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Found 9849 publications. Showing page 182 of 394:

Publication  
Year  
Category

Luftforurensinger.

Tørseth, K.; Berg, T.

2010

Luftforurensing i norske byer.

Høiskar, B.A.K.; Sundvor, I.; Tarrasón, L.; Endregard, G.

2011

Luften vi puster

Grossberndt, Sonja; Liu, Hai-Ying

2018

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria

2019

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria; Gray, Laura

2019

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria; Gray, Laura

2019

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria; Gray, Laura

2019

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria; Gray, Laura

2019

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria; Gray, Laura

2019

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria; Gray, Laura

2019

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria; Gray, Laura

2019

Lufta er for alle!

Grossberndt, Sonja; Castell, Nuria; Gray, Laura

2019

Lufta er for alle - NILU med nytt prosjekt

Solbakken, Christine Forsetlund (interview subject); Ridola, Hilde Nilsson (journalist)

2019

LP-39 TWINALT project – an excellent opportunity to exchange knowledge on alternative methods for toxicity assessment

Roszak, J.; Sobańska, Z.; Wolniakowska, A.; Marinovich, M.; Dusinska, Maria; Rundén-Pran, Elise; Vanhaecke, T.; Reszka, E.

Elsevier

2022

Low-Processing Data Enrichment and Calibration for PM2.5 Low-Cost Sensors

Stojanović, Danka B.; Kleut, Duška N.; Davidović, Miloš D.; De Vito, Saverio; Jovasević-Stojanović, Milena V.; Bartonova, Alena; Lepioufle, Jean-Marie

Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.

2023

Low-cost sensors and networks. Overview of current status by the Norwegian Reference Laboratory for Air Quality.

Castell, Nuria

Denne rapporten går igjennom status innen mikrosensorteknologien. Den vurderer tilgjengelighet av produkter på markedet, målte parametere og datakvalitet, samt hvilke muligheter sensorene åpner for når de blir brukt riktig.

NILU

2021

Low-cost sensors and Machine Learning aid in identifying environmental factors affecting particulate matter emitted by household heating

Hassani, Amirhossein; Bykuć, Sebastian; Schneider, Philipp; Zawadzki, Paweł; Chaja, Patryk; Castell, Nuria

Poland continues to rely heavily on coal and fossil fuels for household heating, despite efforts to reduce Particulate Matter (PM) levels. The availability of reliable air quality data is essential for policymakers, environmentalists, and citizens to advocate for cleaner energy sources. However, Polish air quality monitoring is challenging due to the limited coverage of reference stations and outdated equipment. Here, we report the results of a study on the spatio-temporal variability of Particulate Matter in Legionowo, Poland, using residents’ network of low-cost sensors. Along with identifying the hotspots of household-emitted PM, (1) we propose a data quality assurance scheme for PM sensors, (2) suggest an approach for estimating the Relative Humidity-induced uncertainty in the sensors without co-location with reference instruments, and (3) develop an interpretable Machine Learning (ML) model, a Generalized Additive Model (RMSE = 6.16 μg m−3, and R2 = 0.88), for unveiling the underlying relations between PM2.5 levels and other environmental parameters. The results in Legionowo suggest that as air temperature and wind speed increase by 1 °C and 1 km h−1, PM2.5 would respectively decrease by 0.26 μg m−3 and 0.14 μg m−3 while PM2.5 increases by 0.03 μg m−3 as RH increases by 1%.

Elsevier

2023

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