Environmental concerns have been raised on the adverse health effects of vehicle emissions in micro-scale traffic-crowded street canyons, especially for pedestrians and residents living in near-road buildings. Viaduct design is sometimes used to improve transportation efficiency but possibly affects urban airflow and the resultant exposure risk, which have been rarely investigated so far. The personal intake fraction (P_IF) is defined as the average fraction of total emissions that is inhaled by each person of a population (1 ppm = 1 × 10), and the daily carbon monoxide (CO) pollutant exposure (E) is estimated by multiplying the average concentration of a specific micro-environment within one day. As a novelty, by considering time activity patterns and breathing rates in various micro-environments for three age groups, this paper introduces IF and E into computational fluid dynamic (CFD) simulation to quantify the impacts of street layouts (street width/building height W/H = 1, 1.5, 2), source location, viaduct settings and noise barriers on the source-exposure correlation when realistic CO sources are defined. Narrower streets experience larger P_IF (1.51-5.21 ppm) and CO exposure, and leeward-side buildings always attain higher vehicular pollutant exposure than windward-side. Cases with a viaduct experience smaller P_IF (3.25-1.46 ppm) than cases without a viaduct (P_IF = 5.21-2.23 ppm) if the single ground-level CO source is elevated onto the viaduct. With two CO sources (both ground-level and viaduct-level), daily CO exposure rises 2.80-3.33 times but P_IF only change slightly. Noise barriers above a viaduct raise concentration between barriers, but slightly reduce vehicular exposure in near-road buildings. Because people spend most of their time indoors, vehicular pollutant exposure within near-road buildings can be 6-9 times that at pedestrian level. Although further studies are still required to provide practical guidelines, this paper provides effective methodologies to quantify the impacts of street/viaduct configurations on human exposure for urban design purpose.

No abstract

No abstract

Background: Cycling to work has been promoted as a green commute in many countries because of its reduced congestion relative to that of cars and its reduced environmental impact on air pollution. However, cyclists might be exposed to higher air pollution, causing adverse health effects. Few studies have examined the respiratory effects of traffic-related air pollution exposure during short-term cycling, especially in developing countries with heavy air pollution. The aim of this study was to assess the impact of air pollution exposure on lung function while cycling in traffic. Methods: Twenty-five healthy adults in total cycled on a specified route in each of three Chinese cities during four periods of a day. Lung function measures were collected immediately before and after cycling. Real-time particulate matter (PM) and the particle number count (PNC) for particles with different sizes were measured along each cycling route, while ambient sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) levels were measured at the nearest stations. Mixed-effect models were used to estimate the impact of short-term air pollution exposure on participants’ lung function measures during cycling. Results: We found that an interquartile increase in particulate matter consisting of fine particles (PM1, aerodynamic diameter £ 1 mm; and PM2.5, aerodynamic diameter £ 2.5 mm) was associated with a significant decrease in forced vital capacity (FVC) (PM1, –5.61%, p = 0.021; PM2.5, –5.57%, p = 0.022). Interquartile increases in the 99th percentile of PNC for fine particles (aerodynamic diameter 0.3–0.4 mm) also had significant negative associations with FVC (0.3 mm, –5.13%, p = 0.041; 0.35 mm, –4.81%, p = 0.045; 0.4 mm, –4.59%, p = 0.035). We also observed significant inverse relationships between ambient CO levels and FVC (–5.78%, p = 0.015).Conclusions: Our results suggest that short-term exposure to fine particles and CO while cycling in traffic contributes to a reduction in FVC of cyclists.

The recent advancements in multimodal dialogue systems have been gaining importance in several domains such as retail, travel, fashion, among others. Several existing works have improved the understanding and generation of multimodal dialogues. However, there still exists considerable space to improve the quality of output textual responses due to insufficient information infusion between the visual and textual semantics. Moreover, the existing dialogue systems often generate defective knowledge-aware responses for tasks such as providing product attributes and celebrity endorsements. To address the aforementioned issues, we present a Transformer-based Multimodal Infusion Dialogue (TMID) system that extracts the visual and textual information from dialogues via a transformer-based multimodal context encoder and employs a cross-attention mechanism to achieve information infusion between images and texts for each utterance. Furthermore, TMID uses adaptive decoders to generate appropriate multimodal responses based on the user intentions it has determined using a state classifier and enriches the output responses by incorporating domain knowledge into the decoders. The results of extensive experiments on a multimodal dialogue dataset demonstrate that TMID has achieved a state-of-the-art performance by improving the BLUE-4 score by 13.03, NIST by 2.77, image selection Recall@1 by 1.84%.

Rapid urbanization, dense urban configuration and increasing traffic emissions have caused severe air pollution, resulting in severe threats to public health. Particularly, photochemical pollution is associated with chemical transformation introducing more complexity. The understanding of the combined effects of pollutant sources, urban configuration and chemical transformation is still insufficient because most previous studies focused on non-reactive pollutant dispersions. In this study, we adopt a simplified street network model including complex photochemical reactions, i.e., the Model of Urban Network of Intersecting Canyons and Highways (MUNICH), with the real traffic and street data of a region in Guangzhou to investigate the combined effects of the three factors above on photochemical pollution. Our simulations show that the overall reduction in traffic emissions decreases NOx pollution while increasing O3 concentration. Controlling VOC emission can effectively mitigate O3 pollution. Moreover, irregular building heights and arrangements can lead to certain hot spots of air pollution. High-rise buildings will obstruct ventilation and exacerbate pollution. If higher buildings have lower vehicle use, the deep canyon can offset the effect of lower emissions. In conclusion, urban planners and policy makers should avoid deep canyons and irregular street networks to achieve better pollutant dispersion and pay attention to controlling VOC emissions.