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This study investigated the performance of a passive biocover system at a Danish landfill. The overall methane oxidation efficiency of the system was assessed by comparing annual whole-site methane emissions before and after biocover installation. Annual whole-site methane emission predictions were calculated based on empirical models developed by a discrete number of tracer gas dispersion measurements. Moreover, a series of field campaigns and continuous flux measurements was carried out to evaluate the functionality of an individual biowindow. The results indicated that biocover system performance highly depended on barometric pressure variations. Under decreasing barometric pressure, estimated efficiency declined to 20%, while under increasing barometric pressure, nearly 100% oxidation was achieved. In-situ measurements on a specific biowindow showed a similar oxidation efficiency pattern in respect to barometric pressure changes despite the difference in spatial representation. Eddy covariance results revealed pronounced seasonal variability in the investigated biowindow, measuring higher methane fluxes during the cold period compared to the warm period. Results from the in-situ campaigns confirmed this finding, reporting a threefold increase in the biowindow's methane oxidation capacity from April to May. The annual average oxidation efficiency of the system was estimated to range between 51% and 65%, taking into consideration the impact of changes in barometric pressure and seasonal variability. This indicated an annual reduction in landfill's methane emissions between 24 and 35 tonnes. This study revealed the challenge facing current approaches in documenting accurately the performance of a passive biocover system, due to the short-term variability of oxidation efficiency, which is influenced by barometric pressure changes.

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Eddy covariance method has become a key technique to measure CH4 flux continuously in lakes. A large number of CH4 flux data was missing due to variable reasons. In order to reconstruct a complete time series of CH4 flux, it is necessary to find an appropriate gap-filling method to insert the CH4 flux data gap. Based on the routine meteorological data and CH4 flux data measured at Bifenggang site in the eastern part of the Taihu eddy flux network during 2014 to 2017, we analyzed the control factors of CH4 flux at the half-hour scale and daily scale. With those data, we tested that whether nonlinear regression method and two machine learning methods, random forest algorithm and error back propagation algorithm, could fill the CH4 flux gap at the half-hour scale and daily scale. The results showed that CH4 flux at the half-hour scale was mainly influenced by sediment temperature, friction velocity, air temperature, relative humidity, latent heat flux and water temperature at 20 cm in the growing season, and was mainly affected by relative humidity, latent heat flux, wind speed, sensible heat flux and sediment temperature in non-growing season. The CH4 flux at the daily scale was mainly affected by latent heat flux and relative humidity. Random forest model was the best in CH4 flux data gap filling at both time scales. The random forest model with the input variables of day of year, solar elevation angle, sediment temperature, friction velocity, air temperature, water temperature at 20 cm, relative humidity, air pressure, and wind speed was more suitable for filling the CH4 flux data gap at the half-hour scale. The random forest model with the input variables of day of year, sediment temperature, friction velocity, air temperature, water temperature at 20 cm, relative humidity, air pressure, wind speed, and downward shortwave radiation was more suitable for filling CH4 flux data gap at the day scale. The interpolation models could fill the data gap better at daily scale than that at the half-hour scale.

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An empirical model was developed and employed to estimate annual methane (CH4) emissions from two Danish landfills (Skellingsted and AV Miljø). The overall aim was to provide accurate annual CH4 emission estimates based on discrete emission field measurements and to address temporal variability caused by the impact of barometric pressure. Four non-linear regression models were developed, corresponding to the two landfills as well as to the western and eastern waste sections of AV Miljø. A comparison of model predictions with on-site eddy covariance fluxes showed that the models can accurately predict short-term emission variability. Predicted annual CH4 emissions for the Skellingsted and AV Miljø landfills were 69 ± 4 and 80 ± 4 tonnes, respectively, whereas for the western and eastern sections of the AV Miljø landfill, emissions were estimated at 63 ± 3 and 19 ± 1 tonnes, respectively. The results demonstrate that even though maximum emissions from Skellingsted were approximately threefold compared to AV Miljø, annual predicted CH4 emissions for Skellingsted were lower. This was because during the most frequently occurring pressure change events, emission rates were higher at AV Miljø in comparison to Skellingsted. An optimised sampling strategy was proposed, targeting the determination of an empirical emission model though the effective use of discrete field measurements. Analysis of annual emission estimates, based on the number of the tracer dispersion method (TDM) measurements, showed that both the number as well as the distribution of performed TDM measurements across the range of expected dP/dt influence the uncertainty.

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Total solar radiation is an important factor affecting carbon exchange in forest ecosystem. In order to understand the effects of radiation change on carbon exchange in Chinese fir plantation, long-term monitoring data of carbon dioxide flux and meteorological factors measured by open eddy covariance system and meteorological gradient observation system were used in this study. The clearness index (kt) was used to represent the condition of solar radiation. We analyzed the effects of kt on net ecosystem exchange of carbon dioxide (NEE) in the central subtropical Chinese fir plantation during the growing season (from April to October). The results showed that total solar radiation in clear sky was usually higher in the morning than that in the afternoon, and that NEE was lower in the morning than in the afternoon. Such difference in NEE reached the maximum when the solar elevation angle was about 50°. At the medium kt(0.42-0.52), carbon absorption of Chinese fir plantation was the strongest. The ave-rage maximum relative change of NEE in 10 years in different solar elevation angles ranged from 11.0% to 29.4%, while the minimum and maximum critical values appeared at 35°-40° and 45°-50°, respectively. When kt was at the moderate degree due to the existence of clouds, carbon absorption and diffuse photosynthetically active radiation of Chinese fir plantation reached the maximum, and the latter might be the main reason for the former. Moderate radiation condition with the presence of cloud clould promote NEE of Chinese fir plantation and lead to largest carbon absorption.

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We report the development of a laser gas analyzer that measures gas concentrations at a data rate of 100 Hz. This fast data rate helps eddy covariance calculations for gas fluxes in turbulent high wind speed environments. The laser gas analyzer is based on derivative laser absorption spectroscopy and set for measurements of water vapor (H2O, at wavelength ~1392 nm) and carbon dioxide (CO2, at ~2004 nm). This instrument, in combination with an ultrasonic anemometer, has been tested experimentally in both marine and terrestrial environments. First, we compared the accuracy of results between the laser gas analyzer and a high-quality commercial instrument with a max data rate of 20 Hz. We then analyzed and compared the correlation of H2O flux results at data rates of 100 Hz and 20 Hz in both high and low wind speeds to verify the contribution of high frequency components. The measurement results show that the contribution of 100 Hz data rate to flux calculations is about 11% compared to that measured with 20 Hz data rate, in an environment with wind speed of ~10 m/s. Therefore, it shows that the laser gas analyzer with high detection frequency is more suitable for measurements in high wind speed environments.

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The dynamic variations of evapotranspiration (ET) and weather data during summer maize growing season in 2013-2015 were monitored with eddy covariance system, and the applicability of two operational models (FAO-PM model and KP-PM model) based on the Penman-Monteith model were analyzed. Firstly, the key parameters in the two models were calibrated with the measured data in 2013 and 2014; secondly, the daily ET in 2015 calculated by the FAO-PM model and KP-PM model was compared to the observed ET, respectively. Finally, the coefficients in the KP-PM model were further revised with the coefficients calculated according to the different growth stages, and the performance of the revised KP-PM model was also evaluated. These statistical parameters indicated that the calculated daily ET for 2015 by the FAO-PM model was closer to the observed ET than that by the KP-PM model. The daily ET calculated from the revised KP-PM model for daily ET was more accurate than that from the FAO-PM model. It was also found that the key parameters in the two models were correlated with weather conditions, so the calibration was necessary before using the models to predict the ET. The above results could provide some guidelines on predicting ET with the two models.

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As an important part of inland waters,small pond is a neglected source of greenhouse gas.The main objective of the study was to quantify greenhouse gas fluxes (CO2 and CH4) from small pond in the Yangtze Delta using flux-gradient method.The results showed that:① zero-gradient test indicated that the flux measurement precision for water vapor,CO2,and CH4 was 7.525 W·m-2,0.022 mg·(m2·s)-1,and 0.054 µg·(m2·s)-1,respectively.During the test period,84%,80%,and 94% of half-hourly flux data for H2O,CO2,and CH4 were higher than the zero-gradient measurement precision.② Based on the measurement,the small pond was the source of CO2 and CH4 for the atmosphere in summer,the mean emission flux of CO2 and CH4 was 0.038 mg·(m2·s)-1 and 0.889 µg·(m2·s)-1,respectively.The CH4 emission fluxes from the small pond were more higher than the median value of emission for global lakes.The results indicated that greenhouse gas emission from small pond was an important part for estimating inland water greenhouse gas emissions,especially for CH4 emission.These results can provide scientific reference for making emission inventory of regional greenhouse gas.

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The behaviour of tritium in the environment is linked to the water cycle. We compare three methods of calculating the tritium evapotranspiration flux from grassland cover. The gradient and eddy covariance methods, together with a method based on the theoretical Penmann-Monteith model were tested in a study carried out in 2013 in an environment characterised by high levels of tritium activity. The results show that each of the three methods gave similar results. The various constraints applying to each method are discussed. The results show a tritium evapotranspiration flux of around 15 mBq m(-2) s(-1) in this environment. These results will be used to improve the entry parameters for the general models of tritium transfers in the environment.

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Clearcutting a forest ecosystem can result in a drastic reduction of stand productivity. Despite the severity of this disturbance type, past studies have found that the productivity of young regenerating stands can quickly rebound, approaching that of mature undisturbed stands within a few years. One of the obvious reasons is increased leaf area (LA) with each year of recovery. However, a less obvious reason may be the variability in species composition and distribution during the natural regeneration process. The purpose of this study was to investigate to what extent the increase in gross ecosystem productivity (GEP), observed during the first 4 years of recovery in a naturally regenerating clearcut stand, was due to (i) an overall expansion of leaf area and (ii) an increase in the canopy's photosynthetic capacity stemming from either species compositional shifts or drift in physiological traits within species. We found that the multi-year rise in GEP following harvest was clearly attributed to the expansion of LA rather than a change in vegetation composition. Sizeable changes in the relative abundance of species were masked by remarkably similar leaf physiological attributes for a range of vegetation types present in this early-successional environment. Comparison of upscaled leaf-chamber estimates with eddy-covariance-based estimates of light-response curves revealed a broad consistency in both maximum photosynthetic capacity and quantum yield efficiency. The approaches presented here illustrate how chamber- and ecosystem-scale measurements of gas exchange can be blended with species-level LA data to draw conclusive inferences about changes in ecosystem processes over time in a highly dynamic environment.

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