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Gas/particle (G/P) partitioning is a determining factor that drives the behavior of polycyclic aromatic hydrocarbons (PAHs) in the atmosphere. To comprehend the variations and impacts of PAHs G/P partitioning on inhalation risks from different sources, atmospheric PAHs in Chengdu were studied from January 2009 to January 2021. Chengdu's air quality demonstrated improvement, with a noticeable decline in PM 10 (from 238 to 103 μg m−3), PM 10 -bound PAHs mass concentrations (from 0.08 to 0.01 μg m−3), as well as PAHs inhalation non-cancer (from 63.5 to 7.1) and cancer risks (from 6.2 × 10−5 to 7.7 × 10−6). Decreasing trends were also observed in the concentrations of organic carbon (OC) and the values of absorptive G/P partitioning coefficient (K p, OM) from 2009 to 2020, indicating a slight decrease in the PM absorptive capacity. A positive matrix factorization (PMF) model utilized three distinct datasets: PMF pp (particle-phase PAHs only), PMF tot (total PAHs concentrations), and PMF pp/gas (particle-phase in parallel with gas-phase PAHs). PMF pp and PMF pp/gas provide reasonable source apportionment and source-specific risk results. However, daily variations in source contributions of PMF tot were found to be unreasonable. PMF pp/gas and PMF tot could identify a source of low molecular weight PAHs (LPAHs) related to temperature, and a significantly positive correlation between LPAHs and temperature was discovered. Gas/particle partitioning of PAHs showed a notable impact on source-specific risks associated with coal and biomass combustion (CC&BC). The findings contribute to our understanding of the variation in PAHs G/P partitioning and the PM absorptive capacity resulting from effective air pollution control in China. Additionally, they offer prerequisite information for incorporating semi-volatile organic compounds in source apportionment and source-specific risk assessment. [Display omitted] • A slight decrease of the PM absorptive capacity was observed from 2009 to 2020. • Including the gaseous PAHs in PMF can resolve the temperature-related source. • G/P partitioning had a great effect on specific risk of coal&biomass combustion. • Cancer and non-cancer risks of PAHs in PM 10 showed a downward trend. • Traffic emissions played a dominant role in both cancer and non-cancer risks. [ABSTRACT FROM AUTHOR]

A thorough understanding of the relationship between urbanization and PM 2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µ m) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a rapidly developing Chinese megacity, Chengdu, as the study area to investigate the spatiotemporal and policy-related variations of PM 2.5 composition and sources based on long-term observation at multiple sites. A total of 836 samples were collected from 19 sites in winter 2015–2019. According to the specific characteristics, 19 sampling sites were assigned to three layers. Layer 1 was the most urbanized area and referred to the core zone of Chengdu, layer 2 was located in the outer circle of layer 1, and layer 3 belonged to the outermost zone with the lowest urbanization level. The average PM 2.5 concentrations for 5 years were in the order of layer 2 (133 µ g m -3) > layer 1 (126 µ g m -3) > layer 3 (121 µ g m -3). Spatial clustering of the chemical composition at the sampling sites was conducted for each year. The PM 2.5 composition of layer 3 in 2019 was found to be similar to that of the other layers 2 or 3 years ago, implying that urbanization levels had a strong effect on air quality. During the sampling period, a decreasing trend was observed for the annual average concentration of PM 2.5 , especially at sampling sites in layer 1, where the stricter control policies were implemented. The SO42-/NO3- mass ratio at most sites exceeded 1 in 2015 but dropped to less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu, and these values can be further supported by temporal variations of the SO 42- and NO 3- concentrations. The positive matrix factorization (PMF) model was applied to quantify PM 2.5 sources. A total of five sources were identified, with average contributions of 15.5 % (traffic emissions), 19.7 % (coal and biomass combustion), 8.8 % (industrial emissions), 39.7 % (secondary particles), and 16.2 % (resuspended dust). From 2015 to 2019, a dramatic decline was observed in the average percentage contributions of coal and biomass combustion, but the traffic emission source showed an increasing trend. For spatial variations, the high coefficient of variation (CV) values of coal and biomass combustion and industrial emissions indicated their higher spatial difference in Chengdu. High contributions of resuspended dust occurred at sites with intensive construction activities, such as subway and airport construction. Combining the PMF results, we developed the source-weighted potential source contribution function (SWPSCF) method for source localization. This new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was evaluated. [ABSTRACT FROM AUTHOR]

A Chemical Mass Balance (CMB) model has been applied to source apportionment of PM 2.5 in the Chinese megacity of Chengdu. The study explored the sensitivity of the CMB model to the adoption of different organic source profiles, and to the use of organic markers only (OM-CMB), compared with using a combination of organic and inorganic markers (IOM-CMB). A comprehensive comparison of OM-CMB and IOM-CMB shows that PM 2.5 mass concentrations from gasoline vehicles, diesel vehicles, industrial coal combustion, biomass burning, cooking, and SOA which shared same markers in the two methods are in fair to good agreement between the two methods, with the relative biases ranging from 2.2% to 17.3%. The average contributions of sulfate and nitrate sources are more sensitive to the choice of model because inorganic ions were not inputted directly into the OM-CMB. The temporal variations of PM 2.5 contributions from sulfate, nitrate, SOA, gasoline vehicles, and biomass burning, characterized by unique markers and low collinearity, were in good agreement between the OM-CMB and IOM-CMB results with the Pearson's r above 0.91 (p < 0.01). However, resuspended dust estimates from OM-CMB had a relatively weak correlation with that from IOM-CMB (Pearson's r = 0.73, p < 0.01), due to the different tracers used. When replacing the source profile for industrial coal combustion with that for residential sources, the contributions of resuspended dust and residential coal combustion were higher, and the contributions of other sources were lower compared with the result for the industrial coal combustion. Different source profiles for gasoline vehicles showed considerable sensitivity of the model to the choice of source profile, even when using data from within a single emissions study. Our results emphasize the value of combining inorganic and organic tracers in minimizing error, and in using up-to-date locally-relevant source profiles in source apportionment of PM. [Display omitted] • A Chemical Mass Balance model is applied to particulate matter from Chengdu, China. • Inclusion of both inorganic and organic source tracers gives better results. • There is considerable sensitivity to organic source profiles adopted. • Recent, locally sampled source profiles give the best outcomes. [ABSTRACT FROM AUTHOR]