Environmental perceptions, self-regulation, and coping with noise mediate the associations between children's physical environment and sleep and mental health problems

Environmental perceptions, self-regulation, and coping with noise mediate the associations between children’s physical environment and sleep and mental health problems Peter Lercher a,1 , Angel M. Dzhambov a,b,1,* , Kerstin Persson Waye c a Institute of Highway Engineering and Transport Planning, Graz University of Technology, Graz, Austria b Environmental Health Division, Research Institute at Medical University of Plovdiv, Medical University of Plovdiv, Plovdiv, Bulgaria c School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden ARTICLE INFO Keywords: Air pollution Emotion regulation Green space Noise annoyance Restorative quality Traffic noise ABSTRACT Background: Children face various challenges in their home and extended neighborhood settings. In this study, we examine the impact of the built and social environments on sleep/mental health and the potential mediating role of environmental perceptions, self-regulation, and coping with noise. Methods: Cross-sectional data for 1251 schoolchildren (8–12 years) were sampled in the Tyrol region of Austria/ Italy. Questionnaires provided information on sociodemographic and housing factors, perceived neighborhood quality, coping with noise during homework, self-regulation, sleep, and mental health problems. A built environment score was based on modeled levels of road and rail traffic noise, nitrogen dioxide, and imperviousness density. Home garden represented availability of accessible greenspace. Associations between predictors and mental health/sleep problems were examined using quantile regressions and structural equation modeling (SEM). Results: In multivariate regressions, poor neighborhood quality, poor self-regulation, low traffic safety, and higher coping efforts were associated with more mental health and sleep problems. Good family relations acted in the opposite direction. In SEM, the built environment score was associated with lower neighborhood quality and lower traffic safety, which in turn led to higher coping efforts, and then to mental health/sleep problems. Home gardens related to less sleep problems through higher perceived neighborhood quality and lower coping efforts. Good family relations were associated with better mental health/sleep directly and via better selfregulation and lower coping efforts. Conclusions: Children forced to engage in coping activities when disturbed by noise during homework show poorer mental health. Good family relations, good neighborhood quality, and close-by greenspace may be factors to alleviate built environment stressors. The negative association of required coping with noise during homework suggests that children, in contrast to adults, may be limited in their coping abilities. Our findings call for further inquiries, as children and their environments may vary with respect to coping efficiency. 1. Introduction Child mental health is a multifaceted construct that is dynamically shaped by transactions between the child and their surrounding physical and social environments (Persson Waye et al., 2023; Gudi-Mindermann et al., 2023). Throughout their development, children encounter various challenges and demands in their home and extended neighborhood settings (Persson Waye et al., 2023). Adversities that can undermine psychological functioning involve for example socioeconomic deprivation and family instability, but physical stressors can also harm child mental health via biological pathways (Basu and Banerjee, 2020) and through restricted opportunities for recreation, psychological restoration, and sensory and experiential enrichment (Christian et al., 2015). For example, traffic noise can impair cognitive development, reading * Corresponding author. Environmental Health Division, Research Institute at Medical University of Plovdiv, Medical University of Plovdiv, 15A Vasil Aprilov Blvd. 4002, Plovdiv, Bulgaria. E-mail addresses: [email protected], [email protected] (A.M. Dzhambov). 1 Equal first co-authorship. Contents lists available at ScienceDirect Environmental Research journal homepage: www.elsevier.com/locate/envres https://doi.org/10.1016/j.envres.2024.120414 Received 10 August 2024; Received in revised form 5 November 2024; Accepted 20 November 2024 Environmental Research 264 (2025) 120414 Available online 21 November 2024 0013-9351/© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). comprehension, sleep and school achievement (Klatte et al., 2017; Stansfeld and Clark, 2015; Clark and Paunovic, 2018; Foraster et al., 2022) as well as neurodevelopment in general, though the evidence of that is heterogeneous (Zare Sakhvidi et al., 2018). Although exposure to traffic noise at night is a known critical stressor undermining normal sleep, only few studies exist in children (¨ Ohrstr¨ om et al., 2006; Bruni et al., 2011; Tiesler et al., 2013; Skrzypek et al., 2017; Weyde et al., 2017) and results are inconsistent. It has been suggested that children are less likely to awaken by noise events than adults (Eberhardt, 1988), while noise-related cardiovascular reactions and motility are more pronounced (Muzet, 2007). A study by ¨ Ohrstr¨ om et al. (2006) provided contradictive results between self-reported data and actigraphy measured sleep in so far that sleep logs indicated that children reported better perceived sleep quality and fewer awakenings than parent reports on child sleep. It is well-known that the disruption of normal sleep of children can result in a broader spectrum of adverse health effects, among which are neurodevelopmental problems, poor emotional-behavioral regulation and psychosocial health (Chaput et al., 2017; Matricciani et al., 2019). However, studies on noise-related sleep problems in children have linked only classic acoustic indicators to sleep problems and not investigated potential mediating effects of sleep on mental health problems. This applies also to the literature on the potential effects of air pollution on children’s sleep. Although some studies have observed relationships between air pollution and sleep (Lawrence et al., 2018; Mayne et al., 2021), very few studies have investigated how air pollution affects mental health via sleep. Coping, which refers to purposeful efforts to regulate processes like cognition, emotions, behavior, physiology, and environmental context when challenged with a stressor (Compas et al., 2017), is important for child adaptation and normal development. However, children can be differentially affected by stressors depending on the coping styles that they employ to manage stress (Compas et al., 2017). Since children are in a sensitive neurodevelopmental stage and exhibit a broad range of vulnerabilities (Diderichsen et al., 2019), it is important to understand how the built and natural environments that they grow up in influence cognitive resources and strategies required to support mental health. The ability to cope with stress effectively often requires attentional control, regulation of emotions, behavior, and impulses; those resources may be undermined by the home environment through increased allostatic load caused by chaotic living conditions, air pollution, and noise contributing to chronic stress and dysregulating processes in the brain (e.g., in the prefrontal cortex) that underly executive functioning and attentional processes involved in self-regulation (Ursache et al., 2022; Bagais and Pati, 2023). Conversely, exposure to natural outdoor environments encourages longer play time and physical activity, greater exposure to daylight, and allows for restoration of attentional mechanisms that become fatigued as a result of ongoing efforts to adapt to stressors (Buczyłowska et al., 2023). Self-regulatory faculties are a finite resource requiring periodic restoration (Muraven and Baumeister, 2000). Therefore, stress-dampening and restorative experiences in environments perceived as calm, safe, and socially supportive can offset the activation of physiological stress pathways that disrupt normal neurodevelopment, sleep patterns, and cognition. Nature contact can also decrease maladaptive emotion regulation strategies such as rumination and worry (Vitale and Bonaiuto, 2024). However, direct evidence for the impact of the physical environment on child self-regulation is much more limited than for psychosocial stressors (e.g., Ursache et al., 2022). For example, living in a noisy (Zare Sakhvidi et al., 2018) and air polluted (Castagna et al., 2022) areas has been linked to neurodevelopmental problems. Other influences can be protective, such as greener neighborhoods activating pathways to better cognitive functioning (Buczyłowska et al., 2023) and self-regulatory capacity (Weeland et al., 2019; Vitale and Bonaiuto, 2024). As a closely related perceptual aspect of the neighborhood environment, the sense of safety plays a role in the development of children’s emotional processing and self-regulation (McCoy et al., 2016), engagement in physical activity and active mobility behavior (Carver et al., 2008), and in general, mental health (Meltzer et al., 2007). In a longitudinal UK study, indoor dampness, second-hand smoke, and TV noise predicted emotional dysregulation in early and middle childhood (Oloye and Flouri, 2021), but garden access and neighborhood greenspace did not come up as protective (Mueller and Flouri, 2020; Oloye and Flouri, 2021), even though garden activities have been found to support adolescents’ wellbeing (van Lier et al., 2017). Overall, built environment indicators in combination with coping activities have been explored only in a few studies with children. Moreover, there have been few attempts to examine indirect pathways between the physical environment, coping, and mental health in an integrated way. In the present study, we leverage existing data from schoolchildren living in several Alpine valleys (Dzhambov et al., 2024) to examine the relative impact of the built and social environments on sleep and mental health and the potential mediating role of environmental perceptions, self-regulation, and coping with noise. The study is embedded within the Equal-Life project, part of the European Human Exposome Network, which undertakes to explore the effects of the exposome on mental health and cognitive development in children and adolescents (van Kamp et al., 2021). Equal-Life applies an often neglected but required holistic perspective to children’s mental health and cognition. 2. Methods 2.1. Study procedures and sampling We collected a cross-sectional sample of 1251 children aged 8–12 years from 3rd and 4th grade. Children were sampled in 2004–2005 from 49 public schools in the Tyrol region of Austria and Northern Italy. The study area stretched across several alpine valleys including the Lower Inn valley, Wipp valley, and three side valleys extending outwards from the Wipp valley. The area is mostly low in urbanization and brings together heavy-duty traffic lines running along the Lower Inn and Wipp valley floors and high vegetation cover of the mountain slopes. Meteorological conditions and local topography vary across these valleys, which leads to differential patterns of sound propagation and air pollution dispersion. See Dzhambov et al. (2019) for details on study design and setting. Information on living conditions, perceptions of the neighborhood environment, mental wellbeing and other psychological and contextual variables was collected with questionnaires that children and their mothers filled out. Survey data were then linked to geographic variables characterizing the built and natural neighborhood environments. Ethical approval was obtained from the Ethics committee of the Medical University Innsbruck (Ethics commission number 2105/2004). 2.2. Built environment and greenspace Traffic-related noise and air pollution were calculated at the residential address coordinates using bespoke modelling tailored to the specifics of the study area. Modelling details are reported elsewhere (Dzhambov et al., 2019). Briefly, we assigned each address day-evening-night sound levels (L den ) from traffic on the highway and other roads, and railway traffic in the valleys. Long-term exposure to traffic-related air pollution was represented by mean annual concentration of nitrogen dioxide (NO 2 ) calculated with a meteorological model, Graz Mesoscale Model, at a horizontal resolution of 10 ×10 m and a vertical resolution of 2 m. Greyspace or built-up area was measured as the percentage of artificially sealed soil in a 100 m buffer area around the home address. This imperviousness density indicator ranged from 0 to 100%, and data were sourced from the Copernicus repository with a pixel resolution of 20 ×20 m (https://land.copernicus.eu/user-corner/technical-librar y/hrl-imperviousness-technical-document-prod-2015). Imperviousness density was calculated using automatic derivation P. Lercher et al. Environmental Research 264 (2025) 120414 2 based on calibrated Normalized Difference Vegetation Index (NDVI), therefore its correlation with NDVI did not allow for testing both variables together. Instead, the potentially positive influence of residential greenspace on sleep quality and mental health was represented by the presence of a domestic garden (no vs. yes) reported by the child’s mother (cf. van Lier et al., 2017). Gardens included private greenspace, green yards, orchards, floral gardens, or vegetable gardens. In a sensitivity analysis, we used NDVI derived for a 100 m buffer around the residence instead of home garden, and excluded imperviousness density from the model to avoid collinearity. Briefly, NDVI was calculated at a spatial resolution of 30 ×30 m using cloud-free images from summertime (July–August in 2003) from the Landsat 4–5 Thematic Mapper. All pixels with open/surface water as indicated in Open Street Map data (https://www.openstreetmap.org/) were set to missing values to prevent suppressing the effect of greenspace (Dzhambov et al., 2019). Spatial analyses were conducted in ArcMap 10.6 and QGIS v3.8. 2.3. Mental health and sleep problems Selected items from an early version of the KINDL questionnaire (Ravens-Sieberer and Bullinger, 1998) were used to operationalize mental health problems based on the child’s agreement with statements about (1) feeling alone, (2) laughing a lot and having fun (reversed), (3) feeling bored, and (4) feeling anxious (experiencing fear). The child reported the frequency of occurrence of these feelings on a 5-point Likert scale (1 =never, 2 =seldom, 3 =sometimes, 4 =often, or 5 =very often). McDonald’s ω for this scale was 0.57. Children were asked about recent (1) problems falling asleep, (2) uneasy sleep, and (3) feeling tired in the morning. These items tap the core symptoms of insomnia according to the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition and are typically a core part of questionnaires on sleep quality (Fabbri et al., 2021). Answers were provided on the same 5-point scale as for mental health problems. A summary sleep problems scale was created from responses to these items. McDonald’s ω for this scale was 0.55. 2.4. Perceived neighborhood quality Given that perceptions of the neighborhood environment are likely to shape outdoor behavior and stress response of children, we asked children to rate neighborhood characteristics that we consider may be restorative and their sense of road safety. Albeit conceptually related, these constructs can both predict overall perceived restorativeness of a setting (Strag` a et al., 2023). A perceived neighborhood quality scale was computed as the sum of responses to seven items asking about neighborhood characteristics that could mediate the effect of traffic emissions, built-up areas, and home gardens. Children reported if their neighborhood (1) had a lot of space to play, (2) meadows and trees, and (3) clean air, if it was (4) quiet, if (5) children were allowed to run around, if (6) people were helpful to children, and (7) if children generally enjoyed living there. Responses were given on a Likert scale ranging from 1 to 4 (1 =not right at all, 2 =rather not right, 3 =rather right, 4 =fully right). McDonald’s ω for this scale was 0.70. In addition, children were asked about their perception of safety on the way to school using three items (“I need to watch out for cars”, “My way to school is dangerous”, and “I am afraid of a traffic accident when walking to school”). Responses were given on the same four-point scale. The item scoring was reversed and a summary neighborhood traffic safety scale was generated. McDonald’s ω for this scale was suboptimal (0.55). 2.5. Coping and self-regulation Seven questions related to the frequency of coping with noise were asked after children reported if they were disturbed by road and rail traffic noise during homework (i.e., children had a response option “I am never disturbed by noise during homework”). If the child reported no noise during homework, the coping activity item was set to a value of zero. For a sensitivity analysis, we excluded children reporting no noise exposure to make sure the coding of the coping variable did not serve as a proxy for noise exposure. If there was noise reported but no disturbance reported, coping was set to one. Of note, inspection of the data revealed that some children first reported no disturbance to the filtering question, but later they marked a few coping activities. In such instances, we corrected the “not disturbed” response to the reported frequency of the respective coping behavior. The list of coping behaviors when the child felt disturbed by noise during homework is shown in Supplementary Section S1. Reported frequencies of these behaviors (never, sometimes, often, most of the time) were summed up. Although we recognize that different classifications of coping styles have been proposed (Holen et al., 2012), in our sample all items seemed to reflect a unidimensional latent construct, as suggested by an exploratory factor analysis (EFA) (See Supplementary Section S1). The items were thus allowed to form one latent factor tapping the demands to cope with noise (McDonald’s ω =0.92). Self-regulation was probed with five items from the Needleman questionnaire on school behavior (Needleman et al., 1979). School teachers rated three behavioral and two emotional aspects of self-regulation using a yes/no scale. See Supplementary Section S2 for the wording of specific items. EFA suggested that the items belonged to one factor (See Supplementary Section S2). This scale had acceptable internal consistency (McDonald’s ω =0.72). 2.6. Family relations Two items referring to good family relations during the past week were used from the KINDL questionnaire (“My child got on well with us as parents”, “My child felt fine at home”; r =0.58) (Ravens-Sieberer and Bullinger, 1998). Reponses to these items were rated by the parent on a five-point scale (1 =never, 2 =seldom, 3 =sometimes, 4 =often, or 5 = very often) and these were summed up for the analysis. 2.7. Other covariates Information on potential confounding factors and effect modifiers was collected through child and parent-completed questionnaires. Sociodemographic variables included child’s age, sex, and maternal education reported by the mother. Maternal education was categorized as basic for ≤9 years of school, skilled labor, vocational, and higher/Alevel, and served as a proxy for the family’s socioeconomic status. Noise sensitivity was reported by the child on a single-item five-point scale (1 =not at all to 5 =very much). 2.8. Statistical analysis Variables were first described and their distribution was examined. Due to missing data on different variables, some statistical tests used a smaller analysis sample (i.e., 1190 observations in the main analysis). We then followed with bivariate tests of association between the variables using Spearman, point-biserial, and phi correlations. Quantile regression was used to examine the associations between the main predictor, mediator, and the conditional median of mental health problems and sleep problems. This model was selected as it makes no distributional assumptions about the dependent variables. L den , NO 2 , and greyspace were entered as observed variables in the predictor set, and individual items measuring latent psychological constructs were summed up and used as summary scores in the regressions. Variance inflation factors <5 and tolerance values >0.2 suggested no collinearity. Structural equation modelling (SEM) was used as the main technique to test how well the data fitted the conceptual model we developed (see Fig. 1). For the main analysis, i.e., a SEM, we constructed a latent Built P. Lercher et al. Environmental Research 264 (2025) 120414 3 environment variable that loaded onto the three indicators – L den , NO 2 and greyspace. Coping, traffic safety, family relations, self-regulation, mental health problems, sleep problems, and perceived neighborhood quality were also modeled as latent variables. We anticipated that higher level of exposure to the built environment and lack of access to a home garden would be associated with more mental health and sleep problems not only directly but also that these associations would be mediated by more unfavorable perceptions of the residential environment and its safety, poorer self-regulation, and lower efforts to cope with noise. At the same time, good family relations would lead to better mental health and sleep both directly and through better self-regulation and lower requirements to cope with noise. We also assumed direct pathways from these variables to mental health and sleep in order to evaluate how much of the total effect was explained by the putative mediators. High noise sensitivity was also hypothesized to lead to mental health and sleep problems and to higher coping efforts. In the sensitivity analysis with NDVI instead of home garden (conceptual diagram not shown), we added another pathway from NDVI to traffic safety. In another sensitivity analysis, we refitted the SEM after excluding children who had reported no noise exposure. SEM was used as a confirmatory technique to determine how well this theory-based model fitted the data. All constructs operationalized with multiple items were specified as single-factor latent variables loading onto observed items. We assumed a-priory correlations between mental health and sleep problems, between perceived neighborhood quality and safety, and between built environment and domestic garden presence. All observed variables were standardized and the model was estimated from polychoric correlations using the diagonally weighted least squares estimator (DiStefano and Morgan, 2014). Robust standard errors and respective confidence intervals were computed for all path coefficients. Presence of significant indirect effects (i.e., the product of the path coefficients associated with their constituent paths) was assumed when the 95% confidence interval around the estimate did not contain zero. Model fit was evaluated on the basis of a set of commonly used indices and corresponding cut-off criteria proposed by Hu and Bentler (1999): non-significant χ 2 (p >0.05); comparative fit index (CFI) ≥0.95; root mean square error of approximation (RMSEA) ≤0.06 with a 90% CI ≤0.06; and standardized root mean squared residual (SRMSR) ≤0.08. A parsimony normed fit index (PNFI), which takes into account the complexity of the model, was expected to be >0.50 (Iacobucci, 2010). Inspection of model residuals and modification indices would be used to refine the model in case points of ill fit were identified. Statistically significant findings were evaluated at the p <0.05 level (two-tailed). SEM was conducted using the lavaan package v. 0.6.17 for R v 4.3.3. Stata MP v. 18 was used for all other statistical tests. 3. Results 3.1. Sample characteristics and bivariate associations Children’s characteristics are presented in Table 1. The age range was rather narrow with most children being around 9 years old; almost half of them were male. Different levels of maternal education were equally represented in the sample. Most children lived in houses with a garden and they were exposed to moderate levels of road and rail traffic noise and air pollution. Bivariate correlations show that poor mental health was associated with poor sleep, lower neighborhood quality, higher coping with noise, higher noise sensitivity, poor family relations, low traffic safety, not having a home garden, and higher NO 2 . The same patterns were present Fig. 1. Conceptual model of the hypothesized associations between children’s physical residential environment and potential mediators leading to mental health and sleep problems. Note. Arrows in red indicate inverse hypothesized associations, and green arrows positive hypothesized associations. Table 1 Summary of child characteristics. Characteristics Median (Q 25th-75th) or N (%) Socio-demographics Age [years] 9.35 (8.85–9.87) Male sex 623 (49.80) Maternal education Basic 279 (23.08) Skilled labor 396 (32.75) Vocational 287 (23.74) A-level 247 (20.43) Perceptual variables Mental health problems [4–19 scale] 6.00 (5.00–8.00) Sleep problems [3–15 scale] 7.00 (4.00–9.00) Neighborhood quality [10–28 scale] 24.00 (22.00–26.00) Low traffic safety [3–12 scale] 6.00 (4.00–7.00) Poor self-regulation [0–5 scale] 1.00 (0.00–2.00) Coping with noise [0–28 scale] 11.00 (0.00–16.00) Good family relations [2–10 scale] 10.00 (8.00–10.00) Noise sensitivity [1–5 scale] 2.00 (1.00–2.00) Physical environment NO 2 [ μ g/m³] 14.35 (9.86–22.19) L den [dB] 49.89 (42.54–59.54) Greyspace [0–100%] 26.31 (8.81–46.84) Home garden 927 (74.82) NDVI 100 m 0.43 (0.33–0.53) Note. Abbreviations: L den – address day-evening-night sound level from all traffic sources in dB, NDVI – normalized difference vegetation index, NO 2 – annual mean nitrogen dioxide level in μ g/m³. P. Lercher et al. Environmental Research 264 (2025) 120414 4 for poor sleep, and in addition, it was associated to lower greenness level and higher imperviousness in the 100 m buffer (see Fig. 2). Other interesting correlations were those between the perceptual variables, where environmental characteristics perceived as unfavorable related to worse self-regulation and coping with noise, while good family relations seemed to counter poor self-regulation, coping, and noise sensitivity. Associations between the geographic variables were expected, with traffic emissions and greyspace correlating strongly among them and inversely with NDVI and home garden presence. 3.2. Regression modeling Multivariate associations between neighborhood environment, perceptions, and children’s mental health and sleep problems are given in Table 2. Following mutual adjustment, poor neighborhood quality, low traffic safety, poor self-regulation, noise coping efforts, and poor family relations were associated with more mental health problems. Interestingly, higher L den was associated with less mental health problems. Sleep problems were predicted by poor neighborhood quality, low traffic safety, and coping with noise. These models did not explain much of the dependent variables though (R 2 =0.07 for mental health problems and 0.08 for sleep problems). 3.3. Structural equation modeling The model converged normally after 102 iterations. No post hoc modifications were indicated as the model had a reasonably good fit to the data: χ 2 (650) =1273.143, p <0.001; CFI =0.972; PNFI =0.829; RMSEA =0.028 (90% CI: 0.025, 0.030); SRMR =0.039. A good amount of the variance in mental health problems (29%) and sleep problems (23%) was explained by this model. Better mental health and sleep were observed with higher levels of perceived traffic safety, lower noise sensitivity, better family relations, and lower demand to cope with noise (Fig. 3). Table 3 shows the estimated total and indirect effects of the exposures of interest (see Supplementary Table S1 and Fig. S1 for all pathways in the model). High built environment scores were associated with poor mental health and sleep problems only indirectly via perceptions of lower neighborhood quality and lower traffic safety in the neighborhood, which in turn led to higher coping efforts. Having a garden was related to less sleep problems through higher perceived neighborhood quality and less coping efforts. Good family relations were associated with better self-regulation and lower demand to cope with noise, and thus with better mental health and sleep. Overall, the estimated total effects showed that gardens and good family relations related to better mental health and less sleep problems, while higher built environment exposure related to more sleep Fig. 2. Spearman correlations between key variables in the study. Note. Abbreviations: L den – day-evening-night sound level from all traffic sources in dB, NO 2 – annual mean nitrogen dioxide level in μ g/m³. Minimum number of observations per variable pair =1167. The color ramp from dark blue to dark red colors represents the strengths of correlation ranging from −1 to +1, respectively. Table 2 Multivariate associations between perceptions, neighborhood environment and children’s mental health and sleep problems. Predictors Outcomes Mental health problems (N = 1063) Sleep problems (N = 1073) Neighborhood quality ¡0.10 (-0.16, -0.04)* ¡0.08 (-0.16, -0.003)* Low traffic safety 0.21 (0.13, 0.29)* 0.21 (0.10, 0.33)* Poor self-regulation 0.23 (0.11, 0.36)* 0.12 (−0.05, 0.29) Coping with noise 0.05 (0.02, 0.07)* 0.07 (0.04, 0.10)* Good family relations ¡0.24 (-0.42, -0.07)* −0.24 (−0.48, 0.00) Noise sensitivity 0.13 (−0.05, 0.31) 0.11 (−0.14, 0.36) NO 2 0.02 (−0.001, 0.05) −0.01 (−0.05, 0.02) L den ¡0.03 (-0.05, -0.003)* 0.03 (−0.01, 0.06) Greyspace −0.01 (−0.02, 0.0002) 0.01 (−0.01, 0.02) Home garden −0.11 (−0.53, 0.32) −0.18 (−0.76, 0.40) Age 0.003 (−0.27, 0.26) −0.01 (−0.38, 0.35) Male sex −0.27 (−0.62, 0.08) −0.33 (−0.82, 0.15) Maternal education Basic Ref.  Skilled labor −0.43 (−0.89, 0.04) −0.44 (−1.08, 0.20) Vocational −0.18 (−0.69, 0.33) −0.03 (−0.73, 0.67) A-level −0.16 (−0.69, 0.37) 0.31 (−0.41, 1.03) Note. Effect estimates shown are unstandardized beta coefficients from multivariate quantile regressions with their 95% confidence interval (CI). All predators are tested at the same time. P. Lercher et al. Environmental Research 264 (2025) 120414 5 problems. A sensitivity SEM, using NDVI instead of home garden, also fit the data well: χ 2 (612) =1143.329, p <0.001; CFI =0.976; PNFI =0.827; RMSEA =0.026 (90% CI: 0.024, 0.029); SRMR =0.038. The findings of significant pathways did not differ much (see Supplementary Fig. S2). However, NDVI led to better mental health and sleep via higher neighborhood quality and traffic safety, and in turn lower noise coping efforts. Total indirect effects of NDVI on these outcomes were significant, but not the total effects. In the other sensitivity analysis (N =754) that excluded children reporting no noise exposure, the model fit was also good: χ 2 (650) = 1026.606, p <0.001; CFI =0.963; PNFI =0.794; RMSEA =0.028 (90% CI: 0.024, 0.031); SRMR =0.044. Notably, this model explained mental health (R 2 =34%) and sleep problems (R 2 =30%) to a higher degree. The role of coping in this model did not differ and it was still associated with more mental health and sleep problems (see Supplementary Fig. S3). 4. Discussion 4.1. General overview of results This study investigated pathways linking physical neighborhood characteristics, coping with noise, and family climate to mental health and sleep problems in schoolchildren. Good family relations were robustly associated with less problems, and the opposite was observed with higher levels of traffic emissions and greyspace or not having access to a garden at home. Unlike family relations, the physical environment worked only indirectly through a sequence of processes involving perceptions of neighborhood quality, traffic safety, and self-regulation and coping with noise. Throughout childhood, supportive and cohesive family relationships are crucial for normal neurodevelopment (Bush et al., 2020). The pronounced role of the family-level factor in our study is aligned with a large body of evidence. We observed better self-regulation, lower coping efforts, and better mental health when children had more positive interactions with their parents. It is well accepted that parental support helps children develop effective strategies for regulating their behavior and emotional responses to environmental challenges and daily hassles (Morris et al., 2007). Additionally, the sense of security and stability at home supports sleep health (Tsai et al., 2018; Covington et al., 2021). Conversely, prior evidence shows that children living in dysfunctional families, exposed to chaotic living conditions, abuse, or neglect are at higher risk of developing psychopathology such as anxiety and behavioral problems (Basu and Banerjee, 2020). In part, this is explained by the impact of chronic stress on the developing brain caused by such family conditions (Bush et al., 2020); the resulting chronic disruption of sleep patterns further intensifies the negative effect on child mental health (Tsai et al., 2018; Covington et al., 2021). Functional alterations in the prefrontal cortex, which exerts top-down control over subcortical regions (Dixon et al., 2017; Friedman and Robbins, 2022) involved in adaptation to situational demands, can adversely affect the ability of children to actively deal with stressful situations in ways that contribute to personal growth and adaptation rather than reinforcing stress response (Zalewski et al., 2011). There is growing appreciation that social influences do not act in Fig. 3. Path diagram showing prominent pathways linking the neighborhood environment to children’s sleep and mental health problems in the structural equation model (N =1190). Note. Path coefficients shown are statistically significant at p <0.05; Percentage values indicate the variance explained in the respective variable. Control variables, indicators of latent variables, and pathways from control variables and those associated with non-significant coefficients are not shown to enhance clarity. The full path diagram with structural and measurement relationships are reported in Supplementary Fig. S1. Table 3 Estimated pathways from the structural equation model linking the neighborhood environment to children’s sleep and mental health problems. Path Estimate P 95% CI Lower bound Upper bound Total indirect effects Built environment → Mental health problems 0.107 0.000 0.062 0.153 Garden → Mental health problems −0.013 0.073 −0.027 0.001 Good family relations → Mental health problems ¡0.028 0.002 ¡0.046 ¡0.01 Built environment → Sleep problems 0.093 0.000 0.05 0.136 Garden → Sleep problems −0.004 0.593 −0.017 0.01 Good family relations → Sleep problems ¡0.018 0.042 ¡0.036 ¡0.001 Total effects Built environment → Mental health problems 0.033 0.42 −0.047 0.112 Garden → Mental health problems ¡0.045 0.049 ¡0.089 0.000 Good family relations → Mental health problems ¡0.149 0.000 ¡0.221 ¡0.078 Built environment → Sleep problems 0.114 0.003 0.04 0.188 Garden → Sleep problems ¡0.047 0.045 ¡0.093 ¡0.001 Good family relations → Sleep problems ¡0.106 0.003 ¡0.177 ¡0.035 P. Lercher et al. Environmental Research 264 (2025) 120414 6 isolation in shaping child neurodevelopment and have to be studied within the physical environment in which they are embedded (Christian et al., 2015; Gudi-Mindermann et al., 2023). For example, proximal child environment factors like secondhand smoke (Oloye and Flouri, 2021) have been found to negatively affect brain development, and as a child grows older, the extended neighborhood environment starts to impact neurodevelopment more strongly. Physical exposures like noise (Zare Sakhvidi et al., 2018) and air pollution (Castagna et al., 2022) trigger physiological stress responses, which if they become chronic, can alter neural development and neurotransmitter signaling, thereby undermining the substrate for normal cognitive processes involved in emotion regulation and coping behaviors needed to buffer other stressors. Nighttime noise can also reduce sleep quality and through that lead to mental health problems (Tiesler et al., 2013). In our study, there were only indirect pathways from the built environment to mental health/sleep problems and those went through perceptions of low environmental quality and safety. Additionally, self-regulation was worse in highly exposed children, which in turn also led to mental health/sleep problems. Beyond direct physiological effects, built environment stressors may work though behavior modification, which in part may explain these observed effects. Traffic safety in particular is an important determinant of child active travel behavior (Aarts et al., 2012; Amiour et al., 2022). A traffic-dominated neighborhood is seen as less conducive to outdoor play and social interaction with other people, and instead may increase the time children spend in sedentary activities (Aarts et al., 2012). Thus, children are not only deprived of the metabolic benefits of physical activity but they experience reduced sensory, cognitive, and emotional stimulation, which are important inputs for cognitive performance and mental health (cf. Dzhambov et al., 2023). This is especially true when the neighborhood is perceived as unsafe (Mayne et al., 2021). Diminished feelings of neighborhood safety can also induce sustained arousal, and thereby negatively influence sleep quality (Carson and Janssen, 2012; Bagley et al., 2016; Chaparro et al., 2019; Mayne et al., 2021). Our findings supported the important role of safety, which is one of the most consistently studied neighborhood correlates of child mental health and sleep (Mayne et al., 2021). As a positive environmental feature, accessible greenspace may activate the same pathways that presence of traffic disrupts. Greener surroundings foster recreational physical activity in children, create opportunities to meet and socialize with others, and dampen stress levels (Sprague et al., 2022; Zare Sakhvidi et al., 2023). At the same time, restoration following attentional fatigue is facilitated by spending time in green areas such as home gardens, where the fatiguing challenges of daily life are absent and one’s attention is effortlessly drawn to other pleasant aspects of the surroundings (Stevenson et al., 2019; Dzhambov et al., 2022). In earlier studies using the same sample, we found that having a home garden was associated with less school behavior problems (Dzhambov et al., 2022) and better sleep (Dzhambov et al., 2024). Here, we extended that model to encompass self-regulatory and coping pathways to mental health and sleep. Home gardens were overall associated with more favorable mental health and sleep outcomes through perceptions of higher neighborhood quality, and lower coping with noise. This finding is in line with systematic reviews on this subject covering studies across different contexts (Luque-García et al., 2022; Zare Sakhvidi et al., 2022). Mental health benefits for children living in greener areas can to some extent be attributed to better emotion and behavior regulation (Vitale and Bonaiuto, 2024; Bratman et al., 2024). Neuroimaging studies have also inferred a positive effect of greenspace exposure on neuroanatomical structures underpinning self-regulation (Dadvand et al., 2018; Kühn et al., 2023). However, we are unaware of a previous study in children that has studied the mediating role of self-regulation in the larger context of multiple physical and family-level exposures. Our study revealed that among the constructs considered, coping was an intermediary variable connecting both physical and family environments to mental health and sleep. The role of coping however calls for a judicious interpretation. Intuitively, one may assume that higher levels of coping would be protective and that coping would show similar patterns of associations with the other constructs as self-regulation. Contrary to this expectation, having to cope with noise led to worse mental health and sleep. That is not at all unlikely since coping is a complex multifaceted construct, where different coping styles may act in different directions with respect to mental health (Zalewski et al., 2011). Empirical correlations between the seven coping items in our dataset supported a one-factor structure, but since they referred to a very specific stressor (i.e., noise), there is no basis for comparison in the literature. According to research on more generally defined coping strategies, they can be oriented towards problem solving, emotion modulation, cognitive restructuring and reappraisal, problem avoidance etc., though these are not orthogonal and partly overlap (cf. Holen et al., 2012). Of those, disengagement, emotional suppression, denial, and avoidance are seen as maladaptive, while problem-focused active engagement with the stressor is associated with better mental health (Compas et al., 2017). In our case, the coping items were worded so that they captured efforts to reduce noise levels, anger towards the noise, and trying to adapt the performance of the task to increase efficiency despite the persisting noise. One could argue, the positive correlation between coping and poor mental health and sleep may in part be due to the coping construct merely capturing noise exposure. However, the correlation of overall noise annoyance with coping in the dataset was not that high (r =0.48) and weaker with annoyances during specific activities (r =0.11 to 0.31), and the associations between coping and mental health and sleep problems persisted even after we excluded children who reported no noise exposure. It should also be noted that our measure of coping represented the frequency of strategies employed to cope with noise rather than the efficiency of those coping efforts. Another plausible explanation for the positive associations between coping and mental health/sleep problems is that children reporting more efforts to cope with noise were those who were easily distracted by it in the first place. Thus, coping efforts may have reflected mental ill health or both may have been caused by some unmeasured neurodevelopment risk factor (cf. Compas et al., 2017). It is well known that unsuccessful coping with chronic stress from noise or other environmental or social threats could both strain psycho-biological response systems and undermine self-regulatory capacities (Muraven and Baumeister, 2000; Evans and Kim, 2013). In Bronfenbrenner’s ecological system model, the interaction between children and their environment can shape the development of self-regulation (Bronfenbrenner and Evans, 2000; Bagais and Pati, 2023), and perceived loss of control in coping with the noise environment can impair motivation (Evans et al., 1995; Lercher, 2003; Evans and Stecker, 2004; Lercher et al., 2013), followed by learned helplessness (Dohmen et al., 2022) and eventually end up in mental health problems. These interpretations offer intriguing hypotheses for future research, but it was not possible to address them here with the data we had. We have previously argued that a holistic understanding of alpine children’s physical environment is needed to inform land use changes that will maximize health benefits in this area where both traffic stressors and natural greenspace levels are high (Lercher et al., 2000; Lercher, 2003; Dzhambov et al., 2022, 2023). The present study further supports this proposition. However, findings show that it is necessary to expand this idea to encompass relational dynamics in the family since positive parent-child interaction can contribute to mechanisms leading to better sleep and mental health beyond environmental influences (cf. Gudi-Mindermann et al., 2023). Results are also suggestive that coping strategies employed by children against disturbing noise may not be effective in terms of health and need to be tailored to the specific circumstances considering the child’s developmental stage (cf. Persson Waye et al., 2023) and its specific home environment. That is, coping efficiency in children is restricted due the limitations they have (Andringa and Lanser, 2013; Evans and Kim, 2013; Compas et al., 2017). Therefore, it is not surprising that the results for coping efficiency with P. Lercher et al. Environmental Research 264 (2025) 120414 7 noise in children differ from those in adults (Lercher, 1998; Botteldooren and Lercher, 2004; Hahad et al., 2024). Unfortunately, no comparable studies are available in children. School-based interventions and intervention campaigns could teach children to appraise traffic noise in an adaptive way, recognize environmental threats to their health better, and develop adaptive responses within various micoenvironments. Nevertheless, we recognize that this idea is rather generic and requires more focused efforts to be implemented. Further, family relations may not act as an exogenous factor that independently affects model outcomes. Rather, it is possible that the environmental context in which a family is embedded acts as a formative influence on the parent-child relationship. In light of the Relational restoration theory put forth by Hartig (2021), a family with access to a garden or other nearby green areas, where they like to spent time, may more easily replenish relational resources that become depleted in their daily lives. There have been no such studies in children but a recent multi-country study found that adults visiting greenspace had better mental health via relationship and community satisfaction (Pasanen et al., 2023). It is worth exploring how greenspace and the built environment can drive not only individual child behavior but also collective decisions that a family makes to spent time together outdoors, enjoy themselves, and overcome daily frustrations and routines in the proximal home environment. 4.2. Strengths and limitations We advise caution when drawing direct conclusions from our findings as they are based on cross-sectional data that prevents causal interpretation. It is possible that the imposed conceptual direction of some pathways in our model ignores bidirectional processes that dynamically shape each other over time, such as the association between mental health/sleep and self-regulation, or environmental perceptions. Further, we only measured coping with a scale constructed ad hoc, which in spite of its high internal consistency and construct validity may not distinguish between different coping styles in a way that would allow disentangling their differential roles in mental health. Moreover, the coping items were very specific and not only referred to interference by noise but specifically during homework activities. Still, as others have suggested, coping during a specific activity can be reflective of the general patterns of coping strategies that a child employs when faced with a stressor (cf. Donaldson et al., 2000; Holen et al., 2012). The same can be argued about our measure of self-regulation, which was teacher-rated and referred to classroom performance. Sleep quality was also measured with ad hoc items that we believe have adequately captured the core aspects of insomnia covered in self-reported sleep quality questionnaires (Fabbri et al., 2021). Next, we lacked information on personal exposure to the built and natural environment factors and instead relied on static exposure assessment at the residential address. In our main analysis, we also deliberately focused on only one greenspace indicator, presence of a home garden, since other satellite or land use-based measures tend to be highly correlated with soil sealing, which we already had as a built environment indicator. Moreover, in an earlier study we have found that home gardens were relevant for behavioral problems of children in the sample (Dzhambov et al., 2022). Here, NDVI was indirectly associated with mental health and sleep, but total effects with NDVI were not supported. This leads us to suggest that home gardens may be more supportive of the studied outcomes than overall vegetation cover near the home, though our dichotomous variable ignored features such as size, quality, and actual use. Importantly, in the study area, which is predominantly rural, having a domestic garden is not strongly associated with a family’s socioeconomic status (Dzhambov et al., 2022). We lacked control for additional socioeconomic indicators beyond maternal education. We did not observe direct associations between noise/air pollution and sleep disturbance. This may be due to lack of sufficient exposure contrast to detect an effect of air pollution, as well as to classic acoustic indicators not being sensitive enough to discern direct effects on child sleep. Our model included only home-based built environment indicators, and the school context was not considered. Since most of the constructs were anchored to the home environment (coping, sleep, family relations), and given the general proximity of children’s homes to their school in the area, we do not see this a major limitation. Finally, the internal consistency of some multivariate scales was suboptimal. We nevertheless combined them into summary scores as we deemed the constituent items to theoretically belong together. This was supported by analysis of the factor structure of those scales and the respective measurement models in the SEM. Limitations notwithstanding, we believe that this study advances the field by bringing in both family and area-level exposures, providing an expanded exposome perspective on children’s mental health and sleep quality. From an operational perspective, we reduced common method bias for the constructs included in our SEM: physical factors were objectively measured, environmental perceptions and outcomes were child-reported, parents reported on family relationships, and teachers on child self-regulation. Though the implications of such different reporting sources in this study are not entirely clear, we believe that the bias would be nondifferential. With additional evidence pooled across different geographies that is expected from the Equal-Life project (van Kamp et al., 2021), upcoming analyses will increase statistical power in more geospatially heterogeneous samples adding to our understanding of the association between the studied constructs. 5. Conclusions This study found that good family relations were robustly associated with less health problems, and the opposite was observed with higher levels of traffic emissions and greyspace or not having access to a garden at home. Unlike family relations, the physical environment worked only indirectly through a sequence of processes involving perceptions of neighborhood quality, traffic safety, and self-regulation and the required amount of coping with noise interference during homework. Good family relations, good neighborhood quality, and close-by green space (home gardens) may be factors to alleviate noise burden. The negative association of required coping with noise during homework suggests that children, in contrast to adults, may be limited in their coping abilities. Our findings call for further inquiries, as children and their environments may vary with respect to coping efficiency. CRediT authorship contribution statement Peter Lercher: Writing – original draft, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Angel M. Dzhambov: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Kerstin Persson Waye: Writing – review & editing, Methodology, Investigation. 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. Acknowledgements We want to thank the inhabitants of the Lower Inn and Wipp valleys. Our thanks also go to the Austrian Ministry of Science and Transportation for funding the framework of the Environmental Health Impact Assessment (EHIA), the government of the Tyrol region for P. Lercher et al. Environmental Research 264 (2025) 120414 8 providing GIS data and informational support from the BEG (Brenner Eisenbahn Gesellschaft). The BBT survey got support from the BBT company within a legally required EHIA through EU-support. The noise mapping was done by Ghent University, and the air pollution assessment by an Italian-Austrian consortium. We also thank the large EHIA-teams in both studies who did the fieldwork. Finally, we thank Iana Markevych and Matthew Browning for calculating the normalized difference vegetation index and other geographic variables. This research was partly funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 874724 (Equal-Life). Equal-Life is part of the European Human Exposome Network. Appendix A. 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