Qatar Dust Atlas Project: Deployment of a National Field Soiling and Environmental Parameters Monitoring Network

The impact of soiling on PV power production exhibits significant spatial and temporal variability on both large and small scales, even within the radius of a solar power plant [1]. Variations in soiling are prominent across different locations in a country like Qatar, influenced by specific environmental and meteorological conditions. To enhance the accuracy, cost-effectiveness, and reliability of soiling monitoring, a new class of sensors has emerged, leveraging the optical characteristics of dust particles on PV glass. These stand-alone sensors, exemplified by Atonometrics' Mars Soiling Sensor and Kipp & Zonen's Dust IQ, require minimal maintenance and have no moving parts. Since 2013, Qatar environment and energy research institute (QEERI) has become a regional leader in PV soiling research, with focus put on desert environments, exploring both experimental (characterization, innovative measurement methods, mapping) and modeling aspects (sandstorm prediction, dust deposition) [2]. The Qatar Dust Atlas project, carried out at QEERI's Energy Center, is dedicated to evaluating the spatio-temporal mapping of soiling across Qatar and forecasting it using Machine Learning and WRF-CHEM modeling. Key elements addressed in the Dust Atlas encompass a thorough understanding of soiling in Qatar through real world data, incorporating atmospheric aerosol (primarily PM10), field-soiling data, and Albedo [3]. This effort leads to the creation of a spatio-temporal map of dust particles, alongside the development of a land suitability index and a soiling-risk map for prospective PV installations. This paper provides an overview of recent developments and common outcomes from the Dust Atlas project, with a focus on the initiation and establishment of a "National Soiling and Environmental Parameters Monitoring Network". This network integrates monitoring stations and advanced sensors to directly assess soiling and other influential factors, playing a pivotal role in crafting AI-based solutions for forecasting soiling on PV solar panels at diverse spatial and temporal resolutions throughout Qatar.