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Name (Pinyin):zhenzhen

School/Department:林学院

Education Level:With Certificate of Graduation for Doctorate Study

Degree:Doctoral Degree in Agriculture

Status:Employed

Discipline:
Forest Management

Honors and Titles:
2024年10月 东北林业大学2023~2024年度优秀本科生导师奖
2023年11月 获得2023年东北林业大学青年教师教学竞赛(农林组)二等奖
2023年04月 第十届“共享杯”大学生科技资源共享服务创新大赛优秀指导教师奖
2023年08月 东北林业大学2022~2023年度优秀本科生导师奖
2023年07月 指导本科生参加“挑战杯”黑龙江省大学生课外学术科技作品大赛荣获三等奖
2022年10月 东北林业大学2021~2022年度优秀本科生导师奖
2021年09月 东北林业大学2020~2021年度教学质量二等奖
2021年05月 指导本科生参加美国大学生数学建模大赛(ICM)获得一等奖(M奖)
2020年10月 东北林业大学2019~2020年度教学质量二等奖
2019年12月 东北林业大学林学院2019年度本科课程建设优秀奖
2018年06月 第七届梁希青年论文奖三等奖
2017年10月 东北林区主要树种(组)林木及林分动态预测技术,黑龙江省科学技术奖,二等奖(第8完成人),黑龙江省人民政府
2017年04月 东北林区主要树种(组)基础模型系统的研究,梁希林业科学技术奖,二等奖(第6完成人),国家林业局,中国林学会
2016年12月 GIScience & Remote Sensing杂志最佳审稿人
2016年09月 第六届梁希青年论文奖三等奖
2015年12月 第三届“共享杯”大学生科技资源共享服务创新大赛优秀指导教师奖
2015年09月 东北林业大学2014~2015年度教学质量二等奖
2014年09月 第五届梁希青年论文奖二等奖

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Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018

Date:2025-06-02 clicks:

Impact Factor:4.614

DOI number:10.3390/ijerph191811627

Journal:International Journal of Environmental Research and Public Health

Key Words:PCA; GTWR; GWR; TWR; particulate matter; meteorological factors; NDVI

Abstract:Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial–temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014–2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCAGTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.

Co-author:Qibang Wang,Jianhui Li,Huiran Jin

First Author:Q1, Longhui Fu

Indexed by:Journal paper

Correspondence Author:Qingbin Wei*,Zhen Zhen*

Volume:19

Issue:18

Page Number:11627

ISSN No.:1660-4601

Translation or Not:no

Date of Publication:2022-01-01

Included Journals:SCI

Pre One:Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests Next One:Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix Olgensis Henry) Using a Hierarchical Bayesian Approach