Spatiotemporal dynamics of urban climate during the wet-dry season transition in a tropical African city.

The Urban Heat Island effect has been the focus of several studies concerned with the effects of urbanisation on human and ecosystem health. Humidity, however, remains much less studied, although it is useful for characterising human thermal comfort, the Urban Dryness Island effect and vegetation development. Furthermore, variability in microscale climate due to differences in land cover is increasingly crucial for understanding urbanisation effects on the health and wellbeing of living organisms. We used regression analysis to investigate the spatial and temporal dynamics of temperature, humidity and heat index in the tropical African city of Kampala, Uganda. We gathered data during the wet to dry season transition from 22 locations that represent the wide range of urban morphological differences in Kampala. Our analysis showed that the advancement of the dry season increased variability of climate in Kampala and that the most built-up locations experienced the most profound seasonal changes in climate. This work stresses the need to account for water availability and humidity to improve our understanding of human and ecosystem health in cities.

Spatial dimensions of the influence of urban green-blue spaces on human health: A systematic review.

BACKGROUND: There is an increasing volume of literature investigating the links between urban environments and human health, much of which involves spatial conceptualisations and research designs involving various aspects of geographical information science. Despite intensifying research interest, there has been little systematic investigation of pragmatic methodological concerns, such as how studies are realised in terms of the types of data that are gathered and the analytical techniques that are applied, both of which have the potential to impact results. The aim of this systematic review is, therefore, to understand how spatial scale, datasets, methods, and analytics are currently applied in studies investigating the relationship between green and blue spaces and human health in urban areas. METHOD: We systematically reviewed 93 articles following PRISMA protocol, extracted information regarding different spatial dimensions, and synthesised them in relation to various health indicators. RESULTS AND DISCUSSION: We found a preponderance of the use of neighbourhood-scale in these studies, and a majority of the studies utilised land-use and vegetation indices gleaned from moderate resolution satellite imagery. We also observed the frequent adoption of fixed spatial units for measuring exposure to green and blue spaces based on physical proximity, typically ranging between 30 and 5000 m. The conceptual frameworks of the studies (e.g., the focus on physical vs. mental health or the definition of exposure to green space) were found to have an influence on the strength of association between exposure and health outcomes. Additionally, the strength and significance of associations also varied by study design, something which has not been considered systematically. CONCLUSION: On the basis of our findings, we propose a set of recommendations for standardised protocols and methods for the evaluation of the impact of green-blue spaces on health. Our analysis suggests that future studies should consider conducting analyses at finer spatial scales and employing multiple exposure assessment methods to achieve a comprehensive and comparable evaluation of the association between greenspace and health along multiple pathways.

Relationships between health outcomes in older populations and urban green infrastructure size, quality and proximity.

BACKGROUND: There is a growing body of literature supporting positive associations between natural environments and better health. The type, quality and quantity of green and blue space ('green-space') in proximity to the home might be particularly important for less mobile populations, such as for some older people. However, considerations of measurement and definition of green-space, beyond single aggregated metrics, are rare. This constitutes a major source of uncertainty in current understanding of public health benefits derived from natural environments. We aimed to improve our understanding of how such benefits are conferred to different demographic groups through a comprehensive evaluation of the physical and spatial characteristics of urban green infrastructure. METHODS: We employed a green infrastructure (GI) approach combining a high-resolution spatial dataset of land-cover and function with area-level demographic and socio-economic data. This allowed for a comprehensive characterization of a densely populated, polycentric city-region. We produced multiple GI attributes including, for example, urban vegetation health. We used a series of step-wise multi-level regression analyses to test associations between population chronic morbidity and the functional, physical and spatial components of GI across an urban socio-demographic gradient. RESULTS: GI attributes demonstrated associations with health in all socio-demographic contexts even where associations between health and overall green cover were non-significant. Associations varied by urban socio-demographic group. For areas characterised by having higher proportions of older people ('older neighbourhoods'), associations with better health were exhibited by land-cover diversity, informal greenery and patch size in high income areas and by proximity to public parks and recreation land in low income areas. Quality of GI was a significant predictor of good health in areas of low income and low GI cover. Proximity of publicly accessible GI was also significant. CONCLUSIONS: The influence of urban GI on population health is mediated by green-space form, quantity, accessibility, and vegetation health. People in urban neighbourhoods that are characterised by lower income and older age populations are disproportionately healthy if their neighbourhoods contain accessible, good quality public green-space. This has implications for strategies to decrease health inequalities and inform international initiatives, such as the World Health Organisation's Age-Friendly Cities programme.

Synergistic Surface Passivation of CH3 NH3 PbBr3 Perovskite Quantum Dots with Phosphonic Acid and (3-Aminopropyl)triethoxysilane.

CH3 NH3 PbBr3 perovskite quantum dots (PQDs) are synthesized by using four different linear alkyl phosphonic acids (PAs) in conjunction with (3-aminopropyl)triethoxysilane (APTES) as capping ligands. The resultant PQDs are characterized by means of XRD, TEM, Raman spectroscopy, FTIR spectroscopy, UV/Vis, photoluminescence (PL), time-resolved PL, and X-ray photoelectron spectroscopy (XPS). PA chain length is shown to control the PQD size (ca. 2.9-4.2 nm) and excitonic absorption band positions (λ=488-525 nm), with shorter chain lengths corresponding to smaller sizes and bluer absorptions. All samples show a high PL quantum yield (ca. 46-83 %) and high PL stability; this is indicative of a low density of band gap trap states and effective surface passivation. Stability is higher for smaller PQDs; this is attributed to better passivation due to better solubility and less steric hindrance of the shorter PA ligands. Based on the FTIR, Raman, and XPS results, it is proposed that Pb2+ and CH3 NH3 + surface defects are passivated by R-PO3 2- or R-PO2 (OH)- , whereas Br- surface defects are passivated by R-NH3 + moieties. This study establishes the combination of PA and APTES ligands as a highly effective dual passivation system for the synergistic passivation of multiple surface defects of PQDs through primarily ionic bonding.

Evaluating the impact of improvements in urban green space on older adults' physical activity and wellbeing: protocol for a natural experimental study.

BACKGROUND: Creating or improving urban green space has the potential to be an effective, sustainable and far-reaching way to increase physical activity and improve other aspects of wellbeing in the population. However, there is a dearth of well-conducted natural experimental studies examining the causal effect of changing urban green space on physical activity and wellbeing. This is especially true in older adults and in the United Kingdom. This paper describes a natural experimental study to evaluate the effect of four small-scale urban street greening interventions on older adults' physical activity and wellbeing over a 1-year period, relative to eight matched comparison sites. All sites are located in deprived urban neighbourhoods in Greater Manchester, United Kingdom. METHODS: Components of the interventions include tree and flower planting, and artificial tree decorations. Eight unimproved comparison sites were selected based on a systematic process of matching using several known objective and subjective environmental correlates of physical activity in older adults. The outcome measures are physical activity and two other behavioural indicators of wellbeing (Connect: connecting with other people; and Take Notice: taking notice of the environment), collected using a newly developed observation tool. The primary outcome is Take Notice behaviour due to largest effects on this behaviour being anticipated from improvements in the aesthetic quality of green space at the intervention sites. Baseline data collection occurred in September 2017 before the interventions were installed in November 2017. Follow-up data collection will be repeated in February/ March 2018 (6 months) and September 2018 (12 months). DISCUSSION: The present study permits a rare opportunity to evaluate the causal effects of small-scale changes in urban green space in an understudied population and setting. Although the interventions are expected to have small effects on the outcomes, the present study contributes to developing natural experiment methodology in this field by addressing key methodological weaknesses causing high risk of bias in previous natural experimental studies. Key improvements to reduce risk of bias in the present study are rigorous matching of multiple comparison sites and appropriate statistical control of key confounders. TRIAL REGISTRATION: Retrospectively registered with study ID NCT03575923 . Date of registration: 3 July 2018.

Customizing the coefficients of urban domestic pollutant discharge and their driving mechanisms: Evidence from the Taihu Basin, China.

Discharge of urban domestic pollution has risen sharply during China's extensive urbanization. Together with understanding the complexity of influencing factors underpinning this rise, it has become a pressing issue to estimate total discharge and illustrate its driving mechanism scientifically. This paper reports on the monitoring of discharge from 36 sampling sites in selected residential districts in the heavily polluted Taihu Basin, China. The data were used to estimate the total amount of discharge, to develop corresponding urban domestic pollutant discharge coefficients and to analyse associated spatial patterns. Data from a questionnaire survey of over 1000 households in downtown, suburb and market town areas were then used to apply an econometric model in order to distinguish driving mechanisms. The urban domestic pollutant discharge coefficients developed in this paper are generally smaller than those reported nationally for China, based on more generalised data, decaying from city centres to the urban periphery. This study quantifies the amount of discharge and also demonstrates that urban domestic pollutant discharge is driven by multiple factors. For example, urban domestic pollution discharge rates were positively correlated with income and female-dominated households also tend to discharge more wastewater. Other factors were found to have negative correlations, such as sewage treatment rates, awareness of environmental protection, age and degree of education. As well as providing new and refined data on urban pollution discharge characteristics, the research in this paper also demonstrates the utility of combining household questionnaire and sample monitoring data in order to yield greater insights into the causes of typical polluting behaviour in Chinese neighbourhoods.

Towards understanding the unusual photoluminescence intensity variation of ultrasmall colloidal PbS quantum dots with the formation of a thin CdS shell.

In this study, we report anomalous size-dependent photoluminescence (PL) intensity variation of PbS quantum dots (QDs) with the formation of a thin CdS shell via a microwave-assisted cation exchange approach. Thin shell formation has been established as an effective strategy for increasing the PL of QDs. Nonetheless, herein we observed an unusual PL decrease in ultrasmall QDs upon shell formation. We attempted to understand this abnormal phenomenon from the perspective of trap density variation and the probability of electrons and holes reaching surface defects. To this end, the quantum yield (QY) and PL lifetime (on the ns-µs time scales) of pristine PbS QDs and PbS/CdS core/shell QDs were measured and the radiative and non-radiative recombination rates were derived and compared. Moreover, transient absorption (TA) analysis (on the fs-ns time scale) was performed to better understand exciton dynamics at early times that lead to and affect longer time dynamics and optical properties such as PL. These experimental results, in conjunction with theoretical calculations of electron and hole wave functions, provide a complete picture of the photophysics governing the core/shell system. A model was proposed to explain the size-dependent optical and dynamic properties observed.

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data.

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR2: 0.33-0.38). For NO2 CTM improved prediction modestly (adjR2: 0.58) compared to models without SAT and CTM (adjR2: 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.

Organolead Halide Perovskite Nanocrystals: Branched Capping Ligands Control Crystal Size and Stability.

CH3 NH3 PbBr3 perovskite nanocrystals (PNCs) of different sizes (ca. 2.5-100 nm) with high photoluminescence (PL) quantum yield (QY; ca. 15-55 %) and product yield have been synthesized using the branched molecules, APTES and NH2 -POSS, as capping ligands. These ligands are sterically hindered, resulting in a uniform size of PNCs. The different capping effects resulting from branched versus straight-chain capping ligands were compared and a possible mechanism proposed to explain the dissolution-precipitation process, which affects the growth and aggregation of PNCs, and thereby their overall stability. Unlike conventional PNCs capped with straight-chain ligands, APTES-capped PNCs show high stability in protic solvents as a result of the strong steric hindrance and propensity for hydrolysis of APTES, which prevent such molecules from reaching and reacting with the core of PNCs.

A multicentre study of air pollution exposure and childhood asthma prevalence: the ESCAPE project.

The aim of this study was to determine the effect of six traffic-related air pollution metrics (nitrogen dioxide, nitrogen oxides, particulate matter with an aerodynamic diameter <10 µm (PM10), PM2.5, coarse particulate matter and PM2.5 absorbance) on childhood asthma and wheeze prevalence in five European birth cohorts: MAAS (England, UK), BAMSE (Sweden), PIAMA (the Netherlands), GINI and LISA (both Germany, divided into north and south areas). Land-use regression models were developed for each study area and used to estimate outdoor air pollution exposure at the home address of each child. Information on asthma and current wheeze prevalence at the ages of 4-5 and 8-10 years was collected using validated questionnaires. Multiple logistic regression was used to analyse the association between pollutant exposure and asthma within each cohort. Random-effects meta-analyses were used to combine effect estimates from individual cohorts. The meta-analyses showed no significant association between asthma prevalence and air pollution exposure (e.g. adjusted OR (95%CI) for asthma at age 8-10 years and exposure at the birth address (n=10377): 1.10 (0.81-1.49) per 10 µg · m(-3) nitrogen dioxide; 0.88 (0.63-1.24) per 10 µg · m(-3) PM10; 1.23 (0.78-1.95) per 5 µg · m(-3) PM2.5). This result was consistently found in initial crude models, adjusted models and further sensitivity analyses. This study found no significant association between air pollution exposure and childhood asthma prevalence in five European birth cohorts.

Meta-analysis of air pollution exposure association with allergic sensitization in European birth cohorts.

BACKGROUND: Evidence on the long-term effects of air pollution exposure on childhood allergy is limited. OBJECTIVE: We investigated the association between air pollution exposure and allergic sensitization to common allergens in children followed prospectively during the first 10 years of life. METHODS: Five European birth cohorts participating in the European Study of Cohorts for Air Pollution Effects project were included: BAMSE (Sweden), LISAplus and GINIplus (Germany), MAAS (Great Britain), and PIAMA (The Netherlands). Land-use regression models were applied to assess the individual residential outdoor levels of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5), the mass concentration of particles between 2.5 and 10 µm in size, and levels of particulate matter with an aerodynamic diameter of less than 10 µm (PM10), as well as measurement of the blackness of PM2.5 filters and nitrogen dioxide and nitrogen oxide levels. Blood samples drawn at 4 to 6 years of age, 8 to 10 years of age, or both from more than 6500 children were analyzed for allergen-specific serum IgE against common allergens. Associations were assessed by using multiple logistic regression and subsequent meta-analysis. RESULTS: The prevalence of sensitization to any common allergen within the 5 cohorts ranged between 24.1% and 40.4% at the age of 4 to 6 years and between 34.8% and 47.9% at the age of 8 to 10 years. Overall, air pollution exposure was not associated with sensitization to any common allergen, with odds ratios ranging from 0.94 (95% CI, 0.63-1.40) for a 1 × 10(-5) ∙ m(-1) increase in measurement of the blackness of PM2.5 filters to 1.26 (95% CI, 0.90-1.77) for a 5 µg/m(3) increase in PM2.5 exposure at birth address. Further analyses did not provide consistent evidence for a modification of the air pollution effects by sex, family history of atopy, or moving status. CONCLUSION: No clear associations between air pollution exposure and development of allergic sensitization in children up to 10 years of age were revealed.

The Experimentalist's Guide to Machine Learning for Small Molecule Design.

Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: the ESCAPE project.

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.

Development of land use regression models for particle composition in twenty study areas in Europe.

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Paper2GIS: improving accessibility without limiting analytical potential in Participatory Mapping.

Participatory Mapping encompasses a broad spectrum of methods, each with advantages and limitations that can influence the degree to which the target audience is able to participate and the veracity of the data collected. Whilst being an efficient means to gather spatial data, the accessibility of online methods is limited by digital divides. Conversely, whilst non-digital approaches are more accessible to participants, data collected in this way are typically more challenging to analyse and often necessitate researcher interpretation, limiting their use in decision-making. We therefore present 'Paper2GIS', a novel sketch mapping tool that automatically extracts mark-up drawn onto paper maps and stores it in a geospatial database. The approach embodied in our tool simultaneously limits the technical burden placed on the participant and generates data comparable to that of a digital system without the subjectivity of manual digitisation. This improves accessibility, whilst simultaneously facilitating spatial analyses that are usually not possible with paper-based mapping exercises. A case study is presented to address two energy planning questions of the residents in the Outer Hebrides, UK. The results demonstrate that accessibility can be improved without impacting the potential for spatial analysis, widening participation to further democratise decision-making.

Integrating knowledge on green infrastructure, health and well-being in ageing populations: Principles for research and practice.

Ageing and urbanisation pose significant challenges for public health and urban planning. Ageing populations are at particular risk from hazards arising from urbanisation processes, some of which are in turn exacerbated by climate change. One approach for mitigating the negative effects of urbanisation on ageing populations is the leveraging of the beneficial effects of urban green infrastructure as a public health intervention in the planning process. We assessed the potential of available theoretical frameworks to provide the context for such leverage. This involved active engagement with academics and practitioners specialising in ageing, green infrastructure and health and well-being through a knowledge-brokering approach. We concluded that an integrated and comprehensive framework on the socio-cultural-ecological determinants of health is lacking. To address this, we present a set of principles for overcoming challenges to knowledge integration when working at the intersection of green infrastructure, ageing, health and well-being. Our findings-and the co-production process used to generate them-have wider significance for trans-disciplinary research into the benefits of the natural environment to human health and well-being as well as other complex and interconnected topics associated with global grand challenges.

Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project.

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

Estimating multiple greenspace exposure types and their associations with neighbourhood premature mortality: A socioecological study.

BACKGROUND: Greenspace exposures are often measured using single exposure metrics, which can lead to conflicting results. Existing methodologies are limited in their ability to estimate greenspace exposure comprehensively. We demonstrate new methods for estimating single and combined greenspace exposure metrics, representing multiple exposure types that combine impacts at various scales. We also investigate the association between those greenspace exposure types and premature mortality. METHODS: We used geospatial data and spatial analytics to model and map greenspace availability, accessibility and eye-level visibility exposure metrics. These were harmonised and standardised to create a novel composite greenspace exposure index (CGEI). Using these metrics, we investigated associations between greenspace exposures and years of potential life lost (YPLL) for 1673 neighbourhoods applying spatial autoregressive models. We also investigated the variations in these associations in conjunction with levels of socioeconomic deprivation based on the index of multiple deprivations. RESULTS: Our new CGEI metric provides the opportunity to estimate spatially explicit total greenspace exposure. We found that a 1-unit increase in neighbourhood CGEI was associated with approximately a 10-year reduction in YPLL. Meaning a 0.1 increment or 10% increase in the CGEI is associated with an approximately one year lower premature mortality value. A single 1-unit increase in greenspace availability was associated with a YPLL reduction of 9.8 years, whereas greenness visibility related to a reduction of 6.14 years. We found no significant association between greenspace accessibility and YPLL. Our results further identified divergent trends in the relations between greenspace exposure types (e.g. availability vs. accessibility) and levels of socioeconomic deprivation (e.g. least vs. most). CONCLUSION: Our methods and metrics provide a novel approach to the assessment of multiple greenspace exposure types, and can be linked to the broader exposome framework. Our results showed that a higher composite greenspace exposure is associated with lower premature mortality.

Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions.

The visibility of natural greenness is associated with several health benefits along multiple pathways, including stress recovery and attention restoration mechanisms. However, existing methodologies are inadequate for capturing eye-level greenness visibility exposure at high spatial resolutions for observers located on the ground. As a response, we developed an innovative methodological approach to model and map eye-level greenness visibility exposure for 5 m interval locations within a large study area. We used multi-source spatial data and applied viewshed analysis in conjunction with a distance decay model to compute a novel Viewshed Greenness Visibility Index (VGVI) at more than 86 million observer locations. We compared our eye-level visibility exposure map with traditional top-down greenness exposure metrics such as Normalised Differential Vegetation Index (NDVI) and a Street view based Green View Index (SGVI). Furthermore, we compared greenness visibility at street-only locations with total neighbourhood greenness visibility. We found strong to moderate correlations (r = 0.65-0.42, p < 0.05) between greenness visibility and mean NDVI, with a decreasing trend in correlation strength at increasing buffer distances from observer locations. Our findings suggest that top-down and eye-level measurements of greenness are two distinct metrics for assessing greenness exposure. Additionally, VGVI showed a strong correlation (r = 0.481, p < 0.01) with SGVI. Although the new VGVI has good agreement with existing street view based measures, we found that street-only greenness visibility values are not wholly representative of total neighbourhood visibility due to the under-representation of visible greenness in locations such as backyards and community parks. Our new methodology overcomes such underestimations, is easily transferable, and offers a computationally efficient approach to assessing eye-level greenness exposure.

Spatiotemporal Temperature and Pressure in Thermoplasmonic Gold Nanosphere-Water Systems.

We offer a detailed investigation of the photophysical properties of plasmonic solid and hollow gold nanospheres suspended in water by combining ultrafast transient absorption (TA) spectroscopy with molecular dynamics (MD) simulations. TA reveals that hollow gold nanospheres (HGNs) exhibit faster excited state relaxation and larger amplitude acoustic phonon modes than solid gold nanoparticles of the same outer diameter. MD simulation carried out on full scale nanoparticle-water models (over 10 million atoms) to simulate the temporal evolution (0-100 ps) of the thermally excited particles (1000 or 1250 K) provides atomic-scale resolution of the spatiotemporal temperature and pressure maps, as well as visualization of the lattice vibrational modes. For the 1000 K HGN, temperatures upward of 500 K in the vicinity of the shell surface were observed, along with pressures up to several hundred MPa in the inner cavity, revealing potential use as a photoinduced nanoreactor. Our approach of combining TA and MD provides a path to better understanding how thermal-structural properties (such as expansion and contraction) and thermal-optical properties (such as modulated dielectrics) manifest themselves as TA signatures. The detailed picture of heat transfer at interfaces should help guide nanoparticle design for a wide range of applications that rely on photothermal conversion, including photothermal coupling agents for nanoparticle-mediated photothermal therapy and photocatalysts for light-driven chemical reactions.