Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily CO concentration as a function of 12 input variables. Then, forward selection (FS) technique was applied to reduce the number of input variables. After converting 12 input variables to 7 using the FS, they were fed to SVM models (FS-(-SVM) and FS-(-SVM)). Finally, a comparison among SVM models operation and previously developed techniques, i.e. classical regression model and artificial intelligent methods such as ANN and adaptive neuro-fuzzy inference system (ANFIS) was carried out. Determination of coefficient (R2) and mean absolute error (MAE) for -SVM (-SVM) were 0.87 (0.40) and 0.87 (0.41), respectively, while they were 0.90 (0.39) and 0.91 (0.35) for ANN and ANFIS, respectively. Results of developed SVM models indicated that both FS-(-SVM) and FS-(-SVM) regression techniques were superior. Furthermore, it was founded that the performance of FS-(-SVM) and FS-(-SVM) models were generally a bit better than the best FS-ANFIS and FS-ANN solutions for short term forecasting of CO concentrations.

The life cycle of diesel fuel in non-passenger vehicles was assessed for all registered vehicles in Qatar as of November 2017. The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model was used as a source of normalized data to evaluate diesel fuel emissions for all non-passenger vehicle categories. This work aims at estimating the emissions from all non-passenger diesel vehicles in Qatar and evaluating the impact of the fuel life cycle assessment. The emissions of CO2, NOx, CO, SO2, VOC, black carbon (BC), organic carbon, fine particulates PM2.5, and coarse particulates PM10 were evaluated. SO2 emissions were found to be dominant during the well to pump (WTP) stage of the life cycle assessment (LCA) process, while the pump to wheel (PTW) stage was found to be dominated by CO, VOC, PM10, PM2.5, and BC emissions. NOx and organic carbon emissions were virtually the same during both stages. Total greenhouse gas emissions amounted to 5367 kt of CO2 equivalent (CO2-eq) in 2017 as compared with that in 2014 (5277 kt), the only reported value in Qatar for transportation emissions. In addition, several mitigation strategies are proposed to ensure sustainability in the transport sector and to minimize the negative impact of diesel fuel emissions in the country.

Mitigation of atmospheric pollution has been a topic of concern over the past decades. In this study, tree rings have been used to reconstruct past climates as well as to assess the effects of recent climatic and environmental changes on tree growth. Vehicular emission is one of the major sources of pollutants in the atmosphere and this study focused on the Haatso-Atomic road which over the years has been a spot for heavy vehicular traffic. Swietenia mahagoni (Mahogany) tree was logged and the rings counted and age determined to be 61 years spanning from 1957 to 2018. X-ray fluorescence (XRF) was used to investigate the presence of the following heavy metals. Heavy metals (Cu, Mn, Zn, Pb, Cd and Ni) which ranged from (3.15—9.84mg/kg), (2.58 – 5.49 mg/kg), (8.18 – 15.78mg/kg), (0.12—0.60 mg/kg), (0.01—0.09 mg/kg) and (0.10 – 0.99 mg/kg) respectively, from vehicular emissions were determined for annual rings spanning from 1957 to 2018 and surprisingly an increasing trend was observed with some the heavy metals exceeding WHO guidelines. Tree growth rates were calculated through ring width measurements and related to annual precipitation data spanning over the sampling period. It was observed that wet seasons correlate with high growth rates of trees while low precipitations seasons related to low or no growth rate of trees.

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.

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.

A comprehensive modelling approach has been developed to predict population exposure to the ambient air PM₂.₅ concentrations in different microenvironments in London. The modelling approach integrates air pollution dispersion and exposure assessment, including treatment of the locations and time activity of the population in three microenvironments, namely, residential, work and transport, based on national demographic information. The approach also includes differences between urban centre and suburban areas of London by taking account of the population movements and the infiltration of PM₂.₅ from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM₂.₅ at street scale for the year 2008, including both regional and urban contributions. Model analysis of the exposure in the three microenvironments shows that most of the total exposure, 85%, occurred at home and work microenvironments and 15% in the transport microenvironment. However, the annual population weighted mean (PWM) concentrations of PM₂.₅ for London in transport microenvironments were almost twice as high (corresponding to 13–20 μg/m³) as those for home and work environments (7–12 μg/m³). Analysis has shown that the PWM PM₂.₅ concentrations in central London were almost 20% higher than in the surrounding suburban areas. Moreover, the population exposure in the central London per unit area was almost three times higher than that in suburban regions. The exposure resulting from all activities, including outdoor to indoor infiltration, was about 20% higher, when compared with the corresponding value obtained assuming inside home exposure for all times. The exposure assessment methodology used in this study predicted approximately over one quarter (−28%) lower population exposure, compared with using simply outdoor concentrations at residential locations. An important implication of this study is that for estimating population exposure, one needs to consider the population movements, and the infiltration of pollution from outdoors to indoors.

Both air pollution and dyslipidemias contributed to large number of deaths and disability-adjusted life lost years. Long-term air pollution exposure was related to changed blood lipids and risk of dyslipidemias. This study was designed to evaluate relationships between air pollutants, blood lipids and prevalence of dyslipidemias in a Chinese rural population exposed to high-level air pollution based on baseline data of The Henan Rural Cohort study. An amount of 39,057 participants from rural areas in China were included. The 3-year average exposure of air pollutants (PM2.5, PM10, NO2) was estimated by a spatiotemporal model. Logistic and linear regression models were employed to explore relationships between air pollutants, blood lipids (TC, TG, HDL-C and LDL-C) and prevalence of dyslipidemias. The three-year concentration of PM2.5, PM10 and NO2 was 72.8 ± 2.3 μg/m3, 131.5 ± 5.7 μg/m3and 39.1 ± 3.1 μg/m3, respectively. Overall, increased air pollution exposure was related to increased TC and LDL-C, while decreased TG and HDL-C. Each 1-μg/m3 increment of PM2.5 was related to 0.10% (0.07%–0.19%) increase in TC, 0.63% (0.50%–0.77%) increase in LDL-C, 2.93% (2.70%–3.16%) decrease in TG, 0.49% (0.38%–0.60%) decrease in HDL-C; and 5.7% (95%CI: 3.7%–7.6%), 4.0% (95%CI: 2.1%–6.0%) and 3.8% (95%CI: 2.5%–5.1%) increase in odds for hypercholesterolemia, hyperbetalipoproteinemia and hypoalphalipoproteinemia, respectively. Stronger associations were found in male and older participants. Findings suggest that air pollutants were associated with changed blood lipid levels and higher risk of dyslipidemias among rural population. Male and elder people should pay more attention to personal safety protection.

Particulate air pollution is a continuing challenge in China, and its adverse effects on chronic obstructive pulmonary disease (COPD) have been widely reported. However, epidemiological evidence on the associations between size-fractionated particle number concentrations (PNCs) and COPD mortality is limited. In this study, we utilized a time-series approach to investigate the associations between PNCs of particles at 0.25–10 μm in diameter and COPD mortality in Shanghai, China. Quasi-Poisson regression generalized additive models were applied to evaluate these associations, with adjustment of time trend, day of week, holidays, temperature and relative humidity. Stratification analyses were performed by season and gender. There were a total of 3238 deaths due to COPD during the study period. We found that daily COPD deaths were significantly associated with PNCs of particles <0.5 μm, and the magnitude of associations increased with decreasing particle size. An interquartile range (IQR) increase in PNC₀.₂₅—₀.₂₈, PNC₀.₂₈—₀.₃, PNC₀.₃—₀.₃₅, PNC₀.₃₅—₀.₄, PNC₀.₄—₀.₄₅ and PNC₀.₄₅—₋₀.₅ was associated with increments of 7.51% (95%CI: 2.45%, 12.81%), 7.22% (95%CI: 2.16%, 12.53%), 6.95% (95%CI: 1.81%, 12.35%), 6.26% (95%CI: 1.25%, 11.52%), 5.24% (95%CI: 0.56%, 10.13%) and 4.15% (95%CI: 0.14%, 8.32%), respectively. The associations remained robustness after controlling for the mass concentrations of gaseous air pollutants. In stratification analyses, significant associations between PNCs and COPD mortality were observed in the cold seasons, and in males. Our results suggested that particles <0.5 μm in diameter might be most responsible for the adverse effects of particulate air pollution on COPD mortality, and COPD patients are more susceptible to PM air pollution in the cold seasons, especially for males.

The determination of the spatiotemporal patterns and driving factors of PM₂.₅ is of great interest to the atmospheric and climate science community, who aim to understand and better control the atmospheric linkage indicators. However, most previous studies have been conducted on pollution-sensitive cities, and there is a lack of large-scale and long-term systematic analyses. In this study, we investigated the spatiotemporal evolution of PM₂.₅ and its influencing factors by using an exploratory spatiotemporal data analysis (ESTDA) technique and spatial econometric model based on remote sensing imagery inversion data of the Yangtze River Economic Belt (YREB), China, between 2000 and 2016. The results showed that 1) the annual value of PM₂.₅ was in the range of 23.49–37.67 μg/m³ with an inverted U-shaped change trend, and the PM₂.₅ distribution presented distinct spatial heterogeneity; 2) there was a strong local spatial dependence and dynamic PM₂.₅ growth process, and the spatial agglomeration of PM₂.₅ exhibited higher path-dependence and spatial locking characteristics; and 3) the endogenous interaction effect of PM₂.₅ was significant, where each 1% increase in the neighbouring PM₂.₅ levels caused the local PM₂.₅ to increase by at least 0.4%. Natural and anthropogenic factors directly and indirectly influenced the PM₂.₅ levels. Our results provide spatial decision references for coordinated trans-regional air pollution governance as well as support for further studies which can inform sustainable development strategies in the YREB.

Exposure to air pollutants has been associated with respiratory and cardiovascular mortality, but the underlying molecular mechanisms remain inadequately understood. We aimed to examine molecular-level inflammatory and oxidative stress responses to personal air pollutant exposure. Fifty-three healthy adults aged 22–52 were measured three times for their blood inflammatory cytokines and urinary malondialdehyde (MDA, an oxidative stress biomarker) within 2 consecutive months. Pollutant concentrations monitored indoors and outdoors were combined with the time-activity data to calculate personal O₃, PM₂.₅, NO₂, and SO₂ exposures averaged over 12 h, 24 h, 1 week, and 2 weeks, respectively, prior to biospecimen collection. Inflammatory cytokines and MDA were associated with pollutant exposures using linear mixed-effects models controlling for various covariates. After adjusting for a co-pollutant, we found that concentrations of proinflammatory cytokines were significantly and negatively associated with 12-h O₃ exposures and significantly but positively associated with 2-week O₃ exposures. We also found significant and positive associations of proinflammatory cytokines with 12-h and 24-h NO₂ exposures, respectively. However, we did not find clear associations of PM₂.₅ and SO₂ exposure with proinflammatory cytokines and with MDA. The removal of an O₃-generating electrostatic precipitator in the mechanical ventilation systems of the offices and residences of the subjects was associated with significant decreases in IL-1β, IL-2, IL-6, IL-8, IL-17A, and TNF-α. These findings suggest that exposure to O₃ for different time durations may affect systemic inflammatory responses in different ways.