Changes in Air Quality from the COVID to the Post-COVID Era in the Beijing-Tianjin-Tangshan Region in China

Guo, Qingchun; Wang, Zhaosheng; He, Zhenfang; Li, Xinzhou; Meng, Jingjing; Hou, Zhanfang; Yang, Jiaoxue

doi:10.4209/aaqr.210270

Cited by 26 publications

(8 citation statements)

References 32 publications

(38 reference statements)

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“…The proposed model depends on a contextual analysis of China's Hubei territory. A few significant parameters like greatest daily temperature, least daily temperature, normal day-to-to-day temperature, density, relative density, and speed of the wind as well as the quality of the air [ 11 ] were parsed as the informational index and picked the number of affirmed cases as the result of information collection for thirty days. They were of the view that the parallel order model gives more prominent ability to exactness in anticipating the announced cases.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data

Daniyal

Tawiah

Muhammadullah

et al. 2022

Journal of Healthcare Engineering

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The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout the globe suffered from this pandemic, forcing various governments to apply different restrictions to reduce its impact. In this study, we compare different time-series models with the neural network autoregressive model (NNAR). The study used COVID-19 data in Pakistan from February 26, 2020, to February 18, 2022, as a training and testing data set for modeling. Different models were applied and estimated on the training data set, and these models were assessed on the testing data set. Based on the mean absolute scaled error (MAE) and root mean square error (RMSE) for the training and testing data sets, the NNAR model outperformed the autoregressive integrated moving average (ARIMA) model and other competing models indicating that the NNAR model is the most appropriate for forecasting. Forecasts from the NNAR model showed that the cumulative confirmed COVID-19 cases will be 1,597,180 and cumulative confirmed COVID-19 deaths will be 32,628 on April 18, 2022. We encourage the Pakistan Government to boost its immunization policy.

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Section: Introductionmentioning

confidence: 99%

Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data

Daniyal

Tawiah

Muhammadullah

et al. 2022

Journal of Healthcare Engineering

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“…As a result, reducing social and economic activities not only had a positive effect on epidemic prevention and control but also had a positive effect on epidemic prevention and control. Reducing social and economic activities not only helps to prevent and control epidemics but also helps to improve air quality [30].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Model for the Impact of COVID-19 Prevention Measures on the Sustainable Development of the Aviation System

Zhu

Yang²,

Zhao

et al. 2022

Mathematical Problems in Engineering

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An airport is a complicated system that serves as a vital link in the aviation system. Airplanes are also a popular mode of transportation for daily travel and tourism. Now that COVID-19 is spreading over the world, many airlines are under severe financial strain. Therefore, long-term air traffic planning is critical, especially since the airport is a key region for COVID-19 to expand. To anticipate, simulate, and estimate the medical and health expenditures caused by the propagation of the epidemic under lax, mild, and strict social contact intervention, we utilize a hybrid model of the agent-based model and discrete event simulation. An agent-based model can capture individual behavior, and discrete event simulation can be used to explain the entire boarding process. Limiting the rate of wearing masks while traveling and upgrading airport facilities are the most effective strategies to manage the epidemic, given the convenience of travelers and the swift recovery of the national economy. Simulating passenger boarding will aid government agencies in improving airport equipment and facilities, facilitating passengers’ reasonable travel plans, avoiding large-scale contact, preventing the spread of epidemic diseases, and promoting the long-term sustainability of densely populated public transportation systems such as airports.

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“…The abrupt COVID-19 pandemic offers a chance to research the impact of urban blockade policies on the change of air pollutants, and to describe the normal modes of air pollution under the disappearance of the epidemic [ 24 ]. The COVID-19 epidemic has had an unprecedented influence on the air pollution in the Beijing and Tianjin districts [ 25 ]. During the period of the COVID-19, different urban blockade policies significantly improved the air quality of all four mega cities in China [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Guo

Wang

2023

Toxics

Self Cite

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Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

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Changes in Air Quality from the COVID to the Post-COVID Era in the Beijing-Tianjin-Tangshan Region in China

Cited by 26 publications

References 32 publications

Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data

Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data

A Hybrid Model for the Impact of COVID-19 Prevention Measures on the Sustainable Development of the Aviation System

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

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