Climate zoning: identifying suitable regions for the occurrence of Alternaria Brown spot in tangerine trees in Brazil.

BACKGROUND: Brazilian citrus farming has been migrating to nontraditional citrus-growing regions, which can be considered a challenge for citrus growers, as not all these areas are suitable for tangerine cultivation. Thus, the mapping of regions exhibiting favorable climatic conditions for Alternaria brown spot has become crucial in the selection of appropriate locations for the establishment of new orchards. This mapping enables the implementation of an avoidance strategy, which entails steering clear of areas where the disease is prevalent, aligning with fundamental principles of disease control. RESULTS: Thus, this study seeks to zone areas with high and low climatic favorability for the occurrence of Alternaria brown spot in tangerine trees in Brazil. Historical climate data series from the NASA-POWER database were used for all municipalities in Brazil. Agrometeorological variables used to determine the development of Alternaria brown spot were average monthly air temperature (Tmean) and duration of leaf wetness period (LWD). Areas were considered unsuitable climatically when Tmean was <17 °C or >33 °C, relatively suitable when Tmean was between 13 °C and 33 °C and LWD <10%, and climatically suitable when Tmean was between 13 °C and 33 °C and LWD >10%. The states of Paraná, Santa Catarina and Rio Grande do Sul showed greater thermal amplitude within months and throughout the year. The southern region of the country has harsher winters, with minimum temperatures below 15 °C, which is unfavorable for the disease incidence. CONCLUSION: The states with the greatest favorability for Alternaria brown spot were Paraná, Santa Catarina, and Rio Grande do Sul, mainly from May to September. Rio Grande do Sul was the state in this region that showed the greatest favorability, as a consequence of leaf wetness exceeding 10 h. The main tangerine-producing regions in Brazil, including the southern part of Minas Gerais, the state of São Paulo and the metropolitan region of Porto Alegre, were mostly classified as relatively favorable areas for the occurrence of Alternaria brown spot. It is recommended that when establishing new tangerine orchards, regions with lower favorability for the occurrence of Alternaria brown spot, such as the North and Central-West regions of Brazil, particularly the states of Amazonas, Pará and Mato Grosso, should be selected. © 2023 Society of Chemical Industry.

The performance of artificial neural networks for modeling daily concentrations of particulate matter from meteorological data.

The use of techniques based on artificial intelligence and machine learning for the simulation of many processes is becoming increasingly important in environmental sciences, with applications in the study of time series of atmospheric properties, such as pollution levels. The present work aimed to evaluate the efficiency of a model based on Artificial Neural Networks (ANN) in the simulation PM10 from meteorological data observed between 2018 and 2019 in Guaíba, southern Brazil, thus also having an estimate of the influence of atmospheric conditions on local air pollution. For this purpose, meteorological and PM10 data obtained from the stations Parque 35, sustained by Celulose Riograndense (CMPC), and A-801, sustained by the National Institute of Meteorology (INMET), were used. The ANN used for the simulation was of the Multilayer Perceptron type, trained by the backpropagation algorithm with cross-validation. The results obtained indicate that the simulation was satisfactory with a Nash-Sutcliffe index (NSE) of 0.64, a linear correlation coefficient (R) of 0.81, a relative error (Er) of 26% and a root mean square error (RMSE) of 7.40 µg/m3. Thus, even with some difficulty in estimating extreme concentrations, the model was suitable for the largest range observed, of 10 µg/m3 to 50 µg/m3. For this dataset, the model proved to be an useful assessment tool and has the potential to be applied operationally to contribute to the monitoring and control of air quality levels both in the study area and in other regions of Brazil and the world.

Evolving Drivers of Brazilian SARS-CoV-2 Transmission: A Spatiotemporally Disaggregated Time Series Analysis of Meteorology, Policy, and Human Mobility.

Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil's 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (R t ) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire 15-month period and several shorter, 3-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and R t . We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and increased specific humidity with increased transmission. However, the impacts of meteorology, policy, and mobility on R t varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.

Using climate reanalysis and remote sensing-derived data to create the basis for predicting the occurrence of algal blooms, harmful algal blooms and toxic events in Santa Catarina, Brazil.

This study aimed to form a basis for future predictive modeling efforts in support of the harmful algal blooms (HAB) surveillance program currently in force in the Brazilian State of Santa Catarina (SC). Data from monitoring toxin-producing algae were merged with both meteorological and oceanographic data and analyzed. Data from four sources were used in this study: climate reanalysis (air temperature, pressure, cloud cover, precipitation, radiation, U and V winds); remote sensing (chlorophyll concentration and sea surface temperature); Oceanic Niño Index; and HAB monitoring data (phytoplankton counts and toxin levels in shellfish samples obtained from 39 points located in shellfish farms distributed along the SC coastline). This study analyzed the period from 2007-01-01 to 2019-12-31 (7035 records in the HAB database) and used descriptive, bivariate and multivariate analyses to draw correlations among environmental parameters and the occurrence of algal blooms (AB), HAB and toxic events. Dinophysis spp. AB were the most registered type of event and tended to occur during the late autumn and winter months. These events were associated with high atmospheric pressure, predominance of westerly and southerly winds, low solar radiation and low sea and air temperature. An inverted pattern was observed for Pseudo-nitzschia spp. AB, which were mostly registered during the summer and early autumn months. These results give evidence that the patterns of occurrence of highly prevalent toxin-producing microalgae reported worldwide, such as the Dinophysis AB during the summer, differ along the coast of SC. Our findings also show that meteorological data, such as wind direction and speed, atmospheric pressure, solar radiation and air temperature, might all be key predictive modeling input parameters, whereas remote sensing estimates of chlorophyll, which are currently used as a proxy for the occurrence of AB, seem to be a poor predictor of HAB in this geographic area.

Spatio-temporal analysis of PM2.5 and policies in Northwestern South America.

This paper analyzes the spatio-temporal variations, and exceedances of the PM2.5 concentrations in Northwestern South America at different scales to assess the implemented policies and identify the involved phenomena. Through reanalysis and ground-based data, we found that high PM2.5 levels in most cities of the region are caused by wildfires and local emissions, including the capital cities of Venezuela, Ecuador, Colombia, and Panamá. In-situ measurements suggest that the majority of the cities comply with the local but not with the WHO guidelines, indicating that local annual limits should be more restrictive. Two peaks in the daily variations of PM2.5 (related to vehicle emissions) and also a steeper decrease around noon (associated with an increase in wind speed and in the boundary layer height) were identified. The trend-analysis shows that Bogotá and Medellín have a decreasing PM2.5 annual-trend (between -0.8µgm-3 and -1.7µgm-3) that corresponds to effective policies. In contrast, Cali has a positive annual-trend (0.8µgm-3) most likely because of Short-Range Transport produced by a northerly-flow from a highly polluted neighboring city, which also affects Cali's PM2.5 diurnal cycle, or by local-dynamics. The exceedances show that the policies are working on an annual but not at a daily time-scale. These results serve as a first input for additional studies, with the aim of gaining a better understanding of the contaminant before adapting current policies or implementing new policies and measures that need to include a joint international, regional, and inter-city efforts regarding pollution transport.

An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil.

BACKGROUND: The recent studies of the variations in the atmospheric column-averaged CO2 concentration ([Formula: see text]) above croplands and forests show a negative correlation between [Formula: see text]and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO2. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on [Formula: see text] above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO2. RESULTS: The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual [Formula: see text] cycle. The daily model of [Formula: see text] estimated from Qg and RH predicts daily [Formula: see text] with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01). CONCLUSION: The obtained results imply that a significant part of daily [Formula: see text] variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions.

Low cost climate station for smart agriculture applications with photovoltaic energy and wireless communication.

Measuring climatic conditions is a fundamental task for a wide array of scientific and practical fields. Weather variables change depending on position and time, especially in tropical zones without seasons. Additionally, the increasing development of precision or smart agriculture makes it necessary to improve the measurement systems while widely distributing them at the location of crops. For these reasons, in this work, the design, construction and fabrication of an adaptable autonomous solar-powered climatic station with wireless 3G or WiFi communication is presented. The station measures relative humidity, temperature, atmospheric pressure, precipitation, wind speed, and light radiation. In addition, the system monitors the charge state of the main battery and the energy generated by the photovoltaic module to act as a reference cell for solar energy generation capability and agrivoltaic potential in the installation area. The station can be remotely controlled and reconfigured. The collected data from all sensors can be uploaded to the cloud in real-time. This initiative aims at enhancing the development of free and open source hardware that can be used by the agricultural sector and that allows professionals in the area to improve harvest yield and production conditions.

Beach strandings of Tehuelche scallop, Aequipecten tehuelchus, in Patagonia: Multi-scale processes and management implications.

The magnitude and causal mechanisms of a massive beach stranding of Tehuelche scallops that occurred in November 2017 in San José Gulf, Argentina, were investigated with the long-term goal of improving the assessment and management of the scallop fishery. The biomass of scallops washed ashore and deposited over a 10-km stretch of coast was estimated by quadrat sampling and compared with the results of a scallop stock assessment survey conducted three months prior to the stranding event. The resulting estimate of total biomass loss was in the order of 200 t, representing 10% of the estimated total scallop biomass in the San José gulf. The stranding coincided with persistent strong southerly winds (13 m/s) blowing for 24 h in San José Gulf, and large-scale windstorms that affected the southern tip of South America. Surface waves predicted under such windstorm conditions could generate strong bottom orbital velocities at shallow waters (<10 m depth), sufficient to drag and transport ashore scallops by Stokes drift (600-2000 m in 24 h). Analysis of local wind data recorded over a 6.8-year period indicated that such windstorm conditions occurred with an average frequency of 7.7 times per year, implying that beach strandings could have a significant impact on the scallop resource and its fishery. The actual impact of windstorms would depend on the location, depth and size composition of scallop beds, shallow beds (<10 m depth) being more susceptible to stranding risks. The use of spatial harvest control rules, instead of the global total allowable catch used at present, could reduce the risks of yield loss by directing the harvest to the more vulnerable scallop beds.

Effect of meteorological factors on first episode and recurrence of primary spontaneous pneumothorax

SUMMARY OBJECTIVE: In this study, we evaluated the clinical characteristics and seasonal distribution of patients with primary spontaneous pneumothorax and examined the relationships between meteorological factors and pneumothorax development overall and in terms of first episode and recurrence. METHODS: The hospital records of 168 pneumothorax patients treated in our clinic between January 2016 and December 2020 were reviewed retrospectively. A cluster was defined as two or more patients with pneumothorax presenting within three consecutive days. Meteorological factors were compared between days with and without pneumothorax patients. This comparison was based on meteorological data from the day of symptom onset (D), the day before symptom onset (D1), and the difference between those days (D-D1). Meteorological data from the index day (D) were also compared between patients with first episode and recurrence of pneumothorax. RESULTS: The study included 149 (88.7%) men and 19 (11.3%) women. The mean age was 25.02±6.97 (range, 17-35; median, 26) years. Of note, 73 (43.4%) patients underwent surgery. The highest number of patients presented in November (n=19, 11.3%). In terms of season, most presentations occurred in autumn. Humidity was significantly lower on recurrence days compared with first episode (p=0.041). CONCLUSION: Our results indicated that meteorological factors (i.e., atmospheric pressure, humidity, wind speed, temperature, and precipitation) were not associated with pneumothorax development. By comparing the patients with first episode and recurrence, the humidity was significantly lower in the recurrence group.

Negative-Binomial and quasi-poisson regressions between COVID-19, mobility and environment in São Paulo, Brazil.

Brazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to monitor quarantines and prevent the transmissions of SARS-CoV-2. In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, Brazil. We applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between RMI and COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 4 and 21 days, and 2) the association between COVID-19 and exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility. We found that an RMI of 45.28% results in 1212 cases (95% CI: 1189 to 1235) and 44 deaths (95% CI: 40 to 47). Increasing the isolation from 45.28% to 50% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 µg⋅m-³ of PM2.5 results in a risk of 1.140 (95% CI: 1.021 to 1.274) for cases and 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively. We compared our results with observations and literature review, finding well agreement. Policymakers can use such mobility indices as tools to control social distance activities. Spatial distancing is an important factor to control COVID-19, however, measuring face-mask usage would enhance the understanding the pandemic dynamic. Small increments of air pollution result in an increased number of COVID-19 cases and deaths.

A better understanding of air quality resulting from the effects of the 2020 pandemic in a city in the equatorial region (Fortaleza, Brazil).

The year 2020 was atypical due to the pandemic caused by the SARS-CoV-2 virus (COVID-19), providing a unique opportunity to understand changes in air quality due to the reduction in urban activity. Therefore, the aim of the present study was to perform an integrated evaluation on the influence of the effects of the 2020 pandemic on air quality in the city of Fortaleza, investigating levels of PM2.5, PM10, NO2, NO, SO2, CO, and O3, corresponding health risks, as well as the influence of meteorological variables and urban activity. In all phases analyzed, significant reductions were found in NOx, NO, NO2, and CO. A considerable reduction in PM2.5 and PM10 was found in the early phases, with an increase in the later phases. These findings are explained by the nearly 50% reduction in vehicular traffic and the consequent reduction in fossil fuel emissions, mainly in the partial lockdown and total lockdown periods, as well as reductions in commercial (stores/shops) and industrial activities. The variation in O3 was initially non-significant, followed by a considerable increase in the last three phases analyzed; this increase was influenced by changes in temperature and the incidence of sunlight. SO2 concentrations increased in the period studied, demonstrating that the vehicular fleet, local commerce, and other activities are not the predominant sources of this compound. Estimated health risks were reduced by half during the lockdown period, especially for non-smokers, followed by a drastic increase in the last three phases. The planetary boundary layer was positively correlated with O3 and PM10 and negatively correlated with NOx, NO2, and NO, indicating its influence on the distribution of pollutants in the lower atmosphere and, consequently, air quality.

Relative Humidity Predicts Day-to-Day Variations in COVID-19 Cases in the City of Buenos Aires.

Possible links between the transmission of COVID-19 and meteorology have been investigated by comparing positive cases across geographical regions or seasons. Little is known, however, about the degree to which environmental conditions modulate the daily dynamics of COVID-19 spread at a given location. One reason for this is that individual waves of the disease typically rise and decay too sharply, making it hard to isolate the contribution of meteorological cycles. To overcome this shortage, we here present a case study of the first wave of the outbreak in the city of Buenos Aires, which had a slow evolution of the caseload extending along most of 2020. We found that humidity plays a prominent role in modulating the variation of COVID-19 positive cases through a negative-slope linear relationship, with an optimal lag of 9 days between the meteorological observation and the positive case report. This relationship is specific to winter months, when relative humidity predicts up to half of the variance in positive case count. Our results provide a tool to anticipate possible local surges in COVID-19 cases after events of low humidity. More generally, they add to accumulating evidence pointing to dry air as a facilitator of COVID-19 transmission.

Impact of the COVID-19 Lockdown on Air Quality and Resulting Public Health Benefits in the Mexico City Metropolitan Area.

Meteorology and long-term trends in air pollutant concentrations may obscure the results from short-term policies implemented to improve air quality. This study presents changes in CO, NO2, O3, SO2, PM10, and PM2.5 based on their anomalies during the COVID-19 partial (Phase 2) and total (Phase 3) lockdowns in Mexico City (MCMA). To minimise the impact of the air pollutant long-term trends, pollutant anomalies were calculated using as baseline truncated Fourier series, fitted with data from 2016 to 2019, and then compared with those from the lockdown. Additionally, days with stagnant conditions and heavy rain were excluded to reduce the impact of extreme weather changes. Satellite observations for NO2 and CO were used to contrast the ground-based derived results. During the lockdown Phase 2, only NO2 exhibited significant decreases (p < 0.05) of between 10 and 23% due to reductions in motor vehicle emissions. By contrast, O3 increased (p < 0.05) between 16 and 40% at the same sites where NO2 decreased. During Phase 3, significant decreases (p < 0.05) were observed for NO2 (43%), PM10 (20%), and PM2.5 (32%) in response to the total lockdown. Although O3 concentrations were lower in Phase 3 than during Phase 2, those did not decrease (p < 0.05) from the baseline at any site despite the total lockdown. SO2 decreased only during Phase 3 in a near-road environment. Satellite observations confirmed that NO2 decreased and CO stabilised during the total lockdown. Air pollutant changes during the lockdown could be overestimated between 2 and 10-fold without accounting for the influences of meteorology and long-term trends in pollutant concentrations. Air quality improved significantly during the lockdown driven by reduced NO2 and PM2.5 emissions despite increases in O3, resulting in health benefits for the MCMA population. A health assessment conducted suggested that around 588 deaths related to air pollution exposure were averted during the lockdown. Our results show that to reduce O3 within the MCMA, policies must focus on reducing VOCs emissions from non-mobile sources. The measures implemented during the COVID-19 lockdowns provide valuable information to reduce air pollution through a range of abatement strategies for emissions other than from motor vehicles.

Implementation and evaluation of WRF simulation over a city with complex terrain using Alos-Palsar 0.4 s topography.

Air quality modeling requires an accurate representation of meteorology, and in cities with complex topography, the performance of meteorological modeling can be improved by using an alternative global digital elevation model (GDEM) such as Alos-Palsar 0.4 s instead of the default elevation data. Bogotá is a city with complex topography geographically located over the Andes Mountains at 2600 m.a.s.l. A reliable meteorological simulation model is critical for performing a suitable air quality modeling in any case of study. Previous researches have been developed using the standard Weather Research and Forecast (WRF) topography (GTOPO 30 s). These studies have been developed with different configurations for the representation of meteorology. The aim of this study is to evaluate Alos-Palsar 0.4 s topography with WRF, and two domain configurations with horizontal spatial resolutions up to 1000 m, to establish a reliable and accurate way to simulate the meteorology in the city of Bogotá. The evaluation quantitative parameters: IOA, r (Pearson), RMSE, MGE, and MB were calculated for the quantitative evaluation of temperature, relative humidity, wind speed, wind direction, and solar radiation. An additional evaluation using Taylor diagrams was performed. Spatial differences were identified in the same locations as well the differences between the elevation from Alos-Palsar 0.4 s and GTOPO30. The results and evaluation suggest that simulations based on Alos-Palsar 0.4 s topography lead to a significant improvement in the meteorology representation by WRF in a region with complex topography such as Bogotá, Colombia.

Cielos australes, tormentas sociales y pestilencias: astronomía y meteorología en el debate parlamentario argentino, 1869-1872 / Southern skies, social storms and pestilences: astronomy and meteorology in Argentine parliamentary debates,1869-1872

Resumen Entre 1869 y 1872 se debatieron los proyectos de creación del Observatorio Nacional Argentino y de la Oficina Meteorológica Argentina. Las propuestas de financiamiento de nuevas instituciones nacionales, así como su articulación con las políticas públicas, estaban atravesadas por conflictos propios de la etapa de construcción del Estado. Oponiéndose a los proyectos elevados al Congreso Nacional, algunos sectores argumentaban recursos escasos. El presidente, Domingo Sarmiento, los acusaba de ser aliados de un gobierno anterior, considerado como "bárbaro". No obstante, mostraremos que quienes defendían los proyectos, consideraban también inadmisible un gasto elevado, remarcaban que era poco y lograban su aprobación articulando el discurso con políticas entonces en curso que apuntaban a otras inquietudes como la educación, la inmigración y las epidemias.

Abstract From 1869 to 1872, there was debate about bills to create the Observatorio Nacional Argentino and the Oficina Meteorológica Argentina. The proposed funding for these new national institutions, as well as their connection to public policies, were riven by conflicts inherent to that phase of the construction of the State. Some sectors opposed the bills before the Congreso Nacional, arguing that resources were scarce. President Domingo Sarmiento charged that they were allies of the previous government, which he described as "barbarous." This article shows that the bills' supporters stressed that they were low-cost. They achieved passage of the bills by linking their discourse with contemporary policies aimed at other concerns, such as education, immigration and epidemics.

Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities.

This study aimed to analyze how meteorological conditions such as temperature, humidity and rainfall can affect the spread of COVID-19 in five Brazilian (São Paulo, Rio de Janeiro, Brasília, Manaus and Fortaleza) cities. The cities selected were those with the largest number of confirmed cases considering data of April 13. Variables such as number of cumulative cases, new daily cases and contamination rate were employed for this study. Our results showed that higher mean temperatures and average relative humidity favored the COVID-19 transmission, differently from reports from coldest countries or periods of time under cool temperatures. Thus, considering the results obtained, intersectoral policies and actions are necessary, mainly in cities where the contamination rate is increasing rapidly. Thus, prevention and protection measures should be adopted in these cities aiming to reduce transmission and the possible collapse of the health system.

Estresse por calor na Atenção Primária à Saúde: uma revisão clínica / Heat stress in Primary Health Care: A clinical review / Estrés por calor en la Atención Primaria a la Salud: una revisión clínica

Introdução: A exposição nociva ao calor ganha mais relevância com a progressão do aquecimento global antropogênico e a Atenção Primária à Saúde (APS) tem um papel crescente nesse cenário. No Brasil, as ondas de calor entre 2014 e 2015 duraram mais tempo que nos anos prévios, além disso, entre 2000-2015 a associação entre temperatura e hospitalizações variou de acordo com a duração da exposição ao calor. Nesse contexto, o objetivo desta revisão é realizar uma atualização sobre manejo clínico de patologias relacionadas ao calor na APS. Metodologia: Realizou-se a busca na base de dados ACCESSS, que utiliza a pirâmide 5.0 da assistência à saúde baseada em evidências. Foram identificados 103 sumários sintetizados para referência clínica com as palavras "Heat stress", "Heat Stroke", "Heat Wave" e "Heat Exhaustion", mas apenas três entravam no escopo deste estudo. Resultados e Discussão: O estresse pelo calor é uma condição comum, negligenciada e evitável que afeta diversos pacientes, iniciando-se com uma má adaptação ao calor que se não for corrigida pode gerar uma cascata de eventos inflamatórios. O estresse pelo calor é caracterizado por sintomas inespecíficos, como mal-estar, cefaleia e náusea. O tratamento envolve o resfriamento do paciente e monitoramento, garantindo hidratação adequada. A exaustão pelo calor, se não tratada, pode evoluir para insolação, uma doença grave que pode levar ao coma e morte, envolvendo disfunção do sistema nervoso central - necessitando de um tratamento mais agressivo além do resfriamento

Introduction: The nocive exposure to heat gets more attention with anthropogenic global warming, and Primary Health Care (PHC) has a growing role in this scenario. In Brazil heat waves between 2014 and 2015 lasted longer than in previous years. Further, in addition between 2000-2015 the association between temperature and hospitalizations varied according to the duration of heat exposure. Therefore, the aim of this review is to perform an update on clinical management of heat related pathologies in PHC. Methodology: The ACCESSS database was searched using the evidence-based health care pyramid 5.0, where we identified 103 synthesized summaries for clinical reference with words "Heat stress", "Heat Stroke", "Heat Wave" and "Heat Exhaustion", but only three fell within the scope of this study. Results and Discussion: Heat stress is a common, neglected and preventable condition that affects several patients, it starts with a poor adaptation to heat that if it is not adjusted it can generate a cascade of inflammatory events. Heat stress is characterized by nonspecific symptoms such as malaise, headache and nausea. The treatment involves patient monitoring and cooling, ensuring adequate hydration. Heat exhaustion, if untreated, can progress to heatstroke, a serious illness that can lead to coma and death, involving central nervous system dysfunction - requiring more aggressive treatment than cooling.

Introducción: La exposición nociva al calor gana más destaque con la progresión del calentamiento global antropogénico, y la Atención Primaria a la Salud tienen un papiel cresciente en este escenario. En Brasil las olas de calor entre 2014 y 2015 duraron más tiempo que en los años previos, además entre 2000-2015 la asociación entre temperatura y hospitalizaciones ha variado de acuerdo con la duración de la exposición al calor. En este contexto, el objetivo de esta revisión es realizar una actualización sobre manejo clínico de patologías relacionadas al calor en la APS. Metodología: Se realizó la búsqueda en la base de datos ACCESSS, que utiliza la pirámide 5.0 de la asistencia a la salud basada en evidencias. Se han identificado 103 sumarios sintetizados para referencia clínica con las palabras "Heat stress", "Heat Stroke", "Heat Wave" y "Heat Exhaustion", pero sólo tres son considerados en el ámbito de este estudio. Resultados y Discusión: El estrés por el calor es una condición común, descuidada y evitable que afecta a varios pacientes, iniciándose con una mala adaptación al calor que si no se corrige puede generar una cascada de eventos inflamatorios. El estrés por el calor se caracteriza por síntomas inespecíficos, como malestar, cefalea y náuseas. El tratamiento implica el enfriamiento del paciente y el monitoreo, garantizando la hidratación adecuada. El agotamiento por el calor, si no se trata, puede evolucionar hacia la insolación, una enfermedad grave que puede llevar al coma y a la muerte, involucrando disfunción del sistema nervioso central - necesitando un tratamiento más agresivo además del enfriamiento.

Atmospheric blockages as trigger of environmental contingencies in Mexico City.

Atmospheric pollution in cities is due to several human factors, for instance the number of cars in circulation, fuel efficiency and industrial waste, as well as orographic and meteorological conditions that determine air circulation. Ozone contingencies cause health disorders on the population, making it important to understand the factors that trigger such contingencies. Here, we analyze meteorological (wind, temperature, relative humidity) and atmospheric composition (ozone, and NOx) data of five atmospheric monitoring stations on Mexico City, from March 2004 to May 2018, comparing normal days with the extreme days in the 90th percentile of ozone. Moreover, we present the synoptic patterns of the seasonal differences of geopotential height at 500 hPa between extreme and control days. We found that, in the dry-hot season (from March to May) an atmospheric blockage with meteorological conditions of almost no wind, low relative humidity, and small temperature fluctuations occurs. Because the air in the city permanently contains large amounts of ozone precursors like NOx, this meteorological scenario raises ozone levels to those of an environmental contingency. Thus, during the dry-hot season on Mexico City, ozone contingencies are triggered by atmospheric blocking. This scenario will be present in cities surrounded by mountains with high levels of Ozone precursors.

Seasonality of Rotavirus Hospitalizations at Costa Rica's National Children's Hospital in 2010-2015.

Rotavirus is a leading cause of acute diarrhea in children worldwide. Costa Rica recently started universal rotavirus vaccinations for infants with a two-dose schedule in February 2019. We aimed to study the seasonality of rotavirus during the pre-vaccination era. We retrospectively studied a six-year period of hospital admissions due to rotavirus gastroenteritis. We estimated seasonal peak timing and relative intensities using trend-adjusted negative binomial regression models with the δ-method. We assessed the relationship between rotavirus cases and weather characteristics and estimated their effects for the current month, one-month prior and two months prior, by using Pearson correlation coefficients. A total of 798 cases were analyzed. Rotavirus cases predominated in the first five months of the year. On average, the peak of admissions occurred between late-February and early-March. During the seasonal peaks, the monthly count tended to increase 2.5-2.75 times above the seasonal nadir. We found the strongest negative association of monthly hospitalizations and joint percentiles of precipitation and minimal temperature at a lag of two months (R = -0.265, p = 0.027) and we detected correlations of -0.218, -0.223, and -0.226 (p < 0.05 for all three estimates) between monthly cases and the percentile of precipitation at lags 0, 1, and 2 months. In the warm tropical climate of Costa Rica, the increase in rotavirus hospitalizations coincided with dry and cold weather conditions with a two-month lag. The findings serve as the base for predictive modeling and estimation of the impact of a nation-wide vaccination campaign on pediatric rotaviral infection morbidity.

Aplicación de métodos multivariados para la caracterización de periodos de sequía meteorológica en Venezuela / Application of multivariate methods for the characterization of meteorological drought periods in Venezuela

El análisis multivariado consiste en determinar si existen maneras más simples de representar un conjunto de datos complejo, además de explorar si las observaciones se concentran en grupos y si existe una interdependencia entre los elementos. Este tipo de técnicas se han utilizado ampliamente para analizar datos climatológicos. Es por ello que el objetivo de esta investigación fue caracterizar la ocurrencia de períodos de sequía por medio de series temporales del Índice Normalizado de Precipitación (SPI) para siete localidades agrícolas de Venezuela, mediante el uso de dos métodos multivariados. A través del SPI, se cuantificaron las condiciones de déficit o exceso de precipitación en las localidades agrícolas con una escala mensual del periodo 1980-2014. Para el análisis, se usó la combinación de dos métodos multivariados: el Análisis de Coordenadas Principales de las matrices de datos usando distancia Euclídea y el Análisis de Conglomerados. En las siete localidades se describieron dos o tres grupos de años de SPI. En el caso de las localidades que resultaron con tres grupos (CENIAP, El Cují y Yaritagua) estos se categorizaron en años húmedos, años intermedios y años con déficit hídrico significativo. En tanto que, en el caso de las localidades que resultaron con dos grupos (Turén, Quíbor, Mucuchíes y Bramón) se clasificaron en años húmedos y años asociados al déficit hídrico. La aplicación de estos métodos multivariados permitió identificar los patrones espaciales mensuales dominantes del SPI sobre las localidades estudiadas, además de estar relacionados con la ocurrencia de sequías locales de gran importancia desde el punto de vista agrícola.

Multivariate analysis consists in determining if there are simpler ways to represent a complex set of data, besides exploring if the observations are concentrated in groups and if there is interdependence between the elements. These types of techniques have been widely used to analyze climatological data. That is why the objective of this research was to characterize the occurrence of drought periods by means of time series of the Standard Precipitation Index (SPI) for seven agricultural locations in Venezuela, by using two multivariate methods. The conditions of deficit or excess of precipitation in the agricultural localities were quantified through the SPI with a monthly scale of the period 1980-2014. The combination of two multivariate methods was used for the analysis: Principal Coordinate Analysis of the data matrices using Euclidean distance and Cluster Analysis. Two or three groups of years of SPI were described in the seven locations. In the case of the locations that resulted with three groups (CENIAP, El Cují and Yaritagua), these were categorized in wet years, intermediate years and years with significant water deficit. Meanwhile, in the case of the locations that resulted with two groups (Turén, Quíbor, Mucuchíes and Bramón), they were classified into wet years and years associated with the water deficit. The application of these multivariate methods made it possible to identify the dominant monthly spatial patterns of the SPI on the studied locations, besides being related to the occurrence of local droughts of great importance from the agricultural point of view.