Fant 10084 publikasjoner. Viser side 22 av 404:
2024
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
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
Monitoring of environmental contaminants in freshwater food webs (MILFERSK), 2023
This report presents data from the third year of a 5-year period of the MILFERSK program. In 2023 the monitoring program reports on the sampling and analyses of the pelagic food chain in Lake Mjøsa, with the following sample types: zooplankton, Mysis, E. smelt, vendace, and brown trout, in addition to brown trout from Lake Femunden. A total of 205 single compounds/isomers were determined, and frequent detections were found of specific PFAS, PBDEs, Hg and siloxanes through the food chain with biomagnifying properties. Some contaminants, such as octocrylene is found in higher concentrations in the lower trophic levels. A slight downwards trend is observed from 2014 – 2023 for PFOS in Lake Mjøsa. We also observe a lower length adjusted mercury concentration for brown trout in Lake Mjøsa for the period 2014 to 2023, compared to the 9 years prior (2006 – 2014).
Norsk institutt for vannforskning (NIVA)
2024
2024
Integrating Low-cost Sensor Systems and Networks to Enhance Air Quality Applications
Low-cost air quality sensor systems (LCS) are emerging technologies for policy-relevant air quality analysis, including pollution levels, source identification, and forecasting. This report discusses LCS use in networks and alongside other data sources for comprehensive air quality applications, complementing other WMO publications on LCS operating principles, calibration, performance assessment, and data communication.
The LCS’s utility lies in their ability to provide new insights into air quality that existing data sources may not offer. While LCS data must be verified, their integration with other data sources can enhance understanding and management of air quality. In areas without reference-grade monitors, LCS can identify factors affecting local air quality and guide future monitoring efforts. Combining LCS data with satellite and other air quality systems can improve data reliability and establish corroborating evidence for observed trends. LCS can extend the spatial coverage of existing monitoring networks, offering localized insights and supporting effective air quality management policies. Co-locating LCS with reference-grade monitors helps quantify measurement uncertainties and apply LCS data appropriately for forecasting, source impact analysis, and community engagement.
World Meteorological Organization
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
Copernicus Atmosphere Monitoring Servicice
2024
2024
2024
2024