Particulate matter source apportionment in a village situated in industrial region of Central Europe

Influence of the Aryl Hydrocarbon Receptor Activating Environmental Pollutants on Autism Spectrum Disorder

Autism spectrum disorder (ASD) is an umbrella term that includes many different disorders that affect the development, communication, and behavior of an individual. Prevalence of ASD has risen exponentially in the past couple of decades. ASD has a complex etiology and traditionally recognized risk factors only account for a small percentage of incidence of the disorder. Recent studies have examined factors beyond the conventional risk factors (e.g., environmental pollution). There has been an increase in air pollution since the beginning of industrialization. Most environmental pollutants cause toxicities through activation of several cellular receptors, such as the aryl hydrocarbon receptor (AhR)/cytochrome P450 (CYPs) pathway. There is little research on the involvement of AhR in contributing to ASD. Although a few reviews have discussed and addressed the link between increased prevalence of ASD and exposure to environmental pollutants, the mechanism governing this effect, specifically the role of AhR in ASD development and the molecular mechanisms involved, have not been discussed or reviewed before. This article reviews the state of knowledge regarding the impact of the AhR/CYP pathway modulation upon exposure to environmental pollutants on ASD risk, incidence, and development. It also explores the molecular mechanisms involved, such as epigenesis and polymorphism. In addition, the review explores possible new AhR-mediated mechanisms of several drugs used for treatment of ASD, such as sulforaphane, resveratrol, haloperidol, and metformin.

Spatial-temporal variability of aerosol sources based on chemical composition and particle number size distributions in an urban settlement influenced by metallurgical industry

The Moravian-Silesian region of the Czech Republic with its capital city Ostrava is a European air pollution hot spot for airborne particulate matter (PM). Therefore, the spatiotemporal variability assessment of source contributions to aerosol particles is essential for the successful abatement strategies implementation. Positive Matrix Factorization (PMF) was applied to highly-time resolved PM_0.15-1.15 chemical composition (1 h resolution) and particle number size distribution (PNSD, 14 nm - 10 μm) data measured at the suburban (Ostrava-Plesná) and urban (Ostrava-Radvanice) residential receptor sites in parallel during an intensive winter campaign. Diel patterns, meteorological variables, inorganic and organic markers, and associations between the chemical composition factors and PNSD factors were used to identify the pollution sources and their origins (local, urban agglomeration and regional). The source apportionment analysis resolved six and four PM_0.15-1.15 sources in Plesná and Radvanice, respectively. In Plesná, local residential combustion sources (coal and biomass combustion) followed by regional combustion sources (residential heating, metallurgical industry) were the main contributors to PM_0.15-1.15. In Radvanice, local residential combustion and the metallurgical industry were the most important PM_0.15-1.15 sources. Aitken and accumulation mode particles emitted by local residential combustion sources along with common urban sources (residential heating, industry and traffic) were the main contributors to the particle number concentration (PNC) in Plesná. Additionally, accumulation mode particles from local residential combustion sources and regional pollution dominated the particle volume concentration (PVC). In Radvanice, local industrial sources were the major contributors to PNC and local coal combustion was the main contributor to PVC. The source apportionment results from the complementary datasets elucidated the relevance of highly time-resolved parallel measurements at both receptor sites given the specific meteorological conditions produced by the regional orography. These results are in agreement with our previous studies conducted at this site. Graphical abstract.

Ambient Air Quality in the Czech Republic: Past and Present

Based on an analysis of related core papers and reports, this review presents a historical perspective on ambient air pollution and ambient air quality development in the modern-day Czech Republic (CR) over the past seven decades, i.e., from the 1950s to the present. It offers insights into major air pollution problems, reveals the main hot spots and problematic regions and indicates the principal air pollutants in the CR. Air pollution is not presented as a stand-alone problem, but in the wider context of air pollution impacts both on human health and the environment in the CR. The review is arranged into three main parts: (1) the time period until the Velvet Revolution of 1989, (2) the transition period of the 1990s and (3) the modern period after 2000. Obviously, a major improvement in ambient air quality has been achieved since the 1970s and 1980s, when air pollution in the former Czechoslovakia culminated. Nevertheless, new challenges including fine aerosol, benzo[a]pyrene and ground-level ozone, of which the limit values are still vastly exceeded, have emerged. Furthermore, in spite of a significant reduction in overall emissions, the atmospheric deposition of nitrogen, in particular, remains high in some regions.

Assessment of air pollution origin based on year-long parallel measurement of PM2.5 and PM10 at two suburban sites in Prague, Czech Republic

From 2nd April 2008 to 28th March 2009, a total 248 daily samples of the PM_2.5 and PM₁₀ were collected every sixth day parallel at two suburban sites (Libuš and Suchdol) located at the two opposite sides (south and north, respectively) of Prague, Czech Republic. The PM_2.5 samples were analyzed for ions by ion chromatography (IC), organic and elemental carbon (OC and EC) by OC/EC analyzer and PM₁₀ samples also for 56 elements by inductively coupled plasma-mass spectrometry (ICP-MS). The average annual PM_2.5 and PM₁₀ was 24.4 ± 13.0 μg m^-3 and 26.7 ± 15.1 μg m^-3, respectively, in Prague-Libuš, and 25.1 ± 22.1 μg m^-3 and 27.1 ± 23.2 μg m^-3, respectively, in Prague-Suchdol. Since the species forming large part of the aerosol mass were strongly correlated (Spearman's rank correlation coefficient r_s > 0.80), the variability of PM_2.5 and PM₁₀ concentration was mainly driven by the local meteorology or regional and/or long range transport. PM₁₀ mass closure was calculated based on analytical results with the average percentage of recalculated mass of 77 ± 19% in Prague-Libuš and 86 ± 16% in Prague-Suchdol. The most abundant groups in PM₁₀ at both sites during the four seasons were OM (Prague-Libuš 34% and Prague-Suchdol 37%) and SIA (Prague-Libuš 30% and Prague-Suchdol 34%). The Positive Matrix Factorization (PMF) was applied to the chemical composition of PM₁₀ from both sites (124 samples) together to determine its sources. The nine factors were assigned as: mixed factor secondary sulphate and biomass burning, secondary sulphate, traffic, secondary nitrate, road dust, residential heating, aged sea salt, industry and mixed factor road salt along with aged sea salt. According to the polar plots and ventilation index (VI) east/west classification analysis the sources were separated based on origin to four categories local, urban agglomeration, regional and long range transport (LRT). The mixed source secondary sulphate and biomass burning, residential heating and industry were common sources of local origin at both sites. Prague-Suchdol was influenced by traffic related pollution from the urban agglomeration more than Prague-Libuš where the traffic and road dust/salt were of local origin. The regional pollution by secondary sulphates and nitrate was also relevant at both sites along with long range transport of sea salt from North Atlantic Ocean, Norwegian Sea and North Sea. The contribution of the local sources to PM₁₀ was significant mainly at Prague-Libuš site. However, the sources of regional origin were also important and influence of urban agglomeration pollution to PM₁₀ is not negligible as well.

New comprehensive approach for airborne asbestos characterisation and monitoring

High concentrations of airborne asbestos in the ambient air are still a serious problem of air quality in numerous localities around the world. Since 2002, elevated concentrations of asbestos minerals of unknown origin have been detected in the ambient air of Pilsen, Czech Republic. To determine the asbestos fibre sources in this urban air, a systematic study was conducted. First, 14 bulk dust samples were collected in Pilsen at nine localities, and 6 bulk samples of construction aggregates for gravel production were collected in a quarry in the Pilsen-Litice district. The quarry is the largest quarry in the Pilsen region and the closest quarry to the built-up urban area. X-ray diffraction of the asbestos minerals revealed that monoclinic amphibole (MA, namely actinolite based on subsequent SEM-EDX analysis) in the bulk samples accounted for < 1-33% of the mass and that the highest values were found in the bulk dust samples from the railway platform of the Pilsen main railway station. Simultaneously, 24-h samples of airborne particulate matter (PM) at three localities in Pilsen were collected. Actinolite was identified in 40% of the PM samples. The relationship between the meteorology and presence of actinolite in the 24 PM₁₀ samples was not proven, probably due to the long sampling integration time. Therefore, highly time-and-size-resolved PM sampling was performed. Second, sampling of size-segregated aerosols and measurements of the wind speed (WS), wind direction (WD), precipitation (P) and hourly PM₁₀, PM_2.5 and PM₁ were conducted in a suburban locality near the quarry in two monthly highly time-resolved periods (30, 60, 120 min). Three/eight PM size fractions were sampled by a Davis Rotating-drum Uniform-size-cut Monitor (3/8DRUM) and analysed for the presences of asbestos fibres by scanning electron microscopy with energy dispersive x-ray spectroscopy (SEM-EDX). Asbestos fibre detection in highly time-resolved PM samples and current WD and WS determination allows the apportionment directionality of asbestos fibre sources. The number of critical actinolite asbestos fibres (length ≥ 5 μm and width < 3 μm, 3:1) increased with the PM_1-10/PM₁₀ and PM_2.5-10/PM₁₀ ratios, WS > 2 m s^-1 and precipitation < 1 mm. Additionally, the number of critical actinolite asbestos fibres was not related to a specific WD. Therefore, we conclude that the sources of airborne critical actinolite asbestos fibres in Pilsen's urban area are omnipresent. Frequent use of construction aggregates and gravel from the metamorphic spilite quarries in the Pilsen region and in many localities around the urban area is a plausible explanation for the omnipresence of the critical actinolite asbestos fibres concentration in Pilsen's ambient air. Mitigation strategies to reduce the concentrations of critical actinolite asbestos fibres must be developed. Continuous monitoring and performing SEM-EDX analysis of highly time-and-size-resolved PM samples, correlated with fast changing WS and WD, seems to be a strong tool for efficiently controlling the mitigation strategies of critical actinolite asbestos fibres.

Comparison of PM2.5 chemical composition and sources at a rural background site in Central Europe between 1993/1994/1995 and 2009/2010: Effect of legislative regulations and economic transformation on the air quality

From December 1993 to January 1995 and from October 2009 to October 2010, a total of 320 and 365 daily samples of the PM_2.5 were collected at a rural background site (National Atmospheric Observatory Košetice) in Central Europe. The PM_2.5 samples were analyzed for 29 and 26 elements respectively by Particle-Induced X-ray Emission (PIXE) and water-soluble inorganic ions by Ion Chromatography (IC) in 2009/2010. The Positive Matrix Factorization (PMF) was applied to the chemical composition of PM_2.5 to determine its sources. The decreasing trends of almost all elements concentrations, especially the metals regulated by the EU Directive (2004/107/EC) are evident. The annual median ratios indicate a decrease in concentrations of the PM_2.5 elements. The slight increase of K concentrations and Spearman's rank correlation coefficient r_s 0.09 K/Se points to a rise in residential wood combustion. The S concentrations are nearly comparable (higher mean in 2009/2010, while the annual median ratio is under 1). The five major source types in the mid-1990s were ascribed to brown coal combustion, oil combustion, sea salt and dust - long-range transport, re-suspended dust and black coal combustion. The industrial combustion of brown and/or black coal (r_s 0.75 Se/As, r_s 0.57 Ga/Ge and r_s 0.20 As/Zn) and oil (r_s 0.72 V/Ni) of the regional origin dominated. In the 1990s, the potential source regions were the border area of Czech Republic, German and Poland (brown coal), the Moravia-Silesia region at the Czech-Polish border (black coal), and Slovakia, Austria, Hungary, and the Balkans (oil). In 2009/2010, the apportioned sources were sulfate, residential heating, nitrate, industry, re-suspended dust, and sea salt and dust - long-range transport. The secondary sulfate from coal combustion and residential biomass burning (r_s 0.96, K/K⁺) of local origin dominated. The declining trend of the elemental concentrations and change in the source pattern of the regional background PM2.5 in Central Europe between the mid-1990s and 2009/10 reflects the economic transformation and impact of stricter legislation in Central Europe.

Chemical characterization and receptor modeling of PM10 in the surroundings of the opencast lignite mines of Western Macedonia, Greece

The Western Macedonian Lignite Center (WMLC) in northwestern Greece is the major lignite center in the Balkans feeding four major power plants of total power exceeding 4 GW. Concentrations of PM₁₀ (i.e., particulate matters with diameters ≤10 μm) are the main concern in the region, and the high levels observed are often attributed to the activities related to power generation. In this study, the contribution of fugitive dust emissions from the opencast lignite mines to the ambient levels of PM₁₀ in the surroundings was estimated by performing chemical mass balance (CMB) receptor modeling. For this purpose, PM₁₀ samples were concurrently collected at four receptor sites located in the periphery of the mine area during the cold and the warm periods of the year (November-December 2011 and August-September 2012), and analyzed for a total of 26 macro- and trace elements and ionic species (sulfate, nitrate, chloride). The robotic chemical mass balance (RCMB) model was employed for source identification/apportionment of PM₁₀ at each receptor site using as inputs the ambient concentrations and the chemical profiles of various sources including the major mine operations, the fly ash escaping the electrostatic filters of the power plants, and other primary and secondary sources. Mean measured PM₁₀ concentrations at the different sites ranged from 38 to 72 μg m^-3. The estimated total contribution of mines ranged between 9 and 22% in the cold period increasing to 36-42% in the dry warm period. Other significant sources were vehicular traffic, biomass burning, and secondary sulfate and nitrate aerosol. These results imply that more efficient measures to prevent and suppress fugitive dust emissions from the mines are needed.

A review of AirQ Models and their applications for forecasting the air pollution health outcomes

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.

Source identification and apportionment of PM2.5 and PM2.5-10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM2.5 and PM2.5-10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m(3) for PM2.5 and 331.36, 190.01, and 184.60 μg/m(3) for PM2.5-10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM2.5-10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM2.5-10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.

Source apportionment of size resolved particulate matter at a European air pollution hot spot

Positive Matrix Factorization-PMF was applied to hourly resolved elemental composition of fine (PM0.15-1.15) and coarse (PM1.15-10) aerosol particles to apportion their sources in the airshed of residential district, Ostrava-Radvanice and Bartovice in winter 2012. Multiple-site measurement by PM2.5 monitors complements the source apportionment. As there were no statistical significant differences amongst the monitors, the source apportionment derived for the central site data is expected to apply to whole residential district. The apportioned sources of the fine aerosol particles were coal combustion (58.6%), sinter production-hot phase (22.9%), traffic (15%), raw iron production (3.5%), and desulfurization slag processing (<0.5%) whilst road dust (47.3%), sinter production-cold phase (27.7%), coal combustion (16.8%), and raw iron production (8.2%) were resolved being sources of the coarse aerosol particles. The shape and elemental composition of size-segregated aerosol airborne-sampled by an airship aloft presumed air pollution sources helped to interpret the PMF solution.