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2.
J Environ Radioact ; 165: 103-114, 2016 Dec.
Article in English | MEDLINE | ID: mdl-27676361

ABSTRACT

The variability of the atmospheric concentration of the 7Be and 210Pb radionuclides is strongly linked to the origin of air masses, the strength of their sources and the processes of wet and dry deposition. It has been shown how these processes and their variability are strongly affected by climate change. Thus, a deeper knowledge of the relationship between the atmospheric radionuclides variability measured close to the ground and these atmospheric processes could help in the analysis of climate scenarios. In the present study, we analyze the atmospheric variability of a 14-year time series of 7Be and 210Pb in a Mediterranean coastal city using a synergy of different indicators and tools such as: the local meteorological conditions, global and regional climate indexes and a lagrangian atmospheric transport model. We particularly focus on the relationships between the main pathways of air masses and sun spots occurrence, the variability of the local relative humidity and temperature conditions, and the main modes of regional climate variability, such as the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO). The variability of the observed atmospheric concentrations of both 7Be and 210Pb radionuclides was found to be mainly positively associated to the local climate conditions of temperature and to the pathways of air masses arriving at the station. Measured radionuclide concentrations significantly increase when air masses travel at low tropospheric levels from central Europe and the western part of the Iberian Peninsula, while low concentrations are associated with westerly air masses. We found a significant negative correlation between the WeMO index and the atmospheric variability of both radionuclides and no significant association was observed for the NAO index.


Subject(s)
Air Pollutants, Radioactive/analysis , Beryllium/analysis , Lead Radioisotopes/analysis , Radiation Monitoring , Radioisotopes/analysis , Atmosphere/chemistry , Climate Change , Europe
3.
Trop Med Int Health ; 21(11): 1481-1488, 2016 Nov.
Article in English | MEDLINE | ID: mdl-27580403

ABSTRACT

OBJECTIVE: Tropical highland malaria intensifies and shifts to higher altitudes during exceptionally warm years. Above-normal temperatures associated with El Niño during boreal winter months (December-March) may intensify malaria in East African highlands. We assessed the malaria risk for Oromia, the largest region of Ethiopia with around 30 million inhabitants. METHODS: Simple linear regression and spatial analyses were used to associate sea surface temperatures (SST) in the Pacific and surface temperatures in Ethiopia with annual malaria risk in Oromia, based on confirmed cases of malaria between 1982 and 2005. RESULTS: A strong association (R2 = 0.6, P < 0.001) was identified between malaria and sea surface temperatures in the Pacific, anticipating a 70% increase in malaria risk for the period from August 2016 to July 2017. This forecast was quantitatively supported by elevated land surface temperatures (+1.6 °C) in December 2015. When more station data become available and mean March 2016 temperatures from meteorological stations can be taken into account, a more robust prediction can be issued. CONCLUSION: An epidemic warning is issued for Oromia, Ethiopia, between August 2016 and July 2017 and may include the pre-July short malaria season. Similar relationships reported for Madagascar point to an epidemic risk for all East African highlands with around 150 million people. Preparedness for this high risk period would include pre-emptive intradomestic spraying with insecticides, adequate stocking of antimalarials, and spatial extension of diagnostic capacity and more frequent reporting to enable a rapid public health response when and where required.


Subject(s)
Disease Outbreaks , El Nino-Southern Oscillation , Malaria/epidemiology , Ethiopia/epidemiology , Forecasting , Humans , Temperature
4.
Proc Natl Acad Sci U S A ; 103(15): 5829-34, 2006 Apr 11.
Article in English | MEDLINE | ID: mdl-16571662

ABSTRACT

The incidence of malaria in the East African highlands has increased since the end of the 1970s. The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. With both nonparametric and parametric statistical analyses, we find evidence for a significant warming trend at all sites. To assess the biological significance of this trend, we drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least 1 order of magnitude larger than that in the environmental variable. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models.


Subject(s)
Malaria/epidemiology , Africa, Eastern/epidemiology , Altitude , Climate , Greenhouse Effect , Humans , Temperature
5.
Science ; 289(5485): 1766-9, 2000 Sep 08.
Article in English | MEDLINE | ID: mdl-10976073

ABSTRACT

Analysis of a monthly 18-year cholera time series from Bangladesh shows that the temporal variability of cholera exhibits an interannual component at the dominant frequency of El Niño-Southern Oscillation (ENSO). Results from nonlinear time series analysis support a role for both ENSO and previous disease levels in the dynamics of cholera. Cholera patterns are linked to the previously described changes in the atmospheric circulation of south Asia and, consistent with these changes, to regional temperature anomalies.


Subject(s)
Cholera/epidemiology , Climate , Models, Statistical , Bangladesh/epidemiology , Cholera/transmission , Endemic Diseases , Forecasting , Humans , Incidence , Neural Networks, Computer , Nonlinear Dynamics , Seasons , Statistics, Nonparametric , Temperature , Weather
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