Abstract
The dataset deals with the air quality perceived by citizens before and during the enforcement of COVID-19 restrictions in ten countries around the world: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. An online survey conveniently translated into Chinese, English, Italian, Norwegian, Persian, Portuguese collected information regarding the perceived quality of air pollution according to a Likert scale. The questionnaire was distributed between 11-05-2020 and 31-05-2020 and 9 394 respondents took part. Both the survey and the dataset (stored in a Microsoft Excel Worksheet) are available in a public repository. The collected data offer the people's subjective perspectives related to the objective improvement in air quality occurred during the COVID-19 restrictions. Furthermore, the dataset can be used for research studies involving the reduction in air pollution as experienced, to a different extent, by populations of all the ten countries.
Keywords: Survey data, COVID-19, Environmental pollution, Air quality, Psychometric perception
Specification table
| Subject | Social Sciences |
|---|---|
| Specific subject area | Health psychology, Perceived air pollution |
| Type of data | Primary data, Table |
| How data were acquired | The data were collected by an online survey hosted on two platforms: Google Forms (English, Italian, Norwegian, Persian, Portuguese versions) and WenJuanXing (Chinese version). An English copy is available in the data repository. The survey was distributed by means of professional and social networks |
| Data format | Raw Analyzed |
| Parameters for data collection | The survey data were obtained from 9 394 respondents older than 18 years old having internet access |
| Description of data collection | The online survey was distributed using a combination of purposive and snowball techniques |
| Data source location | Countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States |
| Data accessibility | Dataset is uploaded on Mendeley Data Repository name: Perceived air pollution in Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa, USA before and during COVID-19 restrictions Data identification number: DOI: 10.17632/fb38h4tyzn.2 Direct URL to data: https://data.mendeley.com/datasets/fb38h4tyzn/2 |
Value of the data
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•
The data are related to the perception of air quality and air pollution during the COVID-19 restrictions as experienced by a large pool comprising 9 394 respondents located in ten countries on six continents
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The data can be useful for researchers dealing with the environmental and tropospheric changes occurring during the COVID-19 restrictions
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The data can be used to assess the relationship between the perceived and the quantified change in air quality and air pollution during the COVID-19 restrictions
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The data can be of interest to both citizens and policymakers to realise the tremendous lesson learned during COVID-19, being air quality a key indicator for sustainable development
1. Data description
The dataset provides information regarding the quantity of air pollution perceived before and during the restrictions enforced in ten countries around the world as a consequence of the COVID-19 pandemic: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States (also referred to as AU, BR, CH, GH, IN, IR, IT, NO, ZA and USA, respectively). The dataset is stored in a public repository as Microsoft Excel Worksheet [1]. The total amount of the respondents who joined the survey is 9 394, their geographical distribution is reported in Table 1. Information regarding gender and age are reported in Fig. 1 with box-and-whisker plots: overall, the largest portion of the surveyed population is composed of young and middle-aged individuals. Furthermore, the participants have high education (Fig. 2). The two questions of the survey are “How do you regard the amount of air pollution before the epidemic?” and “How do you regard the amount of air pollution during the restrictions?”: the respondents expressed their opinions according to a 7-point Likert scale varying from “extremely low/absent air pollution” to “extremely high air pollution”. The responses pertaining to before and during the applications of the COVID-19 restrictions are reported in Fig. 3a and Fig. 3b, respectively.
Table 1.
Geographical distribution of survey respondents.
| AUSTRALIA - AU (N = 387) | |||
| Victoria | New South Wales | Queensland | South Australia |
| 40.6 % | 29.2 % | 16.3 % | 11.9 % |
| Western Australia | Tasmania | Northern Territory | Australian Capital Territory |
| 0.8 % | 0.5 % | 0.5 % | 0.3 % |
| BRAZIL - BR (N = 930) | |||
| Minas Gerais | São Paulo | Rio de Janeiro | Bahia |
| 60.0 % | 21.6 % | 3.7 % | 2.4 % |
| Distrito Federal | Santa Catarina | Paraná | Espírito Santo |
| 2.3 % | 1.7 % | 1.3 % | 1.1 % |
| Goiás | Mato Grosso | Rio Grande do Sul | Pernambuco |
| 1.0 % | 1.0 % | 0.9 % | 0.5 % |
| Rio Grande do Norte | Alagoas | Pará | Amazonas |
| 0.5 % | 0.4 % | 0.4 % | 0.3 % |
| Mato Grosso do Sul | Paraíba | Tocantins | Ceará |
| 0.3 % | 0.2 % | 0.2 % | 0.1 % |
| Piauí | other | ||
| 0.1 % | 0.0 % | ||
| CHINA - CH (N = 1731) | |||
| Guangdong | Shaanxi | Jiangsu | Hunan |
| 14.9 % | 13.1 % | 11.9 % | 6.9 % |
| Anhui | Gansu | Hebei | Hubei |
| 4.9 % | 4.7 % | 4.2 % | 3.8 % |
| Shandong | Beijing | Shanxi | Heilongjiang |
| 3.6 % | 3.5 % | 3.0 % | 2.7 % |
| Sichuan | Henan | Inner Mongolia | Fujian |
| 2.0 % | 1.8 % | 1.8 % | 1.7 % |
| Jiangxi | Guangxi | Tianjin | Hainan |
| 1.6 % | 1.3 % | 1.2 % | 1.1 % |
| Jilin | Chongqing | Liaoning | Guizhou |
| 1.1 % | 1.0 % | 1.0 % | 1.0 % |
| Shanghai | Xinjiang | Ningxia | Zhejiang |
| 1.0 % | 0.9 % | 0.9 % | 0.8 % |
| Qinghai | Yunnan | Taiwan | Tibet |
| 0.6 % | 0.5 % | 0.5 % | 0.5 % |
| Macau | Hong Kong | ||
| 0.4 % | 0.3 % | ||
| GHANA - GH (N = 437) | |||
| Greater Accra | Ashanti | Northern | Eastern |
| 29.7 % | 27.0 % | 10.3 % | 8.5 % |
| Central | Western Region | Volta Region | Bono Region |
| 6.4 % | 5.0 % | 3.4 % | 2.1 % |
| Upper East | Bono East Region | Upper West | Ahafo Region |
| 2.1 % | 1.6 % | 1.6 % | 1.1% |
| Oti | Savannah | North East | Western North |
| 0.5 % | 0.2 % | 0.2% | 0.2% |
| INDIA - IN (N = 1334) | |||
| West Bengal | Maharashtra | NCR Delhi | Rajasthan |
| 15.0 % | 13.2 % | 9.2 % | 7.4 % |
| Uttar Pradesh | Tamil Nadu | Karnataka | Bihar |
| 6.8 % | 6.7 % | 6.7 % | 6.6 % |
| Madhya Pradesh | Haryana | Uttarakhand | Gujarat |
| 4.9 % | 3.9 % | 3.7 % | 2.8 % |
| Assam | Telangana | Punjab | Jammu & Kashmir |
| 2.0 % | 1.7 % | 1.6 % | 1.3 % |
| Andhra Pradesh | Odisha | Himachal Pradesh | Kerala |
| 1.2 % | 0.9 % | 0.8 % | 0.8 % |
| Goa | Jharkhand | Chhattisgarh | Meghalaya |
| 0.7 % | 0.7 % | 0.4 % | 0.3 % |
| Chandigarh | Ladakh | Puducherry | Tripura |
| 0.1 % | 0.1 % | 0.1 % | 0.1 % |
| other | |||
| 0.0 % | |||
| IRAN - IR (N = 778) | |||
| Kerman | Tehran | Fars | Razavi Khorasan |
| 48.7 % | 28.5 % | 5.1 % | 5.0 % |
| Isfahan | Yazd | Mazandaran | East Azarbaijan |
| 3.3 % | 1.5 % | 1.4 % | 1.2 % |
| Alborz | Hormozgan | Hamedan | West Azerbaijan |
| 0.8 % | 0.6% | 0.6 % | 0.5 % |
| Qazvin | Sistan Baluchestan | Kermanshah | Kohg. B.-Ahmad |
| 0.5 % | 0.4 % | 0.4 % | 0.3% |
| Golestan | Ilam | Bushehr | North Khorasan |
| 0.3 % | 0.1 % | 0.1 % | 0.1 % |
| South Khorasan | Zanjan | Semnan | other |
| 0.1 % | 0.1 % | 0.1 % | 0.0 % |
| ITALY - IT (N = 604) | |||
| Emilia-Romagna | Lombardiao | Lazio | Veneto |
| 32.5 % | 17.7 % | 12.1 % | 9.8 % |
| Piemonte | Toscana | Campania | Puglia |
| 8.8 % | 3.6 % | 2.5 % | 2.3 % |
| Friuli-Venezia Giulia | Sicilia | Marche | Calabria |
| 2.2 % | 1.7 % | 1.3 % | 1.2 % |
| Liguria | Sardegna | Trentino-Alto Adige | Abruzzo |
| 1.0 % | 0.8 % | 0.8 % | 0.5 % |
| Molise | Umbria | Valle d'Aosta | other |
| 0.5 % | 0.5% | 0.3% | 0.0 % |
| NORWAY - NO (N = 681) | |||
| Trøndelag | Rogaland | Oslo | Viken |
| 54.2 % | 13.4 % | 9.0% | 5.9 % |
| Agder | Innlandet | Møre og Romsdal | Vestland |
| 5.4 % | 5.0 % | 2.8 % | 1.9% |
| Troms og Finnmark | Vestfold og Telemark | other | |
| 1.6 % | 0.9 % | 0.0 % | |
| SOUTH AFRICA - ZA (N = 582) | |||
| KwaZulu-Natal | Gauteng | Western Cape | Eastern Cape |
| 61.7 % | 16.0% | 10.5% | 6.4 % |
| North West | Mpumalanga | Free State | Limpopo |
| 2.4 % | 1.2 % | 1.0% | 0.9 % |
| other | |||
| 0.0 % | |||
| UNITED STATES - USA (N = 1928) | |||
| Connecticut | Ohio | Texas | California |
| 13.9 % | 13.6 % | 12.7 % | 11.3 % |
| Idaho | Florida | Virginia | Washington |
| 6.9 % | 6.8 % | 6.7 % | 5.9 % |
| North Carolina | Illinois | Arizona | New York |
| 2.7 % | 2.1 % | 1.3 % | 1.3 % |
| Colorado | Oregon | Pennsylvania | Michigan |
| 1.2 % | 1.2 % | 1.1 % | 1.0 % |
| Massachusetts | New Jersey | Wisconsin | Georgia |
| 1.0 % | 1.0 % | 0.6 % | 0.6 % |
| Maryland | Vermont | Indiana | Iowa |
| 0.5 % | 0.5 % | 0.4 % | 0.4 % |
| Nevada | South Carolina | Minnesota | Missouri |
| 0.4 % | 0.4 % | 0.4 % | 0.4 % |
| Tennessee | Kentucky | Washington D.C. Columbia | Alaska |
| 0.4 % | 0.3 % | 0.3 % | 0.3 % |
| West Virginia | Alabama | Arkansas | Kansas |
| 0.3 % | 0.2 % | 0.2 % | 0.2 % |
| Louisiana | New Hampshire | Montana | North Dakota |
| 0.2 % | 0.2 % | 0.2 % | 0.1 % |
| Maine | Rhode Island | Wyoming | Hawaii |
| 0.1 % | 0.1 % | 0.1 % | 0.1 % |
| Nebraska | New Mexico | Oklahoma | South Dakota |
| 0.1 % | 0.1 % | 0.1 % | 0.1 % |
| Utah | Guam | US Virgin Islands | other |
| 0.1 % | 0.1 % | 0.1 % | 0.0 % |
Fig. 1.
Age and gender of the respondents for each country.
Fig. 2.
Education of the respondents for each country.
Fig. 3.
Perceived amount of air pollution before (a) and during (b) the COVID-19 restrictions as experienced by the survey respondents in each country.
2. Experimental design, materials, and methods
The online survey has assessed the air quality as subjectively perceived by citizens in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. The online questionnaire was hosted on two platforms: Google Forms (English, Italian, Norwegian, Persian, Portuguese versions) and WenJuanXing (Chinese version) and promoted on professional and social networks. The survey content was the same for each language; only the question regarding the respondents’ geographical location was tailored for each country. A Likert scale was employed to collect information about subjective perceptions [2] regarding both the situation before and during the enforcement of the restrictions due to the COVID-19 pandemic [3,4]. The online survey was distributed using a combination of purposive and snowball techniques between 11-05-2020 and 31-05-2020. Previously, other opinion surveys at regional and national scale also dealt with the perception of air quality [5], [6], [7] and examined the psychological impacts on people's subjective emotional state [8]. The created dataset can allow to explore how air quality was experienced by the populations dealing with different levels of air pollution before the COVID-19 outbreak [9], [10], [11].
Ethics statement
All the survey respondents informed their consent before joining the survey consistent with the Declaration of Helsinki.
Credit Author Statement
Diego Maria Barbieri
Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - Original Draft, Visualization, Project administration
Baowen Lou
Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - Original Draft, Visualization
Marco Passavanti
Conceptualization, Methodology, Investigation, Writing - Original Draft, Visualization
Cang Hui
Investigation, Data curation, Writing - Review & Editing, Visualization, Supervision
Daniela Antunes Lessa
Investigation, Data curation
Brij Maharaj
Investigation, Data curation
Arunabha Banerjee
Investigation, Data curation
Fusong Wang
Investigation, Data curation
Kevin Chang
Investigation, Data curation
Bhaven Naik
Investigation, Data curation
Lei Yu
Investigation, Data curation
Zhuangzhuang Liu
Investigation, Data curation
Gaurav Sikka
Investigation, Data curation
Andrew Tucker
Investigation, Data curation
Ali Foroutan Mirhosseini
Investigation, Data curation
Sahra Naseri
Investigation, Data curation
Yaning Qiao
Investigation, Data curation
Akshay Gupta
Investigation, Data curation
Montasir Abbas
Investigation, Data curation
Kevin Fang
Investigation, Data curation
Navid Ghasemi
Investigation, Data curation
Prince Peprah
Investigation, Data curation
Shubham Goswami
Investigation, Data curation
Amir Hessami
Investigation, Data curation
Nithin Agarwal
Investigation, Data curation
Louisa Lam
Investigation, Data curation
Solomon Adomako
Investigation, Data curation
Declaration of competing interest
This research has not received any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Acknowledgments
The precious support kindly provided by the following academics, researchers and professionals has been greatly appreciated: Mr. Fabio Selva (Heilongjiang International University, China), Mr. Marius Tangerås (Norwegian National Railway Administration Bane NOR, Norway), Dr. Azadeh Lak (Shahid Beheshti University, Iran), Dr. Barbara Stolte Bezerra (Universidade Estadual Paulista, Brazil), Dr. Xiaolong Sun (Guangdong University of Technology, China), Dr. Kasun Wijayaratna (University of Technology Sydney, Australia), Dr. Abdul Rahaman (Bharathidasan University, India), Dr. Dok Yen David Mbabil (Tamale Technical University, Ghana), Mr. Smit Bharat Thakkar (Queensland University of Technology, Australia), Mr. Solomon Kwadwo Achinah (University of Cape Coast, Ghana), Dr. Olaf Weyl (South African Institute for Aquatic Biodiversity, South Africa), Mr. Ayush Dhankute (Atkins Ltd., India), Mr. Mohammadreza Zare Reisabadi (University of Adelaide, Australia), Dr. Sachin Gunthe (Indian Institute of Technology Madras, India), Dr. Issam Qamhia (University of Illinois at Urbana-Champaign, United States), Dr. Parama Bannerji (West Bengal College, India), Mr. Amirhosein Mousavi (University of Southern California, United States), Mr. Anshu Bamney (Rewa Engineering College, India), Dr. Yuefeng Zhu (Shijiazhuang Tiedao University, China), Dr. Jorge Ubirajara Pedreira Junior (Federal University of Bahia, Brazil), Dr. Andrea Colagrossi (Politenico di Milano, Italy) and Dr. Akhilesh Kumar Maurya (Indian Institute of Technology Guwahati, India).
Footnotes
Declarations of interest: none
Initial submission date: 14/07/2020
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.dib.2020.106169.
Contributor Information
Diego Maria Barbieri, Email: diego.barbieri@ntnu.no.
Baowen Lou, Email: loubaowen@chd.edu.cn.
Marco Passavanti, Email: m.passavanti@campus.unimib.it.
Cang Hui, Email: chui@sun.ac.za.
Daniela Antunes Lessa, Email: daniela.lessa@ufop.edu.br.
Brij Maharaj, Email: maharajb@ukzn.ac.za.
Fusong Wang, Email: wangfs@whut.edu.cn.
Kevin Chang, Email: kchang@uidaho.edu.
Bhaven Naik, Email: naik@ohio.edu.
Lei Yu, Email: yulei26@mail2.sysu.edu.cn.
Zhuangzhuang Liu, Email: zzliu@chd.edu.cn.
Andrew Tucker, Email: andrew.tucker@uconn.edu.
Ali Foroutan Mirhosseini, Email: ali.mirhosseini@ntnu.no.
Yaning Qiao, Email: yaning.qiao@cumt.edu.cn.
Akshay Gupta, Email: akshay_g@ce.iitr.ac.in.
Montasir Abbas, Email: abbas@vt.edu.
Kevin Fang, Email: fangk@sonoma.edu.
Navid Ghasemi, Email: navid.ghasemi3@unibo.it.
Shubham Goswami, Email: gshubham@iisc.ac.in.
Amir Hessami, Email: hessami_amir@tamu.edu.
Nithin Agarwal, Email: nithin.agarwal@ufl.edu.
Louisa Lam, Email: l.lam@federation.edu.au.
Solomon Adomako, Email: solomon.adomako@uia.no.
Appendix. Supplementary materials
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