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Performance evaluation of air quality dispersion models at urban intersection of an Indian city: a case study of Delhi city

机译: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

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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 (NO_2) 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 NO_2 concentration when compared with ISCST3 and AERMOD having d value as 0.49. In case of PM_(2.5), the AERMOD, ISCST3 and ADMS- urban have performed satisfactorily having d values as 0.46,0.45 and 0.43 respectively.
机译:空气质量建模通过提供更好,更有效的空气质量规划指南,在制定空气污染控制和管理策略中起着重要作用。几种空气质量扩散模型用于评估城市空气质量。空气质量模型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,使用了四个最新的空气质量模型(例如AERMOD,ADMS-Urban,ISCST3和CALINE4)以及两个代码GFLSM和DFLSM(基于高斯原理)来预测德里城市交叉口的空气质量。印度,然后进行绩效评估。这些模型可用于预测一氧化碳(CO),二氧化氮(NO_2)和PM2.5(尺寸小于2.5微米)的浓度,它们是车辆废气排放的主要成分之一。所有模型/代码的性能已使用标准统计描述符进行了评估,例如一致性指数(d),2因子(FAC2),分数偏差(FB),归一化均方误差(NMSE),几何均值偏差和几何均值方差。一氧化碳浓度的一致性指数(d)表示,与冬季的AERMOD(d = 0.50)和ADMS-Urban(d = 0.45)相比,ISCST3模型(d = 0.69)的性能令人满意。 d值小于0.4时,观察到CALINE 4,DFLSM和GFLSM的性能不令人满意。此外,与d值为0.49的ISCST3和AERMOD相比,ADMS-Urban在预测NO_2浓度方面表现令人满意。在PM_(2.5)的情况下,AERMOD,ISCST3和ADMS-urban的d值分别为0.46、0.45和0.43,令人满意。

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