Impact of terephthalic acid emissions from intensive nocturnal biomass incineration on oxidative potential in Seoul, South Korea.

This study investigated the impact of large-scale incineration facilities on PM2.5 levels in Seoul during winter. Due to the challenge of obtaining accurate combustion data from external sources, heat supply records were used as a proxy for combustion activity. To assess health risks, dithiothreitol-oxidative potential (DTT-OP) was analyzed to identify potential hazards to human health. By comparing DTT-OP with PM2.5 sources related to combustion, the study aimed to understand the impact of local pollution sources on human health in Seoul. The diurnal analysis showed that oxidative potential (0.19 µM/m3) and the biomass burning factor (5.53 µg/m3) peaked between 4:00 and 8:00 AM, with lower levels observed from 12:00 to 20:00. A significant correlation was found between combustion sources and oxidative potential, with a high correlation coefficient (r2 = 0.92). The presence of terephthalic acid (TPA) in the Cellulose combustion source profile, which is produced by the pyrolysis of plastics like polyester fiber and polyethylene terephthalate (PET), further supported the link to emissions from incineration facilities. These findings suggest that the biomass burning source is strongly correlated with DTT-OP, indicating a significant association with health risks among various local sources of PM2.5 in Seoul.

Effects of long-range transport on carboxylic acids, chlorinated VOCs, and oxidative potential in air pollution events.

In the context of air quality research, the collection and analysis of fine particulate matter (PM2.5, with a diameter less than 2.5 µm) and volatile organic compound (VOCs) play a pivotal role in understanding and addressing environmental issues across the Korean Peninsula. PM2.5 and VOCs were collected over 4-hr intervals from October 17 to November 26, 2021 during the 2021 Satellite Integrated Joint Monitoring of Air Quality campaign at Olympic Park in the Republic of Korea to understand the factors controlling air quality over the Seoul Metropolitan Area. Source apportionment was performed using the positive matrix factorization (PMF) model incorporating PM2.5 and VOCs. The factor identified by chlorinated VOCs as a major component was presumed to be due to transboundary influx and was referred to as the long-range transport factor. The long-range transport factor of PM2.5 was composed of NO3-, SO42-, NH4+, and di-carboxylic acids. Back trajectory analysis showed that the airflows originated from China and passed through the west coast of Korea to the Korean Peninsula. In the PMF results using PM2.5 and VOCs, long-range transport factors were identified in both analyses, and the high correlation observed between these factors confirms that they were transported from abroad. The dithiothreitol oxidation potential normalized to quinine showed the highest oxidation potential during the same period as the long-range transport factors increased. In conclusion, PM2.5 from external sources significantly contribute to elevated levels of dithiothreitol assay-oxidative potential (DTT-OP) in Korea. The toxic concentration, expressed as the mean ± standard deviation, was determined to be 0.29 ± 0.05 µM/m³, peaking at 0.39 µM/m³. This level is 1.8 times higher than that observed outside the event period. A notable increase in secondary pollutants was observed during these periods. These pollutants are known to enhance oxidative potential, thereby potentially impacting human health.

High loadings of carbonaceous aerosols from wood smoke in the atmosphere of Beijing from 2015 to 2017: Implications for energy transition policy.

Recently, biomass has been regarded as a promising option for solid energy in China, which is promoted in the residential sector and firing power plants. We collected 200 PM2.5 samples (particulate matter with a aerodynamic diameter smaller than 2.5 µm) at multi-sites across Beijing from three individual sampling cases from 2015 to 2017. The levels of OC, OC fractions, EC, EC fractions, as well as K+ were measured. Then, we adopted the Positive Matrix Factorization 5.0 to apportion the sources of carbonaceous aerosols. The source apportionment results were compared with the estimates of source contribution using the bottom-up technical method with the latest emission inventories after the Action Plan was put into effect in 2013. Our results demonstrate that high pollution of carbonaceous aerosols originated from wood smoking based on the receptor modeling and bottom-up technical method in Beijing from 2015 to 2017. Future energy transition policy should focus on the technologies and regulations for reducing emissions from renewable biomass fuel combustion. This study highlights the importance of regulations that address emissions controls on fuels replacing coal combustion to meet the needs to mitigate air pollution from primary energy use.

Elucidating Molecular-Scale Principles Governing the Anchoring of Liquid Crystal Mixtures on Solid Surfaces.

Computational chemistry calculations are broadly useful for guiding the atom-scale design of hard-soft material interfaces including how molecular interactions of single-component liquid crystals (LCs) at inorganic surfaces lead to preferred orientations of the LC far from the surface. The majority of LCs, however, are not single-component phases but comprise of mixtures, such as a mixture of mesogens, added to provide additional functions such as responsiveness to the presence of targeted organic compounds (for chemical sensing). In such LC mixtures, little is understood about the near-surface composition and organization of molecules and how that organization propagates into the far-field LC orientation. Here, we address this broad question by using a multiscale computational approach that combines density functional theory (DFT)-based calculations and classical molecular dynamics (MD) simulations to predict the interfacial composition and organization of a binary LC mixture of 4'-cyano-4-biphenylcarbolxylic acid (CBCA) and 4'-n-pentyl-4-biphenylcarbonitrile (5CB) supported on anatase (101) titania surfaces. DFT calculations determine the surface composition and atomic-scale organization of CBCA and 5CB at the titania surface, and classical MD simulations build upon the DFT description to describe the evolution of the near-surface order into the bulk LC. A surprising finding is that the 5CB and CBCA molecules adopt orthogonal orientations at the anatase surface and that, above a threshold concentration of CBCA, this mixture of orientations evolves away from the surface to define a uniform far-field homeotropic orientation. These results demonstrate that molecular-level knowledge achieved through a combination of computational techniques permits the design and understanding of functional LC mixtures at interfaces.

Impact of Beijing's "Coal to Electricity" program on ambient PM2.5 and the associated reactive oxygen species (ROS).

The Beijing "Coal to Electricity" program provides a unique opportunity to explore air quality impacts by replacing residential coal burning with electrical appliances. In this study, the atmospheric ROS (Gas-phase ROS and Particle-phase ROS, abbreviated to G-ROS and P-ROS) were measured by an online instrument in parallel with concurrent PM2.5 sample collections analyzed for chemical composition and cellular ROS in a baseline year (Coal Use Year-CUY) and the first year following implementation of the "Coal to Electricity" program (Coal Ban Year-CBY). The results showed PM2.5 concentrations had no significant difference between the two sampling periods, but the activities of G-ROS, P-ROS, and cellular ROS in CBY were 8.72 nmol H2O2/m3, 9.82 nmol H2O2/m3, and 2045.75 µg UD /mg PM higher than in CUY. Six sources were identified by factor-analysis from the chemical components of PM2.5. Secondary sources (SECs) were the dominant source of PM2.5 in the two periods, with 15.90% higher contribution in CBY than in CUY. Industrial Emission & Coal Combustion sources (Ind. & CCs), mainly from regional transport, also increased significantly in CBY. The contributions of Aged Sea Salt & Residential Burning sources to PM2.5 decreased 5.31% from CUY to CBY. The correlation results illustrated that Ind. & CCs had significant positive correlations with atmospheric ROS, and SECs significantly associated with cellular ROS, especially nitrates (r = 0.626, p = 0.000). Therefore, the implementation of the "Coal to Electricity" program reduced PM2.5 contributions from coal and biomass combustion, but had little effect on the improvement of atmospheric and cellular ROS.

Multi-site, multi-pollutant atmospheric data analysis using Riemannian geometry.

We demonstrate the benefits of using Riemannian geometry in the analysis of multi-site, multi-pollutant atmospheric monitoring data. Our approach uses covariance matrices to encode spatio-temporal variability and correlations of multiple pollutants at different sites and times. A key property of covariance matrices is that they lie on a Riemannian manifold and one can exploit this property to facilitate dimensionality reduction, outlier detection, and spatial interpolation. Specifically, the transformation of data using Reimannian geometry provides a better data surface for interpolation and assessment of outliers compared to traditional data analysis tools that assume Euclidean geometry. We demonstrate the utility of using Riemannian geometry by analyzing a full year of atmospheric monitoring data collected from 34 monitoring stations in Beijing, China.

Comparison of the sources and oxidative potential of PM2.5 during winter time in large cities in China and South Korea.

Regional air pollution is rising in Northeast Asia due to increasing energy consumption resulting from a growing population and intensifying industrialization. This study analyzes the sources of air pollution using fine particulate matter (PM2.5) sampling from the atmosphere over Korea and China. We then use this analysis to further investigate the relationship between organic compounds (source tracers) and the oxidative potential of PM2.5. The PM2.5 concentration during winter measured at a measurement stations in Korea showed no significant variation year-to-year. The PM2.5 concentrations measured during winter at a site near Beijing, China were 62.45 µg/m3 in 2018 and 33.07 µg/m3 in 2020. The sources, as determined from PMF, were analyzed at a site in Korea, the sources as secondary nitrate (34.10 %), secondary sulfate (20.20 %), coal combustion (4.01 %), vehicle emission (8.55 %), cooking and biomass burning (18.39 %), dust (8.45 %), and SOA (6.29 %) were identified. At a site in China, secondary nitrate (17.54 %), secondary sulfate (12.03 %), coal combustion (15.53 %), vehicle emission (12.43 %), cooking and biomass burning (9.25 %), dust (26.40 %), secondary organic aerosol (6.82 %) were identified. Our results show secondary organic carbon had a positive association with oxidative potential in Korea while primary organic carbon presented higher correlation with oxidative potential in China. Further, the ECMWF Reanalysis v5 (ERA5) wind field during the high PM2.5 events demonstrated airflow from the west coast of China resulting in high polar organic compounds at the Korean monitoring site. The results further support that aged PM2.5, which contains secondary products, leads to increased oxidative potential. The results presented explain the high concentrations of secondary products and the impact on the biological activities of PM2.5, supporting additional actions to address the impacts of long-range transport of PM2.5.

Enhanced commercial cooking inventories from the city scale through normalized emission factor dataset and big data.

Cooking emission inventories always have poor spatial resolutions when applying with traditional methods, making their impacts on ambient air and human health remain obscure. In this study, we created a systematic dataset of cooking emission factors (CEFs) and applied it with a new data source, cooking-related point of interest (POI) data, to build up highly spatial resolved cooking emission inventories from the city scale. Averaged CEFs of six particulate and gaseous species (PM, OC, EC, NMHC, OVOCs, VOCs) were 5.92 ± 6.28, 4.10 ± 5.50, 0.05 ± 0.05, 22.54 ± 20.48, 1.56 ± 1.44, and 7.94 ± 6.27 g/h normalized in every cook stove, respectively. A three-field CEF index containing activity and emission factor species was created to identify and further build a connection with cooking-related POI data. A total of 95,034 cooking point sources were extracted from Beijing, as a study city. In downtown areas, four POI types were overlapped in the central part of the city and radiated into eight distinct directions from south to north. Estimated PM/VOC emissions caused by cooking activities in Beijing were 4.81/9.85 t per day. A 3D emission map showed an extremely unbalanced emission density in the Beijing region. Emission hotspots were seen in Central Business District (CBD), Sanlitun, and Wangjing in Chaoyang District and Willow and Zhongguancun in Haidian District. PM/VOC emissions could be as high as 16.6/42.0 kg/d in the searching radius of 2 km. For PM, the total emissions were 417.4, 389.0, 466.9, and 443.0 t between Q1 and Q4 2019 in Beijing, respectively. The proposed methodology is transferrable to other Chinese cities for deriving enhanced commercial cooking inventories and potentially highlighting the further importance of cooking emissions on air quality and human health.

Direct measurement of the deposition of submicron soot particles on leaves of Platanus acerifolia tree.

Submicron soot particles (<1.0 µm in aerodynamic diameter) are responsible for global warming and health burdens worldwide. However, studies on bio-monitoring of submicron soot particles and their associated sources by using tree leaves are not comprehensively illustrated. Here, we determined the seasonal trends of submicron soot particles on the leaves of the Platanus acerifolia collected from two cities (Lu'an, Anhui Province, and Nanjing, Jiangsu Province) in the Yangtze River Delta region, China. The source apportionment of submicron soot particles was performed using stable carbon isotopic analyses. Significant seasonal trends of submicron soot particles were observed in two cities with averaged levels of 0.41-1.36 mg m-2 in cold seasons and averaged levels of 0.13-0.24 mg m-2 in warm seasons. The levels of δ13C for submicron soot at the suburban site of Lu'an city were observed to be in the range of -25.6‰ to -18.2‰ with fossil fuels dominated (∼58%) in summer and -23.0‰ to -15.6‰ with biomass burning dominated in winter (∼67%). In comparison, the ranges in the levels of δ13C in submicron soot were found to be from -26.5‰ to -20.4‰ in winter, and -24.2‰ to -17.9‰ in summer at the urban site of Nanjing. Fossil fuels accounted for a large fraction of submicron soot with average contributions of 53% in winter and 73% in summer, respectively. These findings demonstrate that Platanus acerifolia trees could be used as an effective and low-cost bio-monitoring tool for monitoring the pollution status of submicron soot and associated source contribution.

Ordering Transitions of Liquid Crystals Triggered by Metal Oxide-catalyzed Reactions of Sulfur Oxide Species.

Liquid crystals (LCs), when supported on reactive surfaces, undergo changes in ordering that can propagate over distances of micrometers, thus providing a general and facile mechanism to amplify atomic-scale transformations on surfaces into the optical scale. While reactions on organic and metal substrates have been coupled to LC-ordering transitions, metal oxide substrates, which offer unique catalytic activities for reactions involving atmospherically important chemical species such as oxidized sulfur species, have not been explored. Here, we investigate this opportunity by designing LCs that contain 4'-cyanobiphenyl-4-carboxylic acid (CBCA) and respond to surface reactions triggered by parts-per-billion concentrations of SO2 gas on anatase (101) substrates. We used electronic structure calculations to predict that the carboxylic acid group of CBCA binds strongly to anatase (101) in a perpendicular orientation, a prediction that we validated in experiments in which CBCA (0.005 mol %) was doped into an LC (4'-n-pentyl-4-biphenylcarbonitrile). Both experiment and computational modeling further demonstrated that SO3-like species, produced by a surface-catalyzed reaction of SO2 with H2O on anatase (101), displace CBCA from the anatase surface, resulting in an orientational transition of the LC. Experiments also reveal the LC response to be highly selective to SO2 over other atmospheric chemical species (including H2O, NH3, H2S, and NO2), in agreement with our computational predictions for anatase (101) surfaces. Overall, we establish that the catalytic activities of metal oxide surfaces offer the basis of a new class of substrates that trigger LCs to undergo ordering transitions in response to chemical species of relevance to atmospheric chemistry.

Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals Using 3D Convolutional Neural Networks.

We report how analysis of the spatial and temporal optical responses of liquid crystal (LC) films to targeted gases, when performed using a machine learning methodology, can advance the sensing of gas mixtures and provide important insights into the physical processes that underlie the sensor response. We develop the methodology using O3 and Cl2 mixtures (representative of an important class of analytes) and LCs supported on metal perchlorate-decorated surfaces as a model system. Although O3 and Cl2 both diffuse through LC films and undergo redox reactions with the supporting metal perchlorate surfaces to generate similar initial and final optical states of the LCs, we show that a three-dimensional convolutional neural network can extract feature information that is encoded in the spatiotemporal color patterns of the LCs to detect the presence of both O3 and Cl2 species in mixtures and to quantify their concentrations. Our analysis reveals that O3 detection is driven by the transition time over which the brightness of the LC changes, while Cl2 detection is driven by color fluctuations that develop late in the optical response of the LC. We also show that we can detect the presence of Cl2 even when the concentration of O3 is orders of magnitude greater than the Cl2 concentration. The proposed methodology is generalizable to a wide range of analytes, reactive surfaces, and LCs and has the potential to advance the design of portable LC monitoring devices (e.g., wearable devices) for analyzing gas mixtures using spatiotemporal color fluctuations.

Household air pollution from solid fuel use as a dose-dependent risk factor for cognitive impairment in northern China.

The relationship between exposure to household air pollution (HAP) from solid fuel use and cognition remains poorly understood. Among 401 older adults in peri-urban northern China enrolled in the INTERMAP-China Prospective Study, we estimated the associations between exposure to HAP and z-standardized domain-specific and overall cognitive scores from the Montreal Cognitive Assessment. Interquartile range increases in exposures to fine particulate matter (53.2-µg/m3) and black carbon (0.9-µg/m3) were linearly associated with lower overall cognition [- 0.13 (95% confidence interval: - 0.22, - 0.04) and - 0.10 (- 0.19, - 0.01), respectively]. Using solid fuel indoors and greater intensity of its use were also associated with lower overall cognition (range of point estimates: - 0.13 to - 0.03), though confidence intervals included zero. Among individual cognitive domains, attention had the largest associations with most exposure measures. Our findings indicate that exposure to HAP may be a dose-dependent risk factor for cognitive impairment. As exposure to HAP remains pervasive in China and worldwide, reducing exposure through the promotion of less-polluting stoves and fuels may be a population-wide intervention strategy to lessen the burden of cognitive impairment.

An improved method for sampling and analytical measurement of aerosol platinum in ambient air and workplace environments.

In this study we critically examined with both field and laboratory experiments key components of extant methods for measurement of aerosol soluble platinum in ambient air and workplace environments. Our goal was to develop an improved method for soluble platinum measurement that could be readily implemented in the field and laboratory using readily available modern analytical tools, and in parallel provide insight into factors influencing the robustness of specific aspects of measurement methods for soluble platinum. Experiments addressed sampler type, filter media and pre-cleaning, extraction solvent and volume, extraction time & energy and materials composition, with the objective of optimizing each specific component and promulgating strategies for improving signal/noise and precision. We used basic clean-room protocols and applied ICPMS tools to address these objectives. We document a method that provides for measurement of soluble platinum at the 0.02 ng/m3 level (8-h sample at 2 L/min). Of the four samplers evaluated (IOM, closed-face cassette, and two parallel particle impactors), the IOM exhibited the best precision. The three filter substrates evaluated (Teflon, MCE, PVC) performed similarly in most challenges, however, overall, we conclude that MCE media is the most robust collection substrate for soluble platinum measurements. To achieve the lowest detection levels, it is critical to pre-clean the filter substrates. The use of a 0.07 M HCl extractant (in preference to a water extractant) is recommended - platinum recoveries, particularly from real-world samples, are higher and more consistent with the HCl extractant. The outcomes of the extraction kinetics experiments suggest that an extraction time of 60 min may improve the method performance with 0.07 M HCl but degrade the performance with water, in comparison with a 30-min extraction period. The use of sonication in preference to a table-top shaker is recommended for energy input during extraction.

Elemental composition of fine and coarse particles across the greater Los Angeles area: Spatial variation and contributing sources.

The inorganic components of particulate matter (PM), especially transition metals, have been shown to contribute to PM toxicity. In this study, the spatial distribution of PM elements and their potential sources in the Greater Los Angeles area were studied. The mass concentration and detailed elemental composition of fine (PM2.5) and coarse (PM2.5-10) particles were assessed at 46 locations, including urban traffic, urban community, urban background, and desert locations. Crustal enrichment factors (EFs), roadside enrichments (REs), and bivariate correlation analysis revealed that Ba, Cr, Cu, Mo, Pd, Sb, Zn, and Zr were associated with traffic emissions in both PM2.5 and PM2.5-10, while Fe, Li, Mn, and Ti were affected by traffic emissions mostly in PM2.5. The concentrations of Ba, Cu, Mo, Sb, Zr (brake wear tracers), Pd (tailpipe tracer), and Zn (associated with tire wear) were higher at urban traffic sites than urban background locations by factors of 2.6-4.6. Both PM2.5 and PM2.5-10 elements showed large spatial variations, indicating the presence of diverse emission sources across sampling locations. Principal component analysis extracted four source factors that explained 88% of the variance in the PM2.5 elemental concentrations, and three sources that explained 86% of the variance in the PM2.5-10 elemental concentrations. Based on multiple linear regression analysis, the contribution of traffic emissions (27%) to PM2.5 was found to be higher than mineral dust (23%), marine aerosol (18%), and industrial emissions (8%). On the other hand, mineral dust was the dominant source of PM2.5-10 with 45% contribution, followed by marine aerosol (22%), and traffic emissions (19%). This study provides novel insight into the spatial variation of traffic-related elements in a large metropolitan area.

Oral cavity response to air pollutant exposure and association with pulmonary inflammation and symptoms in asthmatic children.

Exposure to fine particulate matter (PM2.5) and ozone (O3) may lead to inflammation and oxidative damage in the oral cavity, which is hypothesized to contribute to the worsening of airway inflammation and asthma symptoms. In this panel study of 43 asthmatic children aged 5-13 years old, each child had 4 clinic visits with a 2-week interval between two consecutive visits. At each visit, saliva samples were collected and subsequently analyzed for interleukin 6 (IL-6) and eosinophil cationic protein (ECP) as biomarkers of inflammation and malondialdehyde (MDA) as a biomarker of oxidative stress in the oral cavity. At each visit, children were measured for fractional exhaled nitric oxide (FeNO) as a marker of pulmonary inflammation. Asthma symptoms of these children were measured using the Childhood Asthma Control Test (C-ACT). We found that an interquartile range (IQR) increase in 24-h average personal exposure to PM2.5 measured 1 and 2 days prior was associated with increased salivary IL-6 concentration by 3.0% (95%CI: 0.2%-6.0%) and 4.2% (0.7%-8.0%), respectively. However, we did not find a clear association between personal O3 exposure and any of the salivary biomarkers, except for a negative association between salivary MDA and O3 exposure measured 1 day prior. An IQR increase in salivary IL-6 concentration was associated with significantly increased FeNO by 28.8% (4.3%-53.4%). In addition, we found that increasing salivary IL-6 concentrations were associated with decreased individual and total C-ACT scores, indicating the worsening of asthma symptoms. We estimated that 13.2%-22.2% of the associations of PM2.5 exposure measured 1 day prior with FeNO and C-ACT scores were mediated by salivary IL-6. These findings suggest that the induction of inflammation in the oral cavity may have played a role in linking air pollution exposure with the worsening of airway inflammation and asthma symptoms.

Prediction of the oxidation potential of PM2.5 exposures from pollutant composition and sources.

The inherent oxidation potential (OP) of atmospheric particulate matter has been shown to be an important metric in assessing the biological activity of inhaled particulate matter and is associated with the composition of PM2.5. The current study examined the chemical composition of 388 personal PM2.5 samples collected from students and guards living in urban and suburban areas of Beijing, and assessed the ability to predict OP from the calculated metrics of carcinogenic risk, represented by ELCR (excess lifetime cancer risk), non-carcinogenic risk represented by HI (hazard index), and the composition and sources of the particulate matter using multiple linear regression methods. The correlations between calculated ELCR and HI and the measured OP were 0.37 and 0.7, respectively. HI was a better predictor of OP than ELCR. The prediction models based on pollutants (Model_1) and pollution sources (Model_2) were constructed by multiple linear regression method, and Pearson correlation coefficients between the predicted results of Model_1 and Model_2 with the measured volume normalized OP are 0.81 and 0.80, showing good prediction ability. Previous investigations in Europe and North America have developed location-specific relationships between the chemical composition of particulate matter and OP using regression methods. We also examined the ability of relationships between OP and composition, sources, developed in Europe and North America, to predict the OP of particulate matter in Beijing from the composition and sources determined in Beijing. The relationships developed in Europe and North America provided good predictive ability in Beijing and it suggests that these relationships can be used to predict OP from the chemical composition measured in other regions of the world.

Source Apportionment of Fine Organic Particulate Matter (PM2.5) in Central Addis Ababa, Ethiopia.

The development of infrastructure, a rapidly increasing population, and urbanization has resulted in increasing air pollution levels in the African city of Addis Ababa. Prior investigations into air pollution have not yet sufficiently addressed the sources of atmospheric particulate matter. This study aims to identify the major sources of fine particulate matter (PM2.5) and its seasonal contribution in Addis Ababa, Ethiopia. Twenty-four-hour average PM2.5 mass samples were collected every 6th day, from November 2015 through November 2016. Chemical species were measured in samples and source apportionment was conducted using a chemical mass balance (CMB) receptor model that uses particle-phase organic tracer concentrations to estimate source contributions to PM2.5 organic carbon (OC) and the overall PM2.5 mass. Vehicular sources (28%), biomass burning (18.3%), plus soil dust (17.4%) comprise about two-thirds of the PM2.5 mass, followed by sulfate (6.5%). The sources of air pollution vary seasonally, particularly during the main wet season (June-September) and short rain season (February-April): From motor vehicles, (31.0 ± 2.6%) vs. (24.7 ± 1.2%); biomass burning, (21.5 ± 5%) vs. (14 ± 2%); and soil dust, (11 ± 6.4%) vs. (22.7 ± 8.4%), respectively, are amongst the three principal sources of ambient PM2.5 mass in the city. We suggest policy measures focusing on transportation, cleaner fuel or energy, waste management, and increasing awareness on the impact of air pollution on the public's health.

Chemical Investigation of Household Solid Fuel Use and Outdoor Air Pollution Contributions to Personal PM2.5 Exposures.

In communities with household solid fuel use, transitioning to clean stoves/fuels often results in only moderate reductions in fine particulate matter (PM2.5) exposures; the chemical composition of those exposures may help explain why. We collected personal exposure (men and women) and outdoor PM2.5 samples in villages in three Chinese provinces (Shanxi, Beijing, and Guangxi) and measured chemical components, including water-soluble organic carbon (WSOC), ions, elements, and organic tracers. Source contributions from chemical mass balance modeling (biomass burning, coal combustion, vehicles, dust, and secondary inorganic aerosol) were similar between outdoor and personal PM2.5 samples. Principal component analysis of organic and inorganic components identified analogous sources, including a regional ambient source. Chemical components of PM2.5 exposures did not differ significantly by gender. Participants using coal had higher personal/outdoor (P/O) ratios of coal combustion tracers (picene, sulfate, As, and Pb) than those not using coal, but no such trend was observed for biomass burning tracers (levoglucosan, K+, WSOC). Picene and most levoglucosan P/O ratios exceeded 1 even among participants not using coal and biomass, respectively, indicating substantial indirect exposure to solid fuel emissions from other homes. Contributions of community-level emissions to exposures suggest that meaningful exposure reductions will likely require extensive fuel use changes within communities.

Cytotoxicity and chemical composition of women's personal PM2.5 exposures from rural China.

Personal exposure PM samples aid in determining the sources and chemical composition of real-world exposures, particularly in settings with household air pollution. However, their use in toxicological research is limited, despite uncertainty regarding health effects in these settings and evidence of differential toxicity among PM2.5 sources and components. This study used women's PM2.5 exposure samples collected using personal exposure monitoring in rural villages in three Chinese provinces (Beijing, Shanxi, and Sichuan) during summer and winter. Water-soluble organic carbon, ions, elements, and organic tracers (e.g. levoglucosan and polycyclic aromatic hydrocarbons [PAHs]) were quantified in water and organic PM2.5 extracts. Human lung epithelial cells (A549) were exposed to the extracts. Cell death, reactive oxygen species (ROS), and gene expression were measured. Biomass burning contributions were higher in Sichuan samples than in Beijing or Shanxi. Some PM characteristics (total PAHs and coal combustion source contributions) and biological effects of organic extract exposures (cell death, ROS, and cytokine gene expression) shared a common trend of higher levels and effects in winter than in summer for Shanxi and Beijing but no seasonal differences in Sichuan. Modulation of phase I/AhR-related genes (cyp1a1 and cyp1b1) and phase II/oxidative stress-related genes (HO-1, SOD1/2, NQO-1, and catalase) was either low or insignificant, without clear trends between samples. No significant cell death or ROS production was observed for water extract treatments among all sites and seasons, even at possible higher concentrations tested. These results support organic components, particularly PAHs, as essential drivers of biological effects, which is consistent with some other evidence from ambient PM2.5.

Estimation of commercial cooking emissions in real-world operation: Particulate and gaseous emission factors, activity influencing and modelling.

Measurements of real-world cooking emission factors (CEFs) were rarely reported in recent year's studies. However, the needs for accurately estimating CEFs to produce cooking emission inventories and further implement controlling measures are urgent. In this study, we collected cooking emission aerosols from real-world commercial location operations in Beijing, China. 2 particulate (PM2.5, OC) and 2 gaseous (NMHC, OVOCs) CEF species were examined on influencing activity conditions of cuisine type, controlling technology, operation scales (represented by cook stove numbers), air exhausting volume, as well as location and operation period. Measured NMHC emission factors (Non-barbecue: 8.19 ± 9.06 g/h and Barbecue: 35.48 ± 11.98 g/h) were about 2 times higher than PM2.5 emission factors (Non-barbecue: 4.88 ± 3.43 g/h and Barbecue: 15.48 ± 7.22 g/h). T-test analysis results showed a significantly higher barbecued type CEFs than non-barbecued cuisines for both particulate and gaseous emission factor species. The efficacy of controlling technology was showing an average of 50 % in decreasing PM2.5 CEFs while a 50 % in increasing OC particulate CEFs. The effects of controlling equipment were not significant in removing NMHC and OVOCs exhaust concentrations. CEF variations within cook stove numbers and air exhausting volume also reflected a comprehensive effect of operation scale, cuisine type and control technology. The simulations among activity influencing factors and CEFs were further determined and estimated using hierarchical multiple regression model. The R square of this simulated model for PM2.5 CEFs was 0.80 (6.17 × 10-9) with standardized regression coefficient of cuisine type, location, sampling period, control technology, cook stove number (N) and N2 of 5.18 (0.02), 5.33 (0.02), 1.93 (0.19), 9.29 (4.18 × 10-6), 9.10 (1.71 × 10-3) and -1.18 (2.43 × 10-3), respectively. In perspective, our study provides ways of better estimating CEFs in real operation conditions and potentially highlighting much more importance of cooking emissions on air quality and human health.