The issue of agricultural pollution has become one of the most important environmental concerns worldwide because of its relevance to human survival and health. Microbial remediation is an effective method for treating heavy metal pollution in agriculture, but the evaluation of its effectiveness has been a difficult issue. Machine learning (ML), a widely used data processing technique, can improve the accuracy of assessments and predictions by analyzing and processing large amounts of data. In microbial remediation, ML can help identify the types of microbes, mechanisms of action and adapted environments, predict the effectiveness of microbial remediation and potential problems, and assess the ecological benefits and crop growth after remediation. In addition, ML can help optimize monitoring programs, improve the accuracy and effectiveness of heavy metal pollution monitoring, and provide a scientific basis for the development of treatment measures. Therefore, ML has important application prospects in assessing the effectiveness of microbial remediation of heavy metal pollution in agriculture and is expected to be an effective pollution management technology.

With the dynamic development of renewable energies, energy storage devices, and electric vehicles, microgrids have been playing an increasingly vital role in smart power grids. Under the recent development of carbon neutralisation, microgrid systems containing multiple clean energy sources have become significant modules for energy conservation and emission reduction. Considering technological and environmental elements, we investigated the economic operation of microgrids with the integration of electric vehicles. In this paper, carbon trading mechanisms and operation scheduling strategies are analysed in the simulation models. Then, transaction costs and power balance are discussed. Industrial applications and policy implications are also presented.

Solar-driven steam generation as a potential green technology has attracted extensive attention to solve the freshwater scarcity crisis. Photothermal materials as the key section of solar steam generation have been widely reported. However, there is still a challenge in developing easily prepared, environmental-friendly, and low-cost materials. Herein, the simple, scalable, and porous carbonized cotton was prepared as an evaporator to enhance solar-based evaporation, which has excellent light absorption ability in the range of the full spectrum (300–2,500 nm). Benefiting from 95% solar absorption and the pores between the cellulose tubes, the carbonized cotton heated by plate carbonization reaches a steam generation rate of 0.8 kg m⁻² h⁻¹, which is about 5 times that of untreated cotton. Compared with tube furnace carbonization, flat plate heating carbonization of cotton requires lower equipment requirements and does not need nitrogen protection and cleaning tar, and the photothermal conversion efficiencies of both are similar. In addition, carbonized cotton as an evaporator was heated up rapidly under 1 sun irradiation and reached a stable temperature in 20 s, greatly improving the photothermal conversion rate. Therefore, plate heating carbonized cotton provides a good idea for preparing solar photothermal conversion materials and a novel strategy for simplifying the production of biomass thermal evaporators.

In 2013, the first electric taxis were launched in Hong Kong. In 2021, the HKSAR government announced that no new registration of fuel-propelled private cars, including hybrid vehicles, will be granted by the year 2035. Local public policies have shown a breakthrough milestone in the passenger transport sector and fostered the development of a smart city in Hong Kong as an international metropolis. This paper aims to study the feasibility of gradually introducing electric vehicles (EVs) in the Hong Kong taxi industry in the next decade. In particular, taxi license owners’ intentions of purchasing electric vehicles for operational use and the driving behavior of taxi drivers in Hong Kong are investigated. Multiagent modelling with an integrated behavioral model is then adopted to analyze the survey data collected from 250 taxi drivers in Hong Kong, followed by interviews with industrial experts. The implications for future public policies are then discussed. To achieve emission reduction, the paper suggests a gradual, step-by-step promotion and transfer from conventional taxis to electric taxis in Hong Kong, with consideration of various factors and the interests of different stakeholders in the community.

Background: The brain–computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection. Specifically, different driving modes indicate different driving styles which can be selected according to the road condition or the preference of individual drivers. Methods: In this paper, a steady-state visual-evoked potential (SSVEP)-based driving mode selection system is proposed. Upon this system, drivers can select the intended driving modes by only gazing at the corresponding SSVEP stimuli. A novel EEG processing algorithm named inter-trial distance minimization analysis (ITDMA) is proposed to enhance SSVEP detection. Both offline and real-time experiments were carried out to validate the effectiveness of the proposed system. Conclusion: The results show that a high selection accuracy of up to 92.3% can be realized, although this depends on the specific choice of flickering duration, the number of EEG channels, and the number of training signals. Additionally, energy consumption is investigated in terms of which the proposed brain-controlled system considerably differs from a traditional driving mode selection system, and the main reason is shown to be the existence of a detection error.