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Roudreo B, Puangthongthub S. A decreased impact of air pollution on hospital pneumonia visits during COVID-19 outbreak in northeastern Thailand. J Thorac Dis 2024; 16:133-146. [PMID: 38410600 PMCID: PMC10894424 DOI: 10.21037/jtd-23-1051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/24/2023] [Indexed: 02/28/2024]
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic had effects on changes in people, society, and pollutant sources. This was a unique research opportunity to assess the effects on the risk of pneumonia resulted from the changes in air pollution and personal hygiene regarding city lockdown. Methods This study, we estimated time-series relative risks (RRs) of pneumonia (n=94,288) associated with PM10, PM2.5, NO2, and O3 in Khon Kaen province and its vicinity, using Poison regression with generalized additive model and compared air pollutant-associated risk of pneumonia before vs. during the COVID-19 outbreak [2018-2021]. Results During the COVID-19 period, pneumonia cases, PM2.5, PM10, and NO2 levels were lower than those before the COVID-19 but the O3 level was significantly higher. The single-pollutant analyses showed that the increase in PM10, PM2.5, and NO2 were significantly associated with pneumonia risks at single-day lag 0 in the earlier two years (2018-2019). For multi-pollutant analyses, there were higher RRs in PM2.5 at lag 0 [RR =1.078, 95% confidence interval (CI): 1.004 to 1.157], lag 4 (RR =1.054, 95% CI: 1.011 to 1.098) and lag 5 (RR =1.090, 95% CI: 1.021 to 1.165) and for all cumulative-day lags, greatest was at lag 0-5 (RR =1.314, 95% CI: 1.200 to 1.439) before the COVID-19 period while there were lower pneumonia RRs of a 10-µg/m3 increase in PM2.5 at single-day lag 1 (RR =1.064, 95% CI: 1.002 to 1.130) and for all cumulative-day lags, greatest was at lag 0-5 (RR =1.201, 95% CI: 1.073 to 1.344) during the COVID-19 outbreak. Multi-pollutant of NO2 significantly increased pneumonia risk in cumulative day exposure before the COVID-19 outbreak at lag 0-3 (RR =1.050, 95% CI: 1.001 to 1.100). It was significantly greater than that risk during the outbreak. Conclusions This study revealed that the lockdown measures to control COVID-19 were effective in improving air quality and lowering associated pneumonia risk. These findings would help raise awareness about measures and policies to preserve the air quality to increase respiratory health benefits.
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Affiliation(s)
- Benjawan Roudreo
- Industrial Toxicology and Risk Assessment Graduate Program, Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | - Sitthichok Puangthongthub
- Industrial Toxicology and Risk Assessment Graduate Program, Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
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Bagaria P, Mahapatra PS, Bherwani H, Pandey R. Environmental management: a country-level evaluation of atmospheric particulate matter removal by the forests of India. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1306. [PMID: 37828295 DOI: 10.1007/s10661-023-11928-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023]
Abstract
Particulate matter (PM) is a critical air pollutant, responsible for an array of ailments leading to premature mortality worldwide. Nature-based solutions for mitigation of PM and especially role of forests in mitigating PM from an ecosystem perspective are less explored. Forests provide a natural pollution abatement strategy by providing a surface area for the deposition of PM. Depending on their structure and composition, forests have varying capacities for PM adsorption, which is again less explored. Hence, in the present study, we evaluate the removal capacity of PM by the forest-type groups of India. Deposition flux and total PM removal across sixteen forest types were estimated based on the 2019 dataset of PM using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. Externality values and PM removal costs by industrial equipment were used for associating an economic value to the air pollution abatement service by forests. The total PM2.5 removal by forests in 2019 was estimated to be 1361.28 tons and PM10 was estimated to be 303,658.27 tons. Deposition of PM was found to be high in littoral and swamp forests, tropical semi-evergreen forests, tropical moist deciduous forests, and sub-tropical pine forests. Tropical dry deciduous forests had the highest net weight % removal of PM with 39% removal for PM2.5 and 39% removal for PM10. The air pollution abatement service by forests for PM removal was 188 M US dollars (USD) with externality-based removal service by forests of 2009 M USD. The net PM removed by all forests of India was estimated to be approximately worth ₹ 470-648 Crore (59-81 million dollars) for PM2.5 and worth ₹56,746-1,22,617 Crore (7093-15,327 million dollars) for PM10 based on valuation using value transfer method. The study concludes that forests can be a significant contributor to PM reduction at a global level. Especially for India's National Clean Air Programme and further research and policy considerations, the findings would be extremely useful.
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Affiliation(s)
| | | | | | - Rajiv Pandey
- Indian Council of Forestry Research and Education, Dehradun, India.
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Mirza S, Niwalkar A, Gupta A, Gautam S, Anshul A, Bherwani H, Biniwale R, Kumar R. Is safe distance enough to prevent COVID-19? Dispersion and tracking of aerosols in various artificial ventilation conditions using OpenFOAM. GONDWANA RESEARCH : INTERNATIONAL GEOSCIENCE JOURNAL 2023; 114:40-54. [PMID: 35431597 PMCID: PMC8990448 DOI: 10.1016/j.gr.2022.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/23/2022] [Accepted: 03/09/2022] [Indexed: 05/11/2023]
Abstract
The current COVID-19 pandemic has underlined the importance of learning more about aerosols and particles that migrate through the airways when a person sneezes, coughs and speaks. The coronavirus transmission is influenced by particle movement, which contributes to the emergence of regulations on social distance, use of masks and face shield, crowded assemblies, and daily social activity in domestic, public, and corporate areas. Understanding the transmission of aerosols under different micro-environmental conditions, closed, or ventilated, has become extremely important to regulate safe social distances. The present work attempts to simulate the airborne transmission of coronavirus-laden particles under different respiratory-related activities, i.e., coughing and speaking, using CFD modelling through OpenFOAM v8. The dispersion coupled with the Discrete Phase Method (DPM) has been simulated to develop a better understanding of virus carrier particles transmission processes and their path trailing under different ventilation scenarios. The preliminary results of this study with respect to flow fields were in close agreement with published literature, which was then extended under varied ventilation scenarios and respiratory-related activities. The study observed that improper wearing of mask leads to escape of SARS-CoV-2 containminated aerosols having a smaller aerodynamic diameter from the gap between face mask and face, infecting different surfaces in the vicinity. It was also observed that aerosol propagation infecting the area through coughing is a faster phenomenon compared to the propagation of coronavirus-laden particles during speaking. The study's findings will help decision-makers formulate common but differentiated guidelines for safe distancing under different micro-environmental conditions.
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Affiliation(s)
- Shahid Mirza
- CSIR-National Environmental Engineering Research Institute, Nagpur 440020, Maharashtra, India
| | - Amol Niwalkar
- CSIR-National Environmental Engineering Research Institute, Nagpur 440020, Maharashtra, India
| | - Ankit Gupta
- CSIR-National Environmental Engineering Research Institute, Nagpur 440020, Maharashtra, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Sneha Gautam
- Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore 641114, Tamil Nadu, India
| | - Avneesh Anshul
- CSIR-National Environmental Engineering Research Institute, Nagpur 440020, Maharashtra, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Hemant Bherwani
- CSIR-National Environmental Engineering Research Institute, Nagpur 440020, Maharashtra, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Rajesh Biniwale
- CSIR-National Environmental Engineering Research Institute, Nagpur 440020, Maharashtra, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Rakesh Kumar
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
- Council of Scientific and Industrial Research (CSIR), Anusandhan Bhawan, 2 Rafi Ahmed Kidwai Marg, New Delhi 110001, India
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Ameli M, Shams Esfandabadi Z, Sadeghi S, Ranjbari M, Zanetti MC. COVID-19 and Sustainable Development Goals (SDGs): Scenario analysis through fuzzy cognitive map modeling. GONDWANA RESEARCH : INTERNATIONAL GEOSCIENCE JOURNAL 2023; 114:138-155. [PMID: 35132304 PMCID: PMC8811702 DOI: 10.1016/j.gr.2021.12.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/24/2021] [Accepted: 12/24/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 crisis has immensely impacted the implementation of the 2030 Agenda for Sustainable Development worldwide. This research aims at providing a policy response to support achieving the Sustainable Development Goals (SDGs) taking the COVID-19 long-term implications into account. To do so, a qualitative analytical method was employed in the following four steps. First, a fuzzy cognitive map was developed to specify causal-effect links of the interdependent SDGs in Iran as a developing country in the Middle East. Second, potential effects of the pandemic on the SDGs achievement were analyzed. Third, five strategies were formulated, including green management, sustainable food systems, energizing the labor market, inclusive education, and supporting research and technology initiatives in the energy sector. And finally, different scenarios corresponding to the five proposed strategies were tested based on the identified interconnections among the SDGs. The analysis showed that applying each of the five considered strategies or their combination would mitigate the effect of COVID-19 on the SDGs only in case of a medium pandemic activation level. Moreover, implementing a single strategy with a high activation level leads to better outcomes on the SDGs rather than applying a combination of strategies in low or medium activation levels during the pandemic situation. The provided insights support stakeholders and policy-makers involved in the post-COVID-19 recovery action plan towards implementing the 2030 Agenda for Sustainable Development.
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Affiliation(s)
- Mariam Ameli
- Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
| | - Zahra Shams Esfandabadi
- Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Turin, Italy
- Energy Center Lab, Politecnico di Torino, Turin, Italy
| | - Somayeh Sadeghi
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Meisam Ranjbari
- Department of Economics and Statistics "Cognetti de Martiis", University of Turin, Turin, Italy
- ESSCA School of Management, Lyon, France
| | - Maria Chiara Zanetti
- Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Turin, Italy
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Wathore R, Rawlekar S, Anjum S, Gupta A, Bherwani H, Labhasetwar N, Kumar R. Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters. GONDWANA RESEARCH : INTERNATIONAL GEOSCIENCE JOURNAL 2023; 114:69-77. [PMID: 35431596 PMCID: PMC8990533 DOI: 10.1016/j.gr.2022.03.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 03/17/2022] [Accepted: 03/17/2022] [Indexed: 05/15/2023]
Abstract
The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.
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Affiliation(s)
- Roshan Wathore
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440020, Maharashtra, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Samyak Rawlekar
- Indian Institute of Technology (IIT) Dharwad, Dharwad 580 011, Karnataka, India
| | - Saima Anjum
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440020, Maharashtra, India
| | - Ankit Gupta
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440020, Maharashtra, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Hemant Bherwani
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440020, Maharashtra, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Nitin Labhasetwar
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440020, Maharashtra, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Rakesh Kumar
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
- Council of Scientific and Industrial Research (CSIR), Anusandhan Bhawan, 2 Rafi Ahmed Kidwai Marg, New Delhi 110001, India
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Kolluru SSR, Nagendra SMS, Patra AK, Gautam S, Alshetty VD, Kumar P. Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave? STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2023; 37:795-810. [PMID: 36164666 PMCID: PMC9493175 DOI: 10.1007/s00477-022-02308-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/30/2022] [Indexed: 05/05/2023]
Abstract
The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.
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Affiliation(s)
| | - S. M. Shiva Nagendra
- Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Aditya Kumar Patra
- Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Sneha Gautam
- Department of Civil Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu India
| | - V. Dheeraj Alshetty
- Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Prashant Kumar
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH Surrey UK
- Department of Civil, Structural & Environmental Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
- School of Architecture, Southeast University, 2 Sipailou, Nanjing, 210096 China
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Asif M, Mahajan P. Impact of COVID-19 lockdown and meteorology on the air quality of Srinagar city: A temperate climatic region in Kashmir Himalayas. HYGIENE AND ENVIRONMENTAL HEALTH ADVANCES 2022; 4:100025. [PMID: 37520075 PMCID: PMC9474402 DOI: 10.1016/j.heha.2022.100025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/03/2022] [Accepted: 09/08/2022] [Indexed: 06/17/2023]
Abstract
The deadly transmission of the coronavirus forced all countries to implement lockdowns to restrict the transmission of this highly infectious disease. As a result of these lockdowns and restrictions, many urban centers have seen a positive impact on air quality with a significant reduction in air pollution. Therefore, in this study, the impact of COVID-19 lockdown vis-a-vis meteorological parameters on the ambient air quality of Srinagar city was examined. In this regard, we have evaluated the temporal variation of six different key air pollutants (PM10, PM2.5, SO2, NO2, O3, and NH3) along with meteorological parameters (relative humidity, rainfall, temperature, wind speed, and wind direction). The duration of the study was divided into three periods: Before Lockdown(BLD), Lockdown (LD), and Partial Lockdown(PLD). Daily average data for all the parameters was accessed from one of the real-time continuous monitoring stations of the central pollution control board (CPCB) at Rajbagh Srinagar. Some air pollutants have decreased, according to the results, while others have increased. The air quality index (AQI) decreases overall by 6.15 percent compared to before lockdown, and it never exceeds the "moderate" category. The AQI was in the following order for both lockdown and pre-lockdown periods: satisfactory > moderate > good. However, for partial lockdown, it was moderate > satisfactory > good. It was observed that the maximum decrease was seen in the concentration of NO2, NH3 with 75.11% and 69.18%. A modest decrease was observed in PM10 at 3.8%. While SO2 and O3 had an upward trend of 85.82% and 48.74%, The NO2 to SO2 ratio reveals that the emissions of NO2 have substantially decreased due to the complete restriction of transport systems. From principal component analysis for all three study periods, PM10 and PM2.5 were combined into a single component, inferring their shared behavior and source of origin. SO2 and O3 demonstrated identical behavior during the lockdown and partial lockdown periods of study. According to the findings of the study, it is beneficial for the government, environmentalists, and policymakers to impose rigorous lockdown measures, particularly during extreme air pollution events, in order to reduce the damage caused by automotive and industrial emissions.
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Affiliation(s)
- Mohammad Asif
- Department of Botanical & Environmental Sciences, Guru Nanak Dev University, Amritsar, Punjab 143005, India
| | - Pranav Mahajan
- Punjab School of Economics Guru Nanak Dev University, Amritsar, Punjab 143005, India
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Niwalkar A, Indorkar T, Gupta A, Anshul A, Bherwani H, Biniwale R, Kumar R. Circular economy based approach for green energy transitions and climate change benefits. PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2022. [DOI: 10.1007/s43538-022-00137-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Kaewrat J, Janta R, Sichum S, Rattikansukha C, Tala W, Kanabkaew T. Human Health Risks and Air Quality Changes Following Restrictions for the Control of the COVID-19 Pandemic in Thailand. TOXICS 2022; 10:toxics10090520. [PMID: 36136484 PMCID: PMC9501010 DOI: 10.3390/toxics10090520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/23/2022] [Accepted: 08/29/2022] [Indexed: 05/16/2023]
Abstract
The coronavirus (COVID-19) pandemic first impacted Thailand in early 2020. The government imposed lockdown measures from April to May 2020 to control the spread of infection. Daily lifestyles then morphed into a so-called new normal in which activities were conducted at home and people avoided congregation in order to prevent the spread of an infectious disease. This study evaluated the long-term air quality improvement which resulted from the restrictions enforced on normal human activities in Thailand. The air quality index (AQI) of six criteria pollutants and health risk assessments were evaluated in four areas, including metropolitan, suburban, industrial, and tourism areas in Thailand. The results showed that, after the restriction measures, the overall AQI improved by 30%. The subindex of each pollutant (sub-AQI) of most pollutants significantly improved (by 30%) in metropolitan areas after human activities changed due to the implementation of lockdown measures. With regard to industrial and tourism areas, only the sub-AQI of traffic-related pollutants decreased (34%) while the sub-AQIs of other pollutants before and after lockdown were similar. However, the changes in human activities were not clearly related to air quality improvement in the suburban area. The overall hazard index (HI) after lockdown decreased by 23% because of the reduction of traffic-related pollutants. However, the HI value remained above the recommended limits for the health of the adult residents in all areas. Therefore, strict regulations to control other pollutant sources, such as industry and open burning, will also be necessary for air quality improvement in Thailand.
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Affiliation(s)
- Jenjira Kaewrat
- School of Languages and General Education, Walailak University, Nakhon Si Thammarat 80160, Thailand
- Center of Excellence in Sustainable Disaster Management, Walailak University, Nakhon Si Thammarat 80160, Thailand
| | - Rungruang Janta
- School of Languages and General Education, Walailak University, Nakhon Si Thammarat 80160, Thailand
- Center of Excellence in Sustainable Disaster Management, Walailak University, Nakhon Si Thammarat 80160, Thailand
- Correspondence: ; Tel.: +66-75-672-401
| | - Surasak Sichum
- Center of Excellence in Sustainable Disaster Management, Walailak University, Nakhon Si Thammarat 80160, Thailand
| | - Chuthamat Rattikansukha
- School of Languages and General Education, Walailak University, Nakhon Si Thammarat 80160, Thailand
- Center of Excellence in Sustainable Disaster Management, Walailak University, Nakhon Si Thammarat 80160, Thailand
| | - Wittaya Tala
- Environmental Science Research Center (ESRC), Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
- Environmental Chemistry Research Laboratory (ECRL), Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Thongchai Kanabkaew
- Faculty of Public Health, Thammasat University, Pathum Thani 10120, Thailand
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Bherwani H, Musugu K, Nair M, Gupta A, Kumar R. Valuation of environmental damages of Kasardi River: a case for benefits of timely action. PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2022. [DOI: 10.1007/s43538-022-00068-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Bherwani H, Balachandran D, Das A, Kumar R. Monetary quantification of COVID-19 impacts on sustainable development goals: Focus on air pollution and climate change. COVID-19 AND THE SUSTAINABLE DEVELOPMENT GOALS 2022. [PMCID: PMC9335064 DOI: 10.1016/b978-0-323-91307-2.00018-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
COVID-19 epidemic struck without warning, wreaking havoc on all aspects of society. The chapter discusses the quantification of impacts of COVID-19-induced control strategies and restricted resource consumption on sustainable development goals (SDGs), focusing on SDG-3 (Good Health and Wellbeing) and SDG-13 (Climate Action). The impacts of reduced PM2.5 emission are monitored using moderate resolution imaging spectroradiometer (MODIS) satellite data for India (2020–21) and the reduction in the morbidity and mortality is valued using the cost of illness (COI), disability-adjusted life years (DALY), and value of statistical life (VSL). The reported reduction of CO2e emissions of about 40% during the year, in the cities, is quantified for the country and monetized using the regional values of the social cost of carbon (SCC). The chapter also lays a framework for quantifying and valuing such impacts related to other SDGs and can be used by policymakers for implementation and quantified decision-making.
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Nair M, Bherwani H, Mirza S, Anjum S, Kumar R. Valuing burden of premature mortality attributable to air pollution in major million-plus non-attainment cities of India. Sci Rep 2021; 11:22771. [PMID: 34857768 PMCID: PMC8640062 DOI: 10.1038/s41598-021-02232-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 11/01/2021] [Indexed: 12/20/2022] Open
Abstract
Accelerating growth due to industrialization and urbanization has improved the Indian economy but simultaneously has deteriorated human health, environment, and ecosystem. In the present study, the associated health risk mortality (age > 25) and welfare loss for the year 2017 due to excess PM2.5 concentration in ambient air for 31 major million-plus non-attainment cities (NACs) in India is assessed. The cities for the assessment are prioritised based on population and are classified as ‘X’ (> 5 million population) and ‘Y’ (1–5 million population) class cities. Ground-level PM2.5 concentration retrieved from air quality monitoring stations for the NACs ranged from 33 to 194 µg/m3. Total PM2.5 attributable premature mortality cases estimated using global exposure mortality model was 80,447 [95% CI 70,094–89,581]. Ischemic health disease was the leading cause of death accounting for 47% of total mortality, followed by chronic obstructive pulmonary disease (COPD-17%), stroke (14.7%), lower respiratory infection (LRI-9.9%) and lung cancer (LC-1.9%). 9.3% of total mortality is due to other non-communicable diseases (NCD-others). 7.3–18.4% of total premature mortality for the NACs is attributed to excess PM2.5 exposure. The total economic loss of 90,185.6 [95% CI 88,016.4–92,411] million US$ (as of 2017) was assessed due to PM2.5 mortality using the value of statistical life approach. The highest mortality (economic burden) share of 61.3% (72.7%) and 30.1% (42.7%) was reported for ‘X’ class cities and North India zone respectively. Compared to the base year 2017, an improvement of 1.01% and 0.7% is observed in premature mortality and economic loss respectively for the year 2024 as a result of policy intervention through National Clean Air Action Programme. The improvement among 31 NACs was found inconsistent, which may be due to a uniform targeted policy, which neglects other socio-economic factors such as population, the standard of living, etc. The study highlights the need for these parameters to be incorporated in the action plans to bring in a tailored solution for each NACs for better applicability and improved results of the programme facilitating solutions for the complex problem of air pollution in India.
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Affiliation(s)
- Moorthy Nair
- Asian Development Research Institute (ADRI), Patna, BH, India
| | - Hemant Bherwani
- CSIR-National Environmental Engineering Research Institute (NEERI), Nagpur, MH, India. .,Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.
| | - Shahid Mirza
- CSIR-National Environmental Engineering Research Institute (NEERI), Nagpur, MH, India
| | - Saima Anjum
- CSIR-National Environmental Engineering Research Institute (NEERI), Nagpur, MH, India
| | - Rakesh Kumar
- CSIR-National Environmental Engineering Research Institute (NEERI), Nagpur, MH, India.,Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
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Anser MK, Yousaf SU, Hyder S, Nassani AA, Zaman K, Abro MMQ. Socio-economic and corporate factors and COVID-19 pandemic: a wake-up call. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:63215-63226. [PMID: 34227006 PMCID: PMC8256947 DOI: 10.1007/s11356-021-15275-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/29/2021] [Indexed: 05/05/2023]
Abstract
The novel coronavirus 2019 (COVID-19) emerges from the Chinese city Wuhan and its spread to the rest of the world, primarily affected economies and their businesses, leading to a global depression. The explanatory and cross-sectional regression approach assesses the impact of COVID-19 cases on healthcare expenditures, logistics performance index, carbon damages, and corporate social responsibility in a panel of 77 countries. The results show that COVID-19 cases substantially increase healthcare expenditures and decrease corporate social responsibility. On the other hand, an increase in the coronavirus testing capacity brings positive change in reducing healthcare expenditures, increased logistics activities, and corporate social responsibility. The cost of carbon emissions increases when corporate activities begin to resume. The economic affluence supports logistics activities and improves healthcare infrastructure. It linked to international cooperation and their assistance to supply healthcare logistics traded equipment through mutual trade agreements. The greater need to enhance global trade and healthcare logistics supply helps minimize the sensitive coronavirus cases that are likely to provide a safe and healthy environment for living.
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Affiliation(s)
- Muhammad Khalid Anser
- School of Public Administration, Xi’an University of Architecture and Technology, Xi’an, 710000 China
| | - Sheikh Usman Yousaf
- Hailey College of Banking and Finance, University of the Punjab, Lahore, Pakistan
| | - Shabir Hyder
- Department of Management Sciences, COMSATS University Islamabad, Attock Campus, Attock, Pakistan
| | - Abdelmohsen A. Nassani
- Department of Management, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh, 11587 Saudi Arabia
| | - Khalid Zaman
- Department of Economics, University of Haripur, Haripur Khyber Pakhtunkhwa, Pakistan
| | - Muhammad Moinuddin Qazi Abro
- Department of Management, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh, 11587 Saudi Arabia
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14
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Fang F, Mu L, Zhu Y, Rao J, Heymann J, Zhang ZF. Long-Term Exposure to PM 2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126274. [PMID: 34200600 PMCID: PMC8296095 DOI: 10.3390/ijerph18126274] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/28/2021] [Accepted: 06/05/2021] [Indexed: 12/13/2022]
Abstract
Long-term PM2.5 exposure might predispose populations to SARS-CoV-2 infection and intervention policies might interrupt SARS-CoV-2 transmission and reduce the risk of COVID-19. We conducted an ecologic study across the United States, using county-level COVID-19 incidence up to 12 September 2020, to represent the first two surges in the U.S., annual average of PM2.5 between 2000 and 2016 and state-level facemask mandates and stay home orders. We fit negative binomial models to assess COVID-19 incidence in association with PM2.5 and policies. Stratified analyses by facemask policy and stay home policy were also performed. Each 1-µg/m3 increase in annual average concentration of PM2.5 exposure was associated with 7.56% (95% CI: 3.76%, 11.49%) increase in COVID-19 risk. Facemask mandates and stay home policies were inversely associated with COVID-19 with adjusted RRs of 0.8466 (95% CI: 0.7598, 0.9432) and 0.9193 (95% CI: 0.8021, 1.0537), respectively. The associations between PM2.5 and COVID-19 were consistent among counties with or without preventive policies. Our study added evidence that long-term PM2.5 exposure increased the risk of COVID-19 during each surge and cumulatively as of 12 September 2020, in the United States. Although both state-level implementation of facemask mandates and stay home orders were effective in preventing the spread of COVID-19, no clear effect modification was observed regarding long-term exposure to PM2.5 on the risk of COVID-19.
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Affiliation(s)
- Fang Fang
- Department of Epidemiology, Fielding School of Public Health, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA; (F.F.); (J.R.)
| | - Lina Mu
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA;
| | - Yifang Zhu
- Department of Environmental Health Science, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA;
- Institute of the Environment and Sustainability, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Jianyu Rao
- Department of Epidemiology, Fielding School of Public Health, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA; (F.F.); (J.R.)
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Jody Heymann
- WORLD Policy Analysis Center, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA;
| | - Zuo-Feng Zhang
- Department of Epidemiology, Fielding School of Public Health, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA; (F.F.); (J.R.)
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA
- Center for Human Nutrition, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA
- Correspondence:
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