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Outdoor temperatures experienced by people in urban areas are typically higher than those in rural areas due to the urban heat island, but indoor temperatures also vary as a result of differences between buildings. We combined modelling of temperature at city and building scales, to determine the most important sources of variation in exposure to heat stress across London and Southeast England. We modelled outdoor temperatures using the WRF model with BEP-BEM for the hot summer of 2018, and used the output to force the EnergyPlus building model to estimate indoor temperatures of residential buildings.We investigated variations in the residential building stock across the region, as well as demographics. The latter is important because health risks posed by heat also vary with the population’s age, health, and socioeconomic status.Within the study region, we found large differences in exposure to heat between the urban population of London and the general population of Southeast England, and that as a result outdoor heat exposure is highest for ethnic minority groups, younger people, and the more socioeconomically deprived. However, variations in outdoor temperature within the urban areas of London were much smaller than the variations between London and the rest of the southeast.We compared the variation in the outdoor temperature with the variation in indoor temperature, building on evidence that flats (apartments) are more likely to overheat, and are more often located in hotter parts of the city than houses. This has implications for interventions on the built environment designed to reduce the health impacts of heat.By combining thermal modelling at different scales with demographic data, we aim to improve understanding of the sources of personal variations in heat-hazard within the population.

Irrigation and urban greening can mitigate extreme temperatures and reduce adverse health impacts from heat. However, some recent studies suggest these interventions could actually exacerbate heat stress by increasing humidity. These studies use different heat stress indices (HSIs), hindering intercomparisons of the relative roles of temperature and humidity. Our novel method compares the sensitivity of HSIs to temperature and humidity, independent of HSI units. Using this method, we explain the properties of different HSIs and identify the conditions under which they agree or disagree. We highlight recent studies where the use of different HSIs could have led to opposite conclusions. Our findings have significant implications for the evaluation of irrigation and urban greening as adaptive responses to overheating, and climate adaptation measures in general. We urge researchers to be more critical in their choice of HSIs; our method provides a useful tool for making informed comparisons.

Personal weather stations (PWS) can provide useful data on urban climates by densifying the number of weather measurements across major cities. They do so at a lower cost than official weather stations by national meteorological services. Despite the increasing use of PWS data, little attention has yet been paid to the underlying socio-economic and environmental inequalities in PWS coverage. Using social deprivation, demographic, and environmental indicators in England and Wales, we characterize existing inequalities in the current coverage of PWS. We find that there are fewer PWS in more deprived areas which also observe higher proportions of ethnic minorities, lower vegetation coverage, higher building height and building surface fraction, and lower proportions of inhabitants under 65 years old. This implies that data on urban climate may be of lower quality or more uncertain in particular areas, which may limit the potential for climate adaptation and empowerment in those communities.

Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate-resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine learning algorithm based on U-Net (Ronneberger et al., 2015) to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset (https://doi.org/10.5281/zenodo.7603123, Simpson et al., 2023). We estimate that there was 0.23 km2 of green roof in the Central Activities Zone (CAZ) of London, 1.07 km2 in Inner London, and 1.89 km2 in Greater London in the year 2021. This corresponds to 2.0 % of the total building footprint area in the CAZ and 1.3 % in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9 % of the total building footprint area. Test set accuracy was 0.99, with an F score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.

During the last decades, pastoralist, and agropastoralist populations of the world’s drylands have become exceedingly vulnerable to regional and global changes. Specifically, exacerbated stressors imposed on these populations have adversely affected their food security status, causing humanitarian emergencies and catastrophes. Of these stressors, climate variability and change, land-use and management practices, and dynamics of human demography are of a special importance. These factors affect all four pillars of food security, namely, food availability, access to food, food utilization, and food stability. The objective of this study was to critically review relevant literature to assess the complex web of interrelations and feedbacks that affect these factors. The increasing pressures on the world’s drylands necessitate a comprehensive analysis to advise policy makers regarding the complexity and linkages among factors, and to improve global action. The acquired insights may be the basis for alleviating food insecurity of vulnerable dryland populations. info:eu-repo/semantics/acceptedVersion

Concerns about the cooling requirements of buildings and negative health effects from heat exposure are increasing with the public’s awareness of climate change. Green roofs have been considered a powerful mitigative and/or adaptive tool to reduce negative impacts of heat. In fact, they can reduce summer indoor-temperatures, as evapotranspiration increases latent heat-flux from a building’s roof. The cooling effect seems to be small on outdoor air temperatures, according to past studies, although green roofs have other benefits in terms of biodiversity, carbon storage, improved building thermal performance and flood management. As the need for sustainable and climate resilient building designs becomes the norm in cities, it is important to assess the status of green roofs coverage and explore potential for future implementation. Accurate information on the prevalence and characteristics of existing green roofs is indeed required to estimate any effect of green roofs on outdoor and indoor temperatures, although this information is often lacking.Surveying Greater London, we identified existing green roofs and estimated the potential for buildings to be retrofitted with green roofs. Existing green roofs were identified using automated classification of aerial and satellite imagery. Potential for retrofit is assessed using a geospatial database of building characteristics, together with a digital surface model.The current total green roof area in Greater London is around 1.5 square kilometres (around 0.5% of built area). We estimate that retrofitting existing suitable buildings could add another 3 square kilometres, corresponding to around 2% to the built area in Central London, and around 1% outside Central London. Existing green roofs appear mainly on new buildings rather than being retrofitted, and mainly occur on office and commercial buildings in Central London and residential blocks in redeveloped areas. Potential for retrofit may be highest in the borough of Tower Hamlets, largely on residential blocks with flat roofs. This work has direct relevance to sustainable planning policy, especially the London Plan Overheating and cooling policy, and will enable modelling of the building-stock and city-scale effects of green roofs.

Global temperatures have shown a warming trend over the last century, mainly as a result of anthropogenic activities. Rising temperatures are a potential cause for increase of extreme climate events, such as heat waves, both in severity and frequency. Under an increasing extreme event scenario, the world population of mid- and low-latitude countries is more vulnerable to heat related mortality and morbidity. In India, the events occurred in recent years have made this vulnerability clear, since the numbers of heat related deaths are on a rise.Over India, the heat waves occur during the months of April to June and can impact various sectors including health, agriculture, ecosystems and the national economy. In May 2015, a severe heat wave due to the delayed onset of southwest monsoon affected parts of south-eastern India, which claimed more than 2500 lives. Preliminary results show the prevalence of Heat events in six different regions of India during the pre-monsoon (March, April, May) and transitional (May, June, July) months.We consider daily maximum temperatures and NOAA’s Heat Index (HI), a combination of temperature and relative humidity (also known as apparent temperature) which gives an insight into the discomfort because of increment in humidity. It is important to take HI along with temperature anomalies, since humidity also plays a role in transitional period.Heatwaves over India are known to be linked with mortality and have indirect impacts on human health. To evaluate the heat related risk of mortality on Indian population, indicators and clusters of heat events were computed by taking into account population weighted temperature exposure.

Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering 6 years (2015–2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio-temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ) – a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by about 1.0 °C to 1.5 °C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22±0.96 °C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to 1.57 °C, on average and negatively by down to -1.21 °C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS when considering future urban design. publishedVersion

Graphical abstract, Climate change means the UK will experience warmer winters and hotter summers in the future. Concurrent energy efficiency improvements to housing may modify indoor exposures to heat or cold, while population aging may increase susceptibility to temperature-related mortality. We estimate heat and cold mortality and energy consumption in London for typical (non-extreme) future climates, given projected changes in population and housing. Building physics models are used to simulate summertime and wintertime indoor temperatures and space heating energy consumption of London dwellings for ‘baseline’ (2005–2014) and future (2030s, 2050s) periods using data from the English Housing Survey, historical weather data, and projected future weather data with temperatures representative of ‘typical’ years. Linking to population projections, we calculate future heat and cold attributable mortality and energy consumption with demolition, construction, and alternative scenarios of energy efficiency retrofit. At current retrofit rates, around 168–174 annual cold-related deaths per million population would typically be avoided by the 2050s, or 261–269 deaths per million under ambitious retrofit rates. Annual heat deaths would typically increase by 1 per million per year under the current retrofit rate, and 12–13 per million under ambitious rates without population adaptation to heat. During typical future summers, an estimated 38–73% of heat-related deaths can be avoided using external shutters on windows, with their effectiveness lower during hotter weather. Despite warmer winters, ambitious retrofit rates are necessary to reduce typical annual energy consumption for heating below baseline levels, assuming no improvement in heating system efficiencies. Concerns over future overheating in energy efficient housing are valid but increases in heat attributable mortality during typical and hot (but not extreme) summers are more than offset by significant reductions in cold mortality and easily mitigated using passive measures. More ambitious retrofit rates are critical to reduce energy consumption and offer co-benefits for reducing cold-related mortality.

The ambition to develop sustainable and healthy cities requires city-specific policy and practice founded on a multidisciplinary evidence base, including projections of human-induced climate change. A cascade of climate models of increasing complexity and resolution is reviewed, which provides the basis for constructing climate projections—from global climate models with a typical horizontal resolution of a few hundred kilometres, through regional climate models at 12–50 km to convection-permitting models at 1 km resolution that permit the representation of urban induced climates. Different approaches to modelling the urban heat island (UHI) are also reviewed—focusing on how climate model outputs can be adjusted and coupled with urban canopy models to better represent UHI intensity, its impacts and variability. The latter can be due to changes induced by urbanisation or to climate change itself. City interventions such as greater use of green infrastructure also have an effect on the UHI and can help to reduce adverse health impacts such as heat stress and the mortality associated with increasing heat. Examples for the Complex Urban Systems for Sustainability and Health (CUSSH) partner cities of London, Rennes, Kisumu, Nairobi, Beijing and Ningbo illustrate how cities could potentially make use of more detailed models and projections to develop and evaluate policies and practices targeted at their specific environmental and health priorities. Practice relevance Large-scale climate projections for the coming decades show robust trends in rising air temperatures, including more warm days and nights, and longer/more intense warm spells and heatwaves. This paper describes how more complex and higher resolution regional climate and urban canopy models can be combined with the aim of better understanding and quantifying how these larger scale patterns of change may be modified at the city or finer scale. These modifications may arise due to urbanisation and effects such as the UHI, as well as city interventions such as the greater use of grey and green infrastructures. There is potential danger in generalising from one city to another—under certain conditions some cities may experience an urban cool island, or little future intensification of the UHI, for example. City-specific, tailored climate projections combined with tailored health impact models contribute to an evidence base that supports built environment professionals, urban planners and policymakers to ensure designs for buildings and urban areas are fit for future climates.

Background There is a growing discussion regarding the mortality burdens of hot and cold weather and how the balance between these may alter as a result of climate change. Net effects of climate change are often presented, and in some settings these may suggest that reductions in cold-related mortality will outweigh increases in heat-related mortality. However, key to these discussions is that the magnitude of temperature-related mortality is wholly sensitive to the placement of the temperature threshold above or below which effects are modelled. For cold exposure especially, where threshold effects are often ill-defined, choices in threshold placement have varied widely between published studies, even within the same location. Despite this, there is little discussion around appropriate threshold selection and whether reported associations reflect true causal relationships – i.e. whether all deaths occurring below a given temperature threshold can be regarded as cold-related and are therefore likely to decrease as climate warms. Objectives Our objectives are to initiate a discussion around the importance of threshold placement and examine evidence for causality across the full range of temperatures used to quantify cold-related mortality. We examine whether understanding causal mechanisms can inform threshold selection, the interpretation of current and future cold-related health burdens and their use in policy formation. Methods Using Greater London data as an example, we first illustrate the sensitivity of cold related mortality to threshold selection. Using the Bradford Hill criteria as a framework, we then integrate knowledge and evidence from multiple disciplines and areas- including animal and human physiology, epidemiology, biomarker studies and population level studies. This allows for discussion of several possible direct and indirect causal mechanisms operating across the range of ‘cold’ temperatures and lag periods used in health impact studies, and whether this in turn can inform appropriate threshold placement. Results Evidence from a range of disciplines appears to support a causal relationship for cold across a range of temperatures and lag periods, although there is more consistent evidence for a causal effect at more extreme temperatures. It is plausible that ‘direct’ mechanisms for cold mortality are likely to occur at lower temperatures and ‘indirect’ mechanisms (e.g. via increased spread of infection) may occur at milder temperatures. Conclusions Separating the effects of ‘extreme’ and ‘moderate’ cold (e.g. temperatures between approximately 8–9 °C and 18 °C in the UK) could help the interpretation of studies quoting attributable mortality burdens. However there remains the general dilemma of whether it is better to use a lower cold threshold below which we are more certain of a causal relationship, but at the risk of under-estimating deaths attributable to cold.

In order to build resilient cities in face of climate change in Sub-Saharan Africa, much is to be done to understand the impact of rapid and uncontrolled urbanization on the local climate in the region. Recent efforts by Brousse et al. (2019, 2020) demonstrated that using generic urban parameter information derived out of Local Climate Zones (LCZ ; Stewart and Oke, 2012) maps created through the World Urban Database and Access Portal Tool framework (Ching et al. 2018) may be used to model the impact of Sub-Saharan African cities on their local climate – using the case of Kampala, the capital city of Uganda. These studies showed that despite the characteristic data scarcity on urban typologies that is present in Sub-Saharan Africa, LCZ could be used as a solution for modelling and studying the urban climates in the region.Yet these conclusions were only obtained through the use of the bulk-level urban canopy model TERRA_URB, embedded in the COSMO-CLM regional climate model. We therefore test the applicability of a more complex urban canopy models – the Building Effect Parameterization coupled to the Building Energy Model (BEP-BEM) – over the region. To do so, we focus on short periods with specific meteorological conditions during the dry season spanning from December 2017 to February 2018. These are obtained through a k-means clustering over hourly weather measurements given by the automatic weather station located at the Makerere University, in the city-center of Kampala. Wind direction and speed, 2-meter air temperature, incoming short-wave radiation, precipitation, daily temperature range, 2-meter air relative humidity and near-surface pressure are used to depict 5 weather typologies (ie. clusters) during the dry season. We chose to keep only periods with 5 consecutive days of one weather typology, which results in three 5-day periods of distinct typology. We then run the model for these periods and evaluate its outputs against the state-of-the-art simulation by Brousse et al. (2020) as well as in-situ and satellite observations for certain meteorological variables. After that, we show the effect of the recent urbanization on the local climate for each of those three periods and relate it to the variability in urban heat.This study is the first to model a tropical African city at 1 km horizontal resolution using the BEP-BEM model embedded in WRF. The latter could have major implications as more complex urban canopy models coupled to building energy models could shed light on the impact of the built environment on the livability of indoor and outdoor environments in these cities. Furthermore, insights could indeed be gained on the contribution of air conditioning heat fluxes to outdoor temperatures and the energetic consumption needed to keep indoor environments at an optimal temperature. Additionally, by resolving the urban environment in three dimensions, BEP-BEM could help increase our understanding of how specific urban planning and architectural adaptation strategies (like green or cool roofs, roof top solar panel, new building materials, urban greening etc.) may increase the citizens’ thermal comfort and reduce negative health impacts under specific weather conditions.

As climate changes – potentially to a warmer state than any time during the evolution of humans – heat extremes threatening human health, global ecosystem and socio-economic fabric of our society are occurring at increasing frequency and intensity in most parts of the world. This study examines changes in global land area and population exposed to both tails of temperature distribution in changing climate since heat and cold exposure is directly associated with a range of health impacts and affects thermal comfort and occupational capacity. We first utilise the latest ECMWF atmospheric reanalysis, ERA5, to examine changes over the satellite era (since 1979), and then we explore the equivalent changes in CMIP6 archive of historical runs and future projections. Besides daily maximum and minimum of dry-bulb surface air temperature (SAT), we also consider daily extremes of the universal thermal climate index (UTCI) that includes the influence of humidity, wind and radiation encapsulating the synergetic heat exchanges between the environment and the human body. Our analysis dissects changes in spatial and temporal exposure to both heat waves and cold waves and presents metrics contrasting changes in the opposite extremes of SAT and UTCI distributions. We assess the significance of the observed, modelled and projected changes and relate them to external drivers of climate change.

The Lancet Countdown is an international collaboration that independently monitors the health consequences of a changing climate. Publishing updated, new, and improved indicators each year, the Lancet Countdown represents the consensus of leading researchers from 43 academic institutions and UN agencies. The 44 indicators of this report expose an unabated rise in the health impacts of climate change and the current health consequences of the delayed and inconsistent response of countries around the globe—providing a clear imperative for accelerated action that puts the health of people and planet above all else.\ud \ud The 2021 report coincides with the UN Framework Convention on Climate Change 26th Conference of the Parties (COP26), at which countries are facing pressure to realise the ambition of the Paris Agreement to keep the global average temperature rise to 1·5°C and to mobilise the financial resources required for all countries to have an effective climate response. These negotiations unfold in the context of the COVID-19 pandemic—a global health crisis that has claimed millions of lives, affected livelihoods and communities around the globe, and exposed deep fissures and inequities in the world's capacity to cope with, and respond to, health emergencies. Yet, in its response to both crises, the world is faced with an unprecedented opportunity to ensure a healthy future for all.

Ash, gases and particles emitted from volcanic eruptions cause disruption to air transport, but also have negative impacts on respiratory and cardiovascular health. Exposure to sulphur dioxide (SO2) and sulphate (SO4) aerosols increases the risk of mortality, and respiratory and cardiovascular hospital admissions. Ash and gases can be transported over large distances and are a potential public health risk. In 2014–15, the Barðarbunga fissure eruption at Holuhraun, Iceland was associated with high emissions of SO2 and SO4, detected at UK monitoring stations. We estimated the potential impacts on the UK population from SO2 and SO4 associated with a hypothetical large fissure eruption in Iceland for mortality and emergency hospital admissions. To simulate the effects of different weather conditions, we used an ensemble of 80 runs from an atmospheric dispersion model to simulate SO2 and SO4 concentrations on a background of varying meteorology. We weighted the simulated exposure data by population, and quantified the potential health impacts that may result in the UK over a 6-week period following the start of an eruption. We found in the majority of cases, the expected number of deaths resulting from SO2 over a 6-week period total fewer than ~100 for each model run, and for SO4, in the majority of cases, the number totals fewer than ~200. However, the 6-week simulated period with the highest SO2 was associated with 313 deaths, and the period with the highest SO4 was associated with 826 deaths. The single 6-week period relating to the highest combined SO2 and SO4 was associated with 925 deaths. Over a 5-month extended exposure period, upper estimates are for 3350 deaths, 4030 emergency cardiovascular and 6493 emergency respiratory hospitalizations. These figures represent a worst-case scenario and can inform health protection planning for effusive volcanic eruptions which may affect the UK in the future.

There is a well-established relationship between temperature and mortality, with older individuals being most at risk in high-income settings. This raises the question of the degree to which lives are being shortened by exposure to heat or cold. Years of life lost (YLL) take into account population life expectancy and age at which mortality occurs. However, YLL are rarely used as an outcome-metric in studies of temperature-related mortality. This represents an important gap in knowledge; to better comprehend potential impacts of temperature in the context of climate change and an ageing population, it is important to understand the relationship between temperature and YLL, and also whether the risks of temperature related mortality and YLL have changed over recent years. Gridded temperature data derived from observations, and mortality data were provided by the UK Met Office and the Office for National Statistics (ONS), respectively. We derived YLL for each death using sex-specific yearly life expectancy from ONS English-national lifetables. We undertook an ecological time-series regression analysis, using a distributed-lag double-threshold model, to estimate the relationship between daily mean temperature and daily YLL and mortality between 1996 and 2013 in Greater London, the West Midlands including Birmingham, and Greater Manchester. Temperature-thresholds, as determined by model best fit, were set at the 91st (for heat-effects) and 35th (for cold-effects) percentiles of the mean temperature distribution. Secondly, we analysed whether there had been any changes in heat and cold related risk of YLL and mortality over time. Heat-effects (lag 0-2 days) were greatest in London, where for each 1 °C above the heat-threshold the risk of mortality increased by 3.9% (CI 3.5%, 4.3%) and YLL increased by 3.0% (2.5%, 3.5%). Between 1996 and 2013, the proportion of total deaths and YLL attributable to heat in London were 0.50% and 0.40% respectively. Cold-effects (lag 0-27 days) were greatest in the West Midlands, where for each 1 °C below the cold-threshold, risk of mortality increased by 3.1% (2.4%, 3.7%) and YLL also increased by 3.1% (2.2%, 3.9%). The proportion of deaths and YLL attributable to cold in the West Midlands were 3.3% and 3.2% respectively. We found no evidence of decreasing susceptibility to heat and cold over time. The addition of life expectancy information into calculations of temperature-related risk and mortality burdens for English cities is novel. We demonstrate that although older individuals are at greatest risk of temperature-related mortality, heat and cold still make a significant contribution to the YLL due to premature death.

Towns and cities generally exhibit higher temperatures than rural areas for a number of reasons, including the effect that urban materials have on the natural balance of incoming and outgoing energy at the surface level, the shape and geometry of buildings, and the impact of anthropogenic heating. This localized heating means that towns and cities are often described as urban heat islands (UHIs). Urbanized areas modify local temperatures, but also other meteorological variables such as wind speed and direction and rainfall patterns. The magnitude of the UHI for a given town or city tends to scale with the size of population, although smaller towns of just thousands of inhabitants can have an appreciable UHI effect. The UHI “intensity” (the difference in temperature between a city center and a rural reference point outside the city) is on the order of a few degrees Celsius on average, but can peak at as much as 10°C in larger cities, given the right conditions. UHIs tend to be enhanced during heatwaves, when there is lots of sunshine and a lack of wind to provide ventilation and disperse the warm air. The UHI is most pronounced at night, when rural areas tend to be cooler than cities and urban materials radiate the energy they have stored during the day into the local atmosphere. As well as affecting local weather patterns and interacting with local air pollution, the UHI can directly affect health through heat exposure, which can exacerbate minor illnesses, affect occupational performance, or increase the risk of hospitalization and even death. Urban populations can face serious risks to health during heatwaves whereby the heat associated with the UHI contributes additional warming. Heat-related health risks are likely to increase in future against a background of climate change and increasing urbanization throughout much of the world. However, there are ways to reduce urban temperatures and avoid some of the health impacts of the UHI through behavioral changes, modification of buildings, or by urban scale interventions. It is important to understand the physical properties of the UHI and its impact on health to evaluate the potential for interventions to reduce heat-related impacts.

Background: Disentangling the impact of the environment on infectious diseases is crucial for health protection, preparedness and prevention. This is particularly relevant to campylobacteriosis, one of the most common bacterial food borne diseases worldwide. The incidence displays consistent but, despite many hypotheses, unexplained seasonality.Attributing and quantifying the contribution of environmental factors (e.g. temperature, humidity, wind, day length) to the burden of the disease is challenging. For example, discerning the time of onset and the duration of the impact of environmental factors on the likelihood of infection (time-lag) is difficult. Methods: We developed a novel modeling approach exploiting the linkage of approximately one- million campylobacteriosis cases over 25 years in England and Wales with meteorological datasets from diagnostic laboratory locations. Findings: We were able to identify the non-linear relationships for the incidence of campylobacteriosis cases conditional upon environmental variables.We found that the incidence depends non-monotonically with maximum air temperature and tends to reach a peak for relative humidity at around $80\%$.The analysis demonstrated that different environmental factors resulted in different time-lags and that these lags varied with season rather than being fixed values.Finally, maximum air temperature and relative humidity are estimated to contribute equally as drivers of the burden of campylobacteriosis and their contribution is twice that of day length. Interpretation: This study enhances our understanding of the dynamics at the environment-disease interface, and allows us to predict infectious disease incidence by exploiting the databases and knowledge of the history of relevant environmental variables. Funding Statement: This research was funded in part by the UK Medical Research Council (MRC) and the UK Natural Environment Council (NERC) for the MEDMI Project (MR/K019341/1); the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme; the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Environmental Change and Health at the London School of Hygiene and Tropical Medicine in partnership with Public Health England (PHE), and in collaboration with the University of Exeter, University College London, and the Met Office. Declaration of Interests: None to declare.

We examine the impact of model horizontal resolution on simulated concentrations of surface ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5), and the associated health impacts over Europe, using the HadGEM3–UKCA chemistry–climate model to simulate pollutant concentrations at a coarse (∼ 140 km) and a finer (∼ 50 km) resolution. The attributable fraction (AF) of total mortality due to long-term exposure to warm season daily maximum 8 h running mean (MDA8) O3 and annual-average PM2.5 concentrations is then calculated for each European country using pollutant concentrations simulated at each resolution. Our results highlight a seasonal variation in simulated O3 and PM2.5 differences between the two model resolutions in Europe. Compared to the finer resolution results, simulated European O3 concentrations at the coarse resolution are higher on average in winter and spring (∼ 10 and ∼ 6 %, respectively). In contrast, simulated O3 concentrations at the coarse resolution are lower in summer and autumn (∼ −1 and ∼ −4 %, respectively). These differences may be partly explained by differences in nitrogen dioxide (NO2) concentrations simulated at the two resolutions. Compared to O3, we find the opposite seasonality in simulated PM2.5 differences between the two resolutions. In winter and spring, simulated PM2.5 concentrations are lower at the coarse compared to the finer resolution (∼ −8 and ∼ −6 %, respectively) but higher in summer and autumn (∼ 29 and ∼ 8 %, respectively). Simulated PM2.5 values are also mostly related to differences in convective rainfall between the two resolutions for all seasons. These differences between the two resolutions exhibit clear spatial patterns for both pollutants that vary by season, and exert a strong influence on country to country variations in estimated AF for the two resolutions. Warm season MDA8 O3 levels are higher in most of southern Europe, but lower in areas of northern and eastern Europe when simulated at the coarse resolution compared to the finer resolution. Annual-average PM2.5 concentrations are higher across most of northern and eastern Europe but lower over parts of southwest Europe at the coarse compared to the finer resolution. Across Europe, differences in the AF associated with long-term exposure to population-weighted MDA8 O3 range between −0.9 and +2.6 % (largest positive differences in southern Europe), while differences in the AF associated with long-term exposure to population-weighted annual mean PM2.5 range from −4.7 to +2.8 % (largest positive differences in eastern Europe) of the total mortality. Therefore this study, with its unique focus on Europe, demonstrates that health impact assessments calculated using modelled pollutant concentrations, are sensitive to a change in model resolution by up to ∼ ±5 % of the total mortality across Europe.

In 2020, Covid-19-related mobility restrictions resulted in the most extensive human-made air-quality changes ever recorded. The changes in mobility are quantified in terms of outdoor air pollution (concentrations of PM2.5 and NO2) and the associated health impacts in four UK cities (Greater London, Cardiff, Edinburgh and Belfast). After applying a weather-corrected machine learning (ML) technique, all four cities show NO2 and PM2.5 concentration anomalies in 2020 when compared with the ML-predicted values for that year. The NO2 anomalies are –21% for Greater London, –19% for Cardiff, –27% for Belfast and –41% for Edinburgh. The PM2.5 anomalies are 7% for Greater London, –1% for Cardiff, –15% for Edinburgh, –14% for Belfast. All the negative anomalies, which indicate air pollution at a lower level than expected from the weather conditions, are attributable to the mobility restrictions imposed by the Covid-19 lockdowns. Spearman rank-order correlations show a significant correlation between the lowering of NO2 levels and reduction in public transport ('p' < 0.05) and driving ('p' < 0.05), which is associated with a decline in NO2-attributable mortality. These positive effects of the mobility restrictions on public health can be used to evaluate policies for improved outdoor air quality. 'Policy relevance' Finding the means to curb air pollution is very important for public health. Empirical evidence at a city scale reveals significant correlations between the reduction in vehicular transport and in ambient NO2 concentrations. The results provide justification for city-level initiatives to reduce vehicular traffic. Well-designed and effective policy interventions ('e.g'. the promotion of walking and cycling, remote working, local availability of services) can substantially reduce long-term air pollution and have positive health impacts.

Highlights • Mean city centre winter UHI intensity was +2.3 °C reaching maximum of +9.9 °C. • Winter UHI reduces cold-related mortality by 15% (266 deaths avoided). • This compares with increased mortality of 36% (96 deaths) in summer. • Winter UHI continues to have a protective effect in future climate., Exposure to heat has a range of potential negative impacts on human health; hot weather may exacerbate cardiovascular and respiratory illness or lead to heat stroke and death. Urban populations are at increased risk due to the Urban Heat Island (UHI) effect (higher urban temperatures compared with rural ones). This has led to extensive investigation of the summertime UHI and its effects, whereas far less research focuses on the wintertime UHI. Exposure to low temperature also leads to a range of illnesses, and in fact, in the UK, annual cold-related mortality outweighs heat-related mortality. It is not clearly understood to what extent the wintertime UHI may protect against cold related mortality. In this study we quantify the UHI intensity in wintertime for a heavily urbanized UK region (West Midlands, including Birmingham) using a regional weather model, and for the first time, use a health impact assessment (HIA) to estimate the associated impact on cold-related mortality. We show that the population-weighted mean winter UHI intensity was +2.3 °C in Birmingham city center, and comparable with that of summer. Our results suggest a potential protective effect of the wintertime UHI, equivalent to 266 cold-related deaths avoided (~15% of total cold-related mortality over ~11 weeks). When including the impacts of climate change, our results suggest that the number of heat-related deaths associated with the summer UHI will increase from 96 (in 2006) to 221 in the 2080s, based on the RCP8.5 emissions pathway. The protective effect of the wintertime UHI is projected to increase only slightly from 266 cold-related deaths avoided in 2009 to 280 avoided in the 2080s. The different effects of the UHI in winter and summer should be considered when assessing interventions in the built environment for reducing summer urban heat, and our results suggest that the future burden of temperature-related mortality associated with the UHI is likely to increase in summer relative to winter.