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. Author manuscript; available in PMC: 2022 May 1.
Published in final edited form as: Environ Res. 2020 Oct 29;196:110406. doi: 10.1016/j.envres.2020.110406

Environmental exposures and sleep outcomes: A review of evidence, potential mechanisms, and implications

Jianghong Liu a,*, Lea Ghastine b, Phoebe Um b, Elizabeth Rovit a, Tina Wu a
PMCID: PMC8081760  NIHMSID: NIHMS1642190  PMID: 33130170

Abstract

Environmental exposures and poor sleep outcomes are known to have consequential effects on human health. This integrative review first seeks to present and synthesize existing literature investigating the relationship between exposure to various environmental factors and sleep health. We then present potential mechanisms of action as well as implications for policy and future research for each environmental exposure. Broadly, although studies are still emerging, empirical evidence has begun to show a positive association between adverse effects of heavy metal, noise pollution, light pollution, second-hand smoke, and air pollution exposures and various sleep problems. Specifically, these negative sleep outcomes range from subjective sleep manifestations, such as general sleep quality, sleep duration, daytime dysfunction, and daytime sleepiness, as well as objective sleep measures, including difficulties with sleep onset and maintenance, sleep stage or circadian rhythm interference, sleep arousal, REM activity, and sleep disordered breathing. However, the association between light exposure and sleep is less clear. Potential toxicological mechanisms are thought to include the direct effect of various environmental toxicants on the nervous, respiratory, and cardiovascular systems, oxidative stress, and inflammation. Nevertheless, future research is required to tease out the exact pathways of action to explain the associations between each environmental factor and sleep, to inform possible therapies to negate the detrimental effects, and to increase efforts in decreasing exposure to these harmful environmental factors to improve health.

Keywords: Environmental exposure, Sleep, Heavy metals, Noise and light pollution, Second-hand smoke, Air pollution

1. Introduction

The negative impacts of exposure to environmental pollutants on human health have long been known. Environmental toxicants can arise from common household products, heavy metals such as lead, pesticides, and airborne pollutants, and exposure has been linked to 23% of all estimated global deaths (WHO, 2020). Additionally, everyday encounters with noise and light pollution can contribute to the effect of environmental factors on health. Specifically, exposure to environmental factors has been associated with decreased respiratory health (Bertoldi et al., 2012; Mamane et al., 2015; Recio et al., 2016), kidney disease (Kim et al., 2015; Mezynska and Brzóska, 2018), cardiovascular system damage (Bertoldi et al., 2012; Mezynska and Brzóska, 2018; Recio et al., 2016), metabolic disorders (Recio et al., 2016; Valentino et al., 2016), and neurodegenerative disorders (Chin-Chan et al., 2015; de la Monte et al., 2018; Gilbertson et al., 2006; Lucchini et al., 2007; Migliore and Coppedè, 2009). Furthermore, due to the neurotoxic effects of environmental toxins, the negative impact of environmental exposure has extended to cognitive function across the lifespan, leading to cognitive dysfunction in adults and children (Liu and Lewis, 2014; Lucchini et al., 2012) as well as increased incidence of childhood mental health issues, including attention-deficit hyperactivity disorder (Boucher et al., 2012; Sagiv et al., 2012), autism spectrum disorder (Landrigan et al., 2012), and learning disabilities (Khan et al., 2012; Rauh and Margolis, 2016; Zhang et al., 2013). For example, even at low levels of lead exposure, children are more likely to experience decreased neurocognitive function and increased behavior problems (Dietrich et al., 1991; Liu et al., 2014).

Recently, emerging research has focused attention onto the effects of environmental exposures on sleep health. Poor sleep is a rising public health issue, seen in a third of US adults alone (CDC, 2018a). The consequences of sleep problems, such as low sleep quality, short sleep duration, and sleep disturbances, are associated with numerous health problems such as cardiovascular disease (Fang et al., 2015; Irish et al., 2015), diabetes (Fang et al., 2015; Sears and Zierold, 2017), mental health disorders (Arimura et al., 2010; Banks, 2007; Strine and Chapman, 2005), cognitive dysfunction (Daulatzai, 2015; Fulda and Schulz, 2001; Gozal et al., 2007), and behavioral disorders (der HEIJDEN et al., 2007; Gregory and Sadeh, 2012; Lam et al., 2003). Although the impact of environmental factors such as light and noise pollution on human health has largely been ignored due to the relative lack of a direct physical effect on human health, the detrimental repercussions of these growing threats to human health can be elucidated as we examine these pollutants through the lens of sleep as the mediating variable.

Given the high prevalence and important health implications of environmental exposures and poor sleep problems, this comprehensive integrative review aims to present and synthesize current literature investigating the associations between various environmental exposures and sleep outcomes across the lifespan. These environmental exposures include heavy metals, noise pollution, light pollution, second-hand smoke, and air pollutants. For each exposure, we first present a literature synthesis of current research along with a summary table of the appropriate studies (Tables 14). We then propose potential mechanisms based on both basic science and epidemiological research from published literature. For a clear view, we highlight each exposure and its related sleep outcomes and potential mechanisms in corresponding figures (Figs. 15). Finally, a conclusion with general implications for practice, policy, and research is presented and summarized in Table 5.

Table 1.

Selected studies as categorized by heavy metals exposure measured and related sleep outcomes.

Exposure Category Population Studied Study Sample N (% Female) Age Study Location Study Design and Period Exposure(s) of Interest Measure (Heavy Metals) Sleep Outcome Measure(s) Major Findings
Antimony US adult population Scinicariello et al. (2017) N = 2654 (51.1%)
Age: ≥20 y (M = 44 y)
United States Retrospective cross-sectional
2005–2008
Urinary concentrations of antimony NHANES survey • Elevated urinary antimony levels were found to be associated with sleep problems such as insufficient sleep, increased sleep onset latency. obstructive sleep apnea, and daytime sleepiness
Multiple heavy metals US adult population Shiue (2017) N = 5563 (51.9%)
Age: 18-85 y (M = 42.5 y)
United States Retrospective cross-sectional
2005–2006
Urinary environmental chemical concentrations in urine NHANES survey • Leg jerks and cramps while sleeping were correlated to urinary arsenic, polyaromantic hydrocarbons, phthalates, and polyfluoroalkyl compounds
• Waking during sleep was associated with urinary arsenic, phthalates, and polyfluoroalkyl compounds
Trace minerals and heavy metals Severe obstructive sleep apnea patients Asker et al. (2015) N = 97 (45.4%)
Age: N/A (M = 47.6 y)
Turkey Case-control
May 2013–May 2014
Serum levels of trace metals (Mg, Fe, Zn, Cu, Mn, Co) & heavy metals (Cd, Pb) PSG recordings, echocardiography, carotid ultrasonography • Compared to control participants, OSA patients were found to have higher serum levels of Cd, Co, Cu, Fe, Mg, Mn, Pb, and Zn
• OSA patients also had increased carotid intima-media thickness, which was correlated with levels of Co, Cu, Fe, Mg, Mn, and Zn
Cobalt Adult male Zeynalov et al. (2018) N = 1 (0%)
Age: 56 y
New Mexico Case repost
2009–2016
Heavy metal screen Home sleep apnea test • The patient experienced cobalt toxicity from a hip replacement surgery that used cobalt
• The cobalt toxicity was found to have created OSA via induction of oxidative stress and chronic inflammation
Lead Industrial workers Valentino et al. (2016) N = 406 (0%)
Age: N/A
Michigan Case-control Blood lead levels, zinc protoporphyrin level Performance tests: Block Design, Digit Symbol, and Embedded Figures • A significant correlation between daytime performance test scores among industrial workers and increased lead levels was found
• Increased lead levels/absorption is associated with subjective sleep symptoms such as tiredness, sleep disturbances, and irritability
Mercury Miners Doering et al. (2016) N = 612 (57.9%)
Age; 7-64 y (M = 27.7 y)
Indonesia, Tanzania, & Zimbabwe Cross-sectional
2003–2004
Biomarkers of mercury in urine, blood, & hair Medical exam, interview, in-person questionnaire • Higher inorganic mercury intoxication scores were associated with increased sleep problems at night
Lead fumes Factory workers Mohammadyan et al. (2019) N = 40 (100%)
Age: N/A (M = 35.4 y)
Neyshabur, Iran Cross-sectional
2017–2018
Air samples & workers blood lead levels Questionnaire (PSQI) • There was a significant relationship between sleep quality, air lead, and blood lead levels
• For workers exposed to lead above the threshold, bad sleep quality was 2.4 times higher
• Poorer sleep outcomes are more prevalent among workers with higher lead exposure
Heavy metal fumes Welding vs. office workers Chuang et al. (2018) N = 150 (8.7%)
Age: 22-68 y (M = 46.2 y)
Taipei, Taiwan Case-control
September–November 2015
Metal fume PM2.5 levels, urinary biomarkers Wearable device (Fitbit) • Increased PM2.5 levels from metal fumes were associated with decreased serotonin levels in the urine and decreased sleep duration of welding workers, but not office workers
• Urinary serotonin levels were associated with urinary levels of Cu, Mn, Co, Ni, Cd, and Pb
Coal ash Children living near coal ash facilities Sears and Zierold (2017) N = 111 (44.1%)
Age: 4-17 y (M = 10.3 y)
Louisville, Kentucky Community-based cross-sectional
2013–2014
Proximity to coal combustion electric plant Parent/guardian survey • Compared to children not living near coal ash, those that did experienced greater difficulty falling asleep, frequent night awakenings, teeth grindings, and leg cramps during sleep
• There were a greater odds of sleep talking, sleep latency, night awakenings, and leg cramps when adjusted for covariates
Mercury Children Gump et al. (2014) N = 100 (43%)
Age: 9-11 y (M = 10.0 y)
Oswego County, NY Cross-sectional Blood mercury & serum cytokines levels (TNF-α, IL-6) Wrist actigraphy • Higher levels of blood mercury in children were associated with decreased sleep duration and lower levels of TNF-α
Lead Children Kordas et al. (2007) N = 602 (46.0%)
Age: 6-8 y (M = 6.9 y)
Torreón, Mexico Cross-sectional
February–December 2001
Venous blood lead levels Parental questionnaire; observed classroom behavior, measured physical activity • Higher blood lead levels were associated with later waking time and decreased sleep quantity
• For every 1 μg/dL difference in blood lead level, the likelihood of sleeping less than 7 h increased by 7%
Lead Pre-adolescents Liu et al. (2015) N = 665 (47.0%)
Age: 3-5 y (4.74 y)
Jinyan City, China Longitudinal cohort
Fall 2004
Blood lead levels Parental questionnaire (CSHQ), child questionnaire (AHQ) • Significant and positive correlations were found between blood lead levels and sleep onset, sleep duration, and nighttime waking
• Compared to children who had BLL < 10.0 μg/dL, those with BLL ≥ 10.0 μg/dL had higher rates of excessive daytime sleepiness and use of sleeping pills

Abbreviations: AHQ: Adolescent Health Questionnaire; BLL: blood lead levels; CSHQ: Children’s Sleep Habits Questionnaire; NHANES: National Health and Nutrition Examination survey; PSG: polysomnography; PSQI: Pittsburgh Sleep Quality Index.

Table 4.

Selected studies categorized by secondhand smoke exposure measured and related sleep outcomes.

Exposure Studied Population Studied Study Sample N (% Female) Age Study Location Study Design and Period Exposure(s) of Interest Measure (SHS) Sleep Outcome Measure(s) Major Findings
Prenatal maternal smoking Pregnant mothers Stéphan-Blanchard et al. (2010) N = 37 mother-neonate pairs
Age: (M = 36.1 y for mother)
Amiens, France Case-control Maternal interview and detailed medical record examination PSG, hyperoxic test during active and quiet sleep • Prenatal smoking exposure in neonates was associated with a decreased peripheral chemoreceptor tonic activity during active sleep, but decreased during quiet sleep
• Neonates exposed to maternal smoking impacts the duration of apneic episodes and increased oxygen desaturation
Prenatal maternal smoking Pregnant mothers Katila et al. (2019) N = 1388 (47.6%)
Age: 3 months and 8 months
Pirkanmaa Hospital District, Finland Prospective cohort
April 2011–February 2013
Background questionnaire completed by parents separately Sleep questionnaire completed by parents together • Maternal smoking was concluded to be significantly positively associated with infant snoring
• Maternal smoking during pregnancy increases the risk of infant snoring
Prenatal and postnatal maternal smoking Adolescents O’Callaghan et a. (2019) N = 7223 mother-infant pairs
Age: prenatal-21 years
Brisbane, Australia Prospective community-based birth cohort study Maternal questionnaires at the first clinic visit Offspring sleep problems were reported by mothers at 6 months, 5 and 14 years, and by the child at 14 and 21 years • Prenatal maternal smoking was associated with an increased risk of adolescent parasomnias (sleep walking and talking; nightmares)
• Prenatal and postnatal maternal smoking was also positively correlated to youth self-reports of sleep problems and snoring
Prenatal maternal smoking Children Calhoun et al. (2010) N = 508 (47.0%)
Age: 5-12 y (M = 8.6 y)
Dauphin County, PA Case-control Comprehensive child development questionnaire filled out by a parent Overnight PSG, physical exam • The presence of SDB in children was significantly associated with maternal smoking during pregnancy and delayed motor milestones
Secondhand smoke Toddlers Groner et al. (2019) N = 138 (55.1%)
Age: 2-5 y (M = 3.22 y)
Columbus, Ohio Cross-sectional SHS exposure determined by hair nicotine level SDB determined by parental report • SHS exposure was associated with an increased odds of sleep-related breathing problems (snoring, gasping, difficulty breathing at night)
• A relationship between child BMI and sleep-related breathing problems was not found
Secondhand smoke Children Subramanyam et al. (2020) N = 167
Age:
United States Cross sectional
January 2015–January 2018
Electronical medical record, questionnaire , Electronic medical record, PSG, obstructive apnea hypopnea index (OAHI) • Children with severe OSA with SHS exposure were more likely to have higher OAHI measures than those not exposure
• SHS exposure may worsen the symptoms of OSA in children
Secondhand smoke Children Weinstock et al. (2014) N = 464 (61.5%)
Age: 5-9.9 y (M = 7.0 y)
Clinical referral setting Cross-sectional
2011
Data obtained from Childhood Adenotonsillectomy Trial Parental report of the child’s snoring, PSG • Observed a strong association between ETS and OSAS severity
Maternal and paternal smoking Children Tamanyan et al. (2016) N = 301 (39.5%)
Age: 3-17 y (M = 6.8 y)
Melbourne. Australia Retrospective cohort Parental questionnaire Overnight PSG, parental questionnaire • Moderate and severe OSAS was associated with parental smoking rates (both mother and father separately and together)
• Displayed an addictive effect on SHS exposure on sleep outcomes if more than 1 parent smokes
Secondhand smoke Children Abou-Khadra (2013) N = 276 (56.0%)
Age: 6-13 y (M = 9.26 y)
Egypt Cross-sectional
December 2010
PM10 air pollution data from the Egyptian Environmental Affairs Agency Parents completed SDSC questionnaire • Air pollution (specifically exposure to PM10) adversely affects children’s sleep
• PM10 was found to be associated with sleep hyperhidrosis and DIMS
Caregiver smoking Children Miadich et al. (2018) N = 54 (31.5%)
Age: 7-12 y (M = 9.52 y)
Richmond, Virginia Longitudinal Caregiver questionnaire Daily EMA and smartphone survey application • Children who are under the supervision of a caregiver who smokes are at an increased risk for decreased sleep quality
• Children living in urban areas with persistent asthma and a smoker caregiver use more daily quick-relief asthma medications
Secondhand smoke Children Montaldo et al. (2012) N = 768 (56.1%)
Age: 8-11 y (M = 9.8 y)
Naples, Italy Randomized parallel control Family member personal interview and questionnaire Dental examinations determined sleep bruxism • Sleep bruxism in children is associated to high and moderate levels of SHS exposure
• Exposure increases the risk of sleep bruxism in children
Secondhand smoke Children Włodarska et al. (2020) N = 160 (52%)
Age: 6-18 y (M = 13 y)
Warsaw, Rumia, and Slupsk, Poland Case-control
December 2012–February 2016
Child and parental questionnaire PSG • Children exposed to higher levels of smoke experienced more apnea-hypopnea events
• Children in the exposed group were more lethargic and had more daytime sleepiness than the control group
Secondhand smoke Adolescents Schwartz et al. (2014) N = 1592 (59.0%)
Age: 13-18 y (M = 14.8 y)
British Columbia, Canada Cohort
October–December 2011
Internet-delivered survey self-report Internet-delivered survey self-report • A positive association was observed between SHS exposure and restless sleep; a negative association was found for SHS exposure and sleep duration
• Adolescents who reported a higher frequency of SHS exposure were more likely to report more frequently restless nights
• As frequency of SHS exposure increased, nighttime sleep quantity during the week and weekend decreased
Secondhand smoke Adolescents Morioka et al. (2018) N = 84988 (51.5%)
Age: junior and seniors in high school
Japan Cross-sectional
October 2014–March 2015
Adolescent questionnaire Adolescent questionnaire • Compared to adolescents who have never smoked and do not have SHS exposure, those that have never smoked but are exposed to SHS had higher rates of sleeping problems such as difficulty initiating sleep, trouble maintaining sleep, early morning awakening, insufficient sleep, and short sleep duration
• Adolescents exposed to SHS inside the home had a higher adjusted odds ratio for insomnia than those with SHS exposure outside the house
Secondhand smoke Young adults Toyama et al. (2020) N = 1781 (45.5%)
Age: 18-19 y (M = N/A)
Okayama University, Japan Cross-sectional Questionnaire Oral examination, questionnaire, PSQI, ICSD-3 • Associations were found between SHS exposure and both poor sleep quality and sleep bruxism
• There were more profound effects in young females compared to young men
Secondhand smoke Young adults Valentino et al. (2016) N = 1023 (73.5%)
Age: 18-24 y (M = 19.26)
Midwestern Untied States Cross-sectional Online survey Online survey • Young adults who self-reported higher levels of smoke exposure were also more likely to report sleep disturbances and lower sleep quality
Secondhand smoke Adults Zandy et al. (2020) N = 10,806 (50.3%0
Age: 18-79 y (M = N/A)
Canada Cross-sectional
2007–2013
Urinary cotinine (biological marker of tobacco smoke exposure) Interview • Elevated levels of urinary cotinine concentrations, a proxy for tobacco exposure, were found to be associated with too much or too little sleep duration, sleep dissatisfaction, trouble falling and staying asleep, and number of sleep disturbances.
• Stronger associations were seen in females

Abbreviations: BMI: body mass index; DIMS: disorders of initiating and maintaining sleep; EMA: ecological momentary assessments; ETS: environmental tobacco smoke; ICSD-3: International Classification of Sleep Disorders; OSAS: obstructive sleep apnea syndrome; PM: particulate matter; PSG: polysomnography; SDB: sleep disordered breathing; SDSC: Sleep Disturbance Scale for Children; SHS: second-hand smoke.

Fig. 1.

Fig. 1.

Conceptual framework of possible mechanisms for the association between heavy metal exposure and poor sleep health.

Fig. 5.

Fig. 5.

Conceptual framework of possible mechanisms for the association between air pollution exposure and poor sleep health.

Table 5.

Environmental exposure on sleep outcomes and their implications

Environmental Exposure Implications
Practical/policy implication Research Implication
Heavy metals • The adverse effects of heavy metals should fuel the conversation to eliminate these harmful chemicals from the market where they can affect human health. • More research is needed to establish the biological mechanism of action.
• Long-term effects through the life course should be monitored.
• Research should study the combined effects of heavy metal exposure, as most are closely dependent.
• More evidence to support the risk of daily occupational exposure to heavy metal fumes.
Air pollution • 91% of the global population currently live in areas where ambient air pollution exposure exceeds guidelines set by the WHO.
• Measures ought to be taken to reduce exposure to both ambient and indoor pollutants across the world.
• Despite the quantity of research examining the association between pollutant exposure and sleep outcomes, mechanistic models are limited.
• Current research employs varying methodologies for measuring exposure and sleep outcomes, and most rely upon subjective measures.
• Use of objective measures for both exposure and outcome, as well as investigation on potential causal models, is needed in future research.
Secondhand smoke • Second-hand smoke exposure is known to have numerous harmful effects on health, even in minimal, short-term exposures.
• Steps ought to be taken to ameliorate the health effects in those inadvertently exposed to second-hand smoke.
• Little is known regarding the mechanisms specifically underlying the relationship between smoke exposure and sleep outcomes.
• Further research is required to understand the possible pathways for this association.
Noise pollution • Communities situated closer to environmental noise are at a higher risk of sleep problems and deprivation (environmental justice issue). • Future work should simultaneously study the effects of noise and light pollution, as noise is often found with light.
• More stratification to differentiate the types of noise pollution, as not all adversely affect sleep.
• Research is needed to understand the long-term health effects of chronic noise pollution.
Light • Light and sleep can be a beneficial association in some populations via light therapy (dementia patients and elite athletes) as a therapy
• Light and dark timing is essential for proper circadian rhythm function and overall sleep duration and quality.
• More research is needed to determine the downstream effects of circadian rhythm disruptions from light.
• More studies are needed to determine potential target populations for light therapies in addition to dementia patients, cancer survivors, and elite athletes.

2. Environmental exposures and sleep

2.1. Heavy metals

While heavy metals such as lead, arsenic, mercury, and cadmium are essential components of the earth, industrial processes such as mining, coal combustion, and agriculture have interfered with their natural geologic cycles, allowing harmful amounts of these substances to be continuously released into the environment. As a result, human heavy metal exposure via inhalation, dermal absorption, and ingestion of contaminated food/water is of growing public health concern (Rehman et al., 2018). In the U.S., over 50% of the population exhibits detectable urinary levels of at least three heavy metals (Shim et al., 2017). Studies have demonstrated associations between exposures to heavy metal compounds and numerous adverse physical health effects, including cancers (Nordberg et al., 2005), congenital abnormalities (Golding et al., 2013), immune dysfunction (Mishra, 2009), impaired fertility (Rzymski et al., 2015), and hormonal imbalance (Rana, 2014), as well as cognitive effects such as neurodegenerative disorders (Goldman, 2014) and cognitive dysfunction (Karri et al., 2016; Wu et al., 2016). Relative to other heavy metals, there has been considerably more research done on lead, perhaps due to significant morbidity and mortality: the WHO reports that lead exposure accounted for over 1 million deaths and 24 million years of healthy life lost worldwide in 2017 (WHO, 2019). Furthermore, studies assessing the neurotoxic effects of lead exposure on cognitive behavior have linked lead exposure to behavioral problems in children (Bellinger et al., 1994; Liu et al., 2014) and criminal behavior in adults (Wright et al., 2008). Even at low levels, lead exposure has been associated with neurocognitive abnormalities (Bellinger, 2008). Nevertheless, relatively few studies have explicitly investigated the relationship between heavy metal exposure and sleep problems. The following section presents a discussion of available studies on heavy metals and sleep, and detailed information on each paper is provided in Table 1.

Nationally representative samples from the United States National Health and Nutrition Examination Surveys (NHANES) have demonstrated associations between self-reported sleep disturbances and serum/urinary concentrations of heavy metals (Scinicariello et al., 2017; Shiue, 2017). These cross-sectional studies showed that higher urinary arsenic concentrations were associated with more frequent night awakenings and nocturnal leg cramps (Shiue, 2017), an indicator of poor sleep health (Grandner and Winkelman, 2017). Higher urinary antimony levels were correlated with insufficient sleep, delayed sleep onset, and a diagnosis of obstructive sleep apnea (OSA) (Scinicariello et al., 2017). This association with OSA was also noted in smaller studies (Asker et al., 2015; Zeynalov et al., 2018). These findings collectively suggest a potential link between sleep problems and heavy metal exposure as supported by both nationally representative surveys and smaller, objectively measured research studies.

Industrial workers are particularly at risk for the adverse health effects of heavy metal exposure due to high levels of occupational exposure, and studies of this population generally suggest an association between extended heavy metal exposure and poor sleep health. Sleep outcomes in industrial workers, particularly poor sleep quality, have largely been measured by subjective self-report questionnaires, such as the PSQI, although wearable fitness tracker devices have been used (Chuang et al., 2018). Among factory workers exposed to heavy metals, studies found that increasing serum or urinary heavy metal concentrations were associated with declining sleep quality (Mohammadyan et al., 2019) and decreases in urinary serotonin, a neurotransmitter regulating circadian rhythms (Chuang et al., 2018). Furthermore, a pooled analysis of multiple studies examining the toxic effects of chronic mercury vapor exposure associated with artisanal small-scale gold mining across several countries established sleep problems as one of the chief diagnostic criteria of chronic mercury toxicity (Doering et al., 2016). Although these studies do not account for exposure to other heavy metals or exposure arising from food or drink intake, current literature demonstrates that sleep problems are positively associated with occupational heavy metal exposure.

While occupational exposures are responsible for much of the burden of heavy metal toxicity in adults, children may be especially sensitive to both chemical environmental exposures (Lanphear et al., 2000; Suk William et al., 2016) and sleep problems (Dutil et al., 2018). Albeit limited, emerging research has begun to examine the associations between heavy metal exposure and sleep problems in children, although these preliminary studies largely rely on self-report. Many communities in the U.S. are exposed to coal ash, a heavy metal-containing waste product created by power plants using coal to generate electricity (Rules, 2015). In one study, children living nearer to coal ash facilities were more likely to report difficulty falling asleep, frequent night awakenings, teeth grinding, and leg cramps than children living further away from these facilities (Sears and Zierold, 2017). Moreover, cross-sectional studies in children found that increasing serum levels of mercury were associated with decreasing sleep duration as measured by actigraphy (Gump et al., 2014), and higher blood lead levels were associated with later waking time and decreased sleep duration per parental report (Kordas et al., 2007). Likewise, the China Jintan Child Cohort Study reported that preadolescents who had experienced higher BLL (≥10 μg/dL) in early childhood were more likely to report many sleep problems, such as excessive daytime sleepiness, usage of sleeping pills, and insomnia (Liu et al., 2015), thus underscoring the potential longevity of the adverse effects of lead exposure on child health, including sleep.

2.1.1. Potential mechanisms

The mechanism underlying the link between sleep disturbances and heavy metal exposure remains unclear. Proposed mechanisms relate to two overarching explanations, neurocognitive dysfunction and systemic inflammation, which are presented in Fig. 1.

Heavy metal toxicity in organisms leads to neurodegeneration due to the generation and accumulation of reactive oxygen species (ROS), whether directly or indirectly (Wu et al., 2016). Generation of ROS may result in sleep problems via neurotoxicity (Nava-Ruiz et al., 2012), leading to altered neuronal signaling mechanisms (Gorbachev and Kovalev, 2002) and dysregulated activity of neurotransmitters involved in sleep regulation such as dopamine and serotonin (Lechin et al., 2004; Lidsky and Schneider, 2003). Furthermore, heavy metal toxicity may be linked to cognitive dysfunction (Karri et al., 2016) and increased odds of major depressive and panic disorders (Bouchard et al., 2009), which are often accompanied by sleep abnormalities. However, the causality of this relationship is yet unknown.

In addition to neurotoxicity, sleep disturbances due to heavy metals have also been associated with systemic inflammation. Researchers studying mercury-exposed children speculated that the association between elevated serum mercury levels and shorter sleep duration may be mediated by an increase in TNF- α, an inflammatory marker, while also acknowledging that this relationship could be bidirectional (Gump et al., 2014). It has also been posited that antimony exposure may lead to edema of the larynx, causing acute airway obstruction and thus OSA (Scinicariello et al., 2017). Again, the directionality of this relationship is unclear; researchers have postulated that oxidative stress and inflammation resulting from the OSA cycle of hypoxia and reoxygenation leads to impairment of mineral and heavy metal metabolism (Asker et al., 2015). Further research is needed to uncover the causal relationship between heavy metal exposure, sleep problems, and behavioral problems.

In summary, although the study of heavy metals’ adverse effects on sleep is just beginning, available research supports this relationship in both the general population for adults and children and occupational exposure.

2.2. Noise pollution

Exacerbated by urbanization and population growth, noise pollution is a growing threat to the health and wellbeing of humans living in the modern age. While the US EPA recommends a 24-hr exposure limit of 55 A-weighted decibels (dB) to protect the public from all adverse effects on health and welfare in residential areas, a recent study estimated that 104 million individuals in the US had annual continuous average 24-hr exposures >70 dB in 2013 (Hammer et al., 2013). Both environmental noise pollution from automobiles, railways, and aircrafts and occupational noise exposure have been shown to be associated with numerous adverse health effects, ranging from impaired cognitive function to cardiovascular disease, including hypertension (Babisch and Van Kamp, 2009; Jarup et al., 2008), stroke (Sørensen et al., 2011), and myocardial infarction mortality (Amundsen et al., 2013; Bevan and Hood, 2006; Gislason et al., 2016; Kawai et al., 2019; Nassur et al., 2019; Popp et al., 2015; Rahimi Moghadam et al., 2018; Rocha et al., 2019; Teoh and Olson, 2018). Given the physiological importance of sleep, the effect of noise pollution on sleep is particularly salient. Current literature demonstrates that increased noise exposure is negatively associated with sleep health, and a summary of study findings are highlighted in Table 2.

Table 2.

Selected studies categorized by noise pollution exposure measured and related sleep outcomes.

Exposure Studied Population Studied Study Sample N (% Female) Age Study Location Study Design and Period Exposure(s) of Interest Measure (Noise) Sleep Outcome Measure(s) Major Findings
Road traffic noise Norwegian residents Amundsen et al. (2013) N = 511 (48.7%)
Age: (M = 53.7 y)
Norway Pre- & post-interventional with control
2003–2007
24-h sound pressure of outdoor and indoor noise levels Questionnaire • 6 months after the intervention, the proportion of respondents highly annoyed by road traffic noise was reduced to 15% from 43%
• The proportion reporting poor sleep in general was reduced after the improvements,
Road traffic noise European adults Gislason et al. (2016) N = 12184 (52.3%)
Age: 38-65 y (M = 51.5 y)
Iceland, Norway, Sweden, & Denmark Correlational
2011–2012
Self-reported questionnaire Questionnaire • Self-reported exposure to traffic noise pollution and noise was associated with daytime sleepiness and increased the risk of habitual snoring
Road traffic noise Long-haul truck drivers Popp et al. (2015) N = 10 (0%)
Age: 25-50 y (M = 36.3 y)
Denkendorf, Germany Randomized, crossover, within-subject controlled PAK MKII system in an original truck berth PSG, sleepiness tests, vigilance examinations; (KSS, MCT, PVT, TSS, SSS, PST) • On noisy nights, subjects had less the REM and more sleep stage 1
• Subjects rated their sleep quality as having been better during nights without noise
• On silent nights, the subjects slept more, had greater sleep efficiency, enter REM sleep faster, had fewer arousals/sleep phase changes, and a subjectively better rated sleep quality
Road traffic noise New Yorkers Teoh and Olson (2018) N = 1532 (53%)
Age: ≥18 y
New York City
2017
Retrospective cross-sectional
2016–2017
Automated survey Automated survey • Most adult New Yorkers (82%) reported sleep disturbance of any type at least once per week; 64% reported noise as a cause of their poor sleep
• Traffic was the cause of 53% of sleep disturbances of 3+ nights/week, with sources being subways (7%), buses (9%), sirens (15%), and garbage trucks (11%)
• 75% with noise-disrupted sleep 3+ times/week reported difficulty concentrating due to poor sleep
Road traffic noise Older adults Zhang et al. (2013) N = 83 (52%)
Age: 50-80 y
Harbin, China Correlational
November 2018
801 sound-level meter Questionnaire (PSQI) • Older people are significantly more affected by traffic and other noises than young people
Road traffic noise Younger and older population cohorts Rudzik (2019) N = 26 young (46.2%)
N = 16 old (50%)
Age: 19-33 y (young); 52-70 y (old)
Basel, Switzerland Cohort Continuous noise lab recordings PSG, EEG, questionnaires, sleep staging scoring, body movement measurements • Sleep was more fragmented for older individuals when exposed to noise
• Younger individuals exposed no effects on all-night arousal and awakening rates when compared between noise and noise-free nights
• Sleep spindles showed an age-related decline with more noise-induced sleep changes
Road traffic noise Adolescents Rudolph et al. (2019) N = 4508 (50.6%)
Age: 13-18 y (M = 15.3 y)
United States Cross-sectional observational
2001–2004
Estimation of day-night average sound levels Survey and in-person interviews • Found evidence that noise exceeding the EPA threshold had adverse sleep effects for adolescents
• Living in a high- versus low-noise census block-group was associated with later bedtimes and also had 69% increased odds of an anxiety or depressive disorder
Hospital noise Pediatric PICU patients Kawai et al. (2019) N = 116
Age: N/A (M = 4.55 y)
Ann Arbor, MI Quality improvement initiative
August 2015
Sound sensors Pediatric delirium • PICU noise pollution exists, and using the pediatric delirium bundle led to a significant noise reduction of ~50%, which then meets EPA standards for noise
Hospital noise Hospital employees Rahimi et al. (2015) N = 261 (47.5%)
Age: N/A (M = 54.1 y)
Neyushabur, Iran Cross-sectional
Winter 2015
CELL440 sound meter Questionnaire (PSQI) • Exposure to noise is inversely related sleep quality
• For each unit increase in noise, the score of sleep quality decreased by 0.6 (a significant decrease)
Hospital noise Children-mother pairs Bevan and Hood (2006) N = 40 children (52.5%); N = 32 mothers
Age: M = 9.3 y (children); M = 37.9 y (mothers)
Southampton, UK Observational case-control
February 2012–July 2014
Industry standard sound level meter Actigraphy • Both children and parents are worse sleep quality in the hospital than in home, where median sound levels were much greater than the levels at home
• The hospital’s median sound level exceeded WHO recommendations
• It also took children longer to fall asleep in hospitals than at home
Daytime work noise Cafeteria workers Lin et al. (2018) N = 40 (50%)
Age: N/A (M = 54.1 y)
Taiwan Prospective experimental Personal daily noise exposure level was assessed using the TES-1358 sound analyzer noise dose meter Overnight lab PSG, ANS function tests, serum cortisol tests, interviews, questionnaires, otoscopic exams, & pure tone audiometry • Daytime occupational noise exposure has long-term effects on nighttime sleep quality, importantly with regards to slow wave sleep and sleep efficiency
• Noise exposure at levels commonly encountered in modern life was found to be associated with worse sleep quality
Industrial wind turbine noise Nearby residents to IWT Jalali et al. (2016) N = 16 (62.5%)
Age: 33-78 y (M = 54.25 y)
Ontario, Canada Prospective cohort
June–October 2014
Equivalent and maximum sound pressure levels within the bedroom Questionnaire (PSQI, ISI, & ESS), noise sensitivity scale • There are no major changes in the sleep of participants living near IWTs
• No statistically significant differences between IWT residents and non-IWT residents for objective sleep measures, but subjective measures were based on individual differences and psychological factors/attitudes
Industrial wind turbine noise Participants living close to or far from IWTs Nissenbaum et al. (2012) N = 79 (49.4%)
Age: ≥18 y (M = 57.5 y)
Mars Hill and Vinalhaven, Maine Cross-sectional
March–July 2010
Measured IWT sound levels Questionnaire (PSQI & ESS) and general health survey • Participants living within 1.4 km of an IWT had poorer sleep, were sleepier during the day, and had worse mental health scores compared to those living further away
• Saw significant dose-response relationships between PSQI and ESS scores and distance to nearest IWT
Construction site noise Construction site works Yi Feng et al. (2020) N = 60 (N/A)
Age: N/A (M = N/A)
Klang Valley, Malaysia Longitudinal Questionnaire Questionnaire • Heavy machine operations cause significant levels of noise pollution
• Noise pollution adversely impacts sleep disturbances among construction workers and nearby residents

Abbreviations: ANS: autonomic nervous system; EPA: Environmental Protection Agency; ESS: Epworth Sleepiness Score; ISI: Insomnia Severity Index; IWT: industrial wind turbine; KSS: Karolinska Sleepiness Scale; MCT: Mackworth Clock test; PICU: pediatric intensive care unit; PSG: polysomnography; PSQI: Pittsburgh Sleep Quality Index; PST: Pupillographic Sleepiness Test; PVT: psychomotor vigilance task; REM: rapid eye movement; SSS: Stanford Sleepiness Scale; TSS: Tiredness Symptom Scale; WHO: World Health Organization.

Numerous cross-sectional and cohort studies in both laboratory and environmental settings have reported that higher levels of noise exposure during the night to be associated with poor sleep outcomes. Cross sectional studies have linked exposure to traffic noise to both daytime sleepiness & habitual snoring in adults (Gislason et al., 2016) as well as later bedtimes on both weeknights and weekend nights in adolescents (Rudolph et al., 2019). Data from the Norwegian Mother and Child Cohort Study demonstrated an association between traffic and shorter sleep duration and sleep problems at age 7 that was only significant in girls (McGuire et al., 2016). In a case control study evaluating the effects of installation of fagade-insulating measures that decreased indoor noise in the homes of 511 subjects, there was a significant reduction in the proportion of subjects reporting poor sleep quality at 6 months and 2 years post-intervention (Amundsen et al., 2013). Furthermore, laboratory-based polysomnography studies have shown increased noise exposure during the night to be associated with decreased sleep quality, greater latencies to the rapid eye movement (REM), higher percentages of stage 1 sleep, and poorer perceived sleep (Griefahn et al., 2006; Popp et al., 2015).

Interestingly, studies have also found noise exposure during the day to be disruptive to sleep quality at night. A study in Iran focused on the effect of noise pollution on hospital staff and reported that exposure to sound was inversely associated with sleep quality as measured by the Pittsburgh Sleep Quality Index (Rahimi Moghadam et al., 2018). Similarly, a study done in dayshift cafeteria staff in a Taiwanese hospital showed that subjects experienced greater sustained disturbances to nighttime sleep, specifically to slow wave sleep and sleep efficiency, on high-noise days when compared to low-noise days suggesting a possible residual effect of noise exposure (Lin et al., 2018). These findings were echoed in noise-exposure studies done in intensive care units (ICUs), wherein mean noise levels range between 55 and 65 dB over a 24 h period and can peak as high as 80 dB (Freedman et al., 2001). A 2019 study demonstrated that high noise levels in the pediatric ICU contributed to poor sleep and increased the likelihood of delirium and confusion the following day in a group of 8 pilot patients (Kawai et al., 2019). These studies underscore the importance of further evaluating the lasting effects of sleep disturbances as well as the mechanisms underlying the identified associations between noise-induced sleep disturbance and increased morbidity.

2.2.1. Potential mechanisms

The mechanism underlying the effect of noise pollution on sleep is likely multifactorial. Substantial literature supports two avenues through which elevated noise may adversely affect sleep, specifically via activation of the sympathetic response and neuromodulation of ion channels involved in the regulation of sleep, which are depicted in Fig. 2.

Fig. 2.

Fig. 2.

Conceptual framework of possible mechanisms for the association between noise pollution and poor sleep health.

Even at “ear-safe” levels, noise is perceived as an acute stressor that results in a sympathetic response that activates the pituitary-adrenal-cortical axis and the sympathetic-adrenal-medullary axis (Babisch, 2003). Many studies have found that humans are capable of perceiving graded auditory stimuli and responding proportionately in all stages of sleep as measured by changes in EEG patterns (Zung and Wilson, 1961), vasomotor/autonomic reactions (Krichagin, 1978; Williams et al., 1964), and fMRI activation of auditory cortex, thalamus, and caudate (Pirrera et al., 2010; Portas et al., 2000). Environmental noise in particular has been found to disturb sleep in both animals (Rabat, 2007; Turner et al., 2005) and humans (Amundsen et al., 2013; Nassur et al., 2019; Popp et al., 2015; Rocha et al., 2019). Moreover, several studies have reported autonomic effects following exposure to traffic noise during sleep, including the disruption of normal cortisol response upon wake-up (Waye et al., 2003) and increased incidence of heart rate accelerations during the night (Griefahn et al., 2008). Taken together, these findings strongly support the presence of an active neurological and endocrine response to noise that occurs beneath the level of consciousness which may harm sleep effectiveness.

While the mechanism has yet to be fully elucidated, auditory stimuli have also been shown to induce the creation of K-complexes and sleep spindles during the N2 and N3 stages of sleep (Naitoh et al., 1982). Recent studies have shown higher sleep spindle density to be predictive of higher tolerance to noise during sleep (Astori et al., 2013; Dang-Vu et al., 2010), thus suggesting a possible protective mechanism that prevents disruption of sleep in individuals exposed to auditory stimuli. Sleep spindle generation is mediated by rhythmic thalamic bursting, arising from interactions between Cav3.3-type Ca2+ channels and Ca2+-dependent small-conductance-type 2 (SK2) K+ channels (Wimmer et al., 2012). Mice overexpressing SK2 K+ channels were shown to have less fragmented NREM (non-rapid eye movement) sleep (Wimmer et al., 2012), thus supporting a protective role for SK2 receptors and sleep spindle generation. These findings provide a potential mechanism for observed inter-individual differences in tolerance to noise during sleep, may explain the inability for research to come to a consensus regarding a dose-response relationship between noise levels and sleep disturbance (Nissenbaum et al., 2012). Moreover, these findings also suggest that SK2 receptors could serve as a potential new therapeutic target for sleep disorders with environmental or physiological etiologies.

In summary, noise exposure has been studied to a greater extent as compared to heavy metals, and available research points to both daytime and nighttime noise as negatively impacting a variety of sleep outcomes.

2.3. Light exposure

Over 80% of the world’s population lives with light pollution, or the alteration of natural nighttime lighting by anthropogenic sources of light (Jawale et al., 2018). Nearly 100% of the European and U.S. populations live under light-polluted skies (Falchi et al., 2016), and light pollution continues to intensify around the world as societies expand industry and adopt new technology (Davies and Smyth, 2018). The American Medical Association states that artificial nighttime lighting harms sleep health and may increase risk for health problems such as breast cancer, obesity, diabetes, and depression (Mulvin, 2018). Furthermore, increasing evidence has shown that light exposure greatly impacts sleep patterns and general sleep health. The following section provides a discussion on relevant studies, and additional details are listed in Table 3.

Table 3.

Selected studies categorized by light exposure measured and related sleep outcomes.

Exposure Studied Population Studied Study Sample N (% Female) Age Study Location Study Design and Period Exposure(s) of Interest Measure (Light) Sleep Outcome Measure(s) Major Findings
Bedside light Healthy sleepers Cho et al. (2013) N = 10 (40.0%)
Age: 21-34 y (M = 27.0 y)
Korea Interventional Fluorescent lamp 1 m away from eyes PSG EEG, questionnaire (PSQI) • Sleeping with lights on increased stage 1 sleep, decreased slow-wave sleep, increased sleep arousal, & decreased REM sleep activity (theta power, slow oscillations, & sleep spindles)
• Bedside light causes shallow sleep and arousals in addition to brain oscillation changes
Electronic light Young adults Chang et al. (2015) N = 12 (50.0%)
Age: N/A (M = 24.9 y)
Boston, MA Randomized crossover LE-eBook, radio/power meter, fluorescent lamps Plasma melatonin, PSG, EEG, questionnaires (TSS & KSS), wrist actigraphy • Nighttime exposure to a light-emitting eReader delays the circadian clock, reduces morning alertness, and suppresses melatonin
Electronic light Men undergoing fertility evaluation Green et al. (2020) N = 116 (0%)
Age: 21-59 y (M = 35.2 y)
Tel Aviv, Israel Cross-sectional
September 2018–August 2019
Questionnaire Questionnaire • Increased electronic usage at night (smartphone and tablets) was correlated to short sleep durations and more subjective daytime sleepiness
• Observed link between evening electronic exposure, sleep, and sperm quality
Electrical light Hunter-gather communities De la Iglesia et al. (2015) N = 18 (50%)
Age: 14-49 y (M = 20.4 y)
Formosa, Argentina Case-control
November–December 2012
Based on community Wrist actigraphy, sleep diary • Access to electrical is correlated with later bedtimes/sleep onset, but no difference in rise times
• Artificial light reduces sleep duration
• People who have access to electricity sleep less
Natural light Equatorial workers vs. Arctic workers Marqueze et al. (2015) N = 488 (Brazil) & 1273 (Sweden)
Age: N/A (M = 37.5 y Sweden & M = 38.3 y Brazil)
Equatorial and Artic regions (Sweden & Brazil) Cross-sectional
September 2011–January 2013
Questionnaire Questionnaire • Less exposure to natural light increased the perception of having insufficient sleep
• Higher level of depression in workers with less natural light
Light flashes Healthy young volunteers Zeitzer et al. (2014) N = 13 (53.8%)
Age: N/A (M = 26.9 y)
Stanford, CA Parallel group
November 2010–July 2012
1 moderately bright 2-ms flash of white light every 30s for 60 min EEG, sleep diaries, wrist actigraphy, melatonin levels, PSG • Light flashes delayed the start of the circadian salivary melatonin rhythm, but no other significant changes in the spectral content of sleep
Morning and evening bright light Alzheimer’s patients Ancoli-Israel et al. (2003) N = 92 (68.5%)
Age: 61-99 y (M = 82.3 y)
San Diego, CA Randomized trial Photosensitive cell, “Brite-Lite” boxes, dim red light Wrist actigraphy • Both light in the morning and night resulted in more consolidated sleep at nighttime
• Light in the evening increased circadian activity rhythm quality
• Light exposure has a beneficial effect on sleep in dementia patients
Systematic light Cancer survivors Wu et al. (2018) N = 44 (25.0%)
Age: N/A (M = 53.6 y)
Icahn School of Medicine at Mount Sinai, NY Randomized control Litebook with red or white light frequency Wrist actigraphy, questionnaire (PSQI) • Systematic bright light in the morning has beneficial effects on sleep among cancer survivors suffering from fatigue
• Survivors can use bright light therapy as an easy and affordable tool to improve quality of life
Red light Elite basketball players Zhao et al. (2012) N = 20 (100.0%)
Age: N/A (M = 18.6 y)
Beijing, China Randomized parallel pre-/post-test; cohort Whole-body red-light treatment machine (phototherapy) Chinese version of PSQI, serum melatonin levels • Red-light treatment improved sleep, serum melatonin levels, and endurance performance of professional Chinese female basketball players
• Non-pharmalogical/noninvasive therapy option to prevent sleep disorders
White and amber light Pigeons and Australian magpies (birds) Aulsebrook et al. (2020) N = 9 pigeons & N = 8 magpies
Age: N/A
Melbourne, Australia Experimental White light or amber light laboratory exposure EEG, electromyography, and tri-axial accelerometry • Urban light exposure has different intensities and various adverse effects on birds, even between different species
• Amber light was less detrimental to sleep quality and duration than white light for Australian magpies
Dim light Night shift workers Gumenyuk et al. (2012) N = 10 (60.0%)
Age: N/A (M = 37.4 y)
Detroit, MI Case-control
February–April 2011
Dimly light private room MSLT, saliva melatonin, questionnaires (Horne Östberg, ESS, ISI, Berlin, POMS), sleep diary, wrist actigraphy • Melatonin is a reliable circadian phase marker
• Night workers had a delated circadian rhythm as measured by melatonin levels by more than 7 h (internal physiological delay)
Timed light treatment and/or sunglasses Night-shift workers Thorne et al. (2010) N = 8 (0.0%)
Age: N/A (M = 46.5 y)
Guildford, UK Randomized crossover
May–August 2005
White light from a light box, wrist actigraphy Questionnaire (Horne Östberg), sleep diaries, urine samples, wrist actigraphy • When timed accurately, light and dark exposure has beneficial sleep effects, especially for sleep efficiency and duration following a night shift
• Hastened circadian rhythms improves sleep after a night shift
• Light treatment objectively increased sleep duration
Artificial light Free-living animals (great tits) Raap et al. (2015) N = 27 (48.1%)
Age: N/A
Wilrijk, Belgium Case-control
February–March 2014
Infrared LED lights Recordings of sleep, transponder readers • Artificial light significantly affected most sleep parameters (awakening time, leaving time, total sleep amount, morning and evening latency)
• Artificial light particularly affected sleep in the morning (more subtle evening effects)
Outdoor artificial light Adolescents Paksarian et al. (2020) N = 10,123 (48.7%)
Age: 13-18 y (M = 15.2 y)
United States Population-based cross-sectional
February 2001–January 2004
Satellite-measured outdoor artificial light at night Questionnaire • Increased exposure to artificial light at night was associated with later bedtimes
• Decreased exposure to artificial light at night was associated with longer sleep durations
Light during daytime sleep Young adults Harrison et al. (2011) N = 17 (56.5%)
Age: N/A (M = 23.2 y)
San Diego, CA Experimental Green narrowband light via light masks, exposure to 4 light conditions Questionnaires (Horne Östberg, ESS, KSS) sleep diaries, EEG, EOG, EMG, PSG, DST, wrist actigraphy • Light was not found to have an effect on the ability of people to fall asleep/stay asleep during an afternoon nap
• The alerting effects of light did not affect daytime sleep

Abbreviations: DST: Digit Subtraction Test; EEG: electroencephalography; EOG: electro-oculography; EMG: electromyography; ESS: Epworth Sleepiness Score; ISI: Insomnia Severity Index; KSS: Karolinska Sleepiness Scale; MSLT: Multiple Sleep Latency Test; POMS: Profile of Mood States; PSG: polysomnography; PSQI: Pittsburgh Sleep Quality Index; REM: rapid eye movement; TSS: Tiredness Symptom Scale.

In line with expectations, exposure to natural light has been seen to affect sleep health. One study found that in daytime workers, reduced exposure to natural light was associated with increased perception of insufficient sleep. Not only did this population have compromised sleep, but they also were found to have higher rates of depression compared to people who were exposed to normal amounts of lightness and darkness (Marqueze et al., 2015).

While research has demonstrated the negative association of both natural and artificial light exposure with poor sleep outcomes, most studies reveal that artificial light has a more severe effect than natural light (De La Iglesia et al., 2015; Grainger, 2008). In both animal and human studies, findings have shown that exposure to artificial light during the night reduces sleepiness and increases wakefulness and interferes with future sleep. Additionally, electronic light exposure prior to bed was seen to delay the circadian clock by suppressing melatonin release and reduce sleep duration, latency, and morning alertness and function (Chang et al., 2015; De La Iglesia et al., 2015). Furthermore, an interventional study in Korea found that sleeping with a bedside lamp on disrupted normal sleep phases, resulting in increased stage 1 sleep, decreased slow-wave sleep, decreased REM sleep activity, and increased overall sleep arousal (Cho et al., 2013). Even small exposure to artificial light was shown to delay the production of melatonin, delaying sleep onset (Zeitzer et al., 2014).

The negative association between artificial light exposure and sleep quality in humans is supported by conclusions from animal studies. When artificially exposing great tits (a passerine bird) to light, almost all measured sleep parameters were negatively affected, including awakening time, total sleep amount, and morning and evening latency (Raap et al., 2015). Furthermore, these effects were more significant the next morning, compared to the subtler evening effects (Raap et al., 2015). In mice, exposure to pulses of light resulted in direct and adverse effects on morning alertness and both sleep duration and quality (Hubbard et al., 2013).

Despite the apparent negative effect of natural and artificial light exposure on sleep, recent research has interestingly found that light exposure at specified times of the day can enhance sleep, especially for specific populations. In a randomized trial involving Alzheimer’s patients, exposure to light in the morning and at night resulted in more consolidated sleep and higher quality circadian rhythm activity (Ancoli-Israel et al., 2003). Positive results were also seen in professional Chinese basketball players, nightshift workers, and cancer survivors. Exposure to phototherapy light improved sleep, melatonin levels, and overall endurance and performance among basketball players who suffer from sleep disorders (Zhao et al., 2012). For nightshift workers, when light exposure occurred during morning after the shift, sleep efficiency and duration positively and objectively increased (Thorne et al., 2010). Finally, among cancer survivors suffering from chronic fatigue, systematic bright light in the morning greatly benefited sleep quality and duration (Wu et al., 2018). Future research on other populations in addition to dementia patients and athletes should be conducted to confirm the non-pharmacological and non-invasive therapy option of light, especially to prevent sleep disorders.

2.3.1. Potential mechanisms

The mechanistic way that light exposure has been shown to decrease sleep quality and quantity is largely dependent on the human circadian rhythm. Fig. 3 highlights the relationship between light exposure, potential mechanisms, and sleep outcomes.

Fig. 3.

Fig. 3.

Conceptual framework of possible mechanisms for the association between light pollution and poor sleep health.

Natural light, or light coming from the rays of the sun, is controlled by light perception in the retina, which synchronizes and controls daily activities, such as sleep. Light thus is an important mediator of sleep, as it enables pupillary reaction to light hitting photosensitive retinal ganglion cells containing melanopsin, a photopigment (Marqueze et al., 2015), allowing the eyes to control the natural circadian rhythm crucial to everyday function. It inhibits sleep and encourages alertness during the day when it is bright, and it promotes sleep when it is not. Hence, upon exposure to light when it is naturally dark, the brain and endocrine system quickly adapt and coordinate to relay information to the body that it should not sleep. In this way, light acts as a regulatory mechanism for the natural circadian rhythm; when out of balance, the body reacts. Furthermore, research has discovered intrinsically photosensitive retinal ganglion cells (ipRGCs) (Yamazaki et al., 1999), and evidence shows that these receptors, compared to rods and cones, are in charge of light processing, rather than visual formation, in mammals (Paul et al., 2009). The ipRGC system communicates with the main circadian rhythm manager in the suprachiasmatic nucleus (SCN) of the hypothalamus, which also serves as the primary regulator of the rhythms of all physiological processes (Pezuk et al., 2010). By driving and coordinating internal clocks through humoral and neural signals, the SCN modulates sleep by inducing sleep activity when the ipRGCs detect less visible light.

This mechanism hypothesis is supported by the fact that ipRGCs are the first photoreceptors to develop in mammalian retinas, followed by rods and cones (Hannibal and Fahrenkrug, 2004). The early presence of (ipRGCs in mammals is indicative of its role in an essential function: regulation of the circadian rhythm (Sekaran et al., 2005). Furthermore, the ipRGC system involves melanopsin, the primary photopigment involved in circadian rhythm regulation (Provencio et al., 1998). When light is detected, melanopsin is stimulated, which consequently constricts the pupils and inhibits the release of melatonin, the sleep-inducing hormone, and therefore promoting wakefulness (Weng et al., 2009). This is seen in behavioral animal studies, as removing the gene for melanopsin in mice decreases free-running and increases sleep activity when in dark environments, demonstrating the disruption of normal behavior and sleep patterns (Panda et al., 2002; Ruby et al., 2002). This system likely perceives artificial light as real light, which supports research findings showing that artificial light negatively affects the circadian rhythm by promoting wakefulness at unfitting times.

In summary, similar to noise pollution, light pollution has been well-studied in relation to different sleep outcomes, and relationships exist between both natural and artificial light exposure and sleep health.

2.4. Second-hand smoke exposure

Exposure to second-hand smoke (SHS), including minimal, short-term exposures, has been known to cause numerous adverse health effects across the lifespan (American Lung Association, 2019). Per a CDC report, one in four non-smokers in the U.S., including 14 million children, are exposed to SHS (Tsai et al., 2018). While there is no safe level of exposure, greater levels of SHS exposure may lead to mortality due to lung cancer (Li et al., 2010; Öberg et al., 2011) and heart disease (Dunbar et al., 2013; Jefferis et al., 2010) in adults. Exposure to secondhand smoke during pregnancy has been associated with reproductive consequences such as low birth weight (Ko et al., 2014) and increased incidence of sudden infant death syndrome (Health and Services, 2006; Mitchell and Milerad, 2006). Furthermore, children and adolescents are particularly sensitive and susceptible to SHS exposure. In children, exposure can result in severe respiratory disturbances including asthma (Lewis et al., 2005), respiratory infection (Öberg et al., 2011), and impaired lung function (CDC, 2018b; Tager, 2008). The negative effects of SHS extend to sleep, causing poor sleep health and quality, which impairs cognitive function (Herrmann et al., 2008; Liu et al., 2012) and can lead to behavioral problems (Bauer et al., 2015; Liu et al., 2016). Due to the significant health implications of SHS and the importance of sleep, it is critical to understand the relationship between exposure to SHS and various sleep outcomes. Current literature examining this topic is discussed in the following section, and details for each included paper are presented in Table 4.

Most current literature examining the association between SHS and sleep health in children has investigated the association between prenatal exposures and later respiratory-based sleep disorders. Both cross-sectional and cohort studies have reported that prenatal SHS exposure via maternal smoking during pregnancy is associated with increased sleep apnea (Stéphan-Blanchard et al., 2010) and snoring (Katila et al., 2019) in neonates. Additionally, this exposure is positively associated with SDB (Calhoun et al., 2010) and habitual snoring (Sun et al., 2018) in school-aged children, demonstrating that prenatal exposure to SHS has both immediate and long-lasting effects on respiratory-related sleep outcomes in children.

Other studies have investigated the relationship between environmental SHS exposure during childhood and respiratory sleep disturbances, demonstrating a positive relationship between smoke exposure and SDB (Groner et al., 2019; Jara et al., 2015; Yolton et al., 2010), habitual snoring (Groner et al., 2019; Jara et al., 2015; Sun et al., 2018), obstructive sleep apnea (Groner et al., 2019; Jara et al., 2015; Tamanyan et al., 2016), and hypoxia (Jara et al., 2015). These respiratory related disturbances often occur during sleep, disrupting sleep quality and resulting in poor sleep outcomes (Abou-Khadra, 2013). Additionally, regular encounters with SHS were associated with various other sleep disturbances, such as poor sleep quality (Miadich et al., 2018), short sleep duration (Morioka et al., 2018; Schwartz et al., 2014), delayed sleep onset (Morioka et al., 2018; Yolton et al., 2010), difficulty maintaining sleep (Morioka et al., 2018), restless sleep (Schwartz et al., 2014), sleep bruxism (Montaldo et al., 2012), daytime sleepiness (Yolton et al., 2010), parasomnias (O’Callaghan et al., 2019; Yolton et al., 2010), such as nightmares and sleep walking and talking (O’Callaghan et al., 2019; Yolton et al., 2010), and general sleep difficulties (Treyster and Gitterman, 2011; Yolton et al., 2010) in infants, children, and adolescents as shown through cross-sectional, cohort, case-control, and randomized control studies. Thus, the extensive volume of relevant literature demonstrates that exposure to second-hand smoke is negatively related to sleep health.

2.4.1. Potential mechanisms

Despite extensive research highlighting the association between poor sleep health and exposure to second-hand smoke, less is known regarding mechanisms for this relationship. However, two possible explanations have been proposed, namely changes in the chemistry and/or development of the nervous system and incidence or exacerbation of respiratory problems, which are depicted in Fig. 4.

Fig. 4.

Fig. 4.

Conceptual framework of possible mechanisms for the association between second-hand smoke exposure and poor sleep health.

Sleep behavior is regulated by the central nervous system and thus is sensitive to alterations in brain neurochemistry or disruptions to development. An animal study demonstrated that cigarette smoke, exposed during the prenatal period, can easily cross the placental barrier, interfere with oxygen delivery to the fetus, and disrupt central nervous system development (Lei et al., 2015). Additionally, as a cholinergic receptor agonist, the nicotine found in tobacco smoke binds acetylcholine receptors in the brain involved in regulation of motor function, arousal, cardiopulmonary integration, and sleep control (Chang et al., 2003; O’Callaghan et al., 2019). Activation of cholinergic neurons has been associated with states of arousal and increased attentiveness (Colangelo et al., 2019; Kim et al., 2016). Subsequently, exposure to exogenous nicotine found in SHS may disturb normal sleep patterns by increasing arousal and motor function during sleep or altering expression of other neurotransmitters involved in sleep regulation, such as glutamate, serotonin, and dopamine (Montaldo et al., 2012). Importantly, nicotine’s effect on cholinergic neurotransmission results in changes in control of ventilation, which could cause snoring, apnea or other breathing disruptions during sleep (Sun et al., 2018; Tamanyan et al., 2016).

In addition to its effect on the nervous system, SHS exposure can directly impact the respiratory system, potentially leading to respiratory-related disturbances in sleep. At a basic level, particles found in smoke may simply irritate the upper airway, exacerbating respiratory symptoms and resulting in disturbed or restless sleep (Johansson et al., 2008; Schwartz et al., 2014). Additionally, SHS particles have been shown to cause inflammation in the respiratory system (Flouris et al., 2009), which could indirectly elicit or aggravate breathing problems during sleep, leading to increased snoring (Groner et al., 2019) and lower sleep quality (Sun et al., 2018). Furthermore, children are more prone to SHS-related sleep disturbances due to the still developing nervous and respiratory systems, inhalation of larger volumes of air per body weight on average, and increased permeability of their airway epithelia (Sánchez et al., 2019).

Thus, second-hand smoke exposure is seen to be associated with various sleep outcomes, particularly through pre-natal exposures and in children. Although possible explanations for the effect of second-hand smoke on sleep health have been identified, further research is required to elucidate conclusive mechanisms for this relationship.

2.5. Air pollution

Exposure to air pollution encompasses both ambient and indoor air pollution, including particulate matter, nitrogen dioxide, ozone, sulfur dioxide, and combustion products. With approximately 91% of people worldwide living in places with ambient air pollution exposure levels exceeding guidelines recommended by the World Health Organization (WHO, 2018a), air pollution has been recognized as a major public health concern. Furthermore, the adverse effects of ambient exposures are compounded by further indoor pollutant exposures, arising from occupational exposures or household practices such as the use of polluting stoves and coal- or biomass-based fuels (WHO, 2018b). Air pollutant exposure has been associated with various adverse health outcomes, such respiratory diseases (Kurt et al., 2016), chronic cardiovascular diseases and acute cardiac events (Franklin et al., 2015), and cognitive dysfunction, as manifested by delayed cognitive development in children (Sunyer et al., 2015) and increased risk for dementia in the elderly (Fu et al., 2018; Paul et al., 2019; Peters et al., 2019; Russ et al., 2019; Shou et al., 2019).

A recent systematic review has assessed and analyzed the association between sleep health and air pollutant exposure (Liu et al., 2020); thus, we only provide a brief summary of these results in this present integrated review.

Across numerous studies employing different methodologies and spanning many countries, an overall negative association was observed between exposure to air pollution and sleep health. Specifically, sleep quality and efficiency were seen to decrease in children with increased prenatal exposure to particulate matter (Bose et al., 2019). Additionally, exposure to particulate matter in children and adults was associated with sleep disturbances during the night, including difficulty maintaining sleep (Abou-Khadra, 2013) and nighttime awakening (Chuang et al., 2018; Gislason et al., 2016). Interestingly, opposing associations were observed between sleep duration and particulate matter exposure, with children demonstrating shorter sleep with greater prenatal pollutant exposure (Bose et al., 2019), and young adults displaying longer sleep duration with increased particulate matter exposure (An and Yu, 2018). Thus, a general association between poor sleep outcomes and exposure to various air pollutants can be observed across the lifespan.

Notably, most of the current literature focuses on examining air pollutant exposure on respiratory-related sleep disturbances, such as sleep disordered breathing (SDB) and OSA, in particular. In children, habitual snoring, wheezing, and SDB-related symptoms are increased with exposure to sulfur dioxide (Kheirandish-Gozal et al., 2014; Sánchez et al., 2019), ozone (Kheirandish-Gozal et al., 2014; Sánchez et al., 2019), carbon dioxide (Castañeda et al., 2013), and particulate matter (Castañeda et al., 2013), resulting in poorer sleep quality. Furthermore, symptoms of SDB, as assessed objectively using polysomnography and actigraphy, are exacerbated by increased exposure to ozone, nitrogen dioxide, and particulate matter in adults (Cassol et al., 2012; Martens et al., 2018; Shen et al., 2018; Weinreich et al., 2015; Yıldız Gülhan et al., 2019). Given the possible severity of respiratory-related disturbances during sleep (Weinreich et al., 2015), it is critical to understand the positive association between exposure to air pollutants and these disorders, as seen in both children and adults.

2.5.1. Potential mechanisms

A summary of two potential mechanisms of action underlying air pollution and sleep outcomes is depicted in Fig. 5. Briefly, air pollutants are thought to affect sleep outcomes via central nervous system regulation and/or via changes in respiratory system physiology.

Firstly, air pollutants may directly affect the biochemistry of the central nervous system, resulting in the altered expression and dysregulation of neurochemicals (Abou-Khadra, 2013; Billings et al., 2019; Shen et al., 2018; Zanobetti et al., 2010). Specifically, penetration of air pollutants into the brain has been hypothesized to alter serotonin levels (Chuang et al., 2018), induce the breakdown of protective epithelial barriers (Brockmeyer & D’Angiulli, 2016), and damage nerve cells (Brockmeyer & D’Angiulli, 2016). These changes may in turn disrupt functions of the brain, including sleep regulation.

Secondly, the deposition of pollutant particles in the airways, as a result of inhalation, may damage respiratory cells. This could result in inflammation (Khafaie et al., 2016), infection (Billings et al., 2019; Sears and Zierold, 2017; Wei et al., 2017), and/or increased restriction and obstruction of airflow (Abou-Khadra, 2013; Scinicariello et al., 2017; Shen et al., 2018; Weinreich et al., 2015), causing respiratory-related sleep disturbances and compromised sleep quality.

In summary, the effect of air pollution on health has been an emerging area of research, and current literature demonstrates a positive relationship between pollutant exposure and poor sleep outcomes.

3. Implications and conclusion

As demonstrated in the preceding integrated review, the five presented environmental exposures all have negative consequences on subjective and objective sleep outcomes, including general sleep quality, disturbances during the night, and daytime dysfunction, in both children and adults.

This conclusion is derived from different lines of research designs including cross-sectional observational, longitudinal cohort, population-based, and animal studies. The converging evidence supports positive associations between various environmental exposures, including heavy metals, noise pollution, light pollution, second-hand smoke, and air pollution, and poor sleep health, although studies are still emerging. Associations were observed with many sleep outcomes for each environmental exposure over the course of the lifespan. Nevertheless, the exact relationship between light exposure and sleep is less conclusive relative to the other discussed environmental factors, as some literature suggests that certain light exposure may be beneficial to sleep outcomes. Potential mechanistic pathways broadly point to the disruption of various body systems, including nervous, respiratory, and cardiovascular, as a result exposure to environmental factors, which may affect sleep patterns.

The extensive evidence presented by current research calls for increased awareness, additional research, and accompanying policy changes. Despite the volume of literature studying the relationship between environmental exposures and sleep health, the underlying biological mechanisms of action are still largely unknown. Additional research is required to elucidate these toxicological pathways linking environmental exposures and poor sleep outcomes, which can then in turn inform potential therapeutic measures to counteract the adverse effects of pollutant exposure. Furthermore, despite the abounding research demonstrating the detrimental health outcomes related to exposure to physical pollutants, light, and noise, little has been done to lessen exposure to these pollutants. The current state of knowledge highlights the need for a greater effort to reduce exposure to harmful environmental factors and unnatural amounts of light and noise to promote improved health across the life course. As a major contributor to work productivity and human health, the importance of sleep health is of prioritized concern and should thus be considered in future policy decisions regarding chemical, noise and light pollution regulations.

Acknowledgments

Funding

This work was supported by the National Institutes of Health R25-ES021649 and the University of Pennsylvania Center of Excellence in Environmental Toxicology P30-ES013508 National Institutes of Health R01HD087485.

Footnotes

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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