A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates

Lindström, Johan; Szpiro, Adam A.; Sampson, Paul D.; Oron, Assaf P.; Richards, Mark; Larson, Tim; Sheppard, Lianne

doi:10.1007/s10651-013-0261-4

Cited by 89 publications

(77 citation statements)

References 51 publications

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“…Daily average salinities and temperatures for 2006 through 2012 were estimated on a 200 × 200 m gridcell basis using a spatio-temporal kriging model (Szpiro et al 2009, Lindstrom et al 2011 (Fig. 4).…”

Section: Predicted Daily Salinitymentioning

confidence: 99%

Consequences of large-scale salinity alteration during the Deepwater Horizon oil spill on subtidal oyster populations

Powers¹,

Grabowski²,

Roman³

et al. 2017

Mar. Ecol. Prog. Ser.

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Response actions associated with oil spills often have significant impacts on ecological communities. During the 87 d long Deepwater Horizon oil spill, the State of Louisiana (USA) released vast quantities of Mississippi River water into 2 estuarine basins (Barataria Bay and Black Bay/Breton Sound) in response to the approach of oil. We assessed the impact on subtidal oyster populations of this novel oil spill response action using 3 independent methods: (1) comparison of fisheries-independent post-spill densities to a pre-spill temporal baseline; (2) comparison of oyster density collected during natural resource damage assessment sampling between the area of maximal freshwater impact and reference areas in the 2 basins; and (3) estimation from a doseresponse model derived from an analysis of an in situ mark and recapture study conducted in 2010 to assess the relationship between salinity and oyster mortality. A substantial portion of both basins (483 km 2 of Barataria Bay and 362 km 2 of Black Bay/Breton Sound) experienced prolonged periods of very low (< 5 ppt) salinity in 2010 that lasted at least 1 mo longer than the average duration of low salinity between 2006 and 2009. The 3 approaches all indicate that dramatic losses occurred in the number of market-sized (> 75 mm) oysters as a result of a system-wide lowering of salinities, with an estimated 1.16 to 3.29 billion market-equivalent oysters lost. The efficacy of the large-scale response action of altering hydrographic conditions during the summer oyster growth period should be examined in light of the major perturbation to oyster communities.KEY WORDS: Estuary · Oil spill response · Natural resources damage assessment · Oyster reefs · Gulf of Mexico · Crassostrea virginica · Hydrography OPEN PEN ACCESS CCESS Contribution to the Theme Section 'Response of nearshore ecosystems to the Deepwater Horizon oil spill'Mar Ecol Prog Ser 576: [175][176][177][178][179][180][181][182][183][184][185][186][187] 2017 Too much freshwater reduces local salinity to levels where survivorship, growth, or reproduction of oysters is impaired, whereas too much ocean water generally elevates salinities and is associated with higher levels of predators (e.g. the oyster drill Stramonita haemastoma) or disease (e.g. the protozoan parasite Perkinsus marinus) that leads to reduced survivorship (e.g. Gunter 1955, Davis 1958, 1979, Chatry et al. 1983, Brown & Richardson 1988, Soniat & Brody 1988, Fodrie et al. 2008, La Peyre et al. 2009. Although the specific thresholds of these triggers varied from study to study, the general pattern of oysters thriving in waters whose average annual salinities fall be tween 8 and 22 ppt with frequent brief pulses of freshwater input is well established and accepted by oyster biologists. Because settlement of oysters is gregarious on existing oyster shells, sustainable populations of oysters require that the position of this green zone of water stays relatively fixed over time. A substantial perturbation of this salinity zone away from are...

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Section: Predicted Daily Salinitymentioning

confidence: 99%

Consequences of large-scale salinity alteration during the Deepwater Horizon oil spill on subtidal oyster populations

Powers¹,

Grabowski²,

Roman³

et al. 2017

Mar. Ecol. Prog. Ser.

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“…Cross-validation is used to validate any model that depends on data. In air quality applications it has been used, for example, for mapping and exposure models [6][7][8]. The purpose of this two-part paper is to examine the relative merit of using active or passive observations (or independent observations in general) viewed from different evaluation metrics, but also to develop, in the second part, a mathematical framework to estimate the analysis error, and in doing so, to improve the analysis.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics

Ménard

Deshaies-Jacques

2018

Atmosphere

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Abstract:We examine how passive and active observations are useful to evaluate an air quality analysis. By leaving out observations from the analysis, we form passive observations, and the observations used in the analysis are called active observations. We evaluated the surface air quality analysis of O 3 and PM 2.5 against passive and active observations using standard model verification metrics such as bias, fractional bias, fraction of correct within a factor of 2, correlation and variance. The results show that verification of analyses against active observations always give an overestimation of the correlation and an underestimation of the variance. Evaluation against passive or any independent observations display a minimum of variance and maximum of correlation as we vary the observation weight, thus providing a mean to obtain the optimal observation weight. For the time and dates considered, the correlation between (independent) observations and the model is 0.55 for O 3 and 0.3 for PM 2.5 and for the analysis, with optimal observation weight, increases to 0.74 for O 3 and 0.54 for PM 2.5 . We show that bias can be a misleading measure of evaluation and recommend the use of a fractional bias such as the modified normalized mean bias (MNMB). An evaluation of the model bias and variance as a function of model values also show a clear linear dependence with the model values for both O 3 and PM 2.5 .

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“…With the intention of using SVD to get the EOFs, and facing the problem of missing values, Fuentes et al (2006,) propose an algorithm to compute the SVD and impute the missing values iteratively. The method involves an iterative process of matrix decomposition and regression, and has been applied to deal with missing values in a complex spatio-temporal model for air quality data (Lindström et al, 2013b).…”

Section: Svd Imputation Methodsmentioning

confidence: 99%

“…It is publicly available, widely used and computationally efficient. Further details about the application of this method can be found in Cohen et al (2009);Lindström et al (2013b) and Szpiro et al (2009).…”

Section: Introductionmentioning

confidence: 99%