Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative. Issue 6 (27th October 2022) - Caseiro, Alexandre; Schmitz, Seán; Villena, Guillermo; Jagatha, Janani Venkatraman; von Schneidemesser, Erika
Abstract : Using low-cost systems to obtain indicative measurements when no calibration is possible. Abstract : Low-cost particulate matter systems output airborne particle mass concentrations based on optical particle counter sensors. Because the relationship between the particle number and mass is complex and varies with time and space, the research community has developed correction methodologies which imply co-locating a low-cost system and a reference-grade instrument. Such a requirement may not always be achievable. This work aims at supporting users unable to perform co-locations for calibration, providing characterization in terms of indicative measurements. Nine different PurpleAir units in total were deployed (up to 8 units at a time) in parallel with a GRIMM monitor during three different measurement periods in the cities of Berlin and Potsdam, Germany. The three measurement periods encompass over 80 days in total and span from December 2017 to July 2019. We compare the low cost systems' performances against the research-grade measurements and investigate the transferability of correction functions between systems and campaigns. Given the specificity of the correction functions (systems and campaigns), we explore the possibility of their use as indicative measurements under the European legislation specifications as a function of temperature and relative humidity. The results are compiled into a database, the PurpleAir bidimensional look-up matrix, BLUM-i, that is freely available and comes with software for the analysis of PurpleAir uncalibrated data. The BLUM-i is designed as an open-source database application to provide the probability that measurements meet the criteria for indicative measurements as defined in European legislation. The BLUM-i can be continually improved by including more data.