Fant 10000 publikasjoner. Viser side 92 av 400:
1999
2007
2010
2010
2010
2017
2010
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
1999
2016
2005
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
2012
2012
Damage to painted systems. Statistical analysis of results after 8 years exposure. Umwelt- bundesamt. Texte 24/99
1999
2006
DANIDA. Air pollution in Egypt. Status after the first year of EEAA/EIMP measurements. NILU OR
Air quality in Egypt has been summarized from the first year of measurements undertaken by the EEAA/EIMP air pollution monitoring programme. Exceedances of the air quality limit values given by the Egyptian Law no. 4 have been found in several areas.
1999
2004
2004