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 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.

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.

Air pollution is one of the major threats to environment in the present time. Increase in degree of urbanization is a major cause of this air pollution. Due to urbanization, vehicular activities are continuously increasing at a tremendous rate. Mobile or vehicular pollution is predominantly degrading the air quality worldwide. Thus, air quality management is necessary for dealing with this severe problem. The first step to deal with this air pollution problem is to find out the existing concentration of air pollutants in the atmosphere due to vehicular activities. It is not possible to establish ambient air monitoring stations everywhere, especially in developing countries as it is a costly process. Hence, vehicular air quality models are used to predict the concentration of different pollutants in the atmosphere. This review covers the simulation of vehicular emission by different types of models for estimating the pollutant concentration in ambient air from vehicular emissions. The models predict concentrations of pollutants in time and space and relate it to the dependent variables. These can also be used to predict the concentration of pollutants in the future. These models can be useful for imposing regulations by governments and to test techniques for controlling pollutant emissions. This review also discusses where and how the respective models can be used.

There is increasing controversy on whether acute exposure to ambient carbon monoxide (CO) is hazardous on respiratory health. We therefore performed a longitudinal panel study to evaluate the acute effects of ambient CO on fractional exhaled nitric oxide (FeNO), a well-established biomarker of airway inflammation. We completed 4–6 rounds of health examinations among 75 healthy young adults during April to June in 2013 in Shanghai, China. We applied the linear mixed-effect model to investigate the short-term associations between CO and FeNO. CO exposure during 2–72 h preceding health tests was significantly associated with decreased FeNO levels. For example, an interquartile range increase (0.3 mg/m³) of 2-h CO exposure corresponded to 10.6% decrease in FeNO. This association remained when controlling for the concomitant exposure to co-pollutants. This study provided support that short-term exposure to ambient CO might be related with reduced levels of FeNO, a biomarker of lower airway inflammation.

This study classifies urban environments into types characterized by different exposure to environmental risk factors measured by general sense of discomfort and Heart Rate Variability (HRV). We hypothesize that a set of environmental factors (micro-climatic, CO, noise and individual heart rate) that were measured simultaneously in random locations can provide a better understanding of the distribution of human exposure to environmental loads throughout the urban space than results calculated based on measurements from close fixed stations. We measured micro-climatic and thermal load, CO and noise, individual Heart Rate, Subjective Social Load and Sense of Discomfort (SD) were tested by questionnaire survey.The results demonstrate significant differences in exposure to environmental factors among 8 types of urban environments. It appears that noise and social load are the more significant environmental factors to enhance health risks and general sense of discomfort.

Recent development in the field of nanomaterials has given rise into the inquiries regarding the toxicological characteristics of the nanomaterials. While many individual nanomaterials have been screened for their toxicological effects, composites that accompany nanomaterials are not common subjects to such screening through toxicological assessment. One of the widely used composites that accompany nanomaterials is catalyst composite used to reduce air pollution, which was selected as a target composite with nanomaterials for the multifaceted toxicological assessment. As existing studies did not possess any significant data regarding such catalyst composites, this study focuses on investigating toxicological characteristics of catalyst composites from various angles in both in-vitro and in-vivo settings. Initial toxicological assessment on catalyst composites was conducted using HUVECs for cell viability assays, and subsequent in-vivo assay regarding their direct influence on living organisms was done. The zebrafish embryo and its transgenic lines were used in the in-vivo assays to obtain multifaceted analytic results. Data obtained from the in-vivo assays include blood vessel formation, mutated heart morphology, and heart functionality change. Our multifaceted toxicological assessment pointed out that chemical composites augmented with nanomaterials can too have toxicological threat as much as individual nanomaterials do and alarms us with their danger. This manuscript provides a multifaceted assessment for composites augmented with nanomaterials, of which their toxicological threats have been overlooked.

Rising tropospheric ozone concentrations in Asia affect the yield and quality of rice. This study investigated ozone-induced changes in rice grain quality in contrasting rice genotypes, and explored the associated physiological processes during the reproductive growth phase. The ozone sensitive variety Nipponbare and a breeding line (L81) containing two tolerance QTLs in Nipponbare background were exposed to 100 ppb ozone (8 h per day) or control conditions throughout their growth. Ozone affected grain chalkiness and protein concentration and composition. The percentage of chalky grains was significantly increased in Nipponbare but not in L81. Physiological measurements suggested that grain chalkiness was associated with a drop in foliar carbohydrate and nitrogen levels during grain filling, which was less pronounced in the tolerant L81. Grain total protein concentration was significantly increased in the ozone treatment, although the albumin fraction (water soluble protein) decreased. The increase in protein was more pronounced in L81, due to increases in the glutelin fraction in this genotype. Amino acids responded differently to the ozone treatment. Three essential amino acids (leucine, methionine and threonine) showed significant increases, while seven showed significant treatment by genotype interactions, mostly due to more positive responses in L81. The trend of increased grain protein was in contrast to foliar nitrogen levels, which were negatively affected by ozone. A negative correlation between grain protein and foliar nitrogen in ozone stress indicated that higher grain protein cannot be explained by a concentration effect in all tissues due to lower biomass production. Rather, ozone exposure affected the nitrogen distribution, as indicated by altered foliar activity of the enzymes involved in nitrogen metabolism, such as glutamine synthetase and glutamine-2-oxoglutarate aminotransferase. Our results demonstrate differential responses of grain quality to ozone due to the presence of tolerance QTL, and partly explain the underlying physiological processes.

Polycyclic aromatic hydrocarbons (PAHs) were studied in 230 daily fine particulate matter (PM2.5) samples collected in four seasons at urban and suburban sites of Shanghai, China. This study focused on the emission sources of PAHs and its dynamic results under different weather conditions and pollution levels and also emphasized on the spatial sources of PM2.5 and PAHs at a regional level. Annual concentrations of PM2.5 and 16 EPA priority PAHs were 53 μg/m3 and 6.9 ng/m3, respectively, with highest levels in winter. Positive matrix factorization (PMF) modeling identified four sources of PAHs: coal combustion, traffic, volatilization and biomass combustion, and coking, with contributions of 34.9%, 27.5%, 21.1% and 16.5%, respectively. The contribution of traffic, a local-indicative source, increased from 17.4% to 28.7% when wind speed changed from >2m/s to <2m/s, and increased from 18.3% to 31.3% when daily PAH concentrations changed from below to above the annual mean values. This indicated that local sources may have larger contributions under stagnant weather when poorer dispersion conditions and lower wind speed led to the accumulation of local-emitted pollutants. The trajectory clustering and potential source contribution function (PSCF) and concentration weighted trajectory (CWT) models showed clearly that air parcels moved from west had highest concentrations of PM2.5, total PAHs and high molecular weight (HMW) PAHs. While small differences were found among all five clusters in low molecular weight (LMW) PAHs. Sector analyses determined that regional transport source contributed 39.8% to annual PM2.5 and 52.5% to PAHs, mainly from western regions and varying with seasons. This work may make contribution to a better understanding and control of the increasingly severe air pollution in China as well as other developing Asian countries.