The PM₁₀ prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM₁₀ concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM₁₀ in Kota Bharu, Kelantan. The results from these three methods were compared to find the best method to predict PM₁₀ concentration for the next day by using the maximum daily data from January 2002 to December 2017. To this end, 80% of the data were used for training and 20% for validation of the models. The performance measure of the PM₁₀ concentration was based on accuracy, sensitivity, specificity, and precision for RF, BRT, and DT, respectively, which indicates that these three models were developed effectively, and they are applicable in the prediction of other atmospheric environmental data. The best model to use in predicting the next day’s PM₁₀ concentration classification was the random forest classifier, with an accuracy of 98.37, sensitivity of 97.19, specificity of 99.55, and precision of 99.54, but the result of the boosted regression tree was substantially different from the RF model, with an accuracy of 98.12, sensitivity of 97.51, specificity of 98.72, and precision of 98.71. The best model can assist local governments in providing early warnings to people who are at risk of acute and chronic health consequences from air pollution.

Ground-level ozone (O₃) is mainly produced during daytime in the presence of ultraviolet (UV) light and later destroyed by nitrogen oxides during nighttime. However, light pollution caused by the excessive use of artificial lights may disrupt the chemistry of night-time ground-level O₃ by providing enough energy to initiate nighttime ground-level O₃ production. In this study, nighttime (7 p.m. to 7 a.m.) ground-level O₃, nitrogen oxide (NO), and nitrogen dioxides (NO₂) concentrations were observed for three years (2013, 2014, and 2015). The existence of O₃ was found during nighttime, especially in urban areas with a concentration range of 8–20 ppb. The results suggested that nighttime variations of ground-level O₃ concentrations were higher in urban areas than in suburban areas. The mean nighttime O₃ concentration at urban sites varied, possibly because the distribution of anthropogenic lights around the urban sites is brighter than in suburban locations, as indicated by the data from the light-pollution map. This anthropogenic light has not caused the suspected nighttime photolysis processes, which directly slowed nighttime oxidation. The photochemistry rate of JNO₂/k₃ was supposed to be near zero because of the absence of photochemical reactions at night. However, the minimum concentration in all urban and suburban sites ranged from 2–3 ppb, indicating that O₃ might also form at night, albeit not due to light pollution.

Environmental impact assessment (EIA) is a preemptive tool used by engineers, environmental consultants and planners to avoid the most likely adverse consequences of development projects. As a planning tool, EIA should curb the harmful effects from all stages of a project lifecycle. Landslides and flash floods are the most common problems faced by Malaysians almost yearly due to rapid development, especially that involving modification of watercourses, clearing of land and projects on hill slopes. Despite such issues, existing legislation and new guidelines have been enacted by the Malaysian government and must be followed by any proposing project team before starting development projects. The Department may have accepted an EIA report of the project. Still, several developments may have neglected the guidelines, especially during construction involving earthworks and exposure of the surrounding environment, place and people to a high risk of disaster caused by mishaps and accidents. The incidence of landslides and flash floods as reported in newspapers, journals, reports and books since 1919 is explored in this study to determine the details of the losses and locations. Despite the enactment of the new EIA law, landslides and flash floods continue to occur. This situation justifies the need to revise the approach based on sizes and include other factors, namely, the risk indices for disaster to happen and the effectiveness of EIA in reducing disaster risks in projects.

This study conducts a comparative life cycle assessment (LCA) and life cycle cost (LCC) analysis of battery electric vehicles (BEVs) and gasoline vehicles (GVs) in Heilongjiang Province, China, under cold climate conditions and a coal dominated electricity grid. Environmental impacts were assessed using SimaPro with the ReCiPe 2016 Midpoint (H) method, while cost performance was evaluated over 5-, 10-, and 15-year ownership periods. Results show that BEVs offer lower total ownership costs than GVs, even without subsidies, primarily due to reduced energy and maintenance expenses. In terms of global warming potential, BEVs show a 4.52% reduction compared to GVs. However, BEVs demonstrate higher impacts in several non-climate categories—including ionizing radiation, particulate matter formation, eutrophication, toxicity, and water use—largely due to emissions from coal-based electricity. The derived grid emission factor of 1.498 kg CO₂/kWh underscores the critical role of regional energy structure. These findings suggest that while BEVs provide economic and climate benefits, their overall environmental performance is highly dependent on local grid carbon intensity and seasonal energy demand. Policy recommendations include accelerating grid decarbonization, improving cold weather efficiency, and incorporating multidimensional environmental indicators into transport planning.

To address the challenge of climate change, China declared that its carbon dioxide emissions would peak in 2030 and become carbon neutral by 2060. China is the largest energy supplier and electric vehicles market worldwide and will stop selling combustion vehicles by 2035 and completely phase them out by 2050. To provide a overview of the current research, a bibliometric analysis and systematic review were conducted. The bibliometric analysis utilized data from Web of Science and Scopus, selected based on the PRISMA guidelines. Focusing on studies conducted in China and related to social sciences, environmental sciences, business, or economics. A total of 188 articles met the inclusion criteria. The analysis was performed using VOS viewer software to map co-citations and keyword co-occurrences, identifying core research themes and prominent clusters in the field. However, most researchers have primarily focused on one or two aspects of sustainability, with the hot spots being subsidies, charging costs, charging infrastructure, charging stations, batteries, and emissions. Life cycle assessment is also the most applied theory. This study recommended future research to focus on the usage stage of EVs, incorporating all three sustainability pillars. Additionally, regional assessment should be conducted to consider regional characteristics such as temperature, how policy influences affect electric vehicle emissions, social impacts, and economic advantages. Before promoting electric vehicles, regional assessments should be conducted, so that stakeholders can make decisions.

The outbreak of COVID-19 resulted in many significant restricted social and economic activities. The pandemic provided unprecedented natural experiment in which the study was specifically conducted to investigate the impact on particulate matters with aerodynamic diameter less than 10 µm (PM10) resulting from the restriction measures taken by the university management to manage COVID-19 during convocation. PM10 and meteorological parameters (ambient temperature, relative humidity and wind speed) were monitored using TSI Dusttrak aerosol monitor and Kestrel 4500, respectively. Traffic volume was also considered as the significant source of air pollutants due to higher number of vehicles during the event. Two sets of data were collected during peak hours (7.30-9.30 a.m., 12.30-14.30 p.m. and 16.30-18.30 p.m.) on normal working days (before the outbreak in 2018) and during the convocation (after the outbreak in 2022) at two monitoring points along the major road of Persiaran Universiti 1. The results showed that ambient PM10 concentrations were remarkably reduced by ~65-84%, especially during afternoon peak hours. Parking space allocated far from the main hall area contributed to lower PM10 concentrations. From the parking space, graduands were required to either walk with guidance or take the provided university buses to go to the main hall. Pearson correlation analysis showed that PM10 concentrations have significant relationship with the meteorological parameters (p<0.05). However, in terms of traffic emission, it does not necessarily influence solely by the vehicles. From the observation, walking activities by the graduands, students, visitors, etc. around the monitoring areas also contributed to the dust uplifting, hence influencing the level of PM10. The study elucidated the influence of COVID-19 restriction measures on PM10 concentrations which can be a steppingstone towards a better management in reducing the pollutant in the future convocation events.