Premium
Predicting Particulate Matter (PM 2.5 ) Concentrations in the Air of Shahr‐e Ray City, Iran, by Using an Artificial Neural Network
Author(s) -
Asadollahfardi Gholamreza,
Madinejad Mahdi,
Aria Shiva Homayoun,
Motamadi Vahid
Publication year - 2016
Publication title -
environmental quality management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 27
eISSN - 1520-6483
pISSN - 1088-1913
DOI - 10.1002/tqem.21464
Subject(s) - particulates , air quality index , artificial neural network , environmental science , meteorology , air pollution , wind speed , human health , indoor air quality , environmental engineering , geography , artificial intelligence , computer science , ecology , environmental health , biology , medicine
Particulate matter (PM), along with other air pollutants, pose serious hazards to human health. The Artificial Neural Network (ANN) is a branch of artificial intelligence that has an ability to make accurate predictions. In this article, the authors describe such methods and how historical data on air quality, moisture, wind velocity, and temperature in Shahr‐e Ray City, located at the southern tip of Tehran, was used to train an ANN to provide accurate predictions of PM concentrations. The availability of such predictions can offer significant assistance to those who are working to reduce air pollution.