Thirty sampling stations were surveyed each season during the period 2013-2015 in the objective to determine the water quality in the coastal areas between Saidia and Cala Iris. The CPA Analysis shows 5 groups associated following the water quality of 12 components. Only the sites close to the agglomerations bordering the lagoon of Nador and Al Hoceima show high values of BOD5, Nitrites and PO4. This deterioration of the water quality observed in some coastal sites explains in large part their low richness of the biodiversity of the algae

El Asri, O.

El Khiati, N.

Elmsellem, H.

Haloui, B.

Mabrouki, Y.

Mostareh, M.

Ramdani, M.

Taybi, A. F.

English

Revues Scientifiques Marocaines

Moroccan Journal of Chemistry ISSN: 2351-812X http://revues.imist.ma/?journal=morjchem&page=login Ramdani & al. / Mor. J. Chem. 5 N°2 (2017) 227-235  Mor. J. Chem. 5 N°2 (2017) 227-235 227    The spatial variability of water quality in the Mediterranean of eastern Morocco  M. Ramdani(a)*, A. F. Taybi(a), Y. Mabrouki(a), B. Haloui(a), O. El Asri(b), H. Elmsellem(d)*, N. El Khiati(c) and M. Mostareh(a)  (a) Laboratory of Water, Environment and Sustainable Development, Faculty of Sciences, Mohammed Premier University, Oujda, Morocco (b) Laboratory of Plant Biology and Microorganisms. BP 524, 60000 Oujda, Morocco. (c) Department of Biology, Faculty of Science Ain Chock, University Hassan 2 of Casablanca, Morocco   (d) Laboratory of Applied Chemistry and Environment (LCAE-URAC18), Faculty of Sciences, Mohammed Premier University, Oujda, Morocco.       * Corresponding author: h.elmsellem@yahoo.fr Received 13 Nov 2016, Revised 21 Dec 2016, Accepted  04 April 2017 Abstract Thirty sampling stations were surveyed each season during the period 2013-2015 in the objective to determine the water quality in the coastal areas between Saidia and Cala Iris. The CPA Analysis shows 5 groups associated following the water quality of 12 components. Only the sites close to the agglomerations bordering the lagoon of Nador and Al Hoceima show high values of BOD5, Nitrites and PO4. This deterioration of the water quality observed in some coastal sites explains in large part their low richness of the biodiversity of the algae.      Keywords: , Mediterranean coastal areas, Nador lagoon, Morocco.             Mor. J. Chem. 5 N°2(2017) 227-235 228  Introduction The Moroccan Mediterranean coast, located between Al Hoceima and Saidia, is distinguished by a succession of sandy beaches and rocky substrates interspersed with numerous agglomerations, a mouth and a vast lagoon. The ecological importance of this coastline has been demonstrated by several studies on hydrology and algology [1- 6]. The abiotic factors responsible for the spatial distribution of macroalgae are necessary for the availability of recent data on water quality and for explaining the ecology and geographical distribution of marine algae and phanerogams. Thus, a seasonal monitoring of 12 physicochemical parameters was carried out during the period 2013-2015. The study area is 242 km between Cala Iris in the west and Saidia in the east (Fig 1 and Table 1). This area covers several sites of biological and economic interest which are:  Al Hoceima National Park (4 ° N, 35 ° 14'N), located on the coast of the Mediterranean, about 150 km east of the Strait of Gibraltar, north-east of Morocco, Close to the town of Al Hoceima.  Cap des Trois Fourches, situé à 25 km au nord-ouest de Nador, il s'avance d'une vingtaine de kilomètres dans la mer Méditerranée.  The mouth of the Moulouya, one of the most important rivers of Morocco extends over a distance of 520 km. It comprises five systems: marine, estuarine, lacustrine, riparian and palustrine.  The Nador lagoon (SebkhaBouAreg or Marchica) is the second largest North African lagoon, located between Cap des Trois Fourches and Cap de l'Eau, with an area of 114 km2 and a maximum depth of 6.5 m. The coastline of the Moroccan Mediterranean is of great socio-economic, cultural and ecological importance [5] hence the need to assess the state of water quality in order to determine a typology of the stations based on the environmental affinities.   Figure  1: Geographical location of study stations.      Mor. J. Chem. 5 N°2(2017) 227-235 229  Table 1 : Station names and contact information Number Stations N O 1 Cala -iris 35,1500 4,3733 2 Torres 35,1578 4,3333 3 Sfiha 35,2108 3,8997 4 Laazib 35,2750 3,7428 5 Boujibar 35,2881 3,7003 6 Sidi Hssayen 35,1989 3,4394 7 Shablala 35,2225 3,2361 8 Iazzannen 35,2678 3,1450 9 Lissyakh 35,2822 3,1225 10 Ijja Abdelkader 35,2925 3,0711 11 Dcharrana 35,3889 3,0100 12 Phare 35,4386 2,9769 13 Tiboda 35,4200 2,9581 14 BniAnsar 35,2497 2,9214 15 Attallayoune 35,2125 2,8972 16 Sidi Ali 35,1933 2,9236 17 Nouvelle Passe: Bokana 35,2022 2,8619 18  lagune centre 35,1608 2,8550 19 Chaalla 35,1639 2,9031 20 Oued  Selouane 35,1356 2,8803 21 Oued  Bouareg 35,1256 2,8550 22 Mohandiss Lagune 35,1472 2,7917 23 KariatArekmane 35,1128 2,7544 24 Mohandiss Méditerranée 35,1564 2,7856 25 JarfRoum 35,1044 2,6961 26 Boufadiss 35,0933 2,5128 27 Plage rouge 35,1056 2,4817 28 Cap de l’Eau 35,1483 2,4233 29 Embouchure de Moulouya 35,1242 2,3428 30 Saidia: Rochers 35,0861 2,2144  Material and methods In order to assess the physicochemical quality of the waters of the Mediterranean coast of eastern Morocco, sampling was carried out in thirty sites covering the entire coast. The 12 physico-chemical parameters evaluated concern the conductivity, Ph, BOD5, Ca, Mg, Na, K, Cl, NO3, NO2, SO2 and PO4. The samples are stored in a cooler and   Mor. J. Chem. 5 N°2(2017) 227-235 230  analyzed at the chemistry laboratory at the CNRST in Rabat. Statistical analyzes were carried out using software R (version 3.3.1) in order to establish a typology of the stations according to their environmental affinities. The matrix of twelve abiotic parameters was subjected to a principal component analysis (ACPN). These descriptors represent the average of 4 campaigns each designating a season (Table 2). This method of analysis has already shown its effectiveness in hydrobiology[7], [8], [9], [10](Mabroukiet al., 2016 ; Mabroukiet al., 2017, Taybi and al., 2016 ; Taybi, 2016).  Table 2: Average contents of the 12 physico-chemical parameters used for an ACPN analysis. Units of chemical elements are expressed in ppm.  Cond pH DBO5 Ca Mg Na K Chlorures N03 N02 S02 PO4 S1 50,95 8,1 2,60 241,58 950,66 5365,66 229,04 14797,20 181,49 3154,20 15086,51 684,94 S2 51,25 8,1 2,60 367,08 1658,35 6726,15 348,36 18711,10 86,49 2604,00 15052,03 711,30 S3 48,95 8,2 3,30 331,48 1450,69 6421,60 339,34 17801,30 83,39 2266,50 15256,36 700,14 S4 51,45 8,3 3,30 296,40 1263,81 5692,59 302,16 20487,10 94,19 3266,70 15759,50 475,07 S5 51,55 8,1 4,20 387,22 1840,74 6975,25 378,95 18719,00 142,19 2359,30 15687,63 803,15 S6 50,95 8,1 4,20 330,20 1510,70 6663,04 323,65 19894,50 90,99 2752,40 15734,42 624,43 S7 52,35 8,2 4,20 342,08 1624,00 6287,15 344,83 19361,30 0,09 2389,70 16212,30 358,48 S8 52,45 8,0 2,60 317,87 1480,73 6195,96 319,05 38747,80 84,09 5554,20 16450,57 396,45 S9 52,55 8,1 2,60 318,03 311,40 288,90 318,03 233,90 321,39 311,40 233,90 312,15 S10 52,55 8,3 5,30 337,30 1581,81 6644,08 355,80 24094,50 92,79 3237,20 16921,79 480,73 S11 53,95 8,1 3,10 395,82 1969,46 6980,69 383,59 22750,10 0,09 2969,30 17192,77 15,75 S12 54,95 8,1 6,40 353,37 1737,21 6350,79 355,56 18927,50 0,09 2442,20 17687,73 15,75 S13 52,95 8,3 7,92 379,25 1893,64 6945,25 372,36 20539,10 0,09 2803,60 17779,76 501,91 S14 59,55 8,5 14,35 419,41 2148,71 7287,45 399,78 18467,28 49,15 2519,88 17818,26 194,14 S15 52,4 8,3 10,65 397,33 1941,64 6972,14 377,32 18456,40 92,60 2446,10 16727,24 705,85 S16 56 8,5 4,95 321,40 1498,31 6188,63 325,48 19901,10 76,90 2831,60 16885,78 779,61 S17 51,5 8,3 3,35 340,67 1591,81 6676,58 359,17 24239,00 92,80 3247,20 17103,04 508,73 S18 49,25 8,2 8,23 320,96 1576,09 5959,08 305,56 14240,80 41,28 2855,60 16939,98 1097,38 S19 59,5 8,4 20,25 323,38 1512,64 5506,08 287,89 14563,70 117,60 2422,50 11566,43 125,80 S20 52,5 8,4 16,00 404,07 2032,53 6936,52 372,31 18049,30 32,28 2522,90 17585,61 580,54 S21 59,75 8,3 18,25 418,22 2118,15 7177,49 393,81 20449,10 15,78 2694,20 18380,74 169,82 S22 56,5 8,5 6,60 369,06 1816,95 6757,32 363,61 19943,70 98,90 3219,65 19521,51 511,73 S23 59,05 8,3 9,23 416,83 2105,66 7411,35 407,08 19506,45 112,25 2540,05 16852,05 412,50 S24 52,05 7,8 4,80 362,83 1804,62 6580,93 363,40 20042,50 0,09 2760,90 17460,05 611,44 S25 49,95 8,0 4,70 432,79 2239,29 7517,04 409,60 21672,80 0,09 2728,30 16430,71 511,91 S26 57,95 8,1 4,30 364,33 1820,42 6585,97 366,67 21137,70 0,09 2838,00 17129,88 267,77 S27 52,95 8,0 3,40 358,77 1783,29 6404,33 331,17 17296,30 86,79 2580,60 17643,06 350,49 S28 52,55 7,5 8,40 409,42 2120,92 7236,12 398,55 21990,40 104,69 2804,90 17602,73 254,26 S29 8,04 7,8 4,40 193,15 317,04 1142,90 44,55 2942,40 97,29 651,70 16128,78 117,90 S30 58,95 8,0 4,60 418,63 2143,85 7209,07 396,98 22384,20 0,09 2884,30 16918,47 464,41    Mor. J. Chem. 5 N°2(2017) 227-235 231  Results Principal component analysis of the 12 parameters of the 30 stations studied shows that the first three axes F1, F2 and F3 hold the bulk of the information since they represent 75.26% of the total inertia (Figs 2, 3). The significance of each axis in the structured distribution of the cloud of the stations and the relationship between the typological structure and the habitat variables can be explained by examining the correlations between the axes and the various mesological components (Fig 4). The F1 axis (47.18% of total inertia) shows a good negative correlation with sodium (Na) and magnesium (Mg), followed by potassium and calcium.   Figure  2: Biplot of the PCA in the F1-F2 plane showing the physicochemical parameters and the stations.   Figure  3: Biplot of the PCA in the F1-F3 plane showing the physicochemical parameters and the stations.  The latter are negatively correlated with nitrates. The F1 axis separates the stations globally into two groups, one with high concentrations of Na, Mg, K and Ca, and a group with low concentrations of these elements. The F2 axis (16.16% of total inertia) is positively correlated with BOD5, expressing an increasing gradient of deterioration in water quality. The position of station S19 at the end of this group is explained by the rather high values of the   Mor. J. Chem. 5 N°2(2017) 227-235 232  physicochemical parameters indicating a strong degradation of the water quality. The F3 axis (11.9%) expresses a gradient in relation to the phosphate and nitrate contents. Thus, the two factorial planes F1-F2 and F1-F3 isolate five stations: S1, S8, S9, S19 and S29. These sites have particular characteristics. The common point between S9 and S1 is their highest nitrate concentration of 321 and 181 mg / l, respectively. The S8 site has the highest concentration of chlorides and nitrites, while S29 has the lowest Ca, Mg, Na and K contents.    Figure  4: Correlation ratio between the physicochemical parameters and the first 3 axes of the PCA.  The other stations are organized at the centers of the two axes of the ACP, for a better typology a second ACP was tested by eliminating the stations that contributed most in the formation of the first three axes, these are the five isolated stations In the first ACP S1, S8, S9, S19 and S29. The second analysis of the mesological parameters (dataset = 25 stations / 12 variables) of the different stations surveyed in standardized principal components shows that the first three axes F1, F2 and F3 hold most of the information since they represent 73.37% Of the total inertia (Figs 5 and 6). Examination of the correlations (Fig 7) between the axes and the different mesological components explains the significance of each axis.   Figure  5: Biplot of the 2nd ACP in the plane F1-F2 presenting the physicochemical parameters and the stations.    Mor. J. Chem. 5 N°2(2017) 227-235 233  The F1 axis (25.96% of total inertia) shows a very good negative correlation with the four cations (Mg, Ca, K and Na), followed by an average negative conductivity correlation; And a positive mean correlation with PO4. The F1 axis separates two groups of stations, one group rich in Mg, Ca, K and Na, and a second group showing stations poor in these elements, with a generally high concentration of PO4 for the stations on the upper right. The F2 axis (16.25% of total inertia) expresses a strong negative correlation with NO2 and chlorine. The F2 axis isolates at the bottom a group of stations with high levels of nitrite and chlorine, and at the top the stations least rich in these elements. The F3 axis (14.74% of total inertia), which shows a good pH correlation followed by BOD5, generally expresses a pollution gradient and separates at the top stations with basic pH and high BOD5, such as station S22 and S20, which respectively have a pH of 8.49 and 8.36, and a BOD5 of 6.6 mg / l and 16 mg / l. Thus, the planes F1-F2, and F1-F3 of the two ACPs used make it possible to discriminate five groupings of stations (Figs. 6, 7 and 8).   Figure  6: Biplot of the 2nd ACP in the plane F1-F3 presenting the physicochemical parameters and the stations.    Figure  7: Correlation ratio between the physicochemical parameters and the first 3 axes of the 2nd ACP.  Group 1 consists of seven stations (S2, S3, S5, S6, S7, S24, and S27), whose water characteristic is generally low in calcium, sodium, magnesium, potassium and BOD5 and high in phosphates; The Ca and PO4 values are respectively   Mor. J. Chem. 5 N°2(2017) 227-235 234  between 330.19 and 387.21 ppm, and 350.49 and 803.15ppm. The stations S1, S8 and S9 join this group by recording the lowest values in BOD5. Group 2 consists of 4 stations (S4, S16 and S18 and S29). The waters in this group have lower sodium and potassium values. The S29 joined this set with a potassium value of 44.54 ppm, the lowest of all stations. Group 3 consists of stations S10, S12, S13, S17, S22, and S26, showing mean values of calcium, sodium, magnesium and potassium and high nitrite values. Group 4, represented by S11, S25, S28, and S30, has the specificity of having waters with fairly high values of calcium, sodium, magnesium, potassium and chlorides. Group 5 consists of S14, S15, S20, S21 and S23 which show fairly degraded water quality with BOD5 values between 9.22 and 18.25 mg / l. Station S19 joined this group because of the high value of BOD5 (20.25 mg / l).  Figure  8: ascending hierarchical classification of stations in the study area.  Figure  9: Representation of the five station groups on the F1-F2 factor plane of the second ACP.  Conclusion The multivariate analysis allowed to discriminate five types of stations according to the concentrations of the 12 physicochemical parameters Figs 8 and 9. A group of 5 groups has been individualized showing the stations with deteriorated qualities next to the coastal stations remote from human activities and with values meeting the standards of the sanitary. The lagoon stations show a deteriorated water quality, except for the S16 station. It is a statistical treatment of 30 stations spread over a length of 242 km [11], [12] Remote zones of agglomerations show qualities   Mor. J. Chem. 5 N°2(2017) 227-235 235  meeting the standards of the health of the coastal waters while the stations bordering The lagoon in its mainland shows poor qualities and sanitation is required in the present state.   References [1] M. K. Bloundi, P. Faure, J. Duplay, Comptes Rendus Géoscience, 340 (2008) 840 849. [2] Menioui Mohamed  Mars 2008  Consuilt _ecolo_pdf Vulnérabilité des ecosystems dulittoral Méditerranéen oriental à l'élevation, projet ACCMA. [3] Z. Zerrouqi, M. Sbaa, A. Chafi, et H. Aqil, Bull. L’Institut Sci. Rabat, 35(2013) 51 59. [4]  A.Aknaf, M. Akodad, A. Moumen, K. Ben Chekroun, Elhamouti, A. Bailal, M. J. Mater. Environ. Sci. 6 (2015) 2939-2943. [5] M.M.M. Mostar, H. EL Madani, H. Saleh Ali, Yahya, O. EL Hachemi, S. Abdellaoui, A. Chafi. J. Mater. Environ. Sci.7(2016) 4795-5809. [6] M. Ramdani, F. Sahnouni, B. Haloui, N. E. Roger, F. A. Mesfioui, M. Ramdani, J Biodivers Env. Sci, 7 (2015) 252 261. [7] Y. Mabrouki, A.F. Taybi., H. Bensaad, A. Berrahou. J. Mater. Environ. Sci. 7 (2016) 231-243 [8] Y. Mabrouki, A. Berrahou, K. Chaabane, J. Mater. Environ. Sci. 7 (1) (2016) 272-284. [9] A.F. Taybi, Y. Mabrouki, A. Berrahou, K. Chaabane. J. Mater. Environ. Sci. 7 (2016)  272-284. [10] Taybi, A.F., (2016). - Hydrobiological study of the Moulouya: Structure of the biodiversity and longitudinal zonation of benthic invertebrates. PhD thesis, University Mohamed premier, Oujda Morocco, 260. (in French). [11] M. Ramdani, H. Elmsellem, N. Elkhiati, B. Haloui, A. Aouniti, M. Ramdani, Z. Ghazi, A. Chetouani and B. Hammouti. Der Pharma Chemica, 2015, 7(2015)   67-76. [12] M. Ramdani , M. Ramdani, H. Elmsellem, B. Haloui, M. Ramdani, N. Elkhiati, M. Layachi, A. Mesfioui, B. Hammouti, A. Aouniti, and B. El Mahi. Der Pharma Chemica, 8(2016) 330-337.              

The spatial variability of water quality in the Mediterranean of eastern Morocco

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The spatial variability of water quality in the Mediterranean of eastern Morocco

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