资源与环境遥感团队学术论文在SCIE二区Top期刊《Environmental Pollution》上线发表

2024年05月01日，以刘兰芳（云南师范大学地理学部地图学与地理信息系统专业2021级硕士生）为第一作者，云南师范大学地理学部王金亮教授为通讯作者，刘妍（云南师范大学地理学部地图学与地理信息系统专业2017级硕士生）和云南师范大学地理学部程峰老师等为共同作者，所撰写的题为 “Remote sensing estimation of regional PM_2.5 Based on GTWR Model -A case study of southwest China”的学术论文在SCIE二区Top期刊《Environmental Pollution》上线发表 (https://doi.org/10.1016/j.envpol.2024.124057).

近年来，中国的空气污染日益严重，雾霾事件频发。中国西南地区仍是雾霾高发区，其中 PM_2.5是主要的污染源。确定中国西南地区 PM_2.5 的空间分布对保障区域人类健康、环境质量和经济发展具有重要意义。本研究利用遥感（RS）和地理信息系统（GIS）技术，以及气溶胶光学深度（AOD）、数字高程模型（DEM）、归一化植被差异指数（NDVI）、人口密度和2018年1月至12月的气象数据，对中国西南地区的PM_2.5浓度进行了估算。采用普通最小二乘法回归（OLS）、地理加权回归（GWR）和地理与时间加权回归（GTWR）估算了PM_2.5浓度。研究结果准确剖析了西南地区的PM_2.5污染状况，为有关部门制定有针对性的大气污染防控治理措施，提供一定科学理论支撑。

Fig.1. A map of southwest China

Fig.2. The spatial distribution of remote-sensed PM_2.5 for southwest China as derived from three estimation methods. (a) derived from OLS; (b) derived from GWR and (c) derived from GTWR.

Fig.3.  Correlations between ground monitored PM_2.5 and remote sensing estimated PM_2.5 for southwest China. (a) fitting result from OLS; (b) fitting result from GWR and (c) fitting result from GTWR.

Fig.4.  Correlations between ground monitored PM_2.5 and remote sensing estimated PM_2.5 for southwest China. (a) fitting result from OLS; (b) fitting result from GWR and (c) fitting result from GTWR.

Fig.5. The spatial distribution of monthly average remote-sensed PM_2.5 for southwest China derived from GTWR.

该论文得到了王金亮教授主持的云南省重大科技专项计划（云南省西南联合研究生院科技专项-基础研究和应用基础研究重大项目）：“云南金沙江流域矿区植被多模态遥感动态监测及生态修复模式研究”（项目编号：202302AO370003），国家重点研发计划多政府国际科技创新合作重点项目 “利用地理空间技术进行土地利用/土地覆盖变化对生态安全影响的环境监测与评估”（2018YFE0184300）共同资助。

这是刘兰芳同学硕士期间以第一作者发表的第2篇SCI学术论文（详见附录1、附录2），此篇论文也是王金亮教授导师团队2024年发表的第6篇SCIE论文（详见附录3）。让我们恭喜刘兰芳同学！希望她再接再厉！也热烈祝贺资源与环境遥感研究团队取好成绩！

附录 1 论文相关信息

标题： Remote sensing estimation of regional PM_2.5 Based on GTWR Model -A case study of southwest China

作者： Lanfang Liu^a,c,d, Yan Liu^a,b,c , Feng Cheng ^a,c,d , Yuanhe Yu^a,c,e, Jinliang Wang^a,c,d*, Cheng Wang^a,c,f , Lanping Nong^a,c, Huan Deng^g

通讯作者：Jinliang Wang

作者单位：

^a Faculty of Geography, Yunnan Normal University, Kunming Yunnan 650500, China;

^b Weinan Railway Zili Middle School , Weinan Shanxi 714000, China;

^c Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming 650500, China;

^d Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China;

^e School of Geography, Nanjing Normal University, Nanjing 210023,China;

^f Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;

^g College of Geography and Tourism, Zhaotong University, Zhaotong, Yunnan 657000, China;

Correspondence: jlwang@ynnu.edu.cn

出版物：Environmental Pollution

摘要：Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM_2.5 (particulate matter with diameter ≤ 2.5 µm) being the main contributor. Establishing the spatial distribution of PM_2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), a digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM_2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM_2.5 concentrations. However, the R² values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM_2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM_2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM_2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a “V” -shaped oscillation change.

关键词：PM_2.5; remote sensing estimation; OLS model; GWR model; GTWR model; southwest China

附录2 刘兰芳同学发表论文清单

自 2021 年 9 月攻读硕士至今，刘兰芳同学在王金亮教授指导下发表了 2 篇 SCI 学术论文，信息如下：

[2] Lanfang Liu, Yan Liu, Feng Cheng, Yuanhe Yu, Jinliang Wang*, Cheng Wang , Lanping Nong, Huan Deng, Remote sensing estimation of regional PM2.5 Based on GTWR Model -A case study of southwest China, Environmental Pollution, 2024, 124057. https://doi.org/10.1016/j.envpol.2024.124057  (中科院 SCIE 期刊分区：2023年12月最新升级版二区Top，2024 年 IF 8.9)

[1] Lanfang Liu, Jie Li, Jinliang Wang*, Fang Liu, Janine Cole , Jinming Sha , Yuanmei Jiao , Jingchun Zhou. The establishment of an eco-environmental evaluation model for southwest China and eastern South Africa based on the DPSIR framework [J]. Ecological Indicators, 2022,145, 109687. DOI: https://doi.org/10.1016 /j.ecolind.2022.109687. (中科院SCI期刊分区：2021年12月最新基础版二区，升级版二区，2022年IF 6.263)

附录 3 王金亮教授团队 2024 年发表论文清单

自2024年01月01日至05月01日，王金亮教授导师团队发表学术论文6篇（仅仅统计王金亮教授为通讯作者的论文），其中SCIE论文6篇。具体信息如下：

[6] Lanfang Liu, Yan Liu, Feng Cheng, Yuanhe Yu, Jinliang Wang*, Cheng Wang , Lanping Nong, Huan Deng, Remote sensing estimation of regional PM_2.5 Based on GTWR Model -A case study of southwest China, Environmental Pollution, 2024, 124057. https://doi.org/10.1016/j.envpol.2024.124057 (中科院 SCIE 期刊分区：2023年12月最新升级版二区Top期刊，2024 年 IF 8.9)

[5] Rafael Antonio Chaparro Torres, Jinliang Wang, Jianpeng Zhang, Lanfang Liu, Yongcui Lan, Temporal analysis of land degradation and urban expansion in central Yunnan Province using remote sensing for supporting sustainable development goals 11/15, Ecological Indicators, Volume 163, 2024, 112058, ISSN 1470-160X, https://doi.org/10.1016/j.ecolind.2024.112058  (中科院 SCIE 期刊分区：2023年12月最新升级版二区Top期刊，2024 年 IF 6.9)

[4] Gao, Y., Wang, J. *, Liu, S. *, Yao, X., Qi, M., Liang, P., Xie, F., Mu, J., Ma, X. Monitoring dynamics of Kyagar Glacier surge and repeated draining of Ice-dammed lake using multi-source remote sensing[J]. Science of The Total Environment, 2024, 172467. https://doi.org/10.1016/j.scitotenv.2024.172467 (中科院 SCIE 期刊分区：2023年12月最新升级版一区Top期刊，2024 年 IF 9.8)

[3] Xu, Haichao, Rongqing Han, Jinliang Wang*, and Yongcui Lan.Temporal–Spatial Characteristics and Influencing Factors of Forest Fires in the Tropic of Cancer (Yunnan Section)[J]. Forests, 2024, 15, 661. https://doi.org/10.3390/f15040661 (中科院 SCIE 期刊分区：2023年12月最新升级版二区，2024 年 IF 2.9)

[2] Yanke Zhang, Tengfei Gu, Suling He, Feng Cheng, Jinliang Wang*, et al. Extreme drought along the tropic of cancer (Yunnan section) and its impact on vegetation[J]. Scientific reports, 2024, 14, 7508. https://doi.org/10.1038/s41598-024-58068-w. (中科院 SCIE 期刊分区：2023年12月最新升级版二区，2024 年 IF4.6)

[1] Di Duan, Yuncheng Deng, Jianpeng Zhang, Jinliang Wang*, et al. Influence of VF and SOR-Filtering Methods on Tree Height Inversion Using Unmanned Aerial Vehicle LiDAR Data[J]. Drones, 2024, 8(4), 119. https://doi.org/10.3390/drones8040119. (中科院 SCIE 期刊分区：2023年12月最新升级版二区，2024 年 IF4.8)

（云南省高校资源与环境遥感重点实验室  供稿）