| dc.description.abstract | There has long shown that air pollution has a serious impact on human health. However, due to the highly non-linear and unstable characteristics of air pollutants, and complex chemical reactions affecting air pollutants from different factors, there is still no a complete air quality analysis process and prediction model. Therefore, with the perspective of data exploration, this research analyzes the actual observational data, and combines traffic factors, atmospheric factors, hydrological conditions, rainfall characteristics and hydrological analysis, and proposes a complete machine learning model (Long Short-Term Memory, LSTM) and air quality prediction model.
This study uses the Generalized Additive Model (GAM) to analyze the relationship between traffic factors and air quality, and finds that the low vehicle speed below 40Km/hr will aggravate the primary air pollutants CO, NO, NO2, and
NOX emissions. This phenomenon also reflects road congestion(high traffic volume and low vehicle speed) resulting in a trend of positive correlation to air pollution. In addition, this research also proposes a new analysis process of rainfall characteristics. This research uses Bayesian Network (BN) to establish an air pollution relationship model. It is found that the rainfall factor has a significant effect on PM2.5. If the cumulative rainfall in the event exceeds 4mm, there will be a moderate wet deposition effect to the PM2.5, and if it exceeds 21mm, there will be a more significant wet deposition effect. In the other hand, Type I rainfall, which represents long-term & heavy rainfall, has the most significant wet deposition effect. The deposition effect shows that the duration of the rainfall event is greater than 6 hours, there will be a
significant wet deposition effect. Finally, this research combines the analysis results of GAM and Bayesian network model, and uses stepwise regression model to filter out non-significant factors to establishes an LSTM model to predict air quality. The prediction results are excellent. The primary pollutants CO, NO, NO2, NOX has an accuracy rate of more than 90%, and also an accuracy rate of 80% in the secondary air pollutants O3, PM2.5, and PM10.
The results of this study quantify that the vehicle speed controlled above 40Km/hr and the vehicle flow less than 1,000 vehicles per hours will have the best
emission mode, and rainfall duration more than 6 hours or accumulative rainfall more than 9mm will reduce 20% of PM2.5 in expectation. The results of this research can provide policy and suggestion to the government. | en_US |