A multi-objective framework to select numerical options in air quality prediction models: A case study on dust storm modeling.

Numerical weather prediction models are very important tools in predicting severe weather phenomena such as dust storms. However, the prediction accuracy in these models depends on the options considered in the modeling. In this study, a multi-objective framework is presented to determine the optimal options of the weather research forecasting with chemistry (WRF-Chem) model.

Blockchain Technology Application Challenges in Renewable Energy Supply Chain Management.

With the advent of new technologies and globalization of business, supply chains have turned into indispensable tools for gaining competitive advantage. The application of new technologies like blockchain can benefit sustainable energy supply chains by improving chain and logistics operations in the areas of trust, transparency and accountability, cooperation, information sharing, financial exchanges, and supply chain integration. However, the efforts to adopt such technologies in supply chains tend to face many challenges and challenges, which can seriously threaten their success.

A new application of multi-criteria decision making in identifying critical dust sources and comparing three common receptor-based models.

Dust storms are a common phenomenon in arid and semi-arid regions in West Asia, which has led to high levels of PM in local and remote area. The Yazd city in Iran with a high PM level located downstream of dust sources in the Middle East and Central Asia. In this study, based on meteorological and PM monitoring data, backward trajectory modeling of air parcels related to dust events at Yazd station was performed using the HYSPLIT model in 2012-2019.