Ambient Particulate Concentrations Research Articles

Reduction by air purifier of particulate concentration during orthodontic procedures: a pilot study

BackgroundThe SARS-CoV-2 pandemic has raised awareness of the importance of air quality. This pilot study arose from the need to reduce the concentration of particulate matter in the dental office during orthodontic procedures. To evaluate the efficacy of using an air purifier during orthodontic care in the dental office to reduce the concentration of ambient particulate matter.ResultsSignificant reductions in particle numbers were obtained for all particle sizes except the largest particles counted (10 μm) through use of the air filter. A marked association between higher humidity levels and higher particle counts was also observed.ConclusionsUsing an air purifier during dental care achieves a significant reduction in the concentration of ambient particles in the dental office. There is a correlation between higher relative humidity and higher particle concentration. The probability of obtaining a maximum particulate concentration level of 0.3 and 0.5 μm is 1000 times lower when using an air purifier.

Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1

Abstract. Below-cloud scavenging (BCS) is the process of aerosol removal from the atmosphere between cloud base and the ground by precipitation (e.g., rain or snow), and affects aerosol number or mass concentrations, size distribution, and lifetime. An accurate representation of precipitation phases is important in treating BCS as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. The impact of different representations of BCS on existing model biases was examined through implementing a new aerosol BCS scheme in the Environment and Climate Change Canada (ECCC) air quality prediction model GEM-MACH and comparing it with the existing scavenging scheme in the model. Further, the current GEM-MACH employs a single-phase precipitation for BCS: total precipitation is treated as either liquid or solid depending on a fixed environment temperature threshold. Here, we consider co-existing liquid and solid precipitation phases as they are predicted by the GEM microphysics. GEM-MACH simulations, in a local-area domain over the Athabasca oil sands areas, Canada, are compared with observed precipitation samples, with a focus on the particulate base cation NH4+; acidic anions NO3-, SO4=, HSO3- in precipitation; and observed ambient particulate sulfate, ammonium, and nitrate concentrations. Overall, the introduction of the multi-phase approach and the new scavenging scheme enhances GEM-MACH performance compared to previous methods. Including a multi-phase approach leads to altered SO4= scavenging and impacts the BCS of SO2 into the aqueous phase over the domain. Sulfate biases improved from +46 % to −5 % relative to Alberta Precipitation Quality Monitoring Program wet sulfate observations. At Canadian Air and Precipitation Monitoring Network stations the biases became more negative, from −10 % to −30 % for the tests carried out here. These improvements contrast with prior annual average biases of +200 % for SO4=, indicating enhanced model performance. Improvements in model performance (via scores for correlation coefficient, normalized mean bias, and/or fractional number of model values within a factor of 2 of observations) could also be seen between the base case and the two simulations based on multi-phase partitioning for NO3-, NH4+, and SO4=. Whether or not these improvements corresponded to increases or decreases in NO3- and NH4+ wet deposition varied over the simulation region. The changes were episodic in nature – the most significant changes in wet deposition were likely at specific geographic locations and represent specific cloud precipitation events. The changes in wet scavenging resulted in a higher formation rate and larger concentrations of atmospheric particle sulfate.

Effects of extreme precipitation on bacterial communities and bioaerosol composition: Dispersion in urban outdoor environments and health risks

Concerns about contaminants dispersed by seasonal precipitation have grown due to their potential hazards to outdoor environments and human health. However, studies on the crucial environmental factors influencing dispersion changes in bacterial communities are limited. This research adopted four-season in situ monitoring and sequencing techniques to examine the regional distribution profiles of bioaerosols, bacterial communities, and risks associated with extreme snowfall versus rainfall events in two monsoon cities. In the early-hours of winter snowfall, airborne cultivable bioaerosol concentrations were 4.1 times higher than the reference exposure limit (500 CFU/m3). The concentration of ambient particles (2.5 μm) exceeded 24,910 particles/L (97 μg/m3), positively correlating with the prevalence of cultivable bioaerosols. These bioaerosols contained cultivable bacterial species such as pathogenic Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus pneumoniae, and Escherichia coli. Bioaerosol concentrations increased by 53.0% during 50-mm snow extremes. Taxonomic analysis revealed that Pseudomonas, Staphylococcus, and Veillonella were the most abundant bacterial taxa in the initial snowmelt samples during winter precipitation. However, their abundance decreased by 87.6% as snowing continued (24 h). Reduced water base cation concentration also led to a 1.15-fold increase in the Shannon index, indicating a similar yet heightened bacterial diversity. Seasonally, Pedobacter and Massilia showed higher relative abundance (25% and 18%, respectively), presenting increased bacterial transmission to the soil. Furthermore, Pseudomonas was identified in 60% of spring snowstorm samples, suggesting long-distance dispersal of pathogenic bacteria. When these atmospheric aerosol particles carrying biological entities (0.65–1.1 μm) penetrated human alveoli, the calculated hazard ratio was 0.55, which as observed in inhalation exposures. Consequently, this study underscores the risk of seasonal precipitation-enhanced ambient bacterial transmission.

Particulate Air Pollution and Blood Pressure: Signaling by the Arachidonate Metabolism.

Short-term exposure to ambient particulate matter (PM) can raise blood pressure, but the underlying mechanisms are unclear. We explored whether arachidonate metabolites serve as biological intermediates in PM-associated prohypertensive changes. This panel study recruited 110 adults aged 50 to 65 years living in Beijing, China. The participants' blood pressure, arterial stiffness, and cardiac and endothelial function were measured up to 7 times. The serum concentrations of arachidonate metabolites were quantified by targeted lipidomics. Ambient concentrations of fine PM (PM2.5), black carbon, and accumulation mode particles were continuously monitored at a station and their associations with the health indicators were evaluated. Interquartile range increases in 25 to 96-hour-lag exposure to PM2.5, black carbon, and accumulation mode particles were associated with significant increases in systolic blood pressure (brachial: 0.8-3.2 mm Hg; central: 0.7-2.8 mm Hg) and diastolic blood pressure (brachial, 0.5-1.5 mm Hg; central, 0.5-1.6 mm Hg). At least 1 pollutant was associated with increases in augmentation pressure and heart rate and decreases in reactive hyperemia index and ejection time. The serum concentrations of arachidonate were significantly increased by 3.3% to 14.6% in association with PM exposure, which mediated 9% of the PM-associated increases in blood pressure. The levels of eicosanoids from the cytochrome P450, cyclooxygenase, and lipoxygenase pathways changed with PM exposure, and those from the cytochrome pathway significantly mediated the association between PM exposure and blood pressure. Short-term exposure to particulate air pollution was associated with a prohypertensive change in adults, which was in part mediated by alteration of arachidonate metabolism.

Sensitivity analysis of volatile organic compounds to PM2.5 concentrations in a representative industrial city of Korea

This study aims to analyze the sensitivity of volatile organic compounds (VOCs) to ambient concentrations of fine particles (PM2.5) in the representative industrial city of Ulsan, Korea. For the calculation of sensitivity coefficients between VOCs and PM2.5 (SVOCs-PM2.5), PM2.5 data were obtained from an air quality monitoring station, and their corresponding 6-h average concentrations of VOCs (alkanes, alkenes, aromatics, and total VOCs) were measured at the Yeongnam intensive air monitoring station. The air monitoring period was divided into the warm-hot season (May–October 2020) and the cold season (November 2020–January 2021). The sensitivity coefficients in the low pollution period of PM2.5 (5 < PM2.5 ≤ 15 μg/m3) were higher and much higher than those in the medium pollution period (15 < PM2.5 ≤ 35 μg/m3) and high pollution period (35 < PM2.5 ≤ 50 μg/m3), respectively. This result indicates that the change ratios of PM2.5 concentrations to the background (PM2.5 ≤ 5 μg/m3) per unit concentration change of VOCs (particularly alkenes) in the high PM2.5 pollution period were much higher than those in the low pollution period. This also indicates that PM2.5 concentrations above 35 μg/m3 were more easily affected by the unit concentration change of VOCs (particularly alkenes) than those below 15 μg/m3. The average sensitivity coefficients during the cold season increased in a range of 23–125% as compared to those during the warm-hot season, except the alkenes-PM2.5 sensitivity with a decrease of 7%. It means that the impact of VOCs (except alkenes) on PM2.5 concentrations was relatively low in the cold season. However, in the cold season, the alkenes might contribute more to PM2.5 formation, particularly over the high pollution period, having PM2.5 > 35 μg/m3, than other VOC groups. The result of this study can be a basis for establishing PM2.5 management plans in industrial cities with large VOC emissions.

Quantifying the effects of urban fabric and vegetation combination pattern to mitigate particle pollution in near-road areas using machine learning

There are many intertwined factors such as urban fabric, vegetation combination, and meteorological conditions that influence the spatial distribution of fine particles (PM2.5, aerodynamic diameter less than 2.5 μm) in near-road urban areas, and it is difficult yet important to disentangle them. Identifying the relative importance of those factors could facilitate designing urban fabric and roadside vegetation combination patterns to mitigate the negative impacts of particles. This study first proposes an effective and reliable integrated method of the ENVI-met microclimate model and interpretable machine learning to quantify the multifactorial effects. Data from ENVI-met models with different built environments and meteorological conditions are used to train and test the machine learning (ML) models. All ML models show high accuracy in predicting PM2.5 concentrations with R2 high than 0.87. Results indicate that height level and study area are the two most important features, explaining 22.8% and 20.4% of the variation in PM2.5 concentration, respectively. The feature pairwise interactions account for 45.8% of ambient particle concentrations. The combination of trees and hedges in urban fabrics with longer building length than width could mitigate PM2.5 concentrations in residential areas. The integrated methods in this study could be extrapolated to future research to perform source identification analysis.

Estimating PM2.5 concentrations in a central region of China using a three-stage model

ABSTRACT Owing to uneven environmental monitoring site distribution, there are significant spatial data gaps for concentrations of ambient fine particles with diameters ≤ 2.5 µm (PM2.5) obtained using traditional monitoring methods. Satellite products are an alternative data source for locations where monitoring sites are unavailable. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product has been widely used in PM2.5 assessment for years; however, it has obvious data gaps in winter. Here, the Visible Infrared Imaging Radiometer Suite (VIIRS) AOD was applied to supplement MODIS AOD data to obtain a fused AOD dataset. A three-stage model consisting of a corrected AOD model, mixed effects model, and geographically weighted regression model was developed and used with meteorological and vegetation factors to estimate PM2.5. Results showed overall model fitting by cross-validation (CV) with an R2 of 0.92, mean absolute error of 5.72 µg/m3, and root mean square error of 7.15 µg/m3. The combination of MODIS AOD and VIIRS AOD was a suitable method for enhancing AOD coverage. The CV R2 value of the three-stage model (0.92) was higher than that of the two-stage model (0.9). Hence, the three-stage model could achieve a better fit in estimating PM2.5 on a regional scale.

Road traffic-induced particle matter dispersion in a calm wind environment at the main roundabout in Douala, central Africa

&#x0D; &#x0D; &#x0D; &#x0D; Introduction: Road traffic emissions are among the most significant sources of pollution in Douala, Cameroon's economic town, alongside industrial operations. The morning and the evening are two times of the day when traffic is heavier and the winds are also at their calmest. The majority of the non-exhaust Particulate Matters (PMs) produced by autos is re-suspended road contaminants. The purpose of this research is to estimate fine particle dispersion in conditions of calm winds.&#x0D; Materials and methods: In one of Douala's roundabouts, the Gaussian Plume model is employed to calculate the PM concentration under calm winds conditions. Different vehicle classes (HDV: Heavy Duty Vehicles, LDV: Light Duty Vehicles, PC: Passenger Cars) are used to figure out the amount of PMs they produce. Measurements of ambient fine particle concentrations are made with the OC-300 laser dust particle detector.&#x0D; Results: The results made it possible to compare actual measurements of PM2.5, PM10 (300±150 μg/m3 and 650±150 μg/m3, respectively) with simulated values (PM2.5, PM10: 310 μg/m3 and 631 μg/m3, respectively). The difference between in-situ and computed values can range from 10 to 132 μg/m3. From 6 to 10 AM, the population's exposure to PM pollution is more severe. It has also been demonstrated that there is a significant association between traffic flow and PM Concentration during the dry season (R2=0.921). With increased traffic flow intensity, particle concentration levels rise.&#x0D; Conclusion: The concentration threshold stays above the World Health Organization (WHO) limits regardless of the approach. Furthermore, this paper provides important information about Douala's pollution levels.&#x0D; &#x0D; &#x0D; &#x0D;

Extra-Pulmonary Translocation of Exogenous Ambient Nanoparticles in the Human Body.

As one of the major pollutants in the air, ambient fine particles are gaining considerable attention in terms of public health concerns. Significant progress has been achieved in recent years in understanding the biological effects and mechanisms of ambient fine particles. The airborne particles can enter the human body through various pathways and translocate to a range of different organs and further stay in these organs for extended periods. Current studies are making substantial achievements, while many challenges remain. On one hand, the whole picture of the concurrent exposure pathways and the translocation of particles in the human body should be explored, requiring technological advances and systematic biobanking of human samples for analysis. On the other hand, the correlation between the environmental exposure concentration of ambient particles and internal fate (i.e., dose, distribution patterns, and kinetics) of invasive particles needs to be investigated. Moreover, the biotransformation of particles in vivo should be considered, and more information is needed to differentiate exogenous particles from biological macromolecules and biogenically formed particles. We recapitulate the current knowledge gaps in understanding the fate of exogenous ambient fine particles in extra-pulmonary organs of the human body and the related biological effects and also propose future research directions to support both fundamental studies and policy making.

Measurements and predictions of ambient air particulates dry depositions at Taichung Shuinan Economic and Trade Park (T.S.E.T.P) during summer and autumn seasons

This study collected ambient air particulate concentrations and dry depositions at Taichung Shuinan Economic and Trade Park (T.S.E.T.P.) sampling site. The global dry depositions model was applied with known particle sizes, pollutant concentrations in ambient air pollutants dry deposition estimates at T.S.E.T.P. site in Central Taiwan. Statistical analysis of the correlated relationships among ambient air particulate concentration, dry depositions, and dry deposition velocities with respect to the meteorological conditions at T.S.E.T.P. site was also conducted. The results indicated that the average dry deposition fluxes were 377.81 µg/m2*min, the average TSP concentrations were 41.53 µg/m3, and the average dry depositions velocity (Vd ) was 15.26 cm/s. In addition, 40 μm was selected as the best ambient air particulate size for the calculated dry deposition velocities to estimate ambient air dry deposition fluxes using the Global model at this T.S.E.T.P. sampling site. Moreover, ambient air particulates concentrations were higher in the fall season than that of the summer season. Sincepollutants parcels were not easily diffused during the fall season because of the low mixed atmospheric layer in this season. Therefore, atmospheric particulates concentrations were higher from the northern side than that of southern side in this study. Furthermore, wind speed V.S. particulate dry depositions and wind V.S. dry depositions velocity displayed a moderate correlated relationship, while particulate dry depositions V.S. particulate dry depositions velocity presented the highest correlated relationship at this T.S.E.T.P. sampling site. However, particulate dry depositions and dry depositions velocity presented significant differences. Finally, particulate dry depositions and dry depositions velocity were not only highly correlation related but also displayed significant differences at the T.S.E.T.P. sampling site.

Review: Particulate Matter Emissions from Aircraft

The contribution of aircraft operations to ambient ultrafine particle (UFP) concentration at and around airports can be significant. This review article considers the volatile and non-volatile elements of particulate matter emissions from aircraft engines, their characteristics and quantification and identifies gaps in knowledge. The current state of the art emission inventory methods and dispersion modelling approaches are reviewed and areas for improvement and research needs are identified. Quantification of engine non-volatile particulate matter (nvPM) is improving as measured certification data for the landing and take-off cycle are becoming available. Further work is needed: to better estimate nvPM emissions during the full-flight; to estimate non-regulated (smaller) engines; and to better estimate the emissions and evolution of volatile particles (vPM) in the aircraft exhaust plume. Dispersion modelling improvements are also needed to better address vPM. As the emissions inventory data for both vPM and nvPM from aircraft sources improve, better estimates of the contribution of aircraft engine emissions to ambient particulate concentrations will be possible.

Performance of Valved Respirators to Reduce Emission of Respiratory Particles Generated by Speaking.

Wearing of face coverings serves two purposes: reducing the concentration of ambient particles inhaled and reducing the emission of respiratory particles generated by the wearer. The efficiency of different face coverings depends on the material, design, and fit. Face coverings such as N95 respirators, when worn properly, are highly efficient at filtering ambient particles during inhalation. Some N95 respirators, as well as other face covering types, include a one-way valve to allow easier exhalation while still maintaining a high efficiency of filtration of inhaled ambient particles. The extent to which these valves decrease the efficiency of filtration of emitted respiratory particles is, however, not well established. Here, we show that different valved N95s exhibit highly variable filtration efficiencies for exhaled respiratory particles. As such, valved N95s may not provide reliable source control of respired particles and their use should be discouraged in situations in which such source control is needed.

Health Risk Factors of Emissions from Internal Combustion Engine Vehicles: An Up-to-Date Status of the Problem

Introduction: Motor transport with internal combustion engines powered by diesel fuel and gasoline is one of the main sources of ambient air pollution since its emissions pose an urgent medical and environmental challenge. The objective of the study was to identify priority pollutants from emissions of motor vehicles powered by internal combustion engines based on the results of a systematic review in order to substantiate the main preventive strategy to mitigate the associated public health adverse effects. Methods: We did keyword search for relevant publications in several electronic databases, such as the Russian Science Citation Index, CyberLeninka, Scopus, and WoS. Research papers published in 2000–2021 were selected for the analysis. Out of 103 topical full-text publications, 59 works met the criteria for inclusion in the systematic review. Results: We observed that atmospheric emissions of internal combustion engines represent a complex agglomeration of gases, vapors, and particulate matter. The chemicals present in the emissions impair the oxygen transport function by inhibiting cellular respiration, cause irritation of mucous membranes, have mutagenic and carcinogenic effects, contribute to the occurrence of acid rains and to global warming. The biological effect of airborne particles largely depends on their size. It has been established that an increase in the number of airborne particles with an aerodynamic diameter less than 10 μm is associated with the risk of endothelial inflammation, thrombosis, increased cell permeability, and DNA methylation. It has been also demonstrated that a 5 µg/m3 increment in ambient concentrations of fine particles (&lt; 2.5 μm) causes a 7 % increase in the mortality rate. At the same time, PM2.5 exposure-related risks of excess deaths from cardiovascular diseases are twice as high as those posed by exposure to PM10. Conclusions: Diesel and gasoline engine exhausts are a significant risk factor for human health. An effective preventive strategy should be aimed at replacing heavy hydrocarbon motor fuels by compressed gas using hydrogen cells and electric motors.

LPS Response Is Impaired by Urban Fine Particulate Matter.

Fine particulate matter (PM2.5) is a complex mixture of components with diverse chemical and physical characteristics associated with increased respiratory and cardiovascular diseases mortality. Our study aimed to investigate the effects of exposure to concentrated PM2.5 on LPS-induced lung injury onset. BALB/c male mice were exposed to either filtered air or ambient fine PM2.5 in an ambient particle concentrator for 5 weeks. Then, an acute lung injury was induced with nebulized LPS. The animals were euthanized 24 h after the nebulization to either LPS or saline. Inflammatory cells and cytokines (IL-1β, IL-4, IL-5, IL-6, IL-10, IL-17, TNF) were assessed in the blood, bronchoalveolar lavage fluid (BALF), and lung tissue. In addition, lung morphology was assessed by stereological methods. Our results showed that the PM+LPS group showed histological evidence of injury, leukocytosis with increased neutrophils and macrophages, and a mixed inflammatory response profile, with increased KC, IL-6, IL-1β, IL-4, and IL-17. Our analysis shows that there is an interaction between the LPS nebulization and PM2.5 exposure, differently modulating the inflammatory response, with a distinct response pattern as compared to LPS or PM2.5 exposure alone. Further studies are required to explain the mechanism of immune modulation caused by PM2.5 exposure.

Applying principal component, health risk assessment, source identification for metallic elements of ambient air total suspended particulates at Taiwan Scientific Park.

This study collected ambient air total suspended particle (TSP) concentrations and dry depositions at Taichung Science Park sampling site. The metallic elements concentrations and dry depositions were analyzed. The possible pollutant sources are discussed in this study. In addition, this study used the principal component analysis method to find metallic element sources and their transportation pathway and distributions at Taichung Science Park (T.S.P). The results indicated that the average highest TSP concentrations and dry deposition fluxes occurred in the autumn and winter seasons. The highest average metallic element concentration and dry deposition was Fe, while the lowest average metallic element concentration and dry deposition was Hg(p). The study results further indicated that the lowest metallic element concentrations and dry depositions all occurred in the summer season. The pollutant parcels originated from the northern Taiwan counties and sea surface in the autumn, winter and spring seasons. Factor 1 was responsible for the local industrial emission results and traffic road dust. The metallic elements Hg(p) yield a value greater than 0.7 in Factor 2 which revealed that ambient air particulate pollutants were generated from Taichung Thermal Power Plant (T.T.P.P.) emissions and were transported from the coastal area of mainland China cities. The mean seasonal concentration differences existed for ambient air particulates, and there were no mean seasonal concentrations differences for all metallic elements. In addition, there were no significant mean concentrations differences for all metallic elements and meteorological factors such as temperature, humidity and wind speed. Therefore, the ambient air metallic element emissions were stable and considered primary emissions sources. The health risk value for metallic element Cr was higher than that for the acceptable health risk value suggested by the EPA. Metallic element Cr revealed that it was no mean seasonal concentrations differences. Thus, metallic element Cr was considered came from local emission source at this T.S.P. sampling site.

Quantitative Fit Test of a 3D Printed Frame Fitted Over a Surgical Mask: An Alternative Option to N95 Respirator.

Background COVID-19 has spread worldwide and caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to numerous dead cases. However, with the new COVID-19 outbreaks, there is a shortage of personal protective equipment (PPE) especially N95 masks worldwide including Thailand. This issue had placed the health professional in great need of an alternative mask. Aim This study aimed to measure the fit factor of 3D printed frames by quantitative fit testing (QNFT) to find an alternative facemask by using a mask fitter together with 2 different kinds of the American Society for Testing and Materials (ASTM) level 1 surgical mask. Materials and Methods Two commonly used surgical masks (Sultan Com-Fit Super Sensitive Ear Loop Mask or “White Mask Group,” not water-resistant, and Sultan Blue Com-Fit Super High Filtration Ear Loop Mask or “Blue Mask Group,” water-resistant) with and without 3D printed frame covering. The fit performance was measured by a quantitative fit test (QNFT) device (PortaCount, model 8048, TSI Incorporated, Minnesota, USA) accepted by the Occupational Safety and Health Administration (OSHA). The PortaCount device, which is based on a miniature continuous flow condensation nucleus counter (CNC), assesses the respiratory fit by comparing the concentration of ambient dust particles outside the surgical mask to the concentration that has leaked into the surgical mask. The ratio of these two concentrations (Cout/Cin) is called the fit factor. A fit factor of a 3D printed frame of at least 100 is required and considered as a pass level. Results We found that the mask fitter improves the overall performance of surgical masks significantly. The improved performance is comparable to that of N95. Conclusion The mask fitter improves the performance of surgical masks. The authors suggested that further study on frame material, shape, and expanded sample size would be beneficial to society.

Time Series Analysis and Forecasting of the Air Quality Index of Atmospheric Air Pollutants in Zahleh, Lebanon

During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.

Possible sources of ambient PM10 inside Jadavpur University Campus, Kolkata.

High concentration of particulates in the university and research institutional campus can affect cognitive performance of students and researchers. However, studies on ambient particulate concentration in the campus of universities or research institutes are scarce. The ambient concentration of PM10 was measured in the campus of Jadavpur University, Kolkata, during two different seasons (S1: Post-monsoon; S2: Winter) to identify major sources of pollutant here. Significant seasonal variation of ambient PM10 was recorded in the campus. The average ambient PM10 concentration was recorded higher in S2 compared to S1 of the study period. Morphological characteristics of PM10 during the study period suggest that the roundness of particles was in the range of 0.66 to 0.68, whilst the mean spherical diameter suggests most of the PM10 particles were < 2.5 μ diameter. Based on factorial analysis, three factors were generated which includes factor 1: soil, building material and coal burning particles (53.76% of the variance); factor 2: particles from coal combustion (29.89% of the variance) and factor 3: particles from transport emission (16.33% of the variance). The study suggests that it is important to stop burning coal, reduce vehicular emission and reduce road dust resuspension around the campus to maintain the ambient PM10 concentration within the university campus during the post-monsoon and winter months.

Particle size distributions and seasonal concentrations study of atmospheric pollutants (particulates, Hg(p)) at an agricultural site in Taiwan

The concentrations of ambient air particulates of various sizes (PM1, PM2.5, PM6.25, PM10 and PM18) were measured using a MOUDI sampler at an agricultural sampling site in central Taiwan from January to September, 2020. The concentrations of particulate-bound mercury Hg(p) that were attached to particulates of various sizes (PM1, PM2.5, PM6.25, PM10 and PM18)) were analyzed using a Direct Mercury Analyzer (DMA-80). The results revealed that the mean particulate and Hg(p) concentrations associated with PM18, PM10, PM6.25, PM2.5 and the back filter were all highest in autumn and winter. The mean mass median diameter (m.m.d.) value for ambient air particulates was highest in the summer and lowest for Hg(p). The mean m.m.d. value for ambient air Hg(p) was highest in the winter and lowest for ambient air particulates. The northeast monsoon prevails in the winter season of Taiwan. Thus, The sources of particulates and Hg(p) pollutants might came from the northeast side of Taiwan - especially in spring and winter seasons. Thus, ambient air particulate concentrations are higher in the winter than in the spring. However, ambient air particulate-bound mercury Hg(p) tended to be associated more with coarse particulates than with other particulates. Finally, Hg(p) concentrations in Taiwan have increased over the last 15 years. Reducing atmospheric Hg(p) concentrations has become an important environmental goal in all countries - especially those in Asia.

Transcriptomics reveals the mechanisms of population susceptibility to blood glucose associated with short-term exposure to ambient fine and ultrafine particles

BACKGROUND AND AIM: Chronic obstructive pulmonary disease (COPD) and air pollution are both the leading causes of death in China. However, their interactive effects and underlying mechanisms remain largely unknown. This study aims to examine whether air pollutant-associated blood glucose differs between COPD patients and healthy participants, and provide the underlying biological mechanisms based on transcriptome. METHODS: Based on a panel study named COPDB (COPD in Beijing), blood glucose and whole blood transcriptome were repeatedly measured in 53 COPD patients and 82 healthy participants at up to four clinical visits. Ambient concentrations of fine particulate matter (PM2.5), ultrafine particles (UFPs), temperature, and relative humidity were continuously monitored at a monitoring station. Linear mixed-effects models were used to compare the associations between ln-transformed blood glucose and average 1–14 d concentrations of air pollutants before the clinical visits. Mediating effect models were used to identify transcripts with significant associations between air pollutant and blood glucose. MetaCore was used to conduct the pathway enrichment analyses. RESULTS:Blood glucose was positively associated with interquartile range (IQR) increases in average concentrations of UFPs in all participants. UFPs-associated blood glucose elevations were consistently higher in COPD patients than in healthy participants. Some transcripts were identified to mediate the associations between exposure to UFPs and blood glucose levels. Pathway analyses showed that these transcripts were enriched in negative regulation of hypoxia inducible factor-1 (HIF1A) function and immune response related pathways. CONCLUSIONS:COPD patients are more susceptible to elevated blood glucose associated with UFPs exposure than healthy participants. KEYWORDS: Air pollution, Short-term exposure, Omics technologies, Particulate matter, Molecular epidemiology, Causal inference