The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques

Atmospheric Pollution Research

article

Jasper S. Wijnands¹  Kerry A. Nice¹  Sachith Seneviratne¹  Jason Thompson¹  Mark Stevenson¹ 

[1] Transport, Health and Urban Design Research Lab, Melbourne School of Design, The University of Melbourne;Royal Netherlands Meteorological Institute;Faculty of Engineering and Information Technology, The University of Melbourne;Melbourne School of Population and Global Health, The University of Melbourne

关键词: COVID-19;    Nitrogen dioxide;    Particulate matter;    Ozone;    Mobility;    Decision trees;   

DOI  :  10.1016/j.apr.2022.101438

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

In response to the COVID-19 pandemic, most countries implemented public health ordinances that resulted in restricted mobility and a resultant change in air quality. This has provided an opportunity to quantify the extent to which carbon-based transport and industrial activity affect air quality. However, quantification of these complex effects has proven to be difficult, depending on the stringency of restrictions, country-specific emission source profiles, long-term trends and meteorological effects on atmospheric chemistry , emission levels and in-flow from nearby countries. In this study, confounding factors were disentangled for a direct comparison of pandemic-related reductions in absolute pollutions levels, globally. The non-linear relationships between atmospheric processes and daily ground-level NO 2 2 , PM 10 , PM 2.5 and O 3 3 measurements were captured in city- and pollutant-specific XGBoost models for over 700 cities, adjusting for weather, seasonality and trends. City-level modelling allowed adaptation to the distinct topography, urban morphology , climate and atmospheric conditions for each city, individually, as the weather variables that were most predictive varied across cities. Pollution forecasts for 2020 in absence of a pandemic were generated based on weather and formed an ensemble for country-level pollution reductions. Findings were robust to modelling assumptions and consistent with various published case studies. NO 2 2 reduced most in China, Europe and India, following severe government restrictions as part of the initial lockdowns. Reductions were highly correlated with changes in mobility levels, especially trips to transit stations, workplaces, retail and recreation venues. Further, NO 2 2 did not fully revert to pre-pandemic levels in 2020. Ambient PM 2.5 pollution, which has severe adverse health consequences, reduced most in China and India. Since positive health effects could be offset to some extent by prolonged exposure to indoor pollution, alternative transport initiatives could prove to be an important pathway towards better health outcomes in these countries. Increased O 3 3 levels during initial lockdowns have been documented widely. However, our analyses also found a subsequent reduction in O 3 3 for many countries below what was expected based on meteorological conditions during summer months (e.g., China, United Kingdom, France, Germany, Poland, Turkey). The effects in periods with high O 3 3 levels are especially important for the development of effective mitigation strategies to improve health outcomes.

【 授权许可】

CC BY