Memorias de investigación
Tesis:
Short Term Local Forecasting by Artificial Intelligence Techniques and Assessing related social effects from heterogeneous data
Año:2017

Áreas de investigación
  • Ingenierías

Datos
Descripción
For many years, the air pollution problem has been attracting public, government and scholars' attention since it can cause serious health problems. The United States Environmental Protection Agency (EPA) set particle pollution (frequently referred to as particulate matter) (PM), ground-level ozone (O3), carbon monoxide (CO), sulfur oxides (SO2), nitrogen oxides (NOx), and lead as `critical pollutants'. Among these six pollutants,(PM) and O3 are the ones of the most dangerous pollutants that the whole social society[2]. Especially, they have adverse e ects on public health, then lead to signi?cant economic loss[3, 4]. Some of the existing studies have evidenced that both short-term and long-term PM and O3 pollution exposure have a high correlation with respiratory and cardiovascular mortality and morbidity[5, 6, 7, 8, 9, 10]. Apart from the six pollutants, CO2, one of the greenhouse gas, is another emission that is responsible for the globing warming. The main contributor to CO2 is fossil fuels burning for providing energy to transportation, industry, agriculture, commercial and residential etc. Therefore, the issues related to energy, pollution, and pollutionrelated health impacts have sparked a considerable attention from the citizens, the governments, and an amount of researchers.
Internacional
Si
ISBN
Tipo de Tesis
Doctoral
Calificación
Sobresaliente cum laude
Fecha
11/07/2017

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Participantes

Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Proyectos y Calidad
  • Departamento: Ingeniería de Organización, Administración de Empresas y Estadística