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<jats:p>To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM10 and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management.</jats:p>

<jats:p>PM10 emissions have been a significant concern in rock crushing and quarry operations (study site #1) and iron ore mining projects (study site #2) in certain regions of Malaysia, posing fears to the health and well-being of nearby communities with severe air pollution. To address this issue, it is crucial to develop effective mitigation strategies to reduce dust particle emissions like PM10 in the ambient air. The AERMOD model was applied to predict PM10 emissions during quarry operations and iron ore mining projects, both with and without control measures. The results indicated that PM10 emissions were reduced when control measures were implemented. The modeling result shows the mean PM10 concentration with and without control measures in study site #1 is 74.85 µg/m3 and 20,557.69 µg/m3, respectively. In comparison, the average PM10 concentration with and without control measures in study site #2 is 53.95 µg/m3 and 135.69 µg/m3. Therefore, the control measure has successfully reduced the PM10 concentrations by 99.90% and 60.24% in study sites #1 and #2, respectively, and ensures the air quality complies with the Malaysian Ambient Air Quality Guidelines (MAAQG) 24 h threshold limits at 100 µg/m3. In addition, the AERMOD modeling results showed that mitigation measures performed better in rock crushing and quarry operations than in iron ore mining projects in this case study.</jats:p>

<jats:p>Electric vehicles (EVs) must be used as the primary mode of transportation as part of the gradual transition to more environmentally friendly clean energy technology and cleaner power sources. Vehicle-to-grid (V2G) technology has the potential to improve electricity demand, control load variability, and improve the sustainability of smart grids. The operation and principles of V2G and its varieties, the present classifications and types of EVs sold on the market, applicable policies for V2G and business strategy, implementation challenges, and current problem-solving techniques have not been thoroughly examined. This paper exposes the research gap in the V2G area and more accurately portrays the present difficulties and future potential in V2G deployment globally. The investigation starts by discussing the advantages of the V2G system and the necessary regulations and commercial representations implemented in the last decade, followed by a description of the V2G technology, charging communication standards, issues related to V2G and EV batteries, and potential solutions. A few major issues were brought to light by this investigation, including the lack of a transparent business model for V2G, the absence of stakeholder involvement and government subsidies, the excessive strain that V2G places on EV batteries, the lack of adequate bidirectional charging and standards, the introduction of harmonic voltage and current into the grid, and the potential for unethical and unscheduled V2G practices. The results of recent studies and publications from international organizations were altered to offer potential answers to these research constraints and, in some cases, to highlight the need for further investigation. V2G holds enormous potential, but the plan first needs a lot of financing, teamwork, and technological development.</jats:p>

This current study assessed the toxicity of selected heavy metals in paddy and sediments of non-major production sites in Southern Peninsular Malaysia, complemented by bibliometric analysis of research trends and health implications of rice contamination. Paddy (grains, stems, roots) and soil samples were collected from seven selected sites in the Southern parts of Peninsular Malaysia and analyzed for their heavy metals content. The health risk assessments were conducted based on estimated daily intake, and the Web of Science database was used for bibliometric analysis. The results indicated elevated levels of manganese, Mn (0.4 ± 0.07), especially in the roots, compared to other heavy metals. Generally, the heavy metal levels in paddy grains were below FAO/WHO’s tolerable daily intake levels, indicating minimal non-carcinogenic risks to both adults and children. The bibliometric analysis indicated a significant increase in related publications, reflecting growing academic interest. This study highlights the potential of non-major sites to produce rice with lower contamination levels, provides insights into research trends, and identifies future investigation areas, especially for major production sites and post-COVID-19 periods. Therefore, this study offers a robust scientific context, identifies research gaps, benchmarks findings, and guides future research directions, ensuring an in-depth perception on heavy metal contamination and its health risks. © This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.