Use of fuzzy logic models for prediction of taste and odor compounds in algal bloom-affected inland water bodies

Mechanistic modeling of how algal species produce metabolites (e.g., taste and odor compounds geosmin and 2-methyl isoborneol (2-MIB)) as a biological response is currently not well understood. However, water managers and water utilities using these reservoirs often need methods for predicting metabolite production, so that appropriate water treatment procedures can be implemented. In this research, a heuristic approach using Adaptive Network-based Fuzzy Inference System (ANFIS) was developed to determine the underlying nonlinear and uncertain quantitative relationship between observed cyanobacterial metabolites (2-MIB and geosmin), various algal species, and physical and chemical variables. The model is proposed to be used in conjunction with numerical water quality models that can predict spatial–temporal distribution of flows, velocities, water quality parameters, and algal functional groups. The coupling of the proposed metabolite model with the numerical water quality models would assist various utilities which use mechanistic water quality models to also be able to predict distribution of taste and odor metabolites, especially when monitoring of metabolites is limited. The proposed metabolite model was developed and tested for the Eagle Creek Reservoir in Indiana (USA) using observations over a 3-year period (2008–2010). Results show that the developed models performed well for geosmin (R ²?=?0.83 for all training data and R ²?=?0.78 for validation of all 10 data points in the validation dataset) and reasonably well for the 2-MIB (R ²?=?0.82 for all training data and R ²?=?0.70 for 7 out of 10 data points in the validation dataset).     

Monitoring spatiotemporal variations of diel radon concentrations in peatland and forest ecosystems based on neural network and regression models

Concentrations of outdoor radon-222 (²²²Rn) in temperate grazed peatland and deciduous forest in northwestern Turkey were measured, compared, and modeled using artificial neural networks (ANNs) and multiple nonlinear regression (MNLR) models. The best-performing multilayer perceptron model selected out of 28 ANNs considerably enhanced accuracy metrics in emulating ²²²Rn concentrations relative to the MNLR model. The two ecosystems had similar diel patterns with the lowest ²²²Rn concentrations in the afternoon and the highest ones near dawn. Mean level (5.1?+?2.5 Bq?m^?3 h^?1) of ²²²Rn in the forest was three times smaller than that (15.8?+?9.7 Bq?m^?3) of ²²²Rn in the peatland. Mean ²²²Rn level had negative and positive relationships with air temperature and relative humidity, respectively.     

Influence of Meteorological Factors NO2, SO2, CO and PM10 on the Concentration of O3 in the Urban Atmosphere of Eastern Croatia

Ozone, NO₂, SO₂, CO, PM₁₀ and meteorological parameters were measured simultaneously during the summer?Cautumn season 2007 in Osijek??the eastern, flat, agricultural part of Croatia. Fourier analysis confirms the existence of variation in ozone volume fractions with periods ranging from the usual semi-daily and daily to 7 and 28 daily cycles. The relationships between O₃ and other variables were modelled in three ways: principal component analysis, multiple linear regression and principal component regression. The results of the principal component analysis detected underlying relationships among ozone concentrations and meteorological variables. An extremely simple meteorological model is suitable for the prediction of ozone levels. The meteorological factors, temperature and cloudiness played a main role in the MLR model (R ²?=?0.83). The application of the principal component regression approach confirmed that the original variables associated with the valid principal components were meteorological variables (R ²?=?0.82).     

Hydrometeorological variables predict fecal indicator bacteria densities in freshwater: data-driven methods for variable selection

Statistical models of microbial water quality inform risk management for water recreation. Current research focuses on resource-intensive, location-specific data collection and water quality modeling, but this approach may be cost-prohibitive for risk managers responsible for numerous recreation sites. As an alternative, we tested the ability of two data-driven models, tree regression and random forests with conditional inference trees, to select readily available hydrometeorological variables for use in linear mixed effects (LME) models predicting bacterial density. The study included the Chicago Area Waterway System (CAWS) and Lake Michigan beaches and harbors in Chicago, Illinois, at which Escherichia coli and enterococci were measured seasonally in 2007–2009. Tree regression node variables reduced data dimensionality by >50 %. Variable importance ranks from random forests were used in a forward-step selection based on R ² and root mean squared prediction error (RMSPE). We found two to three variables explained bacteria densities well relative to random forests with all variables. LME models with tree- or forest-selected variables performed reasonably well (0.335?<?R ²?<?0.658). LME models for Lake Michigan had good prediction accuracy with respect to the single sample maximum standard (72–77 %), but limited sensitivity (23–62 %). Results suggest that our alternative approach is feasible and performs similarly to more resource-intensive approaches.     

Urban stream syndrome in a small,lightly developed watershed: a statistical analysis of water chemistry parameters,land use patterns,and natural sources

The relationships among land use patterns, geology, soil, and major solute concentrations in stream water for eight tributaries of the Kayaderosseras Creek watershed in Saratoga County, NY, were investigated using Pearson correlation coefficients and multivariate regression analysis. Sub-watersheds corresponding to each sampling site were delineated, and land use patterns were determined for each of the eight sub-watersheds using GIS. Four land use categories (urban development, agriculture, forests, and wetlands) constituted more than 99 % of the land in the sub-watersheds. Eleven water chemistry parameters were highly and positively correlated with each other and urban development. Multivariate regression models indicated urban development was the most powerful predictor for the same eleven parameters (conductivity, TN, TP, NO $_{3}^-$ , Cl^?, HCO $_{3}^-$ , SO $_{4}^{2-}$ , Na⁺, K⁺, Ca²⁺, and Mg²⁺). Adjusted R ² values, ranging from 19 to 91 %, indicated that these models explained an average of 64 % of the variance in these 11 parameters across the samples and 70 % when Mg²⁺ was omitted. The more common R ², ranging from 29 to 92 %, averaged 68 % for these 11 parameters and 72 % when Mg²⁺ was omitted. Water quality improved most with forest coverage in stream watersheds. The strong associations between water quality variables and urban development indicated an urban source for these 11 water quality parameters at all eight sampling sites was likely, suggesting that urban stream syndrome can be detected even on a relatively small scale in a lightly developed area. Possible urban sources of Ca²⁺ and HCO $_{3}^-$ are suggested.     

Spatio-temporal variations of carbon dioxide and its gross emission regulated by artificial operation in a typical hydropower reservoir in China

Supersaturation and excess emission of greenhouse gases in freshwater reservoirs have received a great deal of attention in recent years. Although impoundment of reservoirs has been shown to contribute to the net emission of greenhouse gases, reservoir age, geographical distribution, submerged soil type and artificial regulation also have a great impact on their emissions. To examine how large scale reservoir operation impact the water column CO₂ and its air–water interface flux, a field study was conducted in 2010 to evaluate potential ecological processes that regulate the partial pressure of CO₂ (pCO₂) in the water column in the Pengxi River backwater area (PBA), a typical tributary in the Three Gorges Reservoir, China. Measurements of total alkalinity (TA), pH and water temperature were applied to compute the pCO₂. And this approach was also validated by calculation of pCO₂ from the dissolved inorganic carbon data of samples. Partial least squares (PLS) regression was used to determine how the dynamics of the water pCO₂ were related to the available variables. The estimated pCO₂ in our sample ranged from 26 to 4,087 μatm in the surface water. During low water operation from July to early September, there was an obvious pCO₂ stratification, and pCO₂ in the surface was almost unsaturated. This phenomenon was also observed in the spring bloom during discharge period. Conversely, there was no significant pCO₂ stratification and the entire water column was supersaturated during high water operation from November to the following February. Significant correlation was observed between the magnitude of pCO₂, DO and chlorophyll a, suggesting that phytoplankton dynamics regulate pCO₂ in the PBA. The average areal rate of CO₂ emissions from the Pengxi River ranged from 18.06 to 48.09 mmol m^?2 day^?1, with an estimated gross CO₂ emission from the water surface of 14–37 t day^?1 in this area in 2010. Photosynthesis and respiration rates by phytoplankton might be the dominant processes that regulated pCO₂ in the water column. We conclude that pCO₂ values in the surface water of Pengxi River could be regarded as potential sources of CO₂ to the atmosphere were smaller or similar to those that have been reported for many other reservoirs to date.     

Support Vector Machine Modeling Using Particle Swarm Optimization Approach for the Retrieval of Atmospheric Ammonia Concentrations

This study was performed in order to improve the estimation accuracy of atmospheric ammonia (NH₃) concentration levels in the Greater Houston area during extended sampling periods. The approach is based on selecting the appropriate penalty coefficient C and kernel parameter σ ². These parameters directly influence the regression accuracy of the support vector machine (SVM) model. In this paper, two artificial intelligence techniques, particle swarm optimization (PSO) and a genetic algorithm (GA), were used to optimize the SVM model parameters. Data regarding meteorological variables (e.g., ambient temperature and wind direction) and the NH₃ concentration levels were employed to develop our two models. The simulation results indicate that both PSO-SVM and GA-SVM methods are effective tools to model the NH₃ concentration levels and can yield good prediction performance based on statistical evaluation criteria. PSO-SVM provides higher retrieval accuracy and faster running speed than GA-SVM. In addition, we used the PSO-SVM technique to estimate 17 drop-off NH₃ concentration values. We obtained forecasting results with good fitting characteristics to a measured curve. This proved that PSO-SVM is an effective method for estimating unavailable NH₃ concentration data at 3, 4, 5, and 6 parts per billion (ppb), respectively. A 4-ppb NH₃ concentration had the optimum prediction performance of the simulation results. These results showed that the selection of the set-point values is a significant factor in compensating for the atmospheric NH₃ dropout data with the PSO-SVM method. This modeling approach will be useful in the continuous assessment of NH₃ sensor discrete data sources.     

The relative influence of nutrients and habitat on stream metabolism in agricultural streams

Stream metabolism was measured in 33 streams across a gradient of nutrient concentrations in four agricultural areas of the USA to determine the relative influence of nutrient concentrations and habitat on primary production (GPP) and respiration (CR-24). In conjunction with the stream metabolism estimates, water quality and algal biomass samples were collected, as was an assessment of habitat in the sampling reach. When data for all study areas were combined, there were no statistically significant relations between gross primary production or community respiration and any of the independent variables. However, significant regression models were developed for three study areas for GPP (r ² = 0.79–0.91) and CR-24 (r ² = 0.76–0.77). Various forms of nutrients (total phosphorus and area-weighted total nitrogen loading) were significant for predicting GPP in two study areas, with habitat variables important in seven significant models. Important physical variables included light availability, precipitation, basin area, and in-stream habitat cover. Both benthic and seston chlorophyll were not found to be important explanatory variables in any of the models; however, benthic ash-free dry weight was important in two models for GPP.     

A geographical approach to tracking Escherichia coli and other water quality constituents in a Texas coastal plains watershed

Diffuse sources of surface water pathogens and nutrients can be difficult to isolate in larger river basins. This study used a geographical or nested approach to isolate diffuse sources of Escherichia coli and other water quality constituents in a 145.7-km² river basin in south central Texas, USA. Average numbers of E. coli ranged from 49 to 64,000 colony forming units (CFU) per 100 mL depending upon season and stream flow over the 1-year sampling period. Nitrate-N concentrations ranged from 48 to 14,041 μg?L^?1 and orthophosphate-P from 27 to 2,721 μg?L^?1. High concentrations of nitrate-N, dissolved organic nitrogen, and orthophosphate-P were observed downstream of waste water treatment plants but E. coli values were higher in a watershed draining an older part of the city. Total urban land use explained between 56 and 72 % of the variance in mean annual E. coli values (p?<?0.05) in nine hydrologically disconnected creeks. Of the types of urban land use, commercial land use explained most of the variance in E. coli values in the fall and winter. Surface water sodium, alkalinity, and potassium concentrations in surface water were best described by the proportion of commercial land use in the watershed. Based on our nested approach in examining surface water, city officials are able to direct funding to specific areas of the basin in order to mitigate high surface water E. coli numbers and nutrient concentrations.     

Assessment of temporal and spatial water quality in international Gomishan Lagoon,Iran, using multivariate analysis

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO₂), nitrate (NO₃), biological oxygen demand (BOD), phosphate (PO₄), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R², RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.     

Water quality time series for Big Melen stream (Turkey): its decomposition analysis and comparison to upstream

Big Melen stream is one of the major water resources providing 268 km³ year^???1 of drinking and municipal water for Istanbul. Monthly time series data between 1991 and 2004 for 25 chemical, biological, and physical water properties of Big Melen stream were separated into linear trend, seasonality, and error components using additive decomposition models. Water quality index (WQI) derived from 17 water quality variables were used to compare Aksu upstream and Big Melen downstream water quality. Twenty-six additive decomposition models of water quality time series data including WQI had R ² values ranging from 88% for log(water temperature) (P?≤?0.001) to 3% for log(total dissolved solids) (P?≤?0.026). Linear trend models revealed that total hardness, calcium concentration, and log(nitrite concentration) had the highest rate of increase over time. Tukey’s multiple comparison pointed to significant decreases in 17 water quality variables including WQI of Big Melen downstream relative to those of Aksu upstream (P?≤?0.001). Monitoring changes in water quality on the basis of watersheds through WQI and decomposition analysis of time series data paves the way for an adaptive management process of water resources that can be tailored in response to effectiveness and dynamics of management practices.     

Monitoring diel dissolved oxygen dynamics through integrating wavelet denoising and temporal neural networks

Diel dissolved oxygen (DO) time series measured continuously using proximal sensors in situ for a temperate lake were denoised using discrete wavelet transform (DWT) with the orthogonal wavelet families of coiflet, daubechies, and symmlet with order of 10. Diel DO time series denoised were modeled using nine temporal artificial neural networks (ANNs) as a function of water level, water temperature, electrical conductivity, pH, day of year, and hour. Our results showed that time-lag recurrent network (TLRN) using denoised data emulated diel DO dynamics better than the best-performing TLRN using the original data, time-delay neural network (TDNN), and recurrent network (RNN). Daubechies basis dealt with diel DO data slightly better than the other bases given its coefficient of determination (r ²?=?87.1 %), while symmlet performed slightly better than the other bases in terms of root mean square error (RMSE?=?1.2 ppm) and mean absolute error (MAE?=?0.9 ppm).     

Seasonal variations in species composition,abundance, biomass and production rate of tintinnids (Ciliata: Protozoa) along the Hooghly (Ganges) River Estuary,India: a multivariate approach

The study is the first documentation of seasonal variations in species composition, abundance and diversity of tintinnid (Ciliata: Protozoa), in relation to water quality parameters along the stretch of the Hooghly (Ganges) River Estuary (HRE), eastern coastal part of India. A total of 26 species (22 agglomerated and 4 non-agglomerated) belonging to 8 genera has been identified from 8 study sites where Tintinnopsis (17 species) represented the most dominant genera, contributing up to 65 % of total tintinnid community followed by Tintinnidium (2 species), Leprotintinnus (2 species) and Dadayiella, Favella, Metacylis, Eutintinnus and Helicostomella (each with solitary species). The maximum (1,666 ind.?l^?1) and minimum (62 ind.?l^?1) abundance of tintinnids was recorded during post-monsoon and monsoon, respectively. A distinct seasonal dynamics in terms of biomass (0.005–2.465 μg C l^?1) and daily production rate (0.04–3.13 μg C l^?1 day^?1) was also noticed, accounting highest value during pre-monsoon. Chlorophyll a and nitrate were found to be potential causative factors for the seasonal variations of tintinnids as revealed by a stepwise multiple regression model. The result of ANOVA showed a significant variation between species abundance and months (F?=?2.36, P?≤?0.05). k-dominance curves were plotted to determine the comparison of tintinnid dominance between the investigated stations. Based on a principal component analysis (PCA), three main groups were delineated with tintinnid ciliates and environmental parameters. The changes in lorica morphology in terms of temperature and salinity, recorded for three dominant species, provided information on the ecological characteristics of the species assemblage in this estuarine system.     

Advantages of Geographically Weighted Regression for Modeling Benthic Substrate in Two Greater Yellowstone Ecosystem Streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m² cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r ². For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r ² values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.     

Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks

China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40 years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32 years and a determination coefficient (R ²) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R ² values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360 %. For the persistent forest area since 1974, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 0.98 t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03 t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.     

Monitoring Water Quality and Quantity of National Watersheds in Turkey

National data from the hydrological network for 38 rivers out of 25 watersheds were used to detect spatial and temporal trends in water quality and quantity characteristics between 1995 and 2002. Assessment of water quality and quantity included flow rate, water temperature, pH, electrical conductivity, sodium adsorption rate, Na, K, Ca+Mg, CO₃, HCO₃, Cl, SO₄, and boron. Among the major ions assessed on a watershed basis, Turkish river waters are relatively high in Ca+Mg, Na and HCO₃, and low in K and CO₃. The watersheds in Turkey experienced a general trend of 16% decrease in flow rates between 1995 and 2002 at a mean annual rate of about 4 m³ s^?1, with a considerable spatial variation. Similarly, there appeared to be an increasing trend in river water temperature, at a mean annual rate of about 0.2°C. A substantial proportion of watersheds experienced an increase in pH, in particular, after 1997, with a maximum increase from 8.1 to 8.4 observed in Euphrates (P?R ² values in accounting for variations of pH and water temperature only. The findings of the study can provide a useful assessment of controls over water quality and quantity and assist in devising integrated and sustainable management practices for watersheds at the regional scale in Turkey.     

Source discrimination of heavy metals in sediment and water of To Lich River in Hanoi City using multivariate statistical approaches

The concentrations of Mn, Fe, Ni, Cr, Cu, Pb, Zn, As, and Cd were determined to evaluate the level of contamination of To Lich River in Hanoi City. All metal concentrations in 0–10-cm water samples, except Mn, were lower than the maximum permitted concentration for irrigation water standard. Meanwhile, concentrations of As, Cd, and Zn in 0–30-cm sediments were likely to have adverse effects on agriculture and aquatic life. Sediment pollution assessment was undertaken using enrichment factor and geoaccumulation index (I _geo). The I _geo results indicated that the sediment was not polluted with Cr, Mn, Fe, and Ni, and the pollution level increased in the order of Cu < Pb < Zn < As < Cd. Meanwhile, significant enrichment was shown for Cd, As, Zn, and Pb. Cluster and principal component analyses suggest that As and Mn in sediment were derived from both lithogenic and anthropogenic sources, while Cu, Pb, Zn, Cr, Cd, and Ni originated from anthropogenic sources such as vehicular fumes for Pb and metallic discharge from industrial sources and fertilizer application for other metals.     

Assessment of cylindrospermopsin toxin in an arid Saudi lake containing dense cyanobacterial bloom

This study reports the presence of the cyanobacterial toxin cylindrospermopisn (CYN) and its producer Cylindrospermopsis raciborskii for the first time in Saudi freshwater sources. C. raciborskii was found in Gazan Dam Lake water with two morphotypes (coiled and straight). The appearance and cell density of this species was significantly positively related to high temperature and high ammonium concentrations, and negatively with nitrate and phosphate concentrations in the lake. Intracellular concentrations of CYN (4–173 μg L^?1) were associated with C. raciborskii rather than other cyanobacteria with a maximal value obtained in June 2011, coinciding with the highest bloom of this species (19?×?10⁷ trichome L^?1). CYN cell quotas (0.6–14.6 pg cell^?1) varied significantly along the study period and correlated with most environmental factors. The results of ELISA and liquid chromatography-mass spectrometry proved that the CYN production by strains of this species was isolated from this lake during the present study, with an amount reaching 568 μg g^?1. Extracellular CYN was also detected in cell-free lake water at concentrations 0.03–23.3 μg L^?1, exceeding the drinking water guideline value of 1 μg L^?1 during the Apr–Jul period. As this lake is an important source for drinking and irrigation waters, CYN monitoring should be included in the environmental and health risk assessment plans of these water bodies.     

遂宁城乡集中式饮用水水源地钡分布特征及健康风险评价

为考察遂宁市辖区内集中式饮用水水源地污染物钡的分布特征和健康风险水平,通过电感耦合等离子体原子发射光谱法对研究区域内市级、县级和乡镇级所有在用的56个集中式饮用水水源地钡的浓度进行分析检测,借助空间分析与统计分析的结果,探讨了其空间分布和浓度差异,并利用环境健康风险评价模型,对不同类型水源地钡的健康风险进行了评价。结果表明,38个地表水水源地钡的浓度范围为0.065~0.180 mg/L,均值为0.110 mg/L;18个地下水水源地钡的浓度范围为0.027~0.370 mg/L,均值为0.130 mg/L。地表水与地下水水源地间钡的浓度差异具有统计学意义(P0.05),钡的空间分布也存在不同程度的差异性。各水源地中的钡经饮用和皮肤暴露两种途径对成人和儿童所引起的非致癌风险值为1.34×10~(-8)~1.62×10~(-8),远低于推荐的最大可接受风险水平(1.0×10~(-6)),各水源地因污染物钡导致的非致癌风险极低。     

Occurrence of haloacetic acids (HAAs) and trihalomethanes (THMs) in drinking water of Taiwan

In this study, water samples were collected from 86 water treatment plants for analysis of haloacetic acids (HAAs) and trihalomethanes (THMs) from February to March, 2007 and from July to August, 2007. Both seasonal and geographical variations of disinfection by-products (DBPs) in drinking water of Taiwan were presented. The results showed that the five HAA concentrations (HAA5) were 1.0–38.9 μg/L in the winter and 0.2–46.7 μg/L in the summer; and the total THMs were ND-99.4 μg/L in the winter and ND-133.2 μg/L in the summer. For samples taken from the main Taiwan island, dichloroacetic acid (29.4–31.7%) and trichloroacetic acid (25.3–27.6%) were the two major HAA species, and trichloromethane was the major THM species (49.9–62.2%) in finished water. For water treatment plants located on the offshore islands outside of Taiwan, high bromide concentration was found in raw water, and higher percentage of brominated THMs and HAAs were formed in the overall formation. A statistically significant (P?<?0.005) logarithmic linear regression model was found to be useful to describe the correlations between TTHM and HAA5 or nine HAAs (HAA5?=?1.219 ×TTHM ^0.754, R ²?=?0.658; HAA9?=?1.824 ×TTHM ^0.735, R ²?=?0.678). No apparent difference was observed for DBPs concentrations between finished water and distribution samples in this study.