Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels

Continuous measurements of the air pollutant concentrations at monitoring stations serve as a reliable basis for air quality regulations. Their availability is however limited only at locations of interest. In most situations, the spatial distribution beyond these locations still remains uncertain as it is highly influenced by other factors such as emission sources, meteorological effects, dispersion and topographical conditions. To overcome this issue, a larger number of monitoring stations could be installed, but it would involve a high investment cost. An alternative solution is via the use of a deterministic air quality model (DAQM), which is mostly adopted by regulatory authorities for prediction in the temporal and spatial domain as well as for policy scenario development. Nevertheless, the results obtained from a model are subject to some uncertainties and it requires, in general, a significant computation time. In this work, a meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations. From a dispersion model, it is suggested that the spatially-distributed pollutant levels (i.e. ozone, in this study) across a region under consideration is a function of the grid coordinates, topographical information, solar radiation and the pollutant's precursor emission. Initially, for training the model, the input-output relationship is extracted from a photochemical dispersion model called The Air Pollution Model and Chemical Transport Model (TAPM-CTM), and some of those input-output data are correlated with the ambient measurements collected at monitoring stations. Here, improved radial basis function networks, incorporating a proposed technique for selection of the network centres, will be developed and trained by using the data obtained and the forward selection approach. The methodology is then applied to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM-CTM model only, when compared to the measurement data collected at monitoring stations. © 2013 Elsevier B.V. All rights reserved

A prospective study of the importance of enteric fever as a cause of non-malarial febrile illness in patients admitted to Chittagong Medical College Hospital, Bangladesh

BACKGROUND: Fever is a common cause of hospital admission in Bangladesh but causative agents, other than malaria, are not routinely investigated. Enteric fever is thought to be common. METHODS: Adults and children admitted to Chittagong Medical College Hospital with a temperature of ≥38.0 °C were investigated using a blood smear for malaria, a blood culture, real-time PCR to detect Salmonella Typhi, S. Paratyphi A and other pathogens in blood and CSF and an NS1 antigen dengue ELISA. RESULTS: We enrolled 300 febrile patients with a negative malaria smear between January and June 2012: 156 children (aged ≤15 years) and 144 adults with a median (interquartile range) age of 13 (5-31) years and median (IQR) illness duration before admission of five (2-8) days. Clinical enteric fever was diagnosed in 52 patients (17.3 %), lower respiratory tract infection in 48 (16.0 %), non-specific febrile illness in 48 (16.0 %), a CNS infection in 37 patients (12.3 %), urinary sepsis in 23 patients (7.7 %), an upper respiratory tract infection in 21 patients (7.0 %), and diarrhea or dysentery in 21 patients (7.0 %). Malaria was still suspected in seven patients despite a negative microscopy test. S. Typhi was detected in blood by culture or PCR in 34 (11.3 %) of patients. Of note Rickettsia typhi and Orientia tsutsugamushi were detected by PCR in two and one patient respectively. Twenty-nine (9 %) patients died during their hospital admission (15/160 (9.4 %) of children and 14/144 (9.7 %) adults). Two of 52 (3.8 %) patients with enteric fever, 5/48 (10.4 %) patients with lower respiratory tract infections, and 12/37 (32.4 %) patients with CNS infection died. CONCLUSION: Enteric fever was confirmed in 11.3 % of patients admitted to this hospital in Bangladesh with non-malaria fever. Lower respiratory tract and CNS infections were also common. CNS infections in this location merit more detailed study due to the high mortality

Chickpea

The narrow genetic base of cultivated chickpea warrants systematic collection, documentation and evaluation of chickpea germplasm and particularly wild Cicer species for effective and efficient use in chickpea breeding programmes. Limiting factors to crop production, possible solutions and ways to overcome them, importance of wild relatives and barriers to alien gene introgression and strategies to overcome them and traits for base broadening have been discussed. It has been clearly demonstrated that resistance to major biotic and abiotic stresses can be successfully introgressed from the primary gene pool comprising progenitor species. However, many desirable traits including high degree of resistance to multiple stresses that are present in the species belonging to secondary and tertiary gene pools can also be introgressed by using special techniques to overcome pre- and post-fertilization barriers. Besides resistance to various biotic and abiotic stresses, the yield QTLs have also been introgressed from wild Cicer species to cultivated varieties. Status and importance of molecular markers, genome mapping and genomic tools for chickpea improvement are elaborated. Because of major genes for various biotic and abiotic stresses, the transfer of agronomically important traits into elite cultivars has been made easy and practical through marker-assisted selection and marker-assisted backcross. The usefulness of molecular markers such as SSR and SNP for the construction of high-density genetic maps of chickpea and for the identification of genes/QTLs for stress resistance, quality and yield contributing traits has also been discussed

Optimally-Tuned Cascaded PID Control using Radial Basis Function Neural Network Metamodeling

Dynamic systems are quite often non-linear and require a complex mathematical model. For their optimal control, it has been always a requirement to tune the controller parameters to achieve the best performance. Parameter tuning in complex systems is predominantly a time-consuming task, even with high performance computers. This paper provides an overview of metamodeling and demonstrates how it can be applied to efficiently tune the control parameters of a typically nonlinear and unstable process, the ball and beam system. Here, the metamodel is realized with a radial basis function (RBF) neural network to derive the PID parameters subject to an optimal criterion. The proposed approach is benchmarked with a commonly-used tuning technique

Computational intelligence estimation of natural background ozone level and its distribution for air quality modelling and emission control

Background ozone, known as the ozone that occurs in the troposphere as a result of biogenic emissions without photochemical influences, has a close relationship with human health risk. The prediction of the background ozone level by an air quality model could cover a wider region, whereas a measurement method can only record at monitoring sites. The problem is that simulation with deterministic models is quite tedious because of the nonlinear nature of some particular chemical reactions involved in the pollutant formulation. In this work, we present a reliable method for determination of the background ozone using the ambient measurement data. Our proposed definition can be used to determine the background level at any part of the globe and in any seasons without relying on data obtained at remote sites. A statistical model approach will be used for the estimation of the background ozone concentration, and a method for extrapolating the site data will be utilised to approximate the spatial distribution on the region. The proposed method will be applied in the Sydney basin to evaluate its effectiveness in background ozone determination. The results show the advantage of the proposed approach as a globally generic and computationally efficient way for the background ozone estimation with a reasonable accuracy

A metamodel for background ozone level using radial basis function neural networks

In air quality modelling, determination of the background ozone level is essential as it highly affects the accuracy of the photochemical air quality model. It is known that the background ozone level, especially in urban areas, has been changing over the years. Unfortunately, the reasons of that alteration were not clear and the background ozone itself was not easily derived in practice. In this paper, a new background ozone model will be developed by using the ozone ambient quality data and the meteorological data at the several stations in the Sydney basin. To accomplish the modelling process, an adaptively-tuned radial basis function neural network metamodel is proposed and utilised in the simulation. Different input parameters are considered to evaluate their influence on the constructed background ozone model. The proposed model, subject to some statistical criteria, demonstrates its capability of estimating the background ozone level with a reasonably good accuracy. ©2010 IEEE

Adaptive neural network metamodel for short-term prediction of background ozone level

Modelling is important in air quality forecasting and control. Before applying an air quality model, it is required to accurately estimate the biogenic emission. The assessment of the background ozone concentration is essential for this estimation. It has been known that the biogenic ozone level in urban areas is changing over the years, and hence information about the temporal trends in air quality data is helpful for the assessment. This paper presents a neural-network metamodel for prediction of the background ozone level in the Sydney basin. Based on measured monitoring data under non-photochemical conditions collected at a number of monitoring stations, the proposed model can reliably provide short-term predictions in the biogenic ozone trends to be used for analysis of ground-level emission impact on air quality. ©2010 IEEE