2019 SCIENCELINE  
Journal of Life Science and Biomedicine  
J Life Sci Biomed, 9 (1): 10-18, 2019  
ISSN 2251-9939  
Analysis of heavy metal content of Cu, Pb, Hg and  
dissolved Sn in coastal of Banyuwangi district,  
Indonesia  
Ervina Wahyu Setyaningrum1, Agustina Tri Kusuma Dewi1, Mega Yuniartik1, and Endang Dewi Masithah2  
  
1Faculty of Agriculture and Fisheries, University of 17 Agustus 1945, Banyuwangi 68416, Indonesia  
2Faculty of Fisheries and Marine, University of Airlangga, Surabaya 60115, Indonesia  
Corresponding author’s Email: ervinawahyu@untag-banyuwangi.ac.id  
ABSTRACT  
Original Article  
PII: S225199391900003-9  
Banyuwangi Regency has the longest coast in East Java of Indonesia with sandy beaches and  
corals and there are various types of coastal and marine resources that can be utilized both in  
terms of economics and environment. But in the current era of industrialization, coastal areas  
Rec. 03 Jan. 2019  
Rev. 24 Jan. 2019  
Pub. 25 Jan. 2019  
in Banyuwangi have become a top priority for industrial development, agribusiness, agro-  
industry, housing, transportation, ports and tourism. The purpose of this study was to  
analyze the content of copper (Cu), lead (Pb), mercury (Hg), and tin (Sn) and the effect of water  
quality on the heavy metal content in the coast of Banyuwangi Regency. The method in this  
study uses descriptive. Data taken along the coast of Banyuwangi Regency include water  
quality (alkalinity, NH4, PO4, DO, pH, NO3, water temperature and salinity), copper (Cu), lead  
(Pb), mercury (Hg), and tin (Sn). Data analysis using multiple linear regression analysis,  
followed by F test and t-test. The results showed that there was an influence between the  
quality of the water on the value of heavy metal of copper (Cu), and the value of R-Square 0.681  
which means that it has an influence proportion on the value of copper (Cu) of 68.1%. Likewise,  
for the quality of water for tin (Sn), there is an influence with the value of R-Square of 0.700,  
Keywords  
Banyuwangi coastal,  
Copper (Cu),  
Heavy metals,  
Lead (Pb),  
which means that the effect is as high as 70%. While the quality of the waters against Lead, Mercury (Hg),  
heavy metal (Pb) and mercury (Hg) has no significant effect. Based on the results of the study,  
Banyuwangi district government needs to take serious actions in controlling heavy metal  
pollution through the implementation of law No. 23 of 1997 concerning to environmental  
management, and the application of environmental quality standards more strictly.  
Tin (Sn),  
Water quality  
INTRODUCTION  
Like other coastal waters, the Coastal of Banyuwangi District has the potential to accumulate anthropogenic  
loads carried from several rivers. This is compounded by the misuse of the river as a waste disposal site so that  
the pollutant load will be distributed to the river estuary also to the sea. The input of waste from land to estuary  
generally comes from human activities such as industry, shipping, anthropogenic and others [1]. This makes  
estuary and coastal areas vulnerable to contamination [2].  
Like fresh water, sea water also has a great ability to dissolve various substances, both in the form of gases,  
liquids, and solids. A sea is a place where the rivers transport various types of substances, which can be  
beneficial nutrients for fish and aquatic organisms, can also be materials that are not useful, even disrupt the  
growth and development of fish and aquatic organisms or can cause a decrease in water quality [3].  
This decrease in water quality is caused by the presence of contaminants, both in the form of organic and  
inorganic components. Inorganic components include dangerous heavy metals. Darmono [4], explained that the  
definition of heavy metals is a metal element with a high molecular weight, which is specific gravity greater  
than 5 g/cm3. However metalloid elements which have dangerous properties are also included in the group.  
Thus, currently elements included in heavy metals reach approximately 40 types of elements.  
One of the pollutants that has the potential to be found in the coastal district of Banyuwangi is heavy  
metal. Pollution of heavy metals is categorized as pollution which causes harmful effects on the environment  
and the organisms in it. Heavy metals have non-degradable properties. In addition, heavy metals will  
accumulate in the environment such as water and sediment columns and be absorbed into marine biota [5].  
Heavy metals can enter the environment in various ways, such as weathering of rocks containing heavy  
metals, volcanic activity and disposal of waste from mining, industry and transportation. The main source of  
heavy metal contaminants comes from air and water that pollute the soil. Certain metals in high concentrations  
Setyaningrum, WE, Dewi KTA, Yuniartik M, Masithah DE. 2019. Analysis of heavy metal content of Cu, Pb, Hg and dissolved Sn in coastal of  
Banyuwangi district, Indonesia. J. Life Sci. Biomed., 9 (1): 10-18. www.jlsb.science-line.com  
will be very dangerous if found in the environment. The main cause of heavy metals being dangerous pollutants  
is because they are non-degradable by living organisms in the environment. As a result, these metals  
accumulate into the environment. Heavy metals are dangerous if they enter the metabolic system in amounts  
exceeding the threshold. The threshold which varies for each type of heavy metal [4]. Some of them are widely  
used in various daily needs, therefore they are produced regularly on an industrial scale. The use of these heavy  
metals in various daily needs, either directly or indirectly, or intentionally or unintentionally, has polluted the  
environment. Some heavy metals that are dangerous and often pollute the environment are mainly mercury  
(Hg), lead (Pb), arsenic (As), copper (Cu), cadmium (Cd), chromium (Cr), and nickel (Ni) [6].  
Cu is a microelement is needed by organisms of both land and water, but in small amounts. The presence  
of Cu in general waters can come from industrial areas around the waters. This metal will be absorbed by  
aquatic biota sustainably if its presence in the water is always available, moreover, for aquatic biota with low  
mobility such as shellfish [7]. Lead (Pb) is gray metal, can be forged and can be formed. Pb has active chemical  
properties so that it can be used to coat metal to prevent corrosion. When mixed with other metals, lead can  
form better mixed metals than pure metal. In addition, lead also has a density exceeding other metals. This  
metal is widely used in the battery, cable, paint (as a coloring agent), gilding, pesticide industry and is the most  
widely used as an anti-dust agent in gasoline. Lead is also used as a constituent substance and as a pipe  
connecting formulation [4]. Tin (Sn) is a silvery white, shiny metal, can be forged and can be formed. Tin melting  
point is 231,930C. This metal is not easily oxidized in the air so it is often used as another metal coating to  
prevent rust. Tin is also often used as another metal coating to prevent rust. Tin is also often used as a mixture  
with other metals such as soft solder [8].  
The presence of heavy metal at high concentration in the water column will endanger marine aquatic  
organisms from inhibiting metabolic process to causing the death of biota [9]. Therefore, this study aims to  
monitor the concentration of dissolved heavy metal along coastal of Banyuwangi district and analyze its  
association with aquatic environmental factors.  
MATERIAL AND METHODS  
This research was conducted in March - June 2018. Data collection methods used purposive sampling along the  
coast of Banyuwangi Regency. The  
location of the study can be seen in  
Figure 1.  
The  
research  
method  
uses  
descriptive methods, which is data  
presented by explaining and describing  
the real situation. Measurement of water  
quality which includes temperature,  
salinity, pH, dissolved oxygen (DO) was  
measured directly at the temporary  
research  
location  
for  
observing  
alkalinity, NH4, PO4, NO3 carried out at  
the faculty of agriculture and fisheries  
laboratory on university 17 August 1945  
Banyuwangi. While taking water samples  
for heavy metals using dark glass bottles  
at each research location point along the  
coast of Banyuwangi Regency, then  
taken to the Surabaya to measure heavy  
metal levels.  
Work procedure for analysis of  
heavy metal copper (Cu) content test  
method  
using  
Standar  
Nasional  
Indonesia (SNI) 6989.6: 2009, Mercury  
(Hg) using SNI 6989.78: 2011 test, Lead  
(Pb) using SNI 6989.46: 2009 test  
method, while Tin (Sn) using test method  
American Public Health Association  
(APHA) Ed. 21,311 B, 2005.  
Figure 1. Banyuwangi Coastal Research Site Map.  
Setyaningrum, WE, Dewi KTA, Yuniartik M, Masithah DE. 2019. Analysis of heavy metal content of Cu, Pb, Hg and dissolved Sn in coastal of  
Banyuwangi district, Indonesia. J. Life Sci. Biomed., 9 (1): 10-18. www.jlsb.science-line.com  
Data analysis using multiple linear regression analysis to determine the degree of influence between  
variables of water quality and heavy metals. The statistical test results are presented in the form of  
mathematical equations, namely the multiple linear regression equation as follows:  
Y = a+b1X1+b2X2+…+bnXn  
Where:  
Y: Dependent variable, a: Constanta, b1,b2: Regression coefficient, X1,X2: Independent variable  
RESULTS AND DISCUSSION  
Water quality parameters  
Water quality data taken in the form of temperature, salinity, pH, DO and NH4 in the waters of  
Banyuwangi coastal with the location of data collection in nine points representing all sub-districts along  
Banyuwangi coastal with twice replications.  
Most of the water quality parameters can affect the concentration, distribution and toxicity of heavy  
metals in the waters referring to Hutagaol [10], which stated that temperature, turbidity, pH, salinity and DO  
are parameters that affect the toxicity of heavy metals in the waters. Environmental parameters are suspected  
affect heavy metal concentrations such as temperature, pH and salinity. The increase in temperature will  
reduce the adsorption of heavy metal compounds in particulates to settle to the bottom. The increase in pH can  
reduce the solubility of heavy metals in water because there is a change from the form of carbonate to  
hydroxide which forms a bond with particles in the water. Increasing salinity causes a decrease in toxic metals  
due to the desalination process. So, the existing heavy metal compounds can occur in the sedimentation process  
[11].  
Table 1. Water quality data of Banyuwangi beach in 2018  
Water Quality  
Alkalinity  
Water  
Temperature  
Water  
pH  
NH4  
(ppm)  
NO3  
(ppm)  
PO4  
(ppm)  
DO  
Salinity  
CO3  
HCO3  
(ppm)  
Research Sites  
(ppm)  
6.7  
6.5  
7.1  
30.3  
29.7  
30.1  
28.8  
27.3  
27.5  
31  
7.6  
7.3  
7.3  
7
26  
25  
22  
23  
20  
20  
25  
25  
24  
27  
26  
26  
27  
27  
23  
18  
0
0
1
0
0
12  
116  
112  
100  
144  
116  
136  
88  
Alas Buluh  
Kampe  
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
36  
24  
12  
0
0
7
0
0.1  
0
8
7.2  
7.1  
9
0
24  
16  
80  
32  
12  
BP3  
6.1  
7.5  
6.4  
7.7  
7.4  
7.2  
6.5  
6.1  
6.4  
7.04  
0.9  
6.9  
6.8  
0
0.1  
0.1  
0
0
Cemara Beach  
Pakem Kertosari  
Santen Island  
Blimbingsari  
PangpangBay  
Lampon  
29.3  
31.6  
29.3  
29.7  
29.2  
30.3  
30.1  
30.3  
29.17  
30.6  
30.7  
8
0
100  
112  
124  
92  
7.2  
7.4  
7.2  
7.4  
8.9  
8.4  
6.9  
0
0
0
0
0
16  
36  
36  
12  
0
0
0
0
100  
120  
140  
98  
0
0.1  
0.1  
0
0
12  
0.7  
0.8  
0
24  
24  
44  
44  
0
116  
84  
7.1  
6.9  
25  
26  
3
0
0
80  
DO = dissolved oxygen; NH4= ammonium; PO4= phosphate; CO3= carbonate; HCO3= bicarbonate; BP3= Balai Pendidikan dan Pelatihan  
Perikanan (Fisheries Education and Training Center)  
Heavy metals Cu, Hg, Pb and Sn in the coastal of Banyuwangi regency  
The heavy metals analyzed in this study were types of Copper (Cu), Mercury (Hg), Lead (Pb) and tin (Sn).  
The following are the results of heavy metal tests carried out in the Surabaya Industrial Research and  
Standardization Center laboratory.  
Setyaningrum, WE, Dewi KTA, Yuniartik M, Masithah DE. 2019. Analysis of heavy metal content of Cu, Pb, Hg and dissolved Sn in coastal of  
Banyuwangi district, Indonesia. J. Life Sci. Biomed., 9 (1): 10-18. www.jlsb.science-line.com  
In general, the range of Cu concentration is 0.0104 mg/l, Hg 0 mg/l, Pb 0.0173 mg/l and Sn 1.3436 mg/l  
obtained from the coastal waters of Banyuwangi Regency. If referring to the Decree of the Minister of  
Environment No. 51 of 2004 concerning Sea Water Quality Standards, Mercury (Hg) 0.001 mg/l, Copper (Cu)  
and Lead (Pb) 0.008 mg/l, and Tin (Sn) 2 mg/l, then the value of heavy metals Hg and Sn is still below the  
threshold while Cu and Pb are above the threshold.  
According to WHO, the highest desirable level in drinking water for Cu is 50 µg/L and the threshold for  
concentration for aquatic life tolerance (safe for most fishes) is 2 x 104 µg/L [12]. As for Pb, the WHO maximum  
permissible level of drinking water is 100 µg/L and the threshold of concentration for aquatic life tolerance (safe  
for most fishes) is 100 µg/L [12].  
Table 2. Test results for heavy metals Cu, Hg, Pb and Sn in Coastal of Banyuwangi regency in 2018.  
Test Result  
P. 2169  
Alas  
buluh  
P. 2170  
Alas  
buluh  
Parameter  
Unit  
Test Methods  
P. 2171  
Kampe  
P. 2172  
Kampe  
P. 2173  
BP 3  
P. 2174  
BP 3  
Copper (Cu)  
Mercury (Hg)  
Lead (Pb)  
mg/l  
mg/l  
mg/l  
mg/l  
<0.0223  
<0.0005  
0.012  
0.026  
<0.0005  
0.015  
0.032  
<0.0005  
0.015  
<0.0223  
<0.0005  
0.016  
0.026  
0.026  
SNI 6989.6 : 2009  
SNI 6989.78 : 2011  
SNI 6989.46:2009  
<0.0005 <0.0005  
0.017  
0.015  
Tin (Sn)*  
<0.1050  
<0.1050  
0.469  
<0.1050  
<0.1050  
<0.1050 APHA Ed.21.311 B.2005  
Test Results  
P. 2177 P. 2178  
Parameter  
Unit  
Test Methods  
P. 2175  
P. 2176  
P. 2179  
P. 2180  
P. Santen P. Santen P. Pakem P. Pakem P. Cemara P. Cemara  
Copper (Cu)  
Mercury (Hg)  
Lead (Pb)  
mg/l  
mg/l  
mg/l  
mg/l  
<0.0223  
<0.0005  
0.017  
<0.0223  
<0.0005  
0.018  
0.026  
<0.0005  
0.017  
<0.0223  
<0.0005  
0.022  
0.03  
<0.0005  
0.018  
<0.0223  
<0.0005  
0.018  
SNI 6989.6 : 2009  
SNI 6989.78 : 2011  
SNI 6989.46:2009  
Tin (Sn)*  
<0.1050  
<0.1050  
<0.1050  
<0.1050  
<0.1050  
4.136  
APHA Ed.21.311 B,2005  
Test Results  
Parameter  
Unit  
Test Methods  
P. 2181  
P. 2182  
P. 2183  
P. 2184  
Teluk Pampang  
<0.0223  
Lampon  
Lampon  
P. Blimbing Sari  
<0.0223  
Copper (Cu)  
Mercury (Hg)  
Lead (Pb)  
mg/l  
mg/l  
mg/l  
mg/l  
<0.0223  
<0.0005  
0.019  
<0.0223  
<0.0005  
0.021  
SNI 6989.6 : 2009  
SNI 6989.78 : 2011  
SNI 6989.46:2009  
<0.0005  
0.018  
<0.0005  
0.018  
Tin (Sn)*  
4.703  
4.791  
3.743  
3.656  
APHA Ed.21.311 B,2005  
Note: Parameters tested according to parameters; “<” shows the limit of quantity value from the test  
Source of heavy metals on the coast can be divided into two, which enter naturally and artificially into  
marine waters. Heavy metals that enter the ocean waters can come from three sources, namely:  
-
Input from the coastal area, which originates from the river and the results of coastal abrasion due to  
wave activity.  
-
Inputs from the deep sea, including metals released as a result of volcanic activity in deep seas and  
metals released from particles through chemical processes.  
-
Inputs from nearshore land environments, including metals originating from the atmosphere as dust  
particles.  
The source of artificial metals is metal that was released during the metal and rock industry process. Some  
industries only use certain heavy metals for their production activities. However, in general, most industries  
use various types of heavy metal elements, making it difficult to trace the origin of sources of pollution. Of the  
four heavy metals mentioned above, different concentrations of heavy metals are obtained in seawater. This  
difference in concentration is possible due to the variability of metals in water caused by currents, adsorption,  
tides, or deposition [13].  
Effect of water quality to heavy metal  
Based on the results of regression analysis of the four types of heavy metals namely Copper (Cu), Lead (Pb),  
Mercury (Hg) and Lead (Pb) on the coast of Banyuwangi Regency, it shows that there is no effect on water  
quality of Lead (Pb) and Mercury (Hg). Whereas two other types of heavy metals, namely Copper (Cu) and Tin  
(Sn) have influence.  
In connection with this, even though the Pb value is at the threshold, it is not caused by the value of water  
quality, but the presence of waste entering the coastal area. Whereas the Hg type value is below the threshold  
Setyaningrum, WE, Dewi KTA, Yuniartik M, Masithah DE. 2019. Analysis of heavy metal content of Cu, Pb, Hg and dissolved Sn in coastal of  
Banyuwangi district, Indonesia. J. Life Sci. Biomed., 9 (1): 10-18. www.jlsb.science-line.com  
and the waste entering the coastal area means that it still contains Hg. According to Anggoro [14], heavy metal  
is one of the waste parameters as a source of impact in coastal waters. Probability value of calculated F (sig.) in  
the table above, the value 0,0001 is smaller than the significance level of 0.05 so it can be concluded that the  
linear regression model that was estimated is worth using to explain the effect of heavy metal copper (Cu) on  
alkalinity, NH4, PO4, DO, pH, NO3, water temperature, and salinity. Copper (Cu) is one of the heavy metals that  
can be found in the aquatic environment and in sediments [15]. Heavy metals naturally have low concentrations  
in waters. High or low concentrations of heavy metals are caused by the maximum amount of heavy metal  
waste into the waters. Heavy metals that enter the waters will experience precipitation, dilution and dispersion,  
then absorbed by organisms that live in the waters. Maslukah [16], states that the process of entering Cu in  
subsequent waters undergoes an adsorption process followed by a process of flocculation and desorption. The  
adsorption process by particles causes the precipitation of material in the sediment and makes the  
concentration near the bottom of the water column become high again.  
From the Table 4, the R-Square value of 0.681, it shows that the proportion of the copper (Cu) variable  
influence to the variables of alkalinity, NH4, PO4, DO, pH, NO3, water temperature, and salinity is 68.1%. This  
means, that the value of the independent variable has an influence proportion on the value of copper (Cu) of  
68.1% while the remaining 31.9% (100% - 68.1%) is influenced by other variables that are not in the linear  
regression model.  
Table 3. F-Test: Water quality to copper (Cu) heavy metal.  
ANOVA a  
Model  
Sum of Squares  
0.0001  
Df  
8
Mean Square  
0.0001  
F
Sig.  
Regression  
Residual  
Total  
0.0001  
31  
39  
0.0001  
1
8.265  
0.000b  
0.0001  
a. Dependent Variable: Tin (Cu); b. Predictors: (Constant), Alkalinity, NH4, PO4, DO, pH, NO3, Water Temperature, Salinity  
Table 4. R Square value: copper (Cu) heavy metal to water quality.  
Model Summary b  
Adjusted R  
Model  
R
R Square  
Std. Error of the Estimate Durbin-Watson  
Square  
1
0.825a  
0.681  
0.598  
0.0017955  
2.587  
a. Predictors: (Constant), Alkalinity, NH4, PO4, DO, pH, NO3, Water Temperature, Salinity; b. Dependent Variable: Copper (Cu)  
Table 5. t-Test: Heavy metal copper (Cu) towards water quality.  
Coefficients a  
Unstandardized  
Coefficients  
Standardized  
Coefficients  
Beta  
Collinearity statistics  
Model  
(Constant)  
t
Sig.  
B
-0.014  
0.000  
Std. Error  
Tolerance  
VIF  
0.011  
0.001  
0.000  
0.001  
0.000  
0.002  
0.001  
0.000  
0.000  
-1.289  
0.683  
4.258  
1.496  
-5.907  
-5.193  
-3.387  
-1.882  
1.711  
0.207  
0.500  
0.000  
0.145  
0.000  
0.000  
0.002  
0.069  
0.097  
DO  
0.079  
0.620  
0.182  
-0.928  
-0.688  
-0.445  
-0.202  
0.231  
0.762  
0.485  
0.692  
0.417  
0.586  
0.597  
0.895  
0.567  
1.312  
2.060  
1.445  
2.396  
1.707  
1.674  
1.118  
water temperature  
pH  
0.002  
0.001  
1
Salinity  
NH4  
-0.001  
-0.011  
NO3  
PO4  
Alkalinity  
-0.004  
-0.001  
4.021E-005  
1.764  
a. Dependent variable: copper (Cu); DO= dissolved oxygen; NH4= ammonium; NO3 = nitrate ; PO4= phosphate; VIF: Variance Inflation  
F=actor  
The probability value of calculated t from the independent variables of dissolved oxygen (DO) is 0.50, pH of  
1.45, PO4, and alkalinity of 0.097 (greater than Sig. 0.05) indicates that the independent variable dissolved  
oxygen (DO) , pH, PO4, and alkalinity have no significant effect on the dependent variable of copper (Cu). The  
probability value of calculated t from the independent variable water temperature of 0.00, salinity of 0.00, NH4  
of 0.00 and NO3 of 0.02 (smaller than Sig. 0.05), indicating that the variable is independent of water  
temperature, salinity , NH4, and NO3 have a significant effect on the dependent variable of copper (Cu).  
Based on the above values, the interpretation of the models of alkalinity, NH4, PO4, DO, pH, NO3, water  
temperature, and salinity of Copper (Cu) heavy metals is as follows:  
Setyaningrum, WE, Dewi KTA, Yuniartik M, Masithah DE. 2019. Analysis of heavy metal content of Cu, Pb, Hg and dissolved Sn in coastal of  
Banyuwangi district, Indonesia. J. Life Sci. Biomed., 9 (1): 10-18. www.jlsb.science-line.com  
Copper (Cu) = -0.14 + 0.00 DO + 0.002 Water Temperature + 0.001 pH - 0.001 salinity – 0.011 NH4 – 0.004 NO3  
– 0.001 PO4 + 4.021 alkalinity  
The regression coefficient of dissolved oxygen (DO) is positive, meaning that when the value of DO rises,  
the value of heavy metals Copper (Cu) will also increase. If the value of DO decreases, the value of Cu will  
decrease too. If the value of dissolved oxygen (DO) rises by 1 mg/l it will increase the total Cu value by 0,000  
mg/l and conversely the decrease in DO by 1 mg/l will reduce the copper (Cu) value by 0,000 mg/l.  
Water temperature regression coefficient is positive, meaning that when the water temperature rises, the  
copper (Cu) value will also increase. If the value of the water temperature drops, the value of copper (Cu) will  
o
decrease. If the value of the water temperature rises by 1 C, it will increase the value of Cu by 0.002 mg/l and  
conversely a decrease in water temperature of 1 oC will reduce the value of Cu by 0.002 mg/l.  
PH regression coefficient is positive, meaning that when the pH value rises, the value of copper (Cu) will  
also increase. If the pH value decreases, the copper (Cu) value will decrease. If the pH value rises by 1, it will  
increase the Cu value by 0.001 mg/l and conversely a decrease in pH of 1 will decrease the value of Cu by 0.001  
mg/l.  
Alkalinity regression coefficient has a positive value, meaning that when the alkalinity value rises, the  
copper (Cu) value will also increase. If the value of alkalinity decreases, the value of copper (Cu) will decrease. If  
the alkalinity value rises by 1, it will increase the Cu value by 4.021 mg/l and conversely a decrease in alkalinity  
of 1 will reduce the Cu value by 4.021 mg/l.  
The salinity regression coefficient is negative, meaning that when the salinity value increases, the copper  
(Cu) value will decrease, whereas when the salinity value drops, the value of copper (Cu) will increase. If the  
salinity value increases by 1 then it will reduce the copper (Cu) value by 0.001 mg/l and conversely a decrease in  
the salinity value of 1 will increase the value of Cu by 0.001 mg/l.  
This is confirmed also by Robin [16], that dissolve Cd and Pb in the coastal water indicated that salinity  
played a major role in the depletion of the dissolved metals during estuarine mixing. As salinity increased, the  
concentrations of dissolved Pb and Cd decreased. The data revealed that large quantum of metals was removed  
from the water column and precipitated as a suspended matter which may contaminate the bottom sediments.  
The decrease in the concentration of heavy metals with salinity showed the contribution from freshwater  
sources was insignificant which indicated that point sources and physical mixing of anthropogenic inputs  
injected by industrial, harbour activity, sewage etc. regulated the metal concentrations along these waters.  
NH4 regression coefficient is negative, meaning that when the value of NH4 increases, the value of copper  
(Cu) will decrease, whereas when the value of NH4 drops, the value of Cu will increase. If the value of NH4  
increases by 1 mg/l it will reduce the value of copper (Cu) by 0.011 mg/l and conversely the decrease in the value  
of NH4 by 1 mg/l will increase the value of Cu by 0.011 mg/l.  
NO3 regression coefficient is negative, meaning that when the NO3 value increases, the Cu value will  
decrease, whereas when the NO3 value drops, the Cu value will increase. If the NO3 value increases by 1 mg/l it  
will reduce the value of Cu by 0.004 mg/l and conversely a decrease in NO3 value of 1 mg/l will increase the  
value of Cu by 0.004 mg/l.  
PO4 regression coefficient is negative, meaning that when the PO4 value rises, the value of Cu will decrease,  
whereas when the PO4 value drops, the value of Cu will increase. If the PO4 value increases by 1 mg/l it will  
reduce the value of Cu by 0.001 mg/l and conversely the decrease in the value of PO4 by 1 mg/l will increase the  
value of Cu by 0.001 mg/l.  
According to Robin, [17], Metals such as Zn, Mn, Cu, Cd, Hg, Pb, silt, clay, organic carbon (OC), pH and  
salinity with a strong factor loading (> 0.700) found to be a significant parameters contributing to the water  
quality of these coastal waters. High and positive scores of dissolved metals and sediment characteristic on  
variable 1 or 2 indicated high anthropogenic inputs from catchments. The presence of multiple variables  
present in the same factor suggested a close association among them and identical source.  
The probability value of F count (Sig.) in the table above, the value is 0.270 greater than the significance  
level of 0.05 so it can be concluded that alkalinity, NH4, PO4, DO, pH, NO3, water temperature, salinity have no  
effect on lead (Pb) in coastal of Banyuwangi Regency.  
The probability value of F count (Sig.) in the table above is 0.221 greater than the significance level of 0.05  
so it can be concluded that alkalinity, NH4, PO4, DO, pH, NO3, water temperature, and salinity have no effect on  
mercury value (Hg) .  
The concentration of mercury (Hg) at each sampling location when the research was carried out was the  
same i.e. <0.0005 mg/l. This value based on the Decree of the State Minister of Environment Number 51 of 2004  
concerning Sea Water Quality Standards is classified as very low and does not interfere with aquatic biota,  
including fish; which has been determined the threshold value is 0.001 mg/l. Komarawidjaja [19], explained that  
the value of the measurement results is still far below the quality standards that apply to any designation so  
that coastal waters are safe to be used as ponds, ports or marine tourism.  
The probability value of counted F (Sig.) in the table above is 0.00 less than the significance level of 0.05, so  
that it can be concluded that the multiple linear regression model that is estimated is feasible to use to explain  
the effect of tin (Sn) on Alkalinity, NH4, PO4 , DO, pH, NO3, Water Temperature, and salinity.  
Setyaningrum, WE, Dewi KTA, Yuniartik M, Masithah DE. 2019. Analysis of heavy metal content of Cu, Pb, Hg and dissolved Sn in coastal of  
Banyuwangi district, Indonesia. J. Life Sci. Biomed., 9 (1): 10-18. www.jlsb.science-line.com  
From Table 8, the value of R-Square which is 0.700, it shows that the proportion of the influence of the  
variable tin (Sn) on the variables Alkalinity, NH4, PO4, DO, pH, NO3, Water Temperature, and salinity by 70%.  
That is, the value of Sn has a proportional effect on Alkalinity, NH4, PO4, DO, pH, NO3, Water Temperature, and  
salinity by 70% while the remaining 30% (100% - 70%) is influenced by other variables that do not exist in a  
linear regression model.  
Table 6. F-Test: Water quality for lead metal (Pb)  
ANOVA a  
Model  
Sum of Squares  
0.0001  
Df  
8
Mean Square  
0.0001  
F
Sig.  
Regression  
Residual  
Total  
1.322  
.270b  
0.0001  
31  
39  
0.0001  
1
0.0001  
a. Dependent Variable: Lead (Pb); b. Predictors: (Constant), Alkalinity, NH4, PO4, DO, pH, NO3, water temperature, salinity  
Table 7. F-Test: Water quality against mercury (Hg)  
ANOVA a  
Model  
Sum of Squares  
0.0001  
Df  
8
Mean Square  
0.0001  
F
Sig.  
Regression  
Residual  
Total  
1
1.437  
0.221b  
0.0001  
31  
39  
0.0001  
0.0001  
a. Dependent Variable: Mercury (Hg); b. Predictors: (Constant), Alkalinity, NH4, PO4, DO, pH, NO3, water temperature, salinity  
Table 8. F-Test: Heavy metal tin (Sn) towards water quality.  
ANOVA a  
Model  
Sum of Squares  
113.151  
Df  
8
Mean Square  
14.144  
F
Sig.  
Regression  
Residual  
Total  
1
9.049  
0.0001b  
48.452  
31  
39  
1.563  
161.603  
a. Dependent Variable: Tin (Sn); b. Predictors: (Constant), Alkalinity, NH4, PO4, DO, pH, NO3, water temperature, salinity  
Table 9. R Square value: heavy metal of tin (Sn) against water quality.  
Model Summary b  
Adjusted R  
Model  
R
R Square  
Std. Error of the Estimate Durbin-Watson  
Square  
1
0.837a  
0.700  
0.623  
1.2501901  
1.453  
a. Predictors: (Constant), Alkalinity, NH4, PO4, DO, pH, NO3, Water Temperature, Salinity; b. Dependent Variable: Tin (Sn)  
Table 10. t Test: Heavy metal of tin (Sn) against water quality.  
Coefficients a  
Unstandardized  
Coefficients  
Standardized  
Coefficients  
Beta  
Collinearity statistics  
Model  
(Constant)  
t
Sig.  
B
Std. Error  
Tolerance  
VIF  
7.143  
7.425  
0.454  
0.264  
0.388  
0.115  
1.520  
0.773  
0.322  
0.016  
0.962  
-2.527  
0.998  
1.356  
-0.483  
2.848  
-1.748  
2.806  
-3.822  
0.343  
0.017  
0.326  
0.185  
0.632  
0.008  
0.090  
0.009  
0.001  
-1.147  
0.264  
0.527  
-0.056  
4.329  
-1.352  
0.904  
-0.063  
-0.285  
0.141  
0.762  
0.485  
0.692  
0.417  
0.586  
0.597  
0.895  
0.567  
1.312  
2.060  
1.445  
2.396  
1.707  
1.674  
1.118  
DO  
water temperature  
pH  
1 Salinity  
NH4  
NO3  
0.160  
-0.074  
0.366  
-0.222  
0.292  
-0.499  
PO4  
Alkalinity  
1.764  
a. Dependent variable: tin (Sn); DO= dissolved oxygen; NH4= ammonium; NO3 = nitrate ; PO4= phosphate; VIF: Variance Inflation F=actor  
Setyaningrum, WE, Dewi KTA, Yuniartik M, Masithah DE. 2019. Analysis of heavy metal content of Cu, Pb, Hg and dissolved Sn in coastal of  
Banyuwangi district, Indonesia. J. Life Sci. Biomed., 9 (1): 10-18. www.jlsb.science-line.com  
The probability value of counted t from the independent variable of water temperature is 0.326, pH is  
0.185, salinity is 0.632, and NO3 is 0.09 (greater than Sig. 0.05) indicating that the independent variables of  
water temperature, pH, salinity and NO3 are not significant effect on the dependent variable tin (Sn). The  
probability value of t count variable dissolved oxygen (DO) is 0.017, NH4 is 0.08, PO4 is 0.009, and alkalinity is  
0.01 (p< 0.05), indicating that the independent variable DO, NH4, PO4, and alkalinity have a significant effect on  
the dependent variable tin (Sn).  
Based on the above values, the interpretation of the models of alkalinity, NH4, PO4, DO, pH, NO3, Water  
Temperature, and Salinity for tin heavy metal (Sn) are as follows:  
Tin (Sn) = 7,143 – 1,147 DO + 0,264 Water Temperature + 0,527 pH – 0,56 Salinity + 4,329 NH4– 1,352 NO3 +  
0,904 PO4 – 0,063 alkalinity  
Water temperature regression coefficient is positive, meaning that when the water temperature rises, the  
value of tin (Sn) will also increase. If the value of the water temperature drops, the value of Sn will decrease. If  
o
the value of the water temperature rises by 1 C, it will increase the value of tin (Sn) by 0.264 mg/l and  
conversely a decrease in water temperature of 1 oC will reduce the value of Sn by 0.264 mg/l.  
The pH regression coefficient is positive, meaning that when the pH value rises, the value of tin (Sn) will  
also increase. If the pH value drops, the value of tin (Sn) will decrease. If the pH value increases by 1, it will  
increase the value of Sn by 0.527 mg/l and conversely a decrease in pH of 1 will decrease the value of Sn by 0.527  
mg/l.  
NH4 regression coefficient is positive, meaning that when the value of NH4 rises, the value of tin (Sn) will  
also increase. If the value of NH4 drops, the value of tin (Sn) will decrease. If the value of NH4 rises by 1 mg/l, it  
will increase the value of Sn by 4.329 mg/l and conversely the decrease in NH4 by 1 mg/l will reduce the value of  
tin (Sn) by 4.329 mg/l.  
PO4 regression coefficients are positive, meaning that when the PO4 value rises, the value of tin (Sn) will  
also increase. If the PO4 value drops, the value of Sn will decrease. If the value of PO4 rises by 1 mg/l, it will  
increase the value of Sn by 0.904 mg/l and conversely a decrease in PO4 of 1 mg/l will decrease the value of Sn by  
0.904 mg/l.  
The regression coefficient of dissolved oxygen (DO) is negative, meaning that when the dissolved oxygen  
value rises, the value of Sn will decrease, whereas when the DO value drops, the value of Sn will increase. If the  
value of DO increases by 1 mg/l it will reduce the value of Sn by 1.147 mg/l and conversely a decrease in DO of 1  
mg/l will increase the tin value by 1,147 mg/l.  
The salinity regression coefficient is negative, meaning that when the salinity value rises, the value of tin  
(Sn) will decrease, whereas when the salinity value drops, the value of tin (Sn) will increase. If the salinity value  
increases by 1 ppt it will reduce the value of Sn by 0.056 mg/l and conversely a decrease in the salinity value of 1  
ppt will increase the value of Sn by 0.056 mg/l.  
NO3 regression coefficient is negative, meaning that when the NO3 value rises, the value of Sn will  
decrease, whereas when the NO3 value drops, the value of Sn will increase. If the NO3 value increases by 1 mg/l it  
will reduce the value of Sn by 1.352 mg/l and conversely a decrease in NO3 value of 1 mg/l will increase the value  
of Sn by 1.352 mg/l.  
The regression coefficient of alkalinity is negative, meaning that when the alkalinity value rises, the value  
of Sn will decrease, whereas when the value of alkalinity decreases, the value of Sn will increase. If the alkalinity  
value increases by 1 mg/l it will reduce the value of Sn by 0.063 mg/l and vice versa the decrease in the value of  
alkalinity by 1 mg/l will increase the value of Sn by 0.063 mg/l.  
The high pH and low dissolved oxygen content in this sampling site can contribute towards this situation.  
Hot Spring waters of Lake Bogoria (BG1), Lake Elementaita (EL1) contained lower concentrations of heavy  
metals. High pH and temperature and very low oxygen can encourage solubilization processes and subsequent  
precipitation [18].  
CONCLUSION  
The concentrations of Cu, Hg, Pb and Sn obtained in the coastal waters of Banyuwangi Regency were Cu  
0.0104 mg/l, Hg 0 mg/l, Pb 0.0173 mg/l and Sn 1.3436 mg/l. If referring to the Keputusan Menteri Lingkungan  
Hidup (Decree of the Minister of Environment) No. 51 of 2004 concerning Sea Water Quality Standards,  
Mercury (Hg) 0.001 mg/l, Copper (Cu) and Lead (Pb) 0.008 mg/l, and Tin (Sn) 2 mg/l, then the value of heavy  
metals Hg and Sn is still below the threshold while Cu and Pb are above the threshold. Whereas based on the  
results of regression analysis, of the four types of heavy metals Copper (Cu), Lead (Pb), Mercury (Hg) and Lead  
(Pb) on the coast of Banyuwangi Regency, indicating water quality that there is no effect on Lead specific  
gravity metals (Pb) and Mercury (Hg). Whereas two other types of heavy metals Cu and Sn had influence.  
Based on the results of the study, Banyuwangi district government needs to take serious actions in  
controlling heavy metal pollution through the implementation of law No. 23 of 1997 concerning to  
environmental management, and the application of environmental quality standards more strictly.  
Setyaningrum, WE, Dewi KTA, Yuniartik M, Masithah DE. 2019. Analysis of heavy metal content of Cu, Pb, Hg and dissolved Sn in coastal of  
Banyuwangi district, Indonesia. J. Life Sci. Biomed., 9 (1): 10-18. www.jlsb.science-line.com  
DECLARATIONS  
Acknowledgements  
The authors are grateful to Ministry of Research and Technology who provided grant funds through  
research on university cooperation, so that one of the outputs of this article was published. Deep appreciation to  
the colleague the Faculty of Fisheries, Airlangga University, Surabaya, who was willing to cooperate in  
facilitating this research through the PKPT grant.  
Authors’ contributions  
All authors contributed equally to this work.  
Competing interests  
The authors declare that they have no competing interests.  
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