Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China

The association between ozone exposure and blood pressure in a general Chinese middle-aged and older population: a large-scale repeated-measurement study

BACKGROUND

The relationship between ozone (O3) exposure and blood pressure (BP) remains inconclusive. Given the scarcity of Chinese epidemiological data, more research on this association is of paramount importance, particularly among middle-aged and older Chinese populations.

METHODS

This study involved 10,875 participants (median age: 60.0 years) in Xiamen, China, from 2013 to 2019, with 34,939 repeated BP measurements. Air pollutant exposure data, including O3, particulate matter, nitrogen dioxide, sulfur dioxide, and carbon monoxide were derived from China High Air Pollutants and High-resolution Air Quality Reanalysis datasets using a k-nearest neighbor algorithm. The relationship between mixed air pollutant exposure and BP was evaluated using Bayesian kernel machine regression model. The effects of daily-specific O3 exposure on BP were assessed by distributed lag models integrated into a linear mixed-effects framework. The mediating role of total cholesterol (TC), serum total bilirubin (STB), triglyceride (TG), and low-density lipoprotein (LDL) were examined using multilevel mediation analysis with a fully adjusted model.

RESULTS

Mixed air pollutant exposure was positively correlated with BP, with O3 being a predominant contributor exhibiting an inverse effect. O3 exposure had immediate effects on pulse pressure (PP), while systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) showed delayed responses, with 3-, 14-, and 8-day lags, respectively. During the study period of up to 30 days, each 10 μg/m3 increase in maximum daily 8-h average O3 concentration was associated with reductions in SBP (β =  - 1.176 mm Hg), DBP (- 0.237 mm Hg), PP (β =  - 0.973 mm Hg), and MAP (β =  - 0.544 mm Hg). Stronger correlations were observed in the older participants (aged ≥ 65 years), overweight/obese individuals, smokers and alcohol consumers, and those with hypertension or type 2 diabetes mellitus. STB and LDL mediated these effects, while TC and TG played mitigating roles.

CONCLUSIONS

Short-term O3 exposure is negatively associated with BP in middle-aged and older Chinese individuals. The findings provide preliminary evidence for the impact of O3 exposure on BP regulation and underscore the urgent need to reassess public health policies in response to O3 pollution.

Association between ambient air pollution and age-related macular degeneration: a meta-analysis

BACKGROUND

Several epidemiological studies have investigated the association between ambient air pollution and age-related macular degeneration (AMD). However, a consensus has not yet been reached. Our meta-analysis aimed to clarify this association.

METHODS

Databases, including PubMed, EMBASE, and Web of Science, were searched for relevant studies from 01 January 2000 to 30 January 2024. English-language, peer-reviewed studies using cross-sectional, prospective, or retrospective cohorts and case-control studies exploring this relationship were included. Two authors independently extracted data and assessed study quality. A random-effects model was used to calculate pooled covariate-adjusted odds ratios. Heterogeneity across studies was also tested.

RESULTS

We identified 358 relevant studies, of which eight were included in the meta-analysis. Four studies evaluated the association between particulate matter less than 2.5 μm in diameter (PM2.5) and AMD, and three studies explored the relationship between nitrogen dioxide (NO2) or ozone (O3) and AMD. The pooled odds ratios were 1.16 (95% confidence interval [CI]: 1.11-1.21), 1.17 (95% CI: 1.09-1.25), and 1.06 (95% CI: 1.05-1.07), respectively.

CONCLUSION

Current evidence suggests a concomitant positive but not causal relationship between PM2.5, NO2, or O3 and AMD risk.

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019-2021

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2-98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

Joint effects of meteorological factors and PM2.5 on age-related macular degeneration: a national cross-sectional study in China

BACKGROUND

Weather conditions are a possible contributing factor to age-related macular degeneration (AMD), a leading cause of irreversible loss of vision. The present study evaluated the joint effects of meteorological factors and fine particulate matter (PM_2.5) on AMD.

METHODS

Data was extracted from a national cross-sectional survey conducted across 10 provinces in rural China. A total of 36,081 participants aged 40 and older were recruited. AMD was diagnosed clinically by slit-lamp ophthalmoscopy, fundus photography, and spectral domain optical coherence tomography (OCT). Meteorological data were calculated by European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and were matched to participants' home addresses by latitude and longitude. Participants' individual PM_2.5 exposure concentrations were calculated by a satellite-based model at a 1-km resolution level. Multivariable-adjusted logistic regression models paired with interaction analysis were performed to investigate the joint effects of meteorological factors and PM_2.5 on AMD.

RESULTS

The prevalence of AMD in the study population was 2.6% (95% CI 2.42-2.76%). The average annual PM_2.5 level during the study period was 63.1 ± 15.3 µg/m³. A significant positive association was detected between AMD and PM_2.5 level, temperature (T), and relative humidity (RH), in both the independent and the combined effect models. For PM_2.5, compared with the lowest quartile, the odds ratios (ORs) with 95% confidence intervals (CIs) across increasing quartiles were 0.828 (0.674,1.018), 1.105 (0.799,1.528), and 2.602 (1.516,4.468). Positive associations were observed between AMD and temperature, with ORs (95% CI) of 1.625 (1.059,2.494), 1.619 (1.026,2.553), and 3.276 (1.841,5.830), across increasing quartiles. In the interaction analysis, the estimated relative excess risk due to interaction (RERI) and the attributable proportion (AP) for combined atmospheric pressure and PM_2.5 was 0.864 (0.586,1.141) and 1.180 (0.768,1.592), respectively, indicating a synergistic effect between PM_2.5 and atmospheric pressure.

CONCLUSIONS

This study is among the first to characterize the coordinated effects of meteorological factors and PM_2.5 on AMD. The findings warrant further investigation to elucidate the relationship between ambient environment and AMD.

Effect of PM2.5 Levels on ED Visits for Respiratory Causes in a Greek Semi-Urban Area

Fine particulate matter that have a diameter of <2.5 μm (PM2.5) are an important factor of anthropogenic pollution since they are associated with the development of acute respiratory illnesses. The aim of this prospective study is to examine the correlation between PM2.5 levels in the semi-urban city of Volos and Emergency Department (ED) visits for respiratory causes. ED visits from patients with asthma, pneumonia and upper respiratory infection (URI) were recorded during a one-year period. The 24 h PM2.5 pollution data were collected in a prospective manner by using twelve fully automated air quality monitoring stations. PM2.5 levels exceeded the daily limit during 48.6% of the study period, with the mean PM2.5 concentration being 30.03 ± 17.47 μg/m3. PM2.5 levels were significantly higher during winter. When PM2.5 levels were beyond the daily limit, there was a statistically significant increase in respiratory-related ED visits (1.77 vs. 2.22 visits per day; p: 0.018). PM2.5 levels were also statistically significantly related to the number of URI-related ED visits (0.71 vs. 0.99 visits/day; p = 0.01). The temperature was negatively correlated with ED visits (r: −0.21; p < 0.001) and age was found to be positively correlated with ED visits (r: 0.69; p < 0.001), while no statistically significant correlation was found concerning humidity (r: 0.03; p = 0.58). In conclusion, PM2.5 levels had a significant effect on ED visits for respiratory causes in the city of Volos.

Machine Learning-Based Approach Using Open Data to Estimate PM2.5 over Europe

Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R2 of 0.69, RMSE of 5 μg/m3, and MAE of 3.3 μg/m3. The outcome of this study is a daily full coverage PM2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies.

The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland

Despite the very restrictive laws, Krakow is known as the city with the highest level of air pollution in Europe. It has been proven that, due to its location, air pollutants are transported to this city from neighboring municipalities. In this study, a complex geostatistical approach for spatio-temporal analysis of particulate matter (PM) concentrations was applied. For background noise reduction, data were recorded during the COVID-19 lockdown using 100 low-cost sensors and were validated based on indications from reference stations. Standardized Geographically Weighted Regression, local Moran's I spatial autocorrelation analysis, and Getis-Ord Gi* statistic for hot-spot detection with Kernel Density Estimation maps were used. The results indicate the relation between the topography, meteorological variables, and PM concentrations. The main factors are wind speed (even if relatively low) and terrain elevation. The study of the PM2.5/PM10 ratio allowed for a detailed analysis of spatial pollution migration, including source differentiation. This research indicates that Krakow's unfavorable location makes it prone to accumulating pollutants from its neighborhood. The main source of air pollution in the investigated period is solid fuel heating outside the city. The study shows the importance and variability of the analyzed factors' influence on air pollution inflow and outflow from the city.