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.

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.

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.