyze synoptic patterns behind the occurrence of PM10 extremes. Finally, we suggest the elaboration of a new climate index, the Saharan Oscillation Index (SaOI), and assess its relationship with PM10, NAO and MO indexes.


MATERIALS AND METHODS



The Study Area

Morocco is the country located in the extreme northwest of Africa (Fig. 1). Because of its location in the southern part of the Mediterranean region, the state is considered among the most vulnerable countries with regard to climate variability, particularly likely increased frequencies in extreme climate events (Khomsi, 2013; Filahi et al., 2015; Khomsi et al., 2016; Filahi et al., 2017; Kamal et al., 2018; Khomsi et al., 2018) and accordingly extreme related pollution episodes.

Casablanca and Marrakech are the first and the fourth most populous cities in Morocco with more than 3,000,000 and 900,000 residents, respectively, as reported by the website of World Population Review (https://worldpopulati
onreview.com/). Casablanca is a coastal city located in the central western part of the country bordering the Atlantic Ocean (Fig. 1). It is the largest city in the Maghreb, as well as one of the most important cities in Africa, both economically and demographically. Marrakech is a major city of the Kingdom of Morocco; it is the capital city of the mid-southwestern region of Marrakech-Safi and is an inland city located to the north of the foothills of the snow-capped Atlas Mountains (Fig. 1). Casablanca and Marrakech were chosen as the urban study area where serious pollution concerns may be met, especially with the important population rate increase that reaches 11% in Casablanca and 12% in Marrakech between 2004 and 2014. 

Fig. 1. (a) Location of Morocco in Africa, (b) location of the cities of Casablanca and Marrakech in Morocco, (c) air quality stations in the city of Casablanca, (d) air quality stations in the city of Marrakech.
Fig. 1. (a) Location of Morocco in Africa, (b) location of the cities of Casablanca and Marrakech in Morocco, (c) air quality stations in the city of Casablanca, (d) air quality stations in the city of Marrakech.


PM10 Data

Daily mean data of PM10 concentrations (in µg m–3) were collected from thirteen air quality stations, ten in Casablanca and three in Marrakech (Fig. 1). These stations are technically managed by the National Weather Service. Before being available, the collected data underwent quality control in accordance with the rules and the recommendations from the Agency for the Environment and Energy Management (Kleinpeter et al., 2003). For the purpose of this work, PM10 spatial averages from the measuring stations in each city are used to represent the city. In Morocco, the air quality network is not old and the temporal coverage of available data is limited, therefore it was difficult to retrieve a long data sample. This study was performed on a daily basis for the period between 2013 and 2016 which may not be sufficient to obtain significant statistics yet it is the most appropriate period allowed by the available data. The analysis was presented on a seasonal basis in order to examine the seasonal variability in the obtained results.


Atmospheric Circulation Indexes

The centers of pressure for the NAO are located in the Atlantic Ocean (Ambaum et al., 2001). This connection consists of a north-south dipole of the sea-level pressure (SLP) anomalies, one centered over Iceland and the other over Azores.

The MO was first suggested by Conte et al. (1989). It was defined as an oscillation between the east and the west of the Mediterranean Basin. According to the authors, this oscillation is the consequence of the behavior of the atmospheric dipole at the 500 hPa level between Algiers (36.4°N, 3.1°E) and Cairo (30.1°N, 31.4°E). The same model was applied by Douguédroit (1995) using SLP.

The annual data of the NAO and MO indexes were gathered from the website of the Climatic Research Unit (CRU; http://www.cru.uea.ac.uk/cru/data) and used in order to study the link between these two atmospheric modes and the seasonal evolution of daily PM10 averages between 2013 and 2016.


Sea-level Pressure Data

Daily data were used in order to retrace SLP patterns related to the identified PM10 seasonal extreme events. This will help to associate the flows over the studied areas with extreme particle pollution conditions.

The SLP data were provided by the ERA5 reanalysis. ERA5 is the latest climate reanalysis data generated by the European Centre for Medium-Range Weather Forecasts (ECMWF). Data contain hourly data on diverse atmospheric and land-surface parameters on 37 pressure levels. ERA5 data are accessible in the Climate Data Store (CDS; https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset) on unvarying latitude-longitude grids at 0.25° × 0.25° resolution.


METHODS


To identify extreme events in PM10, the overall and seasonal 95th percentiles computed across the whole time period were used as thresholds. The 95th percentile is widely employed and recommended for climate and related application studies by the STAtistical and Regional dynamical Downscaling of EXtremes for European regions (STARDEX; http://www.cru.
uea.ac.uk/projects/stardex/) and the Expert Team on Climate Change Detection and Indices (ETCCDI; http://cccma.seos.
uvic.ca/ETCCDI/) projects. Annual and seasonal datasets of mean daily PM10 recorded between 2013 and 2016 were compared to the calculated overall and seasonal thresholds, respectively. Thus, an extreme PM10 event is defined as a day that recorded an average PM10 greater than or equal to the 95th percentile.

The magnitudes of trends in time series were analyzed using the non-parametric approach proposed by Theil (1992) and Sen (1968) for univariate time series. This method implies the preparation of the ordinal time points and the calculation of the slopes for all the pairs and then the computing of the median of the calculated slopes as an estimate of the general slope. Sen’s slope is widely used for the estimation of trends’ magnitudes in climate series, given its robustness against outliers (Kamruzzaman et al., 2016; Almeida et al., 2017; Byakatonda et al., 2018; Kahsay et al., 2018). The statistical significance of the obtained trends is tested using the modified Mann-Kendall test proposed by Hamed and Ramachandra Rao (1998) for autocorrelated time series. The test is performed at a significance level of 5%.

Correlations between time series were estimated employing the Spearman coefficient. This statistical coefficient is used to measure the strength of the association between two variables and is widely used in climate studies (Mendez-Lazaro et al., 2014; Hänsel et al., 2015).


RESULTS AND DISCUSSION



Trends in Average PM10, Thresholds and Frequencies of Extreme PM10 Events

Figs. 2 and 3 show respectively the evolution of annual and seasonal average PM10 and extreme PM10 events, Fig. 4 presents overall and seasonal PM10 thresholds for extreme events in the studied area between 2013 and 2016. Results show that the city of Casablanca has recorded more important annual PM10 averages in comparison with the city of Marrakech. The same finding appears for autumn, winter and spring. In summer, Marrakech has recorded more important averages in the last three years. Marrakech is already found to register more important PM10 concentrations in the summers between 2009 and 2012 (Inchaouh et al., 2017). Concerning extreme events, observed frequencies are of the same order in both cities in winter and summer. Extreme event thresholds are more important in Casablanca than in Marrakech in all seasons but summer, they are more important in winter and they far exceed the WHO guideline level of 20 µg m–3 (WHO, ‎2006). This finding is in agreement with the previous studies of Croitoru and Sarraf (2017) and Inchaouh et al. (2017, 2018) where it is shown that PM10 concentrations in Casablanca and Marrakech exceed the cited WHO guideline level. The year 2014 was with fewer extreme events in both cities. A previous study on heat waves, in the same study area has shown that the year 2014 has recorded the lowest maximum temperature and frequency of hot events (Khomsi et al., 2018). This may highlight the contribution of high recorded temperature to the occurrence of PM10 episodes as found by other previous studies (Analitis et al., 2018; Kalisa et al., 2018; Kliengchuay et al., 2018; Egberts et al., 2019). According to the data collected and the statistical approach used, the magnitudes of most studied trends in PM10 averages and extreme events are positive, but not statistically significant. This may be due to the short period of data availability that served for the present study. 

Fig. 2. Evolution of annual PM10 averages and extreme events.Fig. 2. Evolution of annual PM10 averages and extreme events. 

Fig. 3. Evolution of seasonal PM10 averages and extreme events: (a) autumn, (b) winter, (c) spring, (d) summer.

Fig. 3. Evolution of seasonal PM10 averages and extreme events: (a) autumn, (b) winter, (c) spring, (d) summer. 

Fig. 4. Overall and seasonal PM10 thresholds for PM10 extreme events.Fig. 4. Overall and seasonal PM10 thresholds for PM10 extreme events.


Climate Indexes and PM10 Averages

The relationship between climate indexes and PM10 averages in Casablanca and Marrakech was assessed through the Spearman coefficient of correlation. Annual and seasonal results are shown in Fig. 5 and Table 1, respectively. Relationship between annual climate indexes and PM10 averages is confirmed and is stronger with the MO index for both cities. Seasonal data show that PM10/NAO correlation is significant in autumn; meanwhile PM10 and MO keep to be correlated throughout the four seasons. Stronger PM10/MO correlations were observed in the cold season (autumn and winter). When comparing both cities, coefficients of correlation are of the same order. The positive, moderate and significant correlation between MO index and PM10 averages during the cold season highlight the influence of anticyclonic conditions occurring in the northern Mediterranean coasts of Morocco, on PM10 concentrations over the studied area. This influence weakens but persists during the warm season (spring and summer). In autumn, NAO index fed through the anticyclonic conditions in the Atlantic on the west of Morocco maintains a weak impact on studied PM10 concentrations. Anticyclonic settings are generally related to stagnation conditions that disadvantage dispersion and foster PM10 concentrations. These findings are confirmed by the previous results of Pateraki et al. (2012), Hsu and Cheng (2019) and Toro et al. (2019), that have proved the interactions between anticyclonic conditions and PM10 high concentrations in the Mediterranean area, Taiwan and Chile, respectively. These interactions will be better understood, in the study area, by studying extreme PM10 events mutually with large-scale atmospheric circulation and this is the aim of the next chapter. 

Fig. 5. Correlation between annual climate indexes (NAO and MO) and PM10 averages.Fig. 5. Correlation between annual climate indexes (NAO and MO) and PM10 averages. 


PM10 Extreme Events and Large-scale Atmospheric Circulation

After exploring the correlation between PM10 averages and climate indexes, there was a need for further assessment of how could large-scale atmospheric circulation affect the emergence of extreme PM10 events. Accordingly, and in order to reduce the probability of having the episodes triggered by local pollution in the cities of Casablanca and Marrakech, seasonal PM10 episodes common to both cities were identified. For the purpose of this work, a common PM10 episode is defined as an extreme PM10 event recorded on the same day in the two cities, offset days of episodes’ appearances were not considered. We have identified more than 70 recorded PM10 episodes in each city between 2013 and 2016: 16 were common to both cities, 5 in autumn and spring each and 3 in winter and summer each. The maps of SLP fields from ECMWF were reconstructed and analyzed for all the common episodes, they correspond to the weather types. This step helped to identify atmospheric flows behind detected common extremes.

Fig. 6 shows seasonal weather types related to samples of common extreme PM10 events. For all the reconstructed weather types, Morocco is under continental northeasterly to southwesterly flows. For each season, one weather type coincides with the appearance of all the common seasonal PM10 episodes. In autumn (Fig. 6(a)), Morocco is under the combined influence of the large high-pressure area, merging the high that extends over the whole European continent and the Azores High that covers the Atlantic region in the west of the country, and the Saharan trough strengthening toward the country. Thus, the area is receiving a south to southeasterly flow that favors the transport of particulate matter from the desert and a north to northeasterly flow that favors its stagnation over the studied area mainly the coastal city of Casablanca. The anticyclonic stagnation conditions in the north and the west of the country can clarify the significant correlations found between both NAO and MO indexes and PM10 averages in autumn. In winter (Fig. 6(b)), the high-pressure area in the north of Morocco strengthens and invades the country, it extends over North Africa and causes the recession of the Saharan trough. The flow is northeasterly to easterly, continental from over the desert in the east of Morocco. This is a blocking synoptic pattern that worsens local pollution conditions and may increase sensitivity in patients suffering from respiratory diseases. The influence of the anticyclonic conditions from the north and the weakening of the anticyclonic area in the west may explain the significance and the importance of winter PM10/MO correlations, whereas no correlations were found between PM10 and NAO index. In all autumn and winter episodes, Casablanca has recorded the most important PM10 concentrations. This is in accordance with our previous findings and can partly explain why autumn and winter PM10 averages are more important in Casablanca than in Marrakech. In spring (Fig. 6(c)), the correlation PM10/MO keeps to be statistically significant. Blocking conditions are relieved and the Saharan trough invades the country from the south on account of the northern high which weakens and retreats. The impact of the Saharan flow reaches the European continent as well. With this synoptic pattern, the flow in the study area is southerly continental transporting particulate matter from the desert. The inland city of Marrakech recorded higher PM10 concentrations in most extreme cases, yet Casablanca has recorded higher PM10 averages in spring. In summer (Fig. 6(d)), the Saharan trough extends toward the north of the country, including the northern coasts and reaches the European continent where the observed high may create blocking conditions. The Saharan flow that crosses the study area favors southerly flows and fosters high temporary PM10 concentrations, yet no stagnation conditions are noticed over the country. In all observed summer situations, Marrakech has recorded higher PM10 concentrations than Casablanca in accordance with the fact that Marrakech records higher average PM10 concentrations in summer. Even though they weaken, anticyclonic conditions in the north of the study area keep having an impact on the flow invading it; this explains the weak and statistically significant PM10/MO correlation during summer. 

Fig. 6. Seasonal sea-level pressure (MSLP) related to a sample of recorded extreme PM10 events: MSLP on (a) 28 September 2016 (autumn), (b) 18 December 2015 (winter), (c) 17 April 2013 (spring), (d) 19 July 2016 (summer).
Fig. 6. Seasonal sea-level pressure (MSLP) related to a sample of recorded extreme PM10 events: MSLP on (a) 28 September 2016 (autumn), (b) 18 December 2015 (winter), (c) 17 April 2013 (spring), (d) 19 July 2016 (summer).

The performed analysis has shown that average PM10 concentrations and extreme events observed over the studied area are the repercussion of the interactions between anticyclonic areas in the north and the west of Morocco and the cyclonic conditions in the south of the kingdom. The nature of this high-low interaction controls the level of particle pollution in the south inland of the country, represented by the city of Marrakech, and the north coasts, denoted by the city of Casablanca. This finding emerged the need of building an index, similar to NAO and MO indexes, that may describe the southern anticyclonic-cyclonic oscillation and express the southern flow impact. This index will be called the Saharan Oscillation Index.


The Saharan Oscillation Index: Formulation and Correlations

For the purpose of this work, the Saharan oscillation (SaO) is suggested to be related to the dipole between the Azores High and the Saharan trough. The SaOI is defined as the difference between the normalized pressure of the Azores (37.79°N, –25.5°E) from Portugal, as the supposed center of the Azores High, and the normalized pressure in Niamey (13.51°N, 2.10°E) from Niger, as the theoretical center of the Saharan depression area (Eq. (1)). The calculations were performed on a daily basis in order to prepare SaOI time series between 2013 and 2016.


SaOId
 = Pnd (Azores) – Pnd (Niamey)                                     (1)


SaOId
: daily Saharan Oscillation Index;

Pnd: daily normalized pressure between 2013 and 2016.

The evolution of the SaOI data between 2013 and 2016 is shown in Fig. 7. The index ranges between –0.8 and 0.8 and evolves according to a cyclical pattern. Positive values are preponderant and negative values do not exceed 15% each year. Many oscillations occur all over the studied period. No clear relationship was found between the sign of the SaOI and extreme PM10 events. Spearman coefficient of correlation was calculated between annual and seasonal SaOI and PM10  averages for Casablanca and Marrakech, and between SaOI, NAO and MO indexes. The significance level is 0.05. Results are shown in Table 1. Annual PM10 averages and SaOI datasets are negatively correlated. These correlations are statistically significant and coefficients are of the same order in both cities (Table 1). This informs about seasonal PM10/SaOI relationship. In autumn, PM10/SaOI coefficient of correlation is weak, positive and statistically significant in Casablanca. It is negative and statistically significant in both cities in spring. In winter, PM10/SaOI correlations are negative, moderate and statistically significant for both cities. SaOI keeps to be positively correlated with NAO in all seasons, while it is negatively correlated with the MO in winter. Coefficients of correlation are statistically significant and are of the same order in both cities. 

Fig. 7. Evolution of the daily SaOI data between 2013 and 2016.Fig. 7. Evolution of the daily SaOI data between 2013 and 2016.

The results show that SaO and MO contribute mutually to particle pollution in Morocco, mainly in winter. The transport of particles from the southern desert is ensured by the Saharan flow while the Mediterranean flow controls their concentrations. The role of NAO in this operation remains minor. The level of local pollution by PM10 is accentuated by large-scale atmospheric circulation that either transport fine particles from the southern desert or generate stagnation conditions and then cause PM10 concentrations to increase in both cases. It may also be impacted by humidity. This may explain the difference between recorded extreme PM10 concentrations in the coastal city of Casablanca and the city of Marrakech which is inland. Our findings are somehow similar to those of Pateraki et al. (2012) that showed that high PM10 concentrations, in the Mediterranean area, are closely related to the southwesterly regime, suggesting long-range transport from the polluted south sector.


CONCLUSION


Although previous research has examined PM10 pollution in Casablanca and Marrakech, two highly populated Moroccan cities, our study is the first attempt to assess its potential relationship with large-scale atmospheric circulation. We determined trends in the PM10 by analyzing daily measured concentrations for the period 2013–2016. Identifying extreme events involving this pollutant and following their evolution, we then investigated the contribution of large-scale atmospheric circulation to the PM10 by correlating the time series of the average concentrations with climate indexes (NAO and MO) and establishing weather patterns that coincided with the episodes.

Despite not revealing obvious tendencies for the average concentrations or the extreme events, our study demonstrates that the PM10 level depends both on the specific city—hence, local sources—and on large-scale atmospheric circulation according to the season—thus, meteorological parameters such as the temperature, humidity and wind. When more data becomes available, the contribution from each source may be estimated. Additionally, our results show a relationship between the NAO and MO climate indexes and the average PM10 concentrations, proving that the MO plays a major role in particulate pollution in Morocco, and help to define the Saharan oscillation (SaO) as the interaction between the Azores High and the Saharan depression. With the continual transport of particles from the south, the SaO may be a factor that enhances our understanding of this pollution in Morocco; thus, we formulated the Saharan Oscillation Index (SaOI) at the surface level, a new index for this phenomenon, and calculated the related correlations. Future research will focus on computing this index, obtaining time series for different atmospheric levels (850 hPa, 700 hPa, 500 hPa and 200 hPa) and evaluating the contribution of the SaO to PM10 in Morocco all year round.

Our work takes the first step in exploring the hypothesis that particular weather patterns increase the vulnerability of individuals with respiratory disease. Additional studies may aid the establishment of an alert system and provide recommendations for coping with PM10 episodes.


ACKNOWLEDGEMENTS


The authors thank Dr. Sihem Aouabdi who edited the English language of the paper on behalf of the Science Edit For The Developing World.


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Impact Factor: 2.735

5-Year Impact Factor: 2.827


SCImago Journal & Country Rank

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