High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning Article Swipe
Rong Guo
,
Ying Qi
,
Bu Zhao
,
Ziyu Pei
,
Fei Wen
,
Shun Wu
,
Qiang Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph19138005
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph19138005
Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R2) value of 0.740 for PM2.5, 0.754 for CO and 0.716 for SO2. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijerph19138005
- https://www.mdpi.com/1660-4601/19/13/8005/pdf?version=1656645317
- OA Status
- gold
- Cited By
- 14
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283783145
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283783145Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijerph19138005Digital Object Identifier
- Title
-
High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-29Full publication date if available
- Authors
-
Rong Guo, Ying Qi, Bu Zhao, Ziyu Pei, Fei Wen, Shun Wu, Qiang ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/ijerph19138005Publisher landing page
- PDF URL
-
https://www.mdpi.com/1660-4601/19/13/8005/pdf?version=1656645317Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1660-4601/19/13/8005/pdf?version=1656645317Direct OA link when available
- Concepts
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Air quality index, Environmental science, Pollutant, Air monitoring, Air pollution, Data quality, Computer science, Air pollutants, Remote sensing, Data mining, Meteorology, Environmental engineering, Geography, Engineering, Chemistry, Operations management, Organic chemistry, Metric (unit)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 6, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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