Atmospheric Particulate Matter (PM) is considered one of the most critical air pollutants in terms of its detrimental health impacts, environmental degradations and visibility. Particles size, their chemical composition and atmospheric levels are important factors for determining their adverse health impacts. In this paper various aspects of PM 2.5 are analysed including PM 2.5 /PM 10 ratios and association with meteorological parameters using data collected from January 2014 to September 2015 in Makkah Saudi Arabia. During the study period, mean PM 2.5 /PM 10 ratio was found to be 0.64, whereas median and maximum ratios were 0.69 and 0.99, respectively. Diurnal, weekly and annual cycles of PM 10 , PM 2.5 and their ratios were analysed, which demonstrated considerable variations during various hours of the day, days of the week and months of the year. PM 2.5 /PM 10 ratios were lower in summer (June and July) and higher in winter (November and December), likewise the ratios were lower during afternoon and higher in the morning and evening. As expected, there was a positive correlation between PM 10 and PM 2.5 (r = 0.51) and both PM 10 and PM 2.5 showed negative association with relative humidity and positive with wind speed and temperature. Furthermore, PM 2.5 /PM 10 ratios were lower (< 0.45) at lower relative humidity (< 16%) and higher (> 0.70) at higher relative humidity (35-90%), indicating a shift towards high PM 2.5 concentrations at higher relative humidity. Polar plots showed lowest ratios at high wind speed (> 3 m s -1 ) blowing from west and southwest direction in summer, and highest ratios at low wind speed (< 2 m s -1 ) in winter. Polar plots were successfully applied to show the interaction between various meteorological parameters and PM 2.5 /PM 10 ratios. Further work on source apportionment and receptor modelling of PM is required to help develop air quality index and prepare an effective air quality plan for Makkah.

Particulate matter originates from a variety of sources in Makkah, Saudi Arabia. Since Makkah is situated in an arid region and is a very busy city due to its religious importance in the Muslim world, PM 10 concentrations here exceed the international and national air quality standards set for the protection of human health. The main aim of this paper is to model PM 10 concentrations with the aid of meteorological variables (wind speed, wind direction, temperature, and relative humidity) and traffic related air pollutant concentrations (carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ) and lag_PM 10 concentrations), which are measured at the same location near Al-Haram (the Holy Mosque) in Makkah. A Generalized Additive Model was developed for predicting hourly PM 10 concentrations. Predicted and observed PM 10 concentrations are compared, and several metrics, including the coefficients of determination (R 2 = 0.52), Root Mean Square Error (RMSE = 84), Fractional Bias (FB = -0.22) and Factor of 2 (FAC2 = 0.88), are calculated to assess the performance of the model. The results of these, along with a graphical comparison of the predicted and observed concentrations, show that model is able to perform well. While effects of all the covariates were significant (p-value < 0.01), the meteorological variables, such as temperature and wind speed, seem to be the major controlling factors with regard to PM 10 concentrations. Traffic related air pollutants showed a weak association with PM 10 concentrations, suggesting road traffic is not the major source of these. No modeling study has been published with regards to air pollution in Makkah and thus this is the first work of this kind. Further work is required to characterize road traffic flow, speed and composition and quantify the contribution of each source, which is part of the ongoing project for managing the air quality in Makkah.

Greater Cairo (Egypt) is a megalopolis where the studies of the air pollution events are of extremely high relevance, for the geographical-climatological aspects, the anthropogenic emissions and the health impact. While preliminary studies on the particulate matter (PM) chemical composition in Greater Cairo have been performed, no data are yet available on the PM’s toxicity. In this work, the in vitro toxicity of the fine PM (PM2.5) sampled in an urban area of Greater Cairo during 2017–2018 was studied. The PM2.5 samples collected during spring, summer, autumn and winter were preliminary characterized to determine the concentrations of ionic species, elements and organic PM (Polycyclic Aromatic Hydrocarbons, PAHs). After particle extraction from filters, the cytotoxic and pro-inflammatory effects were evaluated in human lung A549 cells. The results showed that particles collected during the colder seasons mainly induced the xenobiotic metabolizing system and the consequent antioxidant and pro-inflammatory cytokine release responses. Biological events positively correlated to PAHs and metals representative of a combustion-derived pollution. PM2.5 from the warmer seasons displayed a direct effect on cell cycle progression, suggesting possible genotoxic effects. In conclusion, a correlation between the biological effects and PM2.5 physico-chemical properties in the area of study might be useful for planning future strategies aiming to improve air quality and lower health hazards.

Air Q 2.2.3 was used to predicted hospital admissions respiratory disease cases due to SO2 and NO2 exposure in two sectors of Egypt during December 2015 to November 2016. Levels were 19, 22 μg/m3 at Ain Sokhna sector and 92, 78 μg/m3 at Shoubra El-Khaima sector for SO2 and NO2, respectively. These levels were less than the Egyptian Permissible limits (125 µg/m³ in urban and 150 µg/m³ in industrial for SO2, 150 µg/m³ in urban and industrial for NO2). Results showed that relative risks were 1.0330 (1.0246 - 1.0414) and 1.0229 (1.0171 - 1.0287) at Ain Sokhna sector while they were 1.0261 (1.0195 -1.0327) and 1.0226 (1.0169 - 1.0283) at Shoubra El-Khaima sector for SO2 and NO2, respectively.The highest cases of HARD were found in Shoubra El-Khaima sector; 311 cases at 120 - 129 μg/m3 of SO2 and 234 cases at 120 - 129 μg/m3 of NO2. While, in Ain Sokhna, HARD were 18 cases at 50 - 59 μg/m3 of SO2 and 15 cases at 60 - 69 μg/m3 of NO2. The excess cases found in Shoubra El-Khaima sector as compared to those in Ain Sokhna sector, may be attributed to the higher density of population and industries in Shoubra El-Khaima sector.Keywords: AirQ2.2.3 model; Hospital admissions respiratory disease (HARD); Nitrogen dioxide (NO2); Sulfur dioxide (SO2); Coastal Sectors.

This study aims to estimate the association between some heavy metals in suspended particulate matter (SPM) and kidney damage among workers at different departments in a secondary aluminum production plant. It also investigates the association between Xeroderma Pigmentosum complementation group D (XPD) gene polymorphisms and worker’s susceptibility to kidney dysfunction. It was conducted on 30 workers from the administrative departments and 147 workers from different departments in the production line. Estimation of some heavy metals (Al, Co, Ni, Cu, Pb, and Cd) in suspended particulate matter (SPM) is done. Also, urinary levels of those metals were measured for all workers. Kidney injury molecule 1 (KIM-1), clusterin levels, and XPD protein level were estimated. Genotyping of XPD gene polymorphisms was performed. The measured annual average concentrations of the estimated heavy metals were lower than the permissible limits. Gravity area had the maximum concentration of metals with a higher Al average daily dose and hazardous index > 1. Kidney injury biomarkers (clusterin and KIM-1) were increased significantly (p < 0.05) while XPD protein showed the lowest levels among workers at the gravity and cold rolling areas. XPD Asn/Asp genotype was more dominant among those workers (85.7%). Conclusion: aluminum workers are at risk of kidney disorders due to heavy metal exposure. The individual’s susceptibility to the diseases is related to the DNA repair efficiency mechanisms. The defect in XPD protein represents a good indicator of susceptibility to the disease. KIM-1 and clusterin estimation is a predictor biomarker for early-staged kidney diseases.