The present study analyzes air quality for Carbon monoxide (CO), in Esfahan with the measurements taken in three different locations to prepare average data in the city. The average concentrations have been measured every 24 hours, every month and every season with the results showing that the highest concentration of CO occurs generally in the morning and at the beginning of night, while the least concentration has been found in the afternoon and early morning. Monthly concentrations of CO show the highest values in August and the lowest values in February. The seasonal concentrations show the least amounts in spring, while the highest amounts belong to summer. Relations between the air pollutant and some meteorological parameters have been calculated statistically, using the daily average data. The data include Temperature (min, max), precipitation, Wind Direction (max), Wind Speed (max), and Evaporation, considered independent variables. The relations between the pollutant concentration and meteorological parameters have been expressed by multiple linear regression equations for both annual and seasonal conditions, using SPSS software. Analysis of variance shows that both regressions of ‘enter’ and ‘stepwise’ methods are highly significant, indicating a significant relation between the CO and different variables, especially for temperature and wind speed in annual condition. RMSE test shows that among different prediction models, stepwise model is the best option.

The quick growth of vehicles is due to fast urbanization in mega cities during last decades. This phenomenon has serious impacts on air quality, as emission from mobile vehicles is the major source of air pollution. As a result, any attempt to reduce the emitted air pollutants is needed. This study aims at improving the fuel quality in transporting system with particular emphasis on taxis in Tehran in 2014. As a clean fuel, Euro IV is being used to reduce the emission of pollution, toxic substances, and greenhouse gases. A bottom-up approach to evaluate vehicular emission, using IVE (International Vehicle Emission) model in Tehran, has been presented, which employs the local vehicle technology and its distributions, vehicle soak distributions, power based driving factors, and meteorological parameters to evaluate the emission, itself.  Results show that the most abundant air pollutant (CO) has been reduced by 87.6% due to the clean fuel consumption (Euro IV). Also, the emission rates of the predominant toxic pollutant (Benzene) decreased by 98.7%. As a clean fuel, Euro IV managed to increase the emitted amount of CO2 and NH3. It can be concluded that upgrading transportation system with updated fuel quality is an essential step to improve air quality in Tehran.

The present research analyzes air quality in Ahvaz, a city in the south of Iran, paying special attention to sulfur dioxide (SO2). In order to prepare the average data in the city, measurements have been carried out between 2009 and 2010 in two different locations. Relations between sulfur dioxide and some meteorological parameters have been calculated statistically, using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation and rainfall have been considered as independent variables. The RMSE Test showed that among different prediction models, the stepwise one is the best option. The average concentrations have been calculated for every 24 hours, during each month and each season. Results show that the highest concentration of sulfur dioxide occurs generally in the morning while the lowest concentration is found before the sunshine. In case of the monthly concentrations of sulfur dioxide, the highest value belongs to January, while the lowest one occurs in October. And as for the seasonal concentrations, it has been shown that the highest amounts belong to winter. Results show that quantities of SO2 in different seasons as well as the entire year can be estimated by climate parameters. Results also indicate that the relations between the SO2 and meteorological parameters are stronger than the entire year during the seasons.

Previous studies have suggested that short-term exposure to ambient air pollution was associated with pediatric hospital admissions and emergency room visits for certain respiratory diseases; however, there is limited evidence on the association between short-term air pollution exposure and pediatric outpatient visits. Our aim was to quantitatively assess the short-term effects of ambient air pollution on pediatric outpatient visits for respiratory diseases. We conducted a time-series study in Yichang city, China between Jan 1, 2014 and Dec 31, 2015. Daily counts of pediatric respiratory outpatient visits were collected from 3 large hospitals, and then linked with air pollution data from 5 air quality monitoring stations by date. We used generalized additive Poisson models to conduct linear and nonlinear exposure-response analyses between air pollutant exposures and pediatric respiratory outpatient visits, adjusting for seasonality, day of week, public holidays, temperature, and relative humidity. Each interquartile range (IQR) increase in PM2.5 (lag 0), PM10 (lag 0), NO2 (lag 0), CO (lag 0), and O3 (lag 4) concentrations was significantly associated with a 1.91% (95% CI: 0.60%, 3.23%), 2.46% (1.09%, 3.85%), 1.88% (0.49%, 3.29%), 2.00% (0.43%, 3.59%), and 1.91% (0.45%, 3.39%) increase of pediatric respiratory outpatient visits, respectively. Similarly, the nonlinear exposure-response analyses showed monotonic increases of pediatric respiratory outpatient visits by increasing air pollutant exposures, though the associations for NO2 and CO attenuated at higher concentrations. These associations were unlikely modified by season. We did not observe significant association for SO2 exposure. Our results suggest that short-term exposures to PM2.5, PM10, NO2, CO, and O3 may account for increased risk of pediatric outpatient visits for respiratory diseases, and emphasize the needs for reduction of air pollutant exposures for children.

The impact of chronic exposure to air pollution and outcomes in the acute respiratory distress syndrome (ARDS) is unknown. The Nationwide Inpatient Sample (NIS) from 2011 was utilized for this analysis. The NIS is a national database that captures 20% of all US in-patient hospitalizations from 47 states. Patients with ARDS who underwent mechanical ventilation from the highest 15 ozone pollution cities were compared with the rest of the country. Secondary analyses assessed outcomes of ARDS patients for ozone pollution and particulate matter pollution on a continuous scale by county of residence. A total of 8,023,590 hospital admissions from the 2011 NIS sample were analyzed. There were 93,950 patients who underwent mechanical ventilation for ARDS included in the study. Patients treated in high ozone regions had significantly higher unadjusted hospital mortality (34.9% versus 30.8%, p < 0.01) than patients in cities with control levels of ozone. After controlling for all variables in the model, treatment in a hospital located in a high ozone pollution area was associated with an increased odds of in-hospital mortality (OR 1.11, 95% CI 1.08–1.15, p < 0.01). After adjustment for all variables in the model, for each increase in ozone exposure by 0.01 ppm the OR for death was 1.07 (95% CI 1.06–1.08, p < 0.01). Similarly, for each increase in particulate matter exposure by 10 μg/m³, the OR for death was 1.08 (95% CI 1.02–1.16, p < 0.01).Chronic exposure to both ozone and particulate matter pollution is associated with higher rates of mortality in ARDS. These preliminary findings need to be confirmed by further detailed studies.

The most common mass spectrometry approach analyzing contamination of the environment deals with targeted analysis, i.e. detection and quantification of the selected (priority) pollutants. However non-targeted analysis is becoming more often the method of choice for environmental chemists. It involves implementation of modern analytical instrumentation allowing for comprehensive detection and identification of the wide variety of compounds of the environmental interest present in the sample, such as pharmaceuticals and their metabolites, musks, nanomaterials, perfluorinated compounds, hormones, disinfection by-products, flame retardants, personal care products, and many others emerging contaminants. The paper presents the results of detection and identification of previously unreported organic compounds in snow samples collected in Moscow in March 2016. The snow analysis allows evaluation of long-term air pollution in the winter period. Gas chromatography coupled to a high resolution time-of-flight mass spectrometer has enabled us with capability to detect and identify such novel analytes as iodinated compounds, polychlorinated anisoles and even Ni-containing organic complex, which are unexpected in environmental samples. Some considerations concerning the possible sources of origin of these compounds in the environment are discussed.

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management.

This study examined effects of short-term urban air pollution exposures on inflammation, thrombosis, and autonomic imbalance in subjects at risk for cardiovascular disease (CVD). We enrolled 61 patients with multiple CVD risk factors and measured high sensitive C-reactive protein (hs-CRP), fibrinogen, D-dimer, and heart rate variability (HRV) indices. Two health examinations for each participant were performed during December 2002 through September 2003. Changes in inflammation and thrombotic markers and HRV indices with exposures to PM2.5, organic carbon (OC), elemental carbon (EC), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) at 1- to 3-day lags were analyzed using mixed models. The results showed inflammatory and thrombotic markers increased with 1- to 3-day lagged PM2.5 components and gaseous pollutants exposures. hs-CRP maximally increased 0.19 [95% confidence interval (CI): 0.07–0.31] and 0.15 (95% CI: 0.05–0.24) mg/L for an interquartile range (IQR) of 1-day lagged SO2 (2.3 ppb) and CO (0.5 ppm), respectively. D-dimer maximally increased 1.05 (95% CI: 0.13–1.75), 0.72 (95% CI: 0.09–1.21), 0.92 (95% CI: 0.13–1.50), and 0.90 (95% CI: 0.07–1.61) mg/dL for an IQR of 1-day lagged OC (3.9 μg/m3), EC (2.0 μg/m3), SO2, and NO2 (13.4 ppb), respectively. The HRV indices, including low frequency, very low frequency, and the ratio of low frequency to high frequency decreased 19.8 (95% CI: 4.4–32.7), 12.9 (95% CI: 0.8–23.4), and 17.6 (95% CI: 5.4–28.2)% for an IQR of 1-day lagged PM2.5 (20.2 μg/m3), respectively. Our findings demonstrated PM2.5 components and gaseous pollutants exert prolonged inflammatory and thrombotic reactions, while PM2.5 exert an immediate autonomic imbalance.

Air pollution plays a pivotal role in the deterioration of many materials used in buildings and cultural monuments causing an inestimable damage. This study aims to estimate the impacts of air pollution (SO2, HNO3, O3, PM10) and meteorological conditions (temperature, precipitation, relative humidity) on limestone, copper and bronze based on high resolution air quality data-base produced with AMS-MINNI modelling system over the Italian territory over the time period 2003–2010. A comparison between high resolution data (AMS-MINNI grid, 4 × 4 km) and low resolution data (EMEP grid, 50 × 50 km) has been performed. Our results pointed out that the corrosion levels for limestone, copper and bronze are decreased in Italy from 2003 to 2010 in relation to decrease of pollutant concentrations. However, some problem related to air pollution persists especially in Northern and Southern Italy. In particular, PM10 and HNO3 are considered the main responsible for limestone corrosion. Moreover, the high resolution data (AMS-MINNI) allowed the identification of risk areas that are not visible with the low resolution data (EMEP modelling system) in all considered years and, especially, in the limestone case. Consequently, high resolution air quality simulations are suitable to provide concrete benefits in providing information for national effective policy against corrosion risk for cultural heritage, also in the context of climate changes that are affecting strongly Mediterranean basin.

Detailed chemical characterization of fine aerosols (PM2.5) is important for reducing air pollution in densely populated areas, such as the Yangtze River Delta region in China. This study systematically analyzed PM2.5 samples collected during November 2015 to April 2016 in urban Yangzhou using a suite of techniques, in particular, an Aerodyne soot particle aerosol mass spectrometry (SP-AMS). The techniques used here reconstructed the majority of total PM2.5 measured where extracted species comprised on average 91.2%. Source analyses of inorganic components showed that secondary nitrate, sulfate and chloride were the major species, while primary sources including biomass burning, coal combustion, traffic, industry and re-suspended dust due to nearby demolition activities, could contribute to other species. EC-tracer method estimated that the organic matter (OM) was composed of 65.4% secondary OM (SOM) and 34.6% primary OM (POM), while the SP-AMS analyses showed that the OM was comprised of 60.3% water-soluble OM (WSOM) and 39.7% water-insoluble OM (WIOM). Correlation analyses suggested that WSOM might be rich in secondary organic species, while WIOM was likely mainly comprised of primary organic species. We further conducted positive matrix factorization (PMF) analyses on the WSOM, and identified three primary factors including traffic, cooking and biomass burning, and two secondary factors. We found the secondary factors dominated WSOM mass (68.1%), and their mass contributions increased with the increase of WSOM concentrations. Relatively small contribution of primary sources to WSOM was probably due to their low water solubility, which should be investigated further in future. Overall, our findings improve understanding of the complex aerosol sources and chemistry in this region.