# Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Busy Areas of Taiwan and Sources of Noise Pollution

#### 2.2. Bayesian Regression MCMC

#### 2.3. Dataset

## 3. Results

#### 3.1. The Construction Steps of Bayesian MCMC

#### 3.2. Evidence of Noise Pollution Reduction

#### 3.3. Measuring Noise Pollution Using Bayesian MCMC

^{2}84.70%. If we look in more detail at Table 2, the prior setting for EB-local and EB-global has the same value. The objectives of the two priors are to find the global empirical Bayes estimates of g in Zellner’s g-prior and model probabilities [50].

## 4. Conclusions

^{2}84.70%. The Environmental Protection Administration (EPA), Executive Yuan, Taiwan declared on 21 January 2010 the Environmental Sound Level Standard to preserve adequate noise levels. In compliance with Article 20(3) of the Noise Control Act, the EPA has proposed the Environmental Noise Measurement Methods. At the moment, large-scale noise management activities, including local councils, event promoters and local suppliers, are expected to take responsibility for noise reduction and regulation prior to and after activities to maintain a safe atmosphere. Preventive steps include the elimination of noise at the sound source, the modification of the sound propagation route and the preservation of levels of the reception of noise. Any individual in violation of noise reduction requirements will be liable to a fine of NTD 3000–30,000. Several city councils also advised activity planners and local suppliers that they would cooperate with the regulations. City municipalities have already adopted these guidelines into their autonomous rules on the protection of large-scale activities. In a nutshell, as in Taiwan’s pre-COVID-19 pandemic strategy, the Taiwan CDC, in coordination with the Central Epidemic Command Center (CECC), held the responsibility of controlling the pandemic. Taiwanese officials were alerted to the outbreak in China by established action networks, triggering an urgent response, including screening of all airline passengers. We do not know when COVID-19 will end, but between the dynamics that occur, we see a positive impact, especially concerning reducing noise pollution. COVID-19 has had a positive impact on the environment, and this paper has proven that reductions in the amount of noise pollution in Taiwan were related to the number of petition cases. Future research should examine types of emissions and GHG reduction.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

CNS | Chinese National Standard |

COVID-19 | Coronavirus disease |

DNL | day–night level |

EPA | Environmental Protection Administration |

EU | European Union |

Fisher Test | statistical significance test |

IEC | International Electrotechnical Commission |

LOGMARG | values of the log of the marginal likelihood for the models |

MCMC | Markov chain Monte Carlo |

POSTROBS | posterior of Bayesian |

Wilcoxon Test | non-parametric statistical hypothesis test used to compare two related samples |

## Appendix A. Posterior Computation

## References

- Fernandes, N. Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. SSRN Electron. J.
**2020**, 1–33. [Google Scholar] [CrossRef] - Hudaefi, F.A.; Beik, I.S. Digital zakah campaign in time of Covid-19 pandemic in Indonesia: A netnographic study. J. Islam. Mark.
**2021**, 12, 498–517. [Google Scholar] [CrossRef] - Yu, K.D.S.; Aviso, K.B. Modelling the economic impact and ripple effects of disease outbreaks. Process. Integr. Optim. Sustain.
**2020**, 4, 183–186. [Google Scholar] [CrossRef][Green Version] - Djalante, R.; Shaw, R.; DeWit, A. Building resilience against biological hazards and pandemics: COVID-19 and its implications for the Sendai Framework. Prog. Disaster Sci.
**2020**, 6, 100080. [Google Scholar] [CrossRef] - Djalante, R.; Lassa, J.; Setiamarga, D.; Sudjatma, A.; Indrawan, M.; Haryanto, B.; Mahfud, C.; Sinapoy, M.S.; Djalante, S.; Rafliana, I.; et al. Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March. Prog. Disaster Sci.
**2020**, 6, 100091. [Google Scholar] [CrossRef] - Djalante, R.; Nurhidayah, L.; Lassa, J.; van Minh, H.; Mahendradhata, Y.; Phuong, N.T.N.; Trias, A.P.L.; Miller, M.A.; Djalante, S.; Sinapoy, M.S. The ASEAN’s responses to COVID-19: A policy sciences analysis. PsyArXiv
**2020**. [Google Scholar] [CrossRef] - Caraka, R.E.; Lee, Y.; Chen, R.C.; Toharudin, T.; Gio, P.U.; Kurniawan, R.; Pardamean, B. Cluster around latent variable for vulnerability towards natural hazards, non-natural hazards, social hazards in West Papua. IEEE Access
**2021**, 9, 1972–1986. [Google Scholar] [CrossRef] - Zuhairoh, F.; Rosadi, D. Real-time forecasting of the COVID-19 epidemic using the Richards Model in South Sulawesi, Indonesia. Indones. J. Sci. Technol.
**2020**, 5, 456–462. [Google Scholar] [CrossRef] - Siche, R. What is the impact of COVID-19 disease on agriculture? Sci. Agropecu.
**2020**, 11, 3–6. [Google Scholar] [CrossRef][Green Version] - McKibbin, W.; Fernando, R. The global macroeconomic impacts of COVID-19: Seven scenarios. Asian Econ. Pap.
**2021**, 20, 1–30. [Google Scholar] [CrossRef] - Karaye, I.M.; Horney, J.A. The impact of social vulnerability on COVID-19 in the U.S.: An Analysis of spatially varying relationships. Am. J. Prev. Med.
**2020**, 59, 317–325. [Google Scholar] [CrossRef] [PubMed] - Zambrano-Monserrate, M.A.; Ruano, M.A.; Sanchez-Alcalde, L. Indirect effects of COVID-19 on the environment. Sci. Total Environ.
**2020**, 728, 138813. [Google Scholar] [CrossRef] - Kumar, S. Effect of meteorological parameters on spread of COVID-19 in India and air quality during lockdown. Sci. Total Environ.
**2020**, 745, 141021. [Google Scholar] [CrossRef] - Lokhandwala, S.; Gautam, P. Indirect impact of COVID-19 on environment: A brief study in Indian context. Environ. Res.
**2020**, 188, 109807. [Google Scholar] [CrossRef] [PubMed] - Caraka, R.; Lee, Y.; Kurniawan, R.; Herliansyah, R.; Kaban, P.; Nasution, B.; Gio, P.; Chen, R.; Toharudin, T.; Pardamean, B. Impact of COVID-19 large scale restriction on environment and economy in Indonesia. Glob. J. Environ. Sci. Manag.
**2020**, 6, 65–84. [Google Scholar] [CrossRef] - Bhat, S.A.; Bashir, O.; Bilal, M.; Ishaq, A.; Dar, M.U.D.; Kumar, R.; Bhat, R.A.; Sher, F. Impact of COVID-related lockdowns on environmental and climate change scenarios. Environ. Res.
**2021**, 195, 110839. [Google Scholar] [CrossRef] [PubMed] - Basu, B.; Murphy, E.; Molter, A.; Basu, A.S.; Sannigrahi, S.; Belmonte, M.; Pilla, F. Investigating changes in noise pollution due to the COVID-19 lockdown: The case of Dublin, Ireland. Sustain. Cities Soc.
**2021**, 65, 102597. [Google Scholar] [CrossRef] - Díaz, J.; Antonio-López-Bueno, J.; Culqui, D.; Asensio, C.; Sánchez-Martínez, G.; Linares, C. Does exposure to noise pollution influence the incidence and severity of COVID-19? Environ. Res.
**2021**, 195, 110766. [Google Scholar] [CrossRef] - Aletta, F.; Oberman, T.; Mitchell, A.; Tong, H.; Kang, J. Assessing the changing urban sound environment during the COVID-19 lockdown period using short-term acoustic measurements. Noise Mapp.
**2020**, 7, 123–134. [Google Scholar] [CrossRef] - Lee, P.J.; Jeong, J.H. Attitudes towards outdoor and neighbour noise during the COVID-19 lockdown: A case study in London. Sustain. Cities Soc.
**2021**, 67, 102768. [Google Scholar] [CrossRef] - Pan, S.; Jung, J.; Li, Z.; Hou, X.; Roy, A.; Choi, Y.; Gao, H. Air Quality Implications of COVID-19 in California. Sustainability
**2020**, 12, 7067. [Google Scholar] [CrossRef] - Tong, H.; Aletta, F.; Mitchell, A.; Oberman, T.; Kang, J. Increases in noise complaints during the COVID-19 lockdown in Spring 2020: A case study in Greater London, UK. Sci. Total Environ.
**2021**, 785, 147213. [Google Scholar] [CrossRef] - Lynch, J.F. Marine Mammals and Low-frequency Sound: Progress Since. J. Acoust. Soc. Am.
**2001**, 110, 649–650. [Google Scholar] [CrossRef][Green Version] - Basner, M.; Babisch, W.; Davis, A.; Brink, M.; Clark, C.; Janssen, S.; Stansfeld, S. Auditory and non-auditory effects of noise on health. Lancet
**2014**, 383, 1325–1332. [Google Scholar] [CrossRef][Green Version] - Münzel, T.; Kröller-Schön, S.; Oelze, M.; Gori, T.; Schmidt, F.P.; Steven, S.; Hahad, O.; Röösli, M.; Wunderli, J.-M.; Daiber, A.; et al. Adverse cardiovascular effects of traffic noise with a focus on nighttime noise and the new WHO noise guidelines. Annu. Rev. Public Health
**2020**, 41, 309–328. [Google Scholar] [CrossRef][Green Version] - Sahmurova, A.; Gurseli, M.C. Article Effects of Noise Pollution on Anger and Smoking. IIOB J.
**2020**, 11, 42–47. [Google Scholar] - Ferrier-Pagès, C.; Leal, M.C.; Calado, R.; Schmid, D.W.; Bertucci, F.; Lecchini, D.; Allemand, D. Noise pollution on coral reefs?—A yet underestimated threat to coral reef communities. Mar. Pollut. Bull.
**2021**, 165, 112129. [Google Scholar] [CrossRef] - Caraka, R.; Chen, R.; Lee, Y.; Toharudin, T.; Rahmadi, C.; Tahmid, M.; Achmadi, A. Using multivariate generalized linear latent variable models to measure the difference in event count for stranded marine animals. Glob. J. Environ. Sci. Manag.
**2021**, 7, 117–130. [Google Scholar] [CrossRef] - Paviotti, M.; Vogiatzis, K. On the outdoor annoyance from scooter and motorbike noise in the urban environment. Sci. Total Environ.
**2012**, 430, 223–230. [Google Scholar] [CrossRef] - Jakovljevic, B.; Paunovic, K.; Belojevic, G. Road-traffic noise and factors influencing noise annoyance in an urban population. Environ. Int.
**2009**, 35, 552–556. [Google Scholar] [CrossRef] - Peng, J.; Parnell, J.; Kessissoglou, N. Spatially differentiated profiles for road traffic noise pollution across a state road network. Appl. Acoust.
**2021**, 172, 107641. [Google Scholar] [CrossRef] - Szopinska, K. Creation of Theoretical Road Traffic Noise Model with the Help of GIS. In Proceedings of the 10th International Conference “Environmental Engineering”, Vilnius, Lithuania, 27–28 April 2017. [Google Scholar] [CrossRef]
- Brown, A.L. Longitudinal annoyance responses to a road traffic noise management strategy that reduced heavy vehicles at night. J. Acoust. Soc. Am.
**2015**, 137, 165–176. [Google Scholar] [CrossRef][Green Version] - Di, G.; Zhao, C.; Lin, Q.; Fan, M. Study on annoyance and sleep disturbance induced by combined noises from road traffic and viaduct rail transit. Acoust. Aust.
**2019**, 47, 229–237. [Google Scholar] [CrossRef] - Čurović, L.; Jeram, S.; Murovec, J.; Novaković, T.; Rupnik, K.; Prezelj, J. Impact of COVID-19 on environmental noise emitted from the port. Sci. Total Environ.
**2021**, 756, 144147. [Google Scholar] [CrossRef] [PubMed] - Guski, R.; Schreckenberg, D.; Schuemer, R. WHO Environmental noise guidelines for the European region: A systematic review on environmental noise and annoyance. Int. J. Environ. Res. Public Health
**2017**, 14, 1539. [Google Scholar] [CrossRef] [PubMed][Green Version] - Caraka, R.E.; Nugroho, N.T.; Tai, S.-K.; Chen, R.-C.; Toharudin, T.; Pardamean, B. Feature importance of the aortic anatomy on endovascular aneurysm repair (EVAR) using Boruta and Bayesian MCMC. Commun. Math. Biol. Neurosci.
**2020**, 2020, 22. [Google Scholar] [CrossRef] - Salimans, T.; Kingma, D.P.; Welling, M. Markov Chain Monte Carlo and variational inference: Bridging the gap. arXiv
**2015**, arXiv:1410.6460. [Google Scholar] - Dellaportas, P.; Forster, J.J.; Ntzoufras, I. On Bayesian model and variable selection using MCMC. Stat. Comput.
**2002**, 12, 27–36. [Google Scholar] [CrossRef] - Environmental Protection Administration. Draft of Environmental Aircraft Noise Measurement Methods Preannounced. Taiwan, 2017. Available online: https://www.epa.gov.tw (accessed on 10 April 2021).
- Sutton, M. Bayesian Variable Selection. Lévy Matters
**2020**, 6, 121–135. [Google Scholar] - Rabinovich, M.; Angelino, E.; Jordan, M.I. Variational Consensus Monte Carlo; Curran Associates, Inc.: Red Hook, NY, USA, 2015. [Google Scholar]
- Kuha, J. AIC and BIC. Sociol. Methods Res.
**2004**, 33, 188–229. [Google Scholar] [CrossRef] - Gio, P.U.; Rosmaini, E. The Robustness of two independent samples t-test using Monte Carlo simulation with R. IOP Conf. Ser. Mater. Sci. Eng.
**2018**, 300, 012030. [Google Scholar] [CrossRef] - Jaya, I.G.N.M.; Tantular, B.; Ruchjana, B.N. MCMC algorithm for Bayesian heterogeneous coefficients of panel data model. J. Phys. Conf. Ser.
**2019**, 1320, 012014. [Google Scholar] [CrossRef][Green Version] - Andrieu, C.; de Freitas, N.; Doucet, A.; Jordan, M.I. An introduction to MCMC for machine learning. Mach. Learn.
**2003**, 50, 5–43. [Google Scholar] [CrossRef][Green Version] - Summers, J.; Cheng, H.-Y.; Lin, P.H.-H.; Barnard, L.T.; Kvalsvig, A.; Wilson, P.N.; Baker, P.M.G. Potential lessons from the Taiwan and New Zealand health responses to the COVID-19 pandemic. Lancet Reg. Health West. Pac.
**2020**, 4, 100044. [Google Scholar] [CrossRef] [PubMed] - Wang, X.; Yue, Y.; Faraway, J.J. Bayesian Linear Regression. In Bayesian Regression Modeling with Inla; CRC Press: Routledge, UK, 2018; pp. 39–69. [Google Scholar]
- Lee, Y.; Rönnegård, L.; Noh, M. Data Analysis Using Hierarchical Generalized Linear Models with R; Chapman and Hall/CRC: Boca Raton, FL, USA, 2017. [Google Scholar]
- Liang, F.; Paulo, R.; Molina, G.; Clyde, A.M.; Berger, O.J. Mixtures of g priors for Bayesian variable selection. J. Am. Stat. Assoc.
**2008**, 103, 410–423. [Google Scholar] [CrossRef] - Lee, Y.; Noh, M. Modelling random effect variance with double hierarchical generalized linear models. Stat. Model. Int. J.
**2012**, 12, 487–502. [Google Scholar] [CrossRef] - Ha, I.D.; Lee, Y. Estimating frailty models via poisson hierarchical generalized linear models. J. Comput. Graph. Stat.
**2003**, 12, 663–681. [Google Scholar] [CrossRef] - Lee, Y.; Nelder, J.A. Double hierarchical generalized linear models (with discussion). J. R. Stat. Soc. Ser. C Appl. Stat.
**2006**, 55, 139–185. [Google Scholar] [CrossRef] - Kang, W.; Lee, M.-S.; Lee, Y. HGLM versus conditional estimators for the analysis of clustered binary data. Stat. Med.
**2005**, 24, 741–752. [Google Scholar] [CrossRef] - Lee, Y.; Nelder, J.A.; Noh, M. H-likelihood: Problems and solutions. Stat. Comput.
**2007**, 17, 49–55. [Google Scholar] [CrossRef] - Lee, Y.; Nelder, J.A. Likelihood inference for models with unobservables: Another view. Stat. Sci.
**2009**, 24, 255–269. [Google Scholar] [CrossRef][Green Version]

**Figure 1.**Chord diagram noise pollution in Taiwan 2005–2019 (before COVID-19) and 2020 (during COVID-19).

Descriptive | Cases of Over-Standard Noise per Time Frame | Petition Cases | Industry Petitions | Motorcycles | Cars | Density of Vehicles | |
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Before COVID-19 | Min. | 2 | 39,636 | 25,445 | 13,195,265 | 6,667,542 | 549 |

1st | 3 | 58,722 | 29,201 | 13,719,027 | 6,769,454 | 587 | |

Median | 4 | 81,368 | 32,034 | 13,968,198 | 7,287,146 | 595 | |

Mean | 6 | 72,394 | 31,628 | 14,110,811 | 7,351,197 | 593 | |

3rd | 9 | 87,076 | 33,998 | 14,425,164 | 7,869,013 | 606 | |

Max. | 14 | 96,739 | 40,174 | 15,173,602 | 8,193,237 | 617 | |

During COVID-19 | Min. | 6 | 85,457 | 31,142 | 13,992,922 | 8,118,885 | 611 |

1st | 6 | 87,926 | 33,400 | 14,020,632 | 8,137,473 | 612 | |

Median | 7 | 90,394 | 35,658 | 14,048,343 | 8,156,061 | 613 | |

Mean | 7 | 90,394 | 35,658 | 14,048,343 | 8,156,061 | 613 | |

3rd | 7 | 92,863 | 37,916 | 14,076,053 | 8,174,649 | 615 | |

Max. | 8 | 95,331 | 40,174 | 14,103,763 | 8,193,237 | 616 | |

Statistical Test | p-value Wilcoxon test (before and during COVID19) | 0.58680 | 0.00002 | 0.66670 | 0.50000 | 0.50000 | 0.66700 |

Fisher’s test (before and during COVID-19) | 0.43750 | 0.02564 | 0.00020 | 0.00050 | 0.00051 | 0.00050 |

Prior | R^{2} | Dim | LOGMARG | POSTROBS |
---|---|---|---|---|

AIC * | 84.70% | 6 | 2.845605 | 0.0644 |

g-prior | 30.7% | 2 | 1.131336 | 0.0785 |

ZS-null | 30.7% | 2 | 0.062679 | 0.0449 |

ZS-full | 48.2% | 4 | 3.190674 | 0.0316 |

Hyper-g | 64.6% | 6 | 0.820071 | 0.0304 |

Hyper-g-n | 30.7% | 2 | 0.613492 | 0.0414 |

Hyper-g-Laplace | 40.86% | 3 | 0.7213114 | 0.0465 |

Hyper-g-n | 30.7% | 2 | 0.6134929 | 0.0307 |

EB-local | 64.70% | 6 | 1.520647 | 0.0267 |

EB-global | 64.70% | 6 | 1.377268 | 0.0200 |

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**MDPI and ACS Style**

Caraka, R.E.; Yusra, Y.; Toharudin, T.; Chen, R.-C.; Basyuni, M.; Juned, V.; Gio, P.U.; Pardamean, B. Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan. *Sustainability* **2021**, *13*, 5946.
https://doi.org/10.3390/su13115946

**AMA Style**

Caraka RE, Yusra Y, Toharudin T, Chen R-C, Basyuni M, Juned V, Gio PU, Pardamean B. Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan. *Sustainability*. 2021; 13(11):5946.
https://doi.org/10.3390/su13115946

**Chicago/Turabian Style**

Caraka, Rezzy Eko, Yusra Yusra, Toni Toharudin, Rung-Ching Chen, Mohammad Basyuni, Vilzati Juned, Prana Ugiana Gio, and Bens Pardamean. 2021. "Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan" *Sustainability* 13, no. 11: 5946.
https://doi.org/10.3390/su13115946