The relationship between air pollution and COVID-19-related deaths: An application to three French cities

The Role of Meteorological Variables and Aerosols in the Transmission of COVID-19 During Harmattan Season

The role of atmospheric parameters and aerosols in the transmission of COVID-19 within tropical Africa, especially during the harmattan season, has been under-investigated in published papers. The harmattan season within the West African region is associated with significant dust incursion from the Bodele depression and biomass burning. In this study, the correlation between atmospheric parameters (temperature and humidity) and aerosols with COVID-19 cases and fatalities within seven locations in tropical Nigeria during the harmattan period was investigated. COVID-19 infection cases were found to be significantly positively correlated with atmospheric parameters (temperature and humidity) in the southern part of the country while the number of fatalities showed weaker significant correlation with particulate matters only in three locations. The significant correlation values were found to be between 0.22 and 0.48 for particulate matter and -0.19 to -0.32 for atmospheric parameters. Although, temperature and humidity showed negative correlations in some locations, the impact is smaller compared to particulate matter. In December, COVID-19 cases in all locations showed strong correlation with particulate matter except in Kano State. It is suggested that a reduction in atmospheric particulate matter can be used as a control measure for the spread of COVID-19.

COVID-19 pandemic: What can we learn for better air quality and human health?

The COVID-19 lockdown resulted in improved air quality in many cities across the world. With the objective of what could be the new learning from the COVID-19 pandemic and subsequent lockdowns for better air quality and human health, a critical synthesis of the available evidence concerning air pollution reduction, the population at risk and natural versus anthropogenic emissions was conducted. Can the new societal norms adopted during pandemics, such as the use of face cover, awareness regarding respiratory hand hygiene, and physical distancing, help in reducing disease burden in the future? The use of masks will be more socially acceptable during the high air pollution episodes in lower and middle-income countries, which could help to reduce air pollution exposure. Although post-pandemic, some air pollution reduction strategies may be affected, such as car-pooling and the use of mass transit systems for commuting to avoid exposure to airborne infections like coronavirus. However, promoting non-motorized modes of transportation such as cycling and walking within cities as currently being enabled in Europe and other countries could overshadow such losses. This demand focus on increasing walkability in a town for all ages and populations, including for a differently-abled community. The study highlighted that for better health and sustainability there. is also a need to promote other measures such as work-from-home, technological infrastructure, the extension of smart cities, and the use of information technology.

Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford, UK

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national 'lockdown' periods in Oxford, UK. Air quality improvements were most marked during the first lockdown with reductions in observed NO₂ concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM_2.5, PM₁₀ and O₃ concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO₂ and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM_2.5 and O₃ concentration changes were not significant, but PM₁₀ increased in the second lockdown only. City centre traffic volume reduced by 69% and 38% in the first and second lockdown periods. Buses and passenger cars were the major contributors to NO₂ emissions, with relative reductions of 56% and 77% respectively during the first lockdown, and less pronounced changes in the second lockdown. While car and bus NO₂ emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO₂ emissions reduction consistent with the first lockdown could prevent 48 lost life-years among the city population, with economic benefits of up to £2.5 million. Our findings highlight the critical importance of decoupling emissions changes from meteorological influences to avoid overestimation of lockdown impacts and indicate targeted emissions control measures will be the most effective strategy for achieving air quality and public health benefits in this setting.

Effect of environmental pollutants PM2.5, CO, O3 and NO2, on the incidence and mortality of SARS-COV-2 in largest metropolitan cities, Delhi, Mumbai and Kolkata, India

OBJECTIVES

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has developed a challenging situation worldwide. In India, the SARS-CoV-2 cases and deaths have markedly increased. This study aims to evaluate the impact of environmental pollutants "particulate matter (PM 2.5 μm), carbon monoxide (CO), Ozone (O3), and Nitrogen Dioxide (NO2) on daily cases and deaths due to SARS-CoV-2 infection" in Delhi, Mumbai, and Kolkata, India.

METHODS

The day-to-day air pollutants PM2.5, CO, O3, and NO2 were recorded from the metrological web "Real-time Air Quality Index (AQI)." SARS-COV-2 everyday cases and deaths were obtained from the "Coronavirus outbreak in India Web". The PM 2.5, CO, O3, NO2, and daily cases, deaths were documented for more than one year, from March 2, 2020, to March 15, 2021.

RESULTS

Environmental pollutants CO, O3, and NO2, were positively related to SARS-COV-2 cases and deaths. The findings further described that for each one-unit increase in CO, O3, and NO2 levels, the number of cases was significantly augmented by 0.77%, 0.45%, and 4.33%.

CONCLUSIONS

Environmental pollution is a risk factor to SARS-CoV-2 daily cases and deaths. The regional and international authorities must implement the policies to reduce air pollution and the COVID-19 pandemic. The findings can inform health policymakers' verdicts about battling the COVID-19 pandemic in India and globally by minimizing environmental pollution.

Nexuses between crude oil imports, renewable energy, transport services, and technological innovation: a fresh insight from Germany

This research attempts to model the association of crude oil imports with several macroeconomic factors such as renewable energy, transport services, trade, industrial value-added, and patents, using Germany's annual data covering the period of 1990-2020. Employing the Autoregressive Distributed Lag model, this study finds a significant co-integration relationship among targeted variables. Moreover, this study provides empirical evidence on the influence of given macroeconomic factors in determining crude oil imports of Germany. Results reveal that transport services and industrial value-added positively and significantly influence crude oil imports in the long and short run. Similarly, trade is discovered to have a significant positive impact on oil imports only in the long run. In contrast, findings reveal a significant negative association of renewable energy with crude oil imports. Hence, this research implies that the transportation sector and industrial production strongly depend on crude oil consumption. At the same time, promoting renewable energy in these segments could significantly help economies control crude oil demand and achieve sustainability by reducing the economic burden and protecting the environment.

Revisiting air quality during lockdown persuaded by second surge of COVID-19 of megacity Delhi, India

Is the impact of city-scale lockdown in response to 2nd surge of COVID-19, behavioural changes in people owing to yearlong cohabitation with COVID-19, and partial vaccination on air quality different from the impact of nationwide lockdown during COVID-19's 1st surge in March 2020? Targeting this objective, the present work has selected four phases pre-lockdown and lockdown of 1st and 2nd cycles of lockdown taking average air quality index (NAQI) from Central Pollution Control Board (CPCB). The results clearly show that both the nationwide lockdown and the city-scale restriction are responsible for improving air quality in India's megacity Delhi, but the rate of improvement was higher (39%) during the first cycle of lockdown (nationwide) than during the second cycle of lockdown (city-scale). During city-scale lockdown, the disparity in NAQI between the core and the periphery is obvious. Due to the effect of economic activities surrounding Delhi, around 10 km of the city's interior has experienced high NAQI. The reason for the lower NAQI improvement during the second lockdown cycle is likely due to relief from initial fear following a year of cohabitation with COVID-19, partial vaccination, and partial relaxation in industrial sectors to avoid the economic hardships experienced during the first lockdown cycle.

Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy

The consequence of the lockdowns implemented to address the COVID-19 pandemic on human health damage due to air pollution and other environmental issues must be better understood. This paper analyses the effect of reducing energy demand on the evolution of environmental impacts during the occurrence of 2020-lockdown periods in Italy, with a specific focus on life expectancy. An energy metabolism analysis is conducted based on the life cycle assessment (LCA) of all monthly energy consumptions, by sector, category and province area in Italy between January 2015 to December 2020. Results show a general decrease (by ∼5% on average) of the LCA midpoint impact categories (global warming, stratospheric ozone depletion, fine particulate matter formation, etc.) over the entire year 2020 when compared to past years. These avoided impacts, mainly due to reductions in fossil energy consumptions, are meaningful during the first lockdown phase between March and May 2020 (by ∼21% on average). Regarding the LCA endpoint damage on human health, ∼66 Disability Adjusted Life Years (DALYs) per 100,000 inhabitants are estimated to be saved. The analysis shows that the magnitude of the officially recorded casualties is substantially larger than the estimated gains in human lives due to the environmental impact reductions. Future research could therefore investigate the complex cause-effect relationships between the deaths occurred in 2020 imputed to COVID-19 disease and co-factors other than the SARS-CoV-2 virus.

The real economic costs of COVID-19: Insights from electricity consumption data in Hunan Province, China

The COVID-19 pandemic has caused extreme economic fluctuations. However, the magnitude of the economic cost of this extreme event remains challenging to quantify. The impact of the COVID-19 pandemic on the economy is estimated through firm-level electricity consumption data from Hunan province, China. Specifically, a difference-in-differences (DID) model was employed to estimate the real economic costs. The results indicate that electricity consumption in Hunan Province dropped by 27.8% during the early stage of the COVID-19 pandemic. Manufacturing and the transportation industry suffered the most severe declines. Electricity consumption began to recover after the virus was controlled. We suggest that government departments should take full measures to prevent and control COVID-19 outbreaks and associated economic impacts, in conjunction with preparing for economic recovery, deploying targeted measures to support different industries in response to the heterogeneity COVID-19 pandemic impacts. The COVID-19 has changed people's living habits and brought a new direction, the Internet industry, of economic growth. Hunan Province needs to accelerate the digital empowerment of traditional industries, develop the Internet, 5G technology, and new digital infrastructure to offset the negative impact of the COVID-19 pandemic. Electricity consumption is an applicable index in estimate the real economic cost of extreme events.

State of air pollutants and related health risk over Haryana India as viewed from satellite platform in COVID-19 lockdown scenario

COVID-19 driven lockdown has affected air quality worldwide. Changes in air pollutants concentration, Air Quality Index (AQI), and associated Excess Health Risk (ER%) were assessed using satellite data of before (2019), and during (2020) COVID-19 periods in the industrially, agriculturally developed and highly populated area of Haryana in the northern region of Indo-Gangetic Plains. Parameters such as Aerosol Optical Depth (AOD), Particulate matters (PM), Sulphur Di-Oxide (SO2), Nitrogen Di-Oxide (NO2), Carbon Mono-oxide (CO), and Methane (CH4) were derived using satellite data and validated using ground-based observations (n = 23). The coefficient of correlation (r) 0.91, 0.90, 0.95, 0.73, 0.81 and 0.80 were established with AOD, PM2.5, PM10, SO2, NO2 and CO, respectively. Significant reduction (p  < 0.005) in the concentration of air pollutants, viz. 38% in AOD, 55% in PM2.5, 61% in PM10, 31% in SO2, 10% in NO2, 5% in CO and 1% in CH4 were observed during lockdown. Significant (p  < 0.00) improvement in air quality was observed due to a 44% reduction in pollution level, which led to the reduction in ER% by 71%, which is quite significant. AQI and ER% from satellite and ground showed a high r2 i.e. 0.88 and 0.99 respectively, suggesting the potential application of satellite data for periodic AQI and ER% assessment.

Environmental stocks, CEO health risk and COVID-19

During the COVID-19 pandemic, we find that Australian firms with environmentally sustainable practices generated higher abnormal returns. Firms with CEOs who were exposed to significant health risks from COVID-19 experienced poorer stock market performance. Firms with low pre-COVID default risk and high pre-COVID liquidity performed better during the COVID-19 stock market crash. This research signifies the importance of environmental sustainability for Australian firms to endure pandemics such as COVID-19.

The impact of coal combustion, nitrous oxide emissions, and traffic emissions on COVID-19 cases: a Markov-switching approach

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread to more than 200 countries with a current case fatality ratio (CFR) of more than 2% globally. The concentration of air pollutants is considered a critical factor responsible for transmitting coronavirus disease among the masses. The photochemical process and coal combustions create respiratory disorders that lead to coronavirus disease. Based on the crucial fact, the study evaluated the impact of nitrous oxide (N₂O) emissions, coal combustion, and traffic emissions on COVID-19 cases in a panel of 39 most affected countries of the world. These three air pollution factors are considered to form a lethal smog that negatively affects the patient's respiratory system, leading to increased susceptibility to coronavirus worldwide. The study used the Markov two-step switching regime regression model for obtaining parameter estimates. In contrast, an innovation accounting matrix is used to assess smog factors' intensity on possibly increasing coronavirus cases over time. The results show that N₂O emissions, coal combustion, and traffic emissions increase COVID-19 cases in regime-1. On the other hand, N2O emissions significantly increase coronavirus cases in regime-2. The innovation accounting matrix shows that N2O emissions would likely have a more significant share of increasing coronavirus cases with a variance of 33.902%, followed by coal combustion (i.e., 6.643%) and traffic emissions (i.e., 2.008%) over the time horizon. The study concludes that air quality levels should be maintained through stringent environmental policies, such as carbon pricing, sustainable urban planning, green technology advancement, renewable fuels, and pollution less accessible vehicles. All these measures would likely decrease coronavirus cases worldwide.

Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)

Background: the Lombardy region in Italy was the first area in Europe to record an outbreak of COVID-19 and one of the most affected worldwide. As this territory is strongly polluted, it was hypothesized that pollution had a role in facilitating the diffusion of the epidemic, but results are uncertain. Aim: the paper explores the effect of air pollutants in the first spread of COVID-19 in Lombardy, with a novel geomatics approach addressing the possible confounding factors, the reliability of data, the measurement of diffusion speed, and the biasing effect of the lockdown measures. Methods and results: all municipalities were assigned to one of five possible territorial classes (TC) according to land-use and socio-economic status, and they were grouped into districts of 100,000 residents. For each district, the speed of COVID-19 diffusion was estimated from the ambulance dispatches and related to indicators of mean concentration of air pollutants over 1, 6, and 12 months, grouping districts in the same TC. Significant exponential correlations were found for ammonia (NH₃) in both prevalently agricultural (R² = 0.565) and mildly urbanized (R² = 0.688) areas. Conclusions: this is the first study relating COVID-19 estimated speed of diffusion with indicators of exposure to NH₃. As NH₃ could induce oxidative stress, its role in creating a pre-existing fragility that could have facilitated SARS-CoV-2 replication and worsening of patient conditions could be speculated.

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 optimisation algorithm: the case of atmospheric pollutants as confounders 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 (PM2.5, 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 PM2.5, 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.

Sandstorm and its effect on particulate matter PM 2.5, carbon monoxide, nitrogen dioxide, ozone pollutants and SARS-CoV-2 cases and deaths

Sandstorms are a natural metrological phenomenon, frequently occurring in many arid and semi-arid regions of the world. The sandstorm dust contains environmental pollutants, microorganisms including bacteria, fungi, and viruses. These events are the primary sources of air pollution and its long-distance transport. Thus, sandstorms are becoming a greater concern during the COVID-19 pandemic. Therefore, this novel study aimed to investigate the effect of a sandstorm on "environmental pollutants particulate matter (PM2.5), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and day-to-day new cases and deaths due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection" in Riyadh, Saudi Arabia. On March 12, 2021, a sandstorm occurred in the Riyadh region, the capital city of Saudi Arabia. The data on PM 2.5, CO, NO2, and O3 were recorded three weeks before and three weeks after the onset of the sandstorm, from February 20, 2021, to March 12, 2021, and from March 13 to April 2, 2021. The daily PM2.5, CO, NO2, and O3 levels were documented from the metrological websites, and Air Quality Index-AQI, COVID-19 daily cases, and deaths were obtained from Saudi Arabia's official coronavirus website. After sandstorm, the air pollutants, CO level increased by 84.25%; PM2.5: 76.71%; O3: 40.41%; NO2: 12.03%; and SARS-CoV-2 cases increased by 33.87%. However, the number of deaths decreased by 22.39%. The sandstorm event significantly increased the air pollutants, PM2.5, CO, and O3, which were temporally associated with increased SARS-COV-2 cases. However, no significant difference was noticed in NO2 and the number of deaths after the sandstorm. The findings have an important message to health authorities to timely provide information to the public about the sandstorm and its associated health problems, including SARS-CoV-2 cases and deaths.

The effect of air pollution on COVID-19 severity in a sample of patients with multiple sclerosis

Background and purpose

Some studies have shown that air pollution, often assessed by thin particulate matter with diameter below 2.5 µg/m³ (PM2.5), may contribute to severe COVID‐19 courses, as well as play a role in the onset and evolution of multiple sclerosis (MS). However, the impact of air pollution on COVID‐19 has never been explored specifically amongst patients with MS (PwMS). This retrospective observational study aims to explore associations between PM2.5 and COVID‐19 severity amongst PwMS.

Methods

Data were retrieved from an Italian web‐based platform (MuSC‐19) which includes PwMS with COVID‐19. PM2.5 2016–2018 average concentrations were provided by the Copernicus Atmospheric Monitoring Service. Italian patients inserted in the platform from 15 January 2020 to 9 April 2021 with a COVID‐19 positive test were included. Ordered logistic regression models were used to study associations between PM2.5 and COVID‐19 severity.

Results

In all, 1087 patients, of whom 13% required hospitalization and 2% were admitted to an intensive care unit or died, were included. Based on the multivariate analysis, higher concentrations of PM2.5 increased the risk of worse COVID‐19 course (odds ratio 1.90; p = 0.009).

Conclusions

Even if several other factors explain the unfavourable course of COVID‐19 in PwMS, the role of air pollutants must be considered and further investigated.

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.

The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence

While the deployment of technological innovation was able to avert a devastating global supply chain fallout arising from the impact of ravaging COronaVIrus Disease 19 (COVID-19) pandemic, little is known about potential environmental cost of such achievement. The aim of this paper is to identify the determinants of logistics performance and investigate its empirical linkages with economic and environmental indicators. We built a macro-level dataset for the top 25 ranked logistics countries from 2007 to 2018, conducting a set of panel data tests on cross-sectional dependence, stationarity and cointegration, to provide preliminary insights. Empirical estimates from Fully Modified Ordinary Least Squares (FMOLS), Generalized Method of Moments (GMM), and Quantile Regression (QR) model suggest that technological innovation, Human Development Index (HDI), urbanization, and trade openness significantly boost logistic performance, whereas employment and Gross Fixed Capital Formation (GFCF) fail to respond in such a desirable path. In turn, an increase in the Logistic Performance Index (LPI) is found to worsen economic growth. Finally, LPI exhibits a large positive effect on carbon emissions, which is congruent with a strand of the literature highlighting that the modern supply chain is far from being decarbonized. Thus, this evidence further suggest that more global efforts should be geared to attain a sustainable logistics.

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.

Impact of environmental factors and Sahara dust intrusions on incidence and severity of COVID-19 disease in Spain. Effect in the first and second pandemic waves

Scientific evidence suggests that Saharan dust intrusions in Southern Europe contribute to the worsening of multiple pathologies and increase the concentrations of particulate matter (PM) and other pollutants. However, few studies have examined whether Saharan dust intrusions influence the incidence and severity of COVID-19 cases. To address this question, in this study we carried out generalized linear models with Poisson link between incidence rates and daily hospital admissions and average daily concentrations of PM10, NO2, and O3 in nine Spanish regions for the period from February 1, 2020 to December 31, 2020. The models were adjusted by maximum daily temperature and average daily absolute humidity. Furthermore, we controlled for trend, seasonality, and the autoregressive nature of the series. The variable relating to Saharan dust intrusions was introduced using a dichotomous variable, NAF, averaged across daily lags in ranges of 0-7 days, 8-14 days, 14-21 days, and 22-28 days. The results obtained in this study suggest that chemical air pollutants, and especially NO2, are related to the incidence and severity of COVID-19 in Spain. Furthermore, Saharan dust intrusions have an additional effect beyond what is attributable to the variation in air pollution; they are related, in different lags, to both the incidence and hospital admissions rates for COVID-19. These results serve to support public health measures that minimize population exposure on days with particulate matter advection from the Sahara.

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.

COVID-19 and environmental concerns: A rapid review

COVID-19 has slowed global economic growth and consequently impacted the environment as well. Parallelly, the environment also influences the transmission of this novel coronavirus through various factors. Every nation deals with varied population density and size; air quality and pollutants; the nature of land and water, which significantly impact the transmission of coronavirus. The WHO (Ziaeepour et al., 2008) [1] has recommended rapid reviews to provide timely evidence to the policymakers to respond to the emergency. The present study follows a rapid review along with a brief bibliometric analysis of 328 research papers, which synthesizes the evidence regarding the environmental concerns of COVID-19. The novel contribution of this rapid review is threefold. One, we take stock of the diverse findings as regards the transmission of the novel coronavirus in different types of environments for providing conclusive directions to the ongoing debate regarding the transmission of the virus. Two, our findings provide topical insights as well as methodological guidance for future researchers in the field. Three, we inform the policymakers on the efficacy of environmental measures for controlling the spread of COVID-19.

Threshold effects of COVID-19-confirmed cases on change in pollutants changes: evidence from the Chinese top ten cities

A more comprehensive understanding of the impact of the COVID-19 pandemic on changes in pollution could serve us to better deal with the environmental challenges caused by the pandemic. Existing studies mainly focused on the linear impact of the pandemic on the pollutants without considering the impact of other factors. To fill the research gap, the nonlinear relationship between pandemic and pollutants with considering the temperature factor was explored by developing panel threshold regression approach. In the proposed approach, the number of confirmed cases was set as explanatory variable, concentrations of NO2 and PM2.5 were set as explained variables, temperature was used as threshold variable, and other air pollution indicators were used as control variables. The results showed that there is a threshold effect between the changes in confirmed COVID-19 cases and the concentrations of PM2.5 and NO2, confirming the impact of the pandemic on pollutions was nonlinear. The results also show that the negative impact of pandemic on pollution increased when the temperature was rising. This work had theoretical and practical significance. The nonlinear research perspective of this article provided a methodological reference for exploring the relationship between epidemic and pollutant-related variables. Furthermore, this study expanded the scope of application of the threshold panel regression model and enriched the quantitative analysis of epidemics and pollutants.

Health impact assessment of air pollutants during the COVID-19 pandemic in a Brazilian metropolis

Studies around the world have revealed reduced levels of atmospheric particulate matter in periods of greatest human mobility restriction to contain the spread of SARS-CoV-2 during the COVID-19 pandemic. The present study aimed to carry out a health impact assessment in Recife, Brazil, hypothesizing a scenario in which the levels of PM₁₀ and PM_2.5 remained, throughout the year, as in the most restrictive period of human mobility. Particular material data (PM₁₀ and PM_2.5) were measured during the pandemic and population and health (mortality, hospital admissions for heart and respiratory problems) data from 2018 were used. We observed a reduction in the concentration of PM_2.5 in up to 43.7% and PM₁₀ up to 29.5% during the period of social isolation in the city of Recife. The reduction in PM_2.5 would avoid 106 annual deaths from non-external causes and 58 annual deaths from cardiovascular diseases. In this scenario, $ 294.88 million would be saved ($ 114.88 million from heart problems and $ 180 million from non-external causes). When considering hospitalizations avoided by the decrease in PM₁₀, we observed 57 fewer hospitalizations for respiratory diseases, 42 for heart diseases and a reduction of 37 deaths due to non-external causes. The reduction in spending on respiratory and cardiovascular hospitalizations would exceed $ 330,000. Therefore, the reduction of particulate matter could prevent hospital admissions, deaths and consequently there would be a reduction in disease burden in developing countries where economic resources are scarce. In this sense, governments should seek to reduce levels of pollution in order to improve the life quality and health of the population.

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.

Environmental perspective of COVID-19: Atmospheric and wastewater environment in relation to pandemic

The pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major challenge to health systems worldwide. Recently, numbers of epidemiological studies have illustrated that climate conditions and air pollutants are associated with the COVID-19 confirmed cases worldwide. Researches also suggested that the SARS-CoV-2 could be detected in fecal and wastewater samples. These findings provided the possibility of preventing and controlling the COVID-19 pandemic from an environmental perspective. With this review, the main purpose is to summarize the relationship between the atmospheric and wastewater environment and COVID-19. In terms of the atmospheric environment, the evidence of the relationship between atmospheric environment (climate factors and air pollution) and COVID-19 is growing, but currently available data and results are various. It is necessary to comprehensively analyze their associations to provide constructive suggestions in responding to the pandemic. Recently, large numbers of studies have shown the widespread presence of this virus in wastewater and the feasibility of wastewater surveillance when the pandemic is ongoing. Therefore, there is an urgent need to clarify the occurrence and implication of viruses in wastewater and to understand the potential of wastewater-based epidemiology of pandemic. Overall, environmental perspective-based COVID-19 studies can provide new insight into pandemic prevention and control, and minimizes the economic cost for COVID-19 in areas with a large outbreak or a low economic level.

Influence of mobility restrictions on air quality in the historic center of Cuenca city and its inference on the Covid-19 rate infections

At the end of 2019 in Wuhan China city, the outbreak of the virus called SARS-CoV 2 was originated, which later became a pandemic. In Ecuador, patient zero arrived on February 14, 2020 and the first mobility restriction imposed by the Government occurred on Tuesday, March 17 of the same year. Throughout the confinement, vehicle mobility restrictions have been modified by government entities depending on the number of infected people. This article presents an air quality study in the historic center of Cuenca city as consequence of mobility changes caused by Covid-19, where a comparison of concentration levels of polluting gases of the first semester of 2018, 2019 and 2020 is made, that allow differentiating and identifying the influence of vehicular flow on air quality. It can also be verified how the decrease in vehicle mobility restrictions influenced the increase in the rate of daily infections. For the study, air quality data published by the public mobility company of the city of Cuenca (EMOV EP) and the communications issued by the Emergency Operations Committee (COE), before and during the confinement, were collected. The acquisition, classification, analysis and interpretation of the data obtained through machine learning techniques was carried out. It can be concluded that while mobility restrictions were more severe, air quality improved and infections rate of decrease. Obtaining that polluting gases such as NO2 and CO produced by vehicular traffic show correlations of 61% and 60% respectively, which means that after 15 days of lifting the restrictive measures, the pollutants increased as well as the number of infected.

Role of atmospheric particulate matter exposure in COVID-19 and other health risks in human: A review

Due to intense industrialization and urbanization, air pollution has become a serious global concern as a hazard to human health. Epidemiological studies found that exposure to atmospheric particulate matter (PM) causes severe health problems in human and significant damage to the physiological systems. In recent days, PM exposure could be related as a carrier for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus transmission and Coronavirus disease 2019 (COVID-19) infection. Hence, it is important to understand the adverse effects of PM in human health. This review aims to provide insights on the detrimental effects of PM in various human health problems including respiratory, circulatory, nervous, and immune system along with their possible toxicity mechanisms. Overall, this review highlights the potential relationship of PM with several life-limiting human diseases and their significance for better management strategies.

An ANN experiment on the Indian economy: can the change in pollution generate an increase or decrease in GDP acceleration?

In recent years, the concept of sustainable development has enriched numerous scientific researches. Therefore, the combination of economic growth and the environment has been the subject of numerous econometric and statistical models. They demonstrated that there is a two-way relationship between economic growth and pollution. So, we use data from the World Bank database (1971-2014) to assess the possibility that a change (positive or negative) in pollution in India generates a gross domestic product acceleration. Through a Machine Learning approach via artificial neural network analysis, empirical findings show that a deep neural network can predict the outcome under study. The novelty of this paper is to have determined the results through a model based on a comparison with a highly developed country (Japan). The results obtained show that in a country like India, 76% of the time, a change in pollution evolves into a change in the acceleration of the economic growth.

Community factors and excess mortality in first wave of the COVID-19 pandemic in England

Risk factors for increased risk of death from COVID-19 have been identified, but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality in people aged 40 years and older at the community level during the first wave of the pandemic in England, March-May 2020 compared with 2015-2019. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or with a non-white ethnicity. We found no association between population density or air pollution and excess mortality. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed to avoid further widening of inequalities in mortality patterns as the pandemic progresses.

Community factors and excess mortality in first wave of the COVID-19 pandemic in England

Risk factors for increased risk of death from COVID-19 have been identified, but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality in people aged 40 years and older at the community level during the first wave of the pandemic in England, March-May 2020 compared with 2015-2019. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or with a non-white ethnicity. We found no association between population density or air pollution and excess mortality. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed to avoid further widening of inequalities in mortality patterns as the pandemic progresses.

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.

Meteorological factors, COVID-19 cases, and deaths in top 10 most affected countries: an econometric investigation

This paper examines the nexus between the Covid-19 confirmed cases, deaths, meteorological factors, including an air pollutant among the world's top 10 infected countries, from 1 February 2020 through 30 June 2020, using advanced econometric techniques to address heterogeneity across the nations. The findings of the study suggest that there exists a strong cross-sectional dependence between Covid-19 cases, deaths, and all the meteorological factors for the countries under study. The findings also reveal that a long-term relationship exists between all the meteorological factors. There exists a bi-directional causality running between the Covid-19 cases and all the meteorological factors. With Covid-19 death cases as the dependent variable, there exists bi-directional causality running between the Covid-19 death cases and Covid-19 confirmed cases, air pressure, humidity, and temperature. Temperature and air pressure exhibit a statistically significant and negative impact on the Covid-19 confirmed cases. Air pollutant PM2.5 also exhibits a significant but positive impact on the Covid-19 confirmed cases. Temperature indicates a statistically significant and negative impact on the Covid-19 death cases. At the same time, Covid-19 confirmed cases and air pollutant PM2.5 exhibit a statistically significant and positive impact on the Covid-19 death cases across the ten countries under study. Hence, it is possible to postulate that cool and dry weather conditions with lower temperatures may promote indoor activities and human gatherings (assembling), leading to virus transmission. This study contributes both practically and theoretically to the concerned field of pandemic management. Our results assist in taking appropriate measures in implementing intersectoral policies and actions as necessary in a timely and efficient manner. Causal relations of Meteorological factors and Covid-19 (2 models used in the study).

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, PM2.5, and NO2 concentrations in New York state using city-level daily data through two Machine Learning experiments. PM2.5 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 PM2.5 to Deaths, NO2 to Deaths, and economic growth to both PM2.5 and NO2. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM2.5 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 (PM2.5 and NO2) in New York state.

An Italian individual-level data study investigating on the association between air pollution exposure and Covid-19 severity in primary-care setting

BACKGROUND

Several studies have been focusing on the potential role of atmospheric pollutants in the diffusion and impact on health of Covid-19. This study's objective was to estimate the association between ≤10 μm diameter particulate matter (PM10) exposure and the likelihood of experiencing pneumonia due to Covid-19 using individual-level data in Italy.

METHODS

Information on Covid-19 patients was retrieved from the Italian IQVIA® Longitudinal Patient Database (LPD), a computerized network of general practitioners (GPs) including anonymous data on patients' consultations and treatments. All patients with a Covid-19 diagnosis during March 18th, 2020 - June 30th, 2020 were included in the study. The date of first Covid-19 registration was the starting point of the 3-month follow-up (Index Date). Patients were classified based on Covid-19-related pneumonia registrations on the Index date and/or during follow-up presence/absence. Each patient was assigned individual exposure by calculating average PM10 during the 30-day period preceding the Index Date, and according to GP's office province. A multiple generalized linear mixed model, mixed-effects logistic regression, was used to assess the association between PM10 exposure tertiles and the likelihood of experiencing pneumonia.

RESULTS

Among 6483 Covid-19 patients included, 1079 (16.6%) had a diagnosis of pneumonia. Pneumonia patients were older, more frequently men, more health-impaired, and had a higher individual-level exposure to PM10 during the month preceding Covid-19 diagnosis. The mixed-effects model showed that patients whose PM10 exposure level fell in the second tertile had a 30% higher likelihood of having pneumonia than that of first tertile patients, and the risk for those who were in the third tertile was almost doubled.

CONCLUSION

The consistent findings toward a positive association between PM10 levels and the likelihood of experiencing pneumonia due to Covid-19 make the implementation of new strategies to reduce air pollution more and more urgent.

COVID-19 and the Environment, Review and Analysis

We reviewed studies linking COVID-19 cases and deaths with the environment, focusing on relationships with air pollution. We found both short- and long-term observational relationships with a range of regulated pollutants, although only two studies considered both cases (i.e., infections) and deaths within a common analytical framework. Most of these studies were limited to a few months of the pandemic period. Statistically significant relationships were found more often for PM2.5 and NO2 than for other regulated pollutants, but no rationale was suggested for such short-term relationships; latency was seldom considered for long-term relationships. It was also unclear whether confounding had been adequately controlled in either type of study. Studies of air quality improvement following lockdowns found more robust relationships with local (CO, NO2) rather than regional (PM2.5, O3) pollutants, but meteorological confounding was seldom considered. Only one of seven studies of airborne virus transmission reported actual measurements. Overall, we found the existing body of literature to be more suggestive than definitive. Due to these various deficiencies, we assembled a new state-level database of cumulative COVID-19 cases and deaths through March 2021 with a range of potential predictor variables and performed linear regression analyses on various combinations. As single predictors, we found significant (p < 0.05) relationships between cumulative cases and household crowding (+), education (−), face-mask usage (−), or voting Republican (+). For cumulative deaths, we found significant relationships with education (−), black race (+), or previous levels of PM2.5 (+). NOx (+), and elemental carbon (EC, +). We found no relationships between long-term air quality and cumulative COVID-19 cases. Our associations linking air pollution with COVID-19 mortality were not statistically different from those for all-cause mortality in previous studies. In multiple mortality regressions combining air pollution, race, and education, NOx and EC remained significant but PM2.5 did not. We concluded that the current worldwide emphasis on PM2.5 is misplaced. We predicted air pollutant effects of a few percentage points, but individual differences between races, political identification, and post-graduate education were of the order of factors of 2 to 4. In general, the factors predicting infection were personal and related to COVID-19 exposure, while those predicting subsequent mortality tended to be more situational and related to geography. Overall, we concluded that how you live is more important than where you live.

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.

Association between coronavirus disease 2019 (COVID-19) and long-term exposure to air pollution: Evidence from the first epidemic wave in China

People with chronic obstructive pulmonary disease, cardiovascular disease, or hypertension have a high risk of developing severe coronavirus disease 2019 (COVID-19) and of COVID-19 mortality. However, the association between long-term exposure to air pollutants, which increases cardiopulmonary damage, and vulnerability to COVID-19 has not yet been fully established. We collected data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average air pollutant levels as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the period of the emergency response. Other variables included smoking prevalence, climate data, socioeconomic data, education level, and number of hospital beds for 324 cities in China. After adjusting for human mobility and socioeconomic factors, we found an increase of 37.8% (95% confidence interval [CI]: 23.8%-52.0%), 32.3% (95% CI: 22.5%-42.4%), and 14.2% (7.9%-20.5%) in the number of COVID-19 cases for every 10-μg/m³ increase in long-term exposure to NO₂, PM_2.5, and PM₁₀, respectively. However, when stratifying the data according to population size, the association became non-significant. The present results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings suggested that air pollution may be related to population vulnerability to COVID-19 infection, although the extent to which this relationship is confounded by city population density needs further exploration.

Investigating the Linkage between Economic Growth and Environmental Sustainability in India: Do Agriculture and Trade Openness Matter?

This paper assesses the linkage between CO2 emissions and economic growth while taking into account the role of energy consumption, agriculture, and trade openness in India. Using data covering the period between 1965 and 2019, the Bayer and Hanck cointegration and Gradual shift causality tests are applied to assess these economic indicators relationships’. Furthermore, we employed the wavelet coherence test. The advantage of the wavelet coherence test is that it differentiates between short-, medium-, and long-run dynamics over the entire sampling period. To the best of the authors’ understanding, the present paper is the first to apply wavelet analysis to investigate this relationship by incorporating agriculture as a determinant of environmental degradation. The empirical outcomes show that all variables appear to be highly correlated with CO2 emissions with the exemption of trade openness. This is further affirmed by the Gradual shift causality test, which shows that agriculture and energy consumption are crucial determinants of CO2 emissions in India. Accordingly, adequate policy measures are proposed based on these findings.

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.

Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

The main goal of this study is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption in renewable energy markets. The study will be based on the hypothesis that similar changes in human behavior can be expected in the future with the further spread of COVID-19 and/or the introduction of additional quarantine measures around the world. The analysis also yielded additional results. The strongest reductions in energy generation occurred in countries with a high percentage (more than 80%) of urban population (Brazil, USA, the United Kingdom and Germany). This study uses two models created with the Keras Long Short-Term Memory (Keras LSTM) Model, and 76 and 10 parameters are involved. This article suggests that various restrictive strategies reduced the sustainable demand for renewable energy and led to a drop in economic growth, slowing the growth of COVID-19 infections in 2020. It is unknown to what extent the observed slowdown in the spread from March 2020 to September 2020 due to the policy’s impact and not the interaction between the virus and the external environment. All renewable energy producers decreased the volume of renewable energy market supply in 2020 (except China).

Geographically varying relationships of COVID-19 mortality with different factors in India

COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ([Formula: see text]) with smaller Akaike Information Criterion (AICc [Formula: see text]) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran's [Formula: see text] and [Formula: see text]) in the residuals. It is found that more than 86% of local [Formula: see text] values are larger than 0.60 and almost 68% of [Formula: see text] values are within the range 0.80-0.97. Moreover, some interesting local variations in the relationships are also found.

A Spatial Ecosystem Services Assessment to Support Decision and Policy Making: The Case of the City of Bologna

In recent years, both mapping and assessing urban Ecosystem Services (ESs) to support urban planning has been a topic of great debate. This work aims at contributing to this discussion by developing and testing a methodological approach to first assess and map supply and demand of ESs, and then identify areas of priority of intervention. Starting from the existing models, the work develops a tailored approach to map and assess three ESs (water retention and runoff, PM10 removal, and carbon sequestration and storage) that are tested in the city of Bologna and tailored according to available open data. All data are processed in a GIS environment to allow for spatial distribution and visualization of ESs. These maps facilitate defining supply and demands and, consequently, the presence and distribution of ESs deficiencies. Building on mismatches, this paper proposes four clusters by grouping the city’s districts based on predominant land use (built-up, green urban areas) and tree canopy cover. This classification enabled the identification of intervention priority areas and suggestions of relevant nature-based solutions (NBS) to be implemented. The proposed method can serve other urban areas to perform a rapid assessment of their current needs and challenges in terms of ES provision.

How did the German and other European electricity systems react to the COVID-19 pandemic?

The first wave of the COVID-19 pandemic led to decreases in electricity demand and a rising share of Renewable Energy Sources in various countries. In Germany, the average proportion of net electricity generation via Renewable Energy Sources rose above 55 % in the first half of 2020, as compared to 47 % for the same period in 2019. Given these altered circumstances, in this paper we analyze how the German and other European electricity systems behaved during the COVID-19 pandemic. We use data visualization and descriptive statistics to evaluate common figures for electricity systems and markets, comparing developments during the COVID-19 pandemic with those of previous years. Our evaluation reveals noticeable changes in electricity consumption, generation, prices, and imports/exports. However, concerning grid stability and ancillary services, we do not observe any irregularities. Discussing the role of various flexibility options during the COVID-19 pandemic, a relatively higher grid capacity resulting from a decreased electricity consumption, in particular, may have contributed to grid stability.

NO2 levels as a contributing factor to COVID-19 deaths: The first empirical estimate of threshold values

This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO2) concentrations and COVID-19-related deaths in France. The concentration of NO2 linked to COVID-19-related deaths in three major French cities were determined using Artificial Neural Networks experiments and a Causal Direction from Dependency (D2C) algorithm. The aim of the study was to evaluate the potential effects of NO2 in spreading the epidemic. The underlying hypothesis is that NO2, as a precursor to secondary particulate matter formation, can foster COVID-19 and make the respiratory system more susceptible to this infection. Three different neural networks for the cities of Paris, Lyon and Marseille were built in this work, followed by the application of an innovative tool of cutting the signal from the inputs to the selected target. The results show that the threshold levels of NO2 connected to COVID-19 range between 15.8 μg/m3 for Lyon, 21.8 μg/m3 for Marseille and 22.9 μg/m3 for Paris, which were significantly lower than the average annual concentration limit of 40 μg/m³ imposed by Directive 2008/50/EC of the European Parliament.

COVID-19 Pandemic and Environmental Health: Effects and the Immediate Need for a Concise Risk Analysis

COVID-19 pandemic, as another disease emerging in the interface between animals and humans, has revealed the importance of interdisciplinary collaborations such as the One Health initiative. Environmental Health, whose role in the One Health concept is well established, has been associated with COVID-19 pandemic via various direct and indirect pathways. Modern lifestyle, climate change, environmental degradation, exposure to chemicals such as endocrine disruptors, and exposure to psychological stress factors impact human health negatively. As a result, many people are in the disadvantageous position to face the pandemic with an already impaired immune system due to their exposure to environmental health hazards. Moreover, the ongoing pandemic has been associated with outdoor and indoor air pollution, water and noise pollution, food security, and plastic pollution issues. Also, the inadequate infrastructure, the lack of proper waste and wastewater management, and the unequal social vulnerability reveal more linkages between Environmental Health and COVID-19 pandemic. The significant emerging ecological risk and its subsequent health implications require immediate risk analysis and risk communication strategies.

Waste generation, wealth and GHG emissions from the waste sector: Is Denmark on the path towards circular economy?

Municipal solid waste (MSW) is one of the most urgent issues associated with economic growth and urban population. When untreated, it generates harmful and toxic substances spreading out into the soils. When treated, they produce an important amount of Greenhouse Gas (GHG) emissions directly contributing to global warming. With its promising path to sustainability, the Danish case is of high interest since estimated results are thought to bring useful information for policy purposes. Here, we exploit the most recent and available data period (1994-2017) and investigate the causal relationship between MSW generation per capita, income level, urbanization, and GHG emissions from the waste sector in Denmark. We use an experiment based on Artificial Neural Networks and the Breitung-Candelon Spectral Granger-causality test to understand how the variables, object of the study, manage to interact within a complex ecosystem such as the environment and waste. Through numerous tests in Machine Learning, we arrive at results that imply how economic growth, identifiable by changes in per capita GDP, affects the acceleration and the velocity of the neural signal with waste emissions. We observe a periodical shift from the traditional linear economy to a circular economy that has important policy implications.

The Relationship between Renewable Energy and Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the Brazilian Economy

This paper examines the relationship between renewable energy consumption and economic growth in Brazil, in the Covid-19 pandemic. Using an Artificial Neural Networks (ANNs) experiment in Machine Learning, we tried to verify if a more intensive use of renewable energy could generate a positive GDP acceleration in Brazil. This acceleration could offset the harmful effects of the Covid-19 global pandemic. Empirical findings show that an ever-greater use of renewable energies may sustain the economic growth process. In fact, through a model of ANNs, we highlighted how an increasing consumption of renewable energies triggers an acceleration of the GDP compared to other energy variables considered in the model.