Fire and soil temperatures during controlled burns in seasonally dry tropical forests of southern India

Fire and soil temperatures were measured during controlled burns conducted by the Forest Department at two seasonally dry tropical forest sites in southern India, and their relationships with fuel load, fuel moisture and weather variables assessed using stepwise regression. Fire temperatures at the ground level varied between 79 degrees C and 760 degrees C, with higher temperatures recorded at high fuel loads and ambient temperatures, whereas lower temperatures were recorded at high relative humidity. Fire temperatures did not vary with fuel moisture or wind speed. Soil temperatures varied between <79 degrees C and 302 degrees C and were positively correlated with ground-level fire temperatures. Results from the study imply that fuel loads in forested areas have to be reduced to ensure low intensity fires in the dry season. Low fire temperatures would ensure lower mortality of above-ground saplings and minimal damage to root stocks of tree species that would maintain the regenerative capacity of a tropical dry forest subject to dry season wildfires.

CLIGEN非降水要素在黄土塬区的适用性评估

许多基于物理机制的水文和作物模型需要日序列气象数据来驱动,CLIGEN是为WEPP等模型产生气候输入文件的天气发生器,可以产生10个日序列气象变量来满足这种需要,但是其在中国的适用性需要进行评估。研究的目标是利用黄土高原陕西长武1957~2001年的气象数据评估CLIGEN产生非降水要素(最高温度、最低温度、露点温度、太阳辐射和风速)的能力。结果表明,CLIGEN对最高温度、最低温度和露点温度的模拟效果较好,对太阳辐射和极端气候事件的模拟效果较差,对风速的模拟效果最差。相关性检验表明CLIGEN很好地保持了气象要素的季节性,这对模拟农业生产是非常重要的;但是没有保留气象要素逐日的自相关和互相关性,进而导致产生的温度变化不符合连续渐变的规律。

Characteristics of Grass Pollen Seasons in Areas of Southern Spain and the United Kingdom

Spatial and temporal variations in daily grass pollen counts and weather variables are described for two regions with different bio-geographical and climatic regimes, southern Spain and the United Kingdom. Daily average grass pollen counts are considered from six pollen-monitoring sites, three in southern Spain (Ciudad Real, Córdoba and Priego) and three in the United Kingdom (Edinburgh, Worcester and Cambridge). Analysis shows that rainfall and maximum temperatures are important factors controlling the magnitude of the grass pollen season in both southern Spain and the United Kingdom, and that the strength and direction of the influence exerted by these variables varies with geographical location and time.

Do Urban Canyons Influence Street Level Grass Pollen Concentrations

In epidemiological studies, outdoor exposure to pollen is typically estimated using rooftop monitoring station data, whilst exposure overwhelmingly occurs at street level. In this study the relationship between street level and roof level grass pollen concentrations was investigated for city centre street canyon environments in Aarhus, Denmark, and London, UK, during the grass pollen seasons of 2010 and 2011 respectively. For the period mid-day to late evening, street level concentrations in both cities tended to be lower than roof-level concentrations, though this difference was found to be statistically significant only in London. The ratio of street/roof level concentrations was compared with temperature, relative humidity, wind speed and direction, and solar radiation. Results indicated that the concentration ratio responds to wind direction with respect to relative canyon orientation and local source distribution. In the London study, an increase in relative humidity was linked to a significant decrease in street/roof level concentration ratio, and a possible causative mechanism involving moisture mediated pollen grain buoyancy is proposed. Relationships with the other weather variables were not found to be significant in either location. These results suggest a tendency for monitoring station data to overestimate exposure in the canyon environment.

Methodologies for the generation of design summer years for building energy simulation using UKCP09 probabilitic climate projections

Design summer years representing near-extreme hot summers have been used in the United Kingdom for the evaluation of thermal comfort and overheating risk. The years have been selected from measured weather data basically representative of an assumed stationary climate. Recent developments have made available ‘morphed’ equivalents of these years by shifting and stretching the measured variables using change factors produced by the UKCIP02 climate projections. The release of the latest, probabilistic, climate projections of UKCP09 together with the availability of a weather generator that can produce plausible daily or hourly sequences of weather variables has opened up the opportunity for generating new design summer years which can be used in risk-based decision-making. There are many possible methods for the production of design summer years from UKCP09 output: in this article, the original concept of the design summer year is largely retained, but a number of alternative methodologies for generating the years are explored. An alternative, more robust measure of warmth (weighted cooling degree hours) is also employed. It is demonstrated that the UKCP09 weather generator is capable of producing years for the baseline period, which are comparable with those in current use. Four methodologies for the generation of future years are described, and their output related to the future (deterministic) years that are currently available. It is concluded that, in general, years produced from the UKCP09 projections are warmer than those generated previously. Practical applications: The methodologies described in this article will facilitate designers who have access to the output of the UKCP09 weather generator (WG) to generate Design Summer Year hourly files tailored to their needs. The files produced will differ according to the methodology selected, in addition to location, emissions scenario and timeslice.

Evaluation of regional climate simulations for air quality modelling purposes

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional “climate modeling” source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

Climate and construction delays: case study in Chile

Purpose – Construction projects usually suffer delays, and the causes of these delays and its cost overruns have been widely discussed, the weather being one of the most recurrent. The purpose of this paper is to analyze the influence of climate on standard construction work activities through a case study. Design/methodology/approach – By studying the extent at which some weather variables impede outdoor work from being effectively executed, new maps and tables for planning for delays are presented. In addition, a real case regarding the construction of several bridges in southern Chile is analyzed. Findings – Few studies have thoroughly addressed the influences of major climatic agents on the most common outdoor construction activities. The method detailed here provides a first approximation for construction planners to assess to what extent construction productivity will be influenced by the climate. Research limitations/implications – Although this study was performed in Chile, the simplified method proposed is entirely transferable to any other country, however, other weather or combinations of weather variables could be needed in other environments or countries. Practical implications – The implications will help reducing the negative social, economic and environmental outcomes that usually emerge from project delays. Originality/value – Climatic data were processed using extremely simple calculations to create a series of quantitative maps and tables that would be useful for any construction planner to decide the best moment of the year to start a project and, if possible, where to build it.

Susceptibility of mosquito vectors to Dirofilaria immitis on Madeira Island, Portugal

Dirofilaria immitis (Leidy, 1856), an agent of heartworm disease, is an important parasite from both the veterinary standpoint and as a model to study human filariasis. It is a mosquito-borne filarial nematode which inhabits the right ventricle and pulmonary arteries of dogs. D. immitis is an important disease agent on Madeira Island with about 30% of dogs testing positive for this worm. Nevertheless, the vectors of this parasite in Madeira have never been studied, nor has the interaction between pathogen and vector, or the environmental variables that might influence heartworm transmission. Innate susceptibility to infection is only one component of vector competence, and field isolation of naturally infected mosquitoes has shown the capability of D. immitis to exploit a great diversity of vector species under natural conditions. The purpose of this work was to determine which mosquitoes are vectors of heartworm disease, the relation between population density and environment, and the association between immune response of the vector to the filarial parasite. Seasonal abundance of Culex theileri and Culex pipiens molestus was studied. Correlation and canonical correspondence analysis were performed using abundance data of these two species with selected weather variables, including mean temperature, relative humidity and accumulated precipitation. The most important factor determining Cx. theileri abundance was accumulated precipitation, while Cx. pipiens molestus abundance did not have any relationship with weather variables. Field studies were performed to verify whether Cx. theileri Theobald functions as a natural vector of D. immitis on Madeira Island, Portugal. Cx. theileri tested positive for D. immitis for the first time. The same study was made regarding Cx. p. molestus. Two abnormal L2 stage filarial worms were found in Malpighian tubules in field caught Cx. p. molestus. In the laboratory, two strains of Cx. p. molestus were studied for their susceptibility to D. immitis. None presented infective-stage larvae. Finally, because Cx. p. molestus is an autogenous mosquito, we evaluated the reproductive costs when this mosquito mounts an immune response against D. immitis in the absence of a blood meal. This mosquito showed an active immune response when inoculated intrathoracically with microfilariae (mf) of the heartworm. The ovaries from mosquitoes undergoing melanotic encapsulation developed more eggs than those which could not melanize the mf. This fact is contradictory with some previous studies of reproductive costs in Armigeres subalbatus and Ochlerotatus trivittatus, and it was the first time that an autogenous mosquito was used to study this subject.

Índice de área foliar e produtividade do tomate sob condições de ambiente protegido

The tomato cultivation in the greenhouse has been expanded in the last years, mainly, in the South and Southeast regions of Brazil, whose purpose is to improve the productivity and the quality of the agricultural products, offering regularity in the production. The present study aimed to determine, along the crop cycle, the relationship between the leaf area index and the productivity, and at the end of the cycle, the components of production of the tomato in the greenhouse. The models were generated through polynomial equations of 1st and 2nd order, having as independent variable the number of days after the transplanting. It was verified that it is possible to determine, in the greenhouse, through mathematical models, the leaf area index of the tomato crop considering the days after the transplanting. Basing on values of leaf area index, the productivity of the crop and the period of the maximum productivity can be determined, aiding the farmers to determine the best sowing and transplanting time of the tomato crop.

Comportamento, produção e qualidade do leite de vacas Holandês-Gir com climatização no curral

This study was conducted with the objective to evaluate the duration of exposure of animals to the evaporative cooling system in the waiting barn on the weather variables (temperature and relative humidity), animal behavior, production and quality aspects of milk from Girolando cows. Sixteen multiparous lactating cows were used having a genetic composition of 7/8 Holstein-Gir and mean milk production of 18 kg animal-1day-1. A 4 × 4 Latin square design was used. The experiment was conducted during the summer season (February-March 2009) and lasted 56 days. The treatments consisted of different periods of 10, 20 and 30 minutes of exposure to acclimatization and control. Time of 30 minutes of exposure to thermal conditioning system enabled the second milking (evening shift), providing an increase in milk production in the next milking of 0.640 kg animal-1. A higher percentage of time spent on food intake and rumination (17.31 and 30.19%) in 30 minutes treatment, compared to control (15.50 and 27.37%), respectively. There was no significant effect of treatments in the composition of milk.

Estimativa da radiação solar ultravioleta em Botucatu/SP Brasil utilizando técnicas de aprendizado de máquina

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Variabilidade do índice de vegetação por diferença normalizada (NDVI) em áreas reflorestamento: floresta estadual Edmundo Navarro de Andrade (FEENA)/Rio Claro (SP)

Pós-graduação em Geociências e Meio Ambiente - IGCE

Pediatric asthma incidence in Texas and associations with ambient ozone levels in an urban area an analysis using Medicaid claims data

Few recent estimates of childhood asthma incidence exist in the literature, although the importance of incidence surveillance for understanding asthma risk factors has been recognized. Asthma prevalence, morbidity and mortality reports have repeatedly shown that low-income children are disproportionately impacted by the disease. The aim of this study was to demonstrate the utility of Medicaid claims data for providing statewide estimates of asthma incidence. Medicaid Analytic Extract (MAX) data for Texas children ages 0-17 enrolled in Medicaid between 2004 and 2007 were used to estimate incidence overall and by age group, gender, race and county of residence. A 13+ month period of continuous enrollment was required in order to distinguish incident from prevalent cases identified in the claims data. Age-adjusted incidence of asthma was 4.26/100 person-years during 2005-2007, higher than reported in other populations. Incidence rates decreased with age, were higher for males than females, differed by race, and tended to be higher in rural than urban areas. With this study, we were able to demonstrate the utility of MAX data for estimating asthma incidence, and create a dataset of incident cases to use in further analysis. ^ In subsequent analyses, we investigated a possible association between ambient air pollutants and incident asthma among Medicaid-enrolled children in Harris County Texas between 2005 and 2007. This population is at high risk for asthma, and living in an area with historically poor air quality. We used a time-stratified case-crossover design and conditional logistic regression to calculate odds ratios, adjusted for weather variables and aeroallergens, to assess the effect of increases in ozone, NO2 and PM2.5 concentrations on risk of developing asthma. Our results show that a 10 ppb increase in ozone was significantly associated with asthma during the warm season (May-October), with the strongest effect seen when a 6-day cumulative lag period was used to compute the exposure metric (OR=1.05, 95% CI, 1.02–1.08). Similar results were seen for NO2 and PM 2.5 (OR=1.07, 95% CI, 1.03–1.11 and OR=1.12, 95% CI, 1.03–1.22, respectively). PM2.5 also had significant effects in the cold season (November-April), 5-day cumulative lag: OR=1.11, 95% CI, 1.00–1.22. When compared with children in the lowest quartile of O3 exposure, the risk for children in the highest quartile was 20% higher. This study indicates that these pollutants are associated with newly-diagnosed childhood asthma in this low-income urban population, particularly during the summer months. ^

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Temperature and Photoperiod Effects on Vicia faba Phenology Simulated by CROPGRO-Fababean

Models may be useful tools to design efficient crop management practices provided they are able to accurately simulate the effect of weather variables on crop performance. The objective of this work was to accurately simulate the effects of temperature and day length on the rate of vegetative node expression, time to flowering, time to first pod, and time to physiological maturity of faba bean (Vicia faba L.) using the CROPGRO-Fababean model. Field experiments with multiple sowing dates were conducted in northwest Spain during 3 yr (17 sowing dates: 12 used for calibration and five for validation). Observed daily minimum and maximum air temperatures were within the range of ?9.0 and 39.2°C and observed photoperiods within 10.1 to 16.6 h. Optimization of thermal models to predict leaf appearance raised the base temperature (Tb) from the commonly used value of 0.0 to 3.9°C. In addition, photothermal models detected a small accelerating effect of day length on the rate of leaf appearance. Accurate prediction of the flowering date required incorporating day length, but the solved Tb approached negative values, close to ?4°C. All the reproductive phases after flowering were affected only by temperature, but postanthesis Tb was also mayor que0°C and approached values close to 8°C for time to first pod set and 5.5°C for time from first pod to physiological maturity. Our data indicated that cardinal base temperatures are not the same across all phenological phases.