Impact of COVID-19 on the Territory and Demographic Processes: A View from Spanish Rural and Urban Areas

The current physiognomy and problems of rural and urban territories in Spain are directly related to the demographic processes linked to the rural exodus of the 1960s. In the year 2020, a new problem and/or conditioning factor arose, COVID-19, which has modified dynamics, routines, and aspects of the daily life of the population. The objectives of this research are to check whether there are differences in the effect of COVID-19 between urban and rural municipalities and, in turn, to analyse the demographic dynamics of the population between 2020 and 2022, as well as territorial distribution patterns. To this end, population data were extracted from the Population Register and Residential Variation data for the period 2010 and 2022 and demographic and statistical calculations (Student's t-test and Pearson's correlation) were carried out. Among the main results, it is observed that COVID-19 has less of an effect in Spanish rural areas. Moreover, these areas show a positive demographic trend for the period 2020-2022. Population growth has had a direct influence on the improvement of demographic data, although with differences according to autonomous communities. This fact represents a break in the trend in rural areas, but is beginning to show signs of exhaustion and a return to the pre-pandemic trend.

Implementation of seasonal differences and sociodemographic factors on the achievement of agribusiness MSME output in the Bengawan Solo Wonogiri Watershed

The socioeconomic characteristics of the community in the Bengawan Solo Hulu watershed allow the agribusiness MSMEs business process to occur in production and significantly impact aspects of socioeconomic life. The limited reliable sources of income will affect the community's tendency to repressive actions. The purpose of the study is (1) to determine the sociodemographic conditions of agribusiness MSME households, (2) to determine the effect of the season on the achievement of agribusiness MSME output, and (3) to find out the solutions of Agribusiness MSMEs. The research method used was a survey on Agribusiness MSMEs by taking locations in 2 Sub-watershed, Alangunggahan Sub-Watershed (Eramoko District) and Keduang Sub-Watershed (Jatipurno District and Jatisrono District) with a total sample of 60 MSMEs. The analysis used is the input-output analysis and Econometric analysis. The results showed that the R2 value was 87.14%, the F test was significant at 95%, and all sociodemographic variables were significant except the age factor of Agribusiness MSME actors. There is a significant seasonal difference in the achievement of agribusiness MSME output. This phenomenon indicates that post-Covid, efforts have risen from the Covid 19, and seasonal differences are considered in decision-making efforts to increase output achievements in the Agribusiness MSME.

Traffic Impacts of the COVID-19 Pandemic: Statewide Analysis of Social Separation and Activity Restriction

The COVID-19 pandemic resulted in significant social and economic impacts throughout the world. In addition to the health consequences, the impacts on travel behavior have also been sudden and wide ranging. This study describes the drastic changes in human behavior using the analysis of highway volume data as a representation of personal activity and interaction. Same-day traffic volumes for 2019 and 2020 across Florida were analyzed to identify spatial and temporal changes in behavior resulting from the disease or fear of it and statewide directives to limit person-to-person interaction. Compared to similar days in 2019, overall statewide traffic volume dropped by 47.5%. Although decreases were evident across the state, there were also differences between rural and urban areas and between highways and arterials both in terms of the timing and extent. The data and analyses help to demonstrate the early impacts of the pandemic and may be useful for operational and strategic planning of recovery efforts and for dealing with future pandemics.

NEW INSIGHTS ON EPIDEMIOLOGICAL DATA AND IMPACT OF THE COVID-19 PANDEMIC ON IGA VASCULITIS IN CHILDREN AND ADULTS IN FRANCE: A NATIONWIDE COHORT

BackgroundIgA vasculitis (IgAV) is a rare autoimmune disease affecting small vessels. It is well established that the incidence is higher in children (3 to 26 per 100,000 children/year,) [1] than in adults (0.1 to 1.8 per 100,000 individuals/year) [1]. However others epidemiological data and impact of the COVID-19 on IgAV remain overlooked [2].ObjectivesTo collect and analyze epidemiological data on IgAV in both adults and children in France.MethodsWe conducted an observational study using a national database called "BNDMR” [3] (Banque Nationale de Données Maladies Rares) on IgA vasculitis (code ORPHA761), which gathered patients managed in the French rare disease expert network. The incidence was estimated from the date of diagnosis, and we calculated the median annual incidence over the period 2010-2022. We specifically assessed the north/south gradient (latitude of the residence higher/lower than the median of the latitudes), the seasonality, and the impact of the COVID-19 pandemic compared to other patients reported within the same period and addressed in the same expert centers used as controls.ResultsDuring this 12-year period, 1988 patients with IgAV were reported (1498 children;490 adults). The male to female ratio was 1.57 for adults and 1.05 for children. The median IgAV annual incidence was 15 cases/year [IQR 9-30] and 82 cases/year [IQR 72-86] for adult and children cases respectively. Time to diagnosis was less than 1 month for both. Compared with other patients reported in the same expert centers, IgAV was more frequently reported in the southern part of France than in the north (OR 4.88 [95% confidence intervals: 4.17 - 5.74] in adults and OR 1.51 [1.35 - 1.68] in children). IgAV was also more frequently observed in winter than during the rest of the year in both adults (OR 1.60 [1.39 - 1.82]) and children (OR 1.22 [1.01 - 1.48]). The incidence of IgAV decreased during the COVID-19 pandemic period (from March 2020 to September 2022) in children (OR 0.62 [0.47 - 0.81]) but not in the adult population (OR 0.90 [0.76 - 1.06]).ConclusionOur study confirms the winter seasonality and sex ratio in IgAV [4,5], but suggests that the incidence or the reporting of IgAV decreased in children during the COVID19 pandemia, possibly due to barrier measures [6]. The observed north/south gradient need confirmation. The main limitation of this study is a possible IgAV under-reporting as this study rely only on cases addressed in expert centers.References[1]Audemard-Verger A, Pillebout E, Guillevin L, Thervet E, Terrier B. IgA vasculitis (Henoch-Shönlein purpura) in adults: Diagnostic and therapeutic aspects. Autoimmun Rev. 2015;14(7):579-585. doi:10.1016/j.autrev.2015.02.003[2]Deshayes S, Moulis G, Pillebout E, Aouba A, Audemard-Verger A. Positive predictive value of hospital discharge diagnosis code to identify immunoglobulin A vasculitis in France: A validation study. Eur J Intern Med. 2017;43:e18-e19. doi:10.1016/j.ejim.2017.05.025[3]Jannot AS, Messiaen C, Khatim A, Pichon T, Sandrin A, BNDMR infrastructure team. The ongoing French BaMaRa-BNDMR cohort: implementation and deployment of a nationwide information system on rare disease. J Am Med Inform Assoc. 2022;29(3):553-558. doi:10.1093/jamia/ocab237[4]Piram M, Maldini C, Biscardi S, et al. Incidence of IgA vasculitis in children estimated by four-source capture-recapture analysis: a population-based study. Rheumatology (Oxford). 2017;56(8):1358-1366. doi:10.1093/rheumatology/kex158[5]Gardner-Medwin JMM, Dolezalova P, Cummins C, Southwood TR. Incidence of Henoch-Schönlein purpura, Kawasaki disease, and rare vasculitides in children of different ethnic origins. Lancet. 2002;360(9341):1197-1202. doi:10.1016/S0140-6736(02)11279-7[6]Kaya Akca U, Atalay E, Cuceoglu MK, et al. Impact of the COVID-19 pandemic on the frequency of the pediatric rheumatic diseases. Rheumatol Int. 2022;42(1):51-57. doi:10.1007/s00296-021-05027-7Figure.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Economic policy uncertainty, governance institutions and economic performance in Africa: are there regional differences?

In recent years, the global economy has witnessed several uncertainty-inducing events. However, empirical evidence in Africa on the effects of economic policy uncertainty (EPU) on economic activities remains scanty. Besides, the moderating effect of governance institutions on the uncertainty-economic performance relationship in Africa and the likelihood of regional differences in the response of economic activities to EPU on the continent are yet to be investigated. To address these gaps, we applied system GMM and quantile regressions on a panel of forty-seven African countries from 2010 to 2019. We find that while global EPU and EPUs from China, USA and Canada exert considerable influence on economic performance in Africa, the effects of domestic EPU and EPUs from Europe, UK, Japan, and Russia were negligible, suggesting that African economies are resilient to these sources of uncertainty shocks. We also find that governance institutions in Africa are not significantly moderating the uncertainty-economic performance relationship. However, our results highlighted regional differences in the response of economic activities to uncertainty, such that when compared to East and West Africa, economic performance in Central, North and Southern Africa is generally more resilient to global EPU and EPUs from China, USA, Europe and UK. We highlighted the policy implications of these findings.

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h-1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h-1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

Probing environmental and tectonic changes underneath Mexico City with the urban seismic field

The sediments underneath Mexico City have unique mechanical properties that give rise to strong site effects. We investigated temporal changes in the seismic velocity at strong-motion and broadband seismic stations throughout Mexico City, including sites with different geologic characteristics ranging from city center locations situated on lacustrine clay to hillside locations on volcanic bedrock. We used autocorrelations of urban seismic noise, enhanced by waveform clustering, to extract subtle seismic velocity changes by coda wave interferometry. We observed and modeled seasonal, co- and post-seismic changes, as well as a long-term linear trend in seismic velocity. Seasonal variations can be explained by self-consistent models of thermoelastic and poroelastic changes in the subsurface shear wave velocity. Overall, sites on lacustrine clay-rich sediments appear to be more sensitive to seasonal surface temperature changes, whereas sites on alluvial and volcaniclastic sediments and on bedrock are sensitive to precipitation. The 2017 Mw 7.1 Puebla and 2020 Mw 7.4 Oaxaca earthquakes both caused a clear drop in seismic velocity, followed by a time-logarithmic recovery that may still be ongoing for the 2017 event at several sites or that may remain incomplete. The slope of the linear trend in seismic velocity is correlated with the downward vertical displacement of the ground measured by interferometric synthetic aperture radar, suggesting a causative relationship and supporting earlier studies on changes in the resonance frequency of sites in the Mexico City basin due to groundwater extraction. Our findings show how sensitively shallow seismic velocity and, in consequence, site effects react to environmental, tectonic and anthropogenic processes. They also demonstrate that urban strong-motion stations provide useful data for coda wave monitoring given sufficiently high-amplitude urban seismic noise.

Preference Shifts During Health Shocks: Foreign Tourists' Expenditure-Price Elasticity Throughout the Pandemic

This study evaluates the changes in the expenditure-price elasticities of foreign tourists in the summer periods of 2019, 2020, and 2021. We first develop a theoretical characterization that combines microeconomic, loss aversion, price inequality and precautionary savings theories. Next, exploiting microdata for more than 34,000 foreign tourists visiting Spain, we estimate OLS and quantile regressions to empirically examine the expenditure elasticities with respect to the prices of transport services, leisure activities and bars and restaurants at the destination (17 regions). We find that (i) the expenditure-price elasticity of transportation (leisure activities) increases (decreases) during the pandemic, whereas that of bars and restaurants remains unchanged, (ii) foreign tourists are comparatively less expenditure-price elastic at high expenditure levels in transportation and bars and restaurants, and (iii) expenditure-price elasticities are highly heterogeneous depending on the origin country. Managerial and theoretical implications of the findings for firms' pricing strategies are discussed.

Multilevel governance, community and emergency management during the pandemic: migrants in Japan

PurposeBuilding on perspectives from the study of multilevel governance, migrants' inclusion and emergency management, this article asks how differences across national regulations for foreign residents, work eligibility and access to national emergency supports intersected with local approaches in responding to migrants.Design/methodology/approachThis article examines national policy adjustments and parallel subnational governance early in the pandemic for three groups of foreign residents: international students, technical interns and co-ethnics with long-term visas, primarily Brazilians and Peruvians. It uses Japanese-language documents to trace national policy responses. To grasp subnational governance, the article analyzes coverage in six Japanese regional newspapers from northern, central and western Japan, for the period of April 1 to October 1, 2020.FindingsNational policies obstructed or enabled migrants' treatment as members of the local community but did not dictate this membership, which varied according to migrant group. Migrants' relationship to the community affected available supports.Originality/valueThe article brings together perspectives on multilevel governance, emergency management and migrants' inclusion. It exposes how different migrant groups' ties to the local community affected access to supports.

"Home away from home” in pandemic times: how has COVID-19 changed the Airbnb market in Melbourne?

PurposeThis study aims to investigate how the COVID-19 pandemic has impacted and changed Airbnb market in the Greater Melbourne area in terms of its temporal and spatial patterns and identify possible shifts in underlying trends in travel activities.Design/methodology/approachA panel data set of Airbnb listings in Melbourne is analysed to compare temporal patterns, spatial distribution and lengths of stay of Airbnb users before and after the COVID outbreak.FindingsThis study found that the COVID disruption did not fundamentally change the temporal cycle of the Airbnb market. Month-to-month fluctuations peaked at different levels from pre-pandemic times mainly because of lockdowns and other restrictive measures. The impact of COVID-19 disruptions on neighbourhood-level Airbnb revenues is associated with distance to CBD rather than number of COVID cases. Inner city suburbs suffered major loss during the pandemic, whereas outer suburbs gained popularity due to increased domestic travel and long stays. Long stays (28 days or more, as defined by Airbnb) were the fastest growing segment during the pandemic, which indicates the Airbnb market was adapting to increasing demand for purposes like remote working or lifestyle change. After easing of COVID-related restrictions, demand for short-term accommodation quickly recovered, but supply has not shown signs of strong recovery. Spatial distribution of post-pandemic supply recovery shows a similar spatial variation. Neighbourhoods in the inner city have not shown signs of significant recovery, whereas those in the middle and outer rings are either slowly recovering or approaching their pre-COVID levels.Practical implicationsThe COVID-19 pandemic has significantly impacted short-term rental markets and in particular the Airbnb sector during the phase of its rapid development. This paper helps inform in- and post-pandemic housing policy, market opportunity and investment decision.Originality/valueTo the best of the authors' knowledge, this is one of the first attempts to empirically examine both temporal and spatial patterns of the COVID-19 impact on Airbnb market in one of the most severely impacted major cities. It is one of the first attempts to identify shifts in underlying trends in travel based on Airbnb data.

Need of Technological Interventions for Indoor Air Quality and Risk Assessment Upon Short-Term Exposure: A Futuristic Approach

The variation in ambient air pollution hampers indoor air quality (IAQ), and even the short-term variation is very hazardous for the exposed population. Technological interventions including sensors, smartphones and other gadgets are implemented to build smart environments. However, these interventions are still not fully explored in developing countries like India. The COVID-19 pandemic has made it very important to keep a tab on the air we breathe in as those already suffering from respiratory troubles are prone to fall victim to the deadly disease. In such a scenario, even a rise in pollution for a short duration is dangerous to the exposed pollution. Such short-term exposure facilitated by the meteorological creates a disaster for environmental health. The short-term rise in the concentration of pollutants makes things worse for the exposed people, even indoors. It is therefore critical to come up with a concrete solution to predict the IAQ instantly and warn the exposed population which can be only achieved by technological interventions and futuristic Internet of Things-based computational predictions. This chapter is intended to elaborate the health hazards linked to short-term rise in pollutants, which often goes unnoticed but has a critical impact and how with the help of IoT-based applications, the short-term variation can be predicted through different strategies. Similarly, the assessment of the health impact associated with short-term exposure to air pollution is also significant, and different exposure assessment models and computational strategies are discussed in the course of the study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Short-term effect of COVID-19 lockdowns on atmospheric CO2, CH4 and PM2.5 concentrations in urban environment.

The COVID-19 pandemic has changed all areas of human activity as it forced the authorities around the world to enact unprecedented restrictions such as "lockdowns". The low economic activity reduced the anthropogenic impact on the environment, in particular, greenhouse gases and aerosols emissions were decreased. However, the associated change in air quality is difficult to directly observe and quantify, since concentrations of these components in urban areas are affected by many other factors. In this work statistical analysis of atmospheric CO2, CH4 and PM2.5, measured in 2017-2020 in the city of Ekaterinburg, Russia, are presented. A detailed focus was made on the lockdown period from March 28 to April 30, 2020. A significant decrease in concentrations and inter-hourly variations of all studied components were observed only in the short "self-isolation" period from April 6 to April 8. The anthropogenic origin of this effect, primarily associated with the reduction in vehicular traffic, was concluded from mean diurnal cycles and air temperature correlations of all components. A decrease in the difference between measured and background CO2 and CH4 mole fractions was also found during this period. The difference was 1.3±0.2 ppm for CO2 and 8±4 ppb for CH4, which was many times lower than during any other observed periods, suggesting a short-term effect of lockdown restrictions. Overall, a negative impact on the atmosphere quickly resumed after the recovery of economic activity. The approaches in this study can be used to detect weak fluctuations of atmospheric components in other urban territories.

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Vascular and non-vascular UNet systems dominated with sUNet classes with attention. Most of the studies suffered from a low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings.

France 2022: a narrowing gap between births and deaths

Natural increase-the difference between births and deaths-fell fourfold in France between 2012 and 2022, reflecting a 100,000 decrease in the number of births over the last 10 years and a similar increase in the number of deaths. Life expectancy has stagnated over the last 3 years due to the COVID-19 pandemic combined with excess mortality in 2022 linked to several summer heatwaves and a seasonal flu epidemic at the end of the year. It appears that while COVID-19 had little impact on births, it temporarily modified their seasonality.

A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm

Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the "IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm” database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model's performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day.

The COVID'19 Dashboard with Implementation of Face Mask Detection and Social Distancing Detection Algorithm

With the outbreak of the global pandemic, India seemed to reach its peak with regard to the number of confirmed positive cases in the months of April and May. Hence, the decision was made to develop a data visualization project with one of the efficient visualization tools Tableau to help people analyze the scenario of the cases across the country. To contribute to state-wise and country-wise analysis of COVID cases in India, 2 dashboards have been developed. The first dashboard consists of the analysis of cases across the country giving a holistic and overall view of the number of deaths, positive cases, and density of cases in each state which is done through color variation. On the other hand, the second dashboard gives a detailed state-wise analysis of cases with the necessary parameters and details catering to every individual state as per the preference of the user. On merging these components, users can get an all-inclusive analysis based on different parameters on the COVID'19 cases across India at a glance. In order to prevent a further spike in cases, implementing a face mask detection system will also take place after conducting a thorough analysis of the possible machine learning algorithms. Two major object detection algorithms were taken into consideration and based on the conclusion drawn, the best algorithm - RCNN was used to implement the face mask detection system. This project is solely motivated by the current extreme situation in the world and as an attempt to provide a solution to combat the same. © 2023 IEEE.

Personalized Smart Diet Assistance System in Health Care Prosperity with AI and AR

Health care prosperity is the most challenging task for human being in the present dangerous COVID scenario and the discovery proposes an augmented reality based personalized smart diet assistance system which provides diet recommendations, appropriate time, type, quantity and method of consumption of a food item diet based on user health parameters based on location and event activities. The augmented reality based system comprises a user data input, an image processing, food consumption assistance, transmissible disease information retrieval and diet planning modules. The system incorporates an AI based camera to scan a food item before or after cooking and utilizes augmented reality to indicate the nutritional information. The proposed system provides personalized diet recommendations to the user based on personal data such as height, weight, existing medical conditions and thereof of a user. The system retrieves existing transmissible diseases data from world health organizations and data from news articles about any viral infections or diseases to suggest immunity boosting foods to the user to thereby safeguard the user against such diseases or infections.

Sex affects immune response capacity against COVID-19 infection.

The genetic variability of each individual may lead to the identification of completely different genetic polymorphisms which are associated with a different sensitivity to infectious diseases in humans. Such genetic variability allows the immune system to respond differently to viral agents, therefore only a fraction of humans develop severe symptoms, as happened with SARS-CoV-2. Such knowledge is critical to enable the development of appropriate pharmacological solutions to prevent the consequences of insufficient immunity in dealing with serious viral diseases such as SARS-CoV-2. For instance, global epidemiological data show that male sex is a risk factor for the severe evolution of SARS-CoV-2 disease. Men, due to higher production of Testosterone (TLT), are more vulnerable than females. Women, due to greater expression of the TLR7 gene found on the X chromosome, a key innate immunity gene that encodes Toll-like proteins, are able to synthesise more antiviral proteins and interferons in dendritic cells, resulting in a more robust immune system capable of preventing severe SARS-CoV-2 viral disease. This manuscript highlights how human genetic variability can lead to severe infectious symptoms in some individuals who must take appropriate prophylactic actions, such as vaccination, to prevent this.

Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula

In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time.We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from -10 % to -40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from -40 % in summer to +60 % in winter in the most urbanized areas, and from -10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting;this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets.Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.