Residents in the Northeastern United States closely monitored air quality warnings this summer as wildfire smoke cast an orange haze across the skies. Airborne pollutants, including minute particles known as fine particulate matter (PM 2.5), raised concerns due to their potential health risks when inhaled, particularly for individuals with pre-existing heart and lung conditions.

Air Pollution Exposure ModelsTo evaluate PM 2.5 exposure and assist public health authorities in formulating effective strategies, a research team led by Penn State has developed enhanced models utilizing artificial intelligence and mobility data.

“Our findings demonstrate that integrating artificial intelligence and mobility data into air quality models enhances their accuracy. This aids decision-makers and public health officials in identifying areas requiring additional monitoring or safety alerts due to poor air quality or a combination of poor air quality and high pedestrian activity,” explained Manzhu Yu, an assistant professor of geography at Penn State and the study’s lead author.

Published in the journal Frontiers in Environmental Science, the study focused on PM 2.5 measurements in eight major metropolitan areas in the continental United States. Air quality data originated from Environmental Protection Agency (EPA) monitoring stations and affordable sensors typically acquired and distributed by local community groups. The researchers used this data to calculate hourly averages of PM 2.5 in each region.

The scientists incorporated the air quality data into a land use regression model, considering local geographical factors like aerosol optical depth measured by satellites, proximity to roads or streams, elevation, vegetation, and meteorological conditions such as humidity and wind speed. Unlike previous models that followed a linear approach, assigning fixed importance to each geographic factor, Yu and her colleagues opted for a nonlinear approach. This approach, incorporating automated machine learning, considers dynamic factors that change hourly or seasonally and may have intricate interactions affecting air quality.

The automated machine learning technique employed an ensemble method, allowing the machine to run and combine multiple models to identify the most effective model for each region. The researchers analyzed anonymized cell phone mobility data to identify areas with both poor air quality and high visitor numbers.

The study revealed that the automated machine learning method, coupled with data from low-cost sensors and EPA monitoring stations, enhanced the accuracy of air pollution exposure models by an average of 17.5%. This improvement offered greater spatial variation compared to relying solely on regulatory monitors.

Yu attributed the increased accuracy to the method’s ability to better consider dynamic variables such as aerosol optical depth and meteorological factors, which consistently emerged as crucial across all study regions. The inclusion of mobility data enabled the team to identify potential hotspots within regions and times of the day and year when large populations might encounter high PM 2.5 levels.

While some areas may consistently exhibit elevated air pollution levels, like those near industrial facilities and major transportation hubs, this information alone isn’t sufficient to prioritize places for additional monitoring or health alerts, Yu noted. The mobility-based exposure maps generated by the study provide public health officials and decision-makers with insights into hotspots featuring both unhealthy air quality and high visitor traffic. This information can be utilized to send alerts to individuals’ mobile phones when entering areas with notably high PM 2.5 levels, reducing their exposure to unhealthy air quality.

Other posts

  • Comparison of Traditional Regression With Regression Methods of Machine Learning
  • Implementing Machine Learning Algorithms with Python
  • How Machine Learning Affects The Development of Cities
  • The AI System Uses a Huge Database of 10 Million Biological Images
  • Improving the Retail Customer Experience Using Machine Learning Algorithms
  • Travel Venture Layla Snaps Up AI-Driven Trip Planning Assistant Roam Around
  • Adaptive Learning
  • The Role of Machine Learning in Manufacturing Quality Control
  • Bumble's Latest AI Technology Detects And Blocks Fraudulent And Fake Accounts
  • A Revolution in Chemical Analysis With GPT-3
  • An Introductory Guide to Neural Networks and Deep Learning
  • Etsy Introduces Gift Mode, an AI-Powered Tool That Creates Over 200 Custom Gift Collections
  • Machine Learning Programs For People With Disabilities
  • Fingerprint Detection with Machine Learning
  • Reinforcement Learning
  • Google Introduces Lumiere - An Advanced AI-Powered Text-To-Video Tool
  • Transforming Energy Management with Predictive Analytics
  • Image Recognition Using Machine Learning
  • A Machine Learning Study Has Shown That Seagulls Are Changing Their Natural Habitat To An Urban One
  • The Method of Hybrid Machine Learning Increases the Resolution of Electrical Impedance Tomography
  • Comparing Traditional Regression with Machine Learning Regression Techniques
  • Accelerated Discovery of Environmentally Friendly Energy Materials Using a Machine Learning Approach
  • An Award-Winning Japanese Writer Uses ChatGPT in Her Writing
  • Machine Learning in Stock Market Analysis
  • OpenAI to Deploy Counter-Disinformation Measures for Upcoming 2024 Electoral Process
  • Clustering Algorithms in Unsupervised Learning
  • Recommender Systems in Music and Entertainment
  • Scientists Create AI-Powered Technique for Validating Software Code
  • Innovative Clustering Algorithm Aids Researchers in Deciphering Complex Molecular Data
  • An Introduction to SVMs for Beginners
  • Machine Learning in Cybersecurity
  • Bioengineers Constructing the Nexus Between Organoids and Artificial Intelligence Utilizing 'Brainoware' Technology
  • Principal Component Analysis (PCA)
  • AWS AI Unveils Data Augmentation with Controllable Diffusion Models and CLIP Integration
  • Machine Learning Applications in Healthcare
  • Understanding the Essentials of Machine Learning Algorithms
  • Harnessing AI Language Processing to Advance Fusion Energy Studies
  • Leveraging Distributed Ledger Technology to Boost Machine Learning in Crop Phenotyping
  • Understanding Convolutional Neural Networks
  • Using Artificial Intelligence to Identify Subterranean Reservoirs of Renewable Energy
  • Scientists Create Spintronics-Based Probabilistic Computing Systems for Modern AI Applications
  • Natural Language Processing (NLP) and Text Mining Techniques
  • Artificial Intelligence Systems Demonstrate Proficiency in Imitation, But Struggle with Innovation
  • Leveraging Predictive Analytics for Smarter Supply Chain Decisions
  • AI-Powered System Offers Affordable Monitoring of Invasive Plant
  • Using Machine Learning to Track Driver Attention Levels Could Enhance Road Safety
  • K-Nearest Neighbors (KNN)
  • Precision Farming, Crop Yield Prediction, and Machine Learning
  • AI Model Analyzes Characteristics of Potential New Medications
  • Scientists Create Large Language Model for Medicine
  • Introduction to Recurrent Neural Networks
  • Hidden Markov Models (HMMs)
  • Using Machine Learning to Combat Fraud
  • The Impact of Machine Learning on Gaming
  • Machine Learning in the Automotive Industry
  • Recent Research Suggests Larger Datasets May Not Always Enhance AI Model
  • Improving Flood Mitigation Through Machine Learning Innovations
  • Scientists Utilized Machine Learning and Molecular Modeling to Discover Potential Anticancer Medications
  • Improving X-ray Materials Analysis through Machine Learning Techniques
  • Utilizing Machine Learning, Researchers Enhance Vaccines and Immunotherapies for Enhanced Treatment Effectiveness
  • Progress in Machine Learning Transforming Nuclear Power Operations Towards a Sustainable, Carbon-Free Energy Future
  • Machine Learning Empowers Users with 'Superhuman' Capabilities to Navigate and Manipulate Tools in Virtual Reality
  • Research Highlights How Large Language Models Could Undermine Scientific Accuracy with False Responses
  • Algorithm Boosts Secure Communications without Sacrificing Data Authenticity
  • Random Forests in Predictive Modeling
  • Decision Trees
  • Supervised vs. Unsupervised Learning
  • The Evolution of Machine Learning Algorithms Over the Years