Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City

Abstract

Fine particulate matter (PM2.5) has a small particle size, which allows it to directly enter the respiratory mucosa and reach the alveoli and even the blood. Many countries are already aware of the adverse effects of PM2.5, and determination of the sources of PM2.5 is a critical step in reducing its concentration to protect public health. This study monitored PM2.5 in the summer (during the southwest monsoon season) of 2017. Three online monitoring systems were used to continuously collect hourly concentrations of key chemical components of PM2.5, including anions, cations, carbon, heavy metals, and precursor gases, for 24 h per day. The sum of the concentrations of each compound obtained from the online monitoring systems is similar to the actual PM2.5 concentration (98.75%). This result suggests that the on-line monitoring system of this study covers relatively complete chemical compounds. Positive matrix factorization (PMF) was adopted to explore and examine the proportion of each source that contributed to the total PM2.5 concentration. According to the source contribution analysis, 55% of PM2.5 can be attributed to local pollutant sources, and the remaining 45% can be attributed to pollutants emitted outside Taipei City. During the high-PM2.5-concentration (episode) period, the pollutant conversion rates were higher than usual due to the occurrence of vigorous photochemical reactions. Moreover, once pollutants are emitted by external stationary pollutant sources, they move with pollution air masses and undergo photochemical reactions, resulting in increases in the secondary pollutant concentrations of PM2.5. The vertical monitoring data indicate that there is a significant increase in PM2.5 concentration at high altitudes. High-altitude PM2.5 will descend to the ground and thereby affect the ground-level PM2.5 concentration.

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

“…The characteristics of the basin terrain can constrain the diffusion of polluted air masses and thus favor the accumulation of air pollution in urban areas (Yu and Wang, 2010). Local emission sources of air pollutants in the TKMA include vehicular exhaust, industrial emissions, and various sources related to residential activities (e.g., cooking) Ho et al, 2018;Wu et al, 2017). In winter time, the long-distance transport of dust and polluted air masses under the northeast monsoon from the Asian continent results in a significant increase in concentrations of air pollutants (Chi et al, 2017;Chou et al, 2010).…”

“…The final area-specific LUR models consisted of three (for O3), four (for NO2), and five predictor variables (for PM10 and PM2.5) ( Table 2) areas of the TKMA (Lee et al, 2014;Wu et al, 2017). For instance, it was reported that gasoline and diesel vehicle emissions contributed approximately half of PM2.5 concentrations in Taipei City based on source apportionment analysis (Ho et al, 2018). Several previous LUR studies selected the population density variable as the final explanatory variable in their PM2.5 and NO2 LUR models (Ji et al, 2019;Meng et al, 2015;Rahman et al, 2017).…”

Sources of Particulate Matter in the Hunter Valley, New South Wales, Australia

“…Positive matrix factorisation (PMF) is a mathematical method that relies on internal correlations between species in the dataset to identify both the factors contributing to the samples and the amount that each factor contributes to the composition measured on the filter [14,15]. Sources are identified from the factors using the species that contribute to the factor, knowledge of atmospheric chemistry, and other information such as meteorological data.…”

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

“…Air pollution has been reported to be positively associated with a variety of health effect endpoints, such as lung function and respiratory and cardiovascular diseases (Çapraz et al, 2017;Sun et al, 2010;Yin et al, 2020;Zhou et al, 2020). Exposure assessment of air pollution is a critical component of epidemiological studies (Cai et al, 2020;Hoek et al, 2008;Li et al, 2017). Cohort studies focusing on the long-term effect on specific diseases of exposure to air pollution require accurate exposure estimates for a large group of participants (e.g., thousands or more) over a defined time period (Brokamp et al, 2019;Morley and Gulliver, 2018;Zhou et al, 2020).…”