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.

Carbon monoxide (CO) is an important gas that affects human health and causes air pollution. However, the estimates of CO emissions in China are still subject to large uncertainties. Based on the CO mass concentration and the coupled Weather Research and Forecast (WRF) and Stochastic Time-Inverted Lagrangian Transport (STILT) model (WRF-STILT), this study estimates the CO emissions over Zhengzhou, China. The results show that the mean CO mass concentration was 1.17 mg m⁻³ from November 2017 to February 2018, with a clear diurnal variation. There were two periods of rapidly increasing CO concentration in the diurnal variation, which are 06:00–09:00 and 16:00–20:00 local time. The footprint analysis shows that the observation site is highly influenced by local emissions. The most influential regions to the site observations are northeast and northwest Zhengzhou, which are associated with the geographical barrier of the Taihang Mountains in the north and narrow Fenwei Plain in the west. The inversion result shows that the actual emissions are lower than the inventory estimates. Using the optimal scaling factors, the WRF-STILT simulations of CO concentration agree closely with the CO measurements with the linear fitting regression equation y = 0.87x + 0.15. The slopes of the linear fitting regressions between the WRF-STILT-simulated CO concentrations determined using the optimal emissions and the observations range from 0.72 to 0.89 for four months, and all the fitting results passed the significance test (P < 0.001). These results indicate that the new optimal emissions derived with the scaling factors could better represent the real emission conditions than the a priori emissions if the WRF-STILT model is assumed to be reliable.

This study aimed to provide a comprehensive characterisation of the indoor air quality during the sleeping period of 10 couples at Lisbon dwellings, using a multi-pollutant approach, and to understand how the compliance with legislation and guidelines was to assure a good indoor air quality. The assessment of indoor air quality was conducted in the cold season using real time monitors during the sleeping period for comfort parameters (temperature and relative humidity) and air pollutants (carbon dioxide – CO₂, carbon monoxide – CO, formaldehyde – CH₂O, total volatile organic compounds – VOCs, and particulate matter – PM₂.₅ and PM₁₀), together with active sampling of bioaerosols (fungi and bacteria) before and after the sleeping period. Lower compliance (less than 50% of the cases) with the Portuguese legislation was found for temperature, CO₂ (3440 ± 1610 mg m⁻³), VOCs (1.79 ± 0.99 mg m⁻³) and both bioaerosol types. In 70% of the cases, PM₂.₅ (15.3 ± 9.1 μg m⁻³) exceeded the WHO guideline of 10 μg m⁻³. All bedrooms presented air change rates above the recommended minimum value of 0.7 h⁻¹, highlighting that a good indoor air quality during sleep is not guaranteed.

Air pollution is one of the most important branches of environmental science as it affects human health, climate and ecosystems. Emissions of air pollutants from transport (vehicles and ships) in port cities strongly affect air quality at local scales, warranting for a combination of theoretical and experimental studies to identify pollution hotspots. The purpose of this paper is to provide a methodology for developing a hybrid emission inventory from transport sector for two port cities located respectively on the Northern Aegean islands of Chios and Lesvos. Emission inventories were constructed for the year 2014 based on top-down and bottom-up approaches. Official data from local authorities and survey results were used for the calculation of emissions. Traffic emissions were spatially allocated to the road network based on population data and hourly traffic counts, and distributed over time (on an hourly basis) with the use of local temporal coefficients.Regarding carbon monoxide road emissions, the highest quantities are mainly emitted by Passenger Cars (43%,32% in Chios and Lesvos respectively) while for PM₁₀ emissions, trucks have the largest share (66% in Chios and 86% in Lesvos). The pollutants that are emitted in greater quantities from the ships at the ports of Mytilene and Chios are NOₓ, followed by SO₂ and CO. Most of the ship emissions in the ports occur by the ships at berth, as they remain berthed for hours whereas maneuvering lasts 15–20 min. As for the daily contribution of the two transport sources to the pollution profile of Mytilene, road emissions are higher for almost all pollutants. However, the contribution of ship emissions is not negligible, especially during the touristic period when marine traffic increases and emissions close to the port area become more important than those from road transport.

Ozone has become a major atmospheric pollutant in China as the pattern of urban energy usage has changed and the number of motor vehicles has grown rapidly. The Beijing-Tianjin-Hebei Urban Agglomeration, also known as the Jing-Jin-Ji Urban Agglomeration (hereafter, JJJUA), with a precarious balance between protecting the ecological environment and sustaining economic development, is challenged by high levels of ozone pollution. Based on ozone observation data from 13 cities in the JJJUA from 2014 to 2017, the spatio-temporal trends in the evolution of ozone pollution and its associated influencing factors were analyzed using Moran’s I Index, hot-spot analysis, and Geodetector using ArcGIS and SPSS software. Five key results were obtained. 1) There was an increase in the annual average ozone concentration, for the period 2014–2017. Comparing the 13 prefecture-level cities, ozone pollution in Chengde and Hengshui decreased, while it worsened in the remaining 11 cities. 2) Ozone pollution was worse in spring and summer than in autumn and winter; the peak ozone pollution season was from May to September; the average ozone concentration on workdays was higher than that on non-workdays, showing a counter-weekend effect. 3) Annual average concentrations were high in the central and southern parts of the study region but low in the north. 4) Prominent positive spatial correlations were observed in ozone concentration, with the best correlations shown in summer and autumn; concentrations were high in Baoding and Xingtai but low in Beijing and Chengde. 5) Concentrations of PM10, NO2, CO, SO2, and PM2.5, as well as average wind speed, sunshine duration, evaporation, precipitation, and temperature, all had significant effects on ozone pollution, and interactions between these influencing factors increased it.

New environmental regulations are mandating cleaner fuels and lower emissions from all maritime operations. Natural gas (NG) is a fuel that enables mariners to meet regulations; however, emissions data from maritime operations with natural gas is limited. We measured emissions of criteria, toxic and greenhouse pollutants from a dual-fuel marine engine running either on diesel fuel or NG as well as engine activity and analyzed the impacts on pollutants, health, and climate change. Results showed that particulate matter (PM), black carbon (BC), nitric oxides (NOₓ), and carbon dioxide (CO₂) were reduced by about 93%, 97%, 92%, and 18%, respectively when switching from diesel to NG. Reductions of this magnitude provide a valuable tool for the many port communities struggling with meeting air quality standards. While these pollutants were reduced, formaldehyde (HCHO), carbon monoxide (CO) and methane (CH₄) increased several-fold. A health risk assessment of exhaust plume focused on when the vessel was stationary, and at-berth showed the diesel plume increased long-term health risk and the NG plume increased short-term health risk. An analysis of greenhouse gases (GHGs) and BC was performed and revealed that, on a hundred year basis, the whole fuel cycle global warming potential (GWP) per kWh including well-to-tank and exhaust was 50% to few times higher than that of diesel at lower engine loads, but that it was similar at 75% load and lower at higher loads. Mitigation strategies for further reducing pollutants from NG exhaust are discussed and showed potential for reducing short-term health risks and climate impacts.

Fine particulate matter (PM₂.₅) pollution has become a worldwide environmental concern because of its adverse impacts on human health. This study aimed to explore the spatiotemporal variations and influencing factors of PM₂.₅ concentrations in Beijing during the 2013–2018 period, and further analyzed the impacts of environmental protection policies implemented in recent years. Notably, this study employed various statistical methods, i.e., ordinary Kriging interpolation, spatial autocorrelation analysis, time-series analysis and the Bonferroni test, to evaluate the regional and seasonal differences of PM₂.₅ concentrations based on long-term monitoring data. The results illustrated that PM₂.₅ concentrations decreased on a yearly basis, demonstrating that air pollution control policies have achieved initial success. Furthermore, PM₂.₅ concentrations were higher in the winter and in the southern regions. Diurnal variation presented a bimodal distribution, which varied slightly with the season. Relative humidity and wind speed were the principal meteorological factors affecting the distribution of PM₂.₅ concentrations, while precipitation had essentially no effect. A high positive correlation between PM₂.₅ and gaseous pollutants (SO₂, NO₂, and CO) indirectly reflected the contribution of automobile exhaust and coal-fired emissions. Generally, PM₂.₅ concentrations demonstrated strong spatiotemporal variations, and meteorological factors and pollutant emissions played an important role in this.

Existing studies have typically investigated only the association between single pollutants and health outcomes. However, in the real world, people are exposed to multiple air pollutants simultaneously. The effect of air pollutants on emergency department (ED) visits has not been previously studied in the Sichuan Basin, which is one of the most polluted areas. We collected nonaccidental, respiratory and cardiovascular daily ED visits and daily concentrations of PM₂.₅, PMc, CO, SO₂, NO₂ and O₃ in Chengdu, China, from 2014 to 2016. A weighted variable for the combination of multiple air pollutants was constructed to assess the joint adverse health effects. Each air pollutant was assigned a health-related weight, which indicated the pollutant’s relative contribution to the joint effect. The effects on specific subpopulations (males and females; 15–65 years old and >65 years old) were also examined. With an increase of 10 μg/m³ of the combined multiple air pollutants, the daily ED visits for nonaccidental, respiratory and cardiovascular causes increased by 0.96% (95% CI: 0.51%–1.39%), 1.19% (95% CI: 0.53%, 1.85%) and 4.36% (95% CI: 1.06%, 7.76%) at lag 1, respectively. Males presented more pronounced effects, except for cardiovascular disease, than females. Elderly individuals were found to be more sensitive than young individuals. For nonaccidental and respiratory diseases, the contributions of particulate matter (PM) were dominant among the air pollutants, whereas cardiovascular disease was mainly affected by gaseous air pollutants. The combination of multiple air pollutants was significantly associated with ED visits in the Sichuan Basin, China. The joint effect of the combination of multiple air pollutants was highest for cardiovascular disease at lag 1. The relative contributions of individual pollutants varied by disease and subpopulation. These findings suggest that under different pollution scenarios, preventive strategies should target those with different diseases and different subpopulations.

In this study, the positive matrix factorization (PMF) source apportionment model was employed to quantify the contributions of airport activities to particle number concentrations (PNCs) at Amsterdam Schiphol. Time-resolved particle number size distributions in parallel with the concentrations of auxiliary variables, including gaseous pollutants (NOₓ and CO), black carbon, PM₂.₅ mass, and number of arrivals/departures were measured for 32 sampling days over a 6-month period near Schiphol airport to be used in the model. PMF results revealed that airport activities, cumulatively, accounted for around 79.3% of PNCs and our model segregated them into three major groups: (i) aircraft departures, (ii) aircraft arrivals, and (iii) ground service equipment (GSE) (with some contributions of local road traffic, mostly from airport parking lots). Aircraft departures and aircraft arrivals showed mode diameters <20 nm and contributed, respectively, to 46.1% and 26.7% of PNCs. The factor GSE/local road traffic, with a mode diameter of around 60–80 nm, accounted for 6.5% of the PNCs. Road traffic related mainly to the surrounding freeways was characterized with a mode diameter of 30–40 nm; this factor contributed to 18.0% of PNCs although its absolute PNCs was comparable with that of areas heavily impacted by traffic emissions. Lastly, urban background with a mode diameter at 150–225 nm, had a minimal contribution (2.7%) to PNCs while dominating the particle volume/mass concentrations with a contribution of 58.2%. These findings illustrate the dominant role of the airport activities in ambient PNCs in the surrounding areas. More importantly, the quantification of the contributions of different airport activities to PNCs is a useful tool to better control and limit the increased PNCs near the airports that could adversely impact the health of the adjacent urban communities.

China has been seriously affected by particulate matter (PM) and gaseous pollutants in the atmosphere. In this study, we systematically analyse the spatio-temporal patterns of PM₂.₅, PM₁₀, SO₂, CO, NO₂, and O₃ and the associated health risks, using data collected from 1498 national air quality monitoring sites. An analysis of the averaged data from all the sites indicated that, from 2015 to 2018, annual mean concentrations of PM₂.₅, PM₁₀, SO₂ and CO declined by 3.2 μg m⁻³, 3.7 μg m⁻³, 3.9 μg m⁻³, and 0.1 mg m⁻³, respectively. In contrast, those of NO₂ and O₃ increased at rates of 0.4 and 3.1 μg m⁻³, respectively. Except for O₃, the annual mean concentrations of all pollutants were generally the highest in North China and lowest in the Tibetan Plateau. The concentrations were generally higher in the north of the country than in the south. In all regions of China, the pollutant concentrations were the highest in winter and lowest in summer, except for O₃, which showed an opposite seasonal pattern. Overall, the seasonal mean concentrations of all the pollutants (except for O₃) significantly decreased between the same seasons in 2018 and 2015, whereas the seasonal mean O₃ concentrations generally significantly increased, and/or remained at stable levels in all four seasons except for winter. Diurnal variations of all pollutants (except for O₃) exhibited a bimodal pattern with peaks between 8:00 and 11:00 a.m. and 9:00 and 12:00 p.m., whereas O₃ exhibited a unimodal pattern with maximum values between 5:00 and 7:00 p.m. No significant differences in the daily mean concentrations of all pollutants were found between weekdays and weekends in all regions, except for PM₂.₅ and PM₁₀ in Northeast China. In Northwest China and Southeast China, PM₂.₅ showed stronger correlations with NO₂ relative to SO₂, suggesting that NOₓ emission control may be more effective than SO₂ emission control for alleviating PM₂.₅ formation. Compared with 2015, the total PM₂.₅-attributable mortality, number of respiratory and cardiovascular diseases, and incidence of chronic bronchitis decreased overall by 23.4%–26.9% in 2018. In contrast, for O₃-attributable deaths, there was an increase of 18.9%. Our study not only improves the understanding of the spatial and temporal patterns of air pollutants in China, but also highlights that synchronous control of PM₂.₅ and O₃ pollution should be implemented to achieve dual benefits in protecting human health.