Elsevier

Atmospheric Environment

Volume 224, 1 March 2020, 117314
Atmospheric Environment

Decreasing atmospheric visibility associated with weakening winds from 1980 to 2017 over China

https://doi.org/10.1016/j.atmosenv.2020.117314Get rights and content

Highlights

  • The annual mean AV has decreased at most stations and all seasons across China since the 1980s.

  • On average the AV has significant positive correlation with the near-surface and the lower troposphere WS for all seasons.

  • AV exhibits a significant negative correlation center over southeastern China in mid-upper troposphere.

  • Both decreasing WS and increasing T could well account for the decreasing trend in AV.

Abstract

This paper presents the climatology and changes in atmospheric visibility (AV) in China during 1980–2017 based on adjusted data from an extended station network. The relationships between AV and the wind speed (WS) from the near surface to the troposphere (850 hPa to 200 hPa) are analysed. The mean visibility at 582 qualified stations in China is 22.03 km, with the highest in summer (25.37 km) and lowest in winter (18.92 km). The annual mean AV has decreased at most stations and all seasons across China since the 1980s. Most regions show a decreasing AV trend mainly before 2005 and an increasing trend in recent years, possibly related to the emission reduction policies in China. The AV has a significant positive correlation with the near-surface WS in all seasons, with the largest correlation coefficient of 0.84 in summer and the smallest in winter. The AV also has a positive correlation with the WS in the lower troposphere in most of China, except in some inland desert regions. Additionally, the long-term trends of WS, surface temperature (T), relative humidity (RH) and surface pressure (Ps) were analysed at each station across China. The results show that both decreasing WS and increasing T could well account for the decreasing trend in AV. The changes in RH and Ps are at least not the main drivers. Overall, the “best” (“worst”) visibility corresponds to the largest (smallest) WS near the ground and in the lower troposphere, but such correspondence tends to overturn towards the upper troposphere.

Introduction

Atmospheric visibility (AV) usually refers to the horizontal distance at which a target object is visible by the human eye against the sky background. AV can be up to 145–225 km in a clean atmosphere (EPA, 2001). In contrast, very low visibility canoccur at sites under serious air pollution due to the light extinction of aerosol particles and gases, especially the scattering effect of secondary aerosols (Watson and Chow, 2006). For example, AV decreased to a few metres during the London Smog events in 1952 (Wilkins, 1954).

Many studies have been carried out to investigate the variation in AV in different parts of the world and the underlying physical mechanisms at local, regional and global scales (Craig and Faulkenberry, 1979; Sloane, 1982a; b; Husar et al., 2000; Doyle and Dorling, 2002; Ghim et al., 2005; Tsai, 2005; Mahowald et al., 2007; Molnar et al., 2008; Sabetghadam et al., 2012; Deng et al., 2014; Hu et al., 2017; Singh et al., 2017). Rapid industrialization and urbanization have led to increasing air pollution and hence decreasing AV in China since the 1980s (Chang et al., 2009; Deng et al., 2014; Huang et al., 2014). Since 2000, many studies have analysed long-term AV observations and the relationships between visibility and aerosol pollution in different regions of China (Fan et al., 2005; Chang et al., 2009; Zhao et al., 2011; Chen and Xie, 2012; Wu et al., 2012; Deng et al., 2014; Fu et al., 2018).

While meteorological factors such as air temperature, relative humidity and winds do not directly influence clear sky visibility, they affect the source and sink of the pollutants in the atmosphere (Singh et al., 2017). In general, AV increases with increasing temperature and wind speed but decreases with increasing relative humidity and atmospheric pressure (Tsai, 2005; Wen and Yeh. 2010; Deng et al., 2016; Sun et al., 2018). Among the meteorological variables considered, wind speed and relative humidity may be the major meteorological factors that influence AV in China (Zhao et al., 2013; Zhang et al., 2015). However, radiosonde RH observations from the lower to the mid-troposphere showed no significant trends during 1979–2005, while the near-surface wind speed experienced significant weakening in recent decades in China (Guo et al., 2011; Fu et al., 2011; Wu et al., 2018).

In addition to the local meteorological factors, the changes in large-scale climate modes to varying degrees have been recognized to considerably affect the interannual variability (Chen et al., 2019) and long-term trend (Chen et al., 2018; Pei et al., 2018) of visibility. Among those climate modes, the reduction in autumn Arctic sea ice, the local precipitation and the surface wind during winter are considered to be the most influential factors for haze pollution changes in eastern China (Wang et al., 2015; Wang and Chen, 2016). In terms of decadal variability, clear sky AV and surface wind speed also show high negative correlations in China, even seriously polluted areas such as the Yangtze River Delta Region (Sun et al., 2018), while they show weak positive correlations over desert areas (Siélé et al., 2019). AV is closely related to surface wind speed because of the advection effect on aerosol loadings as a result of wind. However, circulation patterns are also the primary drivers of day-to-day variations in pollutant concentrations (Zhang et al., 2012; Van Oldenborgh et al., 2010).

Analyses of reanalysis data indicated that the occurrences of severe low visibility events in winter in eastern China were closely related to the weakened northerly winds in the lower troposphere, the weakened East Asian trough in the middle troposphere and the northward shift of the East Asian jet stream in the upper troposphere (Zhang et al., 2014; Chen and Wang, 2015). However, the possible relationships between AV and wind from the ground to the upper layers throughout China have not been comprehensively revealed thus far.

Previous studies mostly applied visibility observation data before 2013 because visibility observations changed from human observations to automatic observations around 2013 throughout China. Pei et al. (2018) adjusted the historical observations in Beijing according to the theoretical calculation suggested by the WMO. Such adjustments over China were complicated because the instrumental changes and their effects differ among the various meteorological stations, which causes the problem of data homogeneity.

In the present study, we applied a long time series of visibility observations across China with careful adjustments by using the metadata recorded for the observation series. Radiosonde wind profile data are widely used to explain low-visibility events, as they can explicitly characterize the critical boundary layer features and regional atmospheric transportation processes at regional or national-scales across China (Wang et al., 2016; Guo et al., 2017; Li et al., 2018). In this study, long-term series of wind sounding data together with ground observations are applied to explain the trend in the visibility series, as these data can explicitly characterize the critical boundary layer features and regional atmospheric transportation processes at regional or national-scales across China. Section 2 describes the observation data used and introduces the adjustment method. Section 3 demonstrates the trends in the AV series and the relationship between AV and wind speed. Section 4 summarizes the main conclusions.

Section snippets

Visibility and wind speed data

The daily observations, including visibility, relative humidity (RH), wind speed (WS), temperature (T), surface pressure (Ps) and precipitation records at 825 weather stations across mainland China at 14:00 Beijing Time (BJT), were used in this study. However, only 582 stations had more than 75% valid data in the study period were applied in the present study (black circles in Fig. 1). The other stations were not used because they involved complicated changes in observing system or site

Visibility adjustment analysis

To further illustrate the visibility results after adjustment, the time series of the daily visibility at 14:00 BJT at 4 different example stations in China are shown in Fig. 2, and their locations are shown in Fig. 1 (blue dots). The time series of the annual mean visibility before and after data adjustment are also shown in Fig. 2. Before 2014, visibility measurements were performed by human observers, mainly with a maximum visibility value of 30 km at the Zhalantun (ZLT) station in the Inner

Summary and concluding remarks

The atmospheric visibility observations at 582 stations across China are adjusted to eliminate or lessen the influence of changes in the observation system. The annual mean AV series based on the adjusted data shows improved continuity for those stations with sharp increases in the maximum visibility after automatic observations began to be collected around 2014–2015. The maximum visibility adjustment has no influence on the station records where the maximum visibility obtained by automatic

Author Contribution Statement

Yong Zhang: Conceptualization, Methodology, Investigation, Writing and reviewing

Lina Gao: Writing Original draft preparation, Visualization

Lijuan Cao: Data curation, Methodology, Validation, Writing, reviewing and editing

Zhongwei Yan: Supervision, Discussion

Yongxue Wu: Discussion

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was performed under the auspices of the Ministry of Science and Technology of China (Grants 2017YFC1501801, 2017YFC1501702 and 2016YFA0600404), the Megacities Experiment on Integrated Meteorological Observations in China initiated by the China Meteorological Administration (CMA), the National Natural Science Foundation of China (Grants 41705133, 91644223) and the Chinese Academy of Sciences (Grant XDA19030402). Last but not least, we thank the anonymous reviewers for their

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