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.

This study explored the interspecies and seasonal variation of polycyclic aromatic hydrocarbons (PAHs) in the extracted lipids of the leaves of seven local plants in an urban environment of Kolkata (22°33′N and 88°20′E), India. Based on the degree of toxicity and carcinogenicity (expressed in terms of their Benzo(a)pyrene equivalent (BaPeq) concentrations) the overall foliar-PAH accumulation during the study period (September 2018‒;August 2019) in the various plants showed the following order: Nerium oleander (80.96 ± 30.08 ng.gdw−1) > Mangifera indica (74.15 ± 20.34 ng.gdw−1) > Lantana aculeata (60.13 ± 21.71 ng.gdw−1) > Thevetia peruviana (40.97 ± 12.45 ng.gdw−1) > Ixora coccinea (38.11 ± 9.5 ng.gdw−1) > Murraya paniculata (37.1 ± 7.35 ng.gdw−1) > Polyalthia longifolia (25.72 ± 5.71 ng.gdw−1). The PAHs like phenanthrene, anthracene, fluoranthene, pyrene, benzo(a)anthracene, chrysene, benzo (b+k)fluoranthene, benzo(a)pyrene, benzo [ghi]perylene and indeno [1,2,3-cd]pyrene were predominant during the study period over the PAHs like naphthalene, acynaphthylene, acenaphthene, fluorine and dibenz [a,h]anthracene in the extracted lipids. The temperature-dependent partitioning of the PAHs onto leaf-surface and photo-degradation could have affected the availability of the PAHs. The foliar PAH accumulation varied seasonally as winter (December–February) > postmonsoon (September–November) > premonsoon (March–May) > monsoon (June–August). The leaf epicuticular wax determined the PAH uptake and storage, which in turn was affected by the temperature and solar radiation. In consistence with the idea of “nature-based solutions” for deteriorated air quality remediation in an urban environment, this study could be a promising initiative to build up cost-effective biological filters to combat the airborne pollutants and improve urban air quality.

Aerosols have severe effects on climate, human health and ecosystems. The impact of aerosols on climate, ecosystems and human health can be better understood if the variability of optical properties of aerosols is accurately known. In this paper, we have used Moderate Resolution Imaging Spectroradiometer (MODIS) datasets of aerosol optical depth (AOD) at 550 nm, Angstrom exponent (440/870) (AE) and enhanced vegetation index (EVI) over Pakistan. We have also analyzed the relationship between meteorological variables (e.g., temperature, relative humidity (RH) and wind speed (WS)) and aerosol optical properties to acquire a deep knowledge of aerosol variability. The coefficients of determination (R²) between Aqua-AOD and AERONET-AOD are found to be 0.6724 over Lahore and 0.7678 over Karachi. Aqua-AOD has also been validated with AOD data from Terra, MISR and SeaWiFS. High AOD (0.8–1) and low AE (0.4–0.8) have been observed over south and southwestern Pakistan indicating the presence of dust aerosols. In northeastern Pakistan, EVI is negatively correlated with AOD. The northeastern Pakistan is characterized by high values of AOD (~1) during all seasons. AOD showed a significant interannual variation with the lowest values (0.22) in January and the highest (0.58) in July. AE is observed to be lower in spring and summer than in winter and autumn seasons in south and south-western Pakistan. A positive R (≥0.6) is observed between AOD and temperature over the southwestern Pakistan while a negative R (−0.4 ≤) is observed between AOD and RH over the southwestern Pakistan.

Based on the ground-level observed NO₂ concentration, satellite-observed NO₂ column concentration from the Ozone Monitoring Instrument (OMI) and meteorological parameters, we comprehensively consider the seasonal and regional differences in the relationship between NO₂ column concentration and measured NO₂ concentration and establish a two-stage combined ground NO₂ concentration estimation (TSCE-NO₂) model using a support vector machine for regression (SVR) and a genetic algorithm optimized back propagation neural network (GABP). On this basis, the spatial-temporal variation in the modelled ground-level NO₂ concentration over China during the period of 2005–2019 was analysed. The results show that the TSCE-NO₂ model proposed in this study provides a reliable estimation of the modelled ground-level NO₂ concentration over China, effectively filling the spatial and temporal gaps in China's air quality ground monitoring network (the model's correlation coefficient, R, is 0.92, the mean absolute error, MAE, is 3.62 μg/m³, the mean square percentage error, MSPE, is 0.72%, and the root-mean-square error, RMSE, is 5.93 μg/m³). The analysis results of the spatial and temporal variation indicate that (1) the perennial ground-level NO₂ concentration over China is high in the eastern area and low in the western area, and the high values are mainly distributed along the northern coast, the eastern coast, the middle reaches of the Yangtze River, the middle reaches of the Yellow River, the Pearl River Delta and the Sichuan Basin. (2) The modelled ground-level NO₂ concentrations over China are highest in winter, followed by those in autumn and spring, and they are lowest in summer. Before 2011, the ground-level NO₂ concentration over China increased at a rate of 0.348 ± 0.132 μg/(m³∙a) but decreased at a rate of 0.312 ± 0.188 μg/(m³∙a) after 2011. (3) From 2011 to 2019, measures such as energy savings and emission reductions alleviated NO₂ pollution on the premise of ensuring sustained China's GDP growth.