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he 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.
2024
2026
2005
2016
1999
CZ0049 - Activity 2. Implementation of a secondary aerosol module in the CAMx model. NILU TR
Prosjekt CZ0049 har som mål å bedre vår kunnskap og karakteristikk av PM, spesielt dannelse og identifisering av sekundære organiske aerosoler. Rapporten beskriver resultater fra aktivitet 2 "Implementering av en Sekundær Organisk Aerosol modul i CAMx". Vi har studert SOA reaksjonskammereksperimenter fra litteraturen og generert en oppdatert parameterisering av antropogen SOA dannelse. Den nye parameteriseringen er implementert i kjemitransportmodellen CAMx. Kildekode og detaljert arbeidsbeskrivelse er inkludert i rapporten.
2010
2010
2017
2010
2010
2010
2007
1999
This study investigates atmospheric microplastic (MP) exchange between marine and terrestrial compartments and associated deposition patterns at Bushehr Port, Persian Gulf. We combined field sampling of the sea-surface microlayer (SML), bulk seawater, sea foam, deposited particles, and suspended airborne particles with FLEXPART Lagrangian dispersion modelling and exploratory Elastic Net regression to evaluate MP sources, transport pathways, and meteorological controls. The simulations indicate a pronounced seasonal contrast in atmospheric MP transport and suggest that land-based sources collectively represented the largest modelled contribution to the atmospheric MP burden. Within the FLEXPART inventory, textile-related microfibres were the largest modelled source category for suspended MPs (∼61%); for deposited MPs, the estimated microfibre contribution (∼32%) was comparable to bare-soil resuspension (∼31%), while sea spray contributed ∼11% to both fractions. Elastic Net regression repeatedly retained air pressure as a positive predictor and the Lifted Index as a negative predictor; however, these associations are interpreted as exploratory because of the limited number of independent sampling intervals. Sea foam and SML samples were enriched in MPs relative to bulk seawater, although the enrichment pattern varied with wave period and tidal-current conditions. The large difference between field-derived net deposition velocities (Vd) and theoretical terminal velocities (Vt) indicates that turbulence, resuspension, environmental mixing, and particle-shape assumptions substantially affect the apparent removal of atmospheric MPs. Overall, the results suggest that MP cycling at this semi-enclosed coastal margin is influenced by coupled land-based emissions, marine surface processes, and atmospheric dynamics, highlighting the need for mitigation strategies that consider both local terrestrial inputs and air-sea exchange.
2026