Search

Chemical mass balance (CMB) is one of the most popular methods to apportion the sources of PM2.5. However, the source apportionment results are dependent on the choices of measured chemical species and the source profiles. Here, we explore the sensitivity of CMB results to source profiles by comparing CMB modeling based on organic markers only (OM-CMB) with a combination of organic and inorganic markers (IOM-CMB), using organic and inorganic markers in PM2.5 samples collected in the Chinese megacity of Chengdu. OM-CMB results show that gasoline vehicles, diesel vehicles, industrial coal combustion, resuspended dust, biomass burning, cooking, vegetation detritus, SOA, sulphate, and nitrate contributed to 4 %, 10 %, 15 %, 12 %, 5 %, 3 %, 4 %, 9 %, 10 %, and 20 %, in comparison to 4 %, 11 %, 15 %, 17 %, 6 %, 2 %, 5 %, 10 %, 7 %, and 18 % from IOM-CMB modelling. The temporal variations of PM2.5 contributions from sulphate, 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. However, resuspended dust estimates from OM-CMB had a poor correlation with that from IOM-CMB, due to the different tracers used. When replacing the source profile for industrial coal combustion with that for from residential sources, the contributions of resuspended dust and residential coal combustion were overestimated because the residential coal combustion profile contained a higher concentration of OC and organic compounds but lower crustal elements. Different source profiles for gasoline vehicles were also evaluated. Our results confirm the superiority of combining inorganic and organic tracers and using up-to-date locally-relevant source profiles in source apportionment of PM.

A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) 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 PM2.5 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 PM2.5 concentrations for 5 years were in the order of layer 2 (133 µg m−3) > layer 1 (126 µg m−3) > layer 3 (121 µg m−3). Spatial clustering of the chemical composition at the sampling sites was conducted for each year. The PM2.5 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 PM2.5, 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 SO42- and NO3- concentrations. The positive matrix factorization (PMF) model was applied to quantify PM2.5 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.

A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a fast-developing Chinese megacity as the studied area to investigate the spatiotemporal and policy-related variations of PM2.5 compositions and sources based on a long-term observation at multisite. A total of 836 samples were collected at 19 sites in wintertime of 2015–2019. According to the specific characteristics, 19 sampling sites were assigned into three layers. Layer 1 was the most urbanized area referred to the core zone of Chengdu, layer 2 was located in the outside circle of layer 1, and layer 3 belonged to the outer-most zone with the lowest urbanization level. The averaged PM2.5 concentrations for five years were in the order of layer 2 (133 µg m−3) > layer 1 (126 µg m−3) > layer 3 (121µg m−3). And for each year, the spatial clustering of chemical compositions at sampling sites was generally consistent with the classification of layers. PM2.5 compositions for layer 3 in 2019 were found to be similar to that for other layers two or three years ago, implying that the urbanization levels had a strong effect on air quality. During the sampled period, a decreasing trend was observed for the annual averaged PM2.5 concentrations, especially at sampling sites in layer 1, which was caused by the more strict control policies implemented in layer 1. The SO42−/NO3− mass ratio at most sites exceeded 1 in 2015 but dropped less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu. The positive matrix factorization (PMF) model was applied to quantify PM2.5 sources. A total of five sources were identified with the average contributions of 15.5 % (traffic emission), 19.7 % (coal and biomass combustion), 8.8 % (industrial emission), 39.7 % (secondary particles) and 16.2 % (resuspended dust), respectively. From 2015 to 2019, dramatical decline was observed in the average percentage contributions of coal and biomass combustion, but traffic emission source showed an increasing trend. For spatial variations, coal and biomass combustion and industrial emission showed the stronger distribution patterns. High contributions of resuspended dust were occurred at sites with intensive construction activities such as subway and airport constructions. 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 well-evaluated.

Water-soluble inorganic ions such as ammonium, nitrate and sulfate are major components of fine aerosols in the atmosphere and are widely used in the estimation of aerosol acidity. However, different experimental practices and instrumentation may lead to uncertainties in ion concentrations. Here, an intercomparison experiment was conducted in 10 different laboratories (labs) to investigate the consistency of inorganic ion concentrations and resultant aerosol acidity estimates using the same set of aerosol filter samples. The results mostly exhibited good agreement for major ions Cl-, SO2-4, NO-3, NHC4 and KC. However, F-, Mg2C and Ca2C were observed with more variations across the different labs. The Aerosol Chemical Speciation Monitor (ACSM) data of nonrefractory SO2-4, NO-3 and NHC4 generally correlated very well with the filter-analysis-based data in our study, but the absolute concentrations differ by up to 42 %. Cl-from the two methods are correlated, but the concentration differ by more than a factor of 3. The analyses of certified reference materials (CRMs) generally showed a good detection accuracy (DA) of all ions in all the labs, the majority of which ranged between 90 % and 110 %. The DA was also used to correct the ion concentrations to showcase the importance of using CRMs for calibration check and quality control. Better agreements were found for Cl-, SO2-4, NO-3, NHC4 and KC across the labs after their concentrations were corrected with DA; the coefficient of variation (CV) of Cl-, SO2-4, NO-3, NHC4 and KC decreased by 1.7 %, 3.4 %, 3.4 %, 1.2 % and 2.6 %, respectively, after DA correction. We found that the ratio of anion to cation equivalent concentrations (AE/CE) and ion balance (anions-cations) are not good indicators for aerosol acidity estimates, as the results in different labs did not agree well with each other. In situ aerosol pH calculated from the ISORROPIA II thermodynamic equilibrium model with measured ion and ammonia concentrations showed a similar trend and good agreement across the 10 labs. Our results indicate that although there are important uncertainties in aerosol ion concentration measurements, the estimated aerosol pH from the ISORROPIA II model is more consistent.