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Encouraging a cycling culture while reducing car usage can lead to substantial health and environmental benefits. In this exploratory work, we use an agent-based model of commuter cycling focusing on the emergence of social norms due to interactions between agents and their environment (including their social networks). The overall goal is to develop an understanding of change and continuity in cycle commuting, and how this is shaped by dynamic relationships between social expectations and individual attributes. Initial characteristics of agents and the distribution of cycling in the population come from the Census and secondary quantitative data. The theoretical basis of our work comes from qualitative studies on cycling and from 'Theories of Practice', which see practices as enrolling individuals, depending on whether they have the material and cultural resources required to participate. Thus, rather than treating humans as rational individuals who, for example, follow the precepts of utility maximization, we explore how changes in norms surrounding cycling practices (such as social expectations around clothing and accessories) shape uptake, and how uptake then affects social norms relating to cycling., We test policies for increasing cycling usage based on provision of cycling stuff and improvements to the environment.

Recently, there has been a strong interest in the climate emergency and the human health impacts of climate change. Although estimates have been quoted, the modeling methods used have either been simplistic or opaque, making it difficult for policy makers to have confidence in these estimates. Providing central estimates of health impacts, without any quantification of their uncertainty, is deficient because such an approach does not acknowledge the inherent uncertainty in extreme environmental exposures associated with spiraling climate change and related health impacts. Furthermore, presenting only the uncertainty bounds around central estimates, without information on how the uncertainty in each of the model parameters and assumptions contribute to the total uncertainty, is insufficient because this approach hides those parameters and assumptions which contribute most to the total uncertainty. We propose a framework for calculating the catastrophic human health impacts of spiraling climate change and the associated uncertainties. Our framework comprises three building blocks: (A) a climate model to simulate the environmental exposure extremes of spiraling climate change; (B) a health impact model which estimates the health burdens of the extremes of environmental exposures; and (C) an analytical mathematical method which characterizes the uncertainty in (A) and (B), propagates the uncertainty in-between and through these models, and attributes the proportion of uncertainty in the health outcomes to model assumptions and parameter values. Once applied, our framework can be of significant value to policy makers because it handles uncertainty transparently while taking into account the complex interactions between climate and human health.

This systematic review aims to synthesize how agent-based modeling (ABM) has been used in migration and modern slavery research and provide the basis to model development for social science researchers exploring the use of ABM. We searched five bibliographic databases using two terminology categories: (1) migration or modern slavery terminology; (2) complex system methods terminology. Two reviewers conducted independent article screening. Peer-reviewed articles presenting original migration or modern slavery ABMs were included. Data extraction included model development steps and model characteristics. The dataset was synthesized and compared across studies. We identified 28 articles for inclusion. Many of the ABMs tested theories and about half were based on empirical data. Model development varied considerably and reported methods were extremely opaque. Only five studies used a structured development framework. The most common model involved agents deciding whether and where to migrate and attempting migration. Climate change was a common exogenous scenario modeled. Most of the ABMs did not undergo any sensitivity analysis or validation.ABM has a greater capacity to account for heterogeneous and dynamic decision-making than more frequently applied methods in research on migration and modern slavery. However, there is still a paucity of studies adopting ABM methods. These reviewed ABMs highlight gaps in the reporting and implementing of model development. ABM is a promising technique to address many urgent and complex questions in research on migration and modern slavery to better support decision-makers, but addressing current methodological gaps is a critical first step.

The UK has introduced ambitious legislation for reaching net zero greenhouse gas (GHG) emissions by 2050. Improving the energy efficiency of homes is a key priority in achieving this target and solutions include minimising unwanted heat losses and decarbonising heating and cooling. Making a dwelling more airtight and applying insulation can result in a lower energy demand by reducing unwanted heat loss through fabric and openings. However, the supply of sufficient outdoor air is required to dilute indoor airborne pollutants. This research investigates the relationship between dwelling air infiltration and self-reported health at population neighbourhood level for Greater London. This paper links data from a variety of sources including Energy Performance Certificates (EPCs), the Greater London Authorities’ Large Super Output Area (LSOA) Atlas and the Access to Healthy Assets and Hazards (AHAH) database at LSOA level. Beta regression has been performed to assess the influence of air infiltration rate on self-reported health, whilst controlling for other socioeconomic factors. All factors have been ranked in order of their association with self-reported health. Findings indicate that air infiltration rate has a positive association with the percentage of people reporting themselves to be in “good or very good” health.

Additional file 1. Search criteria used for each database.

BACKGROUND: In 2016, 23% of children (155 million) aged [Formula: see text] were stunted. Global-level modeling has consistently found climate change impacts on food production are likely to impair progress on reducing undernutrition. OBJECTIVES: We adopt a new perspective, assessing how climate change may affect child stunting via its impacts on two interacting socioeconomic drivers: incomes of the poorest 20% of populations (due to climate impacts on crop production, health, labor productivity, and disasters) and food prices. METHODS: We developed a statistical model to project moderate and severe stunting in children aged [Formula: see text] at the national level in 2030 under low and high climate change scenarios combined with poverty and prosperity scenarios in 44 countries. RESULTS: We estimated that in the absence of climate change, 110 million children aged [Formula: see text] would be stunted in 2030 under the poverty scenario in comparison with 83 million under the prosperity scenario. Estimates of climate change-attributable stunting ranged from 570,000 under the prosperity/low climate change scenario to [Formula: see text] under the poverty/high climate change scenario. The projected impact of climate change on stunting was greater in rural vs. urban areas under both socioeconomic scenarios. In countries with lower incomes and relatively high food prices, we projected that rising prices would tend to increase stunting, whereas in countries with higher incomes and relatively low food prices, rising prices would tend to decrease stunting. These findings suggest that food prices that provide decent incomes to farmers alongside high employment with living wages will reduce undernutrition and vulnerability to climate change. CONCLUSIONS: Shifting the focus from food production to interactions between incomes and food price provides new insights. Futures that protect health should consider not just availability, accessibility, and quality of food, but also the incomes generated by those producing the food. https://doi.org/10.1289/EHP2916.

OBJECTIVES: We examine if temperatures in winter in English homes meet the recommendation of being at least 18°C at all times. We analyse how many days meet this criterion and calculate the hours per day and night being at/above 18°C. These metrics are compared between households with occupants aged above 64 years or having a long-term disability (LTD) and those younger and without disability. DESIGN: Cross-sectional, observational. SETTING: England. PARTICIPANTS: 635 households. OUTCOMES MEASURES: (1) Mean temperatures, (2) proportion of days of the measurement period meeting the criterion, (3) average hours at/above 18°C, (4) average hours at night at/above 18°C. RESULTS: Mean winter temperatures in the bedroom were MBR=18.15°C (SD=2.51), the living room MLR=18.90°C (SD=2.46) and the hallway MHall=18.25°C (SD=2.57).The median number of days meeting the criterion was 19-31%. For the living room, more days meet the criterion in the group with a LTD (Mdisability=342 vs Mno_disability=301, 95% CI 8 to 74), and with someone over 64 years present (Mabove64=341, Mbelow65=301 95%, CI 8 to 74).The median number of hours/day meeting the criterion was 13-17. In the living room, households with a disability had more hours at 18°C (Mdisability=364, Mno_disability=297, 95% CI 17 to 83) as did the older age group (Mabove64=347, Mbelow65=296, 95% CI 18 to 84). In the hallway, more hours met the criterion in households with a disability (Mdisability=338, Mno_disability=302, 95% CI 3 to 70).247 homes had at least nine hours of at least 18°C at night; no effect of age or disability. CONCLUSIONS: Many households are at risk of negative health outcomes because of temperatures below recommendations.

Societal impact is an increasingly important imperative of academicfunding. However, there is little research to date documenting how impactis accomplished in practice. Drawing on insights from Actor NetworkTheory, we explore the research-policy interface within an inter-disciplinaryresearch project on the relationships between air pollution and humanhealth. Health policy impact was important to the researchers for moral aswell as pragmatic reasons, but it was a goal that was seen as potentially intension with that of doing science. In fields such as air pollution andhealth, networks of policy makers and researchers are inevitably entangledand processes of engagement operated to delineate ‘science’ from‘policy’. ‘Health’ was initially black boxed and under-explicated, used as asignifier in itself for societal impact. By mobilising networks of policyactors, brought together in workshops to rank the importance of policyscenarios for the research team, the connections between air pollution andhealth were materialised and made actionable. This was achieved byframing existing data sets, emission technologies, policy expertise,pollutant species and human health in particular ways and, in doing so,excluding others. The process of linking air pollution and health research toachieve societal impact not only influenced how these phenomena wereknown but, critically, enabled and constrained potential policyresponses. Tracing these research arrangements made the materialdiscursive processes of ‘impact’ visible and analysable as objects of socialscience scholarship, and therefore generated a productive site for criticallyengaging with processes of environment and health science and policy

This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km × 5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model–measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2. 5, and PM10 (daily maximum 8 h running mean for O3). The comparison was temporally and spatially comprehensive, covering a 10-year period (2 years for PM2. 5) and all non-roadside measurement data from the UK national reference monitor network, which applies consistent operational and QA/QC procedures for each pollutant (44, 47, 24, and 30 sites for NO2, O3, PM2. 5, and PM10, respectively). Two important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – together with root mean square error, were evaluated by site type, year, month, and day-of-week. Model–measurement statistics were generally better than, or comparable to, values that allow for realistic magnitudes of measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2, and PM2. 5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2. 5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at rural background sites (median normalized mean bias (NMB) values for daily O3 and NO2 of 8 and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB = −29 %) and PM2. 5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km × 5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions (e.g. closer to traffic sources) than the grid average. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. some components of wind-blown dust. There were instances of monthly and weekday/weekend variations in the extent of model–measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model–measurement differences (aside from grid versus monitor spatial representivity) was inaccuracy of model emissions – both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals – rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.

Background: Globally, there is increasing concern about the potential health and economic consequences of current obesity trends. This study assessed the impact of unchecked obesity and the benefits of reducing population weight levels in Malta. Methods: Body mass index rates, disease burden and direct health care costs for breast and colon cancer, coronary heart disease, diabetes, hypertension and stroke in Malta were projected to 2035 using a two-stage microsimulation model. Two scenarios were modelled for population weight reduction. Research: By 2035, uncontrolled overweight and obesity are projected to result in a 62% increase in stroke prevalence, a 27% increase in obesity-related cancers and a 16% increase in prevalence of hypertension. The prevalence of coronary heart disease developing as a consequence of obesity is expected to double within the next two decades. Associated cumulative direct health care costs will amount to around €814 million by 2035. However, a 5% reduction in the average population body mass index by 2035 is projected to result in a saving of €141 million in health expenditure on obesity-related conditions over the intervening 20 years. Conclusion: These findings have important implications for obesity policy in Malta and other European nations with similar health systems and levels of population obesity, thus highlighting the need for effective population-level preventive strategies., peer-reviewed

Average temperatures worldwide are expected to continue to rise. At the same time, major cities in developing countries are becoming increasingly populated and polluted. Governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of a model, which is able to estimate the occupant exposure to extreme temperatures and high air pollution within domestic buildings. Building physics simulations were performed using the EnergyPlus building physics software. An accurate metamodel is then formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) have been compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus., {"references":["D. J. Rowlands et al. Broad range of 2050 warming from an\nobservationally constrained large climate model ensemble. Nature\nGeoscience, 5:256–260, 03/2012 2012.","S. Hajat, S. Vardoulakis, C. Heaviside, and B. Eggen. Climate change\neffects on human health: projections of temperature-related mortality for\nthe uk during the 2020s, 2050s and 2080s. Journal of Epidemiology and\nCommunity Health, 2014.","World Health Organisation. Ambient (outdoor) air pollution in cities\ndatabase 2014.","A. Mavrogianni, P. Wilkinson, M. Davies, P. Biddulph, and\nE. Oikonomou. Building characteristics as determinants of propensity\nto high indoor summer temperatures in London dwellings. Building and\nEnvironment, (55):117–30, 2012.","J. Taylor, A. 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There is growing evidence that projected climate change has the potential to significantly affect public health. In the UK, much of this impact is likely to arise by amplifying existing risks related to heat exposure, flooding, and chemical and biological contamination in buildings. Identifying the health effects of climate change on the indoor environment, and risks and opportunities related to climate change adaptation and mitigation, can help protect public health. We explored a range of health risks in the domestic indoor environment related to climate change, as well as the potential health benefits and unintended harmful effects of climate change mitigation and adaptation policies in the UK housing sector. We reviewed relevant scientific literature, focusing on housing-related health effects in the UK likely to arise through either direct or indirect mechanisms of climate change or mitigation and adaptation measures in the built environment. We considered the following categories of effect: (i) indoor temperatures, (ii) indoor air quality, (iii) indoor allergens and infections, and (iv) flood damage and water contamination. Climate change may exacerbate health risks and inequalities across these categories and in a variety of ways, if adequate adaptation measures are not taken. Certain changes to the indoor environment can affect indoor air quality or promote the growth and propagation of pathogenic organisms. Measures aimed at reducing greenhouse gas emissions have the potential for ancillary public health benefits including reductions in health burdens related heat and cold, indoor exposure to air pollution derived from outdoor sources, and mould growth. However, increasing airtightness of dwellings in pursuit of energy efficiency could also have negative effects by increasing concentrations of pollutants (such as PM2.5, CO and radon) derived from indoor or ground sources, and biological contamination. These effects can largely be ameliorated by mechanical ventilation with heat recovery (MVHR) and air filtration, where such solution is feasible and when the system is properly installed, operated and maintained. Groups at high risk of these adverse health effects include the elderly (especially those living on their own), individuals with pre-existing illnesses, people living in overcrowded accommodation, and the socioeconomically deprived. A better understanding of how current and emerging building infrastructure design, construction, and materials may affect health in the context of climate change and mitigation and adaptation measures is needed in the UK and other high income countries. Long-term, energy efficient building design interventions, ensuring adequate ventilation, need to be promoted.