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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.