The CO( 2)flux (FCO2) from lakes to the atmosphere is a large component of the global carbon cycle anddepends on the air-water CO2concentration gradient (Delta CO2) and the gas transfer velocity (k). Both Delta CO2 and k can vary on multiple timescales and understanding their contributions toFCO(2)is important for explaining var-iability influxes and developing optimal sampling designs. We measuredFCO2 and Delta CO(2 )and derivedkforone full ice-free period in 18 lakes usingfloating chambers and estimated the contributions of Delta CO2 and k to FCO2 variability. Generally, kcontributed more than Delta CO2to short-term (1-9d) FCO2 variability. With in creased temporal period, the contribution of k to FCO2 variability decreased, and in some lakes resulted in Delta CO2 contrib-uting more thank to FCO2 variability over the full ice-free period. Increased contribution of Delta CO2 to FCO2 vari-ability over time occurred across all lakes but was most apparent in large-volume southern-boreal lakes and indeeper (>2m) parts of lakes, whereaskwas linked to FCO(2 )variability in shallow waters. Accordingly, knowing the variability of bothk and Delta CO(2 )over time and space is needed for accurate modeling of F CO2 from these vari-ables. We conclude that priority in FCO(2 )assessments should be given to direct measurements of FCO2 at multiplesites when possible, or otherwise from spatially distributed measurements of Delta CO(2 )combined with k- models that incorporate spatial variability of lake thermal structure and meteorology.

We present a method to monitor methane at atmospheric concentrations with errors inthe order of tens of parts per billion. We use machine learning techniques and periodic calibrationswith reference equipment to quantify methane from the readings of an electronic nose. The resultsobtained demonstrate versatile and robust solution that outputs adequate concentrations in a varietyof different cases studied, including indoor and outdoor environments with emissions arising fromnatural or anthropogenic sources. Our strategy opens the path to a wide-spread use of low-costsensor system networks for greenhouse gas monitoring.

Reducing emissions of the key greenhouse gas methane (CH4) is increasingly highlighted as being important to mitigate climate change. Effective emission reductions require cost-effective ways to measure CH4 to detect sources and verify that mitigation efforts work. We present here a novel approach to measure methane at atmospheric concentrations by means of a low-cost electronic nose strategy where the readings of a few sensors are combined, leading to errors down to 33 ppb and coefficients of determination, R-2, up to 0.91 for in situ measurements. Data from methane, temperature, humidity, and atmospheric pressure sensors were used in customized machine learning models to account for environmental cross-effects and quantify methane in the ppm-ppb range both in indoor and outdoor conditions. The electronic nose strategy was confirmed to be versatile with improved accuracy when more reference data were supplied to the quantification model. Our results pave the way toward the use of networks of low-cost sensor systems for the monitoring of greenhouse gases.

Appropriate methods to measure greenhouse gas (GHG) fluxes are critical for our ability to detect fluxes, understand regulation, make adequate priorities for climate change mitigation efforts, and verify that these efforts are effective. Ideally, we need reliable, accessible, and affordable measurements at relevant scales. We surveyed present GHG flux measurement methods, identified from an analysis of >11000 scientific publications and a questionnaire to sector professionals and analysed method pros and cons versus needs for novel methodology. While existing methods are well-suited for addressing certain questions, this presentation presents fundamental limitations relative to GHG flux measurement needs for verifiable and transparent action to mitigate many types of emissions. Cost and non-academic accessibility are key aspects, along with fundamental measurement performance. These method limitations contribute to the difficulties in verifying GHG mitigation efforts for transparency and accountability under the Paris agreement. Resolving this mismatch between method capacity and societal needs is urgently needed for effective climate mitigation. This type of methodological mismatch is common but seems to get high priority in other knowledge domains. The obvious need to prioritize development of accurate diagnosis methods for effective treatments in healthcare is one example. This presentation provides guidance regarding the need to prioritize the development of novel GHG flux measurement methods.