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Metropolitan areas may suffer with increase of air pollution due to the growth of urbanization, transportation, and industrial sectors. The Metropolitan Area of Vitória (MAV) in Brazil is facing air pollution problems, especially because of the urbanization of past years and of having many industries inside the metropolitan area. Developing air quality system is crucial to understand the air pollution mechanism over these areas. However, having a good input dataset for applying on photochemical models is hard and requires quite of research. One input file for air quality modeling which can play a key role on results is the lateral boundary conditions (LBC). This study aimed to investigate the influence of LBC over CMAQ simulation for particulate matter and ozone over MAV by applying four different methods as LBC during August 2010. The first scenario (M1) is based on a fixed, time-independent boundary conditions with zero concentrations for all pollutants; the second scenario (M2) used a fixed, time-independent concentration values, with average values from local monitoring stations; the third CMAQ nesting scenario (M3) used the nested boundary conditions varying with time from a previous simulation with CMAQ over a larger modeling domain, centered on MAV; and finally, the fourth GEOS-Chem scenario (M4) used the boundary conditions varying with time from simulations of global model GEOS-Chem. All scenarios runs are based on the same meteorology conditions and pollutant emissions. The air quality simulations were made over a domain 61 × 79 km centered on coordinates - 20.25° S, - 40.28° W with a resolution of 1 km. The results were evaluated with the measured data from the local monitoring stations. Overall, significant differences on concentrations and number of chemical species between the LBC scenarios are shown across all LBC scenarios. The M3 and M4 dynamic LBC scenarios showed the best performances over ozone estimates while M1 and M2 had poor performance. Although no LBC scenarios do not seem to have a great influence on total PM10 and PM2.5 concentrations, individual PM2.5 species like Na, NO3-, and NH4+concentrations are influenced by the dynamic LBC approach, since those hourly individual PM2.5 species from CMAQ nesting approach (M3) and GEOS-Chem model (M4) were used as an input to LBC.

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With the introduction of new technologies that lead to the development of new systems of poultry breeding, have been promoted to the animals better thermal comfort conditions. This lessens the great challenge for industrial poultry, with regard to aviary constructions, resulting in increased productivity. It is known that an animal in heat stress condition presents lower productive performance. A lower feed conversion caused by decreased feed intake is the main cause. Multidisciplinary studies have been developed seeking the deepening the needs and possibilities already available about these new systems. With this, this review seeks to approach in a didactic and simplified way the planning and construction of aviary sheds aiming to provide an suitable thermal conditioning for broiler accommodation.

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With the introduction of new technologies that lead to the development of new systems of poultry breeding, have been promoted to the animals better thermal comfort conditions. This lessens the great challenge for industrial poultry, with regard to aviary constructions, resulting in increased productivity. It is known that an animal in heat stress condition presents lower productive performance. A lower feed conversion caused by decreased feed intake is the main cause. Multidisciplinary studies have been developed seeking the deepening the needs and possibilities already available about these new systems. With this, this review seeks to approach in a didactic and simplified way the planning and construction of aviary sheds aiming to provide an suitable thermal conditioning for broiler accommodation.(AU)

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An early method of biometeorological forecasts was developed for Cuba during the late 90s. It was based on the relationship between the daily occurrence of massive health crisis and the magnitude of the 24-h differences of partial density of oxygen in the air (PODA index). Ten years later, applying new technological facilities, a new model was developed in order to offer operational biometeorological forecast to Cuban health institutions. After a satisfactory validation process, the official bioforecast service to health institutions in Villa Clara province began on February of 2012. The effectiveness had different success levels: for the bronchial asthma crisis (94%), in the hypertensive crisis (88%), with the cerebrovascular illnesses (85%), as well as migraines (82%) and in case of cardiovascular diseases (75%) were acceptable. Since 2008, the application of the model was extended to other regions of the world, including some national applications. Furthermore, it allowed the beginning of regional monitoring of meteor-tropic effects, following the occurrence and movement of areas with higher weather contrasts, defined according to the normalized scale of PODA index. The paper describes the main regional results already available, with emphasis in the observed meteor-tropic effects increasing in all regions during recent years. It coincides with the general increase of energy imbalance in the whole climate system. Finally, the paper describes the current development of new global biometeorological forecast services.

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This paper reports about experiences gathered from five online monitoring campaigns in the sewer systems of Berlin (Germany), Graz (Austria), Lyon (France) and Bogota (Colombia) using ultraviolet-visible (UV-VIS) spectrometers and turbidimeters. Online probes are useful for the measurement of highly dynamic processes, e.g. combined sewer overflows (CSO), storm events, and river impacts. The influence of local calibration on the quality of online chemical oxygen demand (COD) measurements of wet weather discharges has been assessed. Results underline the need to establish local calibration functions for both UV-VIS spectrometers and turbidimeters. It is suggested that practitioners calibrate locally their probes using at least 15-20 samples. However, these samples should be collected over several events and cover most of the natural variability of the measured concentration. For this reason, the use of automatic peristaltic samplers in parallel to online monitoring is recommended with short representative sampling campaigns during wet weather discharges. Using reliable calibration functions, COD loads of CSO and storm events can be estimated with a relative uncertainty of approximately 20%. If no local calibration is established, concentrations and loads are estimated with a high error rate, questioning the reliability and meaning of the online measurement. Similar results have been obtained for total suspended solids measurements.

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It is well known that excessive heat exposure causes heat disorders and can lead to death in some situations. Evaluation of heat stress on workers performing indoor and outdoor activities is, nowadays, conducted worldwide by wet-bulb globe temperature (WBGT) index, which calculation parameters are dry-bulb, natural wet-bulb, and globe temperatures, which must be measured at the same time and in location where the worker is conducting his/her activities. However, for some activities performed in large outdoor areas such as those of agricultural ones, it is not feasible to measure directly those temperatures in all work periods and locations where there are workers. Taking this into account, this work aims to introduce a WBGT index estimation using atmospheric variables observed by automatic meteorological stations. In order to support our estimation method, we used, as a test-bed, data recorded in the State of São Paulo (SP), Brazil. By adding the cloudiness factor in the calculation through measurement of solar radiation, the algorithm proved to be as efficient as those mentioned in this work. It was found that this method is viable, with WBGT-estimated values obtained from meteorological data measured by stations with a distance of less than 80 km. This estimate can be used for monitoring heat stress in real time as well as to investigate heat-related disorders and agricultural work.

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Bridging the gap between the predictions of coarse-scale climate models and the fine-scale climatic reality of species is a key issue of climate change biology research. While it is now well known that most organisms do not experience the climatic conditions recorded at weather stations, there is little information on the discrepancies between microclimates and global interpolated temperatures used in species distribution models, and their consequences for organisms' performance. To address this issue, we examined the fine-scale spatiotemporal heterogeneity in air, crop canopy and soil temperatures of agricultural landscapes in the Ecuadorian Andes and compared them to predictions of global interpolated climatic grids. Temperature time-series were measured in air, canopy and soil for 108 localities at three altitudes and analysed using Fourier transform. Discrepancies between local temperatures vs. global interpolated grids and their implications for pest performance were then mapped and analysed using GIS statistical toolbox. Our results showed that global interpolated predictions over-estimate by 77.5 ± 10% and under-estimate by 82.1 ± 12% local minimum and maximum air temperatures recorded in the studied grid. Additional modifications of local air temperatures were due to the thermal buffering of plant canopies (from -2.7 °K during daytime to 1.3 °K during night-time) and soils (from -4.9 °K during daytime to 6.7 °K during night-time) with a significant effect of crop phenology on the buffer effect. This discrepancies between interpolated and local temperatures strongly affected predictions of the performance of an ectothermic crop pest as interpolated temperatures predicted pest growth rates 2.3-4.3 times lower than those predicted by local temperatures. This study provides quantitative information on the limitation of coarse-scale climate data to capture the reality of the climatic environment experienced by living organisms. In highly heterogeneous region such as tropical mountains, caution should therefore be taken when using global models to infer local-scale biological processes.

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The effect of meteorological variables on surface ozone (O3) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O3 concentration, relative humidity and solar radiation. The threshold model that considers two O3 regimes was the one that correctly describes the effect of important meteorological variables in O3 behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O3 regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O3 concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O3 behaviour, being useful to define policy strategies for human health protection regarding air pollution.

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The relationship between wood growth and environmental variability at the tropical treeline of North America was investigated using automated, solar-powered sensors (a meteorological station and two dendrometer clusters) installed on Nevado de Colima, Mexico (19° 35' N, 103° 37' W, 3,760 m a.s.l.). Pure stands of Pinus hartwegii Lindl. (Mexican mountain pine) were targeted because of their suitability for tree-ring analysis in low-latitude, high-elevation, North American Monsoon environments. Stem size and hydroclimatic variables recorded at half-hour intervals were summarized on a daily timescale. Power outages, insect outbreaks, and sensor failures limited the analysis to non-consecutive months during 2001-2003 at one dendrometer site, and during 2002-2005 at the other. Combined data from the two sites showed that maximum radial growth rates occur in late spring (May), as soil temperature increases, and incoming short-wave radiation reaches its highest values. Early season (April-May) radial increment correlated directly with temperature, especially of the soil, and with solar radiation. Stem expansion at the start of the summer monsoon (June-July) was mostly influenced by moisture, and revealed a drought signal, while late season relationships were more varied.

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