Air quality and freight transport in cities: a dynamic approach to measure the real impact

Defined as PM, NOx and O3, air pollution is considered the largest threat to human health, responsible for 555,000 premature deaths in Europe every year [1, 2]. Its effects have recently gained in public awareness, with several bottom-up actions against (traffic-related) air pollution. A large share of these emissions (and their impact on society, the economy and the environment) has its origin in freight transport: while freight transport only represents 14% of total traffic in the Brussels Capital Region, it is responsible for 33% of traffic related PM emission [3].

Externalities arise when the associated costs of air pollution are not carried by the causer (such as the impact of air pollution on human health), as these changes in wealth are not included in the prices of transport activities [4]. The number of receptors, the people in near vicinity of the emission source, is directly linked to the magnitude of the external costs generated by air pollution. So far, only the static geotemporal link between the presence of the emission source (the driving vehicle) and the number of its vicinal receptors has been considered [5]. Assuming immobile humans, this is often based on the home location, thus making abstraction of individual travel patterns [6]. To our knowledge, combining geotemporal dynamics of both receptors and freight transport vehicles emission sources is novel [7].

This study highlights the impact of air pollution by quantifying particulate matter and mono-nitrogen oxides (PM and NOx) generated by freight transport in the Brussels Metropolitan Region (BMR). Applying the impact-pathway approach [8], the external costs along the path followed by freight vehicles (spatiotemporal emission levels) are derived from the TRABAM freight transport model, enveloping vehicle-, road-, traffic-dependent emissions [11]. Dynamic receptor densities have been obtained from mobile service providers data [9, 10] and contain the number of people on cellular network base cell level per 30-minute interval. In turn, the emitted and dispersed pollutants and number of receptors have been connected to one another using dose-response functions of Devos et al. [12, 13], based on local hospital data from the UZ Brussels. Results were then compared to the conventional static methodology which is current practice in literature.

On local-level analyses, this comparison between the conventional static and proposed dynamic approach results in very large discrepancies in external costs generated by freight transport in terms of PM and NOx, going up to a factor 45. In the inner city region, mostly higher external costs are observed using the dynamic approach compared to the previously assumed static measurements. The findings suggest the proposed dynamic methodology should be used in micro-scale or route-specific air pollution analyses. Hence, facilitating freight transport operations where temporal resolutions are low is advisable. Notwithstanding the micro-level differences, it is worth noting the overall difference between the static and dynamic approach is negligible when applied on the entire Brussels Metropolitan Region (0,5%), as such supporting the application of the conventional static approach on meso- or macro level (entire cities or countries). However, this study being the first of its kind, additional research should be carried out for other regions and for passenger transport to allow generalizable statements on this subject.