Fant 10533 publikasjoner. Viser side 59 av 422:
Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning
Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021–2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM2.5) in the garden of one participant’s house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM2.5 measurements at daily resolution (bias/RMSE: 9.45/11.65 μg m–3). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model’s RMSE, MAE, and bias by 44–56, 38–48, and 41–52%, respectively.
2023
Quality-assured aerosol optical properties (AOP) with high spatiotemporal resolution are vital for the accurate estimation of direct aerosol radiative forcing and solar irradiance under clear skies. In this study, the sky information from an all-sky imager (ASI) is used with machine learning (ML) synergy to estimate aerosol optical depth (AOD) and the Ångström Exponent (AE). The retrieved AODs (AE) revealed good accuracy, with a dispersion error lower than 0.07 (0.15). The retrieved ML AOPs are used to estimate the DNI by applying radiative transfer modeling. The estimated ML DNI calculations revealed adequate accuracy to reproduce reference measurements with relatively low uncertainties.
2023
2023
2023
Skogens helsetilstand i Norge. Resultater fra skogskadeovervåkingen i 2022
Skogens helsetilstand påvirkes i stor grad av klima og værforhold, enten direkte ved tørke, frost og vind, eller indirekte ved at klimaet påvirker omfanget av soppsykdommer og insektangrep. Klimaendringene og den forventede økningen i klimarelaterte skogskader gir store utfordringer for forvaltningen av framtidas skogressurser. Det samme gjør invaderende skadegjørere, både allerede etablerte arter og nye som kan komme til Norge i nær framtid. I denne rapporten presenteres resultater fra skogskadeovervåkingen i Norge i 2022 og trender over tid for følgende temaer:
(i) Landsrepresentativ skogovervåking;
(ii) Intensiv skogovervåking;
(iii) Overvåking av bjørkemålere i Troms og Finnmark;
(iv) Barkbilleovervåkingen;
(v) Furuvednematode;
(vi) Askeskuddsyke;
(vii) Andre spesielle skogskader i 2022.
NIBIO
2023
Deposition of sulfur and nitrogen in Norway 2017-2021
Norwegian Meteorological Institute
2023
2023
2023
Field evaluation of three Vaisala sensor system units (AQT530). Gaseous compounds - O3, NO2, NO.
NILU
2023
2023
2023
Satellite remote sensing of Arctic fires - a literature and data review
The main aim of this report is to prepare for the proposed SGA #17 of the Caroline Herschel Framework Partnership Agreement on Copernicus User Uptake Work Programme 2020 named “Arctic peat- and forest-fire information system”. First, we summarize the scientific background of wildfires in the Arctic and the Northern boreal zone and describe observations of long-range transport of forest fire pollution. This is followed by an overview of satellite data and resources available for fire monitoring in these regions. This covers the fire ECVs, as well as smoke plume tracers. Furthermore, we list CAMS and CEMS resources, i.e., GWIS, EFFIS (including the latest country report for Norway), and GFAS, as well as other fire emission inventories. Knowledge gaps and limitations of satellite remote sen.sing, future missions, Norwegian user uptake and user groups are described.
NILU
2023
2023
Aerosol Optical Properties and Type Retrieval via Machine Learning and an All-Sky Imager
This study investigates the applicability of using the sky information from an all-sky imager (ASI) to retrieve aerosol optical properties and type. Sky information from the ASI, in terms of Red-Green-Blue (RGB) channels and sun saturation area, are imported into a supervised machine learning algorithm for estimating five different aerosol optical properties related to aerosol burden (aerosol optical depth, AOD at 440, 500 and 675 nm) and size (Ångström Exponent at 440–675 nm, and Fine Mode Fraction at 500 nm). The retrieved aerosol optical properties are compared against reference measurements from the AERONET station, showing adequate agreement (R: 0.89–0.95). The AOD errors increased for higher AOD values, whereas for AE and FMF, the biases increased for coarse particles. Regarding aerosol type classification, the retrieved properties can capture 77.5% of the total aerosol type cases, with excellent results for dust identification (>95% of the cases). The results of this work promote ASI as a valuable tool for aerosol optical properties and type retrieval.
2023
2023