Characterization of heavy-metal-contaminated sediment by using unsupervised multivariate techniques and health risk assessment.

This study characterized the sediment quality of the severely contaminated Erjen River in Taiwan by using multivariate analysis methods-including factor analysis (FA), self-organizing maps (SOMs), and positive matrix factorization (PMF)-and health risk assessment. The SOMs classified the dataset with similar heavy-metal-contaminated sediment into five groups. FA extracted three major factors-traditional electroplating and metal-surface processing factor, nontraditional heavy-metal-industry factor, and natural geological factor-which accounted for 80.8% of the variance. The SOMs and FA revealed the heavy-metal-contaminated-sediment hotspots in the middle and upper reaches of the major tributary in the dry season. The hazardous index value for health risk via ingestion was 0.302. PMF further qualified the source apportionment, indicating that traditional electroplating and metal-surface-processing industries comprised 47% of the health risk posed by heavy-metal-contaminated sediment. Contaminants discharged from traditional electroplating and metal-surface-processing industries in the middle and upper reaches of the major tributary must be eliminated first to improve the sediment quality in Erjen River. The proposed assessment framework for heavy-metal-contaminated sediment can be applied to contaminated-sediment river sites in other regions.

Differentiating the Spatiotemporal Distribution of Natural and Anthropogenic Processes on River Water-Quality Variation Using a Self-Organizing Map With Factor Analysis.

To elucidate the historical improvement and advanced measure of river water quality in the Taipei metropolitan area, this study applied the self-organizing map (SOM) technique with factor analysis (FA) to differentiate the spatiotemporal distribution of natural and anthropogenic processes on river water-quality variation spanning two decades. The SOM clustered river water quality into five groups: very low pollution, low pollution, moderate pollution, high pollution, and very high pollution. FA was then used to extract four latent factors that dominated water quality from 1991 to 2011 including three anthropogenic process factors (organic, industrial, and copper pollution) and one natural process factor [suspended solids (SS) pollution]. The SOM revealed that the water quality improved substantially over time. However, the downstream river water quality was still classified as high pollution because of an increase in anthropogenic activity. FA showed the spatiotemporal pattern of each factor score decreasing over time, but the organic pollution factor downstream of the Tamsui River, as well as the SS factor scores in the upstream major tributary (the Dahan Stream), remained within the high pollution level. Therefore, we suggest that public sewage-treatment plants should be upgraded from their current secondary biological processing to advanced treatment processing. The conservation of water and soil must also be reinforced to decrease the SS loading of the Dahan Stream from natural erosion processes in the future.

Spatial pattern assessment of river water quality: implications of reducing the number of monitoring stations and chemical parameters.

The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.

Probability-based nitrate contamination map of groundwater in Kinmen.

Groundwater supplies over 50% of drinking water in Kinmen. Approximately 16.8% of groundwater samples in Kinmen exceed the drinking water quality standard (DWQS) of NO3 (-)-N (10 mg/L). The residents drinking high nitrate-polluted groundwater pose a potential risk to health. To formulate effective water quality management plan and assure a safe drinking water in Kinmen, the detailed spatial distribution of nitrate-N in groundwater is a prerequisite. The aim of this study is to develop an efficient scheme for evaluating spatial distribution of nitrate-N in residential well water using logistic regression (LR) model. A probability-based nitrate-N contamination map in Kinmen is constructed. The LR model predicted the binary occurrence probability of groundwater nitrate-N concentrations exceeding DWQS by simple measurement variables as independent variables, including sampling season, soil type, water table depth, pH, EC, DO, and Eh. The analyzed results reveal that three statistically significant explanatory variables, soil type, pH, and EC, are selected for the forward stepwise LR analysis. The total ratio of correct classification reaches 92.7%. The highest probability of nitrate-N contamination map presents in the central zone, indicating that groundwater in the central zone should not be used for drinking purposes. Furthermore, a handy EC-pH-probability curve of nitrate-N exceeding the threshold of DWQS was developed. This curve can be used for preliminary screening of nitrate-N contamination in Kinmen groundwater. This study recommended that the local agency should implement the best management practice strategies to control nonpoint nitrogen sources and carry out a systematic monitoring of groundwater quality in residential wells of the high nitrate-N contamination zones.

An efficient spatiotemporal data calibration approach for the low-cost PM2.5 sensing network: A case study in Taiwan.

The rapid growth of Internet of Things has provided a new aspect to air quality monitoring system. In Taiwan, over 5000 PM2.5 sensors have been installed in the last two years. The greatest asset of low-cost sensors is possibly mapping spatiotemporal air pollution with finer resolution. But the data quality of low-cost sensors is the most common question that how to take proper interpretation of the measurements. This study proposes an efficient calibration approach based on generalized additive model which is further applied to a particular low-cost PM2.5 sensor in Taiwan. The study carried out a field calibration that collecting both measurements of low-cost sensors and the regulatory stations, and investigated the space/time bias between the low-cost sensors and regulatory stations. Results show that the proposed approach can explain the variability of the biases from the low-cost sensors with R-square of 0.76. In addition, the present calibration model can quantify the uncertainty of the low-cost sensors observations and the average standard deviation is about 13.85% with respect to its adjusted levels. This operational spatiotemporal data calibration approach provides an useful information for local communities and governmental agency to face the new era of IoT sensor for air quality monitoring.

Characterization and risk assessment of PAH-contaminated river sediment by using advanced multivariate methods.

This study applied advanced multivariate methods and risk assessment to evaluate the characteristics of polycyclic aromatic hydrocarbons (PAHs) in the sediment of the severely polluted Erjen River in Taiwan. High-molecular-weight PAHs (HPAHs) dominated in the rainy season. The ecological risk of PAHs in the sediment was low, whereas the total health risk through ingestion and dermal contact was considerably high. The SOM (self-organizing map) analysis clustered the datasets of PAH-contaminated sediment into five groups with similar concentration levels. Factor analysis identified major factors, namely coal combustion, traffic, petrogenic, and petrochemical industry factors, accounting for 88.67% of the variance in the original datasets. The major tributary and the downstream of the river were identified as PAH-contamination hotspots. The PMF (positive matrix factorization) was combined with toxicity assessment to estimate the possible apportionment of sources and the associated toxicity. Spills of petroleum-related products, vehicle exhaust, coal combustion, and exhaust from a petrochemical industry complex constituted respectively 12%, 6%, 74%, and 86% of PAHs in the sediment, but contributed respectively 7%, 15%, 22%, and 56% of toxicity posed by PAHs in the sediment. To improve the sediment quality, best management practices should be adopted to eliminate nonpoint sources of PAHs flushed by storm water into the major tributary and the downstream of the Erjen River. The proposed methodologies and results provide useful information on remediating river PAH-contaminated sediment and may be applicable to other basins with similar properties that are experiencing resembled river environmental issues.