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首页> 外文会议>International symposium on the measurement of toxic and related air pollutants >An Exploratory Analysis of the Relationships Among Fine and Coarse Particulate Matter and Ozone and Meteorological Variables in North Carolina
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An Exploratory Analysis of the Relationships Among Fine and Coarse Particulate Matter and Ozone and Meteorological Variables in North Carolina

机译：北卡罗来纳州细，粗颗粒物与臭氧和气象变量之间关系的初步分析

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The North Carolina Department of Environment and Natural Resources (NCDENR)rnprovided us with 1999 data on PM_(2.5), PM_(10) and ozone at five sites across North Carolina.rnOne site also had meteorological data available, which we used to find relationshipsrnbetween met variables and PM_(2.5)rn. Our objective was to develop equations to predictrnPM_(2.5) as a function of ozone, as well as PM_(10) and relevant meteorological variables.rnWhere monitors for PM_(2.5) do not exist, the relationship between PM_(2.5) and ozone or PM_(10)rncan be used to predict how serious the levels of PM_(2.5) might be. The equations created byrnlinear regression could predict anywhere from 39% to 55% of the variability in PM_(2.5).rnWe also found that the PM and ozone levels at the different sites were highly correlated.rnDurham, Raleigh and Greensboro had the highest correlation and so a single equationrnwas created that predicted 63.5% of the variability in ozone at these sites. Using thernmeteorological variables at the Raleigh site created only a slightly better equation thatrncould be used to predict PM_(2.5) levels. Further analysis of these met variables and morernPM_(10) data will help these equations.rnA thorough analysis of day of the week effects on ozone and PM_(2.5) levels found that therernis not a significant difference between days even when seasonal changes are taken intornaccount.
机译：北卡罗莱纳州环境与自然资源部（NCDENR）向我们提供了北卡罗莱纳州五个地点的1999年PM_（2.5），PM_（10）和臭氧数据。rn一个地点也有可用的气象数据，我们用来查找两者之间的关系。满足变量和PM_（2.5）rn。我们的目标是建立方程式来预测PM_（2.5）作为臭氧的函数以及PM_（10）和相关的气象变量.rn如果不存在PM_（2.5）的监视器，则PM_（2.5）与臭氧之间的关系或PM_（10）rn可用于预测PM_（2.5）的水平可能有多严重。线性回归建立的方程可以预测PM_（2.5）的39％到55％的变化.rn我们还发现不同地点的PM和臭氧水平高度相关.Durham，Raleigh和Greensboro的相关性最高，而因此创建了一个方程，预测了这些站点的臭氧变异性的63.5％。使用罗利站点的气象变量仅创建了一个稍好些的方程，可以用来预测PM_（2.5）水平。对这些满足的变量和更多的PM_（10）数据进行进一步分析将有助于这些方程式。rn对星期几对臭氧和PM_（2.5）水平的影响进行彻底分析后发现，即使考虑到季节性变化，每天之间的间隔也没有显着差异。 。

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