Assessment of Heavy Metals at Mangrove Ecosystem, Applying Multiple Approaches using in-situ and Remote Sensing Techniques, Red Sea, Egypt

doi:10.21203/rs.3.rs-2581939/v1

Heavy metal pollution is one of the threats that impact on Mangrove ecosystem. This study aims to examine if the heavy metals in the mangrove sediment contamination status along the Red Sea coast, and give an interpretation of the origin of these metals. Twenty-two samples were collected and then, analyzed for metals (Mn, Ni, Cu, Fe, Cd, Ag, and Pb) using Inductively Coupled Plasma Mass Spectroscopy (ICP-MS). Integration between the in-situ data, contamination indices, and remote sensing and geographical information science (GIS), and multivariate statistical analysis techniques (PCA) to assess and clarify the spatial origin of heavy metals in sediment at a regional scale. The average concentration is shown to be substantially lower than the referenced value. The heavy metals are naturally origin not anthropogenic origin and, ranging from moderate to significant except the levels of Ag were very high. According to Ag levels, should be exploited economically. Remote sensing and GIS technique successfully contributed to interpreting the pattern of the origin of heavy metals and discharging systems along the red sea coast.

Mangrove ecosystem

heavy metals

Wadis

Red Sea

The coastal zones are significant naturel ecosystems as transitional between terrestrial and aquatic areas. The Red Sea coastal zones have an important role on blue economy varied from mining, shipping, fisheries and biodiversity related tourism, etc. The mangroves habitats are common and dispersed along Egyptian Red Sea. Mangrove play a vital ecological role as one of the most productive marine habitats. The mangroves trees providing shelters and feeding nursery for both land and marine dwellers. The stands of mangroves offer natural protection from erosion and storms. The mangrove forests have an action in carbon cycle, considering as one of the most carbon-rich ecosystems.

There are two mangroves’ species at Egyptian Red Sea the Avicennia marina (gray mangrove) mainly dispreading along the coast from north and Rhizophora mucronata (red mangrove) located only at southern borders (Afefe, et al., 2019). The mangrove zones are facing different threats e.g. oil pollution, overcutting, overgrazing, denudation etc. These impacts are causing damage and loss in the areas of these appreciated plants. The assessment of mangroves habitats requires data on different disciplinaries one of it, is the soil contamination from natural and anthropogenic actions (Afefe, A., (2021).

Rainfall in the Red Sea region is extremely sparse and is usually localized in the form of short showers with semi-periodic flooding on the Red Sea mountains drains towards the sea loading of various metals (Azab, 2009).

The unmanaged development activities particularly industries with interference at coastal zones have increasing impacts on marine environment. On the other hand, the marine sediments work as reservoirs that concentrated the contaminants especially heavy metals of different discharges from terrestrial and marine (Tripathi, et al., 2014; Tang, et al., 2019). Heavy metals are mostly toxic contaminants in the environment resulted from non-biodegradable and bioaccumulation processes (El-Said & Youssef 2013; Tam, et al., 2000; Kumar, et al., 2020; Elnaggar et al. 2022). The high levels of heavy metal contamination in mangrove forests have been studied comprehensively (Marchand, et al., 2006; Praveena, et al., 2008; Al-Mutairi & Yap, 2021) reported that heavy metals are discharged into coastal mangroves areas due to mining practices, industrial and port activities. The concentration of metals in the environment are recognizing its toxicity to biota from normal to lethal or sub-lethal (Li, and Davis, 2008; Ama et al., 2014).

The remote sensing techniques was valuable in mapping environmental conditions and clarifying the resources of impacts especially for the remote areas of mangroves. Mapping of wadis routes with ancillary data of geologic components bring image on natural metals distributed on the Red Sea coast. Integration of remote sensing with in-situ data on contamination of heavy metals. Where, sediment quality assessment tools are respected in identifying sediment quality conditions recognized with the biological effects; geo-accumulation index, enrichment factor, contamination factor, degree of contamination, and pollution load index (Shafe, et al., 2013; Kumar, et al., 2019).

This research aims to evaluate the environmental condition of sediment contamination with heavy metals affecting mangrove areas. Assessment of natural and anthropogenic activities related to metal concentrations interact in mangroves forest along Egyptian Red Sea coast.

2.1 Study area:

The study area extends for about one thousand kilometers along Egyptian Red Sea coast from north Hurghada city to south Shalateen city (Fig. 1). Location of 13 mangrove areas were identified mapped and validated form field visits.

2.2 Sampling

Thirteen sites were visited and in some sites collected more than one sample. So, the total number of samples were twenty-two (Table, 1). The samples collection was from the surface sediment for inspecting the heavy elements. All samples were taken from 0.0 to 10 cm depth using a suitable grab sampler. The collected samples were put directly in air sealed polyethylene bags and kept at 4 ℃ until analyses. Coordinates of sampling points were identified using GPS instrument. The samples were air-dried (at room temperature) and the extraneous materials were removed. Seven heavy metals (Pb, Cd, Ni, Ag, Mn, Cu and Fe) were measured (in ppm) by ICP-MS instrument.

Table (1): the name of the twenty-two, site location along the red sea coast.

Sites	Name	Sites	Name
1	Abou-Shaara	12	Wadi EL Qulaan Delta2
2	K. 17 South Safaga	13	Wadi EL Qulaan Delta3
3	K. 35 North Qusier	14	Hamata1
4	Sharm El-Bahary	15	Hamata2
5	Sharm El-Qebly	16	Hamata3
6	Wadi EL Gemal	17	Wadi Lahmy1
7	Al Raada1	18	Wadi Lahmy2
8	Al Raada2	19	Wadi Lahmy3
9	Wadi Mastourah1	20	Wadi Lahmy4
10	Wadi Mastourah2	21	Marsa EL Hameera
11	Wadi EL Qulaan Delta1	22	Shalatein, Al-Somaa

2.3 Geochemical analysis for the heavy metals in the sediment

The sediment samples were desiccated in a hot air oven at 110°C for 24 h, ground in a mortar, and then passed through a 2 mm plastic sieve. Well-mixed 2 g soil samples were treated with 10 ml of freshly prepared aqua regia (HNO3 + 3HCl) on a sand bath for 2h. After the samples were completely dried, the samples were dissolved in 10 ml of 2% HNO3, filtered via Whatman filter paper No 541, and then diluted to 50 ml with milli-Q water (Chen and Ma, 2001). The acid digested sediment samples were transferred into acid-washed plastic bottles and analyzed for determining the concentration of elements (Mn, Ag, Cd, Cu, Pb, Ni, and Fe) using ICP-MS analytical instrument.

2.4 Indices of sediment contamination

Seven indices were utilized in this study to assess the contamination on the sediment.

2.4.1 Enrichment factor (EF)

Enrichment factor (EF) is used to differentiate metal origin from anthropogenic or natural sources. In the beginning, normalize the sample metal concentrations to reference elements, using iron in this study to determine whether a sediment sample is enriched with metals evaluated by the sample’s background environments. The Eq. (1) used to determine EF values, selected iron (Fe) as normalizing element according to its major sorbent phase for trace metals and a quasi-conservative tracer of the natural metal-bearing phases in fluvial and coastal sediments (Schiff and Weisberg, 1999; Turner and Millward, 2000), expressed as

EF= \(\frac{( \mathbf{C}\mathbf{x} /\mathbf{F}\mathbf{e})\mathbf{s}\mathbf{a}\mathbf{m}\mathbf{p}\mathbf{l}\mathbf{e}}{(\mathbf{C}\mathbf{x} /\mathbf{F}\mathbf{e})\mathbf{b}\mathbf{a}\mathbf{c}\mathbf{k}\mathbf{g}\mathbf{r}\mathbf{o}\mathbf{u}\mathbf{n}\mathbf{d}}\) Eq. (1)

where Cx Sample and Cx background represent the concentration of selected metals. (Cx /Fe) background is the ratio of the background values of Fe. EF value of nearly unity means the elements that are naturally derived, while EF values of several orders indicate elements of anthropogenic origin. Table (2) shows the classification of EF value according to Taylor (1964) to determine the degree of metal contamination.

Table (2): shows the classification of EF value

Classification	Value
Minimal	EF<2
Moderate	2≤EF<5
Significant	5≤EF<20
Very high	20≤EF<40
Extremely high	EF≥40

2.4.2 Contamination factor (CF)

Contamination factor (CF) to assess the status of the surface sediment according to Hakanson, (1980) based on the following equation:

CF = C metal in sediment / C metal background Eq. (2)

The CF values according to the four classes are depicted as follows:

(i) CF < 1 = low, (ii) 1 < CF < 3 = moderate, (iii)3 < CF < 6 = considerable, and (iv) CF > 6 = very high.

2.4.3 Degree of contamination (Cd)

The degree of contamination (Cd) represents the sum of all the CF values for all the sampling sites. It was previously proposed by Hakanson, (1980) as shown below:

Cd = \({\sum }_{{i}=1}^{{n}}\mathbf{C}\mathbf{F}\) Eq. (3)

The degree of contamination; (i) Cd < 6 = low, (ii) 6 < Cd < 12 = moderate, (iii) 12 < Cd < 24 = considerably high, and (iv) Cd > 24 = high.

2.4.4 Modified contamination degree (mCd)

is the sum of all contamination factors for the element samples to the number of elements analyzed. This measure was proposed by Abrahim, (2008) to investigate an unlimited number of heavy metals and is represented as:

mCd = \({\sum }_{\mathbf{i}=1}^{\mathbf{n}}\mathbf{C}\mathbf{F}\) /n Eq. (4)

where n is the number of analyzed elements and i is the element (or pollutant) examined and contamination factor (CF).

2.4.5 Geo-accumulation index (Igeo)

is used to analyze the level of pollution of trace elements and the contamination degree in marine sediments. It was initially described by Muller, (1969) as Eq. (5):

Igeo = log2 ( \(\frac{\text{C}\text{n}}{ (1.5 \times (10\left) \text{B}\text{n}\right)}\)) Eq. (5)

Where; Cn = the trace metals calculated (measured concentrations of the sediment samples, respectively) and Bn = background value (average value of crustal abundance) of a particular element.

Where; to decrease the possibility of variation in the background values for a specific trace element in the environment and minor anthropogenic influences, the concentration of each geochemical background value is multiplied by the factor of 1.5 Muller, (1979). The sediment classification is based on the Igeo value as follows:

Igeo > 5 = extreme contamination,
4–5 = strong to extreme contamination,
3–4 = strong contamination,
2–3 = moderate to strong contamination,
1–2 = moderate contamination,
0–1 = uncontaminated to moderate contamination,
< 0 = uncontaminated.

2.4.6 Pollution load index (PLI)

is a parameter to evaluate metal pollution in marine environment, and it can be calculated from the following equation given by Tomlinson, et al., (1980).

PLI for a station = \(\sqrt[n]{\text{C}\text{F}1 \times \text{C}\text{F}2 \times \text{C}\text{F}3 . . . . . . . . . .\text{C}\text{F}\text{n}}\) Eq. (6)

where CF is contamination factor and n is the number of metals investigated.

A PLI value above one (> 1) indicates that an area is polluted, whereas values < 1 indicates no pollution or only background levels of pollutants are present (Chakravarty and Patgiri, 2009; Mashiatullah, et al., 2013). While an estimation of PLI can be used to identify whether a site is collectively polluted or non-polluted by Metals.

2.3.7 Potential ecological risk factor (E r) and risk index (RI)

Potential ecological risk factor is a method used to explore levels of contamination caused by metals and the risk for the aquatic environment. It was first introduced by Hakanson, (1980). The formula is as follow:

E_r = Ti × Cf Eq. (7)

Where, Ti = toxic response factor and CF = contamination factor

Potential ecological risk index evaluates the environmental behavior and characteristics of heavy metal contaminants in the sediments. This method was previously proposed by Hakanson, (1980) and its primary objective is to specify the agents that cause contamination. The RI is the summation of all risk factors for the detection of heavy metal contaminants in the sediment. The RI is calculated based on the following equation:

RI =\({\sum }_{i=1}^{n}\text{E}\text{r}\)

Hakanson, (1980) proposed a standardized toxic response factor of 1, 5, 5, 5, and 30 for Mn, Ni, Cu, Pb and Cd.

2.5 Multivariate Statistical analysis

Descriptive statistics, correlations and principal component analysis (PCA) are the most common multivariate statistical methods used in environmental studies (Ganugapenta et al., 2018; Islam et al., 2018; Saher and Siddiqui, 2019). These methods were applied to verify significant relationships among the heavy metal’s sediments, and to identify contamination sources (natural and/or anthropogenic). Principal component analysis has been applied on the data set of 22 sediment samples and seven variables (Cd, Cu, Pb, Ni, Ag, pb and Fe). R-mode factor analysis with VARIMAX rotation with the Kaiser-Meyer-Olkin (KMO) test with a > 0.5 KMO (0.5) (Hutcheson and Sofroniou, 1999), as well as the Eigen values > 1 was applied to the measure’s metals in the sediment samples.

2.6 Remote sensing and GIS analysis:

2.6.1 Wadi basins Extracting: The drainage system influencing the 13 mangrove sites were extracted using digital image processing of ASTER satellite data with spatial resolution of 30m utilizing the ArcGIS software environment by the “hydrology spatial analyst tool”; to extract basins in the study area. The Wadi’s have been selected according to every mangrove site, where there is sites that receiving the flooding from more than one wadi. In addition to seawater current that transfer the drains load from north or south wadi to the mangrove site.

2.The ancillary data of mineralization sites were obtained from the “Metallic and non-Metallic deposits” maps with scale of 1:1000000 that was issued by the Scientific research academy in 1998.

3.1 Heavy metals in the sediments

All samples were measured for seven heavy metals, and the fundamental statistical (minimum, maximum, mean, and standard deviation) are made in table (3) to compare that with the concentration’s limits of the elements in the earth’s crust after Turekian, et al., (1961). The concentrations of Ag and Pb are exceeds the standard limits in all sites. Where, Ni concentrations were high in stations; 1, 19 and 22 Abou-Shaar, Wadi Lahmy towards land and Marsa Sha'ab respectively. The Cd was high at two station 9 and 20 Wadi Mastourah and Wadi Lahmy respectively.

Table (3): Heavy Metal Concentrations of Sediment Samples and Limit Values. Metal concentration (mg/kg dry weight) / (ppm) and their descriptive statistics

Sites	Mn [ppm]	Fe [ppm]	Ni [ppm]	Cu [ppm]	Cd [ppm]	Ag [ppm]	Pb [ppm]
1	107.518	3393.66	151.968	6.669	0.588	0.839	81.143
2	310.536	6151.02	50.519	7.297	0.779	1.38	130.021
3	281.892	5242.8	55.048	7.209	0.984	1.309	88.424
4	401.653	5023.01	63.435	7.085	0.651	1.007	103.11
5	152.167	3167.97	43.183	2.922	0.446	0.996	60.046
6	207.862	5228.3	29.897	11.413	0.903	1.944	75.785
7	661.333	23714.35	49.76	25.846	0.721	2.722	188.137
8	255.427	7856.06	28.844	19.805	1.394	1.547	85.907
9	210.346	4770.02	26.678	21.728	2.557	1.46	83.687
10	672.164	15013.65	43.657	21.328	1.212	2.638	139.953
11	525.113	21376.13	48.97	29.769	1.943	6.095	140.105
12	85.705	2332.33	22.206	8.618	1.222	2.592	59.138
13	175.563	3557.87	29.753	15.275	1.443	4.307	68.688
14	407.714	12849.09	37.45	31.824	1.688	3.225	86.38
15	733.352	23511.84	52.64	37.034	1.261	3.883	188.334
16	498.211	17652.62	37.477	17.754	0.751	3.102	137.945
17	371.433	17027.17	43.646	23.155	1.453	2.547	120.983
18	321.86	12596.37	49.311	23.807	1.036	3.184	127.475
19	635.897	20735.64	124.515	40.399	1.551	2.742	120.525
20	635.377	20586.83	87.138	42.218	2.012	2.441	116.934
21	363.249	10529.89	49.974	8.2	0.441	1.111	90.941
22	225.697	4372.23	93.549	13.904	0.504	0.725	82.458
Minimum	85.7	2332.3	22.2	2.9	0.4	0.7	59.1
Maximum	733.4	23714.4	152	42.2	2.6	6.1	188.3
Mean	374.5	11213.1	55.4	19.2	1.2	2.4	108
SD	198.3	7503.9	32.2	11.7	0.6	1.3	36.5
Reference	850	57300	80	60	2	0.07	50

Concentrations limits of the elements in the earth’s crust after Turekian, et al., (1961) and Alloway, (1995).

3.2 Indices of sediment contamination

3.2.1 Enrichment factor (EF)

Enrichment factor analysis results for all samples are listed in table (4). The values of EF related to the heavy metals noted Cu as minimal enrichment, where, Mn, Ni, Cd and Pb showing significant enrichment. However, Ag showed as extremely high enrichment ranged from 202.37 to 1470.15 since it’s over 40[ppm].

3.2.2 Table (4) show Enrichment factor (EF)

Sites	Mn	Ni	Cu	Ag	Cd	Pb
1	2.135741	32.07365	1.876704	202.3719	3.309347	13.70053
2	6.168496	10.6623	1.132923	332.8644	4.384323	21.9533
3	5.599511	11.61817	1.313152	315.7388	5.538092	14.92988
4	7.978447	13.38829	1.347036	242.8945	3.66392	17.40953
5	3.02265	9.114002	0.880851	240.2413	2.510151	10.13842
6	4.128977	6.309921	2.084696	468.9046	5.082212	12.79586
7	13.13674	10.50211	1.040844	656.563	4.05789	31.76585
8	5.07381	6.08768	2.40754	373.1458	7.84563	14.5049
9	4.178319	5.630534	4.350137	352.1609	14.39116	14.13007
10	13.35189	9.214042	1.356648	636.3016	6.821308	23.63026
11	10.43086	10.33538	1.32996	1470.151	10.93548	23.65592
12	1.702447	4.686694	3.528742	625.2062	6.877589	9.985112
13	3.487389	6.279529	4.100101	1038.875	8.121409	11.59757
14	8.098843	7.904022	2.365297	777.8896	9.500304	14.58477
15	14.56733	11.10995	1.504241	936.6032	7.097087	31.79911
16	9.896478	7.90972	0.960485	748.2213	4.226735	23.29122
17	7.378156	9.211721	1.298691	614.3519	8.17769	20.42728
18	6.393437	10.40735	1.804939	768.0002	5.830755	21.52342
19	12.63148	26.27955	1.860615	661.3871	8.729248	20.34995
20	12.62115	18.39094	1.958446	588.784	11.32382	19.74363
21	7.215589	10.54728	0.743692	267.98	2.482011	15.35487
22	4.483252	19.74401	3.036967	174.8744	2.836584	13.92256
	EF<2 minimal	2≤EF<5 moderate	5≤EF<20 significant	20≤EF<40 very high		EF≥40 extremely high

3.2.2 Contamination factor (CF)

The contamination factor analysis is shown in Table (5). The average CF values of Pb were moderately contaminated where Ag with average of (33.63) indicates very high contamination sediment. The other metals Fe, Mn, Ni, Cu and Cd arranged as low contamination. The average CF results were ranked as the following: Fe <Cd < Mn < Cu < Ni < Pb < Ag.

Table (5) show Contamination Factor

Sites	Fe	Mn	Ni	Cu	Ag	Cd	Pb
1	0.05	0.13	1.90	0.22	12.0	0.20	0.81
2	0.09	0.37	0.63	0.24	19.7	0.26	1.30
3	0.08	0.33	0.69	0.24	18.7	0.33	0.88
4	0.08	0.47	0.79	0.24	14.4	0.22	1.03
5	0.05	0.18	0.54	0.10	14.2	0.15	0.60
6	0.08	0.24	0.37	0.38	27.8	0.30	0.76
7	0.36	0.78	0.62	0.86	38.9	0.24	1.88
8	0.12	0.30	0.36	0.66	22.1	0.46	0.86
9	0.07	0.25	0.33	0.72	20.9	0.85	0.84
10	0.23	0.79	0.55	0.71	37.7	0.40	1.40
11	0.33	0.62	0.61	0.99	87.1	0.65	1.40
12	0.04	0.10	0.28	0.29	37.0	0.41	0.59
13	0.05	0.21	0.37	0.51	61.5	0.48	0.69
14	0.20	0.48	0.47	1.06	46.1	0.56	0.86
15	0.36	0.86	0.66	1.23	55.5	0.42	1.88
16	0.27	0.59	0.47	0.59	44.3	0.25	1.38
17	0.26	0.44	0.55	0.77	36.4	0.48	1.21
18	0.19	0.38	0.62	0.79	45.5	0.35	1.27
19	0.32	0.75	1.56	1.35	39.2	0.52	1.21
20	0.32	0.75	1.09	1.41	34.9	0.67	1.17
21	0.16	0.43	0.62	0.27	15.9	0.15	0.91
22	0.07	0.27	1.17	0.46	10.4	0.17	0.82
CF < 1 = low 1 < CF < 3 = moderate 3 < CF < 6 = considerable CF > 6 = very high

3.2.3 Degree of contamination (Cd)

Degree of contamination was agreed from considerably high to very high in all sites as shown in table (6) where, the highest sample was 11 from EL-Qulaan mangrove sediments.

Table (6): show Degree of contamination (Cd).

Sites	Value	Sites	Value
1	15.29	12	38.73
2	22.61	13	63.84
3	21.25	14	49.70
4	17.21	15	60.89
5	15.84	16	47.86
6	29.91	17	40.10
7	43.63	18	49.09
8	24.87	19	44.86
9	23.92	20	40.27
10	41.77	21	18.42
11	91.67	22	13.32
Cd≤6	6≤Cd≤12	12≤Cd≤24	Cd≥24
Low	Moderate	Considerable	Very high

3.2.4 Modified degree of contamination (mCd)

Modified degree of contamination values for all sites as illustrated at table (7), are categorized as moderate, high and very high with value of (13.10) at sample 11 from EL Qulaan mangrove sediments.

Table (7) show Modified degree of contamination (m Cd)

Sites	Value	Sites	Value
1	2.18	12	5.53
2	3.23	13	9.12
3	3.04	14	7.10
4	2.46	15	8.70
5	2.26	16	6.84
6	4.27	17	5.73
7	6.23	18	7.01
8	3.55	19	6.41
9	3.42	20	5.75
10	5.97	21	2.63
11	13.10	22	1.90

mCd < 1.5	1.5 ≤ mCd < 26	2 ≤ mCd < 4	2 ≤ mCd < 4	8 ≤ mCd < 16		16 ≤ mCd < 32	mCd ≥ 32
Nil to very low	Low	Moderate	Moderate	Very high		Extremely high	Ultra-high

3.2.5 Geo-accumulation Index

Geo-accumulation Index values of the heavy metals table (8) are noted that all sites were strongly to extremely contaminated with silver while other sites are practically uncontaminated.

Table (8): show Geo accumulation Index (Igeo)

Sites	Fe	Mn	Ni	Cu	Ag	Cd	Pb
1	-4.663	-3.568	0.341	-2.754	2.998	-2.936	-0.886
2	-3.805	-2.038	-1.248	-2.625	3.716	-2.530	-0.206
3	-4.035	-2.177	-1.124	-2.642	3.640	-2.193	-0.762
4	-4.097	-1.666	-0.920	-2.667	3.262	-2.789	-0.541
5	-4.762	-3.067	-1.474	-3.945	3.246	-3.335	-1.321
6	-4.039	-2.617	-2.005	-1.979	4.211	-2.317	-0.985
7	-1.858	-0.947	-1.270	-0.800	4.696	-2.642	0.327
8	-3.452	-2.320	-2.057	-1.184	3.881	-1.691	-0.804
9	-4.171	-2.600	-2.169	-1.050	3.798	-0.815	-0.842
10	-2.517	-0.924	-1.459	-1.077	4.651	-1.893	-0.100
11	-2.007	-1.280	-1.293	-0.596	5.859	-1.212	-0.098
12	-5.204	-3.895	-2.434	-2.384	4.626	-1.881	-1.343
13	-4.594	-2.860	-2.012	-1.559	5.358	-1.641	-1.127
14	-2.742	-1.645	-1.680	-0.500	4.941	-1.415	-0.796
15	-1.870	-0.798	-1.189	-0.281	5.209	-1.835	0.328
16	-2.284	-1.356	-1.679	-1.342	4.885	-2.583	-0.121
17	-2.336	-1.779	-1.459	-0.959	4.600	-1.631	-0.310
18	-2.770	-1.986	-1.283	-0.919	4.922	-2.119	-0.235
19	-2.051	-1.004	0.053	-0.156	4.707	-1.537	-0.316
20	-2.062	-1.005	-0.462	-0.092	4.539	-1.161	-0.359
21	-3.029	-1.811	-1.264	-2.456	3.403	-3.351	-0.722
22	-4.297	-2.498	-0.359	-1.694	2.788	-3.158	-0.863
	Igeo≤0	0<Igeo<1	1<Igeo<2	2<Igeo<3	3<Igeo<4	4<Igeo<5	Igeo≥5
	practically	uncontaminated	moderately	moderately	strongly	strong	extremely

3.2.6 Pollution Load Index (PLI)

Pollution Load Index results clarify two pure sites (sample 5 & 12) Sharm El-Qebly and EL-Qulaan seaward sample. In contrast, finding 4 values with level of deterioration according to PLI was (11, 15, 19 and 20 samples) from sites of EL-Qulaan and Hamata landward, where at Lahmy site the samples were from landward and seaward mangrove sediments. The other samples were ideal in level of baseline represented at table (9).

Table (9): show Pollution Load Index (PLI)

Sites	Value	Sites	Value
1	0.001	12	0.000
2	0.005	13	0.006
3	0.003	14	0.151
4	0.003	15	1.590
5	0.000	16	0.096
6	0.003	17	0.147
7	0.382	18	0.103
8	0.110	19	1.744
9	0.009	20	1.418
10	0.215	21	0.004
11	1.392	22	0.002
	0	1	>1
	Perfection	Baseline	Deterioration

3.2.7 Potential Ecological Risk factor

Potential Ecological Risk Factor (Eri and RI) for sediments heavy metals are shown in table (10 and 11). All sites are represented low Ecological risk level (Eri). As a result, the RI which is the summation of (Eri) confirmed a Low-grade for all heavy metals excluding Cd with value of (255.4) indicate a moderate potential ecological risk (Eri).

Table (10) Heavy metal contamination categories based on Potential Ecological Risk.

sites	Mn	Ni	Cu	Cd	Pb
1	0.13	9.50	1.11	5.88	4.06
2	0.37	3.16	1.22	7.79	6.50
3	0.33	3.44	1.20	9.84	4.42
4	0.47	3.96	1.18	6.51	5.16
5	0.18	2.70	0.49	4.46	3.00
6	0.24	1.87	1.90	9.03	3.79
7	0.78	3.11	4.31	7.21	9.41
8	0.30	1.80	3.30	13.94	4.30
9	0.25	1.67	3.62	25.57	4.18
10	0.79	2.73	3.55	12.12	7.00
11	0.62	3.06	4.96	19.43	7.01
12	0.10	1.39	1.44	12.22	2.96
13	0.21	1.86	2.55	14.43	3.43
14	0.48	2.34	5.30	16.88	4.32
15	0.86	3.29	6.17	12.61	9.42
16	0.59	2.34	2.96	7.51	6.90
17	0.44	2.73	3.86	14.53	6.05
18	0.38	3.08	3.97	10.36	6.37
19	0.75	7.78	6.73	15.51	6.03
20	0.75	5.45	7.04	20.12	5.85
21	0.43	3.12	1.37	4.41	4.55
22	0.27	5.85	2.32	5.04	4.12
Average of (Eri)	0.44	3.46	3.21	11.61	5.40
RI (Sum of Eri)	9.69	76.23	70.54	255.40	118.81

Table (11): Show classified levels of Eri and RI

Scope of potential ecological risk index (Eri)	Ecological risk level of single factor pollution	Scope of potential toxicity index (RI)	General level of potential ecological risk
Eri< 40	Low	RI < 150	Low-grade
40≤Eri<80	Moderate	150 ≤ RI < 300	Moderate
80≤Eri<160	High	300 ≤ RI < 600	Severe
160≤Eri<320	Higher	600 ≤ RI	Serious
320≥Eri	serious

3.3 Multivariate statistical analyses

By applying the Pearson correlations for the seven parameters revealed the high correlations between Fe with Mn, Cu and Pb 0.9, 0.82 and 0.85 respectively. There is significant correlation type moderate between Fe and Ag 0.6. On the other hand, the high positive correlation appeared between Cu and Mn 0.74 and moderate correlation between Cu and Cd 0.65 (Table 12).

Table (12): Correlation matrix for the estimated parameters n=7

Variables [ppm]	Mn	Fe	Ni	Cu	Cd	Ag	Pb
Mn	1.00
Fe	0.91^**	1.00
Ni	0.12	0.10	1.00
Cu	0.74**	0.82*	0.14	1.00
Cd	0.21	0.29	-0.19	0.65	1.00
Ag	0.45	0.60**	-0.26	0.59	0.49*	1.00
Pb	0.85**	0.85**	0.05	0.56	0.05	0.44*	1.00

Bold characters indicate the significant correlations, usually greater than 0.6 **, and values above 0.4*

Multivariate analysis (Principal component analysis, PCA) has been applied on the data set for 22 sediment samples and seven variables. Factor analysis is used to reducing the amount of data from 7 to 2 that focusing on the most important variables which affect the study. This helps in interpreting the results and could build the appropriate model that represents the problem, in an inexpensive and objective description. The results give 4 factors. Eigen values above 1 used to detect the most factors strength. Accordingly, two factors were extracted after the rotation for data process.

Factor analysis can be summarized in (table 13). The rotated component matrix the factors were performed 0.7 for all 22 sites according to KMO. This also means the selected factor 1 and factor 2 comprises 70% of the total variance.

First factor contributed 59.93% of the total Variance exhibiting high positive loadings on Fe and Mn. On the other hand, the second factor reflected 18.88 % of the total variance and composed of the following parameters Ag, Cd, Ni, Pb, and Cu respectively.

Table (13): Factor scores after Varimax rotation:

Variables	F1	F2
Mn [ppm]	0.95	0.01
Fe [ppm]	0.97	0.13
Ni [ppm]	0.25	-0.66
Cu [ppm]	0.82	0.39
Cd [ppm]	0.24	0.81
Ag [ppm]	0.53	0.65
Pb [ppm]	0.89	-0.07
Variability (%)	52.80	23.98
Cumulative %	52.80	76.78

Rotated Component Matrix, Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization

3.4 Remote sensing and GIS

Remote sensing and GIS are utilized in this work to interpret the source of the trace element. The stream networks of the main wadi’s were extracted and displaying the main mines sites of different elements. Thirteen mangrove sites along coastline stand the termination of different wadis flow from Red Sea mountains of Eastern Desert are listed at figure (2) and table (14). There are mangroves sites affected by more than one wadi flow related to costal geomorphology and sea current flow where wadi discharge the trace elements north or south. The high number of wadis was at south area from wadi Qulaan to wadi Lahmy reflected in long coastal with mangroves presence.

Table (14) show the area of wadis basins drained into mangroves sites.

Name	Area KM²	Name	Area KM²
W. Bali'	779.66	W. Gemal	1967.54
W. Abu Sha'r	63.78	W. Abu Ghusun	395.95
W. El Abyad	386.06	W. Ranga	305.38
W. Safaga	747.78	W. Rod El Adai	218.80
W. Jasus	152.09	W. Qulaan	36.29
W. Salim	33.78	W. Rod El Ashabab	118.66
W. Abu Hamrah Al Bhari	28.16	W. Sheialik	80.53
W. Quwayh	1431.70	W. Lahami	595.02
W. Esel	674.75	W. Kurdum	90.28
W. Sherm El-Bahari	206.45	W. Daqalai	95.29
W. Sherm El-Qibli	160.19	W. Kolaigo	722.30
W. Wizr	114.62	W. Ba'aneit	999.84
W. Um Gheig	886.40	W. Merau	208.30
W. Rimarim	279.93

According to the Metallic and non-Metallic deposits maps detecting the sites of 22 main mines at the wadi’s basins of the study area as showed at figure (3) and table (15). These mines are rich by different trace elements of copper, lead, zinc, iron, manganese and gold. In addition, the silver is detecting with gold on the eastern desert rocks.

Table (15) show the main mining locations at the study area.

code	mines	location	Longitude	latitude
1	Manganese	Jabal Abu Shaar Qibli	33° 33' 41.364" E	27° 22' 8.955" N
2	Gold	Abu Muraywat	34° 17' 39.845" E	25° 50' 14.991" N
3	Gold	Simnah	34° 25' 37.248" E	25° 41' 24.208" N
4	Gold	Al Misikat - jabal Kabb Umayri	34° 19' 3.251" E	25° 38' 4.582" N
5	Gold	Jabal Kabb Umayri	34° 12' 45.371" E	25° 23' 0.207" N
6	Gold	Wadi Saqi	34° 34' 58.006" E	24° 48' 53.606" N
7	Manganese	Kalahin	34° 40' 35.267" E	24° 46' 0.134" N
8	Iron	Wadi Karim	35° 3' 8.133" E	24° 22' 46.259" N
9	Lead	Zuj Al Buhar- Isil	35° 13' 1.082" E	24° 21' 53.607" N
10	Gold	Sharm al Bahari	34° 50' 47.074" E	24° 15' 8.131" N
11	Lead - Zinc	Umm Ghayj	35° 12' 21.500" E	24° 9' 14.447" N
12	Iron	Umm Shaddad	33° 39' 24.014" E	26° 31' 3.214" N
13	Iron	Umm Nar	35° 3' 23.236" E	24° 7' 24.909" N
14	Gold	Jabal Al Hanjaliyah	35° 4' 44.316" E	23° 59' 21.030" N
15	Gold	Jabal Umm Ud	35° 38' 12.058" E	22° 41' 48.080" N
16	Iron	Abu Ghalqah	33° 32' 14.743" E	26° 26' 12.762" N
17	Copper and Lead Zinc	Hamata	33° 28' 57.279" E	26° 19' 18.871" N
18	Copper and Lead Zinc	Umm Samyuki	33° 34' 53.433" E	26° 18' 48.998" N
19	Gold	Abu Rahayah	33° 48' 18.570" E	26° 19' 53.314" N
20	Iron	Umm Ufayn	33° 53' 10.989" E	26° 17' 38.275" N
21	Copper and Lead Zinc	Jabal Darhib	34° 5' 32.275" E	25° 54' 24.058" N
22	Manganese	Jabal at Tiyur	34° 17' 12.056" E	25° 56' 23.274" N

Assessment of heavy metals on mangrove areas along the southern coast of the Red Sea was studied for seven heavy metals of Mn, Ni, Cu, Fe, Cd, Ag, and Pb. The mean concentration of heavy metals was shown to decrease in the following order; Ag > Pb > Ni > Cu > Cd > Mn > Fe. For further assessment of the degree of pollution and health status of the sediment, seven geo-accumulation contamination Factors were used; EF, CF, Cd, Igeo, Eri, RI, and PLI, the result of the previous indices fluctuated between the significant increases in the metal levels to Moderate levels of ecological risk. These results were in the same output for assessment of heavy metals contamination on mangrove sediments on the other side of the Red Sea, Suai Arabia that didn’t exceed the significant range Alzahrani et al., 2018; Alharbi et al., (2019) Aljahdali, & Alhassan, (2020) and Al-Hasawi, (2022). Among the selected heavy metals, silver (Ag) concentrations were the highest according to the Igeo index which gives hints about the geographic sources. In addition to the presence of silver with gold in the most of region's rocks (Zoheir and Lehmann 2011) where the higher concentration of Ag in the sediments usually reaches the coastal mangroves drained from the Red Sea Mountains.

Principle component analysis has found the strength in the presence of Fe and Mn together is attributed to the environmental impacts and naturally occurred on mafic rock and granite respectively (Papachristou, et al., 2014). Where, the lead and Cadmium according to CF was moderately contaminated related to natural sources from Red Sea hills. Hanna, (1992) noted that the total Cd content of the Red Sea sediment from 1934 and 1984 expeditions varied between 0.1 and 2 ppm, and he recommended that its origin relation to marine sediments lithogenous. Correspondingly, Mahdy et al., (2009) noted that Lead-zinc mineralization is distributed between El Qusier and Ras Banas. In addition, cadmium is incorporated into the crystal structure of the zinc-lead minerals as a natural source. In the reverse Nour et al., (2022), that analysis heavy metals at Egyptian beaches on the Red Sea and the Gulf of Aqaba in Sinai and demonstrated that Cu, Pb, Cd, and Hg originated from anthropogenic sources. Moreover, Hanna, (1992) pointed to the increasing concentrations of Ni, Pb, Cu, and Cd during the period (1934–1984) on the Red Sea coast. Where described the reasons from different sources of natural contamination as hot brine pools, and oil and minerals mining. In addition to anthropogenic impact the discharge of domestic, industrial waste, and marine transportation.

On the other hand, The PLI results suggest that the sediments are highly contaminated at Wadi EL-Qulaan, Hamata, and Wadi Lahmy as these are massive wadis loading with elements discharge. Discussing the whole environment sediment metals sources that mangroves in the study area are situated in the coastal zone of the Eastern Desert of Egypt, which is dissected by the presence of rich alteration zones (El-Shafei, 2011; Salem, 2013; Amer, et al., 2016; Ghoneim, et al., 2018). These alteration zones are the perfect environment for several mineralization types including rare and heavy minerals. The mangrove sites are located on the final end of wadi’s that pass through the alteration zones. These wadis act as the pathways/carriers of the washed minerals from the upstream rocks to the downstream outlet where the mangrove is present; thus, the carried minerals are objected to and deposited by the act of flooding to the mangrove areas (Embabi, 2018). The mining sites with several mineralization types fall within the drainage system affecting the mangrove sites. This mineralization include; (copper, lead, zinc, gold, iron, and manganese) verified by Nour et al., (2019), Baioumy (2021), Abdelkareem and El-Shazly (2022), and Hegab et. Al., (2022). The existence of these mineralization sites within the stream network that ends up in the mangrove sites suggests the concentration of several heavy minerals being driven through the drainage network naturally.

Several references around the world mentioned an increase in contamination by heavy metals at mangrove sites (Bodin, et al 2013; Rahman, et al., 2014; Chai, et al., 2019; Bakshi, et al., 2019). Where, Red Sea mangrove sediments is supposed to be naturally origin from local mineralogy rather than contamination (Okbah et al., 2005; El Daba and Abd El Wahab, 2018) indicating weak enrichment.

The heavy metals (Mn, Ni, Cu, Fe, Cd, Ag, and Pb) in the mangrove sediment are showed to be from natural origin, rather than anthropogenic activities. The remote sensing and GIS technique successfully contributed to interpreting the pattern of the origin of heavy metals along the Red Sea coast. In addition, it represents synoptic outline view for the study area with its relevant environments condition and origin. Finally, the investigation discovered high content of silver metal concentrated on mangroves sediments, which necessitates rigorous further research to understand how to apply sustainable exploitation of this metal's economics to ignore negative impact on marine ecosystems.

Acknowledgments

Thanks for HEPCA, NGO for help and support during field visits provide its facilities and lab. Thanks for NARSS for the support and availability of all facilities during field and data analysis.

Funding

This work was supported by the National Authority of Remote Sensing and Space Sciences (NARSS), through research project of “Mapping and environmental evaluation of Mangroves habitats along the southern coast of Egyptian Red Sea”.

Competing Interests

“The authors have no relevant financial or non-financial interests to disclose.”

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Asmaa H. Mohammed; Ahmed M. Khalifa; Hagar M. Mohamed; Kareem H. Abd El-Wahid and Mahmoud H. Hanafy. The first draft of the manuscript was written by Asmaa H. Mohammed and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

-Ethical Approval: Not applicable
-Consent to Participate: Not applicable
-Consent to Publish: Not applicable

-Availability of data and materials: all included on the manuscript

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Assessment of Heavy Metals at Mangrove Ecosystem, Applying Multiple Approaches using in-situ and Remote Sensing Techniques, Red Sea, Egypt

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Material And Methods

3. Results And Discussion