The rapid spread of the SARS-CoV-2 in the COVID-19 pandemic had raised questions on the route of transmission of this disease. Initial understanding was that transmission originated from respiratory droplets from an infected host to a susceptible host. However, indirect contact transmission of viable virus by fomites and through aerosols has also been suggested. Herein, we report the involvement of fine indoor air particulates with a diameter of ≤ 2.5 µm (PM_2.5) as the virus’s transport agent. PM_2.5 was collected over four weeks during 48-h measurement intervals in four separate hospital wards containing different infected clusters in a teaching hospital in Kuala Lumpur, Malaysia. Our results indicated the highest SARS-CoV-2 RNA on PM_2.5 in the ward with number of occupants. We suggest a link between the virus-laden PM_2.5 and the ward’s design. Patients’ symptoms and numbers influence the number of airborne SARS-CoV-2 RNA with PM_2.5 in an enclosed environment.

Palm oil production is a key industry in tropical regions, driven by the demand for affordable vegetable oil. Palm oil production has been increasing by 9% every year, mostly due to expanding biofuel markets. However, the oil palm industry has been associated with key environmental issues, such as deforestation, peatland exploitation and biomass burning that release carbon dioxide (CO₂) into the atmosphere, leading to climate change. This review therefore aims to discuss the characteristics of oil palm plantations and their impacts, especially CO₂ emissions in the Southeast Asian region. The tropical climate and soil in Southeast Asian countries, such as Malaysia and Indonesia, are very suitable for growing oil palm trees. However, due to the scarcity of available plantation areas deforestation occurs, especially in peat swamp areas. Total carbon losses from both biomass and peat due to the conversion of tropical virgin peat swamp forest into oil palm plantations are estimated to be around 427.2 ± 90.7 t C ha⁻¹ and 17.1 ± 3.6 t C ha⁻¹ year⁻¹, respectively. Even though measured CO₂ fluxes have shown that overall, oil palm plantation CO₂ emissions are about one to two times higher than other major crops, the ability of oil palms to absorb CO₂ (a net of 64 tons of CO₂ per hectare each year) and produce around 18 tons of oxygen per hectare per year is one of the main advantages of this crop. Since the oil palm industry plays a crucial role in the socio-economic development of Southeast Asian countries, sustainable and environmentally friendly practices would provide economic benefits while minimizing environmental impacts. A comprehensive review of all existing oil plantation procedures is needed to ensure that this high yielding crop has highly competitive environmental benefits.

An exploration task can be performed by a team of mobile robots more efficiently than human counterparts. They can access and give live updates for hard-to-reach areas such as a disaster site or a sewer. However, they face some issues hindering them from optimal path planning due to the symmetrical shape of the environments. Multiple robots are expected to explore more areas in less time while solving robot localization and collision-avoidance issues. When deploying a multi-robot system, it is ensured that the hardware parts do not collide with each other or the surroundings, especially in symmetric environments. Two types of methods are used for collision avoidance: centralized and decentralized. The decentralized approach has mainly been used in recent times, as it is computationally less expensive. This article aims to conduct a systematic literature review of different collision-avoidance strategies and analyze the performance of innovative collision-avoidance techniques. Different methods such as Reinforcement Learning (RL), Model Predictive Control (MPC), Altruistic Coordination, and other approaches followed by selected studies are also discussed. A total of 17 studies are included in this review, extracted from seven databases. Two experimental designs are studied: empty/open space and confined indoor space. Our analysis observed that most of the studies focused on empty/open space scenarios and verified the proposed model only through simulation. ORCA is the primary method, against which all the state-of-the-art techniques are evaluated. This article provides a comparison between different methods used for multi-robot collision avoidance. It discusses if the methods used are focused on safety or path planning. It also sheds light on the limitations of the studies included and possible future directions.

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

Air quality on the east coast of Peninsular Malaysia is influenced by local anthropogenic and biogenic emissions as well as marine air masses from the South China Sea and aged emissions transported from highly polluted East Asian regions during the winter monsoon season. An atmospheric observation tower has been constructed on this coastline at the Bachok Marine Research Station. Daily PM_2.5 samples were collected from the top of the observation tower over a 3-week period, and ion chromatography was used to make time-resolved measurements of major atmospheric ions present in aerosol. ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ was found to be the most dominant ion present and on average made up 66 % of the total ion content. Predictions of aerosol pH were made using the ISORROPIA II thermodynamic model, and it was estimated that the aerosol was highly acidic, with pH values ranging from −0.97 to 1.12. A clear difference in aerosol composition was found between continental air masses originating from industrialised regions of East Asia and marine air masses predominantly influenced by the South China Sea. For example, elevated ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ concentrations and increased Cl⁻ depletion were observed when continental air masses that had passed over highly industrialised regions of East Asia arrived at the measurement site. Correlation analyses of the ionic species and assessment of ratios between different ions provided an insight into common sources and formation pathways of key atmospheric ions, such as ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ , ${\mathrm{NH}}_{\mathrm{4}}^{+}$ and ${\mathrm{C}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{4}}^{\mathrm{2}-}$ . To our knowledge, time-resolved measurements of water-soluble ions in PM_2.5 are virtually non-existent in rural locations on the east coast of Peninsular Malaysia. Overall this dataset contributes towards a better understanding of atmospheric composition in the Maritime Continent, a region of the tropics that is vulnerable to the effects of poor air quality, largely as a result of rapid industrialisation in East Asia.

Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM₁₀ from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns. The identified clusters were analyzed to gain meaningful information regarding air quality patterns in Malaysia and to identify characterization for each cluster. PM₁₀ time series data from 5 July 2017 to 31 January 2019, obtained from the Malaysian Department of Environment and Dynamic Time Warping as the dissimilarity measure were used in this study. At the same time, k-Means, Partitioning Around Medoid, agglomerative hierarchical clustering, and Fuzzy k-Means were the algorithms used for clustering. The results portray that the categories and activities of locations of the monitoring stations do not directly influence the pattern of the PM₁₀ values, instead, the clusters formed are mainly influenced by the region and geographical area of the locations.