Particulate matter (PM10) is an important pollutant particularly in urban environments in Malaysia. In addition, the level of this pollutant was also seasonally significant in most parts of Malaysia, and therefore concern of its effect towards human health is relevant and crucial. Based on a long-term series of PM10 measurement at 20 monitoring locations in Malaysia, this study analysed the spatial and temporal characteristics of PM10 from 1997 to 2015 using standard deviation ellipse and trend analyses. Satellite data and HYSPLIT model were applied to investigate the seasonal potential sources of the pollutant. Results show that annual PM10 average concentrations were greatly varied with large coefficient variation. In term of trend analysis, 11 monitoring sites had shown significant but small decreasing trends. Meanwhile, 7 monitoring sites had shown no significant trends and only 2 monitoring sites showed increasing trends. Trajectory analysis using the HYSPLIT model for the investigation of potential sources of pollutant has shown that high pollution levels of PM10 in Malaysia corresponded to the biomass burning in neighbouring countries. During the southwest monsoon, high PM levels were observed in the central and southern parts of Peninsular Malaysia and Malaysian Borneo, which corresponded to the biomass burning in Indonesia. Based on the long-term analysis, PM10 pollution in Malaysia was characterised by transboundary pollution as well as local sources, especially in urban areas. Despite the recognition of small but significant decreasing trends of PM10 pollution over long-term period, special attention need to be focused on short-term pollution episode, particularly related to transboundary pollution during extreme weather condition such as El Niño event to ensure that human health on a wider population is protected.
It is very crucial to planters to estimate the yield loss due to Ganoderma basal stem rot (BSR) disease in oil palm. However, currently there is a limited mathematical model available that can be used for that purpose. Therefore, this empirical study was conducted to build a mathematical model which can be used for yield loss estimation due to the disease. Three commercial oil palm plots with different production phases (i.e. steep ascent phase, plateau phase, and declining phase) were selected as the study sites. The yield and disease severity of the selected palms in the three study sites were recorded for the duration of twelve months. Model averaging approach using Bayes theorem was used to build the model. This is also known as Bayesian Model Averaging (BMA). The BMA model revealed that planting preparation technique was the most important predictor of oil palm yield loss, followed by disease progress (measured using area under the disease-progress curve, AUDPC), disease severity, number of infected neighbouring palms, and two interaction terms. By using the developed BMA model, it was estimated that the economic loss can be up to 68% compared to the attainable yields of all the infected palms.
In recent years, oil palm has grown on a major scale as it is a prominent commodity crop that contributes the most to almost every producing country’s gross domestic product (GDP). Nonetheless, existing threats such as the Ganoderma basal stem rot (BSR) disease have been deteriorating the oil palm plantations and suitable actions to overcome the issue are still being investigated. The BSR disease progression in oil palm is being studied using the disease progression through the plant disease triangle idea. This concept looks at all potential elements that could affect the transmission and development of the disease. The elements include pathogenic, with their mode of infection in each studied factor.
The aim of this paper is to set out a strategy for improving the inference for statistical models for the distribution of annual maxima observed temperature data, with a particular focus on past and future trend estimation. The observed data are on a 25-km grid over the UK. The method involves developing a distributional linkage with models for annual maxima temperatures from an ensemble of regional and global climate numerical models. This formulation enables additional information to be incorporated through the longer records, stronger climate change signals, replications over the ensemble and spatial pooling of information over sites. We find evidence for a common trend between the observed data and the average trend over the ensemble with very limited spatial variation in the trends over the UK. The proposed model, which accounts for all the sources of uncertainty, requires a very high-dimensional parametric fit, so we develop an operational strategy based on simplifying assumptions and discuss what is required to remove these restrictions. With such simplifications, we demonstrate more than an order of magnitude reduction in the local response of extreme temperatures to global mean temperature changes.
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