Prediction of Potentially High PM2.5 Concentrations in Chengdu, China

Abstract

Daily exposure to high ambient PM 2.5 increases the mortality rate and contributes significantly to the burden of disease. In basin-situated cities with high local emissions of air pollutants, meteorological conditions play a crucial role in forming air pollution. One such city is Chengdu, which is located in the Sichuan Basin and serves as the economic, educational, and transportation hub of western China. Particulate matter with an aerodynamic diameter of < 2.5 µm (PM 2.5 ) is the most critical pollutant in this city. Although the annually averaged PM 2.5 concentrations declined from 92 to 57 µg m -3 between 2013 and 2017, the city still suffers from haze and smog, with 85 days during 2017 displaying 24-h PM 2.5 concentrations > 75 µg m -3 . To better understand the influence of meteorological factors on PM 2.5 pollution with the goal of easily and reliably predicting the latter, we examined the relationships between the 24-h concentration and a variety of meteorological parameters in Chengdu. We found that the strongest predictors of the PM 2.5 concentration were the temperature, precipitation, wind speed, and trajectory direction and distance. Furthermore, although the same-day sea-level pressure (SLP) was a weak predictor, the SLP 5 days in advance performed better. We developed generalized additive models (GAMs) that predicted the PM 2.5 concentration as a function of multiple meteorological parameters. One of the GAMs developed in this study exhibited an adjusted correlation coefficient (R 2 ) of 0.73 and captured up to 73.9% of the variance in the daily averaged PM 2.5 concentrations. The model performance was improved by using the ΔSLP (i.e., mean pressure difference) for 5 days instead of the SLP, suggesting that ΔSLP 5d is a good predictor of high concentration days in Chengdu. This study provides a useful tool for controlling emissions in advance to prevent heavy pollution days and issuing outdoor activity warnings to protect public health.

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Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach

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A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution

“…(2) Numerical model systems for air quality are used to provide O3 concentration predictions, which involve the use of chemical transport models and can simulate the dynamics of the atmosphere from the mathematical representation of different physical and chemical mechanisms (Hoshyaripour et al, 2016;Zeng et al, 2020;Sayeed et al, 2021). However, this method has a complex calculation process, high requirements for computer performance and high cost.…”