Formation features of nitrous acid in the offshore area of the East China Sea

Nitrous acid (HONO) is an important precursor of hydroxyl radical (OH), which plays a key role in atmospheric chemistry. In this study, a shipboard-based measurement of HONO and related species in the offshore area of the East China Sea (ECS) was performed during June 2017. The HONO concentration ranged from 35 ppt to 1.95 ppb, with an average value of 0.44 ± 0.25 ppb during the entire campaign. HONO displayed a relatively higher level (0.48 ± 0.21 ppb) in the area within 30 km from the coastline (S1), whereas a lower level (0.40 ± 0.18 ppb) in the area between 30 km and 100 km from the coastline (S2). Five distinct hotspots of HONO were identified, including Ningbo Port, Yangshan Port, the Yangtze River estuary, northwest of the Zhoushan city, and the area adjacent to Jinshan Chemical Industry Park, suggesting the impact of local vessel emissions and land industrial emissions on HONO formation. During the nighttime, the direct vessel emissions contributed on average 18% of the HONO concentration. The averaged conversion frequency of NO₂-to-HONO (k_het) estimated from six nighttime cases was 1.18 × 10^-2 h^-1. Daytime budget analysis showed that the unknown HONO production rate (P_unknown) in S1 and S2 was 1.52 ppb h^-1 and 1.14 ppb h^-1, respectively. P_unknown was related to a light-induced HONO source from NO₂ on the sea surface and particulate nitrate. During the cruise campaign, the averaged daytime OH production rate from HONO photolysis was 1.35 ± 0.69 ppb h^-1, about 1.6 times higher than that from the O₃ photolysis (0.87 ± 0.55 ppb h^-1), which suggested an important role of HONO in the atmospheric chemistry of the offshore area of ECS.

Inverse calculation of vessel emission source intensity based on optimized Gaussian puff model and particle swarm optimization algorithm

Maritime transportation has significantly contributed to global economic development but is also a major source of air pollution. This study aims to provide an inverse calculation framework of vessel emission source intensity for emission monitoring. This research enhanced the traditional Gaussian diffusion model by specific characteristics of ship emissions and various influencing factors identified through simulation experiments. An inverse model is developed using pattern search and particle swarm optimization (PSO) algorithms to estimate marine exhaust source strengths. Results indicate that the PSO algorithm is the most accurate and efficient, especially with an iteration step size of 0.1 s. Practical application using data from 86 monitored ships revealed that 76 had fuel sulfur content exceeding the 0.1 % threshold, achieving an accuracy rate of 88.37 %. These findings are crucial for improving the understanding of marine exhaust dispersion and advancing remote monitoring technologies, contributing to better environmental management of maritime transport emissions.