Bioclimatic performance of wet coffee processing facilities: conditions for workers and coffee

This study aimed to evaluate the bioclimatic performance of three wet coffee processing facilities in Colombia, focused on the conditions for workers and coffee parchment, through computer simulation. In addition to temperature and relative humidity, the Wet-Bulb Globe Temperature index (WBGT) was simulated during the highest coffee production month. The proposed simulation model was able to predict hygrothermal behavior within the three coffee processing facilities. Case 3 presented the warmest environment, and case 2 the most humid environment concerning the appropriate conditions for the coffee and the worker. The WBGT index limit was exceeded in case 3. Since this type of facility emits large amounts of heat and steam, constructive modifications are suggested to improve the environmental conditions of workers and coffee. Mainly, the physical separation of the heat exchangers is recommended, which ideally should be outside the post-harvest facility. The steam produced in the drying process should be quickly evacuated with ventilation strategies. Additionally, the use of strategies that reduce the energy gain from solar radiation is suggested.

Evaluating methods for reconstructing large gaps in historic snow depth time series

Historic measurements are often temporally incomplete and may contain longer periods of missing data, whereas climatological analyses require continuous measurement records. This is also valid for historic manual snow depth (HS) measurement time series, for which even whole winters can be missing in a station record, and suitable methods have to be found to reconstruct the missing data. Daily in situ HS data from 126 nivo-meteorological stations in Switzerland in an altitudinal range of 230 to 2536 m above sea level are used to compare six different methods for reconstructing long gaps in manual HS time series by performing a “leave-one-winter-out” cross-validation in 21 winters at 33 evaluation stations. Synthetic gaps of one winter length are filled with bias-corrected data from the best-correlated neighboring station (BSC), inverse distance-weighted (IDW) spatial interpolation, a weighted normal ratio (WNR) method, elastic net (ENET) regression, random forest (RF) regression and a temperature index snow model (SM). Methods that use neighboring station data are tested in two station networks with different density. The ENET, RF, SM and WNR methods are able to reconstruct missing data with a coefficient of determination (r2) above 0.8 regardless of the two station networks used. The median root mean square error (RMSE) in the filled winters is below 5 cm for all methods. The two annual climate indicators, average snow depth in a winter (HSavg) and maximum snow depth in a winter (HSmax), can be reproduced by ENET, RF, SM and WNR well, with r2 above 0.85 in both station networks. For the inter-station approaches, scores for the number of snow days with HS>1 cm (dHS1) are clearly weaker and, except for BCS, positively biased with RMSE of 18–33 d. SM reveals the best performance with r2 of 0.93 and RMSE of 15 d for dHS1. Snow depth seems to be a relatively good-natured parameter when it comes to gap filling of HS data with neighboring stations in a climatological use case. However, when station networks get sparse and if the focus is set on dHS1, temperature index snow models can serve as a suitable alternative to classic inter-station gap filling approaches.

Assessment of indoor thermal environment of Aceh house based on WBGT index

Thermal comfort is one of the standard assessments of building thermal environment. Air movement is an important parameter for in a naturally ventilated to achieve thermal comfort by accelerating the evaporative cooling process on the human body. Aceh House has a standard of thermal comfort with a vernacular architecture with a natural ventilation system. This vernacular architectural building has a fairly high harmonization of the environment because it has undergone a process of adaptation. In this study, observations were made at the Original House (OH), the Adaptive Reuse House (ARH), and the Aceh Modified House (AMH). By using the method of assessing changes in environmental comfort, using Wet Bulb Temperature Index (WBGT) method, the minimum and maximum temperature ranges are 25°C and 30°C. In the WBGT thermal rating, AMH has the higher thermal and is followed by ARH and OH respectively. Thus, OH has lower thermal compared to other Aceh houses.

Forcing of cool and warm subsurface water events in Bahía Salinas, Costa Rica

Introduction: Bahía Salinas, on the north Pacific coast of Costa Rica, is a seasonal upwelling area. Sea temperature in Bahía Salinas could be modulated by synoptic and other large-scale systems. This region belongs to the Central American Dry Corridor (CADC), a sub-region in the isthmus that is relatively drier than the rest of the territory, which extends along the Pacific littoral from western Guatemala through northern Costa Rica. Objective: To study the warm and cold events that could be inferred by studying the sea subsurface temperature in Bahía Salinas, and also analyzing the large-scale conditions and synoptic systems of the historical sources when they occurred in order to identify the atmospheric mechanisms that favored their appearance. Methods: A Sea Subsurface Temperature Index was calculated using hourly data from seven stations located at three different points in Bahía Salinas. Records range from June 19, 2003 to December 5, 2017. Additionally, six meteorological stations, with hourly wind records, were used to create two wind indices. The Sea Subsurface Temperature Index was used to identify the warmest and coldest events in the bay. Wind indices and monthly meteorological bulletins were used to analyze the large-scale conditions and synoptic systems in which cold and warm events occurred in Bahía Salinas. Results: Mean sea temperature in Bahía Salinas is 25.2°C. Colder temperatures were observed in February-March, below 21°C. There were two maxima in May-June and August-October with temperatures above 27°C. In four of the five cold events studied, Northeasterly wind anomalies were observed in the Costa Rican North Pacific, associated with trade wind reinforcements; meanwhile westerly anomalies were observed in all the warm events, associated with weaker trade wind conditions. Conclusions: The main seasonal climate driver in Bahía Salinas is the North Atlantic Subtropical High because its latitudinal migration is associated with the strength of the trade winds over Central America. Seasonal upwelling is modulated also by two synoptic scale climate features, the boreal winter arrival of cold front outbreaks and the winter maximum of the easterly Caribbean Low-Level Jet. El Niño-Southern Oscillation is also an important modulator of the sea temperature variability, since warm and cool events are related with positive and negative sea temperature anomalies.

Evaluation of Thermal Indices as the Indicators of Heat Stress in Dairy Cows in a Temperate Climate

Many thermal indices (TIs) have been developed to quantify the severity of heat stress in dairy cows. Systematic evaluation of the representative TIs is still lacking, which may cause potential misapplication. The objectives of this study were to evaluate the theoretical and actual performance of the TIs in a temperate climate. The data were collected in freestall barns at a commercial dairy farm. The heat transfer characteristics of the TIs were examined by equivalent air temperature change (ΔTeq). One-way ANOVA and correlation were used to test the relationships between the TIs and the animal-based indicators (i.e., rectal temperature (RT), respiration rate (RR), skin temperature (ST), and eye temperature (ET)). Results showed that the warming effect of the increased relative humidity and the chilling effect of the increased wind speed was the most reflected by the equivalent temperature index (ETI) and the comprehensive climate index (CCI), respectively. Only the equivalent temperature index for cows (ETIC) reflected that warming effect of solar radiation could obviously increase with increasing Ta. The THI and ETIC showed expected relationships with the RT and RR, whereas the CCI and ETIC showed expected relationships with the ST and ET. Moreover, CCI showed a higher correlation with RT (r = 0.672, p < 0.01), ST(r = 0.845, p < 0.01), and ET (r = 0.617, p < 0.01) than other TIs (p < 0.0001). ETIC showed the highest correlation with RR (r = 0.850, p < 0.01). These findings demonstrated that the CCI could be the most promising thermal index to assess heat stress for housed dairy cows. Future research is still needed to develop new TIs tp precisely assess the microclimates in cow buildings.

Application of a Radiation-Derived Temperature Index Model to the Willow Creek Watershed in Idaho, USA

The ability to simulate snow accumulation and melting processes is fundamental to developing real-time hydrological models in watersheds with a snowmelt-dominated flow regime. A primary source of uncertainty with this model development approach is the subjectivity related to which historical periods to use and how to combine parameters from multiple calibration events. The Hydrologic Engineering Center, Hydrological Modeling System, has recently implemented a hybrid temperature index (TI) snow module that has not been extensively tested. This study evaluates a radiatative temperature index (RTI) model’s performance relative to the traditional air TI model. The TI model for Willow Creek performed reasonably well in both the calibration and validation years. The results of the RTI calibration and validation simulations resulted in additional questions related to how best to parameterize this snow model. An RTI parameter sensitivity analysis indicates that the choice of calibration years will have a substantial impact on the parameters and thus the streamflow results. Based on the analysis completed in this study, further refinement and verification of the RTI model calculations are required before an objective comparison with the TI model can be completed.

Usage of Arduino-based Datalogger for thermal comfort variables monitoring

Environmental monitoring is an effective tool to identify problems in anthropic areas, and the emergence of cyber-physical sensors contributes to technological advances in the area. This paper introduces a device based on the Arduino cyber-physical platform to monitor air temperature and relative humidity in real-time with high efficiency. With the relationship between these two environmental variables, it will be possible to calculate the Heat Index (CI), the Temperature and Humidity Index (ITU), the Effective Temperature Index (ET), and the Thermal Discomfort Index (IDT). The Datalogger developed is easily programmable and easy to assemble and presented stable operation and proper functioning.

Evaluating methods for reconstructing large gaps in historic snow depth time series

Historic measurements are often temporally incomplete and may contain longer periods of missing data whereas climatological analyses require continuous measurement records. This is also valid for historic manual snow depth (HS) measurement time series, where even whole winters can be missing in a station record and suitable methods have to be found to reconstruct the missing data. Daily in-situ HS data from 126 nivo-meteorological stations in Switzerland in an altitudinal range of 230 to 2536 m above sea level is used to compare six different methods for reconstructing long gaps in manual HS time series by performing a "leave-one-winter-out" cross-validation in 21 winters at 33 evaluation stations. Synthetic gaps of one winter length are filled with bias corrected data from the best correlated neighboring station (BSC), inverse distance weighted (IDW) spatial interpolation, a weighted normal ratio (WNR) method, Elastic Net (ENET) regression, Random Forest (RF) regression and a temperature index snow model (SM). Methods that use neighboring station data are tested in two station networks with different density. The ENET, RF, SM and WNR methods are able to reconstruct missing data with a coefficient of determination (r2) above 0.8 regardless of the two station networks used. Median RMSE in the filled winters is below 5 cm for all methods. The two annual climate indicators, average snow depth in a winter (HSavg) and maximum snow depth in a winter (HSmax), can be well reproduced by ENET, RF, SM and WNR with r2 above 0.85 in both station networks. For the inter-station approaches, scores for the number of snow days with HS ≥ 1 cm (dHS1) are clearly weaker and except for BCS positively biased with RMSE of 18–33 days. SM reveals the best performance with r2 of 0.93 and RMSE of 15 days for dHS1. Snow depth seems to be a relatively good-natured parameter when it comes to gap filling of HS data with neighboring stations in a climatological use case. However, when station networks get sparse and if the focus is set on dHS1, temperature index snow models can serve as a suitable alternative to classic inter-station gap filling approaches.

Climate warming drives divergence of montane butterfly communities in Southern Italy

Because of climate change, many insect species are shifting their altitudinal and latitudinal ranges, including Mediterranean butterflies, particularly in mountainous regions. In this study, we evaluated changes in butterfly communities over time, sampled in 1975, 2004, and 2012, in relation to their altitude and two indices representing the climate envelopes of species within a given community: CTI (Community Temperature Index) and CPI (Community Precipitation Index). The study took place in a protected area where we found strong changes in community compositions over the 37-year study period. There was no vertical stratification of communities in 1975, but became significantly so in 2004 and 2012. Likewise, CTI and CPI were correlated with altitude only in 2004 and 2012. Over time, CTI increased at lower altitudes, indicating an increase in species associated with higher temperatures, and was stable or decreased at higher altitudes. CPI showed opposing trends, decreasing at lower altitude of communities and increasing in higher altitude communities. This resulted in asymmetric changes along the altitudinal gradient. The highest elevations (>1900 m) shifted towards butterfly species that are more associated with colder, wetter habitats, and lower elevations shifted towards species more associated with hotter, drier habitats. In conclusion, changes in butterfly communities were consistent with expectations from observed changes of temperatures and precipitations at low altitudes and mid-altitudes, but not at the highest altitudes. This counter-intuitive result may be due to land-use changes following creation of a national park in 1993 that encompassed the sample sites, but we lack the data to test this hypothesis.

Modeling of Heat Stress in Sows—Part 1: Establishment of the Prediction Model for the Equivalent Temperature Index of the Sows

Heat stress affects the estrus time and conception rate of sows. Compared with other life stages of pigs, sows are more susceptible to heat stress because of their increased heat production. Various indicators can be found in the literature assessing the level of heat stress in pigs. However, none of them is specific to assess the sows’ thermal condition. Moreover, thermal indices are mainly developed by considering partial environment parameters, and there is no interaction between the index and the animal’s physiological response. Therefore, this study aims to develop a thermal index specified for sows, called equivalent temperature index for sows (ETIS), which includes parameters of air temperature, relative humidity and air velocity. Based on the heat transfer characteristics of sows, multiple regression analysis is used to combine air temperature, relative humidity and air velocity. Environmental data are used as independent variables, and physiological parameters are used as dependent variables. In 1029 sets of data, 70% of the data is used as the training set, and 30% of the data is used as the test set to create and develop a new thermal index. According to the correlation equation between ETIS and temperature-humidity index (THI), combined with the threshold of THI, ETIS was divided into thresholds. The results show that the ETIS heat stress threshold is classified as follows: suitable temperature ETIS < 33.1 °C, mild temperature 33.1 °C ≤ ETIS < 34.5 °C, moderate stress temperature 34.5 °C ≤ ETIS < 35.9 °C, and severe temperature ETIS ≥ 35.9 °C. The ETIS model can predict the sows’ physiological response in a good manner. The correlation coefficients R of skin temperature was 0.82. Compared to early developed thermal indices, ETIS has the best predictive effect on skin temperature. This index could be a useful tool for assessing the thermal environment to ensure thermal comfort for sows.