Air quality in greater Algiers, in Algeria was assessed analyzing aerosol particulate matter (PM10 and PM2.5) at a site influenced by heavy road traffic. Particulate matters were collected using a Gent sampler to characterize the atmospheric aerosol of Algiers. An Energy dispersive X ray spectrometer (EDXRF) was used to determine the heavy metal concentrations in the PM2.5 and PM10 size fractions. Principal Component analysis and Enrichment factor were used to identify the major sources of air pollutants for PM10 fraction in the studied area. Backward trajectories were calculated in order to identify potential distant sources that contribute to particulate pollution in our site. Significant concentrations of PM 2.5 and PM10 as well as associated heavy metals have been documented. The mean concentrations of heavy metals contained in PM10 and PM2.5 were, in descending order, Fe>Zn>Ni>Pb>Mn>Co>Cr; Pb>Mn>Co>Fe>Zn>Ni>Cr respectively. The contribution of road traffic to the levels of fine (PM2.5), and coarse (PM10) particles were studied.

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.

A comprehensive modelling approach has been developed to predict population exposure to the ambient air PM₂.₅ concentrations in different microenvironments in London. The modelling approach integrates air pollution dispersion and exposure assessment, including treatment of the locations and time activity of the population in three microenvironments, namely, residential, work and transport, based on national demographic information. The approach also includes differences between urban centre and suburban areas of London by taking account of the population movements and the infiltration of PM₂.₅ from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM₂.₅ at street scale for the year 2008, including both regional and urban contributions. Model analysis of the exposure in the three microenvironments shows that most of the total exposure, 85%, occurred at home and work microenvironments and 15% in the transport microenvironment. However, the annual population weighted mean (PWM) concentrations of PM₂.₅ for London in transport microenvironments were almost twice as high (corresponding to 13–20 μg/m³) as those for home and work environments (7–12 μg/m³). Analysis has shown that the PWM PM₂.₅ concentrations in central London were almost 20% higher than in the surrounding suburban areas. Moreover, the population exposure in the central London per unit area was almost three times higher than that in suburban regions. The exposure resulting from all activities, including outdoor to indoor infiltration, was about 20% higher, when compared with the corresponding value obtained assuming inside home exposure for all times. The exposure assessment methodology used in this study predicted approximately over one quarter (−28%) lower population exposure, compared with using simply outdoor concentrations at residential locations. An important implication of this study is that for estimating population exposure, one needs to consider the population movements, and the infiltration of pollution from outdoors to indoors.

This study aimed to provide a comprehensive characterisation of the indoor air quality during the sleeping period of 10 couples at Lisbon dwellings, using a multi-pollutant approach, and to understand how the compliance with legislation and guidelines was to assure a good indoor air quality. The assessment of indoor air quality was conducted in the cold season using real time monitors during the sleeping period for comfort parameters (temperature and relative humidity) and air pollutants (carbon dioxide – CO₂, carbon monoxide – CO, formaldehyde – CH₂O, total volatile organic compounds – VOCs, and particulate matter – PM₂.₅ and PM₁₀), together with active sampling of bioaerosols (fungi and bacteria) before and after the sleeping period. Lower compliance (less than 50% of the cases) with the Portuguese legislation was found for temperature, CO₂ (3440 ± 1610 mg m⁻³), VOCs (1.79 ± 0.99 mg m⁻³) and both bioaerosol types. In 70% of the cases, PM₂.₅ (15.3 ± 9.1 μg m⁻³) exceeded the WHO guideline of 10 μg m⁻³. All bedrooms presented air change rates above the recommended minimum value of 0.7 h⁻¹, highlighting that a good indoor air quality during sleep is not guaranteed.

Evidence for effects of PM₂.₅ on systemic oxidative stress in pregnant women is limited, especially in early pregnancy. To estimate the associations between ambient PM₂.₅ exposures and biomarkers of lipid peroxidation and total antioxidant capacity (T-AOC) in women with normal early pregnancy (NEP) and women with clinically recognized early pregnancy loss (CREPL), 206 early pregnant women who had measurements of serum malondialdehyde (MDA) and T-AOC were recruited from a larger case-control study in Tianjin, China from December 2017 to July 2018. Ambient PM₂.₅ concentrations of eight single-day lags exposure time windows before blood collection at the women’s residential addresses were estimated using temporally-adjusted land use regression models. Effects of PM₂.₅ exposures on percentage change in the biomarkers were estimated using multivariable linear regression models adjusted for month, temperature, relative humidity, gestational age and other covariates. Unconstrained distributed lag models were used to estimate net cumulative effects. Increased serum MDA and T-AOC were significantly associated with increases in PM₂.₅ at several lag exposure time windows in both groups. The net effects of each interquartile range increase in PM₂.₅ over the preceding 8 days on MDA were significantly higher (p < 0.001) in CREPL [52% (95% CI: 41%, 62%)] than NEP [22% (95% CI: 9%, 36%)] women. Net effects of each interquartile range increase in PM₂.₅ over the preceding 5 days on T-AOC were significantly lower (p = 0.010) in CREPL [14% (95% CI: 9%, 19%)] than NEP [24% (95% CI: 18%, 29%)] women. Exposure to ambient PM₂.₅ may induce systemic lipid peroxidation and antioxidant response in early pregnant women. More severe lipid peroxidation and insufficient antioxidant capacity associated with PM₂.₅ was found in CREPL women than NEP women. Future studies should focus on mechanisms of individual susceptibility and interventions to reduce PM₂.₅-related oxidative stress in the first trimester.

Ambient ultrafine particles (UFPs: particles of diameter less than 100 nm) cause significant adverse health effects. As people spend most time indoors, the outdoor-to-indoor transport of UFPs plays a critical role in the accuracy of personal exposure assessments. Herein, a strategy was proposed to measure and analyze the infiltration factor (Fᵢₙf) of UFPs, an important parameter quantifying the fraction of ambient air pollutants that travel inside and remain suspended indoors. Ninety-three measurements were conducted in 11 residential rooms in all seasons in Beijing, China, to investigate Fᵢₙf of UFPs and its associated influencing factors. A multilevel regression model incorporating eight possible factors that influence infiltration was developed to predict Fᵢₙf and FᵢₙfSOA (defined as the ratio of indoor to outdoor UFP concentrations without indoor sources, but with indoor secondary organic aerosol (SOA) formation). It was found that the air change rate was the most important factor and coagulation was considerable, while the influence of SOA formation was much smaller than that of other factors. Our regression model accurately predicted daily-average Fᵢₙf. The annually-averaged Fᵢₙf of UFPs was 0.66 ± 0.10, which is higher than that of PM₂.₅ and PM₁₀, demonstrating the importance of controlling indoor UFPs of outdoor origin.

Inflammation and the coagulation cascade are considered to be the potential mechanisms of ambient particulate matter (PM) exposure-induced adverse cardiovascular events. Tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), interleukin-8 (IL-8), and fibrinogen are arguably the four most commonly assayed markers to reflect the relationships of PM with inflammation and blood coagulation. This review summarized and quantitatively analyzed the existing studies reporting short- and long-term associations of PM₂.₅(PM with an aerodynamic diameter ≤2.5 μm)/PM₁₀ (PM with an aerodynamic diameter≤10 μm) with important inflammation and blood coagulation markers (TNF-α, IL-6, IL-8, fibrinogen). We reviewed relevant studies published up to July 2020, using three English databases (PubMed, Web of Science, Embase) and two Chinese databases (Wang-Fang, China National Knowledge Infrastructure). The OHAT tool, with some modification, was applied to evaluate risk of bias. Meta-analyses were conducted with random-effects models for calculating the pooled estimate of markers. To assess the potential effect modifiers and the source of heterogeneity, we conducted subgroup analyses and meta-regression analyses where appropriate. The assessment and correction of publication bias were based on Begg’s and Egger’s test and “trim-and-fill” analysis. We identified 44 eligible studies. For short-term PM exposure, the percent change of a 10 μg/m³ PM₂.₅ increase on TNF-α and fibrinogen was 3.51% (95% confidence interval (CI): 1.21%, 5.81%) and 0.54% (95% confidence interval (CI): 0.21%, 0.86%) respectively. We also found a significant short-term association between PM₁₀ and fibrinogen (percent change = 0.17%, 95% CI: 0.04%, 0.29%). Overall analysis showed that long-term associations of fibrinogen with PM₂.₅ and PM₁₀ were not significant. Subgroup analysis showed that long-term associations of fibrinogen with PM₂.₅ and PM₁₀ were significant only found in studies conducted in Asia. Our findings support significant short-term associations of PM with TNF-α and fibrinogen. Future epidemiological studies should address the role long-term PM exposure plays in inflammation and blood coagulation markers level change.

While commuting, individuals are exposed to high concentrations of urban air pollutants that can lead to adverse health effects. This study aims to assess commuters’ exposure to particulate matter (PM) when travelling by car, bicycle, metro and bus in Lisbon. Mass concentrations of PM₂.₅ and PM₁₀ were higher in the metro. On the other hand, the highest BC and PN₀.₀₁₋₁ average concentrations were found in car and bus mode, respectively. In cars, the outdoor concentrations and the type of ventilation appeared to affect the indoor concentrations. In fact, the use of ventilation led to a decrease of PM₂.₅ and PM₁₀ concentrations and to an increase of BC concentrations. The highest inhaled doses were mostly observed in bicycle journeys, due to the longest travel periods combined with enhanced physical activity and, consequently, highest inhalation rates.

MicroRNAs (miRNAs) are a class of small, non-coding RNAs with a post-transcriptional regulatory function on gene expression and cell processes, including proliferation, apoptosis and differentiation. In recent decades, miRNAs have attracted increasing interest to explore the role of epigenetics in response to air pollution. Air pollution, which always contains kinds of particulate matters, are able to reach respiratory tract and blood circulation and then causing epigenetics changes. In addition, extensive studies have illustrated that miRNAs serve as a bridge between particulate matter exposure and health-related effects, like inflammatory cytokines, blood pressure, vascular condition and lung function. The purpose of this review is to summarize the present knowledge about the expression of miRNAs in response to particulate matter exposure. Epidemiological and experimental studies were reviewed in two parts according to the size and source of particles. In this review, we also discussed various functions of the altered miRNAs and predicted potential biological mechanism participated in particulate matter-induced health effects. More rigorous studies are worth conducting to understand contribution of particulate matter on miRNAs alteration and the etiology between environmental exposure and disease development.

Airborne particulate matter (PM) has become a serious health issue causing pulmonary diseases such as asthma. Due to the side effects and non-specificity of conventional drugs, there is a need to develop natural-product-based alternative treatments. Sargassum horneri is a brown alga shown to have anti-oxidant, anti-inflammatory, and anti-allergic effects. Thus, we sought to determine whether ethanol extract of Sargassum horneri (SHE) mitigates the effect of PM exposure on asthma development. To establish a mouse model of asthma, BALB/c mice were sensitized with ovalbumin (OVA, 10 μg) and challenged with PM (5 mg/m³) for 7 days consecutively. SHE (200, 400 mg/kg), Prednisone (5 mg/kg), or PBS was daily administrated orally before PM exposure. SHE mitigated PM exacerbated dendritic cell activation. More importantly, SHE restrained Th2 polarization by attenuating transcription factors GATA3 and STAT5, which further mitigated the expression of Th2 cytokines interleukin (IL)-4, IL-5, and IL-13 in the lung homogenates of PM-exacerbated asthmatic mice. SHE further attenuated PM-exacerbated eosinophil infiltration in the lung, trachea, and BALF. In addition, SHE markedly mitigated the activation of mast cells and the IgE level in serum. Concomitantly, SHE further restrained the Th17 cell response in PM-exposed allergic mice through attenuating expression of transcription factors RORγT, STAT3 and expression of relevant effector cytokines IL-17a. This resulted in mitigated neutrophil infiltration in the lung. Taken together, SHE significantly suppressed PM-exacerbated hypersecretion of mucus in asthmatic mice. These results suggest that SHE has therapeutic potential for treating PM-exacerbated allergic asthma through concomitantly inhibiting Th2/Th17 responses.