资源环境科技发展态势分析平台

A significant portion of the production and consumption of trace gases (e.g. CO2, CH4, N2O, NH3, etc.) by world ecosystems occurs in areas without sufficient infrastructure or easily available grid power to run traditional closed-path flux stations. Open-path analyzer design allows such measurements with power consumption 10-150 times below present closed-path technologies, helping to considerably expand the global coverage and improve the estimates of gas emissions and budgets, informing the remote sensing and modeling communities and policy decisions, all the way to IPCC reports. Broad-band nondispersive infrared devices have been used for open-path CO2 and H2O measurements since the late 1970s, but since recently, a growing number of new narrow-band laser-based instruments are being rapidly developed. The new design comes with its own challenges, specifically: (a) mirror contamination, and (b) uncontrolled air temperature, pressure and humidity, affecting both the gas density and the laser spectroscopy of the measurements. While the contamination can be addressed via automated cleaning, and density effects can be addressed via the Webb-Pearman-Leuning approach, the spectroscopic effects of the in situ temperature, pressure and humidity fluctuations on laser-measured densities remain a standing methodological question. Here we propose a concept accounting for such effects in the same manner as Webb et al. proposed to account for respective density effects. Derivations are provided for a general case of flux of any gas, examined using a specific example of CH4 fluxes from a commercially available analyzer, and then tested using "zero-flux" experiment. The proposed approach helps reduce errors in open-path, enclosed, and temperature- or pressure-uncontrolled closed-path laser-based flux measurements due to the spectroscopic effects from few percents to multiple folds, leading to methodological advancement and geographical expansion of the use of such systems providing reliable and consistent results for process-level studies, remote sensing and Earth modeling applications, and GHG policy decision-making.