Extreme value analysis for evaluating ozone control strategies

Brian Reich, Daniel Cooley, Kristen Foley, Sergey Napelenok, Benjamin Shaby

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Tropospheric ozone is one of six criteria pollutants regulated by the US EPA, and has been linked to respiratory and cardiovascular endpoints and adverse effects on vegetation and ecosystems. Regional photochemical models have been developed to study the impacts of emission reductions on ozone levels. The standard approach is to run the deterministic model under new emission levels and attribute the change in ozone concentration to the emission control strategy. However, running the deterministic model requires substantial computing time, and this approach does not provide a measure of uncertainty for the change in ozone levels. Recently, a reduced form model (RFM) has been proposed to approximate the complex model as a simple function of a few relevant inputs. In this paper, we develop a new statistical approach to make full use of the RFM to study the effects of various control strategies on the probability and magnitude of extreme ozone events. We fuse the model output with monitoring data to calibrate the RFM by modeling the conditional distribution of monitoring data given the RFM using a combination of flexible semiparametric quantile regression for the center of the distribution where data are abundant and a parametric extreme value distribution for the tail where data are sparse. Selected parameters in the conditional distribution are allowed to vary by the RFM value and the spatial location. Also, due to the simplicity of the RFM, we are able to embed the RFM in our Bayesian hierarchical framework to obtain a full posterior for the model input parameters, and propagate this uncertainty to the estimation of the effects of the control strategies. We use the new framework to evaluate three potential control strategies, and find that reducing mobile-source emissions has a larger impact than reducing point-source emissions or a combination of several emission sources.

Original languageEnglish (US)
Pages (from-to)739-762
Number of pages24
JournalAnnals of Applied Statistics
Volume7
Issue number2
DOIs
StatePublished - Jun 1 2013

Fingerprint

Value engineering
Ozone
Extreme Values
Control Strategy
Model
Deterministic Model
Conditional Distribution
Reduced-form model
Control strategy
Extreme values
Value analysis
Monitoring
Semiparametric Regression
Uncertainty
Extreme Value Distribution
Quantile Regression
Point Source
Vegetation
Pollutants
Form

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

Cite this

Reich, B., Cooley, D., Foley, K., Napelenok, S., & Shaby, B. (2013). Extreme value analysis for evaluating ozone control strategies. Annals of Applied Statistics, 7(2), 739-762. https://doi.org/10.1214/13-AOAS628
Reich, Brian ; Cooley, Daniel ; Foley, Kristen ; Napelenok, Sergey ; Shaby, Benjamin. / Extreme value analysis for evaluating ozone control strategies. In: Annals of Applied Statistics. 2013 ; Vol. 7, No. 2. pp. 739-762.
@article{8f0697fb15104cef877839a96b733242,
title = "Extreme value analysis for evaluating ozone control strategies",
abstract = "Tropospheric ozone is one of six criteria pollutants regulated by the US EPA, and has been linked to respiratory and cardiovascular endpoints and adverse effects on vegetation and ecosystems. Regional photochemical models have been developed to study the impacts of emission reductions on ozone levels. The standard approach is to run the deterministic model under new emission levels and attribute the change in ozone concentration to the emission control strategy. However, running the deterministic model requires substantial computing time, and this approach does not provide a measure of uncertainty for the change in ozone levels. Recently, a reduced form model (RFM) has been proposed to approximate the complex model as a simple function of a few relevant inputs. In this paper, we develop a new statistical approach to make full use of the RFM to study the effects of various control strategies on the probability and magnitude of extreme ozone events. We fuse the model output with monitoring data to calibrate the RFM by modeling the conditional distribution of monitoring data given the RFM using a combination of flexible semiparametric quantile regression for the center of the distribution where data are abundant and a parametric extreme value distribution for the tail where data are sparse. Selected parameters in the conditional distribution are allowed to vary by the RFM value and the spatial location. Also, due to the simplicity of the RFM, we are able to embed the RFM in our Bayesian hierarchical framework to obtain a full posterior for the model input parameters, and propagate this uncertainty to the estimation of the effects of the control strategies. We use the new framework to evaluate three potential control strategies, and find that reducing mobile-source emissions has a larger impact than reducing point-source emissions or a combination of several emission sources.",
author = "Brian Reich and Daniel Cooley and Kristen Foley and Sergey Napelenok and Benjamin Shaby",
year = "2013",
month = "6",
day = "1",
doi = "10.1214/13-AOAS628",
language = "English (US)",
volume = "7",
pages = "739--762",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "2",

}

Reich, B, Cooley, D, Foley, K, Napelenok, S & Shaby, B 2013, 'Extreme value analysis for evaluating ozone control strategies', Annals of Applied Statistics, vol. 7, no. 2, pp. 739-762. https://doi.org/10.1214/13-AOAS628

Extreme value analysis for evaluating ozone control strategies. / Reich, Brian; Cooley, Daniel; Foley, Kristen; Napelenok, Sergey; Shaby, Benjamin.

In: Annals of Applied Statistics, Vol. 7, No. 2, 01.06.2013, p. 739-762.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Extreme value analysis for evaluating ozone control strategies

AU - Reich, Brian

AU - Cooley, Daniel

AU - Foley, Kristen

AU - Napelenok, Sergey

AU - Shaby, Benjamin

PY - 2013/6/1

Y1 - 2013/6/1

N2 - Tropospheric ozone is one of six criteria pollutants regulated by the US EPA, and has been linked to respiratory and cardiovascular endpoints and adverse effects on vegetation and ecosystems. Regional photochemical models have been developed to study the impacts of emission reductions on ozone levels. The standard approach is to run the deterministic model under new emission levels and attribute the change in ozone concentration to the emission control strategy. However, running the deterministic model requires substantial computing time, and this approach does not provide a measure of uncertainty for the change in ozone levels. Recently, a reduced form model (RFM) has been proposed to approximate the complex model as a simple function of a few relevant inputs. In this paper, we develop a new statistical approach to make full use of the RFM to study the effects of various control strategies on the probability and magnitude of extreme ozone events. We fuse the model output with monitoring data to calibrate the RFM by modeling the conditional distribution of monitoring data given the RFM using a combination of flexible semiparametric quantile regression for the center of the distribution where data are abundant and a parametric extreme value distribution for the tail where data are sparse. Selected parameters in the conditional distribution are allowed to vary by the RFM value and the spatial location. Also, due to the simplicity of the RFM, we are able to embed the RFM in our Bayesian hierarchical framework to obtain a full posterior for the model input parameters, and propagate this uncertainty to the estimation of the effects of the control strategies. We use the new framework to evaluate three potential control strategies, and find that reducing mobile-source emissions has a larger impact than reducing point-source emissions or a combination of several emission sources.

AB - Tropospheric ozone is one of six criteria pollutants regulated by the US EPA, and has been linked to respiratory and cardiovascular endpoints and adverse effects on vegetation and ecosystems. Regional photochemical models have been developed to study the impacts of emission reductions on ozone levels. The standard approach is to run the deterministic model under new emission levels and attribute the change in ozone concentration to the emission control strategy. However, running the deterministic model requires substantial computing time, and this approach does not provide a measure of uncertainty for the change in ozone levels. Recently, a reduced form model (RFM) has been proposed to approximate the complex model as a simple function of a few relevant inputs. In this paper, we develop a new statistical approach to make full use of the RFM to study the effects of various control strategies on the probability and magnitude of extreme ozone events. We fuse the model output with monitoring data to calibrate the RFM by modeling the conditional distribution of monitoring data given the RFM using a combination of flexible semiparametric quantile regression for the center of the distribution where data are abundant and a parametric extreme value distribution for the tail where data are sparse. Selected parameters in the conditional distribution are allowed to vary by the RFM value and the spatial location. Also, due to the simplicity of the RFM, we are able to embed the RFM in our Bayesian hierarchical framework to obtain a full posterior for the model input parameters, and propagate this uncertainty to the estimation of the effects of the control strategies. We use the new framework to evaluate three potential control strategies, and find that reducing mobile-source emissions has a larger impact than reducing point-source emissions or a combination of several emission sources.

UR - http://www.scopus.com/inward/record.url?scp=84879530303&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879530303&partnerID=8YFLogxK

U2 - 10.1214/13-AOAS628

DO - 10.1214/13-AOAS628

M3 - Article

C2 - 24587842

AN - SCOPUS:84879530303

VL - 7

SP - 739

EP - 762

JO - Annals of Applied Statistics

JF - Annals of Applied Statistics

SN - 1932-6157

IS - 2

ER -