• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI)
【2h】

Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI)

机译：通过包含NO2和增强植被指数（EVI）改进了使用基于卫星的地理加权回归（GWR）模型在中国进行的地面PM2.5估算

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM2.5) is currently quite limited in China. By introducing NO2 and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO2 and EVI, where cross-validation R2 increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO2 and EVI in GWR models could more effectively estimate PM2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China.
机译：目前在中国，有关环境细颗粒物（<2.5μm：PM2.5）的空间分布的高精度数据非常有限。通过将NO2和增强植被指数（EVI）引入到地理加权回归（GWR）模型中，新开发的GWR模型与融合的气溶胶光学深度（AOD）产品和气象参数相结合，可以解释相应地区约87％的变异性PM2.5质量浓度。与没有NO2和EVI的原始GWR模型相比，估计精度明显提高，其中交叉验证R 2 从0.77增加到0.87。当测量值低时，这两个模型都倾向于高估，而测量值高时，这两个模型往往被低估，因为精确的边界值在很大程度上取决于因变量。直到2015年，许多居民区仍然存在严重的PM2.5污染；但是，由政策驱动的节能减排不仅在2014年至2015年期间在一定程度上降低了PM2.5污染的严重程度，而且在一定程度上降低了其空间范围。没有足够的地面监测站和沙漠地区。通常，在GWR模型中使用NO2和EVI可以比以前的GWR模型更有效地在全国范围内估算PM2.5。这项研究的结果可为评估健康影响提供合理的参考，并可用于检验中国正在实施的排放控制策略的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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