Multi-headed CNN-GRU Model for Particulate Matter(PM2.5) Prediction in Smart Cities

The spatiotemporal variations in the atmospheric ventilation index (AVI) with the particulate matter (PM) concentrations in South Korea were investigated using a regional grid model derived from the National Center for AgroMeteorology and PM10 concentration data obtained from AirKorea and the Korea Meteorological Administration. To construct a high-resolution AVI database with 1 h time intervals and a spatial resolution of approximately 2.4 km, a medium-range prediction was performed using a regional model twice a week from December 2018 to November 2019. The resultant dataset was used to explore the seasonal patterns of the areal distribution of a novel index: Ventilation Index coupled with PM (VIP), defined by the ratio of the AVI to PM. To determine the effects of geography on the VIP, diurnal variations of the VIP were examined at three major cities in South Korea. The emphasis of the investigation was on major cities that are planned to be developed into smart cities. This study reveals the specific spatiotemporal structure of the AVI in South Korea for the first time at a high resolution and introduced the potential usefulness of the VIP. The results provide insights that could aid decision making for determining favorable locations for clean or polluted cities on an annual basis and can enable the sustainable management of fine PM in and around the areas of interest.

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Deep Learning Models Conformity Assessment for Particulate Matter Prediction in Smart Cities

KIISE Transactions on Computing Practices ◽

10.5626/ktcp.2019.25.12.610 ◽

2019 ◽

Vol 25 (12) ◽

pp. 610-615

Author(s):

Ik-Hee Shin ◽

Yong-Hyuk Moon ◽

Yong-Ju Lee

Keyword(s):

Deep Learning ◽

Particulate Matter ◽

Smart Cities ◽

Conformity Assessment ◽

Learning Models

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Airborne Particulate Matter Monitoring Using UAVs for Smart Cities and Urban Areas

TENCON 2018 - 2018 IEEE Region 10 Conference ◽

10.1109/tencon.2018.8650293 ◽

2018 ◽

Cited By ~ 3

Author(s):

Gian Paolo Mayuga ◽

Chrisandro Favila ◽

Carlos Oppus ◽

Edgardo Macatulad ◽

Len Herald Lim

Keyword(s):

Particulate Matter ◽

Urban Areas ◽

Smart Cities ◽

Airborne Particulate Matter ◽

Airborne Particulate

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EVALUATION OF PARTICULATE MATTER (PM10) DISTRIBUTIONS IN İZMIR USING GEOGRAPHIC INFORMATION SYSTEMS FOR SMART CITIES APPLICATIONS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w5-2021-545-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 545-555

Author(s):

K. Ulutaş ◽

S. K. M. Abujayyab ◽

İ. R. Karaş

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Smart Cities ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Distribution Map ◽

City Managers ◽

Limit Values ◽

Limit Value ◽

Different Sources

Abstract. In this study, PM10 values from the air quality monitoring station in Izmir was evaluated. 9 stations could be used in this study, since PM10 data are suitable to evaluate for the years 2020-2019-2018. The 4-season and annual PM10 distribution map for 3 years was prepared using ArcGIS. The benefits of these maps to city managers in the smart city application were expressed. In addition, PM10 data of 9 stations were evaluated according to legal limit values. It was determined that Aliağa and Gaziemir stations exceeded the limit values more than other stations. It has been observed that different sources of air pollution such as industry, traffic and heating affect different districts. When the number of days exceeding the limit value and the number of days without measurement are evaluated together, it is seen that the limit values are exceeded by all stations.

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