Aerosol characterization in a city in central China plain and implications for emission control

Extensive studies on aerosol chemistry have been carried out in megacities in China, however, aerosol characterization in Central China Plain (CCP) is limited. Here we conducted real-time measurements of fine particle composition with a time-of-flight aerosol chemical speciation monitor in Kaifeng, Henan province in October 2019. Our results showed that nitrate and organics constituted the major fraction of non-refractory PM_2.5 for the entire study, on average accounting for 34% and 33%, respectively. However, aerosol composition was substantially different among four periods due to different meteorological conditions and chemical processing. For instance, nitrate presented the lowest contribution during the first period due to evaporative loss associated with high temperature (T), and then rapidly increased during polluted periods as a function of relative humidity (RH). Positive matrix factorization analysis showed the dominance of secondary organic aerosol (SOA) in OA, and also the changes in OA composition under different T and RH levels. In addition, this study is unique with two periods of local emission controls. Back trajectory and coefficient of divergence analysis showed that air pollution in CCP was overall homogeneously distributed. As a result, the effectiveness of local emission controls in this region was strongly affected by meteorological conditions and regional transport. We found that one of the periods with emission control even showed the highest concentrations for the entire study. Our results point towards the limited effect of local emission controls in mitigating air pollution in CCP, and highlight the importance of joint emission controls under unfavorable meteorological conditions.     

Study of aerosol characteristics and sources using MAX-DOAS measurement during haze at an urban site in the Fenwei Plain

Atmospheric aerosols have effects on atmospheric radiation assessments, global climate change, local air quality and visibility. In particular, aerosols are more likely transformed and accumulated in winter. In this paper, we used the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument to study the characteristics of aerosol type and contributions of PM_2.5 chemical components to aerosol extinction (AE), vertical distribution of aerosols, and source. From December 30, 2018 to January 27, 2019, we conducted MAX-DOAS observations on Sanmenxia. The proportion of PM_2.5 to PM₁₀ was 69.48%–95.39%, indicating that the aerosol particles were mainly fine particles. By analyzing the ion data and modifying Interagency Monitoring of Protected Visual Environments (IMPROVE) method, we found that nitrate was the largest contributor to AE, accounting for 31.51%, 28.98%, and 27.95% of AE on heavily polluted, polluted, and clean days, respectively. NH₄⁺, OC, and SO₄^2? were also major contributors to AE. The near-surface aerosol extinction retrieved from MAX-DOAS measurement the PM_2.5 and PM₁₀ concentrations measured by an Unmanned Aerial Vehicle (UAV) have the same trend in vertical distribution. AE increased about 3 times from surface to 500 m. With the backward trajectory of the air mass during the haze, we also found that the continuous heavy pollution was mainly caused by transport of polluted air from the northeast, then followed by local industrial emissions and other sources of emissions under continuous and steady weather conditions.     

黄渤海上空气溶胶类型判别及其成因分析

气溶胶是大气环境的重要组成部分,其对天气和气候变化有重要影响,对人类健康也有危害.对气溶胶类型的区分有利于判别气溶胶的来源,这对控制气溶胶污染源、改善区域环境质量有重要的推动作用.本文基于黄渤海区域6个观测点的实测数据和卫星遥感数据,采用聚类分析方法对黄渤海上空的气溶胶进行分类,并采用拉格朗日轨迹追踪法判定气溶胶的来源...     

基于深度全连接网络Himawari-8卫星气溶胶反演研究

利用葵花8(Himawari-8,H8)16个波段数据、卫星、太阳角度数据和深度学习技术,提出一种基于深度全连接网络(Deep Neural Networks,DNN)模型的AOD遥感反演方法(Himawari-DNN).该方法直接建立H8影像本身与AERONET站点AOD数据间的关系,可避免传统AOD遥感反演方法中复...