Spatial distribution characteristics and source apportionment of heavy metal(loid)s in park dust in the Mianyang urban area, China.

Park dust is a carrier of heavy metal pollutants and could potentially harm the health of urban residents. The concentrations of 10 heavy metal(loid)s (HMs) in park dust from the Mianyang urban area were analysed via X-ray fluorescence spectrometry. Based on ArcGIS spatial analysis, Spearman correlation analysis, spatial autocorrelation analysis, and the positive matrix factorization (PMF) model, the spatial distribution and sources of HMs in park dust were studied. The average contents of Zn, Co, Cu, Cr, Pb, and Ba in park dust were 185.0, 33.7, 38.7, 178.7, 51.0, and 662.1 mg/kg, respectively, which are higher than the reference values. The 10 HMs exhibited obvious spatial distribution and local spatial agglomeration patterns. High concentrations of As and Pb were primarily concentrated in the eastern part of the Mianyang urban area. High concentrations of Zn, Cr, and Cu were largely distributed in parks near the Changjiang River and Fujiang River. A high concentration of Co was concentrated in the northern region. The high-value areas of Mn, Ba, V, and Ni occurred far from the city centre and were located in the southwestern region. We found that Pb and As primarily originated from mixed traffic and natural sources; Zn, Cr, and Cu mainly originated from industrial activities; Co largely originated from building sources; and Ba, Ni, Mn, and V were mostly derived from natural sources. Mixed, industrial, building, and natural sources accounted for 24.5%, 24.8%, 24.7%, and 26.0%, respectively, of the HM sources. Co, Cu, Cr, and Zn in the Mianyang urban area were obviously influenced by human activities and should receive close attention.

Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks.

Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. However, most existing methods neglect rich known typing information about these entities in knowledge graphs. To address this issue, we take advantage of knowledge graphs to improve fine-grained entity typing through the use of a copy mechanism. Specifically, we propose a novel deep neural model called CopyFet for FET via a copy-generation mechanism. CopyFet can integrate two operations: (i) the regular way of making type inference from the whole type set in the generation model; (ii) the new copy mechanism which can identify the semantic type of a mention with reference to the type-copying vocabulary from a knowledge graph in the copy model. Despite its simplicity, this mechanism proves to be powerful since extensive experiments show that CopyFet outperforms state-of-the-art methods in FET on two benchmark datasets (FIGER (GOLD) and BBN). For example, CopyFet achieves the new state-of-the-art score of 76.4% and 83.6% on the accuracy metric in FIGER (GOLD) and BBN, respectively.

Contamination and Probabilistic Ecological-Health Risk of Heavy Metal(loid)s in Urban Topsoil of Mianyang, SW China.

Heavy metal(loid) (HM) pollution in urban topsoil seriously endangers the health of urban residents and urban sustainable development. Compared with large cities, the research on the HM pollution of topsoil in emerging medium-sized industrial cities is quite limited. This study focuses on topsoil HM contamination in Mianyang, which is a representative moderate emerging industrial city in Southwest China. The results indicate that Ba, Cr, Cu, and Zn in the samples were much higher than their background values. The hot spots of Ba, As, Cu, Pb, Co, Cr, and Zn showed an obvious enrichment trend. The potential ecological risk of HMs showed a low ecological risk, which was mainly caused by As. The investigated HMs presented no significant non-carcinogenic hazard to local adult residents, but there were three sampling sites which presented a non-carcinogenic hazard to children; the carcinogenic risks of As, Cr, Co, and Ni were acceptable. In this study, a mixed source of industry and traffic was identified to be the priority anthropogenic source, and Cr and As were identified as the priority elements for further risk control. The findings of our study could be beneficial to decision-makers with regard to taking appropriate measures to control and reduce HM pollution in the Mianyang urban area.

Spatial distribution and source apportionment of heavy metal(loid)s in urban topsoil in Mianyang, Southwest China.

Spatial distributions and sources of some commonly concerned heavy metal(loid)s (HMs, As, Ba, Cr, Co, Cu, Ni, Pb, Mn, Zn, and V) in topsoil of Mianyang city, a typical medium-sized emerging industrial city in Southwest China, were determined to explore the influences of anthropogenic activities on the urban environment. The contents of the 10 HMs in 101 topsoil samples were analyzed using an X-ray fluorescence spectrometer, and their sources were analyzed by positive matrix factorization and statistical analysis. The spatial distributions of the HMs and the source contributions were mapped using GIS technology. The results showed that the mean contents of Ba, Cr, Cu, and Zn in the topsoil were significantly higher than their background values. Industrial activities resulted in high contents of Ba, Zn, Cu, and Cr. As, Co, Ni, and V that primarily came from natural sources; Pb, Cr, Cu, and Zn were chiefly derived from a mixed source of industry and traffic; and Ba and Mn primarily originated from industrial sources. Natural sources, mixed sources, and industrial sources contributed 32.6%, 34.4%, and 33.0% of the total HM contents, respectively. Industrial sources and mixed sources of industry and traffic were the main anthropogenic sources of HMs in the urban topsoil and should be the focus of pollution control.

Multi-granularity heterogeneous graph attention networks for extractive document summarization.

Extractive document summarization is a fundamental task in natural language processing (NLP). Recently, several Graph Neural Networks (GNNs) are proposed for this task. However, most existing GNN-based models can neither effectively encode semantic nodes of multiple granularity level apart from sentences nor substantially capture different cross-sentence meta-paths. To address these issues, we propose MHgatSum, a novel Multi-granularity Heterogeneous Graph ATtention networks for extractive document SUMmarization. Specifically, we first build a multi-granularity heterogeneous graph (HetG) for each document, which is better to represent the semantic meaning of the document. The HetG contains not only sentence nodes but also multiple other granularity effective semantic units with different semantic levels, including keyphrases and topics. These additional nodes act as the intermediary between sentences to build the meta-paths involved in sentence node (i.e., Sentence-Keyphrase-Sentence and Sentence-Topic-Sentence). Then, we propose a heterogeneous graph attention networks to embed the constructed HetG for extractive summarization, which enjoys multi-granularity semantic representations. The model is based on a hierarchical attention mechanism, including node-level and semantic-level attentions. The node-level attention can learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. Moreover, to better integrate sentence global knowledge, we further incorporate sentence node global importance in local node-level attention. We conduct empirical experiments on two benchmark datasets, which demonstrates the superiority of MHgatSum over previous SOTA models on the task of extractive summarization.

Theoretical investigations on mechanisms and kinetics of methylketene with O(3P) reaction in the atmosphere.

The O(3P)-initiated conversion mechanism and dynamics of CH3CHCO were researched in atmosphere by executing density functional theory (DFT) computations. Optimizations of all the species and single-point energy computations were implemented at the B3LYP/6-311++G(d,p) and CCSD(T)/cc-pVTZ level, respectively. The explicit oxidation mechanism was introduced and discussed. The results state clearly that the O(3P) association was more energetically beneficial than the abstraction of H. The rate coefficients over the probable temperature range of 200-3000 K were forecasted by implementing Rice-Ramsperger-Kassel-Marcus (RRKM) theory. Specifically, the total rate coefficient of O(3P) association reactions is 1.19 × 10-11 cm3 molecule-1 s-1 at 298 K, which is consistent with the experimental results (1.16 × 10-11 cm3 molecule-1 s-1). The rate coefficients for the O(3P) with CH2CO, CH3CHCO, and (CH3)2CCO suggest that rate coefficient of ketene derivatives increase with the increase of methylation degree. Graphical abstract.