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The interactions between temperature and relative humidity are the main focus of this article. These two meteorological observations are of great significance due to their direct effects on humans and their environment. A series of studies on meteorological variables was carried out over a decade with emphasis on their effects on humans and their e...
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... The choice of the Gaussian distribution is due to its computational advantages unlike other distributions with complex computational process and implementation. A plethora of studies on climatic variables have been investigated by several authors using different statistical approaches and with novel findings [50][51][52]. ...
... It is well known that both temperature and relative humidity are inversely proportional. Hence, the pollutants showing a positive association with Temperature and negative association with relative humidity work as an influencer for the climate change and vice versa (Khan et al., 2022;Sein et al., 2022;Uzuazor et al., 2021). Table 1 indicates the beta values that came out from this study. ...
The continuous change in climatic conditions has created a challenging situation for people living all over the World. The rising temperature and humidity have the worst impacts on the cities with high populations, and poor air quality in an urban environment significantly affects climatic variables. Delhi, which tops the list of air pollution hotspots among the top polluted cities around the World, is selected for this study. This study assessed a correlation between criteria air pollutants and meteorological parameters. It was hypothesized that criteria air pollutants would positively predict the change in temperature and relative humidity (pillars of climate change) during the daily dataset (January 01, 2015-December 31, 2021) and average annual dataset (2000 to 2021) in Delhi. This study uses elastic net-applied regularization in model exploration and coefficient estimation using EVIEWS 12. It was observed that during the selected study period, most of the criteria air pollutants played an important part in increasing the changes in the climatic conditions of Delhi. This research further explains the interlinkage between air pollution and climate change with the help of available literature.
... The most important explanation of the likelihood of a refractivityrelated influence required for LRWP prediction techniques is provided by local coverage, refractivity gradient (RG) in N-units/km, and other statistics of refractivity [9,14,8]. It is worth noting that WV are also very important for agricultural purposes [15,16,17,18,19,20,21], as well as other atmospheric purposes [1,3]. Albeit, in this study, the measurement results of air temperature (T), relative humidity (RH), and atmospheric pressure (P) were made at a low altitude (height level) of 50 m above sea level at a low-rise building located within the Ekpoma area of Edo State, Nigeria, utilizing a set of self-implemented, cost-effective portable weather/meteorological monitoring device (WMD) for a period of twelve months (January to December, 2022). ...
Refractivity indices (RI) such as the surface refractivity (SR), the refractivity gradient (RG), and the effective earth radius factor (k-factor) are valuable components for predicting the local radio wave propagation (LRWP) conditions in the troposphere. Hence, in this study, the estimation and analysis of the RI (SR, the RG, and k-factor) from the measured m weather/meteorological variables (WV); air temperature (T) , relative humidity (RH), and atmospheric pressure which was reduced to mean sea level pressure (MSLP) in the Ekpoma area of Edo State, Nigeria, which is located within Latitude 6.74° N and Longitude 6.14° E for a period of twelve months (January to December, 2022), so as to infer the radio propagation conditions using a self-designed weather/meteorological monitoring device (WMD), were investigated. The WMD was positioned at a low altitude (height level) of 50 m above sea level at a low-rise building located within the area so as to measure the needed WV. The results show that the SR and the k-factor values were generally higher during the months with high RH (rainy periods) compared to those of the months with lower relative humidity (dry periods), while the RG, values were higher in the dry periods compared to the rainy periods. The average values of the RI for the period under consideration are 355.58 N-units,-61.58 N-units/km, and 1.51 for the SR, the RG, and k-factor respectively. Thus, it is inferentially concluded that the LRWP condition for the Ekpoma is mostly super-refractive.
... Several research investigations on rainfall across the globe depicts that agricultural activities are often affected by the pattern and amount of rainfall. The effects of the variability of rainfall and changes in temperature has become a critical subject by researchers because rainfall variability is occasioned by variation in temperature [12][13][14][15][16]. The significance of rainfall and temperatures as essential climatic factors that are critical for the existence of plants and animals in the ecosystem is vividly established, hence it is imperative to study their interdependence or correlation. ...
... The applications of kernel estimator are mostly in multivariate case with emphasis majorly on the bivariate estimator whose density estimates can be viewed in twodimensional form or three-dimensional form. The popularity of the bivariate kernel estimator in higher dimensional density estimation is due to the simplification of the presentation of its estimates as surface plots (familiar perspective known as wire frame) or contour plots (Silverman, 2018;Siloko et al., 2021). Another factor that accounts for the application of the bivariate kernel estimator is the presentation of the observations with respect to their direction. ...
The two-dimensional kernel estimators are very important because graphical presentation of data beyond three dimensional forms is oftentimes not too frequently employed in data visualizations. The frequency of the bivariate estimator in the multivariate setting is attributed to the sparseness of data that is associated with increase in dimension. The performance of bivariate kernel is reliant on the smoothing parameter and other statistical parameters. While the smoothness of the estimates generated by the kernel estimator is primarily regulated by the smoothing parameter, its performance numerically may be depended on other statistical parameters. One of the popular performance metrics in kernel estimation is the asymptotic mean integrated squared error (AMISE) whose popularity is occasioned by its mathematical tractability and the inclusion of dimension with respect to performance evaluation. The computation of the bivariate kernel AMISE besides the smoothing parameter depends on basic statistical properties such as correlation coefficient and standard deviations of the observations. This paper compares the performance of the bivariate kernel using the correlation coefficient, standard deviations and the smoothing parameter. The results of the comparison show that for bivariate observations with independent standard deviations and correlated, the AMISE values is smaller than the AMISE values computed with the smoothing parameter. Introduction The estimation of bivariate data involves the application of statistical tools in the derivation of statistical properties from the observations for the purpose of predicting the behaviour of the data.
... The overall result of this study indicates that both log-normal and Cost-231 models could be the most suitable models for path loss estimation in the studied region, as precisely reported by Ojo et al. [1], while both the normal and the Weibull distribution functions were the best distribution functions for modelling the urban pathloss values in the studied locations. However, it must be noted that other factors such as climate variables and refractivity indices as reported by previous studies [18][19][20][21][22][23][24][25], could also affect the base-mobile propagation systems. ...
The role of propagation models in the planning of wireless network, evaluation of cell parameters and frequency assignment cannot be overemphasized. One of the major difficulties with the application of path loss predicting models for any environment is that no two environments are the same in building patterns, terrain, atmospheric conditions, etc. It is therefore impracticable to formulate a single path loss model for all environments. In this study, an assessment of microwave frequency band measurement results based on received signal strength (RSS) values from four base stations in four urban environments in Osun State, Nigeria, are presented. The measured path loss values of each base station were extracted from the RSS values and compared with the results estimated from five conventional path loss models. Model comparison results based on three metric measures and fitting accuracy showed that a log-normal shadowing model exhibited a better agreement with the measured path loss with RMSE of less than 8 dB, the lowest RE, and R 2 closer to one, in all the environments monitored. The best probable probability distribution for modelling the path loss at the investigated urban environments was also determined. The result of the various distribution functions tested using three goodness of fits showed that the normal distribution function offered the best match with the path loss values based on RMSE, RE, and R 2 values calculated and fitting accuracy for both environments. Practical path loss parameters were also estimated for each of the base stations considered. The overall results should be useful for planning future mobile network channels. Keywords received signal strength; path loss; path loss models; probability distribution functions; wireless network
The influence of climate change on agriculture, especially as it relates to the production of food, changes with reverence to duration and space, of which most of these influences are diverse and remarkably uncertain. Undoubtedly, the application of food innovation technology (FIT) in the agricultural processes is an important response for operative and objective adaptation and mitigation of climate change. Consequently, there is a need to urgently re-evaluate the procedures for FIT so as to address the diversities and uncertainties ensuing from these influences of climate change on agriculture with the aim of improving the production of food. Therefore, the application of climate-smart agricultural (CSA) activities with resilience in agricultural events as well as more aids in the application of resources for both in the adaptation and mitigation of climate change by means of FIT will be of great assistance in this regard. Hence, this study presents a facile review of some of the topical developments in the production of food with reverence to the influence of climate change on FIT. Some legal framework on climate change with respect to FIT are also been discussed.
