Management of solid wastes is considered as an economic and environmental issue in the building stone industry. The current study uses raw and calcined calcareous sludge, generated in the stone cutting factories, in order to remove sulfur dioxide. Sludge characterization has been performed, using X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX) analyses. The removal experiments of sulfur dioxide have conducted under different humid contents and adsorbent doses. The results showed that the higher the adsorbent dosage and humidity content, the greater the SO2 adsorption.. The calcination process at temperatures of 400, 500, 600, and 700℃ revealed that with rising calcination temperature and humidity content, the adsorbent capability is enhanced considerably. This method could be developed for the management of stone sludge produced from the stone cutting industry through its conversion into an effective and low-cost adsorbent for desulfurization process.

In the present study, air quality analyses for ozone (O3) were conducted in Shiraz, a city in the south of Iran. The measurements were taken from 2011 through 2012 in two different locations to prepare average data in the city. The average concentrations were calculated for every 24 hours, each month and each season. Results showed that the highest concentration of ozone occurs generally in the afternoon while the least concentration was found in the morning and at midnight. Monthly concentrations of ozone showed the highest value in August and June while the least value was in December. The seasonal concentrations showed the least amounts in autumn while the highest amounts were in spring. Relations between the air pollutant and some meteorological parameters were calculated statistically using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation, dew point, and rainfall were considered as independent variables. The relationships between concentration of pollutant and meteorological parameters were expressed by multiple linear regression equations for both annual and seasonal conditions using SPSS software. Root mean square error (RMSE) test showed that among different prediction models, stepwise model is the best option.

Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level ozone in Karaj City in Iran. Input data included humidity, hour temperature, wind speed, wind direction, PM2.5, PM10 and benzene, which were monitored in 2014. The coefficient of determination between the observed and predicted data was 0.955 and 0.999 for the TDNN and RBF, respectively. The Index of Agreement (IA) between the observed and predicted data was 0.921 for TDNN and 0.9998 for RBF. Both methods determined reliable results. However, the RBF neural network performance had better results than the TDNN neural network. The sensitivity analysis related to the TDNN neural network indicated that the PM2.5 had the greatest and benzene had the minimum effect on prediction of ground-level ozone concentration in comparison with other parameters in the study area.

Atmospheric models today represent all significant aerosol components. Atmospheric aerosols play an important role in the air, globally through their action on the Earth's radiation balance and locally through their effects on health in heavily polluted areas, they vary considerably in their properties that affect the way they absorb and disperse radiation, and they can thus have a cooling or warming effect, they impact on the formation and life of clouds is one example. Among the main sectors of activity releasing emissions of PM10 (fine particles with a diameter of less than 10 µm) and a diameter of less than 2.5 µm (PM2.5) is the industrial sector, in particular the extraction industry of building materials. The aerosols emitted by this type of industry are composed mainly of a mixture of dust, sulphates, carbon black and nitrates, is clearly perceptible in many continental regions of the northern hemisphere. Improvements in in situ, satellite and surface measurements are needed. However, the mechanisms by which aerosols interact with the environment are extremely complex and still poorly understood. This study is based on satellite atmospheric models to have spatiotemporal variability of concentrations of fine particles smaller than 10 µm in diameter (PM10) and smaller than 2.5 µm in diameter (PM2.5) at the level of the western Rif part of Morocco, home to a large number of extraction quarries and thus offering a high-resolution particle capture system (PM10 and PM2.5).

This study carries the evaluation of tolerance of sixteen plants against pollution. These plants have been selected and assessed for several phyto-socio-economic (tree height, canopy, type of tree, laminar structure, hardiness and economic value) and biochemical qualities (pH, Relative water content (R), Ascorbic acid (AA), Chlorophyll a (Chl a), (Chl b), Carotenoids (Car) and total Chlorophyll (TC)) and tested for Air pollution tolerance index (APTI) and Expected performance Index (EPI) and then EPI score used as grades of plants (Not Recommended, Very poor, Poor, Moderate, Good, Very good, Excellent and Best Plus Plant). Statistical analysis tool like correlation matrix among plant parameters and ANNOA test has been applied to understand the relationship among plant parameters and plant species. The best EPI score means best suited plant for the area for plantation and green belt development while plant with lower EPI may be used as bioindicators for the pollution because they are very sensitive for the air pollution.

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.

Ozone(O3), and its precursors, Benzene (C6H6), Nitrogen Dioxide(NO2), Carbon Monoxide (CO) and meteorological parameters Temperature, Relative Humidity and Wind Speed were measured in urban air of two sites of significant spatial variations, Delhi Milk Scheme (DMS), Sadipur and Netaji Subhash Chander Institute of Technology(NSIT) Dwarka, during 2017–2018. Samples collected by Central Pollution Control Board (CPCB) has been analysed. The concentrations of Benzene, Nitrogen dioxide and Carbon monoxide were found to be more at DMS than NSIT site in winter season (11.137±3.258, 5.540±1.441, 55.333±12.741, 44.667±10.066μg/m3, 1.433±0.058, 1.033±0.287mg/m3 respectively) and summer season (3.167±1.222, 2.233±0.929, 50.333±2.082, 31.333±6.658μg/m3, 0.743±0.151, 0.443±0.051mg/m3 respectively) while Ozone was found to be more at NSIT than DMS site (40.333±3.215, 34.433±2.503μg/m3 respectively). The maximum concentrations of Benzene for the DMS and NSIT sites, respectively, were 32.4μg/m3 and 17.7μg/m3 and was observed in the month of November while minimum were 1.0μg/m3 and 0.6μg/m3 and was observed in the month of June. For Ozone, the maximum concentrations for the DMS and NSIT sites, respectively, were 100μg/m3 and 101μg/m3 and was observed in the month of June while minimum were 33.0μg/m3 and 28.0μg/m3 and was observed in the month of February and December respectively. Regression analyses were performed to correlate O3 concentrations with C6H6, NO2 and CO in order to infer their possible sources. The study reveals that there is significant correlation of O3 with C6H6 (r2=0.475) and CO (r2=0.985) in summer at DMS and with C6H6 (r2=0.902) & NO2(r2=0.728) in winter at NSIT. The correlation of O3, C6H6, NO2 and CO with Temperature, Relative Humidity and Wind Speed has also been investigated to understand their influence on these pollutants.

The method of air pollution level evaluation of urban landscapes on the basis of D. melanogaster cohort analysis has been suggested. The method implies the binding to the landscape areas of the city. Within each landscape area traps and cultivators for D. melanogaster have been installed in sanitary-protective zones of various enterprises as well as on the background territory with the least level of anthropogenic load serving as the control. Based on specifically elaborated technique for field conditions, the amount of eggs, third instar larvae, pupae and imago has been calculated. Then, using the computer program ImageJ, the square under the curves of cohort survival has been determined which is considered overall cohort viability (OCVD.m). The previously mentioned indicator considers cohort survival at all stages of ontogenesis. In addition, the expressed in percentage indicator of oppression (IO OCVD.m) in relevance to the control OCVD. m affects the level of air pollution of urban landscapes by emissions of various enterprises. The relevance between these indicators is determined by a four-level scale elaborated specifically for the purpose. The method has been tested based on technogenic landscapes of Chernivtsi, Ukraine. The sensitivity of the suggested indicator for a wide range of pollutants has been proved and its ability to respond to different levels of greening of similar enterprises has been shown.

The main objectives of the present study are to both evaluate the level of awareness about air pollution and examine the determinants, likely to affect this awareness. As a result, it discusses influential factors on air pollution awareness, presenting findings from a case study, conducted in the city of Isfahan, Iran, wherein 400 individuals have been selected via proportional random sampling and the data has been collected by means of a questionnaire, provided by the authors, the validity of which has been confirmed by a panel of experts. As for the assessment of the questionnaire’s reliability, this study has used Cronbach's alpha to find out that it has been beyond 0.7 for all variables. The data have been analyzed, using descriptive and inferential statistics, such as the extent of mean, standard deviation, the coefficient of variation, correlation analysis, and regression analysis. Results from the latter show that level of education, level of using information sources, membership, motivation, and participation could explain 50% of the variations in the level of awareness concerning air pollution.

The purpose of this study is to use both agent-based modeling as a new method in modeling dynamic phenomena and GIS to show the effects of carbon monoxide (CO) on individuals in the city of Tehran. After collecting the latest information about the severity of carbon monoxide pollutants on different days, one of the days with a very high severity of this pollutant has been selected for investigation and the interpolation map of its data has been developed via IDW method in ArcGIS software environment, which is then re-classified with the NetLogo software environment used to run the agent-based model. At this stage, the agents are randomly produced in four different age groups in the environment and begin moving with the onset of the running process in the environment. Also, the symptoms, caused by the pollution effects on the agents, appear in form of changes in color and are based on carboxyhemoglobin (COHb) levels (percentage) of each. The results indicate that among the considered older age groups, the members of the age group above 65, have had been mostly affected by pollution and the effect of pollution on the agents of the age group of 13 to 30 years old has been less than the other groups.