Short-term effects of air pollution on cause-specific mental disorders in three subtropical Chinese cities

Effects of ambient fine particulate matter on the exacerbation of psychiatric disorders in southern Taiwan: a case-crossover study

This study investigated the impact of short-term exposures to ambient fine particulate matter 2.5 (PM2.5) on the exacerbation of psychiatric disorders (PDs) in southern Taiwan between 2014 and 2020. Data on emergency room visits (ERVs) for PDs and air pollutant levels were obtained from the Chang Gung Research Database and the Environmental Protection Administration, respectively. A time-stratified case-crossover design was adopted to estimate the risks of ERVs for PDs. At lag4 days, a 10-μg/m3 increase in PM2.5 was associated with significant increases in ERVs in both single- and multi-pollutant models, with odds ratio (OR) of 1.18 [95% confidence interval (95% CI): 1.00, 1.38] (PM2.5), 1.20 (95% CI: 1.00, 1.43) (PM2.5 + SO2), 1.23 (95% CI: 1.03, 1.46) (PM2.5 + O3), and 1.25 (95% CI: 1.03, 1.52) (PM2.5 + SO2 + O3). For cumulative lags (lag0-6), a 10-μg/m3 increase in PM2.5 was associated with significant increases in ERVs only for multi-pollutant model (PM2.5 + SO2), with OR of 1.41 (95% CI: 1.03, 1.93). Among males, significant increases in ERVs were observed at lag4 and lag0-6 days; however, no significant associations were observed in females. In conclusion, short-term exposure to PM2.5 increased the risk of PDs exacerbation, exhibiting both delayed and cumulative effects, with male patients found to be more sensitive.

Long-term exposure to low-concentration sulfur dioxide and mental disorders in middle-aged and older urban adults

The World Health Organization loosened the air quality guideline for daily sulfur dioxide (SO2) concentrations from 20 μg/m3 to 40 μg/m3. However, the guideline for SO2 concentrations in 2021 raised public concerns since there was no sufficient evidence that low-concentration SO2 exposure is harmless to the population's health, including mental health. We analyzed the associations between low-concentration SO2 exposure and incidence risks of total and cause-specific mental disorders, including depressive disorder, anxiety disorder, bipolar disorder, and schizophrenia spectrum disorder. 245,820 urban participants with low-concentration SO2 exposure (<8 μg/m3) at baseline were involved in the analyses from the UK Biobank. SO2 exposure (2006-2022) was estimated using high-resolution annual mean concentration maps from the Department for Environment, Food and Rural Affairs. Mental disorders and corresponding symptoms were identified using healthcare records and an online questionnaire, respectively. Associations were examined using both time-independent (2006-2010) and time-dependent (from 2006 to 2022) Cox regression models and logistic regression models with full adjustments for potential confounders. Stratification analyses were further conducted to identify vulnerable populations. Long-term exposure to low-concentration SO2 (per 1.36 μg/m3) was associated with increased risks of mental disorders, depressive disorder, and anxiety disorder with hazard ratios of 1.02 (95% confidence interval [CI]: 1.00, 1.03), 1.11 (95% CI: 1.07, 1.16), and 1.10 (95% CI: 1.06, 1.14) in the time-independent model, respectively. Associations were stronger for younger individuals. Additionally, the low-concentration SO2 exposure was linked to several psychiatric symptoms, such as trouble concentrating and restlessness, with odds ratios of 1.07 (95% CI: 1.04, 1.10) and 1.11 (95% CI: 1.07, 1.14), respectively. This study demonstrated significant associations of long-term exposure to low-concentration SO2 with mental disorders, highlighting the need for stricter regulations for SO2 to better protect public health and improve air quality in urban areas, in support of the Sustainable Development Goals.

Short-Term Effect of Air Pollution on Daily Hospital Visits for Anxiety Disorders in Southern China with Low Pollution Concentrations

The global prevalence and burden of anxiety disorders (ADs) are increasing. However, findings on the acute effects of air pollution on ADs remain inconclusive. We evaluated the effects of short-term exposure to ambient air pollutants, including fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3), on daily hospital visits for ADs. A generalized additive model was used to perform a time-series analysis on data from a Southern China city's medical insurance system between 1 March 2021, and 31 July 2023. Although the daily levels of most pollutants (PM10, SO2, CO, NO2 and O3) were consistently below China and WHO's Ambient Air-Quality Standards, significant associations were observed between daily hospital visits for ADs and all six air pollutants. Each interquartile range increase in concentrations resulted in the largest odds ratios of 1.08 (95% CI: 1.01, 1.16) at lag1 for PM2.5, 1.19 (95% CI: 1.05, 1.34) at lag07 for NO2, 1.14 (95% CI: 1.05, 1.23) at lag02 for CO, 1.12 (95% CI: 1.01, 1.25) at lag07 for PM10, 1.06 (95% CI: 1.01, 1.12) at lag7 for SO2 and 1.08 (95% CI: 1.01, 1.15) at lag7 for O3, respectively. The effects of NO2 and CO remained robust across subgroup analyses and sensitivity analyses. Females and middle-aged individuals showed stronger associations than other subgroups. The findings underscore the necessity for public health efforts to alleviate the impact of air pollution on mental health, even in low-concentration settings.

Air pollution mixture exposure during pregnancy and postpartum psychological functioning: racial/ethnic- and fetal sex-specific associations

BACKGROUND

Prenatal air pollution (AP) exposure has been linked to postpartum psychological functioning, impacting health outcomes in both women and children. Extant studies primarily focused on individual pollutants.

OBJECTIVE

To assess the association between prenatal exposure to a mixture of seven AP components and postpartum psychological functioning using daily exposure data and data-driven statistical methods.

METHODS

Analyses included 981 women recruited at 24.0 ± 9.9 weeks gestation and followed longitudinally. We estimated prenatal daily exposure levels for constituents of fine particles [elemental carbon (EC), organic carbon (OC), nitrate (NO3-), sulfate (SO42-), ammonium (NH4+)], nitrogen dioxide (NO2), and ozone (O3) using validated global 3-D chemical-transport models and satellite-based hybrid models based on residential addresses. Edinburgh Postnatal Depression Scale (EPDS) was administered to participants to derive a total EPDS score and the subconstruct scores for anhedonia and depressive symptoms. A distributed lag model (DLM) was employed within Bayesian Kernel Machine Regression (BKMR) to develop time-weighted exposure profile for each pollutant. These exposures were then input into a Weighted Quantile Sum (WQS) regression to estimate an overall mixture effect, adjusted for maternal age, education, race/ethnicity, season of delivery, and delivery year. Effect modification by race/ethnicity and fetal sex was also examined.

RESULTS

Women were primarily Hispanic (51%) and Black (32%) reporting ≤12 years of education (58%). Prenatal exposure to an AP mixture was significantly associated with increased anhedonia subscale z-scores, particularly in Hispanics (β = 0.07, 95%CI = 0.004-0.13, per unit increase in WQS index). It was also borderline associated with increased total EPDS (β = 0.11, 95%CI = 0.00-0.22) and depressive symptom subscale (β = 0.09, 95%CI = -0.02 to 0.19) z-scores, particularly among Hispanic women who gave birth to a male infant. Sulfate (SO42-), O3 and OC were major contributors to these associations.

IMPACT

This study utilizes an advanced data-driven approach to examine the temporally- and mixture-weighted effects of prenatal air pollution exposure on postpartum psychological functioning. We found that exposure to a prenatal air pollution mixture predicted poorer postpartum psychological functioning, particularly anhedonia symptoms in Hispanic women. Findings underscore the importance of considering both exposure mixtures as well as potential modifying factors to better help identify particular pollutants that drive effects and susceptible populations, which can inform more effective intervention strategies.

Air pollutants, genetic susceptibility and the risk of schizophrenia: large prospective study

BACKGROUND

Evidence linking air pollutants and the risk of schizophrenia remains limited and inconsistent, and no studies have investigated the joint effect of air pollutant exposure and genetic factors on schizophrenia risk.

AIMS

To investigate how exposure to air pollution affects schizophrenia risk and the potential effect modification of genetic susceptibility.

METHOD

Our study was conducted using data on 485 288 participants from the UK Biobank. Cox proportional hazards models were used to estimate the schizophrenia risk as a function of long-term air pollution exposure presented as a time-varying variable. We also derived the schizophrenia polygenic risk score (PRS) utilising data provided by the UK Biobank, and investigated the modification effect of genetic susceptibility.

RESULTS

During a median follow-up period of 11.9 years, 417 individuals developed schizophrenia (mean age 55.57 years, s.d. = 8.68; 45.6% female). Significant correlations were observed between long-term exposure to four air pollutants (PM2.5; PM10; nitrogen oxides, NOx; nitrogen dioxide, NO2) and the schizophrenia risk in each genetic risk group. Interactions between genetic factors and the pollutants NO2 and NOx had an effect on schizophrenia events. Compared with those with low PRS and low air pollution, participants with high PRS and high air pollution had the highest risk of incident schizophrenia (PM2.5: hazard ratio = 6.25 (95% CI 5.03-7.76); PM10: hazard ratio = 7.38 (95% CI 5.86-9.29); NO2: hazard ratio = 6.31 (95% CI 5.02-7.93); NOx: hazard ratio = 6.62 (95% CI 5.24-8.37)).

CONCLUSIONS

Long-term exposure to air pollutants was positively related to the schizophrenia risk. Furthermore, high genetic susceptibility could increase the effect of NO2 and NOx on schizophrenia risk.

Differential effects of air pollution exposure on mental health: Historical redlining in New York State

Growing evidence suggests that ambient air pollution has adverse effects on mental health, yet our understanding of its unequal impact remains limited, especially in areas with historical redlining practices. This study investigates whether the impact of daily fluctuations in ambient air pollutant levels on emergency room (ER) visits for mental disorders (MDs) varies across neighborhoods affected by redlining. Furthermore, we explored how demographic characteristics and ambient temperature may modify the effects of air pollution. To assess the disproportional short-term effects of PM2.5, NO2, and O3 on ER visits across redlining neighborhoods, we used a symmetric bidirectional case-crossover design with a conditional logistic regression model. We analyzed data from 2 million ER visits for MDs between 2005 and 2016 across 17 cities in New York State, where redlining policies were historically implemented. A stratified analysis was performed to examine potential effect modification by individuals' demographic characteristics (sex, age, and race/ethnicity) and ambient temperature. We found that both PM2.5 and NO2 were significantly associated with MD-related ER visits primarily in redlined neighborhoods. Per 10μgm-3 increase in daily PM2.5 and per 10 ppb increase in NO2 concentration were associated with 1.04 % (95 % Confidence Interval (CI): 0.57 %, 1.50 %) and 0.44 % (95 % CI: 0.21 %, 0.67 %) increase in MD-related ER visits in redlined neighborhoods, respectively. We also found significantly greater susceptibility among younger persons (below 18 years old) and adults aged 35-64 among residents in grade C or D, but not in A or B. Furthermore, we found that positive and statistically significant associations between increases in air pollutants (PM2.5 and NO2) and MD-related ER visits exist during medium temperatures (4.90 °C to 21.11 °C), but not in low or high temperature. Exposures to both PM2.5 and NO2 were significantly associated with MD-related ER visits, but these adverse effects were disproportionately pronounced in redlined neighborhoods.

Effects of ambient air pollution on the hospitalization risk and economic burden of mental disorders in Qingdao, China

OBJECTIVE

The aim of this study was to examine the impacts of short-term exposure to air pollutants on hospitalizations for mental disorders (MDs) in Qingdao, a Chinese coastal city, and to assess the corresponding hospitalization risk and economic cost.

METHODS

Daily data on MD hospitalizations and environmental variables were collected from January 1, 2015, to December 31, 2019. An overdispersed generalized additive model was used to estimate the association between air pollution and MD hospitalizations. The cost of illness method was applied to calculate the corresponding economic burden.

RESULTS

With each 10 μg/m3 increase in the concentration of fine particulate matter (PM2.5) at lag05, inhalable particulate matter (PM10) at lag0, sulfur dioxide (SO2) at lag06 and ozone (O3) at lag0, the corresponding relative risks (RRs) and 95% confidence intervals (CIs) were 1.0182 (1.0035-1.0332), 1.0063 (1.0001-1.0126), 1.0997 (1.0200-1.1885) and 1.0099 (1.0005-1.0194), respectively. However, no significant effects of nitrogen dioxide (NO2) or carbon monoxide (CO) were found. Stratified analysis showed that males were susceptible to SO2 and O3, while females were susceptible to PM2.5. Older individuals (≥ 45 years) were more vulnerable to air pollutants (PM2.5, PM10, SO2 and O3) than younger individuals (< 45 years). Taking the Global Air Quality Guidelines 2021 as a reference, 8.71% (2,168 cases) of MD hospitalizations were attributable to air pollutant exposure, with a total economic burden of 154.36 million RMB.

CONCLUSION

Short-term exposure to air pollution was associated with an increased risk of hospitalization for MDs. The economic advantages of further reducing air pollution are enormous.

Heatwave characteristics complicate the association between PM2.5 components and schizophrenia hospitalizations in a changing climate: Leveraging of the individual residential environment

BACKGROUND

In the era characterized by global environmental and climatic changes, understanding the effects of PM2.5 components and heatwaves on schizophrenia (SCZ) is essential for implementing environmental interventions at the population level. However, research in this area remains limited, which highlights the need for further research and effort. We aim to assess the association between exposure to PM2.5 components and hospitalizations for SCZ under different heatwave characteristics.

METHODS

We conducted a 16 municipalities-wide, individual exposure-based, time-stratified, case-crossover study from January 1, 2017, to December 31, 2020, encompassing 160736 hospitalizations in Anhui Province, China. Daily concentrations of PM2.5 components were obtained from the Tracking Air Pollution in China dataset. Conditional logistic regression models were used to investigate the association between PM2.5 components and hospitalizations. Additionally, restricted cubic spline models were used to identify protective thresholds of residential environment in response to environmental and climate change.

RESULTS

Our findings indicate a positive correlation between PM2.5 and its components and hospitalizations. Significantly, a 1 μg/m3 increase in black carbon (BC) was associated with the highest risk, at 1.58% (95%CI: 0.95-2.25). Exposure to heatwaves synergistically enhanced the impact of PM2.5 components on hospitalization risks, and the interaction varied with the intensity and duration of heatwaves. Under the 99th percentile heatwave events, the impact of PM2.5 and its components on hospitalizations was most pronounced, which were PM2.5 (2-4d: 4.59%, 5.09%, and 5.09%), sulfate (2-4d: 21.73%, 23.23%, and 25.25%), nitrate (2-4d: 17.51%, 16.93%, and 20.31%), ammonium (2-4d: 27.49%, 31.03%, and 32.41%), organic matter (2-4d: 32.07%, 25.42%, and 24.48%), and BC (2-4d: 259.36%, 288.21%, and 152.52%), respectively. Encouragingly, a protective effect was observed when green and blue spaces comprised more than 17.6% of the residential environment.

DISCUSSION

PM2.5 components and heatwave exposure were positively associated with an increased risk of hospitalizations, although green and blue spaces provided a mitigating effect.

Short-term exposure to ambient nitrogen dioxide and increased hospitalization burden for depression in China: a multicity analysis

Evidence for the increased hospitalization burden, including admissions, expenditures and length of hospital stay (LOS) for depression attributable to ambient nitrogen dioxide (NO2) is lacking. We investigated the associations between short-term exposure to ambient NO2 and attributable admissions, hospitalization expenditures and LOS for depression in 57 Chinese cities during 2013-2017 using a well-established two-stage time-series study approach. Short-term exposure to ambient NO2 was associated with significantly increased admissions, hospitalization expenditures and LOS for depression, and the attributable fractions were 6.87% (95% CI: 2.90%, 10.65%), 7.12% (3.01%, 11.04%) and 6.12% (2.59%, 9.50%) at lag02, respectively. The projected total attributable admissions, hospitalization expenditures and LOS for depression related to ambient NO2 at the national level were 23,335 (9,863, 36,181) admissions, 318.70 (134.43, 492.21) million CNY and 539.55 (227.99, 836.99) thousand days during the study period, respectively. Short-term exposure to ambient NO2 is associated with increased hospitalization burden for depression.

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.

The association between short-term exposure to nitrogen dioxide and hospital admission for schizophrenia: A systematic review and meta-analysis

BACKGROUND

Ambient air pollution has been identified as a primary risk factor for mental disorders. In recent years, the relationship between exposure to ambient nitrogen dioxide (NO2) and the risk of hospital admissions (HAs) for schizophrenia has garnered increasing scientific interest, but evidence from epidemiological studies has been inconsistent. Therefore, a systematic review and meta-analysis were conducted to comprehensively identify potential correlations.

METHODS

A literature search in 3 international databases was conducted before December 31, 2022. Relative risk (RR) and corresponding 95% confidence intervals (CI) were calculated to evaluate the strength of the associations. Summary effect sizes were calculated using a random-effects model due to the expected heterogeneity (I2 over 50%).

RESULTS

A total of ten eligible studies were included in the meta-analysis, including 1,412,860 participants. The pooled analysis found that an increased risk of HAs for schizophrenia was associated with exposure to each increase of 10 μg/m3 in NO2 (RR = 1.029, 95% CI = 1.016-1.041, P < .001). However, the heterogeneity was high for the summary estimates, reducing the credibility of the evidence. In 2-pollutant models, results for NO2 increased by 0.3%, 0.2% and 2.3%, respectively, after adjusting for PM2.5, PM10 and SO2.

CONCLUSIONS

This study provides evidence that NO2 exposure significantly increases the risk of hospital admission for schizophrenia. Future studies are required to clarify the potential biological mechanism between schizophrenia and NO2 exposure to provide a more definitive result.

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.

Significant association between increased risk of emergency department visits for psychiatric disorders and air pollutants in South Korea

BACKGROUND

The association between air pollutants and psychiatric disorders has been investigated in many countries. However, results for the association between air pollutants and emergency room (ER) visits for psychiatric disorders are inconsistent. Further, systematic large-scale studies relating to the same are lacking, especially in South Korea.

OBJECTIVE

We aimed to investigate the acute and short-term cumulative effect of air pollutants on ER visits for psychiatric disorders in South Korea.

METHODS

The data on nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) and ER visits due to nine representative psychiatric disorders were collected from eight major cities in South Korea for three years. We estimated the relative risk (RR) at lag 0 and a cumulative 11-day RR by increasing a 10-unit for PM and 0.01-unit for NO2 using the Distributed Lag Nonlinear Model.

RESULTS

During the study period, a total of 79,092 ER visits for psychiatric disorders were identified and tested for association with NO2, PM2.5, and PM10. The RR at lag 0 of depression per 0.01-unit increase in NO2 was the highest (3.127; 95% confidence interval [CI] 2.933 to 3.332) among the psychiatric disorders. The RRs at lag 0 of anxiety disorders per 10-unit increase in PM2.5 (1.709; 95% CI 1.424 to 2.053) and PM10 (2.168; 95% CI 1.957 to 2.403) were the highest among the psychiatric disorders.

SIGNIFICANCE

Air pollutants increased ER visits for psychiatric disorders with the highest RR of depression due to NO2 and anxiety disorder due to PM2.5 and PM10. These results contribute evidence to the positive association between ambient exposure to air pollution and aggravation of psychiatric disorders, indicating air pollution may be a modifiable risk factor in mental health management.

IMPACT STATEMENT

We investigated the effect of air pollution on emergency room visits caused by major psychiatric disorders in prominent cities in South Korea. Using the Distributed Lag Nonlinear Model, an advanced analysis method, we calculated the acute effect and short-term cumulative effect. Air pollutants increased ER visits for psychiatric disorders with the highest relative risk of depression due to NO2 and anxiety disorder due to PM2.5 and PM10. These results reveal an association between ambient exposure to air pollution and aggravation of psychiatric disorders and suggest that air pollution may be a modifiable risk factor in mental health management.

Do objective and subjective traffic-related pollution, physical activity and nature exposure affect mental wellbeing? Evidence from Shenzhen, China

Urban environment (e.g. greenspaces, air pollution and traffic noise) and individuals' behaviours (e.g. physical activity) have all been associated with mental wellbeing. The large majority of studies on the influence of nature exposure on mental wellbeing assumed that multiple pathways act independently, ignoring the interactions among potential correlated pathways that engage simultaneously. The parallel mediation approach fails to explore the complex associations of combined exposure to air pollution, traffic noise and nature exposure with physical activity, which in turn affect mental wellbeing. Hence, the interest of understanding the sophisticated interactions among different pathways is warranted. We utilized structural equation modelling to simultaneously evaluate whether actual and perceived traffic-related pollution and physical activity mediate the associations between mental wellbeing and nature exposure, which was assessed by Normalized Difference Vegetation Index (NDVI), green view index (GVI), green space density and park accessibility. In summer 2022, we conducted questionnaires from 1772 adults residing in 117 neighbourhoods in Shenzhen, China. Nature exposure was positively and directly associated with mental wellbeing in the single mediator model that considered physical activity only. The indirect effects of nature exposure on mental wellbeing were observed through all pathways in all models, except through the perceived acoustic quality pathway in the serial mediation model. In addition, the percentage mediated by perceived air quality was higher than that of perceived acoustic quality. The influence of nature exposure on mental wellbeing was only for a small proportion mediated by the physical activity pathway. The associations between nature exposure and mental wellbeing were modified by individual characteristics, such as gender, age, income level and alcohol usage, but not employment status and smoking behaviour. These findings point out the importance of both objective and subjective environmental features and human behaviours on mental wellbeing, as well as the necessity of considering multiple pathways simultaneously.

Neurodevelopmental toxicity induced by PM2.5 Exposure and its possible role in Neurodegenerative and mental disorders

Recent extensive evidence suggests that ambient fine particulate matter (PM2.5, with an aerodynamic diameter ≤2.5 μm) may be neurotoxic to the brain and cause central nervous system damage, contributing to neurodevelopmental disorders, such as autism spectrum disorders, neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, and mental disorders, such as schizophrenia, depression, and bipolar disorder. PM2.5 can enter the brain via various pathways, including the blood-brain barrier, olfactory system, and gut-brain axis, leading to adverse effects on the CNS. Studies in humans and animals have revealed that PM2.5-mediated mechanisms, including neuroinflammation, oxidative stress, systemic inflammation, and gut flora dysbiosis, play a crucial role in CNS damage. Additionally, PM2.5 exposure can induce epigenetic alterations, such as hypomethylation of DNA, which may contribute to the pathogenesis of some CNS damage. Through literature analysis, we suggest that promising therapeutic targets for alleviating PM2.5-induced neurological damage include inhibiting microglia overactivation, regulating gut microbiota with antibiotics, and targeting signaling pathways, such as PKA/CREB/BDNF and WNT/β-catenin. Additionally, several studies have observed an association between PM2.5 exposure and epigenetic changes in neuropsychiatric disorders. This review summarizes and discusses the association between PM2.5 exposure and CNS damage, including the possible mechanisms by which PM2.5 causes neurotoxicity.

The association of air pollutants with hospital outpatient visits for child and adolescence psychiatry in Shenzhen, China

BACKGROUND

Although exposure to ambient air pollution has been associated with mental disorder, little is known about its potential effects on children and adolescents, especially in Chinese population. We aimed to reveal the relationship of air pollutants with hospital outpatient visits for child and adolescence psychiatry (HOVCAP) in Shenzhen.

METHODS

A case-crossover study based on time-series data was applied, and a distributed lag non-linear model (DLNM) was used to evaluate the non-linear and delayed effects of 4 major air pollutants (NO₂, PM_2.5, SO₂ and O₃) on HOVCAP. Least absolute shrinkage and selection operator (LASSO) regression was used to control the multicollinearity between covariates and to filter variables.

RESULT

A total of 94,660 cases aged 3-18 were collected from 2014 to 2019 in the Mental Health Center of Shenzhen. Results of pollutants at mode value (M₀) showed that in the single lag effect result, when the average daily concentration of NO₂ at 24 μg/m³, there was a significant effect on HOVCAP over lag 1, lag 4 and lag 5, respectively. The cumulative RR of NO₂ M₀ value to the outpatient visits were 1.438 (1.137-1.818) over lag 0-2, 1.454 (1.120-1.887) over lag 0-3, 1.466 (1.084-1.982) over lag 0-4, 1.680 (1.199-2.354) over lag 0-5, 1.993 (1.369-2.903) over lag 0-6, and 2.069 (1.372-3.119) over lag 0-7. However, PM_2.5, SO₂, O₃ were not associated with HOVCAP over neither single lag effects nor cumulative effects. The RR values both shown an increase either when NO₂ increases by 10 units or when the maximum concentration of NO₂ is reached.

CONCLUSION

Our study suggests that exposure to the normal air quality of NO2 in Shenzhen may associated with the risk of HOVCAP. However, PM2.5, SO2, O3 were not associated with HOVCAP.

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.

Short-term exposure to air pollution is an emerging but neglected risk factor for schizophrenia: A systematic review and meta-analysis

OBJECTIVE

This meta-analysis aimed to explore the association between short-term exposure to air pollution and schizophrenia (SCZ)1, and investigate the susceptible population and the lag characteristics of different pollutants.

METHODS

A systematic review and meta-analysis was conducted by searching PubMed, Cochrane, Web of Sciences, and CNKI for relevant literature published up to 28 Feb 2022. Meta-analysis was performed separately to investigate the association of ambient particulates (diameter ≤ 2.5 μm (PM2.5)2, 2.5 μm < diameter < 10 μm (PMC)3, ≤10μm (PM10)4) and gaseous pollutants (nitrogen dioxide (NO2)5, sulfur dioxide (SO2)6, carbon monoxide (CO)7) with SCZ. Relative risk (RR)8 per 10 μg/m3 increase in air pollutants concentration was used as the effect estimate. Subgroup analyses were conducted by age, gender, country, median pollutant concentration, and median temperature.

RESULTS

We identified 17 articles mainly conducted in Asia, of which 13 were included in the meta-analysis. Increased risk of SCZ was associated with short-term exposure to PM2.5 (RR: 1.0050, 95 % confidence interval (CI)9: 1.0017, 1.0083), PMC (1.0117, 1.0023, 1.0211), PM10 (1.0047, 1.0025, 1.0070), NO2 (1.0275, 1.0132, 1.0420), and SO2 (1.0288, 1.0146, 1.0432) exposure. Subgroup analyses showed that females may be more susceptible to SO2 and NO2, and the young seem to be more sensitive to PM2.5 and PM10. Gaseous pollutants presented the immediate risk, and particulates showed the delayed risk.

CONCLUSIONS

The present meta-analysis suggests that short-term exposure to PM2.5, PMC, PM10, SO2, and NO2 exposure may be associated with an elevated risk of SCZ.

Association of NO2 with daily hospital admissions for mental disorders: Investigation of the modification effects of green spaces and long-term NO2 exposure

Air pollution is a risk factor for increased hospital admissions due to mental disorders, while green spaces have been linked with better mental health. We linked daily hospital admission records from Wuhan's 74 municipal hospitals from 2017 to 2019 with modeled annual average NO₂ concentrations and added data on the residential surrounding green spaces with 250 m and 500 m buffers based on the normalized difference vegetation index (NDVI) using a land use regression model (LUR). The conditional logistic regression model was used to estimate the acute effect of short-term NO₂ exposure, and stratification analyses were applied to explore the modification effect of long-term NO₂ exposure and green spaces by estimating the odds ratios in the single- and dual-environmental factor groups. A total of 42,705 hospital admissions for mental disorders were identified. Short-term exposure to NO₂ was associated with an increased risk of hospital admission for mental disorders. A 10 μg/m³ increase in NO₂ (lag01 day) was associated with an increase in hospital admissions of 2.86% (95% CI, 2.05-3.68) for the total mental disorders. Compared with patients in the "low-NDVI/low-NO₂" group (ER = 2.27%, 95% CI, 0.27-4.31), patients in the "high-NDVI/low-NO₂" group (ER = 1.93%, -0.10-3.99) showed a lower and insignificant increase in hospitalizations for the total mental disorders, while greenness had a slight moderating effect in the high-level long-term NO₂ exposure areas. This study suggested that green spaces may moderate the acute effect of NO₂ exposure for mental disorder hospitalizations, especially in low-level long-term NO₂ exposure areas.

The impact of cold spells on schizophrenia admissions and the synergistic effect with the air quality index

OBJECTIVES

Under current global climate conditions, there are insufficient studies on the health influences of cold spells, especially on mental health. This study aimed to examine the effect of cold spells on schizophrenia admissions and to analyze the potential interaction effect with the air quality index (AQI).

METHODS

Daily data on schizophrenia admissions and climatic variables in Hefei were collected from 2013 to 2019. Based on 20 definitions, the impacts of cold spells were quantified separately to find the most appropriate definition for the region, and meta-regression was used to explore the different effect sizes of the different days in a cold spell event. In addition, the potential interaction effect was tested by introducing a categorical variable, CSH, reflecting the cold spell and AQI level.

RESULTS

The cold spell defined by temperature below the 6th centile while lasting for at least three days produced the optimum model fit performance. In general, the risk of schizophrenia admissions increased on cold spell days. The largest single-day effect occurred on the 12th day with RR = 1.081 (95% CI: 1.044, 1.118). In a single cold spell event, the effect of the 3rd and subsequent days of a cold spell (RR = 1.082, 95% CI: 1.036, 1.130) was higher than that on the 2nd day (RR = 1.054, 95% CI: 1.024, 1.085). Similarly, the effect of the 2nd day was also higher than that of the 1st day (RR = 1.027, 95% CI: 1.012, 1.042). We found a synergistic effect between cold spells and high AQI in the male group, and the relative excess risk due to interaction (RERI) was 0.018 (95% CI: 0.005-0.030).

CONCLUSIONS

This study suggested that the impacts of cold spells should be considered based on the definition of the most appropriate for the region when formulating targeted measures of schizophrenia. The discovery of the synergistic effect was referred to help the selection of the timing of precautions for susceptible people.

Research on adaption to air pollution in Chinese cities: Evidence from social media-based health sensing

Air pollution seriously threats to human health. Understanding the health effects of air pollution is of great importance for developing countermeasures. However, little is known about the real-time impacts of air pollution on the human heath in a comprehensive way in developing nations, like China. To fill this research gap, the Chinese urbanites' health were sensed from more than 210.82 million Weibo (Chinese Twitter) data in 2017. The association between air pollution and the health sensing were quantified through generalized additive models, based on which the sensitivities and adaptions to air pollution in 70 China's cities were assessed. The results documented that the Weibo data can well sense urbanites' health in real time. With the different geographical characteristics and socio-economic conditions, the Chinese residents have adaption to air pollution, indicated by the spatial heterogeneity of the sensitivities to air pollution. Cities with good air quality in South China and East China were more sensitive to air pollution, while cities with worse air quality in Northwest China and North China were less sensitive. This research provides a new perspective and methodologies for health sensing and the health effect of air pollution.

An Evaluation of Risk Ratios on Physical and Mental Health Correlations due to Increases in Ambient Nitrogen Oxide (NOx) Concentrations

Nitrogen oxides (NOx) are gaseous pollutants contributing to pollution in their primary form and are also involved in reactions forming ground-level ozone and fine particulate matter. Thus, NOx is of great interest for targeted pollution reduction because of this cascade effect. Primary emissions originate from fossil fuel combustion making NOx a common outdoor and indoor air pollutant. Numerous studies documenting the observed physical health impacts of NOx were reviewed and, where available, were summarized using risk ratios. More recently, the literature has shifted to focus on the mental health implications of NOx exposure, and a review of the current literature found five main categories of mental health-related conditions with respect to NOx exposure: common mental health disorders, sleep, anxiety, depression, and suicide. All the physical and mental health effects with available risk ratios were organized in order of increasing risk. Mental health concerns emerged as those most influenced by NOx exposure, with physical health impacts, such as asthma, only beginning to surface as the fourth highest risk. Mental health conditions occupied seven of the top ten highest risk health ailments. The results summarized in this narrative review show that there are clear positive correlations between NOx and negative physical and mental health manifestations, thus strengthening the argument in support of the reduction in ambient NOx levels.

Investigating the relationship between satisfaction of basic psychological needs, general health, and some background variables in the Iranian older adults: a cross-sectional study

BACKGROUND

Promoting the health and mental health (MH) of the older adults making up a large part of the world's population in the coming years can provide the necessary conditions for their health and well-being of them. This study aimed to investigate the relationship between the satisfaction of basic psychological needs (BPNs), general health (GH), and some variables in Iranian older adults.

METHODS

The present descriptive-correlational study was conducted on 780 older adults from Sarpol-e Zahab (Kermanshah) in 2019 including the study by multi-stage cluster random sampling. The data collection tool was BPNs satisfaction and GH questionnaire and a researcher-made questionnaire of individual and background information. Was used for data analysis using the SPSS version 16 program and descriptive statistics and tests Pearson correlation coefficient, chi-square test, independent-sample T-test, and multivariate linear regression.

RESULTS

In the present study, participating a total of 780 older adult men aged 73.0 ± 29.32 years. There was a significant relationship between the satisfaction of BPNs and GH (p <  0.001). Also, 41% of the older adults were in poor GH and 30% were high in BPNs. Multiple logistic regression showed that the BPNs, age, income satisfaction, weather, and war zone were strong predictors of GH. the adjusted R2 value of 0.55 shows that the model described 55% of changes in the GH score.

CONCLUSION

According to the findings of the study on the relationship between the satisfaction of BPNs and GH, providing insurance, social and economic support by developing health policies, creating supportive health environments, strengthening community action, and developing individual skills in the older adults can help improve their MH and that of the community.

Short-term effects and economic burden assessment of ambient air pollution on hospitalizations for schizophrenia

The evidence on the health and economic impacts of air pollution with schizophrenia is scarce, especially in developing countries. In this study, we aimed to systemically examine the short-term effects of PM_2.5 (particulate matter ≤ 2.5 μm in diameter), PM₁₀ (≤ 10 μm in diameter), NO₂ (nitrogen dioxide), SO₂ (sulfur dioxide), CO (carbon monoxide), and O₃ (ozone) on hospital admissions for schizophrenia in a Chinese coastal city (Qingdao) and to further assess the corresponding attributable risk and economic burden. A generalized additive model (GAM) was applied to model the impact of air pollution on schizophrenia, and the corresponding economic burden including the direct costs (medical expenses) and indirect costs (productivity loss). Stratified analyses were also performed by age, gender, and season (warm or cold). Our results showed that for a 10 μg/m³ increase in the concentrations of PM_2.5, PM₁₀, SO₂, and CO at lag5, the corresponding relative risks (RRs) were 1.0160 (95% CI: 1.0038-1.0282), 1.0097 (1.0018-1.0177), 1.0738 (1.0222-1.01280), and 1.0013 (1.0001-1.0026), respectively. However, no significant effect of NO₂ or O₃ on schizophrenia admissions was found. The stratified analysis indicated that females and younger individuals (< 45 years old) appeared to be more vulnerable, but no significant difference was found between seasons. Furthermore, 12.41% of schizophrenia hospitalizations were attributable to exposure to air pollution exceeding the World Health Organization (WHO) air quality standard, with a total economic burden of 89.67 million RMB during the study period. At the individual level, excessive air pollution exposure resulted in an economic burden of 8232.08 RMB per hospitalization. Our study found that short-term exposure to air pollutants increased the risk of hospital admissions for schizophrenia and resulted in a substantial economic burden. Considerable health benefits can be achieved by further reducing air pollution.

Coordinated health effects attributable to particulate matter and other pollutants exposures in the North China Plain

Hebei Province, located in the North China Plain (NCP) and encircling Beijing and Tianjin, has been suffering from severe air pollution. The monthly average fine particulate matter (PM_2.5) concentration was up to 276 μg/m³ in Hebei Province, which adversely affects human health. However, few studies evaluated the coordinated health impact of exposure to PM (PM_2.5 and PM₁₀) and other key air pollutants (SO₂, NO₂, CO, and surface ozone (O₃)). In this study, we systematically analyzed the health risks (both mortality and morbidity) due to multiple air pollutants exposures in Hebei Province. The economic loss associated with these health consequences was estimated using the value of statistical life (VSL) and cost of illness (COI) methods. Our results show the health burden and economic loss attributable to multiple ambient air pollutants exposures in Hebei Province is substantial. In 2017, the total premature mortality from multiple air pollutants exposures in Hebei Province was 69,833 (95% CI: 55,549-83,028), which was 2.9 times higher than that of the Pearl River Delta region (PRD). Most of the potential economic loss (79.65%) was attributable to premature mortality from air pollution. The total economic loss due to the health consequences of multiple air pollutants exposures was 175.16 (95% CI: 134.61-224.61) billion Chinese Yuan (CNY), which was 4.92% of Hebei Province's annual gross domestic product (GDP). Thus, the adverse health effects and economic loss caused by exposure to multiple air pollutants should be seriously taken into consideration. To alleviate these damages, Hebei's government ought to establish more stringent measures and regulations to better control air pollution.

Interactive effects of cold spell and air pollution on outpatient visits for anxiety in three subtropical Chinese cities

BACKGROUND

Although low temperature and air pollution exposures have been associated with the risk of anxiety, their combined effects remain unclear.

OBJECTIVE

To investigate the independent and interactive effects of low temperature and air pollution exposures on anxiety.

METHOD

Using a case-crossover study design, the authors collected data from 101,636 outpatient visits due to anxiety in three subtropical Chinese cities during the cold season (November to April in 2013 through 2018), and then built conditional logistic regression models based on individual exposure assessments [temperature, relative humidity, particulate matter (PM_2.5, PM₁₀), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂)] and twelve cold spell definitions. Additive-scale interactions were assessed using the relative excess risk due to interaction (RERI).

RESULTS

Both cold spell and air pollution were significantly associated with outpatients for anxiety. The effects of cold spell increased with its intensity, ranging from 8.98% (95% CI: 2.02%, 16.41%) to 15.24% (95% CI: 6.75%, 24.39%) in Huizhou. Additionally, each 10 μg/m³ increase of PM_2.5, PM₁₀, NO₂ and SO₂ was associated with a 1.51% (95% CI: 0.61%, 2.43%), 1.58% (95% CI: 0.89%, 2.28%), 13.95% (9.98%, 18.05%) and 11.84% (95% CI: 8.25%, 15.55%) increase in outpatient visits for anxiety. Synergistic interactions (RERI >0) of cold spell with all four air pollutants on anxiety were observed, especially for more intense cold spells. For particulate matters, these interactions were found even under mild cold spell definitions [RERI: 0.11 (95% CI: 0.02, 0.21) for PM_2.5, and 0.24 (95% CI: 0.14, 0.33) for PM₁₀]. Stratified analyses yielded a pronounced results in people aged 18-65 years.

CONCLUSIONS

These findings indicate that both cold spell and air pollution are important drivers of the occurrence of anxiety, and simultaneous exposure to these two factors might have synergistic effects on anxiety. These findings highlight the importance of controlling air pollution and improving cold-warning systems.

Air pollution and increased number of psychiatric emergency room visits: A case-crossover study for identifying susceptible groups

BACKGROUND

Ambient particulate matter is a leading risk factor for disease globally. Particulate matter 10 (PM₁₀) and particulate matter 2.5 (PM_2.5) are derived from different sources, including operating motor vehicles as well as from industrial activities. In this study we investigate the association between increased concentrations of PM and total daily visits to the psychiatric emergency unit (PEV). Further, the aim is to identify specific risk groups who are more susceptible to the effects of air pollution exposure by studying sex, age, ongoing psychiatric follow-up and diagnoses of depression/anxiety or substance use.

MATERIAL AND METHODS

The sample was comprised of data from 2740 days to 81 548 PEVs at Sahlgrenska University Hospital in Gothenburg and daily mean concentrations of PM₁₀ and PM_2.5. A time-stratified case-crossover design was used to analyse associations between air pollution and PEVs.

RESULTS

Mean number of daily PEVs were 35 and sex distribution was even. PM exposure was associated with total PEV at lag 0 (the same day), by RR 1.016 (95% confidence interval [CI] 1.004-1.028) and RR 1.020 (95%CI 1.003-1.038) per 10 μg/m³ increase in PM₁₀ and PM_2.5, respectively. In females, PEV were increased at lag 0 and lag 1, and in males at lag 1 and lag 2. In the age-stratified analysis, PEVs significantly increased following PM exposure amongst individuals aged 35-65 years by lag 0-2 and in individuals who had contact with outpatient care at lag 0 to lag 1. There were no associations between air pollution and PEVs for any specific diagnostic group evaluated (amongst depression, anxiety and substance use disorder).

CONCLUSIONS

The results indicate that acute exposure to PM₁₀ and PM_2.5 may trigger acute worsening in mental health in both males and females, especially among 35-65 year old individuals. However, in subgroups of the most common psychiatric diagnoses, we did not observe statistically significant associations with PM exposure.

Gaseous air pollutants and hospitalizations for mental disorders in 17 Chinese cities: Association, morbidity burden and economic costs

The short-term morbidity effects of gaseous air pollutants on mental disorders (MDs), and the corresponding morbidity and economic burdens have not been well studied. We aimed to explore the associations of ambient sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃) and carbon monoxide (CO) with MDs hospitalizations in 17 Chinese cities during 2015-2018, and estimate the attributable risk and economic costs of MDs hospitalizations associated with gaseous pollutants. City-specific relationships between gaseous pollutants and MDs hospitalizations were evaluated using over-dispersed generalized additive models, then combined to obtain the pooled effect. Concentration-response (C-R) curves of gaseous pollutants with MDs from each city were pooled to allow regional estimates to be derived. The morbidity and economic burdens of MDs hospitalizations attributable to gaseous pollutants were further assessed. A total of 171,939 MDs hospitalizations were included. We observed insignificant association of O₃ with MDs. An interquartile range increase in SO₂ at lag0 (9.1 μg/m³), NO₂ at lag0 (16.7 μg/m³) and CO at lag2 (0.4 mg/m³) corresponded to a 3.02% (95%CI: 0.72%, 5.38%), 5.03% (95%CI: 1.84%, 8.32%) and 2.18% (95%CI: 0.40%, 4.00%) increase in daily MDs hospitalizations, respectively. These effects were modified by sex, season and cause-specific MDs. The C-R curves of SO₂ and NO₂ with MDs indicated nonlinearity and the slops were steeper at lower concentrations. Overall, using current standards as reference concentrations, 0.27% (95%CI: 0.07%, 0.48%) and 0.06% (95%CI: 0.02%, 0.10%) of MDs hospitalizations could be attributable to extra SO₂ and NO₂ exposures, and the corresponding economic costs accounted for 0.34% (95%CI: 0.08%, 0.60%) and 0.07% (95%CI: 0.03%, 0.11%) of hospitalization expenses, respectively. Moreover, using threshold values detected from C-R curves as reference concentrations, the above mentioned morbidity and economic burdens increased a lot. These findings suggest more strict emission control regulations are needed to protect mental health from gaseous pollutants.

Short-term exposure to ambient air pollution and risk of daily hospital admissions for anxiety in China: A multicity study

The potential impact of short-term exposure to ambient air pollution on risk of anxiety remains uncertain. We performed a detailed evaluation based on data from national insurance databases in China. Daily hospital admissions for anxiety disorders were identified in 2013-2017 from the national insurance databases covering up to 261 million urban residents in 56 cities in China. A two-stage time-series study was conducted to evaluate the associations between short-term exposure to major ambient air pollutants, including fine particles, inhalable particles, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone, and carbon monoxide, and risk of daily hospital admissions for anxiety. Significant associations between short-term exposures to ambient NO₂ and SO₂ and risk of daily hospital admissions for anxiety were found in the overall analysis. Per 10 μg/m³ increases in NO₂ at lag0 and SO₂ at lag6 were associated with significant increases of 1.37% (95% CI: 0.14%, 2.62%) and 1.53% (95% CI: 0.59%, 2.48%) in anxiety admissions, respectively. Stronger associations were found in the southern region and patients <65 years for SO₂. Short-term exposure to ambient air pollution is associated with increased risk of anxiety admissions, which may provide important implications for promotion of mental health in the public.

Air pollution exposure and depression: A comprehensive updated systematic review and meta-analysis

We provide a comprehensive and updated systematic review and meta-analysis of the association between air pollution exposure and depression, searching PubMed, Embase, and Web of Sciences for relevant articles published up to May 2021, and eventually including 39 studies. Meta-analyses were performed separately according to pollutant type [particulate matter with diameter ≤10 μm (PM₁₀) and ≤2.5 μm (PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and carbon monoxide (CO)] and exposure duration [short- (<30 days) and long-term (≥30 days)]. Test for homogeneity based on Cochran's Q and I² statistics were calculated and the restricted maximum likelihood (REML) random effect model was applied. We assessed overall quality of pooled estimates, influence of single studies on the meta-analytic estimates, sources of between-study heterogeneity, and publication bias. We observed an increased risk of depression associated with long-term exposure to PM_2.5 (relative risk: 1.074, 95% confidence interval: 1.021-1.129) and NO₂ (1.037, 1.011-1.064), and with short-term exposure to PM₁₀ (1.009, 1.006-1.012), PM_2.5 (1.009, 1.007-1.011), NO₂ (1.022, 1.012-1.033), SO₂ (1.024, 1.010-1.037), O₃ (1.011, 0.997-1.026), and CO (1.062, 1.020-1.105). The publication bias affecting half of the investigated associations and the high heterogeneity characterizing most of the meta-analytic estimates partly prevent to draw very firm conclusions. On the other hand, the coherence of all the estimates after excluding single studies in the sensitivity analysis supports the soundness of our results. This especially applies to the association between PM_2.5 and depression, strengthened by the absence of heterogeneity and of relevant publication bias in both long- and short-term exposure studies. Should further investigations be designed, they should involve large sample sizes, well-defined diagnostic criteria for depression, and thorough control of potential confounding factors. Finally, studies dedicated to the comprehension of the mechanisms underlying the association between air pollution and depression remain necessary.

Ambient air pollution and hospitalization for chronic obstructive pulmonary disease: Benefits from Three-Year Action Plan

BACKGROUND

Chronic obstructive pulmonary disease (COPD) hospitalization has been linked with ambient air pollution. However, the evidence on respiratory health benefits from air pollution control policy in China is limited.

OBJECTIVE

To investigate benefits from the Three-Year Action Plan to Win the Battle for a Blue Sky (TYAP) for tackling COPD hospitalization due to ambient air pollution.

METHODS

We conducted a time-stratified case-crossover study of 138,015 COPD hospitalizations aged ≥ 60 years in Guangdong province, China during 2016-2019 to investigate respiratory health benefits from TYAP. Inverse distance weighting method was used to assess daily individual-level exposures to ambient air pollutants including particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5), particulate matter with an aerodynamic diameter ≤ 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). Conditional logistic regression model was applied to analyze the associations between ambient air pollutants and COPD hospitalization.

RESULTS

TYAP can modify the associations. Each 10 μg/m3 increase of exposure to PM2.5, PM10, and NO2 and 1 mg/m3 increase of exposure to CO were significantly associated with 2.5%, 2.0%, 3.0%, and 14.4% increase in odds of COPD hospitalization before TYAP, respectively; while 1.0%, 0.9%, 1.5%, and 5.8% increase in odds during TYAP. We found prominent declines in health burden of COPD hospitalizations due to air pollution among the elderly after TYAP implication when compared with that before TYAP.

CONCLUSION

Reduced levels of ambient air pollutants by TYAP can effectively lower the risk for COPD hospitalization among the elderly, which provides evidence on the respiratory health benefits from consistent and effective air pollution control policy.

Effect of extreme temperatures on daily emergency room visits for mental disorders

Relatively few studies investigated the effects of extreme temperatures (both heat and cold) on mental health (ICD-9: 290-319; ICD-10: F00-F99) and the potential effect modifications by individuals' age, sex, and race. We aimed to explore the effect of extreme temperatures of both heat and cold on the emergency room (ER) visits for mental health disorders, and conducted a stratified analysis to identify possible susceptible population in Erie and Niagara counties, NY, USA. To assess the short-term impacts of daily maximum temperature on ER visits related to mental disorders (2009-2015), we applied a quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM). The model was adjusted for day of the week, precipitation, long-term time trend, and seasonality. We found that there were positive associations between short-term exposure to extreme ambient temperatures and increased ER visits for mental disorders, and the effects can vary by individual factors. We found heat effect (relative risk (RR) = 1.16; 95% confidence intervals (CI), 1.06-1.27) on exacerbated mental disorders became intense in the study region and subgroup of population (the elderly) being more susceptible to extreme heat than any other age group. For extreme cold, we found that there is a substantial delay effect of 14 days (RR = 1.25; 95% CI = 1.08-1.45), which is particularly burdensome to the age group of 50-64 years old and African-Americans. Our findings suggest that there is a positive association between short-term exposure to extreme ambient temperature (heat and cold) and increased ER visits for mental disorders, and the effects vary as a function of individual factors, such as age and race.

Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images

Urban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications.