Estimating traffic contribution to particulate matter concentration in urban areas using a multilevel Bayesian meta-regression approach

Heydari, Shahram; Tainio, Marko; Woodcock, James; Nazelle, Audrey de

doi:10.1016/j.envint.2020.105800

Cited by 46 publications

(34 citation statements)

References 30 publications

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“…The effects of road traffic on air quality in large cities have been studied for years and the main pollutants emitted by vehicles are: particulate matter with a diameter lower than 10 μm (PM 10 ) ( Heydari et al, 2020 ; Ionescu et al, 2013 ; Pant and Harrison, 2013 ; Thorpe and Harrison, 2008 ), black carbon ( Ali et al, 2020 ; Invernizzi et al, 2011 ), CO 2 ( Zheng et al, 2020 ) and nitrogen dioxide (NO 2 ) ( Agudelo-Castañeda et al, 2020 ; Degrauewe et al, 2019 ). In the literature there are many works on the quantification and modelling of the dispersion of pollutants from their sources.…”

Section: Introductionmentioning

confidence: 99%

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

Collivignarelli

Rose

Abbà

et al. 2021

Process Safety and Environmental Protection

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Section: Introductionmentioning

confidence: 99%

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

Collivignarelli

Rose

Abbà

et al. 2021

Process Safety and Environmental Protection

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“…Motor vehicle emissions are one of the primary sources of atmospheric particulate pollution in urban areas [ 10 , 35 , 36 ]. The particulate concentration distribution in the urban road environment generally exceeds that in the adjacent urban environment.…”

Section: Discussionmentioning

confidence: 99%

Measuring and Quantifying Impacts of Environmental Parameters on Airborne Particulate Matter in Under-Viaducts Spaces in Wuhan, China

Yin

Hang

Qin

et al. 2021

IJERPH

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Particulate pollution caused by urban traffic emissions has become a significant public hazard. Many urban roads of under-viaduct spaces (UVSs) have become concentrated areas of particulate pollution. This study aims to explore the effects of landscape parameters on particulate matter in UVSs in Wuhan, China. We selected 14 types of UVS sections and nine potential environmental parameters to monitor four types of particulate matter (PM1.0, PM2.5, PM10, and TSP). Finally, linear regression analysis was employed to quantify the relative contributions of environmental parameters to the reduction in the concentration of the four types of particulate matter in the summer and winter. The results showed that particulate matter concentrations exhibit spatial and seasonal differences in UVSs. A single landscape parameter was correlated with particulate matter concentration, while compound environmental parameters had significant effects on the particulate matter concentration in UVSs. Meteorological factors and greening structures had a dominant impact on the particulate matter concentrations in summer and winter, respectively. Therefore, adjusting and optimizing the environmental parameters could reduce particulate pollution in UVSs and could have practical significance for the planning and design of UVSs.

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“…Another potentially relevant information was whether a study reported sea salt contribution to PM2.5. To better capture variability in the data, cities were assigned to 12 different regions mostly according to geographic proximity, and studies conducted by Karagulian et al (2015) and Heydari et al (2020). These are North America, Central Europe, East Asia, East/West Africa, Middle East, North-western Europe, Oceania/Japan, South/Central America, South-eastern Asia, Southern Asia, Southwestern Europe, and Western Europe.…”

Section: Review Framework and Study Selectionmentioning

confidence: 99%

“…Since the dose-response functions are non-linear, with a curve that is steep at low concentrations and flattens towards higher concentrations, there are diminishing returns of reduction in pollution levels at higher concentrations (Apte et al, 2018). The two factors (proportion of traffic and overall PM2.5 concentrations) vary greatly across the world (Heydari et al, 2020;Karagulian et al, 2015;WHO, 2018). For example, Heydari et al (2020) showed that traffic contribution estimates as well as uncertainties around these estimates vary largely across various cities and regions worldwide.…”

Section: Introductionmentioning

confidence: 99%

“…The two factors (proportion of traffic and overall PM2.5 concentrations) vary greatly across the world (Heydari et al, 2020;Karagulian et al, 2015;WHO, 2018). For example, Heydari et al (2020) showed that traffic contribution estimates as well as uncertainties around these estimates vary largely across various cities and regions worldwide. Many LMIC cities have high concentrations of pollution because of contributions from multiple sectors, of which transport is only one of them (Guttikunda et al, 2019;WHO, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Potential Health Benefits of Eliminating Traffic Emissions in Urban Areas

Heydari

Asgharian

Kelly

et al. 2021

Preprint

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Traffic is one of the major contributors to PM2.5 in cities worldwide. Quantifying the role of traffic is an important step towards understanding the impact of transport policies on the possibilities to achieve cleaner air and accompanying health benefits. We carried out a meta-analysis using the World Health Organisation (WHO) database of source apportionment studies of PM2.5 concentrations. Specifically, we used a Bayesian meta-regression approach, modelling both overall and traffic-related (tailpipe and non-tailpipe) concentrations simultaneously. We obtained the distributions of expected PM2.5 concentrations (posterior densities) of different types for 117 cities worldwide. For each city, we calculated the probabilities of exceeding the WHO-recommended concentration of PM2.5 if all of traffic emissions were removed. Using the non-linear Integrated Exposure Response (IER) function of PM2.5, we estimated percent reduction in different disease endpoints for a scenario with complete removal of traffic emissions. We found that this results in achieving the WHO-recommended concentration of PM2.5 only for a handful of cities that already have low concentrations of pollution. The percentage reduction in prevented mortality for cardiovascular and respiratory diseases increases up to a point (30-40 ug/m3), and above this concentration, it flattens off. For Diabetes-related mortality, the percentage reduction in mortality decreases with increasing concentrations—a trend that is opposite to other outcomes. For cities with high concentrations of pollution, the results highlight the need for multi-sectoral strategies to reduce pollution. The IER functions of PM2.5 result in diminishing returns of health benefits at high concentrations, and in case of Diabetes, there are even negative returns. The results show the significant effect of the shape of IER functions on health benefits. Overall, despite the diminishing results, a significant burden of deaths can be prevented by policies that aim to reduce traffic emissions even at high concentrations of pollution.

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Estimating traffic contribution to particulate matter concentration in urban areas using a multilevel Bayesian meta-regression approach

Cited by 46 publications

References 30 publications

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

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

Measuring and Quantifying Impacts of Environmental Parameters on Airborne Particulate Matter in Under-Viaducts Spaces in Wuhan, China

Potential Health Benefits of Eliminating Traffic Emissions in Urban Areas

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