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PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts.

The simplification operation has always been a challenging research problem in the field of cartography generalization and expression. Continuous tessellation area data of land cover, whose source data come from high-resolution remote sensing satellite sensors, are an important type of remote sensing application data to express the spatial distribution information of natural and artificial objects on Earth's surface. In this paper, a novel and practical simplification method for continuous tessellation area data is proposed. The first step is area discretization, in which a general topological structure containing semantic features is established, and land patches are divided into multiple arcs on the basis of this topological structure. The second step is arc simplification, in which arcs are classified into artificial and natural types according to their semantic and morphological features, and corresponding simplification algorithms are assigned. The final step is area reconstruction, based on the semantic and topological information, and simplified arcs are reconstructed to patch polygons. The geography and national conditions census data of a city in Guizhou Province, China, are used to verify the rationality and effectiveness of the method. Experimental results showed that the method proposed in this article is suitable for the simplification processing of artificial and natural land features. The simplification results can maintain the overall shape of the patches and reduce the complexity of the arcs effectively, and the semantic information of each patch after simplification is also correct. [ABSTRACT FROM AUTHOR]