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The dietary footprints and transition priorities of over 12 000 urban centers
Urban consumption plays a central role in driving global carbon emissions and is associated with a wide range of environmental pressures, including deforestation, water use, and biodiversity loss. As cities and networks of cities work to define and achieve quantitative sustainability goals, the lack of insight into their unique food footprints, shaped by each city’s specific dietary patterns and demographic composition, remains a significant obstacle. Here we combine demographic, group-specific diet, and supply-chain environmental footprint data to estimate the dietary footprints in 12 228 urban centers across 159 countries. We show that diet-related greenhouse gas emissions (GHGE) are highly concentrated in a small number of cities: The 200 urban centers with the largest footprints account for 39.8% of global urban population and 45.6% of total global urban dietary emissions. Overall, adoption of the EAT-Lancet 2.0 diet across all studied centers delivers a modest net reduction in total footprints (≈5%–16%). However, this aggregate outcome masks substantial variability: high-income centers achieve considerable reductions through lower meat and dairy consumption, while lower-income centers presently consuming below EAT-Lancet recommended levels experience footprint increases, partially offsetting global gains. Within transnational city networks (C40 Cities, Eurocities, and Milan Urban Food Policy Pact members), where member centers are predominantly high-income, EAT-Lancet 2.0 adoption delivers substantially larger reductions of 22%–50% in GHGE, underscoring the policy relevance of targeted urban-level dietary interventions. Complementary supply-side interventions, including decarbonization of agricultural production, processing, and distribution, offer additional reductions, indicating that dietary and supply-side levers must be deployed jointly. Our results highlight the need for context-specific urban dietary strategies. The resulting database, MATILDA-City, provides a globally harmonized evidence base to benchmark, rank, and monitor urban-level dietary footprints, supporting municipalities to coordinate actions toward sustainable food systems.
2026
2013
2016
2012
We investigate the concentration fluctuations of passive scalar plumes emitted from small, localised (point-like) steady sources in a neutrally stratified turbulent boundary layer over a rough wall. The study utilises high-resolution large-eddy simulations for sources of varying sizes and heights. The numerical results, which show good agreement with wind-tunnel studies, are used to estimate statistical indicators of the concentration field, including spectra and moments up to the fourth order. These allow us to elucidate the mechanisms responsible for the production, transport and dissipation of concentration fluctuations, with a focus on the very near field, where the skewness is found to have negative values – an aspect not previously highlighted. The gamma probability density function is confirmed to be a robust model for the one-point concentration at sufficiently large distances from the source. However, for ground-level releases in a well-defined area around the plume centreline, the Gaussian distribution is found to be a better statistical model. As recently demonstrated by laboratory results, for elevated releases, the peak and shape of the pre-multiplied scalar spectra are confirmed to be independent of the crosswind location for a given downwind distance. Using a stochastic model and theoretical arguments, we demonstrate that this is due to the concentration spectra being directly shaped by the transverse and vertical velocity components governing the meandering of the plume. Finally, we investigate the intermittency factor, i.e. the probability of non-zero concentration, and analyse its variability depending on the thresholds adopted for its definition.
2024
2015
2019
2005
2010
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2003
2003
2002
2000
The effect of controlled indoor activities on the particulate matter mass and number concentrations.
2013
2017
2013
2004
2021
2020
2011