Search Results (36)

This study analyzes changes in NO_x concentrations due to the aerodynamic and dry deposition effects of roadside trees in the Jongno area, a central business district of Seoul, Republic of Korea, using a computational fluid dynamics (CFD) model. The simulation results indicate that the on-road NO_x concentration was slightly increased (2.09%) due to the aerodynamic effect of roadside trees. However, the dry deposition effect of roadside trees had a greater impact on reducing NO_x concentrations (−2.77%) along sidewalks. It was observed that the reduction in NO_x concentration due to the dry deposition effect of roadside trees was likely to offset the increase in NO_x concentrations due to the aerodynamic effect of roadside trees, resulting in an overall decrease in NO_x concentrations. Furthermore, sensitivity tests showed that the increase in NO_x concentrations due to the aerodynamic effects of roadside trees was intensified along sidewalks when ambient wind speeds were high, while the decrease in NO_x concentration was proportional to the deposition velocity of roadside trees. Therefore, roadside trees should be planted where aerodynamic effects do not significantly increase NO_x concentrations in order to improve near-road air quality. Full article

NOx pollution is one of the greatest air quality issues that urban areas face today, particularly within the European Union (EU), yet currently this pollutant is only controlled through the homologation process. There is currently no periodic technical inspection (PTI) process for NOx emissions within the EU, leaving a weakness in the legislation that is currently allowing high polluters to negatively impact air quality. Work needs to be performed to incorporate a simple, quick, inexpensive, and representative test to accurately identify these high emitters within the on-road vehicle fleet. This paper investigates options for the incorporation of a NOx test into the EU PTI test procedures. In a trial constituting over 600 vehicles, a 3DATX parSYNC was used to measure the NOx emissions over a series of short test types. These are an idle test, two types of high idle test (a constant high idle and a rapid high idle), and an on-road driving dynamic acceleration test. The repeatability of all three test types was good. The NOx concentrations have strong correlations to the mass emissions for each test type, with the use of mean concentrations being deemed more representative than the use of maximum concentrations. The mean results across the tested fleet are calculated and used to define pass/fail thresholds for different vehicle types. The findings of this work show that multiple test methods have the potential to characterize NOx emissions from a vehicle, but in order to catch high emitters on a PTI test, the unloaded idle and high idle test types are not suitable substitutes for a dynamic acceleration test, particularly for petrol vehicles. Full article

Air toxic emissions from on-road mobile sources are significant contributors to the degradation of air quality in urban and dense population centers. Research led by the United States Environmental Protection Agency (EPA) identified more than 1162 hazardous air pollutants (HAPs) in the exhaust and evaporative emissions from on-road mobile sources. However, less than 70 hazardous air pollutants are monitored by regulatory agencies. HAPs emitted from Mobile Sources are known as Mobile Source Air Toxics (MSATs). The EPA estimates that approximately half of the cancer risk and 74% of noncancer health impacts from air toxics is attributed to mobile sources. The quantification of the risk associated with MSATs exposure remains limited to date, and only a few MSATs have ambient air quality standards to protect human health and welfare. This work presents a novel and validated methodology to quantify the myriad health risks associated with exposure to on-road mobile emissions. This methodology is introduced in the form of a pipelined analysis process, which may be employed in existing and new transportation projects. The proposed new methodology integrates results from three different types of models: on-road vehicle emissions inventory models such as MOVES and IVE, air dispersion models such as AERMOD and SCIPUFF, and risk estimate models for human and ecological receptors such as the 2005 Final U.S. EPA Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities. The result of this research work is a new methodology that provides regulators and risk analysts with a more detailed awareness of the health impacts of MSATs. A case study of Saint Paul, Minnesota, validated the air dispersion modeled results against monitored data, and the agreement was acceptable (i.e., the estimates were within a factor of two of the observations). Three high-population locations in the Saint Paul area were evaluated for human health risk, with the observation that at two of these locations, the Saint Paul—Ramsey Health Center and Anderson Office Building, the calculated cancer risk is in excess of the target risk level of 1.0E-05 for benzo(a)pyrene. The methodology presented in this paper allows regulators, risk analysts, and air quality engineers to better estimate multi-pathway cancer and noncancer risk associated with acute and chronic exposure to MSATs. Moreover, this work provides a science-based aid to policy decision makers when considering factors that most significantly affect population health and ecology. Full article

The automotive sector is greatly contributing to pollutant emissions and recent regulations introduced the need for a major control of, and reduction of, internal combustion engine emissions. Artificial intelligence (AI) algorithms have proven to hold the potential to be the thrust in the state-of-the-art for engine-out emission prediction, thus enabling tailored calibration modes and control solutions. More specifically, the scientific literature has recently witnessed strong efforts in AI applications for the development of nitrogen oxides (NOx) virtual sensors. These latter replace physical sensors and exploit AI algorithms to estimate NOx concentrations in real-time. Still, the calibration of the algorithms, together with the appropriate choice of the specific metric, strongly affects the prediction capability. In the present paper, a machine learning-based virtual sensor for NOx monitoring in diesel engines was developed, based on the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The latter is commonly used in the literature to deploy virtual sensors due to its high performance, flexibility and robustness. An experimental campaign was carried out to collect data from the engine test bench, as well as from the engine electronic control unit (ECU), for the development and calibration of the virtual sensor at steady-state conditions. The virtual sensor has, since then, been tested throughout on an on-road driving mission to assess its prediction performance in dynamic conditions. In stationary conditions, its prediction accuracy was around 98%, whereas it was 85% in transient conditions. The present study shows that AI-based virtual sensors have the potential to significantly improve the accuracy and reliability of NOx monitoring in diesel engines, and can, therefore, play a key role in reducing NOx emissions and improving air quality. Full article

Simulation outputs from chemical transport models (CTMs) are essential to plan effective air quality policies. A key strength of these models is their ability to separate out source-specific components which facilitate the simulation of the potential impact of policy on future air quality. However, configuring and running these models is complex and computationally intensive, making the evaluation of multiple scenarios less accessible to many researchers and policy experts. The aim of this work is to present how Gaussian process emulation can provide a top-down approach to interrogating and interpreting the outputs from CTMs at minimal computational cost. A case study is presented (based on fine particle sources in the southwest of Western Australia) to illustrate how an emulator can be constructed to simultaneously evaluate changes in emissions from on-road transport and electricity sectors. This study demonstrates how emulation provides a flexible way of exploring local impacts of electric vehicles and wider regional effects of emissions from electricity generation. The potential for emulators to be applied to other settings involving air quality research is discussed. Full article

Formaldehyde (HCHO), a carcinogenic carbonyl compound and precursor of tropospheric ozone, can be found in vehicle exhaust. Even though the continuous monitoring of HCHO has been recommended, the real-world emissions from the road transport sector are not commonly available. The main reason for this knowledge gap has been the difficulty to measure HCHO in real-time and during real-world testing. This, for instance, increases the uncertainty of the O₃ simulated by air quality models. The present study investigates real-time HCHO measurements comparing three Fourier Transform InfraRed spectrometers (FTIRs) and one Quantum Cascade Laser InfraRed spectrometer (QCL-IR) directly sampling from the exhaust of one gasoline passenger car, one Diesel commercial vehicle and one Diesel heavy-duty vehicle, all meeting recent European emission standards (Euro 6/VI). Non-negligible emissions of HCHO were measured from the Diesel light-duty vehicle, with emissions increasing as temperature decreased. Relatively low emissions were measured for the gasoline car and the Diesel heavy-duty vehicle. The results showed a good correlation between the different instruments under all the conditions tested (in most cases R² > 0.9). Moreover, it was shown that HCHO can be accurately measured during on-road and real-world-like tests using instruments based on FTIR and QCL-IR technologies. Full article

Traffic-related air pollution (TRAP) was monitored using a mobile sensor network on 125 urban taxis in Shanghai (November 2019/December 2020), which provide real-time patterns of air pollution at high spatial resolution. Each device determined concentrations of carbon monoxide (CO), nitrogen dioxide (NO₂), and PM_2.5, which characterised spatial and temporal patterns of on-road pollutants. A total of 80% road coverage (motorways, trunk, primary, and secondary roads) required 80–100 taxis, but only 25 on trunk roads. Higher CO concentrations were observed in the urban centre, NO₂ higher in motorway concentrations, and PM_2.5 lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO₂, and PM_2.5 in Shanghai decreased by 32, 31 and 41%, compared with the previous period. Local contribution related to traffic emissions changed slightly before and after COVID-19 restrictions, while changing background contributions relate to seasonal variation. Mobile networks are a real-time tool for air quality monitoring, with high spatial resolution (~200 m) and robust against the loss of individual devices. Full article

Plug-in hybrid electric vehicles (PHEVs) are promoted as an alternative to conventional vehicles to meet European decarbonisation and air quality targets. However, several studies have shown that gasoline PHEVs present similar criteria and particulate emissions as their conventional gasoline counterparts. In the present work, we investigate the environmental performance of a modern plug-in hybrid Diesel-fuelled vehicle meeting the Euro 6d standard under a large variety of driving patterns, ambient temperatures, and battery states of charge (SOC). Emissions of regulated pollutants, currently unregulated pollutants, and CO₂ were measured in the laboratory and following various on-road routes. The vehicle, whose electric range was 82 km, presented emissions below the Euro 6 regulatory limits in all the different driving cycles performed at 23 °C and all the on-road tests at the different battery SOC. The emissions were lower than the average of the conventional Diesel vehicles tested at JRC in 2020–2021 for all the SOC tested, the exception being solid particle number emissions >23 nm (SPN₂₃) emissions that were comparable at all SOC. Moreover, the emissions obtained with the high voltage battery fully charged during on-road tests were comparable to those obtained with the battery at the minimum SOC for the entire test (ca. 91 km) as well as for the urban section (ca. 36 km). Overall, NOx and SPN₂₃ emissions increased at lower temperatures, showing that at very low temperatures, there is no benefit in terms of particulate emissions from the electric range. Finally, it is shown that the emissions of N₂O, the only unregulated pollutant presenting relevant emissions for this vehicle, and which are of catalytic nature, were proportional to the utilisation of the internal combustion engine. The scope of the manuscript is thus to deepen the knowledge on the emission performances of Diesel PHEVs through the systematic testing of a modern representative of this class of vehicles in a wide range of driving and environmental conditions. Full article

On-road vehicle emissions play a crucial role in affecting air quality and human exposure, particularly in megacities. In the absence of comprehensive traffic monitoring networks with the general lack of intelligent transportation systems (ITSs) and big-data-driven, high-performance-computing (HPC) platforms, it remains challenging to constrain on-road vehicle emissions and capture their hotspots. Here, we established an intelligent modelling and visualization system driven by ITS traffic data for real-world, on-road vehicle emissions. Based on the HPC platform (named “City Brain”) and an agile Web Geographic Information System (WebGISs), this system can map real-time (hourly), hyperfine (10~1000 m) vehicle emissions (e.g., PM_2.5, NO_x, CO, and HC) and associated traffic states (e.g., vehicle-specific categories and traffic fluxes) over the Xiaoshan District in Hangzhou. Our results show sharp variations in on-road vehicle emissions on small scales, which even fluctuated up to 31.2 times within adjacent road links. Frequent and widespread emission hotspots were also exposed. Over custom spatiotemporal scopes, we virtually investigated and visualized the impacts of traffic control policies on the traffic states and on-road vehicle emissions. Such results have important implications for how traffic control policies should be optimized. Integrating this system with chemical transport models and air quality measurements would bridge the technical gap between air pollutant emissions, concentrations, and human exposure. Full article

The past 60 years have seen large reductions in vehicle emissions of particulate matter (PM), nitrogen oxides (NO_x), carbon monoxide (CO), hydrocarbons (HCs), sulfur dioxide (SO₂), and lead (Pb). Advanced emission after-treatment technologies have been developed for gasoline and diesel vehicles to meet increasingly stringent regulations, yielding absolute emission reductions from the on-road fleet despite increased vehicle miles traveled. As a result of reduced emissions from vehicles and other sources, the air quality in cities across the U.S. and Europe has improved greatly. Turn-over of the on-road fleet, increasingly stringent emission regulations (such as Tier 3 in the U.S., LEV III in California, Euro 6 in Europe, and upcoming rules in these same regions), and the large-scale introduction of electric vehicles will lead to even lower vehicle emissions and further improvements in air quality. We review historical vehicle emissions and air quality trends and discuss the future outlook. Full article

Achieving net-zero carbon in the UK by 2050 will necessitate the decarbonisation of the transportation systems. However, there are challenges to this, especially for vehicles in cities where the charging infrastructure is at its minimum. Overcoming these challenges will undoubtedly encourage electrical vehicle (EV) use, with commensurate reductions in emission coupled with better environmental conditions in cities, e.g., air quality. Drivers, on the whole, are reluctant to invest in an EV if they cannot access a convenient charging point within their living area. This research provides a methodology to support the planning for the optimum siting of charging infrastructure, so it is accessible to as many citizens as possible within a city. The work focuses on Winchester City and District in the UK. The multi-criteria decision approach is based on the Analytical Hierarchy Process (AHP) linked to site spatial assessment using Geographical Information System (GIS). The assessment considered key criteria such as road type, road access, on-road parking availability, road slope, proximity to fuel stations, current/planned charging points, car parks and population distributions. The process contains two suitability filters, namely, restricted road and suitability mask. In the first, all restricted roads were excluded from further analysis, which resulted in reducing the road segments from over 9000 to around 2000. When applying the second filter an overall result of 44 suitable EV charging point locations was achieved. These locations were validated using the Google Earth^® imaging platform to check actual locations against those predicted by the analysis. The presented methodology is accurate and is generalisable to other cities or regions. Full article

The current U.S. emission control requirements for on-road motor vehicles are driven by the ozone problem in the South Coast Air Basin (SoCAB) in southern California. Based on ozone modeling performed for Air Quality Management Plans (AQMPs), the SoCAB ozone attainment plan requires large (>80%) amounts of emission reductions in oxides of nitrogen (NOx) from current levels with more modest (~40%) controls on Volatile Organic Compounds (VOC). The shelter in place orders in response to the 2020 COVID-19 pandemic resulted in an immediate reduction in emissions, but instead of ozone being reduced, in 2020 the SoCAB saw some of the highest observed ozone levels in decades. We used the abrupt emissions reductions from 2019 to 2020 caused by COVID-19 to conduct a dynamic model evaluation of the Community Multiscale Air Quality (CMAQ) model to evaluate whether the models used to develop ozone control plans can correctly simulate the ozone response to the emissions reductions. Ozone modeling was conducted for three scenarios: 2019 Base, 2020 business-as-usual (i.e., without COVID reductions), and 2020 COVID. We found that modeled ozone changes between 2019 and 2020 were generally consistent with the observed ozone changes. We determined that meteorology played the major role in the increases in ozone between 2019 and 2020; however, the reduction in NO_X emissions also caused ozone increases in Los Angeles County and into western San Bernardino County, with more widespread ozone decreases further to the east. Full article

The Iberian Peninsula, located in southwestern Europe, is exposed to frequent exceedances of different threshold and limit values of air pollution, mainly related to particulate matter, ozone, and nitrous oxide. Source apportionment modeling represents a useful modeling tool for evaluating the contribution of different emission sources or sectors and for designing useful mitigation strategies. In this sense, this work assesses the impact of various emission sectors on air pollution levels over the Iberian Peninsula using a source contribution analysis (zero-out method). The methodology includes the use of the regional WRF + CHIMERE modeling system (coupled to EMEP emissions). In order to represent the sensitivity of the chemistry and transport of gas-phase pollutants and aerosols, several emission sectors have been zeroed-out to quantify the influence of different sources in the area, such as on-road traffic or other mobile sources, combustion in energy generation, industrial emissions or agriculture, among others. The sensitivity analysis indicates that large reductions of precursor emissions (coming mainly from energy generation, road traffic, and maritime-harbor emissions) are needed for improving air quality and attaining the thresholds set in the European Directive 2008/50/EC over the Iberian Peninsula. Full article

We present the mapping at fine spatial scale of aerosol optical properties using a mobile laboratory equipped with LIDAR (Light Detection And Ranging), sun photometer and in situ instruments for performing on-road measurements. The mobile campaign was conducted from 9 May to 19 May 2017 and had the main objective of mapping the distribution of pollutants in the Beijing and North China Plain (NCP) region. The highest AOD (Aerosol Optical Depth) at 440 nm of 1.34 and 1.9 were recorded during two heavy pollution episodes on 18 May and 19 May 2017, respectively. The lowest Planetary Boundary Layer (PBL) heights (0.5–1.5 km) were recorded during the heavy pollution events, correlating with the highest AOD and southern winds. The transport of desert dust from the Gobi Desert was captured during the mobile measurements, impacting Beijing during 9–13 May 2017. Exploring the NCP outside Beijing provided datasets for regions with scarce ground measurements and allowed the mapping of high aerosol concentrations when passing polluted cities in the NCP (Baoding, Tianjin and Tangshan) and along the Binhai New Area. For the first time, we provide mass concentration profiles from the synergy of LIDAR, sun photometer and in situ measurements. The case study along the Binhai New Area revealed mean extinction coefficients of 0.14 ± 0.10 km⁻¹ at 532 nm and a mass concentration of 80 ± 62 μg/m³ in the PBL (<2 km). The highest extinction (0.56 km⁻¹) and mass concentrations (404 μg/m³) were found in the industrial Binhai New Area. The PM₁₀ and PM_2.5 fractions of the total mass concentration profiles were separated using the columnar size distribution, derived from the sun photometer measurements. This study offers unique mobile datasets of the aerosol optical properties in the NCP for future applications, such as satellite validation and air quality studies. Full article

On-road mobile sources of emissions make important contributions to particulate matter pollution (PM_2.5–PM₁₀) in cities. The quantification of such pollution is, however, highly challenging due to the number of interacting factors that affect emissions such as vehicle category, emission standard, vehicle speed and weather conditions. The proper identification of individual sources of emission is particularly necessary for air quality management areas. In this study, we estimated exhaust and non-exhaust traffic-related PM_2.5 and PM₁₀ contributions to total ambient pollution in Banská Bystrica (Slovak republic) by simulation based on the AERMOD dispersion model. Emission rates of particular vehicle categories were obtained through vehicle population statistics, traffic data survey and emission factors from the EMEP/EEA air pollutant emission inventory guidebook. Continuous PM₁₀ and PM_2.5 data from air quality monitoring stations were analysed for the years 2019–2020 and compared with modelled concentrations. The annual concentration values of PM_2.5 and PM₁₀ in the study area reached 16.71 μg/m³ and 15.57 μg/m³, respectively. We found that modelled PM_2.5 peak concentration values exceeded the WHO air quality guideline annual mean limit. Traffic-related PM_2.5 and PM₁₀ contributions to ambient pollution at the reference point located nearby to a busy traffic route were approximately 25% and 17%, respectively. The reference point located outside the main transport corridors showed an approximately 11% contribution, both for PM_2.5 and PM₁₀ concentrations. The simulations showed that PM pollution is greatly contributed to by on-road mobile sources of emissions in the study area, and especially non-exhaust emissions, which require serious attention in association with their health impacts and the selection of Banská Bystrica as an air quality management area. Full article