Low Emission Zones Research Articles

Meteorological variables and PM10 exceedances effect on aerosol particles in a low emission zone using compositional data analysis

Particulate air pollution is a significant environmental concern due to its potential effects on the environment and human health. Meteorological variables can affect greatly on PM accumulation, also establishing a possible obstacle to the achievement of air quality standards. The objective of the study is to evaluate the effect of meteorological variables such as temperature, relative humidity and daily precipitation on coarse and fine size fraction of PM with and without considering PM10 exceedances conditions using a multiple compositional linear model. The study was conducted in an environmental traffic site placed in a low emission zone (LEZ) of Milan, which is located in the Po Valley (Northern Italy), during the cold season. The results indicated that an increasing in temperature of about 4.7 °C determined a decrease in fine size fraction of PM of about 6.2 % and 24.1 % with and without exceedances, respectively. Whereas an increase in relative humidity of about 19 % determined an increase in fine size fraction of PM of about 10.5 %. In addition, the analysis showed that the PM10 measured when there were PM10 exceedances was compositionally different from that measured when there were not PM10 exceedances as it was richer in fine particles of about 17.2 %. The study showed that PM10 exceedances were mainly caused by an increase in the fine size fraction of PM rather than the coarse one. Furthermore, research results indicated that changes in temperature and relative humidity could affect the particle size fractions of PM. A decrease in temperature and an increase in relative humidity led to an increase in the PM fine size fraction. The study provides insights into the underlying mechanisms affecting both fine and coarse size PM fractions.

How do drivers' attitudes to low emission zones change after experiencing it? – A pilot study in Norway

Air quality is a problematic issue in many urban areas in the world, and transport contributes to this. This study investigates attitudes towards a piloted low emission zone (LEZ) and distance-based differentiated road user charging (RUC) system. Both policy instruments are likely to be implemented in combination in Norway, aiming at curbing local environmental issues in the largest cities and cover other external costs of car use. The piloted system uses GNSS to track the vehicles and we focus specifically on plug-in hybrid vehicles (PHEVs). The drivers in the experiment were encouraged to use electricity as much as possible within the LEZs, and the drivers were given a monetary incentive which simulates a distance-based RUC within the zones. Forty participants tested the system for several weeks. The technical data from the vehicles, and the participants' attitudes were measured both before and after testing the system and were analysed together to measure the participants' behavioural change and how this correlated with their attitudes towards the system. The results revealed a significant change in the drivers' attitudes before and after testing the system. The participants who were successful in using the LEZ system as intended (i.e. increased use of the electricity mode within the LEZ) gained a more positive attitude. There was no difference between the successful and the unsuccessful groups in their attitudes towards distance-based RUC. These findings highlight the importance of understanding how innovative transport policies aiming at regulating negative externalities from car use are affecting people's everyday life, as this is the major explanatory variable for user acceptance. Furthermore, the study also shows that using distance-based RUC as an incentive for stimulating to a mode shift to the electric engine in PHEVs is a promising avenue.

The elasticity and efficiency of carbon reduction strategies in transportation

ABSTRACT Transportation significantly contributes to carbon emissions, prompting the need for effective mitigation policies. This study addresses the knowledge gaps in assessing the effectiveness of transport carbon policies and offers the lack of a holistic comparative overview. The study used a model composed of a mixed-effects meta-regression and carbon elasticity to investigate policies, like shared bikes, mobility hubs, low emission zones, congestion pricing, electric vehicles, and hydrogen vehicles. This model included seven control variables: year, GDP, implementation costs, geographic scale, environmental benefits, and transport share of energy consumption and carbon emissions. Mobility hubs and electric vehicles ranked are top effective policies with carbon elasticities of 3.73 and 3.72, effect sizes of 127.47 and 86.73, and confidence intervals of [65.55, 107.93] and [106.17, 148.78], respectively. Followed by the low emission zone of 16.3 carbon elasticity, proving its cost-effectiveness, effect size of 10.16, and a confidence interval of [−2.48, 22.80]. Congestion pricing, despite having the highest effect size of 873.39, its confidence interval [−354.01, 2100.80] is wide, indicating the uncertainty of this effect. Shared bikes and hydrogen vehicles ranked lowest, suggesting a need for deeper life cycle-based analysis. Although this model displayed high accuracy, the findings’ interpretation should consider the inherent data limitations.

Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

Current emission models primarily focus on traditional combustion vehicles and may not accurately represent emissions from the increasingly diverse vehicle fleet. The growing presence of hybrid and electric vehicles requires the development of accurate emission models to measure the emissions and energy consumption of these vehicles. This issue is particularly relevant for low-emission zones within cities, where effective mobility planning relies on simulation models using continuously updated databases. This research presents a two-dimensional emission model for hybrid vehicles, employing artificial neural networks for low-emission zones. The key outcome is the methodology developed to create a CO2 emission model tailored for hybrid vehicles, which can be used to simulate various road solutions. The CO2 emission model achieved an R2 coefficient of 0.73 and an MSE of 0.91, offering valuable information for further advancements in emission modelling.

Are low emission zones and on-street parking management effective in reducing parking demand for most polluting vehicles and promoting greener ones?

Are low emission zones and on-street parking management effective in reducing parking demand for most polluting vehicles and promoting greener ones?

On the geography of vintage-specific restrictions

Persistent air-pollution problems have led authorities in many cities around the world to impose limits on car use by means of vintage-specific restrictions or low-emission zones. Any vintage restriction must establish not only the cars that face a restriction but also its geographic area of application. As a result of the restriction, a fraction of restricted cars are exported outside the restricted area. Because restricted cars become cheaper, emissions in the restricted area could increase if exported cars remain too close to it. The extent to which such emissions leakage can occur crucially depends on transaction costs in the car market. We study this possibility with a model of the car market that allows for transaction costs and data from Santiago’s 2017 vintage restriction. We fail to find emissions leakage, at least severe enough to undo the 2017 policy effects. Interestingly, transaction costs are shown to have a non-monotonic impact on emissions, and hence, on welfare.

Public acceptability of policies to reduce urban air pollution: A population-based survey experiment

Low-emission zones, congestion charges and pedestrian areas have the potential to improve urban air quality. However, the lack of public and political support for these policies is often a significant obstacle to their introduction. In this article, we present the results of two studies. First, we investigated the public acceptability of three policies to reduce urban air pollution: pedestrianization, low emission zones and congestion charges. Second, based on an experimental online survey, we examined the effect of various framing conditions on the acceptability of congestion charges. The results show, first, that public acceptability is higher for pedestrianization and low emission zones and lower for congestion charging. Second, we find a positive effect on acceptability of information emphasizing the benefits of congestion charges and of positive labelling, comparing the wording “environmental contribution” with “urban toll”; and a negative effect of making political ideology prominent. Our findings suggest that while some interventions aimed at mitigating urban air pollution may be more readily supported than others, their acceptability can be enhanced (or diminished) through careful design and effective communication.

The impact of Low Emission Zones on retail activity: Madrid Central lessons

The impact of Low Emission Zones on retail activity: Madrid Central lessons

Optimizing Vehicle Replacement in Sustainable Urban Freight Transportation Subject to Presence of Regulatory Measures

Since the 1990s, studies and pilot tests have been conducted to reduce traffic, accidents, and pollution due to urban freight transport (UFT). These ended up in several policies, regulations, and restrictions for UFT, such as low emission zones, delivery time windows, and vehicle size and weight restrictions. However, issues in UFT under regulatory measures still persist. This study introduces an optimization framework for deriving an optimal combination of various types of vehicles with different capacities for vehicle replacement with UFT. This framework allows an understanding of how an urban freight company with a limited budget efficiently satisfies its freight demand within an urban area in the presence of regulatory measures by urban administrators. The introduced formulation, which is mixed-integer linear programming, will assist the operator in choosing the best investment strategy for introducing new vehicles of certain types and sizes, for operation in different zones, into its fleet while gaining economic benefits and having a positive impact on the liveability of the urban area. Furthermore, an elasticity analysis is performed to consider the effects of specific uncertain parameters on the total cost. The numerical results show that the share of electric vehicles in the fleet increases, and they are more competitive than diesel vehicles.

Low emission and congestion charge zones are linked to improved health outcomes, review finds

Low emission and congestion charge zones are linked to improved health outcomes, review finds

Modeling the Impacts of Low Emission Zone on Route Diversion and Emissions of Heavy-Duty Trucks: A Southern California Case Study

Heavy-duty diesel trucks (HDDTs), the majority of which are used for freight movement, are major contributors of nitrogen oxides (NO x) and fine particulate matter (PM2.5) emissions. As a result, communities close to freight hubs such as ports, railyards, and distribution centers are disproportionately affected by diesel-related air pollution. This study evaluates the potential for emission-based access restriction and pricing strategy to reduce HDDT emissions in one such community in Southern California, U.S. Using a regional travel demand model with a dedicated heavy-duty truck submodel, it is assumed that a $10 emission fee is collected from HDDTs that do not meet the 2007 emission standards (i.e., model years 2009 and older) when they enter the community, which is considered to be a low emission zone (LEZ). The modeling results show that the emission fee diverts 60% of the pass-through trips made by HDDTs that do not meet the required emission standards away from the LEZ. The emission fee is found to reduce NO x, PM2.5, and carbon dioxide (CO2) emissions from HDDTs inside the LEZ by 37%, 38%, and 25%, respectively. Also, it is found that the emission fee has minimal impacts on the total emissions in the modeling area. The total NO x and CO2 emissions remain unchanged, while the total PM2.5 emission increases by 1%. These results demonstrate that emission-based access restriction and pricing can be an effective strategy for reducing truck emissions in communities heavily affected by truck traffic without resulting in adverse impacts on the regional emission inventory.

Study of Mobile Source Pollutants Based on Multi-Source Data Fusion: A Case Study of Zibo City, China

The primary pollutants emitted from mobile sources have become one of the main sources of urban air pollution. In this study, the primary pollutants CO, HC, NOx and PM from mobile sources in Zibo city, China are studied. Firstly, the localized mobile-source pollutant emission factors are corrected using vehicle emission experiments; secondly, multi-source data fusion is performed using road network data, road traffic data, air quality and meteorological data; then, the corrected pollutant emission factors and multi-source data are used to construct a localized emission measurement model and dispersion simulation model, visualize the emission distribution and propose residual concentration correction to accurately obtain the pollutant sharing rate. The results show that the pollutant emission trends are highly coupled with the distribution of urban residents’ working hours. Based on the localized dispersion model used to analyze the pollutant traceability at air monitoring stations, the emission sharing rate of NOx is the largest, and according to the analysis of the changing trend of mobile-source emission sharing rate, the mobile source pollutants in Zibo are mainly influenced by pollutant emissions from road motor vehicles. This study provides data support and a theoretical basis for the management of the transportation industry to carry out exhaust gas control of motor vehicles and non-road mobile machinery and to make decisions related to improving the air environment (delineating the scope of low emission zones).

The Influence of Passenger Car Banning Policies on Modal Shifts: Rotterdam’s Case Study

Low-emission zones (LEZs), incentivizing electric cars, park-and-ride systems, and other traffic reduction schemes, are all single measures aimed at achieving low-/zero-emission mobility. This paper aims to investigate the impact of LEZs’ passenger car banning and argues that such single measures cannot achieve significant traffic or emission reduction levels without being integrated into a well-designed policy package that ensures the sufficient provision of mobility alternatives. Featuring Rotterdam as a single case study, this paper follows a mixed methodology consisting of (1) quantitative real-time data on transport usage that mirror the users’ behavior and (2) qualitative data acquired from in-depth interviews and documents that explain the government’s intention and the users’ behavior. The results show that between 2016 and 2020, after applying the LEZ policy for restricting passenger cars and vans of Euro 3 and lower, there was a 50% decrease in the number of polluting passenger cars and vans entering Rotterdam. However, this decrease is insignificant, since the number of vehicles targeted by the policy was less than 2% of the overall vehicles that entered the LEZ area. The results also shed light on the role of systematic policy packaging in ensuring a change in user behavior. Among other initiatives, a successful LEZ implantation should be supported by inner-city parking reduction, electric vehicle charging facilities, incentive schemes, and alternative sustainable mobility options. Above all, the number of restricted vehicles must be significant. The conclusion and discussion develop a well-structured, educational, evaluative framework and recommend a comprehensive package of policy measures for cities seeking low-emission mobility. The research, however, did not consider the impact of different land-use distributions on the application of the LEZ, which can be an interesting angle for future researchers.

Is vehicle scrapping affected by low-emission zones? The case of Madrid

This paper provides an initial empirical evaluation of the possible impact a low-emission zone (LEZ) has on vehicle scrappage rates. The analysis is based on a quasi-experimental design, which is applied to the Spanish case known as Madrid Central. Our results, which are quite robust to reasonable changes in the geographic areas used as controls, reveal an excess of scrapping decisions by vehicle owners in the city of Madrid following the implementation of this urban policy. This excess scrapping is transitory and particularly notable for old vehicles subject to major traffic restrictions. The results suggest that establishing a LEZ in a relatively small area can generate some social gains, in terms of transport safety and removal of the vehicles that pollute the most, which may outweigh the simple benefit of improving air quality in the area.

The impact of low emission zones on personal exposure to ultrafine particles in the commuter environment

Auckland is a city with limited industrial activity, road traffic being the dominant source of air pollution. Thus, the time periods when social contact and movement in Auckland were severely curtailed due to COVID-19 restrictions presented a unique opportunity to observe impacts on pedestrian exposure to air pollution under a range of different traffic flow scenarios, providing insights into the impacts of potential future traffic calming measures.Pedestrian exposure to ultrafine particles (UFPs), was measured using personal monitoring along a customised route through Central Auckland during different COVID-19-affected traffic flow conditions. Results showed that reduced traffic flows led to statistically significant reductions in average exposure to UFP under all traffic reduction scenarios (TRS). However, the size of the reduction was variable in both time and place. Under the most stringent TRS (traffic reduction of 82 %), median ultrafine particle (UFP) concentrations reduced by 73 %. Under the less stringent scenario, the extent of reduction varied in time and space; a traffic reduction of 62 % resulted in a 23 % reduction in median UFP concentrations in 2020 but in 2021 similar traffic reductions led to a decrease in median UFP concentrations of 71 %.Under all scenarios, the magnitude of the impact of traffic reductions on UFP exposure varied along the route, with areas dominated by emissions from construction and ferry/port activities showing little correlation between traffic flow and exposure. Shared traffic spaces, previously pedestrianised, also recorded consistently high concentrations with little variability observed.This study provided a unique opportunity to assess the potential benefits and risks of such zones and to help decision-makers evaluate future traffic management interventions (such as low emissions zones). The results suggest that controlled traffic flow interventions can result in a significant reduction in pedestrian exposure to UFPs, but that the magnitude of reductions is sensitive to local-scale variations in meteorology, urban land use and traffic flow patterns.

15‐minute cities and the denial(s) of auto‐freedom

15‐minute cities and the denial(s) of auto‐freedom

An Offline Closed-Form Optimal Predictive Power Management Strategy for Plug-In Hybrid Electric Vehicles

This article develops an optimal predictive power management strategy (OP-PMS) for plug-in hybrid electric vehicles (PHEVs) to manage the remaining battery level throughout a defined journey. Apart from using the battery before charging it again at the destination, this approach also supports transit through an Ultra Low Emission Zone (ULEZ), where a PHEV needs to operate in pure electric mode to avoid emissions. This type of PHEV power management strategy (PMS) design problem is a noncausal control problem, where the power demand of the remaining driving cycle and the final battery energy level targets influence the current power management action. Rather than solving the whole optimization problem online, this article presents a simplification that leads to a closed-form analytic solution with parameters that can be computed offline. This article makes three key contributions. First, a novel input-linearization method is developed, which converts the OP-PMS into a convex quadratic programming (QP) problem that captures the essential model nonlinearities using an input transformation and a quadratic cost function. Second, the influence of future power demands and final targets on current energy management policy is explicitly quantified from a dynamic optimal control perspective. Third, the noncausal convex QP is approximated with a closed-form analytic solution that is calculated offline. This leads to a real-time implementable OP-PMS with coefficients that can be precalculated and stored in the engine control unit. To demonstrate the viability of this approach, numerical examples are provided based on a generic PHEV model to verify the efficacy of the proposed OP-PMS.

Quantifying the importance of socio-demographic, travel-related, and psychological predictors of public acceptability of low emission zones

As ambient air pollution increases, governments are imposing traffic management strategies to improve air quality. A common strategy is the implementation of Low Emission Zones (LEZs), which have generated considerable public debate. Nonetheless, little research has explored which factors determine their public acceptability. Previous empirical studies have also typically lacked power for regression analyses and have not determined the relative importance of different predictors. After conducting a large online survey in a UK city, well-powered multiple regression and dominance analyses demonstrated that psychological factors, such as environmental moral obligation, were the most important predictors of LEZ acceptability. However, travel-related and socio-demographic factors, such as distance lived from the LEZ and having dependent children, were also unique and important predictors. Overall, we argue that, whilst psychological factors are important, travel-related and socio-demographic barriers must not be overlooked during LEZ implementation.

Last mile distribution using cargo bikes: a simulation study in Padova

The recent growth of e-Commerce has induced an increasing freight demand, which could lead to negative externalities, in particular in urban areas. To foster sustainable development of cities and increase their livability, many local authorities are implementing urban vehicle access regulations, such as low-emission zones, banning the circulation of polluting vehicles. These measures prompted the adoption of new sustainable freight transport solutions for last mile deliveries, such as cargo bikes. The aim of this paper is to describe the study for the implementation of such a system. The procedure was tested (1) to define the location of a transshipment facility where parcels are moved from vans to cargo bikes, (2) to estimate the environmental and economic sustainability of the system, and (3) to quantify the effects of uncertainty in the final results. The framework was applied to the city center of Padova (Italy), where two sets of delivery system were considered: the first with traditional vans starting from an existing urban consolidation center and the second with manual and electric cargo bikes starting from a micro-depot. In particular, demand of home deliveries was estimated for a typical weekday; routes of freight transport means were defined by an optimization procedure; these data were used as input to a Discrete Event Simulation model. A sensitivity analysis was carried out modelling the potential uncertainty associated with load/unload times and travel speed of means, due to traffic congestion. Several scenarios were tested considering three locations as potential transshipment points. Outcomes of the simulations were used to estimate key performance indicators, evaluating the environmental and economic effects of the two delivery schemes. Results highlighted the potentiality of cargo bikes as a sustainable delivery system, and the impacts of uncertainty on the ranking of alternative options (i.e. micro-hubs).

Classification of noisy vehicles from unsupervised measurements

The NEMO-project (https://nemo-cities.eu/) aims to identify noisy and polluting road and rail vehicles, using remote sensing technology. Noise levels from individual road vehicles are measured from the roadside, in normal traffic. Road authorities may use these data to enforce noise limits, to limit access to Low Emission Zones or to influence driving behaviour. Whether a vehicle is a 'high noise emitter' is a complex question, as the noise level depends on vehicle type and condition, driving style, weather and location-specific characteristics. From a legal perspective, the question may be answered in relation to type approval noise limits, or in relation to local noise disturbance regulations. Within NEMO, a classification model is developed from a large dataset of unsupervised pass-by noise measurements, from different locations. The model labels noisy vehicles based on the noise measurements, technical vehicle data, driving conditions, and external factors. Several modeling and machine learning techniques were evaluated, to find the most accurate solution. This paper presents the results, and it looks forward to how the technological solution could be applied to enforce regulations, leading to a reduction of traffic noise annoyance.