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Abstract: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 concentration and distribution of atmospheric particulate matter depend primarily on the meteorological conditions associated with a fixed pollution source. The effects of meteorological factors on particulate matter have been analyzed on the meteorological seasonal scale, but few researchers have considered the climatic season, which is divided based on the distribution feature of climatic factors. In addition, the hysteresis effect of meteorological factors is easily neglected. Here, we reviewed the characteristics and influential factors of particle pollution based on particle concentration and meteorological data from January 2013 through December 2013. Results from nonparametric tests and Spearman's nonparametric correlation coefficient showed that particle pollution exhibited a statistically significant seasonal trend. The pollution on workdays was slightly less than that on holidays, but no significant difference was found. The air pressure 1-2 days earlier showed a higher positive correlation with the current particle concentrations (except in winter), and the temperature 2-3 days earlier in summer and fall showed a stronger negative correlation with the particle concentration. Lower moisture and frequent precipitation would significantly reduce the pollution on the current day and the next day (except in summer). The variation of particulate matter concentration in summer exhibited a high-low-high variation, caused mainly by temperature and precipitation; the air quality during the plum rain period was significantly better than that in the period before the plum rain. The fine particle pollution level during the high-temperature and heat wave days was the lowest, after which the concentration increased.

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