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We investigated the relationship between porosity and flexoelectricity for aluminum (Al)/polyvinylidene fluoride (PVDF) composites. Neat PVDF, composites of micron aluminum (μAl)/PVDF, and composites of nano aluminum (nAl)/PVDF were 3D printed, and the flexoelectric response was measured using a cantilever beam test setup. Voids (up to 72.4 mm3) were incorporated into the samples by decreasing the infill percent of the 3D printed material. We found that increasing the porosity via millimeter scale voids incorporated into the infill pattern decreased the average effective flexoelectric coefficient relative to the near full-density (100% infill) control samples. This contrasts with other studies that have shown increasing micron scale porosity increases the flexoelectric coefficient. In addition, we measured higher flexoelectric responses for nAl/PVDF than μAl/PVDF as well as for samples printed by the Hyrel 3D SR printer as opposed to the Ender 3 V2 printer. These results indicate that charge generation due to flexoelectricity can be altered by changing parameters such as porosity, particle size of inclusions, or manufacturing method. Smaller voids and fine particles can induce larger strain gradients than larger inhomogeneities, leading to increased flexoelectric coefficients. A competing effect is that more porosity leads to less materials, which can decrease the flexoelectric coefficient. [ABSTRACT FROM AUTHOR]

[Display omitted] • The PM generated by MSW gasification and pyrolysis exhibited a trimodal distribution. • Higher temperatures promote hydrocarbon gas products conversion to PAHs and soot. • Soot showed high specific surface area and well-developed pore structure. • The soot and ultrafine particle formation pathway and mechanism were revealed. This research investigates the formation mechanism of soot and particulate matter during the pyrolysis and gasification of waste derived from Municipal Solid Waste (MSW) in a laboratory scale drop tube furnace. Compared with CO 2 gasification atmosphere, more ultrafine particles (PM 0.2 , aerodynamic diameter less than 0.2 μm) were generated in N 2 atmosphere at 1200℃, which were mainly composed of polycyclic aromatic hydrocarbons (PAHs), graphitic carbonaceous soot and volatile alkali salts. High reaction temperatures promote the formation of hydrocarbon gaseous products and their conversion to PAHs, which ultimately leads to the formation of soot particles. The soot particles generated by waste derived from MSW pyrolysis and gasification both have high specific surface area and well-developed pore structure. Compared with pyrolysis, the soot generated by gasification of waste derived from MSW had smaller size and higher proportion of inorganic components. The higher pyrolysis temperature led to the collapse of the mesoporous structure of submicron particles, resulting in a decrease in total pore volume and an increase in specific surface area. Innovatively, this research provides an explanation for the effect of reaction temperature/ CO 2 on the formation pathways and physicochemical properties of soot and fine particulate matter. [ABSTRACT FROM AUTHOR]

Environmental stresses are increasingly recognized as significant risk factors for adverse health outcomes. In particular, various forms of pollution and climate change are playing a growing role in promoting noncommunicable diseases, especially cardiovascular disease. Given recent trends, global warming and air pollution are now associated with substantial cardiovascular morbidity and mortality. As a vicious cycle, global warming increases the occurrence, size, and severity of wildfires, which are significant sources of airborne particulate matter. Exposure to wildfire smoke is associated with cardiovascular disease, and these effects are underpinned by mechanisms that include oxidative stress, inflammation, impaired cardiac function, and proatherosclerotic effects in the circulation. In the first part of a 2-part series on pollution and cardiovascular disease, this review provides an overview of the impact of global warming and air pollution, and because of recent events and emerging trends specific attention is paid to air pollution caused by wildfires. [Display omitted] • Global warming and air pollution are associated with increased cardiovascular morbidity and mortality. • Global warming is also increasing the occurrence of wildfires, which further drives air pollution and promotes cardiovascular disease through mechanisms that involve oxidative stress, inflammation, atherogenic effects, and impaired cardiac function. • These issues underscore the urgent need to address global warming and air pollution. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) containing environmentally persistent free radicals (EPFR) is formed by the incomplete combustion of organic wastes, resulting in the chemisorption of pollutants to the surface of PM containing redox-active transition metals. In prior studies in mice, EPFR inhalation impaired endothelium-dependent vasodilation. These findings were associated with aryl hydrocarbon receptor (AhR) activation in the alveolar type-II (AT-II) cells that form the air-blood interface in the lung. We thus hypothesized that AhR activation in AT-II cells promotes the systemic release of mediators that promote endothelium dysfunction peripheral to the lung. To test our hypothesis, we knocked down AhR in AT-II cells of male and female mice and exposed them to 280 µg/m3 EPFR lo (2.7e + 16 radicals/g) or EPFR (5.5e + 17 radicals/g) compared with filtered air for 4 h/day for 1 day or 5 days. AT-II-AhR activation-induced EPFR-mediated endothelial dysfunction, reducing endothelium-dependent vasorelaxation by 59%, and eNOS expression by 50%. It also increased endothelin-1 mRNA levels in the lungs and peptide levels in the plasma in a paracrine fashion, along with soluble vascular cell adhesion molecule-1 and iNOS mRNA expression, possibly via NF-kB activation. Finally, AhR-dependent increases in antioxidant response signaling, coupled to increased levels of 3-nitrotyrosine in the lungs of EPFR-exposed littermate control but not AT-II AhR KO mice suggested that ATII-specific AhR activation promotes oxidative and nitrative stress. Thus, AhR activation at the air-blood interface mediates endothelial dysfunction observed peripheral to the lung, potentially via release of systemic mediators. [ABSTRACT FROM AUTHOR]

[Display omitted] • PM 2.5 and its components is associated with elevated blood pressure and hypertension prevalence. • These pollutants produced a stronger association with the rise of SBP than DBP, PP and MAP. • BC and SOIL were most significant with adverse outcomes in different models. • Reduce the concentrations of PM 2.5 and its components may provide benefits in rural areas. The toxicity of fine particulate matter (PM 2.5) is determined by its components, while the evidence regarding associations of PM 2.5 components with blood pressure (BP) is limited, especially in rural areas. This study aimed to explore the associations of PM 2.5 and its chemical components with systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP), mean artery pressure (MAP) levels and hypertension prevalence, and to identify key components in Chinese rural areas. 39,211 adults from the Henan Rural Cohort were included during 2015–2017. Different periods of PM 2.5 and chemical components were estimated by hybrid satellite model. The single-pollutant, component-PM 2.5 model, component-residual model and component-proportion model were applied to explore the associations of pollutants with BP levels and hypertension prevalence. Exposure-response (E-R) relationships, stratified analyses and sensitivity analyses were used to explore these associations further. 12,826 (32.71%) were identified with hypertension. For each 1 μg/m3 increase of pollutants, the adjusted odds ratio (OR) for hypertension prevalence was 1.03 for PM 2.5 mass, 1.40 for BC, 1.16 for NH 4 +, 1.08 for NO 3 –, 1.17 for OM, 1.12 for SO 4 2− and 1.25 for SOIL in the single-pollutant model. BC and SOIL were statistically significant in the component-PM 2.5 model, component-residual model and component-proportion model. Similarly, associations of these pollutants with elevated BP levels were also found in aforementioned four models. These pollutants produced a stronger association with SBP than DBP, PP and MAP. Most of associations were non-linear in E-R relationships. The groups of older, the men, with lower per capita monthly income, lower educational level and higher BMI were more vulnerable to these pollutants in stratified analyses. The results remained stable in sensitivity analyses. Long-term exposure to PM 2.5 and its components, especially BC and SOIL, was associated with elevated BP and hypertension prevalence in rural adults, and decreasing pollutants may provide additional benefits. [ABSTRACT FROM AUTHOR]

• A comprehensive demonstration of how to characterize and understand the self-assembly behavior of amphiphilic molecules in solution-based parenteral drug products, including CMC determination by DOSY NMR. • An innovative use of computational tools and multiple orthogonal experimental techniques to inform robust drug product design, contribute to impurity control strategies and help enable rapid progression of an anti-infective parenteral drug product to the clinic. • A case study of lipophilic impurity solubilization by prodrug self-assembly that informs when impurities might precipitate, leading to undesired particulate matter formation, for example during dose administration or on stability. PF-07304814 is a water-soluble phosphate ester prodrug of a small molecule inhibitor for the SARS CoV-2 3CL protease designed for the treatment of COVID-19. The amphiphilicity and self-assembly behavior of the prodrug was investigated computationally and experimentally via multiple orthogonal techniques to better design formulations for intravenous infusion. The self-assembly of PF-07304814 into micellar structures enabled an increase in the solubility of lipophilic impurities by up to 1900x in clinically relevant formulations. The observed solubilization could help extend the drug product shelf-life and in use stability through inhibition of precipitation, without the need for solubilizing excipients. The work presented in this manuscript provides a roadmap for the characterization of prodrug self-assembly and highlights the potential for prodrug modifications to enhance solubility of both active ingredients and impurities and to extend drug product shelf-life. [ABSTRACT FROM AUTHOR]

The traditional way of transporting iron ore from mine to end-use plants often causes the industry significant economic hardship and degradation of the ecological system. Hydrotransportation of slurry through a pipeline is a remarkably viable strategy for effectively transporting iron ore in terms of economics, dependability, and safety, with minimal environmental impact. In the present investigation, a blend of bimodal samples is prepared by mixing medium and coarse particles of Indian iron ore of size 38–75 (I-2) and 75–106 (I-3) µm, respectively, with finer particles of size <38 µm (I-1) in 10–40% (by weight) proportion. Rheological characterization of the blend samples is done to finalize the optimum particle size. To enhance the flowability and stability of the concentrated iron ore samples, they are amalgamated with a mixture of plant-based nonionic saponin extracted from Sapindus mukorossi (Ritha) and the anionic sodium dodecyl sulfate (SDS) surfactants. The correct proportion of SDS in saponin is obtained through a thorough analysis of critical micellar concentration, zeta potential, and rheological parameters. Finally, the stability and slurryability of iron ore suspension with a Saponin–SDS mixture are established through rheological properties, dispersant concentration, and stabilization mechanisms. It is found that the optimum proportion of a mixture of saponin–SDS significantly improved the slurryability and stability of iron ore suspension and the maximum iron ore loading was enhanced by up to 81% by weight. The head loss and specific energy consumption analysis successfully evidence the significance of the surfactants in transporting the slurry through pipelines. [ABSTRACT FROM AUTHOR]

Relevant studies have indicated the association of HCG18 with tumour occurrence and progression. In this study, we observed that PM2.5 can enhance the growth of lung adenocarcinoma cells by modulating the expression of HCG18. Further investigations, including overexpression and knockout experiments, elucidated that HCG18 suppresses miR‐195, which in turn upregulates the expression of ATG14, resulting in the upregulation of autophagy. Consequently, exposure to PM2.5 leads to elevated HCG18 expression in lung tissues, which in turn increases Atg14 expression and activates autophagy pathways through inhibition of miR‐195, thereby contributing to oncogenesis. [ABSTRACT FROM AUTHOR]

Existing methods for fitting spatial autoregressive models have various strengths and weaknesses. For example, the maximum likelihood estimation (MLE) approach yields efficient estimates but is computationally burdensome. Computationally efficient methods, such as generalized method of moments (GMMs) and spatial two‐stage least squares (2SLS), typically require exogenous covariates to be significant, a restrictive assumption that may fail in practice. We propose a new estimating equation approach, termed combined moment equation (COME), which combines the first moment with covariance conditions on the residual terms. The proposed estimator is less computationally demanding than MLE and does not need the restrictive exogenous conditions as required by GMM and 2SLS. We show that the proposed estimator is consistent and establish its asymptotic distribution. Extensive simulations demonstrate that the proposed method outperforms the competitors in terms of bias, efficiency, and computation. We apply the proposed method to analyze an air pollution study, and obtain some interesting results about the spatial distribution of PM2.5 concentrations in Beijing. [ABSTRACT FROM AUTHOR]

This study examined the incompatibility of high-intensity conditioning (HIC) conditions required for fine and coarse coal particles. The effects of speed and time of stirring on the recovery of coarse and fine particles were investigated by using three different flotation processes. The results showed that the coarse particle recovery of surface cleaning (SC) flotation reached the maximum value of 69.71% at the stirring condition combination of '1400 rpm +2 min.' However, the coarse particle recovery reached the maximum value of ~69.37% at a stirring condition combination of '1600 rpm +3 min' or '2000 rpm +2 min' in HIC flotation. Achieving the same coarse particle recovery, SC flotation consumes less energy than HIC flotation. The maximum recovery of fine particle 65.37% was achieved at a stirring condition combination of '1600 rpm +2 min' in HIC flotation. SC failed to promote the recovery of fine particles and even produced some negative effects in some cases. Fine particles consume less energy than coarse particles with respect to their maximum recovery. In addition, the optimum stirring speed and time for SC were 1400 rpm and 2 min, respectively, while 2000 rpm and 3 min for HIC, respectively. The optimum energy requirements involving stirring speed and time for SC do not match that for reagent dispersion and particle-reagent collision. The best flotation result achieved by HIC is a compromise of the above three aspects. It is suggested that SC, reagent dispersion, and particle-reagent collision should be carried out separately. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) broke out in 2019 and spread rapidly to the whole world. Lockdown policies were adopted by Pakistan to control the rapid dispersion of COVID-19. These lockdown measures restricted economic and human activities immensely to lower number of the COVID-19 cases and associated illnesses. Additionally, air pollution and meteorological conditions also play a crucial role in the spread of COVID-19 in Pakistan. Therefore, this study mainly focuses on the changes in concentrations of selected air pollutants (coarse particulate matter (PM10), fine particulate matter (PM2.5), sulphur dioxide (SO2) and Ozone (O3)) along with meteorological parameters (relative humidity, temperature, and wind speed) during March 1 to April 25 from 2018 to 2020. The present study acquired available air pollutants data from Environmental Protection Department, meteorological parameters from the wunderground, vertical profiles and backward trajectories from National Oceanic and Atmospheric Administration (NOAA) portal. Ground-based observations showed a decline in the mean concentrations of PM2.5 and PM10 by 51.3 and 65.7% and an increase in concentrations of O3 and SO2 by 82.6 and 21.1%, respectively in 2020 compared to those measured in the year 2019. During phase 4 (lockdown) of 2020, PM10 and PM2.5 decreased by 88.0 and 82.9% compared to 2019 and 2018, respectively. Moreover, the SO2 concentrations were reduced by 59.1% in phase 3 of 2020 compared to those measured in 2019. In phase 4, O3 concentrations decreased by 29.4% in 2020 compared to 2018 but no reduction was observed in O3 compared to 2019. The findings indicate a decline in concentrations of air pollutants during the lockdown due to the restrictions on human, transportation, commercial and industrial activities. These results would be important for policymakers to develop some air pollutant control action plans to mitigate air pollution problems in Pakistan. [ABSTRACT FROM AUTHOR]

Dust resuspension from four typical fabrics (cotton, linen, silk, and polyester) exposed to airflow with common velocities within 0–10 m/s, including a moving fabric case and a fixed fabric case, was experimentally studied in this article. A set of empirical correlations, involving air velocity, fabric motion mode, fabric type, and airflow duration was developed to describe and predict the moving fabric case. It was found that a stronger-than-expected resuspension was triggered by short-term accelerating airflow. The resuspension enhancement of over 90% was reported for the moving fabrics compared with the fixed ones. Fabric motion induced by airflow was proposed to account for these resuspension findings. Fabric acceleration was then demonstrated to be a key factor in evaluating the resuspension for such a scenario. This article not only reveals an inconspicuous phenomenon of dust resuspension from fabrics under the impact of airflow and consequent fabric motion, but also provides a theoretical basis for particulate matter assessment and regulation. Copyright © 2024 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Diesel particulate matter (DPM) is a common and well-known health hazard in the mining environment. The regulatory method for monitoring both the organic and elemental carbon (OC, EC) portions of DPM is a laboratory-based thermal-optical method with a typical turnaround time of one week. In order to evaluate exposure levels and take corrective action prior to overexposure, a portable real-time device capable of quantifying both OC and EC is needed. To that end, researchers from the National Institute for Occupational Safety and Health (NIOSH) designed and tested the feasibility of a device based on bandpass optical filters that target key infrared wavelengths associated with DPM and its spectroscopic baseline. The resulting device, referred to here as a non-dispersive infrared (NDIR) spectrometer could serve as the basis of a cost-effective, field-portable alternative to the laboratory thermal-optical method. The limits of quantification (LOD) indicate that the NDIR spectrometer can quantify EC, OC, and TC provided they are present at 20, 37, and 46 μg/m3 or more, respectively. In the event that the NDIR spectrometer is integrated with a sampler and filter tape the LOD is estimated to be reduced to 13, 7, and 10 μg/m3 for EC, OC, and TC, respectively. These LOD estimates assume a face velocity of 59 cm/s and a sampling time of 30 min. [ABSTRACT FROM AUTHOR]

The chemical components and mechanisms underlying the toxicity and adverse health effects of particulate matter (PM) in the atmosphere have not been fully elucidated. After designing a small, openable, stainless steel cyclone to collect PM samples effectively in powder form for use in toxicological experiments, we evaluated its performance. We compared it with a commercially available aluminum cyclone of similar dimensions, but which is unopenable. The aerodynamic cutoff diameter of the openable cyclone was found by experimentation to be approximately 0.2 µm at a flow rate of 90 L min−1, which is comparable to the unopenable commercial cyclone. The sampling yields, representing the fraction of obtained sample mass relative to the total mass of PM with aerodynamic diameter smaller than 2.5 μm (PM2.5) drawn into the sampler, were approximately 1.3 times higher, on average, for the openable cyclone than for the unopenable cyclone. The openable design of the cyclone might contribute to a marked increase in the finally obtained amounts of PM samples. Analyses of metal concentrations in the PM samples collected simultaneously using the stainless steel openable cyclone and aluminum unopenable cyclone suggest that the stainless steel cyclone is less likely than the aluminum cyclone to cause sample contamination from its material. The openable cyclone developed for this study facilitates the effective collection of powder-form PM samples suitable for use in toxicological experiments. [ABSTRACT FROM AUTHOR]

Morphological information on fine particles is essential for understanding their transport behavior in the ambient atmosphere and in the human respiratory system. More than 3000 transmission electron microscopy (TEM) images of fine particles were collected from ambient atmosphere and directly from various sources, such as diesel and gasoline engine exhaust, biomass burning, coal combustion, and road dust, and were then morphologically categorized into four major classes (spherical, agglomerate, polygonal, and dendrite). Pre-trained convolutional neural network (CNN) models (DenseNet169, InceptionV3, MobileNetV3Small, ResNet50, and VGG16) and traditional machine learning models (decision trees, random forests, and support vector machines) were trained using the classified particles. The fine-tuned CNN model (DenseNet169) having the deepest feature learning exhibited the best performance among the tested models, with an overall classification accuracy of 89% and an average per-class accuracy ranging from 84% to 97%. The reliable classification of thousands of images was performed within several minutes. The agglomerated class was the least misclassified because of its significantly different features from those of the other classes. The critical regions of the particles for classification decisions varied among the pre-trained models. Our results suggest that the pre-trained CNN models would be useful for the rapid morphological classification of a large number of fine particles. Copyright © 2024 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Black carbon, or soot, is one of the highest contributors to global warming. The International Civil Aviation Organization (ICAO) has adopted regulatory standards for soot from aircraft engines, also referred to as a nonvolatile particulate matter (nvPM), to limit or reduce the harmful impacts of nvPM on the environment. Sustainable aviation fuels (SAF) offer advantages to reduce soot emissions and overall environmental impact but require extensive testing and evaluation before wider adoption. Typical measurements of soot produced by combustion of aviation fuels require full-sized jet engines and large volumes of fuel, which can be prohibitively expensive. This study investigates flame spray pyrolysis (FSP) as a simple bench-top tool for comparison of soot emissions from the combustion of different liquid jet fuels. A sampling assembly is designed for soot collection and analysis. Morphological analysis follows from transmission electron microscopy (TEM) image analysis and mobility (differential mobility analyzer) classification. Morphologies are compared to previous measurements from aircraft turbines. Soot agglomerate size distributions and elemental to total carbon ratios (EC/TC) are measured for three liquid fuels and flame conditions with Reynolds numbers and burner equivalence ratios ranging from 6100 to 9100 and 7 to 13, respectively. Day-to-day variations in the dilution ratio resulted in up to 20% variability in the measured total agglomerate number-based concentration and mobility diameter. Geometric mean primary particle and mobility diameter values are below 21 and 104 nm, respectively, in excellent agreement with those emitted from jet engines and prior work using FSP. EC/TC remains > 0.75 for most flame conditions and fuels and increases with burner equivalence ratio, but values as low as 0.63 are measured from SAF combustion. Copyright © 2024 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Mining activities lead to severe particulate matter (PM) pollution that consequently has a detrimental effect on ecosystem. A study was therefore conducted in a coal mining area of Odisha, India with an objective to assess the particulate matter pollution on the basis of differential aerodynamic size (PM10 and PM2.5) of the particles, the metallic (Zn and Fe) composition, and also to evaluate their nature of deposition on two identified plant species. The results suggest a significant variation in particle and heavy metal fractions in the ambient air of different sites (p < 0.05). Fe dominated the finer particle (PM2.5) fraction while Zn dominated the coarser counterpart (PM10) in the ambient air. When evaluated for the particle and heavy metal deposition on leaf surface, Shorea robusta performed better in trapping the coarser particles (PM10) while Holarrhena floribunda was found to be an efficient scrubber of the finer particles (PM2.5). Fe deposition on surface of leaves was comparatively higher than Zn irrespective of plant species or size fractions. Therefore, it is concluded that both S. robusta and H. floribunda should be planted in a schematic manner to tackle the particulate pollution in coal mining areas. [ABSTRACT FROM AUTHOR]

Reaction-bonded boron carbide composites were fabricated by molten silicon infiltration of diamond-containing boron carbide preforms. The effects of diamond particle size on the microstructure, mechanical and tribological properties of reaction-bonded boron carbide composites were investigated. Fine diamond particles (≤7.7 µm) completely reacted with silicon generating nano silicon carbide particles, which bonded the boron carbide particles forming a 3-D ceramic skeleton and improving the flexural strength of the composites. Coarser diamond (≥24 µm) particles were retained protected by a dense SiC layer generated from the reaction of diamond and molten silicon. A "core-shell" structure with the retained diamond as the "core" and with the reaction formed silicon carbide as the "shell" formed. Relief structures were established on the composites' surface during the wear process, greatly reducing the friction coefficient and improving the wear resistance. [ABSTRACT FROM AUTHOR]

Key points World Health Organisation data suggest that up to 99% of the global population are exposed to air pollutants above recommended levels. Impacts to health range from increased risk of stroke and cardiovascular disease to chronic respiratory conditions, and air pollution may contribute to over 7 million premature deaths a year. Additionally, mounting evidence suggests that in utero or early life exposure to particulate matter (PM) in ambient air pollution increases the risk of neurodevelopmental impairment with obvious lifelong consequences. Identifying brain‐specific cellular targets of PM is vital for determining its long‐term consequences. We previously established that microglial‐like BV2 cells were particularly sensitive to urban (U)PM‐induced damage including reactive oxygen species production, which was abrogated by a mitochondrially targeted antioxidant. Here we extend those studies to find that UPM treatment causes a rapid impairment of mitochondrial function and increased mitochondrial fragmentation. However, there is a subsequent restoration of mitochondrial and therefore cell health occurring concomitantly with upregulated measures of mitochondrial biogenesis and mitochondrial load. Our data highlight that protecting mitochondrial function may represent a valuable mechanism to offset the effects of UPM exposure in the neonatal brain. Air pollution represents a growing risk to long‐term health especially in early life, and the CNS is emerging a target for airborne particulate matter (PM). We previously showed that microglial‐like BV2 cells were vulnerable to urban (U)PM exposure, which impaired cell survival and promoted reactive oxygen species production. Here we find that, following UPM exposure, BV2 mitochondrial membrane potential is rapidly reduced, concomitant with decreased cellular bioenergetics and increased mitochondrial fission. However, markers of mitochondrial biogenesis and mitochondrial mass are subsequently induced, which may represent a cellular mitigation strategy. As mitochondria are more vulnerable in the developing brain, exposure to air pollution may represent a greater risk to lifelong health in this cohort; conversely, promoting mitochondrial integrity may offset these risks. [ABSTRACT FROM AUTHOR]

Background: Extreme weather events like heatwaves and fine particulate matter (PM2.5) have a synergistic effect on mortality, but research on the synergistic effect of cold waves and PM2.5 on outpatient visits for respiratory disease, especially at high altitudes in climate change-sensitive areas, is lacking. Methods: we collected time-series data on meteorological, air pollution, and outpatient visits for respiratory disease in Xining. We examined the associations between cold waves, PM2.5, and outpatient visits for respiratory disease using a time-stratified case-crossover approach and distributional lag nonlinear modeling. Our analysis also calculated the relative excess odds due to interaction (REOI), proportion attributable to interaction (AP), and synergy index (S). We additionally analyzed cold waves over time to verify climate change. Results: Under different definitions of cold waves, the odds ratio for the correlation between cold waves and outpatient visits for respiratory disease ranged from 0.95 (95% CI: 0.86, 1.05) to 1.58 (1.47, 1.70). Exposure to PM2.5 was significantly associated with an increase in outpatient visits for respiratory disease. We found that cold waves can synergize with PM2.5 to increase outpatient visits for respiratory disease (REOI > 0, AP > 0, S > 1), decreasing with stricter definitions of cold waves and longer durations. Cold waves' independent effect decreased over time, but their interaction effect persisted. From 8.1 to 21.8% of outpatient visits were due to cold waves and high-level PM2.5. People aged 0–14 and ≥ 65 were more susceptible to cold waves and PM2.5, with a significant interaction for those aged 15–64 and ≥ 65. Conclusion: Our study fills the gap on how extreme weather and PM2.5 synergistically affect respiratory disease outpatient visits in high-altitude regions. The synergy of cold waves and PM2.5 increases outpatient visits for respiratory disease, especially in the elderly. Cold wave warnings and PM2.5 reduction have major public health benefits. [ABSTRACT FROM AUTHOR]

We construct a continuous 24-h daily fine particulate matter (PM2.5)record with 2×2 km2 resolution over eastern China, South Korea, and Japan for 2011–2022 by applying a random forest (RF) algorithm to aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) I and II satellite instruments. The RF uses PM2.5 observations from the national surface networks as training data. PM2.5 network data starting in 2015 in South Korea are extended to pre-2015 with a RF trained on other air quality data available from the network including PM10. PM2.5 network data starting in 2014 in China are supplemented by pre-2014 data from the US embassy and consulates. Missing AODs in the GOCI data are gap-filled by a separate RF fit. We show that the resulting GOCI PM2.5 dataset is successful in reproducing the surface network observations including extreme events, and that the network data in the different countries are representative of population-weighted exposure. We find that PM2.5 peaked in 2014 (China) and 2013 (South Korea, Japan), and has been decreasing steadily since with no region left behind. We quantify the population in each country exposed to annual PM2.5 in excess of national ambient air quality standards and how this exposure evolves with time. The long record for the Seoul Metropolitan Area (SMA) shows a steady decrease from 2013 to 2022 that was not present in the first five years of AirKorea network PM2.5 measurements. Mapping of an extreme pollution event in Seoul with GOCI PM2.5 shows a predicted distribution indistinguishable from the dense urban network observations, while our previous 6×6 km2 product smoothed local features. Our product should be useful for public health studies where long-term spatial continuity of PM2.5 information is essential. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) has various health effects, including cardiovascular diseases. Exposure to PM and a diagnosis of diabetes mellitus (DM) have been associated with an increased risk of cardiac arrhythmias. However, no comprehensive synthesis has been conducted to examine the modifying effect of DM on the association between PM and arrhythmia events. Thus, the objectives of this review were to investigate whether the association of PM is linked to cardiac arrhythmias and whether DM status modifies its effect in the general population. The search was conducted on PubMed/MEDLINE and Embase until January 18, 2023. We included cohort and case-crossover studies reporting the effect of PM exposure on cardiac arrhythmias and examining the role of diabetes as an effect modifier. We used the DerSimonian and Laird random-effects model to calculate the pooled estimates. A total of 217 studies were found and subsequently screened. Nine studies met the inclusion criteria, and five of them were included in the meta-analysis. The participants numbered 4,431,452, with 2,556 having DM. Exposure to PM of any size showed a significant effect on arrhythmias in the overall population (OR 1.10, 95% CI 1.04–1.16). However, the effect modification of DM was not significant (OR 1.18 (95% CI 1.01–1.38) for DM; OR 1.08 (95% CI 1.02–1.14) for non-DM; p-value of subgroup difference = 0.304). Exposure to higher PM concentrations significantly increases cardiac arrhythmias requiring hospital or emergency visits. Although the impact on diabetic individuals is not significant, diabetic patients should still be considered at risk. Further studies with larger sample sizes and low bias are needed. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) and near-surface ozone (O3) are the main atmospheric pollutants in China. Long-term exposure to high ozone concentrations adversely affects human health. It is of great significance to systematically analyze the spatiotemporal evolution mechanism and health effects of ozone pollution. Based on the ozone data of 91 monitoring stations in the Central Plains Urban Agglomeration from 2017 to 2020, the research used Kriging method and spatial autocorrelation analysis to investigate the spatiotemporal variations of ozone concentration. Additionally, the study assessed the health effects of ozone on the population using the population exposure risk model and exposure-response relationship model. The results indicated that: (1) The number of premature deaths caused by ozone pollution in the warm season were 37,053 at 95% confidence interval (95% CI: 28,190–45,930) in 2017, 37,685 (95% CI: 28,669–46,713) in 2018, and 37,655 (95% CI: 28,647–46,676) in 2019. (2) The ozone concentration of the Central Plains urban agglomeration showed a decreasing trend throughout the year and during the warm season from 2017 to 2020, there are two peaks monthly, one is June, and the other is September. (3) In the warm season, the high-risk areas of population exposure to ozone in the Central Plains Urban Agglomeration were mainly concentrated in urban areas. In general, the population exposure risk of the south is lower than that of the north. The number of premature deaths attributed to ozone concentration during the warm season has decreased, but some southern cities such as Xinyang and Zhumadian have also seen an increase in premature deaths. China has achieved significant results in air pollution control, but in areas with high ozone concentrations and high population density, the health burden caused by air pollution remains heavy, and stricter air pollution control policies need to be implemented. [ABSTRACT FROM AUTHOR]

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added. [ABSTRACT FROM AUTHOR]

Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand's transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand's strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced. [ABSTRACT FROM AUTHOR]

This paper brings a new insight into understanding the influence of macrocapsules in packing systems, which can be useful in designing the inert structure of self-healing concrete. A variety of tubular macrocapsules, in terms of types and sizes, was used to assess the capsules' effect in the packing, together with various aggregate types and fractions. The voids ratios (U) of aggregate mixtures were evaluated experimentally and compared with the prediction via the particle packing model of Dewar. The packing of coarse particles was found to be considerably affected by the presence of macrocapsules, while no capsules' effect on the packing of fine particles was attained. A higher capsule dosage and capsule aspect ratio led to a higher voids ratio. In the formulation of the inert structure, the packing disturbance due to capsules can be minimised by increasing the content of fine aggregates over coarse aggregates. Dewar's model showed a good compatibility with experimental results in the absence of capsules. However, the model needed to be upgraded for the introduction of tubular macrocapsules. Accordingly, the effect of macrocapsules was extensively analysed and a 'U model' for capsules (with some limitations) was finally proposed, offering a high predicting accuracy. [ABSTRACT FROM AUTHOR]

Oxylipins, the metabolites of polyunsaturated fatty acids, are vital in regulating cell proliferation and inflammation. Among these oxylipins, specialized pro-resolving mediators notably contribute to inflammation resolution. Previously, we showed that the specialized pro-resolving mediators isomer 11,17dihydroxy docosapentaenoic acid (11,17diHDoPE) can be synthesized in bacterial cells and exhibits anti-inflammatory effects in mammalian cells. This study investigates the in vivo impact of 11,17diHDoPE in mice exposed to particulate matter 10 (PM10). Our results indicate that 11,17diHDoPE significantly mitigates PM10-induced lung inflammation in mice, as evidenced by reduced pro-inflammatory cytokines and pulmonary inflammation-related gene expression. Metabolomic analysis reveals that 11,17diHDoPE modulates inflammation-related metabolites such as threonine, 2-keto gluconic acid, butanoic acid, and methyl oleate in lung tissues. In addition, 11,17diHDoPE upregulates the LA-derived oxylipin pathway and downregulates arachidonic acid- and docosahexaenoic acid-derived oxylipin pathways in serum. Correlation analyses between gene expression and metabolite changes suggest that 11,17diHDoPE alleviates inflammation by interfering with macrophage differentiation. These findings underscore the in vivo role of 11,17diHDoPE in reducing pulmonary inflammation, highlighting its potential as a therapeutic agent for respiratory diseases. [ABSTRACT FROM AUTHOR]

[Display omitted] Limited evidence exists on the effect of submicronic particulate matter (PM 1) on hypertension hospitalization. Evidence based on causal inference and large cohorts is even more scarce. In 2015, 36,271 participants were enrolled in South China and followed up through 2020. Each participant was assigned single-year, lag0–1, and lag0–2 moving average concentration of PM 1 and fine inhalable particulate matter (PM 2.5) simulated based on satellite data at a 1-km resolution. We used an inverse probability weighting approach to balance confounders and utilized a marginal structural Cox model to evaluate the underlying causal links between PM 1 exposure and hypertension hospitalization, with PM 2.5 -hypertension association for comparison. Several sensitivity studies and the analyses of effect modification were also conducted. We found that a higher hospitalization risk from both overall (HR : 1.13, 95% CI : 1.05–1.22) and essential hypertension (HR : 1.15, 95% CI : 1.06–1.25) was linked to each 1 µg/m3 increase in the yearly average PM 1 concentration. At lag0–1 and lag0–2, we observed a 17%–21% higher risk of hypertension associated with PM 1. The effect of PM 1 was 6%–11% higher compared with PM 2.5. Linear concentration-exposure associations between PM 1 exposure and hypertension were identified, without safety thresholds. Women and participants that engaged in physical exercise exhibited higher susceptibility, with 4%–22% greater risk than their counterparts. This large cohort study identified a detrimental relationship between chronic PM 1 exposure and hypertension hospitalization, which was more pronounced compared with PM 2.5 and among certain groups. [ABSTRACT FROM AUTHOR]

• The utilization characteristics of gold tailings were reviewed. • The recovery of valuable components from gold tailings was summarized. • The application of gold tailings in the field of materials was adumbrated. • A new process combining magnetic separation and cemented filling was proposed. Gold tailings are characterized by low-grade, complex composition, fine embedded particle size, environmental pollution, and large land occupation. This paper describes the mineralogical properties of gold tailings, including chemical composition, phase composition, particle size distribution, and microstructure; summarizes the recycling and utilization of components such as mica, feldspar, and valuable metals in gold tailings; reviews harmless treatment measures for harmful elements in gold tailings; and adumbrated the research progress of gold tailings in the application fields of building materials, ceramics, and glass materials. Based on these discussions, a new technology roadmap that combines multistage magnetic separation and cemented filling is proposed for the clean utilization of all components of gold tailings. [ABSTRACT FROM AUTHOR]

The need for technologies that can clean the air indoors has grown in pace with the rise in outside pollution. Maintaining interior environment adaptability requires a permanent air purification system that may be utilized to control PM2.5/10. In addition to more traditional methods of air purification, developing advanced control systems that effectively reduce PM levels sustainably is necessary. Pulsed radio waves may expedite the dry deposition of particles having aerodynamic dimensions of less than 30 µm. The charging and coagulation processes are evaluated in an indoor restricted environment. Experimental results reveal a similar pattern to the Monte Carlo models. Distribution of charge due to the nature of the charging environment increases the coagulation rate. Contained experimental testing confirms the filtering system functions as expected, corroborated by the present research. Daily average levels of PM2.5 and PM10 were lowered by between 55 and 41% according to a study done in three indoor settings using the control technology. Research conducted throughout all seasons showed that the approach was consistently efficient in reducing PM2.5/10. It was shown that PM concentrations could be lowered by around 45 percent using pulse radio wave technology, leading to this conclusion. The use of electromagnetic waves (EM waves) to eliminate air pollution has been suggested as a radical new approach. Considering the limitations of already used strategies, this is of paramount significance while considering solutions to control air pollution. [ABSTRACT FROM AUTHOR]

Nano-graphene lubricating oil with appropriate concentration shows excellent performance in reducing friction and wear under different working conditions of diesel engines, and has been widely concerned. Lubricating oil has a significant impact on particulate matter (PM) emissions. At present, there are few studies on the impact of nano-graphene lubricating oil on the physicochemical properties of PM. In order to comprehensively evaluate the impact of nano-graphene lubricating oil on diesel engines, this paper mainly focused on the effects of lubricating oil nano-graphene additives on the particle size distribution and physicochemical properties of PM. The results show that, compared with pure lubricating oil, the total number of nuclear PM and accumulated PM of nano-graphene lubricating oil is significantly increased. The fractal dimension of PM of nano-graphene lubricating oil increases and its structure becomes more compact. The average fringe separation distance of basic carbon particles decreases, the average fringe length increases. The degree of ordering and graphitization of basic carbon particles are higher. The fringe tortuosity of basic carbon particles decreases, and the fluctuation of carbon layer structure of basic carbon particles decreases. Aliphatic substances in PM are basically unchanged, aromatic components and oxygen functional groups increase. The initial PM oxidation temperature and burnout temperature increase, the maximum oxidation rate temperature and combustion characteristic index decrease, and the activation energy increases, making it more difficult to oxidize. This was mainly caused by the higher graphitization degree of PM of nano-graphene lubricating oil and the increased content of aromatic substances. [ABSTRACT FROM AUTHOR]

This study investigated the association between air pollutants and asthma prevalence in male and female Japanese adults. In this retrospective cross-sectional analysis, annual mean exposure levels of air pollutants, specifically nitrogen dioxide (NO2) and particulate matter with a median aerodynamic diameter ≤2.5 μm (PM2.5), were assessed at a local monitoring site. Multivariable logistic regression models, adjusted for genetic and/or lifestyle factors, were used to explore the association between air pollutants and asthma, with stratification by sex. A total of 1,497 participants aged ≥40 years were included. Their mean age was 65.9 ± 12.4 years, with 847 being women. Overall, 91 participants were diagnosed with asthma. In the multivariable model, ambient exposure levels of NO2 and PM2.5 were significantly associated with asthma in women but not in men. This study highlights sex as a significant determinant of the link between air pollutants and asthma exacerbation, particularly among female Japanese adults. [ABSTRACT FROM AUTHOR]

PM2.5 and arsenic are two of the most hazardous substances for humans that coexist worldwide. Independently, they might cause multiple organ damage. However, the combined effect of PM2.5 and arsenic has not been studied. Here, we used an animal model of simultaneous exposure to arsenic and PM2.5. Adult Wistar rats were exposed to PM2.5, As, or PM2.5 + As and their corresponding control groups. After 7, 14, and 28 days of exposure, the animals were euthanized and serum, lungs, kidneys, and hearts were collected. Analysis performed showed high levels of lung inflammation in all experimental groups, with an additive effect in the coexposed group. Besides, we observed cartilaginous metaplasia in the hearts of all exposed animals. The levels of creatine kinase, CK-MB, and lactate dehydrogenase increased in experimental groups. Tissue alterations might be related to oxidative stress through increased GPx and NADPH oxidase activity. The findings of this study suggest that exposure to arsenic, PM2.5, or coexposure induces high levels of oxidative stress, which might be associated with lung inflammation and heart damage. These findings highlight the importance of reducing exposure to these pollutants to protect human health. [ABSTRACT FROM AUTHOR]

Understanding how different physical and chemical atmospheric processes affect the formation of fine particles has been a persistent challenge. Inferring causal relations between the various measured features affecting the formation of secondary organic aerosol (SOA) particles is complicated since correlations between variables do not necessarily imply causality. Here, we apply a state-of-the-art information transfer measure coupled with the Koopman operator framework to infer causal relations between isoprene epoxydiol SOA (IEPOX-SOA) and different chemistry and meteorological variables derived from detailed regional model predictions over the Amazon rainforest. IEPOX-SOA represents one of the most complex SOA formation pathways and is formed by the interactions between natural biogenic isoprene emissions and anthropogenic emissions affecting sulfate, acidity and particle water. Since the regional model captures the known relations of IEPOX-SOA with different chemistry and meteorological features, their simulated time series implicitly include their causal relations. We show that our causal model successfully infers the known major causal relations between total particle phase 2-methyl tetrols (the dominant component of IEPOX-SOA over the Amazon) and input features. We provide the first proof of concept that the application of our causal model better identifies causal relations compared to correlation and random forest analyses performed over the same dataset. Our work has tremendous implications, as our methodology of causal discovery could be used to identify unknown processes and features affecting fine particles and atmospheric chemistry in the Earth's atmosphere. [ABSTRACT FROM AUTHOR]

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5. [ABSTRACT FROM AUTHOR]

India, one of the most dynamic ancient civilizations, possesses a multitude of historical artifacts, with 37 of its notable architectural structures recognized as UNESCO World Heritage Sites. Yet, the ever-changing climate, especially air pollution, expedites the natural deterioration of historic sites and diminishes their aesthetic appeal, causing socio-economic damage. With this in mind, the current study aims to offer a logical scientific foundation for the implications of air pollution, seasonal shifts, and COVID-19 on 14 significant historical places in India during the year 2019-20. Delhi, among the cities most severely affected by atmospheric pollution, recorded an alarming air quality index (AQI) of 102–141, which can intensify the risk of cultural sites to corrode and deteriorate. Analysis reveals that the winter season had elevated levels of NO2 and particle pollution (PM2.5, PM10), whereas summer had the higher levels of O3. Throughout the 5-month lockdown period, ozone levels exhibited an elevation, contrasting with the reduction observed in other air parameters. Notably, there was a substantial 70% decrease in particulate matter concentration, which had previously remained stable over the course of the year. Variations in geographic locales and anthropogenic influences contribute significantly to the dose-response statistics, revealing an unprecedented elevation in corrosion risks to historical limestone and sandstone structures across target sites. Moreover, the research addresses available Governmental initiatives, and effective strategies designed to safeguard heritage sites against the corrosion and material degradation, offering a comprehensive exploration of protective measures. Thereby, the current research is centred on establishing a foundational understanding of the impact of air pollution on cultural heritage, utilizing a comparison to the year with the lowest air pollution levels, which can aid policymakers in enhancing risk management and implementing a robust national mandate for the preservation of cultural heritage sites against corrosion. [ABSTRACT FROM AUTHOR]

Several coal-fired power plants (CFPPs) were built in South Africa, mainly in the central Mpumalanga Province, due to an increase in the demand for Eskom, the national power utility, to keep up with socio-economic growth. The CFPPs, of which 90% are owned by Eskom, generate a significant share of the country’s electricity but contribute to the air pollution experienced in the country. The paper discusses sulphur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter of size less than 10 micrometre (μm) in diameter (PM10), using data from 2014 to 2018. The statistics revealed higher PM10 concentrations during winter than in summer and spring at the Kriel and Komati sites; associated with the higher contribution of domestic burning. The study’s results could influence legislation and policies and help to understand the source of poor ambient air quality by assessing the three pollutants within the area of the selected power plants. [ABSTRACT FROM AUTHOR]

Objectives: To examine whether long-term air pollution exposure is associated with central hemodynamic and brachial artery stiffness parameters. Methods: We assessed central hemodynamic parameters including central blood pressure, cardiac parameters, systemic vascular compliance and resistance, and brachial artery stiffness measures [including brachial artery distensibility (BAD), compliance (BAC), and resistance (BAR)] using waveform analysis of the arterial pressure signals obtained from a standard cuff sphygmomanometer (DynaPulse2000A, San Diego, CA). The long-term exposures to particles with an aerodynamic diameter < 2.5 μm (PM2.5) and nitrogen dioxide (NO2) for the 3-year periods prior to enrollment were estimated at residential addresses using fine-scale intra-urban spatiotemporal models. Linear mixed models adjusted for potential confounders were used to examine associations between air pollution exposures and health outcomes. Results: The cross-sectional study included 2,387 Chicago residents (76% African Americans) enrolled in the ChicagO Multiethnic Prevention And Surveillance Study (COMPASS) during 2013–2018 with validated address information, PM2.5 or NO2, key covariates, and hemodynamics measurements. We observed long-term concentrations of PM2.5 and NO2 to be positively associated with central systolic, pulse pressure and BAR, and negatively associated with BAD, and BAC after adjusting for relevant covariates. A 1-µg/m3 increment in preceding 3-year exposures to PM2.5 was associated with 1.8 mmHg higher central systolic (95% CI: 0.98, 4.16), 1.0 mmHg higher central pulse pressure (95% CI: 0.42, 2.87), a 0.56%mmHg lower BAD (95% CI: -0.81, -0.30), and a 0.009 mL/mmHg lower BAC (95% CI: -0.01, -0.01). Conclusion: This population-based study provides evidence that long-term exposures to PM2.5 and NO2 is related to central BP and arterial stiffness parameters, especially among African Americans. [ABSTRACT FROM AUTHOR]

The global tradition of fireworks at festivals causes significant environmental harm, including air, water, noise and light pollution, and impacts on human health. Diwali and the Chinese New Year fireworks can spike the Air Quality Index and elevate particulate matter (PM2.5) concentrations in capitals during and after celebrations. This commentary examines evidence for the detrimental effects of fireworks on the environment, human health and wildlife. Alternatives like drone displays, laser shows and eco‐friendly decorations are proposed, along with stricter regulations to curb firework impacts. [ABSTRACT FROM AUTHOR]

Current assessment of air quality or control effectiveness is solely based on particulate matter (PM) mass levels, without considering their toxicity differences in terms of health benefits. Here, we collected a total of 465 automobile air conditioning filters from 31 major Chinese cities to study the composition and toxicity of PM at a national scale. Dithiothreitol assay showed that normalized PM toxicity (NIOG) in different Chinese cities varied greatly from the highest 4.99 × 10-3 for Changsha to the lowest 7.72 × 10-4 for Yinchuan. NIOG values were observed to have significant correlations with annual PM10 concentration (r = -0.416, p = 0.020) and some PM components (total fungi, SO4 2-and calcium element). The concentrations of different elements and water-soluble ions in PM also varied by several orders of magnitude for 31 cities in China. Endotoxin concentrations in PM analyzed using limulus amebocyte lysate assay ranged from 2.88 EU/mg PM (Hangzhou) to 62.82 EU/mg PM (Shijiazhuang) among 31 Chinese cities. Besides, real-time qPCR revealed 10 ~100-fold differences in total bacterial and fungal levels among 31 Chinese cities. The concentrations of chemical (water soluble ions and trace elements) and biological (fungi, bacteria and endotoxin) components in PM were found to be significantly correlated with some meteorological factors and gaseous pollutants such as SO2 . Our results have demonstrated that PM toxicity from 31 major cities varied greatly up to 6.5 times difference; and components such as fungi and SO4 2-in PM could play important roles in the observed PM toxicity. The city-specific air pollution control strategy that integrates toxicity factors should be enacted in order to maximize health and economic co-benefits. This work also provides a comprehensive view on the overall PM pollution situation in China. [ABSTRACT FROM AUTHOR]

The gut microbiota of poultry is influenced by a variety of factors, including feed, drinking water, airborne dust, and footpads, among others. Gut microbiota can affect the immune reaction and inflammation in the lungs. To investigate the effect of gut microbiota variation on lung inflammation induced by PM2.5 (fine particulate matter) in broilers, 36 Arbor Acres (AA) broilers were randomly assigned to three groups: control group (CON), PM2.5 exposure group (PM), and PM2.5 exposure plus oral antibiotics group (PMA). We used non-absorbable antibiotics (ABX: neomycin and amikacin) to modify the microbiota composition in the PMA group. The intervention was conducted from the 18th to the 28th day of age. Broilers in the PM and PMA groups were exposed to PM by a systemic exposure method from 21 to 28 days old, and the concentration of PM2.5 was controlled at 2 mg/m3. At 28 days old, the lung injury score, relative mRNA expression of inflammatory factors, T-cell differentiation, and dendritic cell function were significantly increased in the PM group compared to the CON group, and those of the PMA group were significantly decreased compared to the PM group. There were significant differences in both α and β diversity of cecal microbiota among these three groups. Numerous bacterial genera showed significant differences in relative abundance among the three groups. In conclusion, gut microbiota could affect PM2.5-induced lung inflammation in broilers by adjusting the capacity of antigen-presenting cells to activate T-cell differentiation. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) persist as a significant contributor to global morbidity and mortality despite advances in medical technology. Air pollution has emerged as a significant contemporary challenge due to increased energy consumption and rapid economic development. The study utilized multivariable Poisson regression and Distributed Lag Models (DLM) to assess the link between brief exposure to outdoor air pollutants (PM10—particulate matter with a diameter ≤ 10 μm, NO2—nitrogen dioxide, and O3—ozone) and the risk of acute myocardial infarction with ST-segment elevation (STEMI) hospitalization, stratified by season. The research was conducted from January 2019 to December 2021 at the University Hospital in Timisoara, Romania, and daily records were collected for STEMI admissions, atmospheric pollutant levels, and meteorological parameters. The most pronounced impacts were observed with each 10 μg/m3 increase at lag 07 for PM10 during summer, leading to a 2% increase in STEMI admissions, and for NO2 during spring at lag 07, resulting in a 0.9% rise in CVD incidence. Men, middle-aged adults, and older adults exhibited greater susceptibility to elevated NO2 and PM10 concentrations than women and younger individuals. Brief exposure to diverse air pollutants heightens the likelihood of hospitalization due to STEMI, particularly among men and adults over 45. Effective measures must be implemented to mitigate these impacts, especially for vulnerable populations. [ABSTRACT FROM AUTHOR]

The gradual opening of the Arctic shipping route has made navigation possible. However, the harm caused by ship exhaust emissions is increasingly severe. Therefore, it is necessary to study the diffusion characteristics of ship exhaust plumes during Arctic navigation. The study focuses on a merchant vessel as the subject of investigation, employing computational fluid dynamics (CFD) simulation techniques to analyze the diffusion characteristics of particulate matter (PM) within ship exhaust plumes under Arctic environmental conditions. The diffusion law of ship exhaust plume PM is clarified, and the influence of three factors, synthetic wind speed, yaw angle and chimney angle, on the PM diffusion is analyzed. It was found that after the PM was discharged from the chimney, the majority of the PM dispersed directly backward along with the external flow field, while a minor fraction lingered at the stern of the ship for an extended period before eventually diffusing backward. Among them, 1235 particles were captured within a range of 200 m from the stern, with a capture rate of 0.6%. When the synthetic wind shows a yaw angle, the capture rate of PM in the interval increases rapidly with the increase of yaw angle, while other factors have less influence on the capture rate of PM. This study provides foundational guidance for predicting PM diffusion from ship exhaust plumes in Arctic environments, thereby enabling more effective strategies for managing such emissions. [ABSTRACT FROM AUTHOR]

This study investigates the influences of air temperature (T), relative humidity (RH), wind speed (WS), clearance index (KT), particulate matter (PM) concentrations (PM10 and PM2.5), and aerosol optical depth (AOD) on ultraviolet (UV) radiation during clear skies in Riyadh, central Saudi Arabia. The observational dataset utilized in this study comprises global solar radiation (G), UVA radiation, AOD measurements, and PM concentrations. The data were collected from 2014 to 2015 at the King Abdulaziz City for Science and Technology (KACST) campus in Riyadh. Regression analyses were conducted to investigate the relationships between UV radiation and the considered variables. The methodology is based on the least square method and associated statistical tests. The findings of this study provide valuable insights into the impacts of meteorological variables and aerosols on UV radiation, contributing to the understanding of environmental and industrial applications in the Arabian Peninsula. The analyses showed that the strengths and magnitudes differed from one variable to another. No significant correlations between UVA radiation (315–400 nm) and hourly and daily values of PM2.5 were found. Moreover, no significant correlations were seen between daily values of the UVA radiation and RH and between the UVB (280–315 nm) and PM10. The rest of the correlations (between UV radiation and the PM10 and meteorological variables) were found to be significant. While WS, the ratio of the PM concentrations (PM2.5/PM10), KT, and T exhibited positive correlations with UV radiation, the rest of the variables had anti-correlations with UV radiation. The influences of T, WS, and RH on ambient PM concentrations during the considered period were taken into account, and it was found that the PM concentrations correlate, with different magnitudes and strengths, positively with T and negatively with RH and WS. [ABSTRACT FROM AUTHOR]

Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM0.1; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM2.5; fine particles) and ≤0.1 µm (PM0.1; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors' emissions, and Earth's crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring. [ABSTRACT FROM AUTHOR]

Air pollution remains a significant issue, particularly in urban areas. This study explored the prediction of hourly point-based PM10 concentrations using the XGBoost algorithm to assimilate them into a geostatistical land use regression model for spatially and temporally high-resolution prediction maps. The model configuration and training incorporated meteorological data, station metadata, and time variables based on statistical values and expert knowledge. Hourly measurements from approximately 400 stations from 2009 to 2017 were used for training. The selected model performed with a mean absolute error (MAE) of 6.88 μg m−3, root mean squared error (RMSE) of 9.95 μg m−3, and an R² of 0.65, with variations depending on the siting type and surrounding area. The model achieved a high accuracy of 98.54% and a precision of 73.96% in predicting exceedances of the current EU-limit value for the daily mean of 50 μg m−3. Despite identified limitations, the model can effectively predict hourly values for assimilation into a geostatistical land use regression model. [ABSTRACT FROM AUTHOR]

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO's Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone's propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem. [ABSTRACT FROM AUTHOR]

Chemical transport models (CTMs) are widely used for air pollution modeling, which suffer from significant biases due to uncertainties in simplified parameterization, meteorological fields, and emission inventories. Accurate diagnosis of simulation biases is critical for the improvement of models, interpretation of results, and management of air quality, especially for the simulation of fine particulate matter (PM 2.5). In this study, an efficient method with high speed and a low computational resource requirement based on the tree-based machine learning (ML) method, the light gradient boosting machine (LightGBM), was designed to diagnose CTM simulation biases. The drivers of the Community Multiscale Air Quality (CMAQ) model biases are compared to observations obtained by simulating PM 2.5 concentrations from the perspectives of meteorology, chemical composition, and emission sources. The source-oriented CMAQ was used to diagnose the influences of different emission sources on PM 2.5 biases. The model can capture the complex relationship between input variables and simulation bias well; meteorology, PM 2.5 components, and source sectors can partially explain the simulation bias. The CMAQ model underestimates PM 2.5 by - 19.25 to - 2.66 µgm-3 in 2019, especially in winter and spring and during high-PM 2.5 events. Secondary organic components showed the largest contribution to the PM 2.5 simulation bias for different regions and seasons (13.8 %–22.6 %) of all components. Relative humidity, cloud cover, and soil surface moisture were the main meteorological factors contributing to PM 2.5 bias in the North China Plain, Pearl River Delta, and northwestern China, respectively. Primary and secondary inorganic components from residential sources showed the two largest contributions to this bias (12.05 % and 12.78 %), implying large uncertainties in this sector. The ML-based methods provide valuable complements to traditional-mechanism-based methods for model improvement, with high efficiency and low reliance on prior information. [ABSTRACT FROM AUTHOR]

Background: The Children's Assessing Imperial Valley Respiratory Health and the Environment (AIRE) study is a prospective cohort study of environmental influences on respiratory health in a rural, southeastern region of California (CA), which aims to longitudinally examine the contribution of a drying saline lake to adverse health impacts in children. Objectives: This cohort was established through a community–academic partnership with the goal of assessing the health effects of childhood exposures to wind‐blown particulate matter (PM) and inform public health action. We hypothesize that local PM sources are related to poorer children's respiratory health. Population: Elementary school children in Imperial Valley, CA. Design: Prospective cohort study. Methods: Between 2017 and 2019, we collected baseline information on 731 children, then follow‐up assessments yearly or twice‐yearly since 2019. Data have been collected on children's respiratory health, demographics, household characteristics, physical activity and lifestyle, via questionnaires completed by parents or primary caregivers. In‐person measurements, conducted since 2019, repeatedly assessed lung function, height, weight and blood pressure. Exposure to air pollutants has been assessed by multiple methods and individually assigned to participants using residential and school addresses. Health data will be linked to ambient and local sources of PM, during and preceding the study period to understand how spatiotemporal trends in these environmental exposures may relate to respiratory health. Preliminary Results: Analyses of respiratory symptoms indicate a high prevalence of allergies, bronchitic symptoms and wheezing. Asthma diagnosis was reported in 24% of children at enrolment, which exceeds both CA state and US national prevalence estimates for children. Conclusions: The Children's AIRE cohort, while focused on the health impacts of the drying Salton Sea and air quality in Imperial Valley, is poised to elucidate the growing threat of drying saline lakes and wind‐blown dust sources to respiratory health worldwide, as sources of wind‐blown dust emerge in our changing climate. [ABSTRACT FROM AUTHOR]