This study investigated the spatial and temporal variations in dissolved oxygen (DO) in the Cha-Am wastewater treatment ponds to assess treatment dynamics and to identify possible areas where the treatment train could be improved. Cha-Am is a small resort town with extensive beaches, located on the west coast of the Gulf of Thailand. The wastewater treatment system for Cha-Am consists of four ponds in sequence: aeration pond, sedimentation pond, extended aeration pond, and evaporation pond. Two YSI 6920 datasondes were installed near the inlet of the aeration pond and in the sedimentation pond, to measure dissolved oxygen (DO), pH, conductivity, temperature, and turbidity at 30 min time intervals over a 3 month period. DO averaged 3.09 mg/L and 3.33 mg/L, respectively in the aeration pond and in the sedimentation pond. DO generally varied over a diel cycle with higher values observed in midafternoon and lower values observed after midnight. DO often increased after a rainfall event. Ordinary Kriging (OK) interpolation in ArcGIS10.1 was used to map the spatial distribution of DO at different depths based on YSI spot measurements. OK indicated the highest DO concentrations were near the surface (0.5 m to 1.0 m); averaging 18.53 mg/L, 20.5 mg/L, 17.31 mg/L and 9.7 mg/L in the four ponds, but sometimes the concentrations were <2 mg/L near the bottom of the ponds. Two of the ponds are used as a wild catch fishery and low DO seems to negatively impact the fish. The spatial trend of DO shows that normally DO is lower at the inlet of the aeration pond than at its outlet even though mechanical aerators are operated through part of the day. Improved aeration and sunlight penetration through enhanced particle settling may be of benefit.
Population growth, urbanization, and infrastructure development activities have resulted in the land conversion of forests and farmlands to residential and commercial zones. Such land conversion causes changes in the land cover, as experienced in the Ayung Watershed, in the island of Bali, Indonesia. Here, the land cover undergoes rapid changes due to the growing tourism sector, affecting the runoff coefficient. This study evaluated the changing land cover patterns and surface runoff in the Ayung Watershed between 2012 and 2019. An increase in the surface runoff during the high rainfall events may lead to flooding in the area. The identification of land change patterns in the Ayung Watershed was carried out by a manual digitizing process on Google Earth maps. The runoff coefficient was calculated by Cook’s method using the four physical characteristics of the watershed: land cover, infiltration rate, land slope and drainage density; showing significant changes in the land cover in the study area. Farmlands and forests were reduced by 647.8 ha and 553.1 ha respectively, converted into fast growing grasslands or unproductive land. Such land cover changes have a negative impact by increasing the runoff coefficient in the area. During the study period, the runoff coefficient was consistently found to be more than 0.6 (high-risk category). Several sections in the city of Denpasar experienced an increase in the runoff coefficient by more than 5%. Consequently, there was a high-risk of flooding in the area because of the increasing surface runoff.
Cha am, a popular beach destination in Thailand, uses an aerated lagoon system with four ponds in series to treat its municipal wastewater. This study investigated the spatial pattern of heavy metal concentrations in the sediment deposited at the bottom of the four ponds and along the river receiving the treated wastewater discharge. Using a stratified random sampling scheme, between 11 and 14 surface grab samples were collected from each of the four ponds on two different dates in September and October 2016 (94 samples in total). An additional 17 samples were collected in December 2016 along the 1.8 km river section connecting the ponds to the ocean. A Bruker S1 Titan 600 X-ray fluorescence (XRF) analyser was used to determine metal concentrations in the air dried sediment samples. Ordinary kriging in ArcGIS10.1 indicated that while metal concentrations were greater in the middle areas of each pond, from pond to pond the metal concentrations exhibited different spatial trends. The ponds provide treatment for most of the metals analysed, with Student t-tests showing that mean concentrations of arsenic, chlorine and zinc decreased significantly from the first pond to the third pond but increased significantly in the fourth pond. Chromium concentration changed insignificantly between ponds; lead concentration decreased significantly from the first to the second pond, but there were insignificant changes in mean lead concentration thereafter. Concentrations of cadmium, cobalt, mercury and selenium were below the XRF limit of detection, but the mean levels of arsenic, chromium, copper, lead and manganese in each of the four ponds frequently exceeded Ontario Ministry of the Environment and Climate Change lowest effect level (LEL) guidelines for sediment. Metal levels in the upper reach of the river, closest to the pond discharge, were similar to the pond levels and generally decreased downstream. With the exception of zinc, metal levels detected in the river sediment frequently exceeded the LEL guidelines.
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