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首页> 外文会议>Air and Waste Management Association annual conference and exhibition >10-Year Spatial and Temporal Trends of PM_(2.5) Concentrations in the Southeastern U.S. Estimated Using High-Resolution Satellite Data
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10-Year Spatial and Temporal Trends of PM_(2.5) Concentrations in the Southeastern U.S. Estimated Using High-Resolution Satellite Data

机译：使用高分辨率卫星数据估算的美国东南部PM_（2.5）浓度的10年时空趋势

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In this paper, we used a two-stage spatiotemporal model incorporating MAIAC AOD data, meteorological fields, and land use variables to estimate PM_(2.5) concentrations at 1 km spatial resolution and investigated the 10-year spatial and temporal trends of PM_(2.5) levels in our study region. The model-fitting and CV statistics (e.g. R~2, MPE, and RMSPE) showed that R~2 ranges from 0.71 to 0.85, MPE is from 1.73 to 2.50 μg/m~3, RMSPE ranges from 2.75 to 4.10 ug/m~3, and relative accuracy ranges from 72.9% to 80.7%, which indicate a good fit between predicted values from the fitted models and the observations. Moreover, a regression with zero intercept was performed to fit the predicted values against the observations. The results showed that at high concentration levels, both model fitting and cross validation under-predicted the PM_(2.5) concentrations by 3-7% (e.g. fitted/CV PM_(2.5)=97% to 93% observed PM_(2.5)). In addition, the results revealed a reasonable spatial pattern of PM_(2.5) levels in the study area as well as in the Atlanta metro area. For instance, high concentrations occur in large urban centers and along major highways, while low concentrations appear in rural and mountainous area. PM_(2.5) estimates at high spatial resolutions can provide more details in small geographic regions and reduce exposure misclassification in air pollution and epidemiological studies. The spatial trends of changes in PM_(2.5) concentrations indicated that higher pollution reduction occurred in areas with generally higher PM_(2.5) levels (e.g. in urban areas and along major highways), while areas with generally lower pollution levels (e.g., in forest and recreational areas) had lower and moderate reduction of fine particle concentrations. Our time series analysis results indicated that The PM_(2.5) levels in the study region as well as the Atlanta metro area followed a generally declining trend, especially after year 2005 (Figure 1). The PM_(2.5) levels decreased about 20% in the study region and 23% in the Atlanta Metro area during the period between 2001 and 2010, especially after year 2005.
机译：在本文中，我们使用了两阶段时空模型，结合了MAIAC AOD数据，气象场和土地利用变量，以估算1 km空间分辨率下的PM_（2.5）浓度，并研究了PM_（2.5 ）水平在我们的研究区域。模型拟合和CV统计（例如R〜2，MPE和RMSPE）显示，R〜2的范围为0.71至0.85，MPE的范围为1.73至2.50μg/ m〜3，RMSPE的范围为2.75至4.10 ug / m 〜3，相对准确度在72.9％至80.7％之间，这表明拟合模型的预测值与观察值之间具有良好的拟合度。此外，进行了零截距回归以使预测值与观测值相吻合。结果表明，在高浓度水平下，模型拟合和交叉验证均低估了PM_（2.5）浓度3-7％（例如，fit / CV PM_（2.5）= 97％至93％观察到的PM_（2.5）） 。此外，结果表明研究区域以及亚特兰大都会区的PM_（2.5）水平具有合理的空间格局。例如，高浓度发生在大型城市中心和主要公路沿线，而低浓度出现在农村和山区。在高空间分辨率下的PM_（2.5）估计值可以在较小的地理区域提供更多详细信息，并减少空气污染和流行病学研究中的暴露分类错误。 PM_（2.5）浓度变化的空间趋势表明，在通常具有较高PM_（2.5）水平的区域（例如，在城市地区和主要公路沿线），而在总体上具有较低污染水平的区域（例如，在森林中），污染物的减少率更高。和娱乐场所）的微粒浓度降低程度适中。我们的时间序列分析结果表明，研究区域以及亚特兰大都会区的PM_（2.5）含量总体呈下降趋势，尤其是在2005年之后（图1）。在2001年至2010年期间，尤其是2005年之后，研究区域的PM_（2.5）水平降低了约20％，亚特兰大都市区的降低了23％。

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