Pollution derived from car tires is of growing research interest due to its apparent omnipresence in the urban environment and its associated toxicity. Studies have focused largely on the occurrence of these tire materials, deemed tire wear particles (TWPs), and their associated chemicals in the aquatic environment. However, less attention has been paid to atmospheric TWPs, which can remain airborne and be transported over long distances. In addition, there are few studies pertaining to the gaseous contaminants originating from tire wear, creating a significant knowledge gap. This review aims to summarize the current state of knowledge surrounding atmospheric tire wear pollution by detailing relevant studies conducted under both laboratory and ambient environmental conditions. Organic chemicals that are associated with this form of pollution, including diphenylamine antioxidants, phthalates, benzothiazole, benzotriazoles, and alkylphenols were highlighted for their potential implications for air. While a number of studies have investigated oxidation in aquatic environments, the current review highlights a clear absence of oxidation product information relevant to air. There is also a critical research gap surrounding the physico-chemical properties of these potential atmospheric pollutants. As a result, the environmental behaviour and fate of these contaminants are largely unknown. Based on these knowledge gaps, we propse recommendations for future work to advance this area of research.

In some international cities, winter air pollution can manifest into a local-scale brown air pollution haze which has been associated with negative health outcomes. Land-sea breezes are known to impact urban air quality through the recirculation of air pollution and the formation of internal boundary layers (IBLs). However, research into land-sea breezes has primarily focused on summer air pollution and little is known about the influence of land-sea breezes on local-scale winter brown haze. Using continuous data (including surface meteorology, surface air quality, satellite-derived sea surface temperatures, and ceilometer-derived boundary-layer depths) observed over seven winters from 2013 to 2019, we present a novel investigation of the influence of land-sea breezes on brown haze in Auckland, New Zealand. Severe brown haze days are significantly more likely to coincide with a land-sea breeze circulation simultaneously occurring at both the east and west coasts when compared with days on which brown haze is expected but not observed (based on favourable meteorology and high surface air pollution levels). Both severe brown haze and high surface level PM₂.₅ concentrations (previously associated with the presence of brown haze) are found to be associated with a high degree of horizontal recirculation at Auckland's east coast. No relationship is found between the occurrence of sea-breeze-induced IBLs and the formation of brown haze. The results presented in this study offer insights into the physical mechanisms that influence the formation and persistence of local-scale winter brown haze in a complex coastal setting with correspondingly complex land-sea breezes.

Cities located on Eastern Mediterranean is exposed to both local and distant anthropogenic and natural sources. In this study, to determine the effect of these sources on Particulate Matter (PM) concentrations in a coastal city, particulate matter with diameters less than 2.5 μm (PM2.5) and with diameters between 2.5 and 10 μm (PM2.5−10) were collected once in a two-day period for 24 h between July 2014 and July 2015 in downtown Antalya which is located on the Mediterranean coast of Turkey. Antalya is one of the fast growing city of Turkey with a population of 2.3 million. Samples were analyzed using an energy-dispersive X-ray fluorescence for 15 elements (Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe, Cu, Zn, As, Pb). Statistical parameters were calculated for all measured elements in fine (PM2.5) and coarse (PM2.5−10) fraction. Crustal and marine elements, such as Ti, Ca, Al and Na were abundant in the course fraction. Only S was found in higher concentration in the fine fraction. Monthly variation of Crustal Enrichment Factor (EFC) results of Si showed that the area was under influence of non-local crustal dust especially during spring and late summer. EFC also indicated that during winter season, fine fraction K was due to local wood combustion. Source regions of S was determined using Potential Source Contribution Function (PSCF) and compared with previous studies conducted at a rural site of Antalya approximately twenty years ago. Most of the source regions affecting S concentrations at the Eastern Mediterranean region were found out to be same: western Anatolia, Marmara region, the Aegean Sea coasts of Greece and some parts of Bulgaria and Romania. However, due to decrease in SO2 emissions over the northeast coast of Black Sea and between Caspian Sea and Ukraine, the region was not turned up to be a source region.

The inappropriate combustion of furniture-industry waste can be a source of serious environmental problems. We proposed a method which is capable to distinguish the emission resulting from the combustion of wood-based materials, the essential component of such waste. The originality of the approach consists in the classification of gas mixtures instead of focussing on the individual pollutants emitted to the atmosphere. The classification of emission was based on the measurements applying differential ion mobility spectrometry (DMS) and Fourier transform infrared (FTIR) spectrometry, for comparison. There were successfully distinguished emissions associated with combustion of wood-based materials: OSB board, MDF board and plywood (≥95% correct classifications in the class (ccc)) and wood: pellet and kindling wood (≥92% ccc). Results of classification based on DMS and FTIR measurements were similar. Emissions from the combustion of individual materials were best distinguished using DMS (100% ccc), as compared with FTIR, which offered lower performance, mostly >90% ccc. Regarding pairwise classification, the most distinctive were emissions from the combustion of plywood (DMS: 99% ccc; FTIR: 99% ccc), MDF board (DMS: 99% ccc; FTIR: 99% ccc) and OSB board (DMS: 99% ccc; FTIR: 98% ccc). Emissions form kindling wood (DMS: 100% ccc; FTIR: 95% ccc) and pellet (DMS: 97% ccc; FTIR: 98% ccc) caused a bit more confusion. In most cases, results of classification based on DMS and FTIR measurements were comparable. The success of classification based on DMS measurements proved that it is possible to detect the harmful emission without determining the chemical composition of the flue gas. This solution represents a new approach to air quality monitoring, which recently attracts increasing attention.

This paper presents model results for the dispersion of radionuclides released into the atmosphere by intense forest fires in the Chernobyl Exclusion Zone in April 2020. The ¹³⁷Cs activity concentration in the surface air is calculated on a regional scale (in Ukraine) and a local scale (within the Chernobyl Exclusion Zone). The ¹³⁷Cs activity in the surface air of Kyiv was found to have reached 2–4 mBq m⁻³ during the period April 4–20. The results presented in this paper are generally consistent with measured data pertaining to radioactive contamination in Kyiv and areas around several nuclear power plants in Ukraine. The total effective dose to the population of Kyiv during the fire period was estimated to be 5.7 nSv from external exposure and the inhalation of ¹³⁷Cs and ⁹⁰Sr, rising to 30 nSv by the end of 2020. This is about 0.003% of the annual permissible level of exposure of the population. A committed effective dose of about 16 nSv was estimated for the personnel of the Chernobyl nuclear power plant from the inhalation of ¹³⁷Cs and ⁹⁰Sr during the 2020 forest fires. A method for estimating the radionuclide activity emissions during wildland fires in radioactively contaminated areas is proposed. This method is based on satellite measurement data of the fire radiative power, the radionuclide inventory in the fire area, and an emission factor for radioactive particles.

Accuracy prediction of air quality is of crucial importance for people to take precautions and improve environmental conditions. By introducing adjacency matrix in Long Short-Term Memory (LSTM) cell, we propose in this research a Graph-based Long Short-Term Memory (GLSTM) model to predict PM2.5 concentration in Gansu Province of Northwest China. We regard all air quality monitoring stations as a graph, and construct a parameterized adjacency matrix on the basis of the adjacency matrix of the graph. Through the combination of parameterized adjacency matrix and LSTM, we introduce spatiotemporal information to achieve PM2.5 prediction. The advantage of GLSTM is that it can realize synchronous operation of all stations, making it unnecessary to train different model for each monitoring station to obtain the overall PM2.5 variation of a certain area. The parameterized adjacency matrix also enhances the interpretability of the model. By visualizing the parameterized adjacency matrix obtained from the end-to-end PM2.5 prediction task in training, the importance of introducing spatial information, i.e. the distribution importance of surrounding stations to a specific station is clearly demonstrated. We compared our model with several newly reported methods, and found that it achieved the best results on PM2.5 prediction tasks at almost all stations, which proved the effectiveness of the GLSTM model.

Statistical learning models have been applied to study the spatial patterns of ambient Nitrogen Dioxide (NO2), which is a highly dynamic, traffic-related air pollutant. Commonly, the validation process in most studies is based on bootstrapped split-sampling of training and test sets from fixed ground station measurements. As the ground stations distribute mostly sparsely over a region or country, this kind of cross-validation validation method does not consider how well models are capable of representing spatial variations in air pollution mostly occurring over distances shorter than the ground station sampling spacing. This may lead to inadequate hyperparameter optimisation and bias when comparing different statistical models. External mobile measurements are therefore needed for more reliable model evaluations as these provide detailed and spatially continuous information on air pollution patterns. However, most current designs of mobile NO2 sensing instruments suffer from the trade-off between flexibility and measurement accuracy, as high-end sensors are commonly too heavy to be carried by a person or on a bike. In addition, sufficient repetitions over time are needed so that the measurements are representative to concentrations over a relatively long-term period. In this study, we installed a mobile air quality station onboard a cargo-bike to collect a dataset suitable for external validation. With the external validation dataset the model hyperparameter setting and statistical model comparison results alter. Our model comparison results also differ from previous studies relying only on ground stations for cross-validation.

Cities in southern Chile are facing high levels of PM₂.₅ because of wood burning pollution. We quantify the contribution of wood smoke to fine particles in two mid-size cities: Molina and Valdivia, located in different climate zones. The sampling campaigns were carried out during austral winter (July to September) in 2018 (Molina) and 2019 (Valdivia). 24-h filter samples were analyzed for carbonaceous compounds, secondary ions, metals, and particle-phase organic molecular markers. Average winter concentrations of PM₂.₅ were 53 ± 32 μg/m³ (average ± standard deviation) in Molina and 89 ± 55 μg/m³ in Valdivia. The major component of fine particles was organic matter, representing more than 70% of PM₂.₅. Concentrations of organic molecular markers were used in a receptor model (US EPA CMB8.2) to identify and quantify primary sources of PM₂.₅. The major source of PM₂.₅ was wood smoke, which accounted for 41.55 ± 9.77 μg/m³ (62.9 ± 15.3%) in Molina and 43.65 ± 24.06 μg/m³ (51.7 ± 21.1%) in Valdivia. Secondary organic aerosols (SOA) generated from inefficient wood burning, contributed 20.4 ± 17.7% in Molina and 28.9 ± 27.6% in Valdivia. Secondary inorganic ions and dust are minor sources of PM₂.₅. The total contribution of wood smoke (adding primary wood smoke and SOA) could be as much as 83% in Molina and 81% in Valdivia, during the winter season.

Solar eclipse (with maximum obscuration of 85.3% and magnitude of 0.893) occurred on 26 December 2019 during morning hours (08:10 to 11:15 LT with a peak at 09:33 LT) over Gadanki (13.5ᵒN, 79.2ᵒE) has provided a unique opportunity to test the hypothesis of ‘Extended Fumigation Effect’ or ‘Second Fumigation’ on the surface and boundary layer pollutants. To capture this event, a campaign using multi-instrument (AWS, Aethalometer, PM sensors, ceilometer, radiosonde) on multi-platform (surface, surface based remote sensing, drone, tethered balloon, in-situ balloon) was conducted. Eclipse obscuration caused decrease in surface temperature by 4.3 °C around 10:00 LT. Boundary layer remained shallow until 09:00 LT (between 500 m and 900 m) but near the termination of the eclipse and soon after the termination a convective boundary layer showed a rapid increase to above 1 km within a short time (1 h). A Fumigation peak (common phenomenon in normal days) in black carbon occurred with a sharp peak concentration of 9.4 μg/m³ at around 07:00 LT and then started decreasing. However, concentration started to increase unusually again at around 08:20 LT and remained at the range of 4–6 μg/m³instead of a normal decreasing trend, which is about 2–3 times of the mean concentration at this period of time. Similar variation in PM₁, PM₂.₅, and PM₁₀are also observed. Background instability estimated using radiosonde measurements suggests Fumigation, Fumigation/Lofting and Trapping before, during and after the eclipse, respectively.

The UK may be required to expand its bioenergy production in order to make a significant contribution towards the delivery of its ‘net zero’ greenhouse gas emissions target by 2050. However, some trees grown for bioenergy are emitters of volatile organic compounds (VOCs), including isoprene and terpenes, precursors in the formation of tropospheric ozone, an atmospheric pollutant, which require assessment to understand any consequent impacts on air quality. In this initial scoping study, VOC emission rates were quantified under UK climate conditions for the first time from four species of eucalypts suitable for growing as short-rotation forest for bioenergy. An additional previously characterised eucalypt species was included for comparison. Measurements were undertaken using a dynamic chamber sampling system on 2-3 year-old trees grown under ambient conditions. Average emission rates for isoprene, normalised to 30 °C and 1000 μmol m⁻² s⁻¹ PAR, ranged between 1.3 μg C gdw⁻¹ h⁻¹ to 10 μg C gdw⁻¹ h⁻¹. All the eucalypt species measured were categorised as ‘medium’ isoprene emitters (1–10 μg C gdw⁻¹ h⁻¹). Total normalised monoterpene emission rates were of similar order of magnitude to isoprene or approximately one order of magnitude lower. The composition of the monoterpene emissions differed between the species and major compounds included eucalyptol, α-pinene, limonene and β-cis-ocimene. The emission rates presented here contribute the first data for further studies to quantify the potential impact on UK atmospheric composition, if there were widespread planting of eucalypts in the UK for bioenergy purposes.