Abstract

Weihai, on the east coast of China, is the only city in Shandong Province that the air quality has reached the Chinese National Ambient Air Quality Class II Standard. However, heavy pollution events still occurred in winter and spring. In this study, the Integrated Source Apportionment Method (ISAM), a source analysis tool coupled with the Regional Atmospheric Modeling System and the Community Multiscale Air Quality modeling system (RAMS–CMAQ), was used to quantify the contribution of regional transport to PM2.5 and its main components in Weihai during February to March in 2018. The results showed that regional transport dominated PM2.5 pollution in Weihai, and the contributions of transport outside Weihai in February and March were 55.6–65.5% and 61.3–68.6%, respectively. Different components in PM2.5 showed the proportion of pollutants varies enormously. Secondary particles were mainly transported via long distances. The proportion of long-range transportation of nitrate had the highest value, and the contribution of regional transportation outside Weihai was 87.5–92.5% and 88.2–91.7%, followed by sulfate, which was 67.9–80.6% and 80.8–86.2%, and the proportion of ammonium salt is the lowest, contributed for 38.8–70.8% and 41.1–71.9% in February and March, respectively. Black carbon (BC), organic carbon (OC), and ions (ions here include chloride, sodium, magnesium, potassium, calcium, iron, aluminium, silicon, titanium, manganese, and other major nonspecific fine particles) were mainly influenced by short-range transportation and local emissions. The contributions of BC outside Weihai were 26.0–49.8% and 34.2–55.2%, the values for OC were 33.5–52.9% and 41.0–53.0%, those for ions were 37.8–49.9% and 44.1–54.4%, respectively. The trajectories indicate that PM2.5 pollution in Weihai is significantly affected by transport from Shandong Province, North China, Northeast China and other regions Thus, the improvement in air quality in Weihai requires strengthening interregional joint pollution prevention.

Highlights

  • The fine particulate matter (PM2.5) in the atmosphere is both generated by primary particulate matter emissions and the transformation of gaseous precursors such as SO2, NOx, VOCs and NH3 in primary emissions (Yang et al, 2000; Kanakidou et al, 2005; Hallquist et al, 2009; Liu et al, 2015)

  • The results showed that regional transport dominated PM2.5 pollution in Weihai, and the contributions of transport outside Weihai in February and March were 55.6– 65.5% and 61.3–68.6%, respectively

  • Integrated Source Apportionment Method (ISAM), a source analysis tool coupled with the RAMS-CMAQ model, was used to simulate and quantify the contribution of regional transport of PM2.5 and the main components in Weihai during February and March, 2018

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Summary

INTRODUCTION

The fine particulate matter (PM2.5) in the atmosphere is both generated by primary particulate matter emissions and the transformation of gaseous precursors such as SO2, NOx, VOCs and NH3 in primary emissions (Yang et al, 2000; Kanakidou et al, 2005; Hallquist et al, 2009; Liu et al, 2015). Weihai city is located in the easternmost part of Shandong Province, surrounded by the sea on three sides, and it has jurisdiction over 8 major economic areas, including Wendeng (WD), Rongcheng (RC), Rushan (RS), Huancui (HC), Lingang (LG), Gaoqu (GQ), Jingqu (JQ) and the coastal area (YH). It is the only city in Shandong Province whose annual mean values of all criteria pollutant are below the second-level standard of China's national environmental air quality standard (GB 3095-2012) (Li et al, 2020). The contribution of various components of PM2.5 from local and surrounding areas to Weihai city were calculated through the model, which could provide a reference for the development of localized and efficient fine particulate control measures

The RAMS-CMAQ Model System
Cluster Analysis of Back-trajectory Airmass
RESULTS AND DISCUSSION
Transport Contribution of Primary Particulate Matter Components
Regional Transport of Secondary Particulate Matter Components
CONCLUSIONS
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