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The identification of atmospheric CO₂ observation data which are minimally influenced by very local emissions/removals is essential for trend analysis, for the estimation of regional sources and sinks, and for the modeling of long-range transport of CO₂. In this study, four approaches are used to filter the atmospheric CO₂ observation records from 2009 to 2011 at one World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) regional station (Lin'an, LAN) in China. The methods are based on the concentration of atmospheric black carbon (BC), on a statistical approach (robust extraction of baseline signal, REBS), on CH₄ as an auxiliary tracer (AUX), and on meteorological parameters (MET). All approaches do suitably well to capture the seasonal CO₂ cycle at LAN. Differences are observed in the average regional mole fractions with annual values in the REBS method at least 1.7 ± 0.2 ppm higher than the other methods. The BC method may underestimate the regional CO₂ mole fractions during the winter–spring period and should be treated with caution. The REBS method is a purely statistical method and it may also introduce errors on the regional CO₂ mole fraction evaluations, as the filtered trend may be influenced by the "noisy" raw data series. Although there are correlations between CH₄ and CO₂ mole fractions at LAN, the different source/sink regimes may introduce bias on the regional CO₂ estimation in the AUX method, typically in summer. Overall, the MET method seems to be the most favorable because it mainly focuses on the influence of potential local sources and sinks, and considers diurnal variations and meteorological conditions. Using the MET method, the annual growth rate of regional CO₂ at LAN is determined to be 3.1 ± 0.01 ppm yr⁻¹ (standard error) from 2009 to 2011.