Short-term effects of exposure to particulate matter and air pollution on hospital admissions for asthma and chronic obstructive pulmonary disease in Gyeonggi-do, South Korea, 2007-2018

Short-term exposure to sulfur dioxide and the occurrence of chronic obstructive pulmonary disease: An updated systematic review and meta-analysis based on risk of bias and certainty of evidence

Several studies have documented a relationship between short-term exposure to atmospheric sulfur dioxide (SO2) and chronic obstructive pulmonary disease (COPD). However, findings vary across different regions. This meta-analysis employed a random-effects model to calculate the combined risk estimate for each 10-μg/m3 increase in ambient SO2 concentration. Subgroup analysis aimed to identify sources of heterogeneity. To assess potential bias, studies were evaluated using a domain-based assessment tool developed by the World Health Organization. Sensitivity analyses, based on bias risk, explored how model assumptions influenced associations. An evidence certainty framework was used to evaluate overall evidence quality. The study protocol was registered with PROSPERO (CRD42023446823). We thoroughly reviewed 191 full-text articles, ultimately including 15 in the meta-analysis. The pooled relative risk for COPD was 1.26 (95 % CI 0.94-1.70) per 10-μg/m3 increase in ambient SO2. Eleven studies were deemed high risk due to inadequate handling of missing data. Overall evidence certainty was rated as medium. Given SO2's significant public health implications, continuous monitoring is crucial. Future research should include countries in Africa and Oceania to enhance global understanding of atmospheric SO2-related health issues.

Short-Term Effects of Ambient Air Pollution on Chronic Obstructive Pulmonary Disease Admissions in Jiuquan, China

Recent findings indicate that air pollution contributes to the onset and advancement of chronic obstructive pulmonary disease (COPD). Nevertheless, there is insufficient research indicating that air pollution is linked to COPD in the region of inland northwest China. Daily hospital admission records for COPD, air pollutant levels, and meteorological factor information were collected in Jiuquan for this study between 1 January 2018 and 31 December 2019. We employed a distributed lag non-linear model (DLNM) integrated with the generalized additive model (GAM) to assess the association between air pollution and hospital admissions for COPD with single lag days from lag0 to lag7 and multiday moving average lag days from lag01 to lag07. For example, the pollutant concentration on the current day was lag0, and on the prior 7th day was lag7. The present and previous 7-day moving average pollutant concentration was lag07. Gender, age, and season-specific stratified analyses were also carried out. It is noteworthy that the delayed days exhibited a different pattern, and the magnitude of associations varied. For NO2 and CO, obvious associations with hospitalizations for COPD were found at lag1, lag01-lag07, and lag03-lag07, with the biggest associations at lag05 and lag06 [RR = 1.015 (95%CI: 1.008, 1.023) for NO2, RR = 2.049 (95%CI: 1.416, 2.966) for CO], while only SO2 at lag02 was appreciably linked to hospitalizations for COPD [1.167 (95%CI: 1.009, 1.348)]. In contrast, short-term encounters with PM2.5, PM10, and O3 were found to have no significant effects on COPD morbidity. The lag effects of NO2 and CO were stronger than those of PM2.5 and PM10. Males and those aged 65 years or older were more vulnerable to air pollution. When it came to the seasons, the impacts appeared to be more pronounced in the cold season. In conclusion, short-term encounters with NO2 and CO were significantly correlated with COPD hospitalization in males and the elderly (≥65).

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33μg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Effects of air pollutants and temperature on the number of asthma outpatient visits in Hohhot, China

Although numerous studies have linked asthma to air temperature and pollution, few studies have examined their interactive effects on asthma outpatient visits. This study investigated how air pollutants and their interactions with temperature affect asthma outpatient visits in a city in northern Chinaduring the time period 2018 - 2020 . . As the results, 24,163 asthma outpatients were recorded, a 10-μg/m3 increase in PM10, PM2.5, SO2, and NO2 concentrations was associated with significant increases in visits of 3.47% (95% CI: 2.35%-4.60%), 0.83% (95% CI: 0.36%-1.30%), 3.17% (95% CI: 1.47%-4.90%), and 8.90% (95% CI: 6.09%-11.79%), respectively. The effect was stronger in females than males, and stronger in the elderly (≥65 years) than among the young. The interaction between low temperatures and high air pollution levels significantly increased the number of asthma outpatient visits. This study emphasizesthe importance of reducing air pollution in order to lessen the effects of cold.

Exposure to outdoor particulate matter and risk of respiratory diseases: a systematic review and meta-analysis

According to epidemiological studies, particulate matter (PM) is an important air pollutant that poses a significant threat to human health. The relationship between particulate matter and respiratory diseases has been the subject of numerous studies, but these studies have produced inconsistent findings. The purpose of this systematic review was to examine the connection between outdoor particulate matter (PM2.5 and PM10) exposure and respiratory disorders (COPD, lung cancer, LRIs, and COVID-19). For this purpose, we conducted a literature search between 2012 and 2022 in PubMed, Web of Science, and Scopus. Out of the 58 studies that were part of the systematic review, meta-analyses were conducted on 53 of them. A random effect model was applied separately for each category of study design to assess the pooled association between exposure to PM2.5 and PM10 and respiratory diseases. Based on time-series and cohort studies, which are the priorities of the strength of evidence, a significant relationship between the risk of respiratory diseases (COPD, lung cancer, and COVID-19) was observed (COPD: pooled HR = 1.032, 95% CI: 1.004-1.061; lung cancer: pooled HR = 1.017, 95% CI: 1.015-1.020; and COVID-19: pooled RR = 1.004, 95% CI: 1.002-1.006 per 1 μg/m3 increase in PM2.5). Also, a significant relationship was observed between PM10 and respiratory diseases (COPD, LRIs, and COVID-19) based on time-series and cohort studies. Although the number of studies in this field is limited, which requires more investigations, it can be concluded that outdoor particulate matter can increase the risk of respiratory diseases.

Associations between air pollutants and hospital admissions for chronic obstructive pulmonary disease in Jinan: potential benefits from air quality improvements

Evidence between air pollution and chronic obstructive pulmonary disease (COPD) is inconsistent and limited in China. In this study, we aim to examine the associations between air pollutants and hospital admissions for COPD, hoping to provide practical advice for prevention and control of COPD. Hospital admissions for COPD were collected from a Grade-A tertiary hospital in Jinan from 2014 to 2020. A generalized additive model (GAM) was used to examine the associations between air pollutants and hospital admissions for COPD. Stratified analysis was also conducted for gender, age (20-74 and ≥75 years), and season (warm and cold). The avoidable number of COPD hospital admissions was calculated when air pollutants were controlled under national and WHO standards. Over the study period, a total of 4,012 hospital admissions for COPD were recorded. The daily hospital admissions of COPD increased by 2.36% (95%CI: 0.13-4.65%) and 2.39% (95%CI: 0.19-4.65%) for per 10 μg/m³ increase of NO₂ and SO₂ concentrations at lag2, respectively. There was no statistically significant difference in health effects caused by increased concentrations of PM_2.5, PM₁₀, CO, and O₃. The health effects of increased SO₂ concentration were stronger in women, the ≥75 years old people and the cold season. About 2 (95%CI: 0-3), 64 (95%CI: 4-132) and 86 (95%CI: 6-177) COPD admissions would be avoided when the SO₂ concentration was controlled below the NAAQS-II (150 μg/m³), NAAQS-I (50 μg/m³), and WHO's AQG2021 standard (40 μg/m³), respectively. These findings suggest that short-term exposure to NO₂ and SO₂ was associated with increased risks of daily COPD admissions, especially for females and the elderly. The control of SO₂ and NO₂ under the national and WHO standards could avoid more COPD admissions and obtain greater health benefits.

Spatial distribution of unscheduled hospital admissions for chronic obstructive pulmonary disease in the central area of Asturias, Spain

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is one of the major causes of mortality worldwide and also reports high morbidity rates and the global burden COPD has continued to rise over the last several decades. The best-known COPD risk factors are tobacco smoke and air pollution, but genetics, age, sex, and socioeconomic status are additional factors. This study aimed to assess the spatial distribution of unscheduled COPD hospital admissions of men and women in the central area of Asturias during 2016-2018 and identify trends, spatial patterns, or clusters in the area.

METHODS

Unscheduled COPD hospital admissions in the central area of Asturias were registered, geocoded, and grouped by census tracts (CTs), age, and sex. Standardized admission ratio, smoothed relative risk, posterior risk probability, and spatial clusters between relative risks throughout the study area were calculated and mapped.

RESULTS

The spatial distribution of COPD hospital admissions differed between men and women. For men, high-risk values were located primarily in the northwestern area of the study, whereas for women the cluster pattern was not as clear and high-risk CTs also reached central and southern areas. In both men and women, the north-northwest area included the majority of CTs with high-risk values.

CONCLUSIONS

The present study showed the existence of a spatial distribution pattern of unscheduled COPD hospital admissions in the central area of Asturias that was more pronounced for men than for women. This study could provide a starting point for generating knowledge about COPD epidemiology in Asturias.

Airborne Exposure of the Cornea to PM10 Induces Oxidative Stress and Disrupts Nrf2 Mediated Anti-Oxidant Defenses

The purpose of this study is to test the effects of whole-body animal exposure to airborne particulate matter (PM) with an aerodynamic diameter of <10 μm (PM10) in the mouse cornea and in vitro. C57BL/6 mice were exposed to control or 500 µg/m3 PM10 for 2 weeks. In vivo, reduced glutathione (GSH) and malondialdehyde (MDA) were analyzed. RT-PCR and ELISA evaluated levels of nuclear factor erythroid 2-related factor 2 (Nrf2) signaling and inflammatory markers. SKQ1, a novel mitochondrial antioxidant, was applied topically and GSH, MDA and Nrf2 levels were tested. In vitro, cells were treated with PM10 ± SKQ1 and cell viability, MDA, mitochondrial ROS, ATP and Nrf2 protein were tested. In vivo, PM10 vs. control exposure significantly reduced GSH, corneal thickness and increased MDA levels. PM10-exposed corneas showed significantly higher mRNA levels for downstream targets, pro-inflammatory molecules and reduced Nrf2 protein. In PM10-exposed corneas, SKQ1 restored GSH and Nrf2 levels and lowered MDA. In vitro, PM10 reduced cell viability, Nrf2 protein, and ATP, and increased MDA, and mitochondrial ROS; while SKQ1 reversed these effects. Whole-body PM10 exposure triggers oxidative stress, disrupting the Nrf2 pathway. SKQ1 reverses these deleterious effects in vivo and in vitro, suggesting applicability to humans.

Short-Term Air Pollution Exposure and Risk of Acute Exacerbation of Chronic Obstructive Pulmonary Disease in Korea: A National Time-Stratified Case-Crossover Study

We investigated the association between short-term exposure to air pollution and the risk of acute exacerbation of chronic obstructive pulmonary disease (AE-COPD) in seven metropolitan cities in Korea. We used national health insurance claims data to identify AE-COPD cases in 2015. We estimated short-term exposure to particulate matter (PM) with a diameter of ≤2.5 μm (PM_2.5), PM with diameters of ≤10 μm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) obtained from the Ministry of Environment. We conducted a time-stratified, case-crossover study to evaluate the effect of short-term exposure to air pollution on hospital visits for AE-COPD, using a conditional logistic regression model. The risk of hospital visits for AE-COPD was significantly associated with interquartile range increases in PM₁₀ in a cumulative lag model (lag 0–2, 0.35%, 95% confidence interval (CI) 0.06–0.65%; lag 0–3, 0.39%, 95% CI 0.01–0.77%). The associations were higher among patients who were men, aged 40–64 years, with low household income, and with a history of asthma. However, other air pollutants were not significantly associated with the risk of hospital visits for AE-COPD. Short-term exposure to air pollution, especially PM₁₀, increases the risk of hospital visits for AE-COPD.

Ambient PM Concentrations as a Precursor of Emergency Visits for Respiratory Complaints: Roles of Deep Learning and Multi-Point Real-Time Monitoring

Despite ample evidence that high levels of particulate matter (PM) are associated with increased emergency visits related to respiratory diseases, little has been understood about how prediction processes could be improved by incorporating real-time data from multipoint monitoring stations. While previous studies use traditional statistical models, this study explored the feasibility of deep learning algorithms to improve the accuracy of predicting daily emergency hospital visits by tracking their spatiotemporal association with PM concentrations. We compared the predictive accuracy of the models based on PM datasets collected between 1 December 2019 and 31 December 2021 from a single but more accurate air monitoring station in each district (Air Korea) and multiple but less accurate monitoring sites (Korea Testing & Research Institute; KTR) within Guro District in Seoul, South Korea. We used MLP (multilayer perceptron) to integrate PM data from multiple locations and then LSTM (long short-term memory) models to incorporate the intrinsic temporal PM trends into the learning process. The results reveal evidence that predictive accuracy is improved from 1.67 to 0.79 in RMSE when spatial variations of air pollutants from multi-point stations are incorporated in the algorithm as a 9-day time window. The findings suggest guidelines on how environmental and health policymakers can arrange limited resources for emergency care and design ambient air monitoring and prevention strategies.