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Concentration-dependent increase in symptoms due to diesel exhaust in a controlled human exposure study

Particle and Fibre Toxicology volume 19, Article number: 66 (2022) Cite this article

Abstract

Background

Traffic-related air pollution (TRAP) exposure causes adverse effects on wellbeing and quality of life, which can be studied non-invasively using self-reported symptoms. However, little is known about the effects of different TRAP concentrations on symptoms following controlled exposures, where acute responses can be studied with limited confounding. We investigated the concentration–response relationship between diesel exhaust (DE) exposure, as a model TRAP, and self-reported symptoms.

Methods

We recruited 17 healthy non-smokers into a double-blind crossover study where they were exposed to filtered air (FA) and DE standardized to 20, 50, 150 µg/m3 PM2.5 for 4 h, with a ≥ 4-week washout between exposures. Immediately before, and at 4 h and 24 h from the beginning of the exposure, we administered visual analog scale (VAS) questionnaires and grouped responses into chest, constitutional, eye, neurological, and nasal categories. Additionally, we assessed how the symptom response was related to exposure perception and airway function.

Results

An increase in DE concentration raised total (β ± standard error = 0.05 ± 0.03, P = 0.04), constitutional (0.01 ± 0.01, P = 0.03) and eye (0.02 ± 0.01, P = 0.05) symptoms at 4 h, modified by perception of temperature, noise, and anxiety. These symptoms were also correlated with airway inflammation. Compared to FA, symptoms were significantly increased at 150 µg/m3 for the total (8.45 ± 3.92, P = 0.04) and eye (3.18 ± 1.55, P = 0.05) categories, with trends towards higher values in the constitutional (1.49 ± 0.86, P = 0.09) and nasal (1.71 ± 0.96, P = 0.08) categories.

Conclusion

DE exposure induced a concentration-dependent increase in symptoms, primarily in the eyes and body, that was modified by environmental perception. These observations emphasize the inflammatory and sensory effects of TRAP, with a potential threshold below 150 µg/m3 PM2.5. We demonstrate VAS questionnaires as a useful tool for health monitoring and provide insight into the TRAP concentration–response at exposure levels relevant to public health policy.

Introduction

Traffic-related air pollution (TRAP) exposure causes adverse health effects and is a risk factor for morbidity worldwide [1,2,3]. The effects of pollution on subjective wellbeing and quality of life [4], in particular, can be assessed non-invasively using self-reported symptoms questionnaires [5,6,7]. Symptoms are also robust indicators of the pollution-associated exacerbation of cardiopulmonary diseases [6, 8,9,10] and are correlated with other clinical health measures, such as fractional exhaled nitric oxide (FeNO) [11] and forced expiratory volume in 1 s (FEV1) [12, 13].

Diesel exhaust (DE) consists of gases and particulate matter (PM), including a particularly harmful fraction with a diameter < 2.5 µm (PM2.5), that interacts with cells at mucosal surfaces to instigate inflammation, oxidative stress, epithelial damage and sensory nerve activation [1, 14]. In addition to the systemic response mobilized by these interactions, PM with diameter < 0.1 µm (PM0.1) may directly translocate into the blood to propagate systemic inflammation [15, 16]. Additional roles have been proposed for psychological factors, such as exposure perception [17], in affecting pollution-associated symptoms.

Concentration–response (C–R) relationships help elucidate the link between exposures and effects and have been used to investigate symptom responses over a broad range of TRAP concentrations in epidemiological studies [9, 10, 18, 19]. In controlled human exposure (CHE) studies, where residual confounding is limited, the acute effects of TRAP exposure on symptoms have also been studied, commonly using diesel exhaust (DE) as a model of TRAP [20,21,22,23,24]. However, the C–R relationship between TRAP exposure and symptoms is relatively unexplored in CHE studies [25]. An improved understanding of the link between air pollution and symptoms, and the role of perception in this relationship, is crucial in evaluating the impacts of air pollution on wellbeing.

In this study, we investigated the C–R relationship for self-reported symptoms after controlled human exposures. We hypothesized that higher DE concentrations would increase symptoms. Additionally, we studied the role of environmental perception in this C–R relationship. Lastly, we investigated the relationship between symptoms and clinical measures of airway function and inflammation. We report concentration-dependent effects of TRAP on symptoms that could inform future strategies to assess the impacts of air exposure non-invasively.

These results have previously been reported in the form of a conference abstract [26].

Methods

Controlled diesel exhaust exposures

The Diesel Induces Concentration-dependent Effects (DICE) study (NCT03234790) was a double-blind crossover study approved by the University of British Columbia Research Ethics Board (H16-03053). Healthy non-smokers aged 19–49 were recruited using referrals and online advertisements. Following informed consent, participants were screened for respiratory and cardiac abnormalities by the study physician. Participants were included if they were healthy, aged 19–49, non-smokers, and able to communicate and complete study procedures. Exclusion criteria included pregnancy/breast-feeding, conflicting time commitments and inhaled corticosteroid use. Before each study visit participants completed a standard common cold questionnaire to confirm that they did not have upper respiratory tract infection symptoms and were asked to withhold caffeine and bronchodilator use. Visits were postponed by at least 4 weeks if a possible respiratory infection was reported. Participants were exposed to filtered air (FA) and DE standardized to 20, 50, 150 µg/m3 PM2.5 over four separate visits at the Air Pollution Exposure Laboratory [27]. These PM2.5 concentrations are common in DE exposure studies [28], and approximate real world urban [29] and occupational [30] levels. Exposures were completed in randomized orders with each separated by a ≥ 4-week washout period. In the event of substantial spikes in ambient air pollution, exposures were postponed by at least 4 weeks. DE was generated by an EPA Tier 3-compliant, 6.0 kW Coliseum GY6000 generator, with a 406 cc Yanmar L 100 EE 4-stroke diesel engine with a constant 2.5 kW load, which upon failure in February 2021, was replaced by a 4.5 kW 1B30E Hatz EPA/Euro-Stage Tier 5-compliant engine (for all exposures for participants 14–17) to reflect contemporary technology. Exposure details for the diesel engines are presented in Additional file 1: Table S1. During the exposures, participants exercised intermittently on a stationary bike for 15 min/h at a power-to-weight ratio estimated to achieve a ventilation rate of 15 L/min/m2.

Questionnaires

Symptoms typically associated with air pollution exposure in the literature [17, 24, 31] were evaluated by the participants using a visual analog scale (VAS) [32, 33] questionnaire (Additional File 1: Figure S1) pre-exposure, 4 h and 24 h post-exposure. To assess exposure perception, participants responded to a VAS questionnaire about the environment in the exposure booth and were asked if they thought their exposure was to FA or DE.

Spirometry

Lung function was measured by spirometry before, and at 4 h and 24 h from the start of the exposure according to American Thoracic Society/European Respiratory Society guidelines [34]. Airway responsiveness was measured by the methacholine response before and at 24 h from the start of the exposure using the 2-min tidal breathing technique [35]. Novo-Salbutamol HFA (TEVA; ON, CA) was administered following the baseline methacholine challenge to restore lung function. Methacholine provocation concentration to cause a 20% drop in FEV1 (PC20) was estimated using the appropriate equations [36, 37].

FeNO

FeNO was measured using a NIOX VERO® machine (NIOX, ON, CA) machine before and at 4 h and 24 h from the start of exposures according to American Thoracic Society/European Respiratory Society guidelines [38].

Statistical analyses

Measurements of VAS questionnaires were completed by at least 2 technicians independently, entered into REDCAP 10.4.0 (© 2021 Vanderbilt University) and checked for consistency and accuracy using in-built REDCAP tools. Baseline values for all outcomes were subtracted from values at subsequent timepoints to obtain delta values. To limit the penalty for multiple comparisons, symptoms were analyzed at the category level (summarized in Table 1) similar to Carlsten et al. [17].

Table 1 Symptom questions and categories
Full size table

Linear mixed effects models, with a participant-specific intercept to adjust for repeated measures, were used to assess the effects of DE exposure on symptoms (package nlme_3.1-157). To estimate the C–R, a model (1) of symptom category delta values and PM2.5 was fitted, while in model (2) symptom category delta values and controlled exposure condition groups were fitted to identify potential effects thresholds. To evaluate the effect of perception on symptoms from DE exposure, a model (3) was fit with participant’s perception of environment/exposure as a modifier of the relationship between symptoms and PM2.5.

  • \({Y}_{i,j}={\beta }_{1}\left({PM}_{2.5}\right)+{\beta }_{0}+{\mu }_{i}+{\varepsilon }_{i,j}\)

  • \({Y}_{i,j}={\beta }_{1}\left(exposure\, condition\, group\right)+{\beta }_{0}+{\mu }_{i}+{\varepsilon }_{i,j}\)

  • \({Y}_{i,j}={\beta }_{1}\left({PM}_{2.5}\right)* perception+{\beta }_{0}+{\mu }_{i}+{\varepsilon }_{i,j}\)

where i = ith individual, j = jth repeated measurement, β1 = slope, β0 = overall intercept, µ = participant intercept, ε = error term.

Model assumptions were checked and where appropriate, data were log-transformed. Correlations between outcomes were calculated using repeated measures correlations (rmcorr package V.0.4.5). All statistical analysis was performed using R version 4.2.0. P ≤ 0.05 were considered statistically significant, while P values 0.051–0.1 were considered to be “trending towards significance”.

Results

Study population

Of the 20 participants enrolled in the study, 15 completed all exposures and were included in the analysis. Additionally, 2 participants who did not complete one of four exposures (one in each of the DE50 and DE150 categories) were included in the analysis to give a total of 17 participants (Table 2).

Table 2 A summary of participant demographics
Full size table

The remaining 3 participants withdrew from the study due to scheduling constraints. Details are summarized in flow diagram in Additional file 1: Figure S2.

Exposure characteristics

Exposure data are presented by nominal PM2.5 exposure group and diesel engine in Additional file 1: Table S1. While there were differences between the engines across some measures (most notably more ultrafine particles and hence total particles with the newer Tier 5 engine, at similar levels of PM2.5, as expected given the older engine was subject to combustion inefficiency over years of operation), engine type did not significantly modify symptom responses (Additional file 1: Table S2).

DE induced a concentration-dependent increase in total, eye, and constitutional symptoms

At 4 h post-exposure, DE induced a concentration-dependent increase in total symptoms (β ± standard error = 0.05 ± 0.03, P = 0.04), driven by the constitutional (0.01 ± 0.01, P = 0.03) and eye (0.02 ± 0.01, P = 0.05) symptom categories (Fig. 1). Of the underlying questions, participants primarily reported itching and stinging in the eyes (P = 0.03) and itchiness or dryness of the skin (P = 0.06).

Fig. 1
figure 1figure 1

Diesel exhaust (DE) concentration–response for symptom categories. Symptoms were recorded before, and at 4 and 24 h after the start of exposures to filtered air and diesel exhaust (DE) standardized to 20, 50 and 150 µg/m3 PM2.5. X axes show change in symptom scores from baseline, while Y axes show PM2.5 concentrations (µg/m3). Shaded grey regions represent 95% confidence intervals, and the horizontal dashed lines represent 0 (no change from baseline). Linear mixed effects models were fitted with participant ID as a random effect: *P ≤ 0.05

Full size image

Redoing this analysis with only the 15 participants that completed all four exposures did little to change the output, but eye category symptoms were no longer significantly changed (P > 0.1) and hence total symptoms moved to borderline significance (P = 0.05–0.1). However, this analysis is adversely affected by outliers in the smaller dataset.

Compared to FA, DE at 150 µg/m3 induced an increase in total (8.45 ± 3.92, P = 0.04) and eye (3.18 ± 1.55, P = 0.05) symptoms, and a trend towards significance in the constitutional (1.49 ± 0.86, P = 0.09) and nasal (1.71 ± 0.96, P = 0.08) symptoms (Fig. 2). These effects were all absent at 24 h.

Fig. 2
figure 2

Effects of diesel exhaust (DE) on symptom categories by exposure group. Symptoms were recorded before, and at 4 and 24 h after the start of exposures to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5. X axes show represent change in symptom scores from baseline; Y axes show nominal exposure conditions. Horizontal dashed lines represent 0 (no change from baseline). Linear mixed effects models were fitted with participant ID as a random effect: **P ≤ 0.05, *P = 0.051–0.1

Full size image

The symptom concentration–response was modified by environmental perception

Increasing perception of noise (− 0.07 ± 0.03, P = 0.01) and temperature (− 0.06 ± 0.03, P = 0.02) attenuated the concentration-dependent increase in total symptoms, driven by effects in both the eye (noise effect = − 0.02 ± 0.01, P = 0.02; temperature effect = − 0.03 ± 0.01, P = 0.01) and constitutional categories (noise effect = − 0.01 ± 0.01, P = 0.03) (Table 3). Anxiety enhanced the concentration-dependent increase in constitutional symptoms (0.01 ± 0.00, P = 0.03).

Table 3 Concentration–response effect modification by environmental perception
Full size table

However, symptom responses were not modified by participants perception of whether they were exposed to DE or FA (Fig. 3). Participant sex did not modify symptoms (data not shown).

Fig. 3
figure 3

Effect of perception on the concentration–response between diesel exhaust (DE) and symptoms. Symptoms data was recorded before, and at 4 and 24 h after the start of exposures to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5. Y axes show change in symptom scores from baseline; X axes show PM2.5 concentrations (µg/m3). Shaded grey regions represent 95% confidence intervals, and the horizontal dashed lines represent 0 (no change from baseline). Linear mixed effects models were fitted with perceived exposure condition as an interaction term and participant ID as a random effect

Full size image

The symptom concentration–response was correlated with airway inflammation

The concentration–response for total symptoms was moderately positively correlated with ΔFeNO at 4 h (r = 0.29 ± 0.13, P = 0.04) and 24 h (r = 0.39 ± 0.12, P < 0.00) driven by effects in the constitutional and eye categories (details summarized in Table 4). Symptoms were not correlated with methacholine PC20 and FEV1.

Table 4 Repeated measures correlations between change in symptoms and airway function measures
Full size table

Discussion

Exposure to air pollution is associated with adverse health effects, whose impact on wellbeing and quality of life can be assessed using symptoms [5,6,7]. Current knowledge on the effects of TRAP on symptoms could be improved by better understanding of their C–R relationship. In this study, we investigated and identified concentration-dependent increases in symptoms that were modified by environmental perception.

We observed a significant concentration-dependent increase in total symptoms driven primary by eye and constitutional symptoms. PM and gases routinely interact with exposed surfaces of the body such as the skin [39, 40] and eyes [41] where they may be inflammatory. In the eyes, these pollutants can cause dryness and irritation [41, 42] through oxidative stress [43], mucin disruption, and loss of microvilli, corneal and goblet cells [14, 44, 45]. In the airways, PM2.5 can enter the alveoli where it induces inflammation and oxidative stress that may result in systemic immune mobilization [15, 46]. This “spill over”, in addition to the penetration of PM0.1 into the blood stream, may cause adverse neurological, constitutional, and systemic effects. PM2.5, the primary surrogate for DE concentration in our analyses, was correlated with other pollutants in the DE mixture. Thus, gases and TVOCs may have a role in the symptom response attributed to PM2.5 here, but readers should not infer cause due to any particular aerosol component as this model exposure is a paradigm of traffic-related air pollution with PM2.5 simply used as a metric for reasonably standardizing conditions upon a common parameter. The correlation between symptoms and airway inflammation in our study lends credence to inflammation as a potential physiological pathway through which air pollutants cause symptoms. However, the symptom response was unaccompanied by changes in lung function, similar to other acute exposure studies assessing similar endpoints [20, 47]. This absence of changes in lung function after acute air pollution exposure is likely due to resilience to acute low-concentration DE exposures in healthy populations and has been corroborated by other controlled exposure studies [28, 48, 49].

Our findings are consistent with reports of DE-induced eye and constitutional symptoms in other controlled exposure studies [21,22,23,24, 50]. Notably, we did not observe any of the neurological or airway symptoms reported by these studies and others [47]. In contrast, other studies, which included rhinitis [51] and metabolic syndrome patients [17], did not report effects on symptoms. Interestingly, Carlsten et al. (2013) also reported a prominent role of perceived exposure condition (DE vs FA) in symptoms after DE exposure, albeit not as an effect modifier [17].

The symptom response that we observed was modified by perceived environmental temperature, noise, and anxiety. Higher temperatures and noise levels attenuated the increase in total, eye and constitutional symptoms, while anxiety enhanced constitutional symptoms. Known relationships between air pollution exposure and temperature [52,53,54,55,56], noise [57, 58] and anxiety [59,60,61] in epidemiological literature are mixed and vary by endpoints. For example, air pollution acts synergistically with and directly on temperature and anxiety respectively in some studies [52, 55, 60], but not others [59, 61]. Notably, these studies investigated ambient (not perceived) temperature and noise, and only explored anxiety as a direct effect of air pollution. In controlled human exposure studies, where ambient temperature and noise are relatively constant, the relationship between air pollution and environmental perception is relatively unexplored. Some studies have investigated the direct effects of air pollution exposure and noise, reporting no significant effect on anxiety symptoms [22] and deleterious effects on other endpoints [62, 63]. Since ambient temperature and noise were consistent throughout our study, the interactions we observed could reflect a delineation between the perceived and true (measured) environment. This “mismatch” could be explained individual or localized psychological and physiological factors like sensitivity, discomfort and annoyance, which independently influence environmental perception and subjective symptoms [64,65,66,67]. While the underlying psychological triggers in our experimental setting are unclear, it is possible that a primary feeling, such as discomfort, influenced perception. For example, discomfort associated with anxiety may be responsible for anxiety-related enhancement of symptoms. Similarly, the discomfort due to perceived coldness, which is associated with eye irritation [68], may explain the attenuation of symptoms with increasing perceived temperature. The attenuation of symptoms by increased perceived noise is surprising, considering that others have reported symptom enhancement [64, 65, 69]. Interestingly, overall perceived exposure condition (DE or FA) did not modify symptoms, which indicates effective experimental blinding of symptom responses, but this analysis may be limited by unbalanced comparison groups. Different participant demographics, in addition to methodological differences, such as statistical approaches, endpoints, exposure levels and durations, limit direct comparisons between our work and others. The unknown psychological triggers and relatively small interaction effect sizes, highlight the need for corroboration and further exploration in future studies. Our study is the first DE exposure study to use multiple exposure concentrations to examine the linear C-R relationship between TRAP and symptoms and examine interactions with environmental perception.

The significant increase in symptoms at 150 µg/m3 PM2.5, compared to FA, is consistent with an effect threshold below 150 µg/m3, similar to a threshold below 140 µg/m3 of total suspended particles suggested by Mølhave et al.[70]. Moreover, Vilcassim et al. reported an increase in symptoms when participants travelled from low (< 35 µg/m3 PM2.5) to high (> 100 µg/m3 PM2.5) pollution cities [18], estimating a 40 µg/m3 threshold. The resolution of symptoms at 24 h after acute exposure in our study is consistent with recovery after the cessation of air pollution exposure observed by Vilcassim et al. and Mølhave et al. [18, 70]. The transient nature of symptoms, relatively small effect sizes and absence of a strong relationship with lung function may indicate that these effects are often subclinical in this healthy population. Nevertheless, our findings are important to biological plausibility and may provide useful estimates and potential thresholds for assessing the health impacts of air pollution in a healthy population.

Although the exposure duration in this study is the longest for a controlled DE study to date, it does not fully replicate complex typically day-to-week long real-world exposures [28]. Secondly, our study recruited a relatively small sample of healthy non-smokers and may not be sufficiently generalizable to other populations [71]. Thus, future studies may delve further into susceptibility factors that modify the C-R relationship, as well as physiological mechanisms associated with reported symptoms. Lastly, we report relatively novel findings of perceived environmental modifiers of the air pollution exposure symptom response that warrant replication in future studies.

In this controlled DE exposure study, we detailed a concentration–response relationship between particulate matter and self-reported symptoms, and identified perceived temperature, noise, and anxiety as potential modifiers of this relationship. Our research not only highlights the utility of visual analog scale questionnaires as non-invasive tools for assessing the health effects of air pollution, but also provides effect estimates and modifiers over a range of epidemiologically relevant PM2.5 levels. This may be crucial in adopting self-reported questionnaires as non-invasive tools for health monitoring and developing public health guidelines for air pollution.

Availability of data and materials

The datasets supporting the conclusions of this article are available from the corresponding author upon reasonable request.

References

  1. Leikauf GD, Kim S-H, Jang A-S. Mechanisms of ultrafine particle-induced respiratory health effects. Exp Mol Med. 2020. https://doi.org/10.1038/s12276-020-0394-0.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Muñoz X, Barreiro E, Bustamante V, Lopez-Campos JL, González-Barcala FJ, Cruz MJ. Diesel exhausts particles: their role in increasing the incidence of asthma. Reviewing the evidence of a causal link. Sci Total Environ Elsevier. 2019;652:1129–38.

    Article  Google Scholar 

  3. Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet. 2017;389:1907–18.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Orru K, Orru H, Maasikmets M, Hendrikson R, Ainsaar M. Well-being and environmental quality: Does pollution affect life satisfaction? Qual Life Res. 2016;25:699–705.

    Article  PubMed  Google Scholar 

  5. Hegseth MN, Oftedal BM, Höper AC, Aminoff AL, Thomassen MR, Svendsen MV, et al. Self-reported traffic-related air pollution and respiratory symptoms among adults in an area with modest levels of traffic. PLoS One. 2019. https://doi.org/10.1371/journal.pone.0226221.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Williams AM, Phaneuf DJ, Barrett MA, Su JG. Short-term impact of PM 25 on contemporaneous asthma medication use: behavior and the value of pollution reductions. Proc Natl Acad Sci USA. 2019;116:5246–53.

    Article  CAS  PubMed  Google Scholar 

  7. Chambers L, Finch J, Edwards K, Jeanjean A, Leigh R, Gonem S. Effects of personal air pollution exposure on asthma symptoms, lung function and airway inflammation. Clin Exp Allergy. 2018;48:798–805. https://doi.org/10.1111/cea.13130.

    Article  CAS  PubMed  Google Scholar 

  8. Pirozzi CS, Mendoza DL, Xu Y, Zhang Y, Scholand MB, Baughman RP. Short-term particulate air pollution exposure is associated with increased severity of respiratory and quality of life symptoms in patients with fibrotic sarcoidosis. Int J Environ Res Public Health. 2018;15:1077.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Yu O, Sheppard L, Lumley T, Koenig JQ, Shapiro GG. Effects of ambient air pollution on symptoms of asthma in seattle-area children enrolled in the CAMP study. Environ Health Perspect JSTOR. 2000;108:1209.

    Article  CAS  Google Scholar 

  10. Wu S, Ni Y, Li H, Pan L, Yang D, Baccarelli AA, et al. Short-term exposure to high ambient air pollution increases airway inflammation and respiratory symptoms in chronic obstructive pulmonary disease patients in Beijing, China. Environ Int. 2016. https://doi.org/10.1016/j.envint.2016.05.004.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bjermer L, Alving K, Diamant Z, Magnussen H, Pavord I, Piacentini G, et al. Current evidence and future research needs for FeNO measurement in respiratory diseases. Respir Med. 2014;2014:830–41.

    Article  Google Scholar 

  12. Torén K, Schiöler L, Lindberg A, Andersson A, Behndig AF, Bergström G, et al. The ratio FEV 1 /FVC and its association to respiratory symptoms—a Swedish general population study. Clin Physiol Funct Imaging. 2021;41:181–91. https://doi.org/10.1111/cpf.12684.

    Article  PubMed  Google Scholar 

  13. Betchley C, Koenig JQ, Van Belle G, Checkoway H, Reinhardt T. Pulmonary function and respiratory symptoms in forest firefighters. Am J Ind Med. 1997;31:503–9.

    Article  CAS  PubMed  Google Scholar 

  14. Tan G, Li J, Yang Q, Wu A, Qu D-Y, Wang Y, et al. Air pollutant particulate matter 2.5 induces dry eye syndrome in mice. Sci Rep. 2018;8:17828.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Huff RD, Carlsten C, Hirota JA. An update on immunologic mechanisms in the respiratory mucosa in response to air pollutants. J Allergy Clin Immunol. 2019. https://doi.org/10.1016/j.jaci.2019.04.012.

    Article  PubMed  Google Scholar 

  16. Glencross DA, Ho T-R, Camiña N, Hawrylowicz CM, Pfeffer PE. Air pollution and its effects on the immune system. Free Radic Biol Med. 2020. https://doi.org/10.1016/j.freeradbiomed.2020.01.179.

    Article  PubMed  Google Scholar 

  17. Carlsten C, Oron AP, Curtiss H, Jarvis S, Daniell W, Kaufman JD. Symptoms in response to controlled diesel exhaust more closely reflect exposure perception than true exposure. PLoS One. 2013;2013:8.

    Google Scholar 

  18. Vilcassim MJR, Thurston GD, Chen LC, Lim CC, Saunders E, Yao Y, et al. Exposure to air pollution is associated with adverse cardiopulmonary health effects in international travellers. J Travel Med. 2019. https://doi.org/10.1093/jtm/taz032/5486057.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Barraza-Villarreal A, Sunyer J, Hernandez-Cadena L, Escamilla-Nunñez MC, Sienra-Monge JJ, Ramírez-Aguilar M, et al. Air pollution, airway inflammation, and lung function in a cohort study of Mexico City Schoolchildren. Environ Health Perspect. 2008;116:832–8.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gren L, Dierschke K, Mattsson F, Assarsson E, Krais AM, Kåredal M, et al. Lung function and self-rated symptoms in healthy volunteers after exposure to hydrotreated vegetable oil (HVO) exhaust with and without particles. Part Fibre Toxicol BioMed. 2022;19:9. https://doi.org/10.1186/s12989-021-00446-7.

    Article  CAS  Google Scholar 

  21. Mudway IS, Stenfors N, Duggan ST, Roxborough H, Zielinski H, Marklund SL, et al. An in vitro and in vivo investigation of the effects of diesel exhaust on human airway lining fluid antioxidants. Arch Biochem Biophys. 2004;423:200–12.

    Article  CAS  PubMed  Google Scholar 

  22. Laumbach RJ, Kipen HM, Kelly-Mcneil K, Zhang J, Zhang L, Lioy PJ, et al. Sickness response symptoms among healthy volunteers after controlled exposures to diesel exhaust and psychological stress. Environ Health Perspect. 2011;119:945–50.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Rudell B, Ledin MC, Hammarström U, Stjernberg N, Lundbäck B, Sandström T. Effects on symptoms and lung function in humans experimentally exposed to diesel exhaust. Occup Environ Med. 1996;53:658–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Muala A, Sehlstedt M, Bion A, Österlund C, Bosson JA, Behndig AF, et al. Assessment of the capacity of vehicle cabin air inlet filters to reduce diesel exhaust-induced symptoms in human volunteers. Environ Heal A Glob Access Sci Sour. 2014;13:1.

    Google Scholar 

  25. Orach J, Rider CF, Carlsten C. Concentration-dependent health effects of air pollution in controlled human exposures. Environ Int. 2021;150:106424. https://doi.org/10.1016/j.envint.2021.106424.

    Article  CAS  PubMed  Google Scholar 

  26. Orach J, Rider CF, Yuen A, Carlsten C. Traffic-related air pollution induces a concentration-dependent increase in symptoms in a controlled human exposure study: B105 more calls to action. AIR Pollut Expo Heal. 2022. https://doi.org/10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A3576.

    Article  Google Scholar 

  27. Birger N, Gould T, Stewart J, Miller MR, Larson T, Carlsten C. The Air Pollution Exposure Laboratory (APEL) for controlled human exposure to diesel exhaust and other inhalants: characterization and comparison to existing facilities. Inhal Toxicol. 2011;23:219–25.

    Article  CAS  PubMed  Google Scholar 

  28. Long E, Carlsten C. Controlled human exposure to diesel exhaust: results illuminate health effects of traffic-related air pollution and inform future directions. Part Fibre Toxicol. 2022;19:1–35. https://doi.org/10.1186/s12989-022-00450-5.

    Article  Google Scholar 

  29. Buzzard NA, Clark NN, Guffey SE. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ Heal A Glob Access Sci Sou. 2009;2009:8.

    Google Scholar 

  30. Pronk A, Coble J, Stewart PA. Occupational exposure to diesel engine exhaust: a literature review. J Expo Sci Environ Epidemiol. 2009;2009:443–57.

    Article  Google Scholar 

  31. Gong H, Linn WS, Sioutas C, Terrell SL, Clark KW, Anderson KR, et al. Controlled exposures of healthy and asthmatic volunteers to concentrated ambient fine particles in Los Angeles. Inhal Toxicol. 2003;15:305–25.

    Article  CAS  PubMed  Google Scholar 

  32. Aitken RCB. A growing edge of measurement of feelings [abridged]: measurement of feelings using visual analogue scales. Proc R Soc Med. 1969;62:989–93. https://doi.org/10.1177/003591576906201006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bausewein C, Farquhar M, Booth S, Gysels M, Higginson IJ. Measurement of breathlessness in advanced disease: a systematic review. Respir Med. 2007;101:399–410.

    Article  CAS  PubMed  Google Scholar 

  34. Graham BL, Steenbruggen I, Barjaktarevic IZ, Cooper BG, Hall GL, Hallstrand TS, et al. Standardization of spirometry 2019 update an official American Thoracic Society and European Respiratory Society technical statement. Am J Respir Crit Care Med. 2019;200:E70-88.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Coates AL, Wanger J, Cockcroft DW, Culver BH, Carlsen KH, Diamant Z, et al. ERS technical standard on bronchial challenge testing: General considerations and performance of methacholine challenge tests. Eur Respir J. 2017. https://doi.org/10.1183/13993003.01526-2016%5D.

    Article  PubMed  Google Scholar 

  36. Sterk PJ, Fabbri LM, Quanjer PH, Cockcroft DW, O’Byrne PM, Anderson SD, et al. Airway responsiveness. Standardized challenge testing with pharmacological, physical and sensitizing stimuli in adults. Eur Respir J. 1993;1993:53–83.

    Article  Google Scholar 

  37. Jokic R, Davis EE, Cockcroft DW. Methacholine PC20 extrapolation. Chest. 1998;114:1796–7.

    Article  CAS  PubMed  Google Scholar 

  38. Dweik RA, Boggs PB, Erzurum SC, Irvin CG, Leigh MW, Lundberg JO, et al. An official ATS clinical practice guideline: Interpretation of exhaled nitric oxide levels (FENO) for clinical applications. Am J Respir Crit Care Med. 2011;2011:602–15. https://doi.org/10.1164/rccm.9120-11ST.

    Article  Google Scholar 

  39. Dijkhoff IM, Drasler B, Karakocak BB, Petri-Fink A, Valacchi G, Eeman M, et al. Impact of airborne particulate matter on skin: a systematic review from epidemiology to in vitro studies. Part Fibre Toxicol. 2020;17:35. https://doi.org/10.1186/s12989-020-00366-y.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Drakaki E, Dessinioti C, Antoniou CV. Air pollution and the skin. Front Environ Sci. 2014;2:156–7. https://doi.org/10.1007/s00105-018-4349-5.

    Article  Google Scholar 

  41. Torricelli AAM, Novaes P, Matsuda M, Braga A, Saldiva PHN, Alves MR, et al. Correlation between signs and symptoms of ocular surface dysfunction and tear osmolarity with ambient levels of air pollution in a large metropolitan area. Cornea. 2013;32:e11-5.

    Article  PubMed  Google Scholar 

  42. Mo Z, Fu Q, Lyu D, Zhang L, Qin Z, Tang Q, et al. Impacts of air pollution on dry eye disease among residents in Hangzhou, China: a case-crossover study. Environ Pollut. 2019;246:183–9.

    Article  CAS  PubMed  Google Scholar 

  43. Yoon S, Han S, Jeon K-J, Kwon S. Effects of collected road dusts on cell viability, inflammatory response, and oxidative stress in cultured human corneal epithelial cells. Toxicol Lett. 2018;284:152–60.

    Article  CAS  PubMed  Google Scholar 

  44. Song SJ, Hyun S-W, Lee TG, Park B, Jo K, Kim C-S. New application for assessment of dry eye syndrome induced by particulate matter exposure. Ecotoxicol Environ Saf. 2020. https://doi.org/10.1016/j.ecoenv.2020.111125.

    Article  PubMed  Google Scholar 

  45. Mu N, Wang H, Chen D, Wang F, Ji L, Zhang C, et al. A novel rat model of dry eye induced by aerosol exposure of particulate matter. Investig Opthalmol Vis Sci. 2022;63:39.

    Article  CAS  Google Scholar 

  46. Kwon H-S, Ryu MH, Carlsten C. Ultrafine particles: unique physicochemical properties relevant to health and disease. Exp Mol Med. 2020;2020:1–11.

    Google Scholar 

  47. Xu Y, Barregard L, Nielsen J, Gudmundsson A, Wierzbicka A, Axmon A, et al. Effects of diesel exposure on lung function and inflammation biomarkers from airway and peripheral blood of healthy volunteers in a chamber study. Part Fibre Toxicol. 2013;10:60.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Landwehr KR, Larcombe AN, Reid A, Mullins BJ. Critical review of diesel exhaust exposure health impact research relevant to occupational settings: are we controlling the wrong pollutants? Expo Heal. 2021;2021:141–71. https://doi.org/10.1007/s12403-020-00379-0.

    Article  CAS  Google Scholar 

  49. Ghio A, Sobus J, Pleil J, Madden M. Controlled human exposures to diesel exhaust. Swiss Med Wkly. 2012;2012:142.

    Google Scholar 

  50. Rudell B, Wass U, Hörstedt P, Levin JO, Lindahl R, Rannug U, et al. Efficiency of automotive cabin air filters to reduce acute health effects of diesel exhaust in human subjects. Occup Environ Med. 1999;56:222–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Larsson N, Brown J, Stenfors N, Wilson S, Mudway IS, Pourazar J, et al. Airway inflammatory responses to diesel exhaust in allergic rhinitics. Inhal Toxicol. 2013;25:160–7.

    Article  CAS  PubMed  Google Scholar 

  52. Wu S, Deng F, Hao Y, Wang X, Zheng C, Lv H, et al. Fine particulate matter, temperature, and lung function in healthy adults: findings from the HVNR study. Chemosphere. 2014. https://doi.org/10.1016/j.chemosphere.2014.01.032.

    Article  PubMed  Google Scholar 

  53. Nassan FL, Kelly RS, Kosheleva A, Koutrakis P, Vokonas PS, Lasky-Su JA, et al. Metabolomic signatures of the long-term exposure to air pollution and temperature. Environ Heal. 2021;20:3. https://doi.org/10.1186/s12940-020-00683-x.

    Article  CAS  Google Scholar 

  54. Zanobetti A, Luttmann-Gibson H, Horton ES, Cohen A, Coull BA, Hoffmann B, et al. Brachial artery responses to ambient pollution, temperature, and humidity in people with type 2 diabetes: a repeated-measures study. Environ Health Perspect. 2014. https://doi.org/10.1289/ehp.1206136.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Li H, Bai H, Yang C, Chen R, Wang C, Zhao Z, et al. Acute effects of ambient temperature and particulate air pollution on fractional exhaled nitric oxide: a panel study among diabetic patients in Shanghai, China. J Epidemiol Jpn Epidemiol Assoc. 2017;27:584–9.

    Article  Google Scholar 

  56. Anenberg SC, Haines S, Wang E, Nassikas N, Kinney PL. Synergistic health effects of air pollution, temperature, and pollen exposure: a systematic review of epidemiological evidence. Environ Heal. 2020;19:130. https://doi.org/10.1186/s12940-020-00681-z.

    Article  Google Scholar 

  57. Huang J, Deng F, Wu S, Lu H, Hao Y, Guo X. The impacts of short-term exposure to noise and traffic-related air pollution on heart rate variability in young healthy adults. J Expo Sci Environ Epidemiol. 2013;23:559–64.

    Article  CAS  PubMed  Google Scholar 

  58. Biel R, Danieli C, Shekarrizfard M, Minet L, Abrahamowicz M, Baumgartner J, et al. Acute cardiovascular health effects in a panel study of personal exposure to traffic-related air pollutants and noise in Toronto, Canada. Sci RepoRtS. 2020. https://doi.org/10.1038/s41598-020-73412-6.

    Article  Google Scholar 

  59. Petrowski K, Bührer S, Strauß B, Decker O, Brähler E. Examining air pollution (PM10), mental health and well-being in a representative German sample. Sci Rep. 2021;11:18436.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Power MC, Kioumourtzoglou M-A, Hart JE, Okereke OI, Laden F, Weisskopf MG. The relation between past exposure to fine particulate air pollution and prevalent anxiety: observational cohort study. BMJ. 2015;350:h1111.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Vert C, Sánchez-Benavides G, Martínez D, Gotsens X, Gramunt N, Cirach M, et al. Effect of long-term exposure to air pollution on anxiety and depression in adults: a cross-sectional study. Int J Hyg Environ Health. 2017. https://doi.org/10.1016/j.ijheh.2017.06.009.

    Article  PubMed  Google Scholar 

  62. Stockfelt L, Xu Y, Gudmundsson A, Rissler J, Isaxon C, Brunskog J, et al. A controlled chamber study of effects of exposure to diesel exhaust particles and noise on heart rate variability and endothelial function. 2022.

  63. Hemmingsen JG, Møller P, Jantzen K, Jönsson BAG, Albin M, Wierzbicka A, et al. Controlled exposure to diesel exhaust and traffic noise: effects on oxidative stress and activation in mononuclear blood cells. Mutat Res Fundam Mol Mech Mutagen. 2015;775:66–71.

    Article  CAS  Google Scholar 

  64. Chiarini B, D’Agostino A, Marzano E, Regoli A. The perception of air pollution and noise in urban environments: a subjective indicator across European countries. J Environ Manage. 2020;263:110272. https://doi.org/10.1016/j.jenvman.2020.110272.

    Article  PubMed  Google Scholar 

  65. Martens AL, Reedijk M, Smid T, Huss A, Timmermans D, Strak M, et al. Modeled and perceived RF-EMF, noise and air pollution and symptoms in a population cohort—is perception key in predicting symptoms? Sci Total Environ. 2018;639:75–83. https://doi.org/10.1016/j.scitotenv.2018.05.007.

    Article  CAS  PubMed  Google Scholar 

  66. Peng M, Zhang H, Evans RD, Zhong X, Yang K. Actual air pollution, environmental transparency, and the perception of air pollution in China. J Environ Dev. 2019;28:78–105. https://doi.org/10.1177/1070496518821713.

    Article  Google Scholar 

  67. Pantavou K, Lykoudis S, Psiloglou B. Air quality perception of pedestrians in an urban outdoor Mediterranean environment: a field survey approach. Sci Total Environ. 2017;574:663–70. https://doi.org/10.1016/j.scitotenv.2016.09.090.

    Article  CAS  PubMed  Google Scholar 

  68. Azuma K, Ikeda K, Kagi N, Yanagi U, Osawa H. Prevalence and risk factors associated with nonspecific building-related symptoms in office employees in Japan: relationships between work environment, Indoor Air Quality, and occupational stress. Indoor Air. 2015;25:499–511.

    Article  CAS  PubMed  Google Scholar 

  69. Lercher P, Schmitzberger R, Kofler W. Perceived traffic air pollution, associated behavior and health in an alpine area. Sci Total Environ. 1995;169:71–4.

    Article  CAS  PubMed  Google Scholar 

  70. Mølhave L, Kjærgaard SK, Attermann J. Sensory and other neurogenic effects of exposures to airborne office dust. Atmos Environ. 2000;34:4755–66.

    Article  Google Scholar 

  71. Helena Andersen MG, Loft S, Bønløkke JH, Saber AT, Vogel U, Møller P. Ambient combustion ultrafine particles and health: controlled human exposure studies. 2021. https://www.novapublishers.com/wp-content/uploads/2021/02/Controlled-Human-Exposure-Studies.pdf

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Acknowledgements

We are grateful to the research volunteers that participated in this research study. We appreciate the support of all the staff at the Air Pollution Exposure Laboratory and the Lung Center at the Vancouver General Hospital. We thank Dr. Neeloffer Mookherjee, Ms. Carley Schwartz, Mr. Parteek Johal, Mr. Goldman Lam, Mr. Kevin Lau, Ms. Bora, Ms. Ayman Azhar and Ms. Shuyu Fan for their contributions to the DICE project. Finally, we thank the Vancouver Coastal Health Research Institute and University of British Columbia for institutional support.

Funding

This research study was funded by Occupational Health and Safety Alberta (095221549) and Manitoba Worker’s Compensation Board (RWIP17-03). JO was supported by a WorkSafe BC Training Award (R3019-TG05). CFR was supported by Vancouver Coastal Health Research Institute—Centre for Respiratory, Cardiac and Critical Care Medicine, BC Lung Association/MITACS Accelerate, and Michael Smith Foundation for Health Research Fellowships. CC was supported by the Canada Research Chairs Program.

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Authors and Affiliations

  1. Air Pollution Exposure Laboratory, Division of Respiratory Medicine, Department of Medicine, Vancouver Coastal Health Research Institute, The University of British Columbia, Vancouver, BC, Canada

    Juma Orach, Christopher Francis Rider, Agnes Che Yan Yuen & Christopher Carlsten

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  1. Juma Orach
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  2. Christopher Francis Rider
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  3. Agnes Che Yan Yuen
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  4. Christopher Carlsten
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Contributions

JO: conceptualization, formal analysis, investigation, data curation, writing—original draft, writing—review and editing, visualization. CFR: conceptualization, methodology, formal analysis, writing—review and editing. ACYY: methodology, investigation, writing—review and editing, project administration. CC: conceptualization, methodology, writing—review and editing, supervision, funding acquisition. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Christopher Carlsten.

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This study was approved by the University of British Columbia Research Ethics Board (H16-03053). Informed consent was obtained before volunteer participation.

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The authors declare that they do not have any competing interests.

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Orach, J., Rider, C.F., Yuen, A.C.Y. et al. Concentration-dependent increase in symptoms due to diesel exhaust in a controlled human exposure study. Part Fibre Toxicol 19, 66 (2022). https://doi.org/10.1186/s12989-022-00506-6

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  • DOI: https://doi.org/10.1186/s12989-022-00506-6

Keywords

  • Controlled human exposures
  • Symptoms
  • Diesel exhaust
  • Particulate matter
  • Airways

Particle and Fibre Toxicology

ISSN: 1743-8977

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