Assessing correlations between short-term exposure to atmospheric pollutants and COVID-19 spread in all Italian territorial areas

Long-term air pollution exposure and risk of SARS-CoV-2 infection: A UK-wide cohort study

BACKGROUND

The association between air quality and risk of SARS-CoV-2 infection is poorly understood. We investigated this association using serological individual-level data adjusting for a wide range of confounders, in a large population-based cohort (COVIDENCE UK).

METHODS

We assessed the associations between long-term (2015-19) nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), exposures with SARS-CoV-2 infection, level of antibody response among those infected, and COVID-19 disease severity. We used serological data from 10,489 participants in the COVIDENCE UK cohort, and estimated annual average air pollution exposure at each participant's home postcode.

RESULTS

After controlling for potential confounders, we found a positive association between 5-year NO2 and PM2.5 exposures and the risk of seropositivity: 10 unit increase in NO2 (μg/m3) was associated with an increasing risk of seropositivity by 1.092 (95% CI 1.02 to 1.17; p-for-trend 0.012). For PM2.5, 10 unit increase (μg/m3) was associated with an increasing risk of seropositivity by 1.65 (95% CI 1.015-2.68; p-for-trend 0·049). In addition, we found that NO2 was positively associated with higher antibody titres (p-for-trend 0·013) among seropositive participants, with no evidence of an association for PM2.5.

CONCLUSION

Our findings suggest that the long-term burden of air pollution increased the risks of SARS-CoV-2 infection and has important implications for future pandemic preparedness. This evidence strengthens the case for reducing long-term air pollution exposures to reduce the vulnerability of individuals to respiratory viruses.

Exposure to outdoor particulate matter and risk of respiratory diseases: a systematic review and meta-analysis

According to epidemiological studies, particulate matter (PM) is an important air pollutant that poses a significant threat to human health. The relationship between particulate matter and respiratory diseases has been the subject of numerous studies, but these studies have produced inconsistent findings. The purpose of this systematic review was to examine the connection between outdoor particulate matter (PM2.5 and PM10) exposure and respiratory disorders (COPD, lung cancer, LRIs, and COVID-19). For this purpose, we conducted a literature search between 2012 and 2022 in PubMed, Web of Science, and Scopus. Out of the 58 studies that were part of the systematic review, meta-analyses were conducted on 53 of them. A random effect model was applied separately for each category of study design to assess the pooled association between exposure to PM2.5 and PM10 and respiratory diseases. Based on time-series and cohort studies, which are the priorities of the strength of evidence, a significant relationship between the risk of respiratory diseases (COPD, lung cancer, and COVID-19) was observed (COPD: pooled HR = 1.032, 95% CI: 1.004-1.061; lung cancer: pooled HR = 1.017, 95% CI: 1.015-1.020; and COVID-19: pooled RR = 1.004, 95% CI: 1.002-1.006 per 1 μg/m3 increase in PM2.5). Also, a significant relationship was observed between PM10 and respiratory diseases (COPD, LRIs, and COVID-19) based on time-series and cohort studies. Although the number of studies in this field is limited, which requires more investigations, it can be concluded that outdoor particulate matter can increase the risk of respiratory diseases.

Wildfire and smoke association with COVID-19 cases in the Pantanal wetland, Brazil

OBJECTIVES

In 2020, Brazil experienced two concurrent public health challenges related to respiratory disease: wildfires and increased mortality due to the coronavirus (COVID-19) pandemic. Smoke from these wildfires contributed to a variety of air pollutants, including fine particulate matter (PM2.5). The present study aims to investigate the effects of environmental and socio-economic factors on COVID-19 hospitalisation in the Pantanal.

STUDY DESIGN

Ecological retrospective study.

METHODS

We applied a multilevel negative binomial model to relate monthly hospitalisation data with environmental variables.

RESULTS

We showed that monthly PM2.5 concentration levels had the greatest influence on the increase in hospitalisations by COVID-19 in the elderly (23 % increase). The Gini index, a coefficient that reflects income inequalities, also had a positive association with COVID-19 hospitalisations (18 % increase). Higher temperatures and humidity were protective factors, showing a 15 % and 14 % decrease in hospitalisations, respectively. The results of the present study suggest that high PM2.5 exposure contributed to the increase in COVID-19 hospitalisations, as did the social inequalities of each municipality.

CONCLUSIONS

The present study highlights the importance of gathering evidence supported by multiple information sources to guide decision-making and identify populations needing better public health systems.

Causal relationship between particulate matter 2.5 (PM2.5), PM2.5 absorbance, and COVID-19 risk: A two-sample Mendelian randomisation study

Background

Several observational studies reported on the association between particulate matter ≤2.5μm (PM_2.5) and its absorbance with coronavirus (COVID-19), but none use Mendelian randomisation (MR). To strengthen the knowledge on causality, we examined the association of PM_2.5 and its absorbance with COVID-19 risk using MR.

Methods

We selected genome-wide association study (GWAS) integration data from the UK Biobank and IEU Open GWAS Project for two-sample MR analysis. We used inverse variance weighted (IVW) and its multiple random effects and fixed effects alternatives to generally predict the association of PM_2.5 and its absorbance with COVID-19, and six methods (MR Egger, weighted median, simple mode, weighted mode, maximum-likelihood and MR-PRESSO) as complementary analyses.

Results

MR results suggested that PM_2.5 absorbance was associated with COVID-19 infection (odds ratio (OR) = 2.64; 95% confidence interval (CI) = 1.32-5.27, P = 0.006), hospitalisation (OR = 3.52; 95% CI = 1.05-11.75, P = 0.041) and severe respiratory symptoms (OR = 28.74; 95% CI = 4.00-206.32, P = 0.001) in IVW methods. We observed no association between PM_2.5 and COVID-19.

Conclusions

We found a potential causal association of PM_2.5 absorbance with COVID-19 infection, hospitalisation, and severe respiratory symptoms using MR analysis. Prevention and control of air pollution could help delay and halt the negative progression of COVID-19.

Short-term exposure to ambient air pollution and COVID-19 severity during SARS-CoV-2 Delta and Omicron waves: A multicenter study

Air pollution may affect the clinical course of respiratory diseases, including COVID-19. This study aimed to evaluate the relationship between exposure of adult patients to mean 24 h levels of particulate matter sized <10 μm (PM₁₀ ) and <2.5 μm (PM_2.5 ) and benzo(a)pyrene (B(a)P) during a week before their hospitalization due to SARS-CoV-2 infection and symptomatology, hyperinflammation, coagulopathy, the clinical course of disease, and outcome. The analyses were conducted during two pandemic waves: (i) dominated by highly pathogenic Delta variant (n = 1440) and (ii) clinically less-severe Omicron (n = 785), while the analyzed associations were adjusted for patient's age, BMI, gender, and comorbidities. The exposure to mean 24 h B(a)P exceeding the limits was associated with increased odds of fever and fatigue as early COVID-19 symptoms, hyperinflammation due to serum C-reactive protein >200 mg/L and interleukin-6 >100 pg/mL, coagulopathy due to  d-dimer >2 mg/L and fatal outcome. Elevated PM₁₀ and PM_{2. 5} levels were associated with higher odds of respiratory symptoms, procalcitonin >0.25 ng/mL and interleukin >100 pg/mL, lower oxygen saturation, need for oxygen support, and death. The significant relationships between exposure to air pollutants and the course and outcomes of COVID-19 were observed during both pandemic waves. Short-term exposure to elevated PM and B(a)P levels can be associated with a worse clinical course of COVID-19 in patients requiring hospitalization and, ultimately, contribute to the health burden caused by SARS-CoV-2 variants of higher and lower clinical significance.

Early-phase pandemic in Italy: Covid-19 spread determinant factors

Although the Covid-19 pandemic is still ongoing, the environmental factors beyond virus transmission are only partially known. This statistical study has the aim to identify the key factors that have affected the virus spread during the early phase of pandemic in Italy, among a wide set of potential determinants concerning demographics, environmental pollution and climate. Because of its heterogeneity in pollution levels and climate conditions, Italy provides an ideal scenario for an ecological study. Moreover, the selected period excludes important confounding factors, as different virus variants, restriction policies or vaccines. The short-term relationship between the infection maximum increase and demographic, pollution and meteo-climatic parameters was investigated, including both winter-spring and summer 2020 data, also focusing separately on the two seasonal periods and on North vs Centre-South. Among main results, the importance of population size confirmed social distancing as a key management option. The pollution hazardous role undoubtedly emerged, as NO₂ affected infection increase in all the studied scenarios, PM_2.5 manifested its impact in North of Italy, while O₃ always showed a protective action. Whereas higher temperatures were beneficial, especially in the cold season with also wind and relative humidity, solar irradiance was always relevant, revealing several significant interactions with other co-factors. Presented findings address the importance of the environment in Sars-CoV-2 spread and indicated that special carefulness should be taken in crowded areas, especially if they are highly polluted and weakly exposed to sun. The results suggest that containment of future epidemics similar to Covid-19 could be supported by reducing environmental pollution, achieving safer social habits and promoting preventive health care for better immune system response, as an only comprehensive strategy.

Can vitamin D status influence seroconversion to SARS-COV2 vaccines?

Existing data indicate an association between vitamin D deficiency and increased severity of respiratory distress due to COVID-19 infection, especially in high-risk populations. To date, the effect of vitamin D on immunogenicity to SARS-CoV-2 vaccines has been investigated solely in young healthcare workers in a few studies, yielding conflicting findings, yet highlighting that the response to immunization is inversely related to age. Vitamin D status can potentially influence the antibody titers in people with a previous (or naïve) SARS-CoV-2 infection and vaccination, given its role in immune regulatory functions. From this standpoint, vitamin D supplementation can help reduce the risk of SARS-CoV-2 infection, COVID-19 severity/mortality and rebalance immunological function, particularly in subjects with vigorous T lymphocyte responses to COVID-19. However, more research is needed to establish a correlation between vitamin D status and the generation of protective serological responses to SARS-CoV-2 vaccination.

Relation between PM2.5 pollution and Covid-19 mortality in Western Europe for the 2020-2022 period

The ambient air pollution by particulate matter (PM) has strong negative effects on human health. Recent studies have found correlations between pollution and mortality due to Covid-19. We present here an analysis of such correlation for 32 locations in 6 countries of the Western Europe (France, Germany, Italy, Netherlands, Spain, United Kingdom), for the 2020-2022 period. The data are weekly averaged, and the mortality values were normalized considering the population of the locations. A correlation is qualitatively found for the time-series of PM2.5 pollution and Covid-19 mortality. The higher mortality values occurred during the pollutions peaks, as presented for the city of Paris (France) and the Lombardy regions (Italia), one of the more polluted locations in Western Europe. An almost linear trend with a factor 5.5 ± 1.0 increase in mortality when the pollution increases to ~45 μg.m^-3 is found when considering all data. This leads to an increase of 10.5 ± 2.5 % of mortality per 1 μg.m^-3. More precisely, the trend depends on the period of the analysis and decreases with time (first spread of the pandemic in Spring 2020, mid-2020 - mid 2021 period where the pandemic was better managed, and vaccinal race after mid-2021). Finally, although the initial conditions could differ from one country to another, the relative trend of increase was similar for the countries here considered. Such results can have some implication on the management of the Covid-19 pandemic and other cardiopulmonary diseases during PM pollution events. They also show the importance of reducing the PM pollution in the major cities.

COVID-19 and aerobic exercise: possible role of angiotensin converting enzyme 2

The emergence and circulation of a novel coronavirus (2019-nCoV)—Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—set off a global health crisis. SARS-CoV-2 spreads faster than its two ancestors, SARS-CoV and MERS-CoV. Several modes of transmission have been identified: via respiratory droplets, contact with infected people or contaminated surfaces, and potentially, bioaerosols. Various countries have taken preventive measures that may include partial or total lockdowns lasting weeks. The physical inactivity associated with lockdowns may promote cardiometabolic or other diseases, while physical activity may play a critical role in preventing them. Here we develop the hypothesis of the involvement of aerosols in the contamination process, the role of angiotensin converting enzyme 2 (ACE2), the potential benefits and harm of physical activity during lockdowns, and we suggest directions for future research.

Impact of Different Air Pollutants (PM10, PM2.5, NO2, and Bacterial Aerosols) on COVID-19 Cases in Gliwice, Southern Poland

Many studies have shown that air pollution may be closely associated with increased morbidity and mortality due to COVID-19. It has been observed that exposure to air pollution leads to reduced immune response, thereby facilitating viral penetration and replication. In our study, we combined information on confirmed COVID-19 daily new cases (DNCs) in one of the most polluted regions in the European Union (EU) with air-quality monitoring data, including meteorological parameters (temperature, relative humidity, atmospheric pressure, wind speed, and direction) and concentrations of particulate matter (PM₁₀ and PM_2.5), sulfur dioxide (SO₂), nitrogen oxides (NO and NO₂), ozone (O₃), and carbon monoxide (CO). Additionally, the relationship between bacterial aerosol (BA) concentration and COVID-19 spread was analyzed. We confirmed a significant positive correlation (p < 0.05) between NO₂ concentrations and numbers of confirmed DNCs and observed positive correlations (p < 0.05) between BA concentrations and DNCs, which may point to coronavirus air transmission by surface deposits on bioaerosol particles. In addition, wind direction information was used to show that the highest numbers of DNCs were associated with the dominant wind directions in the region (southern and southwestern parts).

Association between short-term exposure to air pollution and COVID-19 mortality in all German districts: the importance of confounders

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, PM₁₀, CO, NO, NO₂, and O₃ 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 NO₂, and positive associations, at least for the first time period, for O₃ 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.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-022-00657-5.

The association of airborne particulate matter and benzo[a]pyrene with the clinical course of COVID-19 in patients hospitalized in Poland

Air pollution can adversely affect the immune response and increase the severity of the viral disease. The present study aimed to explore the relationship between symptomatology, clinical course, and inflammation markers of adult patients with coronavirus disease 2019 (COVID-19) hospitalized in Poland (n = 4432) and air pollution levels, i.e., mean 24 h and max 24 h level of benzo(a)pyrene (B(a)P) and particulate matter <10 μm (PM₁₀) and <2.5 μm (PM_2.5) during a week before their hospitalization. Exposures to PM_2.5 and B(a)P exceeding the limits were associated with higher odds of early respiratory symptoms of COVID-19 and hyperinflammatory state: interleukin-6 > 100 pg/mL, procalcitonin >0.25 ng/mL, and white blood cells count >11 × 10³/mL. Except for the mean 24 h PM₁₀ level, the exceedance of other air pollution parameters was associated with increased odds for oxygen saturation <90%. Exposure to elevated PM_2.5 and B(a)P levels increased the odds of oxygen therapy and death. This study evidences that worse air quality is related to increased severity of COVID-19 and worse outcome in hospitalized patients. Mitigating air pollution shall be an integral part of measures undertaken to decrease the disease burden during a pandemic of viral respiratory illness.

Effects of environmental parameters and their interactions on the spreading of SARS-CoV-2 in North Italy under different social restrictions. A new approach based on multivariate analysis

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.

The Correlation Analysis between Air Quality and Construction Sites: Evaluation in the Urban Environment during the COVID-19 Pandemic

This research studies the data on air quality and construction activities from 29 January 2020 to 30 April 2020. The analysis focuses on three sample districts of Hangzhou’s Xiacheng, Gongshu, and Xiaoshan districts. The samples, respectively, represent low-level, mid-level, and high-level districts in the scale of construction projects. The correlative relationships are investigated, respectively, in the periods of ‘pandemic lockdown (29 January 2020–20 February 2020)’ and ‘after pandemic lockdown (21 February 2020–30 April 2020)’. The correlative equations are obtained. Based on the guideline values of air parameters provided by the Chinese criteria and standards, the recommended maximum scales of construction projects are defined. The numbers of construction sites are 16, 118, and 311 for the Xiacheng, Gongshu, and Xiaoshan districts during the imposed lockdown period, respectively, and 19, 88, 234, respectively, after the lockdown period. Because the construction site is only one influential factor on the air quality, and the database is not large enough, there are some limitations in the mathematical model and the management plan. Possible problem solving techniques and future studies are introduced at the end of the research study.

Changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China

To prevent the Corona Virus Disease 2019 (COVID-19) spreading, Chinese government takes a series of corresponding measures to restrict human mobility, including transportation lock-down and industries suspension, which significantly influenced the ambient air quality and provided vary rare time windows to assess the impacts of anthropological activities on air pollution. In this work, we divided the studied timeframe (2019/12/24-2020/2/24) into four periods and selected 88 cities from 31 representative urban agglomerations. The indicators of PM_2.5/PM₁₀ and NO₂/SO₂ were applied, for the first time, to analyze the changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China. The results indicated that the ratios of NO₂/SO₂ presented a responding decline, especially in YRD (-5.01), YH (-3.87), and MYR (-3.84), with the sharp reduction of traffic in post-COVID-19 periods (P3-P4: 2.34 ± 0.94 m/m) comparing with pre-COVID-19 periods (P1-P2: 4.49 ± 2.03 m/m). Whereas the ratios of PM_2.5/PM₁₀ increased in P1-P3, then decreased in P4 with relatively higher levels (>0.5) in almost all urban agglomerations. Furthermore, NO₂ presented a stronger association with PM_2.5/PM₁₀ variation than CO; and PM_2.5 with NO₂/SO₂ variation than PM₁₀. In summary, the economic structure, lockdown measures and meteorological conditions could explain the noteworthy variations in different urban agglomerations. These results would be in great help for improving air quality in the post-epidemic periods.

The effect of known and unknown confounders on the relationship between air pollution and Covid-19 mortality in Italy: A sensitivity analysis of an ecological study based on the E-value

Back in December 2019, the novel coronavirus disease 2019 (Covid-19) started rapidly spreading worldwide, especially in Italy that was among the most affected countries. The geographical distribution of air pollution and Covid-19 mortality in Italy suggested atmospheric pollution as a worsening factor of severe Covid-19 health outcomes. The present nationwide ecological study focused on all 107 Italian territorial areas, aiming to assess the potential association between Particulate Matter concentration, less than 2.5 μm in diameter (exposure), and Covid-19 mortality rate (outcome) throughout 2020, by looking at 28 potential confounders. A potential positive association between exposure and outcome was observed when performing a multivariate regression analysis with a Negative Binomial model, suggesting that an increase of 1 μg/m³ in the exposure is associated with an increase of 9.0% (95% CI: 6.5%-11.6%) in the average Covid-19 mortality rate, conditional on all 28 potential confounders. A sensitivity analysis, based on the E-value, shows that a hypothetical unmeasured confounder would have to be associated with both PM_2.5 concentration and Covid-19 mortality rate by a rate ratio of at least 1.40-fold each to explain away the exposure-outcome association, conditional on all 28 covariates included in the main analysis model. Moreover, the Observed Covariate E-value (OCE) was reported to provide a contextualization of the E-value on the observed covariates included in the study. The OCE sensitivity analysis shows that a set of unknown confounders similar in size and magnitude to the set of the considered climatic factors could potentially explain away the estimated exposure-outcome association. Consequently, the role of climatic factors in the Covid-19 pandemic is worth of further investigation.

An Epidemiological Study to Investigate Links between Atmospheric Pollution from Farming and SARS-CoV-2 Mortality

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 (PM_2.5 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.

Do environmental pollutants carrier to COVID-19 pandemic? A cross-sectional analysis

The coronavirus disease (COVID-19) is a highly transmitted disease that spreads all over the globe in a short period. Environmental pollutants are considered one of the carriers to spread the COVID-19 pandemic through health damages. Carbon emissions, PM2.5 emissions, nitrous oxide emissions, GHG, and other GHG emissions are mainly judged separately in the earlier studies in different economic settings. The study hypothesizes that environmental pollutants adversely affect healthcare outcomes, likely to infected people by contagious diseases, including coronavirus cases. The subject matter is vital to analyze the preventive healthcare theory by using different environmental pollutants on the COVID-19 factors: total infected cases, total death cases, and case fatality ratio, in a large cross-section of 119 countries. The study employed the generalized least square (GLS) method for robust inferences. The results show that GHG and CO2 emissions are critical factors likely to increase total coronavirus cases and death rates. On the other hand, nitrous oxide, carbon, and transport emissions increase the case fatality ratio through healthcare damages. The study concludes that stringent environmental policies and improving healthcare infrastructure can control coronavirus cases across countries.

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

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

Variation in Cause-Specific Mortality Rates in Italy during the First Wave of the COVID-19 Pandemic: A Study Based on Nationwide Data

Italy was a country severely hit by the first coronavirus disease 2019 (COVID-19) pandemic wave in early 2020. Mortality studies have focused on the overall excess mortality observed during the pandemic. This paper investigates the cause-specific mortality in Italy from March 2020 to April 2020 and the variation in mortality rates compared with those in 2015-2019 regarding sex, age, and epidemic area. Causes of death were derived from the national cause-of-death register. COVID-19 was the leading cause of death among males and the second leading cause among females. Chronic diseases, such as diabetes and hypertensive, ischemic heart, and cerebrovascular diseases, with decreasing or stable mortality rates in 2015-2019, showed a reversal in the mortality trend. Moreover, mortality due to pneumonia and influenza increased. No increase in neoplasm mortality was observed. Among external causes of death, mortality increased for accidental falls but reduced for transport accidents and suicide. Mortality from causes other than COVID-19 increased similarly in both genders and more at ages 65 years or above. Compared with other areas in Italy, the Lombardy region showed the largest excess in mortality for all leading causes. Underdiagnosis of COVID-19 at the beginning of the pandemic may, to some extent, explain the mortality increase for some causes of death, especially pneumonia and other respiratory diseases.

Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences

In June 2020, we published a review focused on assessing the influence of various air pollutants on the transmission of SARS-CoV-2, and the severity of COVID-19 in patients infected by the coronavirus. The results of most of those reviewed studies suggested that chronic exposure to certain air pollutants might lead to more severe and lethal forms of COVID-19, as well as delays/complications in the recovery of the patients. Since then, a notable number of studies on this topic have been published, including also various reviews. Given the importance of this issue, we have updated the information published since our previous review. Taking together the previous results and those of most investigations now reviewed, we have concluded that there is a significant association between chronic exposure to various outdoor air pollutants: PM2.5, PM10, O3, NO2, SO2 and CO, and the incidence/risk of COVID-19 cases, as well as the severity/mortality of the disease. Unfortunately, studies on the potential influence of other important air pollutants such as VOCs, dioxins and furans, or metals, are not available in the scientific literature. In relation to the influence of outdoor air pollutants on the transmission of SARS-CoV-2, although the scientific evidence is much more limited, some studies point to PM2.5 and PM10 as potential airborne transmitters of the virus. Anyhow, it is clear that environmental air pollution plays an important negative role in COVID-19, increasing its incidence and mortality.

A multiplicity of environmental, economic and social factor analyses to understand COVID-19 diffusion

Research on the impact of the environment on COVID-19 diffusion lacks a full-comprehensive perspective, and neglecting the multiplicity of the human-environment system can lead to misleading conclusions. We attempted to reveal all pre-existing environmental-to-human and human-to-human determinants that influence the transmission of COVID-19. As such, We estimated the daily case incidence ratios (CIR) of COVID-19 for prefectures across mainland China, and used a mixed-effects mixed-distribution model to study the association between the CIR and 114 factors related to climate, atmospheric environmental quality, terrain, population, economic, human mobility as well as non-pharmaceutical interventions (NPIs). Not only the changes in determinants over time as the pandemic progresses but also their lag and interaction effects were examined. CO, O3, PM10 and PM2.5 were found positively linked with CIR, but the effect of NO2 was negative. The temperature had no significant association with CIR, and the daily minimum humidity was a significant negatively predictor. NPIs' level was negatively associated with CIR until with a lag of 15 days. Higher accumulated destination migration scale flow from the epicenter and lower distance to the epicenter (DisWH) were associated with a higher CIR, however, the interaction between DisWH and the time was positive. The more economically developed and more densely populated cities have a higher probability of CIR occurrence, but they may not have a higher CIR intensity.The COVID-19 diffusion are caused by a multiplicity of environmental, economic, social factors as well as NPIs. First, multiple pollutants carried simultaneously on particulate matter affect COVID-19 transmission. Second, the temperature has a limited impact on the spread of the epidemic. Third, NPIs must last for at least 15 days or longer before the effect has been apparent. Fourth, the impact of population movement from the epicenter on COVID-19 gradually diminished over time and intraregional migration deserves more attention.

Outdoor PM2.5 concentration and rate of change in COVID-19 infection in provincial capital cities in China

This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = -0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = -0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = -0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.

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

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

Spread of the COVID-19 In Russia: Regional Peculiarities

The spread of COVID-19 is highly differentiated both between countries and between territories within countries. The main aim of this article is to identify the characteristics of regions that affect the scale of spread of coronavirus infection in the Russian Federation during the second wave of the pandemic. Using econometric modeling methods, the dependences between a number of regional characteristics reflecting the vulnerability of regions and their ability to resist infection were assessed, in addition to the scale of the spread of coronavirus infection. The wealth of a region and social capital of its population became the factors of resistance. The populations of wealthier Russian regions are less exposed to infection, while the most vulnerable are poorer regions, regions with a high population density, poorer environmental quality, and older populations. The article demonstrates the role of social capital in resisting a pandemic: higher quality social capital is associated with lower infection and mortality rates. The findings can be used to inform regional policy measures to potentially reduce COVID-19 and other infections.

Establishing a link between fine particulate matter (PM2.5) zones and COVID -19 over India based on anthropogenic emission sources and air quality data

The spread of coronavirus disease of 2019 (COVID-19) pandemic around the globe is affecting people. The majority of Indian urban complexes are reeling under high emissions of deadly fine particulate matter PM_2.5 and resulting in poor air quality. These fine particles penetrate deep into the body and fuel inflammation in the lungs and respiratory tract, leading to the risk of having cardiovascular and respiratory problems, including a weak immune system. In the present study, we report the first national-scale study over India, which establishes a strong relationship between the PM_2.5 emission load and COVID-19 infections and resulting deaths. We find a significant correlation (R² = 0.66 & 0.60) between the states as well as districts having varied levels of PM_2.5 emissions with corresponding COVID-19 positive cases respectively, and R² = 0.61 between wavering air quality on a longer time scale and the number of COVID-19 related deaths till 5 November 2020. This study provides practical evidence that cities having pollution hotspot where fossil fuel emissions are dominating are highly susceptible to COVID-19 cases.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

COVID-19 and air pollution in Vienna-a time series approach

We performed a time series analysis in Vienna, Austria, investigating the temporal association between daily air pollution (nitrogen dioxide, NO₂ and particulate matter smaller than 10 µm, PM10) concentration and risk of coronavirus disease 2019 (COVID-19) infection and death. Data covering about 2 months (March–April 2020) were retrieved from public databases. Infection risk was defined as the ratio between infected and infectious. In a separate sensitivity analysis different models were applied to estimate the number of infectious people per day. The impact of air pollution was assessed through a linear regression on the natural logarithm of infection risk. Risk of COVID-19 mortality was estimated by Poisson regression. Both pollutants were positively correlated with the risk of infection with the coefficient for NO₂ being 0.032 and for PM10 0.014. That association was significant for the irritant gas (p = 0.012) but not for particles (p = 0.22). Pollutants did not affect COVID-19-related mortality. The study findings might have wider implications on an interaction between air pollution and infectious agents.

Air pollution impacts from COVID-19 pandemic control strategies in Malaysia

Mitigation measures and control strategies relating to novel coronavirus disease 2019 (COVID-19) have been widely applied in many countries in order to reduce the transmission of this pandemic disease. A Movement Control Order (MCO) was implemented in Malaysia starting from the March 18, 2020 as a pandemic control strategy which restricted all movement and daily outdoor activities. To investigate the impact of MCO, air pollutants: particulate matter with an aerodynamic diameter less than 10 μm (PM10), particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO) in nine major cities in Malaysia were measured before and during the implementation of the MCO. The non-carcinogenic health risk assessments of the air pollutants are also determined using the United States Environmental Protection Agency (USEPA) Health Risk Assessment method. Overall, NO2 recorded an average percentage reduction of 40% with the highest reduction observed at Kota Kinabalu (62%). The largest reductions of PM10, PM2.5, SO2, O3 and CO were recorded at Kota Kinabalu (17%), Kuantan (9.5%), Alor Star (38%), Kota Bharu (15%), and Ipoh (27%) respectively. All cities had hazard quotient (HQ) values of <1 suggesting no non-carcinogenic health effects. The highest HQ was observed for PM2.5 during the MCO period (4.53E-02) in Kuala Lumpur. An average hazard index (HI) value of 1.44E-01 (before the MCO) and 1.40E-01 (during the MCO) showed higher human health risks before the MCO than during the MCO. This study gives confidence to regulatory bodies that the reduction of human activities significantly reduces air pollution and increases human health and so good air pollution control strategies can provide crucial impacts, especially in reducing air pollution and improving human health.

A global association between Covid-19 cases and airborne particulate matter at regional level

Evidences of an association between air pollution and Covid-19 infections are mixed and inconclusive. We conducted an ecological analysis at regional scale of long-term exposure to air-borne particle matter and spread of Covid-19 cases during the first wave of epidemics. Global air pollution and climate data were calculated from satellite earth observation data assimilated into numerical models at 10 km resolution. Main outcome was defined as the cumulative number of cases of Covid-19 in the 14 days following the date when > 10 cumulative cases were reported. Negative binomial mixed effect models were applied to estimate the associations between the outcome and long-term exposure to air pollution at the regional level (PM10, PM2.5), after adjusting for relevant regional and country level covariates and spatial correlation. In total we collected 237,749 Covid-19 cases from 730 regions, 63 countries and 5 continents at May 30, 2020. A 10 μg/m3 increase of pollution level was associated with 8.1% (95% CI 5.4%, 10.5%) and 11.5% (95% CI 7.8%, 14.9%) increases in the number of cases in a 14 days window, for PM2.5 and PM10 respectively. We found an association between Covid-19 cases and air pollution suggestive of a possible causal link among particulate matter levels and incidence of COVID-19.

Wildland firefighter exposure to smoke and COVID-19: A new risk on the fire line

Throughout the United States, wildland firefighters respond to wildfires, performing arduous work in remote locations. Wildfire incidents can be an ideal environment for the transmission of infectious diseases, particularly for wildland firefighters who congregate in work and living settings. In this review, we examine how exposure to wildfire smoke can contribute to an increased likelihood of SARS-CoV-2 infection and severity of coronavirus disease (COVID-19). Human exposure to particulate matter (PM), a component of wildfire smoke, has been associated with oxidative stress and inflammatory responses; increasing the likelihood for adverse respiratory symptomology and pathology. In multiple epidemiological studies, wildfire smoke exposure has been associated with acute lower respiratory infections, such as bronchitis and pneumonia. Co-occurrence of SARS-CoV-2 infection and wildfire smoke inhalation may present an increased risk for COVID-19 illness in wildland firefighters due to PM based transport of SARS CoV-2 virus and up-regulation of angiotensin-converting enzyme II (ACE-2) (i.e. ACE-2 functions as a trans-membrane receptor, allowing the SARS-CoV-2 virus to gain entry into the epithelial cell). Wildfire smoke exposure may also increase risk for more severe COVID-19 illness such as cytokine release syndrome, hypotension, and acute respiratory distress syndrome (ARDS). Current infection control measures, including social distancing, wearing cloth masks, frequent cleaning and disinfecting of surfaces, frequent hand washing, and daily screening for COVID-19 symptoms are very important measures to reduce infections and severe health outcomes. Exposure to wildfire smoke may introduce additive or even multiplicative risk for SARS-CoV-2 infection and severity of disease in wildland firefighters. Thus, additional mitigative measures may be needed to prevent the co-occurrence of wildfire smoke exposure and SARS-CoV-2 infection.

How human thermal plume influences near-human transport of respiratory droplets and airborne particles: a review

With mounting evidence and notable cases of large clustered infections, airborne transmission via droplets and particles has been recently acknowledged as an effective mode of transmission for COVID-19. How droplets and aerosol particles disperse are being transported into the human breathing zone-the last few inches for airborne transmission to effectuate-remains a key question which has been widely overlooked. Human thermal plume refers to the constantly rising airflows around the boundary layer of human body due to persisting temperature gradients between the body surfaces and the ambient air. Ample evidence indicated that the thermal plume controls the dispersion and transport of aerosols in the human microenvironment. Given that in calm indoor environments most air inhaled by human comes from the boundary layer where thermal plume flows through constantly, the role of thermal plume needs to be scrutinized to predict the diffusion of droplets, aerosols and other airborne carriers of the novel coronavirus around the human body for prioritizing infection control strategies. Here, we assessed the potential influences of the thermal plume on the transmission of COVID-19 and other airborne pathogens by reviewing the most pertinent evidence and analyzing key variables in the formation of thermal plume in indoor environments, e.g., ambient temperature, human posture and type of clothing. Our reviewed evidence and data indicate that the human thermal plume should facilitate the airborne transmission of COVID-19 in enclosed spaces by elevating small droplets and airborne particles into the breathing zone from lower regions and ascending respiratory droplets from the sources into the upper atmosphere. By drawing attention to aerosol transport dynamics in the human microenvironment, these insights may be useful for understanding COVID-19 transmission in enclosed spaces, especially those intended for public use.