Chemical Composition, Source Appointment and Health Risk of PM2.5 and PM2.5-10 during Forest and Peatland Fires in Riau, Indonesia

Abstract

This study investigated the contributions of particulate matter (PM) from various emission sources during the dry season, which resulted from frequent fires occurring in degraded forests and peatlands in Indonesia. Samples of fine (PM2.5) and coarse (PM2.5-10) particles collected during the dry season in Riau, Indonesia were analyzed to determine the mass concentrations of metallic trace elements, ionic compound, black carbon (BC), and organic carbon (OC). The average concentrations of PM2.5 and PM2.5-10 at Riau, Indonesia were 63.85 ± 3.22 µg m -3 and 27.72 ± 2.40 µg m -3 , respectively. The positive matrix factorization (PMF) model was adopted to identify possible PM sources and their contributions to the ambient PM level. The PMF results identified six major PM2.5 sources, including biomass burning (BB) (28.7%), secondary aerosols (SA) (26.9%), vehicle exhaust (VE) (12.8%), industrial emissions (IE) (12.3%), soil dust (SD) (11.9%), and sea salt (SS) (7.5%). Moreover, there were five primary PM2.5-10 sources, including VE (28.6%) and BB (24%), followed by IE (19.9%), SD (17.2%), and SA (15.3%). A conditional probability function (CPF) analysis revealed that the southeast sector dominated among source directiondependent contributions. The noncarcinogenic health risks for both adults and children resulting from exposure to PM2.5 were mainly contributed by Co, Ni, and Mn, and carcinogenic risks were caused by the toxic metals Cr and Co. Both noncarcinogenic and carcinogenic health risks resulting from cumulative multielement exposure for both adults and children exceeded acceptable levels. Clearly, more attention should be devoted to reducing the noncarcinogenic and carcinogenic health risks caused by particulate-bound toxic elements through inhalation exposure.

Source apportionment of fine atmospheric particles in Bloemfontein, South Africa, using positive matrix factorization

“…The advantage of using PMF analyses is that they can handle incomplete data, such as data with negative values, missing data and data below the limit of detection. However, there were no negative values, missing data or data below the limit of detection in this study (Chueinta et al, 2000, Siregar et al, 2022 ). Adeyemi et al ( 2021 ) performed similar analyses and found that the best results were obtained using the lowest Q value.…”

Temporospatial Variation, Chemical Composition, and Source Resolution of PM2.5 in the Southeastern Taiwan Island

“…Relevant medical and epidemiological researches report that long-term exposure to PM2.5 can cause respiratory, cardiovascular, and even carcinogenic diseases. Medical evidence also show that PM2.5 concentration is positively correlated with the inpatient and mortality rates in the heavily polluted metropolitan areas (Siregar et al, 2022;Wang et al, 2022). Both acute and chronic diseases caused by long-term PM2.5 exposure include asthma, cough, pneumonia, bronchitis, and high blood pressure (Sánchez-Piñero et al, 2022).…”

Assessment of air pollution emitted during cooking using biomass and cleaner fuels in the Shiselweni region of Eswatini (Swaziland)

“…In developing countries, biomass combustion remains a significant source of indoor air pollution since most households using biomass fuels often cook indoors, using open fires in poorly ventilated houses (Bruce et al 2000, Balmes 2019). In general, incomplete combustion of biomass fuels leads to the generation of particulate matter (PM) with aerodynamic diameters ≤ 10 µm (PM 10 ) and 2.5 µm (PM 2.5 ), carbon monoxide (CO) and aerosols that may contain carcinogenic and mutagenic toxins, such as polycyclic aromatic hydrocarbons (PAHs) (e.g., benzo[a]pyrene (BaP)) (Bruce et al 2000, Kim et al 2012, Du et al 2020, Yu et al 2020, Siregar et al 2022.…”