International Journal of Biometeorology最新文献

Yerba mate (Ilex paraguariensis) is renowned for its nutritional and pharmaceutical attributes. A staple in South American (SA) culture, it serves as the foundation for several traditional beverages. Significantly, the pharmaceutical domain has secured numerous patents associated with this plant's distinctive properties. This research delves into the climatic influence on yerba mate by leveraging the CMIP6 model projections to assess potential shifts brought about by climate change. Given its economic and socio-cultural significance, comprehending how climate change might sway yerba mate's production and distribution is pivotal. The CMIP6 model offers insights into future conditions, pinpointing areas that are either conducive or adverse for yerba mate cultivation. Our findings will be instrumental in crafting adaptive and mitigative strategies, thereby directing sustainable production planning for yerba mate. The core objective of this study was to highlight zones optimal for Ilex paraguariensis cultivation across its major producers: Brazil, Argentina, Paraguay, and Uruguay, under CMIP6's climate change forecasts. Our investigation encompassed major producing zones spanning the North, Northeast, Midwest, Southeast, and South of Brazil, along with the aforementioned countries. A conducive environment for this crop's growth features air temperatures between 21 to 25 °C and a minimum precipitation of 1200 mm per cycle. We sourced the current climate data from the WorldClim version 2 platform. Meanwhile, projections for future climatic parameters were derived from WorldClim 2.1, utilizing the IPSL-CM6A-LR model with a refined 30-s spatial resolution. We took into account four distinct socio-economic pathways over varying timelines: 2021-2040, 2041-2060, 2061-2081, and 2081-2100. Geographic information system data aided in the spatial interpolation across Brazil, applying the Kriging technique. The outcomes revealed a majority of the examined areas as non-conducive for yerba mate cultivation, with a scanty 12.25% (1.5 million km²) deemed favorable. Predominantly, these propitious regions lie in southern Brazil and Uruguay, the present-day primary producers of yerba mate. Alarming was the discovery that forthcoming climatic scenarios predominantly forecast detrimental shifts, characterized by escalating average air temperatures and diminishing rainfall. These trends portend a decline in suitable cultivation regions for yerba mate.

The outdoor thermal comfort (OTC) of children is more specific than that of adults, and the complex influence of outdoor activity spaces on children's thermal comfort warrants further investigation. To investigate the outdoor thermal comfort baseline (OTCB) of children in Hangzhou and explore the thermal impact of outdoor surfaces on children, a survey was conducted in six typical outdoor activity spaces in Hangzhou, China, during spring and summer utilizing physical measurements, questionnaire surveys, and the universal thermal climate index (UTCI). This study analyzed the differences in thermal perception among children in Hangzhou in different seasons, their OTCB, and the impact of surface reflectance (R_s) on children's OTC. The results indicated the following: 1) In spring, children in Hangzhou generally felt comfortable, but their discomfort with heat noticeably increased in summer. 2) The neutral UTCIs (NUTCIs) for Hangzhou children were 11.6 °C (spring) and 27.7 °C (summer), and the NUTCI ranges (NUTCIRs) were 9.7–17.5 °C (spring) and 25.7–30.0 °C (summer); additionally, the thermal acceptability ranges (TARs) were 13.2–25.2 °C (spring) and 11.8–34.8 °C (summer). 3) A high R_s made children feel more uncomfortable with heat, which was primarily due to the space's total shortwave and longwave radiation, which peaked between 14:00 and 15:00. 4) Based on the research findings, corresponding bioclimatic design strategies were proposed. Recommendations include using high R_s underlays with shading, composite underlays, or the future adoption of thermochromic coatings. Keeping permeable underlays moist is essential for activating their cooling mechanisms. Fundamental safety measures are imperative. This study provides valuable data for urban planners and landscape designers to create public spaces suitable for children's outdoor activities, contributing to a harmonious and unified living environment.

There is an urgent need for strategies to reduce the negative impacts of a warming climate on human health. Cooling urban neighborhoods by planting trees and vegetation and increasing albedo of roofs, pavements, and walls can mitigate urban heat. We used synoptic climatology to examine how different tree cover and albedo scenarios would affect heat-related morbidity in Los Angeles, CA, USA, as measured by emergency room (ER) visits. We classified daily meteorological data for historical summer heat events into discrete air mass types. We analyzed those classifications against historical ER visit data to determine both heat-related and excess morbidity. We used the Weather Research and Forecasting model to examine the impacts of varied tree cover and albedo scenarios on meteorological outcomes and used these results with standardized morbidity data algorithms to estimate potential reductions in ER visits. We tested three urban modification scenarios of low, medium, and high increases of tree cover and albedo and compared these against baseline conditions. We found that avoiding 25% to 50% of ER visits during heat events would be a common outcome if the urban environment had more tree cover and higher albedo, with the greatest benefits occurring under heat events that are moderate and those that are particularly hot and dry. We conducted these analyses at the county level and compared results to a heat-vulnerable, working-class Los Angeles community with a high concentration of people of color, and found that reductions in the rate of ER visits would be even greater at the community level compared to the county.

Individual heating systems, such as the air-source heat pump (ASHP) air-conditioner or floor heating (FH), are usually used by people living in the hot summer and cold winter (HSCW) zone of China to heat indoor climates in the winter. However, little research has been conducted in the HSCW zone on the thermal comfort difference between indoor climates heated by ASHP air-conditioners and those heated by floor heating, as well as how occupants adapt to different indoor climates. We conducted a comparative field experiment in ASHP-heated and FH-heated apartments in Nanjing to investigate how different types of heating systems influence the thermal sensation of occupants, and we conducted a comparative field experiment in ASHP-heated office buildings and naturally ventilated teaching buildings in Shanghai to investigate how occupants adapt to different indoor thermal environments. Indoor environmental parameters and body surface temperatures were measured using instruments, and occupants’ thermal sensation, activity level, and clothing were evaluated using the questionnaire. The results show that floor heating improves thermal comfort by raising foot temperature compared to the ASHP air-conditioner, and that occupants become acclimatized to different indoor climates by adjusting neutral operative temperature. According to the findings, there is no need to overheat the indoor environment in the HSCW zone because occupants can adapt to their experienced thermal environment and it is critical to maintain warm foot temperature in the cool/cold indoor environment.

Little has been studied about microclimate and the thermal comfort during the implementation of silvopastoral systems. This study aimed to evaluate the microclimate and thermal comfort during the implementation of High Biodiversity Silvopastoral System with Nuclei (SPSnu). Three treatments were investigated, SPSnu with 5 and 10% of the pasture area with nuclei, (SPSnu5 and SPSnu10, respectively), and treeless pasture (TLP). Each treatment was subdivided into 4 areas: within the nuclei, around the nuclei, around the nuclei with shade and internuclei. The analyzed variables were soil surface temperature, air temperature, wind speed, relative humidity, black globe temperature and the Heat Load Index (HLI) at 20 and 120 cm height. We hypothesized that the wind speed reduction associated with insufficient shade projection typical of the first years of SPSs may interfere in microclimate and thermal comfort during the hot seasons. SPSnu5 and SPSnu10 had a reduction in wind speed of 51.58% and 68.47% respectively when compared to TLP at 20 cm. Soil surface temperature and air temperature at 120 cm were higher for SPSnu than TLP. The same effect was observed for the HLI. At 20 cm, HLI indicated better thermal comfort in TLP than in the SPSnu treatments. The lack of shade projection from young nuclei in conjunction with the decrease of wind speed between the nuclei caused a higher air temperature and HLI in the SPSnu treatments, we called this conditions, windbreak countereffect. Farmers must knowledge this effect when implementing SPSs, and when necessary, mitigate with the proper management decisions.

Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making.