» Articles » PMID: 31854910

[Driving Factors of the Significant Increase in Surface Ozone in the Beijing-Tianjin-Hebei Region, China, During 2013-2018]

Overview
Date 2019 Dec 20
PMID 31854910
Citations 2
Authors
Affiliations
Soon will be listed here.
Abstract

Photochemical pollution, which is believed to be influenced by emission changes and meteorological factors, is presently quite serious in the Beijing-Tianjin-Hebei (BTH) region, China. There is a need to ascertain the effectiveness of air quality management in the region based on long-term air quality trends independent from meteorological influences. We apply Kolmogorov-Zurbenko (KZ) filtering, a technique used to separate different scales of motion in a time series, to analyze the time series of the maximum daily 8-hour running average for ozone (O-8h) from 13 cities in the BTH region during 2013-2018, and also discuss trends and driving factors. Results of the KZ filtering revealed that the short-term, seasonal, and long-term components of the O-8h accounted for 32.7%, 63.9%, and 3.4% of the total variance, respectively. The long-term component of the BTH region was much higher than of those reported by others for Berlin, Paris, and London, and was comparable to that of Los Angeles in the early 1990s and in the 4 years previous to our study. Although we found a lower long-term component than of those reported for Shanghai and Nanjing in the Yangtze River Delta, China, the BTH region had higher rates of increase that ranged from 2.31 to 7.12 μg·(m·a)[mean 4.97 μg·(m·a)]. Based on the linear fitting results-that had not been verified by experiments or model simulations-the average increase rates could be mainly attributed to emission changes (90.4%), which may be distinguished into two parts, the decrease of particulate matter (PM) (27.3%) and the emission of O precursors (63.1%). Decreases of PMin Beijing, Langfang, Tianjin, and Hengshui were considered to be responsible for the increase at the levels of 50.8%, 32.5%, 36.7%, and 48.6%, respectively. This suggests that the rapid decrease in PM could be the most important factor in the increasing trend of O in some cities. We conclude that further decreases in the emission of O precursors are required to overcome the effect of decreasing PM causing an increase in O.

Citing Articles

Prediction of respiratory diseases based on random forest model.

Yang X, Li Y, Liu L, Zang Z Front Public Health. 2025; 13:1537238.

PMID: 40027501 PMC: 11868287. DOI: 10.3389/fpubh.2025.1537238.


Air Pollution Characteristics during the 2022 Beijing Winter Olympics.

Chu F, Gong C, Sun S, Li L, Yang X, Zhao W Int J Environ Res Public Health. 2022; 19(18).

PMID: 36141892 PMC: 9517278. DOI: 10.3390/ijerph191811616.


Spatiotemporal Dynamics of Surface Ozone and Its Relationship with Meteorological Factors over the Beijing-Tianjin-Tangshan Region, China, from 2016 to 2019.

Bai L, Feng J, Li Z, Han C, Yan F, Ding Y Sensors (Basel). 2022; 22(13).

PMID: 35808350 PMC: 9268810. DOI: 10.3390/s22134854.