PM10 Fraction Research Articles

Fine dust collection capacity of a moss greening system for the building envelope: An experimental approach

Air pollution is one of the most impactful environmental issues worldwide in terms of mortality. Numerous studies have assessed the benefits of vertical greening systems within urban areas for mitigating air pollution, but their widespread adoption is limited by high costs and maintenance requirements. The current study assesses the air pollution mitigation capacity within urban areas of three moss species: Barbula unguiculata, Grimmia pulvinata and Homalothecium sericeum, used in the moss envelope system called MosSkin. Following a three-months exposure of the three moss species samples in a highly trafficked area, phyllids were analysed with Field Emission Scan Electron microscopy to quantify number, dimension, and nature of particle deposition on leaves on their different portions (base, centre, apex). The moss species chosen collected up to 45,580 particles per mm2 with the trend H. sericeum ≥ G. pulvinata > B. unguiculata, showing a similar tendency in terms of classes and order of magnitudes. However, specific higher affinities for PM fractions 2–7.5 μm were shown by G. pulvinata. The PMx collection can be related to species-specific shape of leaves within stems, and it depends on the leaf portions (highest capture in apex), 45–55 % consisting of PM0.5 μm, indicating a significant collecting capacity of such fractions.

Use of Low-Cost Sensors to Study Atmospheric Particulate Matter Concentrations: Limitations and Benefits Discussed through the Analysis of Three Case Studies in Palermo, Sicily.

The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM2.5 and PM10 fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results obtained from two systems equipped with the same sensor model were compared. Excellent linear correlation was observed between the results, with differences in measurements falling within instrumental accuracy. Two instruments equipped with different sensors, models Novasense SDS011 and Plantower PMSA003, were placed at the same site. These were complemented by a weather station to measure meteorological parameters. Upon comparing the atmospheric particulate matter concentrations measured by the two instruments, it was observed that there was a good linear correlation for PM2.5 and a poor linear correlation for PM10. Additionally, the PMSA003 sensor appeared to consistently record higher concentrations than the SDS011 sensor. During periods influenced by natural sources and/or anthropogenic activities at the regional and/or local scale, i.e., the dispersal of Saharan sands, forest fires, and local events using fireworks, abnormal concentrations of atmospheric particulate matter were detected. Despite the inherent limitations in precision and accuracy, both low-cost instruments were able to identify periods with abnormal concentrations of atmospheric particulate matter, regardless of their source or type.

Modelling of atmospheric concentrations of fungal spores: a 2-year simulation over France using CHIMERE

Abstract. Fungal spore organic aerosol emissions have been recognised as a significant source of particulate matter as PM10; however, they are not widely considered in current air quality models. In this work, we have implemented the parameterisation of fungal spore organic aerosol (OA) emissions introduced by Heald and Spracklen (2009) (H&S) and further modified by Hoose et al. (2010) in the CHIMERE regional chemistry-transport model. This simple parameterisation is based on two variables, leaf area index (LAI) and specific humidity. We have validated the geographical and temporal representativeness of this parameterisation on a large scale by using yearly polyol observations and primary biogenic organic aerosol factors from positive matrix factorisation (PMF) analysis at 11 French measurement sites. For a group of sites in northern and eastern France, the seasonal variation of fungal spore emissions, displaying large summer and small winter values, is correctly depicted. However, the H&S parameterisation fails to capture fungal spore concentrations for a smaller group of Mediterranean sites with less data availability in terms of both absolute values and seasonal variability, leading to strong negative biases, especially during the autumn and winter seasons. Two years of CHIMERE simulations with the H&S parameterisation have shown a significant contribution of fungal spore OA to PM10 mass, which is lower than 10 % during winter and reaches up to 20 % during summer in high-emission zones, especially over large forested areas. In terms of contributions to organic matter (OM) concentrations, the simulated fungal spore contribution in autumn is as high as 40 % and reaches at most 30 % of the OM for the other seasons. As a conclusion, the fungal spore OA contribution to the total OM concentrations is shown to be substantial enough to be considered a major PM10 fraction and should then be included in state-of-the-art chemistry-transport models.

Calculations of the conversion factor from organic carbon to organic matter for aerosol mass balance

Aerosol mass balance studies based on filter samples require a conversion factor to derive organic matter (OM) concentrations from organic carbon (OC) measurements from thermo-optical methods. This factor provides indirect insights on the molecular structure of OM needed in chemical transport models. Site- and season-specific ratios of OC to OM (fOM:OC) were calculated using data from five rural background sites in France between 2012 and 2021 by relating the unidentified chemical fraction in PM2.5 samples to thermo-optical OC concentrations. Further, multiple linear formulations were used to evaluate the impact of possible artefacts on the determination of fOM:OC. The resulting fOM:OC was then compared to other estimates derived from online aerosol mass spectrometry data, showing good agreement. The spatial and temporal variability in fOM:OC is discussed considering factors such as seasonality, meteorological conditions and the atmospheric oxidative potential. Linear-mixed effect models were formulated to quantitatively determine the drivers which influence the fOM:OC at the French rural background sites. Both ozone and relative humidity were variables with statistically significant effects on fOM:OC, indicating that differences in the contributions from both photooxidation and water content, explain the variability in fOM:OC observed at the French rural background sites. Site-specific fOM:OC yielded more accurate PM2.5 mass closure and are therefore recommended in mass-balance exercises. Accurate fOM:OC are critical to maintain consistency in OM time series, especially in cases where filter-based time series may be replaced by state-of-the-art online instrumentation.

Seasonal and spatial variations of arsenic and its species in particulate matter in an urban environment of Brno, Czech Republic

The present paper deals with an analysis of total arsenic concentration using ICP-MS/MS and an analysis of concentration of several arsenic species, arsenite (AsIII), arsenate (AsV), monomethylarsonate (MMA), dimethylarsenite (DMA), and trimethylarsine oxide (TMAO), using HPLC-ICP-MS/MS in the PM10 fraction of airborne urban aerosol. The samples were collected during two campaigns, in the autumn of 2022 and in the winter of 2023, at three locations within the central European city of Brno, with the aim to evaluate the seasonal and spatial variations in the PM10 composition. The results confirmed only the seasonal variability in the content of the methylated arsenic species in PM10 influenced by biomethylation processes. To gain better understanding of the possible arsenic origin, a supplementary analysis of the total arsenic concentrations was performed in samples of different size fractions of particulate matter collected using ELPI + . Local emissions, including industrial activities and heating during the winter season, were suggested as the most likely predominant source contributing to the total As content in PM10.Graphical abstract

Emission rates of particle-bound heavy metals and polycyclic aromatic hydrocarbons in PM fractions from indoor combustion sources

Indoor combustion sources significantly affect the air quality in homes, emitting both airborne particles and related particle-bound chemical compounds, such as heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs). The emission of particle-bound HMs and PAHs from indoor sources has been poorly investigated by the scientific community.This paper aims to fill this knowledge gap by providing emission rates of particle-bound HMs and PAHs in various PM fractions (PM1, PM1-10, and PM10) from typical indoor combustion sources: incense, candle, and mosquito coil combustions and cooking activities.To this end, two experimental analyses were carried out to obtain (i) the emission rates of PM fractions and (ii) the mass fraction of each particle-bound HM and PAH in the PM fractions.Results of the study revealed high PM fraction emission rates, with a significant contribution of PM1 fraction, and high HM and PAH mass fractions with a main contribution of the sub-micrometric range (from 61 % to 100 % for Group 1 carcinogenic compounds). The emission rates of HMs and PAHs were determined based on these measurements, with Group 1 carcinogenic compound emission rates in PM10 ranging from about 3 to >100 ng min−1.Using the emission rate data, indoor concentrations and the corresponding non-carcinogenic and carcinogenic lifetime risks for a typical daily exposure scenario were estimated. The expected concentrations from the use of the investigated sources were higher than those typically measured in homes, and significant carcinogenic risks were associated with the use of sources emitting Group 1 carcinogenic HMs.

Influence of meteorological conditions on the variability of indoor and outdoor particulate matter concentrations in a selected Polish health resort

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.

Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments

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.

Trace Metal Bioaccumulation in Feral Pigeons (Columba livia f. domestica) and Rooks (Corvus frugilegus) Residing in the Urban Environment of Iasi City, Romania.

Nowadays, trace metal contamination within urban atmospheres is a significant and concerning global issue. In the present study, two synanthropic bird species, namely, the feral pigeon (Columba livia f. domestica) and the rook (Corvus frugilegus), were employed as bioindicators to assess the atmospheric trace metal pollution in Iasi City, Romania. The concentrations of Ni, Pb, Cd, Co, Cr, and Cu were determined through high-resolution continuum source graphite furnace atomic absorption spectrometry (HR-CS GF-AAS) of various tissues, including the liver, kidney, lung, heart, muscle, and bone, of feral pigeons and rooks collected in Iasi City. The order of trace metal concentrations in the tissues of feral pigeons and rooks in Iasi City was similar: Cu > Pb > Ni > Cd > Cr > Co. However, trace element values in most tissues were higher in the rook samples than in feral pigeon ones, except for Co, which had elevated levels in feral pigeon renal and cardiac tissues, and Cu, which registered the highest concentrations in feral pigeon liver and kidney tissues. While not statistically significant, Pb concentration values in the PM10 fraction of atmospheric particles positively correlated with Pb concentrations in rook kidney samples (p = 0.05). The concentration levels of Cd, Pb, and Ni in the PM10 fraction of air particles showed a positive correlation with Cd levels in the samples of pigeon heart and rook liver, kidney, and heart, Pb levels in the samples of pigeon kidney, heart, and muscle and rook liver and bone, and Ni levels in the samples of pigeon liver, kidney, and bone and rook liver, muscle, and bone, respectively.

Source Tracing of PM2.5 in a Metropolitan Area Using a Low-Cost Air Quality Monitoring Network: Case Study of Denver, Colorado, USA

Air quality assessments often require source apportioning of the air pollutants observed at the receptor site. Conventional source apportionment models are subject to high uncertainties due to the lack of accurate emission profiles of all the contributing sources and a limited number of measurements at the receptor sites. Recent advances in the development and application of low-cost PM2.5 sensors have facilitated the formation of a more robust database with greater numbers of measurements per location and time. The main objective of this study is to combine a large database of PM2.5 concentration records to records from low-cost sensors in Denver, Colorado, during January 2021. Using wind speed and wind direction at the receptors, we developed a visualization tool for source tracing of PM2.5 with resulting statistical analyses and back-trajectory modeling. For this purpose, a combination of in-house and existing packages of R scripts along with National Oceanic and Atmospheric Administration (NOAA)’s trajectory model and climate and weather toolkits were used. In general, the results show that the PM2.5 measurements obtained from such a network of PM2.5 sensors incorporated with hourly wind field data, which are publicly available, can provide a powerful screening tool to discover the transport pathways of PM2.5 before requiring costly source apportionment approaches. The fraction of PM2.5 concentration detected by each sensor in regard to wind direction and speed bins were quantified using this method. The results of cluster analysis identified the area groups in respect to wind speed and wind direction bins, which shines a light on how far and in which direction polluting sources are. Finally, the back-trajectory modeling outputs illustrated the exact travel path of the PM2.5-laden air parcels of each day to each sensor.

Nature-based solutions for monitoring the impact of vehicular particulate matter and for the preventive conservation of the Palatine Hill archaeological site in Rome, Italy

Magnetic and chemical biomonitoring methodologies were applied to the southern slopes of the Palatine Hill archaeological area in Rome, Italy. Plant leaves and lichen transplants were respectively sampled and exposed between July 2022 and June 2023 to assess the impact of vehicular particulate matter from Via dei Cerchi, a trafficked road coasting Circus Maximus, towards the archaeological area upon the Palatine Hill. The magnetic properties of leaves and lichens, inferred from magnetic susceptibility, hysteresis loops and first order reversal curves, were combined with the concentration of trace elements. It was demonstrated that the bioaccumulation of magnetite-like particles, associated with tracers of vehicular emissions, such as Ba and Sb, decreased with longitudinal distance from the road, without any important influence of elevation from the ground. Lichens demonstrated to be more efficient biomonitors of airborne PM than leaves, irrespective of the plant species. Conversely, leaves intercepted and accumulated all PM fractions, including road dusts and resuspended soil particles. Thus, plant leaves are suitable for providing preventive conservation services that limit the impact of particulate pollution on cultural heritage sites within busy metropolitan contexts.

Enhancing the health benefits of air quality improvement: a comparative study across diverse scenarios.

In many studies, linear methods were used to calculate health benefits of air quality improvement, but the relationship between air pollutants and diseases may be complex and nonlinear. In addition, previous studies using reference number as average number of diseases may overestimate the health benefits. Therefore, the nonlinear model estimation and resetting of the reference number were very important. Hospital admission data for coronary heart disease (CHD), meteorological data, and air pollutant data of Zibo City from 2015 to 2019 were collected. The generalized additive model (GAM) was used to explore the association between air pollutants and hospital admission for CHD, and to evaluate the effects on health benefits under different reference number settings. A total of 21,105 hospitalized cases for CHD were reported in Zibo during the study period. The results of the GAM showed there was a log-linear exposure-response relationship between O3 and hospital admissions for CHD, with RR (relative risk) of 1.0143 (95% CI: 1.0047 ~ 1.0239). There were log-nonlinear exposure-response relationships between PM10, PM2.5, SO2, and hospital admissions for CHD. With the increase of pollutants concentrations, the risk for hospital admission showed a trend of increasing first and then decreasing. Compared with the average hospital admissions as the reference number, health benefits calculated by hospital admissions predicted by the GAM model yielded lower. Using the World Health Organization air quality guidelines as reference, attributable fractions of O3, PM10, and PM2.5 were 1.97% (95% CI: 0.63 ~ 3.40%), 11.82% (95% CI: 8.60 ~ 15.24%), and 11.82% (95% CI: 8.79 ~ 15.04%), respectively. When quantifying health benefits brought by improving air quality, corresponding calculation methods should first be determined according to the exposure-response relationships between air pollutants and outcomes. Then, applying the average hospital admissions as reference number may overestimate health benefits resulting from improved air quality.

Combining analytical techniques to assess the translocation of diesel particles across an alveolar tissue barrier in vitro

BackgroundDuring inhalation, airborne particles such as particulate matter ≤ 2.5 μm (PM2.5), can deposit and accumulate on the alveolar epithelial tissue. In vivo studies have shown that fractions of PM2.5 can cross the alveolar epithelium to blood circulation, reaching secondary organs beyond the lungs. However, approaches to quantify the translocation of particles across the alveolar epithelium in vivo and in vitro are still not well established. In this study, methods to assess the translocation of standard diesel exhaust particles (DEPs) across permeable polyethylene terephthalate (PET) inserts at 0.4, 1, and 3 μm pore sizes were first optimized with transmission electron microscopy (TEM), ultraviolet-visible spectroscopy (UV-VIS), and lock-in thermography (LIT), which were then applied to study the translocation of DEPs across human alveolar epithelial type II (A549) cells. A549 cells that grew on the membrane (pore size: 3 μm) in inserts were exposed to DEPs at different concentrations from 0 to 80 µg.mL− 1 ( 0 to 44 µg.cm− 2) for 24 h. After exposure, the basal fraction was collected and then analyzed by combining qualitative (TEM) and quantitative (UV-VIS and LIT) techniques to assess the translocated fraction of the DEPs across the alveolar epithelium in vitro.ResultsWe could detect the translocated fraction of DEPs across the PET membranes with 3 μm pore sizes and without cells by TEM analysis, and determine the percentage of translocation at approximatively 37% by UV-VIS (LOD: 1.92 µg.mL− 1) and 75% by LIT (LOD: 0.20 µg.cm− 2). In the presence of cells, the percentage of DEPs translocation across the alveolar tissue was determined around 1% at 20 and 40 µg.mL− 1 (11 and 22 µg.cm− 2), and no particles were detected at higher and lower concentrations. Interestingly, simultaneous exposure of A549 cells to DEPs and EDTA can increase the translocation of DEPs in the basal fraction.ConclusionWe propose a combination of analytical techniques to assess the translocation of DEPs across lung tissues. Our results reveal a low percentage of translocation of DEPs across alveolar epithelial tissue in vitro and they correspond to in vivo findings. The combination approach can be applied to any traffic-generated particles, thus enabling us to understand their involvement in public health.

Influence of wildfire emissions to carbon dioxide (CO2) observed at the Mt. Cimone station (Italy, 2165 m asl): A multi-year investigation

This work aimed at investigating on a multi-year framework (2015–2021) the contributions of wildfire emissions to atmospheric CO2 observed at the ICOS atmospheric class-2 station Mt. Cimone (CMN, 2165 m a.s.l. - Italy). Based on the analysis of a case study occurred in March 2020, a methodology providing indications about the possible presence of wildfire plumes from different European source regions was implemented. The methodology used observed CO at CMN, active fire detection by MODIS and air mass back-trajectories. An alternative detection method based on the use of a reanalysis dataset (CAMS) providing information about the amount of PM10 tagged to wildfires was also used.The results suggested that CMN could be affected by wildfire plumes for a fraction of time ranging from 1% to 10% (as a function of the adopted methodology setting). Over the studied period, we found a potentially important contribution of plumes from eastern Europe during October–April, while during May–September there was a prevalence from the Mediterranean sectors.Looking at the possible impact of these events to the observed CO2, we detected a notable increase of CO2 residuals with respect to periods not affected by fire perturbations during October–April (from +1.8 to +3.9 ppm, on average). We did not find evident impacts during the summer months, possibly due to a contribution by biospheric uptake during air mass transport to CMN (at least for a fraction of selected events).We discussed the sensitivity of results as a function of the selection methodology settings, suggesting that the strictest set-up based on the detection of large CO excesses could trace “major” events. A medium level of agreement was found when comparing the results of our selection methodology with CAMS reanalysis (fraction of PM10 emitted by wildfires).Even if it is still preliminary, our study indicated that the observations from CMN can represent, if supported by adequate diagnostic tools, a powerful dataset to evaluate the impact of wildfires to the atmospheric CO2 variability.

Understanding Reductions of PM2.5 Concentration and Its Chemical Composition in the United States: Implications for Mitigation Strategies.

Motivated by the recent tightening of the US annual standard of fine particulate matter (PM2.5) concentrations from 12 to 9 μg/m3, there is a need to understand the spatial variation and drivers of historical PM2.5 reductions. We evaluate and interpret the variability of PM2.5 reductions across the contiguous US using high-resolution estimates of PM2.5 and its chemical composition over 1998-2019, inferred from satellite observations, air quality modeling, and ground-based measurements. We separated the 3092 counties into four characteristic regions sorted by PM2.5 trends. Region 1 (primarily Central Atlantic states, 25.9% population) exhibits the strongest population-weighted annual PM2.5 reduction (-3.6 ± 0.4%/yr) versus Region 2 (primarily rest of the eastern US, -3.0 ± 0.3%/yr, 39.7% population), Region 3 (primarily western Midwest, -1.9 ± 0.3%/yr, 25.6% population), and Region 4 (primarily the Mountain West, -0.4 ± 0.5%/yr, 8.9% population). Decomposition of these changes by chemical composition elucidates that sulfate exhibits the fastest reductions among all components in 2720 counties (76% of population), mostly over Regions 1-3, with the 1998-2019 mean sulfate mass fraction in PM2.5 decreasing from Region 1 (29.5%) to Region 4 (11.8%). Complete elimination of the remaining sulfate may be insufficient to meet the new standard for many regions in exceedance. Additional measures are needed to reduce other PM2.5 sources and components for further progress.

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM10. Routine air quality monitoring includes determining the PM10 concentration as one of the basic measurements. At some air monitoring stations, the PM10 measurement is supplemented by the simultaneous determination of the concentration of PM2.5 as a finer fraction of suspended particles. Since the PM2.5 fraction has a significant share in the PM10 fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM2.5 concentration using PM10 concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM2.5 concentrations. PM10 concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM10 predictor, the RMSE error values were in the range of 2.31–6.86 μg/m3. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m3. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM10 concentration; therefore, the only condition to be met is monitoring the measurement of PM10 concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM2.5 concentrations at another station.

SELECTIVE SODIUM PUMP INHIBITOR CALIX[4]ARENE C-1130 INCREASES CONCENTRATION OF Ca IONS IN SMOOTH MUSCLE CELLS

Aim. This work aimed to determine the biochemical regularities of calix[4]arene C-1130 effect on the activity of Nа+,К+-АТРase and Са2+ level in PM of smooth muscle cells (SMCs). Methods. Calix[4]arene C-1130 was synthesized and characterized in the Institute of Organic Chemistry of NAS of Ukraine. Biochemical studies were carried out in the Department of Muscle Biochemistry of Palladin Institute of Biochemistry, NAS of Ukraine. The experiments were performed on a suspension of myometrial cells and the PM fraction. Determination of the intracellular Са2+ concentration was done by confocal microscopy. The hydrodynamic diameter of myocytes was determined by dynamic light scattering. Results. We have studied the dependence of the activity of Nа+,К+-АТРase of PM on the concentration of Mg ions and ATP at different concentrations of calix[4]arene C-1130. The affinities of Nа+,К+-АТРase for ATP and Mg ions remained unaffected by the presence of calix[4]arene C-1130. However, a notable reduction in the initial maximum velocity of enzymatic ATP hydrolysis was observed. Overall, it indicates a non-competitive mechanism of inhibition of Nа+,К+-АТРase by C-1130. Confocal microscopy analysis showed that calix[4]arene C-1130 elevates Са2+ concentration within myometrial myocytes. Photon-correlation spectroscopy revealed that C-1130 reduces the effective hydrodynamic diameter of SMCs. Conclusions. We have shown that calix[4]arene C-1130 does not affect the activation coefficient for Mg ions and the apparent Michaelis constant Кm for ATP, but it reduces the maximum initial velocity Vmax of the ATP hydrolysis reaction. Thus, calix[4]arene C-1130 acts as a complete non-competitive inhibitor of Nа+,К+-АТРase of PM.

Biomonitoring potentially toxic elements in atmospheric particulate matter of greater Dhaka region using leaves ofhigher plants.

In this study, four different plant species, namely Artocarpus heterophyllus, Mangifera indica, Psidium guajava, and Swietenia mahagoni, were selected from seven different locations to assess the feasibility of using them as a cost-effective alternative for biomonitoring air quality. Atmospheric coarse particulate matter (PM10), soil samples, and leaf samples were collected from residential, industrial, and traffic-congested sites located in the greater Dhaka region. The heavy metal concentrations (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in the leaves of the different species, PM10, and soil samples were analyzed. The highest Pb (718ng/m3) and Zn (15,956ng/m3) concentrations were found in PM10 of Kodomtoli which is an industrial area. On the other hand, the highest Fe (6,152ng/m3) and Ni (61.1ng/m3) concentrations were recorded in the PM10 of Gabtoli, a heavy-traffic area. A significant positive correlation (r = 0.74; p < 0.01) between Pb content in plant leaves and PM fraction was found which indicated that atmospheric PM-bound Pb may contribute to the uptake of Pb by plant leaves. The analysis of the enrichmentfactor (EF)revealed that soils were contaminated with Cd, Ni, Pb, and Zn. The abaxial leaf surfaces of Psidium guajava growing at the polluted site exhibited up to a 40% decrease in stomatal pores compared to the control site. Saet's summary index (Zc) demonstrated that Mangifera indica had the highest bioaccumulation capacity. The metal accumulation index (MAI) was also evaluated to assess the overall metal accumulation capacity of the selectedplants. Of the four species, Swietenia mahagoni (3.05) exhibited the highest MAI value followed by Mangifera indica (2.97). Mangifera indica and Swietenia mahagoni were also found to accumulate high concentrations of Pb and Cr in their leaves and are deemed to be good candidates to biomonitor Pb and Cr contents in ambient air.

Oxidative potential of the inhalation bioaccessible fraction of PM10 and bioaccessible concentrations of polycyclic aromatic hydrocarbons and metal(oid)s in PM10

Atmospheric particulate matter (PM) has been related to numerous adverse health effects in humans. Nowadays, it is believed that one of the possible mechanisms of toxicity could be the oxidative stress, which involves the development of reactive oxygen species (ROS). Different assays have been proposed to characterize oxidative stress, such as dithiothreitol (DTT) and ascorbic acid (AA) acellular assays (OPDTT and OPAA), as a metric more relevant than PM mass measurement for PM toxicity. This study evaluates the OP of the bioaccessible fraction of 65 PM10 samples collected at an Atlantic Coastal European urban site using DTT and AA assays. A physiologically based extraction (PBET) using Gamble’s solution (GS) as a simulated lung fluid (SLF) was used for the assessment of the bioaccessible fraction of PM10. The use of the bioaccessible fraction, instead of the fraction assessed using conventional phosphate buffer and ultrasounds assisted extraction (UAE), was compared for OP assessment. Correlations between OPDTT and OPAA, as well as total and bioaccessible concentrations of polycyclic aromatic hydrocarbons (PAHs) and metal(oid)s, were investigated to explore the association between those compounds and OP. A correlation was found between both OP (OPDTT and OPAA) and total and bioaccessible concentrations of PAHs and several metal(oid)s such as As, Bi, Cd, Cu, Ni, and V. Additionally, OPDTT was found to be related to the level of K+.

Sources, accumulation levels and environmental hazard of heavy metals and metalloids in soils and PM10 fractions of Severobaykalsk

Relevance. The need to study the ecological state of the soil cover of industrial cities, which is the main depositing environment for technogenic emissions of heavy metals and metalloids, to identify the geochemical characteristics of the region and assess the impact on public health. Aim. To assess the sources and accumulation of heavy metals and metalloids in soils and their fraction PM10 in Severobaikalsk and to assess the risks associated with them for public health. Methods. Total content of Zn, As, Cd, Pb, Cr, Co, Ni, Cu, Sb, Mo, V, W, Sr, Bi in soil, coal and ash samples was determined by mass spectral and atomic emission methods with inductive-coupled plasma. Geochemical (KK, Kc, Zc), sanitary and hygienic (Ko) indicators were used. Health risks for adults and children were assessed. Principal component analysis was used. Results. In Severobaikalsk, the priority soil pollutants are Sb, Cu, Pb, Mo, Cr. The soils and their PM10 fraction in the transport zone, where Sb, Cu, Pb, Co, V, W, Ni are accumulated, are the most polluted. The greatest influence on the chemical composition of urban soils and PM10 particles is exerted by emissions from railway infrastructure and coal combustion at the Central Thermal Power Plant (22% for soils in general and 48% for the PM10 fraction). The average total level of contamination of urban soils and PM10 fraction corresponds to a low, non-hazardous level (Zc=6). The total non-carcinogenic risk of soil particles contaminated with heavy metals and metalloids. entering the body of children, exceeded the safe level 1 (from 1.60 in a residential one-story zone to 1.81 in a transport zone). For the adult population, HI values were below the acceptable threshold value. For adult health, there is no carcinogenic risk associated with ingestion or skin contact of As, Cr and Pb. For children, a dangerous risk (ILCRingest 5.56*10–4) of ingestion of carcinogenic heavy metals and metalloids was identified.