Association between green spaces and behavioral problems of children and adolescents: the moderating effects of PM2.5

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This study examines the association between green space exposure and behavioral problems in children and adolescents, considering the potential moderating effects of fine particulate matter (PM 2.5 ). Methods We used the Strengths and Difficulties Questionnaire (SDQ) to assess behavioral problems in children and adolescents. Green space exposure was measured by the Normalized Difference Vegetation Index (NDVI) within a 1500-meter radius of participants' residences. Daily PM 2.5 concentrations were estimated from the Tracking Air Pollution in China website. A generalized linear model (GLM) with a quasi-Poisson link function estimated the association between green spaces and behavioral problems, considering the moderating effects of PM 2.5 . Results The analysis included 4,782 children and adolescents, averaging 12.29 years. A 0.1 increase in NDVI was linked to a 1.19% (95% CI: -2.08 to -0.30%) reduction in total difficulties score and a 1.19% (95% CI: -2.18 to -0.20%) reduction in peer relationship problems. In high PM 2.5 areas, a 0.1 NDVI increase was associated with a 1.78% (95% CI: -3.05 to -0.60%) reduction in total difficulties and a 3.34% (95% CI: -5.92 to -0.60%) reduction in emotional problems. Stronger associations were observed in younger children (< 12 years), girls, and those from non-left-behind or lower-income families. Conclusion Exposure to residential surrounding green space might contributes to the reduction of behavioral problems among children and adolescents, suggesting a protective effect, particularly for those exposed to high levels of PM 2.5 . green space SDQ behavioral problems children adolescents Figures Figure 1 Figure 2 1. INTRODUCTION Behavioral problems in children and adolescents are deviations from expected social norms and include internalizing problems like anxiety, depression, and phobias, as well as externalizing problems, which involve disruptive behaviors that impact others [1,2] . In China, emotional and behavioral problems are highly prevalent among children and adolescents. The 2018 China Youth Development Report reveals that about 30 million youths under 17 are affected. A detailed survey across 15 cities involving 73,992 children aged 6–16 found a 17.6% incidence rate, which increases to 19.0% among adolescents aged 12–16. This survey also noted that boys are more commonly affected than girls and that the frequency of these problems has been rising over time [3] . Internationally, emotional and behavioral problems in children and adolescents are also prevalent, with detection rates ranging from 13.7–50% in various countries [4–7] . The World Health Organization (WHO) ranks childhood behavioral disorders as the second leading cause of disease burden in children aged 10–14 years and the eleventh for adolescents aged 15–19 years [8,9] . Behavioral problems not only impact academic and social functioning but also increase the risk of long-term adverse outcomes, including substance abuse and criminal behavior [10] . The causes of behavioral problems are diverse, including genetic factors [11] , chemical and structural brain differences [12] , and traumatic experiences [13] . Given the challenge of altering these factors, there has been an increased focus on environmental influences such as green spaces, which are believed to offer potential for mitigating behavioral problems. Research on the impact of green spaces on behavioral development has yielded mixed results. Some studies highlight a positive correlation between access to green spaces and behavioral improvements in children [14–16] . However, other research, such as that by Jimenez et al. [17] , found no significant association between greenness exposure and behavioral problems in early adolescents, suggesting that the beneficial effects of green exposure may diminish as children age. Additionally, while there is substantial research from developed countries [18–22] , studies from developing countries like China are sparse, particularly in county areas. This gap underscores the need for more inclusive research that addresses diverse demographic and geographical backgrounds across various cultural settings. Complicating this relationship further is the interaction between green spaces and environmental pollutants, such as PM 2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm). Studies have shown that exposure to PM 2.5 , especially in utero and early life, is linked to impaired cognitive abilities and behavioral function in children, as evidenced by a multi-cohort study in the United States [23] . However, the specific role of PM 2.5 in the relationship between green spaces and behavioral outcomes remains unclear. It is vital to explore how these environmental factors interact and potentially alter the impact of green spaces on behavioral outcomes. Such understanding is essential for developing targeted interventions to promote the well-being of children and adolescents, especially in varied environments like China, where environmental conditions can differ dramatically between urban and county settings. Therefore, our aim is to investigate the relationship between exposure to green spaces and behavioral problems in children and adolescents, with a particular emphasis on understanding how PM 2.5 may modify this association. 2. METHODS 2.1. Population and study design This study was conducted based on the project “Research on the Mental Health Status and Intervention Mode of Left-behind Children in Hubei Province,” focusing on the mental health challenges faced by left-behind children in the province. The research specifically targets two remote counties, chosen for their high prevalence of left-behind children: Tongcheng County, Xianning City and Lichuan City, Enshi Tujia Autonomous Prefecture. Tongcheng County is located approximately 220 kilometers from the provincial capital, Wuhan, and has a population of about 380,000, a total area of 2618 square kilometers, and a GDP of roughly 8.396 billion yuan as of 2020. Lichuan City, about 420 kilometers from Wuhan, is larger, with a population of approximately 750,000, an area of 4190 square kilometers, and a GDP of about 13.56 billion yuan in the same year. A multistage Probability Proportional to Size (PPS) cluster sampling method was employed in this study. The process began with determining the sample sizes for each stratum, based on the proportion of students in grades 4 and 5, as well as 7 and 8, within each administrative district. From this, the minimum number of schools required was selected from each township using a cumulative PPS scale method. Subsequently, the proportions of left-behind children in these grades were used to stratify the numbers of students within each grade at the selected schools. Cluster sampling was then used to determine the specific classes to be included in the survey. The survey ultimately encompassed 60 schools, which included 30 elementary schools, 28 middle schools, and 2 nine-year comprehensive schools. A total of 5,426 eligible students were invited to participate. The students completed the questionnaires independently, either online in the school computer rooms or on paper in their classrooms, ensuring a comprehensive gathering of data from the targeted demographic. The study was approved by Wuhan University Humanities and Social Sciences Ethics Committee. Informed consent was given by all the participants or their parents/guardians prior to participation. 2.2. Exposure assessment The geographical coordinates of each participant's residential address were obtained from the investigated schools and were used to evaluate residential green space and air pollution exposure data in the year before the survey. 2.2.1. Green space NDVI was used to assess residential green space, representing the average density of green vegetation within a circular buffer surrounding each participant's residential address. Derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images collected by NASA's Terra satellite, the NDVI provides insight into the density of green vegetation at a spatial resolution of 250 meters, with data captured every 16 days since 2000 (product number: MOD13Q1). NDVI values range from − 1 to 1, with higher values indicating a higher density of greenness. Negative NDVI values typically denote bodies of water or non-vegetated soil, signifying a lack of green vegetation. Consequently, the negative NDVI values were standardized to zero for this analysis. We calculated the average NDVI in circular buffers of 1500 meters around the participants’ residences for the year preceding the completion of the questionnaires. 2.2.2. PM 2.5 Data on PM 2.5 were obtained from the Tracking Air Pollution in China website ( http://tapdata.org.cn ) [24–27] . TAP has developed a two-stage machine learning model to aimed at forecasting daily PM 2.5 concentrations, ensuring comprehensive spatial coverage. The data sources include PM 2.5 measurements, satellite AOD (aerosol optical depth) retrievals, online CMAQ simulations, meteorological reanalysis data, land use information, and population distribution. By leveraging TAP PM 2.5 data at a 10-km resolution, complete-coverage predictions of daily PM 2.5 at a 1-km spatial resolution have been generated from 2000 to the current date. This was achieved through the integration of high-resolution satellite remote sensing data and environmental spatial data (such as road maps) into a machine learning framework. We computed the average annual PM 2.5 exposure for each participant in the year prior to the survey date based on their residential address. 2.3. Behavioral problems assessment Children and adolescent's behavioral problems were assessed using the Strengths and Difficulties Questionnaire (SDQ), originally devised by American psychologist Goodman R. in 1997 and subsequently revised in 2001 to accommodate its application across more than 40 countries and regions [28] . The SDQ is a brief behavioural screening questionnaire about 2–17 year olds, which comprises 25 questions categorized into five subscales, including emotional problems, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behaviors. The first four subscales assess difficulties, while the last one focuses on strengths. Each item in the questionnaire is scored on a Likert scale with three levels: 0 points (disagree), 1 point (partially agree), and 2 points (fully agree). Reverse scoring is applied to items in the strengths questionnaire. The total difficulties score ranges from 0 to 40, reflecting the overall extent of one's behavioral problems. Higher scores indicate more severe behavior problems. Conversely, the score of the strength questionnaire reflects positive behavior, with higher scores indicating stronger prosocial behaviors. 2.4. Covariates Data involving participants’ personal demographics information, as well as family and social information were acquired through the self-administered questionnaires. The final covariates included in the statistical models were participants’ age (continuous; years), sex (boy, girl), ethnicity (Han, Tujia, other), siblings status (having, not having), school attendance (retension or suspension, not retension or suspension) and left-behind status (left-behind, not left-behind), parental marital status (married, other), family income (better, average, worse) and neighborly relation (good, general, poor). 2.5. Statistical methods Since the dependent variables were non-negative integers and do not follow a normal distribution, a generalized linear model (GLM) with a quasi-Poisson link was applied to evaluate the associations between green space exposure, PM 2.5 exposure and all subscales of SDQ. Models were adjusted for age, sex, ethnicity, siblings status, school attendance and left-behind status, parental marital status, family income and neighborly relation. We also investigate the moderating effects of PM 2.5 in the associations between greenness exposure and all subscales of SDQ. In addition, we conducted a series of sensitivity analyses in order to assess the robustness of our results. First, we performed sub-group analyses to investigate the moderating effects of age, gender, and parental highest education level in the associations between greenness exposure and all subscales of SDQ. Then, we use NDVI within a 800-meter buffer to repeat the analysis. The associations with P < 0.05 were considered statistically significant. All statistical analyses were performed utilizing R 4.3.1. 3. RESULTS 3.1 Descriptive statistics At the outset, we conducted a survey involving 5426 participants. Following the exclusion of 533 participants who did not fully complete the SDQ questionnaire, 21 with missing covariates, and 90 with incomplete environmental exposure data, our final analysis encompassed 4782 participants(Fig. S1). Table 1 demonstrates the characteristics of participants. Among the 4,782 participants, 53.1% (n = 2,538) were boys and 46.9% (n = 2,244) were girls, with an average age of 12.29 years (SD = 1.59). The mean score of SDQ total difficulties was 13.93 (SD = 4.78) and for SDQ subscales ranged from 2.45 to 4.42 (SD = 1.44 - 2.38), except for the prosocial behavior subscale (7.41(SD = 1.97)). Regarding environmental exposures, the average annual PM 2.5 concentration in the year before the survey was 24.95 μg/m 3 (SD = 4.79), while the mean of NDVI within a 1500-meter buffer was 0.50 (SD = 0.11). Table 1 Characteristics of participants ( n =4782). Characteristic n(%) /  mean(SD) Age (mean (SD)) Sex 12.29 (1.59) Boy 2538 (53.1) Girl 2244 (46.9) Ethnicity Han 2235 (46.7) Tujia 2188 (45.8) Other 359 (7.5) Parental marital status Married 3911 (81.8) Other 871 (18.2) Compare family income with classmates Better 758 (15.9) Average 3519 (73.6) Worse 505 (10.6) Having siblings Yes 3944 (82.5) No 838 (17.5) Neighborly relation Good 3697 (77.3) General 801 (16.8) Poor 284 (5.9) Retention or suspension after primary school Yes 233 (4.9) No 4549 (95.1) Left-behind children Yes 2903 (60.7) No 1879 (39.3) Outcomes(mean (SD)) Strengths and Difficulties Questionnaire (SDQ)  Total difficulties score 13.93 (4.78)  Emotional problems 3.01 (2.38)  Conduct problems 2.45 (1.54)  Hyperactivity/inattention 4.42 (1.52)  Peer relationship problems 4.05 (1.44)  Prosocial behavior 7.41 (1.97) Exposures(mean (SD)) PM 2.5 24.95 (4.79) NDVI-1500m 0.50 (0.11) Abbreviations: SD, standard deviation; N: the total number of census tracts included; NDVI: normalized difference vegetation index; PM 2.5 : particulate matter with an aerodynamic diameter less than or equal to 2.5 μm. 3.2 Associations between green space exposure, PM 2.5 exposure and behavioral problems among participants Table 2 illustrates that the associations between NDVI, PM 2.5 and all subscales of SDQ respectively. We found that a 0.1 increase in NDVI within a 1500-meter buffer is significantly associated with a 1.19% (95%CI: -2.08 to -0.30%) reduction in the total difficulties score and 1.19% reduction (95%CI: -2.18 to -0.20%) in peer relationship problems. NDVI were associated with 0.20 to 1.88% reduction in another four subscales of SDQ, including emotional problems, conduct problems, hyperactivity/inattention and prosocial behavior, but the associations were not prominent. In contrast, we observed that a 10 μg/m 3 increase in PM 2.5 was statistically significantly associated with a 2.94% (95%CI: 0.20 to 5.76%) increase in total difficulties score, 9.20% (95%CI: 2.63 to 16.18%) increase in emotional problems and 4.21% (95%CI: -6.20 to -2.18%) reduction in prosocial behavior. In addition, we also conducted a subgroup analysis. Fig.1 presents the associations between NDVI and all subscales of SDQ stratified by age, sex, left-behind status and family income. We observed stronger associations between NDVI and most subscales of SDQ among younger children (age < 12 years), girls, and non-left-behind children and those from families with general or poor income levels compared to their classmates. Table 2 Associations between NDVI-1500m (per 0.1 increase), PM 2.5 (per 10 μg/m 3 increase) and all subscales of SDQ. Strengths and Difficulties Questionnaire (SDQ) ER %(95% CI ) NDVI-1500m PM 2.5 Total difficulties score -1.19(-2.08, -0.30)* 2.94(0.20, 5.76)* Emotional problems -1.88(-3.92, 0.20) 9.20(2.63, 16.18)* Conduct problems -1.00(-2.66, 0.70) 4.29(-0.80, 9.64) Hyperactivity/inattention -0.80(-1.78, 0.10) 0.80(-1.98, 3.67) Peer relationship problems -1.19(-2.18, -0.20)* 0.10(-2.76, 2.94) Prosocial behavior -0.20(-0.10, 0.50) -4.21(-6.20, -2.18)* Abbreviations: β , unstandardized regression coefficient; CI, confidence interval. Adjusted for age, sex, ethnicity, siblings status, school attendance and left-behind status, parental marital status, family income and neighborly relation. ⁎ p < 0.05 Fig. 1. Associations between NDVI-1500m (per 0.1 increase) and all subscales of SDQ stratified by different categories among all participants. 3.3 The moderating effects of PM 2.5 between green space exposure and behavioral problems among participants Table 3 shows that the association between NDVI and all subscales of SDQ stratified by PM 2.5 . Among children and adolescents exposed to low levels of PM 2.5 , the analysis revealed a non-significant association between NDVI and all subscales of SDQ. In contrast, among children and adolescents exposed to high levels of PM 2.5 , 0.1 increase in NDVI was statistically significantly associated with a 1.78% (95%CI: -3.05 to -0.60%) reduction in total difficulties score and 3.34% (95%CI: -5.92 to -0.60%) reduction in emotional problems. No significant associations were observed for conduct problems, hyperactivity/inattention, peer relationship problems, or prosocial behavior in this group. We also examined how these associations vary with different PM 2.5 concentration levels. Among children and adolescents exposed to low levels of PM 2.5 , the associations between NDVI and all SDQ subscales were not statistically significant across all groups, as shown in Fig. S2. In contrast, among those living in areas with high PM 2.5 exposure, the results mirrored those observed in the general participant group, detailed in Fig. 2. Table 3 Associations between NDVI-1500m (per 0.1 increase) and all subscales of SDQ stratified by PM 2.5 . Strengths and Difficulties Questionnaire (SDQ) ER% (95% CI ) Low-PM 2.5 High-PM 2.5 Total difficulties score 0.80(-1.00, 2.74) -1.78(-3.05, -0.60)* Emotional problems 1.71(-2.66, 6.40) -3.34(-5.92, -0.60)* Conduct problems 2.43(-1.19, 80.40) -2.18(-4.40, 0.00) Hyperactivity/inattention 0.50(-1.39, 2.43) -1.09(-2.37, 0.20) Peer relationship problems -0.50(-2.37, 1.51) -1.29(-2.57, 0.00) Prosocial behavior -0.90(-2.27, 0.50) -0.50(-1.49, 0.50) Abbreviations: β , unstandardized regression coefficient; CI, confidence interval. Models were adjusted for age, sex, ethnicity, siblings status, school attendance and left-behind status, parental marital status, family income and neighborly relation. The low/high-PM 2.5 were divided by the median of all of the participants' long-term (1 year) PM 2.5 exposure. ⁎ p < 0.05 Fig. 2. Associations between NDVI-1500m (per 0.1 increase) and all subscales of SDQ stratified by different categories at high-level PM 2.5 exposure. 3.4 Sensitive analyses The sensitivity analysis in this study involved adjusting the green space exposure measurement from a 1500-meter to a 1000-meter buffer zone for NDVI. This adjustment was made to test the robustness of the initial findings. Tables S1 and S2 display results that align with our main analyses, and group analysis identified the same sensitive populations (Fig. S3-S5). The consistency of these results suggests that the observed associations between green space exposure and behavioral outcomes in children and adolescents are stable and not significantly affected by variations in the spatial parameters used to measure NDVI. This consistency underscores the reliability of the positive impact of green space on behavioral issues, thereby enhancing the credibility of the study. 4. DISCUSSION In our study, we explored how green spaces exposure relates to behavioral problems in children and adolescents, with a focus on the modifying effects of PM 2.5 . We uncovered that greater exposure to green spaces was significantly associated with lower mean values of SDQ scores among children and adolescents, especially at high levels of PM 2.5 . We also revealed stronger associations across various subscales of SDQ among younger children (aged < 12 years), girls, and those from non-left-behind families or families with general or poor income levels, compared to their classmates. In line with our results, the majority of previously conducted studies have indicated a negative correlation, demonstrating that increased exposure to green spaces is associated with decreased levels of behavioral problems [ 14,29,30 ] . The study also found a significantly heightened association, specifically regarding peer relationship problems. However, a research conducted in Germany revealed that limited access to urban green spaces was linked to behavioral issues in 10-year-old children, with the most consistent findings observed in relation to hyperactivity/inattention problems [ 30 ] . This discrepancy could potentially be explained by the increased presence of left-behind children in our study cohort, for a study conducted in the Philippines have suggested that left-behind children often experience more profound loneliness when compared to their non-left-behind counterparts [ 31 ] . The beneficial effects of green spaces on behavioral health are attributed to several mechanisms. Green spaces alleviate aggression and impulsivity through relaxation and stress reduction [ 32 ] . Physical activity in green spaces improves mental health, reducing hyperactivity and inattention [ 33 ] . They enhance cognitive function and attention, improving concentration and impulse control [ 34 ] . Social interactions and better air quality in green spaces promote emotional well-being and reduce behavioral problems [ 35,36 ] . Our study also uncovered the moderating role of PM 2.5 in the relationship between green space exposure and behavioral problems in children and adolescents. Specifically, we observed that under conditions of relatively high PM 2.5 exposure, green spaces exerted a significant protective effect on behavioral problems in this population. Although direct comparisons are lacking, several studies have investigated the relationship between green space, PM 2.5 , and children's mental health. A longitudinal study conducted in the Netherlands revealed that increased exposure to PM 2.5 was linked to elevated odds of experiencing poor mental health, which was also consistent with our findings [ 37 ] . However, these associations were notably attenuated following adjustments for factors such as green space availability, traffic noise levels, and urbanization levels. Zhen Xiang et al found that increased exposure to green surroundings around school was inversely associated with depression and anxiety symptoms in adolescents, with PM 2.5 mediating 10.5% of the link between green exposure and depressive symptoms [ 38 ] . This phenomenon can be explained by several mechanisms. Some researchers suggest that green spaces filter pollutants through plant stomata and dry deposition, though the effect may be modest locally. They also enhance ventilation, increasing pollutant dispersion [ 35,39 ] . Moreover, this interaction may be less pronounced in regions with lower PM 2.5 concentrations, where the inherent risk of air pollution is already minimal, thereby making the additional protective effect of green space less discernible. This aligns with the notion that green spaces offer greater health benefits in areas with higher pollution levels or lower socioeconomic status, as they serve as vital mitigators of environmental hazards. Additionally, when exposed to high levels of PM 2.5 , green space was significantly associated with emotional problems subscale scores in children and adolescents, while it appears to have no effect on the peer relationship problems subscale. The possible explanation is the direct and indirect benefits of green spaces might operate differently under varying environmental conditions [ 40,41 ] . Direct benefits, such as the improvement of physical and psychological health through active play and exposure to nature, might become more crucial under stressful conditions like high pollution. Indirect benefits, such as improved social interactions facilitated by more appealing and accommodating spaces for play, might be less perceivable under high pollution levels, where the primary concern might shift towards health preservation rather than social development. The subgroup analysis revealed that at high pollution levels, green spaces significantly protect vulnerable groups, including children under 12, girls, and children from families with general or poor income levels. These findings align with results from other studies [ 38,42 ] . Environmental, economic, and gender-based marginalization of vulnerable groups, such as girls and those from lower-income households, may intensify the connection between mental health and environmental factors [ 43 ] . Additionally, similar significant results were observed in the group of non-left-behind children, which can be attributed to several factors. Firstly, non-left-behind children typically reside in a family environment with parents, providing social support and stability that enhances their ability to benefit from natural surroundings. For instance, they often have greater access to outdoor activities and green spaces, which are linked to increased physical activity and social interaction, both known for their health benefits. Secondly, green spaces offer psychological and physical advantages to all children.But for non-left-behind children, these benefits may be more pronounced due to their greater freedom and opportunities to access and enjoy these environments. The study has some strengths. Initially, we used objectively measured NDVI values to quantify each individual's exposure to green space. Besides, nearly all participants provided accurate residence address, enabling the collection of almost precise residential exposure data. Furthermore, this study stands out as a rare cross-sectional analysis involving a large sample of children and adolescents from Chinese county areas. It is essential to acknowledge the limitations of our study. Firstly, the cross-sectional nature of our analysis limits our ability to infer causality or directionality in the observed associations. Additionally, the reliance on self-reported measures of behavioral problems may introduce bias or measurement error. Despite our rigorous quality control measures, children under 12 years of age inevitably face cognitive challenges. Future research should strive to evaluate children's mental health using varied methods, including clinical diagnoses by doctors. Furthermore, while we controlled for several potential confounding variables, the possibility of residual confounding cannot be entirely ruled out. Finally, similar to many studies, we acknowledge that children and adolescents may encounter environmental exposures in activity locations outside their communities, such as at school, which could obscure the true effects of these exposures on mental health. 5. CONCLUSIONS In conclusion, exposure to green spaces was linked to reduce behavioral problems among children and adolescents, indicating a protective effect, particularly for those exposed to high levels of PM2.5. The benefits were more pronounced in younger children (aged < 12 years), girls, and those from non-left-behind families or families with lower income levels. Overall, the presence of green spaces offers a multitude of benefits for mental health and well-being, collectively contributing to the reduction of behavioral problems among children and adolescents. Declarations Funding This research was supported by the Hubei Population Welfare Foundation[grant numbers YGF-2022-9-2]. Author Contribution Shiqi Huang wrote the original draft of the manuscript. Peizheng Li conceptualized the research and developed the methodology. Juntao Chen designed the software. Yifan Zhang validated the experiments. Jing Wang performed the formal analysis. Qingyu Zhang conducted the investigation. Xiangying Li provided the resources. Chenxi Luo curated the data. Jiayi Diao and Ruoxuan Hong reviewed and edited the manuscript. Kehan Zhong prepared the visualizations. Rui Zhang and Yuqi Hu supervised the research. Suhua Zhou and Chenlu Yang managed the project administration. Lu Ma acquired funding. All authors reviewed the manuscript. Acknowledgement The corresponding authors and first authors of this article are guarantors for this research. Every author made an important scientific contribution to the study, including the collection of data, the statistical analysis, the interpretation of results or the drafting and revising of the manuscript. The authors would like to thank Qinqin Li, Yuwei Rui, Bingdi Yang, Siyi Hu, Xuan Zhou, Shiyu You, Yuxin Gao, Jie Xu, Jinrui Tian and Dongmei Chen of School of Marxism of Wuhan University for their support in providing psychological knowledge. Data Availability The data that support the findings of this study are available from the project “Research on the Mental Health Status and Intervention Mode of Left-behind Children in Hubei Province". The data are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. 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. References Skoog, T. Pubertal Timing and Its Developmental Significance for Mental Health and Adjustment. In Encyclopedia of Mental Health (Third Edition) ; Friedman, H. S., Markey, C. H., Eds.; Academic Press: Oxford, 2023; pp 914–923. https://doi.org/10.1016/B978-0-323-91497-0.00058-8. Starr, R. H.; Dubowitz, H. Chapter 41 - SOCIAL WITHDRAWAL AND ISOLATION. In Developmental-Behavioral Pediatrics (Fourth Edition) ; Carey, W. B., Crocker, A. 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Zach, A.; Meyer, N.; Hendrowarsito, L.; Kolb, S.; Bolte, G.; Nennstiel-Ratzel, U.; Stilianakis, N. I.; Herr, C. Association of Sociodemographic and Environmental Factors with the Mental Health Status among Preschool Children—Results from a Cross-Sectional Study in Bavaria, Germany. Int. J. Hyg. Environ. Health 2016 , 219 (4), 458–467. https://doi.org/10.1016/j.ijheh.2016.04.012. Smeekens, C.; Stroebe, M. S.; Abakoumkin, G. The Impact of Migratory Separation from Parents on the Health of Adolescents in the Philippines. Soc. Sci. Med. 2012 , 75 (12), 2250–2257. https://doi.org/10.1016/j.socscimed.2012.08.025. Zhou, P.; Sun, S.; Chen, T.; Pan, Y.; Xu, W.; Zhang, H. Impacts of Social Inequality, Air Pollution, Rural–Urban Divides, and Insufficient Green Space on Residents’ Health in China: Insight from Chinese General Social Survey Data Analysis. Int. J. Environ. Res. Public. Health 2022 , 19 (21), 14225. https://doi.org/10.3390/ijerph192114225. Bedimo-Rung, A. L.; Mowen, A. J.; Cohen, D. 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Urban Residential Greenspace and Mental Health in Youth: Different Approaches to Testing Multiple Pathways Yield Different Conclusions. Environ. Res. 2018 , 160 , 47–59. https://doi.org/10.1016/j.envres.2017.09.015. Zeng, Y.; W. J. M. Stevens, G.; Helbich, M. Longitudinal Associations of Neighbourhood Environmental Exposures with Mental Health Problems during Adolescence: Findings from the TRAILS Study. Environ. Int. 2023 , 179 , 108142. https://doi.org/10.1016/j.envint.2023.108142. Xiang, Z.; Luo, X.; Zheng, R.; Jiang, Q.; Zhu, K.; Feng, Y.; Xiao, P.; Zhang, Q.; Wu, X.; Fan, Y.; Song, R. Associations of Greenness Surrounding Schools and Self-Reported Depressive and Anxiety Symptoms in Chinese Adolescents. J. Affect. Disord. 2022 , 318 , 62–69. https://doi.org/10.1016/j.jad.2022.08.095. Givoni, B. Impact of Planted Areas on Urban Environmental Quality: A Review. Atmospheric Environ. Part B Urban Atmosphere 1991 , 25 (3), 289–299. https://doi.org/10.1016/0957-1272(91)90001-U. Atiqul Haq, S. M.; Islam, M. N.; Siddhanta, A.; Ahmed, K. J.; Chowdhury, M. T. A. Public Perceptions of Urban Green Spaces: Convergences and Divergences. Front. Sustain. Cities 2021 , 3 . https://doi.org/10.3389/frsc.2021.755313. Semeraro, T.; Scarano, A.; Buccolieri, R.; Santino, A.; Aarrevaara, E. Planning of Urban Green Spaces: An Ecological Perspective on Human Benefits. Land 2021 , 10 (2), 105. https://doi.org/10.3390/land10020105. Putra, I. G. N. E.; Astell-Burt, T.; Cliff, D. P.; Vella, S. A.; Feng, X. Association between Green Space Quality and Prosocial Behaviour: A 10-Year Multilevel Longitudinal Analysis of Australian Children. Environ. Res. 2021 , 196 , 110334. https://doi.org/10.1016/j.envres.2020.110334. Astell-Burt, T.; Mitchell, R.; Hartig, T. The Association between Green Space and Mental Health Varies across the Lifecourse. A Longitudinal Study. J. Epidemiol. Community Health 2014 , 68 (6), 578–583. https://doi.org/10.1136/jech-2013-203767. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialSPPE.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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University","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-07-24 09:18:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4794037/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4794037/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63313980,"identity":"f1321ab2-e1a2-4c1b-9fae-11c2ca98a8c3","added_by":"auto","created_at":"2024-08-26 21:01:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97237,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between NDVI-1500m (per 0.1 increase) and \u0026nbsp;all subscales of SDQ stratified by different categories among all participants.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4794037/v1/ae1cc9a1ea2483fe8c1b208b.png"},{"id":63313981,"identity":"83d2bf31-27d3-489d-8b70-a09b54a0fdc9","added_by":"auto","created_at":"2024-08-26 21:01:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116376,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between NDVI-1500m (per 0.1 increase) and all subscales of SDQ stratified by different categories at high-level PM\u003csub\u003e2.5\u003c/sub\u003e exposure.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4794037/v1/2322f6ec4901c47686021434.png"},{"id":75724901,"identity":"5cb3c18c-c98a-449c-8522-9a7ccfe6611b","added_by":"auto","created_at":"2025-02-07 13:47:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1474267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4794037/v1/50bee975-2e9f-4dce-8b6f-54558cdee051.pdf"},{"id":63313982,"identity":"2a1c0fb6-fada-4216-9e5b-a7140abdb25a","added_by":"auto","created_at":"2024-08-26 21:01:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39677154,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialSPPE.docx","url":"https://assets-eu.researchsquare.com/files/rs-4794037/v1/a45a2027f2c7fdef60d9b334.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between green spaces and behavioral problems of children and adolescents: the moderating effects of PM2.5","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eBehavioral problems in children and adolescents are deviations from expected social norms and include internalizing problems like anxiety, depression, and phobias, as well as externalizing problems, which involve disruptive behaviors that impact others\u003csup\u003e[1,2]\u003c/sup\u003e. In China, emotional and behavioral problems are highly prevalent among children and adolescents. The 2018 China Youth Development Report reveals that about 30\u0026nbsp;million youths under 17 are affected. A detailed survey across 15 cities involving 73,992 children aged 6\u0026ndash;16 found a 17.6% incidence rate, which increases to 19.0% among adolescents aged 12\u0026ndash;16. This survey also noted that boys are more commonly affected than girls and that the frequency of these problems has been rising over time\u003csup\u003e[3]\u003c/sup\u003e. Internationally, emotional and behavioral problems in children and adolescents are also prevalent, with detection rates ranging from 13.7\u0026ndash;50% in various countries\u003csup\u003e[4\u0026ndash;7]\u003c/sup\u003e. The World Health Organization (WHO) ranks childhood behavioral disorders as the second leading cause of disease burden in children aged 10\u0026ndash;14 years and the eleventh for adolescents aged 15\u0026ndash;19 years\u003csup\u003e[8,9]\u003c/sup\u003e. Behavioral problems not only impact academic and social functioning but also increase the risk of long-term adverse outcomes, including substance abuse and criminal behavior\u003csup\u003e[10]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe causes of behavioral problems are diverse, including genetic factors\u003csup\u003e[11]\u003c/sup\u003e, chemical and structural brain differences\u003csup\u003e[12]\u003c/sup\u003e, and traumatic experiences\u003csup\u003e[13]\u003c/sup\u003e. Given the challenge of altering these factors, there has been an increased focus on environmental influences such as green spaces, which are believed to offer potential for mitigating behavioral problems. Research on the impact of green spaces on behavioral development has yielded mixed results. Some studies highlight a positive correlation between access to green spaces and behavioral improvements in children\u003csup\u003e[14\u0026ndash;16]\u003c/sup\u003e. However, other research, such as that by Jimenez et al. \u003csup\u003e[17]\u003c/sup\u003e, found no significant association between greenness exposure and behavioral problems in early adolescents, suggesting that the beneficial effects of green exposure may diminish as children age. Additionally, while there is substantial research from developed countries\u003csup\u003e[18\u0026ndash;22]\u003c/sup\u003e, studies from developing countries like China are sparse, particularly in county areas. This gap underscores the need for more inclusive research that addresses diverse demographic and geographical backgrounds across various cultural settings.\u003c/p\u003e \u003cp\u003eComplicating this relationship further is the interaction between green spaces and environmental pollutants, such as PM\u003csub\u003e2.5\u003c/sub\u003e (particulate matter with an aerodynamic diameter of less than 2.5 \u0026micro;m). Studies have shown that exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, especially in utero and early life, is linked to impaired cognitive abilities and behavioral function in children, as evidenced by a multi-cohort study in the United States\u003csup\u003e[23]\u003c/sup\u003e. However, the specific role of PM\u003csub\u003e2.5\u003c/sub\u003e in the relationship between green spaces and behavioral outcomes remains unclear. It is vital to explore how these environmental factors interact and potentially alter the impact of green spaces on behavioral outcomes. Such understanding is essential for developing targeted interventions to promote the well-being of children and adolescents, especially in varied environments like China, where environmental conditions can differ dramatically between urban and county settings.\u003c/p\u003e \u003cp\u003eTherefore, our aim is to investigate the relationship between exposure to green spaces and behavioral problems in children and adolescents, with a particular emphasis on understanding how PM\u003csub\u003e2.5\u003c/sub\u003e may modify this association.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Population and study design\u003c/h2\u003e \u003cp\u003eThis study was conducted based on the project \u0026ldquo;Research on the Mental Health Status and Intervention Mode of Left-behind Children in Hubei Province,\u0026rdquo; focusing on the mental health challenges faced by left-behind children in the province. The research specifically targets two remote counties, chosen for their high prevalence of left-behind children: Tongcheng County, Xianning City and Lichuan City, Enshi Tujia Autonomous Prefecture. Tongcheng County is located approximately 220 kilometers from the provincial capital, Wuhan, and has a population of about 380,000, a total area of 2618 square kilometers, and a GDP of roughly 8.396\u0026nbsp;billion yuan as of 2020. Lichuan City, about 420 kilometers from Wuhan, is larger, with a population of approximately 750,000, an area of 4190 square kilometers, and a GDP of about 13.56\u0026nbsp;billion yuan in the same year.\u003c/p\u003e \u003cp\u003eA multistage Probability Proportional to Size (PPS) cluster sampling method was employed in this study. The process began with determining the sample sizes for each stratum, based on the proportion of students in grades 4 and 5, as well as 7 and 8, within each administrative district. From this, the minimum number of schools required was selected from each township using a cumulative PPS scale method. Subsequently, the proportions of left-behind children in these grades were used to stratify the numbers of students within each grade at the selected schools. Cluster sampling was then used to determine the specific classes to be included in the survey. The survey ultimately encompassed 60 schools, which included 30 elementary schools, 28 middle schools, and 2 nine-year comprehensive schools. A total of 5,426 eligible students were invited to participate. The students completed the questionnaires independently, either online in the school computer rooms or on paper in their classrooms, ensuring a comprehensive gathering of data from the targeted demographic.\u003c/p\u003e \u003cp\u003e The study was approved by Wuhan University Humanities and Social Sciences Ethics Committee. Informed consent was given by all the participants or their parents/guardians prior to participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Exposure assessment\u003c/h2\u003e \u003cp\u003eThe geographical coordinates of each participant's residential address were obtained from the investigated schools and were used to evaluate residential green space and air pollution exposure data in the year before the survey.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Green space\u003c/h2\u003e \u003cp\u003eNDVI was used to assess residential green space, representing the average density of green vegetation within a circular buffer surrounding each participant's residential address. Derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images collected by NASA's Terra satellite, the NDVI provides insight into the density of green vegetation at a spatial resolution of 250 meters, with data captured every 16 days since 2000 (product number: MOD13Q1). NDVI values range from \u0026minus;\u0026thinsp;1 to 1, with higher values indicating a higher density of greenness. Negative NDVI values typically denote bodies of water or non-vegetated soil, signifying a lack of green vegetation. Consequently, the negative NDVI values were standardized to zero for this analysis. We calculated the average NDVI in circular buffers of 1500 meters around the participants\u0026rsquo; residences for the year preceding the completion of the questionnaires.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eData on PM\u003csub\u003e2.5\u003c/sub\u003e were obtained from the Tracking Air Pollution in China website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tapdata.org.cn\u003c/span\u003e\u003cspan address=\"http://tapdata.org.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e[24\u0026ndash;27]\u003c/sup\u003e. TAP has developed a two-stage machine learning model to aimed at forecasting daily PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, ensuring comprehensive spatial coverage. The data sources include PM\u003csub\u003e2.5\u003c/sub\u003e measurements, satellite AOD (aerosol optical depth) retrievals, online CMAQ simulations, meteorological reanalysis data, land use information, and population distribution. By leveraging TAP PM\u003csub\u003e2.5\u003c/sub\u003e data at a 10-km resolution, complete-coverage predictions of daily PM\u003csub\u003e2.5\u003c/sub\u003e at a 1-km spatial resolution have been generated from 2000 to the current date. This was achieved through the integration of high-resolution satellite remote sensing data and environmental spatial data (such as road maps) into a machine learning framework. We computed the average annual PM\u003csub\u003e2.5\u003c/sub\u003e exposure for each participant in the year prior to the survey date based on their residential address.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Behavioral problems assessment\u003c/h2\u003e \u003cp\u003eChildren and adolescent's behavioral problems were assessed using the Strengths and Difficulties Questionnaire (SDQ), originally devised by American psychologist Goodman R. in 1997 and subsequently revised in 2001 to accommodate its application across more than 40 countries and regions\u003csup\u003e[28]\u003c/sup\u003e. The SDQ is a brief behavioural screening questionnaire about 2\u0026ndash;17 year olds, which comprises 25 questions categorized into five subscales, including emotional problems, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behaviors. The first four subscales assess difficulties, while the last one focuses on strengths. Each item in the questionnaire is scored on a Likert scale with three levels: 0 points (disagree), 1 point (partially agree), and 2 points (fully agree). Reverse scoring is applied to items in the strengths questionnaire. The total difficulties score ranges from 0 to 40, reflecting the overall extent of one's behavioral problems. Higher scores indicate more severe behavior problems. Conversely, the score of the strength questionnaire reflects positive behavior, with higher scores indicating stronger prosocial behaviors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Covariates\u003c/h2\u003e \u003cp\u003eData involving participants\u0026rsquo; personal demographics information, as well as family and social information were acquired through the self-administered questionnaires. The final covariates included in the statistical models were participants\u0026rsquo; age (continuous; years), sex (boy, girl), ethnicity (Han, Tujia, other), siblings status (having, not having), school attendance (retension or suspension, not retension or suspension) and left-behind status (left-behind, not left-behind), parental marital status (married, other), family income (better, average, worse) and neighborly relation (good, general, poor).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical methods\u003c/h2\u003e \u003cp\u003eSince the dependent variables were non-negative integers and do not follow a normal distribution, a generalized linear model (GLM) with a quasi-Poisson link was applied to evaluate the associations between green space exposure, PM\u003csub\u003e2.5\u003c/sub\u003e exposure and all subscales of SDQ. Models were adjusted for age, sex, ethnicity, siblings status, school attendance and left-behind status, parental marital status, family income and neighborly relation. We also investigate the moderating effects of PM\u003csub\u003e2.5\u003c/sub\u003e in the associations between greenness exposure and all subscales of SDQ.\u003c/p\u003e \u003cp\u003eIn addition, we conducted a series of sensitivity analyses in order to assess the robustness of our results. First, we performed sub-group analyses to investigate the moderating effects of age, gender, and parental highest education level in the associations between greenness exposure and all subscales of SDQ. Then, we use NDVI within a 800-meter buffer to repeat the analysis. The associations with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. All statistical analyses were performed utilizing R 4.3.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1 Descriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the outset, we conducted a survey involving 5426 participants. Following the exclusion of 533 participants who did not fully complete the SDQ questionnaire, 21 with missing covariates, and 90 with incomplete environmental exposure data, our final analysis encompassed 4782 participants(Fig. S1).\u003c/p\u003e\n\u003cp\u003eTable 1 demonstrates the characteristics of participants.\u0026nbsp;Among the 4,782 participants, 53.1% (n = 2,538) were boys and 46.9% (n = 2,244) were girls, with an average age of 12.29 years (SD = 1.59).\u0026nbsp;The mean score of SDQ total difficulties was 13.93 (SD = 4.78) and for SDQ subscales ranged from 2.45 to 4.42\u0026nbsp;(SD = 1.44 - 2.38), except for the prosocial behavior subscale (7.41(SD = 1.97)). Regarding environmental exposures, the average annual PM\u003csub\u003e2.5\u003c/sub\u003e concentration in the year before the survey was 24.95 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e (SD = 4.79), while the mean of NDVI within a 1500-meter buffer was 0.50 (SD = 0.11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eCharacteristics of participants (\u003cem\u003en\u003c/em\u003e=4782).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%) /\u003c/strong\u003e \u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003emean(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eAge (mean (SD))\u003c/p\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e12.29 (1.59)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Boy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e2538 (53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Girl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e2244 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eHan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e2235 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tujia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e2188 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e359 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eParental marital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e3911 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e871 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eCompare family income with classmates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Better\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e758 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e3519 (73.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Worse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e505 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eHaving siblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e3944 (82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e838 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eNeighborly relation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e3697 (77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;General\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e801 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e284 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eRetention or suspension after primary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e233 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e4549 (95.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eLeft-behind children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e2903 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e1879 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eOutcomes(mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eStrengths and Difficulties Questionnaire (SDQ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; Total difficulties score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e13.93 (4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; Emotional problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e3.01 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; Conduct problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e2.45 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; Hyperactivity/inattention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e4.42 (1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; Peer relationship problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e4.05 (1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; Prosocial behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e7.41 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eExposures(mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e24.95 (4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.6618444846293%\" valign=\"top\"\u003e\n \u003cp\u003eNDVI-1500m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.338155515370705%\" valign=\"top\"\u003e\n \u003cp\u003e0.50 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation; N: the total number of census tracts included; NDVI: normalized difference vegetation index; PM\u003csub\u003e2.5\u003c/sub\u003e: particulate matter with an aerodynamic diameter less than or equal to 2.5 \u0026mu;m.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Associations between green space exposure, PM\u003csub\u003e2.5\u003c/sub\u003e exposure and behavioral problems among participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 illustrates that the associations between NDVI, PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eand all subscales of SDQ respectively. We found that a 0.1 increase in NDVI within a 1500-meter buffer is significantly associated with a 1.19% (95%CI: -2.08 to -0.30%) reduction in the total difficulties score \u0026nbsp;and 1.19% reduction (95%CI: -2.18 to -0.20%) in peer relationship problems. NDVI were associated with 0.20 to 1.88% reduction in another four subscales of SDQ, including emotional problems, conduct problems, hyperactivity/inattention and prosocial behavior, but the associations were not prominent. In contrast, we observed that a 10 \u0026mu;g/m\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eincrease in \u0026nbsp;PM\u003csub\u003e2.5\u003c/sub\u003e was statistically significantly associated with a 2.94% (95%CI: 0.20 to 5.76%) increase \u0026nbsp;in total difficulties score, 9.20% (95%CI: 2.63 to 16.18%) increase in emotional problems and 4.21% (95%CI: -6.20 to -2.18%) reduction in prosocial behavior.\u003c/p\u003e\n\u003cp\u003eIn addition, we also conducted a subgroup analysis. Fig.1 presents the associations between NDVI and all subscales of SDQ stratified by age, sex, left-behind status and family income. We observed stronger associations between NDVI and most subscales of SDQ among younger children (age \u0026lt; 12 years), girls, and non-left-behind children and those from families with general or poor income levels compared to their classmates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eAssociations between NDVI-1500m\u0026nbsp;(per 0.1 increase),\u0026nbsp;PM\u003csub\u003e2.5\u003c/sub\u003e (per 10 \u0026mu;g/m\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eincrease) and all subscales of SDQ.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrengths and Difficulties Questionnaire (SDQ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eER %(95% \u003cem\u003eCI\u003c/em\u003e)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.79365079365079%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDVI-1500m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.20634920634921%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eTotal difficulties score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\"\u003e\n \u003cp\u003e-1.19(-2.08, -0.30)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.31313131313131%\"\u003e\n \u003cp\u003e2.94(0.20, 5.76)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eEmotional problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\"\u003e\n \u003cp\u003e-1.88(-3.92, 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.31313131313131%\"\u003e\n \u003cp\u003e9.20(2.63, 16.18)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eConduct problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\"\u003e\n \u003cp\u003e-1.00(-2.66, 0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.31313131313131%\"\u003e\n \u003cp\u003e4.29(-0.80, 9.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eHyperactivity/inattention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\"\u003e\n \u003cp\u003e-0.80(-1.78, 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.31313131313131%\"\u003e\n \u003cp\u003e0.80(-1.98, 3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003ePeer relationship problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\"\u003e\n \u003cp\u003e-1.19(-2.18, -0.20)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.31313131313131%\"\u003e\n \u003cp\u003e0.10(-2.76, 2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eProsocial behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\"\u003e\n \u003cp\u003e-0.20(-0.10, 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.31313131313131%\"\u003e\n \u003cp\u003e-4.21(-6.20, -2.18)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u003cem\u003e\u0026beta;\u003c/em\u003e, unstandardized regression coefficient; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eAdjusted for age, sex, ethnicity, siblings status, school attendance and left-behind status, parental marital status, family income and neighborly relation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e⁎ p \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 1.\u0026nbsp;\u003c/strong\u003e Associations between NDVI-1500m (per 0.1 increase) and all subscales of SDQ stratified by different categories among all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 The moderating effects of PM\u003csub\u003e2.5\u003c/sub\u003e between green space exposure and behavioral problems among participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 shows that the association between NDVI and all subscales of SDQ stratified by PM\u003csub\u003e2.5\u003c/sub\u003e. Among children and adolescents exposed to low levels of PM\u003csub\u003e2.5\u003c/sub\u003e, the analysis revealed a non-significant association between NDVI and all subscales of SDQ. In contrast, among children and adolescents exposed to high levels of\u0026nbsp;PM\u003csub\u003e2.5\u003c/sub\u003e, 0.1 increase in \u0026nbsp;NDVI was statistically significantly associated with a 1.78% (95%CI: -3.05 to -0.60%) reduction \u0026nbsp;in total difficulties score and 3.34% (95%CI: -5.92 to -0.60%) reduction in emotional problems. No significant associations were observed for conduct problems, hyperactivity/inattention, peer relationship problems, or prosocial behavior in this group.\u003c/p\u003e\n\u003cp\u003eWe also examined how these associations vary with different PM\u003csub\u003e2.5\u003c/sub\u003e concentration levels. Among children and adolescents exposed to low levels of PM\u003csub\u003e2.5\u003c/sub\u003e, the associations between NDVI and all SDQ subscales were not statistically significant across all groups, as shown in Fig. S2. In contrast, among those living in areas with high PM\u003csub\u003e2.5\u003c/sub\u003e exposure, the results mirrored those observed in the general participant group, detailed in Fig. 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eAssociations between NDVI-1500m\u0026nbsp;(per 0.1 increase)\u0026nbsp;and\u0026nbsp;all subscales of SDQ stratified by PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrengths and Difficulties Questionnaire (SDQ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eER% (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.79365079365079%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.20634920634921%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003eTotal difficulties score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e0.80(-1.00, 2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-1.78(-3.05, -0.60)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003eEmotional problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e1.71(-2.66, 6.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-3.34(-5.92, -0.60)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003eConduct problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e2.43(-1.19, 80.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-2.18(-4.40, 0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003eHyperactivity/inattention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e0.50(-1.39, 2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-1.09(-2.37, 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003ePeer relationship problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e-0.50(-2.37, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-1.29(-2.57, 0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003eProsocial behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e-0.90(-2.27, 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-0.50(-1.49, 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u003cem\u003e\u0026beta;\u003c/em\u003e, unstandardized regression coefficient; CI, confidence interval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModels were adjusted for age, sex, ethnicity, siblings status, school attendance and left-behind status, parental marital status, family income and neighborly relation. The low/high-PM\u003csub\u003e2.5\u003c/sub\u003e were divided by the median of all of the participants\u0026apos; long-term (1 year) PM\u003csub\u003e2.5\u003c/sub\u003e exposure.\u003c/p\u003e\n\u003cp\u003e⁎ p \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 2.\u0026nbsp;\u003c/strong\u003e Associations between NDVI-1500m (per 0.1 increase) and all subscales of SDQ stratified by different categories at high-level PM\u003csub\u003e2.5\u003c/sub\u003e exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Sensitive analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis in this study involved adjusting the green space exposure measurement from a 1500-meter to a 1000-meter buffer zone for NDVI. This adjustment was made to test the robustness of the initial findings. Tables S1 and S2 display results that align with our main analyses, and group analysis identified the same sensitive populations (Fig. S3-S5). The consistency of these results suggests that the observed associations between green space exposure and behavioral outcomes in children and adolescents are stable and not significantly affected by variations in the spatial parameters used to measure NDVI. This consistency underscores the reliability of the positive impact of green space on behavioral issues, thereby enhancing the credibility of the study.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn our study, we explored how green spaces exposure relates to behavioral problems in children and adolescents, with a focus on the modifying effects of PM\u003csub\u003e2.5\u003c/sub\u003e. We uncovered that greater\u0026nbsp;exposure to green spaces was significantly associated with lower mean values of SDQ scores among children and adolescents, especially at high levels of PM\u003csub\u003e2.5\u003c/sub\u003e. We also revealed stronger associations across various subscales of SDQ among younger children (aged \u0026lt; 12 years), girls, and those from non-left-behind families or families with general or poor income levels, compared to their classmates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn line with our results, the majority of previously conducted studies have indicated a negative correlation, demonstrating that increased exposure to green spaces is associated with decreased levels of behavioral problems\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e14,29,30\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The study also found a significantly heightened association, specifically regarding peer relationship problems. However, a research conducted in Germany revealed that limited access to urban green spaces was linked to behavioral issues in 10-year-old children, with the most consistent findings observed in relation to hyperactivity/inattention problems\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This discrepancy could potentially be explained by the increased presence of left-behind children in our study cohort, for a study conducted in the Philippines have suggested that left-behind children often experience more profound loneliness when compared to their non-left-behind counterparts\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The beneficial effects of green spaces on behavioral health are attributed to several mechanisms. Green spaces alleviate aggression and impulsivity through relaxation and stress reduction\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Physical activity in green spaces improves mental health, reducing hyperactivity and inattention\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. They enhance cognitive function and attention, improving concentration and impulse control\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Social interactions and better air quality in green spaces promote emotional well-being and reduce behavioral problems\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e35,36\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study also uncovered the moderating role of PM\u003csub\u003e2.5\u003c/sub\u003e in the relationship between green space exposure and behavioral problems in children and adolescents. Specifically, we observed that under conditions of relatively high PM\u003csub\u003e2.5\u003c/sub\u003e exposure, green spaces exerted a significant protective effect on behavioral problems in this population. Although direct comparisons are lacking, several studies have investigated the relationship between green space, PM\u003csub\u003e2.5\u003c/sub\u003e, and children\u0026apos;s mental health. A longitudinal study conducted in the Netherlands revealed that increased exposure to PM\u003csub\u003e2.5\u003c/sub\u003e was linked to elevated odds of experiencing poor mental health, which was also consistent with our findings\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. However, these associations were notably attenuated following adjustments for factors such as green space availability, traffic noise levels, and urbanization levels. Zhen Xiang et al found that increased exposure to green surroundings around school was inversely associated with depression and anxiety symptoms in adolescents, with PM\u003csub\u003e2.5\u003c/sub\u003e mediating 10.5% of the link between green exposure and depressive symptoms\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This phenomenon can be explained by several mechanisms. Some researchers suggest that green spaces filter pollutants through plant stomata and dry deposition, though the effect may be modest locally. They also enhance ventilation, increasing pollutant dispersion\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e35,39\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Moreover, this interaction may be less pronounced in regions with lower PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, where the inherent risk of air pollution is already minimal, thereby making the additional protective effect of green space less discernible. This aligns with the notion that green spaces offer greater health benefits in areas with higher pollution levels or lower socioeconomic status, as they serve as vital mitigators of environmental hazards.\u003c/p\u003e\n\u003cp\u003eAdditionally, when exposed to high levels of PM\u003csub\u003e2.5\u003c/sub\u003e, green space was significantly associated with emotional problems subscale scores in children and adolescents, while it appears to have no effect on the peer relationship problems subscale.\u0026nbsp;The possible explanation is the direct and indirect benefits of green spaces might operate differently under varying environmental conditions\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e40,41\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Direct benefits, such as the improvement of physical and psychological health through active play and exposure to nature, might become more crucial under stressful conditions like high pollution. Indirect benefits, such as improved social interactions facilitated by more appealing and accommodating spaces for play, might be less perceivable under high pollution levels, where the primary concern might shift towards health preservation rather than social development.\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis revealed that at high pollution levels, green spaces significantly protect vulnerable groups, including children under 12, girls, and children from families with general or poor income levels. These findings align with results from other studies\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e38,42\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Environmental, economic, and gender-based marginalization of vulnerable groups, such as girls and those from lower-income households, may intensify the connection between mental health and environmental factors\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e43\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Additionally, similar significant results were observed in the group of non-left-behind children, which can be attributed to several factors. Firstly, non-left-behind children typically reside in a family environment with parents, providing social support and stability that enhances their ability to benefit from natural surroundings. For instance, they often have greater access to outdoor activities and green spaces, which are linked to increased physical activity and social interaction, both known for their health benefits. Secondly, green spaces offer psychological and physical advantages to all children.But for non-left-behind children, these benefits may be more pronounced due to their greater freedom and opportunities to access and enjoy these environments.\u003c/p\u003e\n\u003cp\u003eThe study has some strengths. Initially, we used objectively measured NDVI values to quantify each individual\u0026apos;s exposure to green space. Besides, nearly all participants provided accurate residence address, enabling the collection of almost precise residential exposure data. Furthermore, this study stands out as a rare cross-sectional analysis involving a large sample of children and adolescents from Chinese county areas.\u003c/p\u003e\n\u003cp\u003eIt is essential to acknowledge the limitations of our study. Firstly, the cross-sectional nature of our analysis limits our ability to infer causality or directionality in the observed associations. Additionally, the reliance on self-reported measures of behavioral problems may introduce bias or measurement error. Despite our rigorous quality control measures, children under 12 years of age inevitably face cognitive challenges. Future research should strive to evaluate children\u0026apos;s mental health using varied methods, including clinical diagnoses by doctors. Furthermore, while we controlled for several potential confounding variables, the possibility of residual confounding cannot be entirely ruled out. Finally, similar to many studies, we acknowledge that children and adolescents may encounter environmental exposures in activity locations outside their communities, such as at school, which could obscure the true effects of these exposures on mental health.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"In conclusion, exposure to green spaces was linked to reduce behavioral problems among children and adolescents, indicating a protective effect, particularly for those exposed to high levels of PM2.5. The benefits were more pronounced in younger children (aged \u003c 12 years), girls, and those from non-left-behind families or families with lower income levels. Overall, the presence of green spaces offers a multitude of benefits for mental health and well-being, collectively contributing to the reduction of behavioral problems among children and adolescents."},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by the Hubei Population Welfare Foundation[grant numbers YGF-2022-9-2].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShiqi Huang wrote the original draft of the manuscript. Peizheng Li conceptualized the research and developed the methodology. Juntao Chen designed the software. Yifan Zhang validated the experiments. Jing Wang performed the formal analysis. Qingyu Zhang conducted the investigation. Xiangying Li provided the resources. Chenxi Luo curated the data. Jiayi Diao and Ruoxuan Hong reviewed and edited the manuscript. Kehan Zhong prepared the visualizations. Rui Zhang and Yuqi Hu supervised the research. Suhua Zhou and Chenlu Yang managed the project administration. Lu Ma acquired funding. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe corresponding authors and first authors of this article are guarantors for this research. Every author made an important scientific contribution to the study, including the collection of data, the statistical analysis, the interpretation of results or the drafting and revising of the manuscript. The authors would like to thank Qinqin Li, Yuwei Rui, Bingdi Yang, Siyi Hu, Xuan Zhou, Shiyu You, Yuxin Gao, Jie Xu, Jinrui Tian and Dongmei Chen of School of Marxism of Wuhan University for their support in providing psychological knowledge.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the project \u0026ldquo;Research on the Mental Health Status and Intervention Mode of Left-behind Children in Hubei Province\". The data are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003ch2\u003eDeclaration of competing interest \u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eSkoog, T. Pubertal Timing and Its Developmental Significance for Mental Health and Adjustment. In \u003cem\u003eEncyclopedia of Mental Health (Third Edition)\u003c/em\u003e; Friedman, H. S., Markey, C. H., Eds.; Academic Press: Oxford, 2023; pp 914\u0026ndash;923. https://doi.org/10.1016/B978-0-323-91497-0.00058-8.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eStarr, R. H.; Dubowitz, H. Chapter 41 - SOCIAL WITHDRAWAL AND ISOLATION. 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Community Health\u003c/em\u003e \u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e68\u003c/em\u003e (6), 578\u0026ndash;583. https://doi.org/10.1136/jech-2013-203767.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"green space, SDQ, behavioral problems, children, adolescents","lastPublishedDoi":"10.21203/rs.3.rs-4794037/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4794037/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eRecent studies suggest green spaces benefit mental health, yet the interaction between environmental factors and behavioral outcomes remains underexplored. This study examines the association between green space exposure and behavioral problems in children and adolescents, considering the potential moderating effects of fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used the Strengths and Difficulties Questionnaire (SDQ) to assess behavioral problems in children and adolescents. Green space exposure was measured by the Normalized Difference Vegetation Index (NDVI) within a 1500-meter radius of participants' residences. Daily PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were estimated from the Tracking Air Pollution in China website. A generalized linear model (GLM) with a quasi-Poisson link function estimated the association between green spaces and behavioral problems, considering the moderating effects of PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analysis included 4,782 children and adolescents, averaging 12.29 years. A 0.1 increase in NDVI was linked to a 1.19% (95% CI: -2.08 to -0.30%) reduction in total difficulties score and a 1.19% (95% CI: -2.18 to -0.20%) reduction in peer relationship problems. In high PM\u003csub\u003e2.5\u003c/sub\u003e areas, a 0.1 NDVI increase was associated with a 1.78% (95% CI: -3.05 to -0.60%) reduction in total difficulties and a 3.34% (95% CI: -5.92 to -0.60%) reduction in emotional problems. Stronger associations were observed in younger children (\u0026lt;\u0026thinsp;12 years), girls, and those from non-left-behind or lower-income families.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eExposure to residential surrounding green space might contributes to the reduction of behavioral problems among children and adolescents, suggesting a protective effect, particularly for those exposed to high levels of PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e","manuscriptTitle":"Association between green spaces and behavioral problems of children and adolescents: the moderating effects of PM2.5","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 21:01:17","doi":"10.21203/rs.3.rs-4794037/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2153742-90d0-4608-be31-fbf6bc64bcdc","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-07T13:39:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-26 21:01:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4794037","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4794037","identity":"rs-4794037","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}