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Abstract Protecting surface water quality can be complicated by high spatiotemporal variability. Pollutant sources and transport pathways may be identified through sufficiently high‐density monitoring sites and high‐frequency sampling, but practical considerations necessitate tradeoffs between spatial and temporal resolution in water quality monitoring network design. We examined how tradeoffs in sampling density and frequency affect measures of spatiotemporal variability in water quality, emphasizing pattern stability over time. We quantified the spatial stability of stream water quality across >250 monitoring sites in the intensively monitored watershed draining to Lake Okeechobee, FL using Spearman's rank correlations between instantaneous observations and site long‐term means for each parameter. We found that water quality spatial patterns for geogenic, biogenic, and anthropogenic parameters were generally stable on decadal timescales for all solutes, and that sampling densely in space yields more information than sampling frequently in time. Variations in spatial stability decreased with increased sampling density but not with greater sampling frequency, attesting to the dominance of spatial variability over temporal variability. For nutrients, the spatial coefficient of variation (CV) was approximately double the temporal CV. Spatial stability of most solutes was similar across flow conditions, but high‐flow monitoring allows for more sites that effectively capture the long‐term spatial patterns of nutrient sources. Water quality monitoring regimes can be optimized for efficiency in capturing water quality patterns and should be adjusted to focus more on spatial variation. We discuss potential improvements for water quality monitoring, particularly in watersheds where scarce resources necessitate tradeoffs between sampling density and frequency.
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Localized Sampling Enables Monitoring of Cell State via Inline Electrospray Ionization Mass Spectrometry
Abstract Nascent advanced therapies, including regenerative medicine and cell and gene therapies, rely on the production of cells in bioreactors that are highly heterogeneous in both space and time. Unfortunately, advanced therapies have failed to reach a wide patient population due to unreliable manufacturing processes that result in batch variability and cost prohibitive production. This can be attributed largely to a void in existing process analytical technologies (PATs) capable of characterizing the secreted critical quality attribute (CQA) biomolecules that correlate with the final product quality. The Dynamic Sampling Platform (DSP) is a PAT for cell bioreactor monitoring that can be coupled to a suite of sensor techniques to provide real‐time feedback on spatial and temporal CQA content in situ. In this study, DSP is coupled with electrospray ionization mass spectrometry and direct‐from‐culture sampling to obtain measures of CQA content in bulk media and the cell microenvironment throughout the entire cell culture process (≈3 weeks). Post hoc analysis of this real‐time data reveals that sampling from the microenvironment enables cell state monitoring (e.g., confluence, differentiation). These results demonstrate that an effective PAT should incorporate both spatial and temporal resolution to serve as an effective input for feedback control in biomanufacturing.
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- Award ID(s):
- 1648035
- PAR ID:
- 10453150
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Biotechnology Journal
- Volume:
- 16
- Issue:
- 3
- ISSN:
- 1860-6768
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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