Cardiovascular disease, mortality and exposure to particulate matter (PM): a systematic review and meta-analysis

PM10-bound trace elements in pan-European urban atmosphere

Although many studies have discussed the impact of Europe's air quality, very limited research focused on the detailed phenomenology of ambient trace elements (TEs) in PM10 in urban atmosphere. This study compiled long-term (2013-2022) measurements of speciation of ambient urban PM10 from 55 sites of 7 countries (Switzerland, Spain, France, Greece, Italy, Portugal, UK), aiming to elucidate the phenomenology of 20 TEs in PM10 in urban Europe. The monitoring sites comprised urban background (UB, n = 26), traffic (TR, n = 10), industrial (IN, n = 5), suburban background (SUB, n = 7), and rural background (RB, n = 7) types. The sampling campaigns were conducted using standardized protocols to ensure data comparability. In each country, PM10 samples were collected over a fixed period using high-volume air samplers. The analysis encompassed the spatio-temporal distribution of TEs, and relationships between TEs at each site. Results indicated an annual average for the sum of 20 TEs of 90 ± 65 ng/m3, with TR and IN sites exhibiting the highest concentrations (130 ± 66 and 131 ± 80 ng/m3, respectively). Seasonal variability in TEs concentrations, influenced by emission sources and meteorology, revealed significant differences (p < 0.05) across all monitoring sites. Estimation of TE concentrations highlighted distinct ratios between non-carcinogenic and carcinogenic metals, with Zn (40 ± 49 ng/m3), Ti (21 ± 29 ng/m3), and Cu (23 ± 35 ng/m3) dominating non-carcinogenic TEs, while Cr (5 ± 7 ng/m3), and Ni (2 ± 6 ng/m3) were prominent among carcinogenic ones. Correlations between TEs across diverse locations and seasons varied, in agreement with differences in emission sources and meteorological conditions. This study provides valuable insights into TEs in pan-European urban atmosphere, contributing to a comprehensive dataset for future environmental protection policies.

Global impact of particulate matter on ischemic stroke

Objective

This study assesses the worldwide impact of ischemic stroke caused by ambient particulate matter pollution between 1990 and 2019, utilizing data from the Global Burden of Disease (GBD) 2019.

Methods

An analysis was conducted across various subgroups, including region, Socio-demographic Index (SDI) level, country, age, and gender. The study primarily examined metrics such as death cases, death rate, Disability-Adjusted Life Years (DALYs), DALY rate, and age-standardized indicators. The Estimated Annual Percentage Change (EAPC) was calculated to assess trends over time.

Results

The study found a moderate increase in the global burden of ischemic stroke attributed to ambient particulate matter, with the age-standardized DALY rate showing an EAPC of 0.41. Subgroup analyses indicated the most substantial increases in Western Sub-Saharan Africa (EAPC 2.64), East Asia (EAPC 2.77), and Eastern Sub-Saharan Africa (EAPC 3.80). Low and middle SDI countries displayed the most notable upward trends, with EAPC values of 3.36 and 3.58 for age-standardized death rate (ASDR) and DALY rate, respectively. Specifically, countries like Equatorial Guinea, Timor-Leste, and Yemen experienced the largest increases in ASDR and age-standardized DALY rate. Furthermore, both death and DALY rates from ischemic stroke due to particulate matter showed significant increases with age across all regions.

Conclusion

The study highlights the increasing worldwide health consequences of ischemic stroke linked to particulate matter pollution, particularly in Asia and Africa. This emphasizes the critical necessity for tailored public health interventions in these regions.

Estimating PM2.5 utilizing multiple linear regression and ANN techniques

The accurate prediction of air pollutants, particularly Particulate Matter (PM), is critical to support effective and persuasive air quality management. Numerous variables influence the prediction of PM, and it's crucial to combine the most relevant input variables to ensure the most dependable predictions. This study aims to address this issue by utilizing correlation coefficients to select the most pertinent input and output variables for an air pollution model. In this work, PM2.5 concentration is estimated by employing concentrations of sulfur dioxide, nitrogen dioxide, and PM10 found in the air through the application of Artificial Neural Networks (ANNs). The proposed approach involves the comparison of three ANN models: one trained with the Levenberg-Marquardt algorithm (LM-ANN), another with the Bayesian Regularization algorithm (BR-ANN), and a third with the Scaled Conjugate Gradient algorithm (SCG-ANN). The findings revealed that the LM-ANN model outperforms the other two models and even surpasses the Multiple Linear Regression method. The LM-ANN model yields a higher R2 value of 0.8164 and a lower RMSE value of 9.5223.

Urbanicity and psychotic disorders: Facts and hypotheses

In the present qualitative literature review, we summarise data on psychotic disorders and urbanicity, focusing particularly on recent findings. Longitudinal studies of the impact of urbanicity on the risk for psychotic disorders have consistently shown a significant association, with a relative risk between 2 and 2.5. However, most of the original studies were conducted in Western Europe, and no incidence studies were conducted in low- and middle-income countries. European studies suggest that neighbourhood-level social fragmentation and social capital may partly explain this association. Exposure to air pollution (positive association) and green space (negative association) may also be part of the explanation, but to date, available data do not make it possible to conclude if they act independently from urbanicity, or as part of the effect of urbanicity on psychotic disorders. Finally, several studies have consistently shown significant associations between the polygenic risk score for schizophrenia and urbanicity, with several possible explanations (pleiotropic effects, results of prodromic symptoms, or selection/intergenerational hypothesis). Thus, more studies are needed to understand the factors that explain the association between urbanicity and the risk of psychotic disorders. Further studies should account for the interdependence and/or interactions of different psychosocial and physical exposures (as well as gene-environment interactions), and explore this association in low- and middle-income countries.