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The advancement of analytical techniques, such as comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC–MS), enables the efficient separation of complex organics. Developing innovative methods for data processing and analysis is crucial to unlock the full potential of GC × GC–MS in understanding intricate chemical mixtures. In this study, we proposed an innovative method for the semi-automated identification and quantification of complex organic mixtures using GC × GC–MS. The method was formulated based on self-constructed mass spectrum patterns and the traversal algorithms and was applied to organic vapor and aerosol samples collected from the tailpipe emissions of heavy-duty diesel vehicles and the ambient atmosphere. Thousands of compounds were filtered, speciated, and clustered into 26 categories, including aliphatic and cyclic hydrocarbons, aromatic hydrocarbons, aliphatic oxygenated species, phenols and alkylphenols, and heteroatom-containing species. The identified species accounted for over 80 % of all the eluted chromatographic peaks at the molecular level. A comprehensive analysis of quantification uncertainty was undertaken. Using representative compounds, quantification uncertainties were found to be less than 37.67 %, 22.54 %, and 12.74 % for alkanes, polycyclic aromatic hydrocarbons (PAHs), and alkyl-substituted benzenes, respectively, across the GC × GC space, excluding the first and the last time intervals. From a source apportionment perspective, adamantane was clearly isolated as a potential tracer for heavy-duty diesel vehicle (HDDV) emissions. The systematic distribution of nitrogen-containing compounds in oxidized and reduced valences was discussed, and many of them served as critical tracers for secondary nitrate formation processes. The results highlighted the benefits of developing self-constructed models for the enhanced peak identification, automated cluster analysis, robust uncertainty estimation, and source apportionment and achieving the full potential of GC × GC–MS in atmospheric chemistry. [ABSTRACT FROM AUTHOR]

Observation-based data of primary and secondary organic carbon in ambient particulate matter (PM) are essential for model evaluation, climate and air quality research, health effect assessments, and mitigation policy development. Since there are no direct measurement tools available to quantify primary organic (POC) and secondary organic carbon (SOC) as separate quantities, their estimation relies on inference approaches using relevant measurable PM constituents. In this study, we measured hourly carbonaceous components and major ions in PM 2.5 for a year and a half in suburban Hong Kong from July 2020 to December 2021. We differentiated POC and SOC using a novel Bayesian inference approach. The hourly POC and SOC data allowed us to examine temporal characteristics varying from diurnal and weekly patterns to seasonal variations, as well as their evolution characteristics during individual PM 2.5 episodes. A total of 65 city-wide PM 2.5 episodes were identified throughout the entire study period, with SOC contributions during individual episodes varying from 10 % to 66 %. In summertime typhoon episodes, elevated SOC levels were observed during daytime hours, and high temperature and NOx levels were identified as significant factors contributing to episodic SOC formation. Winter haze episodes exhibited high SOC levels, likely due to persistent influences from regional transport originating from the northern region to the sampling site. Enhanced SOC formation was observed with increase in the nocturnal NO3 radical (indicated by the surrogate quantity of [ NO2 ][ O3 ]) and under conditions characterized by high water content and strong acidity. These results suggest that both NO3 chemistry and acid-catalyzed aqueous-phase reactions likely make notable contributions to SOC formation during winter haze episodes. The methodology employed in this study for estimating POC and SOC provides practical guidance for other locations with similar monitoring capabilities in place. The availability of hourly POC and SOC data is invaluable for evaluating and improving atmospheric models, as well as understanding the evolution processes of PM pollution episodes. This, in turn, leads to more accurate model predictions and a better understanding of the contributing sources and processes. [ABSTRACT FROM AUTHOR]

Observation-based data of primary and secondary organic carbon in ambient particulate matter (PM) are essential for model evaluation, climate and air quality research, health effects assessment, and mitigation policy development. Since there are no direct measurement tools available to quantify primary organic (POC) and secondary organic carbon (SOC) as separate quantities, their estimation relies on inference approaches using relevant measurable PM constituents. In this study, we measured hourly carbonaceous components and major ions in PM2.5 for a year and a half in suburban Hong Kong from July 2020 to December 2021. We differentiated POC and SOC using a novel Bayesian inference approach, with sulfate identified as the most suitable SOC tracer. The hourly POC and SOC data allowed us to examine temporal characteristics varying from diurnal and weekly patterns to seasonal variations, as well as their evolution characteristics during individual PM2.5 episodes. A total of 65 city-wide PM2.5 episodes were identified throughout the entire studied period, with SOC contributions during individual episodes varying from 10 % to 66 %. In summertime typhoon episodes, elevated SOC levels were observed during daytime hours, and high temperature and NOx levels were identified as significant factors contributing to episodic SOC formation. Winter haze episodes exhibited high SOC levels, likely due to persistent influences from regional transport originating from the northern region to the sampling site. Enhanced SOC formation was observed with the increase in nocturnal NO3 radical (represented by [NO2][O3]) under conditions of high water content and strong acidity. This suggests that aqueous-phase reactions involving NO3 radical were likely a notable contributor to SOC formation during winter haze episodes. The methodology employed in this study for estimating POC and SOC provides practical guidance for other locations with similar monitoring capabilities in place. The availability of hourly POC and SOC data is invaluable for evaluating and improving atmospheric models, as well as understanding the evolution processes of PM pollution episodes. This, in turn, leads to more accurate model predictions and a better understanding of the contributing sources and processes. [ABSTRACT FROM AUTHOR]

Positive matrix factorization (PMF) has been widely used to apportion the sources of fine particulate matter (PM2.5) by utilizing PM chemical speciation data measured at receptor site(s). Traditional PMF, which typically relies on long-term observational datasets of daily or lower time resolution to meet the required sample size, has its reliability undermined by changes in source profiles, thus it is inherently ill-suited for apportioning sporadic sources or ephemeral pollution events. In this study, we explored short-term source apportionment of PM2.5 using a set of hourly chemical speciation data over a period of thirty-seven days in the winter of 2019–2020. PMF run with campaign-wide data as input (PMFref) was initially conducted to obtain reference profiles for the primary source factors. Subsequently, short-term PMF analysis was performed using the Source Finder Professional (SoFi Pro). The analysis sets a window length as the first 18 d of the campaign and constrained the primary source profiles using the a -value approach embedded in SoFi software. Rolling PMF was then conducted with a fixed window length of 18 d and a step of 1 d using the remaining dataset. By applying the a -value constraints to the primary sources, the rolling PMF effectively reproduced the individual primary sources, as evidenced by the slope values close to unity (i.e., 0.9–1.0). However, the estimation for the firework emission factor in the rolling PMF was lower compared with the PMFref (slope: 0.8). These results suggest the unique advantage of short-term PMF analysis in accurately apportioning sporadic sources. Although the total secondary sources were well-modelled (slope: 1.0), larger biases were observed for individual secondary sources. The variation in source profiles indicated higher variability for the secondary sources, with average relative differences ranging from 42 % to 173 %, while the primary source profiles exhibited much smaller variabilities (relative differences of 8–26 %). This study suggests that short-term PMF analysis with the a -value constraints in SoFi can be utilized to apportion primary sources accurately, while future efforts are needed to improve the prediction of individual secondary sources. [ABSTRACT FROM AUTHOR]

Iodine-initiated new particle formation (I-NPF) has long been recognized in coastal hotspot regions. However, no prior work has studied the exact chemical composition of organic compounds and their role in coastal I-NPF. Here we present an important complementary study to the ongoing laboratory and field research on iodine nucleation in the coastal atmosphere. Oxidation and NPF experiments with vapor emissions from real-world coastal macroalgae were simulated in a bag reactor. On the basis of comprehensive mass spectrometry measurements, we reported for the first time a variety of volatile precursors and their oxidation products in gas and particle phases in such a highly complex system. Organic compounds overwhelmingly dominated over iodine in the new particle growth initiated by iodine species. The identity and transformation mechanisms of organic compounds were proposed in this study to provide a more complete story of coastal NPF from low-tide macroalgal emission. [ABSTRACT FROM AUTHOR]

Iodine-initiated new particle formation (I-NPF) has long been recognized in coastal hotspot regions. However, no prior work has studied the exact chemical composition of organic compounds and their role in the coastal I-NPF. Here we present an important complementary study to the ongoing laboratory and field researches of iodine nucleation in coastal atmosphere. Oxidation and NPF experiments with vapor emissions from real-world coastal macroalgae were simulated in a bag reactor. On the basis of comprehensive mass spectrometry measurements, we reported for the first time a variety of volatile precursors and their oxidation products in gas and particle phases in such a highly complex system. Organic compounds overwhelmingly dominated over iodine in the new particle growth initiated by iodine species. The identity and transformation mechanisms of organic compounds were proposed in this study to provide a more complete story of coastal NPF from low-tide macroalgal emission. [ABSTRACT FROM AUTHOR]

Identification of various emission sources and quantification of their contributions comprise an essential step in formulating scientifically sound pollution control strategies. Most previous studies have been based on traditional offline filter analysis of aerosol major components (usually inorganic ions, elemental carbon – EC, organic carbon – OC, and elements). In this study, source apportionment of PM 2.5 using a positive matrix factorization (PMF) model was conducted for urban Shanghai in the Yangtze River Delta region, China, utilizing a large suite of molecular and elemental tracers, together with water-soluble inorganic ions, OC, and EC from measurements conducted at two sites from 9 November to 3 December 2018. The PMF analysis with inclusion of molecular makers (i.e., MM-PMF) identified 11 pollution sources, including 3 secondary-source factors (i.e., secondary sulfate; secondary nitrate; and secondary organic aerosol, SOA, factors) and 8 primary sources (i.e., vehicle exhaust, industrial emission and tire wear, industrial emission II, residual oil combustion, dust, coal combustion, biomass burning, and cooking). The secondary sources contributed 62.5 % of the campaign-average PM 2.5 mass, with the secondary nitrate factor being the leading contributor. Cooking was a minor contributor (2.8 %) to PM 2.5 mass while a significant contributor (11.4 %) to the OC mass. Traditional PMF analysis relying on major components alone (PMF t) was unable to resolve three organics-dominated sources (i.e., biomass burning, cooking, and SOA source factors). Utilizing organic tracers, the MM-PMF analysis determined that these three sources combined accounted for 24.4 % of the total PM 2.5 mass. In PMF t , this significant portion of PM mass was apportioned to other sources and thereby was notably biasing the source apportionment outcome. Backward trajectory and episodic analysis were performed on the MM-PMF-resolved source factors to examine the variations in source origins and composition. It was shown that under all episodes, secondary nitrate and the SOA factor were two major source contributors to the PM 2.5 pollution. Our work has demonstrated that comprehensive hourly data of molecular markers and other source tracers, coupled with MM-PMF, enables examination of detailed pollution source characteristics, especially organics-dominated sources, at a timescale suitable for monitoring episodic evolution and with finer source breakdown. [ABSTRACT FROM AUTHOR]