Using PMF model and Bayesian model to quantify NOx sources in non-typhoons and Super Typhoon Lekima (2019) in Hangzhou

Abstract

Quantifying and comparing nitrate (NO3−) sources in typhoons and non-typhoons is important for controlling and reducing their emissions. In this study, the chemical compositions and stable isotopes (δ15N–NO3- and δ18O–NO3-) of non-typhoon and Typhoon Lekima precipitation in Hangzhou (from May–August during 2019 and 2020) were determined. The results revealed that severe acid rain occurred in Hangzhou, except during Typhoon Lekima. According to the positive matrix factorization (PMF) model results, the ion contributions of sources in non-typhoons were as follows: secondary inorganic aerosols + biomass burning > sea salt aerosols > agricultural sources > terrestrial sources, whereas sea salt aerosols + biomass burning were the primary contributors of ions in Typhoon Lekima. The δ15N–NO3- values of non-typhoon and Typhoon Lekima ranged from −4.0 to −1.3‰ and −2.7 to 5.8‰, respectively. The δ18O–NO3- values of non-typhoon and Typhoon Lekima ranged from 56.3‰ to 74.7‰ and 32.2–55.3‰, respectively. The calculated contribution of N2O5 pathway in NOx oxidation to NO3− during non-typhoon precipitation was 64.7 ± 12.2%. If the contribution of OH· pathway during Typhoon Lekima precipitation was 87%, the Monte Carlo simulation results demonstrated that the estimated oxidation proportions of NO via O3 and RO2 (or HO2) ranged from 48.8 to 100% and 0–51.2%, respectively. Based on the improved Bayesian model combined with nitrogen isotope fractionation, the NOx contributions from four sources (biomass burning > coal combustion > mobile sources > microbial N cycle) in non-typhoon precipitation ranged from 21 to 30% (Ԑ = 3.6). In Typhoon Lekima (Ԑ = 5.2), lightning (23.6 ± 11.2%) was one of the primary NOx contributors and a lower contribution from microbial N cycle (17.9 ± 5.4%) compared with the PMF model (22.8%). Moreover, for Typhoon Lekima, the sum of biomass burning, coal combustion, and mobile sources in the improved Bayesian model (58.5%) was slightly lower than that of secondary inorganic aerosols in the PMF model (62.4%). Therefore, the coupled PMF–improved Bayesian model is a feasible and reliable method for source identification and apportionment of NO3− in precipitation.