Impact of Desert Dust Events on the Cardiovascular Disease: A Systematic Review and Meta-Analysis

Báez-Ferrer, Néstor; Abreu-González, Pedro; Rodrı́guez, Sergio; Díaz, Rocío; Avanzas, Pablo; Hernández-Vaquero, Daniel

doi:10.3390/jcm10040727

Cited by 29 publications

(16 citation statements)

References 38 publications

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“…The differences under consideration may be the product of various distributions and transport of dust, and reactivity with compounds in the environment, which contribute to the occurrence of uncertainty in the assessment of performance [20][21][22][23]. Also, deposition, interactions and environmental conditions should not be neglected as well as influence and potential effect on human health [24][25][26][27][28]. Whether or not inhalation of PM form desert dust has adverse health effect is insufficiently investigated, there is even studies reviewed assessment of the influence of desert dust on cardiovascular diseases [29][30][31][32].…”

Section: Resultsmentioning

confidence: 99%

Forensic-Engineering Aspect in One Example of Long-Distance Transport of Air Pollutant

2023

Teh. vjesn.

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Forensic engineering methods might be applied in detection and identification characterisation of events according to complexity of their appearance in real environment as it covers different aspects (data, materials, chemistry, monitoring systems, technology) needed to find out the cause and predict effects. The long-distance air pollutant transport represents one challenging event for the analysis over years; specific phenomena are Saharan air masses that can transport high amounts of mineral dust particles and biological material. The dynamism of the systems in real environment, inevitably, includes time frame and spatial referencing, on location of event. However, this is directly related to providing findings and opinions when appealing to a forensic examination of an event. An example of long-distance transport of air pollutants with references to related processes in the atmosphere including time frame and spatial references is presented in the paper with discussion that is aligned with the goals of forensic engineering.

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Section: Resultsmentioning

confidence: 99%

Forensic-Engineering Aspect in One Example of Long-Distance Transport of Air Pollutant

2023

Teh. vjesn.

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“…There are some existing reviews, systematic or not, on the health effect of desert dust, but these studies are limited to specific health outcomes and geographical areas (Domínguez‐Rodríguez et al., 2021 ; Hashizume et al., 2020 ). Evidence from this scoping review indicates that the inconsistent health effect findings are possibly due to differences in study design, exposure measurement methods, statistical methodologies, differing populations, and wide‐ranging geographical locations.…”

Section: Discussionmentioning

confidence: 99%

“…There are some existing reviews, systematic or not, on the health effect of desert dust, but these studies are limited to specific geographical areas and health outcomes. (Hashizume M et al., 2020 ; Domínguez‐Rodríguez A et al., 2021 ). For example, skin symptoms and allergic reactions have not been considered (Otani et al., 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of Desert Dust and Sandstorms on Human Health: A Scoping Review

et al. 2023

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Desert dust and sandstorms are recurring environmental phenomena that are reported to produce serious health risks worldwide. This scoping review was conducted to identify the most likely health effects of desert dust and sandstorms and the methods used to characterize desert dust exposure from the existing epidemiological literature. We systematically searched PubMed/MEDLINE, Web of Science, and Scopus to identify studies that reported the effects of desert dust and sandstorms on human health. Search terms referred to desert dust or sandstorm exposure, names of major deserts, and health outcomes. Health effects were cross‐tabulated with study design variables (e.g., epidemiological design and methods to quantify dust exposure), desert dust source, health outcomes and conditions. We identified 204 studies that met the inclusion criteria for the scoping review. More than half of the studies (52.9%) used a time‐series study design. However, we found a substantial variation in the methods used to identify and quantify desert dust exposure. The binary metric of dust exposure was more frequently used than the continuous metric for all desert dust source locations. Most studies (84.8%) reported significant associations between desert dust and adverse health effects, mainly for respiratory and cardiovascular mortality and morbidity causes. Although there is a large body of evidence on the health effects of desert dust and sandstorms, the existing epidemiological studies have significant limitations related to exposure measurement and statistical analysis that potentially contribute to inconsistencies in determining the effect of desert dust on human health.

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“…Aeolian dust storms degrade visibility and cause several health problems including traffic accidents, degradation in industrial machinery, cardiovascular diseases, and lung cancers (Domínguez-Rodríguez et al 2021;Achakulwisut et al 2019;Al-Hemoud et al 2019;Bhattachan et al 2019). Fine dust (soil dust with particle matter <= 2.5 µm) contributes about 20 -50 % of total fine particulate matter (PM2.5) in the western United States (WUS) (Hand et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

Aryal¹

2022

Preprint

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Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. This study explores the accuracy of Artificial Intelligence (AI) models: adaptive-network-based fuzzy inference system (ANFIS) and multi-layered perceptron artificial neural network (mlp-NN) over the southwestern United States (SWUS) based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations at monthly and seasonal timescale from 1990-2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from North American Regional Reanalysis (NARR). The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BISA). ANFIS model generally performs better than mlp-NN model in predicting regional dustiness over the SWUS region with r of 0.77 and 0.83 for monthly and seasonal fine dust respectively. AI models perform better in predicting regional dustiness at a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust at both monthly and seasonal timescales. Compared to precipitation, the near-surface average temperature is the more important predictor of the regional dustiness at both monthly and seasonal timescale. However, compared to the monthly timescale, air temperature is less more important predictor than precipitation at the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models have a good potential to predict monthly and seasonal fine and coarse dust at acceptable accuracy based on basic climatic data.

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Impact of Desert Dust Events on the Cardiovascular Disease: A Systematic Review and Meta-Analysis

Cited by 29 publications

References 38 publications

Forensic-Engineering Aspect in One Example of Long-Distance Transport of Air Pollutant

Forensic-Engineering Aspect in One Example of Long-Distance Transport of Air Pollutant

Effects of Desert Dust and Sandstorms on Human Health: A Scoping Review

Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

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