通过机器学习技术分析了COVID-19封锁对空气质量的影响
Umer Zukaib1,2, Mohammed Maray3, Saad Mustafa1
1Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, KP, Pakistan.
PeerJ. Computer science
|June 22, 2023
概括
由于COVID-19的封锁,拉合尔的空气质量得到了显著改善,PM2.5和PM10等主要污染物减少了. 机器学习模型,特别是LSTM,准确地预测了这些改进,显示大气污染明显减少.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 由于COVID-19大流行,全球都需要封锁,从而减少了工业和车辆的排放.
- 这项研究调查了这些限制对巴基斯坦拉合尔空气质量的影响.
研究的目的:
- 分析COVID-19封锁对拉合尔空气质量的影响.
- 为了比较各种机器学习模型在预测空气污染物度方面的有效性.
主要方法:
- 从拉合尔的四个地点收集了PM2.5,PM10,NO2和O3的历史空气质量数据.
- 采用多种机器学习模型,包括决策树,SVR,随机森林,ARIMA,CNN,N-BEATS和LSTM.
- 使用RMSE,MAE和R-SQUARE指标评估模型性能,重点关注LSTM的准确性.
主要成果:
- 在封锁期间,拉合尔的空气质量提高了大约20%.
- 观察到的显著降低:PM2.5 (42%),PM10 (72%),NO2 (29%). 这是一个非常明显的变化.
- 臭氧 (O3) 度增加了20%;LSTM在污染物估计方面表现出卓越的准确性.
结论:
- 由于减少有害污染物,COVID-19封锁对拉合尔的空气质量产生了积极影响.
- LSTM被证明是最准确的模型来预测空气污染物水平在封锁期间.
- 调查结果强调了减排战略在改善城市空气质量方面的潜力.
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