Global NEST Journal

Nowadays, due to the development of the world, harmful gases and vapours have begun to enter the atmosphere significantly, so we are faced with the problem of air pollution and its low quality. This paper, therefore, focused on enclosed car parks, a parking system that needs to be well examined from an environmental perspective. It is not possible to predict in advance what levels of pollution from engines burning fossil fuels will reach in indoor environments. Because many factors can affect the concentration, such as vehicle type and fuel ventilation, as well as cumulative vehicle emissions in the environment. This study presents the relationship between indoor meteorology factors and pollutants besides evaluating the air quality in closed car parks by collecting the concentration of emitting pollutants. The data were aggregated from five garages in the centre of Konya province. The data were analyzed by using the program SPSS statistics 25. It has been mentioned the points that must be considered, measurements and suggestions to reduce air pollution in the car parks. Correlation coefficients showing the relationship between indoor air quality parameters and their importance levels were determined. According to the significance level, α = 0.05; It has been determined that there is a highly positive and significant relationship between indoor particulate matter (PM2.5, PM10). In addition, it was determined that there was a moderate and significant relationship between temperature and CO2 value. In addition, it has been determined that there is a weak positive relationship between particulate matter (PM2.5 and PM10) and gases (CO2 and CO) values, and between CO, PM2.5 and PM10 and Temperature value. There was a weak negative significant relationship between temperature and relative humidity. When the measurement results were evaluated, it was observed that the PM2,5 and PM10 levels in all car parks exceeded EPA, WHO specified standards, but the CO2 and CO concentrations were at acceptable levels according to the specified values, suggesting that certain ventilation measures need to be taken to reduce the concentration of pollutants in the garage.

In terms of sustainable land use management, it is extremely important to reveal the land conditions and the changes that occur on the land in certain periods. In this study, using CORINE data for Düzce province, land use status and transitions between land-use classes over 28 years were investigated. In addition, changes in carbon storage in above-ground biomass were determined for each land-use and land transition using Landsat satellite images for the years 1990 and 2018. 28 years, artificial surfaces increased from 3,852 hectares to 9,058 hectares. While agricultural areas increased by 11.5% to 114,650 hectares, forest and semi-natural areas decreased by 14% to 122,955 hectares. 96.7% of the increase in agricultural areas was land converted from forest areas. While the amount of carbon stored in the forest and semi-natural areas increased by 27.9%, it increased by 30.5% in agricultural areas. With land transitions, 17.8% of the total carbon (14,407 tons) was transferred from one land use to another. In terms of annual land-use change rate between 1990 and 2018, the highest rate of land-use change was in the post-earthquake 2000-2006 period. The information in this study will make important contributions to policymakers in land management and planning.

Due to the development of open access medium-high resolution remote sensing data like multispectral remote sensing images, crop classification becomes a hot research topic to be realized on large scale using machine learning (ML) models. At the same time, chlorophyll content is a critical index used for defining crop growth conditions, photosynthetic ability, and physiological position. It has an adaptive characteristic which finds useful to monitor crop growth conditions and understand the procedure of material and energy exchange among crops and the environment. Recently, several research works have been carried out to estimate chlorophyll content on multispectral remote sensing images. The recent advances in deep learning models enable us to effectively classify different crop types and estimate chlorophyll content on multispectral remote sensing images. In this view, this paper presents a new remora optimization with deep learning driven crop classification and chlorophyll content estimation (RODLD-C4E) model using multispectral remote sensing images. The proposed RODLD-C4E model intends to properly identify the crop type and chlorophyll content. For accomplishing this, the proposed RODLD-C4E model initially derives a RO algorithm with NASNetLargemodel for feature extraction process. The utilization of RO algorithm enables to effectually adjust the hyperparameters of the NasNetLarge model. Besides, cascaded gated recurrent unit (CGRU) model is employed for crop type classification. Finally, deep belief network (DBN) model is applied to estimate the chlorophyll content exist in the crop. To demonstrate the better performance of RODLD-C4E model, a wide ranging experimental analysis wasimplemented on benchmark dataset. The comparative analysis pointed out the better outcomes of the RODLD-C4E model under several aspects.

Photocatalytic degradation for wastewater treatment is a method that has recently attracted attention. In this research, a synthesized composite of Fe3O4/SiO2/NiO with magnetic properties was used for the photocatalytic degradation of methylene blue dye under UV light. Furthermore, the composites were characterized using XRD, FTIR, BET surface area, SEM-EDS, VSM, and UV-DRS. The results showed that the Fe3O4/SiO2/NiO composite is magnetic with a saturation magnetization of 53.84 emu/g. The Fe3O4/SiO2/NiO composite has a surface area of 128.8 m2/g, large than Fe3O4 and Fe3O/SiO2. The Fe3O4/SiO2/NiO composite has a band gap of 2.83 eV. The photocatalytic activity of Fe3O4/SiO2/NiO composite against the methylene blue dye exhibited high degradation efficiency reaching 98.51 %. The pseudo-first-order is appropriate to describe the kinetics model of photocatalytic degradation on methylene blue dye. The decrease in the degradation efficiency of the Fe3O4/SiO2/NiO composite after 5 times for the photocatalytic degradation of methylene blue dye from 98.02 % to 94.97 % indicates that the catalyst has high stability. Considering these results, the Fe3O4/SiO2/NiO composites could be used as a potential catalyst in industrial wastewater.