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Air quality traffic-related measures have been implemented worldwide to control the pollution levels of urban areas. Although some of those measures are claiming environmental improvements, few studies have checked their real impact. In fact, quantitative estimates are often focused on reducing emissions, rather than on evaluating the actual measures’ effect on air quality. Even when air quality studies are conducted, results are frequently unclear. In order to properly assess the real impact on air quality of traffic-related measures, a statistical method is proposed. The method compares the pollutant concentration levels observed after the implementation of a measure with the concentration values of the previous year. Short- and long-term impact is assessed considering not only their influence on the average pollutant concentration, but also on its maximum level. To control the effect of the main confounding factors, only the days with similar environmental conditions are analysed. The changeability of the key meteorological variables that affect the transport and dispersion of the pollutant studied are used to identify and group the days categorized as similar. Resemblance of the pollutants' concentration of the previous day is also taken into account. The impact of the road traffic measures on the air pollutants’ concentration is then checked for those similar days using specific statistical functions. To evaluate the proposed method, the impact on PM 2.5 concentrations of two air quality traffic-related measures (M1 and M2) implemented in the city of Beijing are taken into consideration: M1 was implemented in 2009, restricting the circulation of yellow-labelled vehicles, while M2 was implemented in 2014, restricting the circulation of heavy-duty vehicles. To compare the results of each measure, a time-period when these measures were not applied is used as case-control. [ABSTRACT FROM AUTHOR]

E-commerce growth is raising the demand for logistic activities, especially in the last-mile, which is considered the most ineffective part of the supply chain and a negative externalities source. Although various solutions aim to address these issues, selecting the best one is challenging due to multiple perspectives, conflicting criteria, trade-offs, and complex and sensitive urban contexts. This article proposes a 4-level hierarchical model based on the triple bottom line of sustainability that may assist decision-makers in selecting the most adequate last-mile solution for historic centers. The model was defined based on a systematic literature review; evaluated by interviewing a set of experts; and quantified according to an AHP-TOPSIS approach. This quantification focused on the historic center of Porto, Portugal. The experts considered all three sustainability dimensions similarly important. Air pollution was the most valued sub-criterion whereas Visual pollution was the least. 67 decision-maker profiles were defined, showing that environmentally oriented decision-makers prefer cargo bikes, while decision-makers who prioritize economic and social factors prefer parcel lockers. All last-mile solutions considered in the model yielded similar results, therefore suggesting a combined distribution strategy. Nevertheless, the use of parcel lockers is the most favorable solution for Porto's historic center. • The group of experts considered the three sustainability dimensions equally important. • Air pollution is the most valued criterion, whereas Visual pollution is the least. • Cargo bikes are more interesting for environmentally oriented decision-makers. • Parcel lockers are the most favorable solution and seem adequate in Porto. • A combined last-mile distribution strategy might be the most optimal alternative. [ABSTRACT FROM AUTHOR]

Between 1989 and 2011 the aviation traffic has been growing 4.6% per year. The increase on aviation traffic had consequences in terms of greenhouse gases (GHGs) and local pollutant emissions (e.g. carbon monoxide – CO, hydrocarbons – HC, nitrogen oxides – NO x ). In order to minimize this problem, the evaluation of sustainable alternatives to current fuel (jet fuel A) has been discussed but their impact on emissions is still unclear. The main research goals of this paper are: (i) to evaluate if the well-to-wake energy consumption in aviation can be reduced with alternative fuels as liquid natural gas (LNG) and/or liquid hydrogen (LH 2 ) and (ii) to assess if alternative fuels can be used in aviation to minimize carbon dioxide emissions and local pollutants. In this analysis two types of flights were evaluated: short flights (<5000 km) and long flights (>5000 km) for six typical aircrafts. We verified that LH 2 from SMR can provide between 13% and 21% less environmental and social impacts than jet fuel A, in the same aircraft. [ABSTRACT FROM AUTHOR]

Highlights: [•] We use a microscale modeling platform to assess Traffic Management Strategies. [•] We study the impact of HOV/eco-lanes in different road types on emissions. [•] HOV/eco-lanes in a medium European city are feasible in terms of emissions. [•] On freeways, passengers can reduce travel time (2%) and emissions (37% to 43%). [•] On urban and arterials, emissions can be reduced if the AOV increases. [Copyright &y& Elsevier]

The impacts of the e-commerce growth have increased the urgency in designing and adopting new alternative delivery strategies. In this context, it is important to consider the particularities of each city like its terrain conditions. This article aims at exploring the impact of road slopes on parcel delivery operations, and how they condition the adoption and implementation of alternative, more sustainable delivery strategies. To this end, a microscopic traffic simulator was used to evaluate different delivery strategies including ICE vans, electric vans, and cargo bikes in three different slope scenarios. This evaluation was based on a medium-sized European city and conducted by comparing the same parcel delivery route at three levels: operational (route length, duration, and waiting time), energy consumption, and emissions. The results revealed that as the road slopes increased, more time was needed to deliver all packages, waiting times grew longer, and vehicles' energy consumption and emissions levels intensified. From the flat terrain to the most sloped terrain, there was an increase in duration of around 5% for traditional and electric vans, 35% for large cargo bikes, and 14% for small cargo bikes. The ICE van suffers a 105% increase in waiting time; the electric van 71%; the large cargo bike 68% and the small cargo bike 52%. Energy consumption also varied, with ICE vans and small cargo bikes consuming nearly 30% more energy, while electric vans and large cargo bikes consumed 4% and 60% more energy, respectively. The ICE van's emissions of CO, HC, PM x , NO x , and CO 2 are 13%, 10%, 1%, 20%, and 29% higher, respectively. Moreover, in flatter terrains, the better strategies are the electric van or a large cargo bike, while in more sloped terrains, the most adequate one is the electric van. These findings suggest that the electric van is the best overall strategy for different terrains and different decision-making profiles, ranking first in more than 70% of the profiles across all three terrains. • The roads' slopes undoubtedly condition and affect the last-mile delivery operation. • The route's duration and waiting times increase as the roads' slopes increase. • There is more energy consumption and more emissions as the roads' slopes increase. • The electric van is the best strategy for all types of terrains and decision-makers. • In flatter terrains, a large cargo bike can be a suitable delivery strategy. [ABSTRACT FROM AUTHOR]

The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NO x , CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NO x and −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories. [ABSTRACT FROM AUTHOR]

Abstract: Sodium content in bread is a growing interest in public health. This study was undertaken to evaluate the role of sampling in the dispersion of salt content in bread. Ten brands of bread representing the consumed types were collected from the largest retail places in Portugal. Their sodium and potassium contents were determined using flame photometry. The ratio sodium/potassium was used as an indicator of salinity. Sodium content varied from 490±21 to 724±9/100g of white bread. Data from uncertainty budgets demonstrated that sampling is the dominant contributor (>55%). According to our results for sodium in bread, to pool samples from different sampling points can disguise the heterogeneity of the results. Therefore, the importance of the number of primary sample units and of the identification of sampling points is highlighted. This provides the necessary information to the users of Food Composition Databanks who wish to have an overview of the parameters, which influence the estimation of sodium intake values, which may affect the diet-disease relationship. [Copyright &y& Elsevier]