Statistical models for monitoring and regulating ground‐level ozone

Gilleland, Eric; Nychka, Douglas

doi:10.1002/env.720

Cited by 29 publications

(26 citation statements)

References 19 publications

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“…We followed the procedure described in Gilleland and Nychka (2005) and Bevilacqua et al (2012) to pre-process the daily observations. The daily maximum 8-hour ozone measurement at station s and day t is assumed to have the decomposition,…”

Section: Analysis Of the Eastern Us Ozone Datamentioning

confidence: 99%

“…The coefficients in the seasonal effect µ(s, t) were estimated by ordinary least square and σ(s) was estimated using the residuals after removing the seasonal effect. Following Gilleland and Nychka (2005), the estimated coefficients matrix of the seasonal effect were further smoothed over space. We modeled the spatio-temporal component w(s, t) by a Gaussian process with mean 0 and a nonseparable spatio-temporal covariance function as in (3.1), with s defined on the sphere.…”

Section: Analysis Of the Eastern Us Ozone Datamentioning

confidence: 99%

“…We modeled the spatio-temporal component w(s, t) by a Gaussian process with mean 0 and a nonseparable spatio-temporal covariance function as in (3.1), with s defined on the sphere. Since the station locations are on the sphere, the transformed great circle distance (Gneiting (1999); Gilleland and Nychka (2005)) was used to ensure positive-definiteness of the covariance function: d(s, s ′ ) = 2r sin (∆ϕ/2), where r is the radius of the earth and ∆ϕ ∈ [0, π] is the central angle between s and s ′ . We used kilometer as the unit of spatial lags and day as the unit of temporal lags.…”

Section: Analysis Of the Eastern Us Ozone Datamentioning

confidence: 99%

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Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

Zhang¹,

Sang²,

Huang³

2014

STAT SINICA

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. (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica, 25 99-114. Full-scale approximations of spatio-temporal covariance models for large datasets AbstractVarious continuously-indexed spatio-temporal process models have been constructed to characterize spatiotemporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatiotemporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset. Disciplines Engineering | Science and Technology Studies FULL-SCALE APPROXIMATIONS OF SPATIO-TEMPORAL COVARIANCE MODELS FOR LARGE DATASETSBohai Zhang, Huiyan Sang and Jianhua Z. Huang Texas A&M UniversityAbstract: Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.

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Section: Analysis Of the Eastern Us Ozone Datamentioning

confidence: 99%

Section: Analysis Of the Eastern Us Ozone Datamentioning

confidence: 99%

Section: Analysis Of the Eastern Us Ozone Datamentioning

confidence: 99%

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Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

Zhang¹,

Sang²,

Huang³

2014

STAT SINICA

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“…Further, our modeling approach can be used to study site-specific trends by adjusting predicted ozone concentrations for meteorological effects where those have been observed. In this regard, we could attempt direct spatio-temporal modeling of extreme levels and for instance, in recent work of Gilleland and Nychka (2005) using generalized extreme value distributions. However, extremes need not be our only interest; the proposed high resolution modeling enables more general assessment of ozone patterns in space and time.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Space–Time Ozone Modeling for Assessing Trends

Sahu

Gelfand

Holland

2007

Journal of the American Statistical Association

100

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The assessment of air pollution regulatory programs designed to improve ground level ozone concentrations is a topic of considerable interest to environmental managers. To aid this assessment, it is necessary to model the space-time behavior of ozone for predicting summaries of ozone across spatial domains of interest and for the detection of long-term trends at monitoring sites. These trends, adjusted for the effects of meteorological variables, are needed for determining the effectiveness of pollution control programs in terms of their magnitude and uncertainties across space. This paper proposes a space-time model for daily 8-hour maximum ozone levels to provide input to regulatory activities: detection, evaluation, and analysis of spatial patterns of ozone summaries and temporal trends. The model is applied to analyzing data from the state of Ohio which has been chosen because it contains a mix of urban, suburban, and rural ozone monitoring sites in several large cities separated by large rural areas. The proposed space-time model is auto-regressive and incorporates the most important meteorological variables observed at a collection of ozone monitoring sites as well as at several weather stations where ozone levels have not been observed. This problem of misalignment of ozone and meteorological data is overcome by spatial modeling of the latter. In so doing we adopt an approach based on the successive daily increments in meteorological variables. With regard to modeling, the increment (or change-in-meteorology) process proves more attractive than working directly with the meteorology process, without sacrificing any desired inference. The full model is specified within a Bayesian framework and is fitted using MCMC techniques. Hence, full inference with regard to model unknowns is available as well as for predictions in time and space, evaluation of annual summaries and assessment of trends.

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“…Among the published articles concerning the integrated spatial and temporal analysis, Bogaert (1996), Cameron and Hunter (2002), Gilleland and Nychka (2005), Pokrajac et al (2003), Stein et al, (1998), and Tuckfield (1994) have reported the acceptable results obtained from integrated spatial and temporal analysis. However, in this study, applying one way ANOVA (α = 0.01) (2007) 129: [277][278][279][280][281][282][283][284][285][286][287][288][289][290][291][292][293][294] revealed that the data could be assumed separable and independent and the spatial and temporal analysis are sufficiently valid (P-value < 0.0001).…”

Section: Resultsmentioning

confidence: 92%

Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Level

Ahmadi¹,

Sedghamiz

2006

Environ Monit Assess

205

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Groundwater and water resources management plays a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying management tools which can reveal the critical and hot conditions seems necessary due to some limitations such as labor and funding. In this study, spatial and temporal analysis of monthly groundwater level fluctuations of 39 piezometric wells monitored during 12 years was carried out. Geostatistics which has been introduced as a management and decision tool by many researchers has been applied to reveal the spatial and temporal structure of groundwater level fluctuation. Results showed that a strong spatial and temporal structure existed for groundwater level fluctuations due to very low nugget effects. Spatial analysis showed a strong structure of groundwater level drop across the study area and temporal analysis showed that groundwater level fluctuations have temporal structure. On average, the range of variograms for spatial and temporal analysis was about 9.7 km and 7.2 months, respectively. Ordinary and universal kriging methods with cross-validation were applied to assess the accuracy of the chosen variograms in estimation of the groundwater level drop and groundwater level fluctuations for spatial and temporal scales, respectively. Results of ordinary and universal krigings revealed that groundwater level drop and groundwater level fluctuations were underestimated by 3% and 6% for spatial and temporal analysis, respectively, which are very low and acceptable errors and support the unbiasedness hypothesis of kriging. Although, our results demonstrated that spatial structure was a little bit stronger than temporal structure, however, estimation of groundwater level drop and groundwater level fluctuations could be performed with low uncertainty in both space and time scales. Moreover, the results showed that kriging is a beneficial and capable tool for detecting those critical regions where need more attentions for sustainable use of groundwater. Regions in which were detected as critical areas need to be much more managed for using the current water resources efficiently. Conducting water harvesting systems especially in critical and hot areas in order to recharge the groundwater, and altering the current cropping pattern to another one that need less water requirement and applying modern irrigation techniques are highly recommended; otherwise, it is most likely that in a few years no more crop would be cultivated.

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Statistical models for monitoring and regulating ground‐level ozone

Cited by 29 publications

References 19 publications

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

High-Resolution Space–Time Ozone Modeling for Assessing Trends

Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Level

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