Elevated Levels of the Pollutant PM2.5 in Crowded Subways of Cities with High COVID-19 Related Mortality-Has COVID-19 Gone Underground?

  

    
City
    
No. of Stations 
    
Length Km 
    
Ridership/million 
  
  

    
Tehran 
    
127 
    
246 
    
820 
  
  

    
Barcelona 
    
133 
    
122 
    
412 
  
  

    
Stockholm 
    
100 
    
208 
    
355 
  
  

    
Sao Paolo 
    
89 
    
101 
    
1494 
  
  

    
Wuhan 
    
201 
    
339 
    
1222 
  
  

    
Paris 
    
302 
    
214 
    
1560 
  
  

    
Milan 
    
106 
    
96 
    
369 
  
  

    
London 
    
270 
    
402 
    
1384 
  
  

    
New York 
    
424 
    
380 
    
1697 
  
  

    
Average 
    
194.6 
    
234.2 
    
1034.7 
  
  

    
SD 
    
115.2 
    
117.8 
    
550.1 
  

Table 4: Cities with high COVID related mortality and characteristics of subway networks assessed.

Statistics

The Statistical Package for Social Sciences (SPSS) was utilized to assess the data retrieved. The data was analysed for normality by the Kolmogoroff-Smirnov test and all the data except for the minimum PM2.5 and the length of the networks were found to be nonparametric. The Mann Whitney U test was applied for comparing nonparametric variables of both groups of cities and the Spearman Rank test was applied for nonparametric correlations.

Discussion

This review article indicated that the minimum and maximum levels of PM2.5 in underground networks correlated with the population percentage mortality rates associated with COVID-19 infection. The correlation between the population percentage mortality progressively increased from minimum (p<0.01) to maximum (p<0.00001) levels of PM2.5. This pattern of significance may increase the biological plausibility of this correlation. Underground stations are characterized by the congestion of numerous commuters confined to an enclosed space in the presence of very high levels the pollutant PM2.5. Elevated PM2.5 levels in overcrowded platforms and train cabins may have synergized possibly making the subways prime sites for seeding COVID-19. PM2.5 concentrations in the London underground were found on average to be 18-times higher than concentrations at the surface. London underground levels of PM2.5 range from 39 μg/m³ to 734 μg/m³ on the deep tunnel lines and 14 μg/m³ to 368 μg/m³ for PM2.5 on the sub-surface lines [6]. In another study, the PM2.5 concentration in the London Underground (mean 88 μg/m³, median 28 μg/m³) was greater than at roadside environments (mean 22 μg/m³, median 14 μg/m³). PM2.5 mass varied up to a magnitude of 90 between lines and locations, with the subsurface and deepest lines being the District (median 4 μg/m³) and Victoria (median 361 μg/m³ but up to 885 μg/ m³) respectively [6]. PM2.5 in underground networks is generated by several factors. These factors include friction between wheels and the rail and the braking mechanisms producing high levels of PM2.5. In this review the correlation between maximal PM2.5 with the number of stations supports the premise that braking mechanisms are responsible for high platform PM2.5 levels. Through the piston effect while trains travel through tunnels, the PM2.5 levels generated by wheel to rail friction would permeate the platform environment. This latter aspect would explain the PM2.5 correlation with the length of the subway networks. High concentration of PM2.5 is more likely in confined areas as in subways as opposed to the surface. Dispersal by wind and rain do not occur in the underground. The temporary piston effect when trains pass through tunnels may disperse platform air but it may also push tunnel air towards the platform. Air quality of underground platform depends on a variety of factors including ventilation, train speed and frequency, wheel materials and braking mechanisms, and station depth and design [7]. Several studies have shown adverse health impact associated with exposure to particulate matter PM2.5 [8,9]. Inhaled particulate matter has the propensity to create free radicals, which at the pulmonary epithelium may cause oxidative stress, resulting in negative health effects [10,11]. The metallic components of subway PM2.5 have been shown to cause oxidative stress in alveolar macrophage cells [12,13]. The health effects of subway PM2.5 can be assessed by measuring the oxidative potential of particulate matter by assessing two antioxidants commonly found at the surface of the lung, ascorbic acid and glutathione. This has been done by Moreno et al, whereby ascorbic acid oxidation and glutathione oxidation showed low levels of oxidative potential possibly due to copper, arsenic and antimony emanating from brake pads and pantographs. The ferrous component does not seem to have a significant effect on lung pathology [14]. An experimental model has suggested that intervention on subway human traffic would have limited effect on the transmission of respiratory infection [15]. The model simulated the interactions of underground commuters with various sites of their daily activities. This model-utilized information from the 1957-1958 H2N2 influenza epidemic and from New York City travel data. The model results indicated that the 1957-1958 pandemic had only a 4.4% contribution of influenza from subway interaction. This model concluded that public health interventions on the underground would have limited effect in containing the pandemic [15]. A crucial difference when comparing the 1957-58 influenza epidemic to COVID-19 is the reproduction number (R₀) reflecting transmissibility of the viral infection. Whereas the R₀ of the 1957-58 influenza epidemic was 1.1 at the beginning of 1957, the R0 of COVID-19 was estimated to be 3.28 in the initial stages [3]. This is substantially higher than the H2N2 in 1957 and also higher than Severe Acute Respiratory Syndrome (SARS) epidemic (R₀ 2.2) in 2003 [3]. During the severe acute respiratory syndrome 2003 SARS epidemic, the air pollution index was noted to correlate with mortality (Liu et al 2020). Patients who contracted SARS from Chinese provinces with high air pollution index had double the mortality compared to those from provinces with low air pollution index (RR=2.18, 95% CI: 1.31-3.65). More specifically to particulate matter, PM10 and PM2.5 were implicated in the transmission of the Avian flu in 1997 [16]. The above literature may suggest that the higher transmissibility of COVID-19 (R₀ 3.28) may be due the presence of co-factors possibly including the utilization of particulate matter as a vector. COVID-19 genes have been found on particulate matter [2]. This study was carried out in the Bergamo region in Italy, which was hard hit with COVID-19 mortality and also reports an elevated level of particulate matter pollution for both PM10 and PM2.5. As opposed to control samples of particulate matter, 34 RNA extractions for the COVID-19 E, N and RdRP genes, demonstrated 20 positive results for one of the genes [2]. This study suggested that airborne transmission was further augmented by ambient particulate matter. It is therefore biologically plausible that in the significantly higher concentrations of PM2.5 found in subways, the mode of transmission was further accelerated in the presence of overcrowded commuters on station platforms and train cabins. This review indicated that subways in cities with high levels of COVID-19 mortality had significantly higher PM2.5 levels compared to cities with low deaths rates, which concomitantly had low levels of PM2.5. The network characteristics such as the number of stations and rail track length correlated with PM2.5 concentration. The study by [1] showed that long-term exposure to PM2.5 increases COVID-19 related mortality. Just an increase of 1 μg/m³ in long-term PM2.5 exposure correlated with an 8% increase in the COVID-19 death rate. In a previous study by the same authors on a large American population of 65-year-olds, the same small increment in long-term exposure to PM2.5 led to an increase of 0.73% in all-cause mortality. Consequently, for the same 1 μg/m³ increase in PM2.5, the magnitude of COVID related deaths increased eleven-fold [1]. The hypothesis of ambient salinity and the sodium chloride component in PM2.5 as a protective factor [17,18] did not hold for the pandemic in the East Coast of USA. The states of New Jersey, Connecticut and Massachusetts have high levels of ambient salinity [19]. The role of subway interconnectivity and elevated PM2.5 levels may have eclipsed any protective factors causing high death rate in New York State [5] and the adjacent states of New Jersey, Connecticut and Massachusetts. Due to the high connectivity, New York, New Jersey and Connecticut are sometimes referred to as the Tristate. Beside the sodium chloride content of PM2.5, the carbon component of this particulate matter may also be involved in the pathogenesis of COVID-19. Whereas carbon is the major component of surface PM2.5, in subway PM2.5 is a minor constituent in subway PM2.5, with haematite being the more dominant element [20]. Carbon has adsorbant properties and possibly the lack of it in the haematite-rich subway PM2.5 may release the adherent virus more easily within the respiratory tract increasing COVID-19 pathogenicity. This review has a number of limiting factors. Firstly, although mortality related to COVID-19 infection is more robust that actual diagnosis of the infected cases, death rates also depend in part on the testing prevalence and variance of diagnoses, especially in the presence of multiple pathology. Moreover, mortality also depends on the social circumstances and education levels of the population, the accessibility of medical and preventive measures of the various cities and also the proportion of elderly and vulnerable in the cities’ population. Social circumstances, educational background and preventive measures also impact the proportion of the population that indulges in tobacco smoking. The highest human exposure to particulate matter is through tobacco smoking. This article did not go into depth as regards the date and the effect of lockdown on COVID-19 infection and mortality. The implementation of national lockdown may have impacted the spread of infection however there was a spectrum of lockdowns throughout both groups of cities. Hong Kong and Seoul did not implement severely restrictive lockdowns. Both cities implemented tempestuous intervention to endorse mass testing and tracking. Moreover after the SARS epidemic the mindset of the people aided in adhering to physical distancing in all its different facets including the ubiquitous use of masks in subways and while travelling within train cabins [21,22]. Have shown that the use of masks are crucial in determining the spread of disease in Wuhan, Italy and New York. Face protection reduced the number of COVID-19 infections by more than 78,000 in Italy and over 66,000 in New York City during the months of April and May 2020 [22-40]. Other limiting factors may also have affected the variables assessed. The PM2.5 levels in various cities were taken prior to the COVID-19 pandemic, during different seasons and years, and by different researchers and PM2.5 monitoring equipment. The studies showing adhesion of COVID-19 genes to particulate matter were related to surface measurements. The characteristics of the subway networks may have varied prior to the pandemic and some are undergoing structural changes that may affect PM2.5 levels

Conclusion

Overcrowded subways with elevated PM2.5 may expose commuters utilizing this mode of transport to higher viral loads adherent to particulate matter and consequent transmission. Combined with the adverse effects PM2.5 has on respiratory immunity, elevated subway PM2.5 levels in conjunction with inherent underground characteristics may possibly have increased COVID-19 infection spread and related mortality.

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