Combining TDLAS and multi-fusion algorithms for methane gas concentration detection

High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition(VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy(TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively.

Quantification of Central and Eastern China's atmospheric CH_(4) enhancement changes and its contributions based on machine learning approach

Methane is the second largest anthropogenic greenhouse gas,and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks.Therefore,the monitoring of CH_(4) concentration changes and the assessment of underlying driving factors can provide scientific basis for the government’s policy making and evaluation.China is the world’s largest emitter of anthropogenic methane.However,due to the lack of ground-based observation sites,little work has been done on the spatial-temporal variations for the past decades and influencing factors in China,especially for areas with high anthropogenic emissions as Central and Eastern China.Here to quantify atmospheric CH_(4) enhancements trends and its driving factors in Central and Eastern China,we combined the most up-to-date TROPOMI satellite-based column CH_(4)(xCH_(4))concentration from 2018 to 2022,anthropogenic and natural emissions,and a random forest-based machine learning approach,to simulate atmospheric xCH_(4) enhancements from 2001 to 2018.The results showed that(1)the random forest model was able to accurately establish the relationship between emission sources and xCH_(4) enhancement with a correlation coefficient(R^(2))of 0.89 and a root mean-square error(RMSE)of 11.98 ppb;(2)The xCH_(4) enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018,with a relative change of 3.27%±0.13%;(3)The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH_(4) enhancement,contributing 68.00% and 31.21%,respectively,and the decrease of animal ruminants contributed-6.70% of its enhancement trend.

Holocene initiation and expansion of the southern margins of northern peatlands triggered by the East Asian summer monsoon recession

Northern peatlands represent one of the largest biospheric carbon reservoirs in the world.Their southern margins act as new carbon reservoirs,which can greatly influence the global carbon dynamics.However,the Holocene initiation,expansion and climate sensitivity of these peatlands remain intensely debated.Here we used a compilation of basal peat ages across six isolated peatlands at the southern margins of northern peatlands to address these issues.We found that the earliest initiation event of these peatlands occurred after the Younger Dryas(YD,12,800–11,700 years ago)period.The second initiation event and rapid expansion occurred since 5 ka cal.BP.The recession of East Asian summer monsoon(EASM)during the YD period and at around 5 ka cal.BP likely played a major role in controlling the initiation and expansion of these peatlands.The rapid expansion of these peatlands possibly contributed to the significant increases in atmospheric methane concentrations during the late Holocene because of the minerotrophic fens status and rapid expansion of them.These ecological processes are different from northern peatlands,indicating the special carbon sink and source implications of these peatlands in the global carbon cycle.

Quasi-distributed sensing network based on coherence multiplexing and spatial division multiplexing for coal mine security monitoring

A low-cost fiber Bragg grating(FBG) sensing system for coal-mine security monitoring is proposed in this paper.Based on the coherence multiplexing(CM) and spatial division multiplexing(SDM) techniques,this hybrid sensing network can support more than 40 sensors for quasi-distributed detection.It is demonstrated experimentally that the multiplexed sensing signal of each sensor can be clearly distinguished by an optical low-coherence reflectometry(OLCR).Methane concentration is detected with maximum sensitivities of an intensity variation of 10.92% and a concentration variation of 1%,using a well-designed sensor structure.Strain and temperature are also detected by this system,which also exhibits good results in the experiment.