scholarly journals Geostatistical Integration and Uncertainty in Pollutant Concentration Surface under Preferential Sampling

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Laura Grisotto ◽  
Dario Consonni ◽  
Lorenzo Cecconi ◽  
Dolores Catelan* ◽  
Michele Carugno ◽  
...  
Keyword(s):  
Pollutant Concentration ◽  
Preferential Sampling ◽  
Concentration Surface

scholarly journals Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling

2016 ◽  
Vol 11 (1) ◽  
Author(s):  
Laura Grisotto ◽  
Dario Consonni ◽  
Lorenzo Cecconi ◽  
Dolores Catelan ◽  
Corrado Lagazio ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
Quality Data ◽  
Random Component ◽  
Deterministic Models ◽  
Prediction Uncertainty ◽  
Preferential Sampling ◽  
Sampling Locations ◽  
Concentration Surface

In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.

Phytoplankton Adaptation to Environmental Stresses from Toxicants, Nutrients, and Pollutants — a Warning

1976 ◽  
Vol 33 (9) ◽  
pp. 2089-2096 ◽  
Author(s):  
John G. Stockner ◽  
Naval J. Antia
Keyword(s):  
Environmental Protection ◽  
Pollutant Concentration ◽  
Subsequent Growth ◽  
Short Term ◽  
Present Evidence ◽  
Test Chemical ◽  
Successful Adaptation ◽  
Shock Response ◽  
Chronic Pollution

Examples are cited from the literature of phytoplankton-related pollution and nutrition studies where the possibility of successful adaptation and subsequent growth could have been overlooked because of insufficient duration of algal exposure to the pollutant or nutrient tested. We present evidence from our investigations where: a) initial algal exposures as long as 20–40 days to the pollutant or alternative nutrient may be required for successful adaptation, and b) phytoplankters initially tolerating only a low level of pollutant concentration could be trained to accept severalfold higher levels by repeated exposure to gradually increasing pollutant concentration A plea is made for future investigators to recognize the importance of long-term bioassays ascertaining algal potential for adaptation, in order that their results may be ecologically realistic for the purpose of environmental protection against chronic pollution and eutrophication. The short-term "shock" response should be clearly distinguished from the long-term habituation response of phytoplankters to the test chemical in these bioassays. Possible problems raising questionable objections to the long-term bioassay approach are discussed.

Uranium migration through intact sandstone: effect of pollutant concentration and the reversibility of uptake

1996 ◽  
Vol 21 (1-4) ◽  
pp. 215-228 ◽  
Author(s):  
R. Sims ◽  
T.A. Lawless ◽  
J.L. Alexander ◽  
D.G. Bennett ◽  
D. Read
Keyword(s):  
Pollutant Concentration ◽  
Uranium Migration

scholarly journals Synergistic Effect of Nanophotocatalysis and Nonthermal Plasma on the Removal of Indoor HCHO

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Yuanwei Lu ◽  
Dinghui Wang ◽  
Yuting Wu ◽  
Chongfang Ma ◽  
Xingjuan Zhang ◽  
...  
Keyword(s):  
Synergistic Effect ◽  
Indoor Air ◽  
Photocatalytic Oxidation ◽  
Reaction Rate ◽  
Nonthermal Plasma ◽  
Pollutant Removal ◽  
Pollutant Concentration ◽  
Air Purification ◽  
Plate Electrode ◽  
Air Treatment

Photocatalysis is an effective method of air purification at the condition of a higher pollutant concentration. However, its wide application in indoor air cleaning is limited due to the low level of indoor air contaminants. Immobilizing the nanosized TiO2particles on the surface of activated carbon filter (TiO2/AC film) could increase the photocatalytic reaction rate as a local high pollutant concentration can be formed on the surface of TiO2by the adsorption of AC. However, the pollutant removal still decreased quickly with the increase in flow velocity, which results in a decrease in air treatment capacity. In order to improve the air treatment capacity by the photocatalytic oxidation (PCO) method, this paper used formaldehyde (HCHO) as a contaminant to study the effect of combination of PCO with nonthermal plasma technology (NTP) on the removal of HCHO. The experimental results show that HCHO removal is more effective with line-to-plate electrode discharge reactor; the HCHO removal and the reaction rate can be enhanced and the amount of air that needs to be cleaned can be improved. Meanwhile, the results show that there is the synergistic effect on the indoor air purification by the combination of PCO with NTP.

scholarly journals Inertial-particle accelerations in turbulence: a Lagrangian closure

2016 ◽  
Vol 798 ◽  
pp. 187-200 ◽  
Author(s):  
S. Vajedi ◽  
K. Gustavsson ◽  
B. Mehlig ◽  
L. Biferale
Keyword(s):  
Numerical Simulations ◽  
Numerical Data ◽  
Direct Numerical Simulations ◽  
Inertial Particles ◽  
Inertial Particle ◽  
Order Unity ◽  
Heavy Particles ◽  
Preferential Sampling ◽  
Closure Scheme ◽  
Light Particles

The distribution of particle accelerations in turbulence is intermittent, with non-Gaussian tails that are quite different for light and heavy particles. In this article we analyse a closure scheme for the acceleration fluctuations of light and heavy inertial particles in turbulence, formulated in terms of Lagrangian correlation functions of fluid tracers. We compute the variance and the flatness of inertial-particle accelerations and we discuss their dependency on the Stokes number. The closure incorporates effects induced by the Lagrangian correlations along the trajectories of fluid tracers, and its predictions agree well with results of direct numerical simulations of inertial particles in turbulence, provided that the effects induced by inertial preferential sampling of heavy/light particles outside/inside vortices are negligible. In particular, the scheme predicts the correct functional behaviour of the acceleration variance, as a function of $St$, as well as the presence of a minimum/maximum for the flatness of the acceleration of heavy/light particles, in good qualitative agreement with numerical data. We also show that the closure works well when applied to the Lagrangian evolution of particles using a stochastic surrogate for the underlying Eulerian velocity field. Our results support the conclusion that there exist important contributions to the statistics of the acceleration of inertial particles independent of the preferential sampling. For heavy particles we observe deviations between the predictions of the closure scheme and direct numerical simulations, at Stokes numbers of order unity. For light particles the deviation occurs for larger Stokes numbers.

Bias in vegetation databases? A comparison of stratified-random and preferential sampling

2011 ◽  
Vol 22 (2) ◽  
pp. 281-291 ◽  
Author(s):  
Dana Michalcová ◽  
Samuel Lvončík ◽  
Milan Chytrý ◽  
Ondřej Hájek
Keyword(s):  
Vegetation Databases ◽  
Preferential Sampling
Sign in / Sign up

Export Citation Format

Share Document