scholarly journals Analyzing the Potential Risk of Climate Change on Lyme Disease in Eastern Ontario, Canada Using Time Series Remotely Sensed Temperature Data and Tick Population Modelling

2017 ◽  
Vol 9 (6) ◽  
pp. 609 ◽  
Author(s):  
Angela Cheng ◽  
Dongmei Chen ◽  
Katherine Woodstock ◽  
Nicholas Ogden ◽  
Xiaotian Wu ◽  
...  
Author(s):  
Ana Carla dos Santos Gomes ◽  
Maytê Duarte Leal Coutinho ◽  
Fábio de Paula Viana ◽  
Losany Branches Viana ◽  
Sivaldo Filho Seixas Tavares ◽  
...  

This research aims to analyze and estimate future scenarios of maximum air temperature in the capitals of northeastern Brazil, in order to highlight the importance of climate change today and in the future. For this, rainfall, wind speed, relative humidity and maximum air temperature data were used by the database meteorological activities of the National Institute of Meteorology, of the nine capitals of the northeastern region of Brazil from 1980 to 2019, and the dynamic regression technique that combines the dynamics of time series and the effect of explanatory variables.The main results showed that the dynamic regression model satisfactorily adjusted the association between meteorological variables.Trend (without lag) and seasonality (lag) functions were considered in all capitals, presenting the occurrence of different lags according to the capital and the variable. Thus, the highest temperatures among the capitals analyzed occurred in Teresina/PI and the least high, in Salvador/BA. In general terms, the optimistic scenarios (C1) presented temperature between 32.5 and 35 ºC, the pessimists (C2) between 37.5 ºC and extremes (C3) 35 and 39 ºC, evidencing that all future scenarios present danger to the population. It is expected that the results obtained can help public policies.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Laura Cameron ◽  
Rhéa Rocque ◽  
Kailey Penner ◽  
Ian Mauro

Abstract Background Despite scientific evidence that climate change has profound and far reaching implications for public health, translating this knowledge in a manner that supports citizen engagement, applied decision-making, and behavioural change can be challenging. This is especially true for complex vector-borne zoonotic diseases such as Lyme disease, a tick-borne disease which is increasing in range and impact across Canada and internationally in large part due to climate change. This exploratory research aims to better understand public risk perceptions of climate change and Lyme disease in order to increase engagement and motivate behavioural change. Methods A focus group study involving 61 participants was conducted in three communities in the Canadian Prairie province of Manitoba in 2019. Focus groups were segmented by urban, rural, and urban-rural geographies, and between participants with high and low levels of self-reported concern regarding climate change. Results Findings indicate a broad range of knowledge and risk perceptions on both climate change and Lyme disease, which seem to reflect the controversy and complexity of both issues in the larger public discourse. Participants in high climate concern groups were found to have greater climate change knowledge, higher perception of risk, and less skepticism than those in low concern groups. Participants outside of the urban centre were found to have more familiarity with ticks, Lyme disease, and preventative behaviours, identifying differential sources of resilience and vulnerability. Risk perceptions of climate change and Lyme disease were found to vary independently rather than correlate, meaning that high climate change risk perception did not necessarily indicate high Lyme disease risk perception and vice versa. Conclusions This research contributes to the growing literature framing climate change as a public health issue, and suggests that in certain cases climate and health messages might be framed in a way that strategically decouples the issue when addressing climate skeptical audiences. A model showing the potential relationship between Lyme disease and climate change perceptions is proposed, and implications for engagement on climate change health impacts are discussed.


2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.


Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.


2021 ◽  
Vol 193 (4) ◽  
Author(s):  
Stefan Erasmi ◽  
Michael Klinge ◽  
Choimaa Dulamsuren ◽  
Florian Schneider ◽  
Markus Hauck

AbstractThe monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape’s forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.


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