Kitchen emissions are mixed indoor air pollutants with adverse health effects, but the large-scale assessment is limited by costly equipment and survey methods. This study aimed to discuss the application of backpropagation (BP) neural network models in the assessment of kitchen emissions based on the exposure marker. A total of 3686 participants were recruited for the kitchen survey, and their sleep quality was measured by the Pittsburgh sleep quality index (PSQI). After excluding the confounders, 365 participants were selected to assess their urinary hydroxy polycyclic aromatic hydrocarbons (OH-PAHs) concentrations by ultra-high-performance liquid chromatography/tandem mass spectrometry. Two BP neural network models were then set up using the survey and detection data from the 365 participants and used to predict the total urinary OH-PAHs concentrations of all participants. The total urinary OH-PAHs and 1-hydroxy-naphthalene (1-OHNap) concentrations were significantly higher among the 365 participants with poor sleep quality (global PSQI score > 5; P < 0.05). Results from internal and external validation showed that our model has high credibility (model 2). Further, the participants with higher predicted total urinary OH-PAHs concentrations were associated with the global PSQI score of >5 (odds ratio (OR) = 1.284, 95% confidence interval (CI) = 1.082–1.525 for participants with predicted total urinary OH-PAHs concentrations of over 1.897 μg/mmol creatinine in model 1, and OR = 1.467, 95% CI = 1.240–1.735 for participants with predicted total urinary OH-PAHs concentrations of over 2.253 μg/mmol creatinine in model 2) after adjusting for the confounders. Findings suggest that the BP neural network model is suitable for assessing kitchen emissions, and the urinary OH-PAHs concentrations can be taken as the model outlay.

This study aimed to provide a comprehensive characterisation of the indoor air quality during the sleeping period of 10 couples at Lisbon dwellings, using a multi-pollutant approach, and to understand how the compliance with legislation and guidelines was to assure a good indoor air quality. The assessment of indoor air quality was conducted in the cold season using real time monitors during the sleeping period for comfort parameters (temperature and relative humidity) and air pollutants (carbon dioxide – CO₂, carbon monoxide – CO, formaldehyde – CH₂O, total volatile organic compounds – VOCs, and particulate matter – PM₂.₅ and PM₁₀), together with active sampling of bioaerosols (fungi and bacteria) before and after the sleeping period. Lower compliance (less than 50% of the cases) with the Portuguese legislation was found for temperature, CO₂ (3440 ± 1610 mg m⁻³), VOCs (1.79 ± 0.99 mg m⁻³) and both bioaerosol types. In 70% of the cases, PM₂.₅ (15.3 ± 9.1 μg m⁻³) exceeded the WHO guideline of 10 μg m⁻³. All bedrooms presented air change rates above the recommended minimum value of 0.7 h⁻¹, highlighting that a good indoor air quality during sleep is not guaranteed.

Electrohypersensitive people attribute various symptoms to exposure of radiofrequency electromagnetic fields (RF-EMF); sleep disturbance is the most frequently cited. However, laboratory experiments have yielded conflicting results regarding sleep alterations. Our hypothesis was that exposure to RF-EMF alone would lead to slight or non-significant effects but that co-exposure to RF-EMFs and other environmental constraints (such as noise) would lead to significant effects.3-week-old male Wistar rats (4 groups, n = 12 per group) were exposed for 5 weeks to continuous RF-EMF (900 MHz, 1.8 V/m, SAR = 30 mW/kg) in the presence or absence of high-level noise (87.5 dB, 50–20000 Hz) during the rest period. After 5 weeks of exposure, sleep (24 h recording), food and water intakes, and body weight were recorded with or without RF-EMF and/or noise. At the end of this recording period, sleep was scored during the 1 h resttime in the absence of noise and of RF-EMF exposure.Exposure to RF-EMF and/or noise was associated with body weight gain, with hyperphagia in the noise-only and RF-EMF + noise groups and hypophagia in the RF-EMF-only group. Sleep parameters recording over 24 h highlighted a higher frequency of active wakefulness in the RF-EMF-only group and a lower non-rapid eye movement/rapid eye movement sleep ratio during the active period in the noise-only group. There were no differences in sleep duration in either group. During the 1-h, constraint-free sleep recording, sleep rebound was observed in the noise-only group but not in the RF-EMF-only and RF-EMF + noise groups.Our study showed effects of RF-EMF, regardless of whether or not the animals were also exposed to noise. However, the RF-EMF + noise group presented no exacerbation of those effects. Our results did not support the hypothesis whereby the effects of RF-EMF on physiological functions studied are only visible in animals exposed to both noise and RF-EMF.

Traffic noise stresses and disturbs sleep. It has been associated with various diseases, and has recently also been associated with lifestyle. Hence, the association between traffic noise and disease could partly operate via a pathway of lifestyle habits, including smoking and alcohol intake.We investigated associations between modelled residential traffic noise and smoking habits and alcohol consumption.In a cohort of 57,053 participants, we performed cross-sectional analyses using data from a baseline questionnaire (1993-97), and longitudinal analyses of change between baseline and follow-up (2000-02). Smoking status (never, former, current) and intensity (tobacco, g/day) and alcohol consumption (g/day) was self-reported at baseline and follow-up. Address history from 1987-2002 for all participants were found in national registries, and road traffic and railway noise was modelled 1 and 5 years before enrolment, and from baseline to follow-up. Analyses were performed using logistic and linear regression, and adjusted for demographics, socioeconomic variables, leisure-time sports, and noise from the opposite source (road/railway).Road traffic noise exposure 5 years before baseline was positively associated with alcohol consumption (adjusted difference per 10 dB: 1.38 g/day, 95% confidence interval (CI): 1.10–1.65), smoking intensity (adjusted difference per 10 dB: 0.40 g/day, 95% CI: 0.19–0.61), and odds for being a current vs. never/former smoker at baseline (odds ratio (OR): 1.14; 95% CI: 1.10–1.17). In longitudinal analyses, we found no association between road traffic noise and change in smoking and alcohol habits. Railway noise was not associated with smoking habits and alcohol consumption, neither in cross-sectional nor in longitudinal analyses.The study suggests that long-term exposure to residential road traffic is associated with smoking habits and alcohol consumption, albeit only in cross-sectional, but not in longitudinal analyses.

Light pollution or artificial light at night (ALAN) is an increasing, worldwide challenge that affects many aspects of animal behaviour. Interestingly, the response to ALAN varies widely among individuals within a population and variation in personality (consistent individual differences in behaviour) may be an important factor explaining this variation. Consistent individual differences in exploration behaviour in particular may relate to the response to ALAN, as increasing evidence indicates its relation with how individuals respond to novelty and how they cope with anthropogenic modifications of the environment. Here, we assayed exploration behaviour in a novel environment as a proxy for personality variation in great tits (Parus major). We observed individual sleep behaviour over two consecutive nights, with birds sleeping under natural dark conditions the first night and confronted with ALAN inside the nest box on the second night, representing a modified and novel roosting environment. We examined whether roosting decisions when confronted with a camera (novel object), and subsequently with ALAN, were personality-dependent, as this could potentially create sampling bias. Finally, we assessed whether experimentally challenging individuals with ALAN induced personality-dependent changes in sleep behaviour.Slow and fast explorers were equally likely to roost in a nest box when confronted with either a camera or artificial light inside, indicating the absence of personality-dependent sampling bias or avoidance of exposure to ALAN. Moreover, slow and fast explorers were equally disrupted in their sleep behaviour when challenged with ALAN. Whether other behavioural and physiological effects of ALAN are personality-dependent remains to be determined. Moreover, the sensitivity to disturbance of different behavioural types might depend on the behavioural context and the specific type of challenge in question. In our increasingly urbanized world, determining whether the effects of anthropogenic stressors depend on personality type will be of paramount importance as it may affect population dynamics.

Accumulating exposure to quality green space over time is posited to influence child health, yet longitudinal studies are scarce. This study aimed to examine the associations between trajectories of perceived green space quality and child health-related outcomes. We used data from 1874 childrenin the B-cohort of the Longitudinal Study of Australian Children who participated in the Child Health Checkpoint module at 11–12 years. Data on caregiver perceived green space quality measured biennially was assessed using discrete trajectory mixture models to group children by contrasting distributions in green space quality over time. Examination of associations between trajectory groups of perceived green space quality and child biomarkers (i.e., albumin-to-creatinine ratio, total, cholesterol, total triglycerides, and glucose), physical health and behavioural assessments (i.e., anthropometric measurements, blood pressure, sedentary behaviour, physical activity, sleep, aerobic work capacity, and general wellbeing), and health care use were assessed using multilevel models, adjusted for sociodemographic variables. Four perceived green space quality trajectories were identified: “decreasing quality from high to moderate”; “increasing quality from low to high”; “consistently high quality”; “consistently low quality”. Compared with consistently low levels of quality green space, adjusted models indicated consistently high-quality green space was associated with lower total triglycerides (β −0.13; 95%CI -0.25, −0.01). Lower odds of hospital admission was observed among children who accumulated quality green space over time (OR 0.45; 95%CI 0.23, 0.87). These associations were observed in boys only in sex-stratified analyses. Moreover, boys accumulating quality green space through time tended to have lower diastolic blood pressure (β −2.76; 95%CI -5.17, −0.35) and girls who experienced loss in quality green space tended to have a higher percentage of body fat (β 2.81; 95%CI 0.43, 5.20). Accumulating quality green space over time is important for various aspects of child health, with contrasting benefits by sex.

Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM₂.₅) and ≤10 μm (PM₁₀) led to 0.4% (95% confidence interval (CI): 1.002–1.006) and 0.3% (95% CI: 1.001–1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM₂.₅ and PM₁₀ were 2.2% (95% CI: 1.012–1.032) and 2.2% (95% CI: 1.012–1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.

Emerging evidence witnesses the association of air pollution exposure with sleep disorders or the risk of obstructive sleep apnea (OSA); however, the results are not consistent. OSA patients with or without a low arousal threshold (LAT) have different pathology and therapeutic schemes. No study has evaluated the potential diverse effects of air pollution on the phenotypes of OSA. The current study aimed to evaluate the associations of short-term and long-term exposure to air pollution with sleep-disordered measures and OSA phenotypes. This cross-sectional study consisted of 4634 participants from a sleep center in Taipei from January 2015 to April 2019. The personal exposure to ambient PM₂.₅ and NO₂ was assessed by a spatial-temporal model. Overnight polysomnography was used to measure the sleep parameters. According to a developed clinical tool, we defined the low arousal threshold (LAT) and identified the OSA patients with or without LAT. We applied a generalized linear model and multinomial logistic regression model to estimate the change of sleep measures and risk of the OSA phenotypes, respectively, associated with an interquartile range (IQR) increment of personal pollution exposure after adjusting for the essential confounders. In the single-pollutant model, we observed the associations of NO₂ with sleep-disordered measures by decreasing the total sleep time, sleep efficiency, extending the time of wake after sleep onset, and the association of NO₂ with the increased risk of LAT OSA by around 15%. The two-pollutant model with both long-term and short-term exposures confirmed the most robust associations of long-term NO₂ exposure with sleep measures. An IQR increment of NO₂ averaged over the past year (6.0 ppb) decreased 3.32 min of total sleep time and 0.85% of sleep efficiency. Mitigating exposure to air pollution may improve sleep quality and reduce the risk of LAT OSA.

In modern society the night sky is lit up not only by the moon but also by artificial light devices. Both of these light sources can have a major impact on wildlife physiology and behaviour. For example, a number of bird species were found to sleep several hours less under full moon compared to new moon and a similar sleep-suppressing effect has been reported for artificial light at night (ALAN). Cloud cover at night can modulate the light levels perceived by wildlife, yet, in opposite directions for ALAN and moon. While clouds will block moon light, it may reflect and amplify ALAN levels and increases the night glow in urbanized areas. As a consequence, cloud cover may also modulate the sleep-suppressing effects of moon and ALAN in different directions. In this study we therefore measured sleep in barnacle geese (Branta leucopsis) under semi-natural conditions in relation to moon phase, ALAN and cloud cover. Our analysis shows that, during new moon nights stronger cloud cover was indeed associated with increased ALAN levels at our study site. In contrast, light levels during full moon nights were fairly constant, presumably because of moonlight on clear nights or because of reflected artificial light on cloudy nights. Importantly, cloud cover caused an estimated 24.8% reduction in the amount of night-time NREM sleep from nights with medium to full cloud cover, particularly during new moon when sleep was unaffected by moon light. In conclusion, our findings suggest that cloud cover can, in a rather dramatic way, amplify the immediate effects of ALAN on wildlife. Sleep appears to be highly sensitive to ALAN and may therefore be a good indicator of its biological effects.