The effect of toxic air pollutants on fertility men and women, fetus and birth rate

Margiana, Ria; Yousefi, Homayon; Afra, Arghavan; Agustinus, Agustinus; Abdelbasset, Walid Kamal; Kuznetsova, Mariya; Mansourimoghadam, Sara; Ekrami, Hafez Ajam; Mohammadi, Mohammad Javad

doi:10.1515/reveh-2022-0032

Cited by 5 publications

(1 citation statement)

References 63 publications

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“…Exposure to excessive air pollution can lead to respiratory diseases and even cancer. Air pollution adversely affects public health and the image of a city, especially in cities with uncontrolled growth of industrial activity and high fossil fuel consumption (Almetwally et al, 2020;Margiana et al, 2022;Choi and Myong, 2018;Grigorieva and Lukyanets, 2021). Additionally, the health risk resulting from extreme weather events (such as heat waves and cold snaps) has attracted increasing attention (Costello et al, 2009;Kan et al, 2012;Semenza and Paz, 2021).…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Yang

et al. 2023

Environ Sci Pollut Res

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Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluated the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprised meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China from January 1, 2016 to August 20, 2022. We used support vector regression (SVR) to build regression models to analyze the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. To evaluate the model performance, 70% of the dataset was used for training and 30% was used for testing. The Spearman correlation and SVR model results indicated that NO 2 , PM 2.5 , and PM 10 were correlated with the occurrence of respiratory diseases, and the strongest correlation was for pneumonia. An increase in the daily average temperature and daily relative humidity decreased the number of patients with pneumonia and chronic lower respiratory diseases but increased the number of patients with acute upper respiratory infections. The SVR modeling showed potential for predicting the number of respiratory-related hospital visits. This work demonstrated that combining machine learning with meteorological and air pollution data can be used for disease prediction and can serve as a useful tool for policymakers to take preventive measures. HighlightsMachine learning was used to examine correlations between meteorological and air pollution factors and outpatient visits.Pneumonia patient visits were strongly correlated with NO 2 , PM 2.5 , and PM 10 .The SVR model showed the best performance for pneumonia.We examined the link between climate and air pollution factors and respiratory diseases.

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Section: Introductionmentioning

confidence: 99%

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Yang

et al. 2023

Environ Sci Pollut Res

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The Impact of Heavy Metal Contamination in Soils on Soil Microbial Communities and Its Potential Health Risks for Humans

Popoola,

Ogundele,

Ladapo

et al. 2024

Soil Microbiome in Green Technology Sustainability

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Polycyclic aromatic hydrocarbon exposure during pregnancy and changes in umbilical renal function

Hsu,

Liu,

Morozova

et al. 2024

Environ Sci Eur

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Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental pollutants with significant adverse effects on human health, particularly concerning fetal development during pregnancy. This study investigates the relationship between maternal exposure to particulate matter-bound (PM-bound) PAHs and potential alterations in fetal renal function. A cross-sectional investigation was conducted on 450 mother-pair newborns from June 2019 to August 2021. Exposure to PM-bound PAHs was estimated at the residential address using spatiotemporal models based on data from 30 monitoring stations across the study area. Umbilical cord blood samples were collected post-delivery for biochemical analysis of renal function markers, including creatinine (Cr), blood urea nitrogen (BUN), and estimated glomerular filtration rate (eGFR). Multivariable regression models were used to assess the relationship between exposure to each PAHs compound and fetal renal function. Moreover, the mixture effects of exposure to PAHs on fetal renal function were assessed using quantile g-computation analysis. Increased concentrations of various PAH compounds at the residential address correlated with raised levels of umbilical BUN and Cr, suggesting potential renal impairment. Notably, exposure to certain PAHs compounds demonstrated statistically negative significant associations with eGFR levels. An increment of one quartile in exposure to PAHs mixture was correlated with a rise of 1.08 mg/dL (95% CI 0.04, 2.11, p = 0.04) and 0.02 mg/dL (95% CI − 0.00, 0.05, p = 0.05) increase in BUN and Cr, respectively. Moreover, a one-quartile increase in PAHs mixture exposure was associated with − 1.09 mL/min/1.73 m2 (95% CI − 2.03, − 0.14, p = 0.02) decrease in eGFR. These findings highlight the potential impact of PAH exposure on fetal renal function and underscore the importance of considering environmental exposures in assessing neonatal renal health outcomes.

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The effect of toxic air pollutants on fertility men and women, fetus and birth rate

Cited by 5 publications

References 63 publications

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

The Impact of Heavy Metal Contamination in Soils on Soil Microbial Communities and Its Potential Health Risks for Humans

Polycyclic aromatic hydrocarbon exposure during pregnancy and changes in umbilical renal function

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