The composition of diesel exhaust fine particulate matter (PM2.5) is of growing interest because of its impacts on health and climatic factors and its application in source apportionment and aerosol modeling. We characterized the detailed chemical composition of the PM2.5, including the organic carbon (OC), elemental carbon (EC), water-soluble ions (WSIs), and elemental contents, emitted from China III and China IV diesel trucks (nine each) based on real-world measurements in Beijing using a portable emissions measurement system (PEMS). Carbonaceous compounds were the dominant components (totaling approximately 87%) of the PM2.5, similar to the results (greater than 80% of the PM2.5) of our previous study of on-road China III diesel trucks. In general, the amounts of individual component groups (carbonaceous compounds, WSIs, and elements) and PM2.5 emissions for China IV diesel trucks were lower than those of China III diesel trucks of the same size, except for the WSIs and elements for the light- and medium-duty diesel trucks. The EC/OC mass ratios were strongly dependent on the emission standards, and the ratios of China IV diesel trucks were higher than those of China III diesel trucks of the same size. The chemical species in the PM2.5 were significantly affected by the driving conditions. Overall, the emission factors (EFs) of the PM2.5 and OC under non-highway (NHW) driving conditions were higher than those under highway (HW) driving conditions, and the EC/OC mass ratios presented an increasing trend, with decreasing OC/PM2.5 and increasing EC/PM2.5 from NHW to HW driving conditions; similar trends were reported in our previous study. In addition, Pearson's correlation coefficients among the PM2.5 species were analyzed to determine the relationships among the various chemical components.

This study visualizes and quantifies extant publications of rural landscape research (RLR) in Web of Science using CiteSpace for a wide range of research topics, from a multi-angle analysis of the overall research profile, while providing a method and approach for quantitative analysis of massive literature data. First, it presents the number of papers published, subject distribution, author network, the fundamental condition of countries, and research organizations involved in RLR through network analysis. Second, it identifies the high-frequency and high betweenness-centrality values of the basic research content of RLR through keyword co-occurrence analysis and keyword time zones. Finally, it identifies research fronts and trending topics of RLR in the decade from 2009 to 2018 by using co-citation clustering, and noun-term burst detection. The results show that basic research content involves protection, management, biodiversity, and land use. Five clearer research frontier pathways and top 20 research trending topics are extracted to show diversified research branch development. All this provides the reader with a general preliminary grasp of RLR, showing that cooperation and analysis involving multiple disciplines, specialties, and angles will become a dominant trend in the field.

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