Relationship between the number of hospitalized cardiovascular and respiratory disease and the average concentration of criteria air pollutants (CAP) in Ahvaz

Short-term association between air pollution and daily genitourinary disorder admissions in Lanzhou, China

The aim of this study was to determine the relationship between short-term exposure to ambient air pollution and the number of daily hospital admissions for genitourinary disorders in Lanzhou. Hospital admission data and air pollutants, including PM2.5, PM10, SO2, NO2, O38h and CO, were obtained from the period 2013 to 2020. A generalized additive model (GAM) combined with distribution lag nonlinear model (DLNM) based on quasi-Poisson distribution was used by the controlling for trends, weather, weekdays and holidays. Short-term exposure to PM2.5, NO2 and CO increased the risk of genitourinary disorder admissions with RR of 1.0096 (95% CI 1.0002-1.0190), 1.0255 (95% CI 1.0123-1.0389) and 1.0686 (95% CI 1.0083-1.1326), respectively. PM10, O38h and SO2 have no significant effect on genitourinary disorders. PM2.5 and NO2 are more strongly correlated in female and ≥ 65 years patients. CO is more strongly correlated in male and < 65 years patients. PM2.5, NO2 and CO are risk factors for genitourinary morbidity, and public health interventions should be strengthened to protect vulnerable populations.

Observational evidence for detrimental impact of inhaled ozone on human respiratory system

The detrimental influence of inhaled ozone on human respiratory system is ambiguous due to the complexity of dose response relationship between ozone and human respiratory system. This study collects inhaled ozone concentration and respiratory disease data from Shenzhen City to reveal the impact of ozone on respiratory diseases using the Generalized Additive Models (GAM) and Convergent Cross Mapping (CCM) method at the 95% confidence level. The result of GAM exhibits a partially significant lag effect on acute respiratory diseases in cumulative mode. Since the traditional correlation analysis is incapable of capturing causality, the CCM method is applied to examine whether the inhaled ozone affects human respiratory system. The results demonstrate that the inhaled ozone has a significant causative impact on hospitalization rates of both upper and lower respiratory diseases. Furthermore, the harmful causative effects of ozone to the human health are varied with gender and age. Females are more susceptible to inhaled ozone than males, probably because of the estrogen levels and the differential regulation of lung immune response. Adults are more sensitive to ozone exposure than children, potentially due to the fact that children need longer time to react to ozone stress than adults, and the elderly are more tolerant than adults and children, which may be related to pulmonary hypofunction of the elderly while has little correlation with ozone exposure.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R² = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R² = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.

Association between ambient PM2.5 and outpatient visits of children's respiratory diseases in a megacity in Central China

Objective

To explore the relationship between ambient PM_2.5 level and outpatient visits of children with respiratory diseases in a megacity, Zhengzhou, in central China.

Methods

We collected daily outpatient visit data, air pollutant data, and meteorological data at the monitoring points of Zhengzhou from the time period 2018 to 2020 and used Spearman's rank correlation to analyze the correlation between children's respiratory outpatient visits and air pollutants and meteorological factors. Generalized additive models were used to analyze the association between PM_2.5 exposures and children's respiratory outpatient visits. A stratified analysis was further carried out for the seasons.

Results

From 2018 to 2020, the total number of outpatients with children's respiratory diseases was 79,1107, and the annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h in Zhengzhou were respectively 59.48 μg/m³, 111.12 μg/m³, 11.10 μg/m³, 47.77 μg/m³, 0.90 mg/m³ and 108.81 μg/m³. The single-pollutant model showed that the risk of outpatient visits for children with respiratory disease increased by 0.341% (95%CI: 0.274-0.407%), 0.532% (95%CI: 0.455-0.609%) and 0.233% (95%CI: 0.177-0.289%) for every 10 μg/m³ increase in PM_2.5 with a 3-day lag, 1-day lag, and 1-day lag respectively for the whole year, heating period, and non-heating period. The multi-pollutant model showed that the risk of PM_2.5 on children's respiratory disease visits was robust. The excess risk of PM_2.5 on children's respiratory disease visits increased by 0.220% (95%CI: 0.147-0.294%) when SO₂ was adjusted. However, the PM_2.5 effects were stronger during the heating period than during the non-heating period.

Conclusion

The short-term exposure to PM_2.5 was significantly associated with outpatient visits for children's respiratory diseases. It is therefore necessary to strengthen the control of air pollution so as to protect children's health.

Effects of air pollutant emission on the prevalence of respiratory and circulatory system diseases in Linyi, China

As a typical industrial city, Linyi has suffered severe atmospheric pollution in recent years. Meanwhile, a high incidence of respiratory and circulatory diseases has been observed in Linyi. The relationship between air pollutants and the prevalence of respiratory and circulatory system diseases in Linyi is still unclear, and therefore, there is an urgent need to assess the human health risks associated with air pollutants. In this study, the number of outpatient visits and spatial distribution of respiratory and circulatory diseases were first investigated. To clarify the correlation between diseases and air pollutant emissions, the residential intake fraction (IF) of air pollutants was calculated. The results showed that circulatory and respiratory diseases accounted for 62.32% of the total causes of death in 2015. The incidence of respiratory diseases was high in the winter, and outpatient visits were observed for more males (60.9%) than females (39.1%). The spatial distribution suggested that outpatient visits for respiratory and circulatory diseases were concentrated in the main urban area of Linyi, including the Hedong District, Lanshan District, and Luozhuang District, and especially at the junction of these three areas. After calculating the IF combined with the characteristics of pollution sources, meteorological conditions, and population data, a high IF value was concentrated in urban and suburban areas, which was consistent with the high incidence of diseases. Moreover, high R values and a significant correlation (R > 0.6, p < 0.05) between outpatient visits and residential IF of air pollutants imply similar spatial distributions of outpatient visits and IF value of residents. The spatial similarity of air pollution and outpatient visits suggested that future air pollution control policies should better reflect the health risks of spatial hotspots. This study can provide a potentially important reference for environmental management and air pollution-related health interventions.