Performance evaluation of air quality dispersion models at urban intersection of an Indian city: a case study of Delhi city
Performance evaluation of air quality dispersion models at urban intersection of an Indian city: a case study of Delhi city
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印度城市交叉口空气质量扩散模型的性能评估：以德里市为例
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
S. Gulia
中科院分区：
文献类型：
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作者：
M. Khare;S. Nagendra;S. Gulia
空气质素模型在制订空气污染管制和管理策略方面发挥重要作用，为更妥善和更有效率的空气质素规划提供指引。城市空气质量评价中常用几种空气质量扩散模式。空气质量模式的性能和效率主要取决于对空气污染问题所涉及的各种大气、排放和地形参数之间复杂相互作用的准确解释。在本文中，四个国家的最先进的空气质量模型，如AERMOD，ADMS-城市，ISCST 3和CALINE 4和两个代码，即GFLSM和DFLSM（基于高斯原理）已被用来预测城市交叉口的空气质量，印度德里市，其次是他们的性能评价。这些模型被应用于预测一氧化碳（CO），二氧化氮（NO2）和PM2.5（尺寸小于2.5微米）的浓度，这是汽车尾气排放的主要组成部分之一。使用标准统计描述符（如一致性指数（d）、因子2（FAC 2）、部分偏倚（FB）、归一化均方误差（NMSE）、几何平均偏倚和几何平均方差）评价了所有型号/代码的性能。CO浓度的一致性指数（d）值表明，ISCST 3模式（d=0.69）与AERMOD模式（d=0.50）和ADMS-Urban模式（d=0.45）相比，在冬季的表现令人满意。已经观察到CALINE 4、DFLSM和GFLSM的性能不令人满意，d值小于0.4。此外，ADMS -城市在预测方面表现令人满意
Air quality modelling plays an important role in formulating air pollution control and management strategies by providing guidelines for better and more efficient air quality planning. Several air quality dispersion models are used to evaluate the urban air quality. The performance and efficiency of an air quality model are mainly depends upon the accurately interpretations of the complex interactions between various atmospheric, emission and topographic parameters involved in the air pollution problem. In this paper, four state-of art air quality models like AERMOD, ADMS- Urban, ISCST3 and CALINE4 and two codes i.e. GFLSM and DFLSM (based on Gaussian principle) have been used to predict the air quality of an urban intersection of Delhi city, India, followed by their performance evaluation. These models are applied to predict the concentration of Carbon monoxide (CO), Nitrogen dioxide (NO2) and PM2.5 (size less than 2.5 micron) which are one of the major components of vehicular exhaust emissions. The performance of all models/codes have been evaluated using standard statistical descriptor like Index of Agreement (d), Factor of 2 (FAC2), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Geometric Mean Bias and Geometric Mean Variance. The index of agreement (d) value for CO concentration indicates that ISCST3 model (d=0.69) performs satisfactorily when compared with AERMOD (d=0.50) and ADMS-Urban (d=0.45) for winter period. The performances of CALINE 4, DFLSM and GFLSM have been observed not satisfactory having d values less than 0.4. Further, the ADMS – urban has performed satisfactorily in predicting