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Interventions to Support Tourism and its Impact on Air Quality - A Case Study of the Go To Travel Campaign in Japan –
Abstract
The effects of COVID-19 measures on the quantitative changes in atmospheric substances associated with economic activities have been explored in various studies. However, the specific relationship between the changes in economic activities due to government interventions and the associated changes in atmospheric substances remains unclear. This relationship needs to be understood in detail, especially in the tourism industry where conflicting responses are needed, such as promoting people flow and socioeconomic development while adopting measures to prevent the spread of infectious diseases. Therefore, this study focused on the tourism industry in Japan, used Google Earth Engine to calculate the changes in five atmospheric substances before and during the government’s Go To Travel Campaign, which was implemented to support domestic travel, and analyzed the correlations with statistical data such as the number of travelers, use of buses, and taxis. As a result, a series of connected behaviors clarified the fact that socioeconomic activities affected by the government’s Go To Travel Campaign surfaced with the changes in the atmospheric substances. To further understand the relationship between interventions to promote tourism and air quality, additional datasets and approaches are needed, including data on the operation status of facilities and the behavioral characteristics of Japanese travelers.
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A questionnaire survey was distributed via the Internet to 600 respondents. Preliminary results revealed that most Japanese people regularly washed their hands and had low resistance to wearing masks even before the COVID-19 pandemic. Internet news was the most common source of information. Half of the respondents said they would “stay at home evacuation” if a disaster occurred during the COVID-19 pandemic, reflecting the strategy promoted to reduce crowding in evacuation shelters. If a state of emergency must be reinstated, one-third of respondents said they could bear it for a few months and another one-third for a few weeks.
Recent studies have indicated that certain atmospheric pollutants had significantly reduced in several countries during the lockdown period imposed to curb the spread of SARS-CoV-2-Virus. The Government of India declared the first lockdown from the end of March 2020, which continued till June 2020 in most Indian states. The present study compares the air quality indicators nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ) across India, during the months of March–August 2020 and the same period in 2019. The application of satellite information from NASA – Ozone Monitoring Instrument and Atmospheric Infrared Sounder were used to compare the quantum of air pollutants. The temporal variation of the air pollutants was studied using satellite imagery and geo-statistics on a monthly, national average basis, to assess the overall impact of the lockdown. NO 2 , SO 2 , and O 3 showed some level of reduction during the period of study in 2020 when compared to 2019, whereas CO levels had gone up in 2020. NO 2 , a pollutant mainly arising from motor vehicle combustion, reduced by 3.98–12.1% in 2020 as compared to the same study period in 2019 and in April 2020, when there was a complete lockdown, it had dropped maximally (by 12.1%). The reduction in SO 2 levels in 2020 ranged from around 0.5–9% but only during April–June 2020, whereas there was an increase in March, July, and August 2020 when compared to 2019. Despite a reduction in NO 2 , the O 3 levels (which are dependent on NO 2 levels) saw an increase in the atmosphere during March–May 2020 by 1.9–5%, and decreased during June–August 2020. The CO levels in the atmosphere did not reduce during lockdown; instead, it peaked in March, April, and May 2020, when compared to 2019, possibly due to incomplete combustion of materials containing carbon materials like wood, plastics, etc. This study demonstrates that it is possible to rapidly reduce atmospheric pollution in India. However, since the level of certain pollutants like O 3 are dependent on others like NO 2 , reducing the atmospheric pollution globally is a sustained and concerted effort by all concerned.
In November 2013, Super Typhoon Haiyan (Yolanda) hit the Philippines. It caused heavy loss of lives and extensive damages to buildings and infrastructure. When collapsed buildings are focused on, it is interesting to find that these buildings did not collapse for the same reasons after the landfall of the typhoon and storm surge. The objective of this study is to develop a statistical model for building damage due to Super Typhoon Haiyan and its storm surge. The data were collected in collaboration with Tanauan Municipality, the Philippines. The data for the inundation map were obtained by field surveys conducted on-site to determine the cause of the damages inferred from satellite data. The maximum wind speed was derived from the Holland parametric hurricane model based on the Japan Meteorological Agency (JMA) typhoon track data and the inundation depth of storm surge was calculated using the MIKE model. Multinomial logistic regression was used to develop a model to identify the significant factors influencing the damage to buildings. The result of this work is expected to be used to prepare urban plans for preventing damage from future storms.
In order to fight against the spread of COVID-19, the most hard-hit countries in the spring of 2020 implemented different lockdown strategies. To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2, SO2, CO, PM2.5 and PM10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown. This work shows the reduction of primary pollutants, especially NO2, is mainly due to lockdown policies. However, preexisting local environmental policy regulations also contributed to declining NO2, SO2 and PM2.5 emissions, especially in Asian countries. In addition, higher rainfall during the lockdown period could cause decline of PM2.5, especially in Johannesburg. By contrast, the changes of AQI in ground-level O3 were not significant in most of cities, as meteorological variability and ratio of VOC/NOx are key factors in ground-level O3 formation.
Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.
Various interventions were made by the Thai government to prevent the COVID-19 spread by controlling socioeconomic activities, but the effectiveness of these interventions and other factors have not yet been fully clarified. Thus, this study aims to provide further scientific evidence on those potential factors which affect the socioeconomic activities changes during the pandemic, by using spatial analysis on atmospheric composition. By taking Phra Nakhon Si Ayutthaya Province, Thailand as the case area. Results of the government's COVID-19 measures and statuses of industries were compared with changes in aerosols, including PM 2.5 which was analyzed by Google Earth Engine with nine open datasets including meteorological and hydrological factors. The analysis revealed that the aerosol index in ueban area of the province decreased at 28.03% in 2020 compared in 2019. Besides, PM 2.5 drastically decreased from March 2020, even without the influence of wind speed which as the highest causal relationship, and kept low level compared with previous years. The reason of the tendency would be explained that other than government interventions including national-level state of the emergency decree, reduction of factories’ activities at Rojana Industrial Park and reduction of the number of tourists had significant influence to reduce the mean value of PM 2.5.
In the year 2020, SARS-CoV-2, the virus behind the coronavirus disease (COVID-19) pandemic, affected many lives and businesses worldwide. COVID-19, which originated in Wuhan City, China, at the end of December 2019, spread over the entire world in approximately four months. By October 2020, approximately 20 million people were infected and millions had died from this disease. Many health organizations such as the World Health Organization and Centers for Disease Control and Prevention made COVID-19 their primary focus. Many industries, especially, the tourism industry, were affected by the pandemic as many flight and hotel reservations were canceled. Thailand, a country considered one of the world’s most popular tourist destinations, suffered much losses because of this pandemic. Many events and travel bookings were canceled and/or postponed. Many people expressed their views and emotions related to this situation over social media, which is considered a powerful media for spreading news and information. In this research, the views of people who were planning to travel to Bangkok, the capital city and most popular destination in Thailand, were retrieved from Twitter for the dates between April 3 and 30, 2020, the period during which the country underwent nationwide lockdown. Sentiment analysis was performed using the support vector machine algorithm. The results showed 71.03% classification accuracy based on three sentiment classifications: positive, negative, and neutral. This study could thus provide an insight into travelers’ opinions and sentiments related to the tourism business. Based on the significant terms in each sentiment extracted, strengths and weaknesses of each tourism issue could be obtained, which could be used for making recommendations to the related tourism organizations.
The transmission of novel coronavirus (COVID-19) can be reduced by implementing lockdown policy, which has also been proven as an effective control measure for air pollution in an urban city. In this study, we applied ground- and satellite-based data of five criteria air pollutants (PM2.5, NO2, SO2, O3, and CO) and meteorological factors from March 8 to May 15, 2020 (before, partial-, and full-lockdown). The generalized additive models (GAMs), wavelet coherence, and random forest (RF) model were employed to explore the relationship between air quality indicators and COVID-19 transmission in Dhaka city. Results show that there are no significant relationships between air pollutants and COVID-19 cases during this study. Overall, 26, 20.4, 17.5, 9.7 and 8.8% declined in PM 2.5, NO2, SO2, O3, and CO concentrations, respectively, in Dhaka City during the partial and full lockdown compared to the period before the lockdown. The implementation of lockdown policy for containing COVID-19 transmission played a key role to reduce the air pollution. The findings of wavelet coherences demonstrate a coupling coherence effect of air pollutants and meteorological factors on the COVID-19 outbreak. Interestingly, outcomes of GAMs indicated that an increase of 1-unit in long-term exposure to O3 and CO (lag1) was associated with a 2.9% (95% CI: -0.3%, -5.6%), and 53.9% (95% CI: 0.2%, -107.9%)] decreased risk of COVID-19 infection rate during the full-lockdown period. Whereas, COVID-19 infection and MT (mean temperature) are modulated by a peak during full-lockdown, which is mostly attributed to contact transmission in Dhaka city. RF model revealed among the parameters being studied, MT, RH (relative humidity), and O3 were the dominant factors that could be associated with COVID-19 cases during the study period. The outcomes reported here could elucidate the effectiveness of lockdown scenarios for COVID-19 containment along with the air pollution control in Dhaka city.
The Severe Acute Respiratory Syndrome Coronavirus 2 is a virus causing the COVID-19 pandemic around the world. The World Health Organization (WHO) raised it to the highest level of global alert. The English, Chinese, and Japanese language Twitter data related to this disease during the first period after the WHO started releasing the situation reports were collected and compared with the tweet trends. This study also used quantitative text analysis to extract and analyze the co-occurrence network of English tweets. The findings show that trends and public concerns in social media are related to the breaking news and global trends such as the confirmed cases, the reported death tolls, the quarantined cruise news, the informer, etc.
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Name:
Akira Kodaka
Affiliation:
Project Assistant Professor, Graduate School of
System Design and Management, Keio University
Address:
Collaboration Complex, 4-1-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa
223-8526, Japan
Brief Career:
2010-Loei Fund for Nature Conservation and Sustainable Development
2014-Project Researcher, International Center for Urban Safety
Engineering, The University of Tokyo
2016-Project Assistant Professor, Graduate School of System Design and
Management, Keio University
Brief Career:
2018-2020 Google Earth Engine Ambassador, Google, Japan
2020-Researcher, Graduate School of System Design and Management,
Keio University
Selected Publications:
Numerical study of electric potential formation in a weakly ionized plasma flowing supersonically through open magnetic field lines
• "Numerical study of electric potential formation in a weakly ionized
plasma flowing supersonically through open magnetic field lines,"
Japanese J. of Applied Physics, Vol.56, No.1, 2016.
Name:
Kumpol Saengtabtim
Affiliation:
Ph.D. Student, Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn
University
Address:
254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand
Brief Career:
2019-Research Assistant, Disaster and Risk Management Information
Systems Research Unit, Chulalongkorn University
Selected Publications: