[Spatial Variability and Source Apportionment of PM2.5 Carbon Components in Tianjin].

The contents of eight carbonaceous subfractions were determined by simultaneously collecting PM2.5 samples from four sites in different functional areas of Tianjin in 2021. The results showed that the organic carbon (OC) concentration was 3.7 µg·m-3 to 4.4 µg·m-3, and the elemental carbon (EC) concentration was 1.6 µg·m-3 to 1.7 µg·m-3, with the highest OC concentration in the central urban area. There was no significant difference in EC concentration. The concentration of PM2.5 showed the distribution characteristics of the surrounding city>central city>peripheral area. The OC/EC minimum ratio method was used to estimate the concentrations of secondary organic carbon (SOC) in PM2.5, and the results showed that the secondary pollution was more prominent in the surrounding city, with SOC accounting for 48.8%. The correlation between carbon subcomponents in each functional area showed the characteristics of the peripheral area>central area>surrounding area, all showing the strongest correlation between EC1 and OC2 and EC1 and OC4. By including the carbon component concentration into the positive definite matrix factorization (PMF) model for source apportionment, the results showed that road dust sources(9.7%-23.5%), coal-combustion sources (10.2%-13.3%), diesel vehicle exhaust (12.6%-20.2%)and gasoline vehicle exhaust (18.9%-38.8%)were the main sources of carbon components in PM2.5 in Tianjin. The pollution sources of carbon components were different in different functional areas, with the central city and peripheral areas mainly affected by gasoline vehicle exhaust; the surrounding city was more prominently affected by the secondary pollution and diesel vehicle exhaust.

Assessing emission-driven changes in health risk of source-specific PM2.5-bound heavy metals by adjusting meteorological covariates.

Heavy metals (HMs) in PM2.5 gain much attention for their toxicity and carcinogenic risk. This study evaluates the health risks of PM2.5-bound HMs, focusing on how meteorological conditions affect these risks against the backdrop of PM2.5 reduction trends in China. By applying a receptor model with a meteorological normalization technique, followed by health risk assessment, this work reveals emission-driven changes in health risk of source-specific HMs in the outskirt of Tianjin during the implementation of China' second Clean Air Action (2018-2020). Sources of PM2.5-bound HMs were identified, with significant contributions from vehicular emissions (on average, 33.4 %), coal combustion (26.3 %), biomass burning (14.1 %), dust (11.7 %), industrial boilers (9.7 %), and shipping emission and sea salt (4.7 %). The source-specific emission-driven health risk can be enlarged or dwarfed by the changing meteorological conditions over time, demonstrating that the actual risks from these source emissions for a given time period may be higher or smaller than those estimated by traditional assessments. Meteorology contributed on average 56.1 % to the interannual changes in source-specific carcinogenic risk of HMs from 2018 to 2019, and 5.6 % from 2019 to 2020. For the source-specific noncarcinogenic risk changes, the contributions were 38.3 % and 46.4 % for the respective periods. Meteorology exerts a more profound impact on daily risk (short-term trends) than on annual risk (long-term trends). Such meteorological impacts differ among emission sources in both sign and magnitude. Reduced health risks of HMs were largely from targeted regulatory measures on sources. Therefore, the meteorological covariates should be considered to better evaluate the health benefits attributable to pollution control measures in health risk assessment frameworks.

Characteristics of number concentration, size distribution and components of particulate matter emitted from a typical large civil airport.

Civil airports are recognized as significant contributors to fine particulate matter, especially ultra-fine particulate matter (UFP). The pollutants from airport activities have a notable adverse impact on global climate, urban air quality, and public health. However, there is a lack of practical observational studies on the characterization of integrated pollutant emissions from large civil airports. This study aims to focus on the combined emission characteristics of particulate number concentration (PNC), size distribution, and components at a large civil airport, especially UFP. The findings reveal that airport activities significantly contribute to elevated PNC levels during aircraft activity in downwind conditions (four times higher than background levels) and upwind conditions (7.5 times higher). UFP dominates the PNC around the airport. The particle size distribution shows two peaks occurring around 10-30 nm and 60-80 nm. Notably, particles within the ranges of 17-29 nm and 57-101 nm account for 65.9 % and 12.0 % of the total PNC respectively. Aircraft landing has the greatest impact on particles sized between 6 and 17 nm while takeoff affects particles sized between 29 and 57 nm resulting in a respective increase in PNC by factors of approximately 3.27 and 35.4-fold increase compared to background levels. Different aircraft types exhibit varying effects on PNC with A320 and A321 showing more pronounced effects during takeoff and landing.The presence of airports leads to roughly five-fold rise in elemental component concentrations with Si being highest followed by OC, Ca, Al, Fe, Ca2+, EC, and Mg2+. The OC/EC ratio under high aircraft activity in downwind conditions falls within range of approximately 2.5-3.5. These characteristic components and ratio can be considered as identifying species for civil airports. PMF model show about 75 % of the particulate emissions at the airport boundary were related to airport activities.

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.

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.

Quantification of NOx sources contribution to ambient nitrate aerosol, uncertainty analysis and sensitivity analysis in a megacity.

Dual isotopes of nitrogen and oxygen of NO3- are crucial tools for quantifying the formation pathways and precursor NOx sources contributing to atmospheric nitrate. However, further research is needed to reduce the uncertainty associated with NOx proportional contributions. The acquisition of nitrogen isotopic composition from NOx emission sources lacks regulation, and its impact on the accuracy of contribution results remains unexplored. This study identifies key influencing factors of source isotopic composition through statistical methods, based on a detailed summary of δ15N-NOx values from various sources. NOx emission sources are classified considering these factors, and representative means, standard deviations, and 95 % confidence intervals are determined using the bootstrap method. During the sampling period in Tianjin in 2022, the proportional nitrate formation pathways varied between sites. For suburban and coastal sites, the ranking was [Formula: see text] (NO2 + OH radical) > [Formula: see text] (N2O5 + H2O) > [Formula: see text] (NO3 + DMS/HC), while the rural site exhibited similar fractional contributions from all three formation pathways. Fossil fuel NOx sources consistently contributed more than non-fossil NOx sources in each season among three sites. The uncertainties in proportional contributions varied among different sources, with coal combustion and biogenic soil emission showing lower uncertainties, suggesting more stable proportional contributions than other sources. The sensitivity analysis clearly identifies that the isotopic composition of 15N-enriched and 15N-reduced sources significantly influences source contribution results, emphasizing the importance of accurately characterizing the localized and time-efficient nitrogen isotopic composition of NOx emission sources. In conclusion, this research sheds light on the importance of addressing uncertainties in NOx proportional contributions and emphasizes the need for further exploration of nitrogen isotopic composition from NOx emission sources for accurate atmospheric nitrate studies.

Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model.

Ambient particulate matter (PM2.5 and PM10), has been extensively monitored in numerous urban areas across the globe. Over the past decade, there has been a significant improvement in PM2.5 air quality, while improvements in PM10 levels have been comparatively modest, primarily due to the limited reduction in coarse particle (PM2.5-10) pollution. Unlike PM2.5, PM2.5-10 predominantly originates from local emissions and is often characterized by pronounced spatial heterogeneity. In this study, we utilized over one million data points on PM concentrations, collected from >100 monitoring sites within a Chinese megacity, to perform spatial source apportionment of PM2.5-10. Despite the widespread availability of such data, it has rarely been employed for this purpose. We employed an enhanced positive matrix factorization approach, capable of handling large datasets, in conjunction with a Bayesian multivariate receptor model to deduce spatial source impacts. Four primary sources were successfully identified and interpreted, including residential burning, industrial processes, road dust, and meteorology-related sources. This interpretation was supported by a considerable body of prior knowledge concerning emission sources, which is usually unavailable in most cases. The methodology proposed in this study demonstrates significant potential for generalization to other regions, thereby contributing to the development of air quality management strategies.

High aerosol loading over the Bohai Sea: Long-term trend, potential sources, and impacts on surrounding cities.

Air pollution over the oceans has received less attention compared to densely populated urban areas of continents. The Bohai Sea, a semi-enclosed sea in northern China, is surrounded by thirteen industrial cities that have experienced significant improvements in air quality over the past decade. However, the changes in air pollution over the Bohai Sea and its impacts on surrounding cities remain poorly understood. To address this, this study investigated the evolution of air pollution and its chemical composition in the Bohai Sea over four decades, utilizing satellite remote sensing data, reanalysis datasets, emissions inventories, and statistical modeling. Historically, the region has suffered from severe air pollution, resulting from a combination of continental emissions and marine inputs (e.g., sea salt, ports and maritime vessel activities). The aerosol optical depth (AOD) over the sea was higher than the mean levels observed in its surrounding coastal cities. Statistically, 45% of the air masses reaching the Bohai Sea are associated with natural sources (dust- and marine-rich), while the remainder carry anthropogenic pollutants from continental regions. With the exception of Cangzhou city, these coastal cities suffer from air pollutants originating from the Bohai Sea. Cities in the northern region of the sea, spanning from Tianjin to Yingkou, are particularly impacted. The majority of the surrounding cities are affected by a large proportion of anthropogenic aerosol types transported through air masses from the Bohai Sea, including those from biomass burning and industrial activities. These findings emphasize the considerable influence of human-induced sources in the Bohai Sea on neighboring urban areas. Furthermore, being a maritime region, natural sources like sea salt and dust from the sea may also exert a discernible impact on the neighboring environment.

Model Simulations and Predictions of Hydroxymethanesulfonate (HMS) in the Beijing-Tianjin-Hebei Region, China: Roles of Aqueous Aerosols and Atmospheric Acidity.

Hydroxymethanesulfonate (HMS) has been found to be an abundant organosulfur aerosol compound in the Beijing-Tianjin-Hebei (BTH) region with a measured maximum daily mean concentration of up to 10 µg per cubic meter in winter. However, the production medium of HMS in aerosols is controversial, and it is unknown whether chemical transport models are able to capture the variations of HMS during individual haze events. In this work, we modify the parametrization of HMS chemistry in the nested-grid GEOS-Chem chemical transport model, whose simulations provide a good account of the field measurements during winter haze episodes. We find the contribution of the aqueous aerosol pathway to total HMS is about 36% in winter in Beijing, due primarily to the enhancement effect of the ionic strength on the rate constants of the reaction between dissolved formaldehyde and sulfite. Our simulations suggest that the HMS-to-inorganic sulfate ratio will increase from the baseline of 7% to 13% in the near future, given the ambitious clean air and climate mitigation policies for the BTH region. The more rapid reductions in emissions of SO2 and NOx compared to NH3 alter the atmospheric acidity, which is a critical factor leading to the rising importance of HMS in particulate sulfur species.

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.

Source apportionment of consumed volatile organic compounds in the atmosphere.

Conventional source apportionments of ambient volatile organic compounds (VOCs) have been based on observed and initial concentrations after photochemical correction. However, these results have not been related to ozone (O3) and secondary organic aerosol (SOA) formation. Thus, the apportioned contributions could not effectively support secondary pollution control development. Source apportionment of the VOCs consumed in forming O3 and SOA is needed. A consumed VOC source apportionment approach was developed and applied to hourly speciated VOCs data from June to August 2022 measured in Laoshan, Qingdao. Biogenic emissions (56.3%), vehicle emissions (17.2%), and gasoline evaporation (9.37%) were the main sources of consumed VOCs. High consumed VOCs from biogenic emissions mainly occurred during transport from parks to the southwest and northwest of study site. During the O3 pollution period, biogenic emissions (46.3%), vehicle emissions (24.2%), and gasoline evaporation (14.3%) provided the largest contributions to the consumed VOCs. However, biogenic emissions contribution increased to 57.1% during the non-O3 pollution period, and vehicle emissions and gasoline evaporation decreased to 16.5% and 9.01%, respectively. Biogenic emissions and the mixed source of combustion sources and solvent use contributed the most to O3 and SOA formation potentials during the O3 pollution period, respectively.

Comprehensive performance evaluation of coordinated development of industrial economy and its air pollution control.

Exploring coordinated pathways that can promote not only the sustainable development of the industrial economy but also air quality is of great significance for the prevention and control of air pollution in China. Currently, the joint development pathways of the industrial economy-environment nexus remain unclear and poorly evaluated. In this study, we proposed a comprehensive performance evaluation combining objective and subjective weighting to identify industrial enterprises' economic-environment nexus benefits. It would be one of the most important steps to explore the coordinated pathways. Based on data envelopment analysis (DEA), the proposed method integrated with the index integration was used to evaluate the comprehensive performances of 41 industrial sectors in China's 13th five-year plan (2016-2020). Evaluation results showed that the comprehensive performances of the economy-environment nexus of the industrial sectors varied significantly, with the five-year average comprehensive technical efficiency (TE) of 0.11-1. Overall, the best two performances were realized by the industries of equipment manufacturing and living consumption, whereas the worst one belonged to the industry of bulk raw materials, with average comprehensive TE values of 0.50, 0.43, and 0.19, respectively. The results of the quantitative evaluation were consistent with those of the qualitative analysis in terms of the developmental status of the industrial sectors. According to the analyses of pure technical efficiency and scale effect, the proposed method identified the industrial sectors with the highest developmental value and with the highest need to control air pollution. Compared with those of the original DEA model, the results of the proposed method showed pronounced differences in terms of the performances of industrial sectors with high energy consumption and high particulate matter (PM) emissions and with low energy consumption and low PM emissions. The proposed evaluation method combining the weighting was suitable for identifying the comprehensive performance of the industrial economy-environment nexus and provides the basis for the prevention and control of air pollution.

Strategies for the coordinated control of particulate matter and carbon dioxide under multiple combined pollution conditions.

Air pollutants represented by fine particulate matter (PM2.5) and the greenhouse effect caused by carbon dioxide (CO2), are both urgent threats to public health. Tackling the synergistic reduction of PM2.5 and CO2 is critical to achieving improvements in clean air worldwide. A persistent issue is the identification of their common sources and integrated impacts under different environmental conditions. In this study, we investigated the characteristics of the pollution types captured by combined analysis through a comprehensive observational dataset for 2017-2020, and applied machine learning algorithms to quantify the effects of drivers on air pollutants and CO2 formation. More importantly, detailed conclusions were drawn for the joint control of PM2.5-CO2 in multiple pollution types by using ensemble traceability technique. We demonstrated that reducing coal combustion emissions was an effective measure to maximize the benefits of PM2.5-CO2 in weather with low CO2 levels and no PM2.5 pollution. Correspondingly, on days with severe PM2.5 episodes, prioritizing control of vehicle emissions can simultaneously mitigate PM2.5 and CO2. Similar conclusions were found at high CO2 levels, accompanied by a more extensive role of vehicle emissions. Furthermore, a comparison of the differences in source impacts between PM2.5-CO2 and individual species suggests that focusing only on the sources that contribute significantly to one species may result in an underestimation or overestimation of PM2.5-CO2 source impacts. One such implication, as evidenced by our findings, is that synergistic controlling common sources of pollutants should be efficient. Thereby, common source management targeting PM2.5-CO2 under multiple pollution types is a more workable solution to alleviate environmental pollution.

Changes in source apportioned VOCs during high O3 periods using initial VOC-concentration-dispersion normalized PMF.

Ambient volatile organic compounds (VOCs) concentrations are affected by emissions, dispersion, and chemistry. This work developed an initial concentration-dispersion normalized PMF (ICDN-PMF) to reflect the changes in source emissions. The effects of photochemical losses for VOC species were corrected by estimating the initial data, and then applying dispersion normalization to reduce the impacts of atmospheric dispersion. Hourly speciated VOC data measured in Qingdao from March to May 2020 were utilized to test the method and had assessed its effectiveness. Underestimated solvent use and biogenic emissions contributions due to photochemical losses during the O3 pollution (OP) period reached 4.4 and 3.8 times the non-O3 pollution (NOP) period values, respectively. Increased solvent use contribution due to air dispersion during the OP period was 4.6 times the change in the NOP period. The influence of chemical conversion and air dispersion on the gasoline and diesel vehicle emissions was not apparent during either period. The ICDN-PMF results suggested that biogenic emissions (23.1 %), solvent use (23.0 %), motor-vehicle emissions (17.1 %), and natural gas and diesel evaporation (15.8 %) contributed most to ambient VOCs during the OP period. Biogenic emissions and solvent use contributions during the OP period increased by 187 % and 135 % compared with the NOP period, respectively, whereas that of liquefied petroleum gas substantially decreased during the OP period. Controlling solvent use and motor-vehicles could be effective in controlling VOCs in the OP period.

[Health Impacts of Air Pollution in Tianjin].

Ambient air pollution is a dominant determinant of health. The health effects and economic losses due to air pollution are very important for decision-making. Since the implementation of the "Air Pollution Prevention and Control Action Plan" and "blue sky defense war" policies, the air quality of Tianjin has changed significantly. Here, the health effects and economic losses attributable to ambient air pollution in Tianjin from 2013 to 2020 wereestimated. For the particulate matter which has complex components, we assessed the inhalation health risks of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in PM2.5. The variation in the concentration of the main components of PM2.5 was also analyzed. The results showed that improved air quality had positive health benefits. The health benefits from SO2 were the highest among the six air pollutants, and 3786 deaths were avoided in 2020 compared to in 2013 due to lower SO2 concentration. The economic losses caused by air pollutants ranged from several billion to ten billion yuan. Among the six air pollutants, particulate matter and ozone had higher health losses in recent years. The health risks of heavy metals and PAHs in PM2.5 showed a decreasing trend. However, Cr(Ⅵ), As, Cd, and Ni in PM2.5in the winter of 2020 still had respiratorysystem carcinogenic risk, whereas there was no health risk of PAHs in PM2.5in 2019-2020. The concentrations of main components of PM2.5 have decreased significantly. In the future, the reduction of health loss caused by air pollution depends on synergy governance of particulate matter and ozone and further research on health effects.

Catalytic Ozonation of Polluter Benzene from -20 to >50 °C with High Conversion Efficiency and Selectivity on Mullite YMn2O5.

Catalytic decomposition of aromatic polluters at room temperature represents a green route for air purification but is currently challenged by the difficulty of generating reactive oxygen species (ROS) on catalysts. Herein, we develop a mullite catalyst YMn2O5 (YMO) with dual active sites of Mn3+ and Mn4+ and use ozone to produce a highly reactive O* upon YMO. Such a strong oxidant species on YMO shows complete removal of benzene from -20 to >50 °C with a high COx selectivity (>90%) through the generated reactive species O* on the catalyst surface (60 000 mL g-1 h-1). Although the accumulation of water and intermediates gradually lowers the reaction rate after 8 h at 25 °C, a simple treatment by ozone purging or drying in the ambient environment regenerates the catalyst. Importantly, when the temperature increases to 50 °C, the catalytic performance remains 100% conversion without any degradation for 30 h. Experiments and theoretical calculations show that such a superior performance stems from the unique coordination environment, which ensures high generation of ROS and adsorption of aromatics. Mullite's catalytic ozonation degradation of total volatile organic compounds (TVOC) is applied in a home-developed air cleaner, resulting in high efficiency of benzene removal. This work provides insights into the design of catalysts to decompose highly stable organic polluters.

Abundant nitrogen oxide and weakly acidic environment synergistically promote daytime particulate nitrate pollution.

Nitrate is formed through the chemical production of gas-phase nitric acid and subsequent partitioning to the aerosol phase during the daytime. Many studies in the past separated these two aspects, even though they occur simultaneously in the atmosphere. To better understand the nitrate formation mechanism and effectively mitigate its production, it is necessary to consider the synergy between these two mechanisms. For this, we analyze hourly-speciated ambient observations data, with EK&TMA (Empirical Kinetic & Thermodynamic Modeling Approach) map to comprehensively explore the factors controlling nitrate production. Results show that precursor NO2 concentration and aerosol pH, which are related to anthropogenic activities, are the two major factors for chemical kinetics production and gas/particle thermodynamic partitioning processes respectively. Abundant NO2 and weakly acidic environments are favorable conditions for daytime particulate nitrate pollution, thus collaborative control of coal source, vehicle source, and dust source is needed to alleviate nitrate pollution.

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.

Source apportionment of fine particulate matter at a megacity in China, using an improved regularization supervised PMF model.

The source apportionment of particulate matter plays an important role in solving the atmospheric particulate pollution. Positive matrix factorization (PMF) is a widely used source apportionment model. At present, high resolution online datasets are increasingly rich, but acquiring accurate and timely source apportionment results is still challenging. Integrating prior knowledge into modelling process is an effective solution and can yield reliable results. This study proposed an improved source apportionment method for the regularization supervised PMF model (RSPMF). This method leveraged actual source profile to guide factor profile for rapidly and automatically identifying source categories and quantifying source contributions. The results showed that the factor profile from RSPMF could be interpreted as seven factors and approach to actual source profile. Average source contributions were also an agreement between RSPMF and EPAPMF, including secondary nitrate (26 %, 27 %), secondary sulfate (23 %, 24 %), coal combustion (18 %, 18 %), vehicle exhaust (15 %, 15 %), biomass burning (10 %, 9 %), dust (5 %, 4 %), industrial emission (3 %, 3 %). The solutions of RSPMF also exhibited good generalizability during different episodes. This study reveals the superiority of supervised model, this model embeds prior knowledge into modelling process to guide model for obtaining more reliable results.

[Spatial-temporal Variation and Driving Factors of Ozone in China from 2019 to 2021 Based on EOF Technique and KZ Filter].

Based on the hourly O3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O3 and meteorological factors. Ultimately, the long-term component of O3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O3 concentration showed a convergent change, that is, the volatility of O3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.