Modelling & Spatial Mapping of Residential-Sector Emissions for Sub-National & Urban Areas.

The residential sector accounts for 33% of energy-related Greenhouse Gas (GHG) emissions globally and must undergo rapid emissions reductions in order to support broader society-wide sustainability and net-zero transitions. Additionally, urban areas account for approximately 70% of global GHG emissions. To provide a baseline for urban climate action plans and mitigation strategies, sub-national municipalities must quantify their sectoral baseline emissions in detail and develop strategies for reducing emissions relative to these baselines. Therefore, it is important to establish clear methodologies for computing these baselines in accordance with the best available science. This paper establishes a novel methodology for developing a residential sector emissions model using a data-driven and spatial mapping approach. This would form an important component of future multi-sectoral baseline emissions inventories. •The residential sector emissions model combines publicly available census and building energy performance datasets in order to model and visualize the distribution of energy demand and resultant emissions across an urban study domain in Ireland.•The methodology presented was developed in line with the approaches and requirements of the Global Covenant of Mayors and the Intergovernmental Panel on Climate Change.•It is envisioned that this residential sector emissions model methodology could be applied in any urban area worldwide.

Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels.

Unprecedented levels of urbanization have escalated urban environmental health issues, including increased air pollution in cities globally. Strategies for mitigating air pollution, including green urban planning, are essential for sustainable and healthy cities. State-of-the-art research investigating urban greenspace and pollution metrics has accelerated through the use of vast digital data sets and new analytical tools. In this study, we examined associations between Google Street View-derived urban greenspace levels and Google Air View-derived air quality, where both have been resolved in extremely high resolution, accuracy, and scale along the entire road network of Dublin City. Particulate matter of size fraction less than 2.5 µm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and carbon dioxide were quantified using 5,030,143 Google Air View measurements, and greenspace was quantified using 403,409 Google Street View images. Significant (p < 0.001) negative associations between urban greenspace and pollution were observed. For example, an interquartile range increase in the Green View Index was associated with a 7.4% [95% confidence interval: -13.1%, -1.3%] decrease in NO2 at the point location spatial resolution. We provide insights into how large-scale digital data can be harnessed to elucidate urban environmental interactions that will have important planning and policy implications for sustainable future cities.

Towards sustainable and net-zero cities: A review of environmental modelling and monitoring tools for optimizing emissions reduction strategies for improved air quality in urban areas.

Climate change is a defining challenge for today's society and its consequences pose a great threat to humanity. Cities are major contributors to climate change, accounting for over 70% of global greenhouse gas emissions. With urbanization occurring at a rapid rate worldwide, cities will play a key role in mitigating emissions and addressing climate change. Greenhouse gas emissions are strongly interlinked with air quality as they share emission sources. Consequently, there is a great opportunity to develop policies which maximize the co-benefits of emissions reductions on air quality and health. As such, a narrative meta-review is conducted to highlight state-of-the-art monitoring and modelling tools which can inform and monitor progress towards greenhouse gas emission and air pollution reduction targets. Urban greenspace will play an important role in the transition to net-zero as it promotes sustainable and active transport modes. Therefore, we explore advancements in urban greenspace quantification methods which can aid strategic developments. There is great potential to harness technological advancements to better understand the impact of greenhouse gas reduction strategies on air quality and subsequently inform the optimal design of these strategies going forward. An integrated approach to greenhouse gas emission and air pollution reduction will create sustainable, net-zero and healthy future cities.

"Biophilic Cities": Quantifying the Impact of Google Street View-Derived Greenspace Exposures on Socioeconomic Factors and Self-Reported Health.

According to the biophilia hypothesis, humans have evolved to prefer natural environments that are essential to their thriving. With urbanization occurring at an unprecedented rate globally, urban greenspace has gained increased attention due to its environmental, health, and socioeconomic benefits. To unlock its full potential, an increased understanding of greenspace metrics is urgently required. In this first-of-a-kind study, we quantified street-level greenspace using 751 644 Google Street View images and computer vision methods for 125 274 locations in Ireland's major cities. We quantified population-weighted exposure to greenspace and investigated the impact of greenspace on health and socioeconomic determinants. To investigate the association between greenspace and self-reported health, a negative binomial regression analysis was applied. While controlling for other factors, an interquartile range increase in street-level greenspace was associated with a 2.78% increase in self-reported "good or very good" health [95% confidence interval: 2.25-3.31]. Additionally, we observed that populations in upper quartiles of greenspace exposure had higher levels of income and education than those in lower quartiles. This study provides groundbreaking insights into how urban greenspace can be quantified in unprecedented resolution, accuracy, and scale while also having important implications for urban planning and environmental health research and policy.

The Role of Ambient Particle Radioactivity in Inflammation and Endothelial Function in an Elderly Cohort.

BACKGROUND: The mechanisms by which exposure to particulate matter might increase risk of cardiovascular morbidity and mortality are not fully known. However, few existing studies have investigated the potential role of particle radioactivity. Naturally occurring radionuclides attach to particulate matter and continue to release ionizing radiation after inhalation and deposition in the lungs. We hypothesize that exposure to particle radioactivity increases biomarkers of inflammation. METHODS: Our repeated-measures study included 752 men in the greater Boston area. We estimated regional particle radioactivity as a daily spatial average of gross beta concentrations from five monitors in the study area. We used linear mixed-effects regression models to estimate short- and medium-term associations between particle radioactivity and biomarkers of inflammation and endothelial dysfunction, with and without adjustment for additional particulate air pollutants. RESULTS: We observed associations between particle radioactivity on C-reactive protein (CRP), intercellular adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1), but no associations with fibrinogen. An interquartile range width increase in mean 7-day particle radioactivity (1.2 × 10 Bq/m) was associated with a 4.9% increase in CRP (95% CI = 0.077, 9.9), a 2.8% increase in ICAM-1 (95% CI = 1.4, 4.2), and a 4.3% increase in VCAM-1 (95% CI = 2.5, 6.1). The main effects of particle radioactivity remained similar after adjustment in most cases. We also obtained similar effect estimates in a sensitivity analysis applying a robust causal model. CONCLUSION: Regional particle radioactivity is positively associated with inflammatory biomarkers, indicating a potential pathway for radiation-induced cardiovascular effects.

Associations between ambient particle radioactivity and lung function.

Previous studies have suggested increased risk of respiratory diseases and mortality following short-term exposures to ionizing radiation. However, the short-term respiratory effects of low-level environmental radiation associated with air pollution particles have not been considered. Although ambient particulate matter (PM) has been reproducibly linked to decreased lung function and to increased respiratory related morbidity, the properties of PM promoting its toxicity are uncertain. As such, we evaluated whether lung function was associated with exposures to radioactive components of ambient PM, referred to as particle radioactivity (PR). For this, we performed a repeated-measures analysis of 839 men to examine associations between PR exposure and lung function using mixed-effects regression models, adjusting for potential confounders. We examined whether PR-lung function associations changed after adjusting for PM2.5 (particulate matter≤2.5 µm) or black carbon, and vice versa. PR was measured by the USEPA's radiation monitoring network. We found that higher PR exposure was associated with a lower forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1). An IQR increase in 28-day PR exposure was associated with a 2.4% lower FVC [95% confidence interval (CI): 1.4, 3.4% p < 0.001] and a 2.4% lower FEV1 (95% CI: 1.3, 3.5%, p < 0.001). The PR-lung function associations were partially attenuated with adjustment for PM2.5 and black carbon. This is the first study to demonstrate associations between PR and lung function, which were independent of and similar in magnitude to those of PM2.5 and black carbon. If confirmed, future research should account for PR exposure in estimating respiratory health effects of ambient particles. Because of widespread exposure to low levels of ionizing radiation, our findings may have important implications for research, and environmental health policies worldwide.

Recent exposure to particle radioactivity and biomarkers of oxidative stress and inflammation: The Framingham Heart Study.

BACKGROUND: Decay products of radioactive materials may attach to ambient fine particles and form radioactive aerosol. Internal ionizing radiation source from inhaled radioactive aerosol may contribute to the fine particulate matter (PM2.5)-inflammation pathway. However, few studies in humans have examined the associations. OBJECTIVES: To examine the associations between particle radioactivity and biomarkers of oxidative stress and inflammation among participants from the Framingham Offspring and Third Generation cohorts. METHODS: We included 3996 participants who were not current smokers and lived within 50 km from our central air pollution monitoring station. We estimated regional mean gross beta radioactivity from monitors in the northeastern U.S. as a surrogate for ambient radioactive particles, and calculated the 1- to 28-day moving averages. We used linear regression models for fibrinogen, tumor necrosis factor α, interleukin-6, and myeloperoxidase which were measured once, and linear mixed effect models for 8-epi-prostaglandin F2α, C-reactive protein, intercellular adhesion molecule-1 (ICAM-1), monocyte chemoattractant protein-1 (MCP-1), P-selectin, and tumor necrosis factor receptor-2 that were measured up to twice, adjusting for demographics, individual- and area-level socioeconomic positions, time, meteorology, and PM2.5. We also examined whether the associations differed by median age, sex, diabetes status, PM2.5 levels, and black carbon levels. RESULTS: The mean age was 54 years and 54% were women. An interquartile range (3 × 10-3 pCi/m3) higher beta radioactivity level at the 7-day moving average was associated with 5.09% (95% CI: 0.92, 9.43), 2.65% (1.10, 4.22), and 4.71% (95% CI: 3.01, 6.44) higher levels of interleukin-6, MCP-1, and P-selectin, but with 7.01% (95% CI: -11.64, -2.15) and 2.70% (95% CI: -3.97, -1.42) lower levels of 8-epi-prostaglandin F2α and ICAM-1, respectively. CONCLUSIONS: Regional mean particle radioactivity was positively associated with interleukin-6, MCP-1, and P-selectin, but negatively with ICAM-1 and 8-epi-prostaglandin F2α among our study participants.

Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping.

The coordination of humanitarian relief, e.g. in a natural disaster or a conflict situation, is often complicated by a scarcity of data to inform planning. Remote sensing imagery, from satellites or drones, can give important insights into conditions on the ground, including in areas which are difficult to access. Applications include situation awareness after natural disasters, structural damage assessment in conflict, monitoring human rights violations or population estimation in settlements. We review machine learning approaches for automating these problems, and discuss their potential and limitations. We also provide a case study of experiments using deep learning methods to count the numbers of structures in multiple refugee settlements in Africa and the Middle East. We find that while high levels of accuracy are possible, there is considerable variation in the characteristics of imagery collected from different sensors and regions. In this, as in the other applications discussed in the paper, critical inferences must be made from a relatively small amount of pixel data. We, therefore, consider that using machine learning systems as an augmentation of human analysts is a reasonable strategy to transition from current fully manual operational pipelines to ones which are both more efficient and have the necessary levels of quality control.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

Associations Between Ambient Particle Radioactivity and Blood Pressure: The NAS (Normative Aging Study).

BACKGROUND: The cardiovascular effects of low-level environmental radiation exposures are poorly understood. Although particulate matter (PM) has been linked to cardiovascular morbidity and mortality, and elevated blood pressure (BP), the properties promoting its toxicity remain uncertain. Addressing a knowledge gap, we evaluated whether BP increased with higher exposures to radioactive components of ambient PM, herein referred to as particle radioactivity (PR). METHODS AND RESULTS: We performed a repeated-measures analysis of 852 men to examine associations between PR exposure and BP using mixed-effects regression models. As a surrogate for PR, we used gross ß activity, measured by the US Environmental Protection Agency's radiation monitoring network. Higher PR exposure was associated with increases in both diastolic BP and systolic BP, for exposures from 1 to 28 days. An interquartile range increase in 28-day PR exposure was associated with a 2.95-mm Hg increase in diastolic BP (95% confidence interval, 2.25-3.66; P<0.001) and a 3.94-mm Hg increase in systolic BP (95% confidence interval, 2.62-5.27; P<0.001). For models including both PR and PM ≤2.5 µm, the PR-BP associations remained stable and significant. For models including PR and black carbon or PR and particle number, the PR-BP associations were attenuated; however, they remained significant for many exposure durations. CONCLUSIONS: This is the first study to demonstrate the potential adverse effects of PR on both systolic and diastolic BPs. These were independent and similar in magnitude to those of PM ≤2.5 µm, black carbon, and particle number. Understanding the effects of particle-bound radionuclide exposures on BP may have important implications for environmental and public health policy.