Spatio-temporal modeling for real-time ozone forecasting

Paci, Lucia; Gelfand, Alan E.; Holland, David M.

doi:10.1016/j.spasta.2013.04.003

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…Ambient exposure is routinely evaluated through pollutant measurements collected by monitoring stations. Since stations are usually sparse across space, pollutant measurements are customarily interpolated across space and over time (Lee and Shaddick, ; Berrocal et al ., ; Paci et al ., ).…”

Section: Introductionmentioning

confidence: 97%

Quantifying Personal Exposure to Air Pollution from Smartphone-Based Location Data

Finazzi

Paci

2019

Biometrics

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Personal exposure assessment is a challenging task that requires both measurements of the state of the environment as well as the individual's movements. In this paper, we show how location data collected by smartphone applications can be exploited to quantify the personal exposure of a large group of people to air pollution. A Bayesian approach that blends air quality monitoring data with individual location data is proposed to assess the individual exposure over time, under uncertainty of both the pollutant level and the individual location. A comparison with personal exposure obtained assuming fixed locations for the individuals is also provided. Location data collected by the Earthquake Network research project are employed to quantify the dynamic personal exposure to fine particulate matter of around 2500 people living in Santiago (Chile) over a 4-month period. For around 30% of individuals, the personal exposure based on people movements emerges significantly different over the static exposure. On the basis of this result and thanks to a simulation study, we claim that even when the individual location is known with nonnegligible error, this helps to better assess personal exposure to air pollution. The approach is flexible and can be adopted to quantify the personal exposure based on any location-aware smartphone application. K E Y W O R D SDynamic models, Markov chain Monte Carlo, particulate matter, space-time modeling

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Section: Introductionmentioning

confidence: 97%

Quantifying Personal Exposure to Air Pollution from Smartphone-Based Location Data

Finazzi

Paci

2019

Biometrics

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“…I chose to apply a search similar to Paci, Gelfand, and Holland () for the optimal ϕ value by fitting the current dataset with values of ϕ starting at 10 and decreasing by an order of magnitude of 1 for each subsequent fitting. Once the model achieved an acceptance rate closest to 32%, that model was then used to obtain forecasting estimates for year J .…”

Section: Assessment Of Forecast Performance Via Cross‐validationmentioning

confidence: 99%

“…I chose to apply a search similar to Paci, Gelfand, and Holland (2013) for the optimal ϕ value by fitting the current dataset with values of ϕ starting at 10 and decreasing by an order of magni- The A and A 1 models were inadequate for forecasting annual catch by location for any of the four species in the analysis. Previous studies indicated that ARIMA models outperform other linear time series methods when forecasting monthly data (Stergiou, Christou, & Petrakis, 1997); however, ARIMA is less suited to yearly data (Stergiou & Christou, 1996) and nonlinear time series (Koutroumanidis et al, 2006 Notes.…”

Section: Control Of Bayesian Parameter ϕ In Crossvalidationmentioning

confidence: 99%

Spatiotemporally explicit model averaging for forecasting of Alaskan groundfish catch

Correia

2018

Ecology and Evolution

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Fisheries management is dominated by the need to forecast catch and abundance of commercially and ecologically important species. The influence of spatial information and environmental factors on forecasting error is not often considered. I propose a forecasting method called spatiotemporally explicit model averaging (STEMA) to combine spatial and temporal information through model averaging. I examine the performance of STEMA against two popular forecasting models and a modern spatial prediction model: the autoregressive integrated moving averages with explanatory variables (ARIMAX) model, the Bayesian hierarchical model, and the varying coefficient model. I focus on applying the methods to four species of Alaskan groundfish for which catch data are available. My method reduces forecasting errors significantly for most of the tested models when compared to ARIMAX, Bayesian, and varying coefficient methods. I also consider the effect of sea surface temperature (SST) on the forecasting of catch, as multiple studies reveal a potential influence of water temperature on the survival and growth of juvenile groundfish. For most of the preferred models, inclusion of SST in the model improved forecasting of catch. It is advisable to consider both spatial information and relevant environmental factors in forecasting models to obtain more accurate projections of population abundance. The STEMA method is capable of accounting for spatial information in forecasting and can be applied to various types of data because of its flexible varying coefficient model structure. It is therefore a suitable forecasting method for application to many fields including ecology, epidemiology, and climatology.

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“…Instead, our models run locally in real time, while incorporating the novelty in considering first‐ and second‐stage AR specifications along with a periodic component and heterogeneous variances. The only other real‐time forecasting work we are aware was developed using first differences along with spatiotemporally varying coefficients is that by Paci, Gelfand, and Holland (). Our temperature‐driven forecasting can substantially reduce the calibration error propagated from computer models such as η ‐CMAQ in the work of Paci et al ().…”

Section: Introductionmentioning

confidence: 99%

Local real‐time forecasting of ozone exposure using temperature data

Gelfand

Holland

2018

Environmetrics

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Rigorous and rapid assessment of ambient ozone exposure is important for informing the public about ozone levels that may lead to adverse health effects. In this paper, we use hierarchical modeling to enable real‐time forecasting of 8‐hr average ozone exposure. This contrasts with customary retrospective analysis of exposure data. Specifically, our contribution is to show how incorporating temperature data in addition to the observed ozone can significantly improve forecast accuracy, as measured by predictive performance and empirical coverage. We adopt two‐stage autoregressive models, also introducing periodicity and heterogeneity while still maintaining our objective of forecasting in real time. The entire effort is illustrated through modeling data collected at the Village Green monitoring station in Durham, North Carolina.

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Spatio-temporal modeling for real-time ozone forecasting

Cited by 12 publications

References 19 publications

Quantifying Personal Exposure to Air Pollution from Smartphone-Based Location Data

Quantifying Personal Exposure to Air Pollution from Smartphone-Based Location Data

Spatiotemporally explicit model averaging for forecasting of Alaskan groundfish catch

Local real‐time forecasting of ozone exposure using temperature data

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