Impacts of ship emissions and sea-land breeze on urban air quality using chemical characterization, source contribution and dispersion model simulation of PM2.5 at Asian seaport.

Fine particulate matter (PM2.5) emitted from marine transportation, bulk materials handling at the docks, and dust dispersion has garnered increased attention, particularly in the interface between port and urban areas. This study explored the inter-transport of PM2.5 between Kaohsiung Harbor and neighboring Metro Kaohsiung. Chemical analyses of PM2.5 samples from four sites include water-soluble ions, metallic elements, carbons, anhydrosugars, and organic acids to establish PM2.5's chemical fingerprints. The CALPUFF air dispersion model is employed to simulate the spatiotemporal distribution of PM2.5 in Kaohsiung Harbor and adjacent urban areas. A clear seasonal and diurnal variation of PM2.5 concentrations and chemical composition was observed in both harbor and urban areas. The high correlation of nighttime PM2.5 levels between the port and urban areas suggests inter-transport phenomena. Sea salt spray, ship emissions, secondary aerosols, and heavy fuel-oil boilers exhibit higher levels in the port area than in the urban area. In Metro Kaohsiung, mobile sources, fugitive dust, and waste incinerators emerge as major PM2.5 contributors. Furthermore, sea breeze significantly influences PM2.5 dispersion from Kaohsiung Harbor to Metro Kaohsiung, particularly in the afternoon. The average contribution of PM2.5 from ships' main engines in Kaohsiung Harbor ranges from 2.9% to 5.3%, while auxiliary engines contribute 3.8%-8.3% of PM2.5 in Metro Kaohsiung.

Inter-comparison of chemical characteristics and source apportionment of PM2.5 at two harbors in the Philippines and Taiwan.

This study inter-compared the concentration and chemical characteristics of PM2.5 at two harbors in East Asia, and identified the potential sources of PM2.5 and their contribution. Two sites located at the Kaohsiung (Taiwan) and Manila (the Philippines) Harbors were selected for simultaneous sampling of PM2.5 in four seasons. The sampling of 24-h PM2.5 was conducted for continuous seven days in each season. Water-soluble ions, metallic elements, carbonaceous content, anhydrosugars, and organic acids in PM2.5 were analyzed to characterize their chemical fingerprints. Receptor modeling and trajectory simulation were further applied to resolve the source apportionment of PM2.5. The results indicated that the Kaohsiung Harbor was highly influenced by long-range transport (LRT) of polluted air masses from Northeast Asia, while the Manila Harbor was mainly influenced by local emissions. Secondary inorganic aerosols were the most abundant ions in PM2.5. Crustal elements dominated the metallic content of PM2.5, but trace elements were mainly originated from anthropogenic sources. Higher concentrations of organic carbon (OC) than elemental carbon (EC) was found in PM2.5, with secondary OC (SOC) dominant to the former. Levoglucosan in PM2.5 at the Manila Harbor were superior to those at the Kaohsiung Harbor due to biomass burning surrounding the Manila Harbor. Additionally, high mass ratios of malonic and succinic acids (M/S) in PM2.5 indicated the formation of SOAs. Overall, the ambient air quality of Manila Harbor was more polluted than Kaohsiung Harbor. The Kaohsiung Harbor was more severely affected by LRT of polluted air masses from Northeast Asia, while those toward the Manila Harbor came from the oceans. The major sources resolved by CMB and PMF models at the Kaohsiung Harbor were secondary aerosols, ironworks, incinerators, oceanic spray, and ship emissions, while those at the Manila Harbor were secondary aerosols, soil dust, biomass burning, ship emissions, and oceanic spray.

Factors influencing surface CO2 variations in LPRU, Thailand and IESM, Philippines.

Surface carbon dioxide concentrations were measured using a non-dispersive infrared carbon dioxide sensor at Lampang Rajabhat University from April to May 2013 and at the University of the Philippines-Diliman campus starting September 2013. Factors influencing the variations in these measurements were determined using multiple linear regression and a Lagrangian transport model. Air temperature and sea level pressure were the dominant meteorological factors that affect the CO2 variations. However, these factors are not enough. Surface CO2 flux and transboundary transport needs to be considered as well.