<p><strong>Abstract.</strong> Regional air pollution is significantly associated with the dominant weather systems. In this study, the relationship between the particle pollution over the Yangtze River Delta (YRD) region and the weather patterns is investigated. Firstly, the pollution characteristics of particles (PM<sub>2.5</sub> and PM<sub>10</sub>) in YRD are studied by using the in situ monitoring data in 16 cities from December 2013 to November 2014. The results show that the annual average concentrations in the cities of Jiangsu Province all exceed the national air quality standard. The pollution level is higher in the inland areas. Highest values can be found in Nanjing, with the concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> being 79 μg · m<sup>−3</sup> and 130  μg · m<sup>−3</sup>, respectively. The PM<sub>2.5</sub>/PM<sub>10</sub> ratios are usually high in YRD, indicating that PM<sub>2.5</sub> is the overwhelmingly dominant particle pollutant. The wintertime peak of particle concentrations is tightly linked to the increased emissions in the heating season and the poor meteorological condition. Secondly, based on NCEP reanalysis data, synoptic weather classification is conducted to reveal that the weather patterns are easy to cause heavy pollution in YRD. Five typical synoptic patterns are objectively identified, including the East Asian trough rear pattern, the depression inverted trough pattern, the transversal trough pattern, the high-pressure controlled pattern, and the northeast cold vortex pattern. Finally, synthetic analysis of meteorological fields and backward trajectory calculation are used to further clarify how these patterns impact particle concentrations. It is clarified that YRD is largely influenced by polluted air masses from the northern and the southern inland areas when it is at the rear of the East Asian major trough. In this case, the strong northwest wind hinders the vertical outward transport of pollutants. Thus, the East Asian trough rear pattern is quite favorable for the accumulation of pollutants in YRD, and respectively contributes 70.4 % and 78.3 % to the occurrence of large-scale regional PM<sub>2.5</sub> and PM<sub>10</sub> pollution episodes. While under the weather systems for other patterns, the clean marine air masses may play great roles in the mitigation of particle pollution in YRD. The correlation between weather patterns and particle pollution can provide valuable views in the decision-making on pollution control and mitigation strategies.</p>

No abstract

No abstract

It is important to disambiguate names among persons in many scenarios. In this work, we propose an unsupervised method Diting and a semi-supervised method Diting++ for author disambiguation. In Diting, we learn a low-dimensional vector to represent each paper in networks, which are formed by connecting papers with multiple types of relationship (such as co-author). During representation learning, we focus on maximizing the gap between positive edges and negative edges. Further, we propose a clustering algorithm which associates papers to their real-life authors. To make full use of the authorship information, which is easy to obtain from the authors' homepages, we design Diting++ to improve the performance for name disambiguation. Diting++ uses the authorship information listed on the authors' homepages to construct label networks and uses a network representation learning method to learn paper representations based on label networks and other networks. Further, Diting++ uses a semi-supervised clustering method to partition learned paper representations into disjoint groups. Each group belongs to a distinct author. By making use of the label information, the clustering method partitions papers written by the same author in the same group, whereas papers written by different authors locate in different groups. Through extensive experiments, we show that our methods are significantly better than the state-of-the-art author disambiguation methods.INDEX TERMS Network representation learning, network embedding, author disambiguation.1 Diting is a magical creature which is good at distinguishing objects in Chinese mythology.

No abstract