Quantifying stability influences on air pollution in Lanzhou, China, using a radon-based "stability monitor": Seasonality and extreme events

A recently-developed radon-based technique is modified to quantify the seasonal influences of atmospheric stability on urban emissions in Lanzhou, China, based on 11 months of observations at three sites with contrasting pollution characteristics. Near-surface concentrations of primary (CO, SO2, NOx) and secondary (O-3) gas phase pollutants responded to changing atmospheric stability in markedly different ways in winter and summer, primarily because monsoonal fetch changes strongly influenced the distance between measurement sites and their nearest upwind pollutant sources, but also due to mean diurnal changes in mixing depth. Typically, morning peak primary pollution concentrations increased by a factor of 2-5 from the most well-mixed to stable conditions, whereas nocturnal ozone concentrations reduced with increasing stability due to surface loss processes and the progressively reduced coupling between the nocturnal boundary layer and overlying free atmosphere. The majority of pollution exceedance events (cf. China National Air Quality Standard guideline values) occurred in winter, when all measurement stations were downwind of the city's main pollution sources, and were directly attributed to morning periods and stable atmospheric conditions. In the sheltered valley region of Lanzhou, extremes of winter nocturnal stability states represented a change in mean nocturnal wind speed of only 0.25 in s(-1) (from 0.6 to 0.85 m s(-1)). Daily-integrated PM10 concentrations increased by a factor of 2 in winter from the most well-mixed to stable conditions, and were usually above guideline values at the industrial and residential sites for all atmospheric stability conditions. In summer, however, daily mean PM10 exceedances usually only occurred at the industrial site, under stable conditions. Finally, a simple model - based on mean radon concentrations between 1900 and 0400 h - is proposed to predict haze conditions in the city prior to commencement of the peak morning commuting time. (C) 2016 Elsevier Ltd. All rights reserved.