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As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM2.5 and PM10. This review paper presents a comprehensive systematic assessment of machine learning (ML) techniques for estimating PM concentrations, drawing on studies published from 2018 to 2024. Traditional statistical methods often fail to account for the complex dynamics of air pollution, leading to inaccurate predictions, especially during peak pollution events. In contrast, ML approaches have emerged as powerful tools that leverage large datasets to capture nonlinear, intricate relationships among various environmental, meteorological, and anthropogenic factors. This review synthesizes findings from 32 studies, demonstrating that ML techniques, particularly ensemble learning models, significantly enhance estimation accuracy. However, challenges remain, including data quality, the need for diverse and balanced datasets, issues related to feature selection, and spatial discontinuity. This paper identifies critical research gaps and proposes future directions to improve model robustness and applicability. By advancing the understanding of ML applications in air quality monitoring, this review seeks to contribute to developing effective strategies for mitigating air pollution and protecting public health.

Polycyclic aromatic hydrocarbons (PAHs) are a group of organic compounds that are both toxic and hazardous to human health and ecological systems. In recent work, a novel analytical strategy based on the chemometric-assisted HPLC-DAD method was proposed for the quantification and source apportionment of eight PAHs in PM10 samples. Compared to traditional chromatographic methods, this approach does not require the purification of complex PM10 samples. Instead, it utilizes a mathematical separation method to extract analytes’ profiles from overlapping chromatographic peaks, enabling precise quantification of PAHs in PM10. Firstly, 40 PM10 samples collected in Loudi city during two sampling periods were used for analysis. Subsequently, the second-order calibration method based on alternating trilinear decomposition (ATLD) was employed to handle the three-way HPLC-DAD data. Finally, the pollution sources of PAHs were analyzed by the feature component analysis method according to the obtained relative concentration matrix. For the validation model, the average recoveries of eight PAHs were between (88.8 ± 7.6)% and (105.6 ± 7.5)%, and the root-mean-square errors of prediction ranged from 0.03 μg mL−1 to 0.47 μg mL−1. The obtained limits of quantification for eight PAHs were in the range of 0.0050 μg mL−1 to 0.079 μg mL−1. For actual PM10 samples, results of the feature component analysis indicated that the main source of PAHs in PM10 may be traffic emissions and coal combustion. In summary, the proposed method provided a new and rapid analysis method for the accurate determination and source apportionment of PAHs in atmospheric aerosols.

Although fine dust is linked to numerous health issues, including cardiovascular, neurological, respiratory, and cancerous diseases, research on its effects on oral health remains limited. In this study, we investigated the protective effects of mature hemp stem extract-induced exosomes (MSEIEs) on periodontal cells exposed to fine dust. Using various methods, including microRNA profiling, PCR, flow cytometry, immunocytochemistry, ELISA, and Alizarin O staining, we found that MSE treatment upregulated key microRNAs, such as hsa-miR-122-5p, hsa-miR-1301-3p, and hsa-let-7e-5p, associated with vital biological functions. MSEIEs exhibited three primary protective functions: suppressing inflammatory genes while activating anti-inflammatory ones, promoting the differentiation of periodontal ligament stem cells (PDLSCs) into osteoblasts and other cells, and regulating LL-37 and MCP-1 expression. These findings suggest that MSEIEs have potential as functional biomaterials for applications in pharmaceuticals, cosmetics, and food industries.

The COVID-19 pandemic has provided an opportunity to examine the impact of reduced human activity on air quality. This study assesses the levels of particulate matter (PM10) in three cities—Bucharest, Brașov, and Iași—during the pandemic restrictions in 2020, comparing them with data from the pre-pandemic period (2017–2019) and post-lockdown period (2021–2022). The results show a significant decrease in PM10 levels during the lockdown, which is closely associated with reduced traffic and mobility. Notably, while PM10 concentrations initially spiked at the beginning of 2020, they markedly declined following the enforcement of lockdown measures, during which mobility to workplaces in these cities decreased by about 60% in Bucharest, 50% in Brașov, and 45% in Iași. Health risks related to PM10 exposure were evaluated using the hazard quotient method, following EU and WHO guidelines. Despite the reduction in pollution levels in 2020, the findings suggest long-term human health risks for residents of these cities. This research highlights the critical need for sustainable strategies to address air quality issues in urban areas and protect public health.

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population’s exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is used to simulate traffic-related PM10 dispersion in a 1.6 × 2 km2 urban area. Hourly simulations are conducted, and the resulting concentrations are cross-validated against high-quality measurements. By offering detailed 3D information at a micro-scale, the CFD model enables the creation of concentration maps at sensor locations. Through regression analysis, relationships between low-cost sensor (LCS) readings and modeled outcomes are established and used for network calibration. The study demonstrates the methodology’s capability to provide aid to low-cost devices during a representative 24 h period. The precision of a CFD model can also guide optimal sensor placement based on prevailing meteorological and emission scenarios and refine existing networks for more accurate urban air quality representation. The usage of cost-effective air quality networks, high-quality monitoring stations, and high-resolution air quality modeling combines the strengths of both top-down and bottom-up approaches for air quality assessment. Therefore, the work demonstrated plays a significant role in providing reliable pollutant monitoring and supporting the assessment of environmental policies, aiming to address health issues related to urban air pollution.

With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM10 emissions from tire wear under a range of real highway conditions with measurements across different seasons and roadway surface types in Phoenix, Arizona. Tire wear was quantified in the sampled PM10 using benzothiazoles (vulcanization accelerators) as tire markers. The measured emission factors had a range of 0.005–0.22 mg km−1 veh−1 and are consistent with an earlier experimental study conducted in Phoenix. However, these results are lower than values typically found in the literature and values calculated from emissions models, such as MOVES (MOtor Vehicle Emission Simulator). We found no significant difference in tire wear PM10 emission factors for different surface types (asphalt vs. diamond grind concrete) but saw a significant decrease in the winter compared to the summer.

Evidence suggests that air pollution, specifically the particulate matters PM2.5 and PM10, plays a key role in exacerbating the risk of prolonged symptoms following COVID-19 infection. Aim: This study endeavors to elucidate the potential interaction between chronic air pollution exposure and the manifestation of long COVID symptoms within a cohort based in Makkah, Saudi Arabia. Methods: Participants included residents from the Makkah region who had recovered from COVID-19 between 2022 and 2023. A comprehensive questionnaire was utilized to gather detailed demographic data and assess the persistent symptoms seen during the post-COVID period. To gauge the environmental exposure to potential risk factors, air sampling for PM10 and PM2.5 was systematically conducted in various locations in Makkah over a year. Results: Significant positive associations were found between PM2.5 and PM10 exposure and long COVID. Furthermore, specific symptom analysis revealed a significant association between air pollution and shortness of breath (for PM2.5). Only PM2.5 exposure remained statistically significant (RR = 1.32, 95% CI: 1.05, 1.67). In contrast, the association with PM10 remained on the cusp of significance, with an RR of 1.27 (95% CI: 1.00, 1.61). Conclusion: This study highlights the importance of reducing air pollution levels to mitigate the long-term health consequences of COVID-19.

The life quality of populations, especially in large agglomerations, is significantly reduced due to air pollution. Major sources of pollution include motor vehicles, industrial facilities and the burning of fossil fuels. A particularly significant source of pollution is thermal power plants and coal-fired power plants, which are widely used in developing countries. The Astana city in the Republic of Kazakhstan is a fast-growing agglomeration where air pollution is compounded by intensive construction and the use of coal for heating. The research is important for the development of urbanism in terms of ensuring the sustainable development of urban agglomerations, which are growing rapidly. Long memory in time series of concentrations of air pollutants (particulate matter PM10, PM2.5) from four stations in Astana using the fractal R/S analysis method was studied. The Hurst exponents for the studied stations are 0.723; 0.548; 0.442 and 0.462. In addition, the behavior of the Hurst exponent in dynamics is studied by the flow window method based on R/S analysis. As a result, it was found that the pollution indicators of one of the stations are characterized by the presence of long-term memory and the time series is persistent. According to the analysis of recordings from the second station, the series is defined as close to random, and for stations 3 and 4, anti-persistence is characteristic. The calculated Hurst exponent values explain the sharp increase in pollution levels in October 2021. The reason for the increase in polluting substances concentration in the air is the close location of thermal power plants to the city. The method of time series fractal analysis can be the ecological state indicator in the corresponding region. Persistent pollution time series can be used to predict the occurrence of a critical pollution level. One of the reasons for anti-persistence or the occurrence of a temporary contamination level may be the close location of the observation station to the source of contamination. The obtained results indicate that the fractal time series analysis method can be an indicator of the ecological state in the relevant region.

The more economically viable and environmentally sustainable approach for treating the by-products of coal combustion from thermal power plants entails their collective disposal as opposed to individual disposal methods. This aligns with pertinent EU directives and domestic regulations, ensuring compliance with established standards while optimizing resource utilization and minimizing environmental impact. This study evaluated the resistance to wind erosion of the binding properties of a mixture (fly ash (FA), bottom ash (BA), and additives) using an indoor wind tunnel under simulated ambient conditions. Investigations of the mutual impact of ash, bottom ash, and additives (CaO and Ca(OH)2) with a certain percentage of water were carried out with eighteen samples. The samples consisted of the water at six addition rates 5, 8, 10, 15, 20, and 25% (w/w), and additive at three addition rates (1, 2, and 3% (w/w). Based on the obtained results, the optimal ratios of additives (3% (w/w)) and water (15% (w/w)) were determined. Prior to the wind tunnel experiments, and according to the different addition rates of additives and water, eight samples were prepared with different addition rates of ash. The mass concentrations of suspended particles (PM10) and total suspended particles (TSPs) in these samples were measured at three distinct wind velocities: 1 m/s, 3 m/s, and 5 m/s, respectively. The results indicate that the samples containing the optimal content of additives and water demonstrate a maximum increase in PM10 emission zero values of no more than 1.9 times. This finding can be considered satisfactory from the standpoint of environmental protection.

When developing strategies aimed at mitigating air pollution in densely populated urban areas, it is vital to accurately investigate the vertical distribution of airborne particulate matter (PM) and its primary influencing factors. For this study, field experiments were conducted to quantify the vertical distribution and dispersion processes of PM at five vertical heights related to trees—including at street level near vehicular emission sources (0.3 m), pedestrian breathing height (1.5 m), beneath the canopy (6 m), mid-canopy (9 m), and the top of the canopy (12 m)—within a street-facing building in Wuhan, China. Comparing the vertical dispersion patterns of PM with six particle sizes (PM1, PM2.5, PM4, PM7, PM10, and total suspended particulates—TSPs), larger particles exhibited more pronounced variations with height, notably TSPs (correlation coefficient of −0.95) and PM10 (−0.84). The findings consistently revealed a downward trend in PM concentrations across various particle sizes with increasing height, indicating a negative linear correlation between particle concentrations and altitude within the street canyon. For every 1% increase in vertical height, the PM2.5 concentration decreased by approximately 5.44%, the PM10 concentration decreased by 132.1%, and the TSP concentration decreased by 180.6%. These findings show potential for guiding building designers in developing effective strategies, such as optimal vent placement, in order to mitigate the intrusion of outdoor air pollution—particularly PM2.5—into indoor environments. Furthermore, this research provides novel insights for residents living in street-facing buildings and individuals with respiratory diseases, aiding them in the selection of residential floors to minimize health risks associated with exposure to respirable PM.

Fine dust causes various disorders, including cardiovascular, neurological, renal, reproductive, motor, systemic, respiratory, and cancerous diseases. Therefore, it is essential to study functional materials to prevent these issues. This study investigated the beneficial effects of erucic acid against fine dust using methods such as miRNA profiling, quantitative PCR, flow cytometry, ELISA, and Alizarin O staining. Erucic acid effectively suppresses inflammation and upregulates osteogenic activators in fibroblasts exposed to fine dust. Additionally, erucic acid-induced exosomes (EIEs) strongly counteract the negative effects of fine dust on osteocytic differentiation and inflammation. Despite fine dust exposure, EIEs promoted osteocytic differentiation in adipose-derived stem cells (ASCs) and enhanced osteogenesis and phagocytosis in macrophages. The significant upregulation of RunX2 and BMP7 by EIEs indicates its strong role in osteocytic differentiation and protection against the effects of fine dust. EIEs also boosts immune activity and acts as an osteogenic trigger for macrophages. MicroRNA profiling revealed that EIEs dramatically upregulated miRNAs, including hsa-miRNA-1301-3p, hsa-miRNA-1908-5p, hsa-miRNA-423-5p, and hsa-miRNA-122-5p, which are associated with osteogenic differentiation and immunity. Therefore, EIEs show potential as biomaterials to prevent environment-borne diseases.

Assessment of the concentrations of dust pollution resulting from both measurements at reference stations and those determined using mathematical modelling requires accurate identification of the sources of emission. Although the concentration of dust results from several complex transport processes, as well as chemical and microphysical transformations of aerosols, sources of emissions may have a significant impact on the local level of pollution. This pilot study aimed to use measurements of the concentrations of dust (with the specification of the PM10 and PM2.5 fractions) made over a heap/excavation and its surroundings using an airship equipped with equipment for testing the optical and microphysical properties of atmospheric aerosols, and a ground station located at the facility. On the basis of the measurements, the function of the source of emissions of dust was estimated. According to our study, the yearly emission of dust varies between 42,470 and 886,289 kg for PM10, and between 42,470 and 803,893 for PM2.5 (minimum and maximum values). A model of local air quality was also used, which allowed us to verify the parameterization of emissions of dust pollutants for the PM10 and PM2.5 fractions from heaps and excavations based on the modelling results.

Leukemia is the most common childhood cancer and its etiology could be related to various environmental contaminants such as particulate matter (PM). The objective of our study is to evaluate the potential association between exposure to PM during pregnancy and the incidence of childhood leukemia. We established a population-based nationwide cohort using the Spanish Birth Registry Statistics database of the National Statistics Institute. We used spatiotemporal land use random forest models to estimate the concentrations of PM10 and PM2.5 for the entire pregnancy and by trimesters. We conducted logistic regression analyses adjusted for various covariates. In addition, we fitted generalized additive models (GAMs) to estimate the non-linear relationship between PM levels and leukemia incidence. The study included 3,112,123 children and 1066 cases of leukemia. The results for the continuous variable of PM10 exposure levels suggested an increased risk of childhood leukemia to be associated with higher exposure. The results for the categorized PM10 variable suggest an increased risk of childhood leukemia among pregnant women whose exposure levels were higher than the median (third and fourth quartiles). The results for PM2.5 were weaker. We found association between exposure to PM10 during pregnancy and an increased risk of childhood leukemia. Our findings indicate that public health interventions should aim to reduce air pollution to lower the incidence of childhood leukemia.

This study proposes a novel air pollution exposure index (APEI) metric, drawing from traditional elements in rhythmanalysis and public-life studies to help understand how people are exposed to air pollution in their urban environment and when the risks are greatest. It expands on the notion of rhythm as a socially constructed natural phenomenon connected to urban life and spaces. Air quality monitoring data, as well as bus times and in situ traffic and pedestrian counts, from Cable Street and Dalton Square in Lancaster were applied to define the APEI, which combines an air pollution index (NO2 and PM10), a surrogate for ambient air pollution level, with a population index, a surrogate for population flow. The index values were subsequently ranked in numeric order, i.e., a higher ranking shows increased exposure risk and vice versa, to determine total exposure on an individual level. This metric proves valuable in defining air pollution exposure status and recognizing factors associated with high pollution and population levels. Similarly, by comparing APEI values, one could evaluate exposure levels in different locations and seasons to verify when the APEI has increased at a given location and the different rhythms and flows responsible for an individual’s exposure. Hence, it has potential use for urban planners and the city council’s policymakers who are involved in Lancaster Air Quality Management.

The serious densification of human settlements necessitates an increase in the role and importance of green infrastructures in the overall functioning of urban ecosystems. Accordingly, the aim of the present study was to (1) assess the efficiency of air pollutant removal (potentially toxic elements) of three common ornamental trees (Tilia tomentosa Moench, Fraxinus excelsior L. and Pinus nigra J.F. Arnold) and (2) model the air quality regulatory services (removal of PM10, PM2.5 and NO2). Three different approaches were applied—enrichment factor (EF) and metal accumulation factor (MAI) per tree species, as well as simulation modeling for the whole urban forest. The MAI values of the three studied species were found to be very similar, in the range of 22.35 to 23.08, which suggests that these species could be a good choice for planting in urban areas with worsened air quality. The highest EF values were observed for U (3–18), followed by As (1.6–2.56) and Sr (0.87–2.46). The potential of urban forests in countering air pollution was highlighted by three simulated scenarios for PM10, PM2.5 and NO2 removal. The highest removal efficiency was calculated for evergreen species, followed by the mixed composition of deciduous (90%) and evergreen trees (10%), and the scenario with wholly deciduous trees had the lowest one. The contribution of nature-based solutions in meeting air quality standards and enhancing resilience in urban areas was clearly demonstrated. The functional complementarity of the different functional tree groups (coniferous, evergreen and deciduous broad-leaved species) was proven to be crucial for the support of both functional stabilities of the phytocenosis under diverse climatic conditions and during the change of seasonal cycles in the vegetation.

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full “heating season” (15 October 2022–15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = −4.8 and −2.9 μg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 μg/m3) and slightly under-estimated PM10 concentrations (MB = −0.4 μg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68–0.75 vs. 0.59–0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.

Changes in vertical column concentration, size distribution, and surface concentration of aerosol associated with the lockdown imposed by the COVID-19 pandemic in 2020 over the Ahmedabad region in Gujarat State, India, were analyzed. The results are compared with changes over selected Indo-Gangetic Plain (IGP) regions. On 25 March 2020, the prime minister of India declared a complete lockdown throughout the country and later lifted restrictions in a phased manner. Aerosol optical depth (AOD) over the Ahmedabad region on 29 March dropped to as low as 0.11, and in the first two weeks of lockdown, the weekly average AOD was only 0.18. On almost all days of the lockdown period, AOD over the Ahmedabad region was lower than the decadal mean. It was found that the Ahmedabad region responded differently to lockdown conditions compared to the IGP regions. During the first lockdown phase, AOD decreased by about 29% compared to the pre-lockdown period over the Ahmedabad region. However, the average reduction over the IGP was much more, about 50%. The average Angstrom exponent (AE) of 0.96 during the pre-lockdown period over the Ahmedabad region increased phase-wise to 1.36 during the L3 lockdown phase, indicating dominance of fine-mode particles during the lockdown period. It suggests a reduction in anthropogenically produced coarse-mode particles, typically dust produced by vehicular movement, construction, and industrial activities. However, on the other hand, over the IGP region, the high dominance of fine-mode particles during the pre-lockdown period had changed to a high dominance of coarse-mode particles, especially over the Delhi region. This indicates a reduction in anthropogenically produced fine-mode particles, which are mainly generated by fossil and biofuels/biomass combustion, over the IGP region by lockdown conditions. Within a few days of lockdown, PM2.5 was reduced by 64% and 76% over the Ahmedabad and Delhi regions, respectively. The lockdown imposed by the pandemic provided an excellent opportunity to ascertain background aerosol conditions in the atmosphere.

Inhalable particulate matter (PM10) is a major air pollutant that has significant impacts on environmental climate and human health. Land-cover change is also a key factor influencing changes in atmospheric pollution. Changes in land-cover types can lead to changes in the sources and sinks of air pollutants, thus affecting the spatial distribution of PM10, which poses a threat to human health. Therefore, exploring the relationship between PM10 concentration change and land-cover change is of great significance. In this study, we constructed an extreme randomized trees model (ET) based on ground PM10 monitoring data, satellite-based aerosol optical depth (AOD) data, and auxiliary data including meteorological, vegetation, and population data to retrieve ground-level PM10 concentrations across China. The coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE) of the model were 0.878, 5.742 μg/m3, and 8.826 μg/m3, respectively. Based on this, we analyzed the spatio-temporal distribution of PM10 concentrations in China from 2015 to 2021. High PM10 values were mainly observed in the desert areas of northwestern China and the Beijing–Tianjin–Hebei urban agglomeration. The majority of China showed a significant decrease in PM10 concentrations. Additionally, we also analyzed the nonlinear response mechanism of the PM10 concentration change to land-cover change. The PM10 concentration is sensitive to forest and barren land change. Therefore, strengthening the protection of forests and desertification control can significantly reduce air pollution. Attention should also be paid to emission management in agricultural activities and urbanization processes.

Understanding the potential effects of climate change on forest fire behavior and the resulting release of combustion products is critical for effective mitigation strategies in Greece. This study utilizes data from the MAGICC 2.4 (Model for the Assessment of Greenhouse Gas-Induced Climate Change) climate model and the SCENGEN 2.4 (SCENarioGENerator) database to assess these impacts. By manipulating various model parameters such as climate sensitivity, scenario, time period, and global climate models (GCMs) within the SCENGEN 2.4 database, we analyzed climatic trends affecting forest fire generation and evolution. The results reveal complex and nuanced findings, indicating a need for further investigation. Case studies are conducted using the FARSITE 4 (Fire Area Simulator) model, incorporating meteorological changes derived from climate trends. Simulations of two fires in East Attica, accounting for different fuel and meteorological conditions, demonstrate an increase in the rate of combustion product release. This underscores the influence of changing meteorological parameters on forest fire dynamics and highlights the importance of proactive measures to mitigate future risks. Our findings emphasize the urgency of addressing climate change impacts on wildfire behavior to safeguard environmental and public health in Greece.

As fleet electrification progresses, vehicles are continuously becoming heavier, while the used electric motors, with their high torques, enable longitudinal dynamics to be maintained or even increased. This raises the question of what effect electric vehicles have on the emission of tire–road particulate matter (PM). To answer this question, investigations were carried out in this study on a tire internal drum test bench with real road surfaces. In addition to the vertical load, the tire inflation pressure and the driving speed were varied. PM emissions were recorded in real time, resulting in emission factors (emission per kilometer driven) for different load conditions. This allows statements to be made about both the effect on the total emission and on the particle size distribution. It was shown that the PM emission increases linearly with the vertical load at constant longitudinal dynamics. If the tire inflation pressure is increased, the emission also increases linearly, and the increases in emission are equally large for both influences. A clear influence of the driving speed on the emission factor could not be determined. With regard to the particle size distribution, the following correlations were found: higher vertical load and higher tire inflation pressure result in a larger mean particle diameter, while a higher driving speed reduces it. Thus, this study contributes to a better understanding of the expected changes in tire-road PM emissions as a result of electrification.

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM2.5, PM10, NO2, SO2, O3, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R2, demonstrated high accuracy with values of 0.92 for PM2.5, 0.95 for PM10, 0.95 for O3, 0.90 for NO2, 0.79 for SO2, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM2.5, although PM10 exhibited a rebound in northern regions. The concentrations of SO2 and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O3 concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution.

We have previously shown that PM10 exposure causes oxidative stress and reduces Nrf2 protein levels, and SKQ1 pre-treatment protects against this damage in human corneal epithelial cells (HCE-2). The current study focuses on uncovering the mechanisms underlying acute PM10 toxicity and SKQ1-mediated protection. HCE-2 were pre-treated with SKQ1 and then exposed to 100 μg/mL PM10. Cell viability, oxidative stress markers, programmed cell death, DNA damage, senescence markers, and pro-inflammatory cytokines were analyzed. Nrf2 cellular location and its transcriptional activity were determined. Effects of the Nrf2 inhibitor ML385 were similarly evaluated. Data showed that PM10 decreased cell viability, Nrf2 transcriptional activity, and mRNA levels of antioxidant enzymes, but increased p-PI3K, p-NFκB, COX-2, and iNOS proteins levels. Additionally, PM10 exposure significantly increased DNA damage, phosphor-p53, p16 and p21 protein levels, and β-galactosidase (β-gal) staining, which confirmed the senescence. SKQ1 pre-treatment reversed these effects. ML385 lowered the Nrf2 protein levels and mRNA levels of its downstream targets. ML385 also abrogated the protective effects of SKQ1 against PM10 toxicity by preventing the restoration of cell viability and reduced oxidative stress. In conclusion, PM10 induces inflammation, reduces Nrf2 transcriptional activity, and causes DNA damage, leading to a senescence-like phenotype, which is prevented by SKQ1.

This study aims to elucidate the influence of meteorological conditions on particle levels in Cergy-Pontoise. It explores the temporal variability of PM10 pollution days by associating them with the vertical temperature profile derived from conventional radiosondes from 2013 to 2022 (regional station). The results indicate that nearly 80% of exceedance days were associated with thermal inversions, primarily observed in winter and typically lasting 1 to 3 days. Analysis of winter thermal inversion characteristics suggests that those linked to pollution primarily occur near the ground, with higher intensity in December (12.1 °C) and lower in February (10.3 °C). Persistent inversions (extended nocturnal by diurnal inversion) account for 91.4% of the total inversions associated with high concentrations. Captive balloon soundings and temperature measurements at different altitudes were conducted during the winter of 2022/2023 to clarify thermal inversion in the Oise Valley at the center of Cergy-Pontoise. The results highlight three nocturnal wind circulation mechanisms in the valley, including downslope flow, circulation influenced by an urban heat island, and mechanical air evacuation under an inversion layer towards the less steep East side of the valley. Analysis of PM with the temperature gradient in the Oise Valley shows a significant correlation, suggesting an increase in concentrations during locally detected inversions and a decrease during atmospheric disturbance.

Particulate matter (PM) suspended in the air significantly impacts human health. Those of anthropogenic origin are particularly hazardous. Poland is one of the countries where the air quality during the heating season is the worst in Europe. Air quality in small towns and villages far from state monitoring stations is often much worse than in larger cities where they are located. Their residents inhale the air containing smoke produced mainly by coal-fired stoves. In the frame of this project, an air quality monitoring network was built. It comprises low-cost PMS7003 PM sensors and ESP8266 microcontrollers with integrated Wi-Fi communication modules. This article presents research results on the influence of the PM sensor location on their indications. It has been shown that the indications from sensors several dozen meters away from each other can differ by up to tenfold, depending on weather conditions and the source of smoke. Therefore, measurements performed by a network of sensors, even of worse quality, are much more representative than those conducted in one spot. The results also indicated the method of detecting a sudden increase in air pollutants. In the case of smokiness, the difference between the mean and median indications of the PM sensor increases even up to 400 µg/m3 over a 5 min time window. Information from this comparison suggests a sudden deterioration in air quality and can allow for quick intervention to protect people’s health. This method can be used in protection systems where fast detection of anomalies is necessary.

Due to the advantage of high spatial coverage, using satellite-retrieved aerosol optical depth (AOD) data to estimate PM2.5 and PM10 mass concentrations is a current research priority. Statistical models are the common method of PM estimation currently, which do not require the knowledge of complex chemical and physical interactions. However, the statistical models rely on station data, which results in less accurate PM estimation concentrations in areas where station data are missing. Hence, a new hybrid model, with low dependency on on-site data, was proposed for PM2.5 and PM10 mass concentration estimation. The Gaofen-1 satellite and MODIS data were employed to estimate PM2.5 and PM10 concentrations with 100 m spatial resolution in Beijing, China. Then, the estimated PM2.5/10 mass concentration data in 2020 were employed to conduct a spatio-temporal analysis for the investigation of the particulate matter characteristic in Beijing. The estimation result of PM2.5 was validated by the ground stations with R2 ranging from 0.91 to 0.98 and the root mean square error (RMSE) ranging from 4.51 μg/m3 to 17.04 μg/m3, and that for PM10 was validated by the ground stations with R2 ranging from 0.85 to 0.98 and the RMSE ranging from 6.98 µg/m3 to 29.00 µg/m3. The results showed that the hybrid model has a good performance in PM2.5/10 estimation and can improve the coverage of the results without sacrificing the effectiveness of the model, providing more detailed spatial information for urban-scale studies.

Several epidemiological studies have demonstrated that particulate matter (PM) in air pollution can be involved in the genesis or aggravation of different cardiovascular, respiratory, perinatal, and cancer diseases. This study assessed the in vitro effects of PM10 on the secretion of cytokines by a human monocytic cell line (THP-1). We compared the chemotactic, pro-inflammatory, and anti-inflammatory cytokines induced by PM10 collected for two years during three different seasons in five different Mexico City locations. MIP-1α, IP-10, MCP-1, TNF-α, and VEGF were the main secretion products after stimulation with 80 μg/mL of PM10 for 24 h. The THP-1 cells showed a differential response to PM10 obtained in the different sites of Mexico City. The PM10 from the north and the central city areas induced a higher pro-inflammatory cytokine response than those from the south. Seasonal pro-inflammatory cytokine secretion always exceeded anti-inflammatory secretion. The rainy-season-derived particles caused the lowest pro-inflammatory effects. We concluded that toxicological assessment of airborne particles provides evidence supporting their potential role in the chronic exacerbation of local or systemic inflammatory responses that may worsen the evolution of some chronic diseases.

(1) Background: The increase in cardiovascular risk related to air pollution has been a matter of interest in recent years. The role of particulate matter 2.5 (PM2.5) has been postulated as a possible factor for premature death, including cardiovascular death. The role of long-term exposure to PM10 is less known. The aim of the study was to assess the individual relationship between air pollution in habitation and the development of coronary artery disease. (2) Methods: Out of 227 patients who underwent coronary angiography, 63 (38 men and 25 women) with a mean age of 69 (63–74) years, with nonsignificant atherosclerotic changes at the initial examination, were included in the study. The baseline and repeated coronary angiography were compared to reveal patients with atherosclerotic progression and its relation to demographic and clinical factors and exposure to air pollution in the habitation place. (3) Results: In the performed analysis, we found a significant correlation between Syntax score in de novo lesions and BMI (Spearman’s rho −0.334, p = 0.008). The significant and strong correlation between median annual PM10 values of 20 µg/m3 and at least 25 µg/m3 in air pollution and the risk of de novo coronary disease was noticed (Spearman’s rho = 0.319, p = 0.011 and Spearman’s rho = 0.809, p < 0.001, respectively). (4) Conclusions: There is a positive correlation between long-term exposure to PM10 air pollution and coronary artery disease progression, demonstrated by the increase in Syntax score. The presented analysis revealed increased morbidity at lower PM10 concentrations than generally recommended thresholds. Therefore, further investigations concerning air pollution’s influence on cardiovascular risk should be accompanied by promoting lifestyle changes in the population and revisiting the needs for environmental guidelines.

A quantitative analytical method for PFAS determination in airborne particulate matter (PM) has been developed using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), allowing for the determination of 33 compounds. The procedure was applied to ambient PM10 with limits of quantification for PFAS in the fg m−3 range. PM10 samples collected during a year-long campaign conducted in an urban site in Umbria (Central Italy) have been characterized for their PFAS content. Among the seven detected PFASs, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) were the most abundant compounds. Furthermore, this work allowed us to obtain the first seasonal trend of airborne PFASs in Central Italy. Seasonal trend analysis shows that PFAS concentration in the atmosphere peaks in summertime. A comparison with trends of other relevant primary and secondary air pollutants determined at the sampling site suggests a secondary nature of the observed PFAS, which are formed in the atmosphere directly from gaseous precursors and can contribute to worsen the urban air quality in summertime.

This contribution deals with the assessment of air pollution caused by atmospheric aerosol particulate matter fraction PM10 and benzo[a]pyrene (BaP) in the urban agglomeration of Ostrava, located in the Czech Republic in Central Europe. The motivation for this research was the need to identify the sources of air pollution in the area, particularly in locations where the contribution of different sources to concentrations of pollutants of concern has not been elucidated yet. In this study, source apportionment in the vicinity of the industrial hot spot was performed by statistically evaluating measured pollutant concentrations as a function of meteorological variables and using the Positive Matrix Factorization (PMF) receptor model. A significant methodological innovation and improvement over previous assessments was the higher monitoring resolution of benzo[a]pyrene, with samples collected at three-hour intervals instead of the standard 24 h collection period. The key findings indicate that in the cold part of the year, secondary particles—specifically sulfates and ammonium nitrates—were responsible for the most significant portion of PM10 air pollution throughout the area of interest. The contribution of these particles ranged from one-third to two-fifths of the total concentration, except at the industrial site of Ostrava–Radvanice (TORE), where they accounted for approximately one-fifth of the measured pollution concentration level. Emissions from individual household heating were identified as the main source of this type of pollution. With regards to benzo[a]pyrene air pollution, this study found that in the whole area of interest, except for the Ostrava–Radvanice site, it mainly originated from individual heating with coal (90%). In contrast, at the Ostrava–Radvanice site, two-thirds of the benzo[a]pyrene pollution came from the premises of Liberty Ostrava a.s., primarily from coke production, and less than one-third came from local domestic heating. This study also determined the spatial extent of the occurrence of extremely high benzo[a]pyrene concentrations (above 5 ng/m3), which are estimated to affect nearly 10,000 inhabitants. The results confirm that the data from the TORE station are only representative of its immediate surroundings and are not applicable to the assessment of air quality and causes of air pollution in the whole city of Ostrava or the urban area of Ostrava–Radvanice as a whole. After years of research, these findings provide the Ministry of the Environment of the Czech Republic and the government with an accurate basis for implementing measures to address the identified pollution sources in the area of interest. The success of this study was made possible by the adoption of a more detailed sampling approach, which involved a resolution of 3 h instead of 24 h. This methodological improvement is a significant finding and will be useful for future source apportionment studies.

The objective of this study was to experimentally determine the mathematical correlations between the loading of the tire, being longitudinal and lateral forces, and the emission of particulate matter (PM) from the tire–road contact. Existing emission factors (EF, emission per vehicle and distance traveled) are the result of long-term measurements, which means that no conclusion can be drawn about the exact driving condition. To determine meaningful emission factors, extensive driving tests were conducted on an internal drum test bench while measuring PM emissions from the tire–road contact in real-time. This showed that the increases in emission over longitudinal and lateral forces can be approximated with fourth-order functions, with lateral forces leading to significantly higher emissions than longitudinal forces for the summer tire investigated. Using the emission functions obtained, a three-dimensional map was created that assigns an EF to each load condition consisting of different longitudinal and lateral forces for one vertical load. With known driving data, the map can be used for future simulation models to predict the total emission of real driving cycles. Furthermore, the results show that the average particle size increases with increasing horizontal force. The particles collected during the tests were analyzed to determine the proportions of tire and road material. According to the results, the tire contributes only about 20% of the particle mass, while 80% is attributable to the road surface. In terms of volume, these shares are 32% and 68%, respectively.

The air quality of the Himalayan region of India is deteriorating due to the increasing load of particulate matter that is emitted from various local and regional sources, as well as to the transit of dust-related pollutants from the Indo-Gangetic Plain (IGP) and surrounding areas. In this study, the mineralogical characteristics of coarse mode particulate matter (PM10) was analyzed using the X-ray diffraction (XRD) technique from January to December 2019 over Nainital (29.39° N, 79.45° E; altitude: 1958 m above mean sea level), a central Himalayan region of India. XRD analysis of PM10 samples showed the presence of clay minerals, crystalline silicate minerals, carbonate minerals, and asbestiform minerals. It was shown that quartz minerals with significant levels of crystallinity were present in all the samples. Other minerals that are contributing to the soil dust were also observed in the analysis (CaFe2O4, CaCO3, CaMg(CO3)2, calcium ammonium silicate hydrate (C-A-S-H), gypsum, kaolinite, illite, augite, and montmorillonite). The minerals ammonium sulphate, hematite, and magnetite were also found in the samples and are suggested to be from biogenic and anthropogenic activities, including biomass burning, fuel combustion, vehicle exhaust, construction activities, etc. This study indicated that the majority of the minerals in PM10 that were present in this Himalayan region are from soil/crustal dust.

Atmospheric particulate matter (PM) with diameters below 10 µm (PM10) may enter the lungs through inhalation and are linked to various negative health consequences. Emergent evidence emphasizes the significance of cell metabolism as a sensitive target of PM exposure. However, the current understanding of the relationship between PM composition, conventional toxicity measures, and the rewiring of intracellular metabolic processes remains limited. In this work, PM10 sampled at a residential area (urban background, UB) and a traffic-impacted location (roadside, RS) of a Portuguese city was comprehensively characterized in terms of polycyclic aromatic hydrocarbons and plasticizers. Epithelial lung cells (A549) were then exposed for 72 h to PM10 organic extracts and different biological outcomes were assessed. UB and RS PM10 extracts dose-dependently decreased cell viability, induced reactive oxygen species (ROS), decreased mitochondrial membrane potential, caused cell cycle arrest at the G0/G1 phase, and modulated the intracellular metabolic profile. Interestingly, the RS sample, richer in particularly toxic PAHs and plasticizers, had a greater metabolic impact than the UB extract. Changes comprised significant increases in glutathione, reflecting activation of antioxidant defences to counterbalance ROS production, together with increases in lactate, NAD+, and ATP, which suggest stimulation of glycolytic energy production, possibly to compensate for reduced mitochondrial activity. Furthermore, a number of other metabolic variations hinted at changes in membrane turnover and TCA cycle dynamics, which represent novel clues on potential PM10 biological effects.

Respirable particulate matter (PM10) is atmospheric particulate matter with a kinetic diameter of less than or equal to 10 μm in air. According to the definition of the World Health Organization, it is called thoracic-enterable particulate matter because it can enter the body through the respiratory tract and be deposited into the lungs or absorbed into the blood and lymphatic systems. The toxic substances in it can enter the bloodstream directly and cause serious harm to human health. In addition, PM10 has unique physiological and biological effects, making it an important area of atmospheric chemistry research. In this study, two urban neighborhoods and sports companies were selected for the purpose of investigating the effects of PM10 concentrations in the air of neighborhoods and workplaces on people living and working in these environments for a long period of time, as well as synergistic effects between PM10 concentrations and changes in temperature and the incidence of related diseases. By assessing the extent of PM10’s impact on the respiratory system, this study provides basic data for assessing the health hazards of particulate matter in community environments. This study also analyzed the synergistic effects between air pollutant concentrations, temperature changes, and the incidence of related diseases in two cities to investigate the spatial and temporal distribution characteristics of air pollution and the meteorological causes of pollution in China. On this basis, we established a prediction model for related sensitivity diseases to provide theoretical and technical support for the prediction of related sensitivity diseases on a nationwide scale. Meanwhile, our study also provides support to relevant government departments to formulate a scientific basis and preventive and control measures for dealing with air pollution and its effects on human health.

PM10 samples were collected at the Kraków air quality traffic monitoring station during two periods: February–May 2018 and February–June 2020. The PM10 concentrations dropped by 50% from 74 ± 29 µg/m3 to 37 ± 13 µg/m3 in 2018 and 2020, respectively. The elemental concentrations were determined by the energy-dispersive X-ray fluorescence (EDXRF) method, and the ion concentrations were determined by ion chromatography (IC). The concentration ratios in 2018 to 2020 were greater than 1.7 for the following elements: S, Cl, K, Zn, Br, and the ions SO42−, Na+, and NH4+. Similar concentrations were observed in 2018 and 2020 for the following chemical species: Ca, Ti, Mn, Ni, Rb, Sr, K+, Mg2+, Ca2+, and PO43−. The Cr concentration was higher in 2020 compared to 2018. Four source profiles were obtained from the PMF (Positive Matrix Factorization) modelling. The following sources were attributed to this: solid fuel combustion, secondary inorganic aerosols, traffic/industry/construction work, and soil. The contributions of solid fuel combustion and secondary inorganic aerosols (SIA) were significantly lower in February and March 2020 than in February and March 2018. The relative differences were in the range 70–98%. Traffic/industry/construction work contributions were 6% and 36% lower in March and May 2020 compared to the same months in 2018, respectively. Two factors affected the characteristics of PM10: one was the ban of using coal and wood for heating purposes introduced in Krakow in September 2019, observed mainly in February and March, and the COVID-19 pandemic that was observed mainly in April and May.

Multivariate regression models for real-time coastal air quality forecasting were suggested from 18 to 27 March 2015, with a total of 15 kinds of hourly input data (three-hours-earlier data of PM and gas with meteorological parameters from Kangnung (Korea), associated with two-days-earlier data of PM and gas from Beijing (China)). Multiple correlation coefficients between the predicted and measured PM10, PM2.5, NO2, SO2, CO and O3 concentrations were 0.957, 0.906, 0.886, 0.795, 0.864 and 0.932 before the yellow sand event at Kangnung, 0.936, 0.982, 0.866, 0.917, 0.887 and 0.916 during the event and 0.919, 0.945, 0.902, 0.857, 0.887 and 0.892 after the event. As the significance levels (p) from multi-regression analyses were less than 0.001, all correlation coefficients were very significant. Partial correlation coefficients presenting the contribution of 15 input variables to 6 output variables using the models were presented for the three periods in detail. Scatter plots and their hourly distributions between the predicted and measured values showed the quite good accuracy of the modeling performance for the current time forecasting of six output values and their high applicability.

Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, such as the risk of becoming ensnared in local minimal within multi-objective loss functions, substantial computational requirements, and the requirement for continuous objective functions. To tackle these challenges, this paper introduces a novel approach that combines the binary gray wolf optimizer (BGWO) with DNN to improve the optimization of models for air pollution prediction. The BGWO algorithm, inspired by the behavior of gray wolves, is used to optimize both the weight and bias of the DNN. In the proposed BGWO, a novel sigmoid function is proposed as a transfer function to adjust the position of the wolves. This study gathers meteorological data, topographic information, PM10 pollution data, and satellite images. Data preparation includes tasks such as noise removal and handling missing data. The proposed approach is evaluated through cross-validation using metrics such as correlation rate, R square, root-mean-square error (RMSE), and accuracy. The effectiveness of the BGWO-DNN framework is compared to seven other machine learning (ML) models. The experimental evaluation of the BGWO-DNN method using air pollution data shows its superior performance compared with traditional ML techniques. The BGWO-DNN, CapSA-DNN, and BBO-DNN models achieved the lowest RMSE values of 16.28, 19.26, and 20.74, respectively. Conversely, the SVM-Linear and GBM algorithms displayed the highest levels of error, yielding RMSE values of 36.82 and 32.50, respectively. The BGWO-DNN algorithm secured the highest R2 (88.21%) and accuracy (93.17%) values, signifying its superior performance compared with other models. Additionally, the correlation between predicted and actual values shows that the proposed model surpasses the performance of other ML techniques. This paper also observes relatively stable pollution levels during spring and summer, contrasting with significant fluctuations during autumn and winter.

The impact of air pollution on people and the environment is a severe issue that has recently been the subject of extensive research. This study has looked at the factors that contribute to the seasonal and spatial variability of pollutant concentration over Delhi NCR from 2019 to 2021. Additionally, the causes of changes in air quality during the COVID-19’s lockdown period in 2020 have been discussed, along with comparisons to the pre-lockdown year (2019) and the post-lockdown year (2021). Seven pollutant parameters, viz., (PM2.5, PM10, NOx, CO, SO2, NH3, and O3) were retrieved from the air quality monitoring stations spread over Delhi NCR. The results show a significant temporal (seasonal) and spatial variability in the air pollutants’ concentration. The highest pollutant level was observed in winter and the lowest in summer seasons. The results suggest that the concentration of atmospheric pollutants was already lower (20–30%) before the implementation of the lockdown. Meteorology played an important role in emission reduction during the lockdown, in particular, and seasonal, in general. The results also suggest that Bhiwadi is not the most polluted city, as claimed in the World Air Quality Report 2022. The most polluted sites in terms of pollutant concentration were observed over Delhi in all the years considered.

Environmental pollution of megacities can cause early biological damage such as DNA strand breaks and micronuclei formation. Comet assay tail length (TL) reflects exposure in the uterus to high levels of air pollution, primarily ozone and air particles (PM10), including mothers’ smoking habits during pregnancy, conditions which can lead to low birth weight. In this biomonitoring study, we evaluated basal DNA damage in the cord blood cells of newborn children from Mexico City. We found a correlation between DNA damage in mothers and their newborns, including various parameters of environmental exposure and complications during pregnancy, particularly respiratory difficulties, malformations, obstetric trauma, neuropathies, and nutritional deficiencies. Mothers living in the southern part of the city showed double DNA damage compared to those living in the northern part (TL 8.64 μm vs. 4.18 μm, p < 0.05). Additionally, mothers’ DNA damage correlates with exposure to NOx (range 0.77–1.52 ppm) and PM10 (range 58.32–75.89 μg/m3), as well maternal age >29. These results highlight the sensitivity of the comet assay in identifying differential in utero exposure for newborns whose mothers were exposed during pregnancy. They also suggest the importance of antioxidants during pregnancy and the role of the placental barrier in protecting the newborn from the DNA-damaging effects of oxidative pollution.

The proposed approach based on compositional data analysis was applied on simultaneous measurements of the mineral element concentrations of PM10 and PM2.5 from a typical suburban site with and without a Saharan event. The suburban site is located in the city of Rome. The selected mineral elements were Al, Si, Ca, Fe, Ti, Mg, and Sr. The data relating to these elements are reported in a previous study. The considered elements are mainly related to mineral matter. The proposed approach allows statistically validating that the mineral element concentrations of PM during days with a Saharan event differ from those without a Saharan event in terms of mineral element composition and size distribution. In particular, the results showed that the compositional data analysis applied to simultaneous measurements of mineral element concentrations of PM10 and PM2.5 is a helpful technique that can be used to study environmental sites affected by natural sources such as Saharan events. Moreover, the presented technique can be handy in all those conditions where it is important to discriminate whether the occurrence of an exceedance or a violation of the daily limit value established for PM could also be due to natural sources.

Background/Objectives: Childhood obesity has emerged as a global health concern with profound implications for long-term health outcomes. In recent years, there has been increasing interest in the potential role of environmental factors in the development of childhood obesity. This comprehensive review aims to elucidate the intricate relationship between various components of air pollution and childhood obesity. Methods: We systematically analyze the existing literature from the past 5 years to explore the mechanistic pathways linking air pollution, including particulate matter (PM), nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), and polycyclic aromatic hydrocarbons (PAHs), to childhood obesity. This systematic review examines 33 epidemiological studies on the link between air pollution and childhood obesity, published from 1 January 2018, to 31 January 2024. Results: Studies from counties with low overall air pollution noticed only low to no impact of the exposure to childhood obesity, unlike studies from countries with higher levels of pollution, suggesting that the mitigation of air pollutants can reduce the chance of it being a negative factor for the development of obesity. This relationship was noticed for PM 2.5 , PM 1 , PM 10 , NO x , and SO 2 but not for PAHs, which showed a negative effect on children's health across 10 out of 11 studies. Conclusions: This review underscores the need for interdisciplinary approaches to address both environmental and socio-economic determinants of childhood obesity. Efforts aimed at reducing air pollution levels and promoting healthy lifestyle behaviors are essential for safeguarding the health and well-being of children worldwide.

The government decision to promote the change of energy mix has led to a serious decrease in industrial activities in the energy basin of Western Macedonia and subsequently had a positive influence on the air quality of the region. To examine the fact and estimate the current atmospheric emission sources, PM10 samples were collected at different sites in Western Macedonia, Greece, from February 2022 to May 2023 and were analyzed for metallic components, major ions, and PAHs. The dataset was then analyzed using PMF and CMB model to identify the possible emission sources. In this study, the first results are presented and discussed.

This study demonstrates the feasibility of a mobile aerial drone particle monitoring system (DPMS) to measure and detect changes in harvest dust levels based on moderate adjustments to harvester settings. When compared to an earlier harvester, a new harvester operated at standard settings produced 35% fewer PM2.5s, 32% fewer PM10s, and 42% fewer TSPs. Increasing the ground speed had an adverse effect on dust mitigation, while reducing it by half only offered a slightly more favorable margin. The mutual effects of some meteorological factors were found to be slightly correlated with PM10 and TSP readings and caused significant variability in PM2.5 readings. The current findings show similar trends to PM reduction estimates of previous studies, with only a nominal difference of 10 to 15% points. Overall, the DPMS was found to perform well within an acceptable statistical confidence level. The use of DPMSs could reduce the logistical needs, complexity issues, and feedback times often experienced using the Federal Reference Method (FRM). Further investigation is needed to verify its robustness and to develop potential correlations with the FRM under different orchard location and management practices. At this stage, the current aerial DPMS should be considered a rapid screening tool not to replace the FRM, but rather to complement it in evaluating the feasibility of dust abatement strategies for the almond industry.

The construction industry is a major contributor to dust, greenhouse gases, and other air pollutants. Implementing effective and sustainable practices in managing construction site operations can greatly mitigate the environmental effects of a project. To achieve this, a collaboration between a scientific research institution and a construction company enabled the real-time monitoring of air quality parameters at a construction site using Internet of Things (IoT) technologies. They implemented an IoT-based system framework that integrated a distributed sensor network to collect real-time data from the construction site. Various sensors were utilized to gather data on the concentration of NO2 and particulate matter (PM2.5 and PM10), as well as meteorological parameters such as wind speed, wind direction, humidity, pressure, and temperature. The real-time measurements yielded insights into the level of air pollution at the construction site and its association with earth excavation, the primary construction activity. This information can be utilized to manage excavation work and reduce the levels of polluting gases (NO2) and suspended particles. By conducting an on-site monitoring of these three pollutants, the study discovered that the dust levels resulting from excavation activities were relatively high. When comparing the wind direction with NO2 and PM concentrations, it was concluded that earth excavation significantly influenced the air quality in the construction area. However, in terms of the primary factors affecting NO2 and construction dust concentrations, the analysis revealed that meteorological factors did not exhibit a significant correlation with NO2 and dust levels at the construction site. The multiple linear regression (MLR) and the artificial neural network (ANN) models for predicting PM2.5, PM10 and NO2 concentration in air using meteorological parameters as predictors were applied. The ANN model showed greater accordance with the measured concentrations in air than the MLR model.

High PM10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50 μg/m3 is the permissible threshold for a daily average PM10 concentration. The number of people affected by this threshold’s exceedance is challenging to estimate and requires high-resolution concentration maps. This paper presents an application of random forests for downscaling regional model air quality results. As policymakers and other end users are eager to receive detailed-resolution PM10 concentration maps, we propose a technique that utilizes the results of a regional CTM (GEM-AQ, with 2.5 km resolution) and a local Gaussian plume model. As a result, we receive a detailed, 250 m resolution PM10 distribution, which represents the complex emission pattern in a foothill area in southern Poland. The random forest results are highly consistent with the GEM-AQ and observed concentrations. We also discuss different strategies of training random forest on data using additional features and selecting target variables.

Several studies have evaluated PM concentrations in single specific microenvironments as a measure of exposure in the entire house. In this study, PM10 was monitored at the same time in three microenvironments (bedroom, living room, and kitchen) from three dwellings located in a small inland town of the Iberian Peninsula to assess whether exposure varies significantly between them. Real-time optical instruments and low-volume gravimetric samplers were employed. A multi-wavelength absorption instrument was used to determine black carbon (BC) concentrations on the filters. The Multiple-Path Particle Dosimetry Model (MPPD) was applied to evaluate the deposition of PM10 and BC in the airways of adults. For all dwellings, the highest PM10 concentrations were recorded in bedrooms (B1 = 22.7 µg m−3; B2 = 19.5 µg m−3; and B3 = 68.1 µg m−3). Houses 1 and 3 did not show significant differences between microenvironments. This did not happen in house 2, suggesting that ventilation is a determining factor for concentrations. BC originated mainly from fossil fuel emissions (90%), while biomass burning represented a minor contribution (10%). MPPD showed that PM10 is predominantly deposited in the head region (≥85% of the total dose), while BC is mainly deposited in the pulmonary region (14%). Higher doses were estimated for males than for females.

Extreme meteorological events can influence air quality. In March 2021, northern China experienced a severe dust event, leading to widespread air quality deterioration. Using reanalysis datasets and station data, we investigate the synoptic weather patterns, dust transport characteristics, and associated impacts on air quality during this event. The results are as follows. (1) The dust event is closely linked to the Mongolian cyclone, providing favorable conditions for dust emission and long-distance transport. (2) The Gobi Desert in Mongolia is the primary source, with dust particles transported from Mongolia to northern China via the northwesterly flow. Dust transport exhibits a complex three-dimensional structure, with the most intense dust transport at approximately 2500 m altitude. (3) The impact of this dust event on air quality was characterized by its remarkable intensity, extensive spatial coverage, and prolonged duration. Additionally, 58.8% of the stations in northern China experienced pollution, 36.3% of them reached severe pollution levels or higher, and 35.9% of the sites experienced more than 12 h of pollution. (4) The visibility at the northern stations near the dust source rapidly decreases due to solid dust particles. In contrast, the southern stations, with higher moisture content, are primarily affected by liquid particles in terms of visibility. After the frontal passage, wet deposition from the precipitation process gradually improves visibility.

The impact of heavy metals in particulates plays an assignable role in human health—especially in the northwest region of China, which is affected by severe dust storms—and the bioaccessibility and health risks of heavy metals in particulate matter have not yet been quantified and evaluated. This study used Gamble’s solution and PBET (physiologically based extraction test) experiments to simulate the human respiratory and digestive systems, and analyzed the concentrations and bioaccessibility of Pb, Mn, Ni, Cd and As in PM2.5 and PM10 samples in the urban area of Hotan City during summer (July) and winter (January). The result shows that Mn and Pb are the most abundant elements in five metals. The bioaccessibility of Mn in gastric fluid was the highest in both summer (PM2.5: 64.1%, PM10: 52.0%) and winter (PM2.5: 88.0%, PM10: 85.1%). Meanwhile, in the respiratory system, the highest bioaccessibility of PM2.5 and PM10 in summer was Ni (53.3%), and Pb (47.9%), respectively. Although the concentration of Cd is low in winter, its bioaccessibility in lung fluid was the highest (PM2.5: 74.7%, PM10: 62.3%). The USEPA standard model and Monte Carlo simulation results show that the heavy metals in PM2.5 and PM10 would give rise to non-carcinogenic risk for both adults and children through the respiratory system in summer but had little risk in winter. However, the metal may have non-carcinogenic risk to children through intake. In addition, there is a cancer risk to adults through the respiratory system in winter (PM2.5: CR = 1.80 × 10−6, PM10: CR = 2.82 × 10−6), while there is a carcinogenic risk through the digestive system regardless of season and age.

This study focuses on particulate matter emissions from tire–road contact and their investigation using an internal drum test bench. The test bench is equipped with real-road surfaces and has been upgraded to enable real-time measurements of particulate matter. It was found that the road surface changes during the tests due to constant rolling over, influencing the level of emissions significantly. To account for this effect, the micro roughness was characterized before, during, and after the tests. Specific emission values consisting of particle mass and number were determined with summer, all-season, and winter tires for different road conditions, as well as specific longitudinal and lateral forces. It turned out that emissions increase disproportionately with load for both force directions. The winter tire led to the highest emissions across all loads, and the summer tire led to the lowest ones. While lateral forces caused emissions many times higher than longitudinal forces for the summer tire, forces in both directions led to comparable emissions for the all-season and winter tires. Regarding the ambient temperature, a lower one seems to be favorable for summer tires and a higher one seems to be favorable for winter tires. Lastly, particle size distributions during different load conditions show a dependence on load, such that larger particles are emitted with increasing load.

In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measurements and 24 h predictions from nine models by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM10 predictions for the following 24 h. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multilayer perceptrons (MLPs). Regardless of the type of memory cell chosen, our results consistently show that the proposed framework outperforms the CAMS models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Furthermore, we examine the impact of outliers on the overall performance of the model.

People spend most of their time indoors, particularly in their houses where daily activities are carried out, enhancing particulate matter (PM) emissions with consequent adverse health impacts. This study intended to appraise the toxicological and mutagenic responses of particulate matter with a diameter less than 10 μm (PM10) released from cooking and ironing activities under different conditions. The cytotoxicity of the PM10 total organic extracts was tested in A549 cells using the WST-8 and the lactate dehydrogenase (LDH) assays, while the interference in cell cycle dynamics and reactive oxygen species (ROS) production was analysed by flow cytometry. The S. typhimurium TA98 and TA100 Ames tester strains with and without metabolic activation were employed to determine the mutagenic potential of the PM10-bound polycyclic aromatic hydrocarbons (PAHs). PM10 organic extracts decreased the metabolic activity of A549 cells; however, no effects in the LDH release were observed. An increase in ROS levels was registered only for cells treated with PM10 at IC20 from steam ironing, in low ventilation conditions, while cell cycle dynamics was only affected by exposure to PM10 at IC20 from frying horse mackerel and grilling boneless pork strips. No mutagenic effects were observed for all the PM10-bound PAHs samples.