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Towards crowd-sourced air quality and physical activity monitoring by a low-cost mobile platform. Lecture Notes in Computer Science, 9677
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The main goal for the “Towards better exploitation of Satellite data for monitoring Air Quality in Norway using
downscaling techniques” (Sat4AQN) project was to evaluate the potential of spatially downscaling satellite data using a
high-resolution Chemical Transport Model (CTM) to spatial scales that are more relevant for monitoring air quality in
urban areas and regional background sites in Norway. For this demonstration project, we focused on satellite aerosol
optical density (AOD) and particulate matter (PM) estimates.
NILU
2020
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Towards an integrated data-driven infrastructure (InfraNor)
The Arctic is warming almost four times faster
compared to the rest of the world (Rantanen et al.
2022). Svalbard and its surroundings have warmed
faster than most of the Arctic (Cai et al. 2021;
Isaksen et al. 2022). The Svalbard archipelago also
shows large temperature variations from south
to north and east to west (Østby et al. 2017).
Svalbard has good infrastructure, logistics and
communications (airport, port, laboratories), and
excellent possibilities for data transfer. This makes
Svalbard and its surroundings an attractive living
natural laboratory for long-term and campaign-
based Arctic studies.
Svalbard Integrated Arctic Earth Observing System
(SIOS) is a Norwegian-initiated international
cooperation to exploit Svalbard’s research
infrastructure for the purpose of increasing
knowledge about global climate and environmental
c h a n g e s t h ro u g h l o n g - t e r m m o n i t o r i n g
(Christiansen et al. 2024). It currently includes 29
member institutions from 10 different countries
with a research focus relevant to interdisciplinary
earth system studies in and around Svalbard. These
studies explore the complex interrelationships
between ocean currents, atmospheric and
geological conditions, the extent of ice and snow,
and terrestrial food webs of plants and animals.
Within SIOS, researchers collaborate by sharing and
integrating data and research infrastructure to build
an efficient observing system that focuses on long-
term monitoring of parameters that are important
for understanding the Arctic in the context of global
environmental change.
The research infrastructures1 in Svalbard have
mainly been established as independent activities
by projects or research stations. The existing
environmental monitoring and observation
infrastructures in Svalbard are generally maintained
at a high standard and are state-of-the-art.
While the individual observations and research
infrastructures might be of good quality, they
are not optimised and the gathered data are not
harmonised, except for e.g., in COAT (Pedersen
et al 2025). SIOS utilises existing infrastructure,
as well as new infrastructure, instigated by
considerations and deliberations of the working
groups coordinated by the central hub, SIOS-
Knowledge Centre.
SIOS-InfraNor is a regional distributed observing
system utilising versatile infrastructure from in situ
to satellite remote sensing observations (Figure 1).
The project, funded jointly by the Research Council
of Norway and the Norwegian Space Agency
(NoSA), strengthens SIOS with a coordinated and
state-of-the-art observation network for marine,
terrestrial and atmospheric research. This network,
which provides data in accordance with the FAIR
principles (Wikinson et al. 2016), is implemented
and operated in and around Svalbard. The InfraNor
project, as a prioritised infrastructure initiative
identified through a gap analysis study, provides
new and upgraded research facilities to support
addressing Earth System Science (ESS) questions
on global environment change. SIOS offers a single
point of access to infrastructure, data, tools and
services owned or operated by its members.
InfraNor is a response to the ongoing effort to
optimise the SIOS observing system. This effort
builds on the SIOS Strategy for Optimisation,
and draws on work conducted through the SIOS
Science Optimisation Service2 (as described in
the SIOS current state document3). The focus
is on observational measurements to address
regional issues and offer an opportunity for much
more comprehensive monitoring of ESS-relevant
variables throughout the region as articulated in
the SIOS Infrastructure Optimisation Report4. The
report targets vertical and horizontal interactions,
cryosphere–geosphere dynamics, and climate
change impacts on biodiversity and ecosystems.
2025
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Towards a remote-sensing-driven model of isoprene emissions from Alpine tundra
Abstract This study investigates isoprene emissions in a high-latitude Alpine tundra ecosystem, focusing on using near-field remote sensing of surface temperatures, the photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI), and meteorological measurements to model these emissions. Isoprene is a key biogenic volatile organic compound (BVOC) emitted by select plants, which can impact atmospheric chemistry and climate. Increased temperatures, particularly in high latitudes, may enhance isoprene emissions due to extended growing seasons and heightened plant stress. The research was conducted in Finse, Norway, where isoprene and CO 2 fluxes were measured with eddy covariance alongside spectral and meteorological data, and surface temperature. A random forest (RF) model was developed to predict isoprene fluxes, considering the variable importance of different environmental factors. The results showed that surface temperature and CO 2 flux were consistently important predictors, across three differential temporal data aggregations (hourly, daily, weekly), while the PRI demonstrated low predictive power, possibly due to the heterogeneous vegetation and variable light conditions. The NDVI was more effective than anticipated, likely linked to phenological changes in vegetation. Model performance varied with temporal resolution, with weekly data achieving the highest predictive accuracy ( R 2 up to 0.76). The RF model accurately reflected seasonal emission patterns but underestimated short-term peaks, suggesting the potential to combine machine learning with process-based modelling. This research highlights the promise of proxy data from remote sensing for scaling BVOC emission models to regional levels, essential for understanding climate impacts in Arctic ecosystems.
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