AIMC Topic: Particulate Matter
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Surveillance of ship emissions and fuel sulfur content based on imaging detection and multi-task deep learning.
Environmental pollution (Barking, Essex : 1987)
Jul 3, 2021
Shipping makes up the major proportion of global transportation and results in an increasing emission of air pollutants. It accounts for 3.1%, 13%, and 15% of the annual global emissions of CO, SO, and NO, respectively. Hence, effective regulatory me...
Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index.
Environmental pollution (Barking, Essex : 1987)
Jun 14, 2021
Limitations of Normalized Difference Vegetation Index (NDVI) potentially contributed to the inconsistent findings of greenspace exposure and childhood asthma. The aim of this study was to use a novel greenness exposure assessment method, capable of o...
Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning.
Journal of hazardous materials
Jun 6, 2021
Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome ...
A novel multi-model data-driven ensemble approach for the prediction of particulate matter concentration.
Environmental science and pollution research international
May 3, 2021
Accuracy in the prediction of the particulate matter (PM and PM) concentration in the atmosphere is essential for both its monitoring and control. In this study, a novel neuro fuzzy ensemble (NF-E) model was proposed for prediction of hourly PM and P...
Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms.
Environmental science and pollution research international
Apr 9, 2021
This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction va...
A hybrid deep learning technology for PM air quality forecasting.
Environmental science and pollution research international
Mar 23, 2021
The concentration of PM is one of the main factors in evaluating the air quality in environmental science. The severe level of PM directly affects the public health, economics and social development. Due to the strong nonlinearity and instability of ...
Asthma-prone areas modeling using a machine learning model.
Scientific reports
Jan 21, 2021
Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran conside...
Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air.
Analytical chemistry
Jan 5, 2021
Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ mann...
PM2.5 concentration modeling and prediction by using temperature-based deep belief network.
Neural networks : the official journal of the International Neural Network Society
Nov 5, 2020
Air quality prediction is a global hot issue, and PM is an important factor affecting air quality. Due to complicated causes of formation, PM prediction is a thorny and challenging task. In this paper, a novel deep learning model named temperature-ba...
Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations.
Environmental research
Oct 28, 2020
Reliable estimates of outdoor air pollution concentrations are needed to support global actions to improve public health. We developed a new approach to estimating annual average outdoor nitrogen dioxide (NO) concentrations using approximately 20,000...
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