Development of a PM2.5 Forecasting System Integrating Low-cost Sensors for Ho Chi Minh City, Vietnam

Air pollution is a serious concern in urban areas, especially cities such as Ho Chi Minh City (HCMC). Because the air quality directly affects people’s health, air quality monitoring is urgently needed. In this study, the models of Weather Research and Forecasting (WRF), Sparse Matrix Operator Kerne...

Full description

Saved in:
Bibliographic Details
Published in:Aerosol and air quality research 2020-06, Vol.20 (6), p.1454-1468
Main Authors: Phung, Nguyen Ky, Long, Nguyen Quang, Tin, Nguyen Van, Le, Dang Thi Thanh
Format: Article
Language:English
Subjects:
Air monitoring
Air pollution
Air pollution forecasting
Air quality
Boundary conditions
Calibration
Cameras
Emission inventories
Environmental monitoring
Industrial emissions
Internet of Things
Land use
Lidar
Low cost
MODIS
Outdoor air quality
Particulate matter
Sensors
Sparse matrices
Urban areas
Weather forecasting
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Air pollution is a serious concern in urban areas, especially cities such as Ho Chi Minh City (HCMC). Because the air quality directly affects people’s health, air quality monitoring is urgently needed. In this study, the models of Weather Research and Forecasting (WRF), Sparse Matrix Operator Kernel Emission (SMOKE), and Community Multiscale Air Quality (CMAQ) were integrated to develop an air quality forecasting system. Drawing input data from transportation and industrial emission inventories, the forecasting system was calibrated and configured using local parameters to deliver hourly forecasts for HCMC. To increase the accuracy of WRF and the meteorological forecasting, the global DEM and land use data were replaced by Lidar data, and land use data were also retrieved from MODIS. Output from the MOZART model served as the boundary conditions for CMAQ, and AOD values reported by the MODIS Aerosol Product were assimilated to enhance the accuracy of the results. A low-cost PM2.5 sensor connected to a LinkIt ONE, a development board for Internet of things (IoT) devices, was employed for calibration and verification. The strong correlation (R2 = 0.8) between the measured and predicted concentrations indicates that the estimates delivered by the proposed forecasting system are consistent with the values obtained via monitoring.
ISSN:1680-8584
2071-1409
DOI:10.4209/aaqr.2019.10.0490