Issue 6

In recent times, advanced technologies in transportation are developing like connected and automated vehicles and shared mobility services. A rapidly increasing number of vehicles in intelligent transportation system (ITS) and smart cities causes pollution and degrade the quality of air. Owing to the incredible effect of air quality on individual lives, it is indispensable to design a system by which air pollutants (PM2.5, NOx, COx, SOx) are predicted. But predicting air quality and its pollutants was complex since air quality relies on various elements like power plants, weather, and vehicular emissions. Deep learning (DL) and Machine learning (ML) techniques are leveraged for developing an air quality predictive method. This study develops an Air Quality Prediction utilizing Ensemble Voting based Deep Learning with Mud Ring Algorithm (AQP-EDLMRA) technique. The presented AQP-EDLMRA technique follows the ensemble voting model, which exploits three DL classification methods like long short-term memory (LSTM), deep belief network (DBN), and stacked autoencoder (SAE). Then, the new data can be classified by the weighted vote of their prediction outcomes. To adjust the hyperparameter values of the DL methods, the MRA was exploited, showing the novelty of the work. The simulation values of the AQP-EDLMRA approach are tested using a series of air quality data and the comprehensive comparative results demonstrated that the AQP-EDLMRA technique has reached improved forecasting performance.

Permanent damages and spalling occur in conventional concrete due to high temperatures. Also concrete loses its compressive strength amounting to about 50 to 75% of the original strength as reported in most of the research works. As opting for high grade of concrete leads to higher costs and emits CO2, an attempt to develop thermal-resistant geopolymer concrete is done. A new class of silica-alumina binders called geopolymers has the ability to withstand high temperatures. Generally geopolymer concrete (GPC) is having good strength and durability properties. In this paper, the heat resistance and strength performance of geopolymer is improved by adding aluminium oxide as an admixture in the doses of 1% and 2% by weight of rice husk ash. With rice husk ash, fine aggregate and coarse aggregate, thermally resistant GPC was created. As alkali activators, sodium hydroxide and silicate were employed. The molarity of NaOH solution was 12 and 14. The specimens were cast and oven-cured at 60°C for 24 hours. The specimens were tested to determine the strength, permeability, durability and thermal studies of geopolymer concrete.

Various investigations have been carried out to evaluate the infrastructure’s augmented vulnerability owing to concrete carbonation persuaded by the changes in the environment during recent years. Due to global warming and its effect, carbon dioxide emission levels in the atmosphere get increased and this would further increase the probability of carbonation-induced corrosion in concrete structures. In this present study, an empirical model is used to identify the diffusion coefficient of carbon dioxide (CO2) in concrete. Water cement (w/c) ratio of 0.45, 0.5, and 0.55 has been considered with emission scenarios of Representative Concentration Pathway (RCP) 8.5 and 4.5 respectively. The research findings through empirical modeling proved that the depth of carbonation has a direct relationship with water cement ratio and it is also predicted that by the year 2100, considering 2000 as the base year, the carbonation depth may raise up to 78% for reinforced concrete structures subject to various climatic conditions such as temperature, humidity, etc.

This study analyses nighttime (7 p.m. to 6 a.m.) light pollution effects on ground-level ozone production in urban and sub-urban sites in Malaysia. In the absence of solar radiation, no photochemical reaction will occur during nighttime, resulting in zero readings of ground-level ozone (O3). O3 and nitrogen dioxide (NO2) data collected in 2020 were analysed to assess time series and diurnal variability at each site and between sites. The highest nighttime mean O3 concentration is Minden (60 ppb); meanwhile, for nighttime mean NO2 concentrations, the highest is Klang (43 ppb). The results show that sub-urban experienced higher O3 variations compared to urban areas. The monthly mean nighttime O3 and NO2 in urban and suburban areas display a gradual increase in O3 and NO2 variations from March to April, followed by a decreasing trend in the mid of the year. These variations in monthly air pollutants are related to the MCO, CMCO, and RMCO in Malaysia during 2020. Putrajaya (sub-urban site) was the darkest site (average lux: 20) for the whole dataset. In contrast, Minden (sub-urban site) was recorded as the brightest site with maximum light pollution (average lux: 70). The relationship between O3 and NO2 shows a negative correlation during nighttime for urban and suburban sites. Light pollution can reach levels that might affect nocturnal O3 and NO2 concentrations; therefore, the long-term variability of light pollution is essential for air pollution studies.