Our results showed that the exposure to fear chemosignals (vs. rest chemosignals and a no-sweat condition) while not changing vigilance behavior leads to faster answers to threatening events

The Function of Fear Chemosignals: Preparing for Danger. Nuno Gomes, Gün R Semin. Chemical Senses, bjab005, February 11 2021. https://doi.org/10.1093/chemse/bjab005

Abstract: It has been shown that the presence of conspecifics modulates human’s vigilance strategies as is the case with animal species. Mere presence has been found to reduce vigilance. However, animal research has also shown that chemosignals (e.g., sweat) produced during fear-inducing situations modulates individuals’ threat detection strategies. In the case of humans, little is known about how exposure to conspecifics’ fear chemosignals modulates vigilance and threat detection effectiveness. The present study (N= 59) examined how human fear chemosignals affect vigilance strategies and threat avoidance in its receivers. We relied on a paradigm that simulates a “foraging under threat” situation in the lab, integrated with an eye-tracker to examine the attention allocation. Our results showed that the exposure to fear chemosignals (vs. rest chemosignals and a no-sweat condition) while not changing vigilance behavior leads to faster answers to threatening events. In conclusion, fear chemosignals seem to constitute an important warning signal for human beings, possibly leading its receiver to a readiness state that allows faster reactions to threat-related events.

Keywords: Vigilance, Fear chemosignals, Olfaction, Threat detection, Eye-tracking

Reduced decision bias and more rational decision making following cortex damage

Reduced decision bias and more rational decision making following ventromedial prefrontal cortex damage. Sanjay Manohar et al. Cortex, February 11 2021. https://doi.org/10.1016/j.cortex.2021.01.015

Summary: Human decisions are susceptible to biases, but establishing causal roles of brain areas has proved to be difficult. Here we studied decision biases in 17 people with unilateral medial prefrontal cortex damage and a rare patient with bilateral ventromedial prefrontal cortex (vmPFC) lesions. Participants learned to choose which of two options was most likely to win, and then bet money on the outcome. Thus, good performance required not only selecting the best option, but also the amount to bet. Healthy people were biased by their previous bet, as well as by the unchosen option’s value. Unilateral medial prefrontal lesions reduced these biases, leading to more rational decisions. Bilateral vmPFC lesions resulted in more strategic betting, again with less bias from the previous trial, paradoxically improving performance overall. Together, the results suggest that vmPFC normally imposes contextual biases, which in healthy people may actually be suboptimal in some situations.

Discussion

In this study we used a novel reversal learning task in which participants made post-decision wagers on their choices, thereby providing a measure of their confidence in winning, and also rated their surprise at outcomes (Fig.1). Analysis was performed on both performance data as well as with a computational model of value learning. In healthy volunteers, bets tracked the expected chance of winning (Fig.4A), but also showed strong biases: People’s bets tended to be similar to their bets on the previous trial, and were higher when the unchosen option was less likely to win. Patients with unilateral mPFC lesions bet more overall (Fig.3B), but showed weaker biases from the previous trial and from the unchosen option. The bilateral patient MJ also showed a weaker bias from the previous trial (Fig.4B), but crucially had a stronger effect of the chosen option’s probability of winning (Fig.4A). This meant that he won more than any other healthy volunteer or unilateral patient on this task (Fig.2A), despite no difference in learning which option was better. Thus, his performance can be seen as exhibiting a more rational betting strategy than in healthy people.

A large body of evidence has revealed that many aspects of human decision making are seemingly irrational, driven by biases that appear to lead to suboptimal outcomes (Tversky and Kahneman, 1974; De Martino et al., 2006; Talluri et al., 2018; Urai et al., 2019). Evidence that some of these biases are driven by normal cognitive operations underpinned by specific brain processes (De Martino et al., 2006; Wimmer and Shohamy, 2012) raises the possibility that damage to the brain might paradoxically reduce such biases (Akrami et al., 2018; Kapur, 1996) and perhaps lead to more rational behaviour. However, to date only limited causal evidence for such a possibility exists in humans (Greene, 2007; Knoch et al., 2006; Koenigs et al., 2007).

The findings presented here show that it is indeed possible for more rational decision making to emerge – at least on a value based reversal learning task – after bilateral vmPFC lesions. This is not to say that all decisions and behaviours become more rational after such brain damage. Clearly, although he managed to continue to work in a demanding job, patient MJ showed evidence of dysfunction in social cognition and some aspects of decision making and judgment in everyday life, just as previous reported cases (Eslinger and Damasio, 1985; Bechara et al., 2000; Berlin et al., 2004; Shamay-Tsoory et al., 2005).

There is some previous circumstantial evidence that mPFC lesions may reduce decision biases. For example, patients with mPFC damage show smaller biases in probabilistic estimation (O’Callaghan et al., 2018), reduced affective contributions to reasoning (Shamay-Tsoory et al., 2005), and may indeed make more utilitarian moral judgements, suggesting more rational valuation with less affective bias (Ciaramelli et al., 2007; Koenigs et al., 2007; Krajbich et al., 2009). These effects might be underpinned by a more general increase in rationality after damage to this region. One possible explanation for this is that individuals with vmPFC lesions might be free of affective biases that normally contribute to such decision making but this remains to be established.

In line with this, Shiv et al. (2005) asked patients with a variety of lesions (amygdala, orbitofrontal and insula) to opt in or out of gambles with positive expected value. Controls tended to opt out especially after a loss, whereas the patients continued to bet, thus winning more. This can be compared to our win-stay analysis (Fig.3F), where MJ bet more than controls on win-stay choices, but did not bet less on lose-switch choices. Further evidence that biases can depend on specific brain areas comes from patients with insula damage, who may lose the normal tendency towards the gamblers’ fallacy (Clark et al., 2014). With this bias, participants tend to re-choose an option that previously lost (because the history of wins should balance out on average). Transcranial stimulation to lateral prefrontal cortex increases this bias (Xue et al., 2012). In our study, there is a possible analogy with the unchosen option effect (Fig.4C), where people bet less when the alternative was valuable (perhaps also because the two options should balance out on average). Unilateral ventromedial patients lost this bias. However, in our task, lesions did not affect the option decisions themselves.

Biases from previous trials may rely on information retained in working memory. Thus an important null result is that the bilateral patient was unimpaired in working memory accuracy (Table S2). He had considerable difficulty remembering verbal lists (Table 1). This memory deficit might have contributed to his lack of trial history biases. However, against this possibility, performance was normal on a specific working memory task, suggesting that the previous bet effect was not simply memory-related. Furthermore, his normal learning and decision-making indicate he was integrating and retaining the specific value information involved in the biases, making memory deficits less likely to contribute. One interpretation of the loss of bias could be that medial frontal areas are required for normal integration of the biasing or interfering information into the current decision. An alternative interpretation is that normal biases are driven by suboptimal heuristics, and that medial frontal lesions abolish these heuristics.

[Table 1. Summary of standardised neuropsychological scores for patient MJ. Impairments were seen in the verbal learning task for short delay recall and yes-no recognition. WAIS: Wechsler adult intelligence scale; WMS: Wechsler memory scale; WM: working memory; CVLT: California verbal learning test; DKEFS: Delis-Kaplan executive function system; GNT: Warrington graded naming test. Red indicates scores in the “extremely low” range (<2nd centile), and pink indicates scores in the borderline range (<10th centile).]

Patients with vmPFC/OFC lesions have previously been shown to bet more under uncertainty (Clark et al., 2008), being generally less risk averse (Bechara et al., 2000; Levens et al., 2014), and our results directly support this finding. However, bets reflect a combination of general risk seeking, confidence, biases and strategic factors. In our study, increased betting alone was insufficient to explain the bilateral patient’s advantage in this task. Instead, reduced biases may have permitted strategic betting, such as the hot hand effect or loss chasing. Interestingly, a previous study had identified that dorsomedial prefrontal lesions can increase the bias caused by eye movements during decisions (Vaidya and Fellows, 2015), but to our knowledge, no human studies have shown reduced biases after lesions in the way demonstrated here.

Information about unchosen options and recent actions may be disrupted by medial or orbitofrontal lesions (Buckley et al., 2009; Levens et al., 2014), which might thus account for the reduced biases in unilateral patients. The effect parallels a recent rodent study where parietal inactivation also paradoxically improved performance, by reducing the active bias from previous trials (Akrami et al., 2018). However, it is unclear why bilateral lesions did not attenuate this bias in MJ. Our finding of larger surprise differences between winning and losing may also match previous reports of increased emotional responses to stochastic outcomes after vmPFC lesions (Levens et al., 2014) and could parallel increases in reward sensitivity observed in these patients (Manohar and Husain, 2016).

Intriguingly, we found no consistent effects on option-selection in this task. Previous studies of classical reversal learning in patients with vmPFC lesions have shown varied effects. Patients tend to perseverate, maladaptively repeating their previous choices even after reward contingencies reverse (Fellows and Farah, 2003; Rolls et al., 1994), but other studies have found only a marginal effect (Daum et al., 1991), and yet others showed normal performance after unilateral lesions, but impaired reversal after bilateral lesions (Hornak et al., 2004). This is consistent with detailed studies in animals suggesting that impairments after OFC lesions may be mild, with medial lesions only impairing performance when discrimination is harder (Izquierdo et al., 2017; Rudebeck and Murray, 2011), and impairments potentially improved by further lesions (Stalnaker et al., 2007). Yet other work has demonstrated that vmPFC lesions produce unstable choices while preserving subjective valuation of single objects (Henri-Bhargava et al., 2012). However, we did not find any deficits in value-based selection of options in our task. This could be because the paradigm used here crucially tests the use of learned values, rather than subjective valuation or rule-following.

In non-human primate studies, brain areas encoding the values of options also encode decision confidence, such as OFC (Kepecs et al., 2008). In humans, fMRI activation increases with decision confidence in vmPFC (De Martino et al., 2013; Lebreton et al., 2015; Rolls et al., 2010; Yokoyama et al., 2010). Although some studies have demonstrated inaccurate confidence judgements after prefrontal lesions (Fleming et al., 2014), others find no deficits even with bilateral lesions (Lemaitre et al., 2018). Remarkably, disrupting anterior PFC with TMS can actually improve metacognitive confidence judgements (Shekhar and Rahnev, 2018). Thus, if medial PFC encodes variables that might bias valuation, lesions to this area should paradoxically improve performance in some situations, as observed here.

Of course, human lesion studies are inherently limited by the possibility of damage not visible on the MRI scans. Although all patients reported here had brain haemorrhages affecting the mPFC, with very little damage outside this region, the bilateral patient had suffered a traumatic injury, followed by haemorrhage. It is possible that this resulted in a different pattern of microscopic damage: although traumatic injuries may appear focal, often the functional damage can be quite widespread. This limits the conclusions that can be drawn about the causal role of medial frontal cortex specifically. However we suggest that the most likely explanation is the bilateral nature of his lesions: reward value is usually considered to be represented bilaterally in OFC (Hampton and O’Doherty, 2007; Rolls, 2015), suggesting that unilateral lesions are less likely to show manifest impairments. One difficulty with interpreting lesion studies is whether the changes reflect direct lesion effects, or compensatory strategies. The chronic nature of his lesion may be a key difference between MJ and other studies demonstrating deficits in reversal learning after vmPFC lesions (Fellows and Farah, 2003; Rolls et al., 1994). This may have allowed recovery and adaptation, leading to his strategic betting pattern. In this case, it is unclear whether it is vmPFC loss per se, or the network-level consequences of this, that attenuates biases. Functional imaging studies in patients might potentially shed light on this in the future.

In summary, the results suggest that vmPFC may drive biases in healthy people. A patient with bilateral lesions won more than other participants did, coupled with more strategic betting and reduced biases, which were attenuated in unilateral patients too. vmPFC may bring contextual information to influence action, which may be suboptimal in some situations.

We find that experts and lay people fared poorly at predicting social and psychological consequences of the pandemic and misperceive what effects it may have already had

Hutcherson, Cendri, Constantine Sharpinskyi, Michael E. W. Varnum, PhD, Amanda M. Rotella, Alexandra Wormley, Louis Tay, and Igor Grossmann. 2021. “The Pandemic Fallacy: Inaccuracy of Social Scientists’ and Lay Judgments About Covid-19’s Societal Consequences in America.” PsyArXiv. February 11. doi:10.31234/osf.io/g8f9s

Abstract: Effective management of global crises relies on expert judgment of their societal effects. How accurate are such judgments? In the spring of 2020, we asked social scientists (N = 717) and lay Americans (N = 394) to make predictions about COVID-19 pandemic-related societal change across social and psychological domains. Six months later we obtained retrospective assessments for the same domains (Nscientists = 270; NlayP = 411) and compared these judgments to objective data to assess estimation accuracy. Social scientists were no more accurate than lay people, neither in prospective nor retrospective judgments. Across studies and samples, estimates of the magnitude of change were off by more than 20% and less than half of participants accurately predicted the direction of changes. Taken together, we find that experts and lay people fared poorly at predicting social and psychological consequences of the pandemic and misperceive what effects it may have already had.

High-income women may prenatally masculinize their sons at the expense of the fitness of their daughters; low-income women may prenatally feminize their daughters at the fitness expense of their sons

Parental income inequality and children’s digit ratio (2D:4D): a ‘Trivers-Willard’ effect on prenatal androgenization? J.T. Manning et al. Journal of Biosocial Science, February 2021. https://www.cambridge.org/core/journals/journal-of-biosocial-science/article/parental-income-inequality-and-childrens-digit-ratio-2d4d-a-triverswillard-effect-on-prenatal-androgenization/CE6A605963BB8D8D921ECBDD4988B01E

Abstract: Income inequality is associated positively with disease prevalence and mortality. Digit ratio (2D:4D) – a negative proxy for prenatal testosterone and a positive correlate of prenatal oestrogen – is related to several diseases. This study examined the association of income inequality (operationalized as relative parental income) and children’s 2D:4D. Participants self-measured finger lengths (2D=index finger, and 4D=ring finger) in a large online survey conducted in July 2005 (the BBC Internet Study) and reported their parents’ income. Children of parents of above-average income had low 2D:4D (high prenatal testosterone, low prenatal oestrogen) while the children of parents of below-average income had high 2D:4D (low prenatal testosterone, high prenatal oestrogen). The effects were significant in the total sample, present among Whites (the largest group in the sample), in the two largest national samples (UK and USA) and were greater for males than females. The findings suggest a Trivers-Willard effect, such that high-income women may prenatally masculinize their sons at the expense of the fitness of their daughters. Women with low income may prenatally feminize their daughters at the fitness expense of their sons. The effect could, in part, explain associations between low income, high 2D:4D (low prenatal testosterone) and some major causes of mortality such as cardiovascular disease.

Keywords 2D:4D Income disparity Trivers-Willard hypothesis

Discussion

The present study found that parental income affects children’s 2D:4D such that below-average income is related to high 2D:4D (feminization of the fetus) and above-average parental income is associated with low 2D:4D (masculinization of the fetus). These findings applied to the total study sample, the most numerous ethnic group in the study (i.e. Whites) and the most numerous national samples (UK and USA). Regarding the total sample, for male children, the effect was present for right and left 2D:4D and for all pairwise comparisons between parental income groups above and below the population average. For female children, the effect was also present on right and left hand 2D:4D and was found in pairwise comparisons between parental income groups above and below the population average. However, the female effects were weaker than the male effects, with only two significant pairwise comparisons for right hand 2D:4D and three for left hand 2D:4D.

The present findings are consistent with the Trivers-Willard hypothesis concerning maternal resources and its links to the influence of the mother’s sex steroids on fetal 2D:4D. Thus, mothers with high income will secrete elevated levels of T relative to E during the 1^st trimester of their pregnancy, i.e. they will masculinize their male and female children. In contrast, women with low income will secrete low levels of T:E in the early stages of pregnancy. This hormonal milieu will feminize their male and female children. That is, high-income mothers will increase the fitness of their sons at the expense of their daughters while low-income mothers will increase the fitness of their daughters at the expense of their sons. Thus, there will be sexually antagonistic effects on the children of both high- and low-income mothers (Manning et al., 2000). There is evidence for an effect of female condition on the production of sex steroids and sexually antagonistic effects of maternal sex steroids on the developing fetus.

There is only weak support for a link between the 2D:4D of women selected at random from the population and their production of T and E (Muller et al., 2011). However, in contrast to women with low income, high-income women may benefit from high levels of nutrition. There may be associations between high income, good nutrition and the production of androgens in women. Elite women athletes with high standards of nutrition and in good condition (with high lean mass and low body fat levels) show negative relationships between their 2D:4D and the breakdown products of T (Eklund et al., 2020) or salivary levels of T (Crewther & Cook, 2019). It appears that women in good condition can, and probably do, secrete elevated levels of T, particularly if they have low 2D:4D.

The Trivers-Willard hypothesis was originally formulated in the context of maternal manipulation of the sex ratio of progeny. There is indeed evidence that masculinized women (with high WHR and/or low 2D:4D) have more sons than feminized women (low WHR and/or high 2D:4D). This may be the result of the deleterious effects of high prenatal T on female fetuses and/or the advantageous effect of high prenatal T on male fetuses (Manning et al., 1996; Singh & Zambarano, 1997; Manning & Bundred, 2001; Kim et al., 2015).

Maternal manipulation of the prenatal sex steroid environment of their children is likely to have later-life health consequences. For example, male children of low-income mothers will be feminized and more prone to several diseases. Prominent among these is likely to be the poverty influenced male-biased burden of cardiovascular diseases. A low income level has been consistently associated with cardiovascular disease, especially in high-income countries. In addition, disparities based on sex (males>females) have been shown in several studies. High 2D:4D in men has been linked to poor outcomes for cardiovascular disease such as early myocardial infarction, high blood pressure, atherosclerotic plaque development, high fibrinogen levels and markers of obesity (Manning & Bundred, 2001; Fink et al., 2006; Lu et al., 2008, 2015; Ozdogmus et al., 2010; Kyriakidis et al., 2010 ; Wu et al., 2013; Manning et al., 2019; Bagepally et al., 2020). Associated with all of these factors is a high level of parental poverty (Kucharska-Newton et al., 2011; Mosquera et al., 2016).

One possible limitation of the present study is that estimates of parental income in the early years of the family are dependent on their children’s recall. Inaccuracies that may result from faulty recall are likely to reduce the Trivers-Willard influence on 2D:4D. Thus, the reported effects may be conservative estimates of maternal influence on offspring 2D:4D. In order to minimize the recall effects of children’s estimates of parental income it is suggested that future studies should also include parental reports of family income.

In conclusion, inequality in parental income may be associated with the 2D:4D of their offspring. Children of parents of above-average income had low 2D:4D (high prenatal T:E) while the children of parents of below-average income had high 2D:4D (low prenatal T:E). The differences in offspring 2D:4D across income groups may arise because of maternal manipulation of sex steroids. Interpreting the findings of the present study through the lens of the Trivers-Willard hypothesis suggests that high-income mothers may masculinize their sons via increased levels of prenatal T. Male reproductive success shows higher variance than female reproductive success. Therefore, the fitness rewards from the sons of high-income mothers are likely to outweigh the deleterious effects of T on the daughters of high-income mothers. In contrast, mothers with low income are expected to feminize their children via increases in prenatal E. The fitness gain from feminized daughters is likely to outweigh the fitness loss of feminized sons. Moreover, the health costs of maternal manipulation of prenatal sex steroids may include hypertension, cardiovascular disease, high levels of fibrinogen and early myocardial infarction and could be focused on the feminized (low T, high E) sons of low-income mothers.

New York City/New Jersey subway average PM2.5 levels are 65 times greater than EPA standards, worse than the worst Chinese city

PM2.5 Concentration and Composition in Subway Systems in the Northeastern United States. David G. Luglio, Maria Katsigeorgis, Jade Hess, Rebecca Kim, John Adragna, Amna Raja, Colin Gordon, Jonathan Fine, George Thurston, Terry Gordon, and M.J. Ruzmyn Vilcassim. Environmental Health Perspectives, February 10 2021. https://doi.org/10.1289/EHP7202

Abstract

Objectives: The goals of this study were to assess the air quality in subway systems in the northeastern United States and estimate the health risks for transit workers and commuters.

Methods: We report real-time and gravimetric PM2.5 concentrations and particle composition from area samples collected in the subways of Philadelphia, Pennsylvania; Boston, Massachusetts; New York City, New York/New Jersey (NYC/NJ); and Washington, District of Columbia. A total of 71 stations across 12 transit lines were monitored during morning and evening rush hours.

Results: We observed variable and high PM2.5 concentrations for on-train and on-platform measurements during morning (from 0600 hours to 1000 hours) and evening (from 1500 hours to 1900 hours) rush hour across cities. Mean real-time PM2.5 concentrations in underground stations were 779±249, 548±207, 341±147, 327±136, and 112±46.7 μg/m3 for the PATH-NYC/NJ; MTA-NYC; Washington, DC; Boston; and Philadelphia transit systems, respectively. In contrast, the mean real-time ambient PM2.5 concentration taken above ground outside the subway stations of PATH-NYC/NJ; MTA-NYC; Washington, DC; Boston; and Philadelphia were 20.8±9.3, 24.1±9.3, 12.01±7.8, 10.0±2.7, and 12.6±12.6 μg/m3, respectively. Stations serviced by the PATH-NYC/NJ system had the highest mean gravimetric PM2.5 concentration, 1,020 μg/m3, ever reported for a subway system, including two 1-h gravimetric PM2.5 values of approximately 1,700 μg/m3 during rush hour at one PATH-NYC/NJ subway station. Iron and total carbon accounted for approximately 80% of the PM2.5 mass in a targeted subset of systems and stations.

Discussion: Our results document that there is an elevation in the PM2.5 concentrations across subway systems in the major urban centers of Northeastern United States during rush hours. Concentrations in some subway stations suggest that transit workers and commuters may be at increased risk according to U.S. federal environmental and occupational guidelines, depending on duration of exposure. This concern is highest for the PM2.5 concentrations encountered in the PATH-NYC/NJ transit system. Further research is urgently needed to identify the sources of PM2.5 and factors that contribute to high levels in individual stations and lines and to assess their potential health impacts on workers and/or commuters.

Discussion

Our measurements and analyses reveal variable and, in places, very high PM2.5${\mathrm{PM}}_{2.5}$ exposures of commuters and transit workers in the underground subway systems of northeastern U.S. cities. The most extreme exposure, identified in a subway station on the PATH system (serving NJ and NYC), was higher than the previously published values for any subway station in the world (Martins et al. 2016; Moreno et al. 2017; Qiu et al. 2017; Van Ryswyk et al. 2017; Xu and Hao 2017; Lee et al. 2018; Minguillón et al. 2018; Mohsen et al. 2018; Moreno and de Miguel 2018; Choi et al. 2019; Loxham and Nieuwenhuijsen 2019; Pan et al. 2019; Shen and Gao 2019; Velasco et al. 2019; Smith et al. 2020), with a mean gravimetric PM2.5${\mathrm{PM}}_{2.5}$ concentration greater than 1,000 μg/m3$\mathrm{1,000}{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$PM2.5${\mathrm{PM}}_{2.5}$ (Figure 1). The MTA-serviced subway stations in Manhattan also had poor air quality, with an adjusted real-time mean±SD$\text{mean}\pm \text{SD}$PM2.5${\mathrm{PM}}_{2.5}$ concentration of 548±207 μg/m3$548\pm 207{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$.

Our particle measurements were similar to those measured previously in the MTA-NYC stations with high PM2.5${\mathrm{PM}}_{2.5}$ levels (Vilcassim et al. 2014) and much greater than aboveground ambient PM2.5${\mathrm{PM}}_{2.5}$ levels [it must be noted that the MTA-NYC subway stations monitored in the present study were a biased sample and chosen based on the high PM2.5${\mathrm{PM}}_{2.5}$ levels in the Vilcassim study (2014)]. Thus, during rush hour, the underground subway stations targeted in the NYC/NJ’s MTA and PATH subway systems had significantly worse air quality, in terms of PM2.5${\mathrm{PM}}_{2.5}$, than the targeted subway stations in Boston, Philadelphia, and Washington, DC. Philadelphia’s subway stations, for example, had better air quality, although the mean real-time PM2.5${\mathrm{PM}}_{2.5}$ concentration was still several fold greater than the mean ambient PM2.5${\mathrm{PM}}_{2.5}$ concentration measured outside the Philadelphia subway stations. In addition, we cannot rule out spurious differences due to uncontrolled sources of variation related to sampling. However, our findings clearly indicate that PM2.5${\mathrm{PM}}_{2.5}$ concentrations in underground stations and measured on subway trains are much greater than aboveground ambient PM2.5${\mathrm{PM}}_{2.5}$ levels, at least during rush hour periods. In addition, we measured extremely high concentrations in individual underground stations in the MTA (NYC) and PATH (NYC/NJ) subway systems that, even if they represent extreme levels for these stations, raise serious health concerns and warrant additional investigation. In addition, underground PM2.5${\mathrm{PM}}_{2.5}$ concentrations were consistently higher than mean ambient PM2.5${\mathrm{PM}}_{2.5}$ concentrations. Thus, our findings suggest that, at least in the northeastern U.S. transit systems included in our study, commuters are exposed to poor air quality during their time spent in underground subway stations. Moreover, exposures in at least some underground stations may be high enough to increase the risk of the adverse health effects associated with PM2.5${\mathrm{PM}}_{2.5}$, even if they occur for relatively short periods of time.

It should be noted that most subway air pollution studies have relied on real-time data collected with light scattering instruments (Xu and Hao 2017) that have been factory calibrated, in the traditional manner, with Arizona road-dust (Curtis et al. 2008; Wang et al. 2016). Despite their many advantages (e.g., real-time data, autocorrection for temperature and RH), the output of real-time PM2.5${\mathrm{PM}}_{2.5}$ instruments can be affected by particle composition, shape, and water content, all physical factors that will variably affect light scattering. In the present study, we compared real-time and gravimetric PM2.5${\mathrm{PM}}_{2.5}$ concentrations during simultaneous 30- to 60-min sampling sessions conducted in the targeted subway systems (except MTA-NYC or the LIRR) and found, overall, that gravimetric values were 2 4 times greater than what was measured with the real-time light scattering device. This ratio is much higher than what has been reported for other environments and dust types (Wu et al. 2005; Wang et al. 2016, 2018; Patts et al. 2019), and this difference is most likely due to the large (e.g., as high as 60% of the total PM2.5${\mathrm{PM}}_{2.5}$ mass) contribution of iron, a dense metal, to the airborne PM2.5${\mathrm{PM}}_{2.5}$ in the targeted subway systems. Therefore, we adjusted our real-time PM2.5${\mathrm{PM}}_{2.5}$ data with a correction factor. Thus, this real-time/gravimetric ratio issue should be considered when interpreting health risks using published data from air quality studies of subway systems conducted throughout the world. Note that most of the samples collected at underground stations in the present study were selected because they had the highest estimated real-time PM2.5${\mathrm{PM}}_{2.5}$ concentrations in each system.

One of the highest unadjusted real-time mean subway system concentrations previously reported was 265 μg/m3$265{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ in Suzhou, China (Cao et al. 2017), whereas Seaton et al. (2005) and Smith et al. (2020) observed real-time, dust-type calibrated PM2.5${\mathrm{PM}}_{2.5}$ concentrations in a few stations in London, UK, that approached what was observed in PATH stations (Table S2), with a maximum 30-min mean concentration of 480 μg/m3$480{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ at one London station. Notably, Smith et al. (2020) observed a single 1-min peak of 885 μg/m3$885{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$. The high pollution levels measured in London’s subways did not reach the upper range of the PM2.5${\mathrm{PM}}_{2.5}$ levels in the PATH subway stations and particularly in the Christopher Street Station, which had a maximum 1-h gravimetric PM2.5${\mathrm{PM}}_{2.5}$ concentration of 1,780 μg/m3$\mathrm{1,780}{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ during rush hour. The gravimetric PM2.5${\mathrm{PM}}_{2.5}$ concentrations measured at Newport Station, however, were more consistent with the peak values estimated in Smith et al. (2020). Comparison of our underground and ambient PM2.5${\mathrm{PM}}_{2.5}$ data strongly suggests that ambient PM2.5${\mathrm{PM}}_{2.5}$ is not a likely source of the high PM2.5${\mathrm{PM}}_{2.5}$ levels observed in NYC’s underground subway stations and that other sources such as the continual grinding of the train wheels against the rails, the electricity-collecting shoes, and diesel soot emissions from maintenance locomotives are important sources.

The contribution of TC to the PM2.5${\mathrm{PM}}_{2.5}$ mass concentration varied considerably among the two underground stations sampled at each of three transit systems (Table 3). TC constituted 6% of the particle composition in the PATH-NYC/NJ stations, whereas it composed 39% and 22% of PM2.5${\mathrm{PM}}_{2.5}$ in Boston and Washington, DC, respectively (Figure S4). Even within a single urban transit system, the TC concentrations varied between stations as was observed in Boston’s Government Center–Blue Line (177 μg/m3$177{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$) and Broadway stations (70.6±24.7 μg/m3$70.6\pm 24.7{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$), albeit based on one and two samples, respectively. Broadway is an older station on the Red line, and Government Center is a much larger station with separate Blue and Green Line platforms and was renovated in the summer of 2016. Notably, TC, made primarily of the estimated OC component, dominated the Government Center–Blue Line aerosol, although the significance of this is unclear and further investigation into the sources of PM2.5${\mathrm{PM}}_{2.5}$ and the role of the mechanical design (e.g., ventilation) of each station is needed. Notably, there was relatively little EC (or the roughly equivalent BC2.5${\text{BC}}_{2.5}$) present in any of the six underground subway stations, an unexpected finding given the emphasis that multiple papers (Vilcassim et al. 2014; Choi et al. 2019) have placed on inorganic carbon species. A plausible source of EC would be diesel combustion in subway systems, for example, from diesel maintenance trains that operate in the MTA system. However, these trains are typically active only at night, and therefore their contribution to the composition of subway PM2.5${\mathrm{PM}}_{2.5}$ is unclear.

Iron accounted for the largest fraction of PM2.5${\mathrm{PM}}_{2.5}$ in the targeted subway stations, and frictional forces between the train wheels and rails and collection shoes and the third rail may account for this finding. The relative concentration of other elements was observed to vary among subway systems, suggesting that other sources (e.g., silicon as a marker for crustal material; arsenic as a marker for rodenticides) contribute to the airborne particles encountered by transit workers and commuters in subway stations. A previous report on PM composition in MTA stations in Manhattan agrees with the present findings. Chillrud et al. (2004) found similar ratios of iron/manganese, and chromium/manganese concentrations (i.e., components of different grades of steel), although some of the trace element concentrations in the present study are many times higher than those reported by Chillrud et al. Although other studies have documented low concentrations of noniron and carbon elements in subways (Minguillón et al. 2018; Lee et al. 2018), results in Shanghai, China, found that aluminum, silicon, and calcium made up more than 30% of PM2.5${\mathrm{PM}}_{2.5}$ (Lu et al. 2015a), suggesting that ambient soil particles can contribute to subway PM. Similarly, in Beijing subways, the iron concentration was outweighed by aluminum, potassium, sodium, calcium, magnesium, zinc, and barium (Pan et al. 2019). Thus, significant differences in PM composition exist among the underground subway systems across the globe, and it is likely that these differences are a result of source contributors that vary among systems.

Our results demonstrate considerable variability regarding the air quality that transit workers and commuters may encounter in the subway stations of the major cities in northeastern United States. Not only does the PM2.5${\mathrm{PM}}_{2.5}$ concentration vary among stations and cities, but the elemental composition of PM2.5${\mathrm{PM}}_{2.5}$. Previous studies have demonstrated that underground depth (Vilcassim et al. 2014; Figueroa-Lara et al. 2019), station volume, age (Van Ryswyk et al. 2017), and ventilation (Martins et al. 2016) all affect aerosol loading. Therefore, these subway system- and station-dependent differences were not unexpected in the present study. It is interesting to note that Martins et al. showed that more recently built stations do not necessarily have better air quality: The stations established in 2002 and 2009 in Oporto, Portugal, and Athens, Greece, had higher PM2.5${\mathrm{PM}}_{2.5}$ aerosol concentrations than a station built in 1983 in Barcelona, Spain. Nevertheless, there is evidence that common methods of reducing airborne PM are effective (Park et al. 2019), such as cleaning stations more often (Chen et al. 2017), improving ventilation (Moreno et al. 2014, 2017), using particle removal systems, and installing shields that confine track-generated particles from the boarding passengers (Guo et al. 2014).

Study Limitations

In the present study, there were relatively few interline differences in air quality among the subway lines within each city (Figure S2 and Table S1). One exception was the Blue line, which exhibited the highest PM2.5${\mathrm{PM}}_{2.5}$ concentration of the three targeted Boston subway lines. Because the Blue line is the most recently built of the three targeted Boston subway lines and would presumably have the best ventilation design (based on our subjective observations), this finding was unexpected. A limitation of this observation, however, is that we sampled air quality in two Blue line stations that were high train-traffic areas. A similar intrasubway system difference was, however, also observed in NYC/NJ’s PATH system, where the mean PM2.5${\mathrm{PM}}_{2.5}$ level on the 33rd Street line was significantly greater than the World Trade Center line serviced by the PATH. As noted above, we did not collect information on all potential factors that might explain differences in air quality among different stations, subway lines, or transit systems.

We compared several transit systems using data collected at similar times of the day and generally within the same season. Thus, we have not cataloged the total potential variation of PM2.5${\mathrm{PM}}_{2.5}$ in each system. In particular, we sampled during a small number of days in Boston and Washington, DC, and our data generally represent only summer conditions. Thus, we do not know if the subway air quality changes significantly over season or time, although Van Ryswyk et al. (2017) have shown that the Toronto, Ontario, Canada, metro had higher PM2.5${\mathrm{PM}}_{2.5}$ concentrations than Montréal, Quebec, regardless of season. Another study limitation is that our PM2.5${\mathrm{PM}}_{2.5}$ and BC2.5${\text{BC}}_{2.5}$ sample sizes for each station were relatively small (n=4$n=4$ for most stations). Although this study design allowed us to compare subway systems, we lacked sufficient power to compare PM2.5${\mathrm{PM}}_{2.5}$ concentrations among individual station platforms within a city’s transit system. Nonetheless, certain stations were clearly more polluted than others. PM2.5${\mathrm{PM}}_{2.5}$ concentrations in the underground Christopher Street (PATH-NYC/NJ) were the highest among the 71 northeastern U.S. underground stations included in our study, and to our knowledge, were higher than any levels reported for any subway system across the globe (Martins et al. 2016; Moreno et al. 2017; Qiu et al. 2017; Van Ryswyk et al. 2017; Xu and Hao 2017; Lee et al. 2018; Minguillón et al. 2018; Mohsen et al. 2018; Moreno and de Miguel 2018; Choi et al. 2019; Loxham and Nieuwenhuijsen 2019; Pan et al. 2019; Shen and Gao 2019; Velasco et al. 2019; Smith et al. 2020).

In addition, it must be noted that the methodologies used to assess BC, OC, and EC were developed for measurement of PM composition collected under ambient outdoor conditions. The measurement of these carbon components was hampered, however, by the presence of large amounts, relative to ambient PM, of iron compounds. As demonstrated by other investigators, the dark color of some iron compounds can interfere with the reflectance measurement of BC, and the chemistry of the iron compounds found in subway PM shifts the transition demarcation of OC and EC in the thermal ramp used by the Sunset Instrument analyzer. Therefore, we chose to present our data as TC (total carbon) concentrations to avoid the latter limitation. Regardless, the potential for underestimation of OC (i.e., caused by high levels of iron in PM2.5${\mathrm{PM}}_{2.5}$ collected on quartz filters in the subways; Figure S4) does not lessen the importance of OC as a major component of the PM2.5${\mathrm{PM}}_{2.5}$ collected in several subway stations.

Implications

The key issue with underground subway exposures is whether there is a significant increase in the risk for adverse cardiovascular and respiratory outcomes, given the well-documented association between PM2.5${\mathrm{PM}}_{2.5}$ and adverse health effects. With one notable exception, the PM2.5${\mathrm{PM}}_{2.5}$ concentrations measured in subway stations during morning and evening rush hours were generally 2 to 7 times the U.S. EPA’s 24-h ambient air standard of 35 μg/m3$35{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$. The one exceptional station (Christopher Street Station) on the PATH subway line connecting NJ to lower Manhattan had a maximum 1-h PM2.5${\mathrm{PM}}_{2.5}$ concentration of 1,780 μg/m3$\mathrm{1,780}{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$, with a mean gravimetric concentration of 1,254 μg/m3$\mathrm{1,254}{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ (n=4$n=4$) (Table S3). If we assume that commuters are exposed to this level of PM2.5${\mathrm{PM}}_{2.5}$ for a typical 15-min total time (from/to home) spent on a subway platform and at 100 μg/m3$100{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ for two 20-min rides on the PATH subway trains each day, then a commuter’s 24-h mean PM2.5${\mathrm{PM}}_{2.5}$ exposure concentration would increase from an assumed daily mean of 7.7 μg/m3$7.7{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ (for NYC metropolitan area; U.S. EPA 2020b) to 26.1 μg/m3$26.1{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$. Given an association of a 6% increase in relative risk for each 10 μg/m3$10{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ increase in long-term (e.g., annual average) PM2.5${\mathrm{PM}}_{2.5}$ (Pope et al. 2004), this exposure scenario suggests that a typical commuter would be at an 11% increase in risk for cardiovascular mortality. However, this calculation assumes that the toxicity of underground subway PM2.5${\mathrm{PM}}_{2.5}$ is similar to that of ambient PM, which is uncertain in the absence of much-needed subway–health studies. It must be emphasized that this increase in individual risk for daily commuters differs in comparison to that for transit workers who spend considerably longer periods of time on the subway platforms (e.g., 8-h work shifts). The impact of exposure on transit workers is unclear because although they would be exposed to significantly greater PM2.5${\mathrm{PM}}_{2.5}$ accumulated doses (i.e., increased exposure time and breathing rates), workers are often considered “healthy,” and the most relevant applicable occupational exposure guidelines are for larger-diameter respirable dust, defined as PM4.0${\mathrm{PM}}_{4.0}$ (OSHA’s Permissible Exposure Limit of 5 mg/m3$5{\mathrm{mg}/\mathrm{m}}^3$ and ACGIH’s threshold limit value of 3 mg/m3$3{\mathrm{mg}/\mathrm{m}}^3$). In conclusion, these findings of poor air quality in subway systems should prompt further investigation as to the levels, sources, composition, and human health effects of the PM2.5${\mathrm{PM}}_{2.5}$ pollution in subway systems. However, even in the absence of such data, the results of our study already indicate that the Precautionary Principal (Science for Environment Policy 2017) would call for mitigation efforts, such as improved ventilation to protect the tens of thousands of subway workers and millions of daily commuters from potentially unwarranted health risks.