Research of low-cost air quality monitoring models with different machine learning algorithms

<p>To improve the performance of the calibration model for the air quality monitoring, a low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithms is proposed. The LCS can measure particulate matter (PM<span class="inline-formula">_2.5</span> and PM<span class="inline-formula">₁₀</span>) and gas pollutants (SO<span class="inline-formula">₂</span>, NO<span class="inline-formula">₂</span>, CO and O<span class="inline-formula">₃</span>) simultaneously. The multi-input multi-output (MIMO) prediction model is developed based on the original signals of the sensors, ambient temperature (<span class="inline-formula"><i>T</i></span>) and relative humidity (RH), and the measurements of the reference instrumentations. The performance of the different algorithms (RF, MLR, KNN, BP, GA–BP) with parameters such as determination coefficient <span class="inline-formula"><i>R</i>²</span>, root mean square error (RMSE), and mean absolute error (MAE) are compared and discussed. Using these methods, the <span class="inline-formula"><i>R</i>²</span> of the algorithms (RF, MLR, KNN, BP, GA–BP) for the PM is in the range 0.68–0.99; the RMSE values of PM<span class="inline-formula">_2.5</span> and PM<span class="inline-formula">₁₀</span> are within 2.36–18.68 and 4.55–45.05 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>, respectively; the MAE values of PM<span class="inline-formula">_2.5</span> and PM<span class="inline-formula">₁₀</span> are within 1.44–12.80 and 3.21–23.20 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>, respectively. The <span class="inline-formula"><i>R</i>²</span> of the algorithms (RF, MLR, KNN, BP, GA–BP) for the gas pollutants (O<span class="inline-formula">₃</span>, CO and NO<span class="inline-formula">₂</span>) is within 0.70–0.99; the RMSE values for these pollutants are 4.05–17.79 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>, 0.02–0.18 mg m<span class="inline-formula">⁻³</span>, 2.88–14.54 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>, respectively; the MAE values for these pollutants are 2.76–13.46 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>, 0.02–0.19 mg m<span class="inline-formula">⁻³</span>, 1.84–11.08 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>, respectively. The <span class="inline-formula"><i>R</i>²</span> of the algorithms (RF, KNN, BP, GA–BP, except for MLR) for SO<span class="inline-formula">₂</span> is within 0.27–0.97, the RMSE value is in the range 0.64–5.37 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>, and the MAE value is in the range 0.39–4.24 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>. 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.</p>