Particulate Matter Components Research Articles

Investigating the vital influences of multi-factor synergies on the nitrate formations on the surface of nano MgO particles

With the fast development of motor vehicle quantities in megacities in recent years, nitrate has become a very important component in atmospheric fine particulate matter, and produced significant impacts on...

Exposure to ambient fine particulate matter components during pregnancy and early childhood and its association with asthma, allergies, and sensitization in school-age children.

Exposure to fine particulate matter (PM2.5) has been associated with allergic diseases, including asthma. However, information about the effects of specific PM2.5 components is limited. This study aimed to investigate the relationship of exposure to chemical components of PM2.5 during pregnancy and early childhood with the development of asthma, allergies, and sensitization in school-age children. This study included 2,408 children in the second grade of elementary school. Questionnaire surveys of respiratory/allergic symptoms and measurements of serum total IgE and specific IgE levels to house dust mite (HDM) and animal proteins were conducted. Exposures to ambient PM2.5 mass, sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), elemental carbon (EC), and organic carbon (OC) of PM2.5 in participants' residences from conception to age six were estimated using predictive models. Multiple logistic regression analysis was used to analyze the association of respiratory/allergic symptoms and allergen sensitization with estimated exposure concentrations, after adjustment for survey year, sex, season of birth, feeding method during infancy, presence of siblings, history of lower respiratory tract infection, use of childcare facilities, passive smoking, presence of pets, mother's age, history of allergic diseases, smoking during pregnancy, and annual household income. No significant association was found between PM2.5 and its component concentrations and asthma. However, wheezing significantly increased with mean NO3- concentrations during pregnancy (odds ratio of 1.64 [95% confidence interval: 1.10, 2.47] for an interquartile range increase). Significant associations were also found between EC in the second trimester of pregnancy and PM2.5, NO3-, EC, and OC concentrations in early childhood. Higher PM2.5, SO4-, and NH4+ concentrations during the second trimester increased the risk of rhinitis. Sensitizations to HDM and animal proteins were significantly associated with exposure to components such as SO42- and NH4+ during pregnancy but not with postnatal exposure. Exposures to NO3-, EC, and OC during pregnancy and early childhood were associated with wheezing. SO42- and NH4+ exposures during pregnancy were associated with sensitization to HDM and animal proteins. Asthma was not associated with exposure to PM2.5 and its main components at any period.

Green space and its types can attenuate the associations of PM2.5 and its components with prediabetes and diabetes—— a multicenter cross-sectional study from eastern China

BackgroundThe effect of fine particulate matter (PM2.5) components on prediabetes and diabetes is of concern, but the evidence is limited and the specific role of different green space types remains unclear. This study aims to investigate the relationship of PM2.5 and its components with prediabetes and diabetes as well as the potential health benefits of different types and combinations of green spaces. MethodsA multicenter cross-sectional study was conducted in eastern China by using a multi-stage random sampling method. Health screening and questionnaires for 98,091 participants were performed during 2017–2020. PM2.5 and its five components were estimated by the inverse distance weighted method, and green space was reflected by the Normalized Difference Vegetation Index (NDVI), percentages of tree or grass cover. Multivariate logistic regression and quantile g-computing were used to explore the associations of PM2.5 and five components with prediabetes and diabetes and to elucidate the potential moderating role of green space and corresponding type combinations in these associations. ResultsEach interquartile range (IQR) increment of PM2.5 was associated with both prediabetes (odds ratio [OR]: 1.15, 95%CI [confidence interval]: 1.10–1.20) and diabetes (OR: 1.18, 95% CI: 1.11–1.25), respectively. All five components of PM2.5 were related to prediabetes and diabetes. The ORs of PM2.5 on diabetes were 1.49 (1.35–1.63) in the low tree group and 0.90 (0.82–0.98) in the high tree group, respectively. In the high tree-high grass group, the harmful impacts of PM2.5 and five components were significantly lower than in the other groups. ConclusionOur study suggested that PM2.5 and its components were associated with the increased risk of prediabetes and diabetes, which could be diminished by green space. Furthermore, the coexistence of high levels of tree and grass cover provided greater benefits. These findings had critical implications for diabetes prevention and green space-based planning for healthy city.

Influence of pollution control measures on the reduction of black carbon in an urban site of megacity, Tianjin, China based on ground-monitored and MERRA-2 reanalysis data

The concentration of particulate matter (PM) has been reduced significantly with the implementation of air pollution control plans in Tianjin. However, as an important component of PM that can lead to global warming and adverse health effects, the influence of pollution control measures (PCM) on black carbon (BC) has been less studied. In this study, ten years of BC concentration satellite-based reanalysis data were collected from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and their reliability was verified using ground-monitored BC data. Using the proposed Kolmogorov-Zurbenko and artificial neural network (KZ-ANN) model, the influences of meteorology and emission measures were separated. The results indicated that the overall meteorological conditions were not conducive to BC diffusion, especially in autumn and winter with low temperature, surface solar radiation, boundary layer height, and high atmospheric pressure, all of which increased the BC concentration. This study also found that although a significant reduction in BC emissions was observed in Tianjin (the total emissions of BC in 2020 dropped by 52 % compared with the level in 2013), the change in emission-influenced BC was relatively low (the concentration of emission-influenced BC in 2022 dropped by only 2.39 % compared to that in 2013). The reduction of emission-influenced BC concentration during the air pollution prevention control and action plan (APPC) was higher than the level during of the three-year action plan for winning the blue sky defense war (abbreviated as the Blue Sky Defense War). In addition, the lockdown measures during the Corona Virus Disease 2019 (COVID-19) did not have beneficial effect on the reduction of emission-influenced BC concentration. This phenomenon can be explained by the long-range transport of BC from surrounding areas, which was also proven by the results of the backward trajectory analysis. Therefore, efforts on emissions reduction in Tianjin were diminished. It is necessary to cooperate with the governments in surrounding areas to implement joint BC control measures, especially in autumn and winter.

Diagnosing the Sensitivity of Particulate Nitrate to Precursor Emissions Using Satellite Observations of Ammonia and Nitrogen Dioxide

AbstractParticulate nitrate is a major component of fine particulate matter (PM2.5). Its formation may be varyingly sensitive to emissions of ammonia (NH3), nitrogen oxides (NOx ≡ NO + NO2), and volatile organic compounds (VOCs), depending on local conditions. Diagnosing these sensitivities is critical for successful air quality management. Here, we show that satellite measurements of tropospheric NH3 and NO2 columns can be used as a quick indicator of the dominant sensitivity regime through the NH3/NO2 column ratio together with the NO2 column. We demonstrate the effectiveness of this indicator with the GEOS‐Chem chemical transport model and define thresholds to separate the different sensitivity regimes. Applying the method to wintertime IASI and OMI observations in East Asia reveals that surface nitrate is dominantly VOC‐sensitive in the southern North China Plain (NCP), NOx‐sensitive in most of the East China Plain, and NH3‐sensitive in the northern NCP, southern China, and Korea.

Effects of in-utero personal exposure to PM2.5 sources and components on birthweight

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-h personal PM2.5 exposure monitoring sub-study. The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions evaluated the association between personal PM2.5 sources/components and birthweight, adjusting for gestational age, maternal age, race, infant sex, parity, diabetes status, temperature, maternal education, and smoking history. Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3295.8 g (484.1) and mean PM2.5 exposure was 21.3 (14.4) µg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2 g decrease in birthweight (95% CI − 197.7, − 0.6), and aged sea salt was associated with a 70.1 g decrease in birthweight (95% CI − 141.7, 1.4). Magnesium, sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass. This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

Comparative Hygienic Assessment of the Composition of Ash and Dust Fractions in Ambient Air of the Area Affected by Emissions from a Thermal Power Station: Improving the Accuracy of Human Health Risk Assessment

Introduction: The relevance of the study is determined by a great number of functioning thermal power stations that operate using solid fuels (40 %) and are a source of ambient air pollution posing human health risks. Objective: A comparative hygienic assessment of the component, disperse, and morphological composition of fly ash and airborne particles in the area surrounding a coal-fired power station for the purpose of increasing the accuracy of human health risk assessment. Materials and methods: We have used unified and approved methods for hygienic assessment of ambient air quality; hazard identification and health risk assessment; scanning electron microscopy and micro-X-ray fluorescence spectroscopy of component, disperse, and morphological structure of fly ash and airborne particulate matter. Results and discussion: Large coal-fired thermal power plants emit about 36 chemicals. Over 59 % of brown coal ash particles are calcium, magnesium, iron, silicon, aluminum, sodium, potassium, sulfur, and phosphorus compounds. Particles found in fly ash and ambient air of the surrounding area have similar components and dispersion, are predominantly less than 10 µm in diameter with the sphericity index ranging from 0.4 to 1.0. Maximum allowable concentrations (MAC) of seven chemicals are exceeded in the study area: up to 3.3 single MAC, up to 1.4 average daily MAC, and up to 1.5 average annual MAC, with the estimated contribution of the thermal power stations of about 40 %. We have also established increased non-carcinogenic health risks with the dust fractions alone generating risk levels as high as 5.5 HQac, 2.4 HQch, and 6.9 HIch, which are rated as “high” and “alerting”. Identified solid particles (aluminum, magnesium, calcium, etc.) are more significant risk factors compared to unidentified particulate matter and can cause respiratory and circulatory diseases, diseases of the eye, etc., which requires their quantification. These chemicals are not included in air emissions inventory lists and are, therefore, not monitored. As a result, health risks posed by economic activities of thermal power stations may be underestimated.

Spatiotemporal Analysis of Ozone Concentration Using Semi-Functional Partial Linear Models

As weather warms up in China, ozone pollution rises to the top of the list of air pollutants. In this research, we examine the spatiotemporal variability of particulate matter components using contemporary functional data analysis techniques. The technique models the yearly pollutant profiles to describe their dynamic behavior over time and location. These cutting-edge methods offer dimension reduction for better data display and permit us to forecast annual profiles for locations and years for which data are lacking. In order to accurately estimate hourly ozone concentrations for 12 stations in China over two years (2015-2016), this study set out to showcase the best prediction models currently available. To accurately predict Ozone concentration, several methods are used, including Kernel Functional Classical Estimation (KFCE), Kernel Functional Quantile estimation (KFQE), Semi-Partial Linear Functional Classical Estimation (SPLFCE), Semi-Partial Linear Functional Quantile Estimation (SPLFQE), and Semi-Partial Linear Functional Expectile Estimation (SPLFEE). These functional models were chosen based on their ability to establish a forecast region with a given level of confidence. In terms of prediction accuracy, we may conclude that the Semi-Partial linear models outperform conventional models.

Comparison of Biological Activities and Protective Effects on PAH-Induced Oxidative Damage of Different Coffee Cherry Pulp Extracts.

Polycyclic aromatic hydrocarbons (PAHs) are the main toxic components of ambient air particulate matter (PM), causing oxidative damage to the skin and ultimately resulting in skin aging. This study was conducted to determine the anti-oxidant, anti-aging properties and protective effects of the extracts of coffee cherry pulp (Coffea arabica L.), which is a by-product of the coffee industry, against the oxidative damage induced by PAH exposure in human epidermal keratinocytes (HaCaT). Three different techniques were used to extract the coffee cherry pulp: maceration, Soxhlet and ultrasonication to obtain CCM, CCS and CCU extract, respectively, which were then compared to investigate the total phenolic content (TPC) and total flavonoid content (TFC). The chemical compositions were identified and quantified using high-performance liquid chromatography (HPLC). The results demonstrated that Soxhlet could extract the highest content of chlorogenic acid, caffeine and theophylline. CCS showed the significantly highest TPC (324.6 ± 1.2 mg GAE/g extract), TFC (296.8 ± 1.2 mg QE/g extract), anti-radical activity against DPPH free radicals (98.2 ± 0.8 µM Trolox/g extract) and lipid peroxidation inhibition (136.6 ± 6.2 µM Trolox/g extract). CCS also showed the strongest anti-aging effects based on collagenase, elastase, hyaluronidase and tyrosinase inhibitory enzymes. In addition, CCS can protect human keratinocyte cells from PAH toxicity by increasing the cellular anti-oxidant capacity. This study suggests that CCS has the potential to be used as a cosmetic material that helps alleviate skin damage caused by air pollution.

Predicted and observed changes in summertime biogenic and total organic aerosol in the southeast United States from 2001 to 2010

Biogenic secondary organic aerosol (bSOA) is a major component of atmospheric particulate matter (PM2.5) in the southeast United States especially during the summer, when emissions of biogenic volatile organic compound (VOCs) are high and emissions from anthropogenic sources enhance the formation of secondary particulate matter. In this work we test the hypothesis whether a chemical transport model (PMCAMx) that includes a detailed description of gas-phase chemistry, secondary organic aerosol (SOA) formation based on the volatility basis set (VBS), and interactions among compounds based on partitioning theory can predict organic aerosol (OA) concentrations that are consistent with the observed changes in OA in response to significant changes in anthropogenic emissions during the summers of 2001 and 2010. The model had good performance for OA for both periods and its predictions were consistent with the observed changes in both the urban and rural areas. The fractional error of OA predictions remained practically the same (0.41 and 0.44 at Chemical Speciation Network (CSN) sites and 0.40 to 0.41 at Interagency Monitoring of Protected Visual Environments (IMPROVE) sites in the summers of 2001 and 2010 respectively) in the two examined periods. The fractional bias of OA predictions increased from 0.10 to 0.22 at CSN sites and decreased from 0 to −0.09 at IMPROVE sites between the two periods. Average predicted bSOA concentrations in the southeast US did not change appreciably from the summer of 2001 to the summer of 2010, while the anthropogenic SOA decreased by 45%. As a result, the biogenic fraction of predicted total OA increased from 0.46 in 2001 to 0.63 in 2010. Partitioning effects due to reduced anthropogenic OA from 2001 resulted in 0.4 μg m−3 less biogenic OA on average in the southeast US in the summer of 2010. This was offset by biogenic SOA increases due to higher biogenic vapor emissions in the warmer 2010 summer. Removing the NOx-dependence of SOA formation yields resulted in higher fractional error and fractional bias at both CSN and IMPROVE sites in both summer periods, demonstrating the efficacy of the current formulation of SOA yields. The results of the analysis support the conclusion that a CTM that simulates NOx-dependent SOA chemistry and semivolatile partitioning of SOA material can consistently predict the observed changes in a region rich in biogenic VOCs and SOA.

Characterizing the neuroimmune environment of offspring in a novel model of maternal allergic asthma and particulate matter exposure

Inflammation during pregnancy is associated with an increased risk for neurodevelopmental disorders (NDD). Increased gestational inflammation can be a result of an immune condition/disease, exposure to infection, and/or environmental factors. Epidemiology studies suggest that cases of NDD are on the rise. Similarly, rates of asthma are increasing, and the presence of maternal asthma during pregnancy increases the likelihood of a child being later diagnosed with NDD such as autism spectrum disorders (ASD). Particulate matter (PM), via air pollution, is an environmental factor known to worsen the symptoms of asthma, but also, PM has been associated with increased risk of neuropsychiatric disorders. Despite the links between asthma and PM with neuropsychiatric disorders, there is a lack of laboratory models investigating combined prenatal exposure to asthma and PM on offspring neurodevelopment. Thus, we developed a novel mouse model that combines exposure to maternal allergic asthma (MAA) and ultrafine iron-soot (UIS), a common component of PM. In the current study, female BALB/c mice were sensitized for allergic asthma with ovalbumin (OVA) prior to pregnancy. Following mating and beginning on gestational day 2 (GD2), dams were exposed to either aerosolized OVA to induce allergic asthma or phosphate buffered saline (PBS) for 1 h. Following the 1-h exposure, pregnant females were then exposed to UIS with a size distribution of 55 to 169 nm at an average concentration of 176 ± 45 μg/m3) (SD), or clean air for 4 h, over 8 exposure sessions. Offspring brains were collected at postnatal days (P)15 and (P)35. Cortices and hippocampal regions were then isolated and assessed for changes in cytokines using a Luminex bead-based multiplex assay. Analyses identified changes in many cytokines across treatment groups at both timepoints in the cortex, including interleukin-1 beta (IL-1β), and IL-17, which remained elevated from P15 to P35 in all treatment conditions compared to controls. There was a suppressive effect of the combined MAA plus UIS on the anti-inflammatory cytokine IL-10. Potentially shifting the cytokine balance towards more neuroinflammation. In the hippocampus at P15, elevations in cytokines were also identified across the treatment groups, namely IL-7. The combination of MAA and UIS exposure (MAA–UIS) during pregnancy resulted in an increase in microglia density in the hippocampus of offspring, as identified by IBA-1 staining. Together, these data indicate that exposure to MAA, UIS, and MAA–UIS result in changes in the neuroimmune environment of offspring that persist into adulthood.

Impact of Haze Event on Daily Admission of Respiratory System Patients in Peninsular Malaysia

Diseases of the respiratory system, especially in children and the elderly, are significantly related to air pollution. Exposure to air pollution has led to an increase in the number of patients who need hospital treatment. The purpose of this study was to learn about the effects of changes in the levels of major pollutant components on the number of daily hospital admissions of respiratory system patients. The generalised linear lag model is used in this study to demonstrate the lag structure of the exposure-response impacts. The results show that particulate matter (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) component factors, as well as meteorological factors like wind speed and ultraviolet (UV) radiation, affect the number of hospital admissions of respiratory system patients. The best model is a lag 6 negative binomial regression model. Daily hospital admission is positively correlated with PM10, NO2, and wind speed, and negatively correlated with CO, O3, and UV radiation. According to the findings of the study, fine particulate matter (PM2.5) and sulphur dioxide (SO2), as well as temperature, humidity, and wind direction, are not significantly contributing factors in the number of respiratory system patients admitted to hospitals.

Atmospheric PM2.5 before and After Lockdown in Relation to COVID-19 Evolution and Daily Viral Counts: Could Viral Natural Selection Have Occurred due to Changes in the Airborne Pollutant PM2.5?

Background: Genes coding for SARS-CoV-2 have been detected on the microscopic airborne pollutant particulate matter, which has been suggested as a vector and co-factor for COVID-19 transmission. Lockdown in China has been shown to be associated with significant reduction in pollution including the particulate matter component which coincided with the appearance of a viral mutant (Clade G) which steadily displaced the original Clade D after lockdown. The reason why Clade G developed a fitness advantage is as yet unknown. This paper examines the possible role of airborne particulate matter PM2.5 as selective pressure determining viral Clade predominance and further shedding light on the mode of SARS-CoV-2 transmission. Methods: The average levels of PM2.5 of a number of cities were obtained from the Air Quality Index (AQI), a real-time assessment of atmospheric pollution. The daily average PM2.5 levels were assessed between January 23rd and April 29th 2020 determined by the timeline when viral counts in Beijing and other cities were available. Daily viral counts of Clades D and G were available starting from the 12th February as determined by the scientific literature published in August 2020. The cities chosen were Beijing, Sheffield, Nottingham, Sydney and Cambridge because of their substantially elevated viral counts compared to other cities. Cities as opposed to vaster areas/nations were chosen as PM2.5 levels vary across regions and countries. Results: For the time period assessed, the Beijing PM2.5 pattern initiated with highly elevated mean PM2.5 levels of 147.5µg/ m3 (SD+/-70.5) during high viral counts, followed by 98.5µg/m3 (SD+/-50.7) (p<0.007) when the viral counts decreased. In all the other cities assessed, the pattern differed whereby the PM2.5 levels increased significantly over the preceding baseline contemporaneously with the viral count rise. The changes in these cities’ PM2.5 levels were on average 34.4µg/m3 before viral counts rose and 46.0µg/m3 contemporaneous with viral count rise. As regards the viral counts from data retrieved from Beijing, the latter part of the bell-shaped curve and a subsequent smaller curve of the viral count was available for evaluation. The average viral count for Clade D in Beijing was 11.1(SD+/-13.5) followed by a mean viral count for Clade G was 13.8(SD+/- 9.2). Conversely in all the other cities besides Beijing, the viral counts averaged 45.8 for Clade D and 161 for Clade G. The variation in viral counts between cities suggests the strong possibility of variation in the availability of sampling between cities. The newer variant, Clade G demonstrated viral counts initially appearing in mid-February in Beijing to later displace Clade D as the dominant viral Clade. The appearance of Clade G coincided with the decreasing gradient of PM2.5 levels. A number of significant correlations were obtained between PM2.5 levels and the viral count in all the cities reviewed. Conclusion: COVID-19 viral counts appear to increase concomitant with increasing PM2.5 levels. Viral counts of both Clades correlated differentially with PM2.5 levels in all the cities assessed. The significantly highly elevated PM2.5 levels in Beijing resulted in correlating mainly with Clade D, however Clade G began to appear with decreasing PM2.5 levels, suggesting the beginnings for the initial SARS-CoV-2 Clade evolution. Clade G, the newer variant was able to flourish at lower levels of PM2.5 than Clade D. Clade G may possibly have utilized other sources of particulate matter as a viral vector, and co-factor such as that derived from tobacco smoking, whereby 66% of Chinese males are smokers and 70% of the Chinese non-smoking population are exposed to 2nd hand smoking.

Concentration identification and endpoint-oriented health risk assessments on a broad-spectrum of organic compounds in atmospheric fine particles: A sampling experimental study in Beijing, China

Understanding the complicate chemical components in atmospheric fine particulate matter (PM2.5) helps policy makers for pollutants control track progress and identify disparities in overall health risks. However, till now, information on accurate component detection, source identification, and effect-oriented risk assessment is scarce, especially for the simultaneous analysis of a broad-spectrum of compounds. In this study, a high-throughput target method was employed to distinguish the occurrence and characteristics of 152 chemicals: phthalate esters (PAEs), organophosphate esters (OPEs), carboxylic acid esters (CAEs), nitrophenols (NPs), nitrogen heterocyclic compounds (NHCs), per- and poly-fluoroalkyl substances (PFASs), triclosan and its derivatives (TCSs), and organosulfates (OSs) in ambient PM2.5 collected from Beijing, China. Detection frequencies of 77 targeted compounds were >50 %. Total concentrations of all compounds ranged from 33.1 to 745 ng/m3. The median concentration of ∑PAEs (108 ng/m3) was the highest, followed by ∑CAEs (12.2 ng/m3) and ∑NPs (10.1 ng/m3). Organophosphate diesters (di-OPEs) and TCSs were reported for the first time in ambient PM2.5. The pollutants mainly originated from the local industrial production, release of building materials, and environmental degradation of parent compounds. Based on absorption, distribution, metabolism, excretion, and toxicity (ADMET)-oriented risk evaluations, we found that bis (2-ethylhexyl) phthalate, diisobutyl phthalate, dibutyl phthalate, and di (2-ethylhexyl) adipate have high health risks. Additionally, the high oxidative stress potential of 4-nitrocatechol and the strong blood-brain barrier penetration potential of triclosan cannot be ignored. Our study will facilitate the evaluations of specific health outcomes and mechanisms of pollutants, and suggestion of pollutants priority control to reduce human health hazards caused by atmospheric particles.

The complexation of atmospheric Brown carbon surrogates on the generation of hydroxyl radical from transition metals in simulated lung fluid

Atmospheric particulate matter (PM) poses great adverse effects through the production of reactive oxygen species (ROS). Various components in PM are acknowledged to induce ROS formation, while the interactions among chemicals remain to be elucidated. Here, we systematically investigate the influence of Brown carbon (BrC) surrogates (e.g., imidazoles, nitrocatechols and humic acid) on hydroxyl radical (OH) generation from transition metals (TMs) in simulated lung fluid. Present results show that BrC has an antagonism (interaction factor: 20–90 %) with Cu2+ in OH generation upon the interaction with glutathione, in which the concentrations of BrC and TMs influence the extent of antagonism. Rapid OH generation in glutathione is observed for Fe2+, while OH formation is very little for Fe3+. The compositions of antioxidants (e.g., glutathione, ascorbate, urate), resembling the upper and lower respiratory tract, respond differently to BrC and TMs (Cu2+, Fe2+ and Fe3+) in OH generation and the degree of antagonism. The complexation equilibrium constants and site numbers between Cu2+ and humic acid were further analyzed using fluorescence quenching experiments. Possible complexation products among TMs, 4-nitrocatechol and glutathione were also identified using quadropule-time-of-flight mass spectrometry. The results suggest atmospheric BrC widely participate in complexation with TMs which influence OH formation in the human lung fluid, and complexation should be considered in evaluating ROS formation mediated by ambient PM.

PM2.5-Bound Fatty Acids and Oxidative Potential: A Year-Round Study in Chiang Mai, Upper Northern Thailand

Air pollution study mostly targeted in components of fine particulate matter (PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm) but their health implication is still scarce. Fatty acids could exhibit surfactant properties of PM2.5. We investigated the oxidative potential (OP) of PM2.5-bound fatty acids in Chiang Mai, upper northern Thailand: a year-round study in 2021. A dithiothreitol (DTT), an acellular assay, was used to evaluate the OP of eighty-nine PM2.5 filters collected from January to December 2021. A series of PM2.5-bound fatty acids i.e., C14:0–C30:0 fatty acids were determined by gas chromatography/mass spectrometry (GC/MS). Total fatty acids (C14:0–C30:0 and C18:1, C18:2) accounted for 498.7 ± 196.1 and 206.9 ± 112.8 ng/m³ in high biomass burning (HBB) and low biomass burning (LBB) periods, respectively. Mean values of OPDTTv (OP normalized by air volume of sampling) in HBB and LBB periods were 0.13 ± 0.06 and 0.03 ± 0.03 nmol/min/m³, respectively. Long chain saturated fatty acids (C22:0-26:0) mostly showed moderately significant correlation with OPDTTv (r = 0.33-0.47, P < 0.01) in HBB period but showed no significant correlation in LBB period. Meanwhile, linoleic acid (C18:2) was the only individual compound among unsaturated fatty acids that significantly correlated with OPDTTv (r = 0.39, P < 0.01). These findings suggest that PM2.5-bound fatty acids, particularly during open-biomass burning period, contribute to variations in oxidative potential, potentially influencing the health risks associated with PM2.5 exposure.

Investigating Vertical Distributions and Driving Factors of Black Carbon in the Atmospheric Boundary Layer Using Unmanned Aerial Vehicle Measurements in Shanghai, China

Black carbon (BC) is a significant component of fine particulate matter (PM2.5, with aerodynamic diameters ≤ 2.5 μm), and its spatial distribution greatly affects the global radiation budget. However, the vertical distributions and key driving factors of BC in the atmospheric boundary layer, where BC is mostly concentrated, remain unclear. In this study, gradient measurements of BC were made using an unmanned aerial vehicle (UAV) platform from ground level to 500 m above ground level (AGL) during and after the 2016 G20 control period in Shanghai. Generally, vertical profiles of BC from local time (LT) 9 to 17 on all experimental days demonstrated an upward trend with increasing height. The BC emitted from chimneys was initially released at higher altitudes, resulting in the positive gradients of vertical BC profiles. Furthermore, with the progressive development of the boundary layer height from LT 9 to 15, the average concentration of BC per vertical profile decreased. However, meteorological conditions unfavorable for dispersions caused by particularly high temperatures, low wind speed, unfavorable boundary layer conditions, or especially high relative humidity, and hygroscopic growth owing to the extremely high relative humidity, led to an overall increase in vertical BC and ground-based PM2.5 and BC. Despite the impact of adverse meteorological conditions, emission control measures during the control period not only effectively decreased the BC concentration but also reduced the proportion of BC in PM2.5 in the atmospheric boundary layer. The results of this study can provide valuable observations for evaluating numerical model results and important implications for making control strategies of BC in the future.

Sulfur isotope-based source apportionment and control mechanisms of PM2.5 sulfate in Seoul, South Korea during winter and early spring (2017–2020)

High level of particulate matter (PM) concentrations are a major environmental concern in Seoul, South Korea, especially during winter and early spring. Sulfate is a major component of PM and induces severe environmental pollution, such as acid precipitation. Previous studies have used numerical models to constrain the relative contributions of domestic and trans-boundary sources to PM2.5 sulfate concentration in South Korea. Because of the scarce measurement result of δ34S for PM2.5 sulfate in South Korea, poorly defined δ34S value of domestic sulfur sources, and no application of sulfur isotope fractionation during sulfate formation in previous observation-based studies, source apportionment results conducted by model studies have not been corroborated from independent chemical observations. Here, we examined the δ34S of PM2.5 in Seoul and domestic sulfur sources, and considered the sulfur isotope fractionation for accurate source apportionment constraint. Accordingly, domestic and trans-boundary sulfur sources accounted for approximately (16–32) % and (68–84) % of the sulfate aerosols in Seoul, respectively, throughout the winter and early spring of 2017–2020. Air masses passing through north-eastern China had relatively low sulfate concentrations, enriched δ34S, and a low domestic source contribution. Those passing through south-eastern China had relatively a high sulfate concentrations, depleted δ34S, and high domestic source contribution. Furthermore, elevated PM2.5 sulfate concentrations (>10 μg m−3) were exclusively associated with a weak westerly wind speed of <3 m s−1. From December 2019 to March 2020, Seoul experienced relatively low levels of PM2.5 sulfate, which might be attributed to favorable weather conditions rather than the effects of COVID-19 containment measures. Our results demonstrate the potential use of δ34S for accurate source apportionment and for identifying the crucial role of regional air mass transport and meteorological conditions in PM2.5 sulfate concentration. Furthermore, the data provided can be essential for relevant studies and policy-making in East Asia.

The association of particulate matter components with heart rate variability

The association of particulate matter components with heart rate variability

Fine-scale Disparities in Exposure to Particulate Matter Components in U.S. Urban Areas

Fine-scale Disparities in Exposure to Particulate Matter Components in U.S. Urban Areas