OpenAQ: An open, real-time air quality data hub for the world

Bukit Kototabang, West Sumatera is one of the 34 global global (Global scale) monitoring stations in the world. Bukit Kototabang GAW Station is an implementation of the Global Atmosphere Watch (GAW) program initiated by the World Meteorological Organization (WMO) as an effort to monitor global atmospheric conditions. The Global Atmospheric Watch (GAW) Stations have duty to obtain atmospheric data and air quality data in remote area or relatively clean areas and far away from anthropogenic activity. Measurements of air quality parameters (CO, NO2, SO2, O3 and PM10) are continuously conducted at Bukit Kototabang. The monitoring data at Bukit Kototabang GAW Station in 2012 which is converted to Indonesian Air Pollution Standard Index shows the air quality is still good, shown by 353 days classified as clean (index = 0-50), 10 days is moderate (index = 51-100), and 1 day is very unhealthy (index = 200-299). That means 3% of daily air quality in Bukit Kototabang in 2012 is not good.

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Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment

Journal of Environmental Management ◽

10.1016/j.jenvman.2021.114121 ◽

2022 ◽

Vol 302 ◽

pp. 114121

Author(s):

Ke Li ◽

Kaixu Bai ◽

Zhengqiang Li ◽

Jianping Guo ◽

Ni-Bin Chang

Keyword(s):

Air Pollution ◽

Air Quality ◽

Data Fusion ◽

Real Time ◽

Pollution Assessment ◽

Quality Data ◽

Full Coverage ◽

Air Pollution Assessment ◽

Air Quality Data

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High Density Real-Time Air Quality Derived Services from IoT Networks

Sensors ◽

10.3390/s20185435 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5435

Author(s):

Claudio Badii ◽

Stefano Bilotta ◽

Daniele Cenni ◽

Angelo Difino ◽

Paolo Nesi ◽

...

Keyword(s):

Air Quality ◽

Real Time ◽

Mobile Applications ◽

Data Analytics ◽

High Density ◽

Quality Data ◽

Control Room ◽

Dense Grid ◽

The City ◽

Air Quality Data

In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices’ dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

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