Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence

Dual-strain dynamics of COVID-19 variants in India: Modeling, analysis, and implications for pandemic control

This study introduces a detailed compartmental model developed to understand the complex dynamics of COVID-19 transmission, focusing on the Delta and Omicron variants in India. The model tracks disease progression through different population compartments, considering factors like vaccination, time-dependent transmission, economic burden and COVID-19 death rates, loss of vaccine-induced immunity, and the transition of asymptomatic cases to recovery. The model is validated against established epidemiological knowledge and real-world data, emphasizing dynamic parameterization and accurate representation of immunity dynamics. The basic reproduction number for both variants is calculated, and sensitivity analysis for various parameters is conducted. Time-dependent parameters are estimated using the discrete inverse method. The study also explores the economic burden, impact of different types of masks, vaccine efficacy, and vaccine-induced immunity through numerical analysis.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Impact of the lockdown period during the COVID-19 pandemic on the metal content of the anemone Anemonia sulcata in the Canary Islands (CE Atlantic, Spain)

Anemones, specifically the species Anemonia sulcata, are cnidarians that serve as bioindicators in marine ecosystems, indicating the health of the environment and changes in environmental conditions. Monitoring anemone populations and studying their well-being and distribution provide valuable insights into marine ecosystem conditions. This study aimed to investigate the impact of the SARS-CoV-2 pandemic on the metal content of Anemonia sulcata. Over a six-year period (2017-2022), twenty specimens of Anemonia sulcata were collected in Tenerife, Spain. The results showed that in 2020, during the two-month lockdown in Spain from March to May when tourism was halted, A. sulcata exhibited the lowest concentrations of various metals studied (Al, Cd, Cu, Fe, Pb, and Zn). This finding suggests that the reduced anthropogenic pressure on the coast due to the absence of tourism significantly decreased pollution levels. Therefore, the study emphasizes the importance of promoting sustainable tourism worldwide. The research highlights that minimizing human impact on coastal areas through responsible tourism practices can effectively reduce pollution in marine ecosystems.

Association between Average Vitamin D Levels and COVID-19 Mortality in 19 European Countries-A Population-Based Study

Early epidemic reports have linked low average 25(OH) vitamin D levels with increased COVID-19 mortality. However, there has been limited updated research on 25(OH) vitamin D and its impact on COVID-19 mortality. This study aimed to update the initial report studying the link between vitamin D deficiency and COVID-19 mortality by using multi-country data in 19 European countries up to the middle of June 2023. COVID-19 data for 19 European countries included in this study were downloaded from Our World in Data from 1 March 2020, to 14 June 2023, and were included in the statistical analysis. The 25(OH) vitamin D average data were collected by conducting a literature review. A generalized estimation equation model was used to model the data. Compared to European countries with 25(OH) vitamin D levels of ≤50 nmol/L, European countries with 25(OH) vitamin D average levels greater than 50 nmol/L had lower COVID-19 mortality rates (RR = 0.794, 95% CI: 0.662-0.953). A statistically significant negative Spearman rank correlation was observed between 25(OH) vitamin D average levels and COVID-19 mortality. We also found significantly lower COVID-19 mortality rates in countries with high average 25(OH) vitamin D levels. Randomized trials on vitamin D supplementation are needed. In the meantime, the issue of vitamin D use should be debated in relation to the ongoing discussions of national post-COVID-19 resilience against future pandemics.

Machine learning-based time series models for effective CO2 emission prediction in India

China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org , CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction.

Testing the theory of Kuznet curve on environmental pollution during pre- and post-Covid-19 era

Covid-19 has brought about significant changes in people's daily lives, leading to a slowdown in economic activities and the implementation of restrictions and lockdowns. As a result, there have been noticeable effects on the environment. In this study, we examine the impact of Covid-19 total cases on the monthly average of carbon monoxide emissions in developed economies known for heavy pollution, covering the period from 2014 to 2023. We apply the Ambiental Kuznets curve approach to analyze the data. By employing different panel estimation techniques such as fixed effects and Driscoll-Kraay regressions, we observe a marked shift in environmental dynamics during the post-Covid era. This shift alters the statistical significance of the N-shaped Kuznets curve, rendering the relationship between economic activity and environmental impact non-significant. Interestingly, the Covid-related variables utilized in the various estimations are not statistically significant in explaining the long-term environmental effects.

Closing the loop of a global supply chain through a robust optimal decentralized decision support system

This paper presents a novel decentralized decision support system to optimally design a general global closed-loop supply chain. This is done through an original risk-based robust mixed-integer linear programming that is formulated based on an initial uncertain bi-level programming. Addressing the decision-maker's (DM's) attitude toward risk, a scenario-based conditional value-at-risk is used to deal with demand and return uncertainty. Also, the Karush-Kuhn-Tucker (KKT) conditions are employed to transform the model into its single-level counterpart. The results obtained from solving a numerical example through the proposed framework are compared with those of the corresponding centralized system, which is formulated through deterministic multi-objective programming and solved by the Lp-metric method. The results show that the use of the proposed framework improves the robustness of profit, income, and cost by about 28%, 34%, and 36% on average. However, a more conservative DM faces a larger cost of robustness than an optimistic DM while experiencing a more significant improvement in the system responsiveness. Using the proposed framework, the manager can measure the advantages, disadvantages, and consequences of their decisions before their actual implementation. This is because the model is capable of establishing fundamental trade-offs among risk, cost, profit, income, robustness, and responsiveness according to the DM's attitude toward risk.

Air pollution and respiratory infections: the past, present, and future

Air pollution levels across the globe continue to rise despite government regulations. The increase in global air pollution levels drives detrimental human health effects, including 7 million premature deaths every year. Many of these deaths are attributable to increased incidence of respiratory infections. Considering the COVID-19 pandemic, an unprecedented public health crisis that has claimed the lives of over 6.5 million people globally, respiratory infections as a driver of human mortality is a pressing concern. Therefore, it is more important than ever to understand the relationship between air pollution and respiratory infections so that public health measures can be implemented to ameliorate further morbidity and mortality. This article aims to review the current epidemiologic and basic science research on interactions between air pollution exposure and respiratory infections. The first section will present epidemiologic studies organized by pathogen, followed by a review of basic science research investigating the mechanisms of infection, and then conclude with a discussion of areas that require future investigation.

Ambient air pollution and COVID-19 in Delhi, India: a time-series evidence

This study aimed to explore the short-term health effects of ambient air pollutants PM2.5, PM10, SO2, NO2, O3, and CO on COVID-19 daily new cases and COVID-19 daily new deaths. A time-series design used in this study. Data were obtained from 1 April 2020 to 31 December 2020 in the National Capital Territory (NCT) of Delhi, India. The generalized additive models (GAMs) were applied to explore the associations of six air pollutants with COVID-19 daily new cases and COVID-19 daily new deaths. The GAMs revealed statistically significant associations of ambient air pollutants with COVID-19 daily new cases and COVID-19 daily new deaths. These findings suggest that governments need to give greater considerations to regions with higher concentrations of PM2.5, PM10, SO2, NO2, O3, and CO, since these areas may experience a more serious COVID-19 pandemic or, in general, any respiratory disease.

Assessing environmental concern and its association with carbon trade balances in N11 Do financial development and urban growth matter?

Expanding of complex global supply chains enhances the role of global trade in the deterioration of the environment by production redeployment across nations, which is tightly connected to emission transmission or the carbon trade balance. Although much earlier studies have assessed the link between emissions of carbon dioxide (CO₂) and their influenced variables in the past few years, no substantial attention is available in the literature review concerning the influence of carbon trade balance on the environment in N11 economies. Therefore, via economic progress, renewable/fossil energies consumption, financial development, and urbanization growth as control variables, the influence of the carbon trade balance on emissions of CO₂ in N11 countries is explored from 1990 to 2020. The Co-integration and causality relationships using Panel PMG ARDL and Granger causality techniques are investigated to reach our goal. All of the variables investigated degrade the environment in the long run, whereas renewables alleviate CO₂. As a result, carbon emission countries' regulators should step up their efforts to support green energy subsidies and carbon taxes, as well as, when supply chains outsource emission-intensive production units to partner nations, they should encourage positive externalities of innovative green technologies.

Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model

Recently, global health has seen an increase in demand for assistance as a result of the COVID-19 pandemic. This has prompted many researchers to conduct different studies looking for variables that are associated with increased clinical risk, and find effective and safe treatments. Many of these studies have been limited by presenting small samples and a large data set. Using machine learning (ML) techniques we can detect parameters that help us to improve clinical diagnosis, since they are a system for the detection, prediction and treatment of complex data. ML techniques can be valuable for the study of COVID-19, especially because they can uncover complex patterns in large data sets. This retrospective study of 150 hospitalized adult COVID-19 patients, of which we established two groups, those who died were called Case group (n = 53) while the survivors were Control group (n = 98). For analysis, a supervised learning algorithm eXtreme Gradient Boosting (XGBoost) has been used due to its good response compared to other methods because it is highly efficient, flexible and portable. In this study, the response to different treatments has been evaluated and has made it possible to accurately predict which patients have higher mortality using artificial intelligence, obtaining better results compared to other ML methods.

Tracing out the effect of transportation infrastructure on NO2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns

This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality

The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socio-economic and health issues in manifold countries. The principal goal of this study is to develop a machine learning experiment to assess the effects of vaccination on the fatality rate of the COVID-19 pandemic. Data from 192 countries are analysed to explain the phenomena under study. This new algorithm selected two targets: the number of deaths and the fatality rate. Results suggest that, based on the respective vaccination plan, the turnout in the participation in the vaccination campaign, and the doses administered, countries under study suddenly have a reduction in the fatality rate of COVID-19 precisely at the point where the cut effect is generated in the neural network. This result is significant for the international scientific community. It would demonstrate the effective impact of the vaccination campaign on the fatality rate of COVID-19, whatever the country considered. In fact, once the vaccination has started (for vaccines that require a booster, we refer to at least the first dose), the antibody response of people seems to prevent the probability of death related to COVID-19. In short, at a certain point, the fatality rate collapses with increasing doses administered. All these results here can help decisions of policymakers to prepare optimal strategies, based on effective vaccination plans, to lessen the negative effects of the COVID-19 pandemic crisis in socioeconomic and health systems.

Mathematical Modeling to Determine the Fifth Wave of COVID-19 in South Africa

The aim of this study is to predict the COVID-19 infection fifth wave in South Africa using the Gaussian mixture model for the available data of the early four waves for March 18, 2020-April 13, 2022. The quantification data is considered, and the time unit is used in days. We give the modeling of COVID-19 in South Africa and predict the future fifth wave in the country. Initially, we use the Gaussian mixture model to characterize the coronavirus infection to fit the early reported cases of four waves and then to predict the future wave. Actual data and the statistical analysis using the Gaussian mixture model are performed which give close agreement with each other, and one can able to predict the future wave. After that, we fit and predict the fifth wave in the country and it is predicted to be started in the last week of May 2022 and end in the last week of September 2022. It is predicted that the peak may occur on the third week of July 2022 with a high number of 19383 cases. The prediction of the fifth wave can be useful for the health authorities in order to prepare themselves for medical setup and other necessary measures. Further, we use the result obtained from the Gaussian mixture model in the new model formulated in terms of differential equations. The differential equations model is simulated for various values of the model parameters in order to determine the disease’s possible eliminations.

Analyzing preventive precautions to limit spread of COVID-19

With the global spread of COVID-19, the governments advised the public for adopting safety precautions to limit its spread. The virus spreads from people, contaminated places, and nozzle droplets that necessitate strict precautionary measures. Consequently, different safety precautions have been implemented to fight COVID-19 such as wearing a facemask, restriction of social gatherings, keeping 6 feet distance, etc. Despite the warnings, highlighted need for such measures, and the increasing severity of the pandemic situation, the expected number of people adopting these precautions is low. This study aims at assessing and understanding the public perception of COVID-19 safety precautions, especially the use of facemask. A unified framework of sentiment lexicon with the proposed ensemble EB-DT is devised to analyze sentiments regarding safety precautions. Extensive experiments are performed with a large dataset collected from Twitter. In addition, the factors leading to a negative perception of safety precautions are analyzed by performing topic analysis using the Latent Dirichlet allocation algorithm. The experimental results reveal that 12% of the tweets correspond to negative sentiments towards facemask precaution mainly by its discomfort. Analysis of change in peoples’ sentiment over time indicates a gradual increase in the positive sentiments regarding COVID-19 restrictions.

Determination of the human impact on the drop in NO2 air pollution due to total COVID-19 lockdown using Human-Influenced Air Pollution Decrease Index (HIAPDI)

This study investigates the relationship between territorial human influence and decreases in NO₂ air pollution during a total COVID-19 lockdown in Metropolitan France. NO₂ data from the confinement period and the Human Influence Index (HII) were implemented to address the problem. The relative change in tropospheric NO₂ was calculated using Sentinel-5P (TROPOMI) satellite data. Hotspot-Coldspot analysis was performed to examine the change in NO₂. Moreover, the novel Human-Influenced Air Pollution Decrease Index (HIAPDI) was developed. Weather bias was investigated by implementing homogeneity analysis with χ² test. The correlations between variables were tested with the statistical T-test. Likewise, remote observations were validated with data from in-situ monitoring stations. The study showed a strong correlation between the NO₂ decrease during April 2020 under confinement measures and HII. The greater the anthropogenic influence, the greater the reduction of NO₂ in the regions (R² = 0.62). The new HIAPDI evidenced the degree of anthropogenic impact on NO₂ change. HIAPDI was found to be a reliable measure to determine the correlation between human influence and change in air pollution (R² = 0.93). It is concluded that the anthropogenic influence is a determining factor in the phenomenon of near-surface NO₂ reduction. The implementation of HIAPDI is recommended in the analysis of other polluting gases.

A review of the impact of environmental factors and pollutants on covid-19 transmission

The coronavirus disease (COVID-19) caused an unprecedented loss of life with colossal social and economic fallout over 237 countries and territories worldwide. Environmental conditions played a significant role in spreading the virus. Despite the availability of literature, the consecutive waves of COVID-19 in all geographical conditions create the necessity of reviewing the impact of environmental factors on it. This study synthesized and reviewed the findings of 110 previously published articles on meteorological factors and COVID-19 transmission. This study aimed to identify the diversified impacts of meteorological factors on the spread of infection and suggests future research. Temperature, rainfall, air quality, sunshine, wind speed, air pollution, and humidity were found as investigated frequently. Correlation and regression analysis have been widely used in previous studies. Most of the literature showed that temperature and humidity have a favorable relationship with the spread of COVID-19. On the other hand, 20 articles stated no relationship with humidity, and nine were revealed the negative effect of temperature. The daily number of COVID-19 confirmed cases increased by 4.86% for every 1 °C increase in temperature. Sunlight was also found as a significant factor in 10 studies. Moreover, increasing COVID-19 incidence appeared to be associated with increased air pollution, particularly PM10, PM2.5, and O₃ concentrations. Studies also indicated a negative relation between the air quality index and the COVID-19 cases. This review determined environmental variables' complex and contradictory effects on COVID-19 transmission. Hence it becomes essential to include environmental parameters into epidemiological models and controlled laboratory experiments to draw more precious results.

The Impact of China's Paired Assistance Policy on the COVID-19 Crisis-An Empirical Case Study of Hubei Province

To control the coronavirus pandemic (COVID-19), China implemented the Paired Assistance Policy (PAP). Local responders in 16 cities in Hubei Province were paired with expert teams from 19 provinces and municipalities. Fully supported by the country's top-down political system, PAP played a significant role in alleviating the COVID-19 pandemic in Hubei Province and China as a whole. In this study, we examined PAP using a two-way fixed effects model with the cumulative number of medical support personnel and cumulative duration as measurements. The results show personnel and material support played an active role in the nation's response to the COVID-19 public health crisis.

Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy

The Covid19 pandemic has significantly impacted on our lives, triggering a strong reaction resulting in vaccines, more effective diagnoses and therapies, policies to contain the pandemic outbreak, to name but a few. A significant contribution to their success comes from the computer science and information technology communities, both in support to other disciplines and as the primary driver of solutions for, e.g., diagnostics, social distancing, and contact tracing. In this work, we surveyed the Italian computer science and engineering community initiatives against the Covid19 pandemic. The 128 responses thus collected document the response of such a community during the first pandemic wave in Italy (February-May 2020), through several initiatives carried out by both single researchers and research groups able to promptly react to Covid19, even remotely. The data obtained by the survey are here reported, discussed and further investigated by Natural Language Processing techniques, to generate semantic clusters based on embedding representations of the surveyed activity descriptions. The resulting clusters have been then used to extend an existing Covid19 taxonomy with the classification of related research activities in computer science and information technology areas, summarizing this work contribution through a reproducible survey-to-taxonomy methodology.

Effect of the COVID-19 Pandemic on Renewable Energy Firm’s Profitability and Capitalization

The COVID-19 pandemic has led many governments to impose restrictive measures that have contributed to a decline in the demand for goods and services, leading to an economic crisis. This study proves a novelty that implies a rise in the capitalization of renewable energy companies during the coronavirus pandemic. The study is based on the hypothesis that, at a time of economic crisis, the prospect of investing in clean energy has increased, through the need to protect the environment and ensure clean air. The analysis provided additional results that there is an inverse relationship between two economic indicators of firms, namely, the percentage change in profitability and capitalization of firms between 2020 and 2021. Analysis of data from companies included in TRBC Industry Name Renewable Fuels provided numerical results that show an average increase in firms’ capitalization of 86%. The study uses analysis techniques such as covariance and correlation. The results show an increase in capitalization of renewable energy companies by 150%, while there is a decrease in income by 2%. However, the capitalization of fossil fuel companies has increased, with an average growth rate of 35%. This situation in the fossil energy market is that company revenues fell by 32% while capitalization increased by 35%. It proves a bubble in the non-renewable energy market. This paper suggests that the period of coronavirus infection has seen a slowdown in economic growth in many countries around the world, but a switch to renewable energy will help improve the quality of life of the population and ensure economic growth.

Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review

COVID-19 is deemed as the most critical world health calamity of the 21st century, leading to dramatic life loss. There is a pressing need to understand the multi-stage dynamics, including transmission routes of the virus and environmental conditions due to the possibility of multiple waves of COVID-19 in the future. In this paper, a systematic examination of the literature is conducted associating the virus-laden-aerosol and transmission of these microparticles into the multimedia environment, including built environments. Particularly, this paper provides a critical review of state-of-the-art modelling tools apt for COVID-19 spread and transmission pathways. GIS-based, risk-based, and artificial intelligence-based tools are discussed for their application in the surveillance and forecasting of COVID-19. Primary environmental factors that act as simulators for the spread of the virus include meteorological variation, low air quality, pollen abundance, and spatial-temporal variation. However, the influence of these environmental factors on COVID-19 spread is still equivocal because of other non-pharmaceutical factors. The limitations of different modelling methods suggest the need for a multidisciplinary approach, including the 'One-Health' concept. Extended One-Health-based decision tools would assist policymakers in making informed decisions such as social gatherings, indoor environment improvement, and COVID-19 risk mitigation by adapting the control measurements.

Asian dust storms result in a higher risk of the silicosis hospital admissions

PURPOSE

Previous studies found that silicosis was majorly associated with occupation-related risks. However, little evidence was available to clarify the relation between Asian dust storm (ADS) and silicosis hospital admissions. This present paper aims to investigate the association between ADS events and hospital admissions for silicosis.

METHODS

We applied a Poisson time-series regression on the 2000-2012 National Health Insurance Research Database in Taiwan, linking air quality data and ambient temperature data to estimate the impact of ADS on silicosis hospital admissions in the age-specific groups.

RESULTS

A total of 2154 hospital admissions were recorded for silicosis in Taiwan, for a daily average number of 0.45. The number rises from 0.43 on a day without ADS to 0.70 on the outbreak day and continues increasing to 0.83 one day after outbreak. Among patients under 45, the effect of ADS appears on the event day as well as several post-event days (lag2-6) at the significant level of p < 0.1. There is also a significant lag effect on post-event day 2 (p < 0.05) for those aged above 74.

CONCLUSION

Asian dust storms do result in a rise of silicosis hospital admissions, particularly for those above 74, those under 45, and for females.

Save lives or save livelihoods? A cross-country analysis of COVID-19 pandemic and economic growth

This paper studies whether containing COVID-19 pandemic by stringent strategies deteriorates or saves economic growth. Since there are country-specific factors that could affect both economic growth and deaths due to COVID-19, we first start with a cross-country analysis on identifying risk and protective factors on the COVID-19 deaths using large across-country variation. Using data on 100 countries from 3 January to 27 November 2020 and taking into account the possibility of underreporting, we find that for deaths per million population, GDP per capita, population density, and income inequality are the three most important risk factors; government effectiveness, temperature, and hospital beds are the three most important protective factors. Second, inspired by the stochastic frontier literature, we construct a measure of pandemic containment effectiveness (PCE) after controlling for country-specific factors and rank countries by their PCE scores for deaths. Finally, by linking the PCE score with GDP growth data in Quarters 2 and 3 of 2020, we find that PCE is positively associated with economic growth in major economies. Countries with average PCE scores, such as Malaysia, would gain more GDP growth by 3.47 percentage points if they could improve their PCE scores for deaths to South Korea's level in Q2 of 2020. Therefore, there is not a trade-off between lives and livelihood facing by governments. Instead, to save economy, it is important to contain the pandemic first. Our conclusion is also mainly valid for infections due to COVID-19.

COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets

The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID-19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID-19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID-19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID-19 infection.

The impact of lockdown on nitrogen dioxide (NO2) over Central Asian countries during the COVID-19 pandemic

Nitrogen dioxide (NO₂) is one of the main air pollutants, formed due to both natural and anthropogenic processes, which has a significant negative impact on human health. The COVID-19 pandemic has prompted countries to take various measures, including social distancing or stay-at-home orders. This study analyzes the impact of COVID-19 lockdown measures on nitrogen dioxide (NO₂) changes in Central Asian countries. Data from TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite, as well as meteorological data, make it possible to assess changes in NO₂ concentration in countries and major cities in the region. In particular, the obtained satellite data show a decreased tropospheric column of NO₂. Its decrease during the lockdown (March 19-April 14) ranged from - 5.1% (Tajikistan) to - 11.6% (Turkmenistan). Based on the obtained results, it can be concluded that limitations in anthropogenic activities have led to improvements in air quality. The possible influence of meteorology is not assessed in this study, and the implied uncertainties cannot be quantified. In this way, the level of air pollution is expected to decrease as long as partial or complete lockdown continues.

Airborne magnetic nanoparticles may contribute to COVID-19 outbreak: Relationships in Greece and Iran

This work attempts to shed light on whether the COVID-19 pandemic rides on airborne pollution. In particular, a two-city study provides evidence that PM_2.5 contributes to the timing and severity of the epidemic, without adjustment for confounders. The publicly available data of deaths between March and October 2020, updated it on May 30, 2021, and the average seasonal concentrations of PM_2.5 pollution over the previous years in Thessaloniki, the second-largest city of Greece, were investigated. It was found that changes in coronavirus-related deaths follow changes in air pollution and that the correlation between the two data sets is maximized at the lag time of one month. Similar data from Tehran were gathered for comparison. The results of this study underscore that it is possible, if not likely, that pollution nanoparticles are related to COVID-19 fatalities (Granger causality, p < 0.05), contributing to the understanding of the environmental impact on pandemics.

Economic Uncertainty During COVID-19 Pandemic in Latin America and Asia

The purpose of this article is to analyze the impact of COVID-19 pandemic on inflation and exchange rate volatility and to study the government measures implemented in order to support economies. Based on monthly data from January to September 2020 for 10 countries, the dynamic panel data model is used to study the effect of COVID-19 spread. The results reveal that high infections negatively affect exchange rate and inflation; the responses of governments increase inflation and result in a lower exchange rate. In fact, providing health protocols which entered the countries into a new economic and financial crisis since economic agents could not freely engage in economic activities. Therefore, policy makers in both regions should invest in health infrastructure to improve the capacity of the national health system to resist the epidemic of contagious diseases.

Long-Term Air Pollution Exposure and Ischemic Heart Disease Mortality Among Elderly in High Aging Asian Economies

In the epidemiological literature, the impact of environmental pollution on cardiac mortality has been well documented. There is, however, a paucity of evidence on the impact of air pollution exposure on ischemic heart disease (IHD) mortality among the Asian aged population. In response, this research seeks to investigate the degree of proximity between exposure to ambient PM2.5, household PM2.5, ground-level ozone (O3), and IHD mortality in the top seven Asian economies with the highest aging rates. This investigation is held in two phases. In the first phase, grey modeling is employed to assess the degree of proximity among the selected variables, and then rank them based on their estimated grey weights. In addition, a grey-based Technique for Order of Preference by Similarity to Ideal Solution (G-TOPSIS) is adopted to identify the key influencing factor that intensifies IHD mortality across the selected Asian economies. According to the estimated results, South Korea was the most afflicted nation in terms of IHD mortality owing to ambient PM2.5 and ground-level O3 exposure, whereas among the studied nations India was the biggest contributor to raising IHD mortality due to household PM2.5 exposure. Further, the outcomes of G-TOPSIS highlighted that exposure to household PM2.5 is a key influencing risk factor for increased IHD mortality in these regions, outweighing all other air pollutants. In conclusion, this grey assessment may enable policymakers to target more vulnerable individuals based on scientific facts and promote regional environmental justice. Stronger emission regulations will also be required to mitigate the adverse health outcomes associated with air pollution exposure, particularly in regions with a higher elderly population.

India’s emerging fuel mix for 2050: actions and strategies to decarbonize the transport sector

Purpose

This paper aims to review the role of government initiatives for developing clean fuels in India, decarbonize the transport sector and maximize the use of renewable sources of energy. India’s socio-economic prosperity is dependent on modern energy. The authors examine the role of biofuel in India’s emerging fuel mix.

Design/methodology/approach

A 20-year timeframe between 2000 and 2021 was set to learn about the subject and find the existing gaps. Of the 40 research papers, the authors found using keywords and delimiting criteria in the database, the authors have shortlisted 21 papers, which provided the theoretical framework for the study. Additionally, the authors used the government database to develop future projections using compound annual growth rate and trend analysis.

Findings

The study findings suggest that India should strictly implement the Biofuel Policy to promote indigenous production of biofuel to enhance affordability and accessibility. With blending options available with biofuels and biogas, the country can replace the right proportion of fossil fuel use by 2050. It will not only decrease India’s import dependence but also will create new job opportunities, specifically in tribal and remote locations and promote green energy mix. With emerging options like electric vehicle and hydrogen, the transport sector could be decarbonized to a greater extent.

Social implications

Indigenous cleaner fuel adoption and transport sector will generate additional employment and cut down fossil fuel import. Financial savings through reduced fossil fuel import will be directed toward social development.

Originality/value

The paper carries out critical analysis for the active use of modern green fuels in the present and coming days. Such unique analysis must help India to balance its energy basket.

Foreign direct investment, financial development, energy consumption, and air quality: A way for carbon neutrality in China

Air quality is a social, economical, and health issue for fast-developing countries such as China. Due to the overuse of nonrenewable energy, industrialization, and the population put pressure on air quality, which seriously threatens public health and economic growth. This study focuses on air quality and also aims to investigate the short-and long-run correlation between foreign direct investment, energy consumption, domestic credit, and financial development. The Autoregressive distributed lag model and the Granger non-causality test were carried out over the period from 1985 to 2018. The main findings of this study show a positive and significant long-run impact of energy consumption on air quality. In addition, domestic credit and financial development similarly show a significant positive short-run association with air quality. Moreover, the unidirectional causality correlation running from foreign direct investment and domestic credit to air quality was concluded by the Granger non-causality test. Considering the empirical analysis, this study suggests that domestic financial institutions should offer credit to industries at a low-interest rate in order to help them to switch from non-renewable to renewable energy consumption towards the promotion of sustainable and healthy air quality.

Carbon neutrality target for leading exporting countries: On the role of economic complexity index and renewable energy electricity

In order to contribute to the existing limited energy-environment literature, the present study analyze the carbon neutrality targets of the 16 major exporting economies while considering the role of economic complexity and renewable energy electricity consumption empirically by investigating the most recent dataset covering the period from 1990 to 2019 by employing advanced econometric techniques. This study uses the economic complexity index, connecting the country's productive structure with the amount of knowledge that the products represent. Employing various cointegration and regression techniques such as augmented mean group (AMG) and dynamic ordinary least square (DOLS) confirms the long-run cointegration among the variables such as economic growth, economic complexity, renewable energy consumption, and CO₂ emission. Also, this study provides evidence that confirms the validity of the environmental Kuznets curve hypothesis in the leading exporting economies. Regarding the carbon neutrality target, we found that both economic complexity and renewable electricity, if increase by one percent each, significantly reduce CO₂ emissions by 0.1491 (AMG) and 0.130% (DOLS) and 0.160 (AMG) and 0.203% (DOLS), respectively, that help attain the carbon neutrality target.

Association between ambient air pollutants and meteorological factors with SARS-CoV-2 transmission and mortality in India: an exploratory study

BACKGROUND

The Coronavirus disease 2019 (COVID-19) pandemic poses a serious public health concern worldwide. Certain regions of the globe were severely affected in terms of prevalence and mortality than other. Although the cause for this pattern is not clearly understood, lessons learned from previous epidemics and emerging evidences suggest the major role of ecological factors like ambient air pollutants (AAP) and meteorological parameters in increased COVID-19 incidence. The present study aimed to understand the impact of these factors on SARS-CoV-2 transmission and their associated mortality in major cities of India.

METHODS

This study used secondary AAP, meteorological and COVID-19 data from official websites for the period January-November 2020, which were divided into Pre-lockdown (January-March 2020), Phase I (April to June 2020) and Phase II (July to November 2020) in India. After comprehensive screening, five major cities that includes 48 CPCB monitoring stations collecting daily data of ambient temperature, particulate matter PM_2.5 and ₁₀ were analysed. Spearman and Kendall's rank correlation test was performed to understand the association between SARS-CoV-2 transmission and AAP and, meteorological variables. Similarly, case fatality rate (CFR) was determined to compute the correlation between AAP and COVID-19 related morality.

RESULTS

The level of air pollutants in major cities were significantly reduced during Phase I compared to Pre-lock down and increased upon Phase II in all the cities. During the Phase II in Delhi, the strong significant positive correlation was observed between the AAP and SARS-CoV-2 transmission. However, in Bengaluru, Hyderabad, Kolkata and Mumbai AAP levels were moderate and no correlation was noticed. The relation between AT and SARS-CoV-2 transmission was inconclusive as both positive and negative correlation observed. In addition, Delhi and Kolkata showed a positive association between long-term exposure to the AAP and COVID-19 CFR.

CONCLUSION

Our findings support the hypothesis that the particulate matter upon exceeding the satisfactory level serves as an important cofactor in increasing the risk of SARS-CoV-2 transmission and related mortality. These findings would help public health experts to understand the SARS-CoV-2 transmission against ecological variables in India and provides supporting evidence to healthcare policymakers and government agencies for formulating strategies to combat the COVID-19.

Assessing a Fossil Fuels Externality with a New Neural Networks and Image Optimization Algorithm: The Case of Atmospheric Pollutants as Cofounders to COVID-19 Lethality

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM_2.5, PM₁₀ and CO₂) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM_2.5, PM₁₀ and CO₂ which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

The effect of energy resources on economic growth and carbon emissions: A way forward to carbon neutrality in an emerging economy

Globally, all countries have producing different levels of carbon emissions and also facing both the problems of climate change and global warming due high carbon emissions in the atmosphere. Therefore, it is important to cutting carbon emissions in the atmosphere. This is only possible by switching to cleaner fuels, use of innovation technologies and development of carbon capture storages. These can substantially help the nations to reaching carbon neutrality. Given this background, this paper examines the effect of disaggregated energy consumption, technological innovations, capital on economic output and CO₂ emissions in India for the period of 1990-2018. Based on empirical analysis, our long-run elasticities indicate that disaggregated energy consumption and technological innovations have a positive impact on economic growth, while renewable energy consumption and technological innovations have a positive impact on CO₂ emissions. It implies that more use of energy consumption producing significant amount of CO₂ emissions and by using renewable energy consumption and technological innovations (i.e. carbon capture storages) can significantly lowering CO₂ emissions, which is clearly indicating that India has moving towards carbon neutrality. The causality analysis further indicates a unidirectional causal relationship running from disaggregated energy usage to economic growth and carbon emissions. These empirical findings suggest that the increased consumption of renewable power does not lead to rise carbon emissions, which, in turn, ensures sustainable economic growth.

Effect of Anti-COVID-19 Measures on Atmospheric Pollutants Correlated with the Economies of Medium-sized Cities in 10 Urban Areas of Grand Est Region, France

Using Sentinel-5P data, this study investigated the magnitude of change in the concentration of air pollutants (NO₂, HCHO, SO₂, O₃, CO, and aerosol index) in the air of ten cities and urban areas of the French region of Grand Est as a result of the first lockdown imposed between March 17, 2020 and May 11, 2020. The results showed that the air quality in the urban environments of Grand Est improved significantly compared to the same period in 2019 without lockdown. NO₂, O₃, aerosol index and CO were the pollutants that exhibited maximum reductions by an average of -33.98%, -5.94%, -26.82% and -0.66%, respectively (the observed maximum decreases were -54.7%, -7.7%, -13.1%, and -5.3%, respectively). The largest decrease occurred in the Public Establishments of Inter-municipal Cooperation (EPCI, in French: Établissement public de coopération intercommunale) areas of Eurométropole de Strasbourg, CA Colmar, and CA Mulhouse Alsace. The maximum decrease in air pollution first occurred in land cover classes close to cities, followed by built-up urban areas. In this study, a global depollution index known as the atmospheric clearance index (ACI) was developed, which involved several air pollution parameters, and quantitatively analyzed the decrease in contamination levels of the atmosphere in this region. In addition, the correlation between the novel ACI and other population and economic development indices was studied. The results indicated that there was a negative and statistically significant correlation between ACI and population density, gross domestic product, gross value added (GVA) at basic prices, number of employees, and active enterprises.

Exposure to air pollution and COVID-19 severity: A review of current insights, management, and challenges

Several epidemiological studies have suggested a link between air pollution and respiratory tract infections. The outbreak of coronavirus disease 2019 (COVID-19) poses a great threat to public health worldwide. However, some parts of the globe have been worse affected in terms of prevalence and deaths than others. The causes and conditions of such variations have yet to be explored. Although some studies indicated a possible correlation between air pollution and COVID-19 severity, there is yet insufficient data for a meaningful answer. This review summarizes the impact of air pollution on COVID-19 infections and severity and discusses the possible management strategies and challenges involved. The available literature investigating the correlation between air pollution and COVID-19 infections and mortality are included in the review. The studies reviewed here suggest that exposure to air pollution, particularly to PM_2.5 and NO₂ , is positively correlated with COVID-19 infections and mortality. Some data indicate that air pollution can play an important role in the airborne transmission of SARS-CoV-2. A high percentage of COVID-19 incidences has been reported in the most polluted areas, where patients needed hospital admission. The available data also show that both short-term and long-term air pollution may enhance COVID-19 severity. However, most of the studies that showed a link between air pollution and COVID-19 infections and mortality did not consider potential confounders during the correlation analysis. Therefore, more specific studies need to be performed focusing on some additional confounders such as individual age, population density, and pre-existing comorbidities to determine the impact of air pollution on COVID-19 infections and deaths. Integr Environ Assess Manag 2021;17:1114-1122. © 2021 SETAC.

Machine Learning-Aided Causal Inference Framework for Environmental Data Analysis: A COVID-19 Case Study

Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 severity have been reported worldwide. However, the existing frameworks of data analysis are insufficient or inefficient to investigate the potential causality behind the associations involving multidimensional factors and complicated interrelationships. Thus, a causal inference framework equipped with the structural causal model aided by machine learning methods was proposed and applied to examine the potential causal relationships between COVID-19 severity and 10 environmental factors (NO₂, O₃, PM2.5, PM10, SO₂, CO, average air temperature, atmospheric pressure, relative humidity, and wind speed) in 166 Chinese cities. The cities were grouped into three clusters based on the socio-economic features. Time-series data from these cities in each cluster were analyzed in different pandemic phases. The robustness check refuted most potential causal relationships' estimations (89 out of 90). Only one potential relationship about air temperature passed the final test with a causal effect of 0.041 under a specific cluster-phase condition. The results indicate that the environmental factors are unlikely to cause noticeable aggravation of the COVID-19 pandemic. This study also demonstrated the high value and potential of the proposed method in investigating causal problems with observational data in environmental or other fields.

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients' financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients' transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.

Global assessment of tropospheric and ground air pollutants and its correlation with COVID-19

The declaration of COVID-19 pandemic by the WHO initiated a series of lockdowns globally that varied in stringency and duration; however, the spatiotemporal effects of these lockdowns on air quality remain understudied. This study evaluates the global impact of lockdowns on air pollutants using tropospheric and ground-level indicators over a five-month period. Moreover, the relationship between air pollution and COVID-19 cases and mortalities was examined. Changes in the global tropospheric (NO2, aerosols, and O3) and ground-level (PM2.5, PM10, NO2, and O3) pollutants were observed, and the maximum air quality improvement was observed immediately after lockdown. Except for a few countries, a decline in air pollutants correlated with a reduction in Land Surface Temperature (LST). Notably, regions with higher tropospheric NO2 and aerosol concentrations were also COVID-19 hotspots. Our analysis showed moderate positive correlation for NO2 with COVID-19 cases (R2 = 0.33; r = 0.57, P = 0.006) and mortalities (R2 = 0.40; r = 0.63, P = 0.015), while O3 showed a weak-moderate positive correlation with COVID-19 cases (R2 = 0.22; r = 0.47, P = 0.003) and mortalities (R2 = 0.12; r = 0.35, P = 0.012). However, PM2.5, and PM10 showed no significant correlation with either COVID-19 cases or mortality. This study reveals that humans living under adverse air pollution conditions are at higher risk of COVID-19 infection and mortality.

Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources

Global energy demand increases overtime, especially in emerging market economies, producing potential negative environmental impacts, particularly on the long term, on nature and climate changes. Promoting renewables is a robust policy action in world energy-based economies. This study examines if an increase in renewables production has a positive effect on the Brazilian economy, partially offsetting the SARS-CoV2 outbreak recession. Using data on Brazilian economy, we test the contribution of renewables on the economy via a ML architecture (through a LSTM model). Empirical findings show that an ever-greater use of renewables may sustain the economic growth recovery, generating a better performing GDP acceleration vs. other energy variables.

The effect of environmental diesel exhaust pollution on SARS-CoV-2 infection: The mechanism of pulmonary ground glass opacity

Diesel exhaust particles (DEP) are the major components of atmospheric particulate matter (PM) and chronic exposure is recognized to enhance respiratory system complications. Although the spread of SARS-CoV-2 was found to be associated with the PMs, the mechanism by which exposure to DEP increases the risk of SARS-CoV-2 infection is still under discussion. However, diesel fine PM (dPM) elevate the probability of SARS-CoV-2 infection, as it coincides with the increase in the number of ACE2 receptors. Expression of ACE2 and its colocalized activator, transmembrane protease serine 2 (TMPRSS2) facilitate the entry of SARS-CoV-2 into the alveolar epithelial cells exposed to dPM. Thus, the coexistence of PM and SARS-CoV-2 in the environment augments inflammation and exacerbates lung damage. Increased TGF-β1 expression due to DEP accompanies the proliferation of the extracellular matrix. In this case, "multifocal ground-glass opacity" (GGO) in a CT scan is an indication of a cytokine storm and severe pneumonia in COVID-19.

Early Spread of COVID-19 in the Air-Polluted Regions of Eight Severely Affected Countries

COVID-19 escalated into a pandemic posing several humanitarian as well as scientific challenges. We here investigated the geographical character of the early spread of the infection and correlated it with several annual satellite and ground indexes of air quality in China, the United States, Italy, Iran, France, Spain, Germany, and the United Kingdom. The time of the analysis corresponded with the end of the first wave infection in China, namely June 2020. We found more viral infections in those areas afflicted by high PM 2.5 and nitrogen dioxide values. Higher mortality was also correlated with relatively poor air quality. In Italy, the correspondence between the Po Valley pollution and SARS-CoV-2 infections and induced mortality was the starkest, originating right in the most polluted European area. Spain and Germany did not present a noticeable gradient of pollution levels causing non-significant correlations. Densely populated areas were often hotspots of lower air quality levels but were not always correlated with a higher viral incidence. Air pollution has long been recognised as a high risk factor for several respiratory-related diseases and conditions, and it now appears to be a risk factor for COVID-19 as well. As such, air pollution should always be included as a factor for the study of airborne epidemics and further included in public health policies.

Impact of the COVID-19 pandemic on air pollution in Chinese megacities from the perspective of traffic volume and meteorological factors

During 2020, the COVID-19 pandemic resulted in a widespread lockdown in many cities in China. In this study, we assessed the impact of changes in human activities on air quality during the COVID-19 pandemic by determining the relationships between air quality, traffic volume, and meteorological conditions. The megacities of Wuhan, Beijing, Shanghai, and Guangzhou were selected as the study area, and the variation trends of air pollutants for the period January-May between 2016 and 2020 were analyzed. The passenger volume of public transportation (PVPT) and the passenger volume of taxis (PVT) along with data on precipitation, temperature, relative humidity, wind speed, and boundary layer height were used to identify and quantify the driving force of the air pollution variation. The results showed that the change rates of fine particulate matter (PM_2.5), NO₂, and SO₂ before and during the lockdown in the four megacities ranged from -49.9% to 78.2% (average: -9.4% ± 59.3%), -55.4% to -32.3% (average: -43.0% ± 9.7%), and - 21.1% to 11.9% (average: -10.9% ± 15.4%), respectively. The response of NO₂ to the lockdown was the most sensitive, while the response of PM_2.5 was smaller and more delayed. During the lockdown period, haze from uninterrupted industrial emissions and fireworks under the effect of air mass transport from surrounding areas and adverse climate conditions was probably the cause of abnormally high PM_2.5 concentrations in Beijing. In addition, the PVT was the most significant factor for NO₂, and meteorology had a greater impact on PM_2.5 than NO₂ and SO₂. There is a need for more national-level policies for limiting firework displays and traffic emissions, as well as further studies on the formation and transmission of secondary air pollutants.

Can biomass energy curtail environmental pollution? A quantum model approach to Germany

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO₂ emissions for Germany. We constructed an innovative algorithm, the Quantum model, and applied it with Machine Learning experiments - through a software capable of emulating a quantum system - to data over the period of 1990-2018. This process is possible after eliminating the "irreversibility" of classical computations (unitary transformations) by making the process "reversible". The empirical findings support the powerful role of biomass energy in reducing carbon dioxide emissions, although the effect of renewable energy technology displays a much stronger magnitude. Moreover, income remains an important determinant of environmental pollution in Germany.

The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM₁₀, PM_2.5, and NO₂ concentrations in New York state using city-level daily data through two Machine Learning experiments. PM_2.5 and NO₂ are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis. In line with the causal findings, a unidirectional causal effect is found from PM_2.5 to Deaths, NO₂ to Deaths, and economic growth to both PM_2.5 and NO₂. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM_2.5 to Deaths, NO₂ to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM_2.5 and NO₂) in New York state.

Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 μg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.

Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review

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.

Emerging role of air pollution and meteorological parameters in COVID-19

Exposure to air pollutants has been associated with respiratory viral infections. Epidemiological studies have shown that air pollution exposure is related to increased cases of SARS-COV-2 infection and COVID-19-associated mortality. In addition, the changes of meteorological parameters have also been implicated in the occurrence and development of COVID-19. However, the molecular mechanisms by which pollutant exposure and changes of meteorological parameters affects COVID-19 remains unknown. This review summarizes the biology of COVID-19 and the route of viral transmission, and elaborates on the relationship between air pollution and climate indicators and COVID-19. Finally, we envisaged the potential roles of air pollution and meteorological parameters in COVID-19.

Investigation of the urbanization contribution to the COVID-19 outbreak in Iran and the MECA countries

The main objective of this research was to disclose the correlative contribution of urban-associated factors affecting the COVID-19 outbreak in the macro-scale of MECA countries and the downscaled micro-scale of the provincial divisions in Iran. For this purpose, the correlation coefficients between the variables and clustering analysis were used to expose the possible effects. Results revealed the comparatively strong relationships between some independent variables (e.g., total greenhouse gas emissions, CO2 emissions, nitrous oxide emissions, and urban population) and confirmed cases (R from 0.619 to 0.695), demonstrating the possible effective role of urbanization and its induced GHG emissions on the COVID-19 outbreak in the country level of the MECA region. Therefore, the results significantly confirmed the strong relationships between some independent variables (e.g., total population, urban population, fuel consumption, NO2-CO2 emissions, energy use, and total intra-changed travels) and confirmed cases (R from 0.724 to 0.945), explaining an explicit relationship between urbanization processes and the COVID-19 outbreak in Iran. Besides, the HCA results revealed the substantial role of the urban population and urban-induced energy use and gas emission in clustering locations regarding the COVID-19 outbreak in both the MECA region and Iran. The main implication of this research is to give a practical correlation between Coronavirus infection and urban constitution, aiming to increase the health of urban societies by creating effective planning in the future.