In this study, WO₃–Nb₂O₅ electrochromic films and an ITO/WO₃–Nb₂O₅/Nb₂O₅/NiVO_x/ITO all-solid-state electrochromic device were deposited using fast-alternating bipolar-pulsed magnetron sputtering using tungsten and niobium targets. The influence of different sputtering powers from the niobium target on the refractive index, extinction coefficient, optical modulation, coloration efficiency, reversibility, and durability of the WO₃–Nb₂O₅ films is discussed. The aim of this work is to find the suitable Nb proportion to increase durability and less negative effect in the electrochromic performance of Nb₂O₅-doped WO₃ films. The lifetime of the WO₃–Nb₂O₅ films is 4 times longer than pure WO₃ films when the sputtering power of the Nb target is higher than 250 W. The results show that WO₃–Nb₂O₅ composite films used for an all-solid-state electrochromic device can sustain over 3 × 10⁴ repeated coloring and bleaching cycles while the transmission modulations can be kept above 20%. The coloring and bleaching response times are 7.0 and 0.7 s, respectively.

Scattering visiometers are widely used to measure atmospheric visibility; however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid's law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predicting visibility on basins and plains. In eastern Taiwan, the SVM model is most suitable for predicting visibility on alluvial plains and rift valleys. Thus, the optimal prediction model should be identified according to the conditions in each area. These results can inform decision-making processes or improve visibility predicting in specific areas.

Two low-cost fine particulate matter (PM_2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM_2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM_2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.