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Article

Development of Mangrove Sediment Quality Index in Matang Mangrove Forest Reserve, Malaysia: A Synergetic Approach

by
Ahmad Mustapha Mohamad Pazi
1,*,
Waseem Razzaq Khan
2,
Ahmad Ainuddin Nuruddin
1,
Mohd Bakri Adam
3 and
Seca Gandaseca
1,2,*
1
Faculty of Forestry and Environment, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
2
Institut Ekosains Borneo, Universiti Putra Malaysia Kampus Bintulu, Bintulu 97008, Sarawak, Malaysia
3
Institute for Mathematical Research (INSPEM), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
*
Authors to whom correspondence should be addressed.
Forests 2021, 12(9), 1279; https://doi.org/10.3390/f12091279
Submission received: 7 June 2021 / Revised: 2 September 2021 / Accepted: 10 September 2021 / Published: 17 September 2021
(This article belongs to the Special Issue Mangrove Wetland Restoration and Rehabilitation)
Browse Figures
Versions Notes

Abstract

:
Sediment is an important part of heavy metal cycling in the coastal ecosystem, acting as a potential sink and source of inorganic and organic contaminants as environmental conditions change. The productivity of mangroves is utterly dependent on sediment enrichment. Moreover, mangrove sediment can trap pollutants discharged by households, industries, and agriculture activities. In this regard, it is essential to assess sediment quality in the presence–absence of heavy metals that are toxic to most living organisms. Thus, the question of how sediment quality is used as an index in the mangrove domain has arisen. Due to the many complex characteristics such as seasonal zones, tidal patterns, flora and fauna, and water, no specific method is used in Malaysia for assessing and monitoring mangrove sediment quality. Thus, the current study intended to develop a mangrove sediment quality index (MSQi) in the Matang mangrove forest in Perak, Malaysia. An area was selected based on the distinct level of mangrove disturbances. At 1.5 m depth, sediments were sampled in five segments (0–15, 15–30, 30–50, 50–100, and 100–150 cm). All the sediment physicochemical properties were then analysed. Fourteen variables were chosen and included in MSQi. This index categorises mangrove sediment levels as I = Very Bad, II = Bad, III = Moderate, IV = Good, and V = Excellent. MSQi will be used as a guideline in monitoring mangrove sediment pollution. In conclusion, the data analysis showed that the Sepetang River (SR) was highly disturbed, followed by the Tinggi River (TR) (moderately disturbed), and the Tiram Laut River (TLR) (least disturbed).

1. Introduction

Mangroves are one of the most productive wetlands globally [1] and can be found in the intertidal zones along tropical and subtropical coastlines [2,3]. Mangroves are vital in providing breeding and nursery grounds [4] for commercially and recreationally important fish [5]. Mangroves also help to protect coastlines from erosion, storm damage, wave action [6], and tsunamis [7]. The mangrove ecosystem consists of several significant components, including forest, soil, and the marine ecosystem [8]. Mangrove sediments are complex and highly variable, composed of the river and marine alluvium, transported as sediment and deposited in rivers and seas [9].
Mangrove sediment is an abiotic matrix made up of residues, inorganic, and organic particles that is relatively heterogeneous in terms of physicochemical and biological characteristics [10]. Sediment is also vital in the heavy metals cycling in the coastal ecosystem [11,12]. It acts as a potential sink and source of inorganic and organic contaminants [3,13,14] during changes in environmental conditions [15,16,17,18].
Sediment quality has been assessed and monitored in few sites around the world [2,19,20,21,22]. There were significant differences amongst the studies regarding locations, variables, sampling methods, and parameters. It has been reported that heavy metal pollution has an impact on the quality of mangrove sediment. Analyses of sediment quality showed that metals were deposited on the sediment surface once transported by the water body and cannot be degraded, either biologically or chemically [18]. However, these metals can only be transported from the source location or accumulate in the ecosystem [21]. The increased toxicity of heavy metals in the mangrove ecosystem has become one of the most severe environmental issues [2], causing a decline in the mangrove area [18]. High metal concentrations are derived from anthropogenic sources around mangrove estuaries, such as disturbance areas, industrial activities, agriculture activities, wastewater disposal, and discarded automobiles [2,23,24].
MSQi is an assessment of mangrove forests sediment quality and monitoring standards. The MSQi is measured using two factors: sediment contaminant concentrations and toxicity. It is also helpful in making decisions and conserving resources. MSQi supports the development and revision of the mangrove quality index (MQI). MSQi is based on standard parameters that can be used and measured, allowing for more accurate data comparison between monitoring stations at the regional, national, and global levels. These comparisons enhance the option of engaging further analyses on mangrove quality at broader geographical scales. Developing a practical sediment quality index (SQi) in mangroves is a way toward quickly identifying the extent of disturbances, impacts, and effective mitigation measures to protect resource sustainability [2].
Since there are many complex environmental factors such as seasonal zones, tidal patterns, flora and fauna, and water, there is no specific method used in Malaysia for assessing and monitoring mangrove sediment quality [2]. For example, the season plays a vital role in mangrove ecology by changing the chemical composition of sediment through harmful chemical removal and nutrient transportation. Due to the complex interactions of factors in determining the quality of mangrove sediment, a comprehensive assessment of all integrating factors at the ecosystem level is needed to select appropriate indicators that could adequately reflect its real-time health status [1]. However, not all aspects can be included when establishing the MSQi. Thus, this study was carried out to develop an MSQi for the mangrove ecosystem in Peninsular Malaysia.

2. Materials and Methods

2.1. Information on the Study Areas

This study was conducted at the Matang Mangrove Forest Reserve (MMFR) in Perak, Malaysia. MMFR is located at the borders of Malacca Strait and is shaped like a crescent moon (Figure 1). The MMFR stretches over a distance of 10.00 km from Kuala Sepetang to Taiping town. The main townships in MMFR are Kuala Sepetang, Kuala Trong, and Kerang River. Meanwhile, fishing villages are Bagan Kuala Gula, Bagan Sangga Besar, Bagan Pasir Hitam, and Bagan Panchor. The climate in MMFR is mainly equatorial, with a mean annual temperature of 23–30 °C. The average rainfall ranges from 2000—3000 mm. Moreover, the reserve experiences semidiurnal tides ranging from 1.6–2.9 m. MMFR is dominated by Rhizophora apiculata and Rhizophora mucronata species.
In MMFR, working plans or management have been revised and implemented. The ten-year program provides detailed resources and schedules for harvesting, yield regulation, silvicultural operations, protection, and conservation. MMFR has been managed sustainably based on five work plans since Malaysia’s Independence Day in 1957 [17]. However, the MMFR, with its large expanse of sheltered waters, is home to 7666 floating fish cages, and cockle culture covers an area of 4726 ha, both within and outside the estuaries [25]. Mangrove forest ecosystems provide productive and complex marine habitats for diversified marine life. There are 163 species of fish, 37 species of shrimps and prawns, and 45 species of crabs that have been identified and recorded in the Sixth Revision of the Working Plan. The following are the rivers’ specific characteristics:
TLR is located near the sea mouth at 4°52′30.30″ N and 100°38′8.04″ E (Figure 2). The river’s length is approximately 8.98 km. TLR is classified as least disturbed since most of this area was converted to open water, dryland forest, and waterways for fishing boats [17].
TR is located near Kampung Pasir Hitam between 4°52′30.30″ N and 100°38′8.04″ E (Figure 3). The river’s length is ~8.1 km. Despite being closest to human development, this river is moderately disturbed due to minimal changes in mangrove land to water bodies, dryland forests, human development, agriculture, and aquaculture activities [17].
SR is near the Kuala Sepetang town, at latitude 4°52′30.30″ N and longitude 100°38′8.04″ E (Figure 4). The river’s length is ~20.4 km. As observed during sampling activities, this river is highly disturbed due to its proximity to human settlements, agriculture, aquaculture, industrial operations, and a jetty. The land had been converted into oil palm plantations, horticulture, paddy fields, aquaculture, urban settlements, and dryland forests [17].

2.2. Experimental Design

The soil sampling was conducted in three rivers using the normalised difference vegetation index (NDVI) at different levels of mangrove disturbance (green = least disturbed, yellow = moderately disturbed, and red = highly disturbed) (Figure 1). A systematic sampling [26] was applied in this study, with three main plots of 450 m × 25 m established as the primary study plot from the landward, central, and seaward zones of each river. Each main plot contained five 5 m × 5 m subplots, with a distance of 100 m between each subplot. Five 1 m × 1 m mini subplots were established for sediment sampling (Figure 5). GPSMAP® 60CSx Garmin was used to record the sampling points.

2.3. Sediment Sampling and Laboratory Analysis

Seven hundred and fifty sediment samples were collected from five mini subplots along the same transect. The sediment samples were taken using a peat auger [27,28] in two seasons: November and December 2017 (wet season) and March and April 2018 (dry season). This study obtained a total of 2250 sediment samples at five depths, i.e., 0–15, 15–30, 30–50, 50–100, and 100–150 cm, because sediment depths can also influence pollution [2,28]. The sediment samples were placed into a labelled plastic bag before being transported to the soil laboratory for analysis.
Sediment samples were characterised for physical and chemical properties. Sediment texture was determined using the hydrometer method [29,30]. Sediment pH was measured in a 1:2.5 ratio (sediment: distilled water) using an electrode pH meter (Model MW 100, Milwaukee, Italy) [31,32]. Total Nitrogen was analysed using the Kjeldahl method [26,33]. Phosphorus was determined using the blue method and a double acid method [26,32,34]. Subsequently, samples were examined using an ultraviolet-visible (UV/Vis) spectrophotometer with a specific wavelength (Model Cary 50 Scan UV/V Spectrophotometer) [35]. Aqua regia method was used to extract and digest the sediments [32,36]. Finally, samples were analysed for heavy metals and base cations using an atomic absorption spectrophotometer (AAS, Model Shimadzu AA-6800) with specific flame and wavelength settings.

2.4. Statistical Analysis

The data were analysed using a statistical analysis system (SAS) software version 9.4 for descriptive analysis. The statistical package for the social sciences (SPSS) version 25 was used for principal component analysis (PCA) to reduce the covariance and correlation matrix [37] and identify important MSQi parameters. Microsoft Excel 2020 was used to create the mangrove sediment degree of pollution table (MSDPT) and MSQi formulation model.
PCA was performed on all measured sediment variables to determine variables with the highest score, more significant than 0.75. PCA, when combined with a coefficient of linear correlation, provides a multi-dimensional statistical test of the studied variables [38]. PCA is widely used in various sediment fields, including sediment assessment [2,21,39]. The most significant variables were determined using PCA and are characterised by the highest score component of each principal component (PC). Each PC is derived from a linear combination of the p metrics. The first component has been extracted and accounts for the second-largest amount of variance that remains after associated with the first extracted component. The second extracted accounts for the second-largest amount of variance.

2.5. Development of MSQi

PCA was conducted on all measured sediment parameters to develop the index using the steps outlined below:
STEP 1:
Fourteen of the nineteen parameters were chosen for PCA analysis because they were grouped in one unit (mg/kg) to reduce bias in PCA analysis. The fourteen parameters are Nitrogen (N), Phosphorus (P), Potassium (K), Cal-cium (Ca), Magnesium (Mg), Sodium (Na), Manganese (Mn), Iron (Fe), Lead (Pb), Zinc (Zn), Copper (Cu), Cadmium (Cd), Chromium (Cr), and Nickel (Ni).
STEP 2:
Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy with KMO value >0.600 was tested to ensure that the relationship between the data in the observation is adequate. If the KMO value < 0.600, the data are insufficient to run the PCA [40].
STEP 3:
Essential parameters in the component were identified, where the highest score of the PC grouped these parameters.
STEP 4:
In PCA, two results were obtained: component matrix and rotated component matrix. The proportion of the variability explained by each important component was identified. For example, four crucial parameter factors (PFs) were selected: P(Xi), P(Xii), P(Xiii), and P(Xiv).
STEP 5:
Each suggested critical PF concentration in the sediment was rated as 0 (low), 1 (medium), and 2 (high), based on the permissible limit range for soil and plant [41], as presented in Table A1 (Appendix A).
STEP 6:
The PFs ratings were referred to the MSDPT (see Figure A1, Appendix A).
STEP 7:
These MSDPT was developed by summation of these parameters. Σ MSDPT = P(Xi) + P(Xii) + P(Xiii) + P(Xiv).
STEP 8:
MSQi was developed by classifying MSDPT as I (very bad), II (bad), III (moderate), IV (good), and V (excellent) (see Table A2, Appendix A).
STEP 9:
A simplified format has been developed to facilitate MSQi modelling (see Table A3, Appendix A).

3. Results

3.1. Development of MSQi

Since PCA was used to analyse fourteen parameters, only the highest four were extracted to strengthen MSQi development. The KMO test was performed; the results that were significant at <0.001 with adequacy of 0.747 (Table 1) were used in the PCA analysis [42].
MSQi development involved PCA to interpret sediment chemical composition and calculate the pollution score. PCA’s primary function is to reduce the complexity of the loading factors [37]. PCA also works as an indicator of anthropogenic sediment pollution. Component loading greater than 0.750 indicates “strong”, values between 0.500 to 0.750 indicate “moderate”, and values between 0.500 to 0.000 indicate “weak”. Only the “strong” values were taken in MSQi modelling. In this study, at least 200 data points were selected to run the PCA [37]. Other missing chemical parameters should be included to improve the PCA loading value in the MSQi formulation. Table 2 and Table 3 presents the PCA results.
Table 3 shows the rotated component matrixa, where six PCs were obtained, and shows the contribution of each parameter in the group. As a result, for PC1: Pb and Zn received the highest score values of 0.841 and 0.883, respectively. PC2: Cr and Ni had the highest score values of 0.912 and 0.913, respectively. PC3: N received the highest score value of 0.876. PC4: K had the highest score value of 0.887. PC5: Mg obtained the highest score value of 0.887. Finally, PC6: Ca had the highest score value of 0.796. Only the two most vital PCs, PC1 and PC2, were included in MSQi modelling, with Pb, Zn, Cr, and Ni were the four MSQi parameters. Figure 6 shows the component plot in rotated space. The red circle represents a strong correlation between all parameters.
Table 4 shows the PF. The World Health Organisation (WHO) guidelines for soil and plants were used to calculate the PF values [38]. Permissible limit PF values were divided into three categories: low, medium, and high concentrations using ratings 0, 1, and 2.
Table 5 shows an example simulation using a random MSDPT table (Figure A1) (Appendix A). The obtained code of MSDPT was 1 2 0 2. This score value was used in Table 5 to calculate the MSQi class. When the MSDPT was obtained, the value was then referred to the rating of MSQi Index at Table 6. Our result showed that the MSDPT score is 5 (Table 6) and falls into Class II (Table 6), indicating that the description of the areas is highly polluted.

3.2. MSQi of TLR during Dry and Wet Seasons

The MSQi of TLR during the dry season is illustrated in Table A4 (Appendix A). The result showed that the MSQi of the landward zone at a sediment depth of 0–15 cm was in Class III, followed by a moderate rating, indicating that the sediment was moderately polluted. However, MSQi of sediment depths of 15–30, 30–50, 50–100, and 100–150 cm was obtained under Class IV, indicating a good rating. In central and seaward zones of the TLR, MSQi in all sediment depths was obtained under MSQi Class IV with a good rating, indicating that the sediment in this area was less polluted. During the wet season, MSQi at landward, central, and seaward zones changed to Class IV: less polluted (Table A5 Appendix A) due to heavy metal (HMs) content dilution [43] with flooded river water [39,44]. In TLR, all the heavy metal content (Pb, Zn, Cr, and Ni) during the wet season at all mangrove zones and sediment depths were below the permissible limit of WHO guidelines for soil and plants [41]. Therefore, mangrove sediment pollution in this river was at lower risk than in the surrounding area.

3.3. MSQi of TR during Dry and Wet Seasons

The MSQi of TR during the dry season is illustrated in Table A6 (Appendix A). The results showed that MSQi in landward and central zones in all sediment depths were obtained with Class III and a moderate rating. However, MSQi at seaward in sediment depths of 30–50 and 50–100 cm was obtained under MSQi Class IV with less pollution. Meanwhile, Table A7 (Appendix A) depicts MSQi during the wet season. The results revealed that MSQi in a landward zone at all sediment depths was obtained with a III score, indicating that the area was moderately polluted. In contrast, sediment depths of 30–50 cm were discovered in Class IV with less polluted conditions. The same result was obtained in the central and seaward of TR, where sediment in-depths of 0–15 and 15–30 cm were obtained under MSQi Class III, and the rating was moderate. However, under MSQi Class IV, sediment depths of 30–50, 50–100, and 100–150 cm were obtained, and the rating was good.

3.4. MSQi of SR during Dry and Wet Seasons

The MSQi of SR during the dry season is illustrated in Table A8 (Appendix A). The results showed that MSQi of SR during the dry season was obtained under MSQi Class III, with a moderate rating, at landward, central, and seaward zones. Meanwhile, the MSQi of SR during the wet season is shown in Table A9 (Appendix A). MSQi results in the central zone of SR revealed that all sediment depths ranging from 0–15, 15–30, and 100–150 cm were classified as MSQi Class III (moderate).

4. Discussion

TLR has been classified as least disturbed because most of this area has been converted to open waterand dryland forest [17]. This river also serves as a waterway for fishing boats from Kuala Sepetang Jetty, Kuala Trong Jetty, and Kg. Pasir Hitam Jetty. As this area was categorised as low polluted, the source of heavy metals from landward and seaward during the dry and wet seasons was runoff [39]. The top layer of sediment contained high metal concentrations due to pollutant migration from the landward zone [45]. This pollution migration is influenced by many factors, such as rainfall, tidal, sediment type, the porosity of sediment, type of vegetation cover, and others [43]. Moreover, sediment contaminations with Pb, Zn, Cr, and Ni are common in many environments. For example, lead came from the historical use of leaded fuels, zinc from galvanised steel, and increasing copper content from the passive leaching of antifouling paints [15,35].
From this study, the findings showed that pollution indeed had an impact on TR. Due to land changes into the water body, dryland forest, human settlement, agriculture, and aquaculture practices, this river is classified as moderately disturbed [14]. Furthermore, as a waterway, this river is opened to fishermen’s boats. Overall, the Pb, Cu, Zn, and Ni content of TR during the dry season in all mangrove zones and sediment depths were below the WHO guidelines for soil and plants [41]. Therefore, mangrove sediment pollution in TR is at low risk than in the surrounding communities.
Meanwhile, SR is highly disturbed due to its proximity to human settlements, agriculture, aquaculture, industrial operations, and a jetty. SR had been converted to oil palm, horticulture, paddy field, aquaculture, urban settlement, and dryland [17]. Hence, the tidal process increased most of these metals content during dry seasons. During high tide, river water flows from seaward to landward and vice versa during low tide [43]. Under these circumstances, pollution remains suspended in sediment in both directions (seaward and landward).

5. Conclusions

During the dry season, TLR was moderately polluted (Class III). However, the MSQi became less polluted during the wet season (Class IV). On the other hand, TR was classified as Class III (moderately polluted) during the dry season, except at seaward at sediment depths of 30–50 and 50–100 cm (Class IV: less polluted). During the wet season, the TR was moderately polluted to less polluted. Lastly, during the dry season, the SR was classified as Class III with moderate pollution, while less polluted during the wet season. Therefore, based on the findings of this study, it can be concluded that sediment depths have an impact on pollution. The MSQi development will serve as an essential benchmark and guideline for assessing sediment pollution in Malaysia’s mangrove ecosystem. The application of MSQi will reduce time and cost in monitoring the mangrove sediment quality compared with current practices.

Author Contributions

Conceptualisation, A.M.M.P., S.G., A.A.N. and M.B.A.; methodology, A.M.M.P. and M.B.A.; formal analysis, A.M.M.P. and W.R.K.; writing—original draft preparation, A.M.M.P.; writing—review and editing, S.G., M.B.A. and W.R.K.; visualisation, A.M.M.P., S.G. and M.B.A.; supervision S.G., A.A.N. and M.B.A.; project administration, A.M.M.P.; funding acquisition, A.M.M.P. and S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by IPS Grant: Vot No. 9609900 and FRGS Grant Vot No. 5540232, Universiti Putra Malaysia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available within the article.

Acknowledgments

The authors would like to extend their special thanks to Universiti Putra Malaysia for funding this project. The contribution and assistance from the Perak Forestry Department and all staff from the Faculty of Forestry and Environment, Universiti Putra Malaysia, are greatly appreciated. Our research team really appreciate the contribution of (Late) Mohd Bakri Adam (20 August 2021). No one can replace you.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Simulation in determination of PF for low, medium, and high.
Table A1. Simulation in determination of PF for low, medium, and high.
MSQi
Parameter		Low
(Unit)
Rating 0		Medium
(Unit)
Rating 1		High
(Unit)
Rating 2
P(Xi)X ≤ P(Xi) *P(Xi) * < X < P(Xi) **X ≥ P(Xi) **
P(Xii)X ≤ P(Xii) *P(Xii) * < X < P(Xii) **X ≥ P(Xii) **
P(Xiii)X ≤ P(Xiii) *P(Xiii) * < X < P(Xiii) **X ≥ P(Xiii) **
P(Xiv)X ≤ P(Xiv) *P(Xiv) * < X < P(Xiv) **X ≥ P(Xiv) **
* and ** = Value of MSQi parameters.
Forests 12 01279 g0a1 550
Figure A1. Simulation of MSDPT.
Figure A1. Simulation of MSDPT.
Forests 12 01279 g0a1
Table
Table A2. Simulation MSQi.
Table A2. Simulation MSQi.
MSDPT RangeMSQi ClassDescription
If 7.00 ≥ X ≥ 8.00IHighly Polluted
If 5.00 ≥ X ≥ 6.99IIHigh Polluted
If 4.00 ≥ X ≥ 4.99IIIModerately Polluted
If 2.00 ≥ X ≥ 3.99IVLow Polluted
If 0.0 ≥ X ≥ 1.99VNot Polluted
Table
Table A3. Example Simulation of MSQi.
Table A3. Example Simulation of MSQi.
MANGROVE SEDIMENT QUALITY INDEX (MSQi).
RiverZoneDepth (cm)MSQi ParameterMedianMSDPT∑ MSDPT SCOREMSQi CLASSRating Description
Tiram Laut RiverLandward0–15xxxxxxx
xxx
xxx
xxx
15–30xxxxxxx
xxx
xxx
xxx
30–50xxxxxxx
xxx
xxx
xxx
50–100xxxxxxx
xxx
xxx
xxx
100–150xxxxxxx
xxx
xxx
xxx
Central0–15xxxxxxx
xxx
xxx
xxx
15–30xxxxxxx
xxx
xxx
xxx
30–50xxxxxxx
xxx
xxx
xxx
50–100xxxxxxx
xxx
xxx
xxx
100–150xxxxxxx
xxx
xxx
xxx
Seaward0–15xxxxxxx
xxx
xxx
xxx
15–30xxxxxxx
xxx
xxx
xxx
30–50xxxxxxx
xxx
xxx
xxx
50–100xxxxxxx
xxx
xxx
xxx
100–150xxxxxxx
xxx
xxx
xxx
Table
Table A4. MSQi in TLR of MMFR, Perak during dry season.
Table A4. MSQi in TLR of MMFR, Perak during dry season.
MANGROVE SEDIMENT QUALITY INDEX (MSQi)
RiverZoneDepth (cm)MSQi ParameterMedianMSDPT∑ MSDPT SCOREMSQi CLASSRatingDescription
Tiram Laut RiverLandward0–15Pb3.83613IIIModerateModerately Polluted
Zn25.3101
Cr1.9151
Ni0.9820
15–30Pb1.36802IVGoodLow Polluted
Zn16.9261
Cr1.7551
Ni0.9000
30–50Pb5.01412IVGoodLow Polluted
Zn22.3701
Cr0.4450
Ni2.2800
50–100Pb3.41812IVGoodLow Polluted
Zn22.7301
Cr0.5070
Ni2.6000
100–150Pb1.36801IVGoodLow Polluted
Zn18.9841
Cr0.5770
Ni2.9600
Central0–15Pb5.24212IVGoodLow Polluted
Zn23.2021
Cr1.2770
Ni3.5470
15–30Pb2.59612IVGoodLow Polluted
Zn16.4061
Cr1.1700
Ni3.0000
30–50Pb2.19012IVGoodLow Polluted
Zn17.3361
Cr0.2960
Ni1.5200
50–100Pb4.78612IVGoodLow Polluted
Zn25.6201
Cr0.3380
Ni1.7330
100–150Pb5.01412IVGoodLow Polluted
Zn26.9701
Cr0.3850
Ni1.9730
Seaward0–15Pb5.92612IVGoodLow Polluted
Zn21.7501
Cr0.9570
Ni0.9100
15–30Pb6.52012IVGoodLow Polluted
Zn19.5421
Cr0.8780
Ni0.6800
30–50Pb3.50612IVGoodLow Polluted
Zn16.4921
Cr0.2220
Ni1.1400
50–100Pb5.01412IVGoodLow Polluted
Zn19.1581
Cr0.2540
Ni1.3000
100–150Pb4.55812IVGoodLow Polluted
Zn20.7081
Cr0.2890
Ni1.4800
Table
Table A5. MSQi in TLR of MMFR, Perak during wet season.
Table A5. MSQi in TLR of MMFR, Perak during wet season.
MANGROVE SEDIMENT QUALITY INDEX (MSQi)
RiverZoneDepth (cm)MSQi ParameterMedianMSDPT∑ MSDPT SCOREMSQi CLASSRatingDescription
Tiram Laut RiverLandward0–15Pb9.80012IVGoodLow Polluted
Zn24.2801
Cr1.1110
Ni1.5530
15–30Pb3.29012IVGoodLow Polluted
Zn15.4261
Cr1.0180
Ni0.2200
30–50Pb4.10212IVGoodLow Polluted
Zn20.4981
Cr0.2580
Ni1.3220
50–100Pb6.38012IVGoodLow Polluted
Zn20.8821
Cr0.2940
Ni1.5080
100–150Pb6.38012IVGoodLow Polluted
Zn19.5681
Cr0.3350
Ni1.7170
Central0–15Pb2.73412IVGoodLow Polluted
Zn14.7201
Cr0.7400
Ni3.7970
15–30Pb3.87412IVGoodLow Polluted
Zn19.9641
Cr0.6790
Ni3.4800
30–50Pb4.64612IVGoodLow Polluted
Zn18.3781
Cr0.1720
Ni0.8820
50–100Pb4.10212IVGoodLow Polluted
Zn19.2821
Cr0.1960
Ni1.0050
100–150Pb1.95601IVGoodLow Polluted
Zn14.7441
Cr0.2230
Ni1.1450
Seaward0–15Pb2.87812IVGoodLow Polluted
Zn14.8801
Cr0.5550
Ni2.8480
15–30Pb5.01412IVGoodLow Polluted
Zn17.6461
Cr0.5090
Ni2.6100
30–50Pb2.73412IVGoodLow Polluted
Zn14.8561
Cr0.1290
Ni0.6610
50–100Pb2.96212IVGoodLow Polluted
Zn16.1701
Cr0.1470
Ni0.7540
100–150Pb3.87412IVGoodLow Polluted
Zn14.7441
Cr0.1670
Ni0.8580
Table
Table A6. MSQi in TR of MMFR, Perak during dry season.
Table A6. MSQi in TR of MMFR, Perak during dry season.
MANGROVE SEDIMENT QUALITY INDEX (MSQi)
RiverZoneDepth (cm)MSQi ParameterMedianMSDPT∑ MSDPT SCOREMSQi CLASSRatingDescription
Tinggi RiverLandward0–15Pb4.33013IIIModerateModerately Polluted
Zn16.4181
Cr9.5751
Ni6.4250
15–30Pb5.92613IIIModerateModerately Polluted
Zn19.1341
Cr8.7751
Ni4.7000
30–50Pb7.49213IIIModerateModerately Polluted
Zn23.9581
Cr2.2231
Ni2.8500
50–100Pb7.06413IIIModerateModerately Polluted
Zn25.6561
Cr2.5351
Ni3.2500
100–150Pb7.29213IIIModerateModerately Polluted
Zn22.6441
Cr2.8861
Ni3.7000
Central0–15Pb6.60813IIIModerateModerately Polluted
Zn21.4781
Cr6.3831
Ni8.1830
15–30Pb8.88813IIIModerateModerately Polluted
Zn24.5401
Cr5.8501
Ni7.5000
30–50Pb6.15413IIIModerateModerately Polluted
Zn28.1481
Cr1.4821
Ni1.9000
50–100Pb4.10213IIIModerateModerately Polluted
Zn19.4221
Cr1.6901
Ni2.1670
100–150Pb6.83613IIIModerateModerately Polluted
Zn23.4741
Cr1.9241
Ni2.4670
Seaward0–15Pb9.03613IIIModerateModerately Polluted
Zn24.3301
Cr4.7871
Ni6.1380
15–30Pb4.10213IIIModerateModerately Polluted
Zn15.9101
Cr3.5631
Ni5.6250
30–50Pb7.97612IVGoodLow Polluted
Zn22.6061
Cr1.1120
Ni1.4250
50–100Pb4.55812IVGoodLow Polluted
Zn16.7161
Cr1.2680
Ni1.6250
100–150Pb6.60813IIIModerateModerately Polluted
Zn23.1401
Cr1.4431
Ni1.8500
Table
Table A7. MSQi in TR of MMFR, Perak during wet season.
Table A7. MSQi in TR of MMFR, Perak during wet season.
MANGROVE SEDIMENT QUALITY INDEX (MSQi)
RiverZoneDepth (cm)MSQi ParameterMedianMSDPT∑ MSDPT SCOREMSQi CLASSRatingDescription
Tinggi RiverLandward0–15Pb8.59213IIIModerateModerately Polluted
Zn36.7261
Cr5.5531
Ni7.1200
15–30Pb7.81413IIIModerateModerately Polluted
Zn37.7801
Cr5.1081
Ni6.5250
30–50Pb8.03812IVGoodLow Polluted
Zn38.8361
Cr1.2890
Ni1.6530
50–100Pb7.59213IIIModerateModerately Polluted
Zn38.9021
Cr1.4701
Ni1.8850
100–150Pb8.48413IIIModerateModerately Polluted
Zn46.1101
Cr1.6741
Ni2.1460
Central0–15Pb6.47613IIIModerateModerately Polluted
Zn37.2521
Cr3.7021
Ni4.7460
15–30Pb5.35813IIIModerateModerately Polluted
Zn34.0001
Cr3.3931
Ni4.3500
30–50Pb6.92212IVGoodLow Polluted
Zn33.9341
Cr0.8600
Ni1.1020
50–100Pb7.14612IVGoodLow Polluted
Zn36.1801
Cr1.0180
Ni1.2570
100–150Pb7.36812IVGoodLow Polluted
Zn34.7041
Cr1.1160
Ni1.4310
Seaward0–15Pb6.69813IIIModerateModerately Polluted
Zn32.8141
Cr2.7771
Ni3.5600
15–30Pb8.48413IIIModerateModerately Polluted
Zn34.8801
Cr2.5451
Ni3.2630
30–50Pb8.49412IVGoodLow Polluted
Zn40.1761
Cr0.6450
Ni0.8270
50–100Pb8.26212IVGoodLow Polluted
Zn33.9561
Cr0.7350
Ni0.9430
100–150Pb7.36812IVGoodLow Polluted
Zn37.2521
Cr0.8370
Ni1.1090
Table
Table A8. MSQi in SR of MMFR, Perak during dry season.
Table A8. MSQi in SR of MMFR, Perak during dry season.
MANGROVE SEDIMENT QUALITY INDEX (MSQi)
RiverZoneDepth (cm)MSQi ParameterMedianMSDPT∑ MSDPT SCOREMSQi CLASSRating Description
Sepetang RiverLandward0–15Pb7.59213IIIModerateModerately Polluted
Zn43.5161
Cr4.5961
Ni4.9100
15–30Pb8.48413IIIModerateModerately Polluted
Zn38.5061
Cr3.2801
Ni3.5000
30–50Pb5.58213IIIModerateModerately Polluted
Zn28.4621
Cr2.6681
Ni3.4200
50–100Pb3.79613IIIModerateModerately Polluted
Zn28.4181
Cr3.0421
Ni2.9000
100–150Pb6.02813IIIModerateModerately Polluted
Zn27.0541
Cr3.4631
Ni2.4400
Central0–15Pb7.59213IIIModerateModerately Polluted
Zn34.0441
Cr3.0641
Ni3.2730
15–30Pb6.47613IIIModerateModerately Polluted
Zn34.5281
Cr2.5291
Ni3.0000
30–50Pb5.80513IIIModerateModerately Polluted
Zn31.1861
Cr1.7781
Ni2.2800
50–100Pb8.03813IIIModerateModerately Polluted
Zn37.4501
Cr2.0281
Ni2.6000
100–150Pb5.80613IIIModerateModerately Polluted
Zn30.3521
Cr2.3091
Ni2.9600
Seaward0–15Pb6.25213IIIModerateModerately Polluted
Zn38.2641
Cr5.7451
Ni4.3650
15–30Pb7.59213IIIModerateModerately Polluted
Zn46.0881
Cr5.2651
Ni3.4700
30–50Pb6.47613IIIModerateModerately Polluted
Zn36.3521
Cr1.3341
Ni1.7100
50–100Pb6.47613IIIModerateModerately Polluted
Zn34.8581
Cr1.5211
Ni1.6950
100–150Pb8.26213IIIModerateModerately Polluted
Zn43.0761
Cr1.7321
Ni2.2200
Table
Table A9. MSQi in SR of MMFR, Perak during wet season.
Table A9. MSQi in SR of MMFR, Perak during wet season.
MANGROVE SEDIMENT QUALITY INDEX (MSQi)
RiverZoneDepth (cm)MSQi ParameterMedianMSDPT∑ MSDPT SCOREMSQi CLASSRating Description
Sepetang RiverLandward0–15Pb11.61014IIIModerateModerately Polluted
Zn70.3082
Cr6.6641
Ni4.5430
15–30Pb11.61014IIIModerateModerately Polluted
Zn65.8022
Cr3.3301
Ni3.7050
30–50Pb10.37814IIIModerateModerately Polluted
Zn61.0982
Cr1.5471
Ni2.8100
50–100Pb8.26214IIIModerateModerately Polluted
Zn54.4182
Cr1.7641
Ni2.2620
100–150Pb8.93213IIIModerateModerately Polluted
Zn43.2301
Cr2.0091
Ni2.5750
Central0–15Pb11.61014IIIModerateModerately Polluted
Zn62.5722
Cr4.4431
Ni5.6960
15–30Pb8.48414IIIModerateModerately Polluted
Zn56.1322
Cr4.0721
Ni5.2200
30–50Pb8.70812IVGoodLow Polluted
Zn42.7481
Cr1.0310
Ni1.3220
50–100Pb8.48412IVGoodLow Polluted
Zn32.0441
Cr1.1760
Ni1.5080
100–150Pb8.26213IIIModerateModerately Polluted
Zn27.2741
Cr1.3391
Ni1.7170
Seaward0–15Pb6.69813IIIModerateModerately Polluted
Zn46.2201
Cr3.3321
Ni4.2720
15–30Pb7.36813IIIModerateModerately Polluted
Zn43.8901
Cr3.1761
Ni3.8530
30–50Pb5.13612IVGoodLow Polluted
Zn34.2861
Cr0.7740
Ni2.9050
50–100Pb5.35812IVGoodLow Polluted
Zn28.0441
Cr0.8820
Ni1.1750
100–150Pb6.02812IVGoodLow Polluted
Zn28.0561
Cr1.0040
Ni1.2880

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Forests 12 01279 g001 550
Figure 1. The Location of the Study Area at TLR, TR, and SR in MMFR, Perak (green = least disturbed, yellow = moderately disturbed, and red = highly disturbed). Note: TLR = Sungai Tiram Laut, TR = Sungai Tinggi and SR = Sungai Sepetang.
Figure 1. The Location of the Study Area at TLR, TR, and SR in MMFR, Perak (green = least disturbed, yellow = moderately disturbed, and red = highly disturbed). Note: TLR = Sungai Tiram Laut, TR = Sungai Tinggi and SR = Sungai Sepetang.
Forests 12 01279 g001
Forests 12 01279 g002 550
Figure 2. TLR at MMFR, Perak.
Figure 2. TLR at MMFR, Perak.
Forests 12 01279 g002
Forests 12 01279 g003 550
Figure 3. TR at MMFR, Perak.
Figure 3. TR at MMFR, Perak.
Forests 12 01279 g003
Forests 12 01279 g004 550
Figure 4. SR at MMFR, Perak.
Figure 4. SR at MMFR, Perak.
Forests 12 01279 g004
Forests 12 01279 g005 550
Figure 5. Sampling plot design from landward to seaward.
Figure 5. Sampling plot design from landward to seaward.
Forests 12 01279 g005
Forests 12 01279 g006 550
Figure 6. Component plot in rotated space.
Figure 6. Component plot in rotated space.
Forests 12 01279 g006
Table
Table 1. KMO measure of sampling adequacy.
Table 1. KMO measure of sampling adequacy.
KMO Measure of Sampling AdequacyBartlett’s Test of Sphericity
Approx. Chi-SquaredfSignificance
0.7471436.09891<0.001
Note: KMO > 0.600 shows that the relationship between the data in the observation is very good.
Table
Table 2. Component matrix.
Table 2. Component matrix.
VariablesComponent
123456
N−0.4140.4180.1000.0880.5530.377
P0.670−0.2610.294−0.1100.189−0.239
K0.2650.2060.4840.492−0.2120.413
Ca−0.043−0.353−0.2930.7210.091−0.198
Mg0.1590.6280.212−0.1480.121−0.575
Na−0.695−0.104−0.007−0.1080.3340.252
Mn0.6570.1820.3260.365−0.2360.002
Fe−0.3360.7350.1100.2780.203−0.191
Pb0.778−0.1350.293−0.1250.2530.108
Zn0.757−0.2130.210−0.2200.3370.097
Cu0.566−0.125−0.2370.2940.3060.063
Cd0.4920.034−0.4590.2220.300−0.171
Cr0.6530.432−0.407−0.177−0.1750.204
Ni0.6150.442−0.444−0.135−0.0990.255
Eigenvalues4.2911.8491.3391.2751.021.001
Percent of Variance30.64813.2099.5649.1077.2837.152
Cumulative Percent30.64843.85753.42162.52769.8176.962
Table
Table 3. Rotated component matrix.
Table 3. Rotated component matrix.
ParametersComponent
123456
N128−0.0450.8760.0380.143−0.052
P0.769−0.024−0.3050.0670.1020.097
K0.0680.0260.1000.887−0.051−0.030
Ca−0.189−0.263−0.1110.085−0.1470.796
Mg0.1140.110−0.066−0.0280.887−0.121
Na−0.267−0.3570.567−0.308−0.209−0.145
Mn0.3130.208−0.2930.6940.1830.129
Fe−0.376−0.0210.4240.1690.6840.045
Pb0.8410.199−0.0750.219−0.0280.025
Zn0.8830.200−0.0510.062−0.0790.039
Cu0.4070.2790.0140.102−0.0970.560
Cd0.2460.386−0.051−0.1210.1150.619
Cr0.1430.912−0.1340.0940.0620.010
Ni0.1250.913−0.0380.0840.0400.062
Eigenvalues2.7192.2281.5061.4981.4221.402
Percent of Variance19.41915.91110.75510.70010.16010.017
Cumulative Percent19.41935.3346.08556.78566.94576.962
Note: The component loading; the value >0.750 indicate “strong”, the values of <0.750 to 0.500 indicate “moderate”, and the values of <0.500 to 0.000 indicate “weak”.
Table
Table 4. Permissible limit for PF from WHO guidelines for soil and plants [41].
Table 4. Permissible limit for PF from WHO guidelines for soil and plants [41].
MSQi
Parameter		Low (mg/kg) Rating 0Medium (mg/kg) Rating 1High (mg/kg) Rating 2
PbX ≤ 2.002.00 < X < 85.00X ≥ 85.00
ZnX ≤ 0.600.60 < X < 50.00X ≥ 50.00
CrX ≤ 1.301.30 < X < 100.00X ≥ 100.00
NiX ≤ 10.010.00 < X < 35.00X ≥ 35.00
Table
Table 5. Example simulation using random value of MSDPT range code.
Table 5. Example simulation using random value of MSDPT range code.
Suggested
Parameter		Rating (PFs)MSDPT
012
Pb0101
Zn0022
Cr0000
Ni0022
Total Score MSHT5
Note: Refer to Figure A1: MSDPT.
Table
Table 6. Rating of MSQi.
Table 6. Rating of MSQi.
∑ MSDPTMSQi ClassRatingDescription
0VExcellentNot Polluted
1IVGoodLow Polluted
2IVGoodLow Polluted
3IIIModerateModerately Polluted
4IIIModerateModerately Polluted
5IIBadHigh Polluted
6IIBadHigh Polluted
7IVery BadHighly Polluted
8IVery BadHighly Polluted
Note: ∑ MSDPT = Total MSDPT.
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Mohamad Pazi, A.M.; Khan, W.R.; Nuruddin, A.A.; Adam, M.B.; Gandaseca, S. Development of Mangrove Sediment Quality Index in Matang Mangrove Forest Reserve, Malaysia: A Synergetic Approach. Forests 2021, 12, 1279. https://doi.org/10.3390/f12091279

AMA Style

Mohamad Pazi AM, Khan WR, Nuruddin AA, Adam MB, Gandaseca S. Development of Mangrove Sediment Quality Index in Matang Mangrove Forest Reserve, Malaysia: A Synergetic Approach. Forests. 2021; 12(9):1279. https://doi.org/10.3390/f12091279

Chicago/Turabian Style

Mohamad Pazi, Ahmad Mustapha, Waseem Razzaq Khan, Ahmad Ainuddin Nuruddin, Mohd Bakri Adam, and Seca Gandaseca. 2021. "Development of Mangrove Sediment Quality Index in Matang Mangrove Forest Reserve, Malaysia: A Synergetic Approach" Forests 12, no. 9: 1279. https://doi.org/10.3390/f12091279

APA Style

Mohamad Pazi, A. M., Khan, W. R., Nuruddin, A. A., Adam, M. B., & Gandaseca, S. (2021). Development of Mangrove Sediment Quality Index in Matang Mangrove Forest Reserve, Malaysia: A Synergetic Approach. Forests, 12(9), 1279. https://doi.org/10.3390/f12091279

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