This study developed new procedures to loosely integrate an air dispersion model, AERMOD, and a geographic information system (GIS) package, ArcGIS, to simulate air dispersion from stationary sources in the Bronx, New York City, for five pollutants: PM(10), PM(2.5), NO(x), CO, and SO(2). Plume buffers created from the model results were used as proxies of human exposure to the pollution from th...

Reservoir model needs to be constrained by various data, including dynamic production data. Reservoir heterogeneities are usually described using geostatistical approaches. Constraining geologicaljgeostatistical mode! realizations by dynamic data is generally performed through history matching, which is a complex inversion process and requires a parameterization of the geostatistical realizatio...

Being able to provide a quick and accurate pollutant maps from readings at isolated measurement stations is becoming more important today in light of the European norms on air quality and the public’s demand to be informed. Commonly used algorithms for cartography are quick but their accuracy remains to be determined. Firstly, the choice of method is arbitrary and based on user's subjective per...

This paper presents an overview of geostatistical simulation with particular focus on aspects of importance to its application for quantification of risk in the mining industry. Geostatistical simulation is a spatial extension of the concept of Monte Carlo simulation. In addition to reproducing the data histogram, geostatistical simulations also honour the spatial variability of data, usually c...

This article models, through Geostatistics, a database with space-time characteristics seeking to examine climate variations in the state of Bahia located Northeast region Brazil. dataset consists information from 86 stations recorded by National Institute Meteorology (INMET) January 2010 December 2022 for state. The focus analysis was study time series data related air temperature. Initially, ...

[1] Inverse modeling methods have been used to estimate surface fluxes of atmospheric trace gases such as CFCs, CH4, and CO2 on the basis of atmospheric mass fraction measurements. A majority of recent studies use a classical Bayesian setup, in which prior flux estimates at regional or grid scales are specified in order to further constrain the flux estimates. This paper, on the other hand, exp...

The generation of reliable information for improving the understanding of hydroelectric reservoir dynamics is fundamental for guiding decision-makers to implement best management practices. In this way, we assessed the performance of different interpolation algorithms to map the bathymetry of the Tucuruí hydroelectric reservoir, located in the Brazilian Amazon, as an aid to manage and operate A...

The coupled ̄ow-mass transport inverse problem is formulated using the maximum likelihood estimation concept. An evolutionary computational algorithm, the genetic algorithm, is applied to search for a global or near-global solution. The resulting inverse model allows for ̄ow and transport parameter estimation, based on inversion of spatial and temporal distributions of head and concentration meas...

In modeling spatial processes, a second-order stationarity assumption is often made. However, for data observed on vast domain, the covariance function varies over space, leading to heterogeneous dependence structure, therefore requiring nonstationary modeling. Spatial deformation one of main methods assuming process has stationary counterpart in deformed space. The estimation poses severe chal...

Abstract Machine learning algorithms have been increasingly applied to spatial numerical modeling. However, it is important understand when such methods will underperform. are impacted by dataset shift ; modeling domains of interest present non-stationarities there no guarantee that the trained models effective in unsampled areas. This work aims compare stationarity requirement geostatistical c...