Zukaib Umer, Maray Mohammed, Mustafa Saad, Haq Nuhman Ul, Khan Atta Ur Rehman, Rehman Faisal
Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, KP, Pakistan.
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China.
PeerJ Comput Sci. 2023 Mar 31;9:e1270. doi: 10.7717/peerj-cs.1270. eCollection 2023.
After February 2020, the majority of the world's governments decided to implement a lockdown in order to limit the spread of the deadly COVID-19 virus. This restriction improved air quality by reducing emissions of particular atmospheric pollutants from industrial and vehicular traffic. In this study, we look at how the COVID-19 shutdown influenced the air quality in Lahore, Pakistan. HAC Agri Limited, Dawn Food Head Office, Phase 8-DHA, and Zeenat Block in Lahore were chosen to give historical data on the concentrations of many pollutants, including PM2.5, PM10 (particulate matter), NO2 (nitrogen dioxide), and O3 (ozone). We use a variety of models, including decision tree, SVR, random forest, ARIMA, CNN, N-BEATS, and LSTM, to compare and forecast air quality. Using machine learning methods, we looked at how each pollutant's levels changed during the lockdown. It has been shown that LSTM estimates the amounts of each pollutant during the lockout more precisely than other models. The results show that during the lockdown, the concentration of atmospheric pollutants decreased, and the air quality index improved by around 20%. The results also show a 42% drop in PM2.5 concentration, a 72% drop in PM10 concentration, a 29% drop in NO2 concentration, and an increase of 20% in O3 concentration. The machine learning models are assessed using the RMSE, MAE, and R-SQUARE values. The LSTM measures NO2 at 4.35%, O3 at 8.2%, PM2.5 at 4.46%, and PM10 at 8.58% in terms of MAE. It is observed that the LSTM model outperformed with the fewest errors when the projected values are compared with the actual values.
2020年2月之后，世界上大多数政府决定实施封锁措施，以限制致命的新冠病毒传播。这种限制措施通过减少工业和车辆交通排放的特定大气污染物，改善了空气质量。在本研究中，我们考察了新冠疫情封锁措施对巴基斯坦拉合尔空气质量的影响。选择拉合尔的HAC农业有限公司、黎明食品总部、DHA第8期以及泽纳特街区，以提供包括PM2.5、PM10（颗粒物）、NO2（二氧化氮）和O3（臭氧）等多种污染物浓度的历史数据。我们使用多种模型，包括决策树、支持向量回归、随机森林、自回归整合移动平均模型、卷积神经网络、N-BEATS和长短期记忆网络，来比较和预测空气质量。利用机器学习方法，我们研究了封锁期间每种污染物水平的变化情况。结果表明，长短期记忆网络模型比其他模型更精确地估计了封锁期间每种污染物的含量。结果显示，在封锁期间，大气污染物浓度下降，空气质量指数提高了约20%。结果还显示，PM2.5浓度下降了42%，PM10浓度下降了72%，NO2浓度下降了29%，O3浓度增加了20%。使用均方根误差、平均绝对误差和决定系数值对机器学习模型进行评估。就平均绝对误差而言，长短期记忆网络模型对NO2的测量误差为4.35%，对O3的测量误差为8.2%，对PM2.5的测量误差为4.46%，对PM10的测量误差为8.58%。可以观察到，当将预测值与实际值进行比较时，长短期记忆网络模型的误差最小，表现最佳。
