| Popis: |
Fine particulate matter (PM2.5) remains a major air pollutant of significant public health concerns in urban areas. Long-term monitoring data of PM2.5 chemical composition and source-specific tracers provide essential information for identification of major sources as well as evaluation and planning of control measures. In this study, we present and analyze a ten-year data set of PM2.5 major components and source-specific tracers (e.g., levoglucosan, hopanes, K+, Ni, V, Al, Si, etc) collected over the period of 2008–2017 in an urban site in Hong Kong, China. The time series of pollutants were analyzed by the Seasonal and Trend decomposition by Loess method and Generalized least squared with Autoregressive-Moving average method. Bulk PM2.5 and all its major components displayed significant decline of varying degrees over the decade. PM2.5 was reduced by 40 % and at -1.5 µg m-3yr-1. PM2.5 components that are predominantly influenced by local vehicular emissions showed the steepest decline, with nitrate by -84 %, elemental carbon by -56 %, and hopanes by -66 %, confirming effective control of local vehicular emissions. For components that are significantly impacted by regional transport and secondary formation, they had a notably lower percentage reduction, with sulfate by -33 % and organic carbon by -23 %, reflecting complexity in their region-wide contributing sources and formation chemistry. Levoglucosan and K+, two tracers for biomass burning, differed in their reduction extent, with K+ at -60 % and levoglucosan at -47 %, indicating they likely track different biomass burning types. Dust components in PM2.5 also decreased, by -37 % for Al and -46 % for Si. The year of 2011 was an anomaly in the overall trend in having higher concentrations of PM2.5 and components than its adjacent years, and the long time series analysis attributed the anomaly to unusually lower rainfall associated with strong La Niña events. This ten-year trend analysis based on measurements exemplifies the utility of chemical composition data in support of an evidence-based approach for control policy formulation. |
