Sequence-enabled community-based microbial source tracking in surface waters using machine learning classification: A review

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Source apportionment based on EEM-PARAFAC combined with microbial tracing model and its implication in complex pollution area, Wujin District, China

Further improving the quality of surface water is becoming more difficult after the control of main point-sources, especially in the complex pollution area with mixed industrial and agricultural productions, whereas the pollution source apportionment might be the key to quantify different pollution sources and developing some effective measures. In this study, a technical framework for source apportionment based on three-dimensional fluorescence and microbial traceability model is developed. Based on screening of the main environmental factors and their spatiotemporal characteristics, potential pollution sources have been tentatively identified. Then, the pollution sources are further tested based on the analysis of fluorescence excitation-emission matrix (EEM) and the similarity of fluorescence components in surface water and potential pollution sources. At the same time, the correlation between microbial species and pollution sources is constructed by analyzing the spatiotemporal characteristics of microbial composition and the response of main species to environmental factors. Therefore, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectation-maximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). PCA-APCS-MLR could not effectively distinguish the contributions of different industrial sources in the complex environment of this study, and the contribution of unknown sources was high (average 39.60%). In contrast, the microbial traceability model can accurately identify the contribution of 7 pollution sources and natural sources, effectively reduce the proportion of unknown sources (average of FEAST is 19.81%, SourceTracker is 16.72%), and show better pollution identification and distribution capabilities. FEAST exhibits a more sensitive potential in source apportionment and shorter calculation time than SourceTracker, thus might be used to guide the precise regional pollution control, especially in the complex pollution environments.

Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology

Wastewater Treatment Plants (WWTPs) present complex biochemical processes of high variability and difficult prediction. This study presents an innovative approach using Machine Learning (ML) models to predict wastewater quality parameters. In particular, the models are applied to datasets from both a simulated wastewater treatment plant (WWTP), using DHI WEST software (WEST WWTP), and a real-world WWTP database from Santa Catarina Brewery AMBEV, located in Lages/SC - Brazil (AMBEV WWTP). A distinctive aspect is the evaluation of predictive performance in continuous data scenarios and the impact of changes in WWTP operations on predictive model performance, including changes in plant layout. For both plants, three different scenarios were addressed, and the quality of predictions by random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP) models were evaluated. The prediction quality by the MLP model reached an R2 of 0.72 for TN prediction in the WEST WWTP output, and the RF model better adapted to the real data of the AMBEV WWTP, despite the significant discrepancy observed between the real and the predicted data. Techniques such as Partial Dependence Plots (PDP) and Permutation Importance (PI) were used to assess the importance of features, particularly in the simulated WEST tool scenario, showing a strong correlation of prediction results with influent parameters related to nitrogen content. The results of this study highlight the importance of collecting and storing high-quality data and the need for information on changes in WWTP operation for predictive model performance. These contributions advance the understanding of predictive modeling for wastewater quality and provide valuable insights for future practice in wastewater treatment.

Persistence of location-specific microbial signatures on boats during voyages

Objects collect microorganisms from their surroundings and develop a microbial "fingerprint" that may be useful for determining an object's past location (provenance). It may be possible to use ubiquitous microorganisms for forensics or as environmental sensors. Here, we use microbial communities in the Chesapeake Bay region to demonstrate the use of natural microorganisms as biological sensors to determine the past location of boats. The microbiomes of two boats and of the open water were sampled as these vessels traveled from the Port of Baltimore to the Port of Norfolk, and back to Baltimore. 16S rRNA sequencing was performed to identify microorganisms. Differential abundance and machine learning analyses were utilized to identify microbial signatures and predicted probabilities which were used to determine the vessel's previous location. The work presented here provides a better understanding of how microbes in aquatic systems can be leveraged as utility for object biosensors.

Contribution of microorganisms from pit mud to volatile flavor compound synthesis in fermented grains for nongxiangxing baijiu brewing

BACKGROUND

Nongxiangxing baijiu (NB) is known for its distinct flavor profile, which is attributed to key aroma compounds. The exposed fermentation technique, utilizing daqu and solid-state fermentation in pit muds, plays a crucial role in flavor development. Though previous studies have investigated the impact of microorganisms from pit ?ud and fermented grains on flavor compound production, a comprehensive understanding of microbial functions in the entire pit fermentation system is lacking. Herein, we aimed to explore the role of pit-mud-derived microorganisms in shaping the microbial community and flavor compound synthesis in NB.

RESULTS

There are 76 volatile flavor compounds that have been identified in fermented grains during NB fermentation. The main flavor compounds in NB clustered within the same network module, and 27.27% of microorganisms in the core modules of the fermented grain co-occurrence network originated from pit mud. The relationship between pit mud microorganisms and flavor compounds revealed a significant positive correlation (92%). Notably, Prevotella and Sarocladium were identified as the main contributors to this effect on flavor.

CONCLUSION

Microorganisms originating from pit mud influenced the composition and activity of microorganisms in fermented grains and facilitated the production of flavor compounds in NB. © 2023 Society of Chemical Industry.

Anthropogenic activity remains the main contributor to fecal pollution in managed tropical watersheds as unraveled by PhyloChip microarray-based microbial source tracking

The spread of disease by enteric pathogens associated with fecal contamination is a major concern for the management of urban watersheds. So far, the relative contribution of natural and anthropogenic sources to fecal pollution in managed tropical watersheds remains poorly evaluated. In this study, the microbiomes of water samples collected from managed watersheds in Singapore were elicited using the PhyloChip, a dense 16S rRNA gene-based DNA microarray, and fecal impairment was inferred using a machine-learning classification algorithm (SourceTracker). The predicted contribution of wildlife fecal sources to environmental samples was generally negligible (< 0.01 ± 0.01), indicating a low likelihood of fecal impairment from natural sources. However, sewage showed considerably higher contribution (0.09 ± 0.05) to microbial communities in a subset of watershed samples from canals and rivers, suggesting persistent impairment of certain areas by anthropogenic activity although being managed. Interestingly, the contribution of sewage microbial communities showed decreasing trends from canals/rivers to the connected reservoirs, indicating meaningful auto-mitigation of fecal pollution in canals and rivers. Notably, exclusion of locally derived fecal samples and source categories from the training data set impaired the predictive performance of the classification algorithm despite a high degree of similarity in the phylogenetic composition of microbiomes in biologically similar but geographically distinct sources.

Persistence of human- and cattle-associated Bacteroidales and mitochondrial DNA markers in freshwater mesocosms

Accurate identification of the origins of non-point source pollution is essential for the effective control of fecal pollution. Host-associated Bacteroidales and mitochondrial DNA (mtDNA) markers have been developed to identify the sources of human and cattle fecal pollution. However, the differences in persistence between these two types of markers under different environmental conditions are still poorly understood. Here, we conducted mesocosm experiments to investigate the influence of indigenous microbiota and nutrients on the decay of Bacteroidales and mtDNA markers associated with humans and cattle. Raw sewage or cattle feces were inoculated into mesocosms containing natural eutrophic water, sterile eutrophic water or artificial freshwater. The Bacteroidales markers HF183 (human) and CowM3 (cattle) and mtDNA markers HcytB (human) and QMIBo (cattle) were quantified using the quantitative polymerase chain reaction (qPCR) assays. All markers but HF183 decreased the fastest in the presence of indigenous microbiota. Nutrients caused a decrease in the persistence of HF183; however, no significant nutrient effects were observed for HcytB, CowM3, and QMIBo. The time to reach one log reduction (T90) for HF183 and HcytB was similar; CowM3 reached T90 earlier than QMIBo in all the treatments but eutrophic water. E. coli persisted longer than both Bacteroidales and mtDNA markers in the mesocosms regardless of inoculum type. Additionally, 16S rRNA gene amplicon sequencing was used to determine the changes in bacterial communities accompanying the marker decay. Analysis using the SourceTracker software showed that bacterial communities in the mesocosms became more dissimilar to those in the corresponding inoculants over time. Our results indicate that environmental factors are important determinants of genetic markers' persistence, but their impact can vary depending on the genetic markers. The cattle Bacteroidales markers may be more suitable for determining recent fecal contamination than cattle mtDNA.

Microbial source tracking using molecular and cultivable methods in a tropical mixed-use drinking water source to support water safety plans

Microbial contamination deteriorates source water quality, posing a severe problem for drinking water suppliers worldwide and addressed by the Water Safety Plan framework to ensure high-quality and reliable drinking water. Microbial source tracking (MST) is used to examine different microbial pollution sources via host-specific intestinal markers for humans and different types of animals. However, the application of MST in tropical surface water catchments that provide raw water for drinking water supplies is limited. We analyzed a set of MST markers, namely, three cultivable bacteriophages and four molecular PCR and qPCR assays, together with 17 microbial and physicochemical parameters, to identify fecal pollution from general, human-, swine-, and cattle-specific sources. Seventy-two river water samples at six sampling sites were collected over 12 sampling events during wet and dry seasons. We found persistent fecal contamination via the general fecal marker GenBac3 (100 % detection; 2.10-5.42 log₁₀ copies/100 mL), with humans (crAssphage; 74 % detection; 1.62-3.81 log₁₀ copies/100 mL) and swine (Pig-2-Bac; 25 % detection; 1.92-2.91 log₁₀ copies/100 mL). Higher contamination levels were observed during the wet season (p < 0.05). The conventional PCR screening used for the general and human markers showed 94.4 % and 69.8 % agreement with the respective qPCR results. Specifically, in the studied watershed, coliphage could be a screening parameter for the crAssphage marker (90.6 % and 73.7 % positive and negative predictive values; Spearman's rank correlation coefficient = 0.66; p < 0.001). The likelihood of detecting the crAssphage marker significantly increased when total and fecal coliforms exceeded 20,000 and 4000 MPN/100 mL, respectively, as Thailand Surface Water Quality Standards, with odds ratios and 95 % confidence intervals of 15.75 (4.43-55.98) and 5.65 (1.39-23.05). Our study confirms the potential benefits of incorporating MST monitoring into water safety plans, supporting the use of this approach to ensure high-quality drinking water supplies worldwide.

Development of two microbial source tracking markers for detection of wastewater-associated Escherichia coli isolates

Escherichia coli has been used as an indicator of fecal pollution in environmental waters. However, its presence in environmental waters does not provide information on the source of water pollution. Identifying the source of water pollution is paramount to be able to effectively reduce contamination. The present study aimed to identify E. coli microbial source tracking (MST) markers that can be used to identify domestic wastewater contamination in environmental waters. We first analyzed wastewater E. coli genomes sequenced by us (n = 50) and RefSeq animal E. coli genomes of fecal origin (n = 82), and identified 144 candidate wastewater-associated marker genes. The sensitivity and specificity of the candidate marker genes were then assessed by screening the genes in 335 RefSeq wastewater E. coli genomes and 3318 RefSeq animal E. coli genomes. We finally identified two MST markers, namely W_nqrC and W_clsA_2, which could be used for detection of wastewater-associated E. coli isolates. These two markers showed higher performance than the previously developed human wastewater-associated E. coli markers H8 and H12. When used in combination, W_nqrC and W_clsA_2 showed specificity of 98.9 % and sensitivity of 25.7 %. PCR assays to detect W_nqrC and W_clsA_2 were also developed and validated. The developed PCR assays are potentially useful for detecting E. coli isolates of wastewater origin in environmental waters, though users should keep in mind that the sensitivity of these markers is not high. Further studies are needed to assess the applicability of the developed markers to a culture-independent approach.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Evaluation of FEAST for metagenomics-based source tracking of antibiotic resistance genes

A metagenomics-based technological framework has been proposed for evaluating the potential and utility of FEAST as an ARG profile-based source apportionment tool. To this end, a large panel of metagenomic data sets was analyzed, associating with eight source types of ARGs in environments. Totally, 1089 different ARGs were found in the 604 source metagenomes, and 396 ARG indicators were identified as the source-specific fingerprints to characterize each of the source types. With the source fingerprints, predictive performance of FEAST was checked using "leave-one-out" cross-validation strategy. Furthermore, artificial sink communities were simulated to evaluate the FEAST for source apportionment of ARGs. The prediction of FEAST showed high accuracy values (0.933 ± 0.046) and specificity values (0.959 ± 0.041), confirming its suitability to discriminate samples from different source types. The apportionment results reflected well the expected output of artificial communities which were generated with different ratios of source types to simulate various contamination levels. Finally, the validated FEAST was applied to track the sources of ARGs in river sediments. Results showed STP effluents were the main contributor of ARGs, with an average contribution of 76 %, followed by sludge (10 %) and aquaculture effluent (2.7 %), which were basically consistent with the actual environment in the area.

A proposed framework for the identification of indicator genes for monitoring antibiotic resistance in wastewater: Insights from metagenomic sequencing

Antibiotic resistance is one of the greatest threats to global human and animal health of our time. Municipal wastewater has been identified as a hotspot of antibiotic resistance contamination to water bodies. However, there are numerous potential antibiotic resistant pathogens and their associated antibiotic resistance genes (ARGs), making it difficult to implement routine monitoring that addresses the breadth of the problem. The objective of this study was to identify candidate indicator ARGs for monitoring antibiotic resistance in wastewater and receiving water bodies. We developed a framework to identify indicator ARGs that incorporated clinical relevance, abundance in wastewater, geographic ubiquity, environmental relevance, ARG mobility, associations with mobile genetic elements, and the availability of quantitative analytical methods. To identify indicator ARGs, published metagenomic sequencing data from 191 wastewater samples originating from 64 countries across the world were obtained from online public repositories. Through ARG annotation and network analysis, this framework revealed 56 candidate indicator ARGs distributed across four modules of strongly correlated ARGs, with one ARG from each module (oqxA, ermB, sul1, and mexE) proposed as a minimally redundant monitoring target. The results of this study provide the basis for antibiotic resistance surveillance and monitoring framework in wastewater and contaminated waterways.

The combined effect of an integrated reclaimed water system on the reduction of antibiotic resistome

The reuse of urban reclaimed water is conducive to alleviate the current serious shortage of water resources. However, antibiotic resistance genes (ARGs) in reclaimed water have received widespread attention due to their potential risks to public health. Deciphering the fate of ARGs in reclaimed water benefits the development of effective strategies to control resistome risk and guarantees the safety of water supply of reclaimed systems. In this study, the characteristics of ARGs in an integrated reclaimed water system (sewage treatment plant-constructed wetland, STP-CW) in Beijing (China) have been identified using metagenomic assembly-based analysis, as well as the combined effect of the STP-CW system on the reduction of antibiotic resistome. Results showed a total of 29 ARG types and 813 subtypes were found in the reclaimed water system. As expected, the STP-CW system improved the removal of ARGs, and about 58% of ARG subtypes were removed from the effluent of the integrated STP-CW system, which exceeded 43% for the STP system and 37% for the CW system. Although the STP-CW system had a great removal on ARGs, abundant and diverse ARGs were still found in the downstream river. Importantly, network analysis revealed the co-occurrence of ARGs, mobile genetic elements and virulence factors in the downstream water, implying potential resistome dissemination risk in the environment. Source identification with SourceTracker showed the STP-effluent was the largest contributor of ARGs in the downstream river, with a contribution of 45%. Overall, the integrated STP-CW system presented a combined effect on the reduction of antibiotic resistome, however, the resistome dissemination risk was still non-negligible in the downstream reclaimed water. This study provides a comprehensive analysis on the fate of ARGs in the STP-CW-river system, which would benefit the development of effective strategies to control resistome risk for the reuse of reclaimed water.

Tracking bacterial pollution at a marine wastewater outfall site - A case study from Norway

Coastal marine environments are increasingly affected by anthropogenic impacts, such as the release of sewage at outfall sites and agricultural run-off. Fecal pollution introduced to the sea through these activities poses risks of spreading microbial diseases and disseminating antibiotic resistant bacteria and their genes. The study area of this research, Bore beach, is situated between two such point sources, an outfall site where treated sewage is released 1 km off the coast and a stream that carries run-off from an agricultural area to the northern end of the beach. In order to investigate whether and to what extent fecal contamination from the sewage outfall reached the beach, we used microbial source tracking, based on whole community analysis. Samples were collected from sea water at varying distances from the sewage outfall site and along the beach, as well as from the sewage effluent and the stream. Amplicon sequencing of 16S rRNA genes from all the collected samples was carried out at two time points (June and September). In addition, the seawater at the sewage outfall site and the sewage effluent were subject to shotgun metagenomics. To estimate the contribution of the sewage effluent and the stream to the microbial communities at Bore beach, we employed SourceTracker2, a program that uses a Bayesian algorithm to perform such quantification. The SourceTracker2 results suggested that the sewage effluent is likely to spread fecal contamination towards the beach to a greater extent than anticipated based on the prevailing sea current. The estimated mixing proportions of sewage at the near-beach site (P4) were 0.22 and 0.035% in June and September, respectively. This was somewhat below that stream's contribution in June (0.028%) and 10-fold higher than the stream's contribution in September (0.004%). Our analysis identified a sewage signal in all the tested seawater samples.

Domestic wastewater causes nitrate pollution in an agricultural watershed, China

Excessive quantities of nitrates in the aquatic environment can cause eutrophication and raise water safety concerns. Therefore, identification of the sources of nitrate is crucial to mitigate nitrate pollution and for better management of the water resources. Here, the spatiotemporal variations and sources of nitrate were investigated by stable isotopes (δ¹⁵N and δ¹⁸O), hydrogeochemical variables (e.g., NO₃^- and Cl^-), and exogenous microbial signals (i.e., sediments, soils, domestic and swine sewage) in an agricultural watershed (Changle River watershed) in China. The concentration ranges of δ¹⁵N- and δ¹⁸O-NO₃^- between 3.03‰-18.97‰ and -1.55‰-16.47‰, respectively, suggested that soil nitrogen, chemical fertilizers, and manure and sewage (M&S) were the primary nitrate sources. Bayesian isotopic mixing model suggested that the major proportion of nitrate within the watershed (53.12 ± 10.40% and 63.81 ± 15.08%) and tributaries (64.43 ± 5.03% and 76.20 ± 4.34%) were contributed by M&S in dry and wet seasons, respectively. Community-based microbial source tracking (MST) showed that untreated and treated domestic wastewater was the major source (>70%) of river microbiota. Redundancy analysis with the incorporation of land use, hydrogeochemical variables, dual stable isotope, and exogenous microbial signals revealed domestic wastewater as the dominant cause of nitrate pollution. Altogether, this study not only identifies and quantifies the spatiotemporal variations in nitrate sources in the study area but also provides a new analytical framework by combining nitrate isotopic signatures and community-based MST approaches for source appointment of nitrate in other polluted watersheds.

A Combined Digital PCR and Next Generation DNA-Sequencing Based Approach for Tracking Nearshore Pollutant Dynamics Along the Southwest United States/Mexico Border

Ocean currents, multiple fecal bacteria input sources, and jurisdictional boundaries can complicate pollution source tracking and associated mitigation and management efforts within the nearshore coastal environment. In this study, multiple microbial source tracking tools were employed to characterize the impact and reach of an ocean wastewater treatment facility discharge in Mexico northward along the coast and across the Southwest United States- Mexico Border. Water samples were evaluated for fecal indicator bacteria (FIB), Enterococcus by culture-based methods, and human-associated genetic marker (HF183) and Enterococcus by droplet digital polymerase chain reaction (ddPCR). In addition, 16S rRNA gene sequence analysis was performed and the SourceTracker algorithm was used to characterize the bacterial community of the wastewater treatment plume and its contribution to beach waters. Sampling dates were chosen based on ocean conditions associated with northern currents. Evidence of a gradient in human fecal pollution that extended north from the wastewater discharge across the United States/Mexico border from the point source was observed using human-associated genetic markers and microbial community analysis. The spatial extent of fecal contamination observed was largely dependent on swell and ocean conditions. These findings demonstrate the utility of a combination of molecular tools for understanding and tracking specific pollutant sources in dynamic coastal water environments.

Next Generation High Throughput Sequencing to Assess Microbial Communities: An Application Based on Water Quality

Traditional techniques to identify different contaminants (biological or chemical) in the waters are slow, laborious, and can require specialized expertise. Hence, the rapid determination of water quality using more sensitive and reliable metagenomic based approaches attains special importance. Metagenomics deals with the study of genetic material that is recovered from microbial communities present in environmental samples. In traditional techniques cultivation-based methodologies were used to describe the diversity of microorganisms in environmental samples. It has failed to function as a robust marker because of limited taxonomic and phylogenetic implications. In this backdrop, high-throughput DNA sequencing approaches have proven very powerful in microbial source tracking because of investigating the full variety of genome-based analysis such as microbial genetic diversity and population structure played by them. Next generation sequencing technologies can reveal a greater proportion of microbial communities that have not been reported earlier by traditional techniques. The present review highlights the shift from traditional techniques for the basic study of community composition to next-generation sequencing (NGS) platforms and their potential applications to the biomonitoring of water quality in relation to human health.

Metagenomic Sequencing and Quantitative Real-Time PCR for Fecal Pollution Assessment in an Urban Watershed

Microbial contamination of recreation waters is a major concern globally, with pollutants originating from many sources, including human and other animal wastes often introduced during storm events. Fecal contamination is traditionally monitored by employing culture methods targeting fecal indicator bacteria (FIB), namely E. coli and enterococci, which provides only limited information of a few microbial taxa and no information on their sources. Host-associated qPCR and metagenomic DNA sequencing are complementary methods for FIB monitoring that can provide enhanced understanding of microbial communities and sources of fecal pollution. Whole metagenome sequencing (WMS), quantitative real-time PCR (qPCR), and culture-based FIB tests were performed in an urban watershed before and after a rainfall event to determine the feasibility and application of employing a multi-assay approach for examining microbial content of ambient source waters. Cultivated E. coli and enterococci enumeration confirmed presence of fecal contamination in all samples exceeding local single sample recreational water quality thresholds (E. coli, 410 MPN/100 mL; enterococci, 107 MPN/100 mL) following a rainfall. Test results obtained with qPCR showed concentrations of E. coli, enterococci, and human-associated genetic markers increased after rainfall by 1.52-, 1.26-, and 1.11-fold log₁₀ copies per 100 mL, respectively. Taxonomic analysis of the surface water microbiome and detection of antibiotic resistance genes, general FIB, and human-associated microorganisms were also employed. Results showed that fecal contamination from multiple sources (human, avian, dog, and ruminant), as well as FIB, enteric microorganisms, and antibiotic resistance genes increased demonstrably after a storm event. In summary, the addition of qPCR and WMS to traditional surrogate techniques may provide enhanced characterization and improved understanding of microbial pollution sources in ambient waters.