Zhanyong Wang

Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection

The minute-scale variations of fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations near a road intersection in Shanghai, China were investigated to identify the influencing factors at three traffic periods. Measurement results demonstrate a synchronous variation of pollutant concentrations at the roadside and setbacks, and the average concentration of PM2.5 at the roadside is 7% (44% for CO) higher than that of setbacks within 500 m of the intersection. The pollution level at traffic peak periods is found to be higher than that of off-peak periods, and the morning peak period is found to be the most polluted due to a large amount of diesel vehicles and unfavorable dispersion conditions. Partial least square regressions were constructed for influencing factors and setback pollutant concentrations, and results indicate that meteorological factors are the most significant, followed by setback distance from the intersection and traffic factors. CO is found to be sensitive to distance from the traffic source and vehicle type, and highly dependent on local traffic conditions, whereas PM2.5 originates more from other sources and background levels. These findings demonstrate the importance of localized factors in understanding spatiotemporal patterns of air pollution at intersections, and support decision makers in roadside pollution management and control.

Hybrid Model for Prediction of Carbon Monoxide and Fine Particulate Matter Concentrations near a Road Intersection

Air quality time series near road intersections consist of complex linear and nonlinear patterns and are difficult to forecast. The backpropagation neural network (BPNN) has been applied for air quality forecasting in urban areas, but it has limited accuracy because of the inability to predict extreme events. This study proposed a novel hybrid model called GAWNN that combines a genetic algorithm and a wavelet neural network to improve forecast accuracy. The proposed model was examined through predicting the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations near a road intersection. Before the predictions, principal component analysis was adopted to generate principal components as input variables to reduce data complexity and collinearity. Then the GAWNN model and the BPNN model were implemented. The comparative results indicated that GAWNN provided more reliable and accurate predictions of CO and PM2.5 concentrations. The results also showed that GAWNN performed better than BPNN did in the capability of forecasting extreme concentrations. Furthermore, the spatial transferability of the GAWNN model was reasonably good despite a degenerated performance caused by the unavoidable difference between the training and test sites. These findings demonstrate the potential of the application of the proposed model to forecast the fine-scale trend of air pollution in the vicinity of a road intersection.

Prediction of vertical PM 2.5 concentrations alongside an elevated expressway by using the neural network hybrid model and generalized additive model

A study on vertical variation of PM2.5 concentrations was carried out in this paper. Field measurements were conducted at eight different floor heights outside a building alongside a typical elevated expressway in downtown Shanghai, China. Results show that PM2.5 concentration decreases significantly with the increase of height from the 3rd to 7th floor or the 8th to 15th floor, and increases suddenly from the 7th to 8th floor which is the same height as the elevated expressway. A non-parametric test indicates that the data of PM2.5 concentration is statistically different under the 7th floor and above the 8th floor at the 5% significance level. To investigate the relationships between PM2.5 concentration and influencing factors, the Pearson correlation analysis was performed and the results indicate that both traffic and meteorological factors have crucial impacts on the variation of PM2.5 concentration, but there is a rather large variation in correlation coefficients under the 7th floor and above the 8th floor. Furthermore, the back propagation neural network based on principal component analysis (PCA-BPNN), as well as generalized additive model (GAM), was applied to predict the vertical PM2.5 concentration and examined with the field measurement dataset. Experimental results indicated that both models can obtain accurate predictions, while PCA-BPNN model provides more reliable and accurate predictions as it can reduce the complexity and eliminate data co-linearity. These findings reveal the vertical distribution of PM2.5 concentration and the potential of the proposed model to be applicable to predict the vertical trends of air pollution in similar situations.

Use of Multi-Rotor Unmanned Aerial Vehicles for Radioactive Source Search

In recent years, many radioactive sources have been lost or stolen during use or transportation. When the radioactive source is lost or stolen, it is challenging but imperative to quickly locate the source before it causes damage. Nowadays, source search based on fixed-wing unmanned aerial vehicles (UAVs) can significantly improve search efficiency, but this approach has higher requirements for the activity of the uncontrolled radioactive source and the take-off sites. The aim of this study was to design and demonstrate a platform that uses low-cost multi-rotor UAVs to automatically and efficiently search for uncontrolled radioactive sources even with lower activity. The hardware of this platform consists of a multi-rotor UAV, radiation detection sensor, main control module, gimbal and camera, and ground control station. In the search process, the ground control station and UAV communicate wirelessly in real time. To accommodate different search scenarios, the study proposed three search algorithms with a theoretical comparison. Then, field experiments based on the traversal search algorithm showed that the search system based on multi-rotor UAVs could effectively and accurately conduct contour mapping of a region and locate the radioactive source with an error of 0.32 m. The platform and algorithms enable accurate and efficient searching of radioactive sources, providing an innovative demonstration of future environmental risk assessment and management.