Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II - particulate matter
Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II - particulate matter
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DOI：
10.1016/j.atmosenv.2008.03.036
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发表时间：
2008-08-01
影响因子：
5
通讯作者：
Roselle, Shawn J.
中科院分区：
文献类型：
--
作者：
Appel, K. Wyat;Bhave, Prakash V.;Roselle, Shawn J.
本文是两篇论文的第二部分，这两篇论文检查了社区多尺度空气质量（CMAQ）模型4.5版（v4.5）的结果，并讨论了所见模型性能特征的潜在解释。本文的重点是细颗粒物（PM2.5）及其化学成分。在CMAQ v4.5中对城市沉积速度和云处理进行了改进，解决了36公里模拟中的补偿（SO42-）误差，提高了颗粒硫酸盐4)的预测。秋季颗粒硝酸盐（NO3-）和铵（NH4+）的大量高估可能是由于对季节性氨（NH3）排放的严重高估。在春末和夏季的几个月里，碳质气溶胶浓度被大大低估了，很可能部分是由于模式中缺乏一些二次有机气溶胶（SOA）的形成途径。CMAQ PM2.5预测与实测PM2.5质量的比较显示出不同的季节表现。春季和夏季整体表现最好，冬季和秋季表现相对较差，PM2.5总质量在这两个季节明显高估。PM2.5质量的模型偏差不能用主要无机离子加碳的模型偏差之和来解释。其他未确定的PM2.5 （PMother）的预测误差很大程度上归咎于PM2.5总质量预测的误差，目前正在努力确定这些误差的原因。Elsevier Ltd.出版。
This paper is Part II in a pair of papers that examines the results of the Community Multiscale Air Quality (CMAQ) model version 4.5 (v4.5) and discusses the potential explanations for the model performance characteristics seen. The focus of this paper is On fine particulate matter (PM2.5) and its chemical composition. Improvements made to the city deposition velocity and cloud treatment in CMAQ v4.5 addressing compensating (SO42-) errors in 36-km Simulations improved particulate Sulfate 4) predictions. Large overpredictions Of particulate nitrate (NO3-) and ammonium (NH4+) in the fall are likely due to a gross overestimation of seasonal ammonia (NH3) emissions. Carborlaceous aerosol concentrations are Substantially underpredicted during the late Spring and summer months, most likely due, in part, to a lack of some secondary organic aerosol (SOA) formation pathways in the model. Comparisons of CMAQ PM2.5 predictions with observed PM2.5 mass show mixed seasonal performance. Spring and summer show the best overall performance, while performance in the winter and fall is relatively poor, with significant overpredictions of total PM2.5 mass in those seasons. The model biases in PM2.5 mass cannot be explained by Summing the model biases for the major inorganic ions Plus carbon. Errors in the prediction of other unspeciated PM2.5 (PMother) are largely to blame for the errors in total PM2.5 mass predictions, and efforts are underway to identify the cause of these errors. Published by Elsevier Ltd.