Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States
Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States
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DOI：
10.1029/2003jd003981
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发表时间：
2004-11-24
影响因子：
4.4
通讯作者：
Sarnat, JA
中科院分区：
文献类型：
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作者：
Liu, Y;Park, RJ;Sarnat, JA
[1]2001年利用多角度成像光谱仪（MISR）获取的气溶胶光学厚度（AOT）数据与美国本土16个气溶胶机器人网络（AERONET）站点的AOT测量数据进行了比较。总体而言，MISR和AERONET AOT具有强相关性（r = 0.73）。回归分析表明，MISR AOT的均方根误差（RMSE）为0.05。MISR AOT的总体检索误差在+/-0.04 +/-0.18 x AOT范围内。这一结果以及回归斜率和截距与以前的结果相比，使用AOT检索MISR或中分辨率成像光谱仪（MODIS）。当排除三个西部内陆站点的数据时，MISR和AERONET AOT之间的一致性得到改善（R-2 = 0.90，RMSE = 0.04）。配对t检验表明，MISR系统性高估AOT 0.02 +/-0.007。研究还表明，在春季和夏季以及美国西部，MISR AOT的这种正偏差更大。总之，这些结果表明，MISR AOT测量可能适合于气溶胶丰度的定量分析。最后，当使用替代MISR AOT参数时，当前结果不太可能发生变化，因为我们的分析还显示MISR AOT参数（最佳拟合、区域平均值和加权区域平均AOT）具有高度可比性。[1]我们提出了一个简单的方法来估计地面细颗粒物（PM2.5，颗粒直径小于2.5毫米）浓度的应用当地的比例因子从全球大气化学模型（GEOS-CHEM与GOCART灰尘和海盐数据）气溶胶光学厚度（AOT）检索的多角度成像光谱仪（MISR）。由此产生的MISR PM2.5浓度与美国的测量结果进行了比较。S.美国环境保护署（EPA）2001年PM2.5达标网络。回归分析表明，年平均MISR PM2.5浓度与EPA PM2.5浓度密切相关（相关系数r = 0.81），估计斜率为1.00，截距不显著，排除南加州的三个潜在离群值。MISR PM2.5浓度的均方根误差（RMSE）为2.20 mug/m（3），这相当于约20%的相对误差（相对于EPA PM2.5平均浓度的RMSE）。使用全球模式产生的模拟气溶胶垂直廓线有助于减少由于对流层低层和高层气溶胶之间的相关性变化而导致的PM2.5浓度估计的不确定性，从而提高MISR AOT估计地面PM2.5浓度的能力。估计的季节平均PM2.5浓度表现出很大的不确定性，特别是在西部。通过改进的MISR云筛选算法和全球模式的沙尘模拟，以及更高的模式空间分辨率，我们期望该方法能够在更高的时空分辨率下对季节平均地面PM2. 5浓度进行可靠的估计。
[1] Aerosol optical thickness (AOT) data retrieved by the Multiangle Imaging Spectroradiometer (MISR) in 2001 were compared with AOT measurements from 16 Aerosol Robotic Network (AERONET) sites over the contiguous United States. Overall, MISR and AERONET AOTs were strongly correlated (r = 0.73). Regression analysis showed that the root mean square error (RMSE) of MISR AOT was 0.05. The overall retrieval error of MISR AOT was within +/-0.04 +/- 0.18 x AOT. This result as well as the regression slope and intercept were comparable with previous results using AOT retrievals from MISR or Moderate Resolution Imaging Spectroradiometer (MODIS). The agreement between MISR and AERONET AOTs was improved (R-2 = 0.90, RMSE = 0.04) when data from three western inland sites were excluded. A paired t test indicated that MISR systematically overestimated AOT by 0.02 +/- 0.007. It was also shown that this positive bias in MISR AOT was greater during the spring and summer as well as in western United States. Together, these results suggest that MISR AOT measurements may be suitable for quantitative analysis of aerosol abundance. Finally, it is unlikely that the current results will vary when using alternative MISR AOT parameters since our analysis also showed the MISR AOT parameters (best fit, regional mean, and weighted regional mean AOTs) to be highly comparable.[1] We present a simple approach to estimating ground-level fine particulate matter (PM2.5, particles smaller than 2.5 mm in diameter) concentrations by applying local scaling factors from a global atmospheric chemistry model (GEOS-CHEM with GOCART dust and sea salt data) to aerosol optical thickness (AOT) retrieved by the Multiangle Imaging Spectroradiometer (MISR). The resulting MISR PM2.5 concentrations are compared with measurements from the U. S. Environmental Protection Agency's (EPA) PM2.5 compliance network for the year 2001. Regression analyses show that the annual mean MISR PM2.5 concentration is strongly correlated with EPA PM2.5 concentration ( correlation coefficient r = 0.81), with an estimated slope of 1.00 and an insignificant intercept, when three potential outliers from Southern California are excluded. The MISR PM2.5 concentrations have a root mean square error (RMSE) of 2.20 mug/m(3), which corresponds to a relative error (RMSE over mean EPA PM2.5 concentration) of approximately 20%. Using simulated aerosol vertical profiles generated by the global models helps to reduce the uncertainty in estimated PM2.5 concentrations due to the changing correlation between lower and upper tropospheric aerosols and therefore to improve the capability of MISR AOT in estimating surface-level PM2.5 concentrations. The estimated seasonal mean PM2.5 concentrations exhibited substantial uncertainty, particularly in the west. With improved MISR cloud screening algorithms and the dust simulation of global models, as well as a higher model spatial resolution, we expect that this approach will be able to make reliable estimation of seasonal average surface- level PM2.5 concentration at higher temporal and spatial resolution.