Analysis of Ambient Air PM10-Bound Pollutants Surrounding an Industrial Site and Their Prediction Using Artificial Neural Network

The 2030 Agenda dictated the Sustainable Development Goals. It states waste reduction needs through their reuse, i.e., considering them as secondary raw materials (Objective 12.5). Bottom ashes from municipal or industrial incinerators can be reused partial cement replacement in concrete after preventive physical processes such ferrous metals removal (magnetic separation) and nonferrous (Eddy current separation). Net of principal pollutant containment systems, diffusive emissions fine particles these processes, coupled with several screening steps a final long-time open-air residues stabilization, could impact surrounding environment due to chemical composition particulate matter itself (inorganic organic pollutants). Moreover, may also arise transporting bottom pre-treatment plant (point source). present work aims predict concentration PM10-bound contaminants that are usually sampled weekly (PCDD/Fs, PCBs, PAHs) daily analyzed inorganic pollutants area an solid slag treatment plant, using Artificial Neural Networks (ANNs) forecasting tool. ANNs have been used clustering tool evaluate plant’s environmental on respect other additional emission sources.