Factors Affecting Water Quality and Surface Sediment at the Port of Tanjung Emas Semarang during the East Asian Monsoon
Abstract
Port plays an important role in provisioning commodity needs for the society in a region. Port of Tanjung Emas Semarang (PTES) is the major port on the north coast of Java Island and serves as a port for liquid and solid goods, passengers, and fuel. This descriptive research applied a random sampling technique whose samples were taken from the port basin and control station. The research was carried out at ten stations at both high and low tides during the East Asian Monsoon. This research was aimed: a) to test the correlation of physical-chemical parameters at both high and low tide using Pearson correlation, b) to test the correlation of heavy metals (Pb, Cd, Cu, Zn) in seawater and sediment at both high and low tide, c) to perform cluster analysis on the stations at both high and low tide, d) to determine port waters Index of Pollution (IP), e) to identify factors that affect the port water pollution using PCA (Principal Component Analysis). The findings showed that in waters, Pb was found as the most dominant pollutant at both high and low tides (Pb>Cd>Cu>Zn). In sediment, Zn was the most dominant of all (Zn>Cu>Pb>Cd). Igeo value indicated that the contamination level in the sediment was moderate. Heavy metals accumulate differently at high and low tides, at low tide heavy metals accumulate on the outside toward the port, while at high tide they accumulate at the mouth of the Kali Baru River. This is caused by differences in the flow of tidal or ebb currents. All research stations were classified as having heavy pollution. Both control stations (Siangker and BKT estuaries) showed differences with stations inside the port, including physico-chemical variables, namely: a) TSS, phosphate, nitrate during high tide conditions; b) pH, DO, TDS, turbidity, TSS at low tide.
Keywords: control stations, Port of Tanjung Emas Semarang, East Asian Monsoon, seawater and sediment, index of pollution, principal component analysis.
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