Spatio-Temporal Heterogeneity of Climate Warming in the Chinese Tianshan Mountainous Region

The Chinese Tianshan mountainous region (CTMR) is a typical alpine region with high topographic heterogeneity, characterized by a large altitude span, complex topography, and diverse landscapes. A significant increase in air temperature had occurred in the CTMR during the last five decades. However, the detailed, comprehensive, and systematical characteristics of climate warming, such as its temporal and spatial heterogeneity, remain unclear. In this study, the temporal and spatial heterogeneity of climate warming across the CTMR had been comprehensively analyzed based on the 10-day air temperature data gathered during 1961–2020 from 26 meteorological stations. The results revealed local cooling in the context of general warming in the CTMR. The amplitude of variation (AV) varied from −0.57 to 3.64 °C, with the average value of 1.19 °C during the last six decades. The lapse rates of the elevation-dependent warming that existed annually, and in spring, summer, and autumn are −0.5 °C/100 m, −0.5 °C/100 m, −0.7 °C/100 m, and −0.4 °C/100 m, respectively. The warming in the CTMR is characteristic of high temporal heterogeneity, as represented by the amplified warming at 10-d scale for more than half a year, and the values of AV were higher than 1.09 °C of the global warming during 2011–2020 (GWV2011–2020). Meanwhile, the amplitudes of warming differed greatly on a seasonal scale, with the rates in spring, autumn, and winter higher than that in summer. The large spatial heterogeneity of climate warming also occurred across the CTMR. The warming pole existed in the warm part, the Turpan-Hami basin (below 1000 m asl) where the air temperature itself was high. That is, the warm places were warmer across the CTMR. The cooling pole was also found in the Kuqa region (about 1000 m asl). This study could greatly improve the understanding of the spatio-temporal dynamics, patterns, and regional heterogeneity of climate warming across the CTMR and even northwest China.

Cardiovascular risk factors in rural areas: case of the Mabayi health district hospital in Burundi

Relevance . Noncommunicable diseases are a serious public health problem due to their high incidence and mortality rate. Globally, noncommunicable diseases cause 41 million deaths every year, accounting for 71% of the total number of deaths. Cardiovascular diseases, accounting for 44% of all noncommunicable diseases, are the leading causes of death. Early identification of the main risk factors for cardiovascular diseases and treatment of associated diseases are a prerequisite for maintaining the health of the population. Objective: To identify the main risk factors for cardiovascular disease in patients living in rural areas of the mountainous region and attending the Mabayi District Hospital in Burundi. Patients and Methods . An open-label retrospective study conducted at the Mabayi District Hospital in Burundi from January 2014 to December 2017. The study included patients whose medical examination revealed at least one risk factor for cardiovascular diseases. The study was approved by the ethics committee of the Kamenge University Hospital and the Faculty of Medicine of the University of Burundi. Data analysis was carried out using Microsoft Word 2007 and Epi-Info TM 7.2.1.0 software. Results and Discussion . Among the 20 297 examined patients, the average age was 50 16.7 years, the extreme values were 14 and 101 years. Male patients accounted for 51.1%. 903 patients (4.5%) had at least one risk factor. The main risk factors were high blood pressure (52.6%), diabetes (42.0%) and alcohol abuse (27.4%). Conclusion . Residents of rural areas of the mountainous region of Burundi have a high frequency of risk factors for cardiovascular diseases, which must be taken into account when organizing medical and preventive measures to prevent cardiovascular diseases.

Peculiarities of acid-base properties of peat formed in various agroclimatic zones of the Altai mountainous region

The results of the study of acid-base indicators of peat in the Altai mountainous region are presented. The natural factors that in the aggregate determine the peculiarities of the physico-chemical properties of mountain peat of different agro-climatic zones of the Altai Mountains have been revealed. The variation in the acid values, total absorbed bases, adsorption capacity and the degree of saturation of raised-bog, transitional, fen peat, the number of exchangeable ions Ca2+ and Mg2+ has been estimated. The interrelation among these indicators has been presented. For the first time, regression equations of the relationship between exchangeable acidity pHKC1 and the degree of peat base saturation V, between total absorbed bases S and the degree of peat base saturation V have been obtained using nonlinear regression analysis. The adequacy and stability of the developed models have been verified. The calculated mean errors of approximation of regression models characterise the high accuracy of the forecast and are indicative of a good selection of models for the initial data.

A series of terribly unfortunate events: How environment and infection synergized to cause the Kihansi spray toad extinction

Outbreaks of emerging infectious diseases are trained by local biotic and abiotic factors, with host declines occurring when conditions favour the pathogen. Extinction of the Tanzanian Kihansi spray toad (Nectophrynoides asperginis) in 2004 was contemporaneous with the construction of a dam, implicating habitat modification in the loss of this species. However, high burdens of a globally emerging infection, Batrachochytrium dendrobatidis (Bd) were synchronously observed implicating infectious disease in this toads extinction. Here, by shotgun sequencing skin DNA from archived toad mortalities and assembling chytrid mitogenomes, we prove this outbreak was caused by the BdCAPE lineage and not the panzootic lineage BdGPL that is widely associated with global amphibian extinctions. Molecular dating showed an invasion of BdCAPE across Southern Africa overlapping with the timing of the extinction event. However, post-outbreak surveillance of conspecific species inhabiting this mountainous region showed widespread infection by BdCAPE yet no signs of amphibian ill-health or species decline. Our findings show that despite efforts to mitigate the environmental impact caused by dams construction, invasion of the pathogen ultimately led to the loss of the Kihansi spray toad; a synergism between emerging infectious disease and environmental change that likely heralds wider negative impacts on biodiversity in the Anthropocene.

‘Technology is Everywhere, we have the Opportunity to Learn it in the Valley’: The Appropriation of a Socio-Technical Enabling Infrastructure in the Moroccan High Atlas

This paper describes the appropriation processes involved in establishing a socio-technical enabling infrastructure in a valley in the High Atlas of Morocco. We focus on the challenges of co-establishing such an intervention in a rural/mountainous region that is already undergoing a process of continuous development and profound transformation. We reflect upon the changes and unforeseen appropriation by our local partners and inhabitants in the valley of a computer club primarily used as an informal learning centre for school children. We followed an ethnographic approach and combined research perspectives from both socio-informatics and anthropology. This paper sheds light on what a successful cooperation and intervention in this kind of challenging environment can look like. It does this by taking seriously competing expectations, fragile infrastructural foundations and the socio-cultural context. Despite the challenges, the intervention managed to lead to the establishment of a socio-technical enabling infrastructure that plays a particularly valuable role in local educational endeavours and that is now moving towards supporting other members of the community. The paper thus provides insights regarding what has to be considered to create a mutually beneficial cooperation with all relevant stakeholders as well as a sustainable intervention.

Identification of agricultural land use change based on machine learning for regional food security analysis in the mountainous region of Kulon Progo regency

After New Yogyakarta International Airport (NYIA) opening, Kulon Progo continues to develop, one of which is The Bedah Menoreh route project which passes through the mountainous region. The development encourages agricultural land conversion, which impacts food security in Kulon Progo, especially in the mountainous region. This study aims to identify the conversion of agricultural land in the mountainous region of Kulon Progo Regency in 2005 – 2020 and analyze its impact on regional food security. The method used is a Normalized Difference Vegetation Index (NDVI) on Landsat Imagery using Machine Learning through Google Earth Engine (GEE) to identify land-use change and mathematical calculations in analyzing regional food security. The result of the supervised classification is a land cover map of the mountainous region of Kulon Progo Regency, which shows that every year the area of rice fields, in general, continues to decrease until 2020 the total area is 2,102.79 ha with a rate of agricultural land conversion -114.87 ha/year. It causes regional food security to be in a food-insecure condition, even though the availability of rice fields can be used for food self-sufficiency for up to 53 years. Other factors such as climate, rice seeds, soil, and water quality, in this case, are quite influential in rice production, not only productivity and agricultural land area.

Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia

Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.