Distributions, Sources, and Backward Trajectories of Atmospheric Polycyclic Aromatic Hydrocarbons at Lake Small Baiyangdian, Northern China

Abstract

Air samples were collected seasonally at Lake Small Baiyangdian, a shallow lake in northern China, between October 2007 and September 2008. Gas phase, particulate phase and dust fall concentrations of polycyclic aromatic hydrocarbons (PAHs) were measured using a gas chromatograph-mass spectrometer (GC-MS). The distribution and partitioning of atmospheric PAHs were studied, and the major sources were identified; the backward trajectories of air masses starting from the center of Lake Small Baiyangdian were calculated for the entire year. The following results were obtained: (1) The total concentration of 16 priority controlled PAHs (PAH16) in the gas phase was 417.2 ± 299.8 ng·m−3, in the particulate phase was 150.9 ± 99.2 ng·m−3, and in dust fall was 6930.2 ± 3206.5 ng·g−1. (2) Vehicle emission, coal combustion, and biomass combustion were the major sources in the Small Baiyangdian atmosphere and accounted for 28.9%, 45.1% and 26.0% of the total PAHs, respectively. (3) Winter was dominated by relatively greater PAHs polluted northwesterly air mass pathways. Summer showed a dominant relatively clean southern pathway, whereas the trajectories in autumn and spring might be associated with high pollution from Shanxi or Henan province.

Spatial and Temporal Variation of PM2.5 and Atmospheric PCDD/Fs in Northern Taiwan during Winter Monsoon and Local Pollution Episodes

“…When performing PCA with the sample size smaller than the number of variables, (n < 17 in the case of PCDD/F), the KMO value cannot be calculated. However, some studies showed that principal component extraction can still be performed using PCA without KMO calculation (Larsen and Baker, 2003;Cincinelli et al, 2007;Luo et al, 2008;Qin et al, 2012;Ngo et al, 2017). Therefore, in this research, although, the small sample size made it ineligible to perform Bartlett's test and KMO value calculation, the PCA still showed suitable principal components.…”

Atmospheric PAHs in North China: Spatial distribution and sources

“…By now, most of current studies on atmospheric PAHs focuses on a specific city or limited areas during short time period, such as in early autumn of 2008 , summer and autumn of 2008 (Wang et al, 2011) and in 2012 and 2013 (Lin et al, 2015a) in Beijing, in 2007 and in Hebei (Qin et al, 2012), and in summer of 2009 in Shanxi (Liu et al, 2014). As a result, direct comparison between studies cannot be made, and it is very hard to obtain PAH spatial variation in a large region.…”

Cluster-based analysis of multi-model climate ensembles

“…The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) was a multi-model initiative conducted to investigate the atmospheric abundance of key climate forcing agents, including tropospheric ozone, and their change over time (e.g. Stevenson et al, 2013;Lamarque et al, 2013). For our purposes, we use the ACCMIP climate model data as an example of a typical multi-model ensemble on which to perform the clustering.…”

“…The k-means clustering algorithm, for example, is a relatively simple and popular technique used in several atmospheric science problems (e.g. Mace et al, 2011;Qin et al, 2012;Austin et al, 2013;Arroyo et al, 2017). Specifically related to climate science, clustering has also been used for automated classification of various remote-sensing data (e.g.…”

“…Such initiatives are now common and form an integral part of scientific assessment of atmospheric composition, particularly in international-policyfacing research concerning climate change. For example, recent model inter-comparison studies have considered stratospheric ozone layer recovery (Eyring et al, 2010), the climate impacts of long-term tropospheric ozone trends Stevenson et al, 2013), and palaeoclimatology (Braconnot et al, 2012), among others.…”