Effects of natural organic matters on bioavailability of petroleum hydrocarbons in soil-water environments.

The bioremediation efficiency of petroleum hydrocarbons in natural soil-water systems is regulated by active microbial populations and other system parameters. Relevant factors include the transfer rate of petroleum contaminants from a medium into microorganisms, the partitioning behavior of contaminants from water into the soil organic matter (SOM), and the influence of the dissolved organic matter (DOM) on the contaminant level in water. The objectives of this study was aimed to determine the correlation among bioavailability of petroleum hydrocarbons, SOM content, and DOM level in soil-water systems. Heptadecane, pristane, and decylcyclohexane were selected as model hydrocarbon contaminants. The bioavailability of target contaminants in soil was examined using soils of different SOM contents (2% and 20%) in slurry bioreactors. In addition, the contaminant bioavailability as affected by various DOM levels (0-100 mgC/L) was also examined. The results showed that the SOM content affected the degrading rate of hydrocarbons significantly, where the rate constant was 4 times higher in 2% SOM microcosm than in the 20% SOM bioreactor for heptadecane degradation. Similarly, the pristane degrading efficiency after 240 h operation was 95% for the 2% SOM microcosm and only 38% for the 20% SOM microcosm. The hydrocarbon degradation rates in water phase were found to be enhanced by the added DOM level. A positive correlation existed between the contaminant bioavailability and the contaminant level in water as impacted by the SOM content in soil and the DOM level in water.

Application of molecular biological tools for monitoring efficiency of trichloroethylene remediation.

Trichloroethylene (TCE) is one of the most ubiquitous halogenated organic compounds of concerns of carcinogens in groundwater in Taiwan. Bioremediation has been recognized as a cost-effective approach in reducing TCE concentration. Five pilot-scale wells were constructed to monitor TCE concentrations in contaminated groundwater. With injection of EOS®, TCE was effectively degraded to 42%-93% by the end of 175 days. The biostimulation with EOS® was useful in establishing a micro-site anaerobic but with limited contribution. Dilution of the aquifer movement also caused the TCE reduction among injection and monitoring wells. The degradability was affected by the location and the proximity from the injection well. TCE concentrations found to be negatively correlated with the associated Dehalococcoides spp. and functional genes levels. Dhc concentration of 108 copies L-1 caused the initial 40% of TCE degradation. The well with the optimal degradation owned tceA of 109 cells L-1. T-RFLP results indicate the wells with the superior TCE degradability also performed the highest Shannon index number (means the highest diversity), which occurred on the same day that Dhc levels started to enlarge. Desulfovibrio desulfuricans and Desulfuromonas chloroethenica were predominant species identified in the T-RFLP fingerprint profile. In brief, a variety of different factors including well locations, geochemical indicators, and microbial contribution were useful to explain the site-specific optimal TCE remediation approach. The consistence among TCE degradation, Dhc growing pattern, functional gene levels, and the dynamics of the microbial community structure present the novelty of this study.

Molecular analysis of methanogens involved in methanogenic degradation of tetramethylammonium hydroxide in full-scale bioreactors.

This study investigated methanogenic communities involved in degradation of tetramethylammonium hydroxide (TMAH) in three full-scale bioreactors treating TMAH-containing wastewater. Based on the results of terminal-restriction fragment-length polymorphism (T-RFLP) and quantitative PCR analyses targeting the methyl-coenzyme M reductase alpha subunit (mcrA) genes retrieved from three bioreactors, Methanomethylovorans and Methanosarcina were the dominant methanogens involved in the methanogenic degradation of TMAH in the bioreactors. Furthermore, batch experiments were conducted to evaluate mcrA messenger RNA (mRNA) expression during methanogenic TMAH degradation, and the results indicated that a higher level of TMAH favored mcrA mRNA expression by Methansarcina, while Methanomethylovorans could only express considerable amount of mcrA mRNA at a lower level of TMAH. These results suggest that Methansarcina is responsible for methanogenic TMAH degradation at higher TMAH concentrations, while Methanomethylovorans may be important at a lower TMAH condition.

Biological treatment of TMAH (tetra-methyl ammonium hydroxide) in a full-scale TFT-LCD wastewater treatment plant.

This study evaluated biological treatment of TMAH in a full-scale methanogenic up-flow anaerobic sludge blanket (UASB) followed by an aerobic bioreactor. In general, the UASB was able to perform a satisfactory TMAH degradation efficiency, but the effluent COD of the aerobic bioreactor seemed to increase with an increased TMAH in the influent wastewater. The batch test results confirmed that the UASB sludge under methanogenic conditions would be favored over the aerobic ones for TMAH treatment due to its superb ability of handling high strength of TMAH-containing wastewaters. Based on batch experiments, inhibitory chemicals present in TFT-LCD wastewater like surfactants and sulfate should be avoided to secure a stable methanogenic TMAH degradation. Finally, molecular monitoring of Methanomethylovorans hollandica and Methanosarcina mazei in the full-scale plant, the dominant methanogens in the UASB responsible for TMAH degradation, may be beneficial for a stable TMAH treatment performance.

Effects of soil organic matter and ageing on remediation of diesel-contaminated soil.

Bioremediation of diesel-contaminated soil was investigated for the effects of soil organic matter (SOM) and ageing time in two sets of experiments (Batch I and II, respectively). This study examined degradation efficiency in soil artificially contaminated with diesel oil (maximum total petroleum hydrocarbons (TPH) concentration of 9000 mg/kg soil). Batch I data showed that the values of the first-order degradation rate, k, were relatively high in the low-SOM soil batches. The quantity of SOM negatively correlated with the TPH degradation rates and with the total TPH degradation efficiency (%). Introduction of rhamnolipid to the soil proved to be a useful solution to resolve the problem of the residual TPH in the soil with high SOM. In Batch II, the k values decreased with the length of ageing time: 0.0245, 0.0128 and 0.0090 l/d in samples ST0 (freshly contaminated), ST38 (aged for 38 days) and ST101 (aged for 101 days), respectively. The TPH degradation efficiency (%) also decreased along with the ageing time. The research also applied molecular technology to analyse the bacterial community dynamics during the bioremediation course. Multivariate statistics based on terminal-restriction fragment length data indicated: 1) the soils with different SOM resulted in separate bacterial community structures, 2) ageing time created a variety of bacterial communities, 3) the bacterial community dynamics was associated with the hydrocarbon consumption. The SOM content in soils affected the TPH degradation rate and efficiency and the bacterial community structures. Aged soil is more difficult to remediate than freshly contaminated soil, and the resulting bacterial community was less dynamic and showed a lack of succession.

Source impacts by volatile organic compounds in an industrial city of southern Taiwan.

This study investigates source impacts by airborne volatile organic compounds (VOC) at two sites designated for traffic and industry, in the largest industrial area Kaohsiung, southern Taiwan. The samples were collected at the two sites simultaneously during rush and non-rush hours in summer and autumn seasons. Same pattern of VOC groups were found at both sites: most abundant aromatics (78-95%) followed by alkanes (2-16%) and alkenes (0-6%). The BTEX concentration measured at the two sites ranged from 69 to 301 ppbC. Toluene, isopentane, ethylbenzene, and benzene were found to be the most abundant species. Speciation of VOCs was characterized with several skills including principal component factor analysis and BTEX characteristic ratios. Each of the resulted principal factors at the two sites explained over 80% of the VOCs data variance, and indicated that both of the sampling sites were influenced by both traffic and industrial sources with separately different levels. The remarkable patterns of the first two factors described not only the similarity but also the discrepancy at the two sampling sites, in terms of the source impacts. The high T/B ratios (7.56-14.25) observed at the industrial site implied the important impact from mobile emissions. The indicators, m,p-xylene/benzene and o-xylene/benzene, also confirmed the potential source of motor vehicles at both of the sampling sites. Air age assessment showed that more than half of the total observations located in the domain of fresh air. Low X/E ratios implied somewhat aged air mass transported to the sampling sites. The industrial site might not only encounter emissions from the industry sources, but also under unavoidable impact from the traffic sources.

Preliminary investigation of greenhouse gas emissions from the environmental sector in Taiwan.

The United Nations Framework Conventions on Climate Change (UNFCCC) asks their Parties to submit a National Inventory Report (NIR) for greenhouse gas (GHG) emissions on an annual basis. However, when many countries are quickly growing their economy, resulting in substantial GHG emissions, their inventory reporting systems either have not been established or been able to be linked to planning of mitigation measures at national administration levels. The present research was aimed to quantify the GHG emissions from an environmental sector in Taiwan and also to establish a linkage between the developed inventories and development of mitigation plans. The "environmental sector" consists of public service under jurisdiction of the Taiwan Environmental Protection Administration: landfilling, composting, waste transportation, wastewater treatment, night soil treatment, and solid waste incineration. The preliminary results were compared with that of the United States, Germany, Japan, United Kingdom, and Korea, considering the gaps in the scopes of the sectors. The GHG emissions from the Taiwanese environmental sector were mostly estimated by following the default methodology in the Intergovernmental Panel on Climate Change guideline, except that of night soil treatment and waste transportation that were modified or newly developed. The GHG emissions from the environmental sectors in 2004 were 10,225 kilotons of CO2 equivalent (kt CO2 Eq.). Landfilling (48.86%), solid waste incineration (27%), and wastewater treatment (21.5%) were the major contributors. Methane was the most significant GHG (70.6%), followed by carbon dioxide (27.8%) and nitrous oxide (1.6%). In summary, the GHG emissions estimated for the environmental sector in Taiwan provided reasonable preliminary results that were consistent and comparable with the existing authorized data. On the basis of the inventory results and the comparisons with the other countries, recommendations of mitigation plans were made, including wastewater and solid waste recycling, methane recovery for energy, and waste reduction/sorting.

Establishment of a Box-Jenkins multivariate time-series model to simulate ground-level peak daily one-hour ozone concentrations at Ta-Liao in Taiwan.

Box-Jenkins univariate autoregressive integrated moving average (ARIMA) and regression with time-series error (RTSE) models were established to simulate historical peak daily 1-hr ozone concentrations at Ta-Liao, Taiwan, 1997-2001. During 1995-2003, the 600 days of Pollution Standard Index (PSI) more than 100 (peak daily 1-hr ozone concentrations detected by greater than 120 ppm) at Tao-Liao showed the highest ozone exceedances among the six monitoring stations in Kaohsiung County. To improve the predictability of extremely high ozone, two different principal components, PC1 and PC(1 + 2), were introduced in the RTSE model. Four typical predictors (particular matter with an aerodynamic diameter less than or equal to 10 microm, temperature, wind speed, and wind direction) plus a PC trigger remained significant in the RTSE model. The model performance statistics concluded that the RTSE model with PC1 was optimal, compared with the univariate ARIMA, the RTSE model without PC, and RTSE model with PC(1 + 2). The contingency table shows that the successful predictions of the univariate model were only 12.9% of that of the RTSE model with PC1. Also, the POD value was improved approximately 5-fold when the univariate model was replaced by the RTSE model, and almost 8-fold when it was replaced by the RTSE model with PC1. Moreover, introducing the PC trigger indeed enhanced the ozone predictability. After the PC trigger was introduced in the RTSE model, the POD was increased 69.9%, and the FAR was reduced 8.3%. The overall correlation between the observed and simulated ozone was improved 9.6%. Also, the first principal component was more useful than the first two components in playing the "trigger" role, though it counted only for 58.62% of the environmental variance during the high ozone days.

Forecasting peak daily ozone levels: part 2--A regression with time series errors model having a principal component trigger to forecast 1999 and 2002 ozone levels.

A modified time series approach, a Box-Jenkins regression with time series errors (RTSE) model plus a principal component (PC) trigger, has been developed to forecast peak daily 1-hr ozone (O3) in real time at the University of Wisconsin-Milwaukee North (UWM-N) during 1999 and 2002. The PC trigger acts as a predictor variable in the RTSE model. It tries to answer the question: will the next day be a possible high O3 day? To answer this question, three PC trigger rules were developed: (1) Hi-Low Checklist, (2) Discriminant Function Approach I, and (3) Discriminant Function Approach II. Also, a pure RTSE model without including the PC trigger and a persistence approach were tested for comparison. The RTSE model with DFA I successfully forecast the only two 1-hr federal exceedances (124 ppb), one in 1999 and one in 2002. In terms of the O3 100-ppb exceedances, 60-80% of the incorrect forecasts occurred with incorrect PC decisions. A few others may have been caused by unexpected O3-weather relations. When the three approaches used UWM-N data to forecast a 100-ppb exceedance somewhere in the Milwaukee, WI, metropolitan area, their performance was dramatically improved: the false alarm rate was reduced from 0.89 to 0.44, and the probability of detection was increased from 0.71 to 0.88.

Forecasting peak daily ozone levels--I. A regression with time series errors model having a principal component trigger to fit 1991 ozone levels.

This research was motivated by the need to warn the population of Milwaukee, WI, on high-ozone days. A statistical model for the peak daily 1-hr ozone level is proposed. A Regression with Time Series Errors (RTSE) model, which includes a principal component (PC) trigger, is the basis for forecasting the peak daily 1-hr ozone level. The RTSE model, with a PC trigger, is first employed to estimate daily peak ozone measured at the University of Wisconsin, Milwaukee-North (UWM-N), during the 1991 ozone season. The RTSE model uses peak daily temperature, morning vector average wind direction, and the PC trigger as predictor variables. The PC trigger was designed to summarize atmospheric circumstances when peak ozone was greater than 100 parts per billion (ppb). It is verified that the RTSE model, with a PC trigger, significantly improves the prediction of peak daily ozone, particularly peak ozone greater than 100 ppb. In comparison with the RTSE model without the PC trigger, the RTSE model with a PC trigger raised the R2 from 0.680 to 0.809. It is suggested that the RTSE model, with the PC trigger, is an adequate statistical model that has the potential for real-time ozone forecasting.