Fant 2676 publikasjoner. Viser side 16 av 268:
A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient (babs) to eBC. This study investigates the spatial–temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and babs measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50 %) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m2 g-1 from 12 aethalometers at 880 nm, and 10.6 ± 4.7 m2 g-1 from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasize the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.
2024
We determine the global emission distribution of the potent greenhouse gas sulfur hexafluoride (SF6) for the period 2005–2021 using inverse modelling. The inversion is based on 50 d backward simulations with the Lagrangian particle dispersion model (LPDM) FLEXPART and on a comprehensive observation data set of SF6 mole fractions in which we combine continuous with flask measurements sampled at fixed surface locations and observations from aircraft and ship campaigns. We use a global-distribution-based (GDB) approach to determine baseline mole fractions directly from global SF6 mole fraction fields at the termination points of the backward trajectories. We compute these fields by performing an atmospheric SF6 re-analysis, assimilating global SF6 observations into modelled global three-dimensional mole fraction fields. Our inversion results are in excellent agreement with several regional inversion studies in the USA, Europe, and China. We find that (1) annual US SF6 emissions strongly decreased from 1.25 Gg in 2005 to 0.48 Gg in 2021; however, they were on average twice as high as the reported emissions to the United Nations. (2) SF6 emissions from EU countries show an average decreasing trend of −0.006 Gg yr−1 during the period 2005 to 2021, including a substantial drop in 2018. This drop is likely a direct result of the EU's F-gas regulation 517/2014, which bans the use of SF6 for recycling magnesium die-casting alloys as of 2018 and requires leak detection systems for electrical switch gear. (3) Chinese annual emissions grew from 1.28 Gg in 2005 to 5.16 Gg in 2021, with a trend of 0.21 Gg yr−1, which is even higher than the average global total emission trend of 0.20 Gg yr−1. (4) National reports for the USA, Europe, and China all underestimated their SF6 emissions. (5) Our results indicate increasing emissions in poorly monitored areas (e.g. India, Africa, and South America); however, these results are uncertain due to weak observational constraints, highlighting the need for enhanced monitoring in these areas. (6) Global total SF6 emissions are comparable to estimates in previous studies but are sensitive to a priori estimates due to the low network sensitivity in poorly monitored regions. (7) Monthly inversions indicate that SF6 emissions in the Northern Hemisphere were on average higher in summer than in winter throughout the study period.
2024
Query-driven Qualitative Constraint Acquisition
Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. Recently, we introduced GEQCA-I, which stands for Generic Qualitative Constraint Acquisition, as a new active constraint acquisition method for learning qualitative constraints using qualitative queries. In this paper, we revise and extend GEQCA-I to GEQCA-II with a new type of query, universal query, for qualitative constraint acquisition, with a deeper query-driven acquisition algorithm. Our extended experimental evaluation shows the efficiency and usefulness of the concept of universal query in learning randomly-generated qualitative networks, including both temporal networks based on Allen’s algebra and spatial networks based on region connection calculus. We also show the effectiveness of GEQCA-II in learning the qualitative part of real scheduling problems.
2024
Monitoring aerosol optical depth during the Arctic night: Instrument development and first results
Moon-photometric measurements were made at two locations in the Arctic during winter nights using two different modified Sun photometers; a Carter Scott SP02 and a Precision Filter Radiometer (PFR) developed at PMOD/WRC. Values of aerosol optical depth (AOD) were derived from spectral irradiance measurements made at four wavelengths for each of the devices. The SP02 was located near Barrow, Alaska and recorded data from November 2012 to March 2013, spanning five lunar cycles, while the PFR was deployed to Ny-Ålesund, Svalbard each winter from February 2014 to February 2019 for a total of 56 measurement periods. A methodology was developed to process the raw data, involving calibration of the instruments and normalizing measured spectral irradiance values in accordance with site-specific determinations of the extraterrestrial atmospheric irradiance (ETI) as Moon phase cycled. Uncertainties of the derived AOD values were also evaluated and found to be in the range, 0.006–0.030, depending on wavelength and which device was evaluated.
The magnitudes of AOD determined for the two sites were in general agreement with those reported in the literature for sunlit periods just before and after the dark periods of Arctic night. Those for the PFR were also compared with data obtained using star photometers and a Cimel CE318-T, recently deployed to Ny-Ålesund, showing that Moon photometry is viable as a means to monitor AOD during the Arctic night. Such data are valuable for more complete assessments of the role aerosols play in modulating climate, the validation of AOD derived using various remote sensing techniques, and applications related to climate modeling.
2024
Toward Standardization of a Lung New Approach Model for Toxicity Testing of Nanomaterials
This study represents an attempt toward the standardization of pulmonary NAMs and the development of a novel approach for toxicity testing of nanomaterials. Laboratory comparisons are challenging yet essential for identifying existing limitations and proposing potential solutions. Lung cells cultivated and exposed at the air-liquid interface (ALI) more accurately represent the physiology of human lungs and pulmonary exposure scenarios than submerged cell and exposure models. A triculture cell model system was used, consisting of human A549 lung epithelial cells and differentiated THP-1 macrophages on the apical side, with EA.hy926 endothelial cells on the basolateral side. The cells were exposed to silver nanoparticles NM-300K for 24 h. The model used here showed to be applicable for assessing the hazards of nanomaterials and chemicals, albeit with some limitations. Cellular viability was measured using the alamarBlue assay, DNA damage was assessed with the enzyme-modified comet assay, and the expression of 40 genes related to cell viability, inflammation, and DNA damage response was evaluated through RT2 gene expression profiling. Despite harmonized protocols used in the two independent laboratories, however, some methodological challenges could affect the results, including sensitivity and reproducibility of the model.
2024
Design of multi-luminescent silica-based nanoparticles for the detection of liquid organic compounds
Tracer testing in reservoir formations is utilised to determine residual oil saturation as part of optimum hydrocarbon production. Here, we present a novel detection method of liquid organic compounds by monodisperse SiO2 nanoparticles (NPs) containing two luminophores, a EuIII:EDTA complex and a newly synthesised fluorophore based on the organic boron-dipyrromethene (BODIPY)-moiety. The particles exhibited stable EuIII PL emission intensity with a long lifetime in aqueous dispersion. The fluorescence of the BODIPY was also preserved in the aqueous environment. The ratiometric PL detection technique was demonstrated by using toluene and 1-octanol as model compounds of crude oil. The optimal synthesis conditions were found to give NPs with a diameter of ~100 nm, which is suitable for transport through porous oil reservoir structures. The cytotoxicity of the NPs was confirmed to be very low for human lung cell and fish cell lines. These findings demonstrate the potential of the NPs to replace the hazardous chemicals used to estimate the residual oil saturation. Moreover, the ratiometric PL detection technique is anticipated to be of benefit in other fields, such as biotechnology, medical diagnostics, and environmental monitoring, where a reliable and safe detection of a liquid organic phase is needed.
2024
This study investigates the impact of meteorological variations on the long-term patterns of PM2.5 in Delhi from 2007 to 2022 using the AirGAM 2022r1 model. Generalized Additive Modeling was employed to analyze meteorology-adjusted (removing the influence of inter-annual variations in meteorology) and unadjusted trends (trends without considering meteorology) while addressing auto-correlation. PM2.5 levels showed a modest decline of 14 μg m−3 unadjusted and 18 μg m−3 meteorology-adjusted over the study period. Meteorological conditions and time factors significantly influenced trends. Temperature, wind speed, wind direction, humidity, boundary layer height, medium-height cloud cover, precipitation, and time variables including day-of-week, day-of-year, and overall time, were used as GAM model inputs. The model accounted for 55% of PM2.5 variability (adjusted R-squared = 0.55). Day-of-week and medium-height cloud cover were non-significant, while other covariates were significant (p
2024
2024
Data fusion for enhancing urban air quality modeling using large-scale citizen science data
Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality management by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the priori dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.
2024
Two-Stage Feature Engineering to Predict Air Pollutants in Urban Areas
Air pollution is a global challenge to human health and the ecological environment. Identifying the relationship among pollutants, their fundamental sources and detrimental effects on health and mental well-being is critical in order to implement appropriate countermeasures. The way forward to address this issue and assess air quality is through accurate air pollution prediction. Such prediction can subsequently assist governing bodies in making prompt, evidence-based decisions and prevent further harm to our urban environment, public health, and climate, all of which co-benefit our economy. In this study, the main objective is to explore the strength of features and proposed a two stage feature engineering approach, which fuses the advantage of influential factors along with the decomposition approach and generates an optimum feature combination for five major pollutants including Nitrogen Dioxide (NO 2 ), Ozone (O 3 ), Sulphur Dioxide (SO 2 ), and Particulate Matter (PM2.5, and PM10). The experiments are conducted using a dataset from 2015 to 2020 which is publicly available and is collected from Belfast-based air quality monitoring stations in Northern Ireland, UK. In stage-1, using the dataset new features such as trigonometric and statistical features are created to capture their dependency on the target pollutant and generated correlation-inspired best feature combinations to improve forecasting model performance. This is further enhanced in stage-2 by an optimum feature combination which is an integration of stage-1 and Variational Mode Decomposition (VMD) based features. This study employed a simplified Long Short Term Memory (LSTM) neural network and proposed a single-step forecasting model to predict multivariate time series data. Three performance indicators are used to evaluate the effectiveness of forecasting model: (a) root mean square error (RMSE), (b) mean absolute error (MAE), and (c) R-squared (R 2 ). The results demonstrate the effectiveness of proposed approach with 13% improvement in performance (in terms of R 2 ) and the lowest error scores for both RMSE and MAE.
2024