In this study, a multitude of statistical tools were used to examine PM10 concentration trends and their seasonal behavior from 2015 to 2021 in Tehran. The results of the integrated analysis have led to a better understanding of current PM10 trends which may be useful for future management policies. The Kruskal – Wallis test indicated the significant impact of atmospheric phenomena on the seasonal fluctuations of PM10. The seasonal decomposition of PM10 time series was conducted for better analysis of trends and seasonal oscillations. The seasonal Mann-Kendall test illustrated the significant possibility of a monotonic seasonal trend of PM10 (p = 0.026) while showing its negative slope simultaneously (Sen = -1.496). The forecasting procedure of PM10 until 2024 comprised 15 time series models which were validated by means of 8 statistical criteria. The model validation results indicated that ARIMA (0,1,2) was the most satisfactory case for predicting the future trend of PM10. This model estimated the concentration of PM10 to reach approximately 79.04 (µg/m3) by the end of 2023 with a 95% confidence interval of 51.38 – 107.42 (µg/m3). Overall, it was concluded that the use of the aforementioned analytical tools may help decision-makers gain a better insight into future forecasts of ambient airborne particulate matter.

Exposure to household dust is a common occurrence in all countries and causes various diseases. This study provided information on the number, shape, size distribution, and elemental composition of household dust particles collected in urban homes in Agadir city in Morocco. Moreover, a potential human health risk of exposure has been identified based on current research. Samples were analyzed using computer-controlled scanning electron microscopy and ImageJ image processing program. A total of 3296 particles were analyzed for their size, and 76 particles were classified according to their size and elemental composition. Household dust particles were classified in six types: micro-aggregates (31.6%), biogenic (5.3%), spherical (17.1%), subrounded (7.9%), subangular (11.8%), and angular (26.3%). These particles were determined to have originated from a distant source (Trask classification index between 1 and 2.5). They were large (Skewness asymmetry coefficient > 1), and ranged from 0.2 to 363 µm with an average value of 22.8 ± 0.6 µm in diameter. Dust particles with diameters of 5-10 µm and 10-20 µm were the most abundant, while dust diameters of 10-20 µm, 20-30 µm, and > 100 µm were the highest in volume. The domestic dust deposition rate was 19.8 ± 7.4 g/m2 per year. Household dust is one of the major sources of PM10 in the residential environment (44.6% of the total number of particles), and the studied properties of house dust are highly related to human health. Household dust is a critical element to be considered in the occurrence of respiratory and cardiovascular infections.

Humans spend close to 90% of their time within the indoor environment. Deteriorating indoor air quality, especially high PM10, PM2.5 and PM1 is slowly becoming a major concern. A study was carried out, for two years, to characterize the spatiotemporal variation of PM in the indoor-outdoor environment across different residential setups (R1, R2, R3, and MC) in the Delhi region. The study established correlation between monthly variations of Indoor/Outdoor (I/O) ratios and meteorological factors. The results showed Spatio-temporal variation in the average mass concentrations of PM10 recorded peak values during the winter season (avg. 514± 72.15 µg/m3) and minimum concentration was observed during monsoon (avg. 91.41± 22.64 µg/m3) months. Among all the sites, the mixed cluster (MC), a residential cum commercial zone reported the highest particulate matter concentration (avg. 308.10 ±37.23 µg/m3) and while R2 reported the least concentration (avg. 244.9± 27.65 µg/m3) within the indoor environment. The I/O ratios of particulate matter were observed to be highest in January (I/O ratio1.6) and lowest in June month (I/O ratio 0.8). PM10, PM2.5, and PM1 dynamics were found to be critically influenced by meteorological factors, regular household activities, and diverse building designs. The short- or long-term exposure of particulate pollutants (beyond the permissible limits) can increase the probability of acute health effects, so there is an utmost requirement to collect better and systematic information about actual exposure levels experienced in different urban residential environments.

Real-time monitoring of Black Carbon and Particulate Matter was done by Aerosol Black Carbon Detector (ABCD) and GRIMM portable aerosol Spectrometer in Agra at five different locations (R1, R2 traffic and R3, R4, R5 residential road sites). Major portion of PM mass was contributed by PM10 followed by PM2.5 and PM1.0. Major portion of PM in number mode is contributed by PM10=PM0.25 followed by PM5.0 =PM0.5, PM1.0, and PM2.5. All the PMs mass and number concentration was highly associated with the R1 site due to the vehicular and other anthropogenic activities and was least at R5 except for PM10. The highest concentration of BC was found at R2 site followed by R1 while During the sampling events NO2 and O3 was found highest at R2 site followed by R1. The source of BC, PMs, NO2, O3 at R1& R2 may be vehicular activities, population activities, crowded area, and industrial activities. BC contribution in PM1.0 was highest followed by PM2.5. The children category in the traffic site has high PM deposition mass visualization as compared to the residential road site so they are highly affected by lung diseases instead of the residential road site children category. From health risk assessment results, it was found that no population was at non-carcinogenic risk from chronic exposure to PM10 while children may be at possible risk from acute exposure. However, cancerous risk assessment showed that both children and adult were at risk from exposure of PM2.5 and may develop cancerous diseases.

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.

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.

Today, the cement industry is one of the major air polluting industries in the world. Hence, in this study, owing to the importance and role of contaminants from the plant, an appraisal of the emission contributions in addition to other factors have been discussed. There are several reasons behind the importance of modeling air pollutants. First, the assessment of standards for air pollution, and the fact that the measurement points are limited. Furthermore, in all industrial areas, measurement and installation of assessment and monitoring stations are not feasible. The AERMOD model is a dispersion steady state model which is utilized to determine the concentration of various pollutants in different areas from urban and rural, flat and rough, shallow diffusion in height, from standpoint and different shallow sources. In this model, it is assumed that the dispersion of concentration in Stable Boundary Layer (SBL) in two horizontal and vertical directions are similar to that of horizontal within Gaussian convectional boundary layer (CBL). With regard to assessment of the parameters and pollutants of stack outlet, the amount of particulate matter was measured as the most important pollutant in the region. Then, via dispersion and diffusion modeling of pollution (AERMOD) along with environmental measurements, the nature of dispersion of this pollutant in the analysis of the surrounding areas was verified. According to the presented results, the highest level of concentration for particulate matters in all areas affected by cement factory amounts to 43.68 (μg/m3) which occurred at a distance of 1500 m in the east direction and 2100 m in the north direction.

A simple and rapid method for the highly sensitive determination of polycyclic aromatic hydrocarbons (PAHs) from airborne fine particulate matter (PM2.5) in an urban environment of Delhi was developed. The target compounds were 10 of the 16 United States Environmental Protection Agency (US-EPA) priority PAHs: fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo[a]pyrene, dibenzo(ah)anthracene, benzo(ghi)perylene, indeno(1,2,3-cd)pyrene. For collecting the samples, the following two locations in Delhi (India) were chosen: ITO and Okhla Industrial Area.  Two sets of samples at these locations of were collected for the purpose of investigation. The fine particulate matter samples were collected on glass fiber filter papers for 24h, from which the PAHs were extracted using dichloromethane (DCM) and hexane using ultrasonication method. Comparison of the characteristic emission of spectra of PAHs with standard spectra indicated the degree of condensation of aromatic compounds present in the investigated mixtures. However, this identification could be more effective with the use of the respective values of Δλ parameter for each particular component of the mixture. It has been found that the concentration of the PAHs is maximum during the winter season and minimum during the summer and monsoon seasons at both the locations.

The oxidative potential (OP) of particulate matter (PM) is considered a better health metric of PM exposure than mass concentration since its value is highly dependent on PM composition. OP assays have shown different sensitivities to PM components and particle sizes. In this work, an urban-industrial mixed site with high levels of airborne Mn and Fe, due to the proximity of a ferromanganese alloy plant, was chosen to study the association between PM elements and three OP assays (ascorbic acid (AA), dithiothreitol (DTT), and 2,7-dichlorofluorescein (DCFH)) in size segregated PM samples (PM10-2.5 and PM2.5). Urban samples from a nearby area were also collected. The concentration of 39 elements in both the soluble (in a phosphate buffer aqueous solution) and insoluble fractions of PM10-2.5 and PM2.5 was determined by ICP-MS. Soluble elements were then associated with OP and local sources using Principal Component Analysis (PCA). Four sources of soluble elements have been identified in the urban-industrial site. The main factor was attributed to road traffic; although Cu and Fe, two active transition metals in OP assays, were associated to this factor, their low solubility, mainly in the coarse fraction, has led to low factor loadings of OP; the second factor was attributed to a ferromanganese plant, since it presented the highest factor loadings for soluble Mn in both PM10-2.5 and PM2.5; it was the main factor associated with OP-DTT and OP-DCFH values, mainly in the coarse fraction. Crustal material and sea salt aerosol were also identified as sources. | This work was supported by the Spanish Ministry of Science and Innovation (Project PID2020-114787RB-I00, funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”).

International audience | A severe air quality degradation event occurred in the Santiago Metropolitan Area (SMA), Chile, in June 2014. Meteorological and air quality measurements from 11 stations in the area as well as numerical simulations using the Weather and Research Forecasting (WRF) model were used to explain the main reasons for the occurrence of elevated particulate matter (PM) concentrations. The conditions were characterized with formation of a coastal low in central Chile between the southeastern anticyclone and a high-pressure system over Argentina. At a local scale, these conditions generated a depression at the base of the inversion layer, an increase in the vertical thermal stability, lower humidity and low-wind conditions, which were conducive to a decrease in pollutant dispersion and insufficient ventilation of the polluted air. Measurements and simulations using the WRF model revealed a vertical structure of the boundary layer during these stagnant conditions and provided a basis for a trajectory analysis. The back-trajectory calculation showed that the transport of air parcels was contained in the valley during the highest concentrations. The analysis also enabled the definition of the threshold values of a simple indicator of air pollution (ventilation coefficient, VC), which confirmed the evolution of the episode and divided the observed daily concentrations into two groups, with one including values above the limits prescribed by the national air quality standards (NAQS) and the other including values below these limits. For the SMA, the daily PM concentrations above the NASQ limits were associated with an overall mean threshold value of VC below 500 m2 s−1 (for PM2.5) and 300 m2 s−1 (for PM10). To apply the VC analysis to other pollutants and different geographic locations, different threshold values should be evaluated.