Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning.
Environ Pollut
; : 120798, 2022 Dec 01.
Article
in English
| MEDLINE | ID: covidwho-2246197
ABSTRACT
Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3-VOCs-NOx relationships applicable in different O3 pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O3 formation regime and derive O3 formation sensitivity curves. The algorithm was tested for O3 events during the COVID-19 lockdown, a sandstorm event, and a heavy O3 pollution episode (maximum hourly O3 concentration >200⯵g/m3) from 2019 to 2021. We show that increasing O3 concentrations during the COVID-19 lockdown and the heavy O3 pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O3 levels were mainly attributable to weak sunlight and low precursor levels. O3 formation sensitivity curves demonstrate that O3 formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O3 sensitivity curves can also help make hybrid and timely strategies for O3 abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O3 formation.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
/
Prognostic study
Topics:
Variants
Language:
English
Journal:
Environ Pollut
Journal subject:
Environmental Health
Year:
2022
Document Type:
Article
Affiliation country:
J.envpol.2022.120798
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