Ting Lu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ting Lu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Haitian Wu
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin’an, Hangzhou 311300, China
1 shared publication - Jian Chen
College of Forestry (College of Life Sciences), Zhejiang A&F University, Hangzhou, 311300, PR China; Tianmushan Forest Ecosystem Orientation Observation and Research Station of Zhejiang Province, Hangzhou, 311300, PR China
1 shared publication - Shan Yang
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin’an, Hangzhou 311300, China
1 shared publication - Xintao Lin
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin’an, Hangzhou 311300, China
1 shared publication
Latest Publications
- Open Access
- 2 February 2018
- Published by MDPI AG in Atmosphere
- Vol. 9 (2) , 47
Optimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China
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.
- 2 February 2018
- Published by MDPI AG in Atmosphere
- Vol. 9 (2) , 47
Open AccessOptimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China
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.