Using different spatial scale measurements in a geostatistically based approach for mapping atmospheric nitrogen dioxide concentrations. Application to the French Centre region

Malherbe, Laure; Cardenas, Giovanni; Colin, Patrice; Yahyaoui, Abderrazak

doi:10.1002/env.947

Cited by 7 publications

(4 citation statements)

References 8 publications

(7 reference statements)

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“…Geostatistics can be used to model data that are spatially and temporally correlated in a statistically rigorous framework . Geostatistics was originally developed in the field of mining, and is now widely used in meteorology, − environmental science, − and agriculture. − A number of studies have demonstrated that geostatistical methods often outperform simpler interpolation methods. − Murphy et al used geostatistics for modeling hypoxia in Chesapeake Bay and discussed the advantages of using universal kriging for interpolation . Zhou et al developed a geostatistical model for bottom-water dissolved oxygen (BWDO) in Lake Erie and Obenour et al developed coupled geostatistical models for BWDO and hypoxic thickness to estimate both hypoxic area and volume.…”

Section: Introductionmentioning

confidence: 99%

Space-Time Geostatistical Assessment of Hypoxia in the Northern Gulf of Mexico

Matli

Fang

Guinness

et al. 2018

Environ. Sci. Technol.

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Nearly every summer, a large hypoxic zone forms in the northern Gulf of Mexico. Research on the causes and consequences of hypoxia requires reliable estimates of hypoxic extent, which can vary at submonthly time scales due to hydro-meteorological variability. Here, we use an innovative space-time geostatistical model and data collected by multiple research organizations to estimate bottom-water dissolved oxygen (BWDO) concentrations and hypoxic area across summers from 1985 to 2016. We find that 27% of variability in BWDO is explained by deterministic trends with location, depth, and date, while correlated stochasticity accounts for 62% of observational variance within a range of 185 km and 28 days. Space-time modeling reduces uncertainty in estimated hypoxic area by 30% when compared to a spatial-only model, and results provide new insights into the temporal variability of hypoxia. For years with shelf-wide cruises in multiple months, hypoxia is most severe in July in 59% of years, 29% in August, and 12% in June. Also, midsummer cruise estimates of hypoxic area are only modestly correlated with summer-wide (June-August) average estimates (r 2 = 0.5), suggesting midsummer cruises are not necessarily reflective of seasonal hypoxic severity. Furthermore, summer-wide estimates are more strongly correlated with nutrient loading than midsummer estimates.

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Section: Introductionmentioning

confidence: 99%

Space-Time Geostatistical Assessment of Hypoxia in the Northern Gulf of Mexico

Matli

Fang

Guinness

et al. 2018

Environ. Sci. Technol.

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“…By contrast, an industrial station may be highly influenced by the local emissions, thus its scale representativeness may be less than one kilometer. Simultaneous treatments of observations from stations of multiple spatial scales are far from straightforward (Malherbe et al, 2008). Nevertheless, it is improper to strictly relate the scale representativeness to the station typology.…”

Section: Background Stationsmentioning

confidence: 99%

Optimal redistribution of the background ozone monitoring stations over France

Bocquet

2011

Atmospheric Environment

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International audienceOzone is a harmful air pollutant at ground level, and its concentrations are routinely measured with monitoring networks. The network design problem aims at determining the optimal positioning of the monitoring stations. In this study, the background stations of the French routine pollution monitoring network (BDQA) are partially redistributed over France under a set of design objectives. These background stations report ozone variations at large spatial scale comparable with that of a chemistry-transport model (CTM). The design criterion needs to be defined on a regular grid that covers France, where in general no ozone observations are available for validation. Geostatistical ozone estimation methods are used to extrapolate concentrations to these grid nodes. The geostatistical criteria are introduced to minimize the theoretical error of those geostatistical extrapolations. A physical criterion is also introduced to measure the ability of a network to represent a physical ozone field retrieved from CTM simulations using geostatistical extrapolation methods. A third type of criteria of geometrical nature, e.g. a maximal coverage of the design domain, are based uniquely on the distance between the network stations. To complete the network design methodology, a stochastic optimization method, simulated annealing, is employed in the algorithm to select optimally the stations. Significant improvement with all the proposed criteria has been found for the optimally redistributed network against the original background BDQA network. For instance, the relative improvements in the physical criterion value range from 21% to 32% compared to randomly relocated networks. Different design criteria lead to different optimally relocated networks. The optimal networks under physical criteria are the most heterogeneously distributed. More background stations are displaced to the coast, frontiers, and large urban agglomerations, e.g. Paris and Marseilles. The ozone heterogeneous fields are not as well reconstructed from optimal networks under geostatistical or geometrical criteria as from the optimal network obtained with the physical criterion. The values of the physical criterion for the geostatistically and geometrically optimal networks show deteriorations of about 8% and 17% respectively compared to that of the physically optimal network

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“…In order to provide more comprehensive information to the authorities and the public, the agencies responsible for air quality monitoring are searching for innovative ways of representing background and roadside concentrations together. In recent work, Malherbe et al (199) searched for new ways of jointly representing background and roadside air pollution concentrations by using an approach based on geostatistics and the relationships between seasonal nitrogen dioxide concentrations and auxiliary variables. The approach was applied to data collected in the French Centre region.…”

Section: Environmetricsmentioning

confidence: 99%