Intercomparison between NIOSH, IMPROVE_A, and EUSAAR_2 protocols: Finding an optimal thermal–optical protocol for Philippines OC/EC samples

Thermal–optical analysis is one of the most widely–recognized methods for measuring organic carbon (OC) and elemental carbon (EC) in atmospheric particulates. Up to date however, there is no standard protocol of analysis and different protocols give varying OC/EC apportionments. This study aims to find an optimal thermal–optical analysis protocol for Philippine OC/EC samples by comparing three widely–used protocols: NIOSH, IMPROVE_A and EUSAAR_2. Philippines is particularly interesting because it has one of the highest EC concentration and lowest OC/EC ratio in the region. In terms of total OC and EC quantification, NIOSH and IMPROVE_A show negative and positive EC bias, respectively – NIOSH exhibits premature EC evolution in the OC4 pure He phase, while IMPROVE_A OC4 temperature step (580 °C) is not sufficiently high, causing some OC to be carried over to He/O2 phase to be measured mainly as EC2. EUSAAR_2 minimizes both effects and may be most accurate in this aspect. However, IMPROVE_A is the only method that is capable of properly resolving individual OC and Philippines’s particularly abundant EC fractions owing to the protocol’s variable step durations. Concurrently, IMPROVE_A and EUSAAR_2 yield lowest pyrolized carbon (PC) formation for urban and rural site, respectively. Minimal PC formation is desired to minimize errors associated with its correction. Finally, transmittance laser correction is preferred over reflectance as it is capable of accounting for char formed within filter. The study thus recommends a modified IMPROVE_A, with increased OC4 temperature step (650 °C, adopted from EUSAAR_2) and transmittance laser correction, as optimal. This protocol is expected to give proper OC and EC evolution, fractionation, and measurement with minimized PC formation and proper correction, leading to more accurate results. Preliminary testing shows that recommended protocol meets those expectations. Application to larger number and wider variety of samples is needed to more properly assert these findings.