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Influence of urban air pollution on the population in the Klang Valley,Malaysia:a spatial approach

Ecological Processes 2022年第1期11卷 126-141页

作者： Siti Haslina Mohd Shafie Mastura Mahmud suzani mohamad Nor Lita Fadilah Rameli Ramdzani Abdullah Ahmad Fariz Mohamed Centre for Research in Development Social and Environment(SEEDS)Faculty of Social Sciences and HumanitiesUniversiti Kebangsaan MalaysiaBangiSelangorMalaysia Department of Environment Faculty of Forestry and Envi-ronmentUniversiti Putra MalaysiaSerdangSelangorMalaysia Institute for Environment and Development(LESTARI) Universiti Kebangsaan MalaysiaBangiSelangorMalaysia.

Background:Urban air pollution resulting from economic growth as well as urbanization impacts the health of *** study aims to examine the spatial distribution of particulate matter(PM_(10))and the risk of exposure for the population in the Klang Valley,an urban conglomeration centred in Kuala Lumpur,***:The inverse distance weighted(IDW)interpolation technique in the ArcGIS platform was used to map the air quality from five air monitoring *** statistics of the Global Moran’s I Spatial Autocorrelation and Optimized Hot Spot Analysis were used to estimate the spatial clustering of PM_(10).The population-weighted exposure level(PWEL)technique was used to calculate the population density susceptible to PM_(10)and the AirQ+model was applied to estimate the proportion of the adult population at risk of chronic bronchitis due to long-term exposure to PM_(10)concentrations in the Klang ***:The highest annual mean PM_(10)concentrations at the Klang station ranged between 80 and 100μg/m^(3)from 2000 to 2009 and exceeded the New Malaysia Ambient Air Quality Standard(NMAAQS)and the World Health Organi-zation Air Quality Guidelines(WHOAQG)levels of 40 and 20μg/m^(3),*** Moran’s I results indicated that the statistically significant clusters of high PM_(10)in Klang present a health risk to the local *** PWEL results showed that levels of PM_(10)in Petaling Jaya exceeded the WHOAQG limits,potentially posing a health risk to the high-density *** AirQ+model found Klang to be a significantly high-risk area for chronic bronchitis among the adult population with 804,240 people(with a relative risk of 1.85)in ***:These high-risk values are due to rapid urbanization and high motor vehicle usage in the Klang Valley and therefore the impact of PM_(10)on the population needs to be *** situation should be taken seriously by the Department of Environment and Kuala Lumpur City Hall in addre

关键词： Population Urban air pollution Spatial distribution PM_(10) Adult chronic bronchitis risk

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Recursive Fuzzy Predictions of Future Patient Paths to Support Clinical Decision Making in ICU

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Engineering（科研） 2013年第10期5卷 584-589页

作者： A. Zeghbib M. Mahfouf J. J. Ross G. H. Mills G. G. Panoutsos M. Denai S. suzani Department of Automatic Control & Systems Engineering University of Sheffield Sheffield UK Northern General Hospital Sheffield UK Teesside University Middlesbrough UK

In this paper, we propose a new architecture that combines prediction and decision-making in the form of a hybrid framework aimed at providing clinicians with transparent and accurate maps, or charts, to guide and to support treatment decisions, and to interrogate the clinical patients’ course as it develops. These maps should be patient-specific, with options displayed of possible treatment pathways. They would suggest the optimal care pathways, and the shortest routes to the most efficient care, by predicting clinical progress, testing the ensuing suggestions against the developing clinical state and patient condition, and suggesting new options as necessary. These maps should also mine an extensive database of accumulated patient data, modelled diseases, and modelled patient-responses based on expert-derived rules. These individualized hierarchical targets, which are implemented in order to prevent life-threatening illnesses, will also have to “adapt” to the patient’s altering clinical condition. Therapies that support one system can destabilize others and selecting which specific support to prioritize is an uncertain process, the prioritization of which can vary between clinical experts. Whilst clinical therapeutic decisions can be made with some degree of anticipation of the “likely” outcome (based on the experts’ opinion and judgment), treatment is essentially rooted in the present, and is dependent on analyzing the current clinical condition and available data. The recursive learning approach presented in this paper, allows decision rules to predict the possible future course, and reflects back derived information from such projections to the present time and thus support proactive clinical care rather than reactive clinical care. The proposed framework for such a patient map supports and enables an optimized choice from available options and also ensures that decisions are based on both the available evidence and a database of best clinical practice. Prelimi

关键词： Proactive Treatment Clinical Decision Intensive-Care Patient-Paths Physiological Map

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