Nguyen Ky Phung 1, Nguyen Quang Long2, Nguyen Van Tin3, Dang Thi Thanh Le1

Institute for Computational Science and Technology, District 12, Ho Chi Minh City, Viet Nam
Ho Chi Minh City University of Science, VietNam National Universty, District 5, Ho Chi Minh City, Viet Nam
Sub-Isntitute of Hydrometeorology and Climate Change, District 1, Ho Chi Minh City, Viet Nam

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Phung, N.K., Long, N.Q., Tin, N.V. and Le, D.T.T. (2020). Development of a PM2.5 Forecasting System Integrating Low-cost Sensors for Ho Chi Minh City, Vietnam. Aerosol Air Qual. Res.,


  • PM2.5 levels detected by the PMS3003 sensor and GRIM are highly correlated.
  • Summarized specialized emission rate table for Ho Chi Minh City.
  • CMAQ model was highly consistent with monitoring measurement R2 > 0.8 (0.8–0.84).
  • Integrating WRF-SMOKE-CMAQ with remote sensing and low-cost sensors.


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.

Keywords: WRF; CMAQ; Low-cost sensors; IoT; PM2.5.

Impact Factor: 2.735

5-Year Impact Factor: 2.827

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