Monitoring and controlling air quality parameters form an important subject of atmospheric and environmental research today due to the health impacts caused by the different pollutants present in the urban areas. The support vector machine (SVM), as a supervised learning analysis method, is considered an effective statistical tool for the prediction and analysis of air quality. The work presented here examines the feasibility of applying the SVM to predict the ozone and particle concentrations in two Tunisian cities, namely Tunis and Sfax. We used the SVM with the linear kernel, SVM with the polynomial kernel and SVM with the RBF kernel to predict the ozone and particle concentrations in Tunisia for one year. The RBF kernel produced good results for the two pollutants with 0% error rate. Polynomial and linear kernels produced sufficiently low errors for the pollutants, at 9.09% and 18.18%, respectively. Discriminant Analysis (DA) was selected to analyze the datasets of two air quality parameters, namely ozone O3 and Suspended Particles SP. The DA results show that the spatial characterization allows for the successful discrimination between the two cities with an error rate of 4.35% in the case of the linear DA and 0% in the case of the quadratic DA. A thematic map of Tunisia was created using the MapInfo software.

The idea of systems analysis and mathematical modeling for formulating and resolving river pollution issues is of relatively recent vintage and has been applied widely in the last 3 decades. The present study illustrates the utility of Beck-modified Khanna–Bhutiani model (BMKB) to determine the pollution load due to the presence of organic matter in River Ganga from its course from Devprayag to Roorkee through the holy city of India, Haridwar. The study was conducted over a period of 3 years between 2010 and 2013. The study was aimed to verify the BMKB model for River Ganga. This model was simulated and calibrated through the data obtained by model by comparing it with the field data observed manually. Paired T-test were performed for dissolved oxygen (DO) and biochemical oxygen demand (BOD) between the titrated value and modelled value to determine if there was any statistically significant difference between the means of respective values. The results of T-test revealed statistically significant difference between DO and BOD, i.e., DO t (11)= 3.819, P= 0.003, BOD t(11)= 14.635, P= 0.000. The model presented with a good agreement between the calibrated and observed data, thereby actualizing the validity of the proposed model.

A four year long research programme divided into four phases has been designed for the first time to assess the combined Noise-Air quality and combined exposure to Noise-Air pollution at the curbside open-air microenvironment of Kolkata city, India. The results and the findings of the different phases of study had already been published. The objective of the review work is to fuse the findings of the different phases of studies; to identify the factor(s) influences the degree of correlation between concentration of the air pollutant(s) and traffic noise level at the microenvironment of the city. Incidence of moderate to very strong positive Pearson’s correlation coefficients between concentration of the air pollutant(s) and traffic noise level indicated chances of correlated exposure to these environmental stressors at the microenvironment of the city. Average combined Noise-Air quality and combined exposure to Noise-Air pollution of the microenvironment of the city was very poor (0.17) to fair (0.50) and poor (-0.06) to excellent (3.33) respectively. The best combined Noise-Air quality and the best level of combined exposure to Noise-Air pollution at the microenvironment of the city was prominently evidenced in after-noon, in summer and under variable road geometry. However, the worst combined Noise-Air quality and the worst level of combined exposure to Noise-Air pollution at the microenvironment of the city was prominently evidenced in evening, in winter and under constant road geometry. A principal component analysis revealed that intensity of wind determines the degree of correlation between the environmental stressors at the microenvironment.

Climate change is a complicated issue with many factors playing role in its formation and distribution. Considering this complication, a comprehensive and holistic approach is needed for a better understanding and management of those factors. The causal frameworks are among systemic and integrated methods for addressing the causes of environmental problems and the relationships that exist between the environmental systems in order to propose proper solutions. The DPSIR model is a functional analysis framework that depicts the cause-effect relationships that exist in creating environmental problems. Tehran is one of the major megacities in the Middle East that faces environmental consequences of overpopulation and unplanned urban sprawl, and being located in an arid region, makes it vulnerable to rise of temperature and reduction of precipitation. In this research, by using the DPSIR framework, different aspects of climate condition of Tehran are analyzed and later with the help of this conceptual framework, strategies for controlling climate change are presented.

Identifying Municipal Solid Waste (MSW) disposal sites and appropriately managing them is a challenging task to many developing countries like Bangladesh. It is a complex issue in an urban area, as increasing population levels, rapid economic growth and rise in community living standard, accelerates the generation rate of MSW. The study area is zone-2 (Mirpur-Pallabi) of Dhaka North City Corporation (DNCC) (10.40 km2) is a residential area, from where about 353.34 ton/day solid waste is generated and among them about 57.43% were managed by DNCC. There are 41 different size containers present at 17 locations in study area. The existing site and waste collecting containers are not sufficient, which deteriorates the environment due to illegal waste dumping and about 15 illegal dumping sites were identified. To identify proper waste dumping site and prevent contamination, Geographical Information System (GIS) was used to propose an efficient scenario with relocating the existing waste collecting containers and another scenario was proposed with number of containers (73) to attain an 93.68% waste collection efficiency including optimization and selection of waste collecting routes for the study area. This study also indicate that the application of GIS is an efficient and low cost tool to study and select appropriate dumping site so as to facilitate decision making processes.

The staff working at beauty centers are exposed to various chemicalproducts used daily in their working environment. Both hair dressers and nail techniciansare exposed to chemicals that are known to have an influence on the respiratory system.In order to evaluate such influence, this cross-sectional observational study wasconducted on a randomly selected 14 beauty centers in Dammam City, Saudi Arabia. Atthe investigated salons, both of respiratory symptoms and the quality of air wereevaluated and data were analyzed statistically. 40.5% out of the 79 subjects included inthe study were hairdressers, 17.7% were nail technicians and (41.8%) were working inboth of the previously mentioned areas as part of their everyday practice. The staffcomplained of respiratory symptoms which include dyspnea, cough, phlegm, wheeze, andshortness of breath. Both hairdressers and nail technicians developed respiratorysymptoms while working in the salons. Indoor Air pollution was assessed and many datawere out of the normal standard ranges which include: relative humidity (RH), volatileorganic compounds (VOC), carbon dioxide (CO2), and ammonia (NH3). We conclude thatgood ventilation is required to reduce the influence of such substances on the respiratorysymptoms of the staff working at salons.

The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less than 10 μm in diameter (PM10μm). To do this, we use four geostatistical interpolation methods: Ordinary Kriging, Universal Kriging, Simple Kriging, and Ordinary Cokriging with Gaussian semivariogram, to estimate the spatial distribution surface for three mentioned air pollutants in Tehran’s atmosphere. The data were collected from 21 air quality monitoring stations located in different districts of Tehran during 2012 and 2013 for 00UTC. Finally, we evaluate the Kriging estimated surfaces using three statistical validation indexes: mean absolute error (MAE), root mean square error (RMSE) that can be divided into systematic and unsystematic errors (RMSES, RMSEU), and D-Willmot. Estimated standard errors surface or uncertainty band of each estimated pollutant surface was also developed. The results indicated that using two auxiliary variables that have significant correlation with CO, the ordinary Cokriginga scheme for CO consistently outperforms all interpolation methods for estimating this pollutant and simple Kriging is the best model for estimation of NO2 and PM10. According to optimal model, the highest concentrations of PM10 are observed in the marginal areas of Tehran while the highest concentrations of NO2 and CO are observed in the central and northern district of Tehran.

The Karnafully is one of the most important rivers due to its profound influence on water chemistry and sediment characteristics. The present study intended to assess the quality of water and sediment from intertidal zone of this river in respect to the pollution index. Seasonal water and sediment samples were collected during four seasons (Monsoon, post-monsoon, winter, and pre-monsoon) of 2014. The result indicates that these investigated parameters ranged as water temperature (21.7-36 °C), pH (8.0-8.7), salinity (2.4-8.8‰), total suspended solid (0.08-0.8 g/L), dissolve oxygen (0.00-4.52 mg/L), soil temperature (21.3-33 °C), pH (5.0-6.8), sand (4.13-44.10%), silt (39.93-75.89%), clay (11.98-21.19%), soil organic matter (4.33-6.21%), organic carbon (2.5-3.6%), nitrite-nitrogen (0.69-3.97 µg/L), and phosphate-phosphorus (0.23-3.44 µg/L). Multivariate statistical analyses like post-hoc LSD test, Cluster Analysis (CA), and Principal Component analysis (PCA) brought out the spatial and temporal changing pattern of water chemistry and sediment characteristics with the effect of uprising pollution. CA ascertained the compatibility among different parameters and categorized the monitoring sites into highly and moderately polluted areas. Moreover, PCA brought out five primary components and highlighted the three dormant factors, enormously regulating the river water chemistry such as municipal waste, carbon based nitrogenous compound, and local geomorphological weathering process. This investigation provided an outline on deterioration of water and sediment quality by high anthropogenic impact and suggests national policy maker to take some initiatives for retaining the quality water and sediment properties.

Surface ozone (Surface O3)is a secondary pollutant and there are only limited studies on ozone in South India. Studies have revealed a strong correlation between higher ozone levels and warmer days. Surface O3 along with its precursors like NO2, CO and CH4 are being measured at Kanyakumari (8.0780° N, 77.5410° E), TamilNadu, India and in this paper we present the summer time variation of ozone from 2010to 2014 . Surface O3 showed a clear diurnal variation, but an irregularity was observed during the night time for all the measuring days of Summer 2014.There was a formation of a well pronounced secondary peak in Surface O3 during 0230 hrs accompanied by relatively strong wind patterns. Since the normal diurnal variation cannot explain this phenomenon,this uncertain behavior is probably attributed tolow NOX titrations and the downward mixing of ozone in the ground layer.The daily mean of Surface O3showed an increasing trend in the study area during the summer monthsand a negative correlation was observed with its precursors. The correlation of Surface O3 with temperature and wind speed for the entire summer season was found as r= +0.68, p= 4.314E-05 and r= +0.63, p

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.