Publications
Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleHazzard JAN, Richards FD, 2024,
Antarctic Geothermal Heat Flow, Crustal Conductivity and Heat Production Inferred From Seismological Data
, Geophysical Research Letters, Vol: 51, ISSN: 0094-8276Geothermal heat flow is a key parameter in governing ice dynamics, via its influence on basal melt and sliding, englacial rheology, and erosion. It is expected to exhibit significant lateral variability across Antarctica. Despite this, surface heat flow derived from Earth's interior remains one of the most poorly constrained parameters controlling ice sheet evolution. To obtain a continent-wide map of Antarctic heat supply at regional-scale resolution, we estimate upper mantle thermomechanical structure directly from VS. Until now, direct inferences of Antarctic heat supply have assumed constant crustal composition. Here, we explore a range of crustal conductivity and radiogenic heat production values by fitting thermodynamically self-consistent geotherms to their seismically inferred counterparts. Independent estimates of crustal conductivity derived from VP are integrated to break an observed trade-off between crustal parameters, allowing us to infer Antarctic geothermal heat flow and its associated uncertainty.
-
ReportHalkyard S, Levey S, Amer H, et al., 2024,
Grantham Institute Outlook 2023-2024
, www.imperial.ac.uk/granthamThe Grantham Institute Outlook magazine provides an overview of the climate and environmental research underway at Imperial College London, encompassing both recent achievements and future plans.
-
Journal articleZhang-Zheng H, Adu-Bredu S, Duah-Gyamfi A, et al., 2024,
Contrasting carbon cycle along tropical forest aridity gradients in West Africa and Amazonia
, Nature Communications, Vol: 15, ISSN: 2041-1723Tropical forests cover large areas of equatorial Africa and play a substantial role in the global carbon cycle. However, there has been a lack of biometric measurements to understand the forests’ gross and net primary productivity (GPP, NPP) and their allocation. Here we present a detailed field assessment of the carbon budget of multiple forest sites in Africa, by monitoring 14 one-hectare plots along an aridity gradient in Ghana, West Africa. When compared with an equivalent aridity gradient in Amazonia, the studied West African forests generally had higher productivity and lower carbon use efficiency (CUE). The West African aridity gradient consistently shows the highest NPP, CUE, GPP, and autotrophic respiration at a medium-aridity site, Bobiri. Notably, NPP and GPP of the site are the highest yet reported anywhere for intact forests. Widely used data products substantially underestimate productivity when compared to biometric measurements in Amazonia and Africa. Our analysis suggests that the high productivity of the African forests is linked to their large GPP allocation to canopy and semi-deciduous characteristics.
-
Journal articleWang W, Gulliver J, Beevers S, et al., 2024,
Short-term nitrogen dioxide exposure and emergency hospital admissions for asthma in children: a case-crossover analysis in England
, Journal of Asthma and Allergy, Vol: 17, Pages: 349-359, ISSN: 1178-6965Background:There is an increasing body of evidence associating short-term ambient nitrogen dioxide (NO2) exposure with asthma-related hospital admissions in children. However, most studies have relied on temporally resolved exposure information, potentially ignoring the spatial variability of NO2. We aimed to investigate how daily NO2 estimates from a highly resolved spatio-temporal model are associated with the risk of emergency hospital admission for asthma in children in England.Methods:We conducted a time-stratified case-crossover study including 111,766 emergency hospital admissions for asthma in children (aged 0–14 years) between 1st January 2011 and 31st December 2015 in England. Daily NO2 levels were predicted at the patients’ place of residence using spatio-temporal models by combining land use data and chemical transport model estimates. Conditional logistic regression models were used to obtain the odds ratios (OR) and confidence intervals (CI) after adjusting for temperature, relative humidity, bank holidays, and influenza rates. The effect modifications by age, sex, season, area-level income deprivation, and region were explored in stratified analyses.Results:For each 10 µg/m³ increase in NO2 exposure, we observed an 8% increase in asthma-related emergency admissions using a five-day moving NO2 average (mean lag 0–4) (OR 1.08, 95% CI 1.06–1.10). In the stratified analysis, we found larger effect sizes for male (OR 1.10, 95% CI 1.07–1.12) and during the cold season (OR 1.10, 95% CI 1.08–1.12). The effect estimates varied slightly by age group, area-level income deprivation, and region.Significance:Short-term exposure to NO2 was significantly associated with an increased risk of asthma emergency admissions among children in England. Future guidance and policies need to consider reflecting certain proven modifications, such as using season-specific countermeasures for air pollution control, to protect the at-r
-
Journal articleFlo V, Joshi J, Sabot M, et al., 2024,
Incorporating photosynthetic acclimation improves stomatal optimisation models
, Plant, Cell and Environment, ISSN: 0140-7791Stomatal opening in plant leaves is regulated through a balance of carbon and water exchange under different environmental conditions. Accurate estimation of stomatal regulation is crucial for understanding how plants respond to changing environmental conditions, particularly under climate change. A new generation of optimality-based modelling schemes determines instantaneous stomatal responses from a balance of trade-offs between carbon gains and hydraulic costs, but most such schemes do not account for biochemical acclimation in response to drought. Here, we compare the performance of six instantaneous stomatal optimisation models with and without accounting for photosynthetic acclimation. Using experimental data from 37 plant species, we found that accounting for photosynthetic acclimation improves the prediction of carbon assimilation in a majority of the tested models. Photosynthetic acclimation contributed significantly to the reduction of photosynthesis under drought conditions in all tested models. Drought effects on photosynthesis could not accurately be explained by the hydraulic impairment functions embedded in the stomatal models alone, indicating that photosynthetic acclimation must be considered to improve estimates of carbon assimilation during drought.
-
Journal articleWood D, Evangelopoulos D, Beevers S, et al., 2024,
Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort
, Environmental Health, Vol: 23, ISSN: 1476-069XBackgroundAn increasing number of studies suggest adverse effects of exposure to ambient air pollution on cognitive function, but the evidence is still limited. We investigated the associations between long-term exposure to air pollutants and cognitive function in the English Longitudinal Study of Ageing (ELSA) cohort of older adults.MethodsOur sample included 8,883 individuals from ELSA, based on a nationally representative study of people aged ≥ 50 years, followed-up from 2002 until 2017. Exposure to air pollutants was modelled by the CMAQ-urban dispersion model and assigned to the participants’ residential postcodes. Cognitive test scores of memory and executive function were collected biennially. The associations between these cognitive measures and exposure to ambient concentrations of NO2, PM10, PM2.5 and ozone were investigated using mixed-effects models adjusted for time-varying age, physical activity and smoking status, as well as baseline gender and level of education.ResultsIncreasing long-term exposure per interquartile range (IQR) of NO2 (IQR: 13.05 μg/m3), PM10 (IQR: 3.35 μg/m3) and PM2.5 (IQR: 2.7 μg/m3) were associated with decreases in test scores of composite memory by -0.10 (95% confidence interval [CI]: -0.14, -0.07), -0.02 [-0.04, -0.01] and -0.08 [-0.11, -0.05], respectively. The same increases in NO2, PM10 and PM2.5 were associated with decreases in executive function score of -0.31 [-0.38, -0.23], -0.05 [-0.08, -0.02] and -0.16 [-0.22, -0.10], respectively. The association with ozone was inverse across both tests. Similar results were reported for the London-dwelling sub-sample of participants.ConclusionsThe present study was based on a long follow-up with several repeated measurements per cohort participant and long-term air pollution exposure assessment at a fine spatial scale. Increasing long-term exposure to NO2, PM10 and PM2.5 was associated with a decrease in cognitive function in older adults in England.
-
Journal articleAggarwal E, Whittaker AC, Gupta S, 2024,
Investigating the Influence of River Geomorphology on Human Presence Using Night Light Data: A Case Study in the Indus Basin
, Remote Sensing, Vol: 16Human settlements have historically thrived near rivers due to enhanced navigation and trade, and the availability of water supply and resources. The use of night light data, representing economic activities, provides a novel approach to studying the interactions between human activity and rivers over time. Here, we use the Defense Meteorological Satellite Program (DMSP) stable night light data from 2000 to 2013 as a proxy for human presence and activities to quantify the statistical relationships between night light presence and intensity in the Indus Basin, Asia. We test how these data are affected by proximity to trunk channels and by channel type (single/multi-thread) in the study area. We find that night light presence is enhanced by 26% within a 0 to 5 km proximity range of the Indus River and its tributaries, relative to the basin as a whole. We interpret this to represent increased human presence and activity within this zone. However, the mean intensity is lower near the river and higher away from the river, signifying denser settlements, such as towns and cities, which are preferentially located away from the Indus and its tributaries. Moreover, the enhancement of lit pixels signifying human presence and activities is increased by 18% near single-thread sections of the Indus River, compared to segments of the Indus displaying multi-thread morphologies. We suggest that this is due to the enhanced stability of single-threaded channels, relative to mobile multi-threaded channel reaches. This study demonstrates how night lights are an important tool in studying the relationship between human presence and river dynamics in large catchments such as the Indus, and we suggest that these data will have an important role in assessing differential flood spatial and social vulnerability at a regional scale.
-
Journal articleBhusal JK, Nayava JL, Baidya SK, et al., 2024,
Rainfall threshold for landslide awareness – Focusing on the case study in the landslide EVO pilot area region in western Nepal
, Mausam, Vol: 75, Pages: 461-478, ISSN: 0252-9416Nepal’s rugged topography, unstable young geological formations, and fragile rocks make the country highly vulnerable to water-induced hazards such as landslides, soil erosion, and debris torrents. Hilly watersheds and settlements in hills and river banks are naturally vulnerable during heavy rainfall. The landslide EVO project selected two landslide areas, one the Bajedi landslides in the Bajura district, and another Sunkuda landslides in Bajhang district of Nepal. Automatic rain gauges were installed, and data were recorded for 2019 and 2021. The best-fit trend lines are determined by the observed rainfall depths of different durations. In addition, 24-hour rainfall records and landslide events that occurred in the region outside the pilot areas in the year 2019 were also analyzed and correlated. Rainfall intensities and depths corresponding to maximum, minimum, and average depth are correlated for different durations. The correlation between rainfall depths and durations data showed an excellent fitting observed. The trend line is considered as the rainfall threshold line for landslide risk assessment for the region.
-
Journal articleHajmohammadi H, Talaei M, Fecht D, et al., 2024,
Long-term air pollution exposure and risk of SARS-CoV-2 infection: A UK-wide cohort study
, Respiratory Medicine, Vol: 224, Pages: 107567-107567, ISSN: 0954-6111BACKGROUND: The association between air quality and risk of SARS-CoV-2 infection is poorly understood. We investigated this association using serological individual-level data adjusting for a wide range of confounders, in a large population-based cohort (COVIDENCE UK). METHODS: We assessed the associations between long-term (2015-19) nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), exposures with SARS-CoV-2 infection, level of antibody response among those infected, and COVID-19 disease severity. We used serological data from 10,489 participants in the COVIDENCE UK cohort, and estimated annual average air pollution exposure at each participant's home postcode. RESULTS: After controlling for potential confounders, we found a positive association between 5-year NO2 and PM2.5 exposures and the risk of seropositivity: 10 unit increase in NO2 (μg/m3) was associated with an increasing risk of seropositivity by 1.092 (95% CI 1.02 to 1.17; p-for-trend 0.012). For PM2.5, 10 unit increase (μg/m3) was associated with an increasing risk of seropositivity by 1.65 (95% CI 1.015-2.68; p-for-trend 0·049). In addition, we found that NO2 was positively associated with higher antibody titres (p-for-trend 0·013) among seropositive participants, with no evidence of an association for PM2.5. CONCLUSION: Our findings suggest that the long-term burden of air pollution increased the risks of SARS-CoV-2 infection and has important implications for future pandemic preparedness. This evidence strengthens the case for reducing long-term air pollution exposures to reduce the vulnerability of individuals to respiratory viruses.
-
Journal articleLi S, Robert A, Faisal AA, et al., 2024,
Learning to optimise wind farms with graph transformers
, Applied Energy, Vol: 359, ISSN: 0306-2619This work proposes a novel data-driven model capable of providing accurate predictions for the power generation of all wind turbines in wind farms of arbitrary layout, yaw angle configurations and wind conditions. The proposed model functions by encoding a wind farm into a fully connected graph and processing the graph representation through a graph transformer. The resultant graph transformer surrogate demonstrates robust generalisation capabilities and effectively uncovers latent structural patterns embedded within the graph representation of wind farms. The versatility of the proposed approach extends to the optimisation of yaw angle configurations through the application of genetic algorithms. This evolutionary optimisation strategy facilitated by the graph transformer surrogate achieves prediction accuracy levels comparable to industrially standard wind farm simulation tools, with a relative accuracy of more than 99% in identifying optimal yaw angle configurations of previously unseen wind farm layouts. An additional advantage lies in the significant reduction in computational costs, positioning the proposed methodology as a compelling tool for efficient and accurate wind farm optimisation.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.