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There has been little discussion to date on air pollution and its potential relationship with health in Makassar, Indonesia. This study aims to create a starting point for this discussion by investigating existing data points and the potential correlation between ambient air pollution and health in Makassar, Indonesia. Six months of air quality data (July-December, 2018) on CO, SO2, NO2, O3, PM10, and PM2.5 were provided by the city and were analyzed alongside tuberculosis and pneumonia data provided by the hospital and community health centers in Makassar. Data were analyzed using principal component analysis, dendrogram, and some GIS mapping. Quantitative data from the USAID-funded Building Health Cities project were also used to help explain some of the quantitative findings. Results show that principal component analysis (PCA) gave three statistics factors having eigenvalues exceeding one, which account for 83% of the total variance in the dataset. The three factors accounted for a strong impact by CO, O3, SO2, PM10, and PM2.5 attributed to the incomplete combustion of fuel from automobiles, bush burning, and industrial emission. Air pollution-related illnesses such as tuberculosis and pneumonia are found to prevail in the area. Real-time air quality monitoring is required to benchmark the health impact of extreme conditions. This study also encourages urgent intervention by decision-makers to tackle the level of tuberculosis and pneumonia occurrence that may be favored by the poor air quality in Makassar.

Using rainfall gauge on its own as input carries great uncertainties regarding runoff estimation, especially when the area is large and the rainfall is measured and recorded at irregular spaced gauging stations. Hence spatial interpolation is the key to obtain continuous and orderly rainfall distribution at unknown points to be the input to the rainfall runoff processes for distributed and semi-distributed numerical modelling. It is crucial to study and predict the behaviour of rainfall and river runoff to reduce flood damages of the affected area along the Kelantan river. Thus, a good knowledge on rainfall distribution is essential in early flood prediction studies. Forty six rainfall stations and their daily time-series were used to interpolate gridded rainfall surfaces using inverse-distance weighting (IDW), inverse-distance and elevation weighting (IDEW) methods and average rainfall distribution. Sensitivity analysis for distance and elevation parameters were conducted to see the variation produced. The accuracy of these interpolated datasets was examined using cross-validation assessment.

The Kelantan River Basin, situated in the northeastern Malaysian Peninsula, suffers serious flood/inundation damage, related to the northeast monsoon season (November–January), every few years. In this river basin, rainfall observation systems have been progressively developed since 1948, and long-term time-series data at distributed rainfall stations have been accumulated. This study firstly investigated the homogeneity of the accumulated time-series data for the purpose of constructing a reliable database for various hydrologic analyses. The homogeneity of rainfall time-series data was established using four absolute homogeneity tests: the Pettitt test, standard normal homogeneity test, Buishand range test, and von Neumann ratio test. It was found that among 50 rainfall stations within the river basin, 9 were flagged by the tests. Of these, inhomogeneous time-series data from four stations were omitted from further analysis. Secondly, using the homogenous time-series rainfall data, a trend analysis of annual rainfall variability was conducted by means of the Mann–Kendall (MK) test. To investigate long- and short-term characteristics of rainfall variability, three sampling methods of the MK test were applied: 30-year, sequentially increased, and 10-year moving segmented sampling. The MK test using the 30-year sampling showed a decreasing trend between 1957 and 1987, and an increasing trend between 1981 and 2011. The MK test using the sequentially increased sampling detected neither significant increasing nor decreasing trends through the same 70-year period. The MK test using 10-year moving segmented sampling clearly showed significant rainfall variability, which corresponded to the El Niño and La Niña events of 1972, 1982, 1988, the mid-1990s, and 2000–2004. Extending the plot, by comparing the turning points with the occurrence of El Niño and La Niña events, the results showed that the influence of the El Niño southern oscillation (ENSO) is the most likely to significantly influence rainfall trends in the Kelantan River Basin. [ABSTRACT FROM PUBLISHER]

Debris flows are among the fatal geological hazards in Malaysia, with 23 incidents recorded in the last two decades. To date, very few studies have been carried out to understand the debris flow processes, causes, and runouts nationwide. This study simulated the debris flow at the Mesilau watershed of Kundasang Sabah caused by the prolonged rainfall after the 2015 Ranau earthquake. Several interrelated processing platforms, such as ArcGIS, HEC-HMS, and HyperKANAKO, were used to extract the parameters, model the debris flow, and perform a sensitivity analysis to achieve the best-fit debris flow runout. The debris flow travelled at least 18.6 km to the Liwagu Dam. The best-fit runout suggested that the average velocity was 12.5 m/s and the lead time to arrive at the Mesilau village was 4.5 min. This high debris flow velocity was probably due to the high-water content from the watershed baseflow with a discharge rate of 563.8 m3/s. The flow depth and depositional thickness were both lower than 5.0 m. This study could provide crucial inputs for designing an early warning system, improving risk communication, and strengthening the local disaster risk reduction and resilience strategy in a tectonically active area in Malaysia. [ABSTRACT FROM AUTHOR]

Sabah is prone to seismic activities due to its location, being geographically located near the boundaries of three major active tectonic plates; the Eurasian, India-Australia, and Philippine-Pacific plates. The 6.0 Mw earthquake that occurred in Ranau, Sabah, on 15 June 2015 which caused 18 casualties, all of them climbers of Mount Kinabalu, raised many issues, primarily the requirements for seismic vulnerability assessment for this region. This study employed frequency ratio (FR)–index of entropy (IoE) and a combination of (FR-IoE) with an analytical hierarchy process (AHP) to map seismic vulnerability for Ranau, Sabah. The results showed that the success rate and prediction rate for the areas under the relative operating characteristic (ROC) curves were 0.853; 0.856 for the FR-IoE model and 0.863; 0.906 for (FR-IoE) AHP, respectively, with the highest performance achieved using the (FR-IoE) AHP model. The vulnerability maps produced were classified into five classes; very low, low, moderate, high, and very high seismic vulnerability. Seismic activities density ratio analysis performed on the final seismic vulnerability maps showed that high seismic activity density ratios were observed for high vulnerability zones with the values of 9.119 and 8.687 for FR-IoE and (FR-IoE) AHP models, respectively. [ABSTRACT FROM AUTHOR]