Abstract

Abstract. Quasicontinuous measurements of carbon monoxide (CO) recorded over three years at Mount Waliguan (WLG), a global baseline station in remote western China, were examined using back trajectory analysis. The data include a revision to correct the working reference scale to the WMO2000 scale and corrections for drift in the reference gases. Between July 2004 and June 2007, CO exhibited large fluctuations and the 5 %, 50 % and 95 %-percentiles of relevant CO mixing ratios were 102 ppb, 126 ppb and 194 ppb. Approximately 50 % of all observed data were selected as CO background data using a mathematical procedure of robust local regression, with the remainder affected by regional-scale pollution. The monthly mean background CO mixing ratios showed a minimum in summer and a maximum in late winter, although all seasons were affected by short-term enhancements that exceeded background levels. The CO data were compared to values observed at the high alpine research station at Jungfraujoch, Switzerland. Smaller seasonal amplitudes were observed at WLG compared to the Jungfraujoch due to lower winter and spring CO levels, however, episodic enhancements of polluted air were greater at WLG. The air parcels arriving at WLG came predominately from the west, except in summer when advection from the east and southeast prevailed. Transport from the east or southeast typically brought polluted air to the site, having passed over populated urban areas upwind. A large number of elevated CO mixing ratios could also be associated with advection from the northwest of WLG via the central Xinjiang Uygur Autonomous Region (XUAR) and the Ge'ermu urban area where growing industrial activities as well as crops residue burning provide sources of CO. Air masses passing over northwestern Gansu were associated with relatively high CO values suggesting an anthropogenic influence, which was likely due to anthropogenic emissions from northwestern China (based on back-trajectory and potential source contribution analysis and on the INTEX-B: intercontinental Chemical Transport Experiment-Phase B). Background conditions were observed most frequently in air parcels from remote Tibet west of WLG. The probability that air parcels pass over regions of clean or polluted regions was further identified using potential source contribution function (PSCF) analysis.

Highlights

  • Carbon monoxide (CO) affects the tropospheric oxidizing capacity as it is the main sink of the hydroxyl radical (OH) (Logan et al, 1981; Thompson, 1992)

  • We report a revision to the quasi-continuous observations of carbon monoxide (CO) at WLG to the WMO 2000 scale

  • We have used a “robust local regression” statistical method to distinguish the background data from polluted air and find that approximately 50 % of all observations can be identified as background air

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Summary

Introduction

Carbon monoxide (CO) affects the tropospheric oxidizing capacity as it is the main sink of the hydroxyl radical (OH) (Logan et al, 1981; Thompson, 1992). A gas chromatographic technique combined with a mercuric oxide detector (GC-HgO) has been widely used in quasi-continuous measurements of CO mixing ratios at many locations over the globe because of its low detection limit and good precision (Novelli et al, 1992, 1998). The observed discrepancies between different labs during intercomparison activities have often exceeded the limits stated in the quality objectives of the World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) (WMO, 2010) and have, to a certain extent, limited the accuracy of CO regional source and sink estimation from their spatial and temporal distributions. The location of WLG is influenced both by background and polluted air, from which changes in regional CO abundance and sources can be examined. Using two trajectorybased statistical approaches, we examine how the meteorological conditions and potential source regions influence WLG observation

Description of the site
Instrumentation and analysis
Standards and calibrations
Calibration scale conversions
Aug-07
Time series and monthly variations
80 Jan-04 Aug-04 Feb-05 Sep-05 Mar-06 Oct-06 Apr-07 Nov-07
Trajectories analysis of selected observation episodes
Cluster analysis
Background conditions
PSCF analysis
Conclusions
Full Text
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