Urban heat mitigation by green and blue infrastructure: Drivers, effectiveness, and future needs

The combination of urbanization 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 infrastructure (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 synthesizes the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximize their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorized 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 prioritizing effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience.

An early warning system to predict and mitigate wheat rust diseases in Ethiopia

Wheat rust diseases pose one of the greatest threats to global food security, including subsistence farmers in Ethiopia. The fungal spores transmitting wheat rust are dispersed by wind and can remain infectious after dispersal over long distances. The emergence of new strains of wheat rust has exacerbated the risks of severe crop loss. We describe the construction and deployment of a near realtime early warning system (EWS) for two major wind-dispersed diseases of wheat crops in Ethiopia that combines existing environmental research infrastructures, newly developed tools and scientific expertise across multiple organisations in Ethiopia and the UK. The EWS encompasses a sophisticated framework that integrates field and mobile phone surveillance data, spore dispersal and disease environmental suitability forecasting, as well as communication to policy-makers, advisors and smallholder farmers. The system involves daily automated data flow between two continents during the wheat season in Ethiopia. The framework utilises expertise and environmental research infrastructures from within the cross-disciplinary spectrum of biology, agronomy, meteorology, computer science and telecommunications. The EWS successfully provided timely information to assist policy makers formulate decisions about allocation of limited stock of fungicide during the 2017 and 2018 wheat seasons. Wheat rust alerts and advisories were sent by short message service and reports to 10 000 development agents and approximately 275 000 smallholder farmers in Ethiopia who rely on wheat for subsistence and livelihood security. The framework represents one of the first advanced crop disease EWSs implemented in a developing country. It provides policy-makers, extension agents and farmers with timely, actionable information on priority diseases affecting a staple food crop. The framework together with the underpinning technologies are transferable to forecast wheat rusts in other regions and can be readily adapted for other wind-dispersed pests and disease of major agricultural crops.

Microsatellite Analysis and Urediniospore Dispersal Simulations Support the Movement of Puccinia graminis f. sp. tritici from Southern Africa to Australia

The Australian wheat stem rust (Puccinia graminis f. sp. tritici) population was shaped by the introduction of four exotic incursions into the country. It was previously hypothesized that at least two of these (races 326-1,2,3,5,6 and 194-1,2,3,5,6 first detected in 1969) had an African origin and moved across the Indian Ocean to Australia on high-altitude winds. We provide strong supportive evidence for this hypothesis by combining genetic analyses and complex atmospheric dispersion modeling. Genetic analysis of 29 Australian and South African P. graminis f. sp. tritici races using microsatellite markers confirmed the close genetic relationship between the South African and Australian populations, thereby confirming previously described phenotypic similarities. Lagrangian particle dispersion model simulations using finely resolved meteorological data showed that long distance dispersal events between southern Africa and Australia are indeed possible, albeit rare. Simulated urediniospore transmission events were most frequent from central South Africa (viable spore transmission on approximately 7% of all simulated release days) compared with other potential source regions in southern Africa. The study acts as a warning of possible future P. graminis f. sp. tritici dispersal events from southern Africa to Australia, which could include members of the Ug99 race group, emphasizing the need for continued surveillance on both continents.

Communicating Geographical Risks in Crisis Management: The Need for Research

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.

Quantifying airborne dispersal routes of pathogens over continents to safeguard global wheat supply

Infectious crop diseases spreading over large agricultural areas pose a threat to food security. Aggressive strains of the obligate pathogenic fungus Puccinia graminis f.sp. tritici (Pgt), causing the crop disease wheat stem rust, have been detected in East Africa and the Middle East, where they lead to substantial economic losses and threaten livelihoods of farmers. The majority of commercially grown wheat cultivars worldwide are susceptible to these emerging strains, which pose a risk to global wheat production, because the fungal spores transmitting the disease can be wind-dispersed over regions and even continents ^1-11 . Targeted surveillance and control requires knowledge about airborne dispersal of pathogens, but the complex nature of long-distance dispersal poses significant challenges for quantitative research ^12-14 . We combine international field surveys, global meteorological data, a Lagrangian dispersion model and high-performance computational resources to simulate a set of disease outbreak scenarios, tracing billions of stochastic trajectories of fungal spores over dynamically changing host and environmental landscapes for more than a decade. This provides the first quantitative assessment of spore transmission frequencies and amounts amongst all wheat producing countries in Southern/East Africa, the Middle East and Central/South Asia. We identify zones of high air-borne connectivity that geographically correspond with previously postulated wheat rust epidemiological zones (characterized by endemic disease and free movement of inoculum) ^10,15 , and regions with genetic similarities in related pathogen populations ^16,17 . We quantify the circumstances (routes, timing, outbreak sizes) under which virulent pathogen strains such as 'Ug99' ^5,6 pose a threat from long-distance dispersal out of East Africa to the large wheat producing areas in Pakistan and India. Long-term mean spore dispersal trends (predominant direction, frequencies, amounts) are summarized for all countries in the domain (Supplementary Data). Our mechanistic modelling framework can be applied to other geographic areas, adapted for other pathogens and used to provide risk assessments in real-time ³ .

Large-Scale Atmospheric Dispersal Simulations Identify Likely Airborne Incursion Routes of Wheat Stem Rust Into Ethiopia

In recent years, severe wheat stem rust epidemics hit Ethiopia, sub-Saharan Africa's largest wheat-producing country. These were caused by race TKTTF (Digalu race) of the pathogen Puccinia graminis f. sp. tritici, which, in Ethiopia, was first detected at the beginning of August 2012. We use the incursion of this new pathogen race as a case study to determine likely airborne origins of fungal spores on regional and continental scales by means of a Lagrangian particle dispersion model (LPDM). Two different techniques, LPDM simulations forward and backward in time, are compared. The effects of release altitudes in time-backward simulations and P. graminis f. sp. tritici urediniospore viability functions in time-forward simulations are analyzed. Results suggest Yemen as the most likely origin but, also, point to other possible sources in the Middle East and the East African Rift Valley. This is plausible in light of available field surveys and phylogenetic data on TKTTF isolates from Ethiopia and other countries. Independent of the case involving TKTTF, we assess long-term dispersal trends (>10 years) to obtain quantitative estimates of the risk of exotic P. graminis f. sp. tritici spore transport (of any race) into Ethiopia for different 'what-if' scenarios of disease outbreaks in potential source countries in different months of the wheat season.

An assessment of the doses received by members of the public in Japan following the nuclear accident at Fukushima Daiichi nuclear power plant

The earthquake and tsunami on 11 March 2011, centred off the east coast of Japan, caused considerable destruction and substantial loss of life along large swathes of the Japanese coastline. The tsunami damaged the Fukushima Daiichi nuclear power plant (NPP), resulting in prolonged releases of radioactive material into the environment. This paper assesses the doses received by members of the public in Japan. The assessment is based on an estimated source term and atmospheric dispersion modelling rather than monitoring data. It is evident from this assessment that across the majority of Japan the estimates of dose are very low, for example they are estimated to be less than the annual average dose from natural background radiation in Japan. Even in the regions local to Fukushima Daiichi NPP (and not affected by any form of evacuation) the maximum lifetime effective dose is estimated to be well below the cumulative natural background dose over the same period. The impact of the urgent countermeasures on the estimates of dose was considered. And the relative contribution to dose from the range of exposure pathways and radionuclides were evaluated. Analysis of estimated doses focused on the geographic irregularity and the impact of the meteorological conditions. For example the dose to an infant's thyroid received over the first year was estimated to be greater in Hirono than in the non-evacuated region of Naraha, despite Hirono being further from the release location. A number of factors were identified and thought to contribute towards this outcome, including the local wind pattern which resulted in the recirculation of part of the release. The non-uniform nature of dose estimates strengthens the case for evaluations based on dispersion modelling.

Sensitivity of the modelled deposition of Caesium-137 from the Fukushima Dai-ichi nuclear power plant to the wet deposition parameterisation in NAME

This paper describes an investigation into the impact of different meteorological data sets and different wet scavenging coefficients on the model predictions of radionuclide deposits following the accident at the Fukushima Dai-ichi nuclear power plant in March 2011. Three separate operational meteorological data sets, the UK Met Office global meteorology, the ECMWF global meteorology and the Japan Meteorological Agency (JMA) mesoscale meteorology as well as radar rainfall analyses from JMA were all used as inputs to the UK Met Office's dispersion model NAME (the Numerical Atmospheric-dispersion Modelling Environment). The model predictions of Caesium-137 deposits based on these meteorological models all showed good agreement with observations of deposits made in eastern Japan with correlation coefficients ranging from 0.44 to 0.80. Unexpectedly the NAME run using radar rainfall data had a lower correlation coefficient (R = 0.66), when compared to observations, than the run using the JMA mesoscale model rainfall (R = 0.76) or the run using ECMWF met data (R = 0.80). Additionally the impact of modifying the wet scavenging coefficients used in the parameterisation of wet deposition was investigated. The results showed that modifying the scavenging parameters had a similar impact to modifying the driving meteorology on the rank calculated from comparing the modelled and observed deposition.

Key factors in imprecision in radiological emergency response assessments using the NAME model

In the very early stages of response to an accidental release of radioactivity leading to environmental contamination, it is likely that only limited measurements of radioactivity in the local environment will be available on which to base decisions concerning protection measures and radiation monitoring activities. Model predictions will be used to aid understanding of the radiological situation and to form a basis for emergency health protection decisions. This paper presents an analysis of the relative importance of contributors to the imprecision associated with emergency response calculations based on a few off-site measurements, using predictions from the UK Met Office's NAME III (Numerical Atmospheric dispersion Modelling Environment version 5.2) dispersion model. The results presented extend those from a previous study in which a simple Gaussian plume model was used and confirm the key parameters contributing to imprecision. The potential extent of the sheltering countermeasure resulting from a hypothetical release in real weather conditions occurring in 2007 and 2008 is also presented.