Characterizing carbonaceous aerosols in residential coal combustion: Insights from thermal/spectral carbon analyzer coupled with photoionization mass spectrometry analysis.

This study aims to identify unique signatures from residential coal combustion in China across various combustion conditions and coal types. Using a Thermal/Spectral Carbon Analyzer with a Photoionization Time-of-Flight Mass Spectrometer (TSCA-PI-TOF-MS), we focus on the optical properties and organic mass spectra of the emissions. Bituminous coal emerged as the primary emitter of total carbon, releasing 729 µg C/mg PM2.5 under smoldering and 894 µg C/mg PM2.5 under flaming. Carbon fractions mainly comprised OC1 and OC2, except for anthracite's dominance of EC1 under smoldering. Pyrolysis carbon absorption shifted from 405, 445 and 532 nm during smoldering to near-infrared bands (635-980 nm) during flaming for both bituminous and anthracite coal. Conversely, clean coal exhibited an inverse trend, attributed to additives enhancing oxygen-containing organic compounds and long-chain hydrocarbons released in charring process. Sample of bituminous coal began charring at OC3 step, while anthracite began earlier at OC2 step, particularly pronounced under flaming. Clean coal displayed unconventional charring at OC1 step under smoldering condition, producing signature compounds like butenal, methylfuran, furanylalcohol, and naphthol. The mass spectra of bituminous coal featured characteristic peaks, including m/z 192 (methylphenanthrene), 206, 220 (alkylated phenanthrenes), and 234 (retene). Anthracite coal showed a potential tracer at m/z 223, shifting from OC1 in smoldering to OC2 in flaming. Clean coal under flaming condition exhibited elevated levels of aromatic compounds, indicating potential toxicity, with peaks at m/z 178 (phenanthrene), 228 (chrysene/benz[a]anthracene), 234 (retene), 242 (methylchrysene), and 252 (benzo[a]pyrene, benzo[k]fluoranthene). Results also showed that the broader mass spectra range in the OC3 and OC4 steps across all coal types suggests that high-temperature pyrolysis promotes diversity. These findings contribute to refined source apportionment of carbon emissions from residential coal combustion and provide the scientific basis for the formulation of air pollution prevention strategies, crucial for coal-dependent regions.

Potential toxic components in size-resolved particles and gas from residential combustion: Emission factor and health risk.

Particulate matter (PM) from residential combustion is an existential threat to human health. Emission factors (EFs) of multiple potential toxic components (PTCs) in size-resolved PM and gas from eight residential fuel combustion were measured, and size distribution, gas/particle partitioning and health risks of the PTCs were investigated. Average EFs from clean coal and anthracite coal were PTEs (sum of EFs of 11 Potential Toxic Elements, 6.62 mg/kg fuels) > PAHs (sum of 22 Polycyclic Aromatic Hydrocarbons, 1.12 mg/kg) > OPAHs (sum of 5 Oxygenated Polycyclic Aromatic Hydrocarbons, 0.45 mg/kg) > PAEs (sum of 6 Phthalate Esters, 0.11 mg/kg) > NPAHs (sum of 14 Nitropolycyclic Aromatic Hydrocarbons, 16.84 µg/kg) > OPEs (sum of 7 Organophosphate Esters, 7.57 µg/kg) > PCBs (sum of 6 Polychorinated Biphenyls, 0.07 µg/kg), which were 2-3 and 1-2 orders of magnitude lower than the EFs of PTCs (except PTEs) from bituminous coal and biomass. Most PAHs, OPAHs and NPAHs, which may mainly originate from chemical reactions, showed similar size distributions and averagely 85 % concentrated in PM1. PTEs, PAEs, OPEs and PCBs generated from the release from raw fuels may have a higher proportion, so their size distributions were more complex and varied with combustion temperature, volatility of compounds, binding mode of the raw fuels, and so on. In addition, clean coal and high-quality anthracite coal could reduce the health risks from the potential organic toxic components, but also reveal the stumbling block of PTEs in risk control.

High spatial-resolved source-specific exposure and risk in the city scale: Influence of spatial interrelationship between PM2.5 sources and population on exposure.

Research on High Spatial-Resolved Source-Specific Exposure and Risk (HSRSSER) was conducted based on multiple-year, multiple-site synchronous measurement of PM2.5-bound (particulate matter with aerodynamic diameter<2.5 µm) toxic components in a Chinese megacity. The developed HSRSSER model combined the Positive Matrix Factorization (PMF) and Land Use Regression (LUR) to predict high spatial-resolved source contributions, and estimated the source-specific exposure and risk by personal activity time- and population-weighting. A total of 287 PM2.5 samples were collected at ten sites in 2018-2020, and toxic species including heavy metals (HMs), polycyclic aromatic hydrocarbons (PAHs) and organophosphate esters (OPEs) were analyzed. The percentage non-cancer risk were in the order of traffic emission (48 %) > industrial emission (22 %) > coal combustion (12 %) > waste incineration (11 %) > resuspend dust (7 %) > OPE-related products (0 %) ≈ secondary particles (0 %). Similar orders were observed in cancer risk. For traffic emission, due to its higher source contributions and large population in central area, non-cancer and cancer risk fraction increased from 23 % to 48 % and 20 % to 46 % after exposure estimation; while for industrial emission, higher source contributions but small population in suburb area decreased the percentage non-cancer and cancer risk from 38 % to 22 % and 39 % to 24 %, respectively.

Inhalation bioaccessibility and risk assessment for PM-bound organic components: Co-effects of component physicochemical properties, PM properties, and sources.

Inhalation bioaccessibility and deposition in respiratory tracts of organic components in atmospheric particulate matter (PM) are key factors for accurately estimating health risks and understanding human exposures. This study evaluated the in-vitro inhalation bioaccessibility of polycyclic aromatic hydrocarbons (PAHs) and PAH derivatives, phthalic acid esters (PAEs), polychlorinated biphenyls (PCBs), and organophosphate flame retardants (OPFRs) in size-resolved PM from a Chinese megacity. The bioaccessibility ranged from 0.2% to 77.8% in the heating period (HP), and from 0.7% to 94.2% in the non-heating period (NHP). Result suggests that less hydrophobic organics might be more bioaccessible. Bioaccessibility of medium logKow organics in sizes > 0.65 µm was significantly inhibited by high carbon fractions, indicating the co-effects. Then, this is the first study to explore effects of sources on inhalation bioaccessibility of organics. Coal and biomass combustion in HP and traffic emission in NHP negatively correlated with bioaccessibility. Secondary particles also negatively correlated with bioaccessibility of medium logKow organics. Incremental lifetime cancer risk (ILCR) and non-cancer risk (HQ) for all measured components in PM10 were estimated after considering the bioaccessibility and deposition efficiencies and the HQ and ILCR were within the acceptable range. BaP and DEHP were strong contributors to HQ and ILCR, respectively.

Variations of inhalation risks during different heavy pollution episodes based on 3-year measurement of toxic components in size-segregated particles.

Toxic metals (TMs) and polycyclic aromatic hydrocarbons (PAHs) in size-segregated particles during common days (CD) and different heavy pollution (HP) episodes were measured during 2018-2021 in a Chinese megacity. The Multiple Path Particle Dosimetry Model (MPPD) was performed to estimate deposition efficiency, and then inhalation risks in the human pulmonary region during different types of HP were assessed and compared. The higher pulmonary deposition efficiency of PAHs and TMs during all types of HP than those during CD was confirmed. The accumulative incremental lifetime cancer risk (ILCR) of different HP were 2.42 × 10-5, 1.52 × 10-5, 1.39 × 10-5, 1.30 × 10-5 and 2.94 × 10-6 for HP4 (combustion sources HP), HP1 (ammonium nitrate HP), HP5 (mixed sources HP), HP3 (resuspended dust HP) and HP2 (ammonium sulfate HP), respectively. The accumulative hazard quotient (HQ) during different HP episodes decreased in the order of HP4 (0.32) > HP3 (0.24) > HP1 (0.22) > HP5 (0.18) > HP2 (0.05). The inhalation risks were dominated by Ni and Cr, what's more, the HQ of Ni and ILCR of Cr during the five HP episodes shared a similar size distribution pattern. However, the characteristic components during different HP episodes and their size distributions of them were distinctive. The size distribution of inhalation risks of the related components (Ni, Cr, BaP, and As) from the combustion process during HP4 peaked at fine mode (0.65-2.1 µm). The size distribution of inhalation risks of the dust-related components (Mn and V) and the components (As and BaP) that are likely to volatilize and re-distribution peaked at coarse mode (2.1-3.3 µm) during HP3. Notably, Mn and Co as catalysts at fine mode could increase the degree of secondary formation and toxicity.

Trends of source apportioned PM2.5 in Tianjin over 2013-2019: Impacts of Clean Air Actions.

A long-term (2013-2019) PM2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013-2017 and 2018-2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM2.5 air quality was observed in Tianjin with decreases in PM2.5 at an annual rate of 6.6%/y. PM2.5 from CC decreased by 4.1%/y. The reductions in SO2 concentration, PM2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO2, CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NOX emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NOX controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards.

Contributors to reductions of PM2.5-bound heavy metal concentrations and health risks in a Chinese megacity during 2013, 2016 and 2019: An advanced method to quantify source-specific risks from various directions.

PM2.5-bound heavy metals were measured in a Chinese megacity (Tianjin) in 2013, 2016 and 2019, and analyzed by a new RSDA method (source directional apportionment of risks). Through combining the receptor model, cluster analysis of back trajectories, and risk assessment, the RSDA was developed in this work to quantify source-specific risks from each direction. Concentrations of PM2.5 and most species (especially for heavy metals) underwent various reductions, and the incremental lifetime cancer risk (ILCR) and non-cancer risk (HQ) declined by more than 80% from 2013 to 2019. Pb was the highest contributor to the reduction of HMs mass concentration (58.6%), while Cr (85.5% for cancer risk) and As (26.0% for non-cancer risk) were more prominent for the reduction of HM risks. The coal combustion and industrial emissions were vital contributors to the reduction of both PM2.5 mass concentrations (contributed 34.0% and 7.8% to the reduction respectively) and health risks (contributed 36.1% and 25.7% to the cancer risk reduction respectively). Although the percentage mass contribution of traffic emissions increased (7.7% in 2013 and 21.9% in 2019), the associated risks decreased (contributed 26.8% to the cancer risk reduction). Furthermore, the results of RSDA consistently implied that coal combustion, industrial emissions and traffic emissions controls in the northeast/north-northeast, south and southwest of the studied area played important roles in the risk reductions, which mainly due to the risk reduction of air masses from NE/NNE, S and SW, and their strong influence to Tianjin. The RSDA method can quantify the health risks from different sources and directions, and the evaluation of contributors to the reductions of risks in this work would provide a meaningful reference for policy maker to control PM2.5 emissions and protect population health.

Size distribution, meteorological influence and uncertainty for source-specific risks: PM2.5 and PM10-bound PAHs and heavy metals in a Chinese megacity during 2011-2021.

This study aims at exploring size distribution, meteorological influence and uncertainty for source-specific risks of atmospheric particulate matter (PM), which can improve risk-mitigation strategies for health protection. Heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) in PM2.5 and PM10 were detected in a Chinese megacity during 2011-2021. A new method named as PMFBMR, which combines the Positive Matrix Factorization, Bootstrapping, Mote Carlo and Risk assessment model, was developed to estimate uncertainty of source-specific risks. It was found that PAH risks concentrated in fine PM, while HMs showed high risks in both fine and coarse PMs. For PM2.5, HQ (non-cancer risk hazard quotient) of gasoline combustion (GC), diesel and heavy oil combustion (DC), coal combustion (CC), industrial source (IS), resuspended dust (RD) and secondary and transport PM (ST) were 0.6, 1.4, 0.9, 1.6, 0.3, and 0.3. ILCR (lifetime cancer risk) of sources were IS (9.2E-05) > DC (2.6E-05) = CC (2.6E-05) > RD (2.2E-05) > GC (1.7E-05) > ST (6.4E-06). PM2.5 from GC, DC, CC and IS caused higher risks than coarse PM, while coarse PM from RD caused higher risks. Source-specific risks were influenced not only by emissions, but also by meteorological condition and dominant toxic components. Risks of GC and DC were usually high during stable weather. Some high risks of CC, IS and RD occurred at strong WS due to transport or wind-blown resuspension. GC and DC risks (influenced by both PAHs and HMs) showed strong relationship with T, while IS and RD risks (dominated by HMs) showed weak link with meteorological conditions. For uncertainty of source-specific risks, HQ and ILCR were sensitive for different variables, because they were dominated by components with different uncertainties. When using source-specific risks for risk-mitigation strategies, the focused toxic components, used toxic values, PM sizes and uncertainty are necessary to be considered.

The Lung Microbiota Affects Pulmonary Inflammation and Oxidative Stress Induced by PM2.5 Exposure.

Fine particulate matter (PM2.5) exposure causes respiratory diseases by inducing inflammation and oxidative stress. However, the correlation between the pulmonary microbiota and the progression of pulmonary inflammation and oxidative stress caused by PM2.5 is poorly understood. This study tested the hypothesis that the lung microbiota affects pulmonary inflammation and oxidative stress induced by PM2.5 exposure. Mice were exposed to PM2.5 intranasally for 12 days. Then, pulmonary microbiota transfer and antibiotic intervention were performed. Histological examinations, biomarker index detection, and transcriptome analyses were conducted. Characterization of the pulmonary microbiota using 16S rRNA gene sequencing showed that its diversity decreased by 75.2% in PM2.5-exposed mice, with increased abundance of Proteobacteria and decreased abundance of Bacteroidota. The altered composition of the microbiota was significantly correlated with pulmonary inflammation and oxidative stress-related indicators. Intranasal transfer of the pulmonary microbiota from PM2.5-exposed mice affected pulmonary inflammation and oxidative stress caused by PM2.5, as shown by increased proinflammatory cytokine levels and dysregulated oxidative damage-related biomarkers. Antibiotic intervention during PM2.5 exposure alleviated pulmonary inflammation and oxidative damage in mice. The pulmonary microbiota also showed substantial changes after antibiotic treatment, as reflected by the increased microbiota diversity, decreased abundance of Proteobacteria and increased abundance of Bacteroidota. These results suggest that pulmonary microbial dysbiosis can promote and affect pulmonary inflammation and oxidative stress during PM2.5 exposure.

Seasonal variation and source apportionment of inorganic and organic components in PM2.5: influence of organic markers application on PMF source apportionment.

PM2.5 samples collected over a 1-year period in a Chinese megacity were analyzed for organic carbon (OC), elemental carbon (EC), water-soluble ions, elements, and organic markers such as polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes, and n-alkanes. To study the applicability of organic markers in source apportionment, the relationship between organic and inorganic components was analyzed, and four scenarios were implemented by incorporating different combinations of organic and inorganic tracers. The consistent temporal variations trend of 4-ring PAHs and SO42- prove that coal burning directly emits a portion of sulfate. The concentrations of ∑5-7-ring PAHs, NO3-, and NO2 show a trend of simultaneous increase and decrease, implying collective impacts from the vehicle source. The concentrations of OC and EC positively correlate with the 5-7-ring PAHs and Cu and Zn, which proves that part of Cu and Zn comes from vehicle emissions. Five factors were identified by incorporating only conventional components, including secondary source (SS, 30%), fugitive dust (FD, 14%), construction dust (CD, 4%), traffic source (TS, 19%), and coal combustion (CC, 14%). Six factors were identified by incorporating conventional components and PAHs, including SS (28%), FD (15%), CD (4%), CC (13%), gasoline vehicles (GV, 12%), and diesel vehicles (DV, 10%). Eight factors were identified by incorporating conventional components, PAHs, hopanes, and n-alkanes, including SS (26%), FD (17%), CD (3%), GV (14%), DV (8%), immature coal combustion (ICC, 5%), mature coal combustion (MCC, 10%), and biogenic source (BS, 1%).

Seasonal variation of dissolved bioaccessibility for potentially toxic elements in size-resolved PM: Impacts of bioaccessibility on inhalable risk and uncertainty.

The health effects of potentially toxic elements (PTEs) in airborne particulate matter (PM) are strongly dependent on their size distribution and dissolution. This study examined PTEs within nine distinct sizes of PM in a Chinese megacity, with a focus on their deposited and dissolved bioaccessibility in the human pulmonary region. A Multiple Path Particle Dosimetry (MPPD) model was used to estimate the deposited bioaccessibility, and an in-vitro experiment with simulated lung fluid was conducted for dissolved bioaccessibility. During the non-heating season, the dissolved bioaccessible fraction (DBF) of As, Cd, Co, Cr, Mn, Pb and V were greater in fine PM (aerodynamics less than 2.1 µm) than in coarse PM (aerodynamics between 2.1 and 10 µm), and vice versa for Ni. With the increased demand of heating, the DBF of Pb and As decreased in fine particle sizes, probably due to the presence of oxide/silicate compounds from coal combustion. Inhalation health risks based on the bioaccessible concentrations of PTEs displayed the peaks in <0.43 µm and 2.1-3.3 µm particulate sizes. The non-cancer risk was at an acceptable level (95th percentiles of hazard index (HI) was 0.49), but the cancer risk exceeded the threshold value (95th percentiles of total incremental lifetime cancer risk (TCR) was 8.91 × 10-5). Based on the results of uncertainty analysis, except for the exposure frequency, the total concentrations and DBF of As and Cr in <0.43 µm particle size segment have a greater influence on the uncertainty of probabilistic risk.

Evaluating the performance of chemical transport models for PM2.5 source apportionment: An integrated application of spectral analysis and grey incidence analysis.

Evaluating the performance of source apportionment (SA) models is difficult due to the non-observable nature of source contribution in reality. Here we propose a new approach to assess the performance of Chemical Transport Models (CTMs) for SA based on wavelet time-frequency spectral analysis and Grey Incidence Analysis (GIA). For each source category, certain species that better reflect the periodic characteristics of the emission sources were selected as the chemical tracers. The consistency of the time series between the simulated source contributions and the observed source-specific chemical tracers was then examined using a GIA model based on the perspective of similarity, and characterized by the GIA scores. By applying this method to six typical pollution episodes, we evaluated the performance of the Comprehensive Air Quality Model with Extensions-Particle Source Apportionment Technology (CAMx-PSAT) model for PM2.5 SA from different temporal and spatial scales. The source- and episode-dependent optimal average time and main source regions were obtained. This approach is robust for facilitating a relatively meticulous evaluation of the performance of CTMs for PM2.5 SA, and provides additional insight for decision-making for heavy pollution emergencies.

Sources and uncertainties of health risks for PM2.5-bound heavy metals based on synchronous online and offline filter-based measurements in a Chinese megacity.

Accurate measurements of PM2.5 related heavy metals (HMs) and some components are critical to better understanding the sources and health risks of PM2.5. HMs and other components in PM2.5 were simultaneously measured using online and offline filter-based methods in a Chinese megacity. Online Mn, Pb, Cu, and Zn concentrations exhibited good correspondence with offline results (R2 ≥ 0.7, relative biases = -3%-22%). Some differences were observed for Cr, As, Co, and V. The assessment of online and offline probabilistic health risks indicated that non-cancer and total cancer risks were higher than the acceptable limits. Different analytical methods of As may result in different uncertainties in risk analysis. The positive matrix factorization model (PMF) was used to perform online and offline source apportionment. Five sources (coal combustion, vehicle source, industrial source, secondary source, and resuspended dust) were identified. Lower online percentage contribution of resuspended dust (8%) might relate to the lack of Si and Al measurements. The different rate contributions of vehicle source were potentially linked to the different time resolutions of the online and offline data. The assessment of online and offline source-specific health risks revealed that industrial source and coal combustion were the most important sources. Online and offline source-specific risks were not significantly different (P > 0.05). This contrastive study not only helps decision makers to manage health risk more effectively, but also provides insights into the applicability of online and offline measurements for quantifying source-specific risks.

Potential Risks of PM2.5-Bound Polycyclic Aromatic Hydrocarbons and Heavy Metals from Inland and Marine Directions for a Marine Background Site in North China.

Ambient PM2.5-bound ions, OC, EC, heavy metals (HMs), 18 polycyclic aromatic hydrocarbons (PAHs), 7 hopanes, and 29 n-alkanes were detected at Tuoji Island (TI), the only marine background atmospheric monitoring station in North China. The annual PM2.5 average concentration was 47 ± 31 µg m-3, and the average concentrations of the compositions in PM2.5 were higher in cold seasons than in warm seasons. The cancer and non-cancer risks of HMs and PAHs in cold seasons were also higher than in warm seasons. BaP, Ni, and As dominated the ∑HQ (hazard quotient) in cold seasons, while the non-carcinogenic risk in warm seasons was mainly dominated by Ni, Mn, and As. The ILCR (incremental lifetime cancer risk) values associated with Cr and As were higher in the cold season, while ILCR-Ni values were higher in the warm season. The backward trajectory was calculated to identify the potential directions of air mass at TI. Through the diagnostic ratios of organic and inorganic tracers, the sources of particulate matter in different directions were judged. It was found that ship emissions and sea salt were the main sources from marine directions, while coal combustion, vehicles emissions, industrial process, and secondary aerosols were the main source categories for inland directions. In addition, potential HM and PAH risks from inland and marine directions were explored. The non-cancerous effects of TI were mainly affected by inland transport, especially from the southeast, northwest, and west-northwest. The cancerous effects of TI were mainly simultaneously affected by the inland direction and marine direction of transport.

Size-resolved source apportionment of particulate matter from a megacity in northern China based on one-year measurement of inorganic and organic components.

This research apportioned size-resolved particulate matter (PM) contributions in a megacity in northern China based on a full year of measurements of both inorganic and organic markers. Ions, elements, carbon fractions, n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes and steranes in 9 p.m. size fractions were analyzed. High molecular weight PAHs concentrated in fine PM, while most other organic compounds showed two peaks. Both two-way and three-way receptor models were used for source apportionment of PM in different size ranges. The three-way receptor model gave a clearer separation of factors than the two-way model, because it uses a combination of chemical composition and size distributions, so that factors with similar composition but distinct size distributions (like more mature and less mature coal combustion) can be resolved. The three-way model resolved six primary and three secondary factors. Gasoline vehicles and coal and biomass combustion, nitrate and high relative humidity related secondary aerosol, and resuspended dust and diesel vehicles (exhaust and non-exhaust) are the top two contributors to pseudo-ultrafine (<0.43 µm), fine (0.43-2.1 µm) and coarse mode (>2.1 µm) PM, respectively. Mass concentration of PM from coal and biomass combustion, industrial emissions, and diesel vehicle sources showed a bimodal size distribution, but gasoline vehicles and resuspended dust exhibited a peak in the fine and coarse mode, separately. Mass concentration of sulphate, nitrate and secondary organic aerosol exhibited a bimodal distribution and were correlated with temperature, indicating strong photochemical processing and repartitioning. High relative humidity related secondary aerosol was strongly associated with size shifts of PM, NO3- and SO42- from the usual 0.43-0.65 µm to 1.1-2.1 µm. Our results demonstrated the dominance of primary combustion sources in the <0.43 µm particle mass, in contrast to that of secondary aerosol in fine particle mass, and dust in coarse particle mass in the Northern China megacity.

Source apportionment of PM2.5 during different haze episodes by PMF and random forest method based on hourly measured atmospheric pollutant.

Hourly measured PM2.5-bound species, gases, and meteorological data were analyzed by the PMF receptor model to quantify source contributions, and by the random forest to estimate decisive factors of variations of PM2.5, sulfur oxidation ratio (SOR), and nitrogen oxidation ratio (NOR) during different haze episodes. PM2.5 variation was influenced by CO (17%), SO2 (19%), NH3 (12%), O3 (10%), air pressure (P, 9.9%), and temperature (T, 10%) during the whole period. SOR was determined by SO2 (15%), temperature (T, 9.8%), relative humidity (RHU, 15%), and pondus hydrogenii (pH, 35%), and NOR was influenced by NOx (19%), O3 (14%), NH3 (13%), and RHU (15%). Three types of pollution episodes were captured. Process I was characterized by high CO (contributing 40% of PM2.5 concentration variation estimated by the random forest) due to coal combustion for heating during winter in northern China. According to the PMF, coal combustion (32%) and secondary sources (38%) were both the most important contributors in the first stage, and then, when the RHU increased to above 80%, the highest contribution was from secondary sources (40%). Process II was during the Spring Festival and was characterized by 8.8 µg m-3 firework contribution. High SO2 during this process, especially on the CNY's Eve, was observed due to the firework displays, and SO2 gave a high contribution (24%) to PM2.5 variation. Process III showed high ions and high RHU in summer with sulfate and nitrate contributing 44% and 22%, respectively. Furthermore, meteorological parameters and NH3 play a key role on SOR and NOR.

Organic compound source profiles of PM2.5 from traffic emissions, coal combustion, industrial processes and dust.

Eighteen polycyclic aromatic hydrocarbons (PAHs), 24 n-alkanes, 7 hopanes, 2 cholestanes, inorganic ions, elements and carbon fractions were analyzed in real-world source samples of PM2.5 (fine particulate matter) from traffic emissions (gasoline vehicles-TGV, diesel vehicles-TDV, diesel ship-TDS, and heavy oil ships-THOS), coal combustion (coal-fired industrial boilers-CIB, power plants-CPP, and residential stoves-CRS), industrial process emissions (cement industry-IPCI, and steel industry-IPSI), and dust (soil dust-DSD, road dust-DRD, and construction dust-DCD). High molecular weight (sum of five to seven rings) PAHs accounted for higher fractions for TGV (80%) and THS (61%) than for TDV, TDS and coal combustion sources (31%-47%). Hopane ratios (C29αß/C30αß) in coal related sources were mostly higher than 1, whereas that of traffic emissions was lower than 1. The homohopane index [S/(S + R)], which is a useful index for identifying the maturity of fuels, ranked as TGV > THS > TDV and TDS > coal combustion. For n-alkane profiles, coal related sources showed peaks at C16-C19, TDV, TDS and THS showed similar peaks at C17-C25, but peaks for DSD (C30-C32), DRD (C17-C20, C24-25 and C30-C31), CRS (C16-C18 and C28-C29) and TGV (C24-C26) are different. Organic markers were selected which can best differentiate the subtypes within source categories by considering the component levels and variations. Through a comprehensive review, we showed that it is inadvisable to directly use diagnostic ratios for source attribution, although their trends can assist in identifying influential sources.

Source-specific health risk assessment of PM2.5-bound heavy metals based on high time-resolved measurement in a Chinese megacity: insights into seasonal and diurnal variations.

The health effects of PM2.5 associated heavy metals have caused wide public concern. To more accurately assess source-specific health risks of PM2.5-bound heavy metals, and to formulate a cost-effective control strategy to health risk reduction, it is necessary to have a better understanding of the temporal variation of source-specific health risks. For this purpose, hourly PM2.5 and associated heavy metals were measured during four seasons in 2018-2019 in a Chinese megacity. A method integrating positive matrix factorization (PMF) with the health risk assessment model was used to quantify the source-specific health risks. Results showed that the total hazard index (HI) of PM2.5-bound heavy metals was 1.35, higher than the safety level, the sum cancer risks (R) of carcinogenic elements (Cr, Co, Ni and As) were 2.8 × 10-5, implying nonnegligible risks. Industrial source 1 (61.3%), which was related with Mn posed the largest non-cancer risk, while coal combustion (36.1%) and industrial source 1 (34.9%) posed most of the cancer risk, and slightly fluctuated with seasons. Health risks of most resolved sources were higher in autumn and winter than in other seasons. In terms of the diurnal variation, they were the lowest in the afternoon. Besides, the health risks of vehicle source had a peak value in rush hours. Different scenarios were simulated to understand the influences of time resolutions and sampling periods on source-specific risk assessment. The results showed the cancer risks of coal combustion and industrial source 1 calculated from the dataset with reduced sampling periods were different from those calculated from the whole dataset. We conclude that source-specific health risks of heavy metals show seasonal and diurnal variations, which suggests that targeted strategies should be adopted on the basis of seasonal and diurnal cycles to protect public health. In addition, a sufficient sampling period is required to generate representative and reliable results for source-specific health risk assessment.

Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources.

The submicron particulate matter (PM1) and fine particulate matter (PM2.5) are very important due to their greater adverse impacts on the natural environment and human health. In this study, the daily PM1 and PM2.5 samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin, China. The collected samples were analyzed for the carbonaceous fractions, inorganic ions, elemental species, and specific marker sugar species. The chemical characterization of PM1 and PM2.5 was based on their concentrations, compositions, and characteristic ratios (PM1/PM2.5, AE/CE, NO3-/SO42-, OC/EC, SOC/OC, OM/TCA, K+/EC, levoglucosan/K+, V/Cu, and V/Ni). The average concentrations of PM1 and PM2.5 were 32.4 µg/m3 and 53.3 µg/m3, and PM1 constituted 63% of PM2.5 on average. The source apportionment of PM1 and PM2.5 by positive matrix factorization (PMF) model indicated the main sources of secondary aerosols (25% and 34%), biomass burning (17% and 20%), traffic emission (20% and 14%), and coal combustion (17% and 14%). The biomass burning factor involved agricultural fertilization and waste incineration. The biomass burning and primary biogenic contributions were determined by specific marker sugar species. The anthropogenic sources (combustion, secondary particle formation, etc) contributed significantly to PM1 and PM2.5, and the natural sources were more evident in PM2.5. This work significantly contributes to the chemical characterization and source apportionment of PM1 and PM2.5 in near-port cities influenced by the diverse sources.

Size distributions of source-specific risks of atmospheric heavy metals: An advanced method to quantify source contributions to size-segregated respiratory exposure.

Heavy metals in size-segregated particulate matter (PM) were investigated in a Chinese megacity, and an advanced model was developed to quantify source-specific risks focusing on size-segregated respiratory exposure. Incremental lifetime cancer risk (ILCR) and non-cancer risk (hazard quotient: HQ) based on deposition concentrations of heavy metals displayed a peak at 4.7-5.8 µm. The percentage contributions to cancer risk were as follows: industrial emission (IE, 34%) > secondary and transport (ST, 29%) > resuspended dust (RD, 21%) > coal combustion (CC, 11%) > traffic emission (TE, 4%) during spring and summer (SS), and CC (31%) > ST (26%) > IE (21%) > RD (11%) ≈ TE (11%) during autumn and winter (AW). RD (41% of HQ during SS, 28% during AW) and IE (45% of HQ during SS, 35% during AW) dominated non-cancer risk. ILCR and HQ of CC were high at sizes 1.1-2.1 µm and 0.43-0.65 µm; those of RD were high at sizes > 3.3 µm; and those of IE were bimodal at fine (<2.1 µm) and coarse (>2.1 µm) sizes, respectively. Cancer risk was more susceptible to small particles than non-cancer risk, partly because higher ILCR was from CC, but higher HQ was attributed by RD.