Ammonia is a raw material of fertilizer, refrigerant, and other commercial cleaning products which commonly stored in a large capacity. The higher storage capacity, the higher risk possibly occurs impacted to the population and environment. The study aims to provide a modeling of ammonia release scenarios, escape from the storage facility, Urea Fertilizer Industry X, located in Indonesia. This model is utilizing Area Location Hazardous Atmosphere (ALOHA) software to forecast the threat zone of ammonia release scenario and QGIS to overlay and spatial analysis. The result shows that the incident causes a significant impact on the area of 41.7 km2 and potentially threatens a massive scale of the inhabitant with higher evacuation factor (Ef), lower affected population (Ap). The risk determined by estimated probability and consequence considered as high risk, therefore, besides the main aim of establishing an emergency response plan, this study could also be used as a reference in risk evaluation of chemical release.
The need for good quality information on climate and its future changes has become increasingly important for society. Of particular interest are analysis and predictions of rainfall at pertinent spatial scales. An option is the use of regional climate models (RCMs) as a physics‐based downscaling tool to retrieve higher spatial resolution information from coarser present and future climate datasets. In order to verify if simulations provide added values that result in an appropriate representation of the state of climate at finer spatial resolutions, the adoption of RCMs requires prior optimization. The performance of the Weather Research and Forecasting Model RCM in simulating precipitation over Indonesia is examined by a series of sensitivity experiments using different parameterized convective physics. Among four tested schemes, the best performance is provided by the Betts‐Miller‐Janjic (BMJ) parameterization. RCM multiannual seasonal rainfall bias outperforms or matches the reanalysis. Modeled results provide added value in simulating rainfall‐related climate indices but show low skill in recreating the annual rainfall pattern at monthly resolution. RCM precipitation exhibits complex spatial response to different ENSO phases, with El Niño conditions resulting in a general loss of model skill during the southern hemisphere spring. A series of regional climate simulations using the BMJ convective scheme forced by a future climate projection dataset show changes in rainfall aligned with previous studies.
The Arafura and Timor Seas region is shared by Indonesia, Timor Leste, Australia, and Papua New Guinea (PNG), and is at the intersection of the Pacific and Indian oceans. High coastal population densities, degraded habitats, overexploited fisheries, low profile coasts, shallow continental shelves and macro-tidal conditions mean that coastal and marine environments in the region are currently facing multiple pressures. Climate change is expected to exacerbate these pressures and have profound effects on the status and distribution of coastal and marine habitats, the fish and invertebrates they support and, therefore, dependent communities and industries. Downscaled climate change projections for 2041–2070 for air and sea temperature, ocean chemistry and rainfall were modelled to provide spatially relevant regional data for a structured semi-quantitative vulnerability assessment. Results of the assessment were spatially variable and identified shallow coral reefs as highly vulnerable, particularly in the Timor-Leste and Indonesia-Arafura sub-regions. Seagrass meadows were most vulnerable in the Gulf of Carpentaria, Indonesia-Arafura, and Timor-Leste sub-regions. Mangrove habitats were most vulnerable in Timor-Leste and Western PNG sub-regions. Drivers of vulnerability include poor habitat condition, non-climate pressures, low connectivity, and limited formal management. Marine species vulnerability was also spatially variable, with highly vulnerable and priority species identified for each sub-region, including finfish and marine invertebrates. A key driver of species vulnerability was their stock status, with many species in Timor-Leste, Western PNG and Indonesia, and several in northern Australia, overfished or potentially overfished. Limited management in some sub-regions, as well as non-climate pressures such as habitat decline, poor water quality and illegal, unregulated and unreported fishing were also key drivers. Species of conservation interest (dugong and marine turtles) were also highly vulnerable to climate change, driven by their threatened status and the fact that they are low productivity species that take years to recover from impacts. Priority species and habitats for local action were identified and current pressures that undermine condition and/or resilience, with strategic recommendations aimed at minimising climate change vulnerability.
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