The construction industry plays a significant role in contributing to the economy and development globally. During the process of construction, various hazards coupled with the unique nature of the industry contribute to high fatality rates. This review refers to previous published studies and related Malaysian legislation documents. Four main elements consisting of human, worksite, management and external elements which cause occupational accidents and illnesses were identified. External and management elements are the underlying causes contributing to occupational safety and health (OSH), while human and worksite elements are more apparent causes of occupational accidents and illnesses. An effective OSH management approach is required to contain all hazards at construction sites. An approach to OSH management constructed by elements of policy, process, personnel and incentive developed in previous work is explored. Changes to the sub-elements according to previous studies and the related Malaysian legislation are also covered in this review.
Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.
seterusnya dapat memberi kesan terhadap peningkatan pencapaian maksimum akademik dan sikap pelajar. Kata kunci: elemen BBM multimedia, keberkesanan penggunaan BBM Multimedia, reka bentuk BBM multimedia
Particulate matter (PM) is one of the major pollutants emitted by vehicles that adversely affect human health and the environment. This study evaluates and predicts concentrations and dispersion patterns of PM10 and PM2.5 in Kuala Lumpur city centre. The OML-Highway model calculates hourly time series of PM10 and PM2.5 concentrations and distribution caused by traffic emissions under different scenarios; business as usual (BAU) and 30% traffic reduction to see the impact of traffic reduction for sustainable traffic management. Continuous PM10 and PM2.5 data from a nearby monitoring station were analysed for the year 2019 and compared with modelled concentrations. Annual average concentration at various locations of interest for PM10 and PM2.5 during BAU runs were in the ranges 41.4–65.9 µg/m3 and 30.4–43.7 µg/m3 respectively, compared to during the 30% traffic reduction run ranging at 40.5–59.5 µg/m3 and 29.9–40.3 µg/m3 respectively. The average concentration of PM10 and PM2.5 at the Continuous Air Quality Monitoring Station (CAQMS) was 36.4 µg/m3 and 28.2 µg/m3 respectively. Strong correlations were observed between the predicted and observed data for PM10 and PM2.5 in both scenarios (p < 0.05). This research demonstrated that the reduction of traffic volume in the city contributes to reducing the concentration of particulate matter pollution.
Urban air pollution has been a major concern due to its impact on global public health. Various techniques for monitoring urban air quality have been developed. However, there is currently a dearth of research on how effective bioaccumulators such as lichen are at monitoring urban air pollution. As a result, the purpose of this research is to investigate the use of U. misaminensis as a biological indicator for biomonitoring urban air quality in Malaysia. Three months were spent exposing lichen samples in three Malaysian cities (Kuala Lumpur, Penang, and Johor Bahru). The trace element content and vitality of the lichens were assessed. The results of this study revealed that U. misaminensis is an effective biological indicator for measuring 25 elements of air pollutants in metropolitan areas. They also revealed that all 25 elements accumulated in the urban area sample were greater than in the control sample. The vitality rate of lichens dropped in the urban area sample when compared with the control sample, indicating that an increase in elements in the air will impact the vitality rate of any biological component. In this study, two arguments are made: (a) Lichen is an excellent biological indicator, particularly for urban air pollutants such as potentially toxic elements; and (b) traffic is the primary contributor to urban air pollution; hence, the local government requires a better plan and design for urban areas to decrease air pollutants build-up.
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