Found 9854 publications. Showing page 304 of 395:
The EIMP project is funded by Danida and headed by COWI. The total project includes four components:
· Coastal water monitoring (responsible VKI (Dansih Water Quality Institute) and COW)
· Air pollution monitoring (responsible NILU)
· Reference laboratory (responsible VKI)
· Pollution sources (responsible COWI)
An audit system is being implemented at the reference laboratory which will enable the laboratory to perform audits in the air pollution monitoring network. As an introduction to station audits a workshop was held.
1999
The nineteenth mission to Egypt on the DANIDA EIMP programme included further training monitoring programme auditing, QA/QC procedures and reporting. Monthly and Quarterly air quality data reports were produced and presented. Training in QA/QC operations and reporting was given to the Monitoring Laboratories. Results from evaluations at EEAA were reported in Newsletters. Discussions concerning the future air quality measurement programme for Cairo were reported.
2001
The main purpose of mission 16 was to introduce a new technique for analysis of volatile organic compounds (VOC) in air samples, and to do on the job training of the staff at Centre for Environmental Hazards Mitigation (CEHM) at the Cairo University, Giza. Due to major changes in staff at the laboratory during the visit, it was not possible to complete the training program according to the original plan. The equipment for sampling and analysis has been checqued and tested and further needs of equipment has been evaluated.
2000
2006
Damage to painted systems. Statistical analysis of results after 8 years exposure. Umwelt- bundesamt. Texte 24/99
1999
2012
2012
Fine particulate matter (PM2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24 h PM2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m3) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m3) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m3), while exhibiting a significantly reduced mean bias (MB of −0.3 μg/m3 vs. −1.5 μg/m3 for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m3, S-MESH outperforms the reanalysis (MB of −7.3 μg/m3 and -10.3 μg/m3 respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.
Elsevier
2024
2005
2016
1999
2020
CZ0049 - Activity 2. Implementation of a secondary aerosol module in the CAMx model. NILU TR
The objective of project CZ0049 is to improve the characterisation of particulate matter, especially the formation and identification of secondary particles. This report presents our achievements in Activity 2 "Implementation of a secondary aerosol module in CAMx". We have reviewed available SOA smog chamber experiments from the literature and created a new parameterisation for formation of anthropogenic secondary organic aerosols. The new parameterisation has been implemented in the chemistry transport model CAMx. A detailed description of the work, including source code implementation, is given in the report.
2010
2010
2017
2010
2010
2010
2007
1999
Cyclic volatile methyl siloxanes in the terrestrial and aquatic environment at remote Arctic sites
Cyclic volatile methyl siloxanes (cVMS) are widely used chemicals with high emissions to the atmosphere due to their volatility. They are found in the Arctic atmosphere, indicating potential for long-range transport. This study examined the potential for deposition of cVMS (D4, D5, D6) to surface media via snow in Arctic regions. Results showed low cVMS levels in vegetation, soil, sediment, and marine biota. D4 was detected above detection limits but generally below quantification limits, while D5 and D6 were generally not detected. This aligns with current research, suggesting negligible cVMS input from atmospheric deposition via snow and snow melt.
NILU
2025