Inter-comparison of NIOSH and IMPROVE protocols for OC and EC determination: implications for inter-protocol data conversion

Abstract

Organic carbon (OC) and elemental carbon (EC) are operationally defined by analytical methods. As a result, OC and EC measurements are protocol dependent, leading to uncertainties in their quantification. In this study, more than 1300 Hong Kong samples were analyzed using both National Institute for Occupational Safety and Health (NIOSH) thermal optical transmittance (TOT) and Interagency Monitoring of Protected Visual Environment (IMPROVE) thermal optical reflectance (TOR) protocols to explore the cause of EC disagreement between the two protocols. EC discrepancy mainly (83 %) arises from a difference in peak inert mode temperature, which determines the allocation of OC4NSH, while the rest (17 %) is attributed to a difference in the optical method (transmittance vs. reflectance) applied for the charring correction. Evidence shows that the magnitude of the EC discrepancy is positively correlated with the intensity of the biomass burning signal, whereby biomass burning increases the fraction of OC4NSH and widens the disagreement in the inter-protocol EC determination. It is also found that the EC discrepancy is positively correlated with the abundance of metal oxide in the samples. Two approaches (M1 and M2) that translate NIOSH TOT OC and EC data into IMPROVE TOR OC and EC data are proposed. M1 uses direct relationship between ECNSH_TOT and ECIMP_TOR for reconstruction: M1 : ECIMP_TOR = a × ECNSH_TOT + b; while M2 deconstructs ECIMP_TOR into several terms based on analysis principles and applies regression only on the unknown terms: M2 : ECIMP_TOR = AECNSH + OC4NSH − (a × PCNSH_TOR + b), where AECNSH, apparent EC by the NIOSH protocol, is the carbon that evolves in the He–O2 analysis stage, OC4NSH is the carbon that evolves at the fourth temperature step of the pure helium analysis stage of NIOSH, and PCNSH_TOR is the pyrolyzed carbon as determined by the NIOSH protocol. The implementation of M1 to all urban site data (without considering seasonal specificity) yields the following equation: M1(urban data) : ECIMP_TOR = 2.20 × ECNSH_TOT − 0.05. While both M1 and M2 are acceptable, M2 with site-specific parameters provides the best reconstruction performance. Secondary OC (SOC) estimation using OC and EC by the two protocols is compared. An analysis of the usability of reconstructed ECIMP_TOR and OCIMP_TOR suggests that the reconstructed values are not suitable for SOC estimation due to the poor reconstruction of the OC / EC ratio.