Declines in mental health associated with air pollution and temperature variability in China

Daily temperature variability and mental health-related hospital visits in New York State

BACKGROUND

Despite plausible behavioral and physiological pathways, limited evidence exists on how daily temperature variability is associated with acute mental health-related episodes.

OBJECTIVES

We aimed to explore associations between daily temperature range (DTR) and mental health-related hospital visits using data of all hospital records in New York State during 1995-2014. We further examined factors that may modify these associations, including age, sex, hospital visit type and season.

METHODS

Using a case-crossover design with distributed lag non-linear DTR terms (0-6 days), we estimated associations between ZIP Code-level DTR and hospital visits for mood (4.6 million hospital visits), anxiety (2.4 million), adjustment (∼368,000), and schizophrenia disorders (∼211,000), controlling for daily mean temperature, via conditional logistic regression models. We assessed potential heterogeneity by age, sex, hospital visit type (in-patient vs. out-patient), and season (summer, winter, and transition seasons).

RESULTS

For all included outcomes, we observed positive associations from period minimum DTR (0.1 °C) until 25th percentile (5.2 °C) and between mean DTR (7.7 °C) and 90th percentile (12.2 °C), beyond which we observed negative associations. For mood disorders, an increase in DTR from 0.1 °C to 12.2 °C was associated with a cumulative 16.0% increase (95% confidence interval [CI]: 12.8, 19.2%) in hospital visit rates. This increase was highest during transition seasons (32.5%; 95%CI: 26.4, 39.0%) compared with summer (10.7%; 95%CI: 4.8, 16.8%) and winter (-1.6%; 95%CI: -7.6, 4.7%). For adjustment and schizophrenia disorders, the strongest associations were seen among the youngest group (0-24 years) with almost no association in the oldest group (65+ years). We observed no evidence for modification by sex and hospital visit type.

DISCUSSION

Daily temperature variability was positively associated with mental health-related hospital visits within specific DTR ranges in New York State, after controlling for daily mean temperature. Given uncertainty on how climate change modifies temperature variability, additional research is crucial to comprehend the implications of these findings, particularly under different scenarios of future temperature variability.

Association of short-term exposure to ambient fine particulate matter and ozone with outpatient visits for anxiety disorders: A hospital-based case-crossover study in South China

BACKGROUND

The short-term adverse effects of ambient fine particulate matter (PM2.5) and ozone (O3) on anxiety disorders (ADs) remained inconclusive.

METHODS

We applied an individual-level time-stratified case-crossover study, which including 126,112 outpatient visits for ADs during 2019-2021 in Guangdong province, China, to investigate the association of short-term exposure to PM2.5 and O3 with outpatient visits for ADs, and estimate excess outpatient visits in South China. Daily residential air pollutant exposure assessments were performed by extracting grid data (spatial resolution: 1 km × 1 km) from validated datasets. We employed the conditional logistic regression model to quantify the associations and excess outpatient visits.

RESULTS

The results of the single-pollutant models showed that each 10 μg/m3 increase of PM2.5 and O3 exposures was significantly associated with a 3.14 % (95 % confidence interval: 2.47 %, 3.81 %) and 0.88 % (0.49 %, 1.26 %) increase in odds of outpatient visits for ADs, respectively. These associations remained robust in 2-pollutant models. The proportion of outpatient visits attributable to PM2.5 and O3 exposures was up to 7.20 % and 8.93 %, respectively. Older adults appeared to be more susceptible to PM2.5 exposure, especially in cool season, and subjects with recurrent outpatient visits were more susceptible to O3 exposure.

LIMITATION

As our study subjects were from one single hospital in China, it should be cautious when generalizing our findings to other regions.

CONCLUSION

Short-term exposure to ambient PM2.5 and O3 was significantly associated with a higher odds of outpatient visits for ADs, which can contribute to considerable excess outpatient visits.

Examining the non-linear association between ambient temperature and mental health of elderly adults in the community: evidence from Guangzhou, China

The association between ambient temperature and mental health has been explored previously. However, research on the psychological effect of temperature in vulnerable groups and neighborhood scales have been scarce. Based on the survey and temperature data collected from 20 neighborhoods in Guangzhou, China, this study estimated the association between ambient temperature and community mental health among the elderly, adopting a fixed-effects methodology. According to this empirical analysis, compared to a comfortable temperature range of 20℃-25℃, measures of worse mental health among elderly were significant in high and low temperatures with increases in negative outcomes observable at both ends of the temperature range, leading to the U-shaped relationship. Second, the association between ambient temperature and worse mental health was found in the subcategories of gender, income, and symptom events. Specifically, from the hot temperature aspect, elderly males were more sensitive than elderly females. The effect on the low was far more than on the middle-high income group, and the probability of each symptom of the elderly's mental health significantly increased. From the cool temperature aspect, the temperature in the range of 5ºC-10ºC was significantly associated with the probability of some symptoms (feeling down, not calm, downheartedness, and unhappiness) and the middle-high income group. Our research enriches the empirical research on ambient temperature and mental health from a multidisciplinary perspective and suggests the need for healthy aging and age-friendly planning in Chinese settings.

The Effect of Ambient Temperature on Lipid Metabolism in Children: from a Prospective Cohort Study

BACKGROUND

Dyslipidemia is increasingly recognized as an essential risk factor for cardiovascular diseases. However, few studies illustrated the effects of ambient temperature exposure (TE) on lipid levels in children. The study aimed to examine the association between ambient TE and lipid levels in children.

METHODS

Based on a prospective cohort, a total of 2,423 children (with 4,466 lipids measure person-time) were collected from 2014 to 2019. The meteorological observation data and adjusted variables were collected. Mixed-effect models and generalized additive mixed model (GAMM) were applied to investigate the association between ambient TE and lipid levels.

RESULTS

A significant negative association was observed between TE and low-density lipoprotein cholesterol (LDL-C) or total cholesterol (TC) levels both in all children [LDL-C, β(95%CI)= -0.350(-0.434,-0.265), P<0.001; TC, β(95%CI)= -0.274(-0.389,-0.160), P<0.001] and by different sex group. However, no significant association was found in low-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) levels. The estimated optimal ambient TEs for LDL-C were 18.273 °C and 18.024 °C for girls and boys, respectively. For TC, the optimal ambient TEs were 17.949 °C and 18.024 °C, respectively. With ambient TE decreased, the risk of dyslipidemia increased for both boys [OR=0.032(0.006,0.179), P<0.001] and girls [OR=0.582(0.576,0.587), P<0.001].

CONCLUSION

This study provided a comprehensive illustration about the associations between ambient TE and lipid levels in different sex and ages from a prospective cohort study. The findings will provide evidence for the government to prevent dyslipidemia in vulnerable children through regulating TE.

Associations and attributable burdens in late-life exposure to PM2.5 and its major components and depressive symptoms in middle-aged and older adults: A nationwide cohort study

BACKGROUND

Depression in late life has been associated with reduced quality of life and increased mortality. Whether the chronic fine particular matter (PM2.5) and its components exposure are contributed to the older depression symptoms remains unclear.

METHOD

Middle-aged and older adults (>45 years) were selected from the China Health and Retirement Longitudinal Study during the four waves of interviews. The concentrations of PM2.5 and its major constituents were calculated using near real-time data at a spatial resolution of 10 km during the study period. The depressive symptom was evaluated by the Depression Center for Epidemiologic Studies Depression (CES-D)-10 score. The fix-effect model was applied to evaluate the association between PM2.5 and its major constituents with depressive symptoms. Three three-step methods were used to explore the modification role of sleep duration against the depressive symptoms caused by PM2.5 exposure.

RESULTS

In our study, a total of 52,683 observations of 16,681 middle-aged and older adults were assessed. Each interquartile range (IQR) level of PM2.5 concentration exposure was longitudinally associated with a 2.6 % (95 % confidence interval [CI]: 1.3 %, 4.0 %) increase in the depression CES-D-10 score. Regarding the major components of PM2.5, OM, NO3-, and NH4+ showed the leading toxicity effects, which could increase the depression CES-D-10 score by 2.2 % (95 %CI: 1.0 %, 3.4 %), 2.2 % (0.6 %, 3.9 %), and 2.0 % (95 %CI: 0.6 %, 3.4 %) correspondingly. Besides, males were more susceptible to the worse depressive symptoms caused by PM2.5 and its major components exposure than female subpopulations. Shortened sleep duration might be the mediator of PM2.5-associated depressive symptoms.

CONCLUSION

Our results suggest that long-term exposure to PM2.5 and its major components were associated with an increased risk for depressive symptoms in middle-aged and older adults. Reducing the leading components of PM2.5 may cost-effectively alleviate the disease burden of depression and promote healthy longevity in heavy pollutant countries.

Global climate change: Effects of future temperatures on emergency department visits for mental disorders in Beijing, China

Rising temperatures can increase the risk of mental disorders. As climate change intensifies, the future disease burden due to mental disorders may be underestimated. Using data on the number of daily emergency department visits for mental disorders at 30 hospitals in Beijing, China during 2016-2018, the relationship between daily mean temperature and such visits was assessed using a quasi-Poisson model integrated with a distributed lag nonlinear model. Emergency department visits for mental disorders attributed to temperature changes were projected using 26 general circulation models under four climate change scenarios. Stratification analyses were then conducted by disease subtype, sex, and age. The results indicate that the temperature-related health burden from mental disorders was projected to increase consistently throughout the 21st century, mainly driven by high temperatures. The future temperature-related health burden was higher for patients with mental disorders due to the use of psychoactive substances and schizophrenia as well as for women and those aged <65 years. These findings enhance our knowledge of how climate change could affect mental well-being and can be used to advance and refine targeted approaches to mitigating and adapting to climate change with a view on addressing mental disorders.

Association between green space and multiple ambient air pollutants with depressive and anxiety symptoms among Chinese adolescents: The role of physical activity

OBJECTIVE

To explore the association between green space, multiple ambient air pollutants and depressive/anxiety symptoms and the mediating role of physical activity (PA) in Chinese adolescents.

METHOD

A school-based health survey was conducted in eight provinces in China in 2021. 22,868 students aged 14.64 (±1.77) years completed standard questionnaires to record details of depressive, anxiety symptoms and PA. We calculated the average normalized difference vegetation index (NDVI) in circular buffers of 200 m, 500 m and 1000 m and estimated the concentrations of PM10, PM2.5, CO, NO2, O3, SO2 around the adolescents' school addresses.

RESULTS

The exposure-response curves showed that the lower the NDVI value, the higher the risk of depressive and anxiety symptoms. CO, PM2.5 and SO2 and air pollution score were associated with increased risk of depressive and anxiety symptoms. NDVI in all circular buffers decreased the risk of depressive and anxiety symptoms at low levels of PA, but the associations were not significant at high levels of PA. In the subgroup analysis, PM10, PM2.5, CO, NO2, SO2, AQI and air pollution score increased the risk of depressive and anxiety symptoms at low PA levels, but the associations were not significant at high levels of PA. Mediation analysis indicated that the mediating effect of PA on the association between NDVI, NDVI-200 m NDVI-500 m, CO, PM10, PM2.5, SO2, AQI and depressive/anxiety symptoms was statistically significant（p < 0.05）.

CONCLUSION

Middle-high level PA could reduce the strength of association between air pollution and depressive and anxiety symptoms. Meanwhile, the association between green space/air pollution and depressive/anxiety symptoms was partly mediated by PA.

Defending against environmental threats: Unveiling household adaptation strategies and population heterogeneity

Humanity faces a variety of risks from pollution and environmental degradation. Societal advancement has equipped the public with numerous self-protection measures to mitigate these threats. However, the ways in which individuals deploy and balance self-defence mechanisms within this complex risk landscape and the resulting consequences remain largely unexplored. Drawing on a detailed survey of households' self-defence practices, this study rigorously analyses the heterogeneity and driving factors behind household-level self-defence strategies. Through exploratory latent class modelling, we identified four distinct defence patterns: inaction, water-sensitive, air-sensitive, and multifaceted. These patterns reveal varied defence capabilities among the population. By integrating frameworks from economics and social psychology, significant disparities were found in the driving factors behind these patterns. Practices aimed at combating air pollution are primarily driven by the actual severity of pollution and perceived coping capabilities, whereas measures to enhance water quality are influenced more by perceived threats. This disparity arises from variations in information availability and health awareness. The study also highlights a misalignment between the distribution of defence capabilities and the levels of pollution. Given that income restricts self-defence options, this mismatch indicates that economically disadvantaged groups are disproportionately affected by severe health inequalities.

Potential Early Effect Biomarkers for Ambient Air Pollution Related Mental Disorders

Air pollution is one of the greatest environmental risks to health, with 99% of the world's population living where the World Health Organization's air quality guidelines were not met. In addition to the respiratory and cardiovascular systems, the brain is another potential target of air pollution. Population- and experiment-based studies have shown that air pollution may affect mental health through direct or indirect biological pathways. The evidence for mental hazards associated with air pollution has been well documented. However, previous reviews mainly focused on epidemiological associations of air pollution with some specific mental disorders or possible biological mechanisms. A systematic review is absent for early effect biomarkers for characterizing mental health hazards associated with ambient air pollution, which can be used for early warning of related mental disorders and identifying susceptible populations at high risk. This review summarizes possible biomarkers involved in oxidative stress, inflammation, and epigenetic changes linking air pollution and mental disorders, as well as genetic susceptibility biomarkers. These biomarkers may provide a better understanding of air pollution's adverse effects on mental disorders and provide future research direction in this arena.

Association of Residential Greenness Exposure with Depression Incidence in Adults 50 Years of Age and Older: Findings from the Cohort Study on Global AGEing and Adult Health (SAGE) in China

BACKGROUND

Depression is a social and public health problem of great concern globally. Identifying and managing the factors influencing depression are crucial for preventing and decreasing the burden of depression.

OBJECTIVES

Our objectives are to explore the association between residential greenness and the incidence of depression in an older Chinese population and to calculate the disease burden of depression prevented by greenness exposure.

METHODS

This study was the Chinese part of the World Health Organization Study on Global AGEing and Adult Health (WHO SAGE). We collected the data of 8,481 residents ≥ 50 years of age in China for the period 2007-2018. Average follow-up duration was 7.00 (± 2.51 ) years. Each participant was matched to the yearly maximum normalized difference vegetation index (NDVI) at their residential address. Incidence of depression was assessed using the Composite International Diagnostic Interview (CIDI), self-reports of depression, and/or taking depression medication. Association between greenness and depression was examined using the time-dependent Cox regression model with stratified analysis by sex, age, urbanicity, annual family income, region, smoking, drinking, and household cooking fuels. Furthermore, the prevented fraction (PF) and attributable number (AN) of depression prevented by exposure to greenness were estimated.

RESULTS

Residential greenness was negatively associated with depression. Each interquartile range (IQR) increase in NDVI 500 -m buffer was associated with a 40% decrease [hazard ratio  ( HR ) = 0.60 ; 95% confidence interval (CI): 0.37, 0.97] in the risk of depression incidence among the total participants. Subgroup analyses showed negative associations in urban residents (HR = 0.32 ; 95% CI: 0.12, 0.86) vs. rural residents, in high-income residents (HR = 0.28 ; 95% CI: 0.11, 0.71) vs. low-income residents, and in southern China (HR = 0.50 ; 95% CI: 0.26, 0.95) vs. northern China. Over 8.0% (PF = 8.69 % ; 95% CI: 1.38%, 15.40%) and 1,955,199 (95% CI: 310,492; 3,464,909) new cases of depression may be avoided by increasing greenness exposures annually across China.

DISCUSSION

The findings suggest protective effects of residential greenness exposure on depression incidence in the older population, particularly among urban residents, high-income residents, and participants living in southern China. The construction of residential greenness should be included in community planning. https://doi.org/10.1289/EHP13947.

Visual effect of air pollution on the need for arousal and variety-seeking behavior

Research on air pollution, one of the most common environmental factors, has primarily focused on its effects on physical, mental, and cognitive health. However, air pollution-induced achromatic color of an environment, which is a prominent feature of air pollution, has received little attention. This study explored the visual effects of air pollution on the variety-seeking purchase behavior of consumers through two scenario-based experiments and primed manipulation (Study 1 and Study 2) and one natural experiment using data from a local fruit chain store (Study 3). Study 1 tested the main effect of air pollution on the variety-seeking behavior and found that primed air pollution increased variety-seeking when consumers purchased beverages. Study 2 broadened the category and tested the mechanism, and the results indicated that primed air pollution increased the variety of purchased chocolates and demonstrated the mediating effect of the need for arousal. Study 3 tested the boundary condition and extended the external validity with actual purchases. The results revealed that severe air pollution increased the purchased SKUs by 22.9% and visibility reduced the moderation effect. This research extended the literature on the visual effect of air pollution by providing evidence of the effects of air pollution on variety-seeking behavior through the need for arousal. And, product managers could leverage the results by offering a greater variety of goods on days with air pollution to increase sales.

The economic commitment of climate change

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons1-6. Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes7,8. Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11-29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

Health-related quality of life during the COVID-19 pandemic: The impact of restrictive measures using data from two Dutch population-based cohort studies

OBJECTIVES

We describe health-related quality of life during the COVID-19 pandemic in the general Dutch population and correlations with restrictive measures.

METHODS

Data were obtained from 18-85 year-old participants of two population-based cohort studies (February 2021-July 2022): PIENTER Corona (n = 8,019) and VASCO (n = 45,413). Per cohort, mean scores of mental and physical health and health utility from the SF-12 were calculated by age group, sex and presence of a medical risk condition. Spearman correlations with stringency of measures were calculated.

RESULTS

Both cohorts showed comparable results. Participants <30 years had lowest health utility and mental health score, and highest physical health score. Health utility and mental health score increased with age (up to 79 years), while physical health score decreased with age. Women and participants with a medical risk condition scored lower than their counterparts. Fluctuations were small over time but most pronounced among participants <60 years, and correlated weakly, but mostly positively with measure stringency.

CONCLUSIONS

During the Dutch COVID-19 epidemic, health utility and mental health scores were lower and fluctuated strongest among young adults compared to older adults. In our study population, age, sex and presence of a medical risk condition seemed to have more impact on health scores than stringency of COVID-19 non-pharmaceutical interventions.

Assessment of air pollution and air quality perception mismatch using mobility-based real-time exposure

Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals' real-time mobility-based (MB) exposures and perceptions of air pollution by considering people's daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people's perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536μg/m3 and when the perceived air quality is good but the air pollution level is higher than 15.608μg/m3. The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning.

Climate change, ambient air pollution, and students' mental health

The impact of global climate change and air pollution on mental health has become a crucial public health issue. Increased public awareness of health, advancements in medical diagnosis and treatment, the way media outlets report environmental changes and the variation in social resources affect psychological responses and adaptation methods to climate change and air pollution. In the context of climate change, extreme weather events seriously disrupt people's living environments, and unstable educational environments lead to an increase in mental health issues for students. Air pollution affects students' mental health by increasing the incidence of diseases while decreasing contact with nature, leading to problems such as anxiety, depression, and decreased cognitive function. We call for joint efforts to reduce pollutant emissions at the source, improve energy structures, strengthen environmental monitoring and gover-nance, increase attention to the mental health issues of students, and help student groups build resilience; by establishing public policies, enhancing social support and adjusting lifestyles and habits, we can help students cope with the constantly changing environment and maintain a good level of mental health. Through these comprehensive measures, we can more effectively address the challenges of global climate change and air pollution and promote the achievement of the United Nations Sustainable Development Goals.

The impact of foreign direct investment on green innovation efficiency: Evidence from Chinese provinces

Improving green innovation efficiency (GIE) is the key to achieve high-quality economic development in China, and the introduction of foreign direct investment (FDI) has become an important path choice to promote the GIE. Based on the data of 30 provinces in China, this paper explores the linear and nonlinear effects of FDI on GIE from both quantity and quality perspectives, and further analyzes the mediating role of environmental regulation level. The results show that: (1) From 2011 to 2020, the GIE of all provinces in China generally shows an upward trend. (2) The quantity and quality of FDI have a significant positive impact on the improvement of GIE in China's provinces, and this impact has regional heterogeneity. (3) The quantity and quality of FDI can promote the improvement of GIE in China through the level of environmental regulation (ER). (4) With the level of knowledge accumulation and GIE as the threshold variables, the quantity and quality of FDI have a single threshold effect on the GIE of China's provinces. The conclusions of this study provide some policy implications for local governments to make full use of FDI to perform green innovation activities.

Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality

Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.

Heatwave exposure in relation to decreased sleep duration in older adults

Few studies have delved into the effects of heatwaves on sleep duration loss among older adults. Our study examined correlations between heatwave exposure and sleep duration reductions in this demographic. Utilizing data of 7,240 older adults drawn from the China Health and Retirement Longitudinal Study (CHARLS) from 2015 to 2018, we assessed sleep duration differences between the baseline year (2015) and follow-up year (2018). Absolute reductions in sleep duration were defined as differences of ≥ 1, 1.5, or 2 h. Changes in sleep duration were categorized based on cut-offs of 5 and 8 h, including excessive decrease, moderate to short and persistent short sleep duration types. 12 heatwave definitions combining four thresholds (90th, 92.5th, 95th, and 97.5th percentiles of daily minimum temperature) and three durations (≥2, ≥3 and ≥ 4 days) were used. Heatwave exposure was determined by the difference in the number of 12 preceding months' heatwave days or events in 2015 and the number of 12 preceding months' heatwave days or events in 2018. The results showed that increased heatwave events (defined as ≥ P90th percentile & lasting three days) were associated with a higher likelihood of ≥ 1-hour sleep reduction and persistent short sleep duration. An increase in heatwave event (defined as ≥ P95th percentile & lasting three days) was linked to shifts from moderate to short sleep duration. For the association between an absolute reduction in sleep duration and heatwave exposure, while higher thresholds signified greater sleep reduction risks, the effect estimates of longer durations were not uniformly consistent. We observed that air pollution and green space modified the relationship between heatwaves and sleep duration. Females, urban residents, and individuals with chronic diseases were identified as vulnerable populations. This study found that increased heatwave exposure was associated with a higher risk of sleep duration loss in older adults.

Income inequality and depressive symptoms among Chinese adults: a quasi-experimental study

OBJECTIVE

There is a lack of causal evidence on the impact of income inequality on depressive symptoms. The impact of China's Targeted Poverty Alleviation (TPA) policy on depressive symptoms is also unclear. Using a quasi-experimental design, this study aims to investigate the causal effects of TPA and income inequality on depressive symptoms among Chinese adults.

STUDY DESIGN

This is a population-based study.

METHODS

Three waves (2012, 2016, and 2018) of the China Family Panel Studies (CFPS), a nationally representative sample of China, were included in this study. We performed difference-in-difference (DID) models to assess the effect of TPA and income inequality on depressive symptoms. We further conducted the mixed effect models to examine the impact of income inequality on depressive symptoms. The study considered a range of spatial factors and spatial splines to address spatial autocorrelations.

RESULTS

This study included valid measures of depressive symptoms (Center for Epidemiologic Studies Depression Scale [CES-D-8] score) from 14,442 adults of CFPS. The DID results indicated that at the provincial level, the CES-D-8 score of the TPA treatment group was on average 0.570 (95% confidence interval [CI]: 0.358-0.783) less than the control group. Furthermore, a 0.1 increase in Gini index would lead to a 0.256 (95% CI: 0.064-0.448) increase in CES-D-8 score. The mixed effect model showed that income inequality was a risk factor for depressive symptoms at the provincial level (excess risk = 5.602% [95% CI: 3.047%-8.219%]).

CONCLUSIONS

Our findings suggest that income inequality adversely affects mental health, but China's Targeted Poverty Alleviation improves the mental health of the Chinese population.

Large-scale data reveal disparate associations between leisure time physical activity patterns and mental health

BACKGROUND

Leisure time physical activity (LTPA) is known to be associated with a lower risk for mental health burden, while whether the underlying mechanisms vary across populations is unknown. We aimed to explore the disparate associations between LTPA and mental health based on large-scale data.

METHODS

In this study, we analyzed data including 711,759 individuals aged 15 years or above from the latest four rounds (2003, 2008, 2013, and 2018) of the National Health Service Survey (NHSS) in China. We used multiple logistic regression models adjusted for potential confounders to investigate associations between LTPA and mental health in the total population and subgroups by measuring a diverse set of activity frequencies, intensities, and types. To examine the dose-response associations between total activity volume and mental health, we conducted restricted cubic splines to investigate possible nonlinearity.

RESULTS

LTPA was associated with remarkably lower self-reported mental health burden (OR 0.56, 95% CI 0.54-0.58). The dose-response relationship between total activity volume and mental health was highly nonlinear (p < 0.001), presenting L-shaped with first 1200 metabolic equivalents of task (METs)-min/week for significant risk reduction (OR 0.58, 95% CI 0.56-0.60). Notably, merely exercising 3-5 times per week with moderate swimming was significantly associated with lower mental health burden among younger people, while the association was strongly large in older adults aged 60 years or above doing 55-min moderate apparatus exercise at least six times a week.

CONCLUSIONS

In a large Chinese sample, LTPA was meaningfully and disparately associated with mental health burden across different people. Policy targeted at prompting activity may be effective for reducing mental health burden, but importantly, tailored strategies are needed based on population contexts.

Long-Term PM2.5 Exposure, Lung Function, and Cognitive Function Among Middle-Aged and Older Adults in China

BACKGROUND

Long-term exposure to PM2.5 is related to poor lung function and cognitive impairment, but less is known about the pathway involved in this association. We aimed to explore whether the effect of PM2.5 on cognitive function was mediated by lung function.

METHODS

A total of 7 915 adults older than 45 years old were derived from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2011 and 2015. PM2.5 exposure was estimated using a geographically weighted regression model. Lung function was measured by peak expiratory flow (PEF). Cognitive function was evaluated through a structured questionnaire with 4 dimensions: episodic memory, attention, orientation, and visuoconstruction. Under the counterfactual framework, causal mediation analysis was applied to examine direct and indirect associations.

RESULTS

An interquartile range (IQR) increase in PM2.5 change was significantly related to an 8.480 (95% confidence interval [CI]: 3.116, 13.845) decrease in PEF change and a 0.301 (95% CI: 0.100, 0.575) decrease in global cognitive score change. The direct and indirect effects of PM2.5 exposure on global cognitive performance were -0.279 (95% CI: -0.551, -0.060) and -0.023 (95% CI: -0.041, -0.010), respectively. The proportion of the indirect effect was 7.48% (p = .010). The same significant association appeared in only 2 dimensions, episodic memory and attention, which were both mediated by PEF.

CONCLUSIONS

Lung function played a partially mediating role in the association between long-term PM2.5 exposure and cognition. More clean air actions should be undertaken to improve lung function and cognitive function in older adults.

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

The effects of long-term exposure to air pollution on incident mental disorders among patients with prediabetes and diabetes: Findings from a large prospective cohort

BACKGROUND

The association between air pollution and mental disorders has been widely documented in the general population. However, the evidence among susceptible populations, such as individuals with prediabetes or diabetes, is still insufficient.

METHODS

We analyzed data from 48,515 participants with prediabetes and 24,393 participants with diabetes from the UK Biobank. Annual pollution data were collected for fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), and nitrogen dioxides (NOx) during 2006-2021. The exposure to air pollution and temperature for each participant were estimated by the bilinear interpolation approach and time-weighted method based on their geocoded home addresses and time spent at each address. We employed the generalized propensity score model based on the generalized estimating equation and the time-varying covariates Cox model to assess the effects of air pollution.

RESULTS

We observed causal links between air pollutants and mental disorders among both prediabetic and diabetic participants, with stronger effects among those with diabetes than prediabetes. The hazard ratios were 1.18 (1.12, 1.24), 1.15 (1.10, 1.20), 1.18 (1.13, 1.23), and 1.15 (1.11, 1.19) in patients with prediabetes, and 1.21 (1.13, 1.29), 1.17 (1.11, 1.24), 1.19 (1.13, 1.25), and 1.17 (1.12, 1.23) in patients with diabetes per interquartile range elevation in PM2.5, PM10, NO2, and NOx. Furthermore, the effects were more pronounced among people who were older, alcohol drinkers, and living in urban areas.

CONCLUSIONS

Our study indicates the potential causal links between long-term exposure to air pollution and incident mental disorders among those with prediabetes and diabetes. Reducing air pollution levels would significantly benefit this vulnerable population by reducing the incidence of mental disorders.

Synergetic roadmap of carbon neutrality and clean air for China

It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction, air quality improvement, and improved health. In the context of carbon peak, carbon neutrality, and clean air policies, this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators. The 18 indicators cover the following five aspects: air pollution and associated weather-climate conditions, progress in structural transition, sources, inks, and mitigation pathway of atmospheric composition, health impacts and benefits of coordinated control, and synergetic governance system and practices. By tracking the progress in each indicator, this perspective presents the major accomplishment of coordinated control, identifies the emerging challenges toward the synergetic governance, and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.

Climatic and Economic Background Determine the Disparities in Urbanites' Expressed Happiness during the Summer Heat

Climate-change-induced extreme weather events increase heat-related mortality and health risks for urbanites, which may also affect urbanites' expressed happiness (EH) and well-being. However, the links among EH, climate, and socioeconomic factors remain unclear. Here we collected ∼6 million geotagged tweets from 44 Chinese prefecture-level cities based on Sina Weibo and performed a quadratic regression model to explore the relationships between summer heat and EH. A three-stage analysis was developed to examine spatiotemporal heterogeneity and identify factors contributing to disparities in urbanites' EH. Results show that all cities exhibited a similar hump-shaped relationship, with an overall optimal temperature (OT) of 22.8 °C. The estimated OT varied geographically, with 25.3, 23.8, and 20.0 °C from north to south. Moreover, a 1 standard deviation increase in heatwave intensity was associated with a 0.813 (95% CI: 0.177, 1.449) standard deviation decrease in EH. Notably, within the geographic scope of this study, it was observed that urbanites in northern China and economically underdeveloped cities faced significantly lower heat risks during the summer heat. This research provides insight for future studies and practical applications concerning extreme weather events, urbanites' mental health, and sustainable urban development goal.

Ecological correlation between short term exposure to particulate matter and hospitalization for mental disorders in Shijiazhuang, China

The associations between particulate matter (PM) and overall and specific mental disorders (MDs) are investigated using data from two general hospitals in Shijiazhuang, China, from January 2014 to December 2019. A longitudinal time series study, as one type of ecological study, is conducted using a generalized additive model to examine the relationship between short-term exposure to PM2.5, PM10, and daily hospital admissions for MDs, and further stratification by subtypes, age, and gender. A total of 10,709 cases of hospital admissions for MDs have been identified. The significant short-time effects of PM2.5 on overall MDs at lag01 and PM10 at lag05 are observed, respectively. For specific mental disorders, there are substantial associations of PM pollution with mood disorders and organic mental disorders. PM2.5 has the greatest cumulative effect on daily admissions of mood disorders and organic mental disorders in lag01, and PM 10 has the greatest cumulative effect in lag05. Moreover, the effect modification by sex or age is statistically significant, with males and the elderly (≥ 45 years) having a stronger effect. Short-term exposure to PM2.5 and PM10can be associated with an increased risk of daily hospital admissions for MDs.

COVID-19 infection rate but not severity is associated with availability of greenness in the United States

Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban-rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.

Unpacking the inter- and intra-urban differences of the association between health and exposure to heat and air quality in Australia using global and local machine learning models

Environmental stressors including high temperature and air pollution cause health problems. However, understanding how the combined exposure to heat and air pollution affects both physical and mental health remains insufficient due to the complexity of such effects mingling with human society, urban and natural environments. Our study roots in the Social Ecological Theory and employs a tri-environmental conceptual framework (i.e., across social, built and natural environment) to examine how the combined exposure to heat and air pollution affect self-reported physical and mental health via, for the first time, the fine-grained nationwide investigation in Australia and highlight how such effects vary across inter- and intra-urban areas. We conducted an ecological study to explore the importance of heat and air quality to physical and mental health by considering 48 tri-environmental confounders through the global and local random forest regression models, as advanced machine learning methods with the advantage of revealing the spatial heterogeneity of variables. Our key findings are threefold. First, the social and built environmental factors are important to physical and mental health in both urban and rural areas, and even more important than exposure to heat and air pollution. Second, the relationship between temperature and air quality and health follows a V-shape, reflecting people's different adaptation and tolerance to temperature and air quality. Third, the important roles that heat and air pollution play in physical and mental health are most obvious in the inner-city and near inner-city areas of the major capital cities, as well as in the industrial zones in peri-urban regions and in Darwin city with a low-latitude. We draw several policy implications to minimise the inter- and intra-urban differences in healthcare access and service distribution to populations with different sensitivity to heat and air quality across urban and rural areas. Our conceptual framework can also be applied to examine the relationship between other environmental problems and health outcomes in the era of a warming climate.

Do Individuals with High Climate Anxiety Believe That They Will Die Earlier? First Evidence from Germany

OBJECTIVES

To examine the association between climate anxiety and perceived longevity in the general adult German population (also stratified by age group).

STUDY DESIGN

Nationally representative survey.

METHODS

Data were used of the general adult German population, with n = 3015 individuals (18 to 74 years; data collection: March 2022). Climate anxiety was assessed using the validated Climate Anxiety Scale. It was adjusted for a wide array of covariates in linear-log regression analysis.

RESULTS

Even after adjusting for various covariates, there was an association between higher (log) climate anxiety and a lower perceived longevity in the total sample (β = -1.41, p < 0.01). Stratified by age group, a significant association was only present among individuals aged 18 to 29 years (β = -3.58, p = 0.01), whereas it was not present in the other age groups (i.e., individuals aged 30 to 49 years, individuals aged 50 to 64 years, and individuals aged 65 years and over).

CONCLUSIONS

This study showed an association between higher climate anxiety and lower perceived longevity, particularly among younger individuals. More clearly, younger individuals with a higher climate anxiety think they will die earlier. This is the first study on this topic and could serve as a foundation for upcoming research. For example, longitudinal studies are needed to confirm our findings.

Short-term effect of PM2.5 on stroke in susceptible populations: A case-crossover study

BACKGROUND

Fine particulate matter (PM2.5) is a risk factor for stroke, and patients with pre-existing diseases appear to be particularly susceptible. We conducted a case-crossover study to examine the association between short-term exposure to fine particulate matter (PM2.5) and hospital admission for stroke in individuals with atrial fibrillation (AF), hypertension, diabetes, or hyperlipidemia.

METHODS

Patients diagnosed with acute ischemic stroke (AIS) were recruited from 2015 to 2017 in Chinese Stroke Center Alliances. We estimated daily PM2.5 average exposures with a spatial resolution of 0.1° using a data assimilation approach combining satellite measurements, air model simulations, and monitoring values. Conditional logistic regression was used to assess PM2.5-related stroke risk in patients with pre-existing medical co-morbidities.

RESULTS

A total of 155,616 patients diagnosed with AIS were admitted. Patients with a history of AF (n = 15,430), hypertension (n = 138,220), diabetes (n = 43,737), or hyperlipidemia (n = 16,855) were assessed separately. A 10 µg/m³ increase in daily PM2.5 was associated with a significant increase in AIS for individuals with AF at lag 4 (odds ratio (OR), 1.008; 95% confidence interval (CI), 1.002-1.014), and with hypertension (OR, 1.008; 95% CI, 1.006-1.010), diabetes (OR, 1.006; 95% CI, 1.003-1.010), and hyperlipidemia (OR, 1.007; 95% CI, 1.001-1.012) at lags 0-7. Elderly (⩾ 65 years old) and female patients with AF had significantly higher associations at lag 5 (OR, 1.009; 95% CI, 1.002-1.015) and lag 5 (OR, 1.010; 95% CI, 1.002-1.018), respectively.

CONCLUSION

Short-term exposure to PM2.5 is significantly associated with hospital admission for stroke in individuals with pre-existing medical histories, especially in older or female patients with AF. Preventive measures to reduce PM2.5 concentrations are particularly important in individuals with other medical co-morbidities.

The role of residential greenness levels, green land cover types and diversity in overweight/obesity among older adults: A cohort study

BACKGROUND

Few studies have investigated the effects of greenness exposure, green land cover types and diversity and their interaction with particulate matter (PM) to adiposity.

METHOD

Cohort data were collected from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Baseline data on greenness levels, green land cover types and diversity were assessed by the Normalized Difference Vegetation Index (NDVI), three greenery types (trees, shrublands and grassland) and Shannon's diversity index, respectively. Body mass index (BMI) and waist circumference (WC) were separately used as dependent variables and represented for peripheral overweight/obesity and central obesity, respectively. The mixed Cox model with random intercept was used to estimate the effects of greenness levels, types and diversity on overweight/obesity using single and multiple exposure models. We also examined the interaction of PM and the aforementioned indicators on overweight/obesity on both additive and multiplicative scales.

RESULTS

Single exposure models showed that higher levels of residential greenness, tree coverage and ratio of trees to shrublands/grassland were inversely associated with peripheral overweight/obesity and central obesity. An increase in shrublands, grassland and diversity of green was related to lower odds of peripheral overweight/obesity. Multiple exposure models confirmed the association between greenness levels and peripheral overweight/obesity. Males, educated participants and elderly who lived in southern regions and areas with cleaner air environments acquired more benefits from greenspace exposure. Single and multiple exposure models indicated that an antagonistic effect of increasing PM and decreasing greenness levels on peripheral overweight/obesity and central obesity. Single exposure models showed the potential interaction of tree coverage, ratio of trees to grassland and PM_2.5 exposures on the risk of peripheral overweight/obesity.

CONCLUSION

Increasing residential greenness and diversity of green were associated with healthy weight status. The relationship between greenery and overweight/obesity varied, and the effects of greenspace exposure on overweight/obesity were associated with air pollution.

Temperature variability and common diseases of the elderly in China: a national cross-sectional study

BACKGROUND

In the context of climate change, it has been well observed that short-term temperature variability (TV) could increase the overall and cause-specific mortality and morbidity. However, the association between long-term TV and a broader spectrum of diseases is not yet well understood, especially in the elderly.

METHODS

Our study used data from the fourth Urban and Rural Elderly Population (UREP) study. Long-term TV was calculated from the standard deviation (SD) of daily minimum and maximum temperatures within the study periods (2010-2014, 2011-2014, 2012-2014, 2013-2014, and 2014). Ten self-reported diseases and conditions were collected by questionnaire, including cataract, hypertension, diabetes, cardio-cerebrovascular diseases, stomach diseases, arthritis, chronic lung disease, asthma, cancer, and reproductive diseases. The province-stratified logistic regression model was used to quantify the association between long-term TV and the prevalence of each disease.

RESULTS

A total of 184,047 participants were included in our study. In general, there were significant associations between TV and the prevalence of most diseases at the national level. Cardio-cerebrovascular disease (OR: 1.16, 95% CI: 1.13, 1.20) generated the highest estimates, followed by stomach diseases (OR: 1.15, 95% CI: 1.10, 1.19), asthma (OR: 1.14, 95% CI: 1.06, 1.22), chronic lung diseases (OR: 1.08, 95% CI: 1.03, 1.13), arthritis (OR: 1.08, 95% CI: 1.05, 1.11), and cataract (OR: 1.06, 95% CI: 1.02, 1.10). Moreover, the associations varied by geographical regions and across subgroups stratified by sex, household income, physical activity, and education.

CONCLUSIONS

Our study showed that long-term exposure to TV was associated with the prevalence of main diseases in the elderly. More attention should be paid to the elderly and targeted strategies should be implemented, such as an early warning system.

Are there joint effects of different air pollutants and meteorological factors on mental disorders? A machine learning approach

Exposure to air pollutants is considered to be associated with mental disorders (MD). Few studies have addressed joint effect of multiple air pollutants and meteorological factors on admissions of MD. We examined the association between multiple air pollutants (PM_2.5, PM₁₀, O₃, SO₂, and NO₂), meteorological factors (temperature, precipitation, relative humidity, and sunshine time), and MD risk in Yancheng, China. Associations were estimated by a generalized linear regression model (GLM) adjusting for time trend, day of the week, and patients' average age. Empirical weights of environmental exposures were judged by a weighted quantile sum (WQS) model. A machine learning approach, Bayesian kernel machine regression (BKMR), was used to assess the overall effect of mixed exposures. We calculated excess risk (ER) and 95% confidence interval (CI) for each exposure. According to the effect of temperature on MD, we divided the exposure of all factors into different temperature groups. In the high temperature group, GLM found that for every 10 μg/m³ increase in O₃, PM_2.5 and PM₁₀ exposure, the ERs were 1.926 (95%CI 0.345, 3.531), 1.038 (95%CI 0.024, 2.062), and 0.780 (95% CI 0.052, 1.512) after adjusting for covariates. Temperature, relative humidity, and sunshine time also reported significant results. The WQS identified O₃ and temperature (above the threshold) had the highest weights among air pollutants and meteorological factors. BKMR found a significant positive association between mixed exposure and MD risks. In the low temperature group, only O₃ and temperature (below the threshold) showed significant results. These findings provide policymakers and practitioners with important scientific evidence for possible interventions. The association between different exposures and MD risk warrants further study.

Electrospun Filtering Membrane Designed as Component of Self-Decontaminating Protective Masks

The 2019 coronavirus outbreak and worsening air pollution have triggered the search for manufacturing effective protective masks preventing both particulate matter and biohazard absorption through the respiratory tract. Therefore, the design of advanced filtering textiles combining efficient physical barrier properties with antimicrobial properties is more newsworthy than ever. The objective of this work was to produce a filtering electrospun membrane incorporating a biocidal agent that would offer both optimal filtration efficiency and fast deactivation of entrapped viruses and bacteria. After the eco-friendly electrospinning process, polyvinyl alcohol (PVA) nanofibers were stabilized by crosslinking with 1,2,3,4-butanetetracarboxylic acid (BTCA). To compensate their low mechanical properties, nanofiber membranes with variable grammages were directly electrospun on a meltblown polypropylene (PP) support of 30 g/m². The results demonstrated that nanofibers supported on PP with a grammage of around only 2 g/m² presented the best compromise between filtration efficiencies of PM_0.3, PM_0.5, and PM_3.0 and the pressure drop. The filtering electrospun membranes loaded with benzalkonium chloride (ADBAC) as a biocidal agent were successfully tested against E. coli and S. aureus and against human coronavirus strain HCoV-229E. This new biocidal filter based on electrospun nanofibers supported on PP nonwoven fabric could be a promising solution for personal and collective protection in a pandemic context.

Air pollution impacts on public health: Evidence from 110 cities in Yangtze River Economic Belt of China

This study intends to further reveal the relationship between air pollution and public health on a city scale in China and explore the spillover effect among cities. On the basis of collecting the panel data of 110 cities in the Yangtze River Economic Belt from 2010 to 2018, we establish a spatial econometric model to analyze the impacts of air pollution, economic development, and other factors on public health. According to the results, a significant spatial correlation exists between the public health and air pollution levels in all of the cities in the Yangtze River Economic Belt. Air pollution also shows a spillover effect among these cities; the relationships between the industrial fume (dust) emissions, industrial sulfur dioxide emissions, and particulate matter (PM 2.5) concentration and the public health level are not simple linear relationships, but instead U-shaped curvilinear relationships. The economic development in recent years has contributed to the improvement of the public health level of the cities in the Yangtze River Economic Belt. The economic development of their neighboring cities, however, has adversely affected the public health levels of these cities.

Association between fine particulate matter and coronary heart disease: A miRNA microarray analysis

Several studies have reported an association between residential surrounding particulate matter with an aerodynamic diameter ≤2.5 μm (PM_2.5) and coronary heart disease (CHD). However, the underlying biological mechanism remains unclear. To fill this research gap, this study enrolled a residentially stable sample of 942 patients with CHD and 1723 controls. PM_2.5 concentration was obtained from satellite-based annual global PM_2.5 estimates for the period 1998-2019. MicroRNA microarray and pathway analysis of target genes was performed to elucidate the potential biological mechanism by which PM_2.5 increases CHD risk. The results showed that individuals exposed to high PM_2.5 concentrations had higher risks of CHD than those exposed to low PM_2.5 concentrations (odds ratio = 1.22, 95% confidence interval: 1.00, 1.47 per 10 μg/m³ increase in PM_2.5). Systolic blood pressure mediated 6.6% of the association between PM_2.5 and CHD. PM_2.5 and miR-4726-5p had an interaction effect on CHD development. Bioinformatic analysis demonstrated that miR-4726-5p may affect the occurrence of CHD by regulating the function of RhoA. Therefore, individuals in areas with high PM_2.5 exposure and relative miR-4726-5p expression have a higher risk of CHD than their counterparts because of the interaction effect of PM_2.5 and miR-4726-5p on blood pressure.

Association of ambient air pollution exposure and its variability with subjective sleep quality in China: A multilevel modeling analysis

Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM_2.5) and ≤10 μm (PM₁₀) led to 0.4% (95% confidence interval (CI): 1.002-1.006) and 0.3% (95% CI: 1.001-1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM_2.5 and PM₁₀ were 2.2% (95% CI: 1.012-1.032) and 2.2% (95% CI: 1.012-1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.

Associations of long-term ambient air pollution and traffic-related pollution with blood pressure and hypertension defined by the different guidelines worldwide: the CHCN-BTH study

The assessment of the generalization of the strict hypertension definition in the 2017 ACC/AHA Hypertension Guideline from environmental condition remains sparse. The aims of this study are to investigate and compare the associations of ambient air pollution and traffic-related pollution (TRP) with hypertension defined by the different criteria. A total of 32,135 participants were recruited from the baseline survey of the CHCN-BTH in 2017. We defined hypertension as SBP/DBP ≥ 140/90 mmHg according to the hypertension guidelines in China, Japan, Europe and ISH (traditional criteria) and defined as SBP/DBP ≥ 130/80 mmHg according to the 2017 ACC/AHA Hypertension Guideline (strict criteria). A two-level generalized linear mixed models were applied to investigate the associations of air pollutants (i.e. PM_2.5, SO₂, NO₂) and TRP with blood pressure (BP) measures and hypertension. Stratified analyses and two-pollutant models were also performed. The stronger associations of air pollutants were found in the hypertension defined by the strict criteria than that defined by the traditional criteria. The ORs per an IQR increase in PM_2.5 were 1.17 (95% CI: 1.09, 1.25) for the strict criteria and 1.14 (95% CI: 1.06, 1.23) for the traditional criteria. The similar conditions were also observed for TRP. The above results were robust in both stratified analyses and two-pollutant models. Our study assessed the significance of the hypertension defined by the strict criteria from environmental aspect and called attention to the more adverse effects of air pollution and TRP on the earlier stage of hypertension.

Association between built environments and quality of life among community residents: mediation analysis of air pollution

OBJECTIVES

The aim of this study was to analyze the relationship between built environments and quality of life (QoL), and the mediating role of air pollution in that relationship.

STUDY DESIGN

This was a cross-sectional population-based study.

METHODS

Data of 5196 adults residing in 148 communities in three cities in Liaoning Province, China, were analyzed. Objective measures of traffic design included street connectivity, road network density, bus station density, and parking lot density; residential greenness was controlled as a confounder. QoL was evaluated using the 12-Item Short Form Health Survey. The average concentrations of PM_2.5 and SO₂ one month before QoL collection for each community were calculated.

RESULTS

Road network density and parking lot density were negatively associated with the Physical Component Summary (PCS), but street connectivity was positively associated with PCS for the participants. Bus station density, street connectivity, and parking lot density were negatively associated with the Mental Component Summary (MCS), and PM_2.5 and SO₂ mediated this association. In addition, gender and road network density and parking lot density had an interactive effect on the MCS of the participants.

CONCLUSIONS

Dense traffic affects people's health not only directly but also indirectly through air pollution. The effects of built environments and air pollution should be considered when building healthy, supportive communities, and healthy cities.

Recent Techniques in Determining the Effects of Climate Change on Depressive Patients: A Systematic Review

Climate change is amongst the most serious issues nowadays. Climate change has become a concern for the scientific community as it could affect human health. Researchers have found that climate change potentially impacts human mental health, especially among depressive patients. However, the relationship is still unclear and needs further investigation. The purpose of this systematic review is to systematically evaluate the evidence of the association between climate change effects on depressive patients, investigate the effects of environmental exposure related to climate change on mental health outcomes for depressive patients, analyze the current technique used to determine the relationship, and provide the guidance for future research. Articles were identified by searching specified keywords in six electronic databases (Google Scholar, PubMed, Scopus, Springer, ScienceDirect, and IEEE Digital Library) from 2012 until 2021. Initially, 1823 articles were assessed based on inclusion criteria. After being analyzed, only 15 studies fit the eligibility criteria. The result from included studies showed that there appears to be strong evidence of the association of environmental exposure related to climate change in depressive patients. Temperature and air pollution are consistently associated with increased hospital admission of depressive patients; age and gender became the most frequently considered vulnerability factors. However, the current evidence is limited, and the output finding between each study is still varied and does not achieve a reasonable and mature conclusion regarding the relationship between the variables. Therefore, more evidence is needed in this domain study. Some variables might have complex patterns, and hard to identify the relationship. Thus, technique used to analyze the relationship should be strengthened to identify the relevant relationship.

The effects of greenness exposure on hypertension incidence among Chinese oldest-old: a prospective cohort study

BACKGROUND

Although the oldest-old (those aged over 80 years) are vulnerable to environmental factors and have the highest prevalence of hypertension, studies focusing on greenness exposure and the development of hypertension among them are insufficient. The aim of this study was to explore the association between residential greenness and hypertension in the oldest-old population.

METHODS

This cohort study included data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The oldest-old were free of hypertension at baseline (2008), and hypertension events were assessed by follow-up surveys in 2011, 2014, and 2018. The one-year averages of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at 500-m buffer before the interview year of incident hypertension or last censoring interview were collected at the level of 652 residential units (district or county). The linear or nonlinear association between greenness and hypertension incidence was analyzed using the Cox proportional hazards model with penalized splines. The linear links between greenness and hypertension incidence were determined using the Cox proportional hazards model included a random effect term.

RESULTS

Among 5253 participants, the incidence rate of hypertension was 7.25 (95% confidence interval [CI]: 6.83-7.67) per 100 person-years. We found a nonlinear association between greenness exposure and hypertension risk, and the exposure-response curve showed that 1 change point existed. We examined the linear effect of greenness on hypertension by categorizing the NDVI/EVI into low and high-level exposure areas according to the change point. We found more notable protective effects of each 0.1-unit increase in greenness on hypertension incidence for participants living in the high-level greenness areas (hazard ratio (HR) = 0.60; 95% CI: 0.53-0.70 for NDVI; HR = 0.46; 95% CI: 0.37-0.57 for EVI). In contrast, no significant influence of greenness exposure on hypertension risk was found for participants living in the low-level greenness areas (HR = 0.77; 95% CI: 0.38-1.55 for NDVI; HR = 0.73; 95% CI: 0.33-1.63 for EVI).

CONCLUSIONS

Greenness exposure is nonlinearly associated with hypertension risk among the oldest-old, presenting its relationship in an inverse "U-shaped" curve. Greenness is a protective factor that decreases the risk of hypertension.

Environmentally vulnerable or sensitive groups exhibiting varying concerns toward air pollution can drive government response to improve air quality

Air pollution seriously threatens human health, and its consequences are particularly prevalent among environmentally vulnerable or sensitive groups. However, whether the concerns among these groups are different and how they affect air pollution governance remain unclear. Here, we extract 3.8 million haze-related posts from China’s Sina Weibo and analyze the concerns raised by these groups by constructing an air pollution notability index. The results show that protection is the key theme for women aged 20–35 years, while elderly individuals are easily influenced by haze-related product ads yet lack awareness of scientific-based protection. Concerns shared by young individuals are more effective in pressuring the government in cities that experience higher levels of pollution. Concerns shared by women are more effective in cities that experience lower levels of pollution. This study evidences the influence of the public concerns conveyed via social media on air pollution governance in China.

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Online haze concerns from environmentally vulnerable groups can drive air clean

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Government response to their concern plays the key role in improving air quality

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When PM2.5 exceeds 200 μg/m³, their concern shifts from self-protection to governance

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Elders are easily drawn by haze-related ads yet lack aware of scientific protection

The Impact of Sustained Exposure to Air Pollutant on the Mental Health: Evidence from China

Emerging evidence suggests that poor mental health is particularly pronounced among Chinese residents, who are exposed to nearly the worst air pollution worldwide. However, the correlations between air pollutant concentration and mental health have not been consistently reported in previous studies. Methodologically speaking, a sufficiently rigorous design is required to demonstrate the causal relationship between the two factors. In this study, we aimed to infer the causal relationship between air pollutant concentration and mental health. In this panel research, the data were compiled through a combination of statistics from the China Family Panel Study, China Environmental Statistics Yearbook, World Meteorological Association, and China National Bureau of Statistics. Ultimately, this study enrolled 65,326 individuals whose mental health, air pollutant concentration, and other demographic information was available and robust. The RD design of this study utilizes the discontinuous variation in air pollutant concentrations and mental health as one crosses the Huai River boundary, which is an arbitrary heating policy that caused the difference in air pollutant concentrations between the north and south of China. In this study, we found that a 10 μg/m3 increase in air pollutant concentrations (air particulate matter smaller than 10 μm (PM10)) leads to a 4.9-unit decrease in the mental health of the Chinese residents(coeff = 0.49, SD = 0.07, p < 0.05), equivalent to 36% of the average of Chinese residents. In the heterogeneity model, the impairment of mental health by air pollutant concentrations was more pronounced in male residents (coeff = 1.37, SD = 0.10, p < 0.05) compared to female residents (coeff = 0.42, SD = 0.04, p < 0.05) and smokers (compared to non-smokers). The robustness of the results is ensured by changing the RD bandwidth and polynomial order, and by two unique sensitivity analyses. The results indicate that air pollutant concentrations significantly impair the mental health of Chinese residents, which provides empirical evidence supporting the Chinese government’s decision to invest more in combating air pollution and ensuring the mental health of Chinese residents.

Cross-Sectional Associations between Living and Built Environments and Depression Symptoms among Chinese Older Adults

In this study, we explored the cross-sectional associations between living and built environments and depression among older Chinese adults. Data from 5822 participants were obtained. Depression symptoms were evaluated through the use of the Patient Health Questionnaire (PHQ-9), with a score higher than 4 categorized as having depression symptoms. The living environment was assessed by asking about dust in the environment and barrier-free facilities. We considered the presence of amenities within a 10 min walking distance and the proportion of green space within an 800 m distance from participants’ dwellings to reflect the built environment. Data were analyzed by multilevel logistic regression. Participants living in a non-dusty environment with proximity to green space had a lower risk of depression (non-dusty environment: OR = 0.784, 95% CI = 0.642, 0.956; green space: OR = 0.834, 95% CI = 0.697, 0.998). However, having no access to barrier-free facilities and hospital proximity increased the depression risk (barrier-free facilities: OR = 1.253, 95% CI = 1.078, 1.457; hospital: OR = 1.318, 95% CI = 1.104, 1.574). Dusty environments, access to barrier-free facilities and proximity to hospitals and green spaces were associated with depression symptoms among older Chinese adults.

A Novel Composite Index to Measure Environmental Benefits in Urban Land Use Optimization Problems

In urban land use optimization problems, different conflicting objectives are applied. One of the most significant goals in urban land use optimization problems is to maximize environmental benefits. To quantify environmental benefits in land use optimization, many researchers have employed a variety of methodologies. According to previous studies, there is no standard approach for calculating environmental benefits in urban land use allocation problems. Against this background, this study aims to (a) identify indicators of environmental benefits and (b) propose a novel composite index to measure environmental benefits in urban land use optimization problems. This study identified four indicators as a measure of environmental benefits based on a literature assessment and expert opinion. These are spatial compactness, land surface temperature, carbon storage, and ecosystem service value. In this work, we proposed a novel composite environmental benefits index (EBI) to quantify environmental benefits in urban land use allocation problems using an ordered weighted averaging (OWA) method. The study results showed that land surface temperature (LST) is the most influential indicator of environmental benefit while carbon storage is the least important factor. Finally, the proposed method was applied in Rajshahi city in Bangladesh. This study identified that, in an average-risk decision, most of the land (64.55%) of the study area falls within the low-environmental-benefit zone due to a lack of vegetated land cover. The result suggests the potential of using EBI in the land use allocation problem to ensure environmental benefits.

Air pollution and individuals' mental well-being in the adult population in United Kingdom: A spatial-temporal longitudinal study and the moderating effect of ethnicity

BACKGROUND

Recent studies suggest an association between ambient air pollution and mental well-being, though evidence is mostly fragmented and inconclusive. Research also suffers from methodological limitations related to study design and moderating effect of key demographics (e.g., ethnicity). This study examines the effect of air pollution on reported mental well-being in United Kingdom (UK) using spatial-temporal (between-within) longitudinal design and assesses the moderating effect of ethnicity.

METHODS

Data for 60,146 adult individuals (age:16+) with 349,748 repeated responses across 10-data collection waves (2009-2019) from "Understanding-Society: The-UK-Household-Longitudinal-Study" were linked to annual concentrations of NO2, SO2, PM10, and PM2.5 pollutants using the individuals' place of residence, given at the local-authority and at the finer Lower-Super-Output-Areas (LSOAs) levels; allowing for analysis at two geographical scales across time. The association between air pollution and mental well-being (assessed through general-health-questionnaire-GHQ12) and its modification by ethnicity and being non-UK born was assessed using multilevel mixed-effect logit models.

RESULTS

Higher odds of poor mental well-being was observed with every 10μg/m3 increase in NO2, SO2, PM10 and PM2.5 pollutants at both LSOAs and local-authority levels. Decomposing air pollution into spatial-temporal (between-within) effects showed significant between, but not within effects; thus, residing in more polluted local-authorities/LSOAs have higher impact on poor mental well-being than the air pollution variation across time within each geographical area. Analysis by ethnicity revealed higher odds of poor mental well-being with increasing concentrations of SO2, PM10, and PM2.5 only for Pakistani/Bangladeshi, other-ethnicities and non-UK born individuals compared to British-white and natives, but not for other ethnic groups.

CONCLUSION

Using longitudinal individual-level and contextual-linked data, this study highlights the negative effect of air pollution on individuals' mental well-being. Environmental policies to reduce air pollution emissions can eventually improve the mental well-being of people in UK. However, there is inconclusive evidence on the moderating effect of ethnicity.

ORIGINAL ARTICLE: Associations of antidepressant medication with its various predictors including particulate matter: Machine learning analysis using national health insurance data

This study uses machine learning and population-based data to analyze major determinants of antidepressant medication including the concentration of particulate matter under 2.5 μm (PM_2.5). Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants, who were aged 15-79 years, lived in the same districts of Seoul and had no history of antidepressant medication during 2002-2012. The dependent variable was antidepressant-free months during 2013-2015 and the 30 independent variables for 2012 were included (demographic/socioeconomic information, health information, district-level information including PM_2.5). Random forest variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of antidepressant-free months. Based on random forest variable importance, the top 15 determinants of antidepressant medication during 2013-2015 included cardiovascular disease (0.0054), age (0.0047), household income (0.0037), gender (0.0027), the district-level proportion of recipients of national basic living security program benefits (0.0019), district-level social satisfaction (0.0013), diabetes mellitus (0.0012), January 2012 PM_2.5 (0.0011), district-level street ratio (0.0010), drinker (0.0009), chronic obstructive pulmonary disease (0.0008), district-level economic satisfaction (0.0006), exercise (0.0005), March 2012 PM_2.5 (0.0005) and November 2012 PM_2.5 (0.0004). Besides these predictors, smoker and district-level deprivation index are found to be influential most widely, given that they ranked within the top 10 most often in sub-group analysis. In conclusion, antidepressant medication has strong associations with neighborhood conditions including socioeconomic satisfaction and the seasonality of particulate matter. Strong interventions for these factors are really needed for the effective management of major depressive disorder.

The Cradle-to-Cradle Life Cycle Assessment of Polyethylene terephthalate: Environmental Perspective

Over the last several years, the number of concepts and technologies enabling the production of environmentally friendly products (including materials, consumables, and services) has expanded. One of these ways is cradle-to-cradle (C2C) certified^TM. Life cycle assessment (LCA) technique is used to highlight the advantages of C2C and recycling as a method for reducing plastic pollution and fossil depletion by indicating the research limitations and gaps from an environmental perspective. Also, it estimates the resources requirements and focuses on sound products and processes. The C2C life cycle measurements for petroleum-based poly (ethylene terephthalate) (PET) bottles, with an emphasis on different end-of-life options for recycling, were taken for mainland China, in brief. It is considered that the product is manufactured through the extraction of crude oil into ethylene glycol and terephthalic acid. The CML analysis method was used in the LCIA for the selected midpoint impact categories. LCA of the product has shown a drastic aftermath in terms of environmental impacts and energy use. But the estimation of these consequences is always dependent on the system and boundary conditions that were evaluated throughout the study. The impacts that burden the environment are with the extraction of raw material, resin, and final product production. Minor influences occurred due to the waste recycling process. This suggests that waste degradation is the key process to reduce the environmental impacts of the production systems. Lowering a product’s environmental impact can be accomplished in a number of ways, including reducing the amount of materials used or choosing materials with a minimal environmental impact during manufacture processes.

Association between greenness and dyslipidemia in patients with coronary heart disease: A proteomic approach

BACKGROUND

Residential surrounding greenness may be protective of dyslipidemia are often theorized but remain poorly quantified. In particular, the underlying biological mechanisms of blood lipid changes with green spaces remain unclear.

METHODS

Our observational epidemiology study included a residentially stable sample of 1035 coronary heart disease patients, and proteomics study included 16 participants. Normalized Difference Vegetation Index (NDVI) was used to evaluate residential greenness exposures. Proteomics technology was used to identify plasma greenness-related proteome disturbance, and the pathway analysis was employed to evaluate the potential biological mechanisms of greenness decreasing dyslipidemia risk.

RESULT

Higher residential surrounding greenness in the 500-m area was associated with lower risks of dyslipidemia (odds ratio (OR) = 0.871, 95% confidence interval (CI): 0.763, 0.994 for per one-quartile NDVI increase). Lymphocytes mediated 18.7% of the association between greenness and dyslipidemia. Greenness related proteins (including PLXDC1, IGFBP2 and LY6D) may regulate the biological functions of lipid metabolism and transport-related proteins (including ADIPOQ and CES1) through a series of biological processes.

CONCLUSION

People in greener surroundings have a lower risk of dyslipidemia, which may be due to their lower inflammation, stronger lipid transporter activity, and normal cholesterol metabolism.