TY - CHAP
T1 - Air contaminant statistical distributions with application to PM10 in Santiago, Chile
AU - Marchant, Carolina
AU - Leiva, Víctor
AU - Cavieres, M. Fernanda
AU - Sanhueza, Antonio
N1 - Funding Information:
The authors wish to thank the editor, Dr. David M. Whitacre, and the referees for their constructive comments on an earlier version of this chapter, which resulted in the current version. C. Marchant gratefully acknowledges support from the scholarship “President of the Republic” of the Chilean government of which she was a recipient during her studies in engineering in statistics in the University of Valparaiso which concluded with this work. The research of V. Leiva was partially supported by FONDECYT 1120879 grant from the Chilean government. The research of A. Sanhueza was partially supported by FONDECYT 1080409.
PY - 2013
Y1 - 2013
N2 - The use of statistical distributions to predict air quality is valuable for determining the impact of air chemical contaminants on human health. Concentrations of air pollutants are treated as random variables that can be modeled by a statistical distribution that is positively skewed and starts from zero. The type of distribution selected for analyzing air pollution data and its associated parameters depend on factors such as emission source and local meteorology and topography. International environmental guidelines use appropriate distributions to compute exceedance probabilities and percentiles for setting administrative targets and issuing environmental alerts. The log-normal distribution is frequently used to model air-pollutant data. This distribution bears a relationship to the normal distribution, and there are theoretical- and physical-based mechanistic arguments that support its use when analyzing air-pollutant data. Other distributions have also been used to model air pollution data, such as the beta, exponential, gamma, Johnson, log-logistic, Pearson, and Weibull distributions. One model also developed from physical-mechanistic considerations that has received considerable interest in recent years is the Birnbaum-Saunders distribution. This distribution has theoretical arguments and properties similar to those of the log-normal distribution, which renders it useful for modeling air contamination data. In this review, we have addressed the range of common atmospheric contaminants and the health effects they cause. We have also reviewed the statistical distributions that have been used to model air quality, after which we have detailed the problem of air contamination in Santiago, Chile. We have illustrated a methodology that is based on the Birnbaum-Saunders distributions to analyze air contamination data from Santiago, Chile. Finally, in the conclusions, we have provided a list of synoptic statements designed to help readers understand the signi finance of air pollution in Chile, and in Santiago, in particular, but that can be useful to other cities and countries.
AB - The use of statistical distributions to predict air quality is valuable for determining the impact of air chemical contaminants on human health. Concentrations of air pollutants are treated as random variables that can be modeled by a statistical distribution that is positively skewed and starts from zero. The type of distribution selected for analyzing air pollution data and its associated parameters depend on factors such as emission source and local meteorology and topography. International environmental guidelines use appropriate distributions to compute exceedance probabilities and percentiles for setting administrative targets and issuing environmental alerts. The log-normal distribution is frequently used to model air-pollutant data. This distribution bears a relationship to the normal distribution, and there are theoretical- and physical-based mechanistic arguments that support its use when analyzing air-pollutant data. Other distributions have also been used to model air pollution data, such as the beta, exponential, gamma, Johnson, log-logistic, Pearson, and Weibull distributions. One model also developed from physical-mechanistic considerations that has received considerable interest in recent years is the Birnbaum-Saunders distribution. This distribution has theoretical arguments and properties similar to those of the log-normal distribution, which renders it useful for modeling air contamination data. In this review, we have addressed the range of common atmospheric contaminants and the health effects they cause. We have also reviewed the statistical distributions that have been used to model air quality, after which we have detailed the problem of air contamination in Santiago, Chile. We have illustrated a methodology that is based on the Birnbaum-Saunders distributions to analyze air contamination data from Santiago, Chile. Finally, in the conclusions, we have provided a list of synoptic statements designed to help readers understand the signi finance of air pollution in Chile, and in Santiago, in particular, but that can be useful to other cities and countries.
UR - http://www.scopus.com/inward/record.url?scp=84871634871&partnerID=8YFLogxK
U2 - 10.1007/978-1-4614-5577-6_1
DO - 10.1007/978-1-4614-5577-6_1
M3 - Chapter
C2 - 23149810
AN - SCOPUS:84871634871
SN - 9781461455769
T3 - Reviews of Environmental Contamination and Toxicology
SP - 1
EP - 31
BT - Reviews of Environmental Contamination and Toxicology Volume 223
PB - Springer Science and Business Media, LLC
ER -
