Introduction

Today, our world is facing huge effects of human activities. Due to the continuous increase in human population and the attendant sophistication in the sort of commodities and materials consumed, the generation of different types of the substantial wastes has reached unprecedented quantities (Krantz and Kifferstein 1995). Water is a valuable natural resource that is essential to human’s survival and the ecosystem’s health. Water is composed of coastal water bodies and fresh water bodies (lakes, rivers and groundwater). Water pollution occurs when a body of water is adversely affected due to the addition of excess amounts of materials to the water. When it is unfit for its intended use, water is considered polluted (Panda et al. 2002; Usali and Ismail 2010; Melegy et al. 2013). Over past few decades, the increasing of anthropogenic activities, especially in industrial areas, has affected water bodies. Although some kinds of water pollution occur through natural processes, pollution is mostly attributed to anthropogenic activities.

From this point of view, the water quality of the aquatic environment is considered as the main factor impacting the state of health and disease in both cultured and wild fish. Pollution of the aquatic environment by inorganic and organic chemicals is a major factor posing a serious threat to the survival of aquatic organisms (Saeed and Shaker 2008). The aquatic environment is subjected to various types of pollutants which enter water bodies and surface sediments; this is a result of human activities that are related to industrial, domestic and agricultural wastes which are dumped into water bodies, and they severely affect the aquatic organisms. Today, the environmental pollution and their deleterious effects have both direct and indirect impacts on natural resources, human activities and socio-economic activities of any area around the world. Lakes can be readily contaminated by human activity without any obvious signs (Oyewale and Musa 2006; Hereher 2014); thus, they have long attracted scientific and environmental interest. Among the various pollutants, heavy metals are the most toxic, persistent and abundant pollutants that can accumulate in aquatic habitats. Furthermore, their concentration increases through bio-magnifications (Sin et al. 2001; Kishe and Machwa 2003; Ahmed et al. 2005; Moore et al. 2009; Abdallah 2014).

The contamination of the natural environment components such as soil, sediment, water resources and biota by heavy metals is one of the major concerns impacting environmental problems worldwide because these metals are indestructible and most of them have toxic effects on living organisms when they exceed a certain concentration. This is particularly true in many of the industrialized countries because of their toxicity, persistence and bioaccumulation (Nuremberg 1984; Forstner 1990; Harte et al. 1991; Schuurmann and Market 1998; MacFarlane and Burchett 2000; Iken et al. 2003; Ghrefat and Yusuf 2006; Abdel-Azeem et al. 2007; Arnous and El-Rayes 2013; Kata and Ramana 2013). Heavy metals are of high ecological significance since they are not removed from water as a result of self-purification, but accumulation in reservoirs can enter the food chain (Loska and Wiechula 2003). The elevation of metal levels in a reservoir is shown mainly by an increase in their concentrations in the bottom sediment. Their occurrence in the environment results primarily from anthropogenic activities.

Lake Manzala (LM) is the largest of the Nile Delta lakes and the most productive for fisheries in Egypt. It is a shallow, brackish water lake located in the north eastern edge of the Nile Delta, and it is an internationally registered important bird area (UNDP 1997). The pollution threatens the lake’s entire ecosystem, in which the heavy metals discharging into a lake from both natural and anthropogenic sources are distributed between bed sediments (as sink and source of heavy metals) and aqueous phases. Thus, in evaluating the pollution condition of a water body, both sediments and water should be considered (Lee et al. 2003). Also, the natural process, such as weathering of rocks and volcanic activities, plays a noticeable role in enriching the water of reservoirs with heavy metals (Forstner and Wittmann 1979, 1983; Nriagu 1989; Veena et al. 1997; Almasoud et al. 2014). Sediments are normally mixtures of several components including different mineral species as well as organic debris. Sediments represent one of the ultimate sinks for heavy metals discharged into the environment (Gibbs 1977; Luoma and Bryan 1981; Bettinetti et al. 2003; Hollert et al. 2003). In order to protect the aquatic life community, comprehensive methods for identifying and assessing the severity of sediment contamination have been introduced over the past 10 years (Van de Guchte 1992; Chapman 2000). Today, the LM is subjected to a gradual shrinkage during the past few decades due to land reclamation and the transformation of significant parts of the lake to fish farms, particularly along the southern regions. Moreover, large parts of the lake are overgrown with aquatic vegetation, which reduces the open water to nearly half of its total area thereby speeding up the process of land transformation (Saeed and Shaker 2008). LM has gradually transformed, with time, from a brackish environment into eutrophic fresh water in response to increased fresh water inputs and nutrient loading associated with agricultural land reclamation processes and due to the urban waste disposal (Bishai and Yossef 1977).

Remote sensing (RS) satellite data inputs have been playing a key role in the management of natural resources along with geographic information system (GIS) over the past two decades. Remotely sensed data provides valuable and near real-time spatial information on natural resources and physical terrain parameters. In this study, an integration of GIS and RS has been used to assess the environmental conditions in and around the lake, monitor changes and derive information to support decision-making about the lake’s valuable resources. The study successfully identified key environmental problems and trends of change that are related to the effect of the heavy metal risk assessment in water and sediments of Eastern Lake Manzala (ELM), Egypt.

The main aim of this study area is to determine the feasibility and accuracy of using GIS and RS data and other ancillary information to detect land use change and its relationship between the water and sediment load of contamination and land use/land cover (LU/LC) change in and around ELM. Field verification and observation, interpretation of water, and bottom sediments of ELM, in addition to the analysis and interpretation of RS satellite data and geostatistical analysis in combination with the GIS techniques, are used in this research to assess, monitor and determine the spatial distribution of LU/LC variation, heavy metal concentration content in the bottom sediments, and the water of ELM as well as to assess the risk of the pollution load in water and sediments and to distinguish the sources of pollution by conducting source apportionment using geostatistical analysis.

Study area

LM is the largest of the delta lagoons and is located on the north-eastern edge of the Nile Delta, between Damietta and Port Said. The lake and its shore lands lie within five governorates and the two macro-economic regions of the Suez Canal and Delta. The present lagoon is 64 km long with a maximum width of 49 km and contains numerous islands. The lake is separated from the Mediterranean Sea by a sandy beach ridge that has three open connections between the lake and the sea. The investigated area is located to the east of the LM, between longitudes 32° 10′–32° 20′ E and latitudes 31° 09′–31° 18′ N (Fig. 1). The lake is very shallow, within an average depth of 1.3 m. The water in the lake is not mixed uniformly, due to the islands. Salt marshes extend southwards and eastwards from the south-eastern corner. LM’s bed belongs to the Late Quaternary sediments deposited within this delta. The central part of LM is located in the centre of the Holocene depositional basin where sediment thickness is more than 40 m. The Holocene deposits thin out towards the edge of the lake basin. The climate of the investigated area is typically Mediterranean; it belongs to the arid province (Ayyad et al. 1983). The study area is characterized by aridity with a long hot rainless summer and a mild winter with low amount of rainfall (Attia 1994). Climatic variables play an important role in controlling the water circulation and the water quality of LM (Thompson et al. 2009; Rasmussen et al. 2009).

Fig. 1
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Location (A) and Landsat ETM+7 image sampling site distribution maps of water and sediments (B) of the ELM area

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Several decades ago, the human impact and utilization were affected in the study area when continuous land reclamation projects (established to meet a rapidly growing population) resulted in a significant decrease in the size of the lake. Land reclamation for agriculture together with the introduction of perennial irrigation, the construction of canals and drains and the discharge of nutrients has greatly modified LM. Some of the salt marshes in the east of the Suez Canal are below the mean sea level. Due to reclamation, the open water area has been decreasing steadily from 170,000 ha at the turn of the century to 90,000 ha in 1983. Today, low saline levels near drain and canal outlets in the south and west characterize the lake. There are saline waters in the extreme north-west and brackish waters over most of the remaining areas (Abdel-Satar 2008). Also, the lake can be divided into five basins, of which four have a different salinity regime (El-Wakeel and Wahby 1970; Shaheen and Yosef 1979).

Material and methods

Water sampling and analysis

Generally, LM receives annually about 6.7 × 109 m3 of raw sewage, agricultural and industrial wastewaters. Metal ions can be incorporated into food chains and concentrated in aquatic organisms to a level that affects their physiological state. The effective pollutions are the heavy metals which have drastic environmental impacts on all organisms. Heavy metals such as Zn, Cu, Mn, Cd and Pb play a biochemical role in the life processes of all aquatic plants. Water samples were collected (n = 65) for measuring heavy metal residues of the study area (Fig. 1). Heavy metal analyses were performed with atomic absorption spectrophotometer (Perkin Elmer Model 2830). All detection limits are given in micrograms per litre and were determined using elemental standards in dilute aqueous solution. Trace element analyses including Zn, Pb, Cu, Mn, and Cd were performed with atomic absorption spectrophotometer (Perkin Elmer). Detection limits are 1.5 μg/l Zn, 5 μg/l Pb, 9 μg/l Cu, 1.5 μg/l Mn and 0.8 μg/l Cd. All detection limits are based on a 98 % confidence level.

Bottom sediments sampling and analysis

Lake bottom sediments are normally the final pathway of both natural and anthropogenic components derived from the environment. Sediment quality is a good indicator of pollution in a water column which tends to concentrate the heavy metals and other organic pollutants. The drainage water transports considerable amounts of autochthonous sediments to the LM. These sediments are deposited on the bottom and constitute, with autochthonous deposits, the total sediments of the lakes. Sediment samples were collected (n = 65) for measuring heavy metal residues of the bottom surface of the study area (Fig. 1). The samples were air-dried and crushed with a wooden pestle and mortar to pass through a 0.5-mm mesh screen. According to the method that is described in Page et al. (1982), the dried grains were digested with a mixture of conc. H2O2, HCl and HNO3. The electrical conductivity (EC) of soils was determined using 1:2 soil to solution (H2O) ratio. Particle size distribution was determined by the pipette method (Gee and Bauder 1986). Soils were also characterized for their carbonate content (Allison and Moodie 1965).

RS and GIS analysis

RS and GIS tools play important roles in which the spatial integration of various datasets including geological map, Landsat satellite images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), and physical and chemical sampling mapping was applied in the present study, to show the distribution and intensities of the contamination of heavy metals in relation to the change of LU/LC activities and its environmental impact by using ERDAS Imagine and ArcGIS software. The image data were acquired at intervals between 1984 and 2006. These images cover a time span of approximately 22 years. The images have been acquired almost in summer season and are of good quality with no effective clouds or image defects such as striping. Three sets of multitemporal Landsat satellite 5 and 7 images (TM and ETM+7), dated 1984, 2001 and 2006 for Path 176 and Row 38 covering the investigated area, have been geometrically and radiometrically corrected and represented in UTM projection WSG84 and were used for the change detection analysis, with spatial resolution 28.5 and 14.25 m, respectively, for change detection of the LU/LC of the human and natural environmental impact activities. Topographic maps at scales 1:50,000; 1:100,000; and geological map of Conoco (EGPC/Conoco 1987) are used as reference maps and digitally processed. Digital elevation model (DEM) was created from the topographic maps and ASTER GDEM to determine and analyze the topographic features of the terrain such as slope, aspect, 3-D view and contour maps as well as to investigate the geomorphological characteristics of the bottom sediment surface terrain of the study area in the eastern part of LM. Digital processing of the multitemporal images generated several products ranging from single-band images to stack image bands. False colour composite images, principal component images, ratio images, brightness inversion, post-classification and change detection techniques were used for detecting the change of the LU/LC thematic map in the study area. Towards this end, spatial analyses and digital mapping of the geographical database by GIS tools are important to analysis of the LU/LC thematic layers and the tabular data of the chemical analysis of water and bottom sediment mapping of the study area in ELM.

Geo-statistical and spatial data analyses

Multivariate statistical analyses have been applied to the obtained chemical data of ELM water and sediment samples of the study area using SPSS software and ArcGIS geostatistical model. The statistical analysis associations include cluster analysis based on the complete linkage method, correlation coefficients and factor analysis. The different relationships among physical and chemical parameters of the ELM area under investigation were studied. Results are shown in an R-mode dendrogram where steps in the hierarchical clustering solution and values of the distances between clusters (squared Euclidean distance) are represented. Details on cluster analysis can be found in standard chemometric textbooks (Brereton 2003). The spatial interpolation of heavy metals in the water’s and sediments’ environmental variables varies in a manner too complex to represent them with simple deterministic functions, and it is somewhat difficult to measure these variables at all positions of interest. Geostatistics is based on the theory of a regionalized variable which is distributed in space (with spatial coordinates) and shows a spatial autocorrelation such that samples close together in space are more alike than those that are further apart (Matheron 1963). Furthermore, spatial analyses encompass a wide variety of data including points of water and bottom sediment samples and their chemical and physical analysis data. These points in its geographic locations (X, Y) are imported into ArcGIS as spatial points in grid; however, the Z values can be interpreted using interpolation tools. The computed Z values of a random variable such as Cu, Pb, Zn, Mn and Cd heavy metals are interpolated depending on the point location (X, Y). Today, the geostatistical methods were developed to create mathematical models of spatial correlation structures with a variogram (or semi-variogram) as a quantitative measure of spatial correlation to measure the spatial variability of a regionalized variable and provide the input parameters for the spatial interpolation of kriging (McGrath et al. 2004; Webster and Oliver 2007; Satapathy et al. 2009; Zheng et al. 2011; Arnous and El-Rayes 2013). For the interpolator kriging, here, the weights are proportional to both the distance and the directional correlation between the data points. In the current study, the geostatistical methodology calculates spatial patterns of contaminants (variograms and spatial covariances) with experimental data of heavy metal contaminants using ordinary kriging within the ArcGIS Geospatial Analyst extension.

Results and discussion

To monitor, assess and prevent ELM heavy metal risk contamination in water and sediments using GIS, geostatistics, hydrochemical and bottom sediment analysis tools, one must be aware of the sources of contamination, understand the movement of contaminants through porous media and understand the technical socio-economic reasons that permit, encourage and (indeed) make ELM contamination the widespread phenomenon that it is today.

Heavy metal concentrations in ELM

The heavy metal characterizations in ELM with maximum, minimum and mean values of the parameters were calculated based on the collected hydrochemical tabular datasets of heavy metal concentration of ELM water and bottom sediments. The average concentration of each element of water and sediment sample was measured by calculating the arithmetic mean concentration values of each metal at all locations. Similarly, the contribution of each metal was measured by calculating the percentage of the average concentration of each metal to the total average concentration of all metals in the ELM sample. Five heavy elements are determined in the sediments and water of the study area (Table 1). A range of ELM contamination problems are correlated with human and natural sources as it is displayed by integrating the geostatistical data analysis of ELM data with the extracted information from LU/LC thematic map using the GIS technique. The nature of the contamination depends on the materials being extracted and the post-extraction processing. Most of the examined ELM samples contain high concentrations of Zn, Cu, Mn, Cd and Pb. The maximum mean values of the measured heavy metals were recorded at each sampling (water and bottom sediments) sites of ELM (Fig. 1). The concentrations of these contaminants are found in risky levels that were higher than the permissible limits (Table 1) recommended by the USEPA (1986), EHCW (1995), the Egyptian laws (Khallaf et al. 1998) and WHO (2010) standards. This may be attributed to the huge amounts of raw sewage, agricultural and industrial wastewater discharged into the lake. Generally, the main natural source of heavy metals in water is weathering of minerals (Klavins et al. 2000). In addition, industrial effluents, non-point sources and changes in atmospheric precipitation can lead to a local increase in heavy metal concentration in water. Also, the total heavy metal concentrations in aquatic ecosystem can be considered as a reflected mirror of the present pollution status of these areas (Haiyan and Stuane 2003; Abdo and El-Nasharty 2010).

Table 1 Concentration of the heavy metals in the sediments and water of ELM
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Geostatistical analysis of ELM heavy metals

Geostatistics provides an advanced methodology which facilitates the quantification of the spatial features of the water’s and sediments’ heavy metal contamination parameters, and it enables spatial interpolation. Also, geostatistics and GIS have become useful tools for the study of spatial uncertainty and hazard assessment. Principal component analysis (PCA), factor analysis and cluster analysis were carried out based on the data matrix of the water and sediment parameters of ELM to differentiate between the different types of pollution and confirm the results obtained by data integration in the GIS. In order to show the relationship between variables, it is needed to integrate statistical analysis into the GIS. PCA is a common multivariate statistical method, and it is widely used to identify pollution sources, to apportion natural versus human contribution and to describe the spatial distribution of pollutants (Atteia et al. 1994; Tao 1995; Carlon et al. 2001; Facchinelli et al. 2001). PCA converts the variables (analytic concentrations) into factors or principal components. The first factor explains the largest variance, the second factor the next largest and so on (Tables 2 and 3).

Table 2 Correlation matrix of non-transformed data input of the heavy metals in the ELM sediments
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Table 3 Varimax normalized factor loading of the heavy metals in the ELM sediments
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Factor analysis was used to identify the potential contamination variables affecting the water and sediments of ELM, such as Pb, Cu, Zn, Mn and Cd as heavy metal contaminants, and biological and chemical absorption processes. The heavy metals’ concentrations in water and sediments of ELM were correlated with other parameters revealing that the concentrations of these metals are dependent on each other from a hydrogeochemistry perspective. According to results of statistical analysis (Tables 1 and 2), the maximum mean values and correlation of the measured heavy metals were recorded at ELM; this may be attributed to the huge amounts of raw sewage, agricultural and industrial wastewater drained into the lake (Abdel-Moati and El-Sammak 1997). The high level of Cd and Pb in water and sediments can be attributed to the industrial and agricultural drainage (Elghobashy et al. 2001; Mason 2002, and Bahnasawy et al. 2011). It is probably due to the paint factories’ tendency to dispose their untreated waste directly into the lake. Furthermore, Cd is one of the ingredients in the phosphatic fertilizer that is used in crop farms. The high level of Pb in ELM water and sediments could be attributed to the industrial and agricultural drainage in addition to the spillage of leaded petrol from fishing. Also, dust holds a huge amount of Pb from the combustion of petrol in automobile cars due to an increase in Pb content which eventually finds its way into the lake (Hardman et al. 1994). Furthermore, the high concentration of Pb and other heavy metals that were recorded will bring into mind a big question mark about the disposal of gas factories nearby. The maximum concentration of Pb in water is possibly due to the disposal from a new natural gas factory located near the area (Hamed et al. 2013). The high concentration of Zn in ELM water samples may be due to considerable amounts of Zn leached from protection plates of boats containing active Zn (Hamed 1998). According to (Beliles 1979), it is possible that the major sources for Mn in air and water are iron and steel manufacturing as well as the burning of diesel fuel in motor cars and boats. Moreover, the engine boats that were distributed in ELM in the last two decades could be a reason for increasing the Pb and Mn in water of ELM. In addition, high level of Mn concentration is probably due to the agricultural drainage water filled with fertilizers being spilled into the drain. Furthermore, the fish farms adjacent to the drain use some chicken farm residuals rich in Mn for feeding fish (Hamed et al. 2013).

Cluster analysis was successfully used to classify water chemistry and determine the hydrochemical groups of distinct populations that may be significant in the geologic context (Suk and Lee 1999; Farnham et al. 2000; Swanson et al. 2001; Davis 2002; El Alfy and Merkel 2006). The R-mode dendrogram of the water and sediment variables of ELM is interpreted and exhibited in two main clusters (Figs. 2 and 3). The first cluster groups, Cd with Zn, and Pb, may point to the human activities, especially industrial and domestic contaminations through the effect of drains, returning flow water rich in heavy metals (Fig. 2).

Fig. 2
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R-mode dendrogram cluster analysis of heavy metal sediments analysis of ELM

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Fig. 3
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R-mode dendrogram cluster analysis of heavy metal water analysis of ELM

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The second cluster has absorption grouped with Cu and Mn. It may reveal the strong contribution of heavy metals by Cu absorption hydrous magnesium oxide processes in the ELM sediments. Furthermore, the indications of Mn oxide or hydroxide apart from the suspended matter plays an important role in the distribution dynamic of heavy metals in ELM that is in line with the results reported by Abdel-Satar (2008) on LM. The analysis of heavy metals in sediments permits the detection of pollutants that may be either absent or in low concentrations in water column (Binning and Baird 2001). Also, the cluster group found between Ec and pH may reflect the role of ELM’s natural water in adding heavy metals to the water and sediments (Fig. 3) in which the accumulation of metals from the overlaying water to the sediments is based on a number of external environmental factors such as pH, Ec, the ionic strength, anthropogenic input, the type and concentration of organic and inorganic ligands and the available surface area for adsorption (mud fractions) caused by variation in grain size distribution (Awfolu et al. 2005).

Change detection and LU/LC analysis

Most digital image processing systems are integrated or compatible with the GIS system and have been proven to be very useful tools for monitoring the environmental impacts, particularly when rapid and repetitive observations are required (Michalak 1993; Coppin et al. 2004; Arnous 2011; Arnous and Green 2011; Arnous et al. 2011; Juman and Ramsewak 2013). The most obvious tool to correlate the spatial extent of contamination with other resource features both concisely and rapidly is the multitemporal Landsat satellite images that are enhanced using change detection and classification techniques to discriminate and construct various LU/LC classes of ELM within a specific time span from 1984 to 2006. The selections of LU classes were based on the LC classification system for the RS data adopted by Anderson et al. (1976). Saad (2003) reported that the current configuration of the Nile Delta Lakes is changing rapidly due to anthropogenic activities and natural processes. Most of their water supply comes from polluted agricultural drains that are due to several impact problems of the Nile Delta Lakes such as pollution, land reclamation, intensive aquatic vegetation, over fishing and coastal erosion. The erections of Aswan High Dam accompanied by a considerable increase in population and, consequently, in human’s activities constitute the main causes of contamination. Based on the RS change detection of the multitemporal data analysis and interpretation of ELM satellite images, the ELM was subjected to a gradual shrinkage during the past few decades due to land reclamation and the transformation of significant parts of the lake to fish farms, particularly along the southern regions. Moreover, large parts of the lake are overgrown with aquatic vegetation which reduces the open water to nearly half of its total area, speeding up the process of land transformation (Fig. 4).

Fig. 4
figure 4

Change detection of the multitemporal enhanced satellite images of ELM area within time span from 1984 to 2006, respectively, showing the major change of the LU/LC classes

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The human activities and constructed new infrastructure inside and around the lake’s margin due to the sediment transportation by water movement and root plant distribution with large areas may have an effect on the settlement, and the sand sediment accumulation inside the lake inlet decreased its depth. This affects water exchange between the lake and Mediterranean Sea, increased rooted plant growth inside the lake and increased sedimentation all over. Due to the high population growth and industrialization and the greater amounts of discharged domestic and industrial effluents, ELM is exposed to high pollution and contamination levels. LU changes are caused by both natural and socio-economic factors (Campbell et al. 2005). LU/LC is perhaps the most prominent form of global environmental change phenomenon occurring at spatial and temporal scales. LC is the physical and biological cover of the surface of land, whereas LU is the syndromes of human activities such as agricultural, industrial and building construction that alters land surface processes (Foley et al. 2005). The conversion of natural land to cropland, pasture, urban area, reservoirs and other anthropogenic landscapes represents the form of human impact on the environment (McGranahan et al. 2005). During the last years, not only industrial activities but also anthropogenic activities have been negative consequences for the natural environment of the ELM. This unfavourable environmental impact especially by heavy metals has also been seen in aquatic ecosystems. Interpreting and conceptualizing the LC/LU changes contribute to complex dynamics of LC and are important for policy and planning actions (Knorr et al. 2011; Reddy and Gebreselassie 2011).

The LU/LC map of the ELM comprises several classes representing the dominant LC types such as fish farms and water bodies, dried fish farms for land reclamation, halophytic plants, gypsiferous soils, industrial, agricultural land, urban land and swamp areas.

The final indicator results based on the multivariate statistics analysis and the results of LU/LC cover interpretations, the water and bottom sediments of ELM are currently threatened by contamination produced by the leakage from the poorly developed sewage system of the villages and drains, the leaching of fertilizers of the agricultural areas and farming and the discharge of industrial liquid wastes near the industrial zone as well as the roads and fishing activities (Fig. 5). Consequently, the highly polluted site with Zn, Pb and Cu in sediment was found in the drain as a result of the industrial disposal. The highly polluted site with Zn and Cu in water and Cd in sediment was found in site near the industrial area as a result of direct disposal of factories nearby, although the site near the connection between the sea and the lake was assumed to be the purest site (Fig. 6).

Fig. 5
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Land use/land cover map of ELM area constructed from enhanced ETM+7 satellite image

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Fig. 6
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Industrial activities along the ELM margin (a, b) and liquid and solid wastes of industrial pollution (c), agricultural wastewater (d) and raw sewage (e) that are disposed directly into the lake water body and human activities (f)

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Spatial distribution mapping of heavy metals

The spatial distribution and the hazard assessment of water and sediments in the ELM were investigated using geostatistics and GIS tools to reveal the spatial distribution patterns and to provide a basis for the hazard assessment of water and sediment contaminations. To understand what factors influence heavy metal distribution in water and sediments is to create the spatial distribution maps of the heavy metal concentrations. The spatial interpolation of heavy metals in the water’s and sediments’ environmental variables varies in a manner too complex to represent them with simple deterministic functions, and it is somewhat difficult to measure these variables at all positions of interest. The geostatistical technique like kriging interpolation in a GIS environment was applied to estimate the spatial extension of the heavy metal pollution sites (Zheng et al. 2011; Arnous and El-Rayes 2013). The spatial dispersion of the heavy metals’ concentrations in water and sediments, via Zn, Pb, Cd, Mn and Cu in addition to the physicochemical circumstances such as Ec, pH and depth to bottom sediments, was estimated, and its spatial distribution was ascertained to the study area of ELM in order to map the polluted sites within the investigated area.

Spatial distribution of heavy metals in the water and bottom sediments (Figs. 7 and 8) was characterized by localized “hotspot” patterns, where high heavy metal content was surrounded by relatively lower contents. The hotspot areas are highly polluted where the water and bottom sediment pollutions of the aquatic environment of ELM by inorganic chemicals have been considered as a major threat to the aquatic organisms. In which, there were enriched with heavy metals mostly coincided with the agriculture drainage water containing pesticides and fertilizers and effluents of industrial activities and run-offs, in addition to sewage effluents, which supply the water bodies and bottom sediment with huge quantities of inorganic anions and heavy metals. The biggest human sources of heavy metals are industrial, petroleum contamination and sewage disposal. ELM bottom sediments are normally the final pathway of both natural and man-made components produced or derived to the aquatic environment. In addition, the bottom sediment quality is a good evidence of pollution in water column, where it tends to concentrate the heavy metals and other organic pollutants. The drainage water and the ELM transports have considerable amounts of autochthonous sediments, which are distributed by currents and water movements with the directions of ELM slope surface throughout most parts of the ELM (Figs. 9 and 10). The velocity vectors indicate that, in the southern zone, the water movement is predominantly directed towards the north. In the northern upper part of the LM, the water movement comes from the west where the water inflow occurs towards the east where the main coastal outlets exist. In this region, the water flows parallel to the shoreline. This movement is motivated by the wind shear stress which is responsible for the water circulation inside the lake. The high velocity at the LM outlet is due to the confluence of the various exiting flows (Bek et al. 2010). These sediments are deposited on the bottom and constitute, with autochthonous deposits, the total sediments of lake (Banat et al. 1998; Mason 2002; ECDG 2002; Santos et al. 2005; Saeed and Shaker 2008). Particularly, the eastward and south-eastward parts of the study area adjacent to the industrial, agricultural, urban and fish farming sewage drain LU areas. This is in addition to the highways that cut the northern part of the ELM where iron steel, petrochemical, soap, paints, battery, plastics, textile dyeing and clothes factories are located alongside a light-industrial area. Furthermore, the gas, oil spills and oil industrial and commercial activities were concentrated around ELM margins where the content of heavy metal concentrations decreased as the distance from the industry area increased (Fig. 5). Although, the statistical analysis depicted a strong association among heavy metals like Cu, Zn and Pb, these heavy metals did not show apparent similarity regarding their spatial distribution. A comprehensive analysis of these maps revealed heavy metal accumulation in almost all water and sediments in the study area. However, for any mono-element, heavy metal enrichment in the sediments and water varied from area to area. The characteristics of the industrial structure in this area, especially the widely scattered township and village industries, small in size and various in types, could have resulted in multiple sources of heavy metal pollution in the ELM. Other human activities such as agriculture, fishing, farming and the waste disposal from drains have also contributed to the metal pollution. For example, the sites near the Bahr El-Baqar drain and Mediterranean Sea connection of ELM are characterized by low concentration values of heavy metals due to their presence far from direct drain discharge.

Fig. 7
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Spatial distribution maps of the water heavy metal concentrations of ELM area

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Fig. 8
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Spatial distribution maps of the bottom sediments heavy metal concentrations of ELM area

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Fig. 9
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LM base case predicted velocity vectors at 0.3-m depth (after Bek et al. 2010)

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Fig. 10
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ELM area water depth map

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The Bahr El-Baqar drain water is a main source for irrigation in the study area in ELM. The Bahr El-Baqar drain accounts for about 25 % of the freshwater input, and it accounts for 60 % of the nutrient loading into LM. The composition of the pollutants includes raw sewage, chemical plant, effluents which contribute a heavy metal loading, agricultural effluents which contribute large concentrations of nitrogenous compounds, phosphate and pesticides. Due to the high organic loading, the Bahr El-Baqar drain water is essentially anaerobic and generates odours due to indigenous hydrogen sulphide and methane production occurring within the sediment because of the high organic loading in this water; it is the most beneficial in the improvement of LM water quality which would result from the treatment of the Bahr El-Baqar drainage water (Rashed and Holmes 1984; Ezzat 1989; Khalil 1990). Consequently, this may be due to the area opposite to drainage being dominated by fine sediments and high-in-organic matter, while the middle region of the lake is dominated by sand fraction and calcareous deposits that are a mixture of sand-silt-clay and low-in-organic carbon. This is in agreement with Abdel-Moati and El-Sammak (1997), Tasi et al. (2003) and Franc et al. (2005), where the variations in the lake’s sediments were reflected on the spatial distribution of the heavy metals and the pollutant concentrations of the heavy metals in sediments increased with decreasing the particle size in sediments. Also, the concentrations of the heavy metal distribution in sediment increased as the amount of organic matter increased. By comparing the spatial accumulation of the heavy metal hotspot (Figs. 7 and 8) in water and sediments, it can be concluded that the heavy metals are more highly accumulated in sediments than in water since the sediments act as a reservoir for all contaminants and dead organic matter descending from the ecosystem above. Also, ELM spatial distribution maps revealed high elevated concentration value of Zn, Cu, Mn, Pb and Cd. In addition, the drainage water may contain some polluting substances leached from the irrigated soil treated with fertilizers, industrial and human activities that normally come into surface water in the forms of animal and human excretions and solid waste dumps. So, the condensed use of fertilizers and pesticides as well as household detergent probably added further enrichment.

The resulted heavy metal concentration spatial maps are then overlaid and integrated together using GIS tools to construct and produce the specific contamination risk maps of water and bottom sediments of the study area and linked with the multivariate statistical and LU/LC result interpretation.

GIS heavy metals data integration and analysis

The purpose of GIS analysis and integration is to assess the relationship between the spatial distribution mapping of heavy metal concentrations, LU/LC, pollutant impact in ELM aquatic environment to prepare the contaminated risk map of water, sediments and contamination potentiality maps of ELM. The effects of LU/LC are apparent across the variety of problems associated with ELM environments. Increased of sediment transport effects can commonly be related to LU/LC changes or to poor land management and due to restoration of the aquatic environment. In the present study, the multivariate contaminated impact GIS weighted maps for each heavy metal were integrated and classified based on the standard permissible level of critical limits of heavy metals in water and soil (Figs. 11, 12 and 13). The integrated contamination risk maps showed that the ELM water quality is poor due to sewage water; agricultural fertilizers and chemicals; animal waste; industrial, oil and gas wastes; and pesticides from agricultural run-off. The bottom sediment quality in lakes is extremely variable geographically. The introduction of excess fine sediment can be addressed in lake tributaries or in the watershed, but the actual sediment quality is difficult to alter because once it is in the lake, it is hard to remove. ELM is sediment sinks and mainly closed systems for sediment, toxins are of great concern. Once an ELM has been polluted, it is difficult to clean. Sediment is important in these environments because many inorganic toxins from drains bind to fine sediments and, also, a large percentage of ELM sediment literature is pointed towards sediment toxicity. Moreover, the integrated risk contamination maps revealed that the sediment represents an important sink for heavy metals in aquatic ELM systems and metal concentrations in sediment can be several orders of magnitude greater than in the overlaying water. Sediment-associated metals pose a direct risk to detrital and deposit feeding benthic organisms and may also represent long-term sources of contamination to higher tropic levels. Consequently, fish living in the polluted water may accumulate toxic heavy metals via ingestion of benthic organisms with elevated metal levels (Arslan et al. 2010). Therefore, the presence of metals in fish and organisms from LM possesses a risk for food contamination. Although some metals such as Cu, Mn and Zn are essential metals since they play important roles in biological systems, some metals, such as Pb and Cd, are toxic even in trace amounts. So, the main highlight topics that may need to be investigated are control of all discharges, regular observation of pollutants and evaluation of the effect of pollutants on the LM ecosystem over the long term. The status of ELM ecosystem-based assistance must be stressed in planning conservation management systems that integrate the effects on sediments, water, air, plants, animals and human activities. The greatest reduction of environmental impacts of sediment on ELM aquatic habitat will happen when environmental conservation management systems are considered and installed on a whole-watershed basis in the study area.

Fig. 11
figure 11

Integrated water contamination zone map of ELM

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Fig. 12
figure 12

Integrated bottom sediment contamination zone map of ELM

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Fig. 13
figure 13

Integrated water and bottom sediment contamination potentiality risky map of ELM

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Conclusion and recommendations

Today, the environmental pollution and their deleterious effects have both direct and indirect impacts on natural resources, human and socio-economic activities of any area around the world. Lakes can be readily contaminated by human activity without any obvious signs; thus, they have long attracted scientific and environmental interest. Among the various risk indicator pollutants, heavy metals are the most toxic, persistent and abundant pollutants that can accumulate in aquatic habitats and, due to that, threat the natural environmental components of any aquatic system. Moreover, their concentration increases through bio-magnifications. The findings of the current study revealed the sediments and water in the ultimate sink for heavy metals in the ELM aquatic system. The water body of ELM is contaminated with heavy metals particularly Zn, Cd, Cu, Mn and Pb that can contaminate the lake food (fish, crops and animals), organisms and, hence, humans. The concentrations of heavy metals indicate that the sediments are more critically contaminated, with respect to heavy metals, than the water concentration and, therefore, pose serious environmental concerns. The statistical change detection of satellite images and LU/LC analysis and interpretation, in addition to the spatial mapping and GIS data integration of the heavy metals, indicated an agreement with abandoned heavy metal parts and effluents from industrial, agricultural, domestic waste, sewage water and commercial activities due to critical environmental contamination of the ELM. So, the ELM study area is contaminated with heavy metals and requires creation of awareness, periodic monitoring and evaluation of most communities living along the margin of lake. Moreover, the present study recommends that the LM depth must be increased to 3 m by dredging bottom sediments to remove polluted sediments, remove dead materials and increase transparency and circulation. Removing rooted plants is very useful to prevent isolation inside the LM in order to increase water movement, decrease sedimentation settlement and maintain navigation. The governorates must prevent more construction of any industrial project outside LM zone so as to overcome any lake water deterioration. The treatment systems of drainage discharge and sewage effluent into the LM should be upgraded to conserve lake water quality. In addition, the governorates must set permissible limits for drainage materials by taking into account the international limits and sustainable development. Finally, a fish farm should be constructed inside the lake body, to support the lake by a good fry to improve fish production and investment of the lake.