ArticleEnvironment international2019
Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea.
Article in Environment international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- A Hybrid Transfer Learning Approach for Ambient PMACS ES&T air · 2026Article
- Integrating Audiovisual Data for Short-Term Particulate Matter Concentration Prediction on Urban Sidewalks.Environmental science & technology · 2026Article
- Assessing the efficacy of a low-cost air pollution monitoring device for environmental and occupational exposure assessments.Environmental monitoring and assessment · 2025Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- A Wearable Sensor Node for Measuring Air Quality Through Citizen Science Approach: Insights from the SOCIO-BEE Project.Sensors (Basel, Switzerland) · 2025Article
- Machine learning in healthcare citizen science: A scoping review.International journal of medical informatics · 2025Article
- Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science.Sensors (Basel, Switzerland) · 2024Article
- Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.Sensors (Basel, Switzerland) · 2024Article
- A DYNAMIC SPATIAL FILTERING APPROACH TO MITIGATE UNDERESTIMATION BIAS IN FIELD CALIBRATED LOW-COST SENSOR AIR POLLUTION DATA.The annals of applied statistics · 2023Article
- Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data.Scientific reports · 2023Article
- Gaps and future directions in research on health effects of air pollution.EBioMedicine · 2023Review
- Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs.Environmental research · 2023Article
- A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic.Scientific reports · 2023Article
- Estimating ground-level PMAir quality, atmosphere, & health · 2023Article
- Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021.Scientific reports · 2022Article
- High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning.International journal of environmental research and public health · 2022Article
- Probabilistic Machine Learning with Low-Cost Sensor Networks for Occupational Exposure Assessment and Industrial Hygiene Decision Making.Annals of work exposures and health · 2022Article
- Applications of artificial intelligence in the field of air pollution: A bibliometric analysis.Frontiers in public health · 2022Article
- Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities.Sensors (Basel, Switzerland) · 2021Article
- Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt.Environmental monitoring and assessment · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.