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As a cost-effective and environmental-friendly transport means, electric vehicle (EV) has received widespread attention in the recent decade. The increasing market share and wider adoption of EV in transportation systems also bring about the energy use issues. In this paper, we propose a method to estimate network-wide EV energy consumption by taking into account battery degradation. We first derive unit energy consumption functions with respect to the battery degradation rate by quadratic regression method using EV operational data in the US. We then put forward an easy-to-implement and tangible method to estimate EV network energy consumption based on EV trajectories and the derived unit energy consumption function. In detail, EVs’ battery degradation rates are assumed to follow a given and known probability distribution and daily travel mileage of each EV is derived from EV trajectories. Based on these, we can get the expected total energy consumption in the network. We also propose a prorated assignment approach to determine the expected energy consumed by EVs through each EV charging point based on the estimated temporal-spatial charging demand distribution from EVs’ trajectories. A case study in Singapore is demonstrated in the end and the importance of incorporating battery degradation is highlighted. The results reveal that overlooking battery degradation would lead to more than 10% of estimation error in energy consumption estimation.

Background: Environmental stressors such as transport noise may contribute to development of obesity through increased levels of stress hormones, sleep deprivation and endocrine disruption. Epidemiological evidence supporting an association of road traffic noise with obesity markers is still relatively scant and confined to certain geographical regions. We aimed to examine the cross-sectional associations between road traffic noise and obesity markers in three large European cohorts involving nearly 500,000 individuals. Methods: Three population-based cohorts (UK Biobank, Lifelines, HUNT3) were established between 2006-2013 in the UK, the Netherlands and Norway respectively. For all three cohorts, residential 24-hour road traffic noise (Lden) for 2009 was modelled from a standardised European noise assessment framework. Residential exposures to NO2 for 2007 and PM2.5 for 2010 were estimated from Europe-wide land use regression models. Obesity markers including body mass index and waist circumference were measured at recruitment. Obesity and central obesity status were subsequently derived. Regression models were fitted in each cohort, adjusting for a harmonised set of demographic and lifestyle covariates, with further adjustments for air pollution in the main model. Results: The main analyses included 412,934 participants of UK Biobank, 61,032 of Lifelines and 30,305 of HUNT3, with a mean age of 43-56 years and Lden ranging 42-89 dB(A) across cohorts. In UK Biobank, per 10 dB(A) higher of Lden: BMI was higher by 0.14kg/m2(95%CI: 0.11-0.18), waist circumference higher by 0.27cm(95%CI: 0.19-0.35), odds of obesity was 1.06(95%CI: 1.04-1.08) and of central obesity was 1.05(95%CI: 1.04-1.07).These associations were robust to most other sensitivity analyses but attenuated by further adjustment of PM2.5 or area-level socioeconomic status. Associations were more pronounced among women, those with low physical activity, higher household income or hearing impairment. In HUNT3, associations were observed for obesity or central obesity status among those exposed to Lden greater than 55dB(A). In contrast, no or negative associations were observed in the Lifelines cohort. Conclusions: This largest study to date providing mixed findings on impacts of long-term exposure to road traffic noise on obesity, which necessitates future analyses using longitudinal data to further investigate this potentially important epidemiological link.

Different modes of governance will certainly have different impacts on the development of the inland waterway network and inland shipping economy on the Yangtze River and Pearl River in China. The inland waterway transport (IWT) economy on both rivers has recently been considered part of the national economic strategy, and it is necessary to analyze the differences between the two modes of governance applied on the arteries of the Yangtze River and the Pearl River and to explore their different impacts on the development of waterway systems. First, this paper considers the Pearl River Economic Belt as an example for comparing the effects of different modes of governance on the development of IWT systems. Second, from the perspective of systems theory, the economic structures of IWT systems under different modes of governance are described. Third, data are collected from inland shipping industries in the Pearl River system, and system dynamic (SD) models of the IWT system are constructed using the Vensim PLE software suite. Finally, the SD models are employed to compare the differences in the development of spatial patterns of waterway systems under two modes of governance and different investment structures.

Introduction Owing to rapid urbanization, residents in urban and rural greater Beijing area may be exposed to a diverse mixture and varying concentrations of ambient air pollution, resulting in dif...

Background Epidemiological studies have confirmed the link between exposure to fine particulate matter (PM2.5) and risk of cardiovascular diseases (CVDs), however, the underlying mechanisms remain ...

Background This study aimed to investigate the effects of long-term exposure to road traffic noise and air pollution on incident cardiovascular disease (CVD) in three large cohorts: HUNT, EPIC-Oxford and UK Biobank. Methods In pooled complete-case sample of the three cohorts from Norway and the United Kingdom (N = 355,732), 21,081 incident all CVD cases including 5259 ischemic heart disease (IHD) and 2871 cerebrovascular cases were ascertained between baseline (1993–2010) and end of follow-up (2008–2013) through medical record linkage. Annual mean 24-hour weighted road traffic noise (Lden) and air pollution (particulate matter with aerodynamic diameter ≤ 10 μm [PM10], ≤2.5 μm [PM2.5] and nitrogen dioxide [NO2]) exposure at baseline address was modelled using a simplified version of the Common Noise Assessment Methods in Europe (CNOSSOS-EU) and European-wide Land Use Regression models. Individual-level covariate data were harmonised and physically pooled across the three cohorts. Analysis was via Cox proportional hazard model with mutual adjustments for both noise and air pollution and potential confounders. Results No significant associations were found between annual mean Lden and incident CVD, IHD or cerebrovascular disease in the overall population except that the association with incident IHD was significant among current-smokers. In the fully adjusted models including adjustment for Lden, an interquartile range (IQR) higher PM10 (4.1 μg/m3) or PM2.5 (1.4 μg/m3) was associated with a 5.8% (95%CI: 2.5%–9.3%) and 3.7% (95%CI: 0.2%–7.4%) higher risk for all incident CVD respectively. No significant associations were found between NO2 and any of the CVD outcomes. Conclusions We found suggestive evidence of a possible association between road traffic noise and incident IHD, consistent with current literature. Long-term particulate air pollution exposure, even at concentrations below current European air quality standards, was significantly associated with incident CVD.

Background/Aim: This study aimed to investigate the effects of long-term exposure to road traffic noise on incident CVD in three large cohorts: HUNT, EPIC-Oxford and UK Biobank. Methods: In a compl...

Introduction Exposure to road traffic noise may increase blood pressure and heart rate. It is unclear to what extent exposure to air pollution may influence this relationship. We investigated associations between noise, blood pressure and heart rate, with harmonized data from three European cohorts, while taking into account exposure to air pollution. Methods Road traffic noise exposure was assessed using a European noise model based on the Common Noise Assessment Methods in Europe framework (CNOSSOS-EU). Exposure to air pollution was estimated using a European-wide land use regression model. Blood pressure and heart rate were obtained by trained clinical professionals. Pooled cross-sectional analyses of harmonized data were conducted at the individual level and with random-effects meta-analyses. Results We analyzed data from 88,336 participants, across the three participating cohorts (mean age 47.0 (±13.9) years). Each 10 dB(A) increase in noise was associated with a 0.93 (95% CI 0.76;1.11) bpm increase in heart rate, but with a decrease in blood pressure of 0.01 (95% CI -0.24;0.23) mmHg for systolic and 0.38 (95% CI -0.53;-0.24) mmHg for diastolic blood pressure. Adjustments for PM10 or NO2 attenuated the associations, but remained significant for DBP and HR. Results for BP differed by cohort, with negative associations with noise in LifeLines, no significant associations in EPIC-Oxford, and positive associations with noise >60 dB(A) in HUNT3. Conclusions Our study suggests that road traffic noise may be related to increased heart rate. No consistent evidence for a relation between noise and blood pressure was found.

Introduction Exposure to road traffic noise may be associated with increased blood pressure and heart rate. It is unclear to what extent exposure to air pollution may influence this relationship. W...

Background and Aims: Chronic bronchitis symptoms are associated with increased risk of COPD and, in some studies, overall as well as COPD mortality. This study aims to assess effects of outdoor air...