21 research outputs found
Combined climate impacts and vulnerability index on coastal ecosystems in prediction of future scenarios: extended sustainable indicator tool for adaptive strategy
No full textThis study presents the coastal vulnerability due to the forecasted climate change impact on the marine environment, including the sea level rise physical trait of risk impact. A combined methodology using Representative Concentration Pathways (RCPs), which corresponds to the greenhouse gas emissions scenarios, is used in this research; combined with Climate Change Vulnerability Index (CCVI) to rank the relative risk for each of the marine ecosystem zones in relation to the potential hazard exacerbated by climate change and sea-level rise. This method presents vulnerability in numerical data, which cannot be calculated directly based on their physical properties. From the results, it shows that the coastal areas of the study area of Marudu Bay would experience a warmer atmosphere both under RCP 4.5 and RCP 8.5 with an increment of 1.0 °C and 1.7 °C; meanwhile, the climate projection for total exhibits of increase in total precipitation by 2.6 mm/day and 1.6. mm/day under RCP 4.5 and RCP 8.5 at the regional measure. At the same time, the projection simulates an increase of sea level by 0.21 m and 0.27 m over the northern region of Marudu Bay under RCP 4.5 and RCP 8.5, respectively. In addition, 43.84 ha and 57.02 ha of land estimated would be potentially inundated by the mid-century year 2050 under RCP 4.5 and RCP 8.5. By the end of the century 2100, the sea level is projected to increase locally at about 0.32 m under RCP 4.5 and 0.38 m under RCP 8.5, consequently resulting in a total of 66.84 ha and 79.78 ha of additional inundation coverage. Therefore, the result from this study can be used when making effective adaptive strategies and conservation planning despite its inherent uncertainties
The regional biogenic emissions response to climate changes and ambient CO₂ in Southeast Asia
No full textThe emissions of isoprene from vegetation in the tropics have been regarded as one of the major sources of the global biogenic emission budget. As this emission is highly sensitive to temperature, one may expect significant changes to the emissions due to climate change. In this study, we explore the impact of regional climate change to the emissions of isoprene in Southeast Asia. The potential role of the combination of climate change and future atmospheric CO₂ concentration on isoprene emissions are also investigated. The latest generation of Hadley Centre regional climate modelling system, PRECIS (Providing Regional Climates for Impact Studies) was used to investigate the climate change in the region. The climate output dataset from the model was then used as input for the BVOC Emission Model, which was developed by Sheffield University and Lancaster University to estimate the emissions of biogenic volatile organic compounds. The projected temperature changes under the A₂ emission scenario was 2.5⁰C, which accounted an increase of 22% of isoprene emission from 29 to 37 TgC/yr if the CO₂ emission factor was excluded. Incorporation of higher concentration in future CO₂ emissions was found to offset the climate change impact on future emissions of isoprene in the region. With the CO₂ effects, the projected regional isoprene emissions in 2100 dropped from 28 to 25 TgC/yr. These results suggest that future emissions of isoprene in the region is largely buffered by a number of competing factors, which are certainly important to be considered in estimating the isoprene global budget. In a wider perspective, the anticipated high concentration of CO₂ in the future could lead to the disruption of the ozone, organic aerosol and methane formation through the competing influence with warmer climate on isoprene emissions from tropical vegetation
Extended air pollution index (API) as tool of sustainable indicator in the air quality assessment: El-Nino events with climate change driven
No full textThe main purpose of this research is to detect the air quality changes with a shorter period of timescale over space that can improve and optimize the risk characterization and conjunctive air quality assessment. Air quality assessment could be based on a very large number of various indicators, including the physical parameter, chemical and biological namely sulphur dioxide (SO2), carbon monoxide (CO), particulate matter (PM), humidity, air pressure and temperature. Nevertheless, often it is not easy to interpret the results of the air quality status when numerous quality elements are analyzed since each parameter indicates different types of quality classes. Moreover, providing appropriate information on air quality to policymakers, including the public, can be challenging. Hence, with this research there is a need to interpret the results in a more simple way and realistic enough by producing one single number for better and more subjective classification on the air quality rather than using the concentrations-based. Therefore, the Air Pollution Index (API) application in this research will overcome this problem by providing a single score that characterizes the air quality and contamination in a more absolute way. In line with that also, the study could help to improve the existing methodology for air quality assessment in a more simplified way and better evaluation of the air quality status, thus can become an alternative way for analysis of changes in air quality, especially in the absence or limitations of the historical or baseline data for comparison, in response for a better and more sustainable indicator in air quality assessment and management. The research shows that the API values across the Regions were recorded largely higher when El-Nino events occurred during the southwest monsoon season with more than 50% frequency of unhealthy days to hazardous status were detected from the API assessment. HYSPLIT model also shows that the air mass has mostly passed through the biomass burning areas from the neighboring country. Hence, the extension application of API was established in this research with the purpose of strengthening the air quality management in Malaysia, and to maximize the usage of the API and at the same time to filling up the gap of the uncertainty on the overall air quality in Malaysia, especially in terms of combine effects of the air pollutants parameters
Non CO2 greenhouse gas sources from managed and natural soils - fluxes and mitigation
No full textThis paper outlines the current knowledge of the processes controlling nitrous oxide (N2O) and methane (CH4) fluxes, methods of measurement, mitigation options and models designed to simulate N2O and CH4 fluxes. Natural and managed soils are globally important sources and sinks of the main non- CO2 greenhouse gases (GHG) N2O and CH4. Compared to CO2 their global warming potential over a 100 year period is 298 and 25 times, respectively, larger than that of CO2
Assessing short term air quality trend in Malaysia based on air pollution index (APi)
No full textAir Pollution Index (API) is used in Malaysia to determine the daily air quality status, which is calculated based on the daily concentrations of particulate matter (PM10), ground-level ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2) and nitrogen dioxide (NO2). This study presents short-term air quality trends based on API from the 52 air quality monitoring stations nationwide between 2010 and 2016. The air quality data and meteorological conditions were obtained from the Department of Environment and used for the API calculation. The API value is classified into six categories, namely: Good (0-50), Moderate (51-100), Unhealthy (101-200), Very Unhealthy (201-300), Hazardous (301-500), and Emergency (above 500). The coefficient of variation (CV) and Mann-Kendall trend test (MK) were used to assess the API variation and trend in each air quality monitoring station. Between the study periods, the API values were largely varied. Observation at 32 air quality monitoring stations have shown significant but small increasing trends, while 12 stations showed significant decreasing trends, and the remaining 8 stations showed no significant trends. The frequency of exceedance (API>50) was used to assess the percentages of unhealthy days. The analysis has found that air quality in Klang Valley was experiencing the highest number of unhealthy days, while the two Malaysian states in Borneo (Sabah and Sarawak) to be relatively less polluted
Upwelling event characteristics of chlorophyll-a concentration in the surface layer of Sabah waters
No full text2532-2540Using satellite-based remote sensing data, the study analysed Chl-a levels throughout Sabah’s coastal waters and correlated the Chl-a levels with the corresponding SST levels for that time and region. It was found that upwelling in Sabah coastal waters was most strongly noted in Labuan during the Northeast Monsoon (NEM), which brings strong northeasterly winds to Sabah’s west and north coast. The strong winds can be cited as the main source of increased upwelling as SST levels did not sufficiently change to suggest a strong relationship between SST and Chl-a levels
Beach erosion: threat and adaptation measures of communities in the Tun Mustapha Park (TMP), Sabah, Malaysia
No full textBeach erosion is among the main phenomena affecting small islands in the Coral Triangle region, particularly in the Tun Mustapha Park (TMP), Malaysia. This study was done to investigate the level of beach erosion and to determine the adaptation measures for the coastal communities to beach erosion. Field trips were carried out in May and July 2017 at seven islands (Banggi, Tiga, Balambangan, Malawali, Molleangan, Tigabu and Mandidarah) of TMP. Semi-structured interviews were conducted with 50 respondents who were the coastal inhabitants of the islands, to gain local knowledge about island beach erosion. Results indicate that beach erosion occurred during the peak of monsoon seasons and extreme events. Wind-induced high waves during the end of the year (northeast monsoon) eroded beaches, damaged houses, fishing structures and uprooted trees. Six of the islands are affected by beach erosion, whereas Mandarah island is experiencing accretion. Karakit beach is the only study site protected by seawall and beach revetment. The identified coastal adaptations to beach erosion were traditional shoreline protection by piling dead corals, sand sacks and woods on the beaches, modification and improvement to damaged building structures. Some local communities opted to move further inland and relocate to other islands or mainland Sabah to avoid the impacts of erosion. This study emphasizes the value of local knowledge shared by the coastal communities which can be incorporated with scientific baseline data for improved sustainable coastal development, protection, and management of the marine protected area
Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model
No full textBACKGROUND AND OBJECTIVES: Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM10 concentration forecasting is important for early prevention and in urban development planning, which is crucial for developing cities. This paper presents the development of PM10 forecasting model using nonlinear autoregressive with exogenous input model.METHODS: To improve performance of nonlinear autoregressive with exogenous input model, principal component analysis is used prior to the model for variable selection. The first stage of principal component analysis involves Scree plot, which determines the number of principal components based on explained variance. This is then followed by selecting variables using a rotated component matrix, based on their strength of contribution towards variation of PM10 concentration. To test the model, PM10 data in Kota Kinabalu from 2003 – 2010 was used. Neural network models are developed using this data by varying number of input variables with the inclusion of temporal variables. The developed forecasting models are evaluated using data PM10 in the city from 2011 to 2012. Four performance indicators, namely root mean square error, mean absolute error, index of agreement and fractional bias are reported.FINDINGS: Results from principal component analysis show that five variables including wind direction index, relative humidity, ambient temperature, concentration of nitrogen dioxide and concentration of ozone strongly contribute to the variation of PM10 concentration. By using these variables together with temporal variables as input in the nonlinear autoregressive with exogenous input models, the resultant model shows good forecasting performance, with root mean square error of 7.086±0.873 µg/m3. The selection of significant variables helps in reducing input variables inside the forecast model without degrading its forecast performance.CONCLUSION: This model shows very promising performance in forecasting PM10 concentration in Kota Kinabalu as it requires fewer input variables and does not require variable transformation
Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis
No full textParticulate matter (PM) has caught scientific attention in scientific research due to its harmful effect on human health. While prediction is essential for future development in Keningau, temporal clustering in Keningau has yet to be studied. Thus, this research aims to determine whether monsoonal clustering is required for meteorological and pollutant concentration data collected in Keningau. Missing data is first imputed using Nearest Neighbour Method (NNM). Then, wind direction and wind speed are converted into northern (Wy) and eastern (Wx) component of wind speed. Data is then temporal clustered based on monsoonal season (NEM, IM4, SWM, IM10). Both clustered and unclustered data are analysed using principal component (PC) analysis (PCA). The findings revealed that humidity in PC1 with average EV (explained variation) of 93.92 ± 0.52 contribute the most variation of PM10, followed by Wx in PC2 with average EV of 3.51 ± 0.48. Regression analysis shows that humidity and PM10 are negatively moderate to strongly correlated except for IM4 (intermonsoon April), which may be due to dry climate during the season. As for Wx, it has weak correlation with PM10. This may be due to location of Keningau at western part of Crocker range. However, the spread of PM10 due to eastern wind causes weak to zero correlation. Due to consideration of dry climate as revealed by the findings from IM4 cluster, there is need for data collected by Keningau to be clustered by monsoon
