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MODELING AND ANALYSIS OF AIR QUALITY DATA.

机译：空气质量数据的建模和分析。

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摘要

Air pollution is monitored in the San Francisco Bay Area at some 30 stations. The data thus collected is intended to be representative of air pollution in the whole air basin. It is also assumed that the manner in which these data are summarized and reported is appropriate for comparisons with air quality standards. These two issues, the representativity of the air pollution data and the validity of certain statistical procedures used to process these data are the subject matter of the present work.; In order to gauge the representativity of the air pollution data collected, it is necessary to assess the variations in pollution levels with time and location. Meteorology is an obvious factor of these variations, so that the statistical interplay between pollution and weather is first investigated. This investigation is limited to oxidant, the worst pollutant in the Bay Area, and to the months of July and August, the worst months for oxidant pollution. It is actually the logarithm of the oxidant level which is used throughout this work, for statistical reasons. There are eight weather variables available to summarize the meteorology of the Bay Area: temperature, wind, speed, percentage sunshine and stability at various locations. The linear effect of the weather on pollution is gauged through regressions. Using all weather factors is not advantageous because of multicollinearity among weather factors. Only the overall effect of the weather on oxidant pollution is captured using the available meteorological data. This is seen by computing partial correlations between stations given the weather.; To test the sufficiency or redundancy of the available data, oxidant pollution at one station is predicted from the weather data and oxidant levels at other levels. Several such prediction methods are used and tested using cross-validation. The most effective method, which in particular predicts well high the oxidant values we are more concerned with, involved only the other monitoring stations as predictors, with no weather variables used, intercept set to zero and rate coefficients adding up to one. This approach could be used for a quick and cheap survey of unmonitored areas: schools, hospitals or other sensitive areas.; Oxidant pollution is summarized using the daily maximum of hourly averages. In practice these hourly averages refer to consecutive, non-overlapping one-hour periods. If continuous hourly averages were used instead, the daily maximum would be different. How different is the question which is next answered using stochastic models: Brownian motion and Ornstein-Uhlenbeck processes. These models show the difference not to be significant. They are then used to predict high oxidant episodes. Those predictions are compared to observed data and shown to be accurate enough to be useful.
机译：在旧金山湾区大约30个站点进行了空气污染监测。这样收集的数据旨在代表整个空气盆中的空气污染。还假定汇总和报告这些数据的方式适合与空气质量标准进行比较。这两个问题，即空气污染数据的代表性和用于处理这些数据的某些统计程序的有效性，是本工作的主题。为了衡量收集到的空气污染数据的代表性，有必要评估污染水平随时间和位置的变化。气象是造成这些变化的明显因素，因此首先研究了污染与天气之间的统计相互作用。这项调查仅限于氧化剂，这是湾区最严重的污染物，而仅限于7月和8月，这是氧化剂污染最严重的月份。实际上，出于统计原因，整个工作中使用的实际上是氧化剂水平的对数。有八个天气变量可用于总结海湾地区的气象：温度，风速，速度，日照百分比和各个位置的稳定性。天气对污染的线性影响通过回归来衡量。由于天气因素之间的多重共线性，因此使用所有天气因素均不利。使用可用的气象数据，只能捕获天气对氧化剂污染的总体影响。通过计算给定天气的站点之间的部分相关性可以看出这一点。为了测试可用数据的充分性或冗余性，从气象数据和其他水平的氧化剂水平预测一个站点的氧化剂污染。使用了几种此类预测方法，并使用交叉验证进行了测试。最有效的方法（尤其是可以很好地预测我们更关注的氧化剂值）仅涉及其他监视站作为预测器，不使用天气变量，将截距设为零，速率系数加为一。这种方法可用于对不受监控的区域进行快速而廉价的调查：学校，医院或其他敏感区域。使用每小时平均值的每日最大值来汇总氧化剂污染。实际上，这些每小时平均值是指连续的，不重叠的一小时周期。如果改为使用连续的每小时平均值，则每日最大值将有所不同。接下来使用随机模型回答的问题有何不同：布朗运动和Ornstein-Uhlenbeck过程。这些模型表明差异并不明显。然后将它们用于预测高氧化剂发作。将这些预测与观察到的数据进行比较，并显示出足够的准确性以至有用。

著录项

  • 作者

    GRIVET, CYRIL DANIEL.;

  • 作者单位

    Stanford University.;

  • 授予单位 Stanford University.;
  • 学科 Environmental Sciences.; Energy.
  • 学位 Ph.D.
  • 年度 1980
  • 页码 176 p.
  • 总页数 176
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 环境科学基础理论;能源与动力工程;
  • 关键词

  • 入库时间 2022-08-17 11:51:39

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