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首页> 外文期刊>Atmospheric Pollution Research >Development of PLS–path model for understanding the role of precursors on ground level ozone concentration in Gulfport, Mississippi, USA
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Development of PLS–path model for understanding the role of precursors on ground level ozone concentration in Gulfport, Mississippi, USA

机译:建立PLS-路径模型,以了解美国密西西比州格尔夫波特的前驱物对地面臭氧浓度的作用

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Ground-level ozone (GLO) is produced by a complex chain of atmospheric chemical reactions that depend on precursor emissions from natural and anthropogenic sources. GLO concentration in a particular location is also governed by local weather and climatic factors. In this work an attempt was made to explore a Partial Least Squares Path Modeling (PLS–PM) approach to quantify the interrelationship between local conditions (weather parameters and primary air pollution) and GLO concentrations. PLS path modeling algorithm was introduced and applied to GLO concentration analyses at Gulfport, Mississippi, USA. In the present analysis, three latent variables were selected: PRC (photochemical reaction catalyst), MP (meteorological factor), and OPP (other primary air pollutants). The three latent variables included 14 indicators for the analysis; with PRC having two (extraterrestrial radiation on horizontal surface, and extraterrestrial radiation normal to the sun), MP having nine (temperature, dew point, relative humidity, pressure, visibility, maximum wind speed, average wind speed, precipitation, and wind direction) and OPP having three (NO2, PM2.5, and SO2) parameters. The resulting model revealed that PRC had significant direct impact on GLO concentration but very small overall effect. This is because PRC had significant indirect negative impact on GLO via MP. Thus, when both direct and indirect effects were taken into account, PRC emerged as having the weakest effect on GLO. The third variable (OPP) also had a positive impact on GLO concentration.
机译:地面臭氧(GLO)是由复杂的大气化学反应链产生的,这些化学反应取决于自然和人为来源的前体排放。特定位置的GLO浓度还受当地天气和气候因素的影响。在这项工作中,尝试探索偏最小二乘路径建模(PLS–PM)方法,以量化局部条件(天气参数和主要空气污染)与GLO浓度之间的相互关系。介绍了PLS路径建模算法,并将其应用于美国密西西比州Gulfport的GLO浓度分析。在本分析中,选择了三个潜在变量:PRC(光化学反应催化剂),MP(气象因子)和OPP(其他主要空气污染物)。这三个潜在变量包括14个分析指标; PRC具有两个(水平面上的地外辐射,垂直于太阳的地外辐射),MP具有九个(温度,露点,相对湿度,压力,能见度,最大风速,平均风速,降水和风向)和OPP具有三个参数(NO 2 ,PM 2.5 和SO 2 )。所得模型表明,PRC对GLO浓度具有显着的直接影响,但总体影响很小。这是因为中国通过MP对GLO产生了间接的重大负面影响。因此,当同时考虑直接和间接影响时,PRC对GLO的影响最弱。第三个变量(OPP)对GLO浓度也有积极影响。

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