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The carcinogenicity of outdoor air pollution

This study reports on an investigation of fine and coarse carbonaceous particulate matters (PM) in Tachileik, eastern Myanmar, during the dry and wet seasons of 2022. The carbon compositions, including organic carbon (OC) and elemental carbon (EC), in each size (PM_10-2.5, PM_2.5-1.0, and PM_1.0-0.5) were determined by a carbon analyzer. The average total mass concentration of the three fractions of particle samples during the dry season was higher (51.23 ± 23.86 μg/m³) than that of the wet season (14.00 ± 5.26 μg/m³). The results indicate that OC and EC levels were highest for PM_1.0-0.5 during the dry season and the sizes were similar for both seasons. The OC/EC ratios varied in different seasons, from 1.93 to 4.40 and 4.13 to 5.25 in the dry and wet seasons, respectively. EC was sub-divided into char-EC and soot-EC; the char-EC/soot-EC in the dry season was higher than in the wet season, signifying that biomass burning is the main particle source during the dry season. However, vehicle emissions dominated during the wet season. The effective carbon ratio (ECR) suggested that finer particles contributed more to global warming. This information is important in terms of air quality control and mitigation in Myanmar and elsewhere.

This article aims to critically review the current state of knowledge on in vitro toxicological assessments of particulate emissions from residential biomass heating systems. The review covers various aspects of particulate matter (PM) toxicity, including oxidative stress, inflammation, genotoxicity, and cytotoxicity, all of which have important implications for understanding the development of diseases. Studies in this field have highlighted the different mechanisms that biomass combustion particles activate, which vary depending on the combustion appliances and fuels. In general, particles from conventional combustion appliances are more potent in inducing cytotoxicity, DNA damage, inflammatory responses, and oxidative stress than those from modern appliances. The sensitivity of different cell lines to the toxic effects of biomass combustion particles is also influenced by cell type and culture conditions. One of the main challenges in this field is the considerable variation in sampling strategies, sample processing, experimental conditions, assays, and extraction techniques used in biomass burning PM studies. Advanced culture systems, such as co-cultures and air-liquid interface exposures, can provide more accurate insights into the effects of biomass combustion particles compared to simpler submerged monocultures. This review provides critical insights into the complex field of toxicity from residential biomass combustion emissions, underscoring the importance of continued research and standardisation of methodologies to better understand the associated health hazards and to inform targeted interventions.

While indoor PM_2.5 concentrations are generally lower than those outdoors, daily exposure indoors can significantly exceed outdoor levels, given that people spend over 90% of their time inside. This study systematically compares traditional methods with machine learning(ML)-based approaches in the context of indoor particulate matter concentration prediction and control research, with the aim of combining their strengths for more effective outcomes.

One-year indoor PM concentration data from a non-interference-operating target office were used to develop ML models. Input variables, including background concentrations (outdoor and hallway), office conditions, and weather parameters, were employed with the CNN-LSTM algorithm. The ML models, enhanced with ensemble techniques, achieved a prediction accuracy of $R^2$ = 0.943 on the test set, not considered during model development.

Using predictions under step-change events in outdoor PM_2.5 levels, the ML model determined the infiltration factor, PM_2.5 removal rates, and particle penetration rates, hence mechanistic model coefficients. Collaborative hybrid models, integrating ML and mechanistic approaches, yielded dynamic models ($R^2$ = 0.928) applicable under general conditions, whereas conventional mechanistic models derived from controlled experiments were limited to controlled conditions.

Air pollution is a worldwide environmental problem with an impact on human health. Particulate matter of ten micrometers or less aerodynamic diameter (PM₁₀) as well as its fine fraction (PM_2.5) is related to multiple pulmonary diseases. The impact of air pollution in Mexico City, and importantly, particulate matter has been studied and considered as a risk factor for two decades ago. Previous studies have reported the composition of Mexico City particulate matter, as well as the biological effects induced by this material. However, material collected and used in previous studies is a limited resource, and sampling and particle recovery techniques have been improved. In this study, we describe the methods used in our laboratory for Mexico City airborne particulate matter PM₁₀ and PM_2.5 sampling, considering the years 2017, 2018 and 2019. We also analyzed the PM₁₀ and PM_2.5 samples obtained to determine their composition. Finally, we exposed lung cell line cultures to PM₁₀ and PM_2.5 to evaluate the biological effect of the material in terms of cell viability, cell death, inflammatory response, and cytogenetic alterations. Our results showed that PM₁₀ composition includes inorganic, organic and biological compounds, while PM_2.5 is a mixture of more enriched organic compounds. PM₁₀ and PM_2.5 treatment in lung cells does not significantly impact cell viability/cell death. However, PM₁₀ and PM_2.5 increase the secretion levels of IL-6. Moreover, PM₁₀ as well as PM_2.5 induce cytogenetic alterations, such as micronuclei, anaphase bridges, trinucleated cells and apoptotic cells in lung cells. Our results update the evidence of the composition and biological effects of Mexico City particulate matter and provide us a reliable basis for future approaches.

The removal effects of multiple discrete dynamic mechanisms on particulate matters (PMs) are of great significance for industrial application and basic research. In this work, a wet phase transition agglomerator (WPTA) is installed between a wet electrostatic precipitator (WESP) and a stack at a 280 t/h coal-fired power plant. The removal effects of the WPTA are compared with those of the WESP by field measurement. Meanwhile, a numerical model for predicting particle size distributions is developed and verified through the experimental data. The results indicate that the WPTA reduces the emission concentrations of PM₁ and PM_2.5 to 0.64 mg/Nm³ and 0.89 mg/Nm³, respectively. The PM₁ removal efficiencies for the independent WPTA, the independent WESP and the combined method are 33.07 %, 40.31 % and 58.36 %, respectively. The microscopic morphologies of the fine PMs in the particle size range of 0.98–1.64 μm exhibit blocky, spherical and crystal structure. The simulated errors of PM₁, PM_2.5 and PM₁₀ are 7.81 %, 7.87 % and 12.27 %, respectively. The boundary layers of the tube bundle are conducive to strengthening thermophoretic force and supersaturation. Consequently, the WPTA can effectively replace the WESP to achieve the emission control of fine PMs in industrial applications. The developed numerical model can accurately predict the particle size distributions in the WPTA.

Poor air quality inside buildings has direct implications for human health. The activity of firefighters causes cancer and promotes the development/aggravation of cardio-respiratory diseases. The indoor air quality inside fire stations remains poorly characterized. This work assesses firefighters’ daily exposure to fine (PM_2.5, PM_156nm) and ultrafine (PM_50.4nm, PM_30.8nm) particulate matter (PM) inside three microenvironments of fire stations, and, for the first time, characterizes the morphology and elemental content of collected PM. Fine PM fractions were predominant in all microenvironments. Fine and ultrafine PM concentrations were increased in the personal protective equipment (PPE) storage room than in the common area and garage [11.84–103.5 μg/m³ versus 18.67–51.89 μg/m³ and 22.57–70.69 μg/m³; p > 0.05 and 4.78–46.02 μg/m³ versus 3.03–40.99 μg/m³ and 5.55–23.30 μg/m³; p > 0.05]; values were below the guideline proposed for occupational exposure to respirable dust (5.0 mg/m³). The morphological analysis revealed the presence of organic matter, microorganisms, crystals, minerals, metals, and other unidentified substances in fine/ultrafine PM. Exposure to total PM-bound metals was increased in the garage comparatively to the common area and the PPE storage room (2765.0–46586.0 pg/m³ versus 2414.2–4568.6 pg/m³; p > 0.5). Cu, Zn, and Cr were the predominant PM-bound metals while Sb, Cd, and Tl were the less abundant. Exposure to PM-bound possible/probable carcinogenic metals (Ni, Cd, Sb, and Pb) accounted for 7.81–10.72% and 0.342–13.07% of fine and ultrafine PM, respectively. The data generated can support regulatory agencies in proposing air quality guidelines that promote occupational safety and health.