Found 9746 publications. Showing page 363 of 390:
Rapid growth in urbanization and industrialization leads to an increase in air pollution and poor air quality. Because of its adverse effects on the natural environment and human health, it’s been declared a “silent public health emergency”. To deal with this global challenge, accurate prediction of air pollution is important for stakeholders to take required actions. In recent years, deep learning-based forecasting models show promise for more effective and efficient forecasting of air quality than other approaches. In this study, we made a comparative analysis of various deep learning-based single-step forecasting models such as long short term memory (LSTM), gated recurrent unit (GRU), and a statistical model to predict five air pollutants namely Nitrogen Dioxide (NO 2 ), Ozone (O 3 ), Sulphur Dioxide (SO 2 ), and Particulate Matter (PM2.5, and PM10). For empirical evaluation, we used a publicly available dataset collected in Northern Ireland, using an air quality monitoring station situated in Belfast city centre. It measures the concentration of air pollutants. The performance of forecasting models is evaluated based on three performance metrics: (a) root mean square error (RMSE), (b) mean absolute error (MAE) and (c) R-squared ( R2 ). The result shows that deep learning models consistently achieved the least RMSE compared to the statistical models with a value of 0.59. In addition, the deep learning model is also found to have the highest R2 score of 0.856.
IEEE (Institute of Electrical and Electronics Engineers)
2023
Volcanoes are known to be important emitters of atmospheric gases and aerosols, which for certain volcanoes can include halogen gases and in particular HBr. HBr emitted in this way can undergo rapid atmospheric oxidation chemistry (known as the bromine explosion) within the volcanic emission plume, leading to the production of bromine oxide (BrO) and ozone depletion. In this work, we present the results of a modelling study of a volcanic eruption from Mt Etna that occurred around Christmas 2018 and lasted 6 d. The aims of this study are to demonstrate and evaluate the ability of the regional 3D chemistry transport model Modèle de Chimie Atmosphérique de Grande Echelle (MOCAGE) to simulate the volcanic halogen chemistry in this case study, to analyse the variability of the chemical processes during the plume transport, and to quantify its impact on the composition of the troposphere at a regional scale over the Mediterranean basin.
The comparison of the tropospheric SO2 and BrO columns from 25 to 30 December 2018 from the MOCAGE simulation with the columns derived from the TROPOspheric Monitoring Instrument (TROPOMI) satellite measurements shows a very good agreement for the transport of the plume and a good consistency for the concentrations if considering the uncertainties in the flux estimates and the TROPOMI columns. The analysis of the bromine species' partitioning and of the associated chemical reaction rates provides a detailed picture of the simulated bromine chemistry throughout the diurnal cycle and at different stages of the volcanic plume's evolution. The partitioning of the bromine species is modulated by the time evolution of the emissions during the 6 d of the eruption; by the meteorological conditions; and by the distance of the plume from the vent, which is equivalent to the time since the emission. As the plume travels further from the vent, the halogen source gas HBr becomes depleted, BrO production in the plume becomes less efficient, and ozone depletion (proceeding via the Br+O3 reaction followed by the BrO self-reaction) decreases. The depletion of HBr relative to the other prevalent hydracid HCl leads to a shift in the relative concentrations of the Br− and Cl− ions, which in turn leads to reduced production of Br2 relative to BrCl.
The MOCAGE simulations show a regional impact of the volcanic eruption on the oxidants OH and O3 with a reduced burden of both gases that is caused by the chemistry in the volcanic plume. This reduction in atmospheric oxidation capacity results in a reduced CH4 burden. Finally, sensitivity tests on the composition of the emissions carried out in this work show that the production of BrO is higher when the volcanic emissions of sulfate aerosols are increased but occurs very slowly when no sulfate and Br radicals are assumed to be in the emissions. Both sensitivity tests highlight a significant impact on the oxidants in the troposphere at the regional scale of these assumptions.
All the results of this modelling study, in particular the rapid formation of BrO, which leads to a significant loss of tropospheric ozone, are consistent with previous studies carried out on the modelling of volcanic halogens.
2023
Arctic Tropospheric Ozone Trends
Observed trends in tropospheric ozone, an important air pollutant and short-lived climate forcer (SLCF), are estimated using available surface and ozonesonde profile data for 1993–2019, using a coherent methodology, and compared to modeled trends (1995–2015) from the Arctic Monitoring Assessment Program SLCF 2021 assessment. Increases in observed surface ozone at Arctic coastal sites, notably during winter, and concurrent decreasing trends in surface carbon monoxide, are generally captured by multi-model median trends. Wintertime increases are also estimated in the free troposphere at most Arctic sites, with decreases during spring months. Winter trends tend to be overestimated by the multi-model medians. Springtime surface ozone increases in northern coastal Alaska are not simulated while negative springtime trends in northern Scandinavia are not always reproduced. Possible reasons for observed changes and model performance are discussed including decreasing precursor emissions, changing ozone dry deposition, and variability in large-scale meteorology.
American Geophysical Union (AGU)
2023
2023
2023
An Unprecedented Arctic Ozone Depletion Event During Spring 2020 and Its Impacts Across Europe
The response of the ozone column across Europe to the extreme 2020 Arctic ozone depletion was examined by analyzing ground-based observations at 38 European stations. The ozone decrease at the northernmost site, Ny-Ålesund (79°N) was about 43% with respect to a climatology of more than 30 years. The magnitude of the decrease declined by about 0.7% deg−1 moving south to reach nearly 15% at 40°N. In addition, it was found that the variations of the ozone column at each of the selected stations in March-May were similar to those observed at Ny-Ålesund but with a delay increasing to about 20 days at mid-latitudes with a gradient of approximately 0.5 days deg−1. The distributions of reconstructed ozone column anomalies over a sector covering a large European area show decreasing ozone that started from the north at the beginning of April 2020 and spread south. Such behavior was shown to be similar to that observed after the Arctic ozone depletion in 2011. Stratospheric dynamical patterns in March–May 2011 and during 2020 suggested that the migration of ozone-poor air masses from polar areas to the south after the vortex breakup caused the observed ozone responses. A brief survey of the ozone mass mixing ratios at three stratospheric levels showed the exceptional strength of the 2020 episode. Despite the stronger and longer-lasting Arctic ozone loss in 2020, the analysis in this work indicates a similar ozone response at latitudes below 50°N to both 2011 and 2020 phenomena.
American Geophysical Union (AGU)
2023
Field data from two latitudinal transects in Europe and Canada were gathered to better characterize the atmospheric fate of three cyclic methylsiloxanes (cVMSs), i.e., octamethyl-cyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5) and dodecamethylcyclohexasiloxane (D6). During a year-long, seasonally resolved outdoor air sampling campaign, passive samplers with an ultra-clean sorbent were deployed at 15 sampling sites covering latitudes ranging from the source regions (43.7–50.7 °N) to the Arctic (79–82.5 °N). For each site, one of two passive samplers and one of two field blanks were separately extracted and analyzed for the cVMSs at two different laboratories using gas-chromatography-mass spectrometry. Whereas the use of a particular batch of sorbent and the applied cleaning procedure to a large extent controlled the levels of cVMS in field blanks, and therefore also the method detection and quantification limits, minor site-specific differences in field blank contamination were apparent. Excellent agreement between duplicates was obtained, with 95% of the concentrations reported by the two laboratories falling within a factor of 1.6 of each other. Nearly all data show a monotonic relationship between the concentration and distance from the major source regions. Concentrations in source regions were comparatively constant throughout the year, while the concentration gradient towards remote regions became steeper during summer when removal via OH radicals is at its maximum. Concentrations of the different cVMS oligomers were highly correlated within a given transect. Changes in relative abundance of cVMS oligomers along the transect were in agreement with relative atmospheric degradation rates via OH radicals.
Royal Society of Chemistry (RSC)
2023
Research communities, engagement campaigns, and administrative agents are increasingly valuing low-cost air-quality monitoring technologies, despite data quality concerns. Mobile low-cost sensors have already been used for delivering a spatial representation of pollutant concentrations, though less attention is given to their uncertainty quantification. Here, we perform static/on-bike inter-comparison tests to assess the performance of the Snifferbike sensor kit in measuring outdoor PM2.5 (Particulate Matter < 2.5 μm). We build a network of citizen-operated Snifferbike sensors in Kristiansand, Norway, and calibrate the measurements using Machine Learning techniques to estimate the concentrations of PM2.5 along the city roads. We also propose a method to estimate the minimum number of PM2.5 measurements required per road segment to assure data representativeness. The co-location of three Snifferbike kits (Sensirion SPS30) at the monitoring station showed a RMSD of 7.55 μg m−3. We approximate that one km h−1 increase in the speed of the bikes will add 0.03 - 0.04 μg m−3 to the Standard Deviation of the Snifferbike PM2.5 measurements. We estimate that at least 27 measurements per road segment are required (50 m here) if the data are sufficiently dispersed over time. We recommend calibrating the mobile sensors when they coincide with reference monitoring stations.
Elsevier
2023
Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019
The Arctic is a critical region in terms of global warming. Environmental changes are already progressing steadily in high northern latitudes, whereby, among other effects, a high potential for enhanced methane (CH4) emissions is induced. With CH4 being a potent greenhouse gas, additional emissions from Arctic regions may intensify global warming in the future through positive feedback. Various natural and anthropogenic sources are currently contributing to the Arctic's CH4 budget; however, the quantification of those emissions remains challenging. Assessing the amount of CH4 emissions in the Arctic and their contribution to the global budget still remains challenging. On the one hand, this is due to the difficulties in carrying out accurate measurements in such remote areas. Besides, large variations in the spatial distribution of methane sources and a poor understanding of the effects of ongoing changes in carbon decomposition, vegetation and hydrology also complicate the assessment. Therefore, the aim of this work is to reduce uncertainties in current bottom-up estimates of CH4 emissions as well as soil oxidation by implementing an inverse modelling approach in order to better quantify CH4 sources and sinks for the most recent years (2008 to 2019). More precisely, the objective is to detect occurring trends in the CH4 emissions and potential changes in seasonal emission patterns. The implementation of the inversion included footprint simulations obtained with the atmospheric transport model FLEXPART (FLEXible PARTicle dispersion model), various emission estimates from inventories and land surface models, and data on atmospheric CH4 concentrations from 41 surface observation sites in the Arctic nations. The results of the inversion showed that the majority of the CH4 sources currently present in high northern latitudes are poorly constrained by the existing observation network. Therefore, conclusions on trends and changes in the seasonal cycle could not be obtained for the corresponding CH4 sectors. Only CH4 fluxes from wetlands are adequately constrained, predominantly in North America. Within the period under study, wetland emissions show a slight negative trend in North America and a slight positive trend in East Eurasia. Overall, the estimated CH4 emissions are lower compared to the bottom-up estimates but higher than similar results from global inversions.
2023
Ammonia (NH3) is one of the most important gases emitted from agricultural practices. It affects air quality and the overall climate and is in turn influenced by long-term climate trends as well as by short-term fluctuations in local and regional meteorology. Previous studies have established the capability of the Infrared Atmospheric Sounding Interferometer (IASI) series of instruments, aboard the Metop satellites, to measure ammonia from space since 2007. In this study, we explore the interactions between atmospheric ammonia, land and meteorological variability, and long-term climate trends in Europe. We investigate the emission potential (Γsoil) of ammonia from the soil, which describes the soil–atmosphere ammonia exchange. Γsoil is generally calculated in-field or in laboratory experiments; here, and for the first time, we investigate a method which assesses it remotely using satellite data, reanalysis data products, and model simulations.
We focus on ammonia emission potential in March 2011, which marks the start of growing season in Europe. Our results show that Γsoil ranges from 2 × 103 to 9.5 × 104 (dimensionless) in fertilized cropland, such as in the North European Plain, and is of the order of 10–102 in a non-fertilized soil (e.g., forest and grassland). These results agree with in-field measurements from the literature, suggesting that our method can be used in other seasons and regions in the world. However, some improvements are needed in the determination of mass transfer coefficient k (m s−1), which is a crucial parameter to derive Γsoil.
Using a climate model, we estimate the expected increase in ammonia columns by the end of the century based on the increase in skin temperature (Tskin), under two different climate scenarios. Ammonia columns are projected to increase by up to 50 %, particularly in eastern Europe, under the SSP2-4.5 scenario and might even double (increase of 100 %) under the SSP5-8.5 scenario. The increase in skin temperature is responsible for a formation of new hotspots of ammonia in Belarus, Ukraine, Hungary, Moldova, parts of Romania, and Switzerland.
2023
2023
Populations of killer whale (Orcinus orca) contain some of the most polluted animals on Earth. Yet, the knowledge on effects of chemical pollutants is limited in this species. Cell cultures and in vitro exposure experiments are pertinent tools to study effects of pollutants in free-ranging marine mammals. To investigate transcriptional responses to pollutants in killer whale cells, we collected skin biopsies of killer whales from the Northern Norwegian fjords and successfully established primary fibroblast cell cultures from the dermis of 4 out of 5 of them. Cells from the individual with the highest cell yield were exposed to three different concentrations of a mixture of persistent organic pollutants (POPs) that reflects the composition of the 10 most abundant POPs found in Norwegian killer whales (p,p’-DDE, trans-nonachlor, PCB52, 99, 101, 118, 138, 153, 180, 187). Transcriptional responses of 13 selected target genes were studied using digital droplet PCR, and whole transcriptome responses were investigated utilizing RNA sequencing. Among the target genes analysed, CYP1A1 was significantly downregulated in the cells exposed to medium (11.6 µM) and high (116 µM) concentrations of the pollutant mixture, while seven genes involved in endocrine functions showed a non-significant tendency to be upregulated at the highest exposure concentration. Bioinformatic analyses of RNA-seq data indicated that 13 and 43 genes were differentially expressed in the cells exposed to low and high concentrations of the mixture, respectively, in comparison to solvent control. Subsequent pathway and functional analyses of the differentially expressed genes indicated that the enriched pathways were mainly related to lipid metabolism, myogenesis and glucocorticoid receptor regulation. The current study results support previous correlative studies and provide cause-effect relationships, which is highly relevant for chemical and environmental management.
Elsevier
2023
2023