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Background: Beneficial effects of greenness on birth outcomes have been reported, but few studies have investigated the associations in both urban and non-urban settings. We aimed to evaluate and compare linear and nonlinear associations between greenness and birth outcomes in urban and non-urban settings.Methods: From October 2015 to December 2018, participants were recruited into the Maoming Birth Cohort Study. A total of 11 258 live birth records were obtained. Greenness exposure was assessed using the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Linear regression and nonlinear restricted cubic spline models were implemented to investigate the associations between greenness and birthweight, birth length, gestational age, preterm birth, low birthweight, small for gestational age and the potential for effect variation under urban or non-urban settings, after adjusting for covariates.Results: A 0.1-unit increase in NDVI-500m was significantly associated with an increase of 35.4 g in birthweight [95% confidence interval (CI): 13.2, 57.7], 0.15 cm in birth length (95% CI: 0.03, 0.26), 0.88 days in gestational age (95% CI: 0.05, 1.71) and lower odds of low birthweight [odds ratio (OR) = 0.69, 95% CI: 0.56, 0.85] and preterm birth (OR = 0.70, 95% CI: 0.58, 0.85). No association with head circumference was observed. For all outcomes, no significant linear associations were observed among non-urban dwellers. Inversed 'U-shaped' associations between greenness exposure and birth outcomes were observed in the total study population.Conclusions: Greenness exposure was associated with increased gestational age, birthweight and birth length in urban dwellers. Nonlinear associations assessed by restricted cubic splines suggested that health benefits could be larger when increasing greenness levels from low to medium compared with increasing greenness from medium to high levels. Further studies adopting nonlinear methods are warranted to verify our findings. [ABSTRACT FROM AUTHOR]

Objectives: Evidence on the link between long-term ambient particulate matter (PM) exposures and childhood sleep disorders were scarce. We examined the associations between long-term exposures to PM 2.5 and PM 1 (PM with an aerodynamic equivalent diameter <2.5 μm and <1 μm, respectively) with sleep disorders in children., Methods: We performed a population-based cross-sectional survey in 177,263 children aged 6 to 18 years in 14 Chinese cities during 2012-2018. A satellite-based spatiotemporal model was employed to estimate four-year annual average PM 2.5 and PM 1 exposures at residential and school addresses. Parents or guardians completed a checklist using the Sleep Disturbance Scale for Children. We estimated the associations using generalized linear mixed models with adjustment for characteristics of children, parents, and indoor environments., Results: Long-term PM 2.5 and PM 1 exposures were positively associated with odds of sleep disorders for almost all domains. For example, increments in PM 2.5 and PM 1 per 10 μg/m 3 were associated with odds ratios of global sleep disorder of 1.24 (95 % confidence interval [CI]: 1.14, 1.35) and 1.31 (95 %CI: 1.18, 1.46), respectively. Similar results were observed for subtypes of sleep disorder. These associations were heterogeneous regionally, with stronger associations among children residing in southeast region than in northeast and northwest regions. Moreover, larger estimates of PM 1 were found than that of PM 2.5 in southeast region., Conclusion: Long-term PM 2.5 and PM 1 exposures are independently associated with higher risks of childhood sleep disorders, and these associations vary by geographical region., Competing Interests: 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., (Copyright © 2024. Published by Elsevier Ltd.)

Ambient particles with aerodynamic diameter <0.1 μm (PM 0.1 ) have been suggested to have significant health impact. However, studies on the association between long-term PM 0.1 exposure and human blood lipid metabolism are still limited. This study was aimed to evaluate such association based on multiple lipid biomarkers and dyslipidemia indicators. We matched the 2006-2009 average PM 0.1 concentration simulated using the neural-network model following the WRF-Chem model with the clinical and questionnaire data of 15,477 adults randomly recruited from 33 communities in Northeast China in 2009. After controlling for social demographic and behavior confounders, we assessed the association of PM 0.1 concentration with multiple lipid biomarkers and dyslipidemia indicators using generalized linear mixed-effect models. Effect modification by various social demographic and behavior factors was examined. We found that each interquartile range increase in PM 0.1 concentration was associated with a 5.75 (95% Confidence interval, 3.24-8.25) mg/dl and a 6.05 (2.85-9.25) mg/dl increase in the serum level of total cholesterol and LDL-C, respectively. This increment was also associated with an odds ratio of 1.25 (1.10-1.42) for overall dyslipidemias, 1.41 (1.16, 1.73) for hypercholesterolemia, and 1.90 (1.39, 2.61) for hyperbetalipoproteinemia. Additionally, we found generally greater effect estimates among the younger participants and those with lower income or with certain behaviors such as high-fat diet. The deleterious effect of long-term PM 0.1 exposure on lipid metabolism may make it an important toxic chemical to be targeted by future preventive strategies., (Copyright © 2022 Elsevier Ltd. All rights reserved.)