AIMC Topic: Particulate Matter

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PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning.

IEEE transactions on neural networks and learning systems
This article devises a photograph-based monitoring model to estimate the real-time PM concentrations, overcoming currently popular electrochemical sensor-based PM monitoring methods' shortcomings such as low-density spatial distribution and time dela...

Air quality prediction using CNN+LSTM-based hybrid deep learning architecture.

Environmental science and pollution research international
Air pollution prediction based on variables in environmental monitoring data gains further importance with increasing concerns about climate change and the sustainability of cities. Modeling of the complex relationships between these variables by sop...

Explainable deep learning predictions for illness risk of mental disorders in Nanjing, China.

Environmental research
Epidemiological studies have revealed the associations of air pollutants and meteorological factors with a range of mental health conditions. However, little is known about local explanations and global understanding on the importance and effect of i...

Surveillance of ship emissions and fuel sulfur content based on imaging detection and multi-task deep learning.

Environmental pollution (Barking, Essex : 1987)
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)
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
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
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
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
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
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...