Dept. of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.
Environ Sci Technol. 2012 Nov 6;46(21):11971-8. doi: 10.1021/es3025319. Epub 2012 Oct 18.
We improve the accuracy of daily ground-level fine particulate matter concentrations (PM(2.5)) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM(2.5). This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM(2.5), and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m(3) + 67%) to ± (1 μg/m(3) + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM(2.5) estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m(3) + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM(2.5) as compared to day-specific relationships.
我们提高了从卫星观测（MODIS 和 MISR）气溶胶光学深度（AOD）和化学输送模型（GEOS-Chem）计算得出的每日地面细颗粒物浓度（PM（2.5））的准确性，这种改进是通过以下两种方法实现的：（1）应用基于与现场 PM（2.5）比较的气候地面区域偏差校正因子，（2）应用空间平滑以降低随机不确定性并扩大覆盖范围。通过应用气候地面区域比例因子，美国和加拿大南部地区的初始每日 1σ均值不确定性从±（1μg/m（3）+67%）降低到±（1μg/m（3）+54%）。当与直接 AOD 反演接近时，空间插值可以在不增加不确定性的情况下增加卫星衍生 PM（2.5）估算的覆盖范围。空间平滑通过限制不确定性的随机分量，进一步将每日 1σ不确定性降低至±（1μg/m（3）+42%）。我们还发现，AOD 与 PM（2.5）的气候关系与特定于天的关系相比具有类似的性能。
