Exposure Response Relationship of Acute Effects of Air Pollution on Respiratory Diseases — China, 2013−2018

Niu, Hongtao; Yu, Tao; Li, Xuexin; Wu, Hanna; Yan, Meilin; Duan, Ruirui; Yang, Ting

doi:10.46234/ccdcw2021.230

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Similarly, Kim et al (2018) show that ambient pollution leads to lung infection. In a recent study, Niu et al (2021) show that exposure to ambient pollution positively leads to asthma and chronic pulmonary obstruction. Kim et al (2018) further suggest that air pollution has acute effects on outpatients and hospital admissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Air Pollution and Health in Africa: The Burden Falls on Children

et al. 2023

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This study sought to examine the impact of air pollution on health in Africa. Air pollution is a major public health concern around the world. Exposure to air pollution has been linked to a slew of negative health consequences, ranging from subclinical effects, physiological changes in pulmonary functions and the cardiovascular system, to clinical symptoms, outpatient and emergency-room visits, hospital admissions, and finally to premature death. Health impact assessments indicate that sub-Saharan Africa suffers a high burden of disease and premature deaths, attributable to environmental pollution in the world. The health and economic repercussions of rapid pollution increases could jeopardize African efforts to enhance economic development, establish human capital, and achieve the Sustainable Development Goals (SDGs). Despite all these, literature on pollution and health is still scanty in the case of Africa. This study was quantitative in nature, and it used a difference GMM approach to estimate its model. The GMM results showed that children are negatively affected by pollution. Children under the age of five are especially at risk, and the effects are believed to be most severe in developing countries, where exposure to high levels of ambient air pollution throughout childhood is thought to lower total life expectancy by an average of 4–5 years. Based on the findings of this study, it is recommended that African countries must not overlook the pollution problem. They must promote and use low carbon technologies and services. In the absence of active intervention, pollution will soon raise morbidity and death.

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Section: Literature Reviewmentioning

confidence: 99%

Air Pollution and Health in Africa: The Burden Falls on Children

et al. 2023

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“…Air pollution is a major threat to mankind worldwide. Long-term exposure to hazardous air pollutants has negative health impacts on people (Niu et al 2021;Ahmad et al 2023). Air pollutants like particulate matter, carbon monoxide, etc.…”

Section: Statements and Declarations Introductionmentioning

confidence: 99%

An ensemble deep learning approach for air quality estimation in Delhi, India

Mohan,

Abraham

2024

Earth Sci Inform

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South Asian megacities are significant contributors to the degrading air quality. In highly populated norther n India, Delhi is a major hotspot for air pollutants that influence health and climate. Effective mitigation of air pollution is impeded by inadequate estimation which emphasizes the need for cost-effective alternatives. This paper proposes an ensemble model based on transformer and Convolutional Neural Network (CNN) models to estimate air quality from images and weather parameters in Delhi. A Data Efficient Image transformer (DeiT) is fine-tuned with outdoor images, and parallelly dark-channel prior extracted from images are fed to a CNN model. Additionally, a 1-dimensional CNN is trained with meteorological features to improve accuracy. The predictions from these three parallel branches are then fused with ensemble learning to classify images into six Air Quality Index (AQI) classes and estimate the AQI value. To train and validate the proposed model, an image dataset is collected from Delhi, India termed 'AirSetDelhi' and properly labeled with ground-truth AQI values. Experiments conducted on the dataset demonstrate that the proposed model outperforms other deep learning networks in the literature. The model achieved an overall accuracy of 89.28% and a Cohen Kappa score of 0.856 for AQI classification, while it obtained an RMSE of 47.36 and an R 2 value of 0.861 for AQI estimation, demonstrating efficacy in both tasks. As a regional estimation model based on images and weather features, the proposed model offers an alternative feasible approach for air quality estimation.

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