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Application of spatial analysis to investigate contribution of VOCs to photochemical ozone creation.
Environmental Science and Pollution Research Pub Date : 2020-01-15 , DOI: 10.1007/s11356-020-07628-4
Mohammad Sakizadeh 1, 2 , Mohamed Mostafa Mohamed 3, 4
Affiliation  

This study was concerned with the temporal analysis of benzene, toluene, ethylbenzene, xylenes (BTEXs), and ozone in Rochester, New York, between 2012 and 2018. Additionally, the influence of ozone precursors (e.g., BTEXs and NO2) and meteorological variables (e.g., relative humidity (RH), temperature along with wind speed) on ozone dispersion was investigated in the eastern half of the USA using the integrated nested Laplace approximation and stochastic partial differential equation (INLA-SPDE). The benzene variability at seasonal scale was characterized by higher values during the cold seasons. On the contrary, the long-term temporal trend of ozone depicted a repetitive cyclic behavior while an episode, with values exceeding 5 μg/m3, was detected associated with benzene in 2015. The spatial analysis by INLA-SPDE indicated that 1,3,5-trimethylbenzene and benzene were the key ozone precursors influencing ozone formation. It was demonstrated that increase of temperature had a considerable impact on ozone build-up whereas the increment of RH leads to decrease in ambient values of ozone. The amounts of root mean squared error (RMSE), mean absolute error (MAE), and bias for the validation data (e.g., 32 samples) were 0.005, 0.004, and 0.0008, exhibiting a reasonable out-of-sample forecasting by the INLA-SPDE model. The distribution map of ozone highlighted a hot spot in the state of Florida.

中文翻译:

应用空间分析研究VOC对光化学臭氧产生的贡献。

这项研究与2012年至2018年间在纽约州罗彻斯特市的苯,甲苯,乙苯,二甲苯(BTEX)和臭氧的时间分析有关。此外,臭氧前体(例如BTEX和NO2)的影响和气象变量使用积分嵌套拉普拉斯近似和随机偏微分方程(INLA-SPDE),在美国东半部调查了臭氧散布(例如,相对湿度(RH),温度和风速)。季节性范围内苯的变异性特征是在寒冷季节较高的值。相反,2015年,臭氧的长期时间趋势表现出重复的循环行为,而2015年检测到的事件与苯相关,其值超过5μg/m3。INLA-SPDE的空间分析表明,1,3,5-三甲基苯和苯是影响臭氧形成的关键臭氧前体。事实证明,温度升高对臭氧积累有相当大的影响,而相对湿度的增加会导致臭氧环境值降低。验证数据(例如32个样本)的均方根误差(RMSE),平均绝对误差(MAE)和偏差的数量分别为0.005、0.004和0.0008,这显示了INLA合理的样本外预测-SPDE模型。臭氧的分布图突出显示了佛罗里达州的一个热点。平均绝对误差(MAE)和验证数据的偏倚(例如32个样本)为0.005、0.004和0.0008,显示了INLA-SPDE模型的合理样本外预测。臭氧的分布图突出显示了佛罗里达州的一个热点。平均绝对误差(MAE)和验证数据的偏倚(例如32个样本)为0.005、0.004和0.0008,显示了INLA-SPDE模型的合理样本外预测。臭氧的分布图突出显示了佛罗里达州的一个热点。
更新日期:2020-01-15
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