The contribution of large-scale atmospheric patterns to pollution 1 with PM 10 : the new Saharan Oscillation Index 2

Direction de la Météorologie Nationale, Boulevard Mohamed Tayeb Naciri, Hay Hassani B.P. 8106 Oasis4 Casablanca, Morocco 5 Laboratory of Drugs Science, Biomedical and Biotechnological Research. Faculty of Medicine and Pharmacy. 6 Hassan II University; Ain Chock. Casablanca; P.B. 5696. Morocco 7 8 Abstract 9 PM10 has natural and anthropogenic sources, it is an urban air pollutant from desertic areas or 10 emitted by industry and traffic activities, it reduces visibility and threatens human wellbeing 11 mainly in big cities. 12 Casablanca concentrates many industrial units and a large vehicle fleet. The rate of 13 urbanization in the metropolis and the population density are the highest in Morocco. Marrakech 14 is one of the most populated cities in the country where the motorization rate has increased during 15 recent years. 16 The present work is based on PM10 daily measurements between 2013 and 2016. The main 17 objective is to assess the concentrations of PM10 in Casablanca and Marrakech and study their 18 relationship with the atmospheric circulation. First, we assessed PM10 correlations with climate 19 indexes (the North Atlantic Oscillation (NAO) and the Mediterranean Oscillation (MO)), then we 20 characterized the contribution of large-scale atmospheric patterns related to PM10 extreme events. 21 The novelty of this research is the creation of a new climate index to characterize the oscillation, 22 in the country’s southern desert, between the Saharan depression and the Azores high. The time 23 series of the new Saharan Oscillation Index (SaOI) were calculated. 24 This study has demonstrated the relationship between MO and PM10 averages and has shown 25 that particulate pollution in the study area is partly induced by continental northeasterly to 26 southwesterly flow. This flow is triggered by the Saharan trough and managed by the high27 pressure area in the north. The assessed correlations related to the SaOI confirm the relationship 28 between this index, PM10 averages, and MO and NAO indexes mainly in winter. 29 30


Introduction
Nowadays, air pollution and climate change are the greatest atmospheric challenges for 35 societies, and they will continue in future decades. These environmental issues are highly 36 connected, mainly through atmospheric processes and meteorological conditions (von 37 Schneidemesser et al., 2015; Kusumaningtyas et al., 2018;Sfîcă et al., 2018). 38 Air pollution is derived from local pollution sources but also affected by large scale movement 39 of air masses that contribute to regional background pollution and air pollution episodes. The  (Douguédroit, 130 1995) using SLP. 131 The annual data of the NAO and MO indexes were gathered from the website of the Climatic 132 Research Unit (CRU, http://www.cru.uea.ac.uk/cru/data) and used in order to study the link 133 between these two atmospheric modes and the seasonal evolution of dailyPM 10 averages between 134 2013 and 2016. 135

Sea Level Pressure (SLP) data 136
Daily data were used in order to retrace SLP patterns related to the identified PM 10 seasonal 137 extreme events. This will help to associate the flows over the studied areas with extreme particle 138 pollution conditions. 139 The SLP data were provided by the ERA 5 reanalysis. ERA5  overall and seasonal thresholds, respectively. Thus, an extreme PM 10 event is defined as a day 154 that recorded an average PM 10 greater than or equal to the 95 th percentile. 155 The magnitudes of trends in time series were analyzed using the non-parametric approach 156 proposed by Theil and Sen (Sen, 1968;Theil, 1992) for univariate time series. This method 157 implies the preparation of the ordinal time points and the calculation of the slopes for all the pairs 158 and then the computing of the median of the calculated slopes as an estimate of the general slope. 159 Sen's slope is widely used for the estimation of trends' magnitudes in climate series, given its  be better understood, in the study area, by studying extreme PM 10 events mutually with large 210 scale atmospheric circulation and this is the aim of the next chapter. 211

PM 10 extreme events and large-scale atmospheric circulation 212
After exploring the correlation between PM 10 averages and climate indexes, there was a need 213 for further assessment of how could large scale atmospheric circulation affect the emergence of 214 extreme PM 10 events. Accordingly, and in order to reduce the probability of having the episodes 215 triggered by local pollution in the cities of Casablanca and Marrakech, seasonal common PM 10 216 episodes to both cities were identified. For the purpose of this work, a common PM 10 episode is 217 defined as an extreme PM 10 event recorded on the same day in the two cities, offset days of 218 episodes appearance were not considered. We have identified more than 70 recorded PM 10 219 episodes in each city between 2013 and 2016, 16 were common to both cities; 5 in autumn and 220 spring each and 3 in winter and summer each. The maps of SLP fields from ECMWF were 221 reconstructed and analyzed for all the common episodes, they correspond to the weather types. 222 This step helped to identify atmospheric flows behind detected common extremes. 223 impact. This index will be called the Saharan Oscillation Index (SaOI). 267

The Saharan Oscillation Index (SaOI): formulation and correlations 268
For the purpose of this work, a Saharan Oscillation (SaO) is suggested to be related to the 269 dipole between the Azores High and the Saharan trough. The SaOI is defined as the difference 270 between the normalized pressure of the Azores (37.79°N, -25.5°E) same order in both cities (Table 1). This informs about seasonal PM 10 /SaOI relationship. In 284 autumn, PM 10 /SaOI coefficient of correlation is weak, positive and statistically significant in 285 Casablanca. It is negative and statistically significant in both cities in spring. In winter, 286

303
Although previous studies have examined particle pollution by PM 10 in Casablanca and 304 Marrakech, this study was the first attempt to assess the possible relationship between this type of 305 pollution and large-scale atmospheric circulation in the two highly populated cities in Morocco. 306 We have assessed trends in average PM 10 by analyzing daily measurements of PM 10 307 concentrations for the period between 2013 and 2016. We identified PM 10 extreme events and 308 assessed their evolution and then we discovered how large-scale atmospheric circulation 309 contributes to pollution by PM 10 ; we first studied the correlation of average PM 10 time series and 310 climate indexes (NAO & MO) and then identified weather patterns that match the occurrence of 311 particle pollution events. 312 Our study did not reveal any accentuated tendencies for PM 10 averages and extreme events; 313 however, it showed that the concentrations of this pollutant depend on the city and thus on the 314 local sources of particles. They also depend on the large-scale atmospheric circulation according 315 to the seasons and thus on meteorological parameters like the temperature, the humidity and the 316 wind. When more data is available, contributions from each source type may be identified. 317 Moreover, this work has shown a relationship between the NAO and the MO climate indexes and 318 PM 10 averages, it has proved that the MO plays a major role in particle pollution in Morocco. It 319 has also helped to define the Saharan Oscillation (SaO) as the interaction between the Azores 320 High and the Saharan Depression, the SaO may be a new factor that helps understand this type of 321 pollution in Morocco, considering the continuous transport of particles from the south.  489  490  491  492  493  494  495  496  497  498  499  500  501  502  503  504  505  506  507  508  509  510  511  512  513  514 515