Temporal and Spatial Monitoring and Forecasting of Suspended Dust Using Google Earth Engine and Remote Sensing Data (Case Study: Qazvin Province)

Dust, the main pillar of air pollution, has always been an important subject of study on several levels. Over the last few years, it has become increasingly concentrated in various regions. The present study, which uses Google Earth engine system and MODIS satellite data with 8-day temporal resolution and spatial resolution of 1km and 250m, as well as statistical methods such as correlation and averaging of Triple Exponential Smoothing (TES), to monitor and predict spatial and temporal changes of airborne dust in Qazvin province. For this purpose, using Aerosol Optical Depth Index (AOD) images, Optimal Vegetation Index (EVI) and Modis Heat Island (HI) change index, and preparing their temporal and spatial monitoring maps during the statistical period 2015-2020 and predict to 2030, the relationship between these factors was examined. The results of the present study showed an increase in the quantity of airborne dust in Qazvin province from 0.461 in 2015 to 0.603 in 2017. his rate was then reduced by 0.493 in 2018 and by 0.575 in 2019. The quantity of airborne dust, then fell slightly to 0.5366 in 2020. The results showed a negative relationship between precipitation, relative humidity and vegetation, and a positive relationship between wind speed, freezing days, temperature, variations in temperature islands and hours of sunshine with AOD. The present study also examined changes in airborne dust concentrations in two high- and low-risk classes, and predicted a high-risk class for 2030. This finding is helping policymakers and planners reduce dust pollution in cities through vegetation management and reduce the heat islands, as well as implement climate programs to manage precipitation and moisture in cities.