Artificial intelligence to predict soil temperatures by development of novel model

Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.

1H HR-MAS NMR chemical profile and chemometric analysis as a tool for quality control of different cultivars of green tea (Camellia sinensis)

Green tea is a product obtained from the processing of fresh leaves of Camellia sinensis (L.) O. Kuntze species. In this study, the influence of climatic parameters on the chemical composition of green tea cultivars ('Yabukita' and 'Yutakamidori') over the harvest was evaluated using HR-MAS NMR. 'Yabukita' showed higher concentrations of epicatechin while higher amounts of theanine and caffeine were found in 'Yutakamidori'. The decline of theanine was associated with high average maximum temperature and solar radiation index, this latter also seemed to be responsible for relevant changes in epicatechin concentrations. It was not possible to associate any trend between climatic parameters and caffeine concentration. Fluctuations in linolenic acid concentration were monitored during the harvest period and were associated with the plant's defense mechanism. Monitoring of green tea over seasons and correlating the fluctuations of compounds to climatic parameters might become an efficient strategy for establishing quality standards for green teas.

Aerosols' variability and their relationship with climatic parameters over West Africa

Aerosols' influences on Earth's climate have been documented by several authors. This ranges from scattering and reflecting of shortwave radiation (direct effect) which is also regarded as the "Whitehouse Effect," to the ability to act as condensation nuclei (indirect effect) which results in cloud droplet formation. This broad summary of aerosol's effect on earth's climate has in turn affected some other weather variables either positively or negatively depending on people's perspectives. This work was done in a view to ascertaining some of these claims by determining the statistical significance of some certain aerosol's relationships with some selected weather variables. This was done over six (6) stations across the West African region to represent the climatic zones from the rainforest around the coasts to the desert of the Sahel. Data used consist of aerosol types (biomass burning, carbonaceous, dust, and PM_2.5) and climatic types (convective precipitation, wind speed, and water vapor) over a period of 30 years, with the python and ferret programs explicitly used for the graphical analyses. Climatologically, locations close to the point source seem to record more of the presence of the pollutants than the farthest ones. Results indicated that aerosols were more pronounced in the dry months of NDJF over the rainforest region depending on the latitudinal position of the location. The relationship result showed a negative correlation between convective precipitation and aerosols, except carbonaceous. But the strongest relationship can be found between water vapor and the selected aerosol types.

Influence of climatic parameters on the probabilistic design of green roofs

Green roofs are effective tools for stormwater control in highly urbanized areas since they allow the reduction of peak runoffs and volumes discharged in sewer systems. Their design is quite standardized, except for the thickness of the growing medium layer, which is strictly related to vegetation type and rainfall regime. The paper proposes an analytical probabilistic approach that relates the climatic variables, the growing medium thickness, and the water content in the condition of fulfilled field capacity to the probability that runoff from green roofs exceeds a fixed threshold. The developed equations also consider the possibility of a reduced retention capacity due to previous rainfall events, that strongly influence the performance of these green infrastructures, especially when short dry periods and/or low evapotranspiration rates occur. This feature, neglected by the traditional design storm approach, and only partially considered by previous analytical probabilistic models, represent a great potentiality of the proposed equations that are also more user-friendly and less time-consuming than continuous simulation analysis. The focus of the paper is on the influence of climatic parameters on runoff probability. To this aim to perform the monthly analysis is fundamental, especially when there is a strong variability of the climatic parameters throughout the year. The model was tested in a case study in Milano, Italy. The application presented a good agreement between the results obtained from the proposed equations and those obtained from the continuous simulation of recorded data. The results also highlighted the importance of performing analysis on a monthly scale.

The detection and monitoring of pollution caused by gold mining using a vegetation cover index

This study explores how a vegetation cover (VC) index can be employed as a pollution warning tool in gold mining areas in the Northwest of Iran. The analysis included the following: (a) the extraction of normalized difference vegetation index (NDVI) maps from Landsat images in three zones, i.e., mining operations, upstream areas without any exploration, and the downstream area of the mining activities, (b) calculation of the zones' VC, (c) investigation of transformation trends in each pixel of VC time series using the Mann-Kendall trend test, (d) determination of the pixels with significant VC reduction and the significant starting points of the trend using the sequential Mann-Kendall test, (e) assessment of the correlation between the zones with significantly reduced VC, and (f) a correlation test between average monthly and annual climate parameters and VC. Our results indicate that although 51 ha of VC has been demolished around the mining activities areas (i.e., zone 1), an overall upward trend in vegetation with no chemical leakage is observed into the downstream area of the basin (i.e., zone 3). This upward trend can be mostly attributed to the increasing precipitation and decreasing temperature in the study period. The fact that the area downstream of the mine shows that the heap leaching method for gold mining in Andaryan mine is currently not damaging the vegetation, this likely means that there is no leakage to the surrounding environment from the mine. Our results further show that using NDVI in a pixel-based scale and statistical methods has a high potential to quantify the effects of human activities on surface biophysical characteristics.

Impact of variation in climatic parameters on hydropower generation: A case of hydropower project in Nepal

Nepal has substantial potential to generate electricity through hydropower projects. Most of the hydropower projects in Nepal are Run-off-River (ROR) types. Significant seasonal variation can be pronounced on its river basins resulting in higher streamflow & higher hydropower generation during the wet/summer season and just reverse scenario in case of the dry/winter season. Thus, ROR-type hydropower in Nepal is more susceptible to Climate Change. This study assesses the impact of variation in climatic parameters on the hydropower generation by implementing WEAP model using the meteorological and hydrological data from 1976 to 2004 under Reference & Climatic Scenarios. The results reveal that the streamflow of Dordi River of Nepal is in increasing trends and can be more pronounced during April, May, June & July of the season under climatic scenarios. The generation of hydropower plant is likely to increase up to 15%, 1%-32% & 1%-51% over the study period under climatic scenario-1, 2 & 3, respectively, as compared to baseline scenario and the increments are observed to be more prominent during April & May of the season which is very crucial finding in current context of Nepal as there is power deficit during the dry season. Therefore, detailed technical and policy level planning can enhance the power generating capability of the future hydropower projects that will be developed in this corridor. This will significantly impacts the national energy planning and implementation.

Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India

Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST₅), 15-cm (ST₁₅), and 30-cm (ST₃₀) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVM-SMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site.

Human activities can drive sulfate-reducing bacteria community in Chinese intertidal sediments by affecting metal distribution

Sulfate-reducing bacteria (SRB), which are ubiquitous in intertidal sediments, play an important role in global sulfur and carbon cycles, and in the bioremediation of toxic metalloids/metals. Pollution from human activities is now a major challenge to the sustainable development of the intertidal zone, but little is known about how and to what extent various anthropic and/or natural factors affect the SRB community. In the current study, based on the dsrB gene, we investigated the SRB community in intertidal sediment along China's coastline. The results showed that dsrB gene abundances varied among different sampling sites, with the highest average abundance of SRB at XHR (near the Bohai Sea). The SRB community structures showed obvious spatial distribution patterns with latitude along the coastal areas of China, with Desulfobulbus generally being the dominant genus. Correlation analysis and redundancy discriminant analysis revealed that total organic carbon (TOC) and pH were significantly correlated with the richness of the SRB community, and salinity, pH, sulfate and climatic parameters could be the important natural factors influencing the composition of the SRB community. Moreover, metals, especially bioavailable metals, could regulate the diversity and composition of the SRB communities. Importantly, according to structural equation model (SEM) analysis, anthropic factors (e.g., population, economy and industrial activities) could drive SRB community diversity directly or by significantly affecting the concentrations of metals. This study provides the first comprehensive investigation of the direct and indirect anthropic factors on the SRB community in intertidal sediments on a continental scale.

Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India

Remote sensing-based observation provides an opportunity to study the spatiotemporal variations of plant phenology across the landscapes. This study aims to examine the phenological variations of different types of sal (Shorea robusta) forests in India and also to explore the relationship between phenology metrics and climatic parameters. Sal, one of the main timber-producing species of India, can be categorized into dry, moist, and very moist sal. The phenological metrics of different types of sal forests were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Enhanced Vegetation Index (EVI) time series data (2002-2015). During the study period, the average start of season (SOS) was found to be 16 May, 17 July, and 29 June for very moist, moist, and dry sal forests, respectively. The spatial distribution of mean SOS was mapped as well as the impact of climatic variables (temperature and rainfall) on SOS was investigated during the study period. In relation to the rainfall, values of the coefficient of determination (R²) for very moist, moist, and dry sal forests were 0.69, 0.68, and 0.76, respectively. However, with temperature, R² values were found higher (R² = 0.97, 0.81, and 0.97 for very moist, moist, and dry sal, respectively). The present study concluded that MODIS EVI is well capable of capturing the phenological metrics of different types of sal forests across different biogeographic provinces of India. SOS and length of season (LOS) were found to be the key phenology metrics to distinguish the different types of sal forests in India and temperature has a greater influence on SOS than rainfall in sal forests of India.

The effect of climate change on depression in urban areas of western Iran

Objective

Human is accustomed to climatic conditions of the environment where they are born and live throughout their lifetime. The aim of this study is to examine mood swings and depression caused by sudden climate changes that have not yet given the humans a chance to adapt.

Results

Our results showed that depression could be affected by climate change and as a result, the behavior of climatic elements and trends has damaged mental health in the western regions of Iran. By investigating the trends and changes of climatic time series and their relationship with the rate of depression in urban areas of western Iran, it can be said that climate change is probably a mental health challenge for urban populations. Climate change is an important and worrying issue that makes the life difficult. Rapid climate changes in western Iran including rising air temperature, changes in precipitation, its regime, changes cloudiness and the amount of sunlight have a negative effects on health. The results showed that type of increasing or decreasing trend, as well as different climatic elements in various seasons did not have the same effect on the rate of depression in the studied areas.

Morphologic characterization of the Blanche de Montagne, an endemic sheep of the Atlas Mountains

The present study aims to investigate the morphological characteristics of the Blanche de Montagne sheep breed, mainly known by its long white fleece. The morphological characterization was performed on a total of 70 unrelated individuals of both sexes, having between 2 and 8 teeth and belonging to 13 farms located in the Ouarzazate region. The body measurements studied were body weight, body length, chest circumference, height at withers, rump height, chest depth, chest width, hip width, head length, head width, ear length, ear width, horn length, cannon circumference, wool length, and tail length. These measurements averaged 28.2±8.10 kg, 65.8±6.25 cm, 73.5±6.71 cm, 59.5±4.39 cm, 59.5±4.72 cm, 27.4±2.90 cm, 19.8±2.70 cm, 21.0±2.14 cm, 19.2±1.57 cm, 11.0±1.01 cm, 9.71±1.02 cm, 5.47±0.53 cm, 43.2±13.8 cm, 7.44±0.77 cm, 10.9±3.56 cm, and 31.8±4.36 cm, respectively. Descriptive statistics presented an overall coefficient of variation less than 15%, showing a homogeneous morphostructure of the breed. Most characters were small in relation with the low productivity of pastures. Moreover, 77.5% of correlation coefficients among the different body measurements were positive and significant, reflecting the strong morphological harmony of the breed, suggesting a long period of adaptation to its environment. The variance analysis showed that sex influenced the measurements, with males having the highest values. Similarly, individuals with 6 or 8 teeth showed higher values than those with 2 or 4 teeth. Through the comparison with other Moroccan breeds, the variation of some morphological traits was found in relation to some climatic parameters (mainly winter temperatures) and feeding supplementation.

Infrared thermography for microclimate assessment in agroforestry systems

In agroforestry systems, trees modify climatic parameters over a given area and create a complex microclimate through interactions between topography, plant composition and organizational structure of trees. In this way, indicators such as surface temperature of tree canopy and pasture, monitored by infrared thermography, are important to monitor the thermal environment of animal production and pasture establishment. Goals of this study were (1) to evaluate temporal and local variations of temperature and humidity leaf surface of tree canopy and pasture in agroforestry systems by infrared remote sensing and, (2) to validate infrared thermography as a potential tool for assessment microclimate in agroforestry systems. The study was carried out between June 2015 and February 2016 in an experimental area located at 54°370'W, 20°270'S and 530 m altitude, in Brazil. Surface temperatures and humidity of tree canopy and pasture in two agroforestry systems with different densities and tree spatial arrangements were determined using infrared thermography. Air, black globe and dew point temperatures, relative humidity and wind speed were measured using digital thermo-hygrometers with datalogger. Moderate to strong associations have been identified between microclimate parameters and those monitored by means of thermography measurements (0.45 ≥ r ≤ 0.78), suggesting positive relationships and equally well explained by air temperature, black globe temperature and relative air humidity (R² = 0.68 ≥ R² ≤ 0.98). Variations in hourly averages of temperatures and humidity of pasture and tree canopy show similar patterns between seasons, with consistently higheraverages during summer and under full sun, indicating the existence of a thermal band with leaf temperatures above air temperature. Therefore, this work's findings support use of infrared thermography as a tool for microclimate assessment in agroforestry systems.

Determining the bioclimatic comfort in Kastamonu City

Bioclimatic comfort defines the optimal climatic conditions in which people feel healthy and dynamic. Bioclimatic comfort mapping methods are useful to urban managers and planners. For the purposes of planning, climatic conditions, as determined by bioclimatic comfort assessments, are important. Bioclimatic components such as temperature, relative humidity, and wind speeds are important in evaluating bioclimatic comfort. In this study of the climate of Kastamonu province, the most suitable areas in terms of bioclimatic comfort have been identified. In this context, climate values belonging to the province of Kastamonu are taken from a total of nine meteorological stations. Altitude (36-1050 m) between stations is noted for revealing climatic changes. The data collected from these stations, including average temperature, relative humidity, and wind speed values are transferred to geographical information system (GIS) using ArcMap 10.2.2 software. GIS maps created from the imported data has designated the most suitable comfort areas in and around the city of Kastamonu. As a result, the study shows that Kastamonu has suitable ranges for bioclimatic comfort zone. The range of bioclimatic comfort value for Kastamonu is 17.6 °C. It is between a comfort ranges which is 15-20 °C. Kastamonu City has suitable area for bioclimatic comfort.