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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences.

María de Lourdes Berrios Cintrón1, Parya Broomandi2, Jafet Cárdenas-Escudero3,4

  • 1Department of Health Sciences, Inter American University of Puerto Rico, Barranquitas Campus, Bo. Helechal Street 156, Barranquitas, Puerto Rico.

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Summary

The kriging geospatial interpolation method is most effective for environmental science applications, showing high accuracy in predicting PM10 concentrations. This research identifies the best algorithm for reliable environmental data analysis.

Keywords:
Air QualityGeostatistical EstimationInterpolation Algorithms and Environmental SciencesPM10 Particles

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Air Quality Monitoring

Background:

  • Geospatial interpolation is crucial for environmental data analysis.
  • A gap exists in identifying the most suitable interpolation algorithms for environmental applications.
  • Accurate spatial data is essential for effective environmental management.

Purpose of the Study:

  • To evaluate and identify the optimal geospatial interpolation algorithm for environmental science.
  • To compare the performance of six different interpolation methods.
  • To provide reliable data for environmental decision-making.

Main Methods:

  • Six geospatial interpolation algorithms were assessed.
  • Annual average PM10 concentrations were used as a reference dataset.
  • Performance was evaluated using metrics like RMSE, MAE, and MAPE.

Main Results:

  • The kriging method demonstrated superior performance among the evaluated algorithms.
  • Spatial similarity between datasets was approximately 70% using kriging.
  • Kriging achieved RMSE of 3.2 µg/m³, MAE of 10.2 µg/m³, and MAPE of 7.3%.

Conclusions:

  • Kriging is the most suitable geospatial interpolation algorithm for environmental sciences.
  • The study fills a knowledge gap in comparing interpolation techniques.
  • Findings support improved environmental management through reliable geospatial data.