This paper is focused on the role of spatial and variographic analysis in the phase of sampling design. In particular, when dealing with environmental variables such as airborne dust concentration all over a selected domain, the best approach to catch the spatial structure of the variable itself, implies the full and more detailed coverage of the domain. In this study this goal is achieved by means of about fifty airborne dust concentration field surveys all over a square area 350 mt wide in a quarry plant in the center of Italy. These data, sampled according with a regular pattern, did not allow to catch the spatial structure of the variable itself thus avoiding a satisfactory variographic modelling. To improve the sampling scheme an infilling procedure was performed by adding an increased number of samples. The selection of these further samples, less than 10% of the total amount, was carried out using sequential Gaussian simulations in those zones of the domain in which the highest variability was recorded. The final outcome shown a good result determining a good upgrade in terms of variographic modelling and final estimation at the cost of few further samples.

Alfaro Degan, G., Coltrinari, G., & Lippiello, D. (2019). Sequential Gaussian Simulation as a tool to improve PM10 sampling scheme in industrial sites. In E3S Web of Conferences (pp.09009). EDP Sciences [10.1051/e3sconf/201912809009].

Sequential Gaussian Simulation as a tool to improve PM10 sampling scheme in industrial sites

Alfaro Degan G.;Coltrinari G.;Lippiello D.
2019

Abstract

This paper is focused on the role of spatial and variographic analysis in the phase of sampling design. In particular, when dealing with environmental variables such as airborne dust concentration all over a selected domain, the best approach to catch the spatial structure of the variable itself, implies the full and more detailed coverage of the domain. In this study this goal is achieved by means of about fifty airborne dust concentration field surveys all over a square area 350 mt wide in a quarry plant in the center of Italy. These data, sampled according with a regular pattern, did not allow to catch the spatial structure of the variable itself thus avoiding a satisfactory variographic modelling. To improve the sampling scheme an infilling procedure was performed by adding an increased number of samples. The selection of these further samples, less than 10% of the total amount, was carried out using sequential Gaussian simulations in those zones of the domain in which the highest variability was recorded. The final outcome shown a good result determining a good upgrade in terms of variographic modelling and final estimation at the cost of few further samples.
Alfaro Degan, G., Coltrinari, G., & Lippiello, D. (2019). Sequential Gaussian Simulation as a tool to improve PM10 sampling scheme in industrial sites. In E3S Web of Conferences (pp.09009). EDP Sciences [10.1051/e3sconf/201912809009].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/358792
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