Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation.
Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation.
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DOI：
10.1016/j.scitotenv.2014.03.107
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发表时间：
2014-07-01
影响因子：
9.8
通讯作者：
Mendola, Pauline
中科院分区：
文献类型：
--
作者：
Chen, Gang;Li, Jingyi;Ying, Qi;Sherman, Seth;Perkins, Neil;Rajeshwari, Sundaram;Mendola, Pauline
关键词：
在本研究中，应用社区多尺度空气质量 (CMAQ) 模型使用 36 公里水平分辨率域来预测 2001 年至 2010 年期间 15 个医院转诊区域 (HRR) 的环境气体和颗粒物浓度。应用基于反距离加权的方法，利用空气质量监测器所在网格单元的观测值和预测值之间的差异，基于观测融合的区域污染物浓度场来产生暴露估计。尽管原始 CMAQ 模型能够根据 EPA 指南对 O3 和 PM2.5 产生令人满意的结果，但使用观测数据融合技术来纠正 CMAQ 预测可以显着提高所有气态和颗粒污染物的模型性能。使用五种不同的方法计算区域平均浓度：1）单独观测数据的反距离加权，2）原始CMAQ结果，3）观测融合CMAQ结果，4）群体平均原始CMAQ结果和5）群体平均融合CMAQ结果。它表明，虽然 HRR 区域的 O3（以及 NOx）监测网络足够密集，足以仅根据监测数据提供一致的区域平均暴露估计，但 PM2.5 观测点（以及 CO、SO2、PM10 和 PM2.5 成分的监测器）通常稀疏，并且通过反距离插值观测、原始 CMAQ 和融合 CMAQ 结果估计的平均浓度之间的差异可能显着不同。应使用人口加权平均值来考虑污染物浓度和人口密度的空间变化。单独使用原始 CMAQ 结果或观察结果可能会导致健康结果分析出现重大偏差。
In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRR regions are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses.
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影响因子：
8.3
作者：
Bravo MA;Fuentes M;Zhang Y;Burr MJ;Bell ML
通讯作者：
Bell ML
影响因子：
5
作者：
Chang, Howard H.;Reich, Brian J.;Miranda, Marie Lynn
通讯作者：
Miranda, Marie Lynn
影响因子：
9.8
作者：
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通讯作者：
Ying, Qi
美国健康效应研究中PM（2.5）化学成分的空间和时间变化。
影响因子：
10.4
作者：
Bell ML;Dominici F;Ebisu K;Zeger SL;Samet JM
通讯作者：
Samet JM
影响因子：
9.8
作者：
Zhang J;Troendle J;Reddy UM;Laughon SK;Branch DW;Burkman R;Landy HJ;Hibbard JU;Haberman S;Ramirez MM;Bailit JL;Hoffman MK;Gregory KD;Gonzalez-Quintero VH;Kominiarek M;Learman LA;Hatjis CG;van Veldhuisen P;Consortium on Safe Labor
通讯作者：
Consortium on Safe Labor