Identification of hotspots using different statistical methods in a region of manufacturing plants

Incident electromagnetic radiation hitting the Earth’s surface shows three phenomena as absorptivity, reflectivity and transmissivity where sum of the three is equal to one. The transmissivity is zero when the surface is opaque. There is a strong relationship between absorptivity and emissivity that is explained by Kirchhoff’s Law. Emissivity is managed by the thermal radiation on the Earth’s surface. Thermal radiation related with the heat transfer of the electromagnetic radiation is controlled by passing energy of atoms and molecules. There are different sources of energy other than the Sun such as geothermal activities, volcanoes and manufacturing plants that contributes to the emissivity of the surface. The thermal radiation produced by manufacturing plants contributes to the Earth’s surface temperature as well.In this study, land surface temperatures were estimated by using inverse Planck function from five Advanced Spaceborne Thermal Emission and Reflection Radiometer remote-sensing satellite sensor thermal infrared bands. It is aimed to highlight hotspots related to manufacturing plants in the region of Kocaeli, Turkey. The hotspots are examined statistically with the minimum noise fraction, the independent component analysis, the local Moran’s I index and the Getis-Ord G_i index methods by using land surface temperatures.     

The Importance of the Chosen Technique to Estimate Diffuse Solar Radiation by Means of Regression

The Ordinary Least Squares (OLS) is one of the widely used methods which is used for estimating the diffuse solar radiation. However, in order to use the OLS method in the estimation, the dataset must provide certain assumptions. In this study, alternative robust methods have been described and they were compared with the OLS method, which is used for estimating diffuse radiation frequently in an application. At the end of the analysis, the R² value obtained by the OLS method is less than the values obtained by M regression models. In other words, the explanation of the dependent value is weak when the OLS method is used. Finally, it can be said that the most appropriate method is Andrews for estimating the diffuse solar radiation.     

The impact of economic,social, and political globalization and democracy on life expectancy in low-income countries: are sustainable development goals contradictory?

The 17 Sustainable Development Goals announced by the United Nations are important guides for the development processes of developing countries. However, achieving all of these goals is only possible if the goals are consistent with each other. It has been observed in the literature that possible contradictions between these goals are ignored. Therefore, the main purpose of this study is to investigate whether two sustainable development goals (SDGs) of the UN are contradictory or supporting each other in low-income countries. These SDGs are “Good Health and Well-Being” (SDG3) and “Partnerships for the Goals” (SDG17). For this purpose, the role of globalization and democracy in life expectancy is empirically investigated in 16 low-income countries over the period 1970–2017. While globalization has been used as an indicator of the partnership between countries, democracy has been used as an indicator of accountability and cooperation between governments and societies. According to estimations of the continuous-updated fully modified (CUP-FM) and bias-adjusted ordinary least squares (BA-OLS), globalization and its subcomponents such as economic, social, and political globalization affect life expectancy positively. Democracy also increases life expectancy in those countries. The GDP per capita is also used as a control variable. Our results show that a higher level of per capita income is positively associated with higher levels of life expectancy. In conclusion, no contradiction was found between SDG3 and SDG17 in those countries. Achieving a healthier society requires economic, social, and political integration between governments and societies.

     

Predicting field capacity,wilting point,and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches

In this study, we examined the ability of reflectance spectroscopy to predict some of the most important soil parameters for irrigation such as field capacity (FC), wilting point (WP), clay, sand, and silt content. FC and WP were determined for 305 soil samples. In addition to these soil analyses, clay, silt, and sand contents of 145 soil samples were detected. Raw spectral reflectance (raw) of these soil samples, between 350 and 2,500-nm wavelengths, was measured. In addition, first order derivatives of the reflectance (first) were calculated. Two different statistical approaches were used in detecting soil properties from hyperspectral data. Models were evaluated using the correlation of coefficient (r), coefficient of determination (R ²), root mean square error (RMSE), and residual prediction deviation (RPD). In the first method, two appropriate wavelengths were selected for raw reflectance and first derivative separately for each soil property. Selection of wavelengths was carried out based on the highest positive and negative correlations between soil property and raw reflectance or first order derivatives. By means of detected wavelengths, new combinations for each soil property were calculated using rationing, differencing, normalized differencing, and multiple regression techniques. Of these techniques, multiple regression provided the best correlation (P?<?0.01) for selected wavelengths and all soil properties. To estimate FC, WP, clay, sand, and silt, multiple regression equations based on first(2,310)-first(2,360), first(2,310)-first(2,360), first(2,240)-first(1,320), first(2,240)-first(1,330), and raw(2,260)-raw(360) were used. Partial least square regression (PLSR) was performed as the second method. Raw reflectance was a better predictor of WP and FC, whereas first order derivative was a better predictor of clay, sand, and silt content. According to RPD values, statistically excellent predictions were obtained for FC (2.18), and estimations for WP (2.0), clay (1.8), and silt (1.63) were acceptable. However, sand values were poorly predicted (RDP?=?0.63). In conclusion, both of the methods examined here offer quick and inexpensive means of predicting soil properties using spectral reflectance data.     

Biology and management of two important <Emphasis Type="Italic">Conyza</Emphasis> weeds: a global review

Weed management is one of the prime concerns for sustainable crop production. Conyza bonariensis and Conyza canadensis are two of the most problematic, noxious, invasive and widespread weeds in modern-day agriculture. The biology, ecology and interference of C. bonariensis and C. canadensis have been reviewed here to highlight pragmatic management options. Both these species share a unique set of biological features, which enables them to invade and adapt a wide range of environmental conditions. Distinct reproductive biology and an efficient seed dispersal mechanism help these species to spread rapidly. Ability to interfere strongly and to host crop pests makes these two species worst weeds of cropping systems. These weed species cause 28–68 % yield loss in important field crops such as soybean and cotton every year. These weeds are more prevalent in no-till systems and, thus, becoming a major issue in conservation agriculture. Cultural practices such as crop rotations, seed rate manipulation, mulching, inter-row tillage and narrow row spacing may provide an effective control of these species. However, such methods are not feasible and applicable under all types of conditions. Different herbicides also provide a varying degree of control depending on crop, agronomic practices, herbicide dose, application time and season. However, both these species have evolved resistance against multiple herbicides, including glyphosate and paraquat. The use of alternative herbicides and integrated management strategies may provide better control of herbicide-resistant C. bonariensis and C. canadensis. Management plans based on the eco-biological interactions of these species may prove sustainable in the future.     

Assessment of heavy metal toxicants in the roadside soil along the N-5, National Highway, Pakistan

The assessment of the toxicants in roadside soil on regular basis has become extremely essential with the increase in awareness for the metal toxicity in the environment. The present study investigates the presence of toxic metals along National Highway (N-5), Pakistan. Averages of about 1.3 million per month of automobile vehicles ply on this route. Lead (Pb), zinc (Zn), copper (Cu), nickel (Ni), cadmium (Cd), cobalt (Co), manganese (Mn), mercury (Hg), and iron (Fe) were analyzed by atomic absorption spectrophotometry in roadside soil at the nine selected locations along the highway. Strong Pearson correlations (α = 0.05) were found between Pb and Zn (r(2)?= 0.887), Fe and Mn (r(2)?= 0.880), Hg and Cd (0.864), Cu and Zn (0.838), and Cu and Pb (0.814). The correlation between the elemental compositions of the main automobile components revealed vehicular traffic as the main non-point source of roadside soil pollution. Extremely high level of mercury, 144.05 mg kg(-1), was found at S5. It was revealed that the unregulated incineration and dumping sites of hazardous waste material along N-5 were also responsible for these contaminations. Multivariate analysis on the obtained data also disclosed the same interpretation. Cluster analysis of the data grouped Pb, Zn, and Cu at 85.23% similarity, whereas, Cd, Hg, and Ni were grouped at 78.75% similarity basis. The findings need swift action against the root cause of soil pollution.     

Do risk perceptions and constraints influence the adoption of climate change practices among small livestock herders in Punjab,Pakistan?

Environmental Science and Pollution Research - Climate change is severely damaging the agricultural system of many food producing regions worldwide. Small/subsistent livestock herders are the most...     

Detecting micronuclei frequency in some aquatic organisms for monitoring pollution of Izmir Bay (Western Turkey)

Micronuclei tests is a system of mutagenicity testing used for determining the pollution and chemicals causing changes in DNA fragments such as micronuclei in the cytoplasm of interphase cells. Damage caused on the DNA by genotoxic pollutants is the first consequence occurring in the aquatic organisms. Thus, it was attempted to determine whether pollution affected the erythrocytes and gills of fish Gobius niger and haemolymph and gills of mussels Mytilus galloprovincialis living in Izmir Bay at the level of DNA by the means of micronuclei (MN) test. Organisms used in the MN test were collected from seven locations (Alsancak, Alaybey Shipyard, Karsiyaka, Bostanli, Göztepe, Konak and Pasaport) which are known as the most polluted part of inner Bay of Izmir (Western Coast of Turkey). According to the results of the present study, frequency of MN was found at high level in Alaybey Shipyard and Pasaport where wastes from existing dockyard contributed to high level of pollution. In conclusion, this study indicates that the micronuclei test gives sensitive results in monitoring the pollution, especially the pollution of harbor, and thus it might be used as standard method in regular monitoring of pollution of coastal ecosystem.     

Stochastic modeling approaches based on neural network and linear–nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers

This paper presents a new mathematical model and a two-layer neural network approach to predict the single droplet collection efficiency (SDCE), η _d, of countercurrent spray towers. SDCE values were calculated using MATLAB^® algorithm for 205 different artificial scenarios given in a large range of operating conditions. Theoretical results were compared with outputs obtained from a two-layer neural network and DataFit^® scientific software. The predicted model developed from linear–nonlinear regression analysis and network outputs agreed with the theoretical data, and all predictions proved to be satisfactory with a correlation coefficient of about 0.921 and 0.99, respectively. By using the proposed model, iterations between Reynolds number (Re), drag coefficient (C _D) and terminal velocity values (v _T) were neglected for a large range of operating conditions. SDCE values were also obtained speedily and practically for five main operating inputs used in the model.     

Investigation of influence of tungsten mine wastes on the elemental composition of some alpine and subalpine plants on Mount Uludağ, Bursa,Turkey

In this study, the effect of mine work wastes on elemental composition (N, P, Ca, K, Mg, Fe, Mn, Cu, Zn) of some alpine and subalpine plant species and in soils were investigated in the surroundings of the Etibank Wolfram Mine Work in Uludag mountain. Soils and plant parts belonging to Thymus praecox Opiz., Acinos alpinus (L.) Moench, Plantago holosteum Scop and Festuca punctoria Sm species collected from three directions around Wolfram Mine Work were used for our investigations. All samples were analysed by flame atomic absorption spectrophotometer (FAAS) for Ca, K, Mg, Mn, Fe, Zn, and Cu. The significant differences among sample sites were in terms of pH, % CaCO3, % C, % N and % P contents of soil. The changes of Ca, Mg, Fe, Mn, Fe, Zn, and Cu contents in soil were determined to be due to mining activity. Of the four species examined, an important change was determined in concentrations of Mg, Mn, Fe and Zn although P. holosteum and F. punctoria were different in above- and below-ground parts, showing that plant element content is specific to species.