Disinfection byproducts (DBPs) formed upon drinkingwater treatment are often cytotoxic and genotoxic, and have beenrelated to health risks, such as bladder cancer. Large DBPs, with morethan two carbon atoms, are produced in large quantities and greatdiversity, and contribute substantially to observed toxicity, but theircomposition and structure remain largely unknown. While a few studieshave explored high-resolution detection methods, we here focus on theextraction and elution procedures of these >2 carbon DBPs, beforeusing negative electrospray ionization (ESI[−]) and detection byFourier transform ion cyclotron resonance mass spectrometry (FT-ICRMS). Samples were collected from three drinking water treatment plants (DWTPs) in Sweden, of which two use hypochlorite and one usesmonochloramine for disinfection. A reversed-phase SPE sorbent (Hyper Sep) showed high complementarity with a hybrid carbonsorbent (Carbon S), which captured a subset of polar saturated DBPs not retained by the commonly used reversed-phase materials.Stepwise elution using solvents of varying polarity reduced sample complexity and ionization suppression and further extended theDBP diversity detected. Extraction at pH 8, investigated for one DWTP, also extended DBP diversity but that impact was smaller.The classic methanol elution on reversed phase may miss proportionally more DBPs in chloraminated drinking water.

Lakes play a significant role in the global carbon cycle, acting as sources and sinks of carbon dioxide (CO2). In situ measurements of CO2 flux (FCO2) from lakes have generally been collected during daylight, despite indications of significant diel variability. This introduces bias when scaling up to whole-lake annual aquatic carbon budgets. We conducted an international sampling program to ascertain the extent of diel variation in FCO2 across lakes. We sampled 21 lakes over 41 campaigns and measured FCO2 at 4-h intervals over a full diel cycle. Rates of FCO2 ranged from -3.16 to 4.39 mmol m-2 h(-1). Integrated over a day, FCO2 ranged from -381.68 to 878.49 mg C m(-2) d(-1) (mean = 76.54) across campaigns. We identified three characteristic diel patterns in FCO2 related to trophic status and show that for half of the campaigns, daily flux estimates were biased by > 50% if based on a single (daytime) measurement.

Global lake and reservoir water surfaces were recently estimated to contribute similar to 10% of global methane (CH4) emissions. The sensitivity of these emissions to climate and environmental change is a growing concern. Here we present data-driven, globally gridded modelling of future open-water CH4 fluxes under different scenarios. We included multiple potential predictor variables and available peer-reviewed flux data focusing on in situ-verified relationships. The results indicate total lake and reservoir CH4 emissions increases of 24-91% under the IPCC Shared Socioeconomic Pathway (SSP) climate change scenarios SSP1-2.6 to SSP5-8.5 by 2080-2099. Effects of changed temperature and seasonality dominated these increases. Area and nutrient load changes also contributed substantially to reservoir emissions. Large absolute changes were predicted at all latitudes. The results demonstrate the urgency in minimizing climate change to avoid substantially increased future inland water CH4 emissions.

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.

Open storages of organic material represent potentially large sources of the greenhouse gas methane (CH4), an emissions source that will likely become more common as a part of societal efforts toward sustainability. Hence, monitoring and minimizing CH4 emissions from such facilities are key, but effective assessment of emissions without disturbing the flux is challenging. We demonstrate the capacity of using a novel high-resolution hyperspectral camera to perform sensitive CH4 flux assessments at such facilities, using as a test case a biofertilizer storage tank for residual material from a biogas plant. The camera and simultaneous conventional flux chamber measurements showed emissions of 6.0 +/- 1.3 and 13 +/- 5.7 kg of CH4 h-1, respectively. The camera measurements covered the whole tank surface of 1104 m2, and the chamber results were extrapolated from measurements over 5 m2. This corresponds to 0.7-1.4% of the total CH4 production at the biogas plant (1330 N m3 h-1 corresponding to 950 kg h(-1)). The camera could assess the entire tank emission in minutes without disturbing normal operations at the plant and revealed additional unknown emissions from the inlet to the tank (17 g of CH4 h(-1)) and during the loading of the biofertilizer into trucks (3.1 kg of CH4 h(-1) during loading events). This study illustrates the importance of adequate measurement capacity to map methane fluxes and to verify that methane emission mitigation efforts are effective. Given the high methane emissions observed, it is important to reduce methane emissions from open storage of organic material, for example by improved digestion in the biogas reactor, precooling of sludge before storage, or building gastight storage tanks with sealed covers. We conclude that hyperspectral, ground-based remote sensing is a promising approach for greenhouse gas monitoring and mitigation.

The assumed dominance of chloride (Cl–) in terrestrial ecosystems is challenged by observations of extensive formation of organically bound Cl (Clorg), resulting in large soil Cl storage and internal cycling. Yet, little is known about the spatial distribution of Cl in ecosystems. We quantified patterns of Cl distribution in different habitats along a boreal hillslope moisture gradient ranging from relatively dry upland coniferous forests to wet discharge areas dominated by alder. We confirmed that dry habitats are important for Cl storage but found that Cl pools tended to be larger in moist and wet habitats. The storage of Clorg was less important in wet habitats, suggesting a shift in the balance between soil chlorination and dechlorination rates. Cl concentrations in the herb layer vegetation were high in wet and moist sites attributed to a shift in plant species composition, indicating plant community-dependent ecosystem Cl cycling. Mass-balance calculations showed that internal Cl cycling increased overall ecosystem Cl residence times at all sites and that plant uptake rates of Cl– were particularly high at wet sites. Our results indicate that habitat characteristics including plant communities and hydrology are key for understanding Cl cycling in the environment.