Dataset Information

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

ABSTRACT: The novel coronavirus (COVID-19), first identified at the end of December 2019, has significant impacts on all aspects of human society. In this study, we aimed to assess the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta (YRD) region using a random forest (RF) model. To estimate the accuracy of the model, the cross-validation (CV), determination coefficient R², root mean squared error (RMSE) and mean absolute error (MAE) were used. The results demonstrate that the RF model achieved the best performance in the prediction of PM₁₀ (R² = 0.78, RMSE = 8.81 μg/m³), PM_2.5 (R² = 0.76, RMSE = 6.16 μg/m³), SO₂ (R² = 0.76, RMSE = 0.70 μg/m³), NO₂ (R² = 0.75, RMSE = 4.25 μg/m³), CO (R² = 0.81, RMSE = 0.4 μg/m³) and O₃ (R² = 0.79, RMSE = 6.24 μg/m³) concentrations in the YRD region. Compared with the prior two years (2018-19), significant reductions were recorded in air pollutants, such as SO₂ (-36.37%), followed by PM₁₀ (-33.95%), PM_2.5 (-32.86%), NO₂ (-32.65%) and CO (-20.48%), while an increase in O₃ was observed (6.70%) during the COVID-19 period (first phase). Moreover, the YRD experienced rising trends in the concentrations of PM₁₀, PM_2.5, NO₂ and CO, while SO₂ and O₃ levels decreased in 2021-22 (second phase). These findings provide credible outcomes and encourage the efforts to mitigate air pollution problems in the future.

SUBMITTER: Hasnain A

PROVIDER: S-EPMC9770002 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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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.

Hasnain Ahmad A Sheng Yehua Y Hashmi Muhammad Zaffar MZ Bhatti Uzair Aslam UA Ahmed Zulkifl Z Zha Yong Y

Chemosphere 20221221

The novel coronavirus (COVID-19), first identified at the end of December 2019, has significant impacts on all aspects of human society. In this study, we aimed to assess the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta (YRD) region using a random forest (RF) model. To estimate the accuracy of the model, the cross-validation (CV), determination coefficient R<sup>2</sup>, root mean squared error (RMSE) and mean absolute error (MAE) were used. The res ...[more]

PMID: 36565760

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Project description:China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.

Dataset Information

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

Publications

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

Similar Datasets

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