Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences

The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM 10 concentrations as a reference dataset. The dataset includes measurements...

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Vydané v:Bulletin of environmental contamination and toxicology Ročník 112; číslo 1; s. 6
Hlavní autori: de Lourdes Berrios Cintrón, María, Broomandi, Parya, Cárdenas-Escudero, Jafet, Cáceres, Jorge O., Galán-Madruga, David
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.01.2024
Springer Nature B.V
Predmet:
Air Pollution
Air quality
Air quality measurements
Algorithms
Aquatic Pollution
bulls
Clustering
data collection
Datasets
decision making
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental management
Environmental Monitoring - methods
Environmental Science
Indicators
kriging
Particulate matter
Pollution
Root-mean-square errors
Soil Science & Conservation
Spatial Analysis
Waste Water Technology
Water Management
Water Pollution Control
Air Quality
Geostatistical Estimation
Interpolation Algorithms and Environmental Sciences
PM
Particles
PM10 Particles
ISSN:0007-4861, 1432-0800, 1432-0800
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Shrnutí:The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM 10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m 3 , 10.2 µg/m 3 , and 7.3%, respectively. This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study’s findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.
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ISSN:0007-4861
1432-0800
1432-0800
DOI:10.1007/s00128-023-03835-0