Quantitative Methods in Environmental and Climate Research

  • Michela Cameletti
  • Francesco Finazzi
Conference proceedings

Table of contents

  1. Front Matter
    Pages i-vii
  2. Khurram Nadeem, Entao Chen, Ying Zhang
    Pages 29-48
  3. Ilia Negri, Alessandro Fassò, Lucia Mona, Nikolaos Papagiannopoulos, Fabio Madonna
    Pages 63-83
  4. Yasmine M. Abdelfattah, Abdel H. El-Shaarawi, Hala Abou-Ali
    Pages 99-120

About these proceedings


This books presents some of the most recent and advanced statistical methods used to analyse environmental and climate data, and addresses the spatial and spatio-temporal dimensions of the phenomena studied, the multivariate complexity of the data, and the necessity of considering uncertainty sources and propagation. The topics covered include: detecting disease clusters, analysing harvest data, change point detection in ground-level ozone concentration, modelling atmospheric aerosol profiles, predicting wind speed, precipitation prediction and analysing spatial cylindrical data.

The volume presents revised versions of selected contributions submitted at the joint TIES-GRASPA 2017 Conference on Climate and Environment, which was held at the University of Bergamo, Italy. As it is chiefly intended for researchers working at the forefront of statistical research in environmental applications, readers should be familiar with the basic methods for analysing spatial and spatio-temporal data. 


spatio-temporal models geostatistics Bayesian modeling functional data analysis health risk uncertainty assessment climate change air pollution satelite earth and atmospheric data big data environmental epidemiology remote sensing cylindrical data LIDAR data weather forecast

Editors and affiliations

  • Michela Cameletti
    • 1
  • Francesco Finazzi
    • 2
  1. 1.Department of Management, Economics and Quantitative MethodsUniversity of BergamoBergamoItaly
  2. 2.Department of Management, Information and Production EngineeringUniversity of BergamoDalmineItaly

Bibliographic information