• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >QUANTIFYING THE RELATIONSHIP BETWEEN NATURAL AND SOCIOECONOMIC FACTORS AND WITH FINE PARTICULATE MATTER (PM2.5) POLLUTION BY INTEGRATING REMOTE SENSING AND GEOSPATIAL BIG DATA
【24h】

QUANTIFYING THE RELATIONSHIP BETWEEN NATURAL AND SOCIOECONOMIC FACTORS AND WITH FINE PARTICULATE MATTER (PM2.5) POLLUTION BY INTEGRATING REMOTE SENSING AND GEOSPATIAL BIG DATA

机译：通过集成遥感和地理空间大数据量化自然和社会经济因素与精细颗粒物（PM2.5）污染之间的关系

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

PMsub2.5/sub pollution is an environmental issue results from various natural and socioeconomic factors, frequently witnessed in the spring and winter across mainland China. However, the dominant influence of natural and socioeconomic factors within a city on PMsub2.5/sub is not extensively studied yet. In this study, the Random Forest Regression (RFR) is utilized to quantify the relationships between PMsub2.5/sub and potential factors within Wuhan city on a typical day turn from winter to spring. Technically, the 24-hour average PMsub2.5/sub concentration in downtown area on February 17th 2017 are collected at 9 sites. In the meantime, we retrieve simultaneous aerosol depth optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The ground measured PMsub2.5/sub and AOD are coupled for the retrieval of near-surface PMsub2.5/sub concentration by Spatial-temporal CoKriging (STCK) with Normalized Vegetation Index (NDVI), Modified Normalized Water Index (MNDWI), Normalized Building Index (NDBI) from Landsat-8 and DEM from Shuttle Radar Topography Mission (SRTM). As the geospatial big data booms, the Internet-collected volunteered geographic information (VGI), representing the urban form and function, are integrating for the regression to obtain the spatial variables importance measures (VIMs) by RFR both in centre and sub-urban region of Wuhan. The results reveal that terrain characteristics and the density of industrial enterprises have obvious relationships with the accumulation of PMsub2.5/sub while the density of roads also contributes to this.
机译：PM 2.5 污染是由于各种自然和社会经济因素造成的环境问题，在中国大陆的春季和冬季经常出现。然而，尚未深入研究城市中自然和社会经济因素对PM 2.5 的主导影响。在这项研究中，利用随机森林回归（RFR）量化了武汉在冬季从春季到春季的典型一天中PM 2.5 与潜在因子之间的关系。从技术上讲，2017年2月17日市区的24小时平均PM 2.5 浓度是在9个地点收集的。同时，我们从中分辨率成像光谱仪（MODIS）中同时获取了气溶胶深度光学深度（AOD）。结合地面测量的PM 2.5 和AOD，通过具有标准化植被指数（NDVI）的时空协同克里格（STCK）修正近地面PM 2.5 浓度，来自Landsat-8的归一化水指数（MNDWI），归一化建筑指数（NDBI）和穿梭雷达地形图任务（SRTM）的DEM。随着地理空间大数据的繁荣，代表城市形态和功能的互联网收集的自愿地理信息（VGI）正在整合以进行回归，以通过RFR获得中部和郊区的空间变量重要性度量（VIM）。武汉结果表明，地形特征和工业企业的密度与PM 2.5 的积累有明显的关系，而道路密度也对此造成了影响。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号