Changes in urban residential and agricultural sources induce a decrease in PM10 levels in cold clean area: A thirteen-year monitoring at the Longfengshan WMO/GAW regional background station in Northeast China

Abstract

Atmospheric background monitoring provides insights into the long-term changes in atmospheric composition resulting from human activities on both global and regional scales. It is important to understand the local characteristics of key air pollutants to evaluate and manage air quality control policies effectively. This study analyzed 13 years of continuous data of PM₁₀ (particulate matter with aerodynamic diameter ≤10 μm) concentration from the only atmospheric background station in Northeastern China, the Longfengshan (LFS) World Meteorological Organization (WMO)/Global Atmosphere Watch (GAW) regional station. The results revealed a significant decrease of 58.02% in PM₁₀ concentrations in the LFS from 2007 to 2019, which was primarily influenced by transport from Changchun and Harbin. Over periods exceeding eight months, the correlation between PM₁₀ levels at the LFS atmospheric background station and those in Changchun and Harbin became more pronounced. The observed decline in PM₁₀ concentrations at the LFS atmospheric background station was largely attributed to reduced emissions from urban residential and agricultural sources. Emissions of carbon monoxide and ammonia from these sources, along with temperature and relative humidity (RH), significantly affected the PM₁₀ concentrations. Using machine learning (ML) methods and PM₁₀ data from the LFS atmospheric background station, this study effectively predicted PM₁₀ concentrations in neighboring cities, demonstrating that the LFS atmospheric background station can effectively assess the trend of PM₁₀ changes in Northeast China. This study quantified the influence of urban areas on atmospheric background stations, highlighting the effectiveness of existing environmental policies, and offering a reference for similar studies and forecasts in other regions.