Analysis of heavy metal and arsenic sources in mangrove surface sediments at Wulishan Port on Leizhou Peninsula, China, using the APCS-MLR model.

ABSTRACT

Mangrove forests are sources and sinks for various pollutants. This study analyzed the current status of heavy metal and arsenic (As) pollution in mangrove surface sediments in rapidly industrializing and urbanizing port cities. Surface sediments of mangroves at Wulishan Port on the Leizhou Peninsula, China, were analyzed using inductively coupled plasma emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for the presence of Cr, Pb, Ni, Zn, Cd, Cu, As, and Hg. The Pollution load index, Nemerow pollution index, and Potential ecological risk index were employed to evaluate the pollutant. Multivariate statistical methods were applied for the qualitative analysis of pollutant sources, and the APCS-MLR receptor model was used for quantification. This study indicated the following results: (1) The average content of eight pollutants surpassed the local background level but did not exceed the Marine Sediment Quality standard. The pollution levels across the four sampling areas were ranked as Ⅲ > Ⅳ > Ⅰ > Ⅱ. The area Ⅱ exhibited relatively lower pollution levels with the grain size of the sediments dominated by sand, which was not conducive to pollutant adsorption and enrichment. (2) The factor analysis and cluster analyses identified three primary sources of contamination. As, Cr, Ni, and Pb originated from nearby industrial activities and their associated wastewater, suggesting that the primary source was the industrial source. Cd, Cu, and Zn stem from the cement columns utilized in oyster farming, alongside discharges from mariculture and pig farming, establishing a secondary agricultural source. Hg originated from ship exhaust burning oil and vehicle emissions in the vicinity, representing the third traffic source. (3) The APCS-MLR receptor model results demonstrated industrial, agricultural, and traffic sources contributing 47.19 %, 33.13 %, and 13.03 %, respectively, with 6.65 % attributed to unidentified sources.