Carbonaceous aerosols in high emission areas attract worldwide attention of the scientific community and the public due to their adverse impacts on the environment, human health and climate. However, long-term continuous hourly measurements are scarce on the regional scale. In this study, a one-year hourly measurement (from December 1, 2016 to November 30, 2017) of organic carbon (OC) and elemental carbon (EC) in airborne fine particles was performed using semi-continuous OC/EC analyzers in Beijing, Tianjin, Shijiazhuang and Tangshan in the Beijing-Tianjin-Hebei (BTH) region in China, which is one of high emission areas in China, even in the world. Marked spatiotemporal variations were observed. The highest concentrations of OC (22.8 ± 30.6 μg/m) and EC (5.4 ± 6.5 μg/m) occurred in Shijiangzhuang while the lowest concentrations of OC (11.0 ± 10.7 μg/m) and EC (3.1 ± 3.6 μg/m) were obtained in Beijing and Tianjin, respectively. Pronounced monthly, seasonal and diurnal variations of OC and EC were recorded. Compared to published data from the past two decades for the BTH region, our OC and EC levels were lower, implying some effect of recent measures for improving the air quality. Significant correlations of OC versus EC (p < 0.001) were found throughout the study period with high slopes and correlation coefficients in winter, but low slopes and correlation coefficients in summer. The estimated secondary OC (SOC), based on the minimum R squared (MRS) method, represented 29%, 47%, 38% and 48% of the OC for Beijing, Tianjin, Shijiazhuang and Tangshan, respectively. These percentages are larger than previous ones obtained for the BTH region in the past decade. There were obvious differences in the potential source regions of OC and EC among the four cities. Obvious prominent potential source areas of OC and EC were observed for Beijing, which were mainly located in the central and western areas of Inner Mongolia and even extended to the Mongolian regions, which is different from the findings in previous studies. For all sites, adjacent areas of the main provinces in northern China were found to be important potential source areas.
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http://dx.doi.org/10.1016/j.envint.2019.02.034 | DOI Listing |
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The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.
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Department of Stomatology, The Second Affiliated Hospital, Hengyang Medical College, University of South China, Hengyang, 421001, Hunan, China.
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