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.

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.

International audience | Biomagnetic monitoring increasingly is applied to assess particulate matter (PM) concentrations, mainly using plant leaves sampled in small geographical area and from a limited number of species. Here, the potential of magnetic analysis of urban tree trunk bark to discriminate between PM exposure levels was evaluated and bark magnetic variation was investigated at different spatial scales. Trunk bark was sampled from 684 urban trees of 39 genera in 173 urban green areas across six European cities. Samples were analysed magnetically for the Saturation Isothermal Remanent Magnetisation (SIRM). The bark SIRM reflected well the PM exposure level at city and local scale, as the bark SIRM (i) differed between the cities in accordance with the mean atmospheric PM concentrations and (ii) increased with the cover of roads and industrial area around the trees. Furthermore, with increasing tree circumferences, the SIRM values increased, as a reflection of a tree age effect related to PM accumulation over time. Moreover, bark SIRM was higher at the side of the trunk facing the prevailing wind direction. Significant relationships between SIRM of different genera validate the possibility to 2 combine bark SIRM from different genera to improve sampling resolution and coverage in biomagnetic studies. Thus, the SIRM signal of trunk bark from urban trees is a reliable proxy for atmospheric coarse to fine PM exposure in areas dominated by one PM source, as long as variation caused by genus, circumference and trunk side is taken into account.

Air is an important medium for all living beings and is essential for the well-being of all. Monitoring of air is important to know the quality of air. The air quality monitoring was carried out in Kolhapur City under the National Air Monitoring Program. The present study involves the assessment of PM10 as described in the National Ambient Air Quality Standards (NAAQS). The source apportionment study related to particulate matter was carried out in Kolhapur City. The study also determined the average PM10 concentration in the city as it will be useful for preparing an action plan to reduce PM10 concentration. PM10 concentration was calculated as per the standard method adopted by CPCB. Sampling was carried out for 8 hours in three shifts twice a week at each sampling site for three consecutive years. Mahadwar Road (MR) and Dabholkar Corner (DC) were selected per the surrounding residential area, population density, and traffic conjunction. The third site Shivaji University (SUK), was selected as a control site. The results indicated that the PM10 level has risen above the prescribed standards of NAAQS. The reason for the rise in PM10 may be due to fossil fuel burning, construction activity, vehicles, and unpaved roads. The Analysis of Variance (ANOVA) technique is used to check the equality of the mean concentration of PM10 at these three locations and found a significant difference between mean concentrations of PM10, suggesting increased particulate matter.

Globally, vehicular pollution is one of the greatest concerns in urban areas. Several studies on air pollution have been conducted using deterministic, statistical, and soft computing methods. However, there has been little research on how soft-computing methods like Artificial Neural Networks (ANN) can help us comprehend vehicular pollution’s non-linear and highly complex dispersion. This study uses an ANN-based vehicular pollution model to investigate the effect of vehicular traffic on PM2.5 concentrations in built-up and open terrain-surrounding environments. Five distinct pollution models were developed for two locations in Delhi, considering PM2.5 pollutants, meteorological variables, traffic flow, and traffic composition into account. The results concluded that under open terrain conditions, the significance of the traffic variable in its association with PM2.5 is almost half the significance observed under built-up conditions. Also, in terms of PM2.5 reductions, the maximum reduction observed at Location-1 (built-up environment), and Location-2 (open terrain environment) is 1.85 and 2.44 times the percent reduction in traffic during peak hours, respectively. The study’s findings have significant ramifications for the current practices of ignoring the contribution of traffic and the built environment to pollution and adopting measures like an odd-even rule and high fuel and parking prices to combat pollution.

Environmental pollution is a serious concern nowadays with its disastrous impact on living organisms. In several types of pollution, Air pollution takes on a crucial role by directly affecting the respiratory system and causing fatal diseases in humans. Air pollution is a mixture of gaseous and particulate matter interweaved by different sources and emanating into the atmosphere. In particular, particle pollutants are critical in growing air pollution in India’s main cities. Forecasting the particulate matter could mitigate the complications caused by it. The employment of a model to predict future values based on previously observed values is known as time series forecasting. In this paper, the PM2.5 pollutant emission data recorded at the Kodungaiyur region of Chennai city were forecasted using three-time series models. The standard ARIMA model is compared with the deep learning-based LSTM model and Facebook’s developed Prophet algorithm. This comparison helps to identify an appropriate forecasting model for PM2.5 pollutant emission. The Root Mean Squared Error (RMSE) acquired from experimental findings is used to compare model performances.