1,721,167 research outputs found
Statistical sensitivity analysis and water quality
Get PDFIn this paper, concepts and methods of statistical sensitivity analysis (SA) of computer models are reviewed and discussed in relation to water quality analysis and modelling.
The starting point of this approach is based on modelling the uncertainty of the computer code by probability distributions. Despite the fact that computer models are generally speaking non-stochastic, in the sense that if we rerun the code we get the same result, the stochastic approach turns out to be useful to understand how the input uncertainty is propagated through the computer code into the output uncertainty.
We follow the standard approach to SA which is based on variance decomposition and consider three levels of SA. At the Örst or preliminary level, we discuss DOE and response surface methodologies in order to get a first estimate of the input influences on the model output.
At the second level, going further into modelling the relationship between computer model inputs and outputs, we assume that different computer runs are independent. We then discuss techniques derived from Monte Carlo input simulations and regression analysis.
At the third level, recognizing that, since the computer model is actually non-stochastic, the errors are often smoother than independent errors, we consider the geostatistical SA which is based on assuming that the error of the computer code emulator is a stochastic process with positive correlation which gets higher as two inputs get closer
Multivariate hierarchical statistical detectors for health surveillance and diagnostics with example of a cable stayed bridge
No full textAir quality impact assessment of traffic policy in Milan
Get PDFThis paper presents a general spatio-temporal model for assessing the air quality impact of environmental policies which are introduced as abrupt changes. The estimation method is based on the EM algorithm and the model allows to estimate the impact on air quality over a region and the reduction of human exposure following the considered environmental policy. Moreover, impact testing is proposed as a likelihood ratio test and the number of observations after intervention is computed in order to achieve a certain power for a minimal reduction. An extensive case study related to the introduction of the congestion charge in Milan city and the monitoring of particulate matters and total nitrogen oxides motivates the methods introduced and illustrate implementation issues and inferential machinery
Managing data diversity in air quality monitoring and dynamical mapping
No full textIn the last decades, air quality monitoring networks have been increasingly installed around the world, with designs which are developed often on a local basis. For example, the European Community gives general rules for the member states which demand local governments to design and manage such local networks. As a result, even if modern instruments are rather precise, the EC monitoring network is very expensive and appears rather etherogeneous from the point of view of spatial representativeness, human risk exposure etc.. Thickening the network at the global scale is an unaffordable task. Satellite measurements are then an interesting data source because of homogeneity over time and space and fixed cost. Along these lines, in this paper, we discuss statistical issues in air quality indexes and spatio-temporal modelling for merging ground level data, computer simulation outputs and satellite data
Recursive least squares with ARCH errors and nonparametric modelling of environmental time series
No full textStatistical assessment of air quality interventions
No full textThis paper presents a general spatio-temporal model for assessing the air quality impact of environmental policies which are introduced as abrupt changes. The estimation method is based on the EM algorithm and the model allows to estimate the impact on air quality over a region and the reduction of human exposure following the considered environmental policy. Moreover, impact testing is proposed as a likelihood ratio test and the number of observations after intervention is computed in order to achieve a certain power for a minimal reduction. An extensive case study is related to the introduction of the congestion charge in Milan city. The consequent estimated reduction of airborne particulate matters and total nitrogen oxides motivates the methods introduced while its derivation illustrates both implementation and inferential issues
- …
