Quality comparison of market waste compost

Accumulation of a large amount of waste may create several problems to inhabiting populations. Composting is an environment friendly way of waste management. Good quality compost is essential to improve the soil fertility and to increase the yield of the crops which eventually contributes to the food security. Urban Council (UC), Vavuniya collected the market vegetable waste, tree loppings and other waste material within the UC and produced compost at Vepankulam, Vavuniya and sold to the farmers in the surroundings. Preliminary analysis was done to find out C/N ratio and low C/N ratio of 8:1 was reported for the compost produced by the UC. This study aims to find out the combination of different waste materials found in the environment to have good quality compost. There were six heaps prepared using market vegetable wastes, paddy husk ash and paddy straw with the dimension of 6' length and 2' width and 1.5' height each. Among the six heaps, three heaps were prepared using market vegetable waste and paddy husk ash with 1:1, 1:2 and 1:3 ratio where in each heap 1 part of market vegetable waste and I part, 2parts and 3parts of paddy husk ash on volume basis respectively and in another three heaps 1 part of paddy straw and 1 part, 2parts and 3parts of marketable waste on volume basis respectively. Organic carbon and total nitrogen were determined using Walkly and Black method and Kjeldahl procedure respectively, taking five samples from a representative sample of each heap. The results revealed that the compost produced with equal parts (1:1) of market vegetable waste and paddy straw produced good quality compost with C/N ratio of 23:1

Preliminary Indications on an Atmospheric Particulate Matter and Rainwater Chemistry in Colombo, Sri Lanka: A Study During Southwestern Monsoon

The atmospheric cleansing nature of rainfall, specifically in the removal of particulate matter, is a determinant of rainwater quality. This study investigated the relationship between atmospheric particulate matter (PM10) concentration and rainwater quality in Colombo during the southwest monsoon period in 2023. The rainwater samples were collected using a manually prepared high- density polyethylene collector and analysed for pH, electrical conductivity (EC), water-soluble cations (Na+, K+, Ca2+, Mg2+, and NH4+), and anions (Cl-, NO3–, and SO42–). The concentration of atmospheric PM10 was obtained from the ambient air quality monitoring station (AQMS) at Battaramulla. The measured data were analysed using multivariate statistical techniques, including principal component analysis and Pearson correlation analysis, to identify relationships between the concentration of atmospheric PM10 and rainwater quality. The atmospheric PM10 showed an insignificant positive correlation with southwest monsoon rainfall (r=0.14, p>0.05). The concentration of atmospheric PM10 increased the total concentration of water-soluble ions (r=0.30) and decreased the pH (r=-0.42) and electrical conductivity (r=0.07) of rainwater samples. The concentration of Na+, K+ and Ca2+ displayed moderate positive correlation, while Mg2+, NH4+, Cl-, NO3- and SO42- showed a weak positive correlation of rainwater samples in the atmospheric PM10. This study contributes valuable insights into the variations of PM10 in the atmosphere and its potential implications on rainwater quality in southwest monsoon, underscoring the importance of comprehensive analyses for a more nuanced understanding of the intricate relationships between atmospheric components and rainwater chemistry.   Keywords: Atmospheric chemistry, Correlation analysis, Particulate matter, Rainwater quality, Southwest monsoon &nbsp

Accident Hotspots in Southern Expressway of Sri Lanka: Interpolation Evaluation using GIS

In this study, the southern expressway, which is the first and lengthiest E class highway (126 km) in Sri Lanka, was analysed for roadside accident incidences. The primary objective of this paper is to identify the best-fit interpolation techniques for the hotspots' most distinctive causes of vehicular crashes. The accident details were collected from the Police Headquarters consisting of 966 accidents that took place during the period from 2015 to 2017. To identify accident hotspots, GIS-based interpolation techniques such as Ordinary Kriging, Kernel Density Estimation (KDE), Inverse Distance Weighting (IDW), and Nearest Neighbour Interpolation methods were used. The spatial interpolation outcome of the four methods was compared based on the standard Prediction Accuracy Index (PAI). The analysis was executed using QGIS and GeoDa. Results of PAI revealed that an IDW and KDE outperformed the other two interpolation methods. The left and right lanes of the expressway, spotted with 11 and 20 hotspots, respectively, indicate the right lane was 50% more prone to accidents than the left lane. Notably, nearly 5% of the entire road stretch is estimated as accident-prone spots in both lanes. Peak accidents were recorded during afternoon and evening hours, and buses were the most active vehicle type. Uncontrolled speeding was the primary reason for more than 50% of the accidents, while unsuccessful overtake accounted for more than 20% of the accidents on the highway. The road design modifications and warning sign placements at appropriate places may be recommended as countermeasures

Five Decadal Trends in Averages and Extremes of Rainfall and Temperature in Sri Lanka

In this study, we used a comprehensive set of statistical metrics to investigate the historical trends in averages and extremes of rainfall and temperature in Sri Lanka. The data consist of 55 years (1961–2015) of daily rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) records from 20 stations scattered throughout Sri Lanka. The linear trends were analyzed using the nonparametric Mann–Kendall test and Sen–Theil regression. The prewhitening method was first used to remove autocorrelation from the time series, and the modified seasonal Mann–Kendall test was then applied for the seasonal data. The results show that, during May, 15% of the stations showed a significant decrease in wet days, which may be due to the delayed southwest monsoon (SWM) to Sri Lanka. A remarkable increase in the annual average temperature of Tmin and Tmax was observed as 70% and 55% of the stations, respectively. For the entire period, 80% of the stations demonstrated statistically significant increases of Tmin during June and July. The daily temperature range (DTR) exhibited a widespread increase at the stations located within the southwestern coast region of Sri Lanka. Although changes in global climate, teleconnections, and local deforestation in recent decades at least partially influence the trends observed in Sri Lanka, a formal trend attribution study should be conducted