Vehicular emissions contribute significantly to air pollution, and the number of vehicles in use is continuing to rise. Policymakers thus need to formulate vehicular emission reduction policies to improve urban air-quality. This study used different vehicle control scenarios to predict the associated potential of mitigating carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxide (NOₓ), particulate matter with an aerodynamic diameter less than 2.5 μm (PM₂.₅), and particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀) in Xi’an China, in 2020 and 2025. One business-as-usual scenario and six control scenarios were established, and vehicular emission inventory was developed according to each scenario. The results revealed that eliminating high-emission vehicles and optimizing after-treatment devices would effectively reduce vehicular emissions. In addition, increasing the number of alternative fuel vehicles, restraining vehicle use, and restraining the growth of the vehicle population would all have certain effects on CO and VOCs emissions, but the effects would not be significant for NOx, PM₂.₅, and PM₁₀. The results also indicated that if all control measures were stringently applied together, emissions of CO, VOCs, NOₓ, PM₂.₅, and PM₁₀ would be reduced by 51.66%, 51.58%, 30.19%,71.12%, and 71.81% in 2020, and 53.55%, 51.44%, 19.09%, 54.88%, and 55.51%, in 2025, respectively.

Poor air quality is an emerging problem in Australia primarily due to ozone pollution events and lengthening and more severe wildfire seasons. A significant deterioration in air quality was experienced in Australia’s most populous cities, Melbourne and Sydney, as a result of fires during the so-called Black Summer which ran from November 2019 through to February 2020. Following this period, social, mobility and economic restrictions to curb the spread of the COVID-19 pandemic were implemented in Australia. We quantify the air quality impact of these contrasting periods in the south-eastern states of Victoria and New South Wales (NSW) using a meteorological normalisation approach. A Random Forest (RF) machine learning algorithm was used to compute baseline time series’ of nitrogen dioxide (NO₂), ozone (O₃), carbon monoxide CO and particulate matter with diameter < 2.5 μm (PM₂.₅), based on a 19 year, detrended training dataset. Across Victorian sites, large increases in CO (188%), PM₂.₅ (322%) and ozone (22%) were observed over the RF prediction in January 2020. In NSW, smaller pollutant increases above the RF prediction were seen (CO 58%, PM₂.₅ 80%, ozone 19%). This can be partly explained by the RF predictions being high compared to the mean of previous months, due to high temperatures and strong wind speeds, highlighting the importance of meteorological normalisation in attributing pollution changes to specific events. From the daily observation-RF prediction differences we estimated 249.8 (95% CI: 156.6–343.) excess deaths and 3490.0 (95% CI 1325.9–5653.5) additional hospitalisations were likely as a result of PM₂.₅ and O₃ exposure in Victoria and NSW. During April 2019, when COVID-19 restrictions were in place, on average NO₂ decreased by 21.5 and 8% in Victoria and NSW respectively. O₃ and PM₂.₅ remained effectively unchanged in Victoria on average but increased by 20 and 24% in NSW respectively, supporting the suggestion that community mobility reduced more in Victoria than NSW. Overall the air quality change during the COVID-19 lockdown had a negligible impact on the calculated health outcomes.

This paper presents the results of experimental research into the component composition of gases and ash residue from the combustion of a set of high-potential coal-water slurries containing petrochemicals. We have established that the use of slurry fuels provides a decrease in the CO₂, CH₄, SO₂, and NOₓ concentrations as compared to those from coal combustion. The content of carbon monoxide and hydrogen in the gas environment from the combustion of slurries is higher due to the intense water evaporation. It is shown that adding biomass allows a further 5–33% reduction in the emissions of nitrogen and sulfur oxides as compared to the coal-water slurry and the composition with added waste turbine oil and a 23–68% decrease as compared to coal (per unit mass of the fuel burnt). The mechanisms and stages of CO₂, SO₂, and NOₓ formation are explained with a view to controlling gaseous anthropogenic emissions and ash buildup. The values of the relative environmental performance indicator are calculated for slurry fuels. It is shown to exceed the same indicator of bituminous coal by 28–56%.

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.

The purpose of this study is to investigate the potential associations between air pollution and dry eye disease (DED). Data of outdoor air pollutants and meteorology as well as outpatient visits for DED were collected. A time-stratified case-crossover approach was used to analyze the associations between ambient air pollutants and outpatient visits for DED. Among the 5062 DED patients studied, 65.45% were female and 34.55% were male. In the single-pollutant model, significant associations were observed between an increase of 10 μg/m³ in the concentrations of fine-particulate matter with a median aerometric diameter of less than 10 μm (PM₁₀), fine-particulate matter with a median aerometric diameter of less than 2.5 μm (PM₂.₅), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO) and outpatient visits for DED. These results were consistent with those of the multipollutant model. The strongest associations between air pollutants and patient visits were observed during the cold season and in patients aged 21–40. The significant association between air pollutants (PM₁₀, PM₂.₅, SO₂, NO₂, and CO) and DED outpatient visits indicates the importance of increased environmental protection.

The rate of deforestation in Brazil increased by 29% between 2015 and 2016, resulting in an increase of greenhouse gas emissions (GHG) of 9%. Deforestation fires in the Amazonia are the main source of GHG in Brazil. In this work, amounts of CO2, CO, main hydrocarbon gases and PM2.5 emitted during deforestation fires, under real conditions directly in Brazilian Amazonia, were determined. A brief discussion of the relationship between the annual emission of CO2 equivalent (CO2,eq) and Paris Agreement was conducted. Experimental fires were carried out in Western Amazonia (Candeias do Jamari, Rio Branco and Cruzeiro do Sul) and results were compared with a previous fire carried out in Eastern Amazonia (Alta Floresta). The average total fresh biomass on the ground before burning and the total biomass consumption were estimated to be 591 ton ha−1 and 33%, respectively. CO2, CO, CH4, and non–methane hydrocarbon (NMHC) average emission factors, for the four sites, were 1568, 140, 8, and 3 g kg−1 of burned dry biomass, respectively. PM2.5 showed large variation among the sites (0.9–16 g kg−1). Emissions per hectare of forest were estimated as 216,696 kg of CO2, 18,979 kg of CO, 1,058 kg of CH4, and 496 kg of NMHC. The average annual emission of equivalent CO2 was estimated as 301 ± 53 Mt year−1 for the Brazilian Amazonia forest. From 2013, the estimated CO2,eq showed a trend to increase in Amazon region. The present study is an alert and provides important information that can be used in the development of the public policies to control emissions and deforestation in the Brazilian Amazonia.

The consequences of indoor and outdoor air pollution on human health are of great concern nowadays. In this study, we firstly evaluated indoor and outdoor air pollution levels (CO, CO₂, NO, NO₂, PM₁₀) at an urban site in Dakar city center and at a rural site. Then, the individual exposure levels to selected pollutants and the variations in the levels of biomarkers of exposure were investigated in different groups of persons (bus drivers, traders working along the main roads and housemaids). Benzene exposure levels were higher for housemaids than for bus drivers and traders. High indoor exposure to benzene is probably due to cooking habits (cooking with charcoal), local practices (burning of incense), the use of cleaning products or solvent products which are important emitters of this compound. These results are confirmed by the values of S-PMA, which were higher in housemaids group compared to the others. Urinary 1-HOP levels were significantly higher for urban site housemaids compared to semirural district ones.Moreover, urinary levels of DNA oxidative stress damage (8-OHdG) and inflammatory (interleukin-6 and -8) biomarkers were higher in urban subjects in comparison to rural ones.The air quality measurement campaign showed that the bus interior was more polluted with PM₁₀, CO, CO₂ and NO than the market and urban or rural households. However, the interior of households showed higher concentration of VOCs than outdoor sites confirming previous observations of higher indoor individual exposure level to specific classes of pollutants.

This is the first study to characterize the variation and emission of C₂-C₅ non-methane volatile organic compounds (NMVOCs) in a semi-urban site of western India based on measurements during February–December 2015. Anthropogenic NMVOCs show clear seasonal dependence with highest in winter and lowest in monsoon season. Biogenic NMVOCs likes isoprene show highest mixing ratios in the pre-monsoon season. The diurnal variation of NMVOC species can be described by elevated values from night till morning and lower values in the afternoon hours. The elevated levels of NMVOCs during night and early morning hours were caused mainly by weaker winds, temperature inversion and reduced chemical loss. The correlations between NMVOCs, CO and NOx indicate the dominant role of various local emission sources. Use and leakage of liquefied petroleum gas (LPG) contributed to the elevated levels of propane and butanes. Mixing ratios of ethylene, propylene, CO, NOx, etc. show predominant emissions from combustion of fuels in automobiles and industries. The Positive Matrix Factorization (PMF) source apportionments were performed for the seven major emission sectors (i.e. Vehicular exhaust, Mixed industrial emissions, Biomass/Fired brick kilns/Bio-fuel, Petrochem, LPG, Gas evaporation, Biogenic). Emissions from vehicle exhaust and industry-related sources contributed to about 19% and 40% of the NMVOCs, respectively. And the rest (41%) was attributed to the emissions from biogenic sources, LPG, gasoline evaporation and biomass burning. Diurnal and seasonal variations of NMVOCs were controlled by local emissions, meteorology, OH concentrations, long-range transport and planetary boundary layer height. This study provides a good reference for framing environmental policies to improve the air quality in western region of India.