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The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.
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The impact of air pollutants on human health is a major issue that developing nations are facing in last decade. Effect of particulate matter especially PM2.5 and PM10 has been severely affecting mortality and morbidity in Rajasthan state as per global lead medical journal Lancet recent publication. Twelve air pollutant-monitoring sta tions in Jaipur city are not enough to predict the exact concentration of air pollutants in each of the 91 wards of Jaipur. In absence of accurate concentration at micro level, it becomes a major challenge for urban planners to remedial strategies. In this paper through GIS spatial distribution, a comparative study of particulate matter at each of the 91 wards during pre-lockdown for the year 2019 and post-lockdown 2020 was done. Results for 2020 showed slightly less pollution;similarly, air purity index, an indicator for healthier environment, was determined for each ward. © 2023 Scientific Publishers. All rights reserved.
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Low quality of the air is becoming a major concern in urban areas. High values of particulate matter (PM) concentrations and various pollutants may be very dangerous for human health and the global environment. The challenge to overcome the problem with the air quality includes efforts to improve healthy air not only by reducing emissions, but also by modifying the urban morphology to reduce the exposure of the population to air pollution. The aim of this contribution is to analyse the influence of the green zones on air quality mitigation through sensor measurements, and to identify the correlation with the meteorological factors. Actually, the objective focuses on identifying the most significant correlation between PM2.5 and PM10 concentrations and the wind speed, as well as a negative correlation between the PM concentrations and wind speed across different measurement locations. Additionally, the estimation of slight correlation between the PM concentrations and the real feel temperature is detected, while insignificant correlations are found between the PM concentrations and the actual temperature, pressure, and humidity. In this paper the effect of the pandemic restriction rules COVID-19 lockdowns and the period without restriction are investigated. The sensor data collected before the pandemic (summer months in 2018), during the global pandemic (summer months 2020), and after the period with restriction measures (2022) are analysed.
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In recent years, the concentration levels of various air pollutants have been constantly increasing, primarily due to the high vehicle flow. In 2020, however, severe lockdowns in Greece were imposed to reduce the spread of the COVID-19 pandemic, which led to a rapid reduction in the concentration levels of air pollutants such as PM2.5 and PM10 in the atmosphere. Initially, this paper seeks to identify the correlation between the concentration levels of PM10 and the traffic flow by acquiring data from low-cost IoT devices which were placed in Thessaloniki, Greece, from March to August 2020. The correlation and the linearity between the two parameters were further investigated by applying descriptive analytics, regression techniques, Pearson correlation, and independent T-testing. The obtained results indicate that the concentration levels of PM10 are strongly correlated to the vehicle flow. Therefore, the results hint that the decrease in the vehicle flow could result in improving the quality of environmental air. Finally, the acquired results point out that the temperature and humidity are weakly correlated with the concentration levels of PM10 present in the atmosphere.
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Background: We aimed to report long-term brain magnetic resonance imaging (MRI) findings in survivors of ICU-treated COVID-19 compared to other groups. Material(s) and Method(s): In this prospective cohort study 70 ICU-treated, 46 ward-treated and 46 home-isolated patients, diagnosed with COVID-19 in 2020, underwent brain MRI 6 months after the acute phase to determine the presence of cerebral microbleeds (CMB) and Fazekas scale. Result(s): CMBs existed in 27 (38.6%) ICU-treated, 13 (28.3%) ward-treated, 8 (17.4%) home-isolated COVID-19 patients, and in 12 (22.6%) non-COVID controls (Figure 1). The number of CMBs in COVID-19 patients and controls was median 2 (IQR 1-4) and 1 (IQR 1-2), respectively. Patients with CMBs differed from those without, regarding age (median 62 vs. 52 years, p < 0.001), history of arterial hypertension (50% vs. 31%, p = 0.03), need of ICU (56% vs. 38%, p = 0.03) and ventilator treatment (42% vs. 22%, p = 0.01), length of hospital stay (median 21 vs. 12 days, p < 0.001), and supplementary oxygen therapy (median 18 vs. 10 days, p = 0.008), respectively. Within the ICU group, patients with and without CMBs differed regarding the duration of ICU stay (median 17 vs. 9 days, p = 0.006), and mechanical ventilation (median 14 vs. 6 days, p = 0.002). In multivariable analysis, only age was associated with CMBs (OR 1.06, 95% CI 1.02-1.09). The majority of subjects in all groups had Fazekas scale one for white matter hyperintensities (Figure 1). Conclusion(s): Although the severity of respiratory failure and history of arterial hypertension were associated with the presence of CMBs, only age was an independent predictor of CMBs.
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This conversation reflects on the importance of transnational Black solidarity in a global moment where the COVID-19 pandemic has exposed structural inequalities that sustain Black death and the global movement for Black lives has focussed attention on anti-Blackness. We reflect on the legacies of past and contemporary Black, Indigenous, People of Colour activism and the role that this has played in strengthening transnational efforts to deal with colonialism, imperialism and patriarchy. In highlighting how anti-Blackness is sustained across different institutions - from the academy to social movements, we centre Black feminist movements' role in building radical visions of equitable and transformative worlds through a focus on the nexus between patriarchy, capitalism and white supremacy. Black feminist visions we argue are geared at disrupting and transforming current power structures to advance justice and create liberatory futures. A central part of these liberatory futures lies in building collective power that is rooted in the political values of solidarity, hope and joy. [ FROM AUTHOR] Copyright of European Journal of Women's Studies is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Air pollution, climate and population health are closely related in terms of their impacts on respiratory health and lung cancer. Air pollutants contribute to the exacerbation of chronic respiratory problems such as COPD and asthma. Air pollutants are also toxic and carcinogenic, initiating and promoting lung cancer development. Climate change in relation to environmental pollution affects the geographical distribution of food supply and diseases such as pneumonia in adults and children. The threat of air pollution, and hence global warming and climate changes, and their effects on population and respiratory health, is an imminent threat to the world and deserves immediate and sustainable combating strategies and efforts. The goals are to increase public awareness and engagement in action, with alignment of international collaboration and policy, and with steering towards further research. Now is the prime time for international collaborative efforts on planning and actions to fight air pollution and climate change before it is too late.Copyright © ERS 2021.
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The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by∼10% because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.
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Societal crises such as the recent COVID-19 pandemic challenge public organizations in many ways. Yet, there is little knowledge of how varying crisis intensity can spark different reactions among public leaders and employees. This study examines the dynamics of the COVID-19 crisis in relation to leadership behaviors and employee outcomes for 920 individuals in 45 organizational units within the police, hospitals, and assisted living facilities. By conducting a panel study that combines nationwide data on crisis intensity with survey data from 2019 to 2021, the study shows that employee outcomes such as public service motivation and job satisfaction are higher in severe stages of the crisis. Likewise, crisis intensity is positively associated with employee perceived use of visionary transformational leadership, but negatively associated with employee perceived use of verbal transactional leadership. Hence, public leaders should be prepared to handle complex employee reactions when the next crisis occurs.
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The results of a preliminary analysis of the relationship between the short-term impact of air pollution exposure on hospitalizations associated with COVID-19 in Tomsk, Russia are presented. The statistical data on air pollution and COVID-19 associated hospitalization were collected and analyzed for the period from March 16, 2022 to April 14, 2022. This period corresponds to a flat plateau of confirmed COVID-19 cases after the main pandemic wave in 2022 in Tomsk and the Tomsk region which were associated with omicron strain of SARS-CoV-2. It was found that all representative peaks in a graph of daily hospitalizations coincide with the peaks in graphs of measured levels of air pollution. The increase in hospitalizations occurred on the same days when air pollution levels increased, or with a slight lag of 1-2 days. This allows us to tentatively conclude that air pollution has a quick effect on infected persons and may provoke an increase in symptoms and severity of the disease. Further detailed research is required. © 2022 SPIE.
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The sudden outbreak of COVID-19 in 2020 causes great impact on the economic development of all countries and even the whole world. Under the background of major public emergencies, a timely dynamic evaluation of regional economic resilience can provide an objective basis for economic regulation and control behavior. Based on the existing evaluation model, an improved dynamic evaluation model of grey incidence projection- fuzzy matter element is proposed in this study. The improved model is a universal evaluation model that can be used in different contexts. This model method can, not only limited to analyzing economic resilience, but also be applied to other different contexts. The evaluation indexes are selected (from the market, industry, investment, foreign trade and finance) to construct an evaluation index system of regional economic resilience under major public emergencies. The improved dynamic evaluation model of grey incidence projection- fuzzy matter element is applied to evaluate economic resilience of Hubei province (with its neighboring areas) in context of COVID-19. At the same time, the relative validity of the model is tested based on the empirical evaluation results.
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Black Lives Matter at School: Community Schools, COVID-19 and Freedom Dreams Edited by Denisha Jones and Jesse Hagopian, I Black Lives Matter at School: An Uprising for Educational Justice i is a must-read for all educators and organizers who are truly committed to centering Black lives in and outside of classrooms. It will be critical moving forward to demonstrate how the knowledge and skills necessary to bring about Black Lives Matter at School and similar Black liberatory curricular efforts can be implemented in teacher education programs, beyond ad-hoc professional development sessions. [Extracted from the article] Copyright of Urban Education is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals. © 2022 IEEE.
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Cerebral microangiopathy (CMA) is one of the significant causes of depression in the elderly. Close associations of the risk of developing depression with white matter hyperintensity, the presence of lacunar infarcts, and other markers of vascular disease are shown. The available data suggest that various vascular mechanisms, in particular, involvement of small vessels of the brain, generalized microvascular and endothelial dysfunction, metabolic risk factors, - are risk factors for the development of depression. Pathogenetic mechanisms include cerebral hypoperfusion and immune dysregulation. Depression is also a common complication of coronavirus infection, occurring both in the acute and post-COVID periods. The same mechanisms as in vascular depression are involved in the pathogenesis of the development of post-COVID depressive disorders. Given the complexity of the mechanisms of development of depressive disorders in patients with CMA, the presence of severe comorbid vascular pathology, antidepressants with an optimal ratio of efficacy and safety should be preferred. Agomelatine (Valdoxan) is one of such drugs.Copyright © 2023 Ima-Press Publishing House. All rights reserved.
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The tenth international symposium on "Large TPCs for low-energy rare event detection” was held in Paris from 15th to 17th of December 2021 at the Institute of Astroparticle physics of the University of Paris. The 2020 issue of this conference series was cancelled due to the Covid pandemic. The symposium was organized in a hybrid mode, which allowed about 40 people to attend in-person among a total of 131 registered participants. Very strict sanitary measures were taken to keep the meeting safe.As in previous events the program included neutrino physics, dark matter and axion searches, related detector R&D and theoretical aspects.To celebrate the tenth edition of the symposium the conference was held on three full days. Special speakers were invited to give a historical overview related to our field. David Nygren shared his memories of the invention and development of the Time Projection Chamber. François Vannucci revealed to us the invisible world of neutrinos. Ioannis Giomataris pointed out various innovative ideas that emerged during presentations and discussions in the last two decades. Jean Iliopoulos retraced the concept and development of the Standard Model and shared his personal vision on the future of particle physics.We wish to thank the many people who contributed to the success of the conference and especially the conveners of the sessions, who allowed for a smooth running of the meeting. We particularly acknowledge the APC management for providing the nice Buffon auditorium and infrastructure. We also thank DSM-Irfu, the University of Zaragoza, the European Research Council and ACAV Ile de France for their valuable support.The organizersList of Organizing Committee is available in this Pdf.
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BACKGROUND AND PURPOSE: Cerebral hypoperfusion has been described in both severe and mild forms of symptomatic Coronavirus Disease 2019 (COVID-19) infection. The purpose of this study was to investigate global and regional cerebral blood flow (CBF) in asymptomatic COVID-19 patients. METHODS: Cases with mild COVID-19 infection and age-, sex-, and race-matched healthy controls were drawn from the Aging Brain Consortium at The University of South Carolina data repository. Demographics, risk factors, and data from the Montreal Cognitive Assessment were collected. Mean CBF values for gray matter (GM), white matter (WM), and the whole brain were calculated by averaging CBF values of standard space-normalized CBF image values falling within GM and WM masks. Whole brain region of interest-based analyses were used to create standardized CBF maps and explore differences between groups. RESULTS: Twenty-eight cases with prior mild COVID-19 infection were compared with 28 controls. Whole-brain CBF (46.7 ± 5.6 vs. 49.3 ± 3.7, p = .05) and WM CBF (29.3 ± 2.6 vs. 31.0 ± 1.6, p = .03) were noted to be significantly lower in COVID-19 cases as compared to controls. Predictive models based on these data predicted COVID-19 group membership with a high degree of accuracy (85.2%, p < .001), suggesting CBF patterns are an imaging marker of mild COVID-19 infection. CONCLUSION: In this study, lower WM CBF, as well as widespread regional CBF changes identified using quantitative MRI, was found in mild COVID-19 patients. Further studies are needed to determine the reliability of this newly identified COVID-19 brain imaging marker and determine what drives these CBF changes.
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Previous studies focused on investigating particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) have shown the risk of disease development, and association with increased morbidity and mortality rates. The current review investigate epidemiological and experimental findings from 2016 to 2021, which enabled the systemic overview of PM2.5's toxic impacts on human health. The Web of Science database search used descriptive terms to investigate the interaction among PM2.5 exposure, systemic effects, and COVID-19 disease. Analyzed studies have indicated that cardiovascular and respiratory systems have been extensively investigated and indicated as the main air pollution targets. Nevertheless, PM2.5 reaches other organic systems and harms the renal, neurological, gastrointestinal, and reproductive systems. Pathologies onset and/or get worse due to toxicological effects associated with the exposure to this particle type, since it can trigger several reactions, such as inflammatory responses, oxidative stress generation and genotoxicity. These cellular dysfunctions lead to organ malfunctions, as shown in the current review. In addition, the correlation between COVID-19/Sars-CoV-2 and PM2.5 exposure was also assessed to help better understand the role of atmospheric pollution in the pathophysiology of this disease. Despite the significant number of studies about PM2.5's effects on organic functions, available in the literature, there are still gaps in knowledge about how this particulate matter can hinder human health. The current review aimed to approach the main findings about the effect of PM2.5 exposure on different systems, and demonstrate the likely interaction of COVID-19/Sars-CoV-2 and PM2.5.
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The present study is an attempt to establish relationship between the concentrations of particulate matter especially (PM2.5) and background meteorological parameters over Delhi, India with the help of statistical and correlative analysis. This work presents the evaluation of air quality in three different locations of Delhi. These locations were selected to fulfil the characteristics as residential, industrial and background locations and performed the analysis for pre and post covid-19, i.e. for 2019 and 2021. The outcome of the study shows that the meteorological parameters have significant influence on the PM2.5 concentration. It was also found that it has a seasonality with low concentration in the monsoon season, moderate in the pre-monsoon season and high during the winters and post-monsoon seasons. However, the statistical and correlative study shows a negative relation with the temperature during the winter, pre-monsoon and post-monsoon and has a positive correlation during the monsoon season. Similarly, it also has been observed that the concentration of PM2.5 shows strong negative correlation with temperature during the high humid conditions, i.e. when the relative humidity is above 50%. However, a weak correlation with ambient temperature has been established during the low humidity condition, i.e. below 50%. The overall study showed that the highest PM2.5 pollution has been observed at residential location followed by industrial and background. The study also concluded that the seasonal meteorology has a complex role in the PM2.5 concentration of the selected areas.
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Air pollution, especially the continual increase in atmospheric particulate matter (PM), is a global environmental challenge. To reduce the PM concentration, a remarkable amount of machine learning-based research has been proposed. However, increasing the accuracy of the predictions and providing clear interpretations of the predictions are challenging. In particular, no studies have addressed models that predict and interpret PM before and during the COVID-19 pandemic. In this paper, we present a two-step predictive and explainable model to obtain insights into reducing PM. We first use attentive multi-task learning to predict the air quality of cities. To accurately predict the concentration of particles with sizes of similar to 10 mu m or similar to 2.5 mu m (PM10 and PM2.5, respectively), we demonstrate a performance difference between single-task and multi-task learning, as well as among the state-of-the art methods. The proposed attentive model with multi-task learning outperformed the others in terms of accuracy performance. We then used Shapley additive explanations, a representative explainable artificial intelligence framework, to interpret and determine the significance of features for predicting PM10 and PM2.5. We demonstrated the superiority of the proposed approach in predicting and explaining both PM10 and PM2.5 concentrations, and observed a statistically significant difference in air pollution before and during the COVID-19 pandemic.
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Abstract: A field study was carried out in the Metropolitan Area of Monterrey (MAM), the second most populated city in Mexico, characterized by increasing urbanization, high traffic density, and intense industrial activity. These characteristics commonly present high concentrations of air pollutants leading to the degradation of air quality. PM2.5 was analyzed for heavy metals at two urban sites located within the MAM (Juarez and San Bernabe) in order to determine sources, health risk, morphology, and elemental content during the COVID-19 pandemic (autumn 2020 and spring 2021). Twenty-four-hour samples of PM2.5 were collected at each site during 30-day periods using high-volume equipment. Gravimetric concentrations and 11 metals were measured (Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Ni, Cr, and Pb) by different analytical techniques (flame atomic absorption spectroscopy, graphite furnace atomic absorption spectroscopy, and inductively coupled plasma optical emission spectroscopy). Selected samples were analyzed by scanning electron microscopy-energy-disperse spectroscopy in order to characterize their morphology and elemental content. PM2.5 concentrations exceeded the Mexican standard and WHO guidelines in Juarez during spring 2021. Cu, Cd, and Co were highly enriched by anthropogenic sources, and Ni, K, Cr, and Pb had a moderate enrichment. Mg, Mn, and Ca were of crustal origin. Bivariate statistics and PCA confirmed that alkaline metals originated from crustal sources and that the main sources of trace metals included traffic emissions, resuspension from soil/road dust, steel industry, smelting, and non-exhaust emissions at both sites. Lifetime cancer risk coefficients did not exceed the permissible levels established by EPA and WHO, implying that local residents are not at risk of developing cancer. Non-carcinogenic risk coefficients revealed that there is a possible risk of suffering cardiovascular and respiratory diseases due to inhalation of cobalt at the study sites. Supplementary Information: The online version contains supplementary material available at 10.1007/s11869-023-01372-7.