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Using cluster algorithms with a machine learning technique and PMF models to quantify local-specific origins of PM2.5 and associated metals in Taiwan.

In Environmental pollution (Barking, Essex : 1987)

The influence of long-range transport (LRT) of air pollutants on neighboring regions and countries has been documented. The magnitude of LRT aerosols and related constituents can misdirect control strategies for local air quality management. In this study, we aimed to quantify PM2.5 (diameter less than 2.5 μm, PM2.5) and associated metals derived from local sources and LRT in different geographic locations in Taiwan using advanced receptor models. We collected daily PM2.5 samples (n = ∼1000) and analyzed 28 metals every three days from 2016 to 2018 in the northern, central-south, eastern, and southern areas of Taiwan. We first used a machine learning technique with a cluster algorithm coupled with a backward trajectory to classify local, regional, and LRT-related aerosols. We then quantified the source contributions with a positive matrix factorization (PMF) model for Taiwan weighted by region-specific populations. The northern and eastern regions were found to be more vulnerable to LRT-related PM2.5 and metals than the central-south and southern regions in Taiwan. The LRT increased Pb and As concentrations by 90-200% and ∼40% in the northern and central-south regions. Ambient PM2.5-metals mainly originated from local traffic-related emissions in the northern, central-south, and southern regions, whereas oil combustion was the primary source of PM2.5-metals in the eastern region. By subtracting the influence from the LRT, the contributions of domestic emission sources to ambient PM2.5 metals in Taiwan were 35% from traffic-related emission, 17% from non-ferrous metallurgy, 13% from iron ore and steel factories, 12% from coal combustion, 12% from oil combustion, 10% from incinerator emissions, and <1% from cement manufacturing emissions. This study proposed an advanced method for refining local source contributions to ambient PM2.5 metals in Taiwan, which provides useful information on regional control strategies.

Hsu Chin-Yu, Soo Jhy-Charm, Lin Sheng-Lun, Wu Chih-Da, Chi Kai-Hsien, Hsu Wen-Chang, Tseng Chun-Chieh, Chen Yu-Cheng

2022-Nov-11

Cluster algorithm, Fine particle, Local emission, Metal, Receptor model