1
|
Ongoma V, Epule TE, Brouziyne Y, Tanarhte M, Chehbouni A. COVID-19 response in Africa: impacts and lessons for environmental management and climate change adaptation. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2023; 26:1-23. [PMID: 36714211 PMCID: PMC9873540 DOI: 10.1007/s10668-023-02956-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 01/14/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 pandemic adds pressure on Africa; the most vulnerable continent to climate change impacts, threatening the realization of most Sustainable Development Goals (SDGs). The continent is witnessing an increase in intensity and frequency of extreme weather events, and environmental change. The COVID-19 was managed relatively well across in the continent, providing lessons and impetus for environmental management and addressing climate change. This work examines the possible impact of the COVID-19 pandemic on the environment and climate change, analyses its management and draws lessons from it for climate change response in Africa. The data, findings and lessons are drawn from peer reviewed articles and credible grey literature on COVID-19 in Africa. The COVID-19 pandemic spread quickly, causing loss of lives and stagnation of the global economy, overshadowing the current climate crisis. The pandemic was managed through swift response by the top political leadership, research and innovations across Africa providing possible solutions to COVID-19 challenges, and redirection of funds to manage the pandemic. The well-coordinated COVID-19 containment strategy under the African Centers for Disease Control and Prevention increased sharing of resources including data was a success in limiting the spread of the virus. These strategies, among others, proved effective in limiting the spread and impact of COVID-19. The findings provide lessons that stakeholders and policy-makers can leverage in the management of the environment and address climate change. These approaches require solid commitment and practical-oriented leadership. Supplementary Information The online version contains supplementary material available at 10.1007/s10668-023-02956-0.
Collapse
Affiliation(s)
- Victor Ongoma
- International Water Research Institute, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150 Ben Guerir, Morocco
| | - Terence Epule Epule
- International Water Research Institute, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150 Ben Guerir, Morocco
| | - Youssef Brouziyne
- International Water Management Institute, Level 3, 7 Abd El-Hady Saleh St., Off Nile Street, Giza, Egypt
| | - Meryem Tanarhte
- Faculty of Sciences and Techniques of Mohammedia, Laboratory of Process Engineering and Environment, Hassan II University of Casablanca, 20650 Mohammedia, Morocco
| | - Abdelghani Chehbouni
- International Water Research Institute, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150 Ben Guerir, Morocco
- Center for Remote Sensing and Applications, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150 Ben Guerir, Morocco
| |
Collapse
|
2
|
Gao M, Yang H, Xiao Q, Goh M. COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts. SOCIO-ECONOMIC PLANNING SCIENCES 2022; 83:101228. [PMID: 35034989 PMCID: PMC8750743 DOI: 10.1016/j.seps.2022.101228] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 12/09/2021] [Accepted: 01/07/2022] [Indexed: 05/17/2023]
Abstract
This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57-18.67%) and a spillover effect (7.07-27.60%).
Collapse
Affiliation(s)
- Mingyun Gao
- School of Business Administration, Hunan University, Changsha, Hunan, 410082, PR China
- NUS Business School and The Logistics Institute-Asia Pacific, National University of Singapore, S(117592), Singapore
| | - Honglin Yang
- School of Business Administration, Hunan University, Changsha, Hunan, 410082, PR China
| | - Qinzi Xiao
- School of Business Administration, Hunan University, Changsha, Hunan, 410082, PR China
- Asper School of Business, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Mark Goh
- NUS Business School and The Logistics Institute-Asia Pacific, National University of Singapore, S(117592), Singapore
| |
Collapse
|
3
|
Spatiotemporal changes in tropospheric nitrogen dioxide hotspot due to emission switch-off condition in the view of lockdown emergency in India. AIR QUALITY, ATMOSPHERE & HEALTH 2022; 15:2123-2135. [PMID: 36061512 PMCID: PMC9424067 DOI: 10.1007/s11869-022-01240-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/22/2022] [Indexed: 10/27/2022]
|
4
|
Ye F, Rupakheti D, Huang L, T N, Kumar Mk S, Li L, Kt V, Hu J. Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 307:119468. [PMID: 35588959 PMCID: PMC9109815 DOI: 10.1016/j.envpol.2022.119468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/25/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
The Community Multi-Scale Air Quality (CMAQ) model was applied to evaluate the air quality in the coastal city of Kannur, India, during the 2020 COVID-19 lockdown. From the Pre1 (March 1-24, 2020) period to the Lock (March 25-April 19, 2020) and Tri (April 20-May 9, 2020) periods, the Kerala state government gradually imposed a strict lockdown policy. Both the simulations and observations showed a decline in the PM2.5 concentrations and an enhancement in the O3 concentrations during the Lock and Tri periods compared with that in the Pre1 period. Integrated process rate (IPR) analysis was employed to isolate the contributions of the individual atmospheric processes. The results revealed that the vertical transport from the upper layers dominated the surface O3 formation, comprising 89.4%, 83.1%, and 88.9% of the O3 sources during the Pre1, Lock, and Tri periods, respectively. Photochemistry contributed negatively to the O3 concentrations at the surface layer. Compared with the Pre1 period, the O3 enhancement during the Lock period was primarily attributable to the lower negative contribution of photochemistry and the lower O3 removal rate by horizontal transport. During the Tri period, a slower consumption of O3 by gas-phase chemistry and a stronger vertical import from the upper layers to the surface accounted for the increase in O3. Emission and aerosol processes constituted the major positive contributions to the net surface PM2.5, accounting for a total of 48.7%, 38.4%, and 42.5% of PM2.5 sources during the Pre1, Lock, and Tri periods, respectively. The decreases in the PM2.5 concentrations during the Lock and Tri periods were primarily explained by the weaker PM2.5 production from emission and aerosol processes. The increased vertical transport rate of PM2.5 from the surface layer to the upper layers was also a reason for the decrease in the PM2.5 during the Lock periods.
Collapse
Affiliation(s)
- Fei Ye
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Dipesh Rupakheti
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Lin Huang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Nishanth T
- Department of Physics, Sree Krishna College Guruvayur, Kerala, 680102, India
| | - Satheesh Kumar Mk
- Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Karnataka, 576104, India
| | - Lin Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Valsaraj Kt
- Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
| |
Collapse
|
5
|
Spatio-Temporal Heterogeneous Impacts of the Drivers of NO2 Pollution in Chinese Cities: Based on Satellite Observation Data. REMOTE SENSING 2022. [DOI: 10.3390/rs14143487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Rapid urbanization in China has led to an increasing problem of atmospheric nitrogen dioxide (NO2) pollution, which negatively impacts urban ecology and public health. Nitrogen dioxide is an important atmospheric pollutant, and quantitative spatio-temporal analysis and influencing factor analysis of Chinese cities can help improve urban air pollution. In this study, the spatio-temporal analysis methods were used to explore the variations of NO2 pollution in Chinese cities from 2005 to 2020. The findings are as follows. In more than half of Chinese cities, NO2 levels remarkably decreased between 2005 and 2020. The effective NO2 reduction strategies contributed to the significant NO2 reduction during the 13th Five-Year Plan (2016–2020). Moreover, we found that the pandemic of COVID-19 alleviated NO2 pollution in China since it reduced the traffic, industrial, and living activities. The NO2 pollution in Chinese cities was found highly spatially clustered. The geographically and temporally weighted regression model was used to analyze the spatio-temporal heterogeneity of NO2 pollution influencing factors in Chinese cities, including natural meteorological and socio-economic factors. The results showed that the GDPPC, population densities, and ambient air pressure were positively correlated with NO2 pollution. In contrast, the ratio of the tertiary to the secondary industry, temperature, wind speed, and relative humidity negatively impacted the NO2 pollution level. The findings of this research contribute to the improvement of urban air quality, stimulating the achievements of the sustainable development goals of Chinese cities.
Collapse
|
6
|
Boluwade A, M. A, Ruheili A. Modeling the contribution of Nitrogen Dioxide, Vertical pressure velocity and PM2.5 to COVID-19 fatalities. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2022; 36:3487-3498. [PMID: 35573160 PMCID: PMC9078208 DOI: 10.1007/s00477-022-02205-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 01/11/2022] [Accepted: 03/03/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 caused by the severe acute respiratory syndrome coronavirus was reported in China in December 2019. The severity and lethality of this disease have been linked to poor air quality indicators such as tropospheric nitrogen dioxide (NO2) and dust surface mass concentration particulate matter (PM2.5) as possible contributors. The Arab League has 22 member countries and is home to almost 420 million people. The primary objective of this study is to assess the relationship between NO2, PM2.5 and vertical pressure velocity (hereafter: OMEGA) (extracted from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) database), socio-economic factors (the population and geographic area of each member country) and COVID-19 deaths using Bayesian model averaging. The total plausible models (25) were estimated. The results show that the posterior inclusion probability (PIP), which indicates the probability that a particular indicator is included in the best model, was 0.69, 0.94, 0.68, 0.47, and 0.61 for OMEGA, PM2.5, NO2, geographical area, and population, respectively, meaning that these variables are important contributors in predicting COVID-19 fatalities in the Arab League states. This study shows that atmospheric satellite measurements from MERRA-2 datasets are capable of being used to quantify trace gases in pandemic studies.
Collapse
Affiliation(s)
- Alaba Boluwade
- Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, Canada
| | - Amna M.
- Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, Canada
| | - Al Ruheili
- Department of Plant Science, College of Agriculture and Marine Science, Sultan Qaboos University, Muscat, Oman
| |
Collapse
|
7
|
Lou B, Barbieri DM, Passavanti M, Hui C, Gupta A, Hoff I, Lessa DA, Sikka G, Chang K, Fang K, Lam L, Maharaj B, Ghasemi N, Qiao Y, Adomako S, Foroutan Mirhosseini A, Naik B, Banerjee A, Wang F, Tucker A, Liu Z, Wijayaratna K, Naseri S, Yu L, Chen H, Shu B, Goswami S, Peprah P, Hessami A, Abbas M, Agarwal N. Air pollution perception in ten countries during the COVID-19 pandemic. AMBIO 2022; 51:531-545. [PMID: 34155609 PMCID: PMC8216327 DOI: 10.1007/s13280-021-01574-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/17/2021] [Accepted: 05/09/2021] [Indexed: 05/28/2023]
Abstract
As largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.
Collapse
Affiliation(s)
- Baowen Lou
- School of Highway, Chang’an University, Nan Er Huan Road (Mid-section), Xi’an, 710064 Shaanxi China
- Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Høgskoleringen 7A, 7491 Trondheim, Trøndelag Norway
| | - Diego Maria Barbieri
- Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Høgskoleringen 7A, 7491 Trondheim, Trøndelag Norway
| | - Marco Passavanti
- Italian Society of Cognitive Behavioural Therapy (CBT-Italy), Mannelli St. 139, 50132 Firenze, Toscana Italy
| | - Cang Hui
- Centre for Invasion Biology, Department of Mathematical Sciences, Stellenbosch University, Matieland, 7602 South Africa
- Biodiversity Informatics Unit, African Institute for Mathematical Sciences, Cape Town, 7945 South Africa
| | - Akshay Gupta
- Department of Civil Engineering, Transportation Engineering Group, Indian Institute of Technology Roorkee, 321-A&B, Roorkee, Uttarakhand 247667 India
| | - Inge Hoff
- Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Høgskoleringen 7A, 7491 Trondheim, Trøndelag Norway
| | - Daniela Antunes Lessa
- Department of Civil Engineering, Federal University of Ouro Preto, Rua Nove, Bauxita, Ouro Preto, Minas Gerais 35400-000 Brazil
| | - Gaurav Sikka
- Department of Geography, Lalit Narayan Mithila University, Darbhanga, Bihar 846004 India
| | - Kevin Chang
- Department of Civil and Environmental Engineering, University of Idaho, 875 Perimeter Drive, Mailstop 1022, Moscow, ID 83844 USA
| | - Kevin Fang
- Department of Geography, Sonoma State University, Environment, and Planning, 1801 East Cotati Avenue, Rohnert Park, CA 94928 USA
| | - Louisa Lam
- School of Health, Federation University Australia, 72-100 Clyde Rd, Berwick, VIC 3806 Australia
| | - Brij Maharaj
- Department of Geography, University of KwaZulu-Natal, Howard College City, Durban, 4000 KwaZulu South Africa
| | - Navid Ghasemi
- Department of Civil Chemical Environmental and Materials Engineering, University of Bologna, Viale del Risorgimento, 2, 40136 Bologna, Emilia-Romagna Italy
| | - Yaning Qiao
- School of Mechanics and Civil Engineering, China University of Mining and Technology, Daxue Road 1, Xuzhou, 22116 Jiangsu China
| | - Solomon Adomako
- Department of Engineering and Science, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Agder Norway
| | - Ali Foroutan Mirhosseini
- Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Høgskoleringen 7A, 7491 Trondheim, Trøndelag Norway
| | - Bhaven Naik
- Department of Civil Engineering/Russ College of Engineering & Technology, Ohio University, 28 W. Green Drive, Athens, OH 45701 USA
| | - Arunabha Banerjee
- Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam 781039 India
| | - Fusong Wang
- State Key Laboratory of Silicate Materials for Architectures, Wuhan University of Technology, Luoshi road 122, Wuhan, 430070 Hubei China
| | - Andrew Tucker
- Connecticut Transportation Safety Research Center, University of Connecticut, 270 Middle Turnpike, Unit 5202 Longley Building, Storrs, CT 06269 USA
| | - Zhuangzhuang Liu
- School of Highway, Chang’an University, Nan Er Huan Road (Mid-section), Xi’an, 710064 Shaanxi China
| | - Kasun Wijayaratna
- School of Civil and Environmental Engineering, University of Technology Sydney, 81, Broadway, Ultimo, NSW 2007 Australia
| | - Sahra Naseri
- School of Medicine, Bam University of Medical Sciences, Bam, 76615-336 Kerman, Iran
| | - Lei Yu
- School of Civil Engineering, Sun Yat-Sen University, Xingang Xi Road 135, Guangzhou, 510275 Guangdong China
| | - Hao Chen
- Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Høgskoleringen 7A, 7491 Trondheim, Trøndelag Norway
| | - Benan Shu
- Foshan Transportation Science and Technology Co. Ltd., Kuiqi Second Road 18, Foshan, 528000 Guangdong China
| | - Shubham Goswami
- Department of Civil Engineering, Indian Institute of Science Bangalore, C V Raman Avenue, Bangalore, Karnataka 560012 India
| | - Prince Peprah
- Department of Social Policy Research Centre, University of New South Wales, John Goodsell Building, Kensington, Sydney, NSW 2052 Australia
| | - Amir Hessami
- Department of Civil and Architectural Engineering, Texas A&M University – , Kingsville, 917 W. Ave B, Kingsville, TX 78363 USA
| | - Montasir Abbas
- Department of Civil and Environmental Engineering, Virginia Tech, 301-D3 Patton Hall, Blacksburg, VA 24061 USA
| | - Nithin Agarwal
- Department of Civil & Coastal Engineering, University of Florida, 2100 NE Waldo Rd., Sta 106, Gainesville, FL 32609 USA
| |
Collapse
|
8
|
Li Y, Li M, Rice M, Yang C. Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective. ENVIRONMENT INTERNATIONAL 2022; 158:106887. [PMID: 34563750 PMCID: PMC8452510 DOI: 10.1016/j.envint.2021.106887] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/13/2021] [Accepted: 09/17/2021] [Indexed: 06/13/2023]
Abstract
The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO2, coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO2 emissions, and fossil fuel consumption have larger NO2 TVCD reductions regarding workplace closures and public transport closures.
Collapse
Affiliation(s)
- Yun Li
- Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USA; NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA
| | - Moming Li
- Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA, USA
| | - Megan Rice
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Chaowei Yang
- Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USA; NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA.
| |
Collapse
|
9
|
Cooper MJ, Martin RV, Hammer MS, Levelt PF, Veefkind P, Lamsal LN, Krotkov NA, Brook JR, McLinden CA. Global fine-scale changes in ambient NO 2 during COVID-19 lockdowns. Nature 2022; 601:380-387. [PMID: 35046607 PMCID: PMC8770130 DOI: 10.1038/s41586-021-04229-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 11/11/2021] [Indexed: 11/23/2022]
Abstract
Nitrogen dioxide (NO2) is an important contributor to air pollution and can adversely affect human health1-9. A decrease in NO2 concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-1910-20. Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO2 concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO2 decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO2 to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.
Collapse
Affiliation(s)
- Matthew J Cooper
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada.
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
| | - Randall V Martin
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
| | - Melanie S Hammer
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Pieternel F Levelt
- Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
- University of Technology Delft, Delft, Netherlands
- National Center for Atmospheric Research, Boulder, CO, USA
| | - Pepijn Veefkind
- Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
- Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands
| | - Lok N Lamsal
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Universities Space Research Association, Columbia, MD, USA
| | | | - Jeffrey R Brook
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | | |
Collapse
|
10
|
Liu J, Law AWK, Duru O. Assessment of COVID-19 pandemic effects on ship pollutant emissions in major international seaports. ENVIRONMENTAL RESEARCH 2021:112246. [PMID: 34699761 PMCID: PMC8539223 DOI: 10.1016/j.envres.2021.112246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/12/2021] [Accepted: 10/17/2021] [Indexed: 06/11/2023]
Abstract
This study aims to investigate the coronavirus disease (COVID-19) pandemic effects and associated restrictive rules on ship activities and pollutant emissions (CO2, SOX, NOX, PM, CO, CH4) in four major seaports, namely the Ports of Singapore, Long Beach, Los Angeles, and Hamburg. We used 2019 as the baseline year to show the business-as-usual emission and compared with the estimated quantity during the July 2020-July 2021 pandemic period. We also project future ship emissions from August 2021-August 2022 to illustrate two potential port congestion scenarios due to COVID-19. The results show that the ship emissions in all four ports generally increased by an average of 79% because of the prolonged turnaround time in port. Importantly, majority of ship emissions occurred during the extended hoteling time at berth and anchorage areas as longer operational times were needed due to pandemic-related delays, with increases ranging from 27 to 123% in the total emissions across ports. The most affected shipping segments were the container ships and dry bulk carriers which the total emissions of all pollutants increased by an average of 94-142% compared with 2019. Overall, the results of this study provide a comprehensive review of the ship emission outlook amid the pandemic uncertainty.
Collapse
Affiliation(s)
- Jiahui Liu
- School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
| | - Adrian Wing-Keung Law
- School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore.
| | - Okan Duru
- School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
| |
Collapse
|
11
|
Rahman MM, Paul KC, Hossain MA, Ali GGMN, Rahman MS, Thill JC. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:72420-72450. [PMID: 34786314 PMCID: PMC8545207 DOI: 10.1109/access.2021.3079121] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 05/07/2021] [Indexed: 05/19/2023]
Abstract
The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.
Collapse
Affiliation(s)
- Md. Mokhlesur Rahman
- The William States Lee College of EngineeringUniversity of North Carolina at CharlotteCharlotteNC28223USA
- Department of Urban and Regional PlanningKhulna University of Engineering and Technology (KUET)Khulna9203Bangladesh
| | - Kamal Chandra Paul
- Department of Electrical and Computer EngineeringThe William States Lee College of EngineeringUniversity of North Carolina at CharlotteCharlotteNC28223USA
| | - Md. Amjad Hossain
- Department of Computer Science, Mathematics and EngineeringShepherd UniversityShepherdstownWV25443USA
| | - G. G. Md. Nawaz Ali
- Department of Applied Computer ScienceUniversity of CharlestonCharlestonWV25304USA
| | - Md. Shahinoor Rahman
- Department of Earth and Environmental SciencesNew Jersey City UniversityJersey CityNJ07305USA
| | - Jean-Claude Thill
- Department of Geography and Earth SciencesSchool of Data ScienceUniversity of North Carolina at CharlotteCharlotteNC28223USA
| |
Collapse
|