Research Papers - Department of Civil Engineering
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Publication Open Access Profiling Microplastic Pollution in Surface Water Bodies in the Most Urbanized City of Sri Lanka and Its Suburbs to Understand the Underlying Factors(Springer, Cham, 2023-02-23) Bandara, R. M. L. S.; Perera, M. D. D.; Gomes, Pattiyage I. A.; Yan, Xu-FengThis study investigated the microplastic pollution of surface waters in and around the most populated and urbanized city in Sri Lanka from 2019 to 2022. The sampling regime was designed to cover the rainfall-driven hydrology and varying levels of urbanization approximated by the built area fraction. Mass and particle concentrations of microplastics ranged from undetected to 0.01 g/L (average ± standard deviation: 0.00464 ± 0.00528 g/L) and from 2 to 36 particles/L (5.3 ± 6.9), respectively. The highest microplastic pollution was observed in the lake; however, in many cases it was without a statistically significant (P < 0.05) difference with canals. Concentrations in the dry state (i.e., at least 30 days after no rainfall) were about 1.5 times more than the wet state (i.e., at least 50 mm/day rainfall for 10 days) in the lake and in the semi-urban canal, but again, the differences were not significant; however, in urban canals, the concentrations were similar in both states. Over 80% of the microplastics were fibre and fragments. Mass concentrations of microplastics showed moderately positive (Pearson’s r > 0.6) correlations with the built area fraction of the contributing catchment in both states but was significant (P < 0.1) only in the dry state. In the case of particle concentrations, none showed even a weak correlation. The independence of microplastic content against built area fraction and rainfall, as well as twice the concentrations found in point source inputs against the surface waters, gave the following insights. Microplastic content in our study area was governed mostly by the modified catchment hydrology spearheaded by stormwater drainages (some cases trans-catchment) and diffusion factors such as non-residential population.Publication Open Access Relationships between climatic factors to the paddy yeild: A case study from North-Western province of Sri Lanka(Smart Computing and Systems Engineering, 2020, 2020-09-23) Wickramasinghe, L; Jayasinghe, J. M. J. W; Rathnayake, U. SClimate variation is one of the major impacting issues for paddy cultivation. It also highly impacts the harvest. Therefore, many researchers try to understand the relationships between climatic factors and harvest using numerous methods. Sri Lanka is still titled as a country with an agricultural-based economy and thus identifying the impact of climate variability on agriculture is very important. However, previous studies reveal a little information in the context of Sri Lanka on the impact of climate variabilities on agriculture. Therefore, this study showcases an artificial neural network (ANN) framework; that is an ordinary machine learning algorithm based on the model of the human neuron system, to evaluate the relationships among the climatic components and the paddy harvest in the North-Western province of Sri Lanka. This on-going study helps to analyze the relationships between the paddy harvest of the North-Western province and climate, including rainfall minimum atmospheric temperature and maximum atmospheric temperature. Correlation coefficient (R) and mean squared error (MSE) are used to test the performance of the ANN model. The results obtained from the analysis revealed that the predicted and real paddy yields have a significant correlation with rainfall, maximum temperature and minimum temperature.Publication Open Access Recent Climatic Trends In Trinidad And Tobago, West Indies(Research and Technology Transfer Affairs Division,, 2020-02) Perera, A; Mudannyake, S; Azamathulla, H; Rathnayake, U. SSeawater level rise is one of the most prevalent adverse environmental impacts of the ongoing global warming process. Island nations are more vulnerable to the impact than the land masses. Two such islands impacted by global warming are Trinidad and Tobago, located in the Atlantic Ocean. However, there is minimal related research in this area in the context of the impact of climate variability. Therefore, it is timely and interesting to assess the climatic trends in islands that are extremely vulnerable like Trinidad and Tobago. This paper presents a detailed non-parametric statistical analysis for well-known climate gauges in Trinidad and Tobago, West Indies. Mann Kendall and Sen’s slope tests were carried out on two identified rain gauges in Trinidad and Tobago. Monthly climatic data including cumulative rainfall and the average of the minimum and maximum atmospheric temperatures were processed to identify the trend analysis using the above stated non-parametric tests. Important results are found from the analysis; most importantly, there is no significant impact on the rainfall in the area due to the climate variability over 30 years. However, the atmospheric temperature behaves in a different way and it has a rising pattern across the total 12 months studied. This can be seen for both the minimum and maximum atmospheric temperatures. Therefore, the warm months are becoming warmer and the cold months are becoming less cold. This is a critical finding that must be considered for any future planning processes.Publication Open Access Rainfall and atmospheric temperature against the other climatic factors – Case study from Colombo, Sri Lanka(2019-12) Perera, A; Rathnayake, U. SClimate prediction is given a high priority by many countries due to its importance in mitigation of extreme weather conditions. However, the prediction is not an easy task as the climatic parameters not only show spatial variations but also temporal variations. In addition, the climatic parameters are interrelated. To overcome these difficulties, soft computing techniques are widely used in prediction of climate variables with respect to the other variables. On the other hand, Colombo, Sri Lanka, is experiencing adverse or extreme weather conditions over the last few years. However, a climate prediction study is yet to be carried out in this tropical climatic zone. Therefore, this paper presents a study, identifying relationships between the two most impacted climate parameters (atmospheric temperature and rainfall) and other climatic parameters. Artificial neural network (ANN) models are developed to define the relationships and then to predict the atmospheric temperature as a function of other parameters including monthly rainfall, minimum and maximum relative humidity, and average wind speed. Same analysis is carried out to define the prediction model to the monthly rainfall. The best algorithm out of several other ANN algorithms is chosen for the analyses. Results revealed that the atmospheric temperature in Colombo can be presented with respect to the other climatic variables. However, the rainfall does not show a greater relationship with the other climatic parameters.