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Hamza AhmadIsiyaka

Bio: Hamza AhmadIsiyaka is an academic researcher. The author has contributed to research in topics: Air quality index & Nonpoint source pollution. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Papers
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01 Jan 2014
TL;DR: In this article, the authors investigated the spatial variation in the source of air pollution; identify the most significant parameters contributing to the air pollution and to develop the best receptor model for predicting air pollution index (API).
Abstract: The quest for industrial and urban development over the years has changed the pattern and source of atmospheric air pollution in Malaysia. This study aims to investigate the spatial variation in the source of air pollution; identify the most significant parameterscontributing to the air pollution and to develop the best receptor model for predicting air pollution index (API). Data from five monitoring stations base on five year's observation (2007-2011) were used. Multivariate techniques such as cluster analysis (HACA), discriminate analysis (DA), principal component analysis (PCA), factor analysis (FA) and modeling techniques comprising of artificial neural network (ANN) and multiple linear regression (MLR) were used in this study. HACA was able to group the five monitoring stations into three clusters, indicating that one station in each cluster can provide a reasonable accurate spatial assessment of air quality within the study area. The result for standard mode, forward stepwise and backward stepwise DA gave a correct assignation of 82.37% (p˂ 0.05) which indicate that all the parameters significantly discriminate spatially. PCA and FA for the three clusters account for more than 62%, 56% and 58% of the total variance respectively indicating that the source of air pollution are from anthropogenic induced point source and nonpoint source. ANN gave a better prediction at R

7 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors investigated and proposed a reduction in the number of water quality monitoring stations, parameters and developed the best input combination for water quality modelling using artificial neural network and multivariate statistical technique.
Abstract: This study investigates and proposes a reduction in the number of water quality monitoring stations, parameters and develops the best input combination for water quality modelling using artificial neural network and multivariate statistical technique. Fourteen water quality physicochemical parameters acquired from eight monitoring sites for 8 years (2006–2013) were investigated. Hierarchical agglomerative cluster analyses (HACA) classify the eight monitoring sites into two significant clusters. Principal component analysis (PCA) accounted for more than 82% of the total variance and attributes the sources of pollution to critical anthropogenic activities, surface run-off and weathering of parent rocks. Furthermore, sensitivity analyses percentage contribution of pollutants revealed dissolved oxygen as the most significant parameter responsible for the pollution (66.3%), followed by ammonia nitrogen (14.4%), chemical oxygen demand (9.4%) and biochemical oxygen demand (5.3%). The result for source category apportionment assigned 39% to rock weathering, 25% anthropogenic activities, 20% surface run-off, 11% faecal waste, 3.4% human and natural factors and 1.4% erosion of river bank. In addition, three input combination models (model 1, 2 and 3) were developed in order to identify the best that can predict water quality index (WQI) at a very high precision. Model 2 using the principal component scores before varimax rotation appears to have the best prediction capability at node eight with coefficient of determination (R2) = 0.999 and root mean square error (RMSE) = 0.159. These findings justify the use of environmetrics modelling technique to reveal the pattern of water quality for decision making by government and stakeholders.

69 citations

Journal ArticleDOI
TL;DR: In this paper, a survey of indoor radon concentration in 174 kindergartens of three Bulgarian cities was carried out in 777 ground floor rooms using alpha tract detectors, exposed for 3 months in cold period of 2014.
Abstract: The study was conducted to assess the spatiality of the building factors’ effect on air quality through evaluation of indoor radon concentration in areas with different geology and geographical position. For that matter, a survey of indoor radon concentration was carried out in 174 kindergartens of three Bulgarian cities. The time-integrated measurements were performed in 777 ground floor rooms using alpha tract detectors, exposed for 3 months in cold period of 2014. The results of indoor radon concentrations vary from 20 to 1117 Bq/m3. The differences in the mean radon concentrations measured in the different cities were related to geology. The effect of building-specific factors: elevator, basement, mechanical ventilation, type of windows, number of floors, building renovation, building materials, type of room, type of heating, construction period, and availability of foundation on radon concentration variations was examined applying univariate and multivariate analysis. Univariate analysis showed that the effects of building-specific factors on radon variation are different in different cities. The influence of building factors on radon concentration variations was more dominant in inland cities in comparison to the city situated on the sea coast. The multivariate analysis, which was applied to evaluate the impact of building factors simultaneously, confirmed this influence too.

17 citations

Journal ArticleDOI
TL;DR: This study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data.
Abstract: An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

17 citations

Journal ArticleDOI
01 Oct 2019-Heliyon
TL;DR: A comprehensive review of relevant scientific journals concerning on the major environmental issues in Malaysia, published between 2013 and 2017, suggested that chemometrics techniques have a greater accuracy, flexibility and efficiency to be applied in environmental modelling.

13 citations

Journal ArticleDOI
TL;DR: In this paper , a review of the use of chemometrics in environmental pollution analysis is presented, highlighting its types, applications, advantages, and limitations in the environmental domain, as well as future trends in future applications.

9 citations