Gas transfer velocities in a tropical reservoir and its river downstream. Wind speed and rainfall effect

We have measured simultaneously the methane (CH4) and carbon dioxide (CO2) surface concentrations and fluxes in the Petit-Saut reservoir (French Guiana) and its tidal river (Sinnamary River) downstream the dam, during two field experiments in wet (May 2003) and dry season (December 2003). Gas fluxes were measured with floating chambers (FC) on the artificial lake and on the river, and with the eddy covariance (EC) technique for CO2 during a 24h experiment on the lake. For each chamber measurement, wind speed was measured at 1m above the water surface and recalculated at 10 m using the formulation proposed by Amorocho et DeVrie (1980). During the 24h EC experiment the wind speed at 10m (U10) and the rainfall rates were recorded by a meteorological station. For each flux measurement the gas transfer velocity normalized for a Schmidt Number of 600 was computed

External validation of a hidden Markov model for gamma‐based classification of anatomical changes in lung cancer patients using EPID dosimetry

Purpose To externally validate a hidden Markov model (HMM) for classifying gamma analysis results ofin vivoelectronic portal imaging device (EPID) measurements into different categories of anatomical change for lung cancer patients. Additionally, the relationship between HMM classification and deviations in dose-volume histogram (DVH) metrics was evaluated. Methods The HMM was developed at CHU de Quebec (CHUQ), and trained on features extracted from gamma analysis maps ofin vivoEPID measurements from 483 fractions (24 patients, treated with three-dimensional 3D-CRT or intensity modulated radiotherapy), using the EPID measurement of the first treatment fraction as reference. The model inputs were the average gamma value, standard deviation, and average value of the highest 1% of gamma values, all averaged over all beams in a fraction. The HMM classified each fraction into one of three categories: no anatomical change (Category 1), some anatomical change (no clinical action needed, Category 2) and severe anatomical change (clinical action needed, Category 3). The external validation dataset consisted of EPID measurements from 263 fractions of 30 patients treated at Maastro with volumetric modulated arc therapy (VMAT) or hybrid plans (containing both static beams and VMAT arcs). Gamma analysis features were extracted in the same way as in the CHUQ dataset, by using the EPID measurement of the first fraction as reference (gamma Q), and additionally by using an EPID dose prediction as reference (gamma M). For Maastro patients, cone beam computed tomography (CBCT) scans and image-guided radiotherapy (IGRT) classification of these images were available for each fraction. Contours were propagated from the planning CT to the CBCTs, and the dose was recalculated using a Monte Carlo dose engine. Dose-volume histogram metrics for targets and organs-at-risk (OARs: lungs, heart, mediastinum, spinal cord, brachial plexus) were extracted for each fraction, and compared to the planned dose. HMM classification of the external validation set was compared to threshold classification based on the average gamma value alone (a surrogate for clinical classification at CHUQ), IGRT classification as performed at Maastro, and differences in DVH metrics extracted from 3D dose recalculations on the CBCTs. Results The HMM achieved 65.4%/65.0% accuracy for gamma Q and gamma M, respectively, compared to average gamma threshold classification. When comparing HMM classification with IGRT classification, the overall accuracy was 29.7% for gamma Q and 23.2% for gamma M. Hence, HMM classification and IGRT classification of anatomical changes did not correspond. However, there is a trend towards higher deviations in DVH metrics with classification into higher categories by the HMM for large OARs (lungs, heart, mediastinum), but not for the targets and small OARs (spinal cord, brachial plexus). Conclusion The external validation shows that transferring the HMM for anatomical change classification to a different center is challenging, but can still be valuable. The HMM trained at CHUQ cannot be used directly to classify anatomical changes in the Maastro data. However, it may be possible to use the model in a different capacity, as an indicator for changes in the 3D dose based on two-dimensional EPID measurements

External validation of a hidden Markov model for gamma-based classification of anatomical changes in lung cancer patients using EPID dosimetry

Purpose To externally validate a hidden Markov model (HMM) for classifying gamma analysis results ofin vivoelectronic portal imaging device (EPID) measurements into different categories of anatomical change for lung cancer patients. Additionally, the relationship between HMM classification and deviations in dose-volume histogram (DVH) metrics was evaluated. Methods The HMM was developed at CHU de Quebec (CHUQ), and trained on features extracted from gamma analysis maps ofin vivoEPID measurements from 483 fractions (24 patients, treated with three-dimensional 3D-CRT or intensity modulated radiotherapy), using the EPID measurement of the first treatment fraction as reference. The model inputs were the average gamma value, standard deviation, and average value of the highest 1% of gamma values, all averaged over all beams in a fraction. The HMM classified each fraction into one of three categories: no anatomical change (Category 1), some anatomical change (no clinical action needed, Category 2) and severe anatomical change (clinical action needed, Category 3). The external validation dataset consisted of EPID measurements from 263 fractions of 30 patients treated at Maastro with volumetric modulated arc therapy (VMAT) or hybrid plans (containing both static beams and VMAT arcs). Gamma analysis features were extracted in the same way as in the CHUQ dataset, by using the EPID measurement of the first fraction as reference (gamma Q), and additionally by using an EPID dose prediction as reference (gamma M). For Maastro patients, cone beam computed tomography (CBCT) scans and image-guided radiotherapy (IGRT) classification of these images were available for each fraction. Contours were propagated from the planning CT to the CBCTs, and the dose was recalculated using a Monte Carlo dose engine. Dose-volume histogram metrics for targets and organs-at-risk (OARs: lungs, heart, mediastinum, spinal cord, brachial plexus) were extracted for each fraction, and compared to the planned dose. HMM classification of the external validation set was compared to threshold classification based on the average gamma value alone (a surrogate for clinical classification at CHUQ), IGRT classification as performed at Maastro, and differences in DVH metrics extracted from 3D dose recalculations on the CBCTs. Results The HMM achieved 65.4%/65.0% accuracy for gamma Q and gamma M, respectively, compared to average gamma threshold classification. When comparing HMM classification with IGRT classification, the overall accuracy was 29.7% for gamma Q and 23.2% for gamma M. Hence, HMM classification and IGRT classification of anatomical changes did not correspond. However, there is a trend towards higher deviations in DVH metrics with classification into higher categories by the HMM for large OARs (lungs, heart, mediastinum), but not for the targets and small OARs (spinal cord, brachial plexus). Conclusion The external validation shows that transferring the HMM for anatomical change classification to a different center is challenging, but can still be valuable. The HMM trained at CHUQ cannot be used directly to classify anatomical changes in the Maastro data. However, it may be possible to use the model in a different capacity, as an indicator for changes in the 3D dose based on two-dimensional EPID measurements

Carbon dioxide and methane emissions and the carbon budget of a 10-year old tropical reservoir (Petit Saut, French Guiana)

International audienceThe emissions of carbon dioxide (CO2) and methane (CH4) from the Petit Saut hydroelectric reservoir (Sinnamary River, French Guiana) to the atmosphere were quantified for 10 years since impounding in 1994. Diffusive emissions from the reservoir surface were computed from direct flux measurements in 1994, 1995, and 2003 and from surface concentrations monitoring. Bubbling emissions, which occur only at water depths lower than 10 m, were interpolated from funnel measurements in 1994, 1997, and 2003. Degassing at the outlet of the dam downstream of the turbines was calculated from the difference in gas concentrations upstream and downstream of the dam and the turbined discharge. Diffusive emissions from the Sinnamary tidal river and estuary were quantified from direct flux measurements in 2003 and concentrations monitoring. Total carbon emissions were 0.37 ± 0.01 Mt yr-1 C (CO2 emissions, 0.30 ± 0.02; CH4 emissions, 0.07 ± 0.01) the first 3 years after impounding (1994-1996) and then decreased to 0.12 ± 0.01 Mt yr-1 C (CO2, 0.10 ± 0.01; CH4, 0.016 ± 0.006) since 2000. On average over the 10 years, 61% of the CO2 emissions occurred by diffusion from the reservoir surface, 31% from the estuary, 7% by degassing at the outlet of the dam, and a negligible fraction by bubbling. CH4 diffusion and bubbling from the reservoir surface were predominant (40% and 44%, respectively) only the first year after impounding. Since 1995, degassing at an aerating weir downstream of the turbines has become the major pathway for CH4 emissions, reaching 70% of the total CH4 flux. In 2003, river carbon inputs were balanced by carbon outputs to the ocean and were about 3 times lower than the atmospheric flux, which suggests that 10 years after impounding, the flooded terrestrial carbon is still the predominant contributor to the gaseous emissions. In 10 years, about 22% of the 10 Mt C flooded was lost to the atmosphere. Our results confirm the significance of greenhouse gas emissions from tropical reservoir but stress the importance of: (1) considering all the gas pathways upstream and downstream of the dams and (2) taking into account the reservoir age when upscaling emissions rates at the global scale