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.

In Turkey, Southeastern Anatolia region is the highest in terms of solar radiation level. However, the provinces in the region are subject to Particulate Matter (PM) coming from the Sahara desert, the Syrian Desert and the Arabian Desert by atmospheric transport. The daily limit of PM₁₀ and PM₂.₅ set by WHO for health is often exceeded in Sanliurfa. PM₁₀ and PM₂.₅ pollutants also accumulate on the Photovoltaic (PV) panels and cause loss of PV panel performance. In this study, the effects of atmospheric dust deposition on the performance of PV panel was determined for both monocrystalline and polycrystalline technologies under Sanliurfa atmospheric conditions. Two panels with the same characteristics were used for each PV panel group from 2 different PV technologies. One of the panels in the group was cleaned by washing with distilled water every Monday while the other was not cleaned. Thus, the effect of the dust accumulation on the PV panel was determined by comparison to the cleaned PV panel. PV panel power is measured with I–V meter. Panel surface temperature, solar radiation and other meteorological parameters are measured simultaneously. The measurements were done every Monday, Wednesday and Friday at 12:00 am from May 1 to December 31, 2019. It is observed that the dust accumulation reduces the PV power output up to 8% depending on the amount of radiation. During the summer months, the power loss on the average is 4.33% for monocrystalline and 4.57% for polycrystalline. In the autumn months, it is less than 1.77%.

This study investigates the contents, distribution patterns, and sources of lanthanoid elements (La to Lu) in aerosols with an aerodynamic diameter ≤10 μm (PM₁₀) in a coastal Caribbean region in order to better constrain the origin of the atmospheric PM contamination. We sampled and analysed PM₁₀ aerosols during 2015 simultaneously at a rural and an urban site in Cienfuegos (Cuba) as well as particles samples from regional contamination sources. Results showed that the sum of the studied lanthanoids concentrations ranged from 0.03 to 13.42 ng m⁻³ and from 0.51 to 18.75 ng m⁻³ at the rural and the urban site, respectively. Time variations for the lanthanoid concentrations displayed similar trends and showed that the highest concentrations corresponded to the influence of the African dust for both sites, but presented distinct variability and lower concentrations when dust intrusions were less frequent. The lanthanoid distribution patterns in the rural and urban sites were significantly different, due to the impact of different local combustion sources. Our results were comforted by comparing the degree of fractionation of the lighter and heavier lanthanoids and the δEu and δCe anomalies between our PM₁₀ samples and those of the local sources of contamination. Ultimately, we highlight the added value of lanthanoid elements as reliable indicators for discriminating emission sources and for tracking the origin of atmospheric particulate matter.

In this paper, we investigate the footprint of the operation of Athens International Airport in loads of air pollutants emitted during the Landing-Take Off phase of incoming and outgoing flights. This part of the flight has the distinctive characteristic that it operates in the human environment, at low altitudes, so it directly affects the air quality at the airport and its surroundings by changing the total amounts of air pollutants involved. The present survey covers the period 2002–2019 and only civil aircrafts flights have been considered. In particular, the concentrations mono-nitrogen oxides (NOX), carbon monoxide (CO), carbon dioxide (CO₂), unburnt hydrocarbons (HC) and total Particulate Matter (PM) consisting of volatile organic PM, volatile sulphuric PM and non-volatile PM have been studied. According to the results obtained more than 6500 kt of CO₂, almost 28 kt of NOX, about 18 kt of CO, almost 1.5 kt of HC and 0.3 kt of PMₜₒₜₐₗ have been released into the atmosphere during the total operating time of the airport. Actions related to the conduct of new measurements of air pollutants are aimed which point to the reduction of their impacts in the coming years.