Negative Air Ions in Neuropsychiatric Disorders

The role of extreme high humidex in depression in chongqing, China: A time series-analysis

As global climate change intensifies, people are paying increasing attention to the impact of temperature changes on adverse mental health outcomes, especially depression. While increasing attention has been paid to the effect of temperature, there is little research on the effect of humidity. We aimed to investigate the association between humidex, an index combining temperature and humidity to reflect perceived temperature, and outpatient visits for depression from 2014 to 2019 in Chongqing, the largest and one of the most hot and humid cities of China. We also aimed to further identify susceptible subgroups. A distributed lag non-linear model (DLNM) was used to explore the concentration-response relationship between humidex and depression outpatient visits. Hierarchical analysis was carried out by age and gender. A total of 155,436 visits for depression were collected from 2014 to 2019 (2191 days). We found that depression outpatient visits were significantly associated with extremely high humidex (≥40). The significant positive single-lag day effect existed at lag 0 (RR = 1.029, 95%CI: 1.000-1.059) to lag 2 (RR = 1.01, 95%CI: 1.004-1.028), and lag 12 (RR = 1.013, 95%CI: 1.002-1.024). The significant cumulative adverse effects lasted from lag 01 to lag 014. Hierarchical analyses showed that females and the elderly (≥60 years) appeared to be more susceptible to extremely high humidex. The attributable numbers (AN) and fraction (AF) of extremely high humidex on depression outpatients were 1709 and 1.10%, respectively. Extremely high humidex can potentially increase the risk of depression, especially in females and the elderly. More protective measures should be taken in vulnerable populations.

Short-term effects of extreme meteorological factors on daily outpatient visits for anxiety in Suzhou, Anhui Province, China: a time series study

Anxiety disorders are a major public health concern in China. Previous studies have provided evidence for associations between ambient temperature and anxiety outpatient visits, but no studies have examined short-term effects of other meteorological factors such as sunshine duration, wind speed, and precipitation on increased anxiety outpatient visits. We aimed to assess the association between climatic factors and outpatient visits for anxiety in Suzhou, a city with a temperate climate in Anhui Province, China. Daily anxiety outpatient visits, meteorological factors, and air pollutants from 2017 to 2019 were collected. A quasi-Poisson generalized linear regression model combined with distributed lag non-linear model (DLNM) was used to quantify the effects of extreme meteorological factors (sunshine duration, wind speed, and precipitation) on anxiety outpatient visits. All effects were presented as relative risk (RR), with the 90th and 10th percentiles of meteorological factors compared to the median. Subgroup analyses by age and gender were performed to identify susceptible subgroups. A total of 11,323 anxiety outpatient visits were reported. Extremely low sunshine duration and low and high wind speed increased the risk of anxiety outpatient visits. The strongest cumulative effects occurred at lag 0-14 days, and the corresponding RRs of extremely low sunshine duration and low and high wind speed were 1.417 (95% CI: 1.056-1.901), 1.529 (95% CI: 1.028-2.275), and 1.396 (95% CI: 1.007-1.935), respectively. Subgroup analyses showed that males and people aged ≥45 years appeared to be more susceptible to the cumulative effects of extremely low sunshine duration. In addition, the adverse effects of extreme wind speed were more pronounced in the cold season. This study provides evidence that extreme climatic factors have a lagged effect on anxiety outpatient visits. In the context of climate change, these findings may help develop weather-based early warning systems to minimize the effects of extreme meteorological factors on anxiety.

A Study on Highly Effective Electromagnetic Wave Shield Textile Shell Fabrics Made of Point Polyester/Metallic Core-Spun Yarns

In this study, stainless steel (SS) filaments are wrapped in Ge fibers to form core-spun yarns. The yarns along with 500 D polyester (PET) fibers undergo weaving, thereby forming functional woven fabrics. The experiment is composed of two parts:yarns and fabrics. The yarns are twisted with TPI of 8, 9, 10, 11, and 12, and then tested for tensile strength and tensile elongation. The yarns possess mechanical properties that are dependent on the TPI—the higher the TPI, the better the mechanical properties. The maximal mechanical properties occur when the core-spun yarns are made of 12 TPI where the maximal tensile strength is 5.26 N and the lowest elongation is 43.2%. As for the functional woven fabrics, they are made of Ge/SS core-spun yarns as the weft yarns and 500 D PET yarns as the warp yarns. The tensile strength, tensile elongation, negative ion release, electromagnetic interference shielding effectiveness (EMI SE), and air permeability tests are conducted, determining the optimal woven fabrics. The 12 TPI core-spun yarns provide the woven fabrics with the maximal tensile strength of 153.6 N and the optimal elongation at break of 10.08%. In addition, the woven fabrics made with 8 or 9 TPI core-spun yarns exhibit an optimal EMI SE of 41 dB, an optimal air permeability of 212 cm³/cm²/s, and an optimal release amount of negative ion of 550–600 ions/cc. The proposed woven fabrics have a broad range of applications, such as functional garments and bedding.

An inversion model for estimating the negative air ion concentration using MODIS images of the Daxing'anling region

The negative air ion (NAI) concentration is an essential indicator of air quality and atmospheric pollution. The NAI concentration can be used to monitor air quality on a regional scale and is commonly determined using field measurements. However, obtaining these measurements is time-consuming. In this paper, the relationship between remotely sensed surface parameters (such as land surface temperature, normalized difference vegetation index (NDVI), and leaf area index) obtained from MODIS data products and the measured NAI concentration using a stepwise regression method was analyzed to estimate the spatial distribution of the NAI concentration and verify the precision. The results indicated that the NAI concentration had a negative correlation with temperature, leaf area index (LAI), and gross primary production while it exhibited a positive correlation with the NDVI. The relationship between land surface temperature and the NAI concentration in the Daxing’anling region is expressed by the regression equation of y = -35.51x₁ + 11206.813 (R² = 0.6123). Additionally, the NAI concentration in northwest regions with high forest coverage was higher than that in southeast regions with low forest coverage, suggesting that forests influence the air quality and reduce the impact of environmental pollution. The proposed inversion model is suitable for evaluating the air quality in Daxing’anling and provides a reference for air quality evaluation in other areas. In the future, we will expand the quantity and distribution range of sampling points, conduct continuous observations of NAI concentrations and environmental parameters in the research areas with different land-use types, and further improve the accuracy of inversion results to analyze the spatiotemporal dynamic changes in NAI concentration and explore the possibility of expanding the application areas of NAI monitoring.