Remote sensing of HDO and CH₄ could provide valuable information on environmental and climatological studies. In a recent contribution, we reported a 3.53 μm distributed feedback (DFB) inter-band cascade laser (ICL)-based heterodyne radiometer. In the present work, we present the details of measurements and inversions of HDO and CH₄ at Dunhuang, Northwest of China. The instrument line shape (ILS) of laser heterodyne radiometer (LHR) is discussed firstly, and the spectral resolution is about 0.004 cm⁻¹ theoretically according to the ILS. Furthermore, the retrieval algorithm, optimal estimation method (OEM), combined with LBLRTM (Line-by-line Radiative Transfer Model) for retrieving the densities of atmospheric HDO and CH₄ are investigated. The HDO densities were retrieved to be less than 1.0 ppmv, while the CH₄ densities were around 1.79 ppmv from 20 to 24 July 2018. The correlation coefficient of water vapor densities retrieved by LHR and EM27/SUN is around 0.6, the potential reasons for the differences were discussed. Finally, in order to better understand the retrieval procedure, the Jacobian value and the Averaging Kernels are also discussed.

The instrument line shape (ILS), as a very important parameter, has a significant influence on the inversion of trace gas concentration. Different levels of ILS degradation for H₂O, CO₂, CH_4, and CO gases were investigated, and the influence of ILS on the inversion of column-averaged dry air mole fractions (DMFs) was assessed. Our results indicate that the averages of XH₂O, XCH_4, and XCO with modulation efficiency (ME) amplitude values have a positive correlation, the correlation coefficients are 0.9925, 0.9968, and 0.9981 respectively, whereas the relationship between the average of XCO₂ and ME is a negative correlation with 0.986 correlation coefficient. For a typical ILS degradation, a decrease of 5% in the modulation efficiency amplitude value results in the average of XCO₂ changing by 0.744%, XCH₄ and XH₂O are less sensitive species, with average values of −0.206% and −0.464%, whereas XCO shows the strongest intraday variability with an average value of −0.238%. However, with a decrease of 2‰ in the phase error (PE) value, the average of XCO changed by −0.150%, XCO₂ and XH₂O almost coincided with the same average value of −0.141%, whereas XCH₄ was the least sensitive species with an average value of −0.133%. At the same time, we measured the ILS for EM27/SUN spectrometers—the mean values of modulation efficiency amplitudes and phase errors were 0.9611 and 0.00593. Compared with standard values, the modulation efficiency amplitudes and the phase error deviations were 2.450% and 0.433%. During the observation period, the daily average of XCO₂ ranged from 415.09 to 421.78 ppm. XCH₄ ranged from 1.96 to 2.02 ppm with a mean of 1.982 ppm, and the daily average of XCO ranged from 0.118 to 0.157 ppm with a mean of 0.137 ppm. For the relationship between XCO₂ and XCH₄, the linear regression line shows a good correlation with the correlation coefficient R² ≥ 0.5. Especially, for the correlation coefficient R² = 0.82 on 8 October, our studies found a weak correlation in the variation of CO₂ and CO during the observations. The correlation coefficient R² ≥ 0.5 was only found on 30 September and 3 October. The trajectories dram at a height of 10 km give a hint of trace gas transport from the bay of western India, Bengal, and the Arabian Sea, whereas for the trajectories dram boundary layer height, trace gases were transported from southwest and east of China. These results provide a theoretical basis to understand the time and space distribution and the changes of greenhouse gas in the atmosphere as well as providing a theoretical basis for calculations of atmospheric radiation transmission.

This study investigates the spatial and temporal dynamics of carbon dioxide (CO₂) and methane (CH₄) in the Hefei region from 2009 to 2020 using satellite-based column ob-servations (GOSAT) and the HYSPLIT model. The results reveal a significant increasing trend for both greenhouse gases, with Hefei's CO₂ growth rate (2.43 ppm/year) exceed-ing the global average. Pronounced seasonal cycles were identified: CO₂ concentrations peak in winter due to enhanced fossil fuel combustion for heating and weakened plant photosynthesis, and reach a minimum in summer owing to strong biospheric carbon uptake. In contrast, CH₄ concentrations are highest in summer and autumn, likely driven by agricultural activities such as rice cultivation, and lowest in winter and spring. Diurnal variations show a synchronous pattern for CO₂ and CH₄, peaking around noon, influenced by the interplay of anthropogenic emission cycles and plane-tary boundary layer dynamics. Backward trajectory analysis and clustering further elu-cidate that the transport pathways and source regions of greenhouse gases are domi-nantly controlled by the East Asian monsoon. The summer is characterized by clean, locally-influenced marine air masses, while the winter is predominantly influenced by long-range transport from the polluted northwestern interior of China, which acts as a critical pollution corridor. This research underscores the combined roles of local an-thropogenic activities, regional transport, and meteorological systems in driving the characteristics of greenhouse gases in an inland subtropical city.

Rapidly industrializing urban regions are critical hotspots for global greenhouse gas (GHG) emissions, yet their specific spatiotemporal dynamics and driving mechanisms remain complex. This study aims to elucidate the long-term variability, co-emission characteristics, and source–sink attributions of atmospheric column-averaged dry-air mole fraction of carbon dioxide (XCO2) and methane (XCH4) in Hefei. To achieve this, a comprehensive source–sink–transport framework was employed, integrating long-term satellite retrievals (2009–2020), high-resolution ground-based measurements, atmospheric backward trajectory modeling, and anthropogenic emission inventories. Satellite observations reveal that the XCO2 growth rate in Hefei (2.41 ± 0.07 ppm/year) exceeded the global average, exhibiting a distinct seasonal maximum in autumn (2.7 ± 0.2 ppm/year). Meanwhile, XCH4 showed substantial interannual variability that was highly sensitive to regional emission anomalies. Ground-based measurements captured a robust synoptic-scale diurnal co-variation in spring (R2 = 0.965; ΔXCH4/ΔXCO2 ≈ 0.007), indicating dominant fossil fuel co-emissions. Seasonally, strong XCO2–XCH4 correlations in spring, autumn, and winter (R2 > 0.86) confirmed the persistent influence of anthropogenic combustion. However, this correlation significantly weakened in summer (R2 = 0.66) due to the decoupling effects of biogenic activities and dilution by marine air masses. Supported by backward trajectory and emission inventory analyses, the industrial, construction, and power generation sectors were identified as the primary drivers, contributing over 75% to the regional GHG emission increments. Ultimately, this study highlights the critical role of industrial and power combustion in local atmospheric GHG variability, providing quantitative insights necessary for targeted emission mitigation in super-regional developing hubs.