Accurate monitoring of atmospheric CO₂ and CH₄ column concentrations is crucial for understanding greenhouse gas source-sink processes， assessing the effectiveness of emission reduction measures， and formulating climate change mitigation strategies. Fourier Transform Infrared （FTIR） spectroscopy， known for its high spectral resolution and sensitivity， is a key ground-based remote sensing technique. The portable EM27/SUN FTIR spectrometer， developed by KIT and Bruker， features a compact and robust design suitable for long-term field observations. Its core is an interferometer： incoming solar radiation is split， reflected by fixed and moving mirrors， and recombined to generate interference signals. Subsequently， the PREPROCESS program and the PROFFAST retrieval algorithm are applied to retrieve the column concentrations of the target gases.

To further improve the retrieval accuracy and reliability of EM27/SUN， this paper evaluates the reliability of the EM27/SUN by comparing its retrieval results in the Hefei area with those from the TCCON high-resolution FTIR spectrometer. Calibration formulas were developed to optimize the EM27/SUN retrieval performance. After calibration， the daily average deviation of the column-averaged dry-air mole fraction of carbon dioxide （XCO₂） between the EM27/SUN and TCCON was 1.259 0 ppm， while that of methane （XCH₄） was 0.003 7 ppm. Furthermore， the observation precision of XCO₂ and XCH₄ was calculated based on continuous observation data from the Dalian observation site during the midday period （11∶00~13∶00） on October 23rd. The observation precision for XCO₂ was found to be 0.12 ppm， and for XCH₄， it was 0.9 ppb， which meets the precision requirements for ground-based remote sensing of greenhouse gas monitoring.

Using the PROFFAST retrieval algorithm， time series of atmospheric XCO₂ and XCH₄ were obtained for selected dates in February， March， September， October， and November 2023 in the Dalian Changxing Island region. The mean XCO₂ was （422.41±0.34） ppm （parts per million by volume）， while the mean XCH₄ was （1.915 2±0.001 8） ppm. The observational data were divided into two periods： the late winter to early spring transition period （late February to March） and the mid-to-late autumn period （late September to early November）. The mean XCO₂ during the late winter to early spring period （422.99 ppm） was higher than that during the mid-to-late autumn period （421.55 ppm）. In contrast， XCH₄ showed an opposite trend， with a lower mean during the late winter to early spring transition period （1.909 9 ppm） compared to the mid-to-late autumn period （1.923 2 ppm）. Although CO₂ and CH₄ have different source-sink mechanisms， their concentration variations exhibited certain correlations in specific months， implying common influences on their variations. The correlation coefficient was 0.73 in the late winter to early spring period and 0.69 in the mid-to-late autumn period. By analyzing the characteristics of CO₂ and CH₄ concentration variations during the observation period and combining this with a backward trajectory model， possible source-sink processes were investigated.

Furthermore， a comparison was conducted between the EM27/SUN observation data and the corrected data from the Japanese GOSAT satellite. The results showed high consistency， with correlation coefficients of 0.87 for XCO₂ and 0.76 for XCH₄， respectively. Additionally， GOSAT satellite data （monthly averages） from December 2021 to November 2024 were analyzed， revealing distinct seasonal variations for both gases： XCO₂ peaked in spring and winter and reached its lowest in summer （spring>winter>autumn>summer）， whereas XCH₄ was lower in spring and higher in summer and autumn （autumn>summer>winter>spring）. Both gases exhibited a clear increasing trend over the years. This study provides important data support for greenhouse gas source-sink analysis in the Dalian region. The portable FTIR spectrometer EM27/SUN can accurately observe atmospheric CO₂ and CH₄ column concentrations and their variations， making it suitable for validating satellite remote sensing data in remote areas.