Issue 5

For the past few decades, owing to human activities, urbanization, and industrialization, air pollution is becoming severe across several countries. Deep Learning (DL) and Machine Learning (ML) techniques had great contribution to the development of methods in various aspects of prediction, planning, and uncertainty analysis of smart cities and urban advancement in the current scenario. Many of the cities which are developed suffered from severe air quality (AQ) because of the rapid growth in industrialization and population. In this paper, we introduce a deep learning based air pollution prediction model for smart environment design planning (DLAPP-SEDP). The presented DLAPP-SEDP technique majorly intends to predict the level of air pollution in the smart environment. It follows a three-stage process namely data pre-processing, air pollution prediction, and hyperparameter tuning. At the initial stage, the presented DLAPP-SEDP technique performs various levels of data pre-processing such as missing value replacement, categorical value encoding, normalization, and feature selection. In the next stage, the DLAPP-SEDP technique employs graph convolutional network (GCN) model. Finally, the DLAPP-SEDP technique utilizes atomic orbital search optimization (AOSO) algorithm for optimal hyperparameter tuning process, showing the novelty of the work. To demonstrate the enhanced predictive efficiency of the DLAPP-SEDP method, a wide-ranging experimental analysis can be carried out. The experimental values assured the enhancements of the DLAPP-SEDP method over other recent techniques.

The aim of this study was to investigate the tensile strength reduction of four different materials: steel, polypropylene, polyethylene and glass fiber/epoxy (GFRP), under the conditions in a chemical scrubber. In detail, the performance of these materials in an acidic environment was assessed. The outcomes of the study revealed some useful information regarding the influence of the acid conditions to the construction materials. Also, the data generated were capable to predict the aging effect on the strength when used to finite element simulations.

Oil Palm Kernel Shell (OPKS) is a residue of the palm oil extraction process, which has good characteristics for the production of activated carbon (AC). Colombia ranks fourth in the world in palm oil production and it is estimated that in 2019 the amount produced of OPKS was approximately 336000 ton per year. Within the contaminants of surface waters, the pesticide paraquat is identified as one of the most dangerous for human health and is at the same time one of the most used in agriculture in Colombia. It is found worldwide in natural waters in the range of 50 µg/l to 5000 µg/l. Oil palm kernel shells were first carbonized in a horizontal oven under N2 atmosphere until 850 °C for 30 min. After that, three batches, each of 300 g of the carbonized OPKS (COPKS) were activated in the same horizontal reactor by means of partial gasification using H2O as reaction agent. The activation process starts with a heating stage by raising the temperature from 20 °C to 850 °C in approximately 280 minutes under a N2 atmosphere. Then in the activation stage the flow of N2 is replaced by a flow of 20,68 l/min steam while maintaining the temperature at 850 ± 10 °C for another 310 minutes. Finally, the flow of steam is changed to N2 and the oven is cooled down until ambient temperature. As a result of the activation process, an activated carbon (AC-CAC) with approximately 50 % degree of activation and a surface area of 1200 m2/g were obtained. The adsorption capacity of the AC-CAC towards paraquat is studied by measuring breakthrough curves in an adsorption column using initial concentrations of paraquat between 600 µg/l and 5000 µg/l. A comparison is performed by using a commercial activated carbon CAC from the company Donau Carbon ref. Hydraffin®. The determination of the concentration of paraquat in water is made by voltammetry.

In this research the arsenic contaminations issues are exposed and suitable measures are recommended through electrokinetic remediation. The arsenic expulsion was assessed using the electro kinetic remediation from two different soil tests; alluvial clay soil and red clay soil taken from the Tamilnadu zone. Three distinct cathodic electrolytes—deionized water (DIW), potassium phosphate (KH2PO4), and sodium hydroxide—were used to test the viability of upgrading experts (NaOH).The consequences of the examinations on the alluvial mud soil shows that the potassium phosphate (0.1N) was the best in extricating arsenic (79.5% removal efficiency), likely because of anion trade of arsenic species by phosphate. Then again, the sodium hydroxide (0.1N) appeared to be the most proficient in with eliminating arsenic (63%) from the red soil. The sodium hydroxide's ability to increase soil pH and hasten the ionic migration of arsenic species through the desorption of arsenic species as well as the disintegration of arsenic-bearing minerals may help to explain and suggest this result. In order to forecast the effectiveness of removing arsenic from polluted soil, studies using response surface technique are also designed using the Box Behnken Method.

The reduction of highly toxic Cr (VI) from industrial wastewater of low concentration by different low-cost ample sorbent materials was investigated. In order to remove Cr (VI) from aqueous solutions, the biosorption properties of natural waste (Betel Seeds) were investigated with varying several parameters like contact period, sorbent dose, sorbate sorption, and pH. The study's contact period, biosorbent dosage, sorbate concentration, and pH were all varied from 0 to 120 minutes, 12 g/l, 40 mg/l, and 2 to 7, respectively. According to the study, chromium sorption by betel seed powder reached equilibrium after 60 minutes, and a small change in chromium removal effectiveness was then noticed (Gode, et.al., 2003). At pH 3 and doses of 12 g/l and 30 mg/l of sorbent and sorbate, the maximum removal of chromium (99.2%) was achieved. The results revealed that chromium(VI) biosorption follows the Langmuir isotherm comparison with a correlation factor of 0.998. Additionally, the biosorption process's kinetics adheres to the pseudo-second-order kinetics model, with a rate constant of 0.721 min-1 and the rate constant was evaluated at 30°C. Hexavalent chromium in water was quantified using a UV-VIS spectrophotometer. A desorption examination was also carried out, and it is vital by 0.5H2SO4. Results indicate that the cheap, readily available, widely cultivated sorbent can be used to effectively remove chromium ions.

An economic system focused on low energy usage, low pollution, and low emission is known as a "low-carbon economy". The concept of environmentally friendly and low-carbon food packaging architecture directs practice growth and reform in accordance with the customer's green psychology. As the primary benchmark for packaging concerned with designing practices, it uses the green conceptual design of contemporary eco-environmental preservation and other meaningful reforms. It is significant need to mix people's developed green psychology with this issue in order to discover a solution for green packaging design. The design and use of eco-friendly and low-carbon food packaging is covered in this research. With the implementation of wireless sensor network (WSN) and Artificial Intelligence (AI) technique, it accomplishes the fusion of market and ecologically responsible design, enhances production effectiveness and product usage. The confidence weight information choice technique is used to determine the type of smart energy-saving packaging, and the fundamental element is that the energy usage vector is formed by an Artificial Neural Network (ANN). Packaging makers must reevaluate how design and the environment interact, eliminate packaging design pollutants, make environmental protection their top concern, and develop a design idea that complies with ecological ethics for the long-term profit of human survival. Environment, politics, economy, and social factors all play a role in how the food system functions and are influenced by them. Compared to traditional techniques for monitoring the environment We use wireless sensor networks to keep an eye on the agricultural environment because they have three key benefits: (1) There is no need to lay wire; (2) the system is only implemented once; (3) the nodes are dense; (4) collected data has good precision; and (5) sensor nodes with a specific calculation and storage capacity, enabling collaboration among nodes, are perfect for unsupervised monitoring system. The incorporation and handling of product packaging data is made possible by smart packaging technology, which is essential in the creation of low-carbon food packaging and helps

The regional tourism economy of Guizhou Province (Latitude 26° 38' 49.1964'' N and Longitude: 106° 37' 47.9892'' E) can be developed by improving the transportation situation. The influence of transportation on the regional tourism economy is the main research content here. A correlation model is established to quantify the relationship between the two. Finally, the model experiment results are analyzed and summarized. Growth rate of total tourism revenue has the largest impact after a positive shock in the initial stage and then drops sharply, turning into the largest negative response. There are obvious shocks in the next few times. The impact gradually decreases and finally tends to 0. The impact of the shock disappears after 20 hits. When it is impacted by the total length of transportation lines, the growth rate of total tourism revenue doesn’t respond at the initial stage. It then responds negatively. The results indicate that there is a coupling relationship between transportation and tourism. The improvement of transportation serves as a strong booster for the tourism economy and supports the development of tourist attractions. The results are significant for promoting transportation and tourism in Guizhou Province.

In concrete industry, the use of concrete solid wastes is becoming further common since it can minimize associated costs and environmental implications. The current research looks at how concrete performs when polypropylene (PP) plastic, produced from pollution garbage items, is used as a partially replacing for coarse aggregate (CA). The proportion of total PP (PPA) (0, 5, 10, 15, and 20%) as well as the water-cement ratio 0.45 of recycled household waste water, are the most important variables. Flow ability, hard porosity, maximum compressive strengths are all factors to consider, and economic feasibility are all discussed. The slump value grew when the proportion of PPA was raised, according to the findings. Compressive and cracking tensile strengths were increased in concrete containing 15% PPA. As a result, suggested to utilize more than 10% PPA in structural concrete, with either crushed stone or PP aggregate. The results showed that 15% is the best alternative to coarse gravel during terms of strength and durability, and hence PPA can be utilized as a partial replacement for coarse aggregate in concrete. To conduct studies to enhance the impact resistance of concrete by utilizing recycled water. Reducing the consumption of nonrenewable resources reducing greenhouse gas emissions and improving the strength and load capacity of industrial materials used in building research. Finally, employing non-biodegradable waste plastic resources to make green concrete will offer up new possibilities.

Rapid urbanisation, industrialization, and population growth have fostered an urgent need for a green, clean-living environment. Among the many pollutants, gaseous pollutants like NO2, SO2, and particle pollutants like PM 2.5 and PM 10 have a profound negative impact on human health. In this investigation, samples of air pollutants like NO2, SO2, PM 2.5, and PM 10 were taken at ten different sites inside the Tirupur corporation boundaries. For the locations taken into consideration, the Air Quality Index has been calculated using the Central Pollution Control Board's model. Utilizing inverse weighted distance (IDW), these data are interpolated and integrated into a geographic information system (GIS). It was discovered that the level of secondary industries and the density of automobiles had a substantial impact on air quality. Results indicate that PM 10 concentrations in the new bus stop and RakkiyapalayamPirivu are 113.7 mg/m3 and 134.73 mg/m3, respectively, which are comparatively higher than the permitted level specified in IS 5182. The areas with the most vehicular traffic and industry include the new bus stop location and RakkiyapalayamPirivu. Thus, the main sources of pollution in the study region are an increase in automobile traffic and industrial development. A clear indication of the area where the concentration of pollutants is high is provided by integrating the pollutant data with geographic information systems, and this enables local government agencies to conduct in-depth environmental evaluations. This aids planners in their decision-making for sustainable development.