A Comparison of Meteorology-Driven Interannual Variations of Surface Aerosol Concentrations in the Eastern United States, Eastern China, and Europe

Abstract

By the GEOS-Chem simulation with fixed anthropogenic and biomass burning emissions, this study exhibits the differences in interannual variations (IAVs) of surface-layer PM 2.5 concentrations among three populated regions, the eastern United States (US), eastern China, and Europe driven by variations in meteorological parameters. In the eastern US, PM 2.5 concentrations have relatively small IAVs with no explicit seasonality, with the absolute percent departure from the mean (APDM) values of 4−16% in four seasons. The IAVs of PM 2.5 are found to be large in North China and the northwestern Europe during winter and spring. The APDM values are 24−28% in winter and 32−36% in spring in eastern China, and 32−36% in winter and 20−24% in spring in Europe. Additionally, we obtain the key meteorological parameters that drive the IAVs of PM 2.5 by the stepwise multiline regression model (SLR) containing 8 meteorological variables. The most important meteorological variables over the eastern US, eastern China, and Europe are, respectively, the westerly at 850 hPa, surface wind speed, and the planetary boundary layer height in winter, and precipitation, relative humidity, and surface temperature in summer.(Citation: Feng, J., H. Liao, and Y. Gu, 2016: A comparison of meteorology-driven interannual variations of surface aerosols in the eastern United States, eastern China, and Europe. SOLA, 12, 146−152,

An Air Stagnation Index to Qualify Extreme Haze Events in Northern China

“…The ASI TS prediction could also combine with the regional meteorological models and regional air quality models to forecast the extreme haze in the subsequent few days. Finally, because of the good relationship between monthly ASI TS and PM 2.5 concentrations and the simple expressions of ASI TS , the ASI TS could be helpful for understanding the impacts of climate change and general atmospheric circulation on long-term variations in haze events over NC, which is one of the frontier study issues (Feng et al 2016;Cai et al 2017;Chen and Wang 2015;Ding and Liu 2014).…”

“…1) has been experiencing severe air pollution during recent years because of high quantities of emissions accompanied by rapid economic development and urbanization (Hu et al 2014;IPCC 2013;Mijling et al 2013;Tie et al 2009;Yuxuan Wang et al 2013;Zhang et al 2012). Aerosol concentrations over NC are highly sensitive to meteorological parameters and show strong variabilities at daily (Quan et al 2014;Guo et al 2017), monthly (Zhang et al 2012;Guo et al 2017), and annual time scales (Mu and Liao 2014;Feng et al 2016) since the meteorological factors, including wind speed, planetary boundary layer height (PBLH), and precipitation, can modulate aerosol dilution, transport, deposition processes, and even chemical reactions (Mu and Liao 2014;Seinfeld and Pandis 2006;Zhao et al 2012). Hence, meteorology plays an important role in the occurrence of extreme haze events, characterized by extremely high concentrations of particulate matter smaller than 2.5 mm in diameter (PM 2.5 ) (Quan et al 2011;Zhao et al 2013;R.…”

“…In summary, two methods were used to establish these indices. The first and most common one is to establish an empirical-statistical model using PM 2.5 concentrations and multivariable meteorological data by multilinear regression (MLR; Tai et al 2010Tai et al , 2012Feng et al 2016) or through other statistical methods, such as the summation of multinormalized atmospheric factors (Cai et al 2017). An empirical-statistical meteorological index is efficient for studying variations at a specific place and time scale but it lacks a sufficient physical background.…”

Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

“…Wang et al, 2011) but remaining stable at synoptic scales, excluding unexpected events such as volcanic activity and emergency lockdowns. Meteorology dominates synoptic scale (i.e., high-frequency) variations in aerosol concentrations (Bei et al, 2016;Zheng et al, 2015;Leung et al, 2018) and regulates aerosol physicochemical processes including their generation, diffusion, transport, and deposition (Feng et al, 2016), thus synchronizing periodic accumulation-removal of aerosol pollution with activities of synoptic systems (Chen et al, 2008;Guo et al, 2014).…”