Connecting microbial, nutrient, physiochemical, and land use variables for the evaluation of water quality within mixed use watersheds

Effects of landscape changes on water quality: A global meta-analysis

Landscape changes resulting from anthropogenic activities and climate changes severely impact surface water quality. A global perspective on understanding their relationship is a prerequisite for pursuing equity in water security and sustainable development. A sequent meta-analysis synthesizing 625 regional studies from 63 countries worldwide was conducted to analyze the impacts on water quality from changing landscape compositions in the catchment and explore the moderating factors and temporal evolution. Results exhibit that total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) in water are mostly concerned and highly responsive to landscape changes. Expansion of urban lands fundamentally degraded worldwide water quality over the past 20 years, of which the arid areas tended to suffer more harsh deterioration. Increasing forest cover, particularly low-latitude forests, significantly decreased the risk of water pollution, especially biological and heavy metal contamination, suggesting the importance of forest restoration in global urbanization. The effect size of agricultural land changes on water quality was spatially scale-dependent, decreasing and then increasing with the buffer radius expanding. Wetland coverage positively correlated with organic matter in water typified by COD, and the correlation coefficient peaked in the boreal areas (r=0.82, p<0.01). Overall, the global impacts of landscape changes on water quality have been intensifying since the 1990s. Nevertheless, knowledge gaps still exist in developing areas, especially in Africa and South America, where the water quality is sensitive to landscape changes and is expected to experience dramatic shifts in foreseeable future development. Our study revealed the worldwide consistency and heterogeneity between regions, thus serving as a research roadmap to address the quality-induced global water scarcity under landscape changes and to direct the management of land and water.

Enhancing phosphorus source apportionment in watersheds through species-specific analysis

Phosphorus (P) is a pivotal element responsible for triggering watershed eutrophication, and accurate source apportionment is a prerequisite for achieving the targeted prevention and control of P pollution. Current research predominantly emphasizes the allocation of total phosphorus (TP) loads from watershed pollution sources, with limited integration of source apportionment considering P species and their specific implications for eutrophication. This article conducts a retrospective analysis of the current state of research on watershed P source apportionment models, providing a comprehensive evaluation of three source apportionment methods, inventory analysis, diffusion models, and receptor models. Furthermore, a quantitative analysis of the impact of P species on watersheds is carried out, followed by the relationship between P species and the P source apportionment being critically clarified within watersheds. The study reveals that the impact of P on watershed eutrophication is highly dependent on P species, rather than absolute concentration of TP. Current research overlooking P species composition of pollution sources may render the acquired results of source apportionment incapable of assessing the impact of P sources on eutrophication accurately. In order to enhance the accuracy of watershed P pollution source apportionment, the following prospectives are recommended: (1) quantifying the P species composition of typical pollution sources; (2) revealing the mechanisms governing the migration and transformation of P species in watersheds; (3) expanding the application of traditional models and introducing novel methods to achieve quantitative source apportionment specifically for P species. Conducting source apportionment of specific species within a watershed contributes to a deeper understanding of P migration and transformation, enhancing the precise of management of P pollution sources and facilitating the targeted recovery of P resources.

Using watershed characteristics to enhance fecal source identification

Fecal pollution is one of the most prevalent forms of pollution affecting waterbodies worldwide, threatening public health and negatively impacting aquatic environments. Microbial source tracking (MST) applies polymerase chain reaction (PCR) technology to help identify the source of fecal pollution. In this study, we combine spatial data for two watersheds with general and host-associated MST markers to target human (HF183/BacR287), bovine (CowM2), and general ruminant (Rum2Bac) sources. Concentrations of MST markers in samples were determined with droplet digital PCR (ddPCR). The three MST markers were detected at all sites (n = 25), but bovine and general ruminant markers were significantly associated with watershed characteristics. MST results, combined with watershed characteristics, suggest that streams draining areas with low-infiltration soil groups and high agricultural land use are at an increased risk for fecal contamination. Microbial source tracking has been applied in numerous studies to aid in identifying the sources of fecal contamination, but these studies usually lack information on the involvement of watershed characteristics. Our study combined watershed characteristics with MST results to provide more comprehensive insight into the factors that influence fecal contamination in order to implement the most effective best management practices.

Microbial source tracking to elucidate the impact of land-use and physiochemical water quality on fecal contamination in a mixed land-use watershed

Escherichia coli has been widely used as a fecal indicator bacterium (FIB) for monitoring water quality in drinking water sources and recreational water. However, fecal contamination sources remain difficult to identify and mitigate, as millions of cases of infectious diseases are reported yearly due to swimming and bathing in recreational water. The objective of this study was to apply molecular techniques for microbial source tracking (MST) to identify sources of fecal contamination in a representative mixed land-use watershed located in the Appalachian Mountains of the United States of America (USA). Monthly samples were collected over one year at 11 sites, including the confluence of key first-order streams in the study watershed representing distinct land-use types and anticipated fecal sources. Results indicated that coupled monitoring of host-specific MST markers with the FIB E. coli effectively identified sources and quantified fecal contamination in the study watershed. Human-associated MST markers were abundant primarily at developed sites, suggesting septic or sewer failure is a key source of fecal input to the watershed. Across the dataset, samples positive for E. coli and human MST markers were associated with a higher pH than those samples from which each target was not detected, thereby suggesting that acid mine drainage in the watershed likely contributed to inactivation or loss of culturability in E. coli. In addition, this research provides the first evidence that the BacCan-UCD marker is present in fox feces and can influence MST results in areas where substantial wildlife activity is present. Identifying the sources of fecal contamination and better understanding the impact of in-stream physiochemistry throughout this study will help to develop sustainable and effective watershed management plans to control fecal contamination to protect drinking water sources and recreational water.

Fecal pollution source characterization in the surface waters of recharge and contributing zones of a karst aquifer using general and host-associated fecal genetic markers

Fecal pollution of surface waters in the karst-dominated Edwards aquifer is a serious concern as contaminated waters can rapidly transmit to groundwaters, which are used for domestic purposes. Although microbial source tracking (MST) detects sources of fecal pollution, integrating data related to environmental processes (precipitation) and land management practices (septic tanks) with MST can provide better understanding of fecal contamination fluxes to implement effective mitigation strategies. Here, we investigated fecal sources and their spatial origins at recharge and contributing zones of the Edwards aquifer and identified their relationship with nutrients in different environmental/land-use conditions. During March 2019 to March 2020, water samples (n = 295) were collected biweekly from 11 sampling sites across four creeks and analyzed for six physico-chemical parameters and ten fecal indicator bacteria (FIB) and MST-based qPCR assays targeting general (E. coli, Enterococcus, and universal Bacteroidales), human (BacHum and HF183), ruminant (Rum2Bac), cattle (BacCow), canine (BacCan), and avian (Chicken/Duck-Bac and GFD) fecal markers. Among physico-chemical parameters, nitrate-N (NO₃-N) concentrations at several sites were higher than estimated national background concentrations for streams. General fecal markers were detected in the majority of water samples, and among host-associated MST markers, GFD, BacCow, and Rum2Bac were more frequently detected than BacCan, BacHum, and HF183, indicating avian and ruminant fecal contamination is a major concern. Cluster analysis results indicated that sampling sites clustered based on precipitation and septic tank density showed significant correlation (p < 0.05) between nutrients and FIB/MST markers, indicating these factors are influencing the spatial and temporal variations of fecal sources. Overall, results emphasize that integration of environmental/land-use data with MST is crucial for a better understanding of nutrient loading and fecal contamination.

Distribution and correlation between antibiotic resistance genes and host-associated markers before and after swine fever in the longjiang watershed

Antibiotic resistance genes (ARGs) are abundantly shed in feces. Thus, it is crucial to identify their host sources so that ARG pollution can be effectively mitigated and aquatic ecosystems can be properly conserved. Here, spatiotemporal variations and sources of ARGs in the Longjiang watershed of South China were investigated by linking them with microbial source tracker (MST) indicators. The most frequently detected ARGs (>90%) were sulI, sulII, bla_TEM, tetW, ermF, and the mobile element intI1. Spatial distribution analyses showed that tributaries contributed significantly more sulI, sulII, and ermF contamination to the Longjiang watershed than the main channel. MST indicator analysis revealed that the Longjiang watershed was contaminated mainly by human fecal pollution. Livestock- and poultry-associated fecal pollution significantly declined after the swine fever outbreak. The occurrence of most ARGs is largely explained by human fecal pollution. In contrast, pig fecal pollution might account for the prevalence of tetO. Moreover, combined human-pig fecal pollution contributed to the observed bla_NDM-1 distribution in the Longjiang watershed. Subsequent analysis of the characteristics of MST markers disclosed that the relatively lower specificities of BacHum and Rum-2-Bac may lead to inaccurate results of tracking ARG pollution source. The present study determined spatiotemporal variations and ARG origins in the Longjiang watershed by combining MST markers. It also underscored the necessity of using multiple MST markers simultaneously to identify and characterize ARG pollution sources accurately.

Improving the accuracy of nonpoint-source pollution estimates in inland waters with coupled satellite-UAV data

Quantitatively and accurately analyzing nonpoint-source (NPS) pollution is essential for efficiently preventing the input of NPS loads into inland waters. However, the accuracy of previous NPS pollution models is limited by the accuracy of ground parameter data. In addition, there are few effective methods that thoroughly verify modeling results at large scales. This paper presents a framework for accurate NPS pollution estimation by coupling satellite and unmanned aerial vehicle (UAV) monitoring data, and the results are verified by both field sampling and a newly developed inlet NPS pollution "observation" simulation method. Fractional vegetation coverage (FVC) data obtained by satellite were used to improve the accuracy of the runoff module of the framework. Satellite and UAV data were coupled to acquire livestock data, determine inlets, and identify reservoir buffer zones and vegetation types. These new data were then used to improve the accuracy of the livestock and runoff modules in the framework. The results show that the estimation accuracy of total nitrogen, total phosphorus, ammonia nitrogen, and chemical oxygen demand with FVC were improved by 39.96%, 69.29%, 54.05% and 47.22% (in relative error), respectively. The high-resolution livestock data acquisition improved the estimation accuracy of the NPS pollution load by 7-53%. The high-resolution inlet extraction improved the accuracy by 3-24%. The high-resolution buffer zone identification improved the accuracy with the estimated NPS pollutant concentration into reservoir decreasing by 60-99%. Finally, the high-resolution vegetation type identification improved the accuracy by 10-72%. The framework performs satisfactorily, which was verified based on the simulated NPS observations with an average relative error of 11.54-24.31%. We found that the FVC, livestock number, and inlet number are key parameters for NPS pollution modeling; the introduction of monthly variation in the FVC makes the modeled NPS pollution load much higher in areas with mature complex forested ecosystems or densely distributed vegetation but much lower in areas with sparsely distributed vegetation. The above methods provide a scientific reference for high-efficiency NPS pollution prevention in inland waters, laying a solid basis for decision-making regarding water quality management in data-scarce regions around the world.

Incorporating receiving waters responses into the framework of spatial optimization of LID-BMPs in plain river network region

Deep insights into the receiving waters responses to optimal spatial allocation of LID-BMPs are considered extremely important. This study addressed the urgent need to incorporate receiving waters responses into the spatial allocation optimization of LID-BMPs and demonstrated the efficiency of the approach to guide watershed management. The integration of an overland-river coupling model and the NSGA-III algorithm resulted in the proposal of a general simulation-optimization framework for the optimal layout of LID-BMPs. The coupled model was swapped out for the surrogates to increase computational efficiency. When 40.71%, 36.06%, and 61.80% reductions in runoff volume, flood volume, and TP concentration are achieved, the newly proposed framework can save 34.44% and 16.31% cost compared to the approach that does not consider receiving waters responses and refined spatial allocation, respectively. Results indicate that the incorporation of receiving waters responses and refined spatial allocation are essential for the optimal design of LID-BMPs. This new framework offers the potential for more cost-effective high-cost solutions. The results of spatial optimization are significantly influenced by imperviousness.