A simple differential capacitive sensor is provided in this paper to measure the absolute positions of length measuring systems. By utilizing a shield window inside the differential capacitor, the measurement range and linearity range of the sensor can reach several millimeters. What is more interesting is that this differential capacitive sensor is only sensitive to one translational degree of freedom (DOF) movement, and immune to the vibration along the other two translational DOFs. In the experiment, we used a novel circuit based on an AC capacitance bridge to directly measure the differential capacitance value. The experimental result shows that this differential capacitive sensor has a sensitivity of 2 × 10⁻⁴ pF/μm with 0.08 μm resolution. The measurement range of this differential capacitive sensor is 6 mm, and the linearity error are less than 0.01% over the whole absolute position measurement range.

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_2.5 and PM₁₀) and gas pollutants (SO₂, NO₂, CO and O₃) simultaneously. The multi-input multi-output (MIMO) 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 parameters such as determination coefficient R², root mean square error (RMSE), and mean absolute error (MAE) 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 RMSE values of PM_2.5 and PM₁₀ are within 2.36–18.68 and 4.55–45.05 µg m⁻³, respectively; the MAE values of PM_2.5 and PM₁₀ are within 1.44–12.80 and 3.21–23.20 µg m⁻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 RMSE values for these pollutants are 4.05–17.79 µg m⁻³, 0.02–0.18 mg m⁻³, 2.88–14.54 µg m⁻³, respectively; the MAE values for these pollutants are 2.76–13.46 µg m⁻³, 0.02–0.19 mg m⁻³, 1.84–11.08 µg m⁻³, respectively. The R² of the algorithms (RF, KNN, BP, GA–BP, except for MLR) for SO₂ is within 0.27–0.97, the RMSE value is in the range 0.64–5.37 µg m⁻³, and the MAE value is in the range 0.39–4.24 µg m⁻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.

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 (PM_2.5 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 PM_2.5 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.