Exposure to Air Pollution in Transport Microenvironments

People spend approximately 90% of their day in confined spaces (at home, work, school or in transit). During these periods, exposure to high concentrations of atmospheric pollutants can pose serious health risks, particularly to the respiratory system. The objective of this paper is to define a framework of the existing literature on the assessment of air quality in various transport microenvironments. A total of 297 papers, published from 2002 to 2021, were analyzed with respect to the type of transport microenvironments, the pollutants monitored, the concentrations measured and the sampling methods adopted. The analysis emphasizes the increasing interest in this topic, particularly regarding the evaluation of exposure in moving cars and buses. It specifically focuses on the exposure of occupants to atmospheric particulate matter (PM) and total volatile organic compounds (TVOCs). Concentrations of these pollutants can reach several hundreds of µg/m3 in some cases, significantly exceeding the recommended levels. The findings presented in this paper serve as a valuable resource for urban planners and decision-makers in formulating effective urban policies

Heterogeneity of passenger exposure to air pollutants in public transport microenvironments

Epidemiologic studies have linked human exposure to pollutants with adverse health effects. Passenger exposure in public transport systems contributes an important fraction of daily burden of air pollutants. While there is extensive literature reporting the concentrations of pollutants in public transport systems in different cities, there are few studies systematically addressing the heterogeneity of passenger exposure in different transit microenvironments, in cabins of different transit vehicles and in areas with different characteristics. The present study investigated PM2.5 (particulate matter with aerodynamic diameters smaller than 2.5μm), black carbon (BC), ultrafine particles (UFP) and carbon monoxide (CO) pollutant concentrations in various public road transport systems in highly urbanized city of Hong Kong. Using a trolley case housing numerous portable air monitors, we conducted a total of 119 trips during the campaign. Transit microenvironments, classified as 1). busy and secondary roadside bus stops; 2). open and enclosed termini; 3). above- and under-ground Motor Rail Transport (MTR) platforms, were investigated and compared to identify the factors that may affect passenger exposures. The pollutants inside bus and MTR cabins were also investigated together with a comparison of time integrated exposure between the transit modes. Busy roadside and enclosed termini demonstrated the highest average particle concentrations while the lowest was found on the MTR platforms. Traffic-related pollutants BC, UFP and CO showed larger variations than PM2.5 across different microenvironments and areas confirming their heterogeneity in urban environments. In-cabin pollutant concentrations showed distinct patterns with BC and UFP high in diesel bus cabins and CO high in LPG bus cabins, suggesting possible self-pollution issues and/or penetration of on-road pollutants inside cabins during bus transit. The total passenger exposure along selected routes, showed bus trips had the potential for higher integrated passenger exposure compared to MTR trips. The present study may provide useful information to better characterize the distribution of passenger exposure pattern in health assessment studies and the results also highlight the need to formulate exposure reduction based air policies in large cities. © 2015 Elsevier Ltd.postprin

A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation

Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as microscopic land-use regression (mu LUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010-2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The mu LUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers

An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London

© 2019 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.A comprehensive modelling approach has been developed to predict population exposure to the ambient air PM2.5 concentrations in different microenvironments in London. The modelling approach integrates air pollution dispersion and exposure assessment, including treatment of the locations and time activity of the population in three microenvironments, namely, residential, work and transport, based on national demographic information. The approach also includes differences between urban centre and suburban areas of London by taking account of the population movements and the infiltration of PM2.5 from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM2.5 at street scale for the year 2008, including both regional and urban contributions. Model analysis of the exposure in the three microenvironments shows that most of the total exposure, 85%, occurred at home and work microenvironments and 15% in the transport microenvironment. However, the annual population weighted mean (PWM) concentrations of PM2.5 for London in transport microenvironments were almost twice as high (corresponding to 13-20 µg/m3) as those for home and work environments (7-12 µg/m3). Analysis has shown that the PWM PM2.5 concentrations in central London were almost 20% higher than in the surrounding suburban areas. Moreover, the population exposure in the central London per unit area was almost three times higher than that in suburban regions. The exposure resulting from all activities, including outdoor to indoor infiltration, was about 20% higher, when compared with the corresponding value obtained assuming inside home exposure for all times. The exposure assessment methodology used in this study predicted approximately over one quarter (-28%) lower population exposure, compared with using simply outdoor concentrations at residential locations. An important implication of this study is that for estimating population exposure, one needs to consider the population movements, and the infiltration of pollution from outdoors to indoors.Peer reviewedFinal Accepted Versio

Comparative study of particulate matter in the transport microenvironment (buses) of Pakistan and UK

Transport microenvironments can contain higher levels of particulate matter due to infiltration from the roads, vehicular exhaust and commuter’s activities. The present study monitored PM, CO2, CO, temperature and relative humidity levels in diesel-powered buses in Pakistan and United Kingdom. Two routes of almost the same travelling distance were selected in Pakistan and the UK. Indoor air quality of the buses was monitored to determine the exposure faced by the commuters on inter-city journeys. While the observed levels in both countries were not in compliance with the WHO guidelines, levels of particulate matter were much higher in Pakistan than the concentrations in UK

Prediction of metal pm emission in rail tracks for condition monitoring application

Exposure to particulate material (PM) is a major health concern in megacities across the world which use trains as a primary public transport. PM emissions caused by railway traffic have hardly been investigated in the past, due to their obviously minor influence on the atmospheric air quality compared to automotive transport. However, the electrical train releases particles mainly originate from wear of rails track, brakes, wheels and carbon contact stripe which are the main causes of cardio-pulmonary and lung cancer. In previous reports most of the researchers have focused on case studies based PM emission investigation. However, the PM emission measured in this way doesn’t show separately the metal PM emission to the environment. In this study a generic PM emission model is developed using rail wheel-track wear model to quantify and characterise the metal emissions. The modelling has based on Archard’s wear model. The prediction models estimated the passenger train of one set emits 6.6mg/km-train at 60m/s speed. The effects of train speed on the PM emission has been also investigated and resulted in when the train speed increase the metal PM emission decrease. Using the model the metal PM emission has been studied for the train line between Leeds and Manchester to show potential emissions produced each day. This PM emission characteristics can be used to monitor the brakes, the wheels and the rail tracks conditions in future

Exploring the use of mobile sensors for noise and black carbon measurements in an urban environment

Mobile measurements have been collected on a bicycle equipped with a global positioning system (GPS) in a few connecting streets in Gent (Belgium). The 1-s sound pressure levels and 1-s black carbon concentrations were measured. In addition, 5 continuous monitoring fixed stations connected to building facades were used. Different processing methods are compared, based on different temporal and spatial weighting aggregations. The possibility to take profit of the fixed stations to refine estimations is tested, according to the noise levels collected at fixed stations and the distance between mobile and fixed sensors. In a last step, route selection based on travel distance, noise levels and black-carbon measurements is explored based on the data obtained

Variability of Personal Exposure to Fine Particulates for Urban Commuters inside an Automobile

Over the last decade, a growing body of evidence has emerged to suggest a causal link between short-duration exposure to elevated levels of fine airborne particulate matter and adverse health consequences. It is believed much of this ‘peak’ exposure occurs in transport microenvironments both because of the higher levels of fine particulates associated with road traffic, primarily from diesel exhaust emissions, and the fact people spend a significant amount of time traveling (for instance, 80 minutes/day for residents of Sydney). While previous studies have suggested substantial differences in exposure rates due to factors such as choice of mode, route, in-vehicle conditions, and meteorological factors, current measurement techniques have restricted insights to fairly coarse sampling intervals (e.g., every half hour, every trip). As a consequence, little tangible evidence is available on how pollution varies over a trip and most critically about the location, duration, and magnitude of peak excursions within trips. The current paper reports on a study in which the capabilities of Global Positioning Systems (GPS) and real-time particle monitors are combined to address this problem for an urban commute trip in Sydney. This ability to precisely spatially reference pollution data and in particular identify ‘hotspots’ holds considerable promise for both our understanding and reporting of such data in the future

Exposure and Respiratory Tract Deposition Dose of Equivalent Black Carbon in High Altitudes

The traffic microenvironment accounts for a significant fraction of the total daily dose of inhaled air pollutants. The adverse effects of air pollution may be intensified in high altitudes (HA) due to increased minute ventilation (MV), which may result in higher deposition doses compared to that at sea level. Despite this, air quality studies in regions with combined high pollution levels and enhanced inhalation are limited. The main goals of this study are to investigate how the choice of travel mode (walking, microbus, and cable car ride) determines (i) the personal exposure to equivalent black carbon (eBC) and (ii) the corresponding potential respiratory deposited dose (RDD) in HA. For this investigation, we chose La Paz and El Alto in Bolivia as HA representative cities. The highest eBC exposure occurred in microbus commutes (13 μg m-3), while the highest RDD per trip was recorded while walking (6.3 μg) due to increased MV. On the other hand, the lowest eBC exposure and RDD were observed in cable car commute. Compared with similar studies done at sea level, our results revealed that a HA city should reduce exposure by 1.4 to 1.8-fold to achieve similar RDD at sea level, implying that HA cities require doubly aggressive and stringent road emission policies compared to those at sea level. © 2020 by the authors

Predicting fine particulate concentrations near a busy intersection in Sydney using artificial neural networks

Scientific evidence of the connection between vehicle emissions and public health outcomes continues to grow. Key to this connection is the accurate monitoring and prediction of pollution concentrations within transport microenvironments at fine levels of spatial and temporal disaggregation. This paper explores the potential for using Artificial Neural Networks for such a purpose, focusing on temporallydisaggregate prediction of PM2.5 concentrations for a busy intersection in Sydney. The main findings are that with knowledge of ambient PM2.5 concentrations, traffic volumes and weather conditions, the approach is able to explain over 90 percent of the variation in PM2.5 concentrations at the intersection, and over 70 percent of the variation when applied to an independent data set collected at the same location