Air quality in enclosed railway stations: Quantifying the impact of diesel trains through deployment of multi-site measurement and random forest modelling

Air quality on UK diesel and hybrid trains

Concentrations of particulate matter (PM10, PM2.5), ultrafine (UFP), particle number (PNC), black carbon (BC), nitrogen dioxide (NO2) and nitrogen oxides (NOX) were measured in train carriages on diesel and bi-mode trains on inter-city and long-distance journeys in the United Kingdom (UK) using a high-quality mobile measurement system. Air quality on 15 different routes was measured using highly-time resolved data on a total of 119 journeys during three campaigns in winter 2020 and summer 2021; this included 13 different train classes. Each journey was sampled 4-10 times with approximatively 11,000 min of in-train concentrations in total. Mean-journey concentrations were 7.552 µg m-3 (PM10); 3.936 µg m-3 (PM2.5); 333-11,300 # cm-3 (PNC); 225-9,131 # cm-3 (UFP); 0.6-11 µg m-3 (BC); 28-201 µg m-3 (NO2); and 130-3,456 µg m-3 (NOX). The impact of different factors on in-train concentrations was evaluated. The presence of tunnels was the factor with the largest impact on the in-train particle concentrations with enhancements by a factor of 40 greater than baseline for BC, and a factor 6 to 7 for PM and PNC. The engine fuel mode was the factor with the largest impact on NO2 with enhancements of up to 14-times larger when the train run on diesel compared to the times running on electric on hybrid trains. Train classes with an age < 10 years observed the lowest in-train PM, BC and NOX concentrations reflecting improvements in aspects of rail technology in recent years. Air quality on UK diesel trains is higher than ambient concentrations but has lower PM2.5 and PNC than most other transport modes, including subway systems, diesel and petrol cars. This paper adds significantly to the evidence on exposure to poor air quality in transport micro-environments and provides the industry and regulatory bodies with reference-grade measurements on which to establish in-train air quality guidelines.

Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6)

Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO2 and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollutant concentrations after 2016 (Euro 6-era) to the estimated concentrations without Euro 6. The concentration without Euro 6 was estimated by first modeling the air quality using various environmental factors related to diesel vehicles, meteorological conditions, temporal information such as date and precursors in 2002-2015 (pre-Euro 6-era), and then applying the model to predict the concentration after 2016. In this study, we used both recurrent neural network (RNN) and random forest (RF) algorithms to model the air quality and showed that RNN can achieve higher R2 (0.634 ~ 0.759 depending on pollutants) than RF, making it more suitable for air quality modeling. According to our results, the measured concentrations during 2016-2019 were lower than the concentrations predicted using RNN by - 1.2%, - 3.4%, and - 4.8% for CO, NO2 and PM10. Such reduction can be attributed to the result of Euro 6.

Energy, economic, and environmental impacts of enhanced ventilation strategies on railway coaches to reduce Covid-19 contagion risks

In the last years, the Covid-19 outbreak raised great awareness about ventilation system performance in confined spaces. Specifically, the heating, ventilation, and air conditioning system design and operating parameters, such as air change per hour, air recirculation ratio, filtration device performance, and vents location, play a crucial role in reducing the spread of viruses, moulds, bacteria, and general pollutants. Concerning the transport sector, due to the impracticability of social distancing, and the relatively loose requirements of ventilation standards, the SARS-COV-19 outbreak brought a reduction of payload (up to 50%) for different carriers. Specifically, this has been particularly severe for the railway sector, where train coaches are typically characterized by relatively elevated occupancy and high recirculation ratios. In this framework, to improve the Indoor Air Quality and reduce the Covid-19 contagion risk in railway carriages, the present paper investigates the energy, economic and environmental feasibility of diverse ventilation strategies. To do so, a novel dynamic simulation tool for the complete dynamic performance investigation of trains was developed in an OpenStudio environment. To assess the Covid-19 contagion risk connected to the investigated scenarios, the Wells-Riley model has been adopted. To prove the proposed approach's capabilities and show the Covid-19 infection risk reduction potentially achievable by varying the adopted ventilation strategies, a suitable case study related to an existing medium-distance train operating in South/Central Italy is presented. The conducted numerical simulations return interesting results providing also useful design criteria.

Intervention of an Upgraded Ventilation System and Effects of the COVID-19 Lockdown on Air Quality at Birmingham New Street Railway Station

High NO2 concentrations (long term average of 383 µg/m3 in 2016/2017) recorded at Birmingham New Street railway station have resulted in the upgrade of the bi-directional fan system to aid wind dispersion within the enclosed platform environment. This paper attempts to examine how successful this intervention has been in improving air quality for both passengers and workers within the station. New air pollution data in 2020 has enabled comparisons to the 2016/2017 monitoring campaign revealing a 23-42% decrease in measured NO2 concentrations. The new levels of NO2 are below the Occupational Health standards but still well above the EU Public Health Standards. This reduction, together with a substantial decrease (up to 81%) in measured Particulate Matter (PM) concentrations, can most likely be attributed to the new fan system effectiveness. Carbon Monoxide levels were well below Occupational and Public Health Standards at all times. The COVID-19 pandemic "initial lockdown" period has also allowed an insight into the resultant air quality at lower rail-traffic intensities, which produced a further reduction in air pollutants, to roughly half the pre-lockdown concentrations. This study shows the scope of improvement that can be achieved through an engineering solution implemented to improve the ventilation system of an enclosed railway station. Further reduction in air pollution would require additional approaches, such as the removal of diesel engine exhaust emissions via the adoption of electric or diesel-electric hybrid powered services.

Increased ozone pollution alongside reduced nitrogen dioxide concentrations during Vienna's first COVID-19 lockdown: Significance for air quality management

BACKGROUND

Lockdowns amid the COVID-19 pandemic have offered a real-world opportunity to better understand air quality responses to previously unseen anthropogenic emission reductions.

METHODS AND MAIN OBJECTIVE

This work examines the impact of Vienna's first lockdown on ground-level concentrations of nitrogen dioxide (NO₂), ozone (O₃) and total oxidant (O_x). The analysis runs over January to September 2020 and considers business as usual scenarios created with machine learning models to provide a baseline for robustly diagnosing lockdown-related air quality changes. Models were also developed to normalise the air pollutant time series, enabling facilitated intervention assessment.

CORE FINDINGS

NO₂ concentrations were on average -20.1% [13.7-30.4%] lower during the lockdown. However, this benefit was offset by amplified O₃ pollution of +8.5% [3.7-11.0%] in the same period. The consistency in the direction of change indicates that the NO₂ reductions and O₃ increases were ubiquitous over Vienna. O_x concentrations increased slightly by +4.3% [1.8-6.4%], suggesting that a significant part of the drops in NO₂ was compensated by gains in O₃. Accordingly, 82% of lockdown days with lowered NO₂ were accompanied by 81% of days with amplified O₃. The recovery shapes of the pollutant concentrations were depicted and discussed. The business as usual-related outcomes were broadly consistent with the patterns outlined by the normalised time series. These findings allowed to argue further that the detected changes in air quality were of anthropogenic and not of meteorological reason. Pollutant changes on the machine learning baseline revealed that the impact of the lockdown on urban air quality were lower than the raw measurements show. Besides, measured traffic drops in major Austrian roads were more significant for light-duty than for heavy-duty vehicles. It was also noted that the use of mobility reports based on cell phone movement as activity data can overestimate the reduction of emissions for the road transport sector, particularly for heavy-duty vehicles. As heavy-duty vehicles can make up a large fraction of the fleet emissions of nitrogen oxides, the change in the volume of these vehicles on the roads may be the main driver to explain the change in NO₂ concentrations.

INTERPRETATION AND IMPLICATIONS

A probable future with emissions of volatile organic compounds (VOCs) dropping slower than emissions of nitrogen oxides could risk worsened urban O₃ pollution under a VOC-limited photochemical regime. More holistic policies will be needed to achieve improved air quality levels across different regions and criteria pollutants.