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      Changes of Air Pollution between Countries Because of Lockdowns to Face COVID-19 Pandemic

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      Applied Sciences
      MDPI AG

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          Abstract

          The goal of this study is to analyze how levels of air pollution changed between countries with their restriction policy of lockdown to cope with the COVID-19 pandemic. The study design compares average changes of CO, NO2, SO2, O3, PM2.5 and PM10 concentrations based on measurements at ground level in January, February, and March for the years 2019, 2020, 2021, and 2022 (during the COVID-19 pandemic crisis) to average values of a 2015–2018 baseline period (ex-ante COVID-19 pandemic) between 300 cities in 19 countries of five geoeconomic regions. Results reveal that the maximum reduction in air pollutant concentrations is given by: CO (−4367.5%) in France, NO2 (−150.5%) in China and Australia, SO2 (−154.1%) in Israel, O3 (−94.1%) in China, PM2.5 (−41.4%) in Germany, and PM10 (−157.4%) in Turkey. Findings show that the effects of restriction policies for COVID-19 pandemic on air quality vary significantly between countries, depending on the different geographical, economic, industrial and social characteristics of the countries. These results clarify the critical relationship between control measures for pandemic crises and levels of air pollution in countries that can support best practices of environmental policy for pathways of sustainable development.

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          Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India

          Amid the COVID-19 pandemic, a nationwide lockdown is imposed in India initially for three weeks from 24th March to 14th April 2020 and extended up to 3rd May 2020. Due to the forced restrictions, pollution level in cities across the country drastically slowed down just within few days which magnetize discussions regarding lockdown to be the effectual alternative measures to be implemented for controlling air pollution. The present article eventually worked on this direction to look upon the air quality scenario amidst the lockdown period scientifically with special reference to the megacity Delhi. With the aid of air quality data of seven pollutant parameters (PM10, PM2.5, SO2, NO2, CO, O3 and NH3) for 34 monitoring stations spread over the megacity we have employed National Air Quality Index (NAQI) to show the spatial pattern of air quality in pre and during-lockdown phases. The results demonstrated that during lockdown air quality is significantly improved. Among the selected pollutants, concentrations of PM10 and PM2.5 have witnessed maximum reduction (>50%) in compare to the pre-lockdown phase. In compare to the last year (i.e. 2019) during the said time period the reduction of PM10 and PM2.5 is as high as about 60% and 39% respectively. Among other pollutants, NO2 (−52.68%) and CO (−30.35%) level have also reduced during-lockdown phase. About 40% to 50% improvement in air quality is identified just after four days of commencing lockdown. About 54%, 49%, 43%, 37% and 31% reduction in NAQI have been observed in Central, Eastern, Southern, Western and Northern parts of the megacity. Overall, the study is thought to be a useful supplement to the regulatory bodies since it showed the pollution source control can attenuate the air quality. Temporary such source control in a suitable time interval may heal the environment.
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            Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID

            This study has two goals. The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 in Italy that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats having accelerated viral infectivity in society. Using data on N = 55 Italian province capitals, and data of infected individuals at as of April 7th, 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 μm or less in diameter) or ozone in previous years. In particular, hinterland cities with average higher number of days exceeding the limits set for PM10 (and a low intensity of wind speed) have a very high number of infected people on 7th April 2020 (arithmetic mean about 2200 infected, with average polluted days greater than 80), than coastal cities also having days of exceeding the limits set for PM10 or ozone but with high intensity of wind speed (arithmetic mean about 944.70 infected individuals, with about 60 average polluted days); moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3350 infected individuals, 7th April 2020), whereas cities having less than 100 days of air pollution, they have a lower average number of infected individuals (about 1014). The findings here also suggest that to minimize the impact of future epidemics similar to COVID-19, the max number of days per year in which Italian provincial capitals can exceed the limits set for PM10 or for ozone, considering their meteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities under study seems to be a more important predictor in the initial phase of diffusion (on 17th March 2020, b1 = 1.27, p < 0.001) than interpersonal contacts (b2 = 0.31, p < 0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7th April 2020 with b′1 = 0.81, p < 0.001) also because of indirect effect of lockdown, whereas coefficient of transmission by interpersonal contacts has stability (b′2 = 0.31, p < 0.01). This result reveals that accelerated transmissions dynamics of COVID-19 is due to mainly to the mechanism of “air pollution-to-human transmission” rather than “human-to-human transmission”. Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study of Italy suggest that a proactive strategy to cope with future epidemics is to also apply especially an environmental and sustainable policy based on reduction of levels of air pollution mainly in hinterland and polluting cities- having low wind speed, high percentage of moisture and fog days-that seem to have an environment that may damage immune system of people and foster a fast transmission dynamics of viral infectivity in society. Hence, in the presence of polluting industrialization in regions that can trigger the mechanism of air pollution-to-human transmission dynamics of viral infectivity, this study must conclude that a comprehensive strategy to prevent future epidemics similar to COVID-19 has to be also designed in environmental and socioeconomic terms, that is also based on sustainability science and environmental science, and not only in terms of biology, healthcare and health sector.
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              Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation

              The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO2, NO x , PM2.5 and VOCs emissions by approximately 16–26%, 29–47%, 27–46% and 37–57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM2.5, NO2 and SO2 decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%–61%, the daily average PM2.5 concentrations range between 15 and 79 μg m−3, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM2.5 comes from industry (32.2–61.1%), mobile (3.9–8.1%), dust (2.6–7.7%), residential sources (2.1–28.5%) in YRD and 14.0–28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.
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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                December 2022
                December 13 2022
                : 12
                : 24
                : 12806
                Article
                10.3390/app122412806
                ae20ac2b-89f3-4d99-bf72-e441649423ea
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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