Performance of NIST SRM® 2917 with 13 recreational water quality monitoring qPCR assays

Willis, Jessica R.; Sivaganesan, Mano; Haugland, Richard A.; Kralj, Jason G.; Servetas, Stephanie L.; Hunter, Monique E.; Jackson, Scott A.; Shanks, Orin C.

doi:10.1016/j.watres.2022.118114

Cited by 15 publications

(9 citation statements)

References 36 publications

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“…Although Japan has a small land area, it has developed earlier in the research and development of water quality monitoring systems. Their main advantages lie in the high degree of automation and the relatively high degree of integration of modules [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things

Dang

Liu

2022

Computational Intelligence and Neuroscience

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Monitoring environmental water quality in an efficient, cheap, and sustainable way can better serve the country’s strategic requirements for water resources and water ecological protection. This paper takes the Shaanxi section of the Weihe River Basin as a pilot project and aims to use the Internet of Things technology to develop water quality monitoring sensors, so as to realize the construction of low-cost, high-reliability water quality monitoring demonstration applications. First of all, we established the design of the water quality collection terminal, designed the low-power water quality sensor node, supported the Internet of Things protocol and the collection of various water quality parameters, and used networking for data transmission. Secondly, we use the ant colony algorithm-based system clustering model to obtain a cluster map of water quality monitoring tasks in a certain section of the Weihe River Basin. We take the task clustering graph as an example for analysis, optimize the monitoring model through the ant colony algorithm, and obtain the weight of the optimization index. The weight of the scheduled task limit of the monitoring point becomes larger, so the release of the monitoring task mainly affects the limit of the scheduled task of the monitoring point. Through the above work, we designed and implemented a set of online water quality monitoring system based on the Internet of Things and data mining technology. The system can provide reference for large-scale water resource protection and water environment governance.

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Section: Introductionmentioning

confidence: 99%

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things

Dang

Liu

2022

Computational Intelligence and Neuroscience

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“…Reference DNA sources consisted of two plasmid constructs and salmon sperm DNA. Plasmid constructs included the National Institute of Standards and Technology Standard Reference Material ® 2917 (SRM 2917; Rockville, MD) for qPCR calibration model generation [ 31 , 32 ] and an internal amplification control (IAC; Integrated DNA Technologies, Coralville, IA). SRM 2917 is a commercially available, ready to use, linearized plasmid consisting of five dilution preparations as follows: Level 1 (10.3 copies/2 μL), Level 2 (1.11·10 2 copies/2 μL), Level 3 (1.06·10 3 copies/2 μL), Level 4 (1.06·10 4 copies/2 μL), and Level 5 (1.04·10 5 copies/2 μL).…”

Section: Methodsmentioning

confidence: 99%

Genetic fecal source identification in urban streams impacted by municipal separate storm sewer system discharges

et al. 2023

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Municipal stormwater systems are designed to collect, transport, and discharge precipitation from a defined catchment area into local surface waters. However, these discharges may contain unsafe levels of fecal waste. Paired measurements of Escherichia coli, precipitation, three land use metrics determined by geographic information system (GIS) mapping, and host-associated genetic markers indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), dog (DG3), and avian (GFD) fecal sources were assessed in 231 urban stream samples impacted by two or more municipal stormwater outfalls. Receiving water samples were collected twice per month (n = 24) and after rain events (n = 9) from seven headwaters of the Anacostia River in the District of Columbia (United States) exhibiting a gradient of impervious surface, residential, and park surface areas. Almost 50% of stream samples (n = 103) were impaired, exceeding the local E. coli single sample maximum assessment level (410 MPN/100 ml). Fecal scores (average log10 copies per 100 ml) were determined to prioritize sites by pollution source and to evaluate potential links with land use, rainfall, and E. coli levels using a recently developed censored data analysis approach. Dog, ruminant, and avian fecal scores were almost always significantly increased after rain or when E. coli levels exceeded the local benchmark. Human fecal pollution trends showed the greatest variability with detections ranging from 9.1% to 96.7% across sites. Avian fecal scores exhibited the closest connection to land use, significantly increasing in catchments with larger residential areas after rain events (p = 0.038; R2 = 0.62). Overall, results demonstrate that combining genetic fecal source identification methods with GIS mapping complements routine E. coli monitoring to improve management of urban streams impacted by stormwater outfalls.

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“…The plasmids were stored at −20 °C, and the SAE was stored at 4 °C. Standard Reference Material 2917 (NIST) was used for control sample DNA (Willis et al, 2022). Initial SAE provided by the Rose Lab was diluted to 2 ng/mL for ddPCR.…”

Section: Methodsmentioning

confidence: 99%

Using Watershed Characteristics for Improving Fecal Source Identification

Hart¹,

Jamison

McNair³

et al. 2022

Preprint

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Fecal pollution is one of the most prevalent forms of pollution affecting waterbodies across the United States, 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-specific MST markers to target human, bovine, and general ruminant sources. Two different PCR technologies were applied for quantifying the targets: quantitative PCR (qPCR) and droplet digital PCR (ddPCR). We found that ddPCR had a higher detection rate (75%) of quantifiable samples compared to qPCR (27%), indicating that ddPCR is more sensitive than qPCR. The three host-specific markers were detected at all sites (n=25) , suggesting that humans, cows, and ruminants are contributing to fecal contamination in these watersheds. MST results, combined with watershed characteristics, suggest that streams draining low-infiltration soil groups, high septic system prevalence, 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 source of fecal contamination, however these studies lacked information on the involvement of watershed characteristics. This study combined MST results, applying more sensitive PCR techniques, in addition to watershed characteristics to provide more comprehensive insight into the factors that influence fecal contamination in order to implement the most effective best management practices.

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Performance of NIST SRM® 2917 with 13 recreational water quality monitoring qPCR assays

Cited by 15 publications

References 36 publications

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things

Genetic fecal source identification in urban streams impacted by municipal separate storm sewer system discharges

Using Watershed Characteristics for Improving Fecal Source Identification

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