Fant 9759 publikasjoner. Viser side 272 av 391:
2006
Esso Slagentangen. Overvåkingsprogram for konsentrasjoner i luft fra utslipp fra Slagen raffineri. NILU OR
2009
Esso Slagentangen. Oppsummering måleprogram 2006-2011. NILU OR
NILU har utført måleprogram ved Slagen raffineri på oppdrag fra Esso Norge. Programmet innebar målinger av meteorologi, SO2, PM10, BTEX og nedbørkvalitet. De meteorologiske målingene viser at vinden er kanalisert nord/sør (topografisk effekt). Monitormålinger av SO2 ved to stasjoner i juni 2006 - des 2007, i 2009 og i 2011 viser at stasjonen nær raffineriet hadde høyeste timemiddelverdi 689,7 µg/m3 i 2007 (20 timeverdier over 350 µg/m3), 540,6 µg/m3 i 2009 (29 timeverdier over 350 µg/m3) og 1167,2 µg/m3 i 2011 (19 timeverdier over 350 µg/m3). Dvs. at luftkvaliteten overskred grenseverdien for timemiddel i 2009. Også grenseverdi for døgn (fem verdier over 1252 µg/m3 mot tre tillatte) og for år (20,3 µg/m3 mot 20 µg/m3 som gitt grense) ble overskredet i 2009. PM10 viste ingen overskridelser av grenseverdier. Benzen var over nasjonalt mål i 2009 (målt verdi 2,6 µg/m3 mot grenseverdi 2 µg/m3).
2013
Prepared by Earth Observation Data Centre for Water Resources Monitoring (EODC) GmbH in cooperation with TU Wien, GeoVille, ETH Zürich, TRANSMISSIVITY, AWST, FMI, UCC and NILU
The ESA Climate Change Initiative Phase 2 Soil Moisture Project
2018
2017
2002
Instead of a flag valid/non-valid usually proposed in the quality control (QC) processes of air quality (AQ), we proposed a method that predicts the p-value of each observation as a value between 0 and 1. We based our error predictions on three approaches: the one proposed by the Working Group on Guidance for the Demonstration of Equivalence (European Commission (2010)), the one proposed by Wager (Journal of Machine Learning Research, 15, 1625–1651 (2014)) and the one proposed by Lu (Journal of Machine Learning Research, 22, 1–41 (2021)). Total Error framework enables to differentiate the different errors: input, output, structural modeling and remnant. We thus theoretically described a one-site AQ prediction based on a multi-site network using Random Forest for regression in a Total Error framework. We demonstrated the methodology with a dataset of hourly nitrogen dioxide measured by a network of monitoring stations located in Oslo, Norway and implemented the error predictions for the three approaches. The results indicate that a simple one-site AQ prediction based on a multi-site network using Random Forest for regression provides moderate metrics for fixed stations. According to the diagnostic based on predictive qq-plot and among the three approaches used in this study, the approach proposed by Lu provides better error predictions. Furthermore, ensuring a high precision of the error prediction requires efforts on getting accurate input, output and prediction model and limiting our lack of knowledge about the “true” AQ phenomena. We put effort in quantifying each type of error involved in the error prediction to assess the error prediction model and further improving it in terms of performance and precision.
MDPI
2021
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