• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Satellite-based ground PM2.5 estimation using timely structure adaptive modeling
【24h】

Satellite-based ground PM2.5 estimation using timely structure adaptive modeling

机译：基于及时结构自适应建模的卫星地面PM2.5估算

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Although ground-level measurement of PM2.5 is relatively accurate, this method is limited in spatial and temporal coverage due to the high costs. Recently, satellite-retrieved aerosol optical depth (AOD), with high-resolution and wide spatial-temporal coverage has been increasingly applied to estimate PM2.5 concentrations. However, these AOD-based PM2.5 concentrations were spontaneously estimated using the structure fixed models across an entire study period. While these 'structure fixed' simplifications greatly facilitated the efficiency of model developments and enhanced their generalizability, they ignored the fact that the 'contributors' of PM2.5 variation are not always coherent with time. For this, we propose a timely structure adaptive modeling (TSAM) method for satellite based ground PM2.5 estimation in this study by considering the timely variations of modeling predictors and magnitude of predictors at respective optimal spatial scales. Meanwhile, the reliability of TSAM for estimating national scale daily PM2.5 concentrations was tested by employing the AOD data from June 1, 2013 to May 31, 2014 over China with other multi-source auxiliary data such as meteorological factors, land use etc. While the fitting degree (R-2) of the daily TSAM models is 0.82, the one in 10-fold validation is 0.80, which are relatively higher than previous studies. These results are significantly better than those from structure-fixed models in this study. Additionally, the TSAM simulated PM2.5 concentrations show that the national annual PM2.5 concentration in China during study period is 69.71 mu g/m(3) with significant seasonal changes. These concentrations exceed the Level 2 of CNAAQS in more than 70% Chinese territory. Therefore, it can be concluded that the TSAM is a promising PM2.5 modeling method that is superior to structure-fixed modeling and could be very useful for air pollution mapping over large geographic areas. (C) 2016 Elsevier Inc. All rights reserved.
机译：尽管PM2.5的地面测量相对准确，但由于成本高昂，该方法在空间和时间范围上受到限制。近来，具有高分辨率和宽时空覆盖范围的卫星探测气溶胶光学深度（AOD）已越来越多地用于估算PM2.5浓度。但是，这些基于AOD的PM2.5浓度是在整个研究期间使用结构固定模型自发估算的。这些“固定结构”的简化极大地提高了模型开发的效率并增强了其通用性，但他们忽略了PM2.5变化的“贡献者”并不总是与时间保持一致的事实。为此，在本研究中，我们考虑了建模预测变量的及时变化和各个最佳空间尺度上预测变量的大小，提出了一种基于卫星的地面PM2.5估计的及时结构自适应建模（TSAM）方法。同时，通过使用中国2013年6月1日至2014年5月31日的AOD数据以及其他多源辅助数据（如气象因素，土地利用等），测试了TSAM估算全国规模每日PM2.5浓度的可靠性。每日TSAM模型的拟合度（R-2）为0.82，十分之一的验证度为0.80，这相对于以前的研究相对较高。这些结果明显好于本研究中的结构固定模型。此外，TSAM模拟的PM2.5浓度显示，研究期间中国全国PM2.5的年度浓度为69.71μg / m（3），具有明显的季节性变化。在中国超过70％的地区中，这些浓度超过了CNAAQS的2级标准。因此，可以得出的结论是，TSAM是一种有前途的PM2.5建模方法，它优于结构固定的建模方法，对于在大范围地理区域进行空气污染制图非常有用。 （C）2016 Elsevier Inc.保留所有权利。

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