Short-term effects of air pollution on daily hospital admissions for cardiovascular diseases in western China

Urban-rural disparities in the association of nitrogen dioxide exposure with cardiovascular disease risk in China: effect size and economic burden

BACKGROUND

Together with rapid urbanization, ambient nitrogen dioxide (NO2) exposure has become a growing health threat. However, little is known about the urban-rural disparities in the health implications of short-term NO2 exposure. This study aimed to compare the association between short-term NO2 exposure and hospitalization for cardiovascular disease (CVD) among urban and rural residents in Shandong Province, China. Then, this study further explored the urban-rural disparities in the economic burden attributed to NO2 and the explanation for the disparities.

METHODS

Daily hospitalization data were obtained from an electronic medical records dataset covering a population of 5 million. In total, 303,217 hospital admissions for CVD were analyzed. A three-stage time-series analytic approach was used to estimate the county-level association and the attributed economic burden.

RESULTS

For every 10-μg/m3 increase in NO2 concentrations, this study observed a significant percentage increase in hospital admissions on the day of exposure of 1.42% (95% CI 0.92 to 1.92%) for CVD. The effect size was slightly higher in urban areas, while the urban-rural difference was not significant. However, a more pronounced displacement phenomenon was found in rural areas, and the economic burden attributed to NO2 was significantly higher in urban areas. At an annual average NO2 concentration of 10 μg/m3, total hospital days and expenses in urban areas were reduced by 81,801 (44,831 to 118,191) days and 60,121 (33,002 to 86,729) thousand CNY, respectively, almost twice as much as in rural areas. Due to disadvantages in socioeconomic status and medical resources, despite similar air pollution levels in the urban and rural areas of our sample sites, the rural population tended to spend less on hospitalization services.

CONCLUSIONS

Short-term exposure to ambient NO2 could lead to considerable health impacts in either urban or rural areas of Shandong Province, China. Moreover, urban-rural differences in socioeconomic status and medical resources contributed to the urban-rural disparities in the economic burden attributed to NO2 exposure. The health implications of NO2 exposure are a social problem in addition to an environmental problem. Thus, this study suggests a coordinated intervention system that targets environmental and social inequality factors simultaneously.

Association Between Air Pollution and Cardiovascular Disease Hospitalizations in Lanzhou City, 2013-2020: A Time Series Analysis

Extensive evidence has shown that air pollution increases the risk of cardiovascular disease (CVD) admissions. We aimed to explore the short-term effect of air pollution on CVD admissions in Lanzhou residents and their lag effects. Meteorological data, air pollution data, and a total of 309,561 daily hospitalizations for CVD among urban residents in Lanzhou were collected from 2013 to 2020. Distributed lag non-linear model was used to analyze the relationship between air pollutants and CVD admissions, stratified by gender, age, and season. PM2.5, NO2, and CO have the strongest harmful effects at lag03, while SO2 at lag3. The relative risks of CVD admissions were 1.0013(95% CI: 1.0003, 1.0023), 1.0032(95% CI: 1.0008, 1.0056), and 1.0040(95% CI: 1.0024, 1.0057) when PM2.5, SO2, and NO2 concentrations were increased by 10 μg/m³, respectively. Each 1 mg/m3 increase in CO concentration was associated with a relative risk of cardiovascular hospitalization of risk was 1.0909(95% CI: 1.0367, 1.1479). We observed a relative risk of 0.9981(95% CI: 0.9972, 0.9991) for each 10 μg/m³ increase in O3 for CVD admissions at lag06. We found a significant lag effects of air pollutants on CVD admissions. NO2 and CO pose a greater risk of hospitalization for women, while PM2.5 and SO2 have a greater impact on men. PM2.5, NO2, and CO have a greater impact on CVD admissions in individuals aged <65 years, whereas SO2 affects those aged ≥65 years. Our research indicates a possible short-term impact of air pollution on CVD. Local public health and environmental policies should take these preliminary findings into account.

Ambient carbon monoxide and the risk of cardiovascular disease emergency room visits: a time-series study in Lanzhou, China

Until now, the epidemiological evidence on the association between short-term exposure to ambient carbon monoxide (CO) and cardiovascular diseases (CVDs) is relatively lacking and controversial. This study aims to examine the relationship between ambient CO and daily emergency room visits (ERVs) for total and cause-specific CVD in Lanzhou, China. A distributed lag nonlinear model was used to examine the association. For every 1 mg/m3 increase in the CO concentration, the relative risks of daily ERVs were 1.041 (95% CI: 1.017, 1.065) for total CVD, 1.065 (95% CI: 1.018, 1.114) for ischemic heart disease (IHD), 1.083 (95% CI: 1.020, 1.149) for heart rhythm disturbances (HRD), 1.062 (95% CI: 1.011, 1.115) for heart failure (HF), and 1.057 (95% CI: 1.017, 1.098) for cerebrovascular diseases (CD). For the two different gender subgroups, the short-term impact of CO on total CVD, IHD, and CD was relatively stronger for the females than for the males, while the opposite was true for HRD and HF. In the age subgroup analyses, the effect of ambient CO on total CVD and IHD appeared to be greater for the age ≥ 65 years group, while the opposite was true for HRD, HF, and CD. The associations for all disease categories were stronger in cold seasons than in warm seasons. We also observed a nearly linear correlation between CO and CVD ERVs. In conclusion, the study showed that exposure to ambient CO may increase the risks of ERVs for total and cause-specific CVD. Besides, CO-ERVs associations may vary by gender and age.

Meandered and muddled: a systematic review on the impact of air pollution on ocular health

From the years 1970-2023, a systematic overview of the diverse consequences of particulate matter on eye health and a disease classification according to acute, chronic, and genetic are presented using the PubMed, Research Gate, Google Scholar, and Science Direct databases. Various studies on medical aspects correlate with the eye and health. However, from an application perspective, there is limited research on the ocular surface and air pollution. The main objective of the study is to uncover the relationship between eye health and air pollution, particularly particulate matter, along with other external factors acting as aggravators. The secondary goal of the work is to examine the existing models for mimicking human eyes. The study is followed by a questionnaire survey in a workshop, in which the exposure-based investigation was tagged based on their activity. This paper establishes a relationship between particulate matter and its influence on human health, leading to numerous eye diseases like dry eyes, conjunctivitis, myopia, glaucoma, and trachoma. The results of the questionnaire survey indicate that about 68% of the people working in the workshop are symptomatic with tears, blurred vision, and mood swings, while 32% of the people were asymptomatic. Although there are approaches for conducting experiments, the evaluation is not well defined; empirical and numerical solutions for particle deposition on the eye are needed. There prevails a broad gap in the arena of ocular deposition modeling.

Ambient nitrogen dioxide and cardiovascular diseases in rural regions: a time-series analyses using data from the new rural cooperative medical scheme in Fuyang, East China

Most of studies relating ambient nitrogen dioxide (NO₂) exposure to hospital admissions for cardiovascular diseases (CVDs) were conducted among urban population. Whether and to what extent these results could be generalizable to rural population remains unknown. We addressed this question using data from the New Rural Cooperative Medical Scheme (NRCMS) in Fuyang, Anhui, China. Daily hospital admissions for total CVDs, ischaemic heart disease, heart failure, heart rhythm disturbances, ischaemic stroke, and haemorrhagic stroke in rural regions of Fuyang, China, were extracted from NRCMS between January 2015 and June 2017. A two-stage time-series analysis method was used to assess the associations between NO₂ and CVD hospital admissions and the disease burden fractions attributable to NO₂. In our study period, the average number (standard deviation) of hospital admissions per day were 488.2 (117.1) for total CVDs, 179.8 (45.6) for ischaemic heart disease, 7.0 (3.3) for heart rhythm disturbances, 13.2 (7.2) for heart failure, 267.9 (67.7) for ischaemic stroke, and 20.2 (6.4) for haemorrhagic stroke. The 10-μg/m³ increase of NO₂ was related to an elevated risk of 1.9% (RR: 1.019, 95% CI: 1.005 to 1.032) for hospital admissions of total CVDs at lag0-2 days, 2.1% (1.021, 1.006 to 1.036) for ischaemic heart disease, and 2.1% (1.021, 1.006 to 1.035) for ischaemic stroke, respectively, while no significant association was observed between NO₂ and hospital admissions for heart rhythm disturbances, heart failure, and haemorrhagic stroke. The attributable fractions of total CVDs, ischaemic heart disease, and ischaemic stroke to NO₂ were 6.52% (1.87 to 10.94%), 7.31% (2.19 to 12.17%), and 7.12% (2.14 to 11.85%), respectively. Our findings suggest that CVD burdens in rural population are also partly attributed to short-term exposure to NO₂. More studies across rural regions are required to replicate our findings.

Effects of Temperatures and Heatwaves on Occupational Injuries in the Agricultural Sector in Italy

The effects of heat on health have been well documented, while less is known about the effects among agricultural workers. Our aim is to estimate the effects and impacts of heat on occupational injuries in the agricultural sector in Italy. Occupational injuries in the agricultural sector from the Italian national workers' compensation authority (INAIL) and daily mean air temperatures from Copernicus ERA5-land for a five-year period (2014-2018) were considered. Distributed lag non-linear models (DLNM) were used to estimate the relative risk and attributable injuries for increases in daily mean air temperatures between the 75th and 99th percentile and during heatwaves. Analyses were stratified by age, professional qualification, and severity of injury. A total of 150,422 agricultural injuries were considered and the overall relative risk of injury for exposure to high temperatures was 1.13 (95% CI: 1.08; 1.18). A higher risk was observed among younger workers (15-34 years) (1.23 95% CI: 1.14; 1.34) and occasional workers (1.25 95% CI: 1.03; 1.52). A total of 2050 heat-attributable injuries were estimated in the study period. Workers engaged in outdoor and labour-intensive activities in the agricultural sector are at greater risk of injury and these results can help target prevention actions for climate change adaptation.

Short-term effects of meteorological factors on childhood atopic dermatitis in Lanzhou, China

Atopic dermatitis (AD) is one of the leading burdens of skin disease in children globally. Meteorological factors are involved in the onset and development of AD. Several studies have examined the effects of meteorological factors on AD, but their results are inconsistent, and the understanding of the link between AD and meteorological factors remains inadequate. In this study, a total of 19,702 children aged 0 to 14 visited the outpatient clinic for AD from 2015 to 2019 in Lanzhou, China. A distributed lag nonlinear model (DLNM) applies to evaluate effects of meteorological factors on childhood AD in Lanzhou, China, and further explored age and gender differences. It was found that extremely high or low temperatures, extremely high diurnal temperature range (DTR), extremely low relative humidity (RH), and extremely high wind speed (WS) increased the risk of outpatient visits for childhood AD. Effects of extremely high DTR and extremely high WS were more intense, with maximum cumulative risks of 2.248 (95% CI 1.798, 2.811) and 3.834 (95% CI 3.086, 4.759) at lag 0-21, respectively. Furthermore, the combination of low temperature and low RH can also contribute to the higher risk of childhood AD. For extreme temperatures, children aged 7-14 years were more vulnerable. For extremely low RH, extremely high DTR and WS, boys and children aged 0-3 years were more vulnerable. Public health departments should strengthen publicity and education about how meteorological factors affect childhood AD and develop sex- and age-specific preventative measures.

Interactive short-term effects of meteorological factors and air pollution on hospital admissions for cardiovascular diseases

A substantial number of studies have demonstrated the association between air pollution and adverse health effects. However, few studies have explored the potential interactive effects between meteorological factors and air pollution. This study attempted to evaluate the interactive effects between meteorological factors (temperature and relative humidity) and air pollution ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) on cardiovascular diseases (CVDs). Next, the high-risk population susceptible to air pollution was identified. We collected daily counts of CVD hospitalizations, air pollution, and weather data in Nanning from January 1, 2014, to December 31, 2015. Generalized additive models (GAMs) with interaction terms were adopted to estimate the interactive effects of air pollution and meteorological factors on CVD after controlling for seasonality, day of the week, and public holidays. On low-temperature days, an increase of [Formula: see text] in [Formula: see text], [Formula: see text], and [Formula: see text] was associated with increases of 4.31% (2.39%, 6.26%) at lag 2; 2.74% (1.65%, 3.84%) at lag 0-2; and 0.13% (0.02%, 0.23%) at lag 0-3 in CVD hospitalizations, respectively. During low relative humidity days, a [Formula: see text] increment of lag 0-3 exposure was associated with increases of 3.43% (4.61%, 2.67%) and 0.10% (0.04%, 0.15%) for [Formula: see text] and [Formula: see text], respectively. On high relative humidity days, an increase of [Formula: see text] in [Formula: see text] was associated with an increase of 5.86% (1.82%, 10.07%) at lag 0-2 in CVD hospitalizations. Moreover, elderly (≥ 65 years) and female patients were vulnerable to the effects of air pollution. There were interactive effects between air pollutants and meteorological factors on CVD hospitalizations. The risk that [Formula: see text], [Formula: see text], and [Formula: see text] posed to CVD hospitalizations could be significantly enhanced by low temperatures. For [Formula: see text] and [Formula: see text], CVD hospitalization risk increased in low relative humidity. The effects of [Formula: see text] were enhanced at high relative humidity.

Association between atmospheric particulate matter and emergency room visits for cerebrovascular disease in Beijing, China

PURPOSE

The association between atmospheric particulate matter and emergency room visits for cerebrovascular disease were evaluated in Beijing.

METHODS

A generalized additive model was used to evaluate the associations between particulate matter and cerebrovascular disease, based on the daily data of meteorological elements, PM concentrations, and emergency room (ER) visits for cerebrovascular disease in Beijing from 2009 to 2012. Long-term trends and the effects of holidays, the day of the week, and confounding factors were controlled to determine the lag effect at 0-6 days. Single- and double-pollutant models were employed for different age and sex groups.

RESULTS

The effect of PM_2.5 concentration on the number of daily ER visits for cerebrovascular disease was much stronger than that of PM₁₀ concentration. PM_2.5 and PM₁₀ had maximum RR values of 1.096 and 1.054 at lag 6 for patients aged 61-75 years. For each inter-quartile range (IQR) increase in PM₁₀ concentration, the maximum RR values for the total, males, females, aged 15-60 years, aged 61-75 years, and aged > 75 years were 1.024, 1.044, 1.043, 1.038, 1.054, and 1.032, respectively. For each IQR increase in PM_2.5 concentration, the maximum RR values for the total, males, females, aged 15-60 years, aged 61-75 years, and aged > 75 years were 1.038, 1.064, 1.076, 1.054, 1.096, and 1.049, respectively. The RR values of the double-pollutant models were lower than those of the single-pollutant models.

CONCLUSION

This study showed that the effects of PM pollution on cerebrovascular disease were different among different gender and age groups, and aged 61-75 years were mostly sensitive to particulate matters. The effects of PM_2.5 on cerebrovascular disease were stronger than those of PM₁₀. Our results can provide scientific evidence for the local government to take effective measures to improve air quality and the health of residents.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-021-00776-w.

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be missing significant periods of measurements. Models built using these datasets can therefore be biased. Although various methods have been proposed to handle missing data in many application areas, more air quality missing data prediction requires additional investigation. This study proposes an autoencoder model with spatiotemporal considerations to estimate missing values in air quality data. The model consists of one-dimensional convolution layers, making it flexible to cover spatial and temporal behaviours of air contaminants. This model exploits data from nearby stations to enhance predictions at the target station with missing data. This method does not require additional external features, such as weather and climate data. The results show that the proposed method effectively imputes missing data for discontinuous and long-interval interrupted datasets. Compared to univariate imputation techniques (most frequent, median and mean imputations), our model achieves up to 65% RMSE improvement and 20–40% against multivariate imputation techniques (decision tree, extra-trees, k-nearest neighbours and Bayesian ridge regressors). Imputation performance degrades when neighbouring stations are negatively correlated or weakly correlated.

Synergistic effects of gaseous pollutants on hospital admissions for cardiovascular disease in Liuzhou, China

Previous studies demonstrated that short-term exposure to gaseous pollutants (nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃)) had a greater adverse effect on cardiovascular disease. However, little evidence exists regarding the synergy between gaseous pollutants and cardiovascular disease (CVD). Therefore, we aimed to estimate the effect of individual gaseous pollutants on hospital admissions for CVD and to explore the possible synergistic effects between gaseous pollutants. Daily hospitalization counts for CVD were collected from January 1, 2014, to December 31, 2015. We also collected daily time series on gaseous pollutants from the Environment of the People's Republic of China, including NO₂, SO₂, and O₃. We used distributed lag nonlinear models (DLNMs) to assess the association of individual gaseous pollutants on CVD hospitalization, after controlling for seasonality, day of the week, public holidays, and weather variables. Then, we explored the variability across age and sex groups. In addition, we analyzed the synergistic effects between gaseous pollutants on CVD. Extremely low NO₂ and SO₂ increase the risk of CVD in all subgroup at lag 7 days. The greatest effect of high concentration of SO₂ was observed in male and the elderly (≥ 65 years) at lag 3 days. Greater effects of high concentration of O₃ were more pronounced in the young (< 65 years) and female at lag 3 days, while the effect of low concentration of O₃ was greater in male and the young (< 65 years) at lag 0 day. We found a synergistic effect between NO₂ and SO₂ for CVD, as well as between SO₂ and O₃. The synergistic effects of NO₂ and SO₂ on CVD were stronger in the elderly (≥ 65) and female. The female was sensitive to synergistic effects of SO₂-O₃ and NO₂-O₃. Interestingly, we found that there was a risk of CVD in the susceptible population even for gaseous pollutant concentrations below the National Environmental Quality Standard. The synergy between NO₂ and SO₂ was significantly associated with cardiovascular disease hospitalization in the elderly (≥ 65). This study provides evidence for the synergistic effect of gaseous pollutants on hospital admissions for cardiovascular disease.

Association between air pollution and hospital admissions for hypertension in Lanzhou, China

Air pollution has been accepted as an important risk factor for hypertension. However, little is known about the association between air pollution and hospitalization for hypertension. In this study, we explored the association between six criteria air pollutants and hypertension hospitalization in Lanzhou, China. An over-dispersed Poisson regression model combined with a distributed lag nonlinear model (DLNM) was used. In addition, we investigated the effect of modification by sex, age, and season. A total of 30,197 hospitalization cases were identified during the study period. A 10μg/m³ increase in PM_2.5, PM₁₀, SO₂, and NO₂ concentrations or 1 mg/m³ increment in CO was significantly associated with relative risks (RRs) of hospital admissions due to hypertension 1.026 [95% confidence interval (CI): 1.010, 1.043], 1.010 (95%CI: 1.005, 1.015), 1.042 (95%CI: 1.001, 1.085), 1.028 (95%CI: 1.003, 1.052), and 1.106 (95%CI: 1.031, 1.186), respectively. No significant influence of O₃8h was found on hypertension hospital admissions. The associations differed by individual characteristics; the elderly (≥ 65 years) and females were highly vulnerable. The effects of PM_2.5, SO₂, and CO were more evident in the cool season than in the warm season. From exposure-response curves, we observe a nearly linear relationship for PM_2.5, PM₁₀, SO₂, NO₂, and CO. This study suggests that exposure to PM_2.5, PM₁₀, SO₂, NO₂, and CO is associated with hypertension morbidity.

Persistent pollution episodes, transport pathways, and potential sources of air pollution during the heating season of 2016-2017 in Lanzhou, China

As one of the most important industrial cities in Northwest China, Lanzhou currently suffers from serious air pollution. This study analyzed the formation mechanism and potential source areas of persistent air pollution in Lanzhou during the heating period from November 1, 2016 to March 31, 2017 based on the air pollutant concentrations and relevant meteorological data. Our findings indicate that particulate pollution was extremely severe during the study period. The daily PM_2.5 and PM₁₀ concentrations had significantly negative correlations with daily temperature, wind speed, maximum daily boundary layer height, while the daily PM_2.5 and PM₁₀ concentrations showed significantly positive correlations with daily relative humidity. Five persistent pollution episodes were identified and classified as either stagnant accumulation or explosive growth types according to the mechanism of pollution formation and evolution. The PM_2.5 and PM₁₀ concentrations and PM_2.5/PM₁₀ ratio followed a growing "saw-tooth cycle" pattern during the stagnant accumulation type event. Dust storms caused abrupt peaks in PM₁₀ and a sharp decrease in the PM_2.5/PM₁₀ ratio in explosive growth type events. The potential sources of PM₁₀ were mainly distributed in the Kumtag Desert in Xinjiang Uygur Autonomous Region, the Qaidam Basin and Hehuang Valley in Qinghai Province, and the western and eastern Hexi Corridor in Gansu Province. The contributions to PM₁₀ were more than 120 μg/m³. The important potential sources of PM_2.5 were located in Hehuang Valley in Qinghai and Linxia Hui Autonomous Prefecture in Gansu; the concentrations of PM_2.5 were more than 60 μg/m³.

Short-term effect of multi-pollutant air quality indexes and PM2.5 on cardiovascular hospitalization in Hamadan, Iran: a time-series analysis

Air pollutants are the most important environmental factors that contributed to cardiovascular disease (CVD). The present study aimed to investigate the number of hospitalization due to heart failure (HF) and myocardial infarction (MI) following the air pollutant exposure using a time-series regression analysis with a distributed lag model in Hamadan, Iran (2015-2019). A total of 2091 cases of CVD were registered. Based on the findings, the highest health effects on HF hospitalization were observed with air quality health index (AQHI) at lag 9 (RR = 1.043, 95% CI 0.991-1.098), and air quality index (AQI) at lags 2, 7, and 9 (RR = 1.001, 95% CI 0.998-1.002), for an increase in 1 unit of the indexes, and with PM_2.5 at lag 0 (RR = 1.001, 95% CI 0.996-1.004) for 10 μg/m³ increase in PM_2.5 levels. The highest health effects on MI hospitalization were calculated with AQHI at lag 10 (RR = 1.059, 95% CI 1.001-1.121) and AQI at lags 1 and 2 (RR = 1.001, 95% CI 0.998-1.002), for an increase in 1 unit of the indexes, and with PM_2.5 at lag 8 (RR = 1.002, 95% CI 0.997-1.005) for 10 μg/m³ increase in PM_2.5 levels. According to a seasonal classification, results showed that hospitalization in the warm season was higher than that of the cold season. Based on our knowledge, the current study is the first study that investigated the effect of air quality indexes on hospitalization due to HF and MI in Iran. Findings can provide basic information to plan preventive measures for reducing exposure chance and hospitalization rate in high-risk people.

Ambient PM2.5 and PM10 Exposure and Respiratory Disease Hospitalization in Kandy, Sri Lanka

Evidence of associations between exposure to ambient air pollution and health outcomes are sparse in the South Asian region due to limited air pollution exposure and quality health data. This study investigated the potential impacts of ambient particulate matter (PM) on respiratory disease hospitalization in Kandy, Sri Lanka for the year 2019. The Generalized Additive Model (GAM) was applied to estimate the short-term effect of ambient PM on respiratory disease hospitalization. As the second analysis, respiratory disease hospitalizations during two distinct air pollution periods were analyzed. Each 10 μg/m³ increase in same-day exposure to PM_2.5 and PM₁₀ was associated with an increased risk of respiratory disease hospitalization by 1.95% (0.25, 3.67) and 1.63% (0.16, 3.12), respectively. The effect of PM_2.5 or PM₁₀ on asthma hospitalizations were 4.67% (1.23, 8.23) and 4.04% (1.06, 7.11), respectively (p < 0.05). The 65+ years age group had a higher risk associated with PM_2.5 and PM₁₀ exposure and hospital admissions for all respiratory diseases on the same day (2.74% and 2.28%, respectively). Compared to the lower ambient air pollution period, higher increased hospital admissions were observed among those aged above 65 years, males, and COPD and pneumonia hospital admissions during the high ambient air pollution period. Active efforts are crucial to improve ambient air quality in this region to reduce the health effects.

Application of Ultrasound Virtual Reality in the Diagnosis and Treatment of Cardiovascular Diseases

Cardiovascular disease is a common chronic disease in the medical field, which has a great impact on the health of Chinese residents (especially the elderly). At present, the effectiveness of the prevention and treatment of cardiovascular diseases in my country is not optimistic. Overall, the prevalence and mortality of CVD are still on the rise. The timely and effective detection and treatment of cardiovascular and cerebrovascular diseases are of great practical significance to improve the health of residents and to carry out prevention and treatment. This article aims to study the application of ultrasound-based virtual reality technology in the diagnosis and treatment of cardiovascular diseases to improve the efficiency and accuracy of the diagnosis of cardiovascular and cerebrovascular diseases by medical staff. The focus is on the application of feature attribute selection related algorithms and classification related algorithms in medical and health diagnosis systems, and a cardiovascular and cerebrovascular disease diagnosis system based on naive Bayes algorithm and improved genetic algorithm is designed and developed. The system builds a diagnostic model for cardiovascular and cerebrovascular diseases and diagnoses and displays the corresponding results based on the patient's examination data. This paper first puts forward the theoretical concepts of ultrasonic virtual reality technology, scientific computing visualization, genetic algorithm, naive Bayes algorithm, and surgery simulation system and describes them in detail. Then, we construct a three-dimensional ultrasonic virtual measurement system, from the collection and reconstruction of image data to the filtering and segmentation of image data, plus the application of three-dimensional visualization and virtual reality technology to construct a three-dimensional measurement system. The experimental results in this paper show that 10 isolated congenital heart disease models with atrial septal defect (ASD) established through the use of three-dimensional visualization and virtual reality technology measured the short diameter, long diameter, and area of the atrial septal defect in the left and right atria. Finally, a value of L less than 0.05 indicates that the statistics are meaningful, and a value of r generally greater than 0.9 indicates that the virtual measurement result is highly correlated with the real measurement result.

Study on Environmental and Lifestyle Factors for the North-South Differential of Cardiovascular Disease in China

Human death and life span are closely related to the geographical environment and regional lifestyle. These factors considerably vary among counties and regions, leading to the geographical disparity of disease. Quantitative studies on this phenomenon are insufficient. Cerebrovascular and heart diseases are the leading causes of death. The mortality rate of cerebrovascular and heart diseases is statistically higher in northern China than in southern China; the p-value of t-test for cerebrovascular and heart diseases was 0.047 and 0.000, respectively. The population attribution fraction of 12 major risk factors for cardiovascular disease (CVD) in each province was calculated based on their exposure and relative risk. The results found that residents in northern China consume high sodium-containing food, fewer vegetables, and less sea food products, and tend to be overweight. Fine particulate matter is higher in northern China than in southern China. Cold temperatures also cause a greater number of deaths than hot temperatures. All these factors have resulted in a higher CVD mortality rate in northern China. The attributive differential for sodium, vegetable, fruit, smoking, PM_2.5, omega-3, obesity, low temperature, and high temperature of heart disease between the two parts of China is 9.1, 0.7, -2.5, 0.1, 1.4, 1.3, 2.0, 4.7, and -2.1%, respectively. Furthermore, the attributive differential for the above factors of cerebrovascular disease between the two parts of China is 8.7, 0.0, -5.2, 0.1, 1.0, 0.0, 2.4, 4.7, and -2.1%. Diet high in sodium is the leading cause of the north-south differential in CVD, resulting in 0.71 less years of life expectancy in northern compared with that in southern China.

Association between atmospheric PM2.5 and daily outpatient visits for children's respiratory diseases in Lanzhou

The relationship between fine particulate matter (PM_2.5) and respiratory disease outcomes among children aged 0 to 14 years in Lanzhou, China, was evaluated. We utilized a generalized additive model linked by a quasi-Poisson distribution to examine the associations between PM_2.5 and paediatric respiratory outpatient visits for time lags of 0 up to 7 days, and stratified by gender, age, and season. Cases of respiratory disease in children were collected from 3 large hospitals for the years 2014-2017 and then linked with air pollutant concentrations from 4 air quality monitoring stations by date. We observed positive and significant associations between PM_2.5 and respiratory disease from the lag to lag 7, and from lag01 to lag07, with ER reaching the maximum value at lag07. For each 10 μg/m³ increase in PM_2.5 (lag07), the associated increment in respiratory diseases was 2.83% (95% CI 1.80%-3.86%). Males were more sensitive to the adverse effects, and the association was more significant in spring (from March to May) and winter (from December to the next February). Overall, the child group (age 3-6 years) demonstrated a higher risk of respiratory disease after PM_2.5 exposure. The associations between ambient PM_2.5 and respiratory hospital outpatients among young children became partially attenuated after the adjustment for gaseous pollutants in subgroups. The exposure-response curves were positive and generally nonlinear but flatted at concentrations over 60 μg/m³. This research found a significant association between ambient PM_2.5 levels and hospital outpatient visits in child with respiratory diseases in Lanzhou, China.

Clinical Manifestations of Ultrasonic Virtual Reality in the Diagnosis and Treatment of Cardiovascular Diseases

On a global scale, cardiovascular disease has become one of the most serious diseases that endangers human health and causes death and seriously threatens human life and health. If we can make accurate, timely, and effective judgments on cardiovascular-related parameters and take corresponding effective measures, the incidence of cardiovascular diseases can be reduced to a large extent. Based on this, this paper proposes the clinical application research of ultrasound virtual reality technology in the diagnosis and treatment of cardiovascular diseases. This article uses literature methods, experimental research methods, mathematical statistical analysis methods, and other research methods and in-depth study of virtual reality technology, cardiovascular disease, and other theoretical knowledge and briefly introduces ultrasound image denoising algorithms, such as bilateral filtering and PM model. And on this basis, it establishes clinical trials of ultrasound virtual reality technology in the diagnosis and treatment of cardiovascular diseases. This article mainly analyzes the application of virtual reality technology, technology comparison, and the experimental results carried out in this article. From the survey results, the total prevalence of hypertension was 25.1%, and the prevalence of males and females was 25.9% and 24.4%, respectively; the diagnostic accuracy rate of the experimental group reached 85.39%, while the diagnostic accuracy rate of the control group was 76.8%. It shows that the use of ultrasound virtual reality technology for disease diagnosis can effectively improve the accuracy of cardiovascular disease diagnosis and reduce the proportion of misdiagnosis and missed detection.

Association between Atrial Fibrillation Incidence and Temperatures, Wind Scale and Air Quality: An Exploratory Study for Shanghai and Kunming

As a common cardiovascular disease, atrial fibrillation has the characteristics of high morbidity, high disability, and high fatality rates, seriously endangering human health and sustainability. Some research has confirmed that environmental factors are related to the risk of illness and death from cardiovascular diseases (including atrial fibrillation), while there is still little comparison on the situation of the two cities in China. This research uses medical data in Shanghai and Kunming establishing, through two-step research, logistic models to compare the impacts on atrial fibrillation incidence to figure out the association between environmental factors (including air pollution, weather, temperature, and wind scales) and atrial fibrillation. Finally, this research shows that environmental impacts on atrial fibrillation prevalence have generality, regionality, and lagging characteristics. The result is significant for atrial fibrillation patients and provides a reliable medical theory basis for nursing measures. Besides, this research provides a prospective method of offering early warning for potential atrial fibrillation patients, helping to maintain human beings’ sustainable development.

Comparison of pre-hospital emergency services time intervals in patients with heart attack in Arak, Iran

Background:

After cardiac arrest, the possibility of death or irreversible complications will highly increase in the absence of cardiac resuscitation within 4 to 6 minutes. Accordingly, measuring the pre-hospital services time intervals is important for better management of emergency medical services delivery. The purpose of this study then was to investigate pre-hospital time intervals for patients with heart attack in Arak city, based on locations and time variables.

Methods:

This is a retrospective descriptive cross-sectional study, which was conducted at the Arak Emergency Medical Services (EMS) during 2017-2018. Data were analyzed by SPSS version 13.

Results:

The total number of heart attack patients registered in Arak emergency medical services was 2,659 of which 51% of patients were males. Six percent of patients were under 25 and about 49 percent were between 46 and 65 years old. The average of activation, response, on-scene, transportation, recovery and total time intervals were 3:30, 7:56, 15:15, 13:34, 11:07, 12:11, and 41:25, respectively. In the city area, the shortest and longest average response time intervals were in spring and winter, respectively. In out of the city area, the shortest average response time interval was in summer and the longest one in autumn. The shortest and the longest average response time intervals in the city area were in June and March, respectively, and in out of the city area, the shortest average response time interval was in June and the longest one in April.

Conclusions:

The shorter response and delivery time interval compared to the other studies may indicate improvement in the provision of EMS. Special attention should be paid to the facilities and equipment of vehicles during cold seasons to be in the shortest possible time. Also, training and informing the staff more about the code of cardiac patients along with general public education can help improve these intervals.

Association of ambient air pollution with cardiovascular disease risks in people with type 2 diabetes: a Bayesian spatial survival analysis

BACKGROUND

Evidence is limited on excess risks of cardiovascular diseases (CVDs) associated with ambient air pollution in diabetic populations. Survival analyses without considering the spatial structure and possible spatial correlations in health and environmental data may affect the precision of estimation of adverse environmental pollution effects. We assessed the association between air pollution and CVDs in type 2 diabetes through a Bayesian spatial survival approach.

METHODS

Taiwan's national-level health claims and air pollution databases were utilized. Fine individual-level latitude and longitude were used to determine pollution exposure. The exponential spatial correlation between air pollution and CVDs was analyzed in our Bayesian model compared to traditional Weibull and Cox models.

RESULTS

There were 2072 diabetic patients included in analyses. PM_2.5 and SO₂ were significant CVD risk factors in our Bayesian model, but such associations were attenuated or underestimated in traditional models; adjusted hazard ratio (HR) and 95% credible interval (CrI) or confidence interval (CI) of CVDs for a 1 μg/m³ increase in the monthly PM_2.5 concentration for our model, the Weibull and Cox models was 1.040 (1.004-1.073), 0.994 (0.984-1.004), and 0.994 (0.984-1.004), respectively. With a 1 ppb increase in the monthly SO₂ concentration, adjusted HR (95% CrI or CI) was 1.886 (1.642-2.113), 1.092 (1.022-1.168), and 1.091 (1.021-1.166) for these models, respectively.

CONCLUSIONS

Against traditional non-spatial analyses, our Bayesian spatial survival model enhances the assessment precision for environmental research with spatial survival data to reveal significant adverse cardiovascular effects of air pollution among vulnerable diabetic patients.

Acute effects of ambient air pollution on hospital outpatients with chronic pharyngitis in Xinxiang, China

We present results on a time-series study that analyzed the acute effects of six criteria air pollutants on hospital outpatient with chronic pharyngitis (CP) in Xinxiang, China. Data on the concentration of air pollutants and CP outpatient records were collected daily in Xinxiang, China, from January 1, 2015 to December 31, 2018. This study identified 62,823 outpatients with CP. The annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ are 75.7, 132.1, 33.2, 48.4, 1377, and 59.4 μg/m³, respectively. Further, a 10 μg/m³ increment in the concentration of PM₁₀, SO₂, NO₂, and CO corresponds to an increase of 0.28% (95% confidence interval (CI): 0.03-0.53%), 1.10% (95% CI: 0.09-2.11%), 1.82% (95% CI: 0.84-2.80%), and 0.03% (95% CI: 0.01-0.06%) in daily CP hospital outpatients, respectively. Furthermore, results indicated that outpatients under the age of 15 are more susceptible to the air pollutants, excluding O₃. Meanwhile, males might be more susceptible, and effect estimates appear slightly stronger in the cool season. Therefore, we should implement effective measures to manage air pollutants and reinforce protection of the high-risk population.

Health Effects of Asian Dust: A Systematic Review and Meta-Analysis

BACKGROUND

Potential adverse health effects of Asian dust exposure have been reported, but systematic reviews and quantitative syntheses are lacking.

OBJECTIVE

We reviewed epidemiologic studies that assessed the risk of mortality, hospital admissions, and symptoms/dysfunction associated with exposure to Asian dust.

METHODS

We performed a systematic search of PubMed and Web of Science to identify studies that reported the association between Asian dust exposure and human health outcomes. We conducted separate meta-analyses using a random-effects model for mortality and hospital admissions for a specific health outcome and assessed pooled estimates for each lag when at least three studies were available for a specific lag.

RESULTS

We identified 89 studies that met our inclusion criteria for the systematic review, and 21 studies were included in the meta-analysis. The pooled estimates (percentage changes) of mortality from circulatory and respiratory causes for Asian dust days vs. non-Asian dust days were 2.33% [95% confidence interval (CI): 0.76, 3.93] increase at lag 0 and 3.99% (95% CI: 0.08, 8.06) increase at lag 3, respectively. The increased risk for hospital admissions for respiratory disease, asthma, and pneumonia peaked at lag 3 by 8.85% (95% CI: 0.80, 17.55), 14.55% (95% CI: 6.74, 22.94), and 8.51% (95% CI: 2.89, 14.44), respectively. Seven of 12 studies reported reduced peak expiratory flow, and 16 of 21 studies reported increased respiratory symptoms associated with Asian dust exposure. There were substantial variations between the studies in definitions of Asian dust, study designs, model specifications, and confounder controls.

DISCUSSION

We found evidence of increased mortality and hospital admissions for circulatory and respiratory events. However, the number of studies included in the meta-analysis was not large and further evidences are merited to strengthen our conclusions. Standardized protocols for epidemiological studies would facilitate interstudy comparisons. https://doi.org/10.1289/EHP5312.

Association between ambient particulate matter and hospital outpatient visits for chronic obstructive pulmonary disease in Lanzhou, China

Until now, a number of epidemiological studies have focused on the association between ambient particulate matter pollution and chronic obstructive pulmonary disease (COPD), especially in developed countries. There are limited evidences on the association between short-term exposure to particulate matters (PM_2.5, PM_C, and PM₁₀) and overall hospital outpatient visits for COPD at the same time in China. Thus, a time-series analysis on the short-term association between three subtypes of PM (PM_2.5, PM_C, and PM₁₀) and daily hospital outpatients for COPD in Lanzhou, China was conducted, from 2014 to 2017.An over dispersed, generalized additive model was used to analyze the associations after controlling for time trend, weather conditions, day of the week, and holidays. Stratified analyses were also performed by age and gender. The results disclosed that a 10-μg/m³ increase in PM_2.5 concentration at a lag of 0-7 days was associated with 1.190% (95% CI 0.176~2.215%). For PM_c, therewere not statistically significant effects at any lag days, but we could find the greatest effect at lag07 that a 10-μg/m³ increase in concentration was associated with 0.014% (95% CI - 0.065~0.093%). PM₁₀ also exerted a high effect for COPD (0.185% increase; 95% CI - 0.046~0.417%) when 6 days of exposures (lag6), however, no significance relationship could be found. For COPD among males, positive results were observed for PM_2.5 with lags of 0-7 days, a 10-μg/m³ increase was 1.184% (95% CI 0.095~2.284%). The effect of PM_2.5 on females was also most significant at lag07, a 10-μg/m³ increase was 1.254% (95% CI 0.053~2.469%). For those aged < 65 years old, PM_2.5 was not statistically significant at all lag days, but it reached the maximum at lag07, a 10-μg/m³ increase was 0.978% (95% CI - 0.139~2.108%). For those aged 65 ≥ years old and older, PM_2.5 had a statistically significant lag effect at lag1, lag2, lag3, lag02, lag03, lag04, lag05, lag06, and lag07, and it was most significant at lag07; a 10-μg/m³ increase was 1.906% (95% CI 0.553~3.277%). Short-term exposure to PM_2.5 was associated with increased risk of hospital visits for COPD. In particular, the elderly (aged ≥ 65 years old) and males were relatively more sensitive to PM_2.5, and were affected right away after the PM_2.5 concentration went up.

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Background

Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017.

Methods

Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models.

Results

The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900–0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783–0.901), SVM (0.834, 95% CI: 0.774–0.894) and ANN (0.890, 95% CI: 0.836–0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879–0.974) and XGBoost (0.930, 95% CI: 0.878–0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32 and 43%, respectively.

Conclusion

This study suggests that ensemble learning models, especially the LightGBM model, can be used to effectively predict the peak events of CVDs admissions, and therefore could be a very useful decision-making tool for medical resource management.

Short-term effects of particulate matter exposure on emergency room visits for cardiovascular disease in Lanzhou, China: a time series analysis

Cardiovascular disease (CVD) has been the leading cause of death in China. Identifying the relationship between particulate matter (PM) and CVD in China is a significant challenge. In this study, daily CVD emergency room visit, environmental monitoring, and weather data from January 1, 2017, to December 31, 2018, in Lanzhou were collected. Generalized additive models (GAMs) were constructed to estimate the short-term effects of daily PM_2.5, PM_C, and PM₁₀ concentrations on CVD emergency room visits with different lag structures after controlling for the influence of meteorological elements and gaseous pollutants. Stratified analyses were conducted according to age (≥ 65 years and < 65 years), sex (male and female), cold season (from November to April), and warm season (from May to October). The results showed that each 10 μg/m³ increase in PM_2.5 was associated with a 1.93% (95% CI 0.12-3.78%) increase in CVD emergency room visits at lag03, and no single lag model was statistically significant. The excess relative risks (ERRs) of PM₁₀ and PM_C were not statistically significant at any lag pattern. The exposure-response curves demonstrated a nonlinear upward trend for these three PM pollutants. When adjusting for other gaseous pollutants, such as NO₂, SO₂, CO, and O₃, in the two-pollutant models, the associations between PM₁₀ and PM_C and CVD emergency room visits did not change compared with the single-pollutant models. The ERRs of PM_2.5 were 1.67% (95% CI 0.03-3.34%) at lag02 after adjustment for NO₂ and 1.65% (95% CI 0.02-3.30%) at lag02 after adjustment for SO₂. The ERRs of PM_2.5 were still statistically significant at lag03 when we adjusted for any one of the gaseous pollutants. Susceptibility to PM_2.5 was increased in people aged < 65 years, in males, and in the warm season. The findings are very important for local governments to develop environmental policies and strategies to reduce ambient PM_2.5 levels.

Hourly associations between ambient air pollution and emergency ambulance calls in one central Chinese city: Implications for hourly air quality standards

BACKGROUND

Most studies on the short-term health effects of air pollution have been conducted on a daily time scale, while hourly associations remain unclear.

METHODS

We collected the hourly data of emergency ambulance calls (EACs), ambient air pollution, and meteorological variables from 2014 to 2016 in Luoyang, a central Chinese city in Henan Province. We used a generalized additive model to estimate the hourly effects of ambient air pollutants (PM_2.5, PM₁₀, SO₂, and NO₂) on EACs for all natural causes and cardiovascular and respiratory morbidity, with adjustment for potential confounding factors. We further examined the effect modification by temperature, relative humidity, wind speed, and atmospheric pressure using stratified analyses.

RESULTS

In the single-pollutant models, PM_2.5, PM₁₀, SO₂, and NO₂ were associated with an immediate increase in all-cause morbidity at 0, 0, 12, 10 h, separately, after exposure to these pollutants (excess risks: 0.19% (95% confidence interval (CI): 0.03%, 0.35%), 0.13% (95% CI: 0.02%, 0.24%), 0.28% (95% CI: 0.01%, 0.54%) and 0.52% (95% CI: 0.06%, 0.99%), respectively). These effects remained generally stable in two-pollutant models. SO₂ and NO₂ were significantly associated with an immediate increase in risk of cardiovascular morbidity, but the effects on respiratory morbidity were relatively more delayed. The stratified analyses suggested that temperature could modify the association between PM_2.5 and EACs, humidity and atmospheric pressure could modify the association between SO₂ and EACs.

CONCLUSIONS

Our study provides new evidence that higher concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ may have transiently acute effects on all-cause morbidity and subacute effects on respiratory morbidity. SO₂ and NO₂ may also have immediate effects on cardiovascular morbidity. Findings of this study have important implications for the formation of hourly air quality standards.

Short-term effects of ambient air pollution and cardiovascular events in Shiraz, Iran, 2009 to 2015

Air pollution and dust storms are associated with increased cardiovascular hospital admissions. The aim of this study was to investigate the association between short-term exposure to ambient air pollutants and CVD (cardiovascular disease) events in a long-term observational period. The study included the events of cardiovascular diseases (namely coronary artery disease, ischemic heart disease, myocardial infarction, and pneumo thrombo embolism) within the population of Shiraz, from March 21, 2009 to March 20, 2015. Also, each patient's demographics were recorded. Main meteorological variables and five ambient pollutants (CO, O₃, SO₂, NO₂, and PM₁₀) were recorded. Statistical analysis was performed using linear regression (GLM) and a generalized additive model (GAM) estimating Poisson distribution and adjusted for the main risk factors and ambient meteorological variables. A mild prevalence (51.5%) of coronary artery disease (CAD) was registered in 6425 events. In GLM analysis, we observed an association among the pollutants with the coronary artery disease hospital admissions which was in the order of CO, NO₂, and PM₁₀. The highest association of each pollutant with hospital admission was observed as PM₁₀ at lag 4 (RR = 1.08; 95% CI 1.02, 1.14 and p < 0.05), NO₂ at lag 0 (RR = 1.22; 95% CI 1.00, 1.48), and CO at lag 0 (RR = 1.52 95% CI = (1.16, 1.99)). However, on dusty days, there were significantly higher numbers of referrals of cardiovascular patients (mean = 7.54 ± 4.44 and p = 0.002,) than on non-dusty days. According to these data, dust storms and some types of pollutants in the air are responsible for more admissions to hospitals for cardiovascular problems.

Health damage assessment of particulate matter pollution in Jing-Jin-Ji region of China

Jing-Jin-Ji is the largest and most dynamic economic region in northern China, and its air pollution has attracted much public attention. Scientific evaluation of health losses caused by air pollution can provide decision-making basis for formulation and improvement of pollution reduction policies in the Jing-Jin-Ji region. This paper estimated the adverse effects of particulate matter pollution on health in the Jing-Jin-Ji region in 2016 by using logarithmic linear exposure-response function, and monetized the health effects by adjusting human capital method and disease cost method. The results show non-ignorable health hazards and economic impacts caused by atmospheric particulate pollution. The economic losses relevant to health hazards by PM2.5 in the Jing-Jin-Ji region are 122.40 billion yuan, and those relevant to PM10 are 118.34 billion yuan, accounting for 1.62% and 1.56% of the region's GDP, respectively. Similar evaluations previously conducted in other countries yielded figures within the same order of magnitude. Considering the difference in economic losses per unit among disease types, the economic losses caused by air pollution in the Jing-Jin-Ji region mainly come from premature deaths. Infants and elderly people are the main victims of particulate matter. Affected by population, pollutant concentration, industrial structure, and other factors, the economic losses of particulate matter pollution in Beijing, Tianjin, Shijiazhuang, Tangshan, and Baoding are large. In order to reduce health hazards and economic impacts caused by particulate matter pollution, this paper put forward to guide the urban population diversion, reduce the outgoing frequency of susceptible groups such as infants and the elderly in haze weather, adopt high-efficiency particulate matter air purifier indoors, and develop public transportation to reduce motor vehicle exhaust emissions. In Tianjin and Hebei, promoting cleaner production in industries such as steel and cement and reducing coal use in the power industry are also suggested.

Contributions of local pollution emissions to particle bioreactivity in downwind cities in China during Asian dust periods

This study investigated the effects of pollution emissions on the bioreactivity of PM_2.5 during Asian dust periods. PM_2.5 during the sampling period were 104.2 and 85.7 μg m^-3 in Xi'an and Beijing, respectively, whereas PM_2.5 which originated from the Tengger Desert was collected (dust background). Pollution conditions were classified as non-dust days, pollution episode (PE), dust storm (DS)-1, and DS-2 periods. We observed a significant decrease in cell viability and an increase in LDH that occurred in A549 cells after exposure to PM_2.5 during a PE and DS-1 in Xi'an and Beijing compared to Tengger Desert PM_2.5. Positive matrix factorization was used to identify pollution emission sources. PM_2.5 from biomass and industrial sources contributed to alterations in cell viability and LDH in Xi'an, whereas vehicle emissions contributed to LDH in Beijing. OC, EC, Cl^-, K⁺, Mg²⁺, Ca, Ti, Mn, Fe, Zn, and Pb were correlated with cell viability and LDH for industrial emissions in Xi'an during DS. OC, EC, SO₄^2-, S, Ti, Mn, and Fe were correlated with LDH for vehicle emissions in Beijing during DS. In conclusion, the dust may carry pollutants on its surface to downwind areas, leading to increased risks of particle toxicity.

Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis

In this study, we estimated the short-term effects of ambient air pollution on respiratory disease hospitalization in Taiyuan, China. Daily data of respiratory disease hospitalization, daily concentration of ambient air pollutants and meteorological factors from 1 October 2014 to 30 September 2017 in Taiyuan were included in our study. We conducted a time-series study design and applied a generalized additive model to evaluate the association between every 10-μg/m³ increment of air pollutants and percent increase of respiratory disease hospitalization. A total of 127,565 respiratory disease hospitalization cases were included in this study during the present period. In single-pollutant models, the effect values in multi-day lags were greater than those in single-day lags. PM_2.5 at lag02 days, SO₂ at lag03 days, PM₁₀ and NO₂ at lag05 days were observed to be strongly and significantly associated with respiratory disease hospitalization. No significant association was found between O₃ and respiratory disease hospitalization. SO₂ and NO₂ were still significantly associated with hospitalization after adjusting for PM_2.5 or PM₁₀ into two-pollutant models. Females and younger population for respiratory disease were more vulnerable to air pollution than males and older groups. Therefore, some effective measures should be taken to strengthen the management of the ambient air pollutants, especially SO₂ and NO₂, and to enhance the protection of the high-risk population from air pollutants, thereby reducing the burden of respiratory disease caused by ambient air pollution.

Spatiotemporal Variations and Driving Factors of Air Pollution in China

In recent years, severe and persistent air pollution episodes in China have drawn wide public concern. Based on ground monitoring air quality data collected in 2015 in Chinese cities above the prefectural level, this study identifies the spatiotemporal variations of air pollution and its associated driving factors in China using descriptive statistics and geographical detector methods. The results show that the average air pollution ratio and continuous air pollution ratio across Chinese cities in 2015 were 23.1 ± 16.9% and 16.2 ± 14.8%. The highest levels of air pollution ratio and continuous air pollution ratio were observed in northern China, especially in the Bohai Rim region and Xinjiang province, and the lowest levels were found in southern China. The average and maximum levels of continuous air pollution show distinct spatial variations when compared with those of the continuous air pollution ratio. Monthly changes in both air pollution ratio and continuous air pollution ratio have a U-shaped variation, indicating that the highest levels of air pollution occurred in winter and the lowest levels happened in summer. The results of the geographical detector model further reveal that the effect intensity of natural factors on the spatial disparity of the air pollution ratio is greater than that of human-related factors. Specifically, among natural factors, the annual average temperature, land relief, and relative humidity have the greatest and most significant negative effects on the air pollution ratio, whereas human factors such as population density, the number of vehicles, and Gross Domestic Product (GDP) witness the strongest and most significant positive effects on air pollution ratio.

Ambient Air Pollution and Morbidity in Chinese

The rapid economic growth in China is coupled with a severe ambient air pollution, which poses a huge threat to human health and the sustainable development of social economy. The rapid urbanization and industrialization over the last three decades have placed China as one of countries with the greatest disease burden in world. Notably, the prevalence rate of chronic noncommunicable diseases (CND), including respiratory diseases, CVD, and stroke, in 2010 reaches 16.9%. The continuous growth of the incidence of CND urgent needs for effective regulatory action for health protection. This study aims to evaluate the impact of rapid urbanization on status of ambient air pollution and associated adverse health effects on the incidence and the burden of CND and risk assessment. Our findings would be greatly significant in the prediction of the risk of ambient air pollution on CND and for evidence-based policy making and risk management in China.