Analysis, monitoring and simulation of dust hazard phenomenon in the northern Persian Gulf, Iran, Middle East

Abstract

Dust is a particulate matter in the atmosphere that is created by natural and human agents. Dust has negative effects on various parts of human life, including agriculture, health and economics. In recent decades, in various areas with a lack of rainfall and drought being contested, dust has happened there. One of these areas is the northern part of the Persian Gulf in Iran which has been exposed to dust in recent years. The purpose of the present study was to evaluate and predict the hazardous phenomenon of dust in the western strip of Iran. Therefore, the data of dust from 14 synoptic stations of the study area (1990–2018) using panel data-hybrid neural network and adaptive neuro-fuzzy inference system (ANFIS) models were used. Finally, TOPSIS and simple additive weighting (SAW) multi-criteria decision-making (MCDM) models were used to prioritize more dust-prone areas. The results showed that the reliability of the panel data-hybrid neural network error estimation models is more than the ANFIS. Based on prediction models, the highest probability of occurrence of the maximum dust in the future was observed at Sarpol-e Zahab and Abadan stations (128.917 and 120.709%, respectively). According to the SAW model, the highest probability of occurrence of dust was at Abadan station (998%) and based on the TOPSIS model, Eslamabad-e Gharb, with 997%. It is necessary the inter-organizational cooperation by contracting an international memorandum with neighbouring countries in addition to domestic actions to reduce the damage caused by the dust phenomenon in the study area.