Quantifying traffic-related carbon emissions on elevated roads through on-road measurements.

Vehicles generally move smoothly and with high speeds on elevated roads, thereby producing specific traffic-related carbon emissions in contrast to ground roads. Hence, a portable emission measurement system was adopted to determine traffic-related carbon emissions. The on-road measurement results revealed that the instantaneous emissions of CO2 and CO from elevated vehicles were 17.8% and 21.9% higher than those from ground vehicles, respectively. Based on it, the vehicle specific power was confirmed to exhibit a positive exponential relationship with instantaneous CO2 and CO emissions. In addition to carbon emissions, carbon concentrations on roads were simultaneously measured. The average CO2 and CO emissions on elevated roads in urban areas were 1.2% and 6.9% higher than those on ground roads, individually. Finally, a numerical simulation was performed, and the results verified that elevated roads could deteriorate the air quality on ground roads but improve the air quality above them. What ought to be paid attention to is that the elevated roads present varied traffic behaviour and cause particular carbon emissions, indicating that comprehensive consideration and further balance among the traffic-related carbon emissions are necessary when building elevated roads to alleviate the traffic congestion in urban areas.

Impacts of viaduct and geometry configurations on the distribution of traffic-related particulate matter in urban street canyon.

Viaduct is a ubiquitous transportation infrastructure in the congested megacities worldwide to improve the accessibility and capacity of urban transportation network. However, there is a lack of understanding of the impacts of the interplay between viaduct-ground emissions and viaduct-canyon configurations on the particle distribution in urban street canyon. To fill the research gap, we conducted vertical measurements of particle number concentrations (PNCs) at different heights of "street canyon along a viaduct" to reveal effect of viaduct on the vertical distribution of PNCs in street canyon. Observation results indicated that the vertical profiles of PNCs exhibited bimodal distribution patterns, which were more significant for coarse particles than fine particles. The one peak appeared at ground level and the other at the viaduct height, indicating the impacts of "double" emission sources (i.e., the emissions on the ground and viaduct) and the hindrance of viaduct to particle diffusion. We further modelled the role of viaduct in street canyon through Computational Fluid Dynamics (CFD) simulations to reveal the vertical distribution of particles under different viaduct-canyon configurations and discern the contributions of viaduct and ground emissions to the particle distribution. Simulation results showed that viaduct changed airflow field and turbulence structure and elevated particle concentrations in street canyon while the optimized viaduct-canyon configurations including higher viaduct height (12 > 10 > 8 m), smaller aspect ratio (0.5 > 0.67 > 1), and shorter centerline distance (0 > 1 > 2 m) between canyon and viaduct could bring better dispersion conditions and lower particle concentrations. Additionally, ground emissions contributed more to the vertical distribution of particles on the leeward side of street canyon than viaduct emissions while the windward side displayed the opposite characteristics to the leeward side. These findings revealed the general patterns of particle diffusion in viaduct-canyon configurations and provided implications into viaduct design and traffic management to alleviate local particulate pollution.

Regional Prediction of Ozone and Fine Particulate Matter Using Diffusion Convolutional Recurrent Neural Network.

Accurate air quality forecasts can provide data-driven supports for governmental departments to control air pollution and further protect the health of residents. However, existing air quality forecasting models mainly focus on site-specific time series forecasts at a local level, and rarely consider the spatiotemporal relationships among regional monitoring stations. As a novelty, we construct a diffusion convolutional recurrent neural network (DCRNN) model that fully considers the influence of geographic distance and dominant wind direction on the regional variations in air quality through different combinations of directed and undirected graphs. The hourly fine particulate matter (PM2.5) and ozone data from 123 air quality monitoring stations in the Yangtze River Delta, China are used to evaluate the performance of the DCRNN model in the regional prediction of PM2.5 and ozone concentrations. Results show that the proposed DCRNN model outperforms the baseline models in prediction accuracy. Compared with the undirected graph model, the directed graph model considering the effects of wind direction performs better in 24 h predictions of pollutant concentrations. In addition, more accurate forecasts of both PM2.5 and ozone are found at a regional level where monitoring stations are distributed densely rather than sparsely. Therefore, the proposed model can assist environmental researchers to further improve the technologies of air quality forecasts and could also serve as tools for environmental policymakers to implement pollution control measures.

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19.

Following the outbreak of the COVID-19 pandemic, various lockdown strategies restrained global economic growth bringing a significant decline in maritime transportation. However, the previous studies have not adequately recognized the specific impacts of COVID-19 on maritime transportation. In this study, a series of analyses of the Baltic Dry Index (BDI), the China Coastal Bulk Freight Index (CCBFI) and of container throughputs with and without the impact of COVID-19 were carried out to assess changing trends in dry bulk and container transportation. The results show that global dry bulk transportation was largely affected by lockdown policies in the second month during COVID-19, and BDI presented a year-on-year decrease of approximately 35.5% from 2019 to 2020. The CCBFI showed an upward trend in the second month during COVID-19, one month ahead of the BDI. The container throughputs at Shanghai Port, the Ports of Hong Kong, the Ports of Singapore and the Ports of Los Angeles from 2019 to 2020 presented the largest year-on-year drops of approximately 19.6%, 7.1%, 10.6% and 30.9%, respectively. In addition, the authors developed exponential smoothing models of BDI, CCBFI, and container transportation, and calculated the percentage prediction error between the observed and predicted values to examine the impact of exogenous effects on the shipping industry due to the outbreak of COVID-19. The results are consistent with the conclusions obtained from the comparison of BDI, CCBFI, and container transportation during the same period in 2020 and 2019. Finally, on the basis of the findings, smart shipping and special support policies are proposed to reduce the negative impacts of COVID-19.

Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China.

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%-48.8% drop in the concentration of NO2 has been observed in the four metropolises compared with the same period in 2018-2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (µg/m3) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality.

Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model.

The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident's outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM2.5, the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.