Sustainable Urban Freight for Energy-Efficient Smart Cities—Systematic Literature Review

Smart cities need energy-efficient and low-emission transportation for people and goods. Most studies focus on sustainable urban-transportation systems for passengers. Freight transportation in cities has increased significantly during the COVID-19 pandemic, leading to greenhouse gases emissions and negative externalities, such as traffic congestion. The purpose of this paper is to identify through a systematic literature review which innovations (hardware and software) applied by logistics service providers (LSPs) in sustainable urban freight (SUF) are suitable to support the transition to energy-efficient smart cities. We propose to classify the existing innovations in last-mile delivery for SUF into categories: (1) urban freight consolidation and/or trans-shipment;(2) the Consumer as a Service Provider (CaaSP);(3) choice of transportation modes. We introduce the concept of CaaSP as an innovative solution in last-mile delivery (LMD), where customers take over some transport operations with the use of smart technologies, and thus reduce the energy demand. We consider the modes of transportation, such as: drones, autonomous delivery robots, autonomous vehicles, cargo bikes (including e-cargo bikes, e-tricycles), electric vehicles (mainly vans), and combined passenger-and-cargo transportation rapid-transit systems. From the analyzed dataset, we find that energy-efficiency in smart cities can be improved by the consolidation of parcels in micro-depots, parcel lockers, and mobile depots. We analyze smart technologies (the Internet of things, big data, artificial intelligence, and digital twins), which enable energy efficiency by reducing the energy demand (fuel) of SUF, due to better operational planning and infrastructure sharing by logistics service providers. We propose a new IEE matrix as an actionable tool for the classification of innovations applied by LSPs in SUF, according to the level of their interconnectivity and energy efficiency. Additionally, this paper contributes to the theory by exploring possible future research directions for SUF in energy-efficient smart cities. © 2023 by the authors.

Heavy pollution incidents in the context of "low emission" in Beijing-Tianjin-Hebei around the Chinese New Year of 2020

Persistent air pollution in the Beijing-Tianjin-Hebei region (BTH) has become an extremely complex challenge due to the combined effects of industrial structure, regional characteristics, weather and climate, and development. Although China's air pollution levels have reduced significantly since the Airborne Pollution Prevention and Control Action Plan and the Blue Sky Protection Campaign were implemented, the BTH remains a sensitive and vulnerable area. Such large decrease in primary pollution was mainly attributed to the substantial reductions in economic activities and associated emissions during the 2019 novel coronavirus (COVID-19) lockdown, i.e., around the Chinese New Year of 2020. Yet two consecutive severely polluting weather processes occurred in the BTH around the Chinese New Year of 2020, which have seeded doubt among the Chinese public and policymakers regarding the current scientific understanding of the mechanisms of haze pollution. The causes of formation and maintenance of pollution processes can differ significantly. The formation and maintenance of heavy pollution weather is caused by various factors, which is a complex process. Thus, it is crucial to distinguish the contribution of emissions and meteorological conditions on polluting weather, as well as distinguish the contribution of various meteorological factors on the formation and maintenance of polluting weather, for conducting effective attribution diagnostic analysis in actually environmental and meteorological impact assessment operation systems, especially in areas that are sensitive or vulnerable to air pollution. Identifying the specific meteorological conditions formed by polluting weather and establishing a comprehensive model for discriminating atmospheric dynamics and thermodynamics can provide a scientific basis for improving numerical models for air pollution potential forecasting in the future. Therefore, in this study, we focused on two consecutive severely polluting weather processes in BTH around the 2020 Chinese New Year (January to February 2020), as an ideal and unique field experiment for the prevention and control of current severe air pollution. We explored the reasons for the formation and maintenance of continuous severely polluting weather in the context of "continuous emissions reduction" and "relatively low social activity levels" from the perspective of the abnormal structure of the high-low atmospheric circulation system. Based on comprehensive diagnostic analyses, we quantified the relative contribution of each key meteorological factor to the continuous severely polluting weather in BTH by using the standardized multiple linear regression method. The results indicated that stable maintenance of low-level coastal high-pressure systems led to higher relative humidity at ground level compared with normal years and blocking systems, which are two key meteorological factors that induced persistent polluting weather in BTH. The abnormally stable blocking situation provided a special circulation background for the occurrence and maintenance of persistent heavy air pollution in BTH. The continuous and stable easterly and southerly water vapor transportation structure provided the BTH with more moisture than normal years, and it was conducive to increased moisture absorption by aerosols, especially under blocking. The "subsidence warming" effect of the high-level blocking high-pressure system facilitated the production of a "warm cover" structure in the middle of the troposphere. The presence of the anomalous warm cover structure in the troposphere facilitated the establishment of stable and high humidity weather, which was conducive to the accumulation of pollutants and continued air pollution. Dynamic systems (blocking systems) and water vapor transportation factors directly explained 46.8% of the meteorological causes of persistent heavy air polluting weather events around the 2020 Chinese New Year in BTH.

Using Machine Learning to estimate the impact of different modes of transport and traffic restriction strategies on urban air quality

Classical pollutant dispersion models, based on the numerical resolution of some approximate form of the momentum, energy and chemical species conservation equations, are usually limited by incomplete descriptions of the atmospheric boundary layer hydrodynamics, partial characterizations of the emission inventories and, often, high computational costs. Using the metropolitan area of Barcelona as benchmark, the Machine Learning aproach presented here alleviates these limitations providing very accurate local predictions of key pollutant concentrations. Originating mostly from Open Data sources, time-series data on road, maritime and air traffic along with meteorological records from October 2017 to June 2021, have allowed, by means of Machine Learning techniques, to create a model capable of estimating the individual contributions of each mode of transport to worsened Air Quality. Also, when used to investigate the impact of recently implemented mitigation measures, model results predict a reduction of approximately 8 μg·m−3 for CO and NOx. In contrast, O3, PM10 and SO2 are found to be unaffected. The COVID-19 lockdown provided an accidental opportunity to improve the model's robustness and predictive capability through unusually low emission rates from transportation. © 2022 The Author(s)

Environmental Benefits and Energy Savings from Gas Radiant Heaters’ Flue-Gas Heat Recovery

This paper demonstrates the need and potential for using waste heat recovery (WHR) systems from infrared gas radiant heaters, which are typical heat sources in large halls, due to the increasing energy-saving requirements for buildings in the EU and the powerful and wide-spread development of the e-commerce market. The types of gas radiant heaters are discussed and the classification of WHR systems from these devices is performed. The article also presents for the first time our innovative solution, not yet available on the market, for the recovery of heat from the exhaust gases of ceramic infrared heaters. The energy analysis for an industrial hall shows that this solution allows for environmental benefits at different levels, depending on the gas infrared heater efficiency, by reducing the amount of fuel and emissions for domestic hot water (DHW) preparation (36.8%, 15.4% and 5.4%, respectively, in the case of low-, standard- and high-efficiency infrared heaters). These reductions, considering both DHW preparation and hall heating, are 16.1%, 7.6% and 3.0%, respectively. The key conclusion is that the innovative solution can spectacularly improve the environmental effect and achieve the highest level of fuel savings in existing buildings that are heated with radiant heaters with the lowest radiant efficiency.

Transportation and Air Quality Perspectives and Projections in a Mediterranean Country, the Case of Greece

This study provides a thorough review and analysis of the evolution of the Greek vehicle fleet over the last ~30 years, which is next used for the generation of high granularity fleet projections, the assessment of associated air pollution and the estimation of relevant environmental benefits by 2030. The integrated methodology developed takes also into account vehicle clustering and the Brown’s Double Simple Exponential Smoothing technique that, together with the adoption of COPERT-based emission factors, allow for the estimation of the anticipated emissions in 2030. Expected 2030 emissions levels suggest a reduction across all pollutants compared to 2018, ranging from 3.7% for PM10 to 54.5% for NMVOC (and 46% for CO, 14% for SO2, 28% for NOX and 21% for CO2). We find that Greece is on track with national goals concerning the reduction of air pollution from the transportation sector, which designates the positive contribution anticipated by EVs and new, “greener” vehicles, and sets new challenges for the further improvement of the sector beyond the 2030 outlook.

Analysis of lockdown for CoViD-19 impact on NO2 in London, Milan and Paris: What lesson can be learnt?

Nitrogen dioxide (NO2) can have harmful effects on human health and can act as a precursor for the formation of other air pollutants in urban environment such as secondary PM2.5 and ozone. The lockdown measures for CoViD-19 allowed to simulate on a large scale the massive and prolonged reduction of road traffic (the main source for NO2 in urban environment). This work aims to selectively assess the maximum impact that total traffic blocking measures can have on NO2. For this reason, three megacities (London, Milan and Paris) were chosen which had similar characteristics in terms of climatic conditions, population, policies of urban traffic management and lockdown measures. 52 air quality control units have been used to compare data measured in lockdown and in the same periods of previous years, highlighting a significant decrease in NO2 concentration due to traffic (London: 71.1 % - 80.8 %; Milan: 8.6 % - 42.4 %; Paris: 65.7 % - 79.8 %). In 2020 the contribution of traffic in London, Milan and Paris dropped to 3.3 ± 1.3 µg m-3, 6.1 ± 0.8 µg m-3, and 13.4 ± 1.5 µg m-3, respectively. Despite the significant reduction in the NO2 concentration, in UT stations average NO2 concentrations higher than 40 µg m-3 were registered for several days. In order to reduce the pollution, the limitation of road traffic could be not enough, but a vision also aimed at rethink the vehicles and their polluting effects should be developed.