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Abstract Air pollution poses a significant global concern, notably impacting pregnancy outcomes through mechanisms such as DNA damage, oxidative stress, inflammation, and altered miRNA expression, all of which can adversely affect trophoblast functions. Cleistocalyx nervosum var. paniala, known for its abundance of anthocyanins with diverse biological activities including anti-mutagenic, antioxidant, and anti-inflammatory properties, is the focus of this study examining its effect on Particulate Matter 10 (PM10) soluble extract-induced trophoblast cell dysfunction via miRNA expression. The study involved the extraction of C. nervosum fruit using 70% ethanol, followed by fractionation with hexane, dichloromethane, and ethyl acetate. Subsequent testing for total phenolics, flavonoids, anthocyanins, and antioxidant activity revealed the ethyl acetate fraction (CN-EtOAcF) as possessing the highest phenolic and anthocyanin content along with potent antioxidant activity, prompting its selection for further investigation. In vitro studies on HTR-8/SVneo cells demonstrated that 5–10 µg/mL PM10 soluble extract exposure inhibited cell proliferation, migration, invasion, and induced apoptosis. However, pretreatment with 20–80 µg/mL CN-EtOAcF followed by 5 µg/mL PM10 soluble extract exposure exhibited protective effects against PM10 soluble extract-induced damage, including inflammation inhibition and intracellular ROS suppression. Notably, CN-EtOAcF down-regulated PM10-induced miR-146a-5p expression, with SOX5 identified as a potential target. Overall, CN-EtOAcF demonstrated the potential to protect against PM10-induced harm in trophoblast cells, suggesting its possible application in future therapeutic approaches.

The Sichuan Basin is one of the most severely air-polluted regions in China. To study the scavenging effect of precipitation on PM2.5 and PM10 in Sichuan Basin, the PM2.5 and PM10 mass concentration data from 90 environmental monitoring stations and observation data from 17 ground meteorological stations in Sichuan Basin from 2016 to 2021 were used. First, the effects of precipitation on the spatial distribution of PM2.5 and PM10 in the Sichuan Basin were analyzed. Then, the influence of precipitation intensity and duration on PM2.5 and PM10 removal effects was revealed based on the removal rates of aerosol particle mass concentration changes before and after precipitation. Finally, by calculating the removal coefficients using the raindrop spectral distribution, raindrop sizes, and end-of-fall velocities, the removal effects of precipitation on four PM2.5 and PM10 pollution events in the Sichuan Basin were investigated. The results are as follows: (1) In the Sichuan Basin, precipitation affects the spatial distribution of PM2.5 and PM10. The greater the precipitation amounts and the longer the duration of precipitation, the greater the removal rates and the more significant the scavenging effect. (2) In cases of heavy rainfall, removal rates are not sensitive to increases in precipitation duration when it exceeds 6 hours. (3) When the raindrop diameter, the total number of raindrops, and the minute rain intensity peak, and the raindrop spectra broaden and intensify, rapid decreases of PM2.5 and PM10 mass concentrations occur. (4) The removal coefficients are indicative of variations in PM2.5 and PM10 mass concentrations. When the peak removal coefficients are above 10 h-1, the scavenging effect of precipitation on PM2.5 and PM10 is significant.

Nomagugu Ndlovu,* Benedicta Ngwenchi Nkeh-Chungag* Department of Biological and Environmental Sciences, Walter Sisulu University, Mthatha, South Africa*These authors contributed equally to this workCorrespondence: Nomagugu Ndlovu, Email nondlovu@wsu.ac.zaAbstract: Indoor air pollution accounts for approximately 3.8 million inopportune deaths annually at global level. Due to spending more time indoors, children and older adults are especially susceptible to the health risks of indoor air pollution. This review seeks to summarise existing knowledge on the cardiovascular health effects of three common indoor air pollutants, namely carbon monoxide (CO), particulate matter (PM2.5 and PM10), and Nitrogen dioxide (NO2), focusing on older adults. We systematically reviewed the literature (PROSPERO CRD42024479220) on PubMed, Google Scholar, Scopus, Web of Science and Embase. The search yielded 20,914 records. Two independent reviewers screened the articles using titles, abstracts, and full-length articles written in English. Upon a detailed assessment of all the records, the review considered 38 full-length articles. Several studies reported mortality, myocardial infarction, stroke, increased hospitalisation and increased emergency room visits due to exposure to indoor air pollution. A few studies reported arrhythmias, hypertension and Ischaemic heart disease due to exposure to indoor air pollutants. The increased mortality, morbidity, hospitalization, and emergency rooms visits resulting from indoor air pollution associated CVDs makes indoor air pollution a health risk for older adults. There is, therefore, a need to synthesize information on studies relate d to how the selected indoor air pollutants affected the cardiovascular health of older adults.Keywords: particulate matter, PM2.5, PM10, nitrogen dioxide, myocardial infarction, stroke, hospitalisation, mortality, older adults

The quality of life in large urban areas is considerably diminished by air pollution, with major contributors being motor vehicles, industrial activities, and fossil fuel combustion. A major contributor to air pollution is coal-fired and thermal power plants, which are commonly found in emerging markets. In Astana, Kazakhstan, a rapidly expanding city's significant reliance on coal for heating and considerable building exacerbate air pollution. This research is essential for improving urban development practices that support sustainable growth in rapidly expanding cities. Using time series data from four monitoring stations in Astana using fractal R/S analysis, the study looks at long-term patterns in air pollutant levels, especially PM10 and PM2.5. The stations' Hurst exponents were determined to be 0.723, 0.548, 0.442, and 0.462. Additionally, the flow window method was used to study the Hurst exponent's dynamic behavior. The findings showed that one station's pollution levels had long-term memory, which suggests that the time series is persistent. While anti-persistence was noted in the third and fourth sites, data from the second station indicated nearly random behavior. The Hurst exponent values explain the October 2021 spike in pollution levels, which is probably caused by thermal power plants close to the city. The fractal analysis of time series could serve as an indicator of environmental conditions in a given region, with persistent pollution trends potentially aiding in predicting critical pollution events. Anti-persistence or temporary pollution spikes may be influenced by the observation station's proximity to pollution sources. Overall, the findings suggest that fractal time series analysis can act as a valuable tool for monitoring environmental health in urban areas.

Introduction: In recent years, remote sensing (RS) products have emerged as effective tools for monitoring air pollution. This study aims to predict the concentrations of particulate matter with a diameter smaller than 10μm (PM10) using a multivariate linear regression (MLR) model, incorporating both Aerosol Optical Depth (AOD) products and meteorological parameters. Material and Methods: In this study, data on PM10 concentrations, Aerosol Optical Depth (AOD), and meteorological parameters (wind speed, temperature, humidity, and horizontal visibility) were used. The study focused on the time 15:00 each day, as this time was identified as having significant data relevance. The methodology section also consisted of three steps: 1) pairwise correlation analysis: The relationship between meteorological parameters, AOD, and PM10 was assessed using the pairwise correlation method. 2) Model development: A MLR model was developed to predict PM10 concentrations. 3) Validation: The model was validated using a separate dataset, ensuring that 70% of the data was used for training, and 30% for testing and validation. Results: The pairwise correlation analysis revealed a strong correlation (0.86) between AOD remote sensing index and PM10. The highest correlation (0.9) was observed during the spring season. The five developed equations to estimate the PM10 index yielded correlation coefficients ranging from 0.86 to 0.90. Notably, the highest correlation was achieved when AOD data and all the meteorological parameters were utilized simultaneously. These results highlighted the utility of remote sensing products and meteorological data in air quality monitoring and prediction. Conclusion: This study demonstrates that a MLR model incorporating AOD and meteorological parameters can effectively predict PM10 concentrations in Ahvaz City, particularly during dust storms in hot seasons. These findings can aid policymakers and public health officials in developing strategies to mitigate the adverse effects of dust storms on air quality and public health.

Abstract Cardiovascular disease remains the leading cause of death worldwide, with air pollution’s impact on cardiovascular health being closely monitored. However, the specific effects of air pollution on the risk of hospital readmission for heart failure (HF) in patients with unstable angina (UA) have not been fully explored. We conducted a retrospective study involving 12,857 consecutive patients diagnosed with acute coronary syndrome (ACS) between January 2015 and March 2023. After rigorous screening, we included 8,737 patients with UA in the analysis. Furthermore, we used a Cox proportional hazards regression model to examine the relationship between air quality indicators and hospital readmission for HF in patients with UA. Additionally, a decision tree model identified air quality indicators levels that had the most significant impact on readmission for HF risk. After adjusting for confounding factors, we found that elevated levels of PM10 [hazard ratio (HR) = 1.003, 95% confidence interval (CI): 1.000-1.005, p = 0.04453] and CO (HR = 1.013, 95% CI: 1.005–1.021, p = 0.00216) were associated with an increased risk of hospital readmission for HF in UA patients. Specifically, patients exposed to PM10 levels above 112.5 µ g/m3 had a 1.61-fold higher risk of readmission for HF in UA patients. (HR = 1.609, 95% CI: 1.190–2.176, p = 0.00201), and those exposed to CO levels above 37.5 mg/m3 had a 2.70-fold higher risk of readmission for HF in UA patients. (HR = 2.681, 95% CI: 1.731–4.152, p

Abstract The article evaluates air pollution by particulate matter (PM) in indoor and outdoor air in one of the Polish health resorts, where children and adults with respiratory diseases are treated. The highest indoor PM concentrations were recorded during the winter season. Therefore, the maximum average daily concentration values in indoor air for the PM10, PM2.5, and PM1 fractions were 50, 42 and 23 µg/m3, respectively. In the case of outdoor air, the highest average daily concentrations of PM2.5 reached a value of 40 µg/m3. The analyses and backward trajectories of episodes of high PM concentrations showed the impact of supra-regional sources and the influx of pollutants from North Africa on the variability of PM concentrations. The correlation between selected meteorological parameters and PM concentrations shows the relationship between PM concentrations and wind speed. For example, the correlation coefficients between PM1(I) and PM1(O) concentrations and wind speed were − 0.8 and − 0.7 respectively. These factors determined episodes of high PM concentrations during winter periods in the outdoor air, which were then transferred to the indoor air. Elevated concentrations in indoor air during summer were also influenced by chimney/gravity ventilation and the appearance of reverse chimney effect.

Abstract Alxa League, located in the western part of Inner Mongolia, China, frequently suffers from sandstorms. Such dusty conditions result in sharp increases in respirable particulate matter (PM10) concentration in outdoor and indoor environments, posing a significant health risk to local inhabitants. Vernacular dwellings in this area feature a specific floor plan that includes an entrance space to safeguard indoor air quality during sandstorms while being compatible with the local climate. This study utilises CONTAM, a multizone indoor air quality and ventilation analysis computer program, and field measurements to quantitatively evaluate the effectiveness of the entrance space in protecting indoor air quality against ambient PM10 pollution under both dusty and normal conditions. The simulation results reveal that the entrance space can effectively mitigate PM10 pollution in the middle room, lowering the average concentration from 47.0 μg/m3 to 37.5 μg/m3 during dust periods. However, PM10 pollution may increase in the entrance space, reaching an average concentration of up to 70.0 μg/m3. Experimental outcomes align with the simulated results. Given that construction activities exacerbate desertification and frequent sandstorms, the above findings help identify the optimal design strategies for energy-efficient green vernacular dwellings in the targeted desert area, promoting environmental harmony and addressing climate change challenges.

Abstract In this study, we have examined the air quality within a revitalized, post-industrial urban area in Łódź, Poland. The use of Dron technology with mobile measurement equipment allowed for accurate assessment of air quality (particulate matter and gaseous pollutants) and factors influencing air quality (wind speed and direction) on a local scale in an area of 0.18 km2 and altitudes from 2 to 50 m. The results show that the revitalization carried out in the Lodz special economic zone area contributed to eliminate internal air pollution emitters through the use of ecological and effective heat sources. The exceedances permissible concentration values were local, and concerned mainly the higher measurement zones of the troposphere (more than 30 m above ground level). In the case of gaseous pollutants, higher wind speeds were associated with a decrease in the concentration of SO2 and an increase in H2S concentration. In both cases, the wind contributed to the occurrence of local areas of accumulation of these gaseous pollutants in the spaces between buildings or wooded areas.

Abstract This study investigates the environmental impact of burning herbicide-contaminated biomass, focusing on atrazine (ATZ) and diuron (DIU) sprayed on rice straw prior to burning. Samples of soil, biomass residues, total suspended particulate (TSP), particulate matter with an aerodynamic diameter ≤ 10 µm (PM10), and aerosols were collected and analyzed. Soil analysis before and after burning contaminated biomass showed significant changes, with 2,4-dichlorophenoxyacetic acid (2,4-D) initially constituting 79.2% and decreasing by 3.3 times post-burning. Atrazine-desethyl, sebuthylazine, and terbuthylazine were detected post-burning. In raw rice straw biomass, terbuthylazine dominated at 80.0%, but burning ATZ-contaminated biomass led to the detection of atrazine-desethyl and notable increases in sebuthylazine and terbuthylazine. Conversely, burning DIU-contaminated biomass resulted in a shift to 2,4-D dominance. Analysis of atmospheric components showed changes in TSP, PM10, and aerosol samples. Linuron in ambient TSP decreased by 1.6 times after burning ATZ-contaminated biomass, while atrazine increased by 2.9 times. Carcinogenic polycyclic aromatic hydrocarbons (PAHs), including benzo[a]anthracene (BaA), benzo[a]pyrene (BaP), and benzo[b]fluoranthene (BbF), increased by approximately 9.9 to 13.9 times after burning ATZ-contaminated biomass. In PM10, BaA and BaP concentrations increased by approximately 11.4 and 19.0 times, respectively, after burning ATZ-contaminated biomass. This study sheds light on the environmental risks posed by burning herbicide-contaminated biomass, emphasizing the need for sustainable agricultural practices and effective waste management. The findings underscore the importance of regulatory measures to mitigate environmental contamination and protect human health.

Relevance. Russian coal exports have been steadily increasing in recent years, and Far Eastern ports, originally not adapted to dusty cargoes, have switched to coal operations. Unoptimized coal handling leads to increased content of coal-containing particles in the air and to various environmental problems. Aim. To analyze the concentration of particulate matter in the air and the chemical composition of particles on the surface of conifer needles in the Posyet settlement (Primorsky Krai, Russia), where a large coal terminal operates. Materials and methods. The chemical composition was studied on the surface of conifer needles by X-ray fluorescence and in washout of conifer needle samples using Raman spectroscopy and energy dispersive analysis. Concentrations of particles with the diameter of 2.5 and 10 μm (PM2.5 and PM10, respectively) were measured using automatic monitoring stations. Results and conclusions. Chemical composition was dominated by mineral particles (calcite, silicates, etc.). However, the presence of coal particles (up to 8.3%) and metal particles was also observed. Mean concentrations of PM2.5 and PM10 were within the Russian and international health standards, but close to their upper limits. Concentrations of both measured particle fractions were highest during the winter months (as well as in March, which is traditionally a cold month in the Far East), followed by a gradual decrease. The study obtained new data on the annual variations of concentrations of PM2.5 and PM10, as well as on the basic composition of particles from the surface of woody vegetation (needles) as an indicator of the state of air pollution in this settlement.

This paper presents results from a comprehensive study of source apportionment of particulate matter (PM) of size PM2.5 and PM10 near a busy highway in Sharjah, United Arab Emirates. Source apportionment was carried out using US Environmental Protection Agency (EPA) Positive Matrix Factorization model. Furthermore, backward trajectory analysis and Potential Source Contribution Function were used to assess air mass transport pathways and identify potential source regions, respectively. The results revealed six major sources for PM2.5, including traffic, sea salt, fugitive dust, secondary aerosols, heavy oil combustion and mineral dust. For PM10, four major sources were identified, including secondary aerosols, traffic, sea salt and mineral dust. Traffic emissions were found to be significant contributors to both PM2.5 and PM10 pollution, along with natural sources like sea salt and mineral dust. Backward trajectory analysis indicated the influence of different wind regimes on air mass transport, with contributions from regions like Arabian Gulf, Arabian Sea, Oman and Iran. The Conditional Bivariate Probability Function analysis further explained the impact of local traffic emissions and other sources on PM pollution under varying wind conditions.

Accumulating observational studies have linked particulate air pollutants to neurodegenerative diseases (NDDs). However, the causal links and the direction of their associations remain unclear. Therefore, we adopted a two-sample Mendelian randomization (TSMR) design using the GWAS-based genetic instruments of particulate air pollutants (PM2.5 and PM10) from the UK Biobank to explore their causal influence on four common neurodegenerative diseases. Estimates of causative relationships were generated by the Inverse variance weighted (IVW) method with multiple sensitive analyses. The heterogeneity and pleiotropy tests were additionally performed to verify whether our findings were robust. Genetically predicted PM2.5 and PM10 could elevate the occurrence of AD (odds ratio [OR] = 2.22, 95 % confidence interval [CI] 1.53–3.22, PIVW = 2.85×10−5, PFalse discovery rate [FDR] = 2.85×10−4 and OR = 2.41, 95 % CI: 1.26–4.60, PIVW = 0.008, PFDR =0.039, respectively). The results were robust in sensitive analysis. However, no evidence of causality was found for other NDDs. Our present study suggests that PM2.5 and PM10 have a detrimental effect on AD, which indicates that improving air quality to prevent AD may have pivotal public health implications.

ObjectiveTo explore the relationship between the exposure level of particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10) in the air of pregnant women during preconception and first trimester of pregnancy and the risk of gestational diabetes mellitus (GDM).MethodsThe data of pregnant women delivered in 22 monitoring hospitals in Hebei Province from 2019 to 2021 were collected, and the daily air quality data of their cities were used to calculate the exposure levels of PM2.5 and PM10 in different pregnancy stages, and logistic regression model was used to analyze the impact of exposure levels of PM2.5 and PM10 on GDM during preconception and first trimester of pregnancy.Results108,429 singleton live deliveries were included in the study, of which 12,967 (12.0%) women had a GDM diagnosis. The prevalence of GDM increased over the course of the study from 10.2% (2019) to 14.9% (2021). From 2019 to 2021, the average exposure of PM2.5 and PM10 was relatively 56.67 and 103.08μg/m3 during the period of preconception and first trimester of pregnancy in Hebei Province. Handan, Shijiazhuang, and Xingtai regions had the most severe exposure to PM2.5 and PM10, while Zhangjiakou, Chengde, and Qinhuangdao had significantly lower exposure levels than other regions. The GDM group had statistically higher exposure concentrations of PM2.5 and PM10 during the period of preconception, first trimester, preconception and first trimester (P

Abstract Introduction PM exposure can induce inflammatory and oxidative responses; however, differences in these adverse effects have been reported depending on the chemical composition and size. Moreover, inflammatory mechanisms such as NLRP3 activation by PM10 have yet to be explored. Objective To assess the impact of PM10 on cell cytotoxicity and the inflammatory response through in vitro and in vivo models. Methodology Peripheral blood mononuclear cells (PBMCs) from healthy donors were exposed to PM10. Cytotoxicity was determined using the LDH assay; the expression of inflammasome components and the production of pro-inflammatory cytokines were quantified through qPCR and ELISA, respectively; and the formation of ASC complexes was examined using confocal microscopy. For in vivo analysis, male C57BL6 mice were intranasally challenged with PM10 and bronchoalveolar lavage fluid was collected to determine cell counts and quantification of pro-inflammatory cytokines by ELISA. RNA was extracted from lung tissue, and the gene expression of inflammatory mediators was quantified. Results PM10 exposure induced significant cytotoxicity at concentrations over 100 µg/mL. Moreover, PM10 enhances the gene expression and release of pro-inflammatory cytokines in PBMCs, particularly IL-1β; and induces the formation of ASC complexes in a dose-dependent manner. In vivo, PM10 exposure led to cell recruitment to the lungs, which was characterized by a significant increase in polymorphonuclear cells compared to control animals. Furthermore, PM10 induces the expression of several inflammatory response-related genes, such as NLRP3, IL-1β and IL-18, within lung tissue. Conclusion Briefly, PM10 exposure reduced the viability of primary cells and triggered an inflammatory response, involving NLRP3 inflammasome activation and the subsequent production of IL-1β. Moreover, PM10 induces the recruitment of cells to the lung and the expression of multiple cytokines; this phenomenon could contribute to epithelial damage and, thus to the development and exacerbation of respiratory diseases such as viral infections.

Atmospheric environmental pollution has gained significant attention recently. Inhalable particulate matter (PM10) impacts societal development and poses serious health threats. Consequently, PM10 has become a primary focus of air pollutant research. In this article, we propose a stacking model incorporating a back propagation neural network and extremely randomized trees (Stacking-BP-ET model). This model combined aerosol optical depth products with auxiliary factors, including meteorology, elevation and population distribution, to construct a global PM10 dataset with a 1 km spatial resolution from 2015 to 2021. The product had an out-of-station and out-of-year spatiotemporal cross-validation coefficient of determination (R2) of 0.833, and the mean absolute error (MAE) and root mean square error (RMSE) were 6.411 and 14.071 μg/m3, respectively. High PM10 concentrations were found in areas with frequent dust storm events such as Mongolia, the Arabian Peninsula and Northwest China and wildfire disasters such as the west coast of the United States and Australia. The PM10 concentrations in these areas had shown an increasing trend in recent years as the disaster frequency has increased. Overall, the global PM10 dataset may be useful for future large-scale studies of air pollution.

Background Gestational diabetes mellitus (GDM) can have negative effects on both the pregnancy and perinatal outcomes, as well as the long-term health of the mother and the child. It has been suggested that exposure to air pollution may increase the risk of developing GDM. This study investigated the relationship between exposure to air pollutants with gestational diabetes.Methods The present study is a retrospective cohort study. We used data from a randomised community trial conducted between September 2016 and January 2019 in Iran. During this period, data on air pollutant levels of five cities investigated in the original study, including 6090 pregnant women, were available. Concentrations of ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOx), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter < 2.5 (PM2.5) or .05). Also, none of the adjusted logistic regression models showed any significant association between PM10 exposure and GDM risk at all different time points after adjusting for various confounders (p > .05).Conclusions This study found no association between GDM risk and exposure to various air pollutants before and during the different trimesters of pregnancy. This result should be interpreted cautiously due to the lack of considering all of the potential confounders.

The relevance of atmospheric particulate matter (PM) to health and the environment is widely known. Long-term studies are necessary for understanding current and future trends in air quality management. This study aimed to assess the long-term PM concentration in the Magdalena department (Colombia). It focused on the following aspects: i) spatiotemporal patterns, ii) correlation with meteorology, iii) compliance with standards, iv) temporal trends over time, v) impact on health, and vi) impact of policy management. Fifteen stations from 2003 to 2021 were analyzed. Spearman-Rho and Mann-Kendall methods were used to correlate concentration with meteorology. The temporal and five-year moving trends were determined, and the trend magnitude was calculated using Teil-Sen. Acute respiratory infection odd ratios and risk of cancer associated with PM concentration were used to assess the impact on health. The study found that the maximum PM10 concentration was 194.5 μg/m3, and the minimum was 3 μg/m3. In all stations, a negative correlation was observed between PM10 and atmospheric water content, while the wind speed and temperature showed a positive correlation. The global trends indicated an increasing value, with five fluctuations in five-year moving trends, consistent with PM sources and socio-economic behavior. PM concentrations were found to comply with national standard; however, the results showed a potential impact on population health. The management regulation had a limited impact on increasing concentration. Considering that national regulations tend to converge towards WHO standards, the study area must create a management program to ensure compliance.

Introduction: Traditional statistical methods cannot find quantitative relationship from environmental data.Methods: We selected gene expression programming (GEP) to study the relationship between pollutant gas and PM2.5 (PM10). They were used to construct the relationship between pollutant gas and PM2.5 (PM10) with environmental monitoring data of Xi’an, China. GEP could construct a formula to express the relationship between pollutant gas and PM2.5 (PM10), which is more explainable. Back Propagation neural networks (BPNN) was used as the baseline method. Relevant data from January 1st 2021 to April 26th 2021 were used to train and validate the performance of the models from GEP and BPNN.Results: After the models of GEP and BPNN constructed, coefficient of determination and RMSE (Root Mean Squared Error) are used to evaluate the fitting degree and measure the effect power of pollutant gas on PM2.5 (PM10). GEP achieved RMSE of [8.7365–14.6438] for PM2.5; RMSE of [13.2739–45.8769] for PM10, and BP neural networks achieved average RMSE of [13.8741–34.7682] for PM2.5; RMSE of [29.7327–52.8653] for PM10. Additionally, experimental results show that the influence power of pollutant gas on PM2.5 (PM10) situates between −0.0704 and 0.6359 (between −0.3231 and 0.2242), and the formulas are obtained with GEP so that further analysis become possible. Then linear regression was employed to study which pollutant gas is more relevant to PM2.5 (PM10), the result demonstrates CO (SO2, NO2) are more related to PM2.5 (PM10).Discussion: The formulas produced by GEP can also provide a direct relationship between pollutant gas and PM2.5 (PM10). Besides, GEP could model the trend of PM2.5 and PM10 (increase and decrease). All results show that GEP can be applied smoothly in environmental modelling.

Rising urbanization necessitates robust air quality monitoring and prediction systems, particularly in developing nations like India, to mitigate adverse health impacts. Previous research primarily focused on machine learning algorithms for Air Quality Index (AQI) prediction and classification. We propose a novel MI-MMA-XGB which coupled features of multimodal imputer(MI) with the features of multi-modal autoencoder (MMA) and fed to an XGBoost(XGB) algorithm for AQI prediction and classification. Moreover, imputation approaches namely, KNN, MICE, and SVD were employed to address problems with null values and outliers. Furthermore, SMOTE is employed to balance the imputed data and then the model was trained on both balanced and unbalanced imputed data to extract predictive features. In this process, our model MI-MMA-XGB achieves significant accuracy, reaching 97.14% and 93.87% with and without SMOTE, respectively. Additionally, it attains an $R^{2}$ score of 0.9578 and an RMSE of 0.203 for AQI prediction in Indian cities. The proposed model outperforms baseline models in both classification and regression tasks across various evaluation metrics.

The quality of indoor air in educational institutions is a critical determinant of the health and well-being of students. The purpose of the study was indoor air quality monitoring carried out for different age groups of students in grades IV-VIII or college classrooms (students used the classroom according to the schedule, the same group uses the room for up to 2 classes), I-III (students stay in the classroom for all classes, leaving only during breaks), kindergarten "0" (students are practically in one and the same room all day). The individual measurements included measurement of CO2 concentration, concentration of PM2.5 and PM10 particles, formaldehyde, volatile organic compounds (VOCs), along with monitoring of changes in temperature and humidity in the classroom. The results indicate that children are exposed to elevated levels of these pollutants for a significant portion of their school day, which can have implications for their health and well-being. The findings reveal significant deviations from recommended levels, emphasizing the need for improved ventilation and monitoring practices.

Abstract Background There is a growing concern that particulate matter (PM) such as PM2.5 and PM10 has contributed to exacerbating psychological disorders, particularly depression. However, little is known about the roles of these air pollutants on depression in elderly. Therefore, this study aimed to examine the association between PM2.5 and PM10, and depression in the elderly population in South Korea. Methods We used panel survey data, the Korean Longitudinal Study of Aging (KLoSA), administered by the Labor Institute during the study period of 2016, 2018, and 2020 covering 217 districts in South Korea (n = 7674). Annual district-specific PM2.5 and PM10 concentrations were calculated for the study period from the monthly prediction concentrations produced by a machine-learning-based ensemble model (cross-validated R2: 0.87), then linked to the people matching with year and their residential district. We constructed a generalized estimating equation (GEE) model with a logit link to identify the associations between each of the long-term PM2.5 and PM10 exposures and depression (CES-D 10) after adjusting for individual and regional factors as confounders. Results In single-pollutant models, we found that long-term 10 $$\mathrm{\mu g}/{m}^{3}$$ μ g / m 3 increments in PM2.5 (OR 1.36, 95% CI 1.20–1.56) and PM10 (OR 1.19, 95% CI 1.10–1.29) were associated with an increased risk of depression in the elderly. Associations were consistent after adjusting for other air pollutants (NO2 and O3) in two-pollutant models. In addition, the impacts substantially differed by regions grouped by the tertile of the population density, for which the risks of particulate matters on depression were substantial in the middle- or high-population-density areas in contrast to the low-population-density areas. Conclusions Long-term exposure to PM2.5 and PM10 was associated with a higher risk of developing depression in elderly people. The impact was modified by the population density level of the region where they reside.

Nghiên cứu này sẽ xác định phạm vi ảnh hưởng của hoạt động giao thông đến nồng độ bụi (TSP, PM10, PM2.5) trong môi trường không khí xung quanh. Nồng độ bụi được đo ở 6 điểm thuộc tuyến đường quốc lộ Tôn Đức Thắng và đường dân sinh Ngô Văn Sở để so sánh nồng độ bụi trong không khí xung quanh ở khu vực hai bên đường. Tại 6 điểm đo tiến hành lấy mẫu ở các vị trí cách mép đường lần lượt 0m, 2m và 4m. Kết quả đo đạc cho thấy, nồng độ bụi giảm dần theo khoảng cách tính từ mép đường tại các điểm đo. Nồng độ bụi tổng, PM10 và PM2.5 ở tuyến đường Tôn Đức Thắng cao hơn nhiều so với tuyến đường Ngô Văn Sở. Nguyên nhân chính là do lượng xe lưu thông trên đường Tôn Đức Thắng nhiều hơn và đa dạng loại xe container, xe tải lớn, xe khách..., tuyến đường Ngô Văn Sở chủ yếu xe máy và ít xe ô tô cá nhân.

Governments worldwide have established lockdowns to limit the spread of COVID-19 during the pandemic. The restrictions on travel and reduction of economic activity have led to a temporary improvement in air quality in several countries, especially in urban areas. This study investigates the changes in concentration levels of air pollutants (PM10, PM2.5, SO2, NO2, and bacterial aerosol) in the Upper Silesia Region of Southern Poland, during three periods: pre (March 2018 and 2019), during (March 2020, and 2021) and post-COVID-19 lockdown period (March 2022 and 2023). Our findings indicate that COVID-19 restrictions had a moderate impact on PM10 levels in comparison to pre- and post-COVID-19 periods. PM2.5 during lockdown was significantly lower than in the pre-COVID period and not significantly higher after COVID. PM10 and PM2.5 average concentrations decreased during COVID-19 restrictions by 27.8% and 12.7%, respectively. Compared with the results from the pre-COVID-19 phase, the reductions in NO2 and SO2 during the lockdown were 9.5% and 34.0%. Among other pollutants, bacterial aerosol (BA) concentrations also decreased during the lockdown by 23.0%, compared to the results from the pre-COVID-19 period.

This article addresses the issue of atmospheric pollution caused by solid particles in urban environments. The presence of PM10 and PM2,5 particles in the air of major cities and industrial areas worldwide has been examined. An evaluation of atmospheric pollution levels with PM10 and PM2,5 particles in Kostopil, considering current air quality standards in Ukraine and the European Union, has been conducted. The authors employed the gravimetric method to measure the levels of suspended dust particles (PM10 and PM2,5) in Kostopil from autumn 2022 to winter 2023. The study revealed an excessive amount of fine dust particles in the city's air, exceeding the maximum permissible values outlined in regulatory laws by 2.1-2.7 times. Furthermore, the monitoring of changes in suspended dust particle levels showed peak values of PM10 = 1.15 mg/m³ in January and PM2,5 = 0.96 mg/m³ in December. The results of the statistical analysis of particle level distribution in Kostopil's urban areas indicated the statistical significance of certain distribution parameters, specifically SW-W and D for PM10 and PM2,5 particle classes.

The purpose of this study was to determine the amount of particulate and microbial contaminants, i.e. bacteria and fungi found in indoor air in a lecture hall in one of Lublin's universities and their classification. In the research part, the amount of particulate and microbial pollutants was measured. Bioaerosols were investigated using an Andersen cascade impactor, which was located in the central part of the room at a height of 1-1.5 m, and single-level impactors. Identification of the microorganisms present in indoor air was carried out. The air in the room was sampled before the start of class to determine the "background," i.e. the concentration level of microbial contaminants in the classroom without the presence of students. Subsequent measurements were taken during teaching activities in the presence of students and the teacher. The study shows that the air condition in the classroom during its operation met the requirements in terms of PM2.5, PM10 and microorganisms. The highest risk was recorded for carbon dioxide. Moreover, the highest recorded readings of this pollutant coincided with the maximum concentrations of the other monitored quantities. Therefore, it can be unequivocally stated that in the case of the analyzed room, monitoring carbon dioxide and adjusting the size of the ventilation airflow to maintain its concentration within the limit of 1000 ppm would guarantee the maintenance of adequate indoor air quality. The study showed no correlation between CO2 concentration and measured concentrations of microbial contaminants.

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.

Background: Environmental pollution is a highly challenging global health concern, affecting the natural ecosystem and human health. The obesity risk is allied to diverse factors, including genetics, behaviour, food, and socio-cultural conditions, however, literature is lacking in exploring the effect of environmental pollution on obesity and the causal pathways are not fully understood. Therefore, this study aimed to investigate the effect of environmental pollutants particulate matter PM2.5 μm, PM10 μm, Nitrogen Dioxide (NO2), and ground level Ozone (O3) on obesity. Methods: This study recorded data on air pollutants and obesity using the electronic platforms Pub Med, Web of Science, Scopus, and Google Scholar. The keywords included for the literature search were based on a combination of two main aspects, which were used to represent exposure (air pollutants), and outcome (obesity). Initially, 324 articles and reports were identified, and after revising the abstracts and full articles, 12 studies were selected for a detailed analysis and discussion. The Odds Ratio (OR) and 95 % confidence intervals (CIs) were extracted to investigate the impact between air pollutants and obesity. The Cochrane chi-squared test (Chi2), fixed-effects design was used when I2 0.05; otherwise, a random-effects model was adopted. Results: Environmental pollutants, particulate matter PM2.5 (OR = 1.18; 95 % CI: 1.10–1.25p

Over recent decades, the adverse impacts of airborne particles on human health have received wide attention. Elevated PM concentrations on underground platforms might pose a significant public health issue within underground metro systems. This study explores the impact of introducing a new type of train on the concentration of airborne particles on an underground metro platform through statistical modelling, analyses interactions between various factors, and estimates air quality on underground platforms after introducing a new type of train. Based on the data from a long-term field measurement, a linear mixed model, the multi-factor interaction model, which is an expansion of a previous multi-factor model, explored the impacts of train operations, passenger flow, urban background PM levels, ventilation, nighttime maintenance work, and their interactions on hourly PM10, PM2.5, and PM1 values on the platform. The model results show a positive correlation between those factors and platform PM10, PM2.5 and PM1 values, with significant interactions among these factors. The new model has a higher estimate quality than the previous model. Based on the combination of the model and measurement results, the levels of underground PM decreased significantly after replacing the old type of trains with new ones.

This article presents research on the model of forecasting the average daily air pollution levels focused mainly on two solutions, artificial neural networks: the NARX model and the LSTM model. The research used an air quality monitoring system. This system includes individually designed and implemented sensors to measure the concentration of pollutants such as PM10, PM2.5, SO2, NO2 and to record weather conditions such as temperature, humidity, pressure, wind strength and speed. Data is sent to a central database server based on the MQTT protocol. Additional weather information in the area covered by pollution monitoring is collected from the weather services of the IMGW and openwethermap.org. The artificial neural network models were built in the MATLAB environment, the process of learning neural networks was performed and the results of pollution prediction for the level of PM10 dust were tested. The models showed good and acceptable results when forecasting the state of PM10 dust concentration in the next 24 hours. The LSTM prediction model were more accurate than the NARX model. The future work will be related to the use of artificial intelligence algorithms to predict the concentration of other harmful substances, e.g. PM2.5, NO2, SO2 etc. A very important task in the future will be to frame the entire system of monitoring and predicting smog in a given area.

This study was designed to assess the concentrations of nine water-soluble ions in PM10 mass at two sites of an open-pit coal mine and to analyze the correlation and variation of the spatial distribution of sulfate ions with the PM10 sulfate aerosol optical depth at 550 nm (suaod550) in two (North and South) stations of the study area. The daily average of PM10 concentrations ranged from 20.48 to 53.10 μg/m3 and thus did not exceed the daily average maximum permissible level of PM10 (100 μg/m3) established in the Colombia standard at any station. The concentrations of nine water-soluble ions in PM10 (Cl−, NO3−, PO43−, SO42– Na+, NH4+, K+, Mg2+, and Ca2+) were determined. The ions under analysis, SO42−, Na+, and NH4+ had the highest concentrations. Combined, they accounted for 75% of the mass of water-soluble ions in a total of 210 samples. The SO42− concentrations in PM10 significantly correlated with suaod550 (r ranging from 0.57 to 0.66), emphasizing the strong effect of suaod550 from Venezuela (Lake Maracaibo) on central and northern Colombia. These results demonstrate that the effects of local sulfate emissions near monitoring sites can be predicted and assessed using satellite data.

Abstract The spatial monitoring of submicron particles has become an essential issue due to their negative effects on human health. However, the use of high-cost and high-grade measurement instruments is a challenging investment cost. Thus, a cost-effective optical particle counter (OPC), which is improved measurement quality, has become a good candidate. In this study, two cost-effective OPCs, coupled with a heated inlet tube to reduce the effect of humidity on its measurements, were applied in the field to measure ambient PM10, PM2.5, and PM1 over 1 month. Their 1-h-average and 24-h-average data were compared with those obtained from a reference-grade OPC and a beta attenuation monitor (BAM). In particular, the correlations between the measurement data obtained from them, the differences in the mean values of these data, and the relative errors were evaluated. The PM2.5 data obtained from cost-effective OPCs were comparable to those from BAM at even under high humidity conditions, except for rainy days. The data obtained from the cost-effective OPCs also showed good correlations and low relative errors (i.e.,

In the present research, a statistical analysis of all pollution incidents occurring from 2015 to 2019 in the cities of the urban agglomeration centered on Shenyang was performed. The results indicated that heavy pollution mainly occurred during the heating season, and the main pollutants were (particulate matter) PM2.5 and PM10. It was also determined that the heavy pollution that occurred during the heating season in Shenyang was of the soot type. The weather research forecast-chemistry (WRF-Chem) was used to simulate the meteorological elements and particle concentration during the two heavy pollution periods in 2019 and compared the simulation data with the monitoring data to verify the simulation performance of the model. Results demonstrated that the model had a better simulation effect on temperature and pressure than on wind speed and wind direction. By comparing the hourly particle concentration data, it was found that the simulation results for pollutants obtained with the WRF-Chem model were lower than the measured values. The simulation effect on PM2.5 was better than that on PM10, and the simulation results were basically consistent in the high- and low-value areas, and the time of peak and valley was basically synchronous. It was proven that the selected parameterization scheme properly simulated the weather situation and changes in pollutants during heavy pollution events in the Shenyang area. These results verified the application value of the WRF-Chem model during the investigation of heavy pollution events.

Abstract In a situation where various policy measures can be used to reduce atmospheric particulates, effectiveness and efficiency may vary depending on how the policy is designed. Therefore, this study evaluated the effectiveness and efficiency of atmospheric particulates reduction policy in order to contribute to effective and efficient policy design. To this end, this study demonstrated the effectiveness of 1st Basic Plan on Metropolitan Area Air Quality Improvement and explored the cause of the effectiveness. As a result of the study, this study did not confirm that the effect of reducing PM10 caused by the plan in the metropolitan area was significantly different from that of the non-metropolitan area where the policy was not implemented. In particular, distinct effect was not confirmed on the installation of DPF, which required a large number of costs. Based on the results, more effective and efficient policy measures will be used based on the causal relationship of atmospheric particulates generation.

Abstract A thick foggy weather and worst visibility in Fukuoka, Japan and Busan, South Korea occurred from the late July to early August 2020 due to the Nishinoshima volcanic eruption. In this study, an intensive measurement was made to clarify the chemical nature of the ambient particulate matter (PM) and rain water collected in Fukuoka and Busan during the Nishinoshima volcanic eruption (episode period) and non-eruption (non-episode period). In this study, one week after volcanic eruption, which recorded the usual PM concentration, was defined as the non-episode period. Compared to non-episode period, the PM2.5 concentration during the episode period increased 4.32 times in Busan and 6.03 times in Fukuoka. The sulfur and chlorine concentrations in the total suspended particles (TSP) and rainwater of episode period were particularly higher than those of non-episode period. The sulfate concentration in PM2.5 was 1.81 and 27.98 µg/m3 in non-episode and episode periods, respectively. The sulfate concentration during the episode period accounted for 55.4% of PM2.5 (50.45 µg/m3). Strong correlation between trace elements in TSP and those in rainwater during the episode period indicates that the volcanic ashes could be incorporated into raindrops.

Abstract This study examines the PM2.5 characteristics in Seoul in relation to those of PM1.0 and PM10. Samples were typically collected daily on filters and a few hours sampling were conducted during a few haze events (March 2007 to June 2008). Mean mass concentrations of PM1.0, PM2.5, and PM10 were 19.7 μg/m3, 26.0 μg/m3, and 48.2 μg/m3, respectively, and PM2.5 was reasonably correlated with PM1.0 (γ=0.79) and PM10 (γ=0.52). Three mass group types were mainly distinguished. Group 1 (31%): linear increase of PM1.0 with PM10 and high OC and NO3−; Group 2 (17%): PM10 considerably higher than PM1.0 and high Ca2+ and SO42−; Group 3 (52%): PM1.0 relatively more enhanced than PM10 and highest carbonaceous fraction against mass. The fine mode fraction was lowest (highest) in Group 2 (Group 3). Haze and dust episodes relating to Chinese outflows were mostly evident in Groups 1 and 2, respectively; average PM2.5 concentrations were visibly higher than in Group 3. Non-Negative Matrix Factorization analysis demonstrated that traffic-related urban primary (28%) and coal-fired industry (27%) emissions equally contributed to the PM2.5 mass, followed by aged urban secondary (19%), soil mineral (16%), and biomass combustion (10%) sources. Seasonal variations were apparent in air mass trajectories. Urban primary and coal-fired industry factors were predominant in Group 3 under stagnant conditions in the warm season and under a strong northerly wind in the cold season, respectively. However, contributions of the other three factors were higher in Groups 1 and 2. This study shows that the PM2.5 mass in Seoul is largely dependent on high concentration episodes occurring mostly in cold seasons. It also shows that local emissions contribute considerably during warm months, while the influence of Chinese outflow predominates during cold months.

The current study aimed to determine the potential sources of distant emissions of PM10 particles that significantly affect PM10 levels at a given site in southeastern Baltic. The EEA Air Quality Monitoring Station in Elk City, northeastern Poland, was selected for this study. This station is located approximately 50 km from the border of the Russian exclave (Kaliningrad Region). In this study, the NOAA HYSPLIT_4 trajectory model, potential source contribution function (PSCF), and concentration-weight trajectory (CWT) were employed to investigate the origin of the measured PM10 mass at a receptor site. PSCF and CWT utilize back-trajectory analysis and Lagrangian particle dispersion simulations to reconstruct the advection pathways of air masses arriving at the site. These reconstructed retroplumes provide detailed information regarding the geographic locations traversed by polluted air masses on their way to the receptor. By integrating trajectory information with concurrent pollutant concentration data, the PSCF and CWT enable the identification of potential source regions and quantification of their impact on the observed atmospheric levels. From January 1, 2021, to December 31, 2022, at 200 m the 72h backward trajectories of air masses entering the receptor point were calculated and categorized by clustering them into 5-4-4-5 clusters. Subsequently, the PM10 levels at the Elk site associated with each air mass cluster were examined during the observation period. The seasonal variation in PM10 was generally characterized by a peak in winter and minimum values in summer. PM10 was lower during warmer periods, particularly during summer, and significantly, higher concentrations were observed during colder periods. Cluster analyses showed that airflow followed a seasonal pattern, with different results obtained in different seasons. According to the PSCF and CWT results, in winter and spring, the receptor site was influenced more by long-range PM10 pollution, particularly from heavily industrialized areas in Central-Eastern Europe. In contrast, in summer and autumn, the receptor site was less influenced by long-range pollution. The findings demonstrate that the seasonal distributions of PM10 source areas obtained using these two methods generally share similar characteristics, suggesting the credibility and accuracy of the analytical results.

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.

Abstract Background Shipping and port-related air pollution has a significant health impact on a global scale. The present study aimed to assess the mortality burden attributable to long-term exposure to ambient particulate matter (PM2.5, PM10) and nitrogen dioxide (NO2) in the city of Ancona (Italy), with one of the leading national commercial harbours. Methods Exposure to air pollutants was derived by dispersion models. The relationship between the long-term exposure of air pollution exposure and cause-specific mortality was evaluated by Poisson regression models, after adjustment for gender, age and socioeconomic status. Results are expressed as percent change of risk (and relative 95% confidence intervals) per 5 unit increases in the exposures. The health impact on the annual number of premature cause-specific deaths was also assessed. Results PM2.5 and NO2 annual concentrations were higher in the area close to the harbour than in the rest of the city. Positive associations between each pollutant and most of the mortality outcomes were observed, with estimates of up to 7.6% (95%CI 0.1, 15.6%) for 10 µg/m3 increase in NO2 and cardiovascular mortality and 15.3% (95%CI-1.1, 37.2%) for 10 µg/m3 increase PM2.5 and lung cancer. In the subpopulation living close to the harbour, there were excess risks of up to 13.5%, 24.1% and 37.9% for natural, cardiovascular and respiratory mortality. The number of annual premature deaths due to the excess of PM2.5 and NO2 exposure (having as a reference the 2021 World Health Organization Air Quality Guidelines) was 82 and 25, respectively. Conclusions Our study confirms the long-term health effects of PM and NO2 on mortality and reveals a higher mortality burden in areas close to shipping and port-related emissions. Estimating the source-specific health burdens is key to achieve a deeper understanding of the role of different emission sources, as well as to support effective and targeted mitigation strategies.

Abstract Coal is the major source of power in India and world over. Coal mining is an essential industry which has a major role in the economic development of the country. Most major mining activities contribute directly or indirectly to air pollution. Coal dust is a major air pollutant which affects the personal working in the mines and also people residing in villages near the mines. Air pollution due to coal particulates can affect human health and cause damages to the environment. Hence effective pollution control mechanisms are needed to keep the pollution levels within permissible levels. The easiest and most common method employed for dust suppression worldwide is sprinkling of water. In majority of mines, water sprinklers are operated manually and can lead to wastage of water due to over sprinkling. It can also prove to be ineffective in dust suppression if sprinkling is not done properly. The paper proposes a system which can be deployed to automate the dust suppressions sprinklers. The system will monitor the concentration of PM10 and PM2.5 in the air and initiate sprinkling operation when the particulate matter content exceeds preconfigured limits.

The major goal of our research was to find statistically relevant mathematical correlations between PM 10 levels in the atmosphere and the meteorological complex over the Moldova - Romania region. The period of analysis was from 2009 to 2020. We worked on daily data on PM10 levels and meteorological parameters from a network of 25 monitoring stations. The correlations between PM10 levels and air temperature, solar radiation, wind and precipitation were negative. The correlations between PM10 concentrations, atmospheric pressure and relative humidity were positive. However, over the medium term (12 years), the overall climatological correlations between PM10 concentrations and meteorological elements (assessed using Pearson coefficients) had values below ±0.5. This suggests that PM10 levels are not primarily influenced by meteorological elements but rather are closely associated with daily and annual patterns of socioeconomic activities. At time intervals of a few days up to 1-2 weeks, in certain favorable geographical situations, the correlative links between PM10 levels and meteorological elements can become strong and relevant (the Pearson index has in these temporal and spatial contexts values above or below 0.9 units).

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.