Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Mar 22, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Quantifying Multi-pollutant Co-exposure via Deep Learning-Based Simultaneous Prediction Using Geostationary Satellite

Eunjin Kang¹, Sihun Jung¹, Jungho Im^1,2,3

¹Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.

Environmental Science & Technology

|March 20, 2026

Summary

Related Concept Videos

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Steroid administration, timing, and dose in patients with septic shock in the emergency department: retrospective analysis of a multicenter prospective cohort study.

Scientific reports·2026

Same author

PM<sub>2.5</sub> air pollution inequities in the US by sector and state: Past trajectories and future directions.

Science advances·2026

Same author

Spatiotemporal changes in inter-city sustainability impacts linked to emission challenges worldwide.

Nature communications·2026

Same author

<i>Kaempferia parviflora</i> Rhizome Extract Improves Skeletal Muscle Glucose Homeostasis by Upregulating the GLUT4 Signaling Pathway In Vitro and In Vivo.

Pharmaceuticals (Basel, Switzerland)·2026

Same author

O-GlcNAcylation reprograms microglial inflammatory states and attenuates Alzheimer's disease pathology.

Cell death & disease·2026

Same author

The urban green carbon index (UGCI): A spatial framework for suggesting urban carbon management.

PloS one·2026

Same journal

Wildland Fires in the US between 2007 and 2018: Characterizing Equity in Exposures and Health Impacts.

Environmental science & technology·2026

Same journal

Profiling Active Low-Abundance Microbes in As/Sb-Contaminated Soils via d-Amino Acid-Based In Situ Labeling.

Environmental science & technology·2026

Same journal

Looking beyond Sorption in GAC Filters: How Extended Contact Times and Functionally Distinct Microbial Biomass Enable Enhanced Micropollutant Biodegradation.

Environmental science & technology·2026

Same journal

Integrating Experiments and Models To Unravel Interactions between Soil Organic Matter and Enhanced Weathering.

Environmental science & technology·2026

Same journal

Role of Advanced Direct Extraction Technologies in Reducing Environmental Impacts of Lithium Production.

Environmental science & technology·2026

Same journal

Comparison of High Spatial Resolution PM<sub>2.5</sub>, PM<sub>10</sub>, and NO<sub>2</sub> Estimates Using a Deep Ensemble Machine Learning Framework in a Low Pollution Setting.

Environmental science & technology·2026

See all related articles

This summary is machine-generated.

DeepMAP, a new deep learning framework, accurately predicts six major air pollutants hourly. It identifies hotspots for co-pollution, aiding air quality management and public health protection.

Area of Science:

Environmental Science
Data Science
Public Health

Background:

Accurate hourly assessment of multiple air pollutants is crucial for understanding human co-exposure and health impacts.
Existing monitoring methods face spatial and temporal limitations, hindering comprehensive analysis.
Pollutant mixtures pose complex health risks that require advanced analytical tools.

Purpose of the Study:

To develop and validate Deep Learning for Multiple Air Pollutant analysis (DeepMAP), a novel deep learning framework.
To enable simultaneous prediction of six major air pollutants (PM10, PM2.5, O3, NO2, CO, SO2) at hourly resolution.
To identify co-pollution hotspots and characterize co-exposure patterns for targeted public health interventions.

Main Methods:

Developed DeepMAP, a deep learning framework for simultaneous multi-pollutant prediction.

Keywords:

air pollution co-exposure deep learning geostationary satellite multi-task learning

Related Experiment Videos

Last Updated: Mar 22, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Validated the model's performance across diverse regions and pollutant types.

Utilized a novel co-exposure index to pinpoint regions with significant pollutant contributions.

Main Results:

DeepMAP achieved robust performance, with normalized RMSE below 0.36 for all pollutants during high-concentration episodes.
Identified frequent co-exceedance events: PM10-PM2.5 (91 days/year) in East Asia, followed by PM10-PM2.5-NO2, PM2.5-O3, and PM10-PM2.5-O3.
Pinpointed hotspots for PM10-PM2.5-NO2-O3 co-exceedance in the North China Plain, East China, and South Korea.
Revealed distinct hotspot regions where NO2 contribution to co-exposure was significantly higher.

Conclusions:

DeepMAP offers a high-resolution, data-driven solution for analyzing multi-pollutant dynamics.
The framework accurately captures complex co-pollution episodes and identifies critical exposure areas.
Findings support enhanced air quality management strategies and public health protection efforts in vulnerable regions.