Understanding the World
In April of this year, I made an animation of the 24-hour average temperature of January 2020, also showing the day-night cycle. My biggest problem was finding a way to project correctly the area at night without breaking the geometry. The easiest solution I found was rasterising the night polygon and then reprojecting it. Indeed, a vector approach could be used, but I have preferred to use raster data here.
The climate of a place is usually presented through climographs that combine monthly precipitation and temperature in a single chart. However, it is also interesting to visualize the climate on a daily scale showing the thermal amplitude and the daily average temperature. To do this, the averages for each day of the year of the daily minimums, maximums and averages are calculated. The annual climate cycle presents a good opportunity to use a radial or polar which allows us to clearly visualize seasonal patterns.
There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.
Background: Many regions of the world are now facing more frequent and unprecedentedly large wildfires. However, the association between wildfire-related PM2·5 and mortality has not been well characterised. We aimed to comprehensively assess the association between short-term exposure to wildfire-related PM2·5 and mortality across various regions of the world. Methods: For this time series study, data on daily counts of deaths for all causes, cardiovascular causes, and respiratory causes were collected from 749 cities in 43 countries and regions during 2000–16. Daily concentrations of wildfire-related PM2·5 were estimated using the three-dimensional chemical transport model GEOS-Chem at a 0·25° × 0·25° resolution. The association between wildfire-related PM2·5 exposure and mortality was examined using a quasi-Poisson time series model in each city considering both the current-day and lag effects, and the effect estimates were then pooled using a random-effects meta-analysis. Based on these pooled effect estimates, the population attributable fraction and relative risk (RR) of annual mortality due to acute wildfire-related PM2·5 exposure was calculated. Findings: 65·6 million all-cause deaths, 15·1 million cardiovascular deaths, and 6·8 million respiratory deaths were included in our analyses. The pooled RRs of mortality associated with each 10 μg/m3 increase in the 3-day moving average (lag 0–2 days) of wildfire-related PM2·5 exposure were 1·019 (95% CI 1·016–1·022) for all-cause mortality, 1·017 (1·012–1·021) for cardiovascular mortality, and 1·019 (1·013–1·025) for respiratory mortality. Overall, 0·62% (95% CI 0·48–0·75) of all-cause deaths, 0·55% (0·43–0·67) of cardiovascular deaths, and 0·64% (0·50–0·78) of respiratory deaths were annually attributable to the acute impacts of wildfire-related PM2·5 exposure during the study period. Interpretation: Short-term exposure to wildfire-related PM2·5 was associated with increased risk of mortality. Urgent action is needed to reduce health risks from the increasing wildfires.
Background: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. Methods: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. Results: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 ºC decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 ºC) to continental (19.3 ºC), temperate (21.7 ºC), arid (24.5 ºC), and tropical (26.5 ºC). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 ºC for a 1 ºC rise in a communitys annual mean temperature, and by 1ºC for a 1ºC rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 ºC rise in a communitys annual mean temperature and by 1.3 for a 1 ºC rise in its SD. Conclusions: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation.
I am a climate scientist and actually, I am post-doctoral fellow at the University of Santiago de Compostela. I am originally from Grevenbroich, near by Cologne, in Germany. I graduated in Geography and Hispanic Philology at the University of Cologne and RWTH-Aachen University in 2010. After I met my wife in Galicia, I came to Santiago de Compostela, in northwest Spain, and made my Ph.D. in 2015 about the relationship between health and weather at the same University.
The main lines of research are, on the one hand, biometeorology and geographies of health, the relationship between human health and the atmospheric environment, and on the other hand, applied physical geography with a focus on atmospheric variables and their spatio-temporal behaviors.
I am an enthusiastic R user, with a lot of curiosity for spatial analysis, data visualizations, management and manipulation, and GIS.
I am a member of the Public Health Research Group at the University of Santiago de Compostela. In addition, I am a close collaborator of two other research groups, Geobiomet at the University of Cantabria and Climatology Group at the University of Barcelona. Since 2019 I am a member of the MCC Collaborative Research Network, an international research program on the associations between environmental stressors, climate, and health.
Feel free to contact me.
PhD in Physical Geography, 2015
University of Santiago de Compostela, Spain
Geography and Hispanic Philology, 2010
University of Cologne | RWTH-Aachen University, Germany