Organochlorine pesticide residues in Songkla Lake

The water quality of Songkla Lake for organochlorine (OC) pesticide contamination was studied from chosen sampling sites (stations) using the lakes geographic data and pollution sources. The water samples were collected monthly from 13 stations in Songkla Lake during September 1991–November 1992. The OC pesticide residues found were Heptachlor, Heptachlor Epoxide, DDD, DDE, DDT and Aldrin in the range 0–0.5690 ppm. This study showed that DDT and its metabolite products (DDD and DDE) dominated OC pesticide residues in the lake. The concentration of OC residues also depended on the season, i.e. at some sampling stations the concentration of OC residues in the dry season (July–September) were higher than in the wet season (October–January).     

Monitoring of arsenic in aquatic plants, water, and sediment of wastewater treatment ponds at the Mae Moh Lignite power plant, Thailand

Mae Moh is a risky area for arsenic contamination caused by the effluent from biowetland ponds in Mae Moh lignite-fuelled power plant. The objective of this study was to investigate the arsenic concentrations of Mae Moh biowetland ponds and determine the main factors which are important for arsenic phytoremediation in the treatment system. The result revealed that arsenic concentrations in the supernant were in the range of less than 1.0 μg As L^???1 to 2.0 μg As L^???1 while those in the sediment were in the range of 25–200 μg As kg soil^???1. Both values were below the Thailand national standard of 0.25 mg As L^???1 for water and 27 mg As kg soil^???1 for the soil. Arsenic accumulation in the biomass of 5 aquatic plants at the biowetland ponds ranged from 123.83 to 280.53 mg As kgPlant^???1. Regarding the result of regression analysis (R ²?= 0.474 to 0.954), high concentrations of organic matter and other soluble ions as well as high pH value in the sediment could significantly enhance the removal of soluble arsenic in the wetland ponds. From the regression equation of accumulated arsenic concentration in each aquatic plant, Eichhornia crassipes (Mart.) Solms. (R ²?= 0.954), Ipomoea aquatica Forsk. (R ²?= 0.850), and Typha angustifolia (L.) (R ²?= 0.841) were found to be preferable arsenic removers for wastewater treatment pond in the condition of low Eh value and high content of solid phase EC and phosphorus. On the other hand, Canna glauca (L.) (R ²?= 0.749) appeared to be favorable arsenic accumulator for the treatment pond in the condition of high Eh value and high concentration of soluble EC.     

Greenhouse gas emissions from production and use of used cooking oil methyl ester as transport fuel in Thailand

Biodiesel, produced from various vegetable and/or animal oils, is one of the most promising alternative fuels for transportation in Thailand. Currently, the waste oils after use in cooking are not disposed adequately. Such oils could serve as a feedstock for biodiesel which would also address the waste disposal issue. This study compares the life cycle greenhouse gas (GHG) emissions from used cooking oil methyl ester (UCOME) and conventional diesel used in transport. The functional unit (FU) is 100 km transportation by light duty diesel vehicle (LDDV) under identical driving conditions. Life cycle GHG emissions from conventional diesel are about 32.57 kg CO₂-eq/FU whereas those from UCOME are 2.35 kg CO₂-eq/FU. The GHG emissions from the life cycle of UCOME are 93% less than those of conventional diesel production and use. Hence, a fuel switch from conventional diesel to UCOME will contribute greatly to a reduction in global warming potential. This will also support the Thai Government's policy to promote the use of indigenous and renewable sources for transportation fuels.     

Seasonal variation, risk assessment and source estimation of PM 10 and PM10-bound PAHs in the ambient air of Chiang Mai and Lamphun, Thailand

Daily PM10 concentrations were measured at four sampling stations located in Chiang Mai and Lamphun provinces, Thailand. The sampling scheme was conducted during June 2005 to June 2006; every 3 days for 24 h in each sampling period. The result revealed that all stations shared the same pattern, in which the PM10 (particulate matters with diameter of less than 10 microm) concentration increased at the beginning of dry season (December) and reached its peak in March before decreasing by the end of April. The maximum PM10 concentration for each sampling station was in the range of 140-182 microg/m(3) which was 1.1-1.5 times higher than the Thai ambient air quality standard of 120 microg/m(3). This distinctly high concentration of PM10 in the dry season (Dec. 05-Mar. 06) was recognized as a unique seasonal pattern for the northern part of Thailand. PM10 concentration had a medium level of negative correlation (r = -0.696 to -0.635) with the visibility data. Comparing the maximum PM10 concentration detected at each sampling station to the permitted PM10 level of the national air quality standard, the warning visibility values for the PM10 pollution-watch system were determined as 10 km for Chiang Mai Province and 5 km for Lamphun Province. From the analysis of PM10 constituents, no component exceeded the national air quality standard. The total concentrations of PM10-bond polycyclic aromatic hydrocarbons (PAHs) are calculated in terms of total toxicity equivalent concentrations (TTECs) using the toxicity equivalent factors (TEFs) method. TTECs in Chiang Mai and Lamphun ambient air was found at a level comparable to those observed in Nagasaki, Bangkok and Rome and at a lower level than those reported at Copenhagen. The annual number of lung cancer cases for Chiang Mai and Lamphun Provinces was estimated at two cases/year which was lower than the number of cases in Bangkok (27 cases/year). The principal component analysis/absolute principal component scores (PCA/APCS) model and multiple regression analysis were applied to the PM10 and its constituents data. The results pointed to the vegetative burning as the largest PM10 contributor in Chiang Mai and Lamphun ambient air. Vegetative burning, natural gas burning & coke ovens, and secondary particle accounted for 46-82%, 12-49%, and 3-19% of the PM10 concentrations, respectively. However, natural gas burning & coke ovens as well as vehicle exhaust also deserved careful attention due to their large contributions to PAHs concentration. In the wet season and transition periods, 42-60% of the total PAHs concentrations originated from vehicle exhaust while 16-37% and 14-38% of them were apportioned to natural gas burning & coke ovens and vegetative burning, respectively. In the dry period, natural gas burning & coke ovens, vehicle exhaust, and vegetative burning accounted for 47-59%, 20-25%, and 19-28% of total PAHs concentrations. The close agreement between the measured and predicted concentrations data (R(2) > 0.8) assured enough capability of PCA/APCS receptor model to be used for the PM10 and PAHs source apportionment.