Public Needs for Wearable Particulate Matter Devices and Their Influencing Factors

Recently, increasing numbers of people have realized the harm that particulate matter (PM) causes to health, especially those with a diameter less than 2.5 μm (PM2.5). With the increasing popularity of wearable devices in recent years, it is believed that wearable technology can contribute feasible solutions to prevent health hazards caused by PM2.5. In order to better understand the public’s needs regarding wearable devices, this study aimed to determine what kinds of PM2.5 wearable devices were needed by the public and the factors that may influence these needs. An online survey was conducted in the Beijing metropolitan area of China of a total of 894 subjects. The results showed that the public’s overall need for wearable PM2.5 purifiers was higher than for wearable PM2.5 trackers. The public’s needs for wearable breathing-zone PM2.5 devices were significantly higher than for any other type, indicating that people care about the quality of the air they actually breathe. It was also found that education, income level, and attitude toward PM2.5 positively affected their needs for wearable devices. In contrast, age had a negative influence on their needs. The results of this study are expected to serve as a valuable reference for related academic and industrial research.

Resident travel mode prediction model in Beijing metropolitan area

With the development of economic integration, Beijing has become more closely connected with surrounding areas, which gradually formed the Beijing metropolitan area (BMA). The authors define the scope of BMA from two dimensions of space and time. BMA is determined to be the built-up area of Beijing and its surrounding 10 districts. Designed questionnaire survey the personal characteristics, family characteristics, and travel characteristics of residents from 10 districts in the surrounding BMA. The statistical analysis of questionnaires shows that the supply of public transportation is insufficient and cannot meet traffic demand. Further, the travel mode prediction model of Softmax regression machine learning algorithm for BMA (SRBM) is established. To further verify the prediction performance of the proposed model, the Multinomial Logit Model (MNL) and Support Vector Machine (SVM), model are introduced to compare the prediction accuracy. The results show that the constructed SRBM model exhibits high prediction accuracy, with an average accuracy of 88.35%, which is 2.83% and 18.11% higher than the SVM and MNL models, respectively. This article provides new ideas for the prediction of travel modes in the Beijing metropolitan area.

Hydrological response in a highly urbanized watershed in China

Urbanization has a strong signal on the hydrologic cycle, leading to reduced infiltration, and faster and larger runoff. However, less is known in watersheds that have been experiencing such a large and rapid urbanization as those in China. Here we focus on the Wenyu watershed, a fast urbanizing basin located in the Beijing metropolitan area. Using a statistical attribution framework, we examine the hydrological response to the increasing urbanization across a wide range of discharge quantiles, from low to high flows; moreover, we perform analyses at the seasonal scale to capture differences in the physical processes at play during the year. In addition to impervious areas, we also consider precipitation, temperature, antecedent wetness, recycled water amount, and groundwater level as potential predictors. Results indicate that our models can capture well the variability in streamflow in this highly urbanized basin. Overall, urbanization played a different role for the different seasons and discharge quantiles. More specifically, we find its strongest impact to be in winter and spring, and for low and median quantiles. The role of precipitation is the strongest in summer, and it increases as we move towards the upper tail of the discharge distribution. Recycled water, on the other hand, tends to play a more dominant role in winter and spring.

Enhancement of Cloud-to-Ground Lightning Activity Caused by the Urban Effect: A Case Study in the Beijing Metropolitan Area

To investigate the possible impact of urban development on lightning activity, an eight-year (2010–2017) cloud-to-ground (CG) lightning dataset provided by the National-Wide Lightning Detection Network in China was analyzed to characterize the CG lightning activity in the metropolitan area of Beijing. There is a high CG flash density area over the downtown of Beijing, but different from previous studies, the downwind area of Beijing is not significantly enhanced. Compared with the upwind area, the CG flash density in the downtown area was enhanced by about 50%. Negative CG flashes mainly occurred in the downtown and industrial area, while positive CG flashes were distributed evenly. The percentage of positive CG flashes with Ipeak ≥ 75 kA is more than six times that of the corresponding negative CG flashes in the Beijing area. The enhancement of lightning activity varies with season and time. About 98% of CG flashes occurred from May to September, and the peak of CG diurnal variation is from 1900 to 2100 local time. Based on the analysis of thunderstorm types in Beijing, it is considered that the abnormal lightning activity is mainly responsible for an enhancement of the discharge number in frontal systems rather than the increase of the number of local thunderstorms. In addition, there is a non-linear relationship between pollutant concentrations and CG flash number, which indicates that there are other critical factors affecting the production of lightning.