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Type 2 diabetes causes early mortality worldwide. Air pollution’s relationship with T2DM has been studied. The association between them is unclear because of inconsistent outcomes. Studies on this topic have been published since 2019, but not thoroughly evaluated. We conducted a systematic review and meta-analysis using relevant data. The study protocol was registered in PROSPIRO and conducted according to MOOSE guidelines. In total, 4510 manuscripts were found. After screening, 46 studies were assessed using the OHAT tool. This meta-analysis evaluated fine particles with T2DM using OR and HR effect estimates. Evaluation of publication bias was conducted by Egger’s test, Begg’s test, and funnel plot analysis. A sensitivity analysis was conducted to evaluate the influence of several studies on the total estimations. Results show a significant association between PM2.5 and PM10 exposure and T2DM. Long-term exposure to fine air particles may increase the prevalence and incidence of T2DM. Fine air pollution increases the chance of developing T2DM mainly via systemic inflammation, oxidative stress, and endoplasmic reticulum stress. [ABSTRACT FROM AUTHOR]

Evidence regarding the combined effects of green space and air pollutants on hypertension remains limited and complex. This study aims to investigate the varying effects of greenness under different air pollution levels in China, using data from the wave 2018 China Health and Retirement Longitudinal Study (CHARLS) involving 17 468 adults (aged ≥ 45 years). As a result, the prevalence rate of hypertension was 42.04%. Logistic regression analyses revealed the positive associations between air pollution concentrations at the city level and prevalent hypertension and the negative associations between NDVI and prevalent hypertension, all of which were more prominent in the populations of the eastern and rural regions. Notably, the negative effect of green space was greater at the lowest quartiles of each air pollutant (OR for PM2.5 quartiles = 0.724, 0.792, 0.740, and 0.931). Improving air quality and greenness could potentially reduce hypertension risk, and minimizing air pollution might optimize the protective effects of greenness. [ABSTRACT FROM AUTHOR]

This paper aimed to investigate the Spatial and Temporal Variation of the air quality index (AQI) in the Amman and Zarqa Metropolitan Areas during the period 2016-2022 following the method adopted by the Environmental Protection Agency of the United States of America (EPA). Air quality data for PM10, PM2.5, O3, NO2, SO2, and CO recorded at five monitoring stations were downloaded from the official website of the Jordanian Ministry of Environment. Calculated AQI values were generally between the Good class (AQI <50) and the Moderate class (AQI 50-100) at all stations, the AQI calculations for PM10 demonstrated a noticeable increase during autumnal months, likely due to natural dust. PM2.5 demonstrated seasonal variation, with higher values in winter months where residents burn fossil fuel for heating. Stabel air in winter due to the cooled land surface, and the weak natural air mix and ventilation contribute to the deterioration of air quality. Calculated individual AQI for SO2 and NO2 reveals that all extent of the study area falls in the Good AQI class. Similarly, CO and ozone-based AQI values fluctuate within the "Good" class, with occasional episodes of compromised air quality at specific stations. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Air pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an air quality index (AQI). On the other hand, accurate future predictions of air pollutant concentration levels can provide valuable information for data-driven decision-making to reduce health risks from short- and long-term exposure when indicators exceed permissible limits. In this paper, five deep learning architectures are evaluated to predict the concentration of particulate matter pollutants (in their fractions PM2.5 and PM10) and carbon monoxide (CO) in consecutive hours. The proposed prediction models are based on recurrent neural networks (RNNs), long short-term memory (LSTM), vanilla LSTM, Stacked LSTM, Bi-LSTM, and encoder–decoder LSTM networks. Moreover, a methodology is presented to guide the construction of the prediction model, encompassing raw data processing, model design and optimization, and neural network training, testing, and evaluation. The results underscore the precision and reliability of the Stacked LSTM model in predicting the hourly concentration level for PM2.5, with an RMSE of 3.4538 μg/m3. Similarly, the encoder–decoder LSTM model accurately predicts the concentration level for PM10 and CO, with an RMSE of 3.2606 μg/m3 and 2.1510 ppm, respectively. These evaluations, with their minimal differences in error metrics and coefficient of determination, validate the effectiveness and superiority of the deep learning models over other reference models, instilling confidence in their potential. [ABSTRACT FROM AUTHOR]

In this contribution, we report the study of nuclear resonance magnetic spectroscopy techniques (1H-NMR, 13C-NMR, and 2D-NMR) efficiency in the characterisation of the functional composition of water-soluble organic compounds (WSOC) from atmospheric aerosols. The chosen site was our scientific and technical center of research (CRAPC) situated in Algerian Bou-Ismail city. where the concentrations of PM10 were found to be between 15.66 and 142.19 µg.m−3. As results, 1H-NMR analysis showed the coexistence of biological material and emissions from urban and biomass burning. The dominant source was identified by quantitative integration of each 1H NMR spectral region. By using the HSQC technique, many peaks are revealed in biogenic samples including biomass burning. On the other hand, the identification of the source of various organic compounds and their functional composition is possible through specific NMR spectra, which can also be used to adjust the surrounding organic aerosol sources. [ABSTRACT FROM AUTHOR]

The contents of water-soluble major's ions (MSA, Cl−, NO3−, SO42−, Na+, NH4+, K+, Mg2+, and Ca2+) in the PM10 particle fraction were investigated thanks to detailed measurements of the main chemical constituents of PM10 in remote coastal areas in Bou-Ismail; in the South-West of the Mediterranean Sea (Algeria), during a 2-year period; from July 2011 to August 2013, under the framework of the ChArMEx project (Chemistry-Aerosol Mediterranean Experiment, http://charmex.lsce.ipsl.fr). The total water-soluble ion concentrations in PM10 at the Bou-Ismail measurement station varied from 3.3 µg/m3 (July 2011) to 49.6 µg/m3 (March 2012). The annual mean mass concentrations of ions in the PM10 particulate fraction were Cl− > Na+ > SO42− > Mg2+ > NO3− > Ca2+ > K+ > NH4+ > Oxalate. The change in potassium nss-K + concentrations in PM10 over the course of a year reveals that biomass burning (BB) has an effect on three separate seasons: the beginning of winter (February and March), the end of summer (August), and the autumn (September and October). The origin periods of biomass burning BB identified employing the mapping of hotspots and fires during periods of August and September 2011, 2012, and 2013 underlined the important fires in the surrounding areas of the Mediterranean Sea (Sardinia Islands from Italy, Corsica from France). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Air pollution is affected by the atmospheric dynamics. This study aims to determine that air pollution concentration values in İstanbul increased significantly and reached peak values due to atmospheric blocking between the 30th of December 2022 and the 5th of January 2023. In this study, hourly pollutant data was obtained from 16 air quality monitoring stations (AQMS), the exact reanalysis data was extracted from ERA5 database, and inversion levels and meteorological and synoptic analyses were used to determine the effects of atmospheric blocking on air pollution. Also, cloud base heights and vertical visibility measurements were taken with a ceilometer. Statistical calculations and data visualizations were performed using the R and Grads program. Omega-type blocking, which started in İstanbul on December 30, 2022, had a significant impact on the 1st and 2nd of January 2023, and PM10 and PM2.5 concentration values reached their peak values at 572.8 and 254.20 µg/m3, respectively. In addition, it was found that the average concentration values in the examined period in almost all stations were higher than the averages for January and February. As a result, air quality in İstanbul was determined as "poor" between these calendar dates. It was found that the blocking did not affect the ozone (µg/m3) concentration. It was also found that the concentrations of particulate matter (PM) 10 µm or less in diameter (PM10) and PM 2.5 µm or less in diameter (PM2.5) were increased by the blocking effect in the İstanbul area. Finally, according to the data obtained using the ceilometer, cloud base heights decreased to 30 m and vertical visibility to 10 m. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Air pollution remains a major public health concern globally, especially in the Western Balkan countries facing severe air quality problems. This study investigates the relationship between air quality, news media sentiment, and public discourse in Macedonia over a ten-year period (2014–2023). We employed sentiment analysis to examine the emotional tone of news coverage related to air pollution, and topic modeling to uncover recurring themes within news articles. Our analysis revealed a distinct seasonal pattern, with negative media sentiments peaking during winter months when PM10 levels were the highest. This finding aligns with the increased reliance on polluting fuels for winter heating. Interestingly, despite a stable number of neutral articles, a rise in positive-sentiment articles suggests a potential decrease in pollution levels or the effectiveness of new government policies. We identified recurring topics like air quality concerns in specific cities, public unease regarding factories, and ongoing scrutiny of government policies. Emerging topics included the impact of the COVID-19 pandemic on air quality, public discourse surrounding heating practices, and growing concerns about waste management. This study contributes to a deeper understanding of the complex interplay between air pollution data, public discourse, and media framing, offering valuable insights for policymakers and media outlets in Macedonia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The port of Cartagena, south-east Spain, is noted for its intense activity. This paper presents the influence of the COVID-19 pandemic on the recorded levels of six pollutants. Fifteen years of measurements were used, with two objectives. The first was to investigate how these pollutants evolved, together with their usual cycles. The second objective was to ascertain whether the COVID-19 pandemic had an impact on the concentrations recorded. The results showed that nitrogen oxide concentrations remained steady in the first half of the measurement period and decreased in the second half. SO2 concentrations decreased irregularly, whereas O3 and PM10 presented steady concentrations. The annual cycle was observed for nitrogen oxides and O3. Only SO2 evidenced no weekly cycle. Finally, the analysis of pre- and post-pandemic concentrations revealed a sharp decrease in nitrogen oxides, whereas the pandemic was not seen to have had any impact on the other pollutants. In addition, NO2 emerged as the best indicator of human activity—represented by car and maritime traffic—due to its response to the relaxation measures. Finally, Lamb weather types were calculated. The unclassified type was the most frequent. However, the greatest concentration changes were observed for anticyclonic and eastern flow types. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, spatio-temporal identification has been analyzed using new probabilistic risk graphs for air quality assessments in Istanbul, Türkiye, for the first time. Detailed assessments can be conducted through the proposed method, which is based on probabilistic models. These models contribute to the objective explanation of variations in air quality. Air quality and its fluctuations have been elucidated using risk graphs across various districts in Istanbul. According to the model results, the air quality at Mecidiyeköy and Göztepe is worse than in the other districts. Conversely, Şile and Sultanbeyli on the Asian side, as well as Sarıyer and Maslak on the European side, exhibit the best air quality in terms of particulate matter (PM10) and NO2 model results. The proposed methodology provides air quality levels at different risk levels, enabling the tracking of temporal variations in air pollution risks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In an attempt to overcome challenges in PM10 surface concentration estimation imposed by sparse ground-based observations over the complex terrain of Brahmaputra Valley (BV), this study has zeroed in on three objective processes. The first objective is to estimate PM10 using a Multiple Linear Regression Model (MLRM) assimilating planetary boundary layer height (PBLH), meteorological variables [air temperature (AT), relative humidity (RH), wind speed (WS)], and AOD 500 nm for Dibrugarh, a microcosm site of the BV. The second objective is to determine the PBLH from radiosonde observations using the vertical gradient of eight fundamental atmospheric fields. The third objective is to analyze the PM10-meteorology, PM10-PBLH, and PM10-AOD 500 nm relations. The MLRM is fitted with the meteorological variables and PBLH from two sources- ERA5 and ground-based, to assess the model's performance and ERA5 data feasibility. Results show that the R2 for ground-based MLRM (R2 = 0.655) is higher than that of reanalysis-fitted MLRM (R2 = 0.563) with RMSE 17.761 and 20.753 respectively. The minimum difference in R2 and RMSE indicates that ERA5 data performs well in estimating PM10. Out of all the vertical gradients applied, the maximum vertical height of WS and maximum vertical gradient of virtual potential temperature best represent the PBLH. [ABSTRACT FROM AUTHOR]

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.

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 <10 µm (PM10) were obtained from air pollution monitoring stations. Exposure to air pollutants during the three months preceding pregnancy and the first, second and third trimesters of pregnancy for each participant was estimated. The odds ratio was calculated based on logistic regression in three adjusted models considering different confounders. Only results that had a p < .05 were considered statistically significant. Results: None of the logistic regression models showed any statistically significant relationship between the exposure to any of the pollutants and GDM at different time points (before pregnancy, in the first, second and third trimesters of pregnancy and 12 months in total) (p > .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. [ABSTRACT FROM AUTHOR]

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.

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.

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. [ABSTRACT FROM AUTHOR]

A cross-sectional study was conducted among 126 preschool children aged 4–6 years in traffic, industrial, and suburban areas of Penang, Malaysia, to determine their exposure to indoor air pollutants and their associations with respiratory symptoms. A standardised and validated questionnaire was used to collect data on respiratory health symptoms among respondents. An indoor air quality assessment was conducted in selected preschools that included temperature, relative humidity, air velocity, volatile organic compounds (VOCs), and particulate matter (PM2.5, and PM10). There were significant differences in median concentrations of PM2.5, PM10 and relative humidity among study groups. Statistical analysis showed significant associations between air pollutants in preschool with respiratory symptoms. Factors that influenced the increased likelihood of cough and phlegm were linked to the increase in PM2.5 exposure. This study suggests regular classroom cleaning and routine maintenance of air conditioners to be done inside the preschools. [ABSTRACT FROM AUTHOR]

Objective To analyze the effects of air pollutants on male semen quality and different stages of sperm development in Taiyuan. Methods Totally 7 614 men who were admitted to the Reproductive Department of the First Hospital of Shanxi Medical University from January 1 to December 31, 2019 were enrolled. Their age, semen collection season, and semen parameters were recorded. At the same time, the monitoring data of six air pollutants (SO2, N02 CO, 03, PM25, and PML0) were collected, and the exposure of air pollutants in the whole developmental period (0 to 90 days before semen collection) and three critical developmental periods (0-9 days, 10-14 days, and 70-90 days) were calculated. Nonparametric Mann-Whitney U test was used to compare the data difference between qualified and unqualified semen parameters. The generalized estimation equation was used to explore the effects of six kinds of air pollutants on the quality parameters of each sperm at different stages of sperm development. Results During the whole period of sperm development, the exposure concentrations of S02!1 NO2, O3, and PM25 were different between the qualified and unqualified groups of normal sperm morphology. The exposure to PMl()at 10 and 14 days before semen collection was positively correlated with semen volume (07=1.245, 95%CZ: 1.057-1.467), sperm concentration (Oi=1.247, 95%CI: 1,027-1.514), total sperm count (OR=1.385, 95%CI: 1.167-1.643), total motility (Oi=L236, 95%CZ: 1.085-1.409), and forward movement (Oi=L207, 95%CZ: 1.059-1.376). The O3 exposure was positively correlated with sperm concentration (02 = 1.250, 95%CI: 1.002-1.55& OR = 1.173, 95% CI: 1.025 -1343), total sperm count (Oi=1.255!) 95%CI: 1.026-1.537; OR二 121 & 95%CI: 1.074-1380), and normal sperm morphology (O/=1.295, 95%CIi 1.047-1.602; OR二1229, 95%CI: 1.076-1.404). Conclusion Exposure to air pollutants increases the risk of unqualified semen parameters at all stages of sperm development, and the critical window period focuses on 10-14 days before semen collection, which is the developmental stage of sperm motility. PMLo and O3 are the main reproductive toxins affecting the decline of male sperm quality in Taiyuan, which need to be paid attention to. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

Industrial and mining operations are classified as the primary sources of particulate matter (PM10) that pose substantial health and environmental risks, with much attention when situated in or near residential areas. The absence of a standardized approach for quantifying air pollutant fluxes in mining areas has been identified as a research gap, particularly given the increasing rate of mining extraction, especially in developing countries. This study used the AERMOD air pollutants dispersion model to analyze PM10 dispersion from the CIMERWA Cement Plant and adjacent mining sites in Rusizi District, Rwanda, throughout 2022. We developed methods to quantify PM10 horizontal and vertical fluxes and their emission rates within the framework of dispersion analysis. Results indicate high concentrations of PM10 in the vicinity of mining areas, with daily, hourly, monthly, and annual maximum mean ground-level concentrations of 158.86 µg m−3, 92.87 µg m−3, 46.15 µg m−3, and 27.65 µg m−3, respectively. Wind erosion, with a daily horizontal speed of 0.66 m s−1 and shear speed of 0.03 m s−1, contributes to a daily mean horizontal flux of 8.50 µg m−2 s−1 and vertical flux of 0.4 µg m−2 s−1 of PM10. Dispersion maps analysis highlights residents' exposure to significant levels of PM10 depicted with 28 discrete receptors in all directions. The circulation of polluted windy air masses substantially impacts regional air quality. The findings offer evidence-based recommendations for strategic mitigation measures targeted at residents, policymakers, stakeholders, and urban planners to address air quality issues arising from industrial mining activities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigates the spatiotemporal dynamics of pollutant concentrations in Delhi through the utilization of land use regression models. Analysis of data for year 2019 from 38 monitoring stations reveal elevated PM10 and PM2.5 levels, peaking in winter ([PM10: 306.90 ± 53.76 μg/m3], [PM2.5: 185.52 ± 31.59 μg/m3]) and dropping in monsoon ([PM10: 107.77 ± 31.19 μg/m3], [PM2.5: 40.86 ± μg/m3]), surpassing national standards ([PM10: 60 μg/m3], [PM2.5: 40 μg/m3]). Spatial distribution analysis indicates higher concentrations in the north and northwest regions, attributed to dense habitation, industrial zones, and vehicular traffic. Analyzing particulate pollutants data for year alongside urban land use/cover features and socioeconomic variables, the study reveals a robust relationship between particulate concentrations and urban attributes, explaining 37–58% of PM2.5 and 38–62% of PM10 concentration variations. The models demonstrate good accuracy, with low RMSE values (PM2.5: 9.55, PM10: 27.49), underscoring the impact of urban landscape and surface conditions on air quality distribution. Understanding this link offers insights for better urban planning strategies that integrate air quality considerations, crucial for effective policy frameworks addressing pollution in urban environments. [ABSTRACT FROM AUTHOR]

Recent regulations on exhaust emissions have led to an increase in non-exhaust emissions, which now surpasses exhaust emissions. Non-exhaust emissions are mainly generated from brake and tire particle abrasion, road wear, and re-suspended road dust. In Asia, non-exhaust emissions have increased significantly over the past 50 years, resulting in almost 92% of the population breathing polluted air, which accounts for 70% of air pollution related-deaths. Most Asian countries with poor air quality are developing or underdeveloped. Taking this into consideration, the current study aims to shed light on particulate pollution from non-exhaust emissions in the Asian context to assess the current status and its health consequences and provides technological solutions. The study is based on an in-depth analysis of existing reviews and research concerning non-exhaust emissions and their health impacts in Asia to pinpoint knowledge gaps. The study found that particulate pollutants had exceeded WHO's standards in many Asian countries, bringing deleterious health consequences among children and the elderly. The findings underscore the significance of future researchers' efforts to devise solutions that curtail non-exhaust emissions, ultimately reducing air pollution, augmenting air quality, fostering better health outcomes, and paving way for a more sustainable future before it is too late. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the organic carbon (OC) content in the PM10 emitted by agricultural soils and rural roads under controlled conditions. Samples were collected from agricultural soils and rural roads. The PM10 was generated and collected using an electrostatic precipitator coupled with the Easy Dust Generator (EDG). This procedure ensures that the PM10 collected come specifically from soil. OC contents were measured in both the soil and PM10. The enrichment ratio (ER) was calculated as the ratio between OC content in the PM10 and OC content in the soil. The results showed that OC content in the PM10 ranged from 2.7% to 3.5% in agricultural soils and from 1.4% to 2.9% in rural roads. These values were comparable with the OC contents observed in fine particles transported by the wind, but lower than OC contents observed in PM10 samples collected in rural areas using active samplers and filters. A quadratic function described the association between OC in PM10 and OC in the soil. A negative potential function described the association between ERs and OC in the soil. Both associations suggested a saturation of OC in PM10 when the OC content in the soil was high. This information is crucial for a better comprehending of the dust emission role in the redistribution of OC within terrestrial ecosystems and to the atmosphere and oceans. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Air pollution is increasingly linked to impaired kidney function in adults. However, little is known about how early-life exposure to air pollutants affects kidney function in adolescents. Cohort study. We leveraged data from the 'Children of 1997' Hong Kong population-representative birth cohort (N = 8327). Residential exposure to average ambient levels of four air pollutants, including inhalable particle (PM 10), sulfur dioxide (SO 2), nitrogen dioxide (NO 2), and nitrogen monoxide (NO), during in utero , infancy, and childhood periods was estimated using the inverse distance weighting. Kidney function was assessed using estimated glomerular filtration rate (eGFR) calculated from age-adjusted equations for adolescents. Generalized linear regression was used to examine the association of air pollutant exposure in each period with kidney function at 17.6 years. Two-pollutant models tested the robustness of the association. Of the 3350 participants included, 51.4% were boys. Exposure to PM 10 was associated with poorer kidney function. Each interquartile range increment in PM 10 was inversely associated with eGFR (β: −2.933, 95% confidence interval −4.677 to −1.189) in utero , −2.362 (−3.992 to −0.732) infancy, −2.708 (−4.370 to −1.047) childhood, and −2.828 (−4.409 to −1.247) overall. Exposure to PM 10 and SO 2 in utero had a stronger inverse association with kidney function in males. The associations were robust to PM 10 exposure in two-pollutant models. Our findings suggest that early-life exposure to ambient PM 10 and SO 2 is associated with reduced kidney function in adolescents, especially exposure in utero. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents the impact of vehicle traffic intensity and the closure of access roads for wheeled vehicles to selected schools in Warsaw at selected hours of the day on changes in air quality. This study focused primarily on analyses related to the pollutant typical of road traffic, i.e., nitrogen dioxide, but also took into account PM10 and PM2.5 concentrations, which are some of the key factors determining the quality of atmospheric air, the sustainability of human beings, and sustainable development. Some kinds of relationships were found between vehicle traffic intensity and air pollutant concentrations, in particular—nitrogen dioxide. Analyses of the pollutant concentrations in the periods before and after the introduction of restrictions on vehicle traffic on working days indicate that, in the case of two of the schools during the morning traffic rush hours, significantly lower NO2 concentrations were observed in the period after the introduction of road traffic restrictions, compared with the period before they were introduced. NO2 concentrations during the morning peak hours (on working days) after introducing restrictions were more than 30% lower than the concentrations recorded during the same hours in the period before introducing restrictions. [ABSTRACT FROM AUTHOR]

The concentrations of anhydrosugars (levoglucosan, mannosan, and galactosan), polyols (inositol, xylitol, sorbitol, and mannitol), and glucose were measured in PM1 and PM10 samples collected during 1 year at a traffic site in the city of Elche (southeastern Spain). Levoglucosan, mannosan, and galactosan were mainly found in the PM1 fraction since they are mainly emitted from biomass burning (BB). Likewise, inositol, xylitol, and sorbitol were primarily distributed in the fine mode, suggesting a non-negligible contribution from anthropogenic sources (specifically BB) to the levels of these compounds. This was supported by their seasonal variations, with higher concentrations during winter, and their correlations with levoglucosan concentrations. The average contributions of biomass burning and biogenic sources to OC and PM levels were calculated using levoglucosan and mannitol, respectively, as tracers. On average, BB accounted for 12% and 16% of the OC in PM1 and PM10, while the estimated contribution of fungal spores to OC and PM10 levels was 1.2 and 0.8%, respectively. The results of the present study suggest that, at least in the study area, most sugar alcohols are not appropriate tracers of biogenic emissions. [ABSTRACT FROM AUTHOR]

As one of the most environmental concerns, inhaled particulate matter (PM 10) causes numerous health problems. However, the associations between anxiety behavior and toxicity caused by PM 10 have rarely been reported so far. To investigate the changes of behavior after PM 10 exposure and to identify the potential mechanisms of toxicity, PM 10 samples (with doses of 15 mg/kg and 30 mg/kg) were intratracheally instilled into rats to simulate inhalation of polluted air by the lungs. After instillation for eight weeks, anxiety-like behavior was evaluated, levels of oxidative stress and morphological changes of hippocampus were measured. The behavioral results indicated that PM 10 exposure induced obvious anxiety-like behavior in the open field and elevated plus maze tests. Both PM 10 concentrations tested could increase whole blood viscosity and trigger hippocampal neuronal damage and oxidative stress by increasing superoxide dismutase (SOD) activities and malondialdehyde levels, and decreasing the expressions of antioxidant-related proteins (e.g., nuclear factor erythroid 2-related factor 2 (Nrf2), SOD1 and heme oxygenase 1). Furthermore, through collecting and analyzing questionnaires, the data showed that the participants experienced obvious anxiety-related emotions and negative somatic responses under heavily polluted environments, especially PM 10 being the main pollutant. These results show that PM 10 exposure induces anxiety-like behavior, which may be related to suppressing the Nrf2/Keap1-SOD1 pathway. [Display omitted] • The participants experienced obvious anxiety emotions and negative somatic responses under heavily polluted environments. • Different concentrations of PM 10 exposure could induce severe anxiety-like behavior in rats. • PM 10 exposure aggravated hippocampal oxidative stress levels and increased whole blood viscosity. • There was a strong correlation between whole blood viscosity and oxidative stress. • The impaired Nrf2/Keap1-SOD1 pathway in hippocampus involved in anxiety-like behavior of PM 10 -exposed rats. [ABSTRACT FROM AUTHOR]