| مستخلص: |
To improve the prediction for the future air quality trends, the demand for low-cost sensor-based air quality gird monitoring is growing gradually. In this study, a low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithm is proposed. The LCS can measure particulate matter (PM2.5 and PM10) and gas pollutants (SO2, NO2, CO and O3) simultaneously. The multi-dimensional multi-response prediction model is developed based on the original signals of the sensors, ambient temperature (T) and relative humidity (RH), and the measurements of the reference instrumentations. The performance of the different algorithms (RF, MLR, KNN, BP, GA-BP) with the parameters such as determination coefficient R2 and Root Mean Square Error (RMSE) are compared and discussed. Using these methods, the R2 of the algorithms (RF, MLR, KNN, BP, GA-BP) for the PM is in the range 0.68 - 0.99; the mean RMSE values of PM2.5 and PM10 are within 3.96 - 16.16 μgm-3 and 7.37 - 28.90 μgm-3, respectively. The R2 of the algorithms (RF, MLR, KNN, BP, GA-BP) for the gas pollutants (O3, CO and NO2) is within 0.70 -- 0.99; the mean RMSE values for these pollutants are 4.06 - 16.07 μgm-3, 0.04 - 0.15 mgm-3, 3.25 - 13.90 μgm-3, respectively. The R2 of the algorithms (RF, KNN, BP, GA-BP, except for MLR) for SO2 is within 0.27 - 0.97, and the mean RMSE value is in the range 1.05 - 3.22 μgm-3. These measurements are consistent with the national environmental protection standard requirement of China, and the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution. [ABSTRACT FROM AUTHOR] |
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