...
首页> 外文期刊>Atmospheric environment >The assessment of return probability of maximum ozone concentrations in an urban environment of Delhi: A Generalized Extreme Value analysis approach
【24h】

The assessment of return probability of maximum ozone concentrations in an urban environment of Delhi: A Generalized Extreme Value analysis approach

机译:德里城市环境中最大臭氧浓度的返回概率评估:广义极值分析方法

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

High ozone episodes have become a serious issue specially in megacities of developing countries. In this study an attempt has been made to understand the changes in the extreme ozone concentration due to different precursor compounds and meteorological variables in a given time period by applying Generalized Extreme Value (GEV) theory. The return probabilities of the extreme ozone concentrations were estimated for both classical stationary assumption as well as nonstationary assumption given the fact that ozone time series have both trend and multiple periodicities. Under stationary case, the distribution parameters were allowed to remain stationary, but in nonstationary case, distribution parameters were allowed to vary as a function of precursor compounds (Benzene, Toluene, mp-Xylene, NO, NO2) and meteorological variables (temperature and relative humidity). Daily maximum ozone concentrations were found to follow heavy tailed Frechet distribution in both stationary and nonstationary conditions. The inclusion of covariate into the classical model explains the dynamic nature of ozone depending on its precursor variables. Principal components were also used as covariates in nonstationary GEV distribution model. The estimated return levels of ozone from the stationary model, were found to be 97.89, 144.34, 188.44, 232.10, 310.33, 366.47 and 429.97 mu g/m(3) for 3, 7, 15, 30, 90, 180 and 365-day return period, respectively. In case of non-stationary GEV model the estimated return levels for 3, 7, 15, 30, 90, 180 and 365 day period were in the range 38.38-140.96, 84.31-184.35, 115.92-225.26, 166.61-337.21, 189.46-388.30 and 214.98-445 mu g/m(3), respectively. The highest median return level of O-3 for 3-day return period (100.35 mu g/m(3)) was observed due to temperature as covariate, for 7-day (146.04 mu g/m(3)) and 15-day (189.85 mu g/m(3)) return period, it was observed due to Benzene as a covariate and for rest of the 4 different return periods, NO was found responsible for the highest median return level of O-3 (233.52, 312.40, 369.02 and 433.06 mu g/m(3) for 30, 90, 180 and 365-day respectively). Seasonal analysis finds O-3 extremes to be high in monsoon and premonsoon seasons and low in winter period. The impact of nonstationary condition is exemplified by the fact that the 365 day return level of maximum ozone concentration was found to exceed within just 20 days for a lower concentration of precursor Benzene (i.e. 214.48 mu g/m(3)). Results underline the role of the principal components of the precursor compounds in governing the maximum ozone concentration in any city.
机译:特别是在发展中国家的特大城市中,臭氧高发已经成为一个严重的问题。在这项研究中,已尝试通过应用广义极值(GEV)理论来了解给定时间内由于不同的前体化合物和气象变量而导致的极端臭氧浓度的变化。考虑到臭氧时间序列具有趋势和多个周期性的事实,估算了经典平稳假设和非平稳假设的极限臭氧浓度的返回概率。在平稳情况下,允许分配参数保持不变,但在非平稳情况下,允许分配参数根据前体化合物(苯,甲苯,对二甲苯,NO,NO2)和气象变量(温度和相对湿度)而变化。湿度)。在固定和非固定条件下,每日最大臭氧浓度都遵循重尾弗雷谢特分布。在经典模型中包含协变量可以解释臭氧的动态性质,具体取决于其前体变量。主成分还用作非平稳GEV分布模型的协变量。从3、7、15、30、90、180和365-g的静态模型中,臭氧的估计返回水平分别为97.89、144.34、188.44、232.10、310.33、366.47和429.97μg / m(3)。天返回期分别。在非平稳GEV模型的情况下,3、7、15、30、90、180和365天的估计回报水平在38.38-140.96、84.31-184.35、115.92-225.26、166.61-337.21、189.46之间分别为388.30和214.98-445μg / m(3)。由于温度作为协变量,观察到3天O-3的最高中值返回水平(100.35 mu g / m(3)),7天(146.04 mu g / m(3))和15- (189.85μg / m(3))的返还期,这是由于苯作为协变量,在4个不同的返还期的其余时间内,未发现NO导致O-3的最高中值返还(233.52,分别在30、90、180和365天分别为312.40、369.02和433.06μg / m(3))。季节性分析发现,O-3极端值在季风和季风季节较高,而在冬季较低。非稳态条件的影响可以通过以下事实得到证明:对于较低浓度的前体苯(即214.48μg / m(3)),在短短20天内发现最大臭氧浓度的365天返回水平超过了。结果强调了前体化合物的主要成分在控制任何城市中最大臭氧浓度中的作用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号