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The combination of urbanisation and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate change. Yet, the risk of urban overheating can be mitigated by urban green-blue-grey infrastructures (GBGI), such as parks, wetlands, and engineered greening, which have the potential to effectively reduce summer air temperatures. Despite many reviews, the evidence bases on quantified GBGI cooling benefits remains partial and the practical recommendations for implementation are unclear. This systematic literature review synthesises the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximise their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorised under 10 main divisions. Certain GBGI (green walls, parks, street trees) have been well-researched for their urban cooling capabilities. However, several other GBGI have received negligible (zoological garden, golf course, estuary) or minimal (private garden, allotment) attention. The most efficient air cooling was observed in botanical gardens (5.0±3.5°C), wetlands (4.9±3.2°C), green walls (4.1±4.2°C), street trees (3.8±3.1°C), and vegetated balconies (3.8±2.7°C). Under changing climate conditions (2070-2100) with consideration of RCP8.5, there is a shift in climate subtypes, either within the same climate zone (e.g., Dfa to Dfb and Cfb to Cfa) or across other climate zones (e.g., Dfb (continental warm-summer humid) to BSk (dry, cold semi-arid) and Cwa (temperate) to Am (tropical)). These shifts may result in lower efficiency for the current GBGI in the future. Given the importance of multiple services, it is crucial to balance their functionality, cooling performance, and other related co-benefits when planning for the future GBGI. This global GBGI heat mitigation inventory can assist policymakers and urban planners in prioritising effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience.

The 2014–2015 Bárðarbunga‐Veiðivötn fissure eruption at Holuhraun produced about 1.5 km3of lava, making it the largest eruption in Iceland in more than 200 years. Over the course of the eruption, daily volcanic sulfur dioxide (SO2) emissions exceeded daily SO2emissions from all anthropogenic sources in Europe in 2010 by at least a factor of 3. We present surface air quality observations from across Northern Europe together with satellite remote sensing data and model simulations of volcanic SO2for September 2014. We show that volcanic SO2was transported in the lowermost troposphere over long distances and detected by air quality monitoring stations up to 2750 km away from the source. Using retrievals from the Ozone Monitoring Instrument (OMI) and the Infrared Atmospheric Sounding Interferometer (IASI), we calculate an average daily SO2mass burden of 99 ± 49 kilotons (kt) of SO2from OMI and 61 ± 18 kt of SO2from IASI for September 2014. This volcanic burden is at least a factor of 2 greater than the average SO2mass burden between 2007 and 2009 due to anthropogenic emissions from the whole of Europe. Combining the observational data with model simulations using the United Kingdom Met Office's Numerical Atmospheric‐dispersion Modelling Environment model, we are able to constrain SO2emission rates to up to 120 kilotons per day (kt/d) during early September 2014, followed by a decrease to 20–60 kt/d between 6 and 22 September 2014, followed by a renewed increase to 60–120 kt/d until the end of September 2014. Based on these fluxes, we estimate that the eruption emitted a total of 2.0 ± 0.6 Tg of SO2during September 2014, in good agreement with ground‐based remote sensing and petrological estimates. Although satellite‐derived and model‐simulated vertical column densities of SO2agree well, the model simulations are biased low by up to a factor of 8 when compared to surface observations of volcanic SO2on 6–7 September 2014 in Ireland. These biases are mainly due to relatively small horizontal and vertical positional errors in the simulations of the volcanic plume occurring over transport distances of thousands of kilometers. Although the volcanic air pollution episodes were transient and lava‐dominated volcanic eruptions are sporadic events, the observations suggest that (i) during an eruption, volcanic SO2measurements should be assimilated for near real‐time air quality forecasting and (ii) existing air quality monitoring networks should be retained or extended to monitor SO2and other volcanic pollutants. The 2014–2015 Icelandic fissure eruption at Bárðarbunga affected air quality in far fieldSatellite‐derived average daily SO2burden of 99 ± 49 kt (OMI) and 61 ± 18 kt (IASI) in September 2014We calculate a total release of 2.0 ± 0.6 Tg of SO2for September 2014 and fluxes up to 120 kt/d

Abstract: The NAME Atmospheric Dispersion Model is a Lagrangian particle model used by the Met Office to predict the propagation and spread of pollutants in the atmosphere. The model is routinely used in emergency response applications, where it is important to obtain results as quickly as possible. This requirement for a short runtime and the increase in core number of commonly available CPUs, such as the Intel Xeon series, has motivated the parallelisation of NAME in the OpenMP shared memory framework. In this work we describe the implementation of this parallelisation strategy in NAME and discuss the performance of the model for different setups. Due to the independence of the model particles, the parallelisation of the main compute intensive loops is relatively straightforward. The random number generator for modelling sub-grid scale turbulent motion needs to be adapted to ensure that different particles use independent sets of random numbers. We find that on Intel Xeon X5680 CPUs the model shows very good strong scaling up to 12 cores in a realistic emergency response application for predicting the dispersion of volcanic ash in the North Atlantic airspace. We implemented a mechanism for asynchronous reading of meteorological data from disk and demonstrate how this can reduce the runtime if disk access plays a significant role in a model run. To explore the performance on different chip architectures we also ported the part of the code which is used for calculating the gamma dose from a cloud of radioactive particles to a graphics processing unit (GPU) using CUDA-C. We were able to demonstrate a significant speedup of around one order of magnitude relative to the serial CPU version. [Copyright &y& Elsevier]