LOW-COST METHODS TO PARTICULATE MATTER PRELIMINARY STUDY IN THECENTER-NORTH OF BUENOS AIRES SUBURBS

Abstract

Air quality is one of the biggest environmental problems today, and airborne particles are a well-studied indicator given their impacts on health and climate. The cost of regulatory measurement equipment leads to limited information availability in many parts of the world, as in Argentina. This work proposes modeling of particulate matter from ArtificialNeural Networks, fed with data from low-cost equipment developed and used for this purpose. In this way, a study of air quality in the Center-North of the Buenos Aires suburbs is presented, providing new information on quantities and types of particles in a region without historical antecedents. Coarse particles were mostly found at low concentrations and a prediction model for particulate matter with good performance was developed.