Mixed deterministic statistical modelling of regional ozone air pollution

Kalenderski, Stoitchko; Steyn, D. G.

doi:10.1002/env.1088

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…These constrains make modeling an important approach for risk assessment. Modeling ambient air quality has a long history (e.g., Kalenderski and Steyn 2011), and ozone photochemical deterministic models have been developed both at large-and local-scale. However, up to now, large-scale deterministic models are typically available with a too coarse spatial resolution (e.g., 50×50-km EMEP), not suited for the local-scale risk assessment (EEA 2009).…”

Section: Introductionmentioning

confidence: 99%

“…2006), photochemical models are data intensive and expensive to keep updated. In addition, scientific understanding of the physical and chemical processes may be incomplete for a given region, either in principle or in practice (Kalenderski and Steyn 2011). As a result, statistical and geostatistical modeling can be an option, and several attempts exist in this respect (e.g., Loibl et al 2004).…”

Section: Introductionmentioning

confidence: 99%

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Estimating ozone risks using forest monitoring networks—results for science, policy, and society

Cristofori

Bacaro

Confalonieri

et al. 2015

Annals of Forest Science

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Abstract& Key message Few integration steps (adding low-cost ozone measurements, link to existing conventional monitors, joint data processing) transformed the traditional forest monitoring network into a multifunctional infrastructure producing information relevant for estimating risk to vegetation and human health & Context Traditionally, forest monitoring networks have been designed to assess status and trends of forest condition. We argue that they can help providing answers to a much broader range of questions for science, policy, and society. Here, we concentrate on the example of ground-level ozone pollution. & Aims The aim of this study is to demonstrate the value of present forest monitoring networks as infrastructures thatwith few integration steps-can provide important data and information to estimate the risk posed by ground-level ozone to vegetation and human health. & Methods We measured ozone concentration by passive samplers at the local (Trentino, northern Italy) plots of the ICP Forests Level I network over the period [2007][2008][2009][2010][2011]. By integrating these data with those from conventional ozone monitors (mostly located in urban areas), we (i) obtained an even distribution of air quality measurements over the investigated area, (ii) estimated international exposure indicators for vegetation and human population, and (iii) obtained data allowing geostatistical modeling and mapping of ozone concentrations, exposure, and associated potential risk. & Results Mean May-July ozone concentration ranged from 58 to 169 μg m −3, depending on forest site and year. ModelingHandling Editor: Pasi RAUTIO Contribution of the co-authors Antonella Cristofori contributed to design the experiment, running field work, and write the paper; Giovanni Bacaro contributed to model ozone concentrations and write the paper; Mauro Confalonieri contributed to organize field work and write the paper; Fabiana Cristofolini contributed to design the experiment, running field work, and write the paper; Luisa Frati contributed to model ozone concentrations, derive and map ozone risk for population, and write the paper; Francesco Geri contributed to map ozone concentrations and write the paper; Elena Gottardini contributed to design the experiment, running field work, coordinating the research project, and write the paper; Gabriele Tonidandel contributed to collect ozone concentration data and write the paper; Fabio Zottele contributed to spatialize temperature data and write the paper; Marco Ferretti contributed to design the experiment, supervise the work, and write the paper. ) provided evidence that the risk threshold for vegetation in terms of AOT40 was exceeded in large parts (90 %) of the study area, and frequently even by two times, depending on the year. With respect to population, up to 43 % of the dwellers were exposed to medium-high risk of exceedances of the information threshold. & Conclusion Ozone measurements carried out at the ICP Forests Level I forest monitoring network permitted mapping ozone levels...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating ozone risks using forest monitoring networks—results for science, policy, and society

Cristofori

Bacaro

Confalonieri

et al. 2015

Annals of Forest Science

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“…Despite its usefulness, the deterministic approach cannot always be implemented, since the input data must satisfy certain conditions (e.g., Denby et al, 2011;Good et al, 2003;Stevenson et al, 1998). When physical and chemical processes cannot be explained with deterministic models because of the nature of the data, statistical models can be an appealing alternative (e.g., Kalenderski and Steyn, 2011;Pires and Martins, 2011).…”

Section: Introductionmentioning

confidence: 98%

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: A case study

Escarela

2012

Journal of the Air & Waste Management Association

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The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.

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Ozone

Trivisano

2012

Encyclopedia of Environmetrics

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After illustrating the differences between tropospheric and stratospheric ozone, ozone production from photochemical reactions is described. Remarks follow on potential damages on human health and vegetation due to ozone toxicity and the need of its control via air quality standards and guidelines. Emphasis is given to monitoring, data collection and statistical analysis of ozone concentrations: statistical models are becoming important for ozone analysis and can be associated to deterministic chemistry transport and meteorological models. They are able to summarize large amounts of data collected in time and space, to account for confounders, like meteorological variables, and to evaluate uncertainties due to measurement errors. Global, regional and local scales are different contexts for analysis. Bayesian methods are very flexible tools for solving non standard problems, like missing values, change of support and misalignment. A variety of modern statistical methods useful for the analysis of ozone concentrations is reviewed: model fitting and extreme value analysis, spatio and spatio‐temporal models, methods for long‐term trends estimation, models for the analysis of the relationship between ozone concentrations and human health.

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Mixed deterministic statistical modelling of regional ozone air pollution

Cited by 6 publications

References 26 publications

Estimating ozone risks using forest monitoring networks—results for science, policy, and society

Estimating ozone risks using forest monitoring networks—results for science, policy, and society

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: A case study

Ozone

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