Improving Source Apportionment with Receptor Models to Foster the Implementation of the Air Quality Directive

Abstract

Receptor Models identify pollution sources by solving a mass balance equation using measured chemical composition of samples in combination with known source profiles. In the last ten years, the number of receptor modelling studies performed on filed air quality data exponentially increased. Among these, 39% are performed with Positive Matrix Factorization (PMF, ME), 23% with Principal Component Analysis (PCA, APCA), 13% with Chemical Mass Balance (CMB), 10% with Factor Analysis (FA, APCFA) and 10% with the APEG model. This approach has been extensively used in North America and South-eastern Asia mainly on particulate matter. Also in Europe it found wide acceptance and contributed to the identification of sources in support of remediation measures design. With the aim of harmonizing the activity in receptor modelling in Europe and supporting the implementation of Directive 2008/50/EC, an evaluation of the current state of this methodology in Europe was carried out highlighting the following needs: improving data collection, harmonizing analytical protocols by setting up common criteria, promoting advanced tools, establishing criteria for the assessment and, comparing receptor model performances. In this study we discuss the sources of uncertainty in the input data and the common approaches used to express them when preparing model input. We also analyze the contribution to the uncertainty deriving from critical model steps and the methodologies used to identify and reduce them. On the basis of the previous discussion we propose a sketch of Common QA/QC protocol. Improving comparability and reliability of receptor models can be achieved by performing inter-comparison exercises. A community-wide inter-comparison organized and evaluated by the JRC within the framework of FAIRMODE is currently in progress. We describe the methodology for the evaluation and comparison of receptor models used in this exercise.JRC.H.2-Air and Climat