Tryptophan-like Fluorescence Research Articles

Tryptophan-Like Fluorescence for Monitoring Fecal Contamination in a Marsh-Dominated Estuary

Tryptophan-Like Fluorescence for Monitoring Fecal Contamination in a Marsh-Dominated Estuary

Fluorescence sensor enabled control of contaminants of emerging concern in reclaimed wastewater using ozone-based treatment processes

Contaminants of emerging concern (CEC) pose significant challenges to environmental and human health. The development of the wastewater reuse sector, coupled with progressively stringent regulations, needs innovative systems that integrate advanced treatment processes with in-situ and real-time monitoring of CEC. This study investigates the use of a tryptophan-like fluorescence sensor for real-time and online monitoring of CEC within a pilot plant employing O3-based advanced oxidation processes (AOPs). Two tertiary wastewater effluents (WW-1 and WW-2) were tested, placing the pilot system downstream of two different wastewater treatment plants (WWTPs). Priority substances and micropollutants detected in the investigated water matrixes such as pharmaceuticals, per- and polyfluoroalkyl substances (PFAS) were selected as targeted compounds in this study. Fluorescence degradation was detected in real-time by the sensor, showing a high capability to detect fast changes in water quality induced by oxidation. Furthermore, the real-time fluorescence showed better sensitivity than lab-scale fluorescence in detecting the fast action of hydroxyl radicals (·OH) during the O3/H2O2 process, highlighting the importance of online monitoring. Selected CEC were degraded by AOPs with different percentages of removal efficiency (RE) (0%<RE<100% in WW-1 and 15%<RE<90% in WW-2) depending on oxidant doses and the reactivity of compounds with O3 and ·OH. Fluorescence data by online sensor enabled accurate prediction of the removal of a wide spectrum of CEC during O3 and O3/H2O2 processes (R2≥0.93). Furthermore, real-time fluorescence data were successfully used to predict observed pseudo-first-order rate constants of CEC with O3 or O3/H2O2. Obtained results suggest that real-time fluorescence monitoring is an excellent tool to control CEC removal during O3-based AOPs and monitor the transferred ozone in wastewater (R2≥0.94), contributing to the optimization of reagent dose, energy and costs.

Application of tryptophan-like fluorescence index to quantify the trace organic compounds removal in wastewater ozonation

The effectiveness of ozonation, one of the techniques known for destroying organic contaminants from wastewater, depends on the composition of the wastewater matrix. The required ozone (O3) dose is determined based on the target compounds during ozonation. Hydroxyl radicals are quantified using a probe compound. The para-chlorobenzoic acid (pCBA) is typically used as a probe compound to measure hydroxyl radicals. However, real-time measurement is impossible, as the analysis process consumes time and resources. This study aimed to evaluate the spectroscopic characteristics of various organic substances in wastewater ozonation through fluorescence excitation-emission matrix and parallel factor analysis. The study also demonstrated that real-time analyzable tryptophan-like fluorescence (TLF) can be used as a hydroxyl radical index. Importantly, the correlation between para-chlorobenzoic acid and TLF was derived, and the results showed a high correlation (R2 = 0.91), confirming the reliability of our findings. Seven trace organic compounds, classified based on their reactivity with O3 and hydroxyl radicals, were selected as target compounds and treated with O3. The TLF index was used as a model factor for the removal rate of the target compounds. The experimental and model values matched when the O3 dose was below 1.0 g O3/g DOC (RMSE: 0.0445–0.0895).

Study of humic-like substances of dissolved organic matter using size exclusion chromatography and chemometrics

The study of dissolved organic matter (DOM) presents a significant challenge for environmental analyses and the monitoring of wastewater treatment plants (WWTPs). This is particularly true for the tracking of recalcitrant to biodegradation dissolved organic matter (rDOM) compounds, which is generated during the thermal pretreatment of sludge. This study aims to develop analytical and chemometric methods to differentiate melanoidins from humic acids (HAs), two components of rDOM that require monitoring at various stages of wastewater treatment processes due to their distinct biological effects. The developed method implements the separation of macromolecules through ultra-high-performance liquid chromatography size-exclusion chromatography (U-HPLC SEC) followed by online UV and fluorescence detection. UV detection was performed at 210, 254, and 280 nm, and fluorescence detection at six excitation/emission pairs: 230/355 nm, 270/355 nm, 240/440 nm, 270/500 nm, 330/425 nm, and 390/500 nm. Chromatograms obtained for each sample from these nine detection modes were integrated and separated into four molecular fractions: >40 kDa, 20–40 kDa, 10–20 kDa, and <10 kDa. To enhance analytical resolution and normalize the data, ratios were calculated from the areas of chromatographic peaks obtained for each detection mode. The results demonstrate the utility of these ratios in discriminating samples composed of HAs, melanoidins, and their mixtures, through principal component analysis (PCA). Low molecular weight fractions were found to be specific to melanoidins, while high molecular weight fractions were characteristic of HAs. For the detection modes specific to melanoidins, UV absorbance at 210, 254, and 280 nm were predominantly present in the numerators, with tryptophan-like fluorescence emissions in the denominators. Conversely, fluorescence emissions largely represented both numerators and denominators for HAs. This online method also enables the discrimination of pseudo-melanoidins, compounds revealing a nitrogen deficiency in their chemical structures.

Electronic tongue and nose sensor coupled with fluorescence spectroscopy to analyze aesthetic water quality parameters in drinking water distribution system

In this study, a new method is proposed for the evaluation of taste and odor of drinking water quality in water distribution systems by means of objective and quantitative indices derived from electronic sensors and fluorescence spectroscopy analysis. This method can be analyzed easily, quickly, and instead of subjective sensory analysis. Taste index (T.I.) and odor index (O.I.) were derived using an electronic tongue and nose. This method is simpler, faster, and less expensive than human panel methods. The tryptophan-like fluorescence index (TLF-I) and pipe deterioration index (PDI) of the water distribution system, derived from fluorescence spectroscopy, were also calculated to determine if the T.I. and O.I. were high or what was causing the difference. The target of comparison was the final water (point 1) that supplies water, and T.I., O.I., TLF-I, and PDI were calculated for the seven sampled points. Points 5 (3.91), 3 (3.82), and 6 (3.66) observed high T.I. O.I. was highest in the order of points 6 (3.65), 3 (3.16), and 5 (2.51). High TLF-I was observed at points 5 (160), 3 (111), and 6 (89), indexing a solid correlation. Other influencing factors at points 5, 3, and 6 included the PDI and water supply of the reservoir type. Point 5 had the highest PDI of 0.47. These results suggest that TLF-I and PDI can serve as indicators for interpreting the causes of changes in T.I. and O.I., which are indicators of aesthetic water quality. This method can assist in managing taste and odor quality data in drinking water distribution networks.

Characterizing dissolved organic matter and bacterial community interactions in a river network under anthropogenic landcover

Anthropogenic landcover could rise nutrient concentrations and impact the characteristics and bioavailability of dissolved organic matter (DOM) in a river network. Exploring the interactions between DOM and microbials might be conducive to revealing biogeochemistry behaviors of organic matter. In this study, synchronous fluorescence spectra (SFS) with Gaussian band fitting and two-dimensional correlation spectroscopy (2D-COS) were employed to identify DOM fractions and reveal their interactions with bacterial communities. DOM was extracted from a river network under eco-agricultural rural (RUR), eco-residential urban (URB), eco-economical town (TOW), and eco-industrial park (IND) regions in Jiashan Plain of eastern China. The overlapping peaks observed in the SFS were successfully separated into four fractions using Gaussian band fitting, i.e., tyrosine-like fluorescence (TYLF), tryptophan-like fluorescence (TRLF), microbial humic-like fluorescence (MHLF), and fulvic-like fluorescence (FLF) materials. Across all four regions, TRLF (44.79% ± 7.74%) and TYLF (48.09% ± 8.85%) were the dominant components. Based on 2D-COS, variations of TYLF and TRLF were extremely larger than those of FLF in RUR-TOW. However, in URB-IND, the former exhibited lower variations compared to the latter. These suggested that FLF be likely derived continuously from lignin and other residue of terrestrial plant origin along the river network, and TYLF and TRLF be originated discontinuously from domestic wastewater in RUR-TOW. By high-throughput sequenced OTUs, the number of organisms in RUR-TOW could be higher than those in URB-IND, while genes associated with carbohydrate metabolism were lower in former than those in the latter. According to co-occurrence networks, microbes could promote the production of TYLF and TRLF in RUR-TOW. In contrast, microbial communities in URB-IND might contribute to decompose FLF. The obtained results could not only reveal interactions between DOM fractions and bacterial communities in the river network, but this methodology may be applied to other water bodies from different landscapes.

Utilization of Tryptophan-like Fluorescence as a Proxy for E. coli Contamination in a Mixed-Land-Use Karst Basin

Karst aquifers are susceptible to contamination by pathogenic microorganisms, such as those found in human and animal waste, because the surface and subsurface drainage are well integrated through dissolution features. Fecal contamination of water is commonly assessed by the concentration of thermotolerant coliform bacteria, especially E. coli. This method is time-consuming, taking ≥18 h between the start of incubation and subsequent enumeration, as well as the time required to collect and transport samples. We examined the utility of continuous monitoring of tryptophan-like fluorescence (TLF) as a real-time proxy for E. coli in a mixed-land-use karst basin in the Inner Bluegrass region of central Kentucky (USA). Two logging fluorometers were sequentially deployed at the outlet spring. During storm flow, TLF typically peaked after discharge, which suggests that TLF transport in the phreatic conduit is likely related to sediment transport. The ability of TLF and other parameters (48 h antecedent precipitation, turbidity, and air temperature) to predict E. coli concentrations was assessed using the Akaike information criterion (AIC) applied to linear regression models. Because both the models and baseline concentrations of TLF differed between fluorometers, TLF and instrument interaction were accounted for in the AIC. TLF was positively correlated with E. coli and, in conjunction with antecedent precipitation, was the best predictor of E. coli. However, a model that included air temperature and antecedent precipitation but not TLF predicted E. coli concentrations similarly well. Given the expense of the fluorometers and the performance of the alternate model, TLF may not be a cost-effective proxy for E. coli in this karst basin.

Multiple fluorescence approaches to identify rapid changes in microbial indicators at karst springs

Karst springs are globally important for drinking water supply but are often also exceptionally vulnerable to contamination. Such springs usually exhibit strong variation in microbial water quality in sharp response to rainfall events, thus, posing a health hazard to consumers of water supplied from these sources. The rapid detection of such changes is extremely important as well as being able to establish a link to the sources of such pollution, so that appropriate measures can be taken both in terms of immediate protection of human health and the management of karst aquifers. In this study, a fluorescence-based multi-parameter approach was trialed in order to evaluate which methods can be used to monitor rainfall-induced rapid changes in microbial water quality at karst springs, as well as determine whether such changes can be linked to sources of human effluent contamination. The results from three monitoring periods at two karst springs revealed marked responses to rainfall events for all of the microbial parameters measured. Total cell count (TCC) measurements using flow cytometry (FCM) showed very strong positive correlations with the more conventionally monitored faecal indicator bacteria (FIB) and total coliforms (TC), indicating that such a fluorescence-based and cultivation-independent technique can be very useful to indicate rapid changes in microbial water quality at karst springs. Furthermore, very strong positive correlations were also found between tryptophan-like fluorescence (TLF) measurements and concentrations of all monitored microbial parameters, again demonstrating that such a fluorescence-based approach can also be useful for detecting rapid changes in concentrations of traditional faecal indicators. Interestingly, it was found that fluorescent whitening compounds (FWCs) signals do not necessarily follow temporal variations of microbial indicators. However, the frequency of detection of positive FWCs signals may still reveal useful information about the overall magnitude of human wastewater effluent impacts on karst aquifer systems.

An evaluation of groundwater vulnerability assessment methods in a rapidly urbanizing city: evidence from Dakar, Senegal

In rapidly growing cities in the tropics, unregulated urban development presents a major risk to groundwater quality. Here, we assess the vulnerability of an unconfined aquifer of Quaternary sands in the Thiaroye area of Dakar (Senegal) to contamination using four GIS-based indices (DRASTIC, DRASTIC_N, SINTACS, SI). Our correlation of assessed vulnerability to observed impact is semi-quantitative, relating observed groundwater quality, based on nitrate concentrations and tryptophan-like fluorescence to vulnerability degrees (i.e. coincidence rates). We show that considerably more of the Thiaroye area has a “very high vulnerability” according to SI (36%) relative to DRASTIC (5%) and SINTACS (9%); “high vulnerability” is estimated using DRASTIC_N (100%), DRASTIC (66%) and SINTACS (69%). Single-parameter sensitivity tests show that groundwater depth, soil, topography, land use and redox parameters strongly influence assessments of groundwater vulnerability. Correlation with observed nitrate concentrations reveals aquifer vulnerability is better represented by SI (coincidence rates of 56%) relative to DRASTIC_N (43%), SINTACS (38%) and DRASTIC (34%). The underestimation of groundwater vulnerability in Dakar using DRASTIC, DRASTIC_N and SINTACS is attributed to their reliance on an assumed capacity of the unsaturated zone to attenuate surface or near-surface contaminant loading, which in the low-income (Thiaroye) area of Dakar is thin and affords limited protection. The inclusion of a land-use parameter in SI improves the characterization of groundwater vulnerability in this low-income, rapidly urbanizing area of Dakar.

A case study: The deployment of a novel in situ fluorimeter for monitoring biological contamination within the urban surface waters of Kolkata, India

The quality and health of many of our vital freshwater systems are poor. To tackle this with ever increasing pressures from anthropogenic and climatic changes, we must improve water quality monitoring and devise and implement more appropriate water quality parameters. Recent research has highlighted the potential for Peak T fluorescence (tryptophan-like fluorescence, TLF) to monitor microbial activity in aquatic systems. The VLux TPro (Chelsea Technologies Ltd., UK), an in situ real-time fluorimeter, was deployed in different urban freshwater bodies within Kolkata (West Bengal, India) during March 2019. This study is the first to apply this technology in surface waters within a densely populated urban area. Spot-sampling was also undertaken at 13 sampling locations enabling physicochemical analysis, bacterial enumeration and determination of nutrient (nitrate and phosphate) concentrations. This case study has demonstrated the ability of an in situ fluorimeter, VLux TPro, to successfully identify both biological contamination events and potential elevated microbial activity, related to nutrient loading, in complex surface freshwaters, without the need for expensive and time-consuming laboratory analysis.

A continuous, in-situ, near-time fluorescence sensor coupled with a machine learning model for detection of fecal contamination risk in drinking water: Design, characterization and field validation

We designed and validated a sensitive, continuous, in-situ, remotely reporting tryptophan-like fluorescence sensor and coupled it with a machine learning model to predict high-risk fecal contamination in water (>10 colony forming units (CFU)/100mL E. coli). We characterized the sensor’s response to multiple fluorescence interferents with benchtop analysis. The sensor’s minimum detection limit (MDL) of tryptophan dissolved in deionized water was 0.05 ppb (p <0.01) and its MDL of the correlation to E. coli present in wastewater effluent was 10 CFU/100 mL (p <0.01). Fluorescence response declined exponentially with increased water temperature and a correction factor was calculated. Inner filter effects, which cause signal attenuation at high concentrations, were shown to have negligible impact in an operational context. Biofouling was demonstrated to increase the fluorescence signal by approximately 82% in a certain context, while mineral scaling reduced the sensitivity of the sensor by approximately 5% after 24 hours with a scaling solution containing 8 times the mineral concentration of the Colorado River. A machine learning model was developed, with TLF measurements as the primary feature, to output fecal contamination risk levels established by the World Health Organization. A training and validation data set for the model was built by installing four sensors on Boulder Creek, Colorado for 88 days and enumerating 298 grab samples for E. coli with membrane filtration. The machine learning model incorporated a proxy feature for fouling (time since last cleaning) which improved model performance. A binary classification model was able to predict high risk fecal contamination with 83% accuracy (95% CI: 78% - 87%), sensitivity of 80%, and specificity of 86%. A model distinguishing between all World Health Organization established risk categories performed with an overall accuracy of 64%. Integrating TLF measurements into an ML model allows for anomaly detection and noise reduction, permitting contamination prediction despite biofilm or mineral scaling formation on the sensor’s lenses. Real-time detection of high risk fecal contamination could contribute to a major step forward in terms of microbial water quality monitoring for human health.

Novel insights into the interaction reactive components and synergistic fouling mechanisms of ultrafiltration by natural organic matter fractions and kaolin

The mechanisms governing interactions among various natural organic matter (NOM) fractions and the subsequently impact on ultrafiltration process have not been systematically studied. In this work, bovine serum albumin (BSA), humic acid (HA), sodium alginate (SA) were applied as model NOM to explore the influence of the interactions among NOM on ultrafiltration process. Results indicated that tryptophan-like fluorescence fraction was the dominant reaction fraction of HA to react with SA and BSA. Different interactions among model NOM not only changed the interception order of fluorescence fractions by ultrafiltration from fulvic acid-like, humic-like and tryptophan-like in BSA/HA mixture to tryptophan-like, humic-like and fulvic acid-like in BSA/HA/SA/kaolin mixture, but also remarkably influence the membrane fouling behavior. In BSA/HA mixture, new-generated aggregates with molecular weight (MW) of 10 kDa could not pass though ultrafiltration membrane and mainly contributed to chemical reversible fouling. In BSA/HA/SA mixture, SA simultaneously reacted with BSA and HA to generate aggregates with larger MW which could be washed down by physical cleaning. In BSA/HA/SA/kaolin mixture, the aggregates with MW of 10 kDa and chemical reversible fouling were disappeared due to the adsorption role of kaolin. These findings could further improve our understanding regarding membrane fouling mechanisms of raw water with different components.

Development of a fluorescence EEM-PARAFAC model for potable water reuse monitoring: Implications for inter-component protein–fulvic–humic interactions

Measuring the surrogate parameters total organic carbon and dissolved organic carbon (TOC/DOC) is not adequate, alone, to reveal nuances in organic character for optimizing treatment in potable water reuse. Alternatively, analyzing each organic compound contributing to the surrogate measurement is not possible. As an additional analytical tool applied between these extremes, the use of excitation-emission matrix fluorescence spectroscopy with PARAllel FACtor (EEM-PARAFAC) analysis was investigated in this research to track categories (components) or families of organic compounds during treatment in recycled water schemes. Although not all organic molecules fluoresce, many do, and fluorescence helps track their fate through water treatment processes. The sites investigated in this research were Lake Lanier, in Gwinnett County, Georgia, USA; the F. Wayne Hill Water Resources Center (FWH WRC) advanced wastewater treatment facility; and two pilot facilities operated in parallel representing the current indirect potable reuse (IPR) scheme as well as a pilot that evaluated direct potable reuse (DPR). A four-component nonnegativity PARAFAC model—elucidating protein-like (including tyrosine- and tryptophan-like fluorescence in a single component), soluble microbial product (SMP)-like, fulvic-like, and humic-like components—was fitted to the data. Each of the four components was spectrally and mathematically separated, implying that the fluorescing SMP-like component was not comprised of protein-, fulvic-, or humic-like components. PARAFAC excitation loadings with dual (double) pairs of fluorescing regions centered at the same emission wavelengths but different excitation wavelengths oriented parallel to the excitation axis and perpendicular to the emission axis were attributed to individual PARAFAC components. Significantly, the observation of PARAFAC emission loadings with multiple peaks—where the protein-like component exhibited fluorescence in both protein and fulvic/humic regions—is proposed to signify an intermolecular energy transfer (< 10 nm). Correct identification of EEM-PARAFAC components is fundamental to understanding water treatment.

Optical Sensor System for Early Warning of Inflow Organic Matter Breach in Large-Scale Irrigation Systems and Water Treatment Systems

A growing world population together with a decrease in water resources have resulted in a deficit in water sources for agriculture. Nations with limited water resources are already using recycled treated water as a vital water source for irrigation. In 2019, treated wastewater made up 45% of the overall agricultural water consumption in Israel. Since recycling systems work continuously and are directly connected to irrigation reservoirs, there is a constant risk of irrigation system malfunctions in the treatment process. In cases of fecal origin, this may result in pathogens entering the irrigation water, the soil, and eventually the food chain. Early warning systems for biological water contamination are important in large-scale processing, yet less studied. This work proposes such a system, with a functional prototype for irrigation-water monitoring by measuring fluorescence spectra in the range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300-520 \textit {nm}$ </tex-math></inline-formula> at excitation wavelengths of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$280 \textit {nm}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$340 \textit {nm}$ </tex-math></inline-formula> , to capture both tryptophan-like fluorescence and humic-like fluorescence. The prototype was successfully tested under simulated field conditions with treated irrigation water as the base substance, and milk injections to simulate dissolved organic matter breaches. Near real-time operation enabled recording biological and chemical dynamics within the substance for the sake of monitoring drift in process control.

Identification of groundwater pollution from livestock farming using fluorescence spectroscopy coupled with multivariate statistical methods

Extensive livestock farming has highly threatened groundwater quality, thereby necessitating a rapid and effective method to identify groundwater quality in such areas. Fluorescence spectroscopy has been recognized as an interpretable method for tracking anthropogenic influences on water quality, but its applicability in identifying the groundwater pollution from livestock farming remains unknown. In this study, the fluorescence characteristics of dissolved organic matter (DOM) in groundwater from a typical livestock farming area were investigated by using fluorescence excitation emission matrix (EEM)-parallel factor analysis (PARAFAC) coupled with multivariate statistical methods. The results showed that livestock farming significantly altered the content and composition of DOM in groundwater, and these effects were mainly observed in shallow groundwater in the study area. Hierarchical cluster analysis based on fluorescence parameters divided the groundwater samples into three clusters with significantly different pollution degrees: Cluster A, unpolluted; Cluster B, highly polluted; Cluster C, moderately polluted. In particular, the intensity of tryptophan-like fluorescence was high in the polluted groundwater but was almost undetectable in the unpolluted groundwater, suggesting that it is a potential indicator of groundwater quality. Principal component analysis based on the fluorescence parameters explained 91.5% of the variance with the first two principal components, and revealed that the degree of pollution dominated the fluorescence characteristics of groundwater in the study area. In addition, NO3– was abundant in Clusters B and C, while it was low in Cluster A, validating the analysis results of fluorescence spectroscopy. These findings indicated that DOM fluorescence was sensitive to livestock farming pollution and could be applied to identify, monitor, and assess groundwater pollution from livestock farming.

Assessing inputs of aquaculture-derived nutrients to streams using dissolved organic matter fluorescence

Salmon aquaculture is an important economic activity globally where local freshwater supplies permit land-based salmon aquaculture facilities to cultivate early life stage salmon. Nitrogen, phosphorus and organic matter in aquaculture effluents contribute to the eutrophication of adjacent and downstream rivers and lakes. This study quantifies the enrichment of nutrients in land-based salmon aquaculture facility effluents compared to receiving waters. We measured nutrient concentrations and dissolved organic matter (DOM) quantity and quality via fluorescence spectroscopy in streams and effluent waters associated with 27 facilities in Chile. We found that facilities added on average 0.9 (s.d. = 2.0) mg-C L-1, 542 (s.d. = 637) μg-total N L-1, and 104 (s.d. = 104) μg-total P L-1 to effluents compared to stream waters. DOM in stream water was enriched in humic-like fluorescence, while aquaculture effluents were enriched in protein-like DOM fluorophores. Principal component and correlation analysis revealed that tryptophan-like fluorescence was a good predictor of total N and P in effluents, but the strength of significant linear relationships varied among individual facilities (r2: 0.2 to 0.9). Agreement between laboratory fluorescence and a portable fluorometer indicates the utility of in-situ sensors for monitoring of both tryptophan-like fluorescence and covarying nutrients in effluents. Thus, continuous in-situ sensors are likely to improve industry management and allow more robust estimates of aquaculture-derived nutrients delivered to receiving waters.

In-situ fluorescence spectroscopy is a more rapid and resilient indicator of faecal contamination risk in drinking water than faecal indicator organisms

Faecal indicator organisms (FIOs) are limited in their ability to protect public health from the microbial contamination of drinking water because of their transience and time required to deliver a result. We evaluated alternative rapid, and potentially more resilient, approaches against a benchmark FIO of thermotolerant coliforms (TTCs) to characterise faecal contamination over 14 months at 40 groundwater sources in a Ugandan town. Rapid approaches included: in-situ tryptophan-like fluorescence (TLF), humic-like fluorescence (HLF), turbidity; sanitary inspections; and total bacterial cells by flow cytometry. TTCs varied widely in six sampling visits: a third of sources tested both positive and negative, 50% of sources had a range of at least 720 cfu/100 mL, and a two-day heavy rainfall event increased median TTCs five-fold. Using source medians, TLF was the best predictor in logistic regression models of TTCs ≥10 cfu/100 mL (AUC 0.88) and best correlated to TTC enumeration (ρs 0.81), with HLF performing similarly. Relationships between TLF or HLF and TTCs were stronger in the wet season than the dry season, when TLF and HLF were instead more associated with total bacterial cells. Source rank-order between sampling rounds was considerably more consistent, according to cross-correlations, using TLF or HLF (min ρs 0.81) than TTCs (min ρs 0.34). Furthermore, dry season TLF and HLF cross-correlated more strongly (ρs 0.68) than dry season TTCs (ρs 0.50) with wet season TTCs, when TTCs were elevated. In-situ TLF or HLF are more rapid and resilient indicators of faecal contamination risk than TTCs.

Insight into temporal\u2013spatial variations of DOM fractions and tracing potential factors in a brackish-water lake using second derivative synchronous fluorescence spectroscopy and canonical correlation analysis

BackgroundInsight into temporal–spatial variations of dissolved organic matter (DOM) fractions were undertaken to trace potential factors toward a further understanding aquatic environment in Lake Shahu, a brackish-water lake in northwest China, using synchronous fluorescence spectroscopy (SFS) combined with principal component analysis (PCA), second derivative and canonical correlation analysis (CCA).ResultFive fluorescence peaks were extracted from SFS by PCA, including tyrosine-like fluorescence (TYLF), tryptophan-like fluorescence (TRLF), microbial humic-like fluorescence (MHLF), fulvic-like fluorescence (FLF), and humic-like fluorescence (HLF), whose relative contents were obtained by second derivative synchronous fluorescence spectroscopy. The increasing order of total fluorescence components contents was July (11,789.38 ± 12,752.61) < April (12,667.58 ± 15,246.91) < November (19,748.87 ± 17,192.13), which was attributed to tremendous enhancement in TYLF content from April (1615.56 ± 258.56) to November (5631.96 ± 634.82). The PLF (the sum of TYLF and TRLF) dominated the fluorescence components, whose proportion was 40.55, 37.09, or 46.91% in April, July, or November. DOM fractions in November were distinguished from April and July, which could be attributed to that water of the Yellow River was continuously loaded into the lake as water replenishment from April to September. From the replenishment period to non-replenishment, the contents of the five components gradually changed from low in the middle and high around the lake to high throughout entire lake. Based on the CCA results, the potential factors included TYLF, TRLF, MHLF, SD, and BOD5 in April, which were relative to organic matter pollution. The potential factors contained TYLF, TRLF, FLF, Chl-a, TP, CODCr, and DO in July, indicating the enrichment of TP lead algae and plants growth. The potential factors in November consisted of TYLF, TRLF, CODCr, SD, TN, and FLF, representing residue of the algae and plants have been deeply degraded.ConclusionThe replenishment of water led to enrichment of TP, resulting in growth of algae and plants, and was the key factor of water quality fluctuations. This work provided a workflow from perspective of DOM to reveal causes of water quality fluctuations in a brackish-water lake and may be applied to other types of waterbodies.

Using machine learning classification to detect simulated increases of de facto reuse and urban stormwater surges in surface water

Water quality events such as increases in stormwater or wastewater effluent in drinking water sources pose hazards to drinking water consumers. Stormwater and wastewater effluent enter Lake Mead—an important drinking water source in the southwest USA—via the Las Vegas Wash. Previous studies have applied machine learning and online instruments to detect contamination in water distribution systems. However, alert systems at drinking water intakes would provide more time for corrective action. An array of online instruments measuring pH, conductivity, redox potential, turbidity, temperature, tryptophan-like fluorescence, UV absorbance (UVA254), TOC, and chlorophyll-a was fed raw water directly from Lake Mead. Wastewater effluent, dry weather Las Vegas Wash, and storm-impacted Las Vegas Wash samples were blended into the instrument inlets at known ratios to simulate three types of adverse water quality events. Data preprocessing was conducted to correct for diurnal patterns or instrument drift. Supervised machine learning was conducted using previously published models in R. Ninety-nine models were screened on the raw data. Eight high-performing models were evaluated in-depth and optimized. Weighted k-Nearest Neighbors, Single C5.0 Ruleset, Mixture Discriminant Analysis, and an ensemble of these three models had accuracy over 97% when assigning test set data among three classes (Normal, Event, or Maintenance). The ensemble detected all event types at the earliest timepoint and had one false positive that was not a lag error (i.e., consecutively following a true positive). Omitting Maintenance, the Adaboost model had over 99% test set accuracy and zero false positives that were not lag errors. Data preprocessing was beneficial, but the optimal methods were model-specific. All nine water quality variables were useful for most models, but UVA254 and turbidity were most important.

Characteristics, Sources, and Seasonal Variability of Dissolved Organic Matter (DOM) in the Ganges River, Bangladesh

This study investigated seasonal variability of sources and fluorescence characteristics of dissolved organic matter (DOM) in the Ganges River using three-dimensional excitation-emission matrix (3DEEM) spectroscopy and parallel factor analysis (PARAFAC). 3DEEM and PARAFAC identified five fluorophores (Peak: A, C, M, T, W) and four fluorescent components, i.e., detergent, fulvic acid (M-type), fulvic acid (C-type), and protein-like, respectively, showing high fluorescence characteristics in the pre-monsoon (March-June) and monsoon (July-October) compared to the post-monsoon (November-February) period. Dissolved organic carbon (DOC) and Specific ultraviolet absorbance (SUVA254) were in the range 1.85-4.7 mg/L and 1.23-5.51 L/mg-m, respectively. DOM components were both fresh and microbial-derived throughout the year. Local hydrological and physicochemical conditions supported the autochthonous and aromatic DOM production in post-monsoon, and richness of terrestrial-derived natural and anthropogenic DOM in the pre-monsoon and monsoon periods. The molecular weight of DOM decreased from the mid of pre-monsoon and continued till monsoon with the increase of terrestrial-derived DOM and photo-irradiation as well as microbial activity. Tryptophan-like fluorescence (TLF) showed high intensity throughout the peak-flow pre-monsoon and monsoon periods, demonstrating a high to intermediate risk of microbial contamination of the Ganges River. The seasonal variability of DOM characteristics suggests that the source and biogeochemistry of DOM in the Ganges Rivers might be sensitive to local hydrology and climate. • DOC concentrations and DOM fluorescence intensity attained their highest values at the beginning of monsoon and showed high seasonal variability. • The compositions of DOM carried by the Ganges River varied at different times of the year, and fresh, autochthonous and less aromatic DOM dominated in the late pre-monsoon and monsoon. • PARAFAC model identified four fluorescent DOM components (detergent-, fulvic- (M like), fulvic- (C like) and protein-like) which showed high seasonal variability with rainfall and river discharge.