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Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy
Abstract
Following the outbreak of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) last December 2019 in China, Italy was the first European country to be severely affected, with the first local case diagnosed on 20 February 2020. The virus spread quickly, particularly in the North of Italy, with three regions (Lombardy, Veneto and Emilia-Romagna) being the most severely affected. These three regions accounted for >80% of SARS-CoV-2 positive cases when the tight lockdown was established (March 8). These regions include one of Europe's areas of heaviest air pollution, the Po valley. Air pollution has been recently proposed as a possible risk factor of SARS-CoV-2 infection, due to its adverse effect on immunity and to the possibility that polluted air may even carry the virus. We investigated the association between air pollution and subsequent spread of the SARS-CoV-2 infection within these regions. We collected NO2 tropospheric levels using satellite data available on the European Space Agency before the lockdown. Using a multivariable restricted cubic spline regression model, we compared NO2 levels with SARS-CoV-2 infection prevalence rate at different time points after the lockdown, namely March 8, 22 and April 5, in the 28 provinces of Lombardy, Veneto and Emilia Romagna. We found little association of NO2 levels with SARS-CoV-2 prevalence up to about 130 μmol/m², while a positive association was evident at higher levels at each time point. Notwithstanding the limitations of the use of aggregated data, these findings lend some support to the hypothesis that high levels of air pollution may favor the spread of the SARS-CoV-2 infection.
... Of the various types of pollutants present in the air, nitrogen dioxide (NO 2 ) gas is one of the main pollutants that affects air quality degradation in many areas of the earth's surface (Chi et al., 2021Filippini et al., 2020;Georgoulias et al., 2019;Schneider et al., 2015). As an important pollutant, NO 2 is dispersed and has an important role in the troposphere and stratosphere (Caraka et al., 2020;Zheng et al., 2019). ...
... As an important pollutant, NO 2 is dispersed and has an important role in the troposphere and stratosphere (Caraka et al., 2020;Zheng et al., 2019). NO 2 harms human health, causing premature birth in children, asthma in toddlers, and pneumonia, causing premature cardiopulmonary death (Bo et al., 2008;Filippini et al., 2020;Hossain et al., 2021;Sudalma et al., 2015;Sun et al., 2019;Zanobetti & Woodhead, 2010). ...
The high concentration of nitrogen dioxide (NO2) is to blame for West Java's poor Air Quality Index (AQI). So, this study aims to determine the influence of industrial activity as reflected by the value of its imports and exports, wind speed, and ozone (O3) on the high concentration of tropospheric NO2. The method used is the econometric Vector Error Correction Model (VECM) approach to capture the existence of a short-term and long-term relationship between tropospheric NO2 and its predictor variables. The data used in this study is in the form of monthly time series data for the 2018-2022 period sourced from satellite images (Sentinel-5P and ECMWF Climate Reanalysis) and publications of the Central Bureau of Statistics (BPS-Statistics Indonesia). The results explained that, in the short-term, tropospheric NO2 and O3 influence each other as they would in a photochemical reaction. In the long-term, exports from the industrial sector and wind speed have a significant effect on the concentration of tropospheric NO2. The short-term effect occurs directly in the first month after the shock, while the long-term effect occurs in the second month after the shock. Wind gusts originating from industrial areas cause air conditions to be even more alarming because tropospheric NO2 pollutants spread throughout the region in West Java. Based on the coefficient correlation result, the high number of pneumonia cases is one of the impacts caused by air pollution.
... The time period we studied corresponded to the first COVID-19 wave in Italy, during the first half of 2020. 3,18 Soon after the first case of COVID-19 in Italy was identified on 20 February 2020, the Government issued a nationwide light lockdown, on February 23. The February 23 decree issuing the light lockdown gave public and health authorities the legal wherewithal to decrease mobility of people and goods throughout Italy. ...
... We also retrieved data from several other sources: (i) provincial data about population structure (including overall resident population, single-family homes and elderly index) from the National Institute of Statistics, 18 (ii) the populationweighted average of ground-level temperature, humidity and ultraviolet radiation from the European Space Agency Copernicus program 22 and (iii) particulate matter with diameter < 10 μm (PM 10 ) from the ENSAMBLE model of the CAMS European air quality forecasts. 23 ...
Background
Italy was the first country after China to be severely affected by the COVID-19 pandemic, in early 2020. The country responded swiftly to the outbreak with a nationwide two-step lockdown, the first one light, and the second one tight. By analysing 2020 national mobile phone movements, we assessed how lockdown compliance influenced its efficacy.
Methods
We measured individual mobility during the first epidemic wave with mobile phone movements tracked through carrier networks, and related this mobility to daily new SARS-CoV-2 infections, hospital admissions, intensive care admissions and deaths attributed to COVID-19, taking into account reason for travel (work-related or not) and the means of transport.
Results
The tight lockdown resulted in an 82% reduction in mobility for the entire country and was effective in swiftly curbing the outbreak as indicated by a shorter time-to-peak of all health outcomes, particularly for provinces with the highest mobility reductions and the most intense COVID-19 spread. Reduction of work-related mobility was accompanied by a nearly linear benefit in outbreak containment; work-unrelated movements had a similar effect only for restrictions exceeding 50%. Reduction in mobility by car and by airplane was nearly linearly associated with a decrease in most COVID-19 health outcomes, while for train travel reductions exceeding 55% had no additional beneficial effects. The absence of viral variants and vaccine availability during the study period eliminated confounding from these two sources.
Conclusions
Adherence to the COVID-19 tight lockdown during the first wave in Italy was high and effective in curtailing the outbreak. Any work-related mobility reduction was effective, but only high reductions in work-unrelated mobility restrictions were effective. For train travel, there was a threshold above which no further benefit occurred. These findings could be particular to the spread of SARS-CoV-2, but might also apply to other communicable infections with comparable transmission dynamics.
... Most of the studies that investigated the impact of COVID-19 on air quality considered only a few cities or countries globally, perhaps due to the lack of global in-situ data. While the bulk of studies used in-situ measurements alone (Adams, 2020;Baldasano, 2020;Bao and Zhang, 2020;Dantas et al., 2020;Kerimray et al., 2020) or in combination with modeled data Griffith et al., 2020;He et al., 2020;Li et al., 2020;Mollalo et al., 2020;Perera et al., 2021), some studies did use remotely sensed data to investigate the impact of lockdown measures on the environment at local, regional, national, and global scales (Filippini et al., 2020;Filonchyk et al., 2020;Mendez-Espinosa et al., 2020;Metya et al., 2020;Mostafa et al., 2021). Satellite observations can help identify air pollutants and GHG emissions globally . ...
... Recently, researchers reported that lockdown measures imposed to reduce the impact of COVID-19 pandemic had shown a considerable reduction in air pollution and greenhouse gas emissions worldwide (Balasubramaniam et al., 2020;Baldasano, 2020;Chekir and Ben Salem, 2021;Chen et al., 2020;Filippini et al., 2020;Griffin et al., 2020;Gulabchandani and Sethi, 2020;Gupta et al., 2020;Ju et al., 2021;Kumari and Toshniwal, 2020;Liu et al., 2020;Mahato and Ghosh, 2020;Mostafa et al., 2021;Singh et al., 2020). Moreover, they found an improvement in the ozone layer and an overall positive impact on several other aspects, including air and water qualities. ...
The coronavirus 2019 (COVID 19, or SARS-CoV-2) pandemic that started in December 2019 has caused an unprecedented impact in most countries globally and continues to threaten human lives worldwide. The COVID-19 and strict lockdown measures have had adverse effects on human health and national economies. These lockdown measures have played a critical role in improving air quality, water quality, and the ozone layer and reducing greenhouse gas emissions. Using Soil Moisture Active Passive (SMAP) Level 4 carbon (SMAP LC4) satellite products, this study investigated the impacts of COVID-19 lockdown measures on annual carbon emissions globally, focusing on 47 greatly affected countries and their 105 cities by December 2020. It is shown that while the lockdown measures significantly reduced carbon emissions globally, several countries and cities observed this reduction as temporary because strict lockdown measures were not imposed for extended periods in 2020. Overall, the total carbon emissions of select 184 countries reduced by 438 Mt in 2020 than in 2019. Since the global economic activities are slowly expected to return to the non-COVID-19 state, the reduction in carbon emissions during the pandemic will not be sustainable in the long run. For sustainability, concerned authorities have to put significant efforts to change transportation, climate, and environmental policies globally that fuel carbon emissions. Overall, the presented results provide directions to the stakeholders and policymakers to develop and implement measures to control carbon emissions for a sustainable environment.
... To illustrate the effects on the results of erroneously using a regression model with normally distributed errors, we used the data in Filippini et al. [25]. Their objective was to investigate the link between the transmission of SARS-CoV-2 infection and long-term exposure to NO 2 in the provinces of three regions of Northern Italy (Lombardia, Venetto and Emilia Romagna), between March 8 and April 5, 2020 (n = 84). ...
... As we said, we excluded 43 studies that were purely descriptive and those that did not include any type of regression model (Additional file 1: Table S1). In the end we were [25] left with 132 studies with which to carry out the qualitative synthesis (Additional file 1: Tables S2 and S3). ...
Background
While numerous studies have assessed the effects of environmental (meteorological variables and air pollutants) and socioeconomic variables on the spread of the COVID-19 pandemic, many of them, however, have significant methodological limitations and errors that could call their results into question. Our main objective in this paper is to assess the methodological limitations in studies that evaluated the effects of environmental and socioeconomic variables on the spread of COVID-19.
Main body
We carried out a systematic review by conducting searches in the online databases PubMed, Web of Science and Scopus up to December 31, 2020. We first excluded those studies that did not deal with SAR-CoV-2 or COVID-19, preprints, comments, opinion or purely narrative papers, reviews and systematic literature reviews. Among the eligible full-text articles, we then excluded articles that were purely descriptive and those that did not include any type of regression model. We evaluated the risk of bias in six domains: confounding bias, control for population, control of spatial and/or temporal dependence, control of non-linearities, measurement errors and statistical model. Of the 5631 abstracts initially identified, we were left with 132 studies on which to carry out the qualitative synthesis. Of the 132 eligible studies, we evaluated 63.64% of the studies as high risk of bias, 19.70% as moderate risk of bias and 16.67% as low risk of bias.
Conclusions
All the studies we have reviewed, to a greater or lesser extent, have methodological limitations. These limitations prevent conclusions being drawn concerning the effects environmental (meteorological and air pollutants) and socioeconomic variables have had on COVID-19 outcomes. However, we dare to argue that the effects of these variables, if they exist, would be indirect, based on their relationship with social contact.
... The NO x emissions are dominated by anthropogenic fossil fuel combustion, and its chemical lifetime in the lower troposphere is relatively short. Consequently, the satellite-observed NO 2 TVCD is highly responsive to perturbations of human activities, including economic recession (Castellanos and Boersma, 2012;Russell et al., 2012), long-and short-term emission regulations (Duncan et al., 2016;Mijling et al., 2009;Witte et al., 2009), and the ongoing global pandemic caused by the coronavirus, or COVID-19 (Bauwens et al., 2020;Liu et al., 2020;Huang and Sun, 2020). ...
... We only use quality-assured level 2 pixels with cloud fraction < 0.3 and solar zenith angle < 70 • . Throughout the OMI mission, its across-track pixels are limited to 5-23 out of 1-60 to avoid the row anomaly and keep the time series analysis consistent (Duncan et al., 2016;Schenkeveld et al., 2017). TROPOMI features 450 pixels across its 2600 km swath and a nadir pixel size of 3.5 × 5.5 km 2 (3.5 × 7 km 2 before 6 August 2019), leading to significantly higher spatial resolution than OMI, whose nadir pixel size is 13 × 24 km 2 . ...
The evolving nature of the COVID-19 pandemic necessitates timely estimates of the resultant perturbations to anthropogenic emissions. Here we present a novel framework based on the relationships between observed column abundance and wind speed to rapidly estimate the air-basin-scale NOx emission rate and apply it at the Po Valley in Italy using OMI and TROPOMI NO2 tropospheric column observations. The NOx chemical lifetime is retrieved together with the emission rate and found to be 15–20 h in winter and 5–6 h in summer. A statistical model is trained using the estimated emission rates before the pandemic to predict the trajectory without COVID-19. Compared with this business-as-usual trajectory, the real emission rates show three distinctive drops in March 2020 (−42 %), November 2020 (−38 %), and March 2021 (−39 %) that correspond to tightened COVID-19 control measures. The temporal variation of pandemic-induced NOx emission changes qualitatively agrees with Google and Apple mobility indicators. The overall net NOx emission reduction in 2020 due to the COVID-19 pandemic is estimated to be 22 %.
... The results obtained are thus part of a broader panorama of studies aimed at evaluating the effects of air pollution on human health (Carugno et al. 2016;Eum et al. 2019;Fattorini and Regoli 2020;Filippini et al. 2020;Ogen 2020) but also highlight the importance of conducting such analyses on a more appropriate geographic scale such as that of local systems. ...
This paper aims to further investigate the relationship between the concentrations of nitrogen dioxide (NO 2 ) and the severity of COVID-19 by analyzing the data of three Italian Regions (Piedmont, Valle d’Aosta and Liguria) during the first wave of the pandemic (February–May 2020). The analyses were conducted at a local scale using Local Labor Systems of ISTAT. The annual average of NO 2 concentrations, obtained from space satellite Sentinel-5P, was used to assess environmental data. While excess mortality data were used to estimate the severity of the pandemic, calculated as the percentage change in deaths recorded in 2020 compared to the average number of deaths of the previous five years (2015–2019). Using quasi-Poisson multivariate regression models, it was possible to estimate the correlation between the incidence rate of the pandemic and some risk factors, including in particular the concentration of NO 2 .
... Air quality index (AQI) 10 polluted cities in the world (25) The concentration of air pollutants has decreased in all world cities during the lockdown period. ...
Objectives
The aim of this study was to evaluate changes in air quality index (AQI) values before, during, and after lockdown, as well as to evaluate the number of hospitalizations due to respiratory and cardiovascular diseases attributed to atmospheric PM 2.5 pollution in Semnan, Iran in the period from 2019 to 2021 during the COVID-19 pandemic.
Methods
Daily air quality records were obtained from the global air quality index project and the US Environmental Protection Administration (EPA). In this research, the AirQ+ model was used to quantify health consequences attributed to particulate matter with an aerodynamic diameter of <2.5 μm (PM 2.5 ).
Results
The results of this study showed positive correlations between air pollution levels and reductions in pollutant levels during and after the lockdown. PM 2.5 was the critical pollutant for most days of the year, as its AQI was the highest among the four investigated pollutants on most days. Mortality rates from chronic obstructive pulmonary disease (COPD) attributed to PM 2.5 in 2019–2021 were 25.18% in 2019, 22.55% in 2020, and 22.12% in 2021. Mortality rates and hospital admissions due to cardiovascular and respiratory diseases decreased during the lockdown. The results showed a significant decrease in the percentage of days with unhealthy air quality in short-term lockdowns in Semnan, Iran with moderate air pollution. Natural mortality (due to all-natural causes) and other mortalities related to COPD, ischemic heart disease (IHD), lung cancer (LC), and stroke attributed to PM 2.5 in 2019–2021 decreased.
Conclusion
Our results support the general finding that anthropogenic activities cause significant health threats, which were paradoxically revealed during a global health crisis/challenge.
... These shortcomings also strongly limit the interpretation of results from most-earlier epidemiological studies carried out in Italy on air pollution and COVID-19 epidemic. [11][12][13][14][15] More recent investigations with improved study designs, mostly individual-based, have addressed some of the aforementioned constraints, adding evidence on the studied association, like the population-based co-hort studies carried out in Catalonia (Spain) 16 and in Rome (Italy), 17 a prospective individual-based study in Varese (Italy), 18 a UK Biobank-based study, 19 and a state-wide population-based study in California. 20 These studies have adopted finer scale spatiotemporal models for exposure assessment, included correction terms for a large list of individual and contextual factors, also addressing issues related to mobility of populations and spread of SARS-CoV-2 infection. ...
Background:
after the outbreak of the SARS-CoV-2 pandemic in 2020, several waves of pandemic cases have occurred in Italy. The role of air pollution has been hypothesized and investigated in several studies. However, to date, the role of chronic exposure to air pollutants in increasing incidence of SARS-CoV-2 infections is still debated.
Objectives:
to investigate the association between long-term exposure to air pollutants and the incidence of SARS-CoV-2 infections in Italy.
Design:
a satellite-based air pollution exposure model with 1-km2 spatial resolution for entire Italy was applied and 2016-2019 mean population-weighted concentrations of particulate matter < 10 micron (PM10), PM <2.5 micron (PM2.5), and nitrogen dioxide (NO2) was calculated to each municipality as estimates of chronic exposures. A principal component analysis (PCA) approach was applied to 50+ area-level covariates (geography and topography, population density, mobility, population health, socioeconomic status) to account for the major determinants of the spatial distribution of incidence rates of SARS-CoV-2 infection. Detailed information was further used on intra- and inter-municipal mobility during the pandemic period. Finally, a mixed longitudinal ecological design with the study units consisting of individual municipalities in Italy was applied. Generalized negative binomial models controlling for age, gender, province, month, PCA variables, and population density were estimated.
Setting and participants:
individual records of diagnosed SARS-2-CoV-2 infections in Italy from February 2020 to June 2021 reported to the Italian Integrated Surveillance of COVID-19 were used.
Main outcome measures:
percentage increases in incidence rate (%IR) and corresponding 95% confidence intervals (95% CI) per unit increase in exposure.
Results:
3,995,202 COVID-19 cases in 7,800 municipalities were analysed (total population: 59,589,357 inhabitants). It was found that long-term exposure to PM2.5, PM10, and NO2 was significantly associated with the incidence rates of SARS-CoV-2 infection. In particular, incidence of COVID-19 increased by 0.3% (95%CI 0.1%-0.4%), 0.3% (0.2%-0.4%), and 0.9% (0.8%-1.0%) per 1 μg/m3 increment in PM2.5, PM10 and NO2, respectively. Associations were higher among elderly subjects and during the second pandemic wave (September 2020-December 2020). Several sensitivity analyses confirmed the main results. The results for NO2 were especially robust to multiple sensitivity analyses.
Conclusions:
evidence of an association between long-term exposure to ambient air pollutants and the incidence of SARS-CoV-2 infections in Italy was found.
... To compute more accurate estimates of the population at risk in the second wave, we subtracted from the provincial population the estimated percentage of residents who contracted COVID-19 during the first wave, given that such individuals were much less susceptible to reinfection, as also shown by the inverse relation of COVID-19 incidence in the Italian provinces between the first and the second wave . Air pollution has been suggested to be linked to increased COVID-19 incidence and severity (Copat et al., 2020;Filippini et al., 2020;Jerrett et al., 2022), and therefore could either confound or mediate the effect of meteorological factors on COVID-19. We analyzed our data controlling for population-weighted mean levels of ambient air PM 2.5 and also without controlling for it, with no appreciable differences in the results. ...
The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m2 showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.
... Indeed, the link between heavy air pollution and the spread of SARS-CoV-2 is well known (Filippini et al., 2020). Ventilation allows the removal of toxic air pollutants and guarantees high levels of indoor air quality, providing comfort and health to occupants (Ng et al., 2015). ...
Climate change is leading to new challenges, especially in the construction sector,
which is considered one of the most energy-intensive sectors.
This study aims to highlight the performance of the same building located
in different climates, under climate change considering the years 2020, 2050,
and 2080.
The locations chosen for the analysis are Miami, Damascus, Izmir, and Yakutsk,
falling within the locations defined by the international Ko¨ppen-Geiger climate
classification as tropical, arid, temperate, and continental.
The building analyzed is a newly constructed residential apartment. The prototype
model is kept the same to decrease the variables of the problem. The objective
is not to find an optimal building per location, but to investigate how the operative
temperature inside the building varies with changing climatic conditions.
The analysis was performed by plotting the operative temperature on an annual
and hourly basis for each location and for each time range. The calculation was
performed in a free-floating regime, focusing on the building envelope as it asserts
that a building with an optimal envelope will need significantly less demand for
air-conditioning systems.
The results show a clear increase in temperatures and therefore a need to implement cooling systems, even where they are not required today.
... Italy (Filippini et al., 2020) No significant association between NO 2 and COVID-19 epidemic growth rate. ...
Air pollution levels across the globe continue to rise despite government regulations. The increase in global air pollution levels drives detrimental human health effects, including 7 million premature deaths every year. Many of these deaths are attributable to increased incidence of respiratory infections. Considering the COVID-19 pandemic, an unprecedented public health crisis that has claimed the lives of over 6.5 million people globally, respiratory infections as a driver of human mortality is a pressing concern. Therefore, it is more important than ever to understand the relationship between air pollution and respiratory infections so that public health measures can be implemented to ameliorate further morbidity and mortality. This article aims to review the current epidemiologic and basic science research on interactions between air pollution exposure and respiratory infections. The first section will present epidemiologic studies organized by pathogen, followed by a review of basic science research investigating the mechanisms of infection, and then conclude with a discussion of areas that require future investigation.
... As a result, over 4 million people die prematurely from illness attributable to household air pollution, with over 50% of premature deaths among infants below five years of age (Lim et al., 2012). In recent times, indoor air pollution has been significantly linked to airborne diseases, including the novel COVID-19 virus, with increased cases among children and the elderly (Filippini et al., 2020;Naglaa et al., 2021). Individual exposure to indoor air pollutants such as particulate matter, carbon monoxide, sulphur dioxide, lead, nitrogen dioxide, and ozone can be regulated by an interaction between their indoor source strengths and the entrapped time in indoor environments (Yousef et al., 2013). ...
This study assessed the health implications of air quality decline in residential, workplace, and school indoor environments and its implications on the health of the inhabitants of the Michika Area of Adamawa State. Pollutants such as PM, CO, NO2, and SO2 were evaluated in the morning and evening hours within 10 days using an in situ gas sampler. The data were compared to WHO and NESREA limits for human exposure. Anova and student t-test statistics were adopted to hypothesize the mean differences in the diurnal and spatial conditions of indoor air pollutants. The air quality index was estimated using the US EPA equation to determine the level of indoor air quality in relation to health implications. The study found, among other things, that ambient pollutant concentrations were statistically different in the respective indoor environments for CO (P value = 0.000), SO2 (P values of 0.02 and 0.000), NO2 (P value = 0.000), and PM2.5 (P = 0.001 and 0.0001). The residential indoor environment was reportedly dangerous for sensible groups due to the poor AQI rating for SO2, NO2 and PM10. In conclusion, air pollution is evident in the Michika area of Adamawa State, and the rate of exposure is particularly higher in the residential indoor space.
... Finally, the present study does not support the view of NO 2 being a major driver for influenza, an idea that was put forward with regards to the current SARS-CoV2 pandemic [45,46]. Instead, we observed a very moderate negative, rather than a positive, association between NO 2 levels and incidence (Fig. 4), which may more likely be due to statistical collinearity with temperature or PM2.5 than the hallmark of NO 2 -related salutogenesis. ...
Influenza seasonality has been frequently studied, but its mechanisms are not clear. Urban in-situ studies have linked influenza to meteorological or pollutant stressors. Few studies have investigated rural and less polluted areas in temperate climate zones.
We examined influences of medium-term residential exposure to fine particulate matter (PM2.5), NO2, SO2, air temperature and precipitation on influenza incidence.
Whilst strong statistical association of temperature with other stressors makes it difficult to distinguish between direct and mediated temperature effects, results confirm genuine effects by fine particulate matter on influenza infections for both rural and urban areas in a temperate climate. Future studies should attempt to further establish the mediating mechanisms to inform public health policies.
... Finally, the present study does not support the view of NO 2 being a major driver for influenza, an idea that was put forward with regards to the current SARS-CoV2 pandemic [45,46]. Instead, we observed a very moderate negative, rather than a positive, association between NO 2 levels and incidence (Fig. 4), which may more likely be due to statistical collinearity with temperature or PM2.5 than the hallmark of NO 2 -related salutogenesis. ...
Background: Influenza seasonality has been frequently studied, but its mechanisms are not clear. Urban in-situ studies have linked influenza to meteorological or pollutant stressors. Few studies have investigated rural and less polluted areas in temperate climate AQ1 zones AQ2 . Objectives: We examined influences of medium-term residential exposure to fine particulate matter (PM ), NO , SO , air temperature and precipitation on influenza incidence. Methods: To obtain complete spatial coverage of Baden-Württemberg, we modeled environmental exposure from data of the Copernicus Atmosphere Monitoring Service and of the Copernicus Climate Change Service. We computed spatiotemporal aggregates to reflect quarterly mean values at post-code level. Moreover, we prepared health insurance data to yield influenza incidence between January 2011 and December 2018. We used generalized additive models, with Gaussian Markov random field smoothers for spatial input, whilst using or not using quarter as temporal input. Results: In the 3.85 million cohort, 513,404 influenza cases occurred over the 9-year period, with 53.6% occurring in quarter 1 (January to March), and 10.2%, 9.4% and 26.8% in quarters 2, 3 and 4, respectively. Statistical modeling yielded highly significant effects of air temperature, precipitation, PM and NO . Computation of stressor-specific gains revealed up to 3499 infections per 100,000 AOK clients per year that are attributable to lowering ambient mean air temperature from 18.71 °C to 2.01 °C. Stressor specific gains were also substantial for fine particulate matter, yielding up to 502 attributable infections per 100,000 clients per year for an increase from 7.49 μg/m to 15.9 AQ4 8 μg/m . Conclusions: Whilst strong statistical association of temperature with other stressors makes it difficult to distinguish between direct and mediated temperature effects, results confirm genuine effects by fine particulate matter on influenza infections for both rural and urban areas in a temperate climate. Future studies should attempt to further establish the mediating mechanisms to inform public health policies.
... . Kim et al., 2006, Kampa andCastanas, 2008 , Theys et al., 2019, Wang et al., 2020 مونوکسید ، کربن ( Safarianzengir et al., 2020, Schneising et al., , Vîrghileanu et al., 2020 ) ، اوزون ( Quesada-Ruiz et al., 2020, Zhao et al., 2021 متان ، (Lorente et al., 2021, Schneising et al., 2019) ، ذرات معلق (Broomandi et al., 2020, Tiwari et al., 2015 نیتروژن مونوکسید ، ( Lorente et al., 2019, Filippini et al., 2020 اکسید دی همچنین و نیتروژن ( Shikwambana et al., 2020, Omrani et al., 2020, Vîrghileanu et al., 2020, Ialongo et al., 2020a, Koukouli et al., 2021 ...
... 4 One controversial and investigated air pollutant is nitrogen dioxide (NO2) 5 , a gas with characteristic yellowish-brown color and an unpleasant odor 2 , established as a criterion pollutant due to its toxic characteristics. 6 This gas indicates anthropic activities because it is the primary pollutant emitted by vehicle mobilization processes 7 and industrial activities. 8 This pollutant is related to diseases such as hyperthyroidism, diabetes, heart and cardiovascular diseases 9 , and even death. ...
This study analyses air quality behavior by considering nitrogen dioxide (NO2) as a reference parameter during atypical conditions associated with the COVID-19 pandemic. NO2 concentrations in 31 departmental capital cities of Colombia (South America) were evaluated during four periods according to government dispositions to face the pandemic: (I) before isolation (normal conditions), (II) mandatory isolation (significant traffic and industrial activity reduction), (III) intelligent isolation (return of some commercial activities), and (IV) selective isolation (Increase of authorized commercial activities). A good fit between ground data and satellite information is observed. Results indicate that most of the cities (45%) present a counterintuitive behavior with concentration increments for Period II with respect to Period I.
... However, areaspecific analyses are crucial to highlight the necessity of policy decisions and more feasible in the presence of large numbers of confounders, all of which could not be easily obtained for large numbers of individuals. Another limitation is that the considered air pollution metrics may be too low to measure a significant effect on the severity of COVID-19 in comparison, for example to the highly industrialized regions, Lombardy, Veneto, and Emilia-Romagna, where the initial surge of infections and deaths in Italy appeared most severely [59]. ...
Background
The focus of many studies is to estimate the effect of risk factors on outcomes, yet results may be dependent on the choice of other risk factors or potential confounders to include in a statistical model. For complex and unexplored systems, such as the COVID-19 spreading process, where a priori knowledge of potential confounders is lacking, data-driven empirical variable selection methods may be primarily utilized. Published studies often lack a sensitivity analysis as to how results depend on the choice of confounders in the model. This study showed variability in associations of short-term air pollution with COVID-19 mortality in Germany under multiple approaches accounting for confounders in statistical models.
Methods
Associations between air pollution variables PM 2.5 , PM 10 , CO, NO, NO 2 , and O 3 and cumulative COVID-19 deaths in 400 German districts were assessed via negative binomial models for two time periods, March 2020–February 2021 and March 2021–February 2022. Prevalent methods for adjustment of confounders were identified after a literature search, including change-in-estimate and information criteria approaches. The methods were compared to assess the impact on the association estimates of air pollution and COVID-19 mortality considering 37 potential confounders.
Results
Univariate analyses showed significant negative associations with COVID-19 mortality for CO, NO, and NO 2 , and positive associations, at least for the first time period, for O 3 and PM 2.5 . However, these associations became non-significant when other risk factors were accounted for in the model, in particular after adjustment for mobility, political orientation, and age. Model estimates from most selection methods were similar to models including all risk factors.
Conclusion
Results highlight the importance of adequately accounting for high-impact confounders when analyzing associations of air pollution with COVID-19 and show that it can be of help to compare multiple selection approaches. This study showed how model selection processes can be performed using different methods in the context of high-dimensional and correlated covariates, when important confounders are not known a priori. Apparent associations between air pollution and COVID-19 mortality failed to reach significance when leading selection methods were used.
... (Bringslimark, Hartig, & Patil, 2009) investigated the effect of incorporating green plants and nature sounds into hospitals to accelerate recovery. During the current COVID-19 pandemic, stay-at-home orders and the emergence of hypotheses that air pollution can increase susceptibility to infection (Filippini et al., 2020;Zhao, Liu, & Chen, 2020) have had a great impact on studies focused on improving IEQ and IAQ to reduce the spread of infection (Awada et al., 2021). Therefore, redesigning indoor spaces in skyscrapers is important to improve IEQ. ...
Can skyscrapers survive after COVID-19? Can the idea of integrating vertical farming (VF) into vertical architecture support the environmental, economic, and social issues in the post-pandemic era? Answering these questions is the main objective of this study. Therefore, it explores a) the impact of the pandemic on the built environment, especially skyscrapers; b) the challenges facing the survival of skyscrapers; c) the design parameters and main components of VF; and d) the expected feasibility of integrating VF into vertical architecture to reduce the effects of the pandemic. The research concludes that the skyscraper-integrated vertical farming (SIVF) paradigm can create a closed ecosystem that preserves the environment by a) supporting food security, b) improving indoor environmental quality, c) enhancing psychological and physical health, d) saving energy, e) reducing greenhouse gas emissions and releasing oxygen, and f) supporting the local economy. Consequently, the SIVF paradigm can inaugurate an innovative approach that provides insights into new research trends and discoveries. However, further constraints in the adoption of SIVF should be addressed, and collaborations between researchers and multidisciplinary experts must be created to achieve suitable solutions.
KEYWORDS
COVID-19; skyscrapers; vertical architecture; vertical farming; food security; skyscraper-integrated vertical farming
... In 2019, due to the lockdown restrictions adopted to contain the COVID-19 pandemic, a reduction of NO 2 tropospheric concentrations has been observed over Europe from ground-based and satellite instruments [5,6]. In Italy, an important pollution reduction has been observed around Rome [7,8] and in the Po Valley [9,10], in the northern part of Italy. The Po Valley is the most industrialized and polluted area of Italy; here, high mountains surround the Po basin, preventing pollution from dispersion, especially in wintertime. ...
Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments are used worldwide to retrieve pollutant information from visible (VIS) and ultra-violet (UV) diffuse solar spectra. A similar instrument, able to meet the Fiducial Reference Measurements for DOAS (FRM4DOAS) standard requirements, is not yet present in the Po Valley (Italy), one of the most polluted regions in Europe. Our purpose is to close this gap exploiting the SkySpec-2D, a FRM4DOAS-compliant MAX-DOAS instrument bought by the Italian research institute CNR-ISAC in May 2021. As a first step, SkySpec-2D was involved in two measurement campaigns to assess its performance: the first one in August 2021 in Bologna where TROPOGAS, a research-grade custom-built MAX-DOAS instrument is located; the second one in September 2021 at the BAQUNIN facility at La Sapienza University (Rome) near the Pandora#117 instrument. Both campaigns revealed a good quality of SkySpec-2D measurements. Indeed, good agreement was found with TROPOGAS (correlation 0.77), Pandora#117 (correlation 0.9) and satellite (TROPOMI and OMI) measurements. Having assessed its performance, the SkySpec-2D was permanently moved to the “Giorgio Fea” observatory in San Petro Capofiume, located in the middle of the Po Valley, where it has been continuously acquiring zenith and off-axis diffuse solar spectra from the 1 October 2021. Nowadays, its MAX-DOAS measurements are routinely provided to the FRM4DOAS team with the purpose to be soon included in the FRM4DOAS validation network.
... Similarly, the observation that the pandemic had faster and wider spread in the most polluted areas contributed to formulating the hypothesis of positive interaction between pollution and pandemic spread. Many recent studies have linked air pollution to the increasing spread of COVID-19 [9][10][11]. Several have focused on Northern Italy, being the region with the first large outbreak in Europe and, at the same time, having high atmospheric pollution levels [12,13]. ...
Background
Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic.
In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km x 12 km), including satellite information.
Methods
We focused on the Province of Reggio Emilia, which was severely hit by the first wave of the epidemic. The outcomes included new SARS-CoV-2 infections and COVID-19 hospital admissions. Pollution, meteorological and mobility data were analyzed. The spatial simulation domain included the Province of Reggio Emilia in a grid of 40 cells of (12 km)². We implemented a ConvLSTM, which is a spatio-temporal deep learning approach, to perform a 7-day moving average to forecast the 7th day after. We used as training and validation set the new daily infections and hospital admissions from August 2020 to March 2021. Finally, we assessed the models in terms of Mean Absolute Error (MAE) compared with Mean Observed Value (MOV) and Root Mean Squared Error (RMSE) on data from April to September 2021. We tested the performance of different combinations of input variables to find the best forecast model.
Findings
Daily new cases of infection, mobility and wind speed resulted in being strongly predictive of new COVID-19 hospital admissions (MAE = 2.72 in the Province of Reggio Emilia; MAE = 0.62 in Reggio Emilia city), whereas daily new cases, mobility, solar radiation and PM2.5 turned out to be the best predictors to forecast new infections, with appropriate time lags.
Interpretation
ConvLSTM achieved good performances in forecasting new SARS-CoV-2 infections and new COVID-19 hospital admissions. The spatio-temporal representation allows borrowing strength from data neighboring to forecast at the level of the square cell (12 km)², getting accurate predictions also at the county level, which is paramount to help optimise the real-time allocation of health care resources during an epidemic emergency.
... The impact of buildings' indoor environmental quality (IAQ) on residents' health is an important topic in civil, architectural, mechanical engineering, and public health. The concept that air pollution can raise human exposure to viral infections such as SARS-CoV-2 is currently supported by recent data [25]. In previous studies, IAQ indicators were included in the suitability of ambient temperature, visual visibility, acoustic properties, humidity, and ventilation on the health of the inhabitants. ...
Sustainable design methods aim to obtain architectural solutions that assure the coexistence and welfare of human beings, inorganic structures, and living things that constitute ecosystems. The novel coronavirus emergence, inadequate vaccines against the present severe acute respiratory syndrome-coronavirus-(SARS-CoV-2), and increases in microbial resistance have made it essential to review the preventative approaches used during pre-antibiotic periods. Apart from low carbon emissions and energy, sustainable architecture for facilities, building designs, and digital modeling should incorporate design approaches to confront the impacts of communicable infections. This review aims to determine how architectural design can protect people and employees from harm; it models viewpoints to highlight the architects’ roles in combating coronavirus disease 2019 (COVID-19) and designing guidelines as a biomedical system for policymakers. The goals include exploring the hospital architecture evolution and the connection between architectural space and communicable infections and recommending design and digital modeling strategies to improve infection prevention and controls. Based on a wide-ranging literature review, it was found that design methods have often played important roles in the prevention and control of infectious diseases and could be a solution for combating the wide spread of the novel coronavirus or coronavirus variants or delta.
... They also found an increase in surface O 3 from 26 ppb to 56.4 ppb during the same period. Many other studies also observe similar changes in pollutants during the COVID-19 period (Abdullah et al. 2020;Tobías et al. 2020;Bashir et al. 2020;Chauhan and Singh 2020;Muhammad et al. 2020;Dutheil et al. 2020;Isaifan 2020;Filippini et al. 2020;Fan et al. 2020;Li et al. 2020;Ryan et al. 2020). Therefore, it is a challenge to include all studies, and thus, we have mentioned those related to air quality, particularly ozone and NO 2 . ...
A nationwide lockdown was imposed in India from 24 March 2020 to 31 May 2020 to contain the spread of COVID-19. The lockdown has changed the atmospheric pollution across the continents. Here, we analyze the changes in two most important air quality related trace gases, nitrogen dioxide (NO2) and tropospheric ozone (O3) from satellite and surface observations, during the lockdown (April–May 2020) and unlock periods (June–September 2020) in India, to examine the baseline emissions when anthropogenic sources were significantly reduced. We use the Bayesian statistics to find the changes in these trace gas concentrations in different time periods. There is a strong reduction in NO2 during the lockdown as public transport and industries were shut during that period. The largest changes are found in IGP (Indo-Gangetic Plain), and industrial and mining areas in Eastern India. The changes are small in the hilly regions, where the concentrations of these trace gases are also very small (0–1 × 10¹⁵ molec./cm²). In addition, a corresponding increase in the concentrations of tropospheric O3 is observed during the period. The analyses over cities show that there is a large decrease in NO2 in Delhi (36%), Bangalore (21%) and Ahmedabad (21%). As the lockdown restrictions were eased during the unlock period, the concentrations of NO2 gradually increased and ozone deceased in most regions. Therefore, this study suggests that pollution control measures should be prioritized, ensuring strict regulations to control the source of anthropogenic pollutants, particularly from the transport and industrial sectors.
Highlights
• Most cities show a reduction up to 15% of NO2 during the lockdown
• The unlock periods show again an increase of about 40–50% in NO2
• An increase in tropospheric O3 is observed together with the decrease in NO2
... per 1-μg/m 3 increase), which is somewhat smaller than the effect size observed in the present study, although the differences in study design, target population, exposure distribution, and statistical methods preclude a direct comparison with our findings. Typically, previous ecological studies used a time-series study design and generalized additive model to quantify the association,[33][34][35][36][38][39][40] which may be limited by the autocorrelation of air pollution concentrations over time and group-and population-level exposure. By using an ...
Importance Mounting ecological evidence shows an association between short-term air pollution exposure and COVID-19, yet no study has examined this association on an individual level.
Objective To estimate the association between short-term exposure to ambient air pollution and SARS-CoV-2 infection among Swedish young adults.
Design, Setting, and Participants This time-stratified case-crossover study linked the prospective BAMSE (Children, Allergy Milieu, Stockholm, Epidemiology [in Swedish]) birth cohort to the Swedish national infectious disease registry to identify cases with positive results for SARS-CoV-2 polymerase chain reaction (PCR) testing from May 5, 2020, to March 31, 2021. Case day was defined as the date of the PCR test, whereas the dates with the same day of the week within the same calendar month and year were selected as control days. Data analysis was conducted from September 1 to December 31, 2021.
Exposures Daily air pollutant levels (particulate matter with diameter ≤2.5 μm [PM2.5], particulate matter with diameter ≤10 μm [PM10], black carbon [BC], and nitrogen oxides [NOx]) at residential addresses were estimated using dispersion models with high spatiotemporal resolution.
Main Outcomes and Measures Confirmed SARS-CoV-2 infection among participants within the BAMSE cohort. Distributed-lag models combined with conditional logistic regression models were used to estimate the association.
Results A total of 425 cases were identified, of whom 229 (53.9%) were women, and the median age was 25.6 (IQR, 24.9-26.3) years. The median exposure level for PM2.5 was 4.4 [IQR, 2.6-6.8] μg/m3 on case days; for PM10, 7.7 [IQR, 4.6-11.3] μg/m3 on case days; for BC, 0.3 [IQR, 0.2-0.5] μg/m3 on case days; and for NOx, 8.2 [5.6-14.1] μg/m3 on case days. Median exposure levels on control days were 3.8 [IQR, 2.4-5.9] μg/m3 for PM2.5, 6.6 [IQR, 4.5-10.4] μg/m3 for PM10, 0.2 [IQR, 0.2-0.4] μg/m3 for BC, and 7.7 [IQR, 5.3-12.8] μg/m3 for NOx. Each IQR increase in short-term exposure to PM2.5 on lag 2 was associated with a relative increase in positive results of SARS-CoV-2 PCR testing of 6.8% (95% CI, 2.1%-11.8%); exposure to PM10 on lag 2, 6.9% (95% CI, 2.0%-12.1%); and exposure to BC on lag 1, 5.8% (95% CI, 0.3%-11.6%). These findings were not associated with NOx, nor were they modified by sex, smoking, or having asthma, overweight, or self-reported COVID-19 respiratory symptoms.
Conclusions and Relevance The findings of this case-crossover study of Swedish young adults suggest that short-term exposure to particulate matter and BC was associated with increased risk of positive PRC test results for SARS-CoV-2, supporting the broad public health benefits of reducing ambient air pollution levels.
... A recent US study found that an increase of just 1 µg/m 3 in long-term exposure to PM 2.5 was associated with an 8% increase in the COVID-19 death rate [10]. Studies in Italy have identified associations between COVID-19 and COVID-19-related death and exposure to PM 10 and PM 2.5 [11][12][13][14], while tropospheric nitrogen dioxide (NO 2 ) in northern Italy was associated with levels of SARS-CoV-2 infection [15]. The COVID-19 pandemic in Italy started in the Po Valley of northern Italy-one of the most polluted areas in the world [16]where intensive livestock rearing and heavy use of fertilizers make major contributions to atmospheric pollution [17]. ...
Exposure to atmospheric particulate matter and nitrogen dioxide has been linked to
SARS-CoV-2 infection and death. We hypothesized that long-term exposure to farming-related air pollutants might predispose to an increased risk of COVID-19-related death. To test this hypothesis, we performed an ecological study of five Italian Regions (Piedmont, Lombardy, Veneto, Emilia-Romagna and Sicily), linking all-cause mortality by province (administrative entities within regions) to data on atmospheric concentrations of particulate matter (PM2.5 and PM10) and ammonia (NH3), which are mainly produced by agricultural activities. The study outcome was change in all-cause mortality during March–April 2020 compared with March–April 2015–2019 (period). We estimated
all-cause mortality rate ratios (MRRs) by multivariate negative binomial regression models adjusting for air temperature, humidity, international import-export, gross domestic product and population density. We documented a 6.9% excess in MRR (proxy for COVID-19 mortality) for each tonne/km2 increase in NH3 emissions, explained by the interaction of the period variable with NH3 exposure, considering all pollutants together. Despite the limitations of the ecological design of the study, following the precautionary principle, we recommend the implementation of public health measures to limit environmental NH3 exposure, particularly while the COVID-19 pandemic continues. Future
studies are needed to investigate any causal link between COVID-19 and farming-related pollution.
... In Italy the pandemic developed earlier in comparison with other countries (Lolli et al., 2020;Wu et al., 2020a), therefore a lot of studies have been published on this topic. These investigations have examined the effect of atmospheric parameters on the COVID-19 diffusion in the most impacted zone (especially Lombardia, and its main city: Milano), and during the most dramatic period, up to March 2020, i.e. the first month (Pivato et al., 2021;Pirouz et al., 2020;Collivignarelli et al., 2021;Haghshenas et al., 2020;Bontempi, 2020;Passerini et al., 2020;Ye et al., 2021;Sfîcȃ et al., 2020;Khursheed et al., 2021;Coker et al., 2020;Bianconi et al., 2020;Accarino et al., 2021;Perone, 2021;Filippini et al., 2021;Kotsiou et al., 2021), or up to April 2020 (Coccia, 2020;Delnevo et al., 2020;Fazzini et al., 2020;Filippini et al., 2020;Fattorini and Regoli, 2020;Zoran et al., 2020;De Angelis et al., 2021;Benedetti et al., 2020;Agnoletti et al., 2020). Only a few works have considered a wider time span, up to the end of spring (Ho et al., 2021;Pansini and Fornacca, 2021;Lolli et al., 2020;Cascetta et al., 2021). ...
In 2020 North Italy suffered the SARS-CoV-2-related pandemic with a high number of deaths and hospitalization. The effect of atmospheric parameters on the amount of hospital admissions (temperature, solar radiation, particulate matter, relative humidity and wind speed) is studied through about 8 months (May–December). Two periods are considered depending on different conditions: a) low incidence of COVID-19 and very few regulations concerning personal mobility and protection (“free/summer period”); b) increasing incidence of disease, social restrictions and use of personal protections (“confined/autumn period”). The “hospitalized people in medical area wards/100000 residents” was used as a reliable measure of COVID-19 spreading and load on the sanitary system. We developed a chemometric approach (multiple linear regression analysis) using the daily incidence of hospitalizations as a function of the single independent variables and of their products (interactions). Eight administrative domains were considered (altogether 26 million inhabitants) to account for relatively homogeneous territorial and social conditions.
The obtained models very significantly match the daily variation of hospitalizations, during the two periods. Under the confined/autumn period, the effect of non-pharmacologic measures (social distances, personal protection, etc.) possibly attenuates the virus diffusion despite environmental factors. On the contrary, in the free/summer conditions the effects of atmospheric parameters are very significant through all the areas. Particulate matter matches the growth of hospitalizations in areas with low chronic particulate pollution. Fewer hospitalizations strongly correspond to higher temperature and solar radiation. Relative humidity plays the same role, but with a lesser extent. The interaction between solar radiation and high temperature is also highly significant and represents surprising evidence. The solar radiation alone and combined with high temperature exert an anti-SARS-CoV-2 effect, via both the direct inactivation of virions and the stimulation of vitamin D synthesis, improving immune system function.
... NO 2 concentrations exceeding 130 µmol m -3 significantly increase mortality among Corvid-19 patients (Filippini et al. 2020). ...
Of the nitrogen oxides, NO2 has the greatest impact on humans. The areas with its highest concentrations within the EU are these most populated and industrialised ones. Excessive nitro-gen dioxide concentrations are responsible for around 75,000 premature deaths of EU residents each year; in Poland, this value reaches almost 1,900. Since 2017, there have been new possibi- lities to measure NO2 concentration, made as part of the Copernicus Sentinels-5P Programme. Hence, it is purposeful to analyse changes in nitrogen dioxide emissions in the most important Polish cities against the EU in spring and summer 2020. The basis for the analysis was satellite data collected during the Copernicus Sentinels-5P Programme. In 2020, there were huge chan- ges in NO2 concentration, which were the consequences of the implementation of restrictive safety measures relating to the COVID-19 pandemic: a significant reduction in road traffic and the closure or limiting of production in many industrial plants. It was found that, as a result of lockdown, nitrogen dioxide concentrations in Poland’s largest cities fell to a much lesser de-gree than in cities of similar size in southern or western Europe. The analyses indicated that data obtained from the Copernicus Sentinel-5P satellite will play a key role in monitoring chan- ges in nitrogen dioxide concentration throughout the EU. Ground-based observations of nitrogen dioxide concentrations, which have dominated until recently, will remain only of comparative importance in the assessment and analysis of the compound concentration.
... 8.8% declined in PM 2.5 , NO 2 , SO 2 , O 3 , and CO concentrations, respectively, in Dhaka City during the partial and full lockdown compared to the period before the lockdown. Late work using different types of remotely sensed images also confirmed the air quality improvement during the COVID-19 lockdown, quarantine, and social distancing, with studies from badly affected countries such as England (Wyche et al. 2021), Italy (Filippini et al. 2020;Sannino et al. 2020), Brazil (Brito et al. 2020), and most recently India (Naqvi et al. 2021;Sathe et al. 2021). ...
The spread of the 2019 novel coronavirus disease (COVID-19) has engulfed the world with a rapid, unexpected, and far-reaching global crisis. In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have played an important role in many aspects, especially in the fight against COVID-19. This review summarises 102 scientific papers on applications of GIS and RS on studies of the COVID-19 pandemic. In this study, two themes of GIS and RS-related applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGISbased mapping, the correlation between the COVID-19 and natural, socio-economic factors, and the environmental impacts. The findings of this study provide insight into how to apply new techniques (GIS and RS) to better understand, better manage the evolution of the COVID-19 pandemic and effectively assess its impacts.
... El NO 2 ha sido evaluado con información satelital para estudios de calidad del aire en series temporales [3], [4] y específicamente para observar la disminución de sus niveles por las restricciones a la circulación establecidas a partir de la pandemia de COVID-19 [5], [6], [7]. La Comisión Nacional de Actividades Espaciales (CONAE), tiene la misión de proponer y ejecutar el Plan Espacial Nacional a través de distintos cursos de acción, uno de los cuales es el uso y gestión de información espacial.Ésté ultimo comprende a la distribución de productos primarios y con valor agregado y al desarrollo de las aplicaciones necesarias para satisfacer los requerimientos de los distintos sectores de la sociedad. ...
Air quality is assessed by determining criteria pollutant levels in the atmosphere. While the most significant measurements are ground based, satellite remote sensing is rising as a complementary technique to reveal spatial distribution of pollutants in the integrated tropospheric column. In this work we present a new CONAE’s value-added monthly product of nitrogen dioxide (NO 2 ) for South America, derived from the tropospheric NO 2 column density estimated by TROPOMI/Sentinel-5p (ESA) data. Dataset generation of monthly mean, median, standard deviation and quantity of data used per pixel, along with distribution formats of downloading and visualizing data, are explained in order to provide to different users their characteristics and access. In addition, a spatial and temporal analysis is made for the Buenos Aires, Santiago and São Paulo cities along with ground measurements, for the august 2018 to may 2021 period and on a monthly basis. For this matter, higher values of nitrogen dioxide were observed in wintertime for the three cities, due to a greater quantity of stagnation episodes. While satellite derived data follows the temporal profile of ground-based concentrations, Santiago was the city of higher levels and bigger contrast to the summer levels. COVID-19 pandemic restrictions to traffic circulation is also noticed in the diminishing of NO 2 in the two datasets, as it was also reported in previous studies. The publication of this new dataset holds the objective of supporting air quality monitoring in South America, helping non specialized users to freely access to interoperational data.
... In summary, a larger susceptible population may lie at the origin of the higher impact of COVID-19 in Lombardy compared with Veneto and Emilia-Romagna, which were simultaneously hit by the outbreak. This may in turn reflect a combination of causes including geographical segregation of the population, lifestyle, social habits, and environmental factors such as air pollution and climate conditions, that may favor the virus persistence and thus individual exposure (1,25). Interestingly, the model inversion uncovered peculiar values for the testing parameters of Veneto, in line with the more effective prevention policies adopted by this region since the beginning of the outbreak, which included testing both symptomatic and asymptomatic subjects, while in other regions, only symptomatic cases were investigated (26,27). ...
The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as “dynamic causal modeling” (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.
... These recurrent local exacerbations of the pandemic present an opportunity to study the spread of the virus within a population. Key features of the pandemic are still not well understood, such as the susceptibility of the population to subsequent waves after the first outbreak, the threshold for herd immunity, the role of superspreaders [1][2][3][4][5][6][7] as well as of meteorological and environmental factors [8][9][10][11]. ...
Background
The relation between the magnitude of successive waves of the COVID-19 outbreak within the same communities could be useful in predicting the scope of new outbreaks.
Methods
We investigated the extent to which COVID-19 mortality in Italy during the second wave was related to first wave mortality within the same provinces. We compared data on province-specific COVID-19 2020 mortality in two time periods, corresponding to the first wave (February 24–June 30, 2020) and to the second wave (September 1–December 31, 2020), using cubic spline regression.
Results
For provinces with the lowest crude mortality rate in the first wave (February–June), i.e. < 22 cases/100,000/month, mortality in the second wave (September–December) was positively associated with mortality during the first wave. In provinces with mortality greater than 22/100,000/month during the first wave, higher mortality in the first wave was associated with a lower second wave mortality. Results were similar when the analysis was censored at October 2020, before the implementation of region-specific measures against the outbreak. Neither vaccination nor variant spread had any role during the study period.
Conclusions
These findings indicate that provinces with the most severe initial COVID-19 outbreaks, as assessed through mortality data, faced milder second waves.
... Italy is one of the first and most severely affected country in Europe, with its first indigenous case identified on February 21, 2020 (4). As a consequence, in the period February-June 2020 Italy experienced a first wave that severely affected mainly the North of the country (5,6), led to a tight lockdown (7), with regional differences possibly related to genetic, clinical, lifestyle, and environmental factors (8)(9)(10)(11)(12)(13)(14)(15), followed by a decline in the summer period (4,5). ...
Background and aim:
In early 2020, SARS-CoV-2 was declared a pandemic by the WHO and Italy was one of the first and most severely affected country in Europe. Despite the global interest about COVID-19 pandemic, several aspects of this infection are still unclear, especially in pediatric population. This study aims to investigate the characteristics of the isolated or quarantined children and adolescents followed by the Public Health Department of the Italian province of Modena during the first wave of COVID-19.
Methods:
The study population included all non-adult subjects aged 0-18 years who underwent isolation or quarantine during the first wave of SARS-CoV-2 pandemic from February 24 to June 18, 2020 in Modena province, Northern Italy.
Results:
In Modena province, 1230 children and adolescents were isolated in case of SARS-CoV-2 infection (6.3%), or quarantined due to close contact with confirmed cases (88.7%) or travelling from a high-risk area (5.0%). Among 349 individuals who underwent swab testing, 294 (84.2%) reported close contact with an infected cohabiting relative and 158 (45.3%) were symptomatic. Among all tested subjects, 78 (22.4%) resulted positive, with a higher proportion of symptomatic subjects compared with the SARS-CoV-2-negative (78.2% vs. 35.8%). Fever was mostly present in SARS-CoV-2-positive children (48.7% vs. 12.6%). Both anosmia (58.3% vs. 41.7%) and dysgeusia (54.5% vs. 45.5%) had only slightly higher frequency in SARS-CoV-2-positive.
Conclusions:
These findings allow to expand the knowledge regarding characteristics of non-adult subjects isolated or quarantined during the first wave of SARS-CoV-2 pandemic. (www.actabiomedica.it).
... Factors associated with increased susceptibility to COVID-19 onset and severity, following the infection with SARS-CoV-2, have been shown to be male sex, and presence of a comorbidity such as hypertension, diabetes, cardiovascular disease, or chronic lung disease (15,16). Also, environmental factors may play a role increasing COVID-19 susceptibility and severity (17)(18)(19)(20)(21) as also reported in previous studies carried out in Northern Italy suggesting a positive association between air pollutant levels with both SARS-CoV-2 incidence and COVID-19 mortality (22)(23)(24). ...
Background and aim:
The exact COVID-19 severity is still not well defined and it is hotly debated due to the a few methodological issues such as the uncertainties about the spread of the SARS-CoV-2 infection.
Methods:
We investigated COVID-19 case-fatality rate and infection-fatality rate in 2020 in Italy, a country severely affected by the pandemic, basing our assessment on publicly available data, and calculating such measures during the first and second waves.
Results:
We found that province-specific crude case-fatality rate in the first wave (February-July 2020) had a median value of 12.0%. Data about infection-fatality rate was more difficult to compute, due to large underestimation of SARS-CoV-2 infection during the first wave when asymptomatic individuals were very rarely tested. However, when using as a reference population-based seroprevalence data for anti-SARS-CoV-2 antibodies collected in May-July 2020, we computed an infection-fatality rate of 2.2%. During the second wave (Sep-Dec 2020), when SARS-CoV-2 testing was greatly increased and extended to many asymptomatic individuals, we could only compute a 'hybrid' case/infection-fatality rate with a value of 2.2%, similar to the infection-fatality rate of the first wave.
Conclusions:
Overall, this study allowed to assess the COVID-19 case- and infection-fatality rates in Italy before of variant spread and vaccine availability, confirming their high values compared with other airborne infections like influenza. Our findings for Italy were similar to those characterizing other Western European countries.
... Further studies revealed correlations between COVID-19 infection and mortality rates and concentrations of NO 2 , CO, O 3 and PM . Associations between COVID-19 infection rate and related fatality with NO 2 exposure levels have been recorded (Ogen 2020;Filippini et al. 2020). PM in particular has received significant attention, not only in view of increasing susceptibility to and morbidity from SARS-CoV-2 related respiratory infections (Comunian et al. 2020;Zoran et al. 2020), but also being an aid to the airborne transmission of SARS-CoV-2 (Setti et al. 2020a (Setti et al. 2020b, van Doremalen et al. 2020Cai et al. 2020). ...
The nexus of COVID-19 and environment is conspicuously deep-rooted. The roles of environmental factors in the origin, transmission and spread of COVID-19 and the mutual impact of the pandemic on the global environment have been the two perspectives to view this nexus. The present paper attempts to systematically review the existing literature to understand and explore the linkages of COVID-19 with environment and proposes conceptual frameworks to underline this nexus. Our study indicates a critical role of meteorological factors, ambient air pollutants and wastewater in severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmission-spread dynamics. The study also focuses on the direct and indirect impacts of COVID-19 on the regional and global environment. Most of the indirect environmental effects of COVID-19 were attributed to global human confinement that resulted from the implementation of the pandemic containment measures. This worldwide anthropogenic ‘pause’ sent ripples to all environmental compartments and presented a unique test bed to identify anthropogenic impacts on the earth’s natural systems. The review further addresses emerging sustainability challenges in the new normal and their potential solutions. The situation warrants critical attention to the environment-COVID-19 nexus and innovative sustainable practices to address the ramifications of short- and long-term environmental impacts of the COVID-19 pandemic.
... The impact of indoor environment quality on occupant health has long been one of the focus of architecture and public health research. Recent findings partially support the hypothesis that air pollution can increase susceptibility to SARS-CoV-2 infection (Filippini et al., 2020;Zhao et al., 2020). Previous studies have identified various indicators of indoor environment quality, including IAQ, thermal comfort, and visual and acoustic conditions. ...
To effectively reduce the spread of SARS-CoV-2, it is crucial to highlight the effectiveness of building design strategies in mitigating threats to occupants. The ongoing pandemic research and actions focus on how poor Indoor Air Quality (IAQ) amplifies the effects of airborne viruses. This review aims to draw architects' attention toward the high risk of airborne transmission of diseases by providing the latest updates and solutions to understand better the environmental and health issues associated with COVID-19. Based on the complexity of the problem and the need for interdisciplinary research, this study presents a conceptual model that addresses the integration of engineering controls, design strategies and, air disinfection techniques required to achieve a better IAQ.
... Several studies indicated a detrimental role of atmospheric environmental factors on human health (Filippini et al., 2020(Filippini et al., , 2021bGabet et al., 2021;Southerland et al., 2021;Vinceti et al., 2016;Wang et al., 2021), including neurodegenerative diseases (Bai et al., 2018;Filippini et al., 2021a;Tsai et al., 2019;Yu et al., 2021). In particular, air pollutants may result in cognitive decline and dementia etiology (Livingston et al., 2020;Ran et al., 2021;Yang et al., 2015), thus it is no surprise that some epidemiological studies have evaluated if these chemicals may induce adverse effects on the hippocampus. ...
Growing epidemiological evidence suggests that air pollution may increase the risk of cognitive decline and neurodegenerative disease. A hallmark of neurodegeneration and an important diagnostic biomarker is volume reduction of a key brain structure, the hippocampus. We aimed to investigate the possibility that outdoor air nitrogen dioxide (NO2) and particulate matter with diameter ≤2.5 μm (PM2.5) and ≤10 μm (PM10) adversely affect hippocampal volume, through a meta-analysis.
We considered studies that assessed the relation between outdoor air pollution and hippocampal volume by structural magnetic resonance imaging in adults and children, searching in Pubmed and Scopus databases from inception through July 13, 2021. For inclusion, studies had to report the correlation coefficient along with its standard error or 95% confidence interval (CI) between air pollutant exposure and hippocampal volume, to use standard space for neuroimages, and to consider at least age, sex and intracranial volume as covariates or effect modifiers. We meta-analyzed the data with a random-effects model, considering separately adult and child populations.
We retrieved four eligible studies in adults and two in children. In adults, the pooled summary β regression coefficients of the association of PM2.5, PM10 and NO2 with hippocampal volume showed respectively a stronger association (summary β −7.59, 95%CI -14.08 to −1.11), a weaker association (summary β −2.02, 95%CI -4.50 to 0.47), and no association (summary β −0.44, 95%CI -1.27 to 0.40). The two studies available for children, both carried out in preadolescents, did not show an association between PM2.5 and hippocampal volume. The inverse association between PM2.5 and hippocampal volume in adults appeared to be stronger at higher mean PM2.5 levels.
Our results suggest that outdoor PM2.5 and less strongly PM10 could adversely affect hippocampal volume in adults, a phenomenon that may explain why air pollution has been related to memory loss, cognitive decline, and dementia.
... As for most European countries, Italy witnessed the second wave of the pandemic in autumn 2020, and it was still affected in early 2021 [6]. The factors affecting the uneven distribution across the territory of many countries are partially unknown, although they are likely to encompass environmental factors [9][10][11] as well as genetic determinants [12]. The SARS-CoV-2 infection, either in symptomatic individuals or in those asymptomatic for COVID-19, is identified following diagnostic molecular RT-PCR tests based on swabs, recognizing the infection status through viral RNA detection. ...
Objectives:
The COVID-19 pandemic is due to SARS-CoV-2 coronavirus infections. It swept across the world in the spring of 2020, and so far it has caused a huge number of hospitalizations and deaths. In the present study, the authors investigated serum anti-SARS-CoV-2 antibody prevalence in the period of June 1-September 25, 2020, in 7561 subjects in Modena, Northern Italy.
Material and methods:
The study population included 5454 workers referred to testing by their companies, and 2107 residents in the Modena area who accessed testing through self-referral.
Results:
The authors found the overall seroprevalence to be 4.7% (95% confidence interval [CI] 4.2-5.2%), which was higher in women (5.4%, 95% CI: 4.5-6.2%) than in men (4.3%, 95% CI: 3.7-4.9%), and in the oldest age groups (7.3%, 95% CI: 5.2-9.3% for persons aged 60-69 years, and 11.8%, 95% CI: 8.6-15.1%, for persons aged ≥70 years). Among the occupational categories, the highest seroprevalence was found in healthcare workers (8.8%, 95% CI: 7.0-10.5%), dealers and vehicle repairers (5.2%, 95% CI: 2.9-7.6%), and workers in the sports sector (4.0%, 95% CI: 1.8-6.1%), while there was little or no such evidence for those employed in sectors such as transport and storage, accommodation and restaurant services, and the school system.
Conclusions:
These results have allowed, for the first time, to assess population seroprevalence in this area of Italy severely hit by the epidemic, while at the same time identifying the subgroups at a higher risk of exposure to SARS-CoV-2.
This paper estimates a global CO2 emissions model using satellite data at 25 km resolution. The model incorporates industrial sources (including power, steel, cement, and refineries), fires, and non-industrial population-related factors associated with household incomes and energy requirements. It also tests the impact of subways in the 192 cities where they operate. We find highly significant effects with the expected signs for all model variables, including subways. In a counterfactual exercise estimating CO2 emissions with and without subways, we find they have reduced population-related CO2 emissions by about 50 % for the 192 cities and about 11 % globally. Extending the analysis to future subways for other cities, we estimate the magnitude and social value of CO2 emissions reductions with conservative assumptions about population and income growth and a range of values for the social cost of carbon and investment costs. Even under pessimistic assumptions for these costs, we find that hundreds of cities realize a significant climate co-benefit, along with benefits from reduced traffic congestion and local air pollution, which have traditionally motivated subway construction. Under more moderate assumptions, we find that, on climate grounds alone, hundreds of cities realize high enough social rates of return to warrant subway construction.
Background: Recent studies indicated the possible relationship between climate change, environmental pollution, and Coronavirus Disease 2019 (COVID-19) pandemic. This study reviewed the effects of air pollution, climate parameters, and lockdown on the number of cases and deaths related to COVID-19. Methods: The present review was performed to determine the effects of weather and air pollution on the number of cases and deaths related to COVID-19 during the lockdown. Articles were collected by searching the existing online databases, such as PubMed, Science Direct, and Google Scholar, with no limitations on publication dates. Afterwards, this review focused on outdoor air pollution, including PM2.5, PM10, NO2, SO2, and O3, and weather conditions affecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19. Results: Most reviewed investigations in the present study showed that exposure to air pollutants, particularly PM2.5 and NO2, is positively related to COVID-19 patients and mortality. Moreover, these studies showed that air pollution could be essential in transmitting COVID-19. Local meteorology plays a vital role in coronavirus spread and mortality. Temperature and humidity variables are negatively correlated with virus transmission. The evidence demonstrated that air pollution could lead to COVID-19 transmission. These results support decision-makers in curbing potential new outbreaks. Conclusions: Overall, in environmental perspective-based COVID-19 studies, efforts should be accelerated regarding effective policies for reducing human emissions, bringing about air pollution and weather change. Therefore, using clean and renewable energy sources will increase public health and environmental quality by improving global air quality.
Background
Air pollution is speculated to increase the risks of COVID-19 spread, severity and mortality.
Objectives
We systematically review studies investigating the relationship between air pollution and COVID-19 cases, non-fatal severity and mortality in North America and Europe.
Methods
We searched PubMed, Web of Science, and Scopus for studies investigating the effects of harmful pollutants, including particulate matter with diameter ≤2.5 or 10 μm (PM2.5 or PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO), on COVID-19 cases, severity, and deaths in Europe and North America through to June 19th, 2021. Articles were included if they quantitatively measured the relationship between exposure to air pollution and COVID-19 health outcomes.
Results
From 2482 articles screened, we included 116 studies reporting 355 separate pollutant-COVID-19 estimates. Approximately half of all evaluations on incidence were positive and significant associations (52.7%); for mortality the corresponding figure was similar (48.1%), while for non-fatal severity this figure was lower (41.2%). Longer-term exposure to pollutants appeared more likely to be positively associated with COVID-19 incidence (63.8%). Specifically, PM2.5, PM10, O3, NO2 and CO were most strongly positively associated with COVID-19 incidence, and PM2.5 and NO2 with COVID-19 deaths. All studies were observational and most exhibited high risk of confounding and outcome measurement bias.
Discussion
Air pollution may associate with worse COVID-19 outcomes. Future research is needed to better test the air pollution-COVID-19 hypothesis, particularly using more robust study designs and COVID-19 measures that are less prone to measurement error and by considering co-pollutant interactions.
Hava kirliliği, şehirlerin sürdürülebilirliği ve halk sağlığı için hayati sorunlardan biridir. COVID-19 ile insan/endüstriyel faaliyetlerindeki değişim hava kirliliğinin gözlemlenebilmesini sağlamıştır. Bu doğrultuda çalışmanın amacı Adana-Mersin kentlerinde COVID-19 dönemini kapsayan nispeten yüksek çözünürlüklü uydu görüntüleri aracılığı ile yerel hava kalitesi/hava kirliliğinin yersel/zamansal ilişkisinin incelenmesidir. Bu amaçla Ocak 2019’dan Ocak 2022'ye kadar olan troposferik nitrojen dioksit (NO2) konsantrasyonuna ilişkin 4 x 7 km yersel çözünürlükteki veriler Sentinel 5P uydusundan elde edilmiştir. Çalışma sonucunda endüstriyel ve ticari birimler en yüksek NO2 konsantrasyonuna sahip olduğu, sürekli şehir yapısı ve karayolları ve ilgili alanlar ise diğer yüksek değere sahip alanlardır. Ayrıca şehir yapılarına ait sınıflarda 2020 yılı en düşük değerlere sahip iken, 2021 yılında süreksiz şehir yapısı ve karayolları sınıfları en yüksek konsantrasyona sahiptir. Bu çalışma Sentinel-5P TROPOMI verilerinin yerel alanlarda hava kalitesi ve hava kirliliğinin izlenmesinde kullanılma kapasitesini göstermiştir. Sonuçta NO2 kirliliğinin izlenmesi ve düzenlenmesi yer bilimleri, iklim bilimleri, kentsel planlamacılar, politikacılar ve sağlık araştırmacılarının çevresel kalite ve nüfus sağlık durumunun iyileştirilmesi için sürdürülebilir bir çözüm için gereklidir.
The present study was conducted to assess the short-term effects of the meteorological factors on the COVID-19 mortality in Qom, Iran. The GAM with a quasi-Poisson link function was used to evaluate the impact of temperature, DTR, relative humidity, and absolute humidity on the COVID-19 mortality, controlling potential confounders such as time trend, air pollutants, and day of the week. The results showed that the risk of COVID-19 mortality was reduced, in single-day lag/multiple-day average lag, per one-unit increase in absolute humidity (percentage change in lag 0=-33.64% (95% CI (-42.44, -23.49)), and relative humidity (percentage change in lag 0=-1.87% (95% CI (-2.52, -1.22)). Also, per one-unit increase in DTR value, COVID death risk increased in single-day and multiple-day average lag. This study demonstrated a significant relationship between the four meteorological variables and the COVID-19 mortality.
This paper investigates the air quality in 107 Italian provinces in the period 2014-2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants-nitrogen dioxide (NO 2), ozone (O 3), and particulate matter (PM 2.5 and PM 10)-were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO 2 , PM 2.5 , PM 10 , benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO 2, O 3 , PM 2.5 , PM 10 , and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics.
Remotely sensed imagery is used as a tool to aid decision makers and scientists in a variety of fields. A recent world event in which satellite imagery was extensively relied on by a variety of stakeholders was the COVID-19 pandemic. In this article we aim to give an overview of the types of information offered through remote sensing (RS) to help address different issues related to the pandemic. We also discuss about the stakeholders that benefited from the data, and the value added by its availability. The content is presented under four sub-sections; namely (1) the use of RS in real-time decision-making and strategic planning during the pandemic; how RS revealed the (2) environmental changes and (3) social and economic impacts caused by the pandemic. And (4) how RS informed our understanding of the epidemiology of SARS-CoV-2, the pathogen responsible for the pandemic. High resolution optical imagery offered updated on-the-ground data for e.g., humanitarian aid organizations, and informed operational decision making of shipping companies. Change in the intensity of air and water pollution after reduced anthropogenic activities around the world were captured by remote sensing - supplying concrete evidence that can help inform improved environmental policy. Several economic indicators were measured from satellite imagery, showing the spatiotemporal component of economic impacts caused by the global pandemic. Finally, satellite based meteorological data supported epidemiological studies of environmental disease determinants. The varied use of remote sensing during the COVID-19 pandemic affirms the value of this technology to society, especially in times of large-scale disasters.
The increasing frequency of zoonotic diseases is amongst several catastrophic repercussions of inadequate environmental management. Emergence, prevalence, and lethality of zoonotic diseases is intrinsically linked to environmental management which are currently at a destructive level globally. The effects of these links are complicated and interdependent, creating an urgent need of elucidating the role of environmental mismanagement to improve our resilience to future pandemics. This review focused on the pertinent role of forests, outdoor air, indoor air, solid waste and wastewater management in COVID-19 dissemination to analyze the opportunities prevailing to control infectious diseases considering relevant data from previous disease outbreaks. Global forest management is currently detrimental and hotspots of forest fragmentation have demonstrated to result in zoonotic disease emergences. Deforestation is reported to increase susceptibility to COVID-19 due to wildfire induced pollution and loss of forest ecosystem services. Detection of SARS-CoV-2 like viruses in multiple animal species also point to the impacts of biodiversity loss and forest fragmentation in relation to COVID-19. Available literature on air quality and COVID-19 have provided insights into the potential of air pollutants acting as plausible virus carrier and aggravating immune responses and expression of ACE2 receptors. SARS-CoV-2 is detected in outdoor air, indoor air, solid waste, wastewater and shown to prevail on solid surfaces and aerosols for prolonged hours. Furthermore, lack of protection measures and safe disposal options in waste management are evoking concerns especially in underdeveloped countries due to high infectivity of SARS-CoV-2. Inadequate legal framework and non-adherence to environmental regulations were observed to aggravate the postulated risks and vulnerability to future waves of pandemics. Our understanding underlines the urgent need to reinforce the fragile status of global environmental management systems through the development of strict legislative frameworks and enforcement by providing institutional, financial and technical supports.
Satellite data are widely used to study the spatial component of epidemics: to monitor their evolution, to create epidemiological risk maps and predictive models. The improvement of data quality, not only in technical terms but also of scientific relevance and robustness, represents in this context one of the most important aspects for health information technology that can make further significant and useful progress in monitoring and managing epidemics. In this regard, this paper intends to address an issue that is not always adequately considered in the use of satellite data for the creation of maps and spatial models of epidemics, namely the preliminary verification of the level of spatial correlation between remote sensing environmental variables and epidemics. Specifically, we intend to evaluate the contribution of exposure to the pollutant nitrogen dioxide (NO 2 ) on the spatial spread of the virus and the severity of the current COVID infection.
The COVID-19 pandemic has a close relationship with local environmental conditions. This study explores the effects of climate characteristics and air pollution on COVID-19 in Isfahan province, Iran. A number of COVID-19 positive cases, main air pollutants, air quality index (AQI), and climatic variables were received from March 1, 2020, to January 19, 2021. Moreover, CO, NO2, and O3 tropospheric levels were collected using Sentinel-5P satellite data. The spatial distribution of variables was estimated by the ordinary Kriging and inverse weighted distance (IDW) models. A generalized linear model (GLM) was used to analyze the relationship between environmental variables and COVID-19. The seasonal trend of nitrogen dioxide (NO2), wind speed, solar energy, and rainfall like COVID-19 was upward in spring and summer. The high and low temperatures increased from April to August. All variables had a spatial autocorrelation and clustered pattern except AQI. Furthermore, COVID-19 showed a significant association with month, climate, solar energy, and NO2. Suitable policy implications are recommended to be performed for improving people’s healthcare and control of the COVID-19 pandemic. This study could survey the local spread of COVID-19, with consideration of the effect of environmental variables, and provides helpful information to health ministry decisions for mitigating harmful effects of environmental change. By means of the proposed approach, probably the COVID-19 spread can be recognized by knowing the regional climate in major cities. The present study also finds that COVID-19 may have an effect on climatic condition and air pollutants.
Several measures have been taken to mitigate the effects of the COVID-19 pandemic. In this context, almost all non-essential activities in Morocco have been halted since March 20, 2020. From that date, Morocco announced the lockdown for one month and it was extended until June 10, 2020. The main objective of this paper is to study the effects of the lockdown measures on air quality, by analyzing dust PM2.5, NO2, and O3. The dust PM2.5 analysis was carried out from 2016 to 2020. NO2 and O3 analysis was carried out in 2019 and 2020. This study, which is based on satellite data from TROPOMI Sentinel 5P and MERRA, has shown that Morocco has experienced an improvement in air quality during the lockdown. A significant reduction in surface dust PM2.5 and tropospheric NO2 was observed (-10%, -4%, respectively on average). The total column of ozone recorded a slight increase on average of around 1%. Moreover, we demonstrate that a significant part of particulate pollution and NO2 emissions are incoming mainly from the northern and northern-eastern borders of Morocco.
Objectives A number of studies have shown that the
airborne transmission route could spread some viruses
over a distance of 2 meters from an infected person.
An epidemic model based only on respiratory droplets
and close contact could not fully explain the regional
differences in the spread of COVID-19 in Italy. On March
16th 2020, we presented a position paper proposing a
research hypothesis concerning the association between
higher mortality rates due to COVID-19 observed in
Northern Italy and average concentrations of PM10
exceeding a daily limit of 50 µg/m3.
Methods To monitor the spreading of COVID-19 in
Italy from February 24th to March 13th (the date of the
Italian lockdown), official daily data for PM10 levels were
collected from all Italian provinces between February 9th
and February 29th, taking into account the maximum lag
period (14 days) between the infection and diagnosis. In
addition to the number of exceedances of the daily limit
value of PM
10, we also considered population data and
daily travelling information for each province.
Results Exceedance of the daily limit value of PM10
appears to be a significant predictor of infection in
univariate analyses (p<0.001). Less polluted provinces had
a median of 0.03 infections over 1000 residents, while the
most polluted provinces showed a median of 0.26 cases.
Thirty-nine out of 41 Northern Italian provinces resulted in
the category with the highest PM10 levels, while 62 out of
66 Southern provinces presented low PM10 concentrations
(p<0.001). In Milan, the average growth rate before the
lockdown was significantly higher than in Rome (0.34
vs 0.27 per day, with a doubling time of 2.0 days vs 2.6,
respectively), thus suggesting a basic reproductive number
R
0>6.0, comparable with the highest values estimated for
China.
Conclusion A significant association has been found
between the geographical distribution of daily PM10
exceedances and the initial spreading of COVID-19 in the
110 Italian provinces
The novel coronavirus disease (COVID-19) is a highly pathogenic, transmittable and invasive pneumococcal disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which emerged in December 2019 and January 2020 in Wuhan city, Hubei province, China and fast spread later on the middle of February 2020 in the Northern part of Italy and Europe.
This study investigates the correlation between the degree of accelerated diffusion and lethality of COVID-19 and the surface air pollution in Milan metropolitan area, Lombardy region, Italy. Daily average concentrations of inhalable particulate matter (PM) in two size fractions PM2.5, PM10 and maxima PM10 ground level atmospheric pollutants together air quality and climate variables (daily average temperature, relative humidity, wind speed, atmospheric pressure field and Planetary Boundary Layer-PBL height) collected during 1 January–30 April 2020 were analyzed. In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces, or direct human-to-human personal contacts, it seems that high levels of urban air pollution, weather and specific climate conditions have a significant impact on the increased rates of confirmed COVID-19 Total number, Daily New and Total Deaths cases, possible attributed not only to indoor but also to outdoor airborne bioaerosols distribution. Our analysis demonstrates the strong influence of daily averaged ground levels of particulate matter concentrations, positively associated with average surface air temperature and inversely related to air relative humidity on COVID-19 cases outbreak in Milan. Being a novel pandemic coronavirus (SARS-CoV-2) version, COVID-19 might be ongoing during summer conditions associated with higher temperatures and low humidity levels. Presently is not clear if this protein “spike” of the new coronavirus COVID-19 is involved through attachment mechanisms on indoor or outdoor airborne aerosols in the infectious agent transmission from a reservoir to a susceptible host in some agglomerated urban areas like Milan is.
Objectives
Although the COVID-19 is known to cause by human-to-human transmission, it remains largely unclear whether ambient air pollutants and meteorological parameters could promote its transmission.
Methods
A retrospective study is conducted to study whether air quality index (AQI), four ambient air pollutants (PM2.5, PM10, NO2 and CO) and five meteorological variables (daily temperature, highest temperature, lowest temperature, temperature difference and sunshine duration) could increase COVID-19 incidence in Wuhan and XiaoGan between Jan 26th to Feb 29th in 2020.
Results
First, a significant correlation was found between COVID-19 incidence and AQI in both Wuhan (R² = 0.13, p < 0.05) and XiaoGan (R² = 0.223, p < 0.01). Specifically, among four pollutants, COVID-19 incidence was prominently correlated with PM2.5 and NO2 in both cities. In Wuhan, the tightest correlation was observed between NO2 and COVID-19 incidence (R² = 0.329, p < 0.01). In XiaoGan, in addition to the PM2.5 (R² = 0.117, p < 0.01) and NO2 (R² = 0.015, p < 0.05), a notable correlation was also observed between the PM10 and COVID-19 incidence (R² = 0.105, p < 0.05). Moreover, temperature is the only meteorological parameter that constantly correlated well with COVID-19 incidence in both Wuhan and XiaoGan, but in an inverse correlation (p < 0.05).
Conclusions
AQI, PM2.5, NO2, and temperature are four variables that could promote the sustained transmission of COVID-19.
Background
The burden of COVID-19 was extremely severe in Northern Italy, an area characterized by high concentrations of particulate matter (PM), which is known to negatively affect human health. Consistently with evidence already available for other viruses, we initially hypothesized the possibility of SARS-CoV-2 presence on PM, and we performed a first experiment specifically aimed at confirming or excluding this research hyphotesys.
Methods
We have colelcted 34 PM10 samples in Bergamo area (the epicenter of the Italian COVID-19 epidemic) by using two air samplers over a continuous 3-weeks period. Filters were properly stored and underwent RNA extraction and amplification according to WHO protocols in two parallel blind analyses performed by two different authorized laboratories. Up to three highly specific molecular marker genes (E, N, and RdRP) were used to test the presence of SARS-CoV-2 RNA on particulate matter.
Results
The first test showed positive results for gene E in 15 out of 16 samples, simultaneously displaying positivity also for RdRP gene in 4 samples. The second blind test got 5 additional positive results for at least one ofthe three marker genes. Overall, we tested 34 RNA extractions for the E, N and RdRP genes, reporting 20 positive results for at least one of the three marker genes, with positivity separately confirmed for all the three markers. Control tests to exclude false positivities were successfully accomplished.
Conclusion
This is the first evidence that SARS-CoV-2 RNA can be present on PM, thus suggesting a possible use as indicator of epidemic recurrence.
Objectives
In areas of SARS-CoV-2 outbreak worldwide mean air pollutants concentrations vastly exceed the maximum limits. Chronic exposure to air pollutants have been associated with lung ACE-2 over-expression which is known to be the main receptor for SARS-coV2. The aim of this study was to analyse the relationship between air pollutants concentration (PM 2.5 and NO2) and COVID-19 outbreak, in terms of transmission, number of patients, severity of presentation and number of deaths.
Methods
COVID-19 cases, ICU admissions and mortality rate were correlated with severity of air pollution in the Italian regions.
Results
The highest number of COVID-19 cases were recorded in the most polluted regions with patients presenting with more severe forms of the disease requiring ICU admission. In these regions, mortality was two-fold higher than the other regions.
Conclusions
From the data available we propose a “double-hit hypothesis”: chronic exposure to PM 2.5 causes alveolar ACE-2 receptor overexpression. This may increase viral load in patients exposed to pollutants in turn depleting ACE-2 receptors and impairing host defences. High atmospheric NO2 may provide a second hit causing a severe form of SARS-CoV-19 in ACE-2 depleted lungs resulting in a worse outcome.
Highlights
• Developmental exposure to environmental factors can disrupt the immune system.
• Long-term low-dose exposure to chemical mixtures is linked to imunodeficiency
• Immunodeficiency contributes to chronic diseases and the current Covid-19 pandemics.
• Environmental chemicals and microorganisms share similar molecular pathomechanisms (AhR pathway).
• Understanding the underlying pathomechanisms helps to improve public health.
Occupational, residential, dietary and environmental exposures to mixtures of synthetic anthropogenic chemicals after World War II have a strong relationship with the increase of chronic diseases, health cost and environmental pollution. The link between environment and immunity is particularly intriguing as it is known that chemicals and drugs can cause immunotoxicity (e.g., allergies and autoimmune diseases). In this review, we emphasize the relationship between long-term exposure to xenobiotic mixtures and immune deficiency inherent to chronic diseases and epidemics/pandemics. We also address the immunotoxicologic risk of vulnerable groups, taking into account biochemical and biophysical properties of SARS-CoV-2 and its immunopathological implications. We particularly underline the common mechanisms by which xenobiotics and SARS-CoV-2 act at the cellular and molecular level. We discuss how long-term exposure to thousand chemicals in mixtures, mostly fossil fuel derivatives, exposure toparticle matters, metals, ultraviolet (UV)–B radiation, ionizing radiation and lifestyle contribute to immunodeficiency observed in the contemporary pandemic, such as COVID-19, and thus threaten global public health, human prosperity and achievements, and global economy. Finally, we propose metrics which are needed to address the diverse health effects of anthropogenic COVID-19 crisis at present and those required to prevent similar future pandemics.
Background
An epidemic model based only on respiratory droplets and close contact could not fully explain the regional differences in the spread of the recent severe acute respiratory syndrome COVID-19 in Italy, which was fast and dramatic only in Lombardy and Po Valley. On March 16 th 2020, we presented a Position Paper proposing a research hypothesis concerning the association between higher mortality rates due to COVID-19 observed in Northern Italy and the peaks of particulate matter concentrations, frequently exceeding the legal limit of 50 µg/m ³ as PM 10 daily average
Methods
To assess environmental factors related to the spread of the COVID-19 in Italy from February 24 th to March 13 th (the date when the lockdown has been imposed over Italy), official daily data relevant to ambient PM 10 levels were collected from all Italian Provinces between February 9 th and February 29 th , taking into account the average time (estimated in 17 days) elapsed between the initial infection and the recorded COVID positivity. In addition to the number of exceedances of PM 10 daily limit value, we considered also population data and daily travelling information per each Province.
Results
PM 10 daily limit value exceedances appear to be a significant predictor (p < .001) of infection in univariate analyses. Less polluted Provinces had a median of 0.03 infection cases over 1000 residents, while most polluted Provinces had a median of 0.26 cases over 1000 residents. Thirty-nine out of 41 Northern Italian Provinces resulted in the category with highest PM 10 levels, while 62 out of 66 Southern Provinces presented low PM 10 concentrations (p< 0.001). In Milan, the average growth rate before the lockdown was significantly higher than Rome (0.34 vs. 0.27 per day, with a doubling time of 2.0 days vs. 2.6), suggesting a basic reproductive number R 0 >6.0, comparable with the highest values estimated for China.
In December 2019, some cases of viral pneumonia were epidemiologically related to a new coronavirus in the province of Hubei, China. Subsequently, there has been an increase in infections attributable to this virus throughout China and worldwide. The World Health Organization (WHO) has officially named the infection coronavirus disease 2019 (COVID-19), and the virus has been classified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This appears to be a virus from Rhinolophus bats, but the intermediate host has not yet been identified. The mechanism of infection of SARS-CoV-2 is not yet known; it appears to have affinity for cells located in the lower airways, where it replicates. The interhuman transmission of coronaviruses mainly occurs through saliva droplets and direct and indirect contact via surfaces. As of March 10, 2020, the number of cases worldwide was 113,702. Along with severe acute respiratory syndrome (SARS) and Middle Eastern respiratory syndrome (MERS), COVID-19 appears to cause a severe clinical picture in humans, ranging from mild malaise to death by sepsis/acute respiratory distress syndrome. The prognosis is worse in elderly patients with comorbidities. To date, there is no specific therapy for COVID-19. Prevention of SARS-CoV-2 infection implies strategies that limit the spread of the virus. WHO and other international and national bodies have developed continuously updated strategic objectives and provisions to contain the spread of the virus and infection.
Objectives: United States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States.
Design: A nationwide, cross-sectional study using county-level data.
Data sources: COVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center.
Main outcome measures: We fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses.
Results: We found that an increase of only 1 μg/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses.
Conclusions: A small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.
By collecting daily data on measles cases, air pollutants, and meteorological data from 2005 to 2009 in Chengguan District of Lanzhou City, semi-parametric generalized additive model (GAM) was used to quantitatively study the impact of air pollutants and meteorological factors on daily measles cases. The results showed that air pollutants and meteorological factors had effect on the number of daily measles cases, and there was a certain lag effect. Except for SO2 and relative humidity, other factors showed statistically significant associations with daily measles cases: NO2 lag 6 days, PM10 and maximum temperature lag 5 days, minimum temperature and average temperature and average air pressure lag 4 days, visibility, and wind speed lag 3 days had the greatest impact on the number of daily measles cases. Under the optimum lag conditions, the number of daily measles cases increased by 15.1%, 17.6%, 7.0%, 116.6%, 98.6%, 85.7%, and 14.4% with the increase of 1 IQR in SO2, NO2, PM10, maximum temperature, minimum temperature, average temperature, and wind speed; with the increase of 1 IQR in average pressure, relative humidity, visibility, and daily measles cases decreased by 12.8%, 9.7%, and 13.1%, respectively. And different factors showed different seasonal effects. The effects of SO2 and temperature factors on daily measles cases were greater in spring and winter, but PM10 in summer.
We present a
comparison between satellite-based TROPOMI (TROPOspheric Monitoring
Instrument) NO2 products and ground-based observations in Helsinki
(Finland). TROPOMI NO2 total (summed) columns are compared with the
measurements performed by the Pandora spectrometer between April and
September 2018. The mean relative and absolute bias between the TROPOMI and
Pandora NO2 total columns is about 10 % and
0.12×1015 molec. cm−2 respectively. The dispersion of these
differences (estimated as their standard deviation) is
2.2×1015 molec. cm−2. We find high correlation
(r = 0.68) between satellite- and ground-based data, but also that
TROPOMI total columns underestimate ground-based observations for relatively
large Pandora NO2 total columns, corresponding to episodes of
relatively elevated pollution. This is expected because of the relatively
large size of the TROPOMI ground pixel (3.5×7 km) and the a priori
used in the retrieval compared to the relatively small field-of-view of the
Pandora instrument. On the other hand, TROPOMI slightly overestimates (within
the retrieval uncertainties) relatively small NO2 total columns.
Replacing the coarse a priori NO2 profiles with high-resolution
profiles from the CAMS chemical transport model improves the agreement
between TROPOMI and Pandora total columns for episodes of NO2
enhancement. When only the low values of NO2 total columns or the
whole dataset are taken into account, the mean bias slightly increases. The
change in bias remains mostly within the uncertainties.
We also analyse the consistency between satellite-based data and in situ NO2 surface concentrations measured at the Helsinki–Kumpula air quality station (located a few metres from the Pandora spectrometer). We find similar day-to-day variability between TROPOMI, Pandora and in situ measurements, with NO2 enhancements observed during the same days. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities (as expected in urban sites). The TROPOMI NO2 maps reveal also spatial features, such as the main traffic ways and the airport area, as well as the effect of the prevailing south-west wind patterns.
This is one of the first works in which TROPOMI NO2 retrievals are validated against ground-based observations and the results provide an early evaluation of their applicability for monitoring pollution levels in urban sites. Overall, TROPOMI retrievals are valuable to complement the ground-based air quality data (available with high temporal resolution) for describing the spatio-temporal variability of NO2, even in a relatively small city like Helsinki.
Nitrogen dioxide (NO2) is a regulated air pollutant that is of particular concern in many cities, where concentrations are high. Emissions of nitrogen oxides to the atmosphere lead to the formation of ozone and particulate matter, with adverse impacts on human health and ecosystems. The effects of emissions are often assessed through modeling based on inventories relying on indirect information that is often outdated or incomplete. Here we show that NO2 measurements from the new, high-resolution TROPOMI satellite sensor can directly determine the strength and distribution of emissions from Paris. From the observed build-up of NO2 pollution, we find highest emissions on cold weekdays in February 2018, and lowest emissions on warm weekend days in spring 2018. The new measurements provide information on the spatio-temporal distribution of emissions within a large city, and suggest that Paris emissions in 2018 are only 5–15% below inventory estimates for 2011–2012, reflecting the difficulty of meeting NOx emission reduction targets.
Tuberculosis (TB) and air pollution both contribute significantly to the global burden of disease. Epidemiological studies show that exposure to household and urban air pollution increase the risk of new infections with Mycobacterium tuberculosis (M.tb) and the development of TB in persons infected with M.tb and alter treatment outcomes. There is increasing evidence that particulate matter (PM) exposure weakens protective antimycobacterial host immunity. Mechanisms by which exposure to urban PM may adversely affect M.tb-specific human T cell functions have not been studied. We, therefore, explored the effects of urban air pollution PM2.5 (aerodynamic diameters ≤2.5µm) on M.tb-specific T cell functions in human peripheral blood mononuclear cells (PBMC). PM2.5 exposure decreased the capacity of PBMC to control the growth of M.tb and the M.tb-induced expression of CD69, an early surface activation marker expressed on CD3+ T cells. PM2.5 exposure also decreased the production of IFN-γ in CD3+, TNF-α in CD3+ and CD14+ M.tb-infected PBMC, and the M.tb-induced expression of T-box transcription factor TBX21 (T-bet). In contrast, PM2.5 exposure increased the expression of anti-inflammatory cytokine IL-10 in CD3+ and CD14+ PBMC. Taken together, PM2.5 exposure of PBMC prior to infection with M.tb impairs critical antimycobacterial T cell immune functions.
The unprecedented 2015 outbreaks of highly pathogenic avian influenza (HPAI) H5N2 in the U.S. devastated its poultry industry and resulted in over $3 billion economic impacts. Today HPAI continues eroding poultry operations and disrupting animal protein supply chains around the world. Anecdotal evidence in 2015 suggested that in some cases the AI virus was aerially introduced into poultry houses, as abnormal bird mortality started near air inlets of the infected houses. This study modeled air movement trajectories and virus concentrations that were used to assess the probability or risk of airborne transmission for the 77 HPAI cases in Iowa. The results show that majority of the positive cases in Iowa might have received airborne virus, carried by fine particulate matter, from infected farms within the state (i.e., intrastate) and infected farms from the neighboring states (i.e., interstate). The modeled airborne virus concentrations at the Iowa recipient sites never exceeded the minimal infective doses for poultry; however, the continuous exposure might have increased airborne infection risks. In the worst-case scenario (i.e., maximum virus shedding rate, highest emission rate, and longest half-life), 33 Iowa cases had > 10% (three cases > 50%) infection probability, indicating a medium to high risk of airborne transmission for these cases. Probability of airborne HPAI infection could be affected by farm type, flock size, and distance to previously infected farms; and more importantly, it can be markedly reduced by swift depopulation and inlet air filtration. The research results provide insights into the risk of airborne transmission of HPAI virus via fine dust particles and the importance of preventative and containment strategies such as air filtration and quick depopulation of infected flocks.
For fulltext: https://rdcu.be/bOEQ0
The impact of long-term exposure to nitrogen dioxide (NO2) on cause-specific mortality is poorly understood.
To assess mortality risks associated with long-term NO2 exposure and evaluate confounding of this association.
We examined the association between 12-month moving average NO2 exposure and cause-specific mortality in 14.1 million US Medicare beneficiaries between 2000 and 2008. Associations were examined using age, gender, and race-stratified and state-adjusted Poisson regression models. We assessed the potential for confounding by PM2.5 and behavioral covariates and unmeasured confounding by decomposing NO2 into its spatial and spatio-temporal components.
We found significant associations between 12-month NO2 exposure and increased mortality from all-causes [risk ratio (RR): 1.052; 95% CI: 1.051, 1.054; per 10 ppb], cardiovascular (CVD) (1.133; 95% CI: 1.130, 1.137) and respiratory disease (1.050; 95% CI: 1.044, 1.056), all cancers (1.021; 95% CI: 1.017, 1.025), ischemic heart disease (IHD) (1.221; 95% CI: 1.217, 1.226), cerebrovascular (CBV) disease (1.092; 95% CI: 1.085, 1.100), and for the first time pneumonia (1.275; 95% CI: 1.263, 1.287). Associations generally remained positive and statistically significant after adjustment for PM2.5 and behavioral factors.
Our findings provide additional evidence of the increased risk posed by long-term NO2 exposures on increased mortality from all-causes, CVD, respiratory disease, IHD, CBV, and cancer and provide new evidence of their impact on mortality from pneumonia. Unmeasured confounding of these associations was present, however, demonstrating the need to understand sources of this confounding.
Inhalation exposure to indoor air pollutants and cigarette smoke increase the risk of tuberculosis (TB) development. Whether exposure to ambient air pollution particulate matter (PM) alters protective human host immune responses against Mycobacterium tuberculosis (M.tb) has been studied poorly. Here we examined the effect of PM from Iztapalapa, a municipality of Mexico City, with aerodynamic diameters below 2.5μm (PM2.5) and 10μm (PM10) on innate antimycobacterial immune responses in human alveolar type II epithelial cells A549. Exposure to PM2.5 or PM10 deregulated the ability of the A549 cells to express the antimicrobial peptides human β-defensin 2 and 3 (HBD-2 and HBD-3) upon infection with M.tb and increased intracellular M.tb growth (colony-forming units). The observed modulation of antibacterial responsiveness by PM exposure was associated with induction of senescence in PM-exposed A549 cells and unrelated to PM-mediated loss of cell viability. Thus, induction of senescence and downregulation of HBD-2 and HBD-3 expression in respiratory PM-exposed epithelial cells leading to the enhanced M.tb growth represent mechanisms by which exposure to air pollution PM may increase the risk of M.tb infection and TB development.
Copyright © 2015, American Society for Microbiology. All Rights Reserved.
Current day concentrations of ambient air pollution have been associated with a range of adverse health effects, particularly mortality and morbidity due to cardiovascular and respiratory diseases. In this review, we summarize the evidence from epidemiological studies on long-term exposure to fine and coarse particles, nitrogen dioxide (NO2) and elemental carbon on mortality from all-causes, cardiovascular disease and respiratory disease. We also summarize the findings on potentially susceptible subgroups across studies. We identified studies through a search in the databases Medline and Scopus and previous reviews until January 2013 and performed a meta-analysis if more than five studies were available for the same exposure metric.
The Po valley in northern Italy is renowned for its high air pollutant concentrations. Measurements of air pollutants from a background site in Modena, a town of 200 thousand inhabitants within the Po valley, are analysed. These comprise hourly data for CO, NO, NO(2), NO(x), and O(3), and daily gravimetric equivalent data for PM(10) from 1998-2010. The data are analysed in terms of long-term trends, annual, weekly and diurnal cycles, and auto-correlation and cross-correlation functions. CO, NO and NO(2) exhibit a strongly traffic-related pattern, with daily peaks at morning and evening rush hour and lower concentrations over the weekend. Ozone shows an annual cycle with a peak in July due to local production; notwithstanding the diurnal cycle dominated by titration by nitrogen oxide, the decreasing long term trend in NO concentration did not affect the long term trend in O(3), whose mean concentration remained steady over the sampling period. PM(10) shows a strong seasonality with higher concentration in winter and lower concentration in summer and spring. Both PM(10) and ozone show a marked weekly cycle in summer and winter respectively. Regressions of PM(10) upon NO(x) show a consistently greater intercept in winter, representing higher secondary PM(10) in the cooler months of the year. There is a seasonal pattern in primary PM(10) to NO(x) ratios, with lower values in winter and higher values in summer, but the reasons are unclear.
Changes in immune status have been suggested as a possible biologic mechanism by which particulate matter (PM) air pollution could lead to adverse health effects. The authors studied associations between ambient PM₂.₅ and immune status among 115 postmenopausal, overweight women in the greater Seattle, Washington, area. The authors evaluated 3-day, 30-day, and 60-day average PM₂.₅ values in relation to inflammation markers (C-reactive protein, serum amyloid A, interleukin-6) and functional assays of cellular immunity (natural killer cell cytotoxicity, T-lymphocyte proliferation) at 3 time points for each woman during 1 year. Three-day averaged PM₂.₅ was inversely associated with anti-CD3-stimulated lymphocyte proliferation. There were no notable associations between the inflammation markers and PM₂.₅. If additional studies confirm our findings, then future health effect assessments for PM₂.₅ should consider changes in cellular immunity as an endpoint that may lead to overt clinical disease.
The spread of influenza and highly pathogenic avian influenza (H5N1) presents a significant threat to human health. Avian influenza outbreaks in downwind areas of Asian dust storms (ADS) suggest that viruses might be transported by dust storms.
We developed a technique to measure ambient influenza and avian influenza viruses. We then used this technique to measure concentrations of these viruses on ADS days and background days, and to assess the relationships between ambient influenza and avian influenza viruses, and air pollutants.
A high-volume air sampler was used in parallel with a filter cassette to evaluate spiked samples and unspiked samples. Then, air samples were monitored during ADS seasons using a filter cassette coupled with a real-time quantitative polymerase chain reaction (qPCR) assay. Air samples were monitored during ADS season (1 January to 31 May 2006).
We successfully quantified ambient influenza virus using the filtration/real-time qPCR method during ADS days and background days. To our knowledge, this is the first report describing the concentration of influenza virus in ambient air. In both the spiked and unspiked samples, the concentration of influenza virus sampled using the filter cassette was higher than that using the high-volume sampler. The concentration of ambient influenza A virus was significantly higher during the ADS days than during the background days.
Our data imply the possibility of long-range transport of influenza virus.
Evidence is increasing that long-term exposure to ambient air pollution is associated with deaths from cardiopulmonary diseases. In a 2002 pilot study, we reported clear indications that traffic-related air pollution, especially at the local scale, was related to cardiopulmonary mortality in a randomly selected subcohort of 5000 older adults participating in the ongoing Netherlands Cohort Study (NLCS) on diet and cancer. In the current study, referred to as NLCS-AIR, our objective was to obtain more precise estimates of the effects of traffic-related air pollution by analyzing associations with cause-specific mortality, as well as lung cancer incidence, in the full cohort of approximately 120,000 subjects. Cohort members were 55 to 69 years of age at enrollment in 1986. Follow-up was from 1987 through 1996 for mortality (17,674 deaths) and from late 1986 through 1997 for lung cancer incidence (2234 cases). Information about potential confounding variables and effect modifiers was available from the questionnaire that subjects completed at enrollment and from publicly available data (including neighborhood-scale information such as income distributions). The NLCS was designed for a case-cohort approach, which makes use of all the cases in the full cohort, while data for the random subcohort are used to estimate person-time experience in the study. Full information on confounders was available for the subjects in the random subcohort and for the emerging cases of mortality and lung cancer incidence during the follow-up period, and in NLCS-AIR we used the case-cohort approach to examine the relation between exposure to air pollution and cause-specific mortality and lung cancer. We also specified a standard Cox proportional hazards model within the full cohort, for which information on potential confounding variables was much more limited. Exposure to air pollution was estimated for the subjects' home addresses at baseline in 1986. Concentrations were estimated for black smoke (a simple marker for soot) and nitrogen dioxide (NO2) as indicators of traffic-related air pollution, as well as nitric oxide (NO), sulfur dioxide (SO2), and particulate matter with aerodynamic diameter < or = 2.5 microm (PM2.5), as estimated from measurements of particulate matter with aerodynamic diameter < or = 10 microm (PM10). Overall long-term exposure concentrations were considered to be a function of air pollution contributions at regional, urban, and local scales. We used interpolation from data obtained routinely at regional stations of the National Air Quality Monitoring Network (NAQMN) to estimate the regional component of exposure at the home address. Average pollutant concentrations were estimated from NAQMN measurements for the period 1976 through 1996. Land-use regression methods were used to estimate the urban exposure component. For the local exposure component, geographic information systems (GISs) were used to generate indicators of traffic exposure that included traffic intensity on and distance to nearby roads. A major effort was made to collect traffic intensity data from individual municipalities. The exposure variables were refined considerably from those used in the pilot study, but we also analyzed the data for the full cohort in the current study using the exposure indicators of the pilot study. We analyzed the data in models with the estimated overall pollutant concentration as a single variable and with the background concentration (the sum of regional and urban components) and the local exposure estimate from traffic indicators as separate variables. In the full-cohort analyses adjusted for the limited set of confounders, estimated overall exposure concentrations of black smoke, NO2, NO, and PM2.5 were associated with mortality. For a 10-microg/m3 increase in the black smoke concentration, the relative risk (RR) (95% confidence interval [CI]) was 1.05 (1.00-1.11) for natural-cause (nonaccidental) mortality, 1.04 (0.95-1.13) for cardiovascular mortality, 1.22 (0.99-1.50) for respiratory mortality, 1.03 (0.88-1.20) for lung cancer mortality, and 1.04 (0.97-1.12) for noncardiopulmonary, non-lung cancer mortality. Results were similar for NO2, NO, and PM2.5. For a 10-microg/m3 increase in PM2.5 concentration, the RR for natural-cause mortality was 1.06 (95% CI, 0.97-1.16), the same as in the results of the American Cancer Society Study reported by Pope and colleagues in 2002. The highest relative risks were found for respiratory mortality, though confidence intervals were wider for this less-frequent cause of death. No associations with mortality were found for SO2. Some of the associations between the traffic indicator variables used to assess traffic intensity near the home and mortality reached statistical significance in the full cohort. For an increase in traffic intensity of 10,000 motor vehicles in 24 hours (motor vehicles/day) on the road nearest a subject's residence, the RR was 1.03 (95% CI, 1.00-1.08) for natural-cause mortality, 1.05 (0.99-1.12) for cardiovascular mortality, 1.10 (0.95-1.26) for respiratory mortality, 1.07 (0.96-1.19) for lung cancer mortality, and 1.00 (0.94-1.06) for noncardiopulmonary, non-lung cancer mortality. Results were similar for traffic intensity in a 100-m buffer around the subject's residence and living near a major road (a road with more than 10,000 motor vehicles/day). Distance in meters to the nearest major road and traffic intensity on the nearest major road were not associated with any of the mortality outcomes. We did not find an association between cardiopulmonary mortality and living near a major road as defined using the methods of the pilot study. In the case-cohort analyses adjusted for all potential confounders, we found no associations between background air pollution and mortality. The associations between traffic intensity and mortality were weaker than in the full cohort, and confidence intervals were wider, consistent with the smaller number of subjects. The lower relative risks of mortality associated with traffic variables in the case-cohort study population could be related to the particular subcohort that was randomly selected from the full cohort, as the risks estimated with the actual subcohort were well below the average estimates obtained for 100 new case-cohort analyses with 100 alternative subcohorts of 5000 subjects each that we randomly selected from the full cohort. Differences in adjusted relative risks between the full-cohort and the case-cohort analyses could be explained by random error introduced by sampling from the full cohort and by a selection effect resulting from the relatively large number of missing data for variables in the extensive confounder model used in the case-cohort analyses. More complete control for confounding probably did not contribute much to the lower relative risks in the case-cohort analyses, especially for the traffic variables, as results were similar when the limited confounder model for the full cohort was used in analyses of the subjects in the case-cohort study population. In additional analyses using black smoke concentrations as the exposure variables, we found that the association between overall black smoke and cardiopulmonary mortality was somewhat stronger for case-cohort subjects who did not change residence during follow-up, and in the full cohort, there was a tendency for relative risks to be higher for subjects living in the three major cities included in the study. Adjustment for estimated exposure to traffic noise did not affect the associations of background black smoke and traffic intensity with cardiovascular mortality. There was some indication of an association between traffic noise and cardiovascular mortality only for the 1.6% of the subjects in the full cohort who were exposed to traffic noise in the highest category of > 65 A-weighted decibels (dB(A); decibels with the sound pressure scale adjusted to conform with the frequency response of the human ear). Examination of sex, smoking status, educational level, and vegetable and fruit intake as possible effect modifiers showed that for overall black smoke concentrations, associations with mortality tended to be stronger in case-cohort subjects with lower levels of education and those with low fruit intake, but differences between strata were not statistically significant. For lung cancer incidence, we found essentially no relation to exposure to NO2, black smoke, PM2.5, SO2, or several traffic indicators. Associations of overall air pollution concentrations and traffic indicator variables with lung cancer incidence were, however, found in subjects who had never smoked, with an RR of 1.47 (95% CI, 1.01-2.16) for a 10-microg/m3 increase in overall black smoke concentration. In the current study, the mortality risks associated with both background air pollution and traffic exposure variables were much smaller than the estimate previously reported in the pilot study for risk of cardiopulmonary mortality associated with living near a major road (RR, 1.95; 95% CI, 1.09-3.51). The differences are most likely due to the extension of the follow-up period in the current study and to random error in the pilot study related to sampling from the full cohort. Though relative risks were generally small in the current study, long-term average concentrations of black smoke, NO2, and PM2.5 were related to mortality, and associations of black smoke and NO2 exposure with natural-cause and respiratory mortality were statistically significant. Traffic intensity near the home was also related to natural-cause mortality. The highest relative risks associated with background air pollution and traffic variables were for respiratory mortality, though the number of deaths was smaller than for the other mortality categories. (ABSTRACT TRUNCATED)
Nitrogen dioxide (NO2) is a common indoor and outdoor air pollutant that may induce deterioration of respiratory health. In this study the effects of repeated daily exposure to NO2 on airway antioxidant status, inflammatory cell and mediator responses, and lung function were examined. Healthy nonsmoking subjects were exposed under controlled conditions to air (once) and to 2 ppm of NO2 for 4 h on four consecutive days. Lung function measurements were made before and immediately after the end of each exposure. Bronchoscopy with endobronchial biopsies, bronchial wash (BW), and bronchoalveolar lavage (BAL) was carried out 1.5 h after the air exposure and after the last exposure to NO2. Repeated NO2 exposure resulted in a decrease in neutrophil numbers in the bronchial epithelium. The BW revealed a twofold increase in content of neutrophils (p < 0.05) and a 1.5-fold increase in myeloperoxidase (MPO) (p < 0.01) indicative of both migration and activation of neutrophils in the airways. After the fourth NO2 exposure, antioxidant status of the airway fluid was unchanged. Significant decrements in FEV1 and FVC were found after the first exposure to NO2, but these attenuated with repeated exposures. Together, these data indicate that four sequential exposures to NO2 result in a persistent neutrophilic inflammation in the airways, whereas changes in pulmonary function and airway antioxidants are resolved. We conclude that NO2 is a proinflammatory air pollutant under conditions of repeated exposure.