Targets for reductions in carbon emissions and energy use are often framed solely in terms of percentage reductions. However, the amount of energy used by households varies greatly, with some using considerably more than others and, therefore, potentially being able to make a bigger contribution towards overall reductions. Using two recently released UK datasets based on combined readings from over 70 million domestic energy meters and vehicle odometers, we present exploratory analyses of patterns of direct household energy usage.Whilst much energy justice work has previously focussed on energy vulnerability, mainly in low consumers, our findings suggest that a minority of areas appear to be placing much greater strain on energy networks and environmental systems than they need. Households in these areas are not only the most likely to be able to afford energy efficiency measures to reduce their impacts, but are also found to have other capabilities that would allow them to take action to reduce consumption (such as higher levels of income, education and particular configurations of housing type and tenure). We argue that these areas should therefore be a higher priority in the targeting of policy interventions

Background Air pollution exposure reduces life expectancy. Air pollution, deprivation and poor-health status combinations can create increased and disproportionate disease burdens. Problems and solutions are rarely considered in a broad public health context, but doing so can add value to air quality management efforts by reducing air pollution risks, impacts and inequalities. Methods An ecological study assessed small-area associations between air pollution (nitrogen dioxide and particulate matter), deprivation status and health outcomes in Wales, UK. Results Air pollution concentrations were highest in ‘most’ deprived areas. When considered separately, deprivation-health associations were stronger than air pollution-health associations. Considered simultaneously, air pollution added to deprivation-health associations; interactions between air pollution and deprivation modified and strengthened associations with all-cause and respiratory disease mortality, especially in ‘most’ deprived areas where most-vulnerable people lived and where health needs were greatest. Conclusion There is a need to reduce air pollution-related risks for all. However, it is also the case that greater health gains can result from considering local air pollution problems and solutions in the context of wider health-determinants and acting on a better understanding of relationships. Informed and co-ordinated air pollution mitigation and public health action in high deprivation and pollution areas can reduce risks and inequalities. To achieve this, greater public health integration and collaboration in local air quality management policy and practice is needed.

Increasing urban air pollution level in Indian cities is one of the major concerns for policy makers due to its impact on public health. The growth in population and increase in associated motorised road transport demand is one of the major causes of increasing air pollution in most urban areas along with other sources e.g., road dust, construction dust, biomass burning etc. The present study documents the development of an urban local air quality management (ULAQM) framework at urban hotspots (non-attainment area) and a pathway for the flow of information from goal setting to policy making. The ULAQM also includes assessment and management of air pollution episodic conditions at these hotspots, which currently available city/regional-scale air quality management plans do not address. The prediction of extreme pollutant concentrations using a hybrid model differentiates the ULAQM from other existing air quality management plans. The developed ULAQM framework has been applied and validated at one of the busiest traffic intersections in Delhi and Chennai cities. Various scenarios have been tested targeting the effective reductions in elevated levels of NOx and PM2.5 concentrations. The results indicate that a developed ULAQM framework is capable of providing an evidence-based graded action to reduce ambient pollution levels within the specified standard level at pre-identified locations. The ULAQM framework methodology is generalised and therefore can be applied to other non-attainment areas of the country.

Countries around the world introduced strict restrictions on movement and activities known as ‘lockdowns’ to restrict the spread of the novel coronavirus disease (COVID-19) from the end of 2019. A sudden improvement in air quality was observed globally as a result of these lockdowns. To provide insight into the changes in air pollution levels in response to the COVID-19 restrictions we have compared surface air quality data in Delhi during four phases of lockdown and the first phase of the restriction easing period (25 March to 30 June 2020) with data from a baseline period (2018–2019). Simultaneously, short-term exposure of PM_2.5 and O₃ attributed premature mortality were calculated to understand the health benefit of the change in air quality. Ground–level observations in Delhi showed that concentrations of PM₁₀, PM_2.5 and NO₂ dropped substantially in 2020 during the overall study period compared with the same period in previous years, with average reductions of ~49%, ~39%, and ~39%, respectively. An overall lower reduction in O₃ of ~19% was observed for Delhi. A slight increase in O₃ was found in Delhi’s industrial and traffic regions. The highest peak of the diurnal variation decreased substantially for all the pollutants at every phase. The decrease in PM_2.5 and O₃ concentrations in 2020, prevented 904 total premature deaths, a 60% improvement when compared to the figures for 2018–2019. The restrictions on human activities during the lockdown have reduced anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world. Implications: I am submitting herewith the manuscript entitled “Unprecedented Reduction in Air Pollution and Corresponding Short-term Premature Mortality Associated with COVID-19 Forced Confinement in Delhi, India” for potential publishing in your journal. The novelty of this research lies in: (1) we utilized ground-level air quality data in Delhi during four phases of lockdown and the first phase of unlocking period (25^th March to 30^th June) for 2020 as well as data from the baseline period (2018–2019) to provide an early insight into the changes in air pollution levels in response to the COVID-19 pandemic, (2) Chatarize the change of diurnal variation of the pollutants and (3) we assess the health risk due to PM_2.5 and O₃. Results from ground-level observations in Delhi showed that concentrations of PM₁₀, PM_2.5 and NO₂ substantially dropped in 2020 during the overall study period compared to the similar period in previous years, with an average reduction of ~49%, ~39%, and ~39%, respectively. In the case of O₃, the overall reduction was observed as ~19% in Delhi, while a slight increase was found in industrial and traffic regions. And consequently, the highest peak of the diurnal variation decreased substantially for all the pollutants. The health impact assessment of the changes in air quality indicated that 904 short-term premature deaths (~60%) were prevented due to the decline in PM_2.5 and O₃ concentrations in the study period. The restrictions on human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world.

The ’MOT’ vehicle inspection test record dataset recently released by the UK Department for Transport (DfT) provides the ability to estimate annual mileage figures for every individual light duty vehicle greater than 3 years old within Great Britain. Vehicle age, engine size and fuel type are also provided in the dataset and these allow further estimates to be made of fuel consumption, energy use, and per vehicle emissions of both air pollutants and greenhouse gases. The use of this data permits the adoption of a new vehicle-centred approach to assessing emissions and energy use in comparison to previous road-flow and national fuel consumption based approaches. The dataset also allows a spatial attribution of each vehicle to a postcode area, through the reported location of relevant vehicle testing stations. Consequently, this new vehicle data can be linked with socio-demographic data in order to determine the potential characteristics of vehicle owners.This paper provides a broad overview of the types of analyses that are made possible by these data, with a particular focus on distance driven and pollutant emissions. The intention is to demonstrate the very broad potential for this data, and to highlight where more focused analysis could be useful. The findings from the work have important implications for understanding the distributional impacts of transport related policies and targeting messaging and interventions for the reduction of car use