Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances; and (ii) estimation of seagrass total aboveground biomass (STAGB). Firstly, the substrate leaving radiances is determined by compensating the water column correction of the pre-processed data because of the inherent errors associated with the geometric and radiometric fidelities including atmospheric perturbations. Secondly, the seagrass leaving radiances were correlated to the corresponding in situ STAGB to predict seagrass biomass. The relationship between STAGB and cover percentage was then established for seagrass meadows occurring in Merambong, Straits of Johor, Malaysia. By applying the above-mentioned approach on Landsat Thematic Mapper (TM) acquired in 2009 and Operational Land Imager (OLI) data acquired in 2013, the resulting maps indicated that submerged STAGB in less clear water can be successfully quantified empirically from Landsat data, and can be utilized in STAGB change detection over time. Data validation showed a good agreement between in situ STAGB and Landsat TM (R² = 0.977, p < 0.001) and OLI (R² = 0.975, p < 0.001) derived water leaving radiances for the studied seagrass meadows. The STAGB was estimated as 803 ± 0.47 kg in 2009, while it was 752.3 ± 0.34 kg in 2013, suggesting a decrease of 50.7 kg within the four-year interval. This could be mainly due to land reclamation in the intertidal mudflat areas performed, with a view to increase port facilities and coastal landscape development. Statistics on dugong sightings also supports changes in STAGB.

Incomplete hydro-meteorological data and insufficient rainfall gauges have caused difficulties in establishing a reliable flood forecasting system. This study attempted to adopt the remotely sensed hydro-meteorological data as an alternative to the incomplete observed rainfall data in the poorly gauged Kuantan River Basin (KRB), the main city at the east coast of Peninsula Malaysia. Performance of Weather Research and Forecasting (WRF) schemes’ combinations, including eight microphysics (MP) and six cumulus, were evaluated to determine the most suitable combination of WRF MPCU in simulating rainfall over KRB. All the obtained results were validated against observed moderate to extreme rainfall events. Among all, the combination scheme Stony Brook University and Betts–Miller–Janjic (SBUBMJ) was found to be the most suitable to capture both spatial and temporal rainfall, with average percentage error of about ±17.5% to ±25.2% for heavy and moderate rainfall. However, the estimated PE ranges of −58.1% to 68.2% resulted in uncertainty while simulating extreme rainfall events, requiring more simulation tests for the schemes’ combinations using different boundary layer conditions and domain configurations. Findings also indicate that for the region where hydro-meteorological data are limited, WRF, as an alternative approach, can be used to achieve more sustainable water resource management and reliable hydrological forecasting.

Rapid growth and increasing demand for building materials have led to a shortage of key resources for conventional building materials. However, concrete is one

Air pollution is a significant environmental issue that poses serious health risks, particularly in industrial areas. This study focuses on evaluating the spatial distribution of air pollutants and their impact on public health in the industrial areas of Shah Alam and Petaling Jaya in the Klang Valley, Malaysia. This study examines concentrations of particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) over a 2021, 2022, and 2023. Utilizing Geographic Information System (GIS) tools, the study maps the spatial distribution of the health risk in industrial are and correlates them with air pollutants data obtained from Department of Environmental, Malaysia (DOE). The trend of air quality in Shah Alam and Petaling Jaya reveals variation in pollutants concentrations, influenced by both natural and anthropogenic factors, with Shah Alam generally exhibiting higher level of SO2, NO2 and O3, while Petaling Jaya shows elevated CO levels. The differences between Shah Alam and Petaling Jaya’s air quality likely from varying meteorological factors, such as wind patterns, temperature, and humidity, which can influence the dispersion and concentration of pollutants. This underscores the critical need for targeted air quality management strategies to mitigate health risk associated with industrial pollution and urbanization in these regions.

Serious health concerns in developing regions are largely driven by air pollution. The Malaysian Department of Environment has measured that air pollution in industrial areas is much greater than in other areas of the country. This pilot study intends to evaluate outdoor air quality and human health effects in the industrial area of Kuantan. Two Continuous Air Quality Monitoring Stations situated in Balok and Indera Mahkota were chosen to explore how air quality varies across different areas of Kuantan. A structured questionnaire was distributed to residents living nearby, aiming to collect information on any health issues they may be experiencing. The spatial distribution of air pollution was mapped using Geographic Information System (GIS), allowing for a clearer understanding of how these pollutants relate to the health symptoms reported by the local population. The findings revealed that concentrations of PM10, PM2.5, and SO₂ were significantly higher in Balok compared to Indera Mahkota, while NO₂ levels were an exception to this trend. These differences are likely tied to variations in land use between the two areas, which appear to have a strong influence on local air pollution levels. The findings support targeted health actions and better urban planning to protect public well-being.