the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange
Juha-Pekka Tuovinen
Markku Kulmala
Ivan Mammarella
Juha Aalto
Henriikka Vekuri
Annalea Lohila
Anna Lintunen
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Boreal rivers are significant sources of carbon dioxide (CO2) and methane (CH4) to the atmosphere but the controls of these emissions are uncertain. We measured four months of CO2 and CH4 exchange between a regulated boreal river and the atmosphere with eddy covariance. We found statistical relationships between the gas exchange and several environmental variables, the most important of which were dissolved CO2 partial pressure in water, wind speed, and water temperature.
We present a novel version of an aerosol number size distribution instrument, showcasing its capability to measure particle number concentration and particle number size distribution between 1 and 12 nm. Our results show that the instrument agrees well with existing instrumentation and allows for both the accurate measurement of the smallest particles and overlap with more conventional aerosol number size distribution instruments.
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climate–smartboreal forest management.
upliftnutrients into the euphotic layer. The origin of the turbulence that was found contrasted along the transect was also determined.