The motivation of this paper is that the effect of landscape pattern information on the accuracy of particulate matter estimation is seldom reported. The landscape pattern indexes were incorporated in a land use regression (LUR) model to investigate the performance of PM_2.5 simulation over Zhejiang Province. The study results show that the prediction accuracy of the model has been improved significantly after the incorporation of the landscape pattern indexes. At class-level, waters and residential areas were clearly landscape components influencing decreasing or increasing PM_2.5 concentration. At landscape-level, CONTAG (contagion index) played a huge negative role in pollutant concentrations. Latitude and relative humidity are key factors affecting the PM_2.5 concentration at province level. If the land use regression model incorporating landscape pattern indexes was used to simulate distribution of PM_2.5, the accuracy of ordinary kriging for the LUR-based data mining was higher than the accuracy of LUR-based ordinary kriging, especially in the area of low pollution concentration.

The growing awareness of the health advantages offered by forests has underscored the significance of forest exposure as an upstream preventive measure against disease. While numerous studies have confirmed the physical and mental health benefits associated with forests, there is still a lack of quantitative understanding regarding the relationship between forest exposure and physiological health benefits (PHB). Particularly, there is insufficient knowledge about the threshold effects derived from short-term forest exposure. In this study, we propose a PHB threshold model for assessing forest exposure that introduces the concepts of efficiency threshold and benefits threshold. A pilot study was conducted in three typical natural forest sites to validate the proposed model. Electroencephalogram (EEG) was continuously measured as the physiological indicator, while meteorological, environmental, and demographic factors were simultaneously collected. The results show that: (1) the proposed PHB threshold model is applicable in a natural forest environment; (2) despite the longer time required to reach the PHB thresholds, forest exposure yielded more significant and prolonged health benefits compared to urban green spaces; (3) meteorological factors, such as temperature and relative humidity, play a crucial role in impacting the PHB threshold model; and (4) exposure to forests is better for deep thinking and relaxation than urban green spaces. These findings emphasize the potential of forests to offer a respite from the stresses of modern life and promote holistic well-being.