3 research outputs found
Impact of school traffic on outdoor carbon monoxide levels
No full textThis paper aims to determine the relationship between carbon monoxide levels with vehicles, including types and motions of vehicles in a school traffic environment. Children are more vulnerable as they spend most of their time in school and their still-developing respiratory system makes them more susceptible to air pollution compared to adults. The research was carried out by direct measurement of carbon monoxide using MultiRAE Lite PGM-6208 and counting of vehicles manually using tally counter with different traffic flow scenarios, type of vehicles, school days, locations and in schools. From the findings, it is found that the measurements of carbon monoxide exposures were significantly greater in town schools compared to rural area; weekdays recorded much higher carbon dioxide levels compared to weekends; moving vehicles had stronger effects compared to idle vehicles; and light-duty vehicles (LDV) had highest among other types of vehicles. The results show a large impact of traffic management and transport mode on carbon monoxide exposures to school children in the schools
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
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
