Optimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China
Open Access
- 2 February 2018
- journal article
- research article
- Published by MDPI AG in Atmosphere
- Vol. 9 (2) , 47
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 PM2.5 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 PM2.5 concentration. At landscape-level, CONTAG (contagion index) played a huge negative role in pollutant concentrations. Latitude and relative humidity are key factors affecting the PM2.5 concentration at province level. If the land use regression model incorporating landscape pattern indexes was used to simulate distribution of PM2.5, 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.Keywords
This publication has 43 references indexed in Scilit:
- Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environmentEnvironmental Research, 2017
- Land use regression modelling of air pollution in high density high rise cities: A case study in Hong KongScience of The Total Environment, 2017
- Relationships between landscape pattern metrics, vertical structure and surface urban Heat Island formation in a Colorado suburbUrban Ecosystems, 2017
- Estimating ground-level PM 10 in a Chinese city by combining satellite data, meteorological information and a land use regression modelEnvironmental Pollution, 2016
- Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles CountyEnvironmental Research, 2015
- Development of a land-use regression model for ultrafine particles in Toronto, CanadaAtmospheric Environment, 2015
- Land-use regression panel models of NO2 concentrations in Seoul, KoreaAtmospheric Environment, 2015
- A land use regression model for estimating the NO2 concentration in shanghai, ChinaEnvironmental Research, 2015
- Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, IranScience of The Total Environment, 2014
- Agreement of Land Use Regression Models with Personal Exposure Measurements of Particulate Matter and Nitrogen Oxides Air PollutionEnvironmental Science & Technology, 2013