This study obtained the first dataset on the solubilities and partitioning of metals and metalloids (MMs), including Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Sb, Sc, Se, Sn, Sr, Th, Ti, Tl, U, V, W, Y, Zn, and Zr, during spring rains in Moscow. MM concentrations were determined using inductively coupled plasma mass spectrometry and atomic emission spectroscopy. The metals Ti, Zr, Al, Sn, Fe, W, Th, Li, V, Cs, Bi, and Y were predominant in the insoluble fraction of the rainwater, whilst Sb, Zn, Ca, Cd were prevalent in the soluble fraction. The end of the winter heating season, together with a weaker dilution effect due to lower precipitation in April, caused higher soluble and insoluble MM concentrations in rainwater than those in May. MM solubilities increased with decreasing pH and longer periods of precipitation, and declined with an increase in precipitation intensity, the length of the antecedent dry period, and the content of solid particles in the rainwater. A 20%–50% increase in solubility in acid rains compared to non-acid rains was observed for Y, Pb, U, Be, Na, Ni, Th, Cu, W, Fe, Mg, Cr, Tl, and Ba. Anthropogenic sources contributed significantly to the concentration of soluble MMs (>90% for Sb, Pb, Se, Cd, Zn, Cu, Bi, Ca, Mo, Sc, Ba, As, W, Sr, Mn, Sn, Co, Tl, Ni, and Be). For the insoluble MM, crustal materials were the important contributors. During public holidays in Russia in the first decade of May, an increase in the proportion of insoluble MMs was observed.

In Belgium, 16 shredding facilities manage annually tens of thousands tons of wastes from different origins (end-of-life vehicles, electronic waste, electrical transformers, …). These materials contain hazardous persistent organic pollutants such as polychlorinated dibenzo-dioxins/furans (PCDD/Fs), polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). The shredding process promotes the production and the emission of dust contaminated by these compounds. The objective of this study is to measure the concentrations of PCDD/Fs, PCBs and PBDEs in fallout dust collected in the vicinity of 3 shredding plants located in Wallonia (French speaking part of Belgium). Samples were collected by using Owen gauges and pollutant levels were measured by GC-MS. The median deposition levels measured for ∑PCDD/Fs, ∑dioxin-like PCBs, 5 × ∑6 DIN PCBs and PBDE 209 were 1.9 pg TEQ/m².day, 4.4 pg TEQ/m².day, 246.5 ng/m².day and 253.8 ng/m².day, respectively. These levels represent high concentrations compared to those observed in most of the remote, rural and urban areas studied around the world and were similar to those measured in other heavily industrialized districts. Consequently, the health effects of this high exposure to pollutants among workers and residents in the vicinity of these shredding facilities are of concern.

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.

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.

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.

With increasing atmospheric pollution and health issues associated with size of the particulate matter, it has become important to look for techniques that may improve the monitoring resolution. Magnetic bio-monitoring of particulate matter has been used in recent years in some countries as an approach for better spatial resolution that provides proxy indicators for the measurements over large areas. Delhi, which is one of the most polluted cities of not just India, but the whole world, is still probing to understand the possible sources. The present magnetic biomonitoring study was therefore, carried across different land use areas in some air pollution hotspots of Delhi, using common roadside tree species Morus alba, Ficus religiosa, Ficus virens and Ficus benghalensis to understand the magnitude and nature of the particulate pollution, and possible sources by studying magnetic properties (Magnetic susceptibility, Frequency-dependent susceptibility, S-ratio, and SIRM) of the dust deposited on leaves. Mass specific magnetic susceptibility (10⁻⁸ m³ kg⁻¹) values were found to follow the order: Traffic intersection area (25.6–66.5) > Industrial area (25.4–41.3) > Residential area (13.2–30.1) > Institutional area serving as control (2.7–6.6). High magnetic susceptibility values indicated particulates with ferrimagnetic grains of anthropogenic or technogenic origin. Frequency-dependent Susceptibility indicated dominance of coarse multidomain (MD) and Pseudo Single Domain (PSD) +MD grains in industrial area and major traffic intersection. Average S ratio across all study sites ranged from 0.92 to 0.99 indicating presence of soft magnetic mineral with low coercivity. High SIRM values (10⁻⁵Am² kg⁻¹) from 58.1 to 862.3 suggested prevalence of magnetite dominating atmospheric particulates particularly in traffic intersection and industrial area, and to some extent in residential area. Morus alba and Ficus religiosa were found more suitable bio-monitors and the technique provided useful information on size, mineralogy and possible source of the particulates.

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.

Chronic atmospheric nitrogen (N) deposition could influence the functioning of ecosystems as well as their biodiversity. However, N deposition in urban forest ecosystems, especially natural evergreen broad-leaved forests, is not well known. In this study, the concentrations and fluxes of dissolved inorganic N (DIN) and dissolved organic N (DON) in bulk deposition, throughfall, and stemflow were assessed in an urban evergreen broad-leaved forest site over three years, in order to clarify the characteristics of N deposition. At the study site, bulk DIN deposition was 3.7 kg N ha⁻¹ year⁻¹ (1.5 kg N ha⁻¹ year⁻¹ for NH₄–N and 2.2 kg N ha⁻¹ year⁻¹ for NO₃ + NO₂–N), which is the same level as that found in rural areas. In contrast, 6.5 kg N ha⁻¹ year⁻¹ for bulk DON deposition contributed to 66% of the bulk N deposition, which suggests the importance of bulk DON deposition in Japanese forest ecosystems. Passing through the tree canopy, DIN was enriched by 8.8 kg N ha⁻¹ year⁻¹ (3.7 kg N ha⁻¹ year⁻¹ for NH₄–N and 5.1 kg N ha⁻¹ year⁻¹ for NO₃ + NO₂–N) and DON was enriched by 1.5 kg N ha⁻¹ year⁻¹ as net throughfall in the evergreen broad-leaved forest. This reveals that dry deposition of DIN dominates the total DIN deposition onto the urban forest floor, compared to that found in the rural areas, due to the non-negligible N emissions from outside and possibly because of the evergreen broad-leaved forest's greater ability to capture N.

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.

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.