Volume 17 Issue 1
Mar.  2024
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Qin Qian, Mengjie He, Frank Sun, Xinyu Liu. 2024: Monitoring and evaluation of the water quality of the Lower Neches River, Texas, USA. Water Science and Engineering, 17(1): 21-32. doi: 10.1016/j.wse.2023.10.002
Citation: Qin Qian, Mengjie He, Frank Sun, Xinyu Liu. 2024: Monitoring and evaluation of the water quality of the Lower Neches River, Texas, USA. Water Science and Engineering, 17(1): 21-32. doi: 10.1016/j.wse.2023.10.002

Monitoring and evaluation of the water quality of the Lower Neches River, Texas, USA

doi: 10.1016/j.wse.2023.10.002

This work was supported by Center for Resiliency (CfR) at Lamar University (Grant No. 22PSS01).

  • Received Date: 2022-11-20
  • Accepted Date: 2023-09-23
  • Available Online: 2024-03-05
  • Increasing bacteria levels in the Lower Neches River caused by Hurricane Harvey has been of a serious concern. This study is to analyze the historical water sampling measurements and real-time water quality data collected with wireless sensors to monitor and evaluate water quality under different hydrological and hydraulic conditions. The statistical and Pearson correlation analysis on historical water samples determines that alkalinity, chloride, hardness, conductivity, and pH are highly correlated, and they decrease with increasing flow rate due to dilution. The flow rate has positive correlations with Escherichia coli, total suspended solids, and turbidity, which demonstrates that runoff is one of the causes of the elevated bacteria and sediment loadings in the river. The correlation between E. coli and turbidity indicates that turbidity greater than 45 nephelometric turbidity units in the Neches River can serve as a proxy for E. coli to indicate the bacterial outbreak. A series of statistical tools and an innovative two-layer data smoothing filter are developed to detect outliers, fill missing values, and filter spikes of the sensor measurements. The correlation analysis on the sensor data illustrates that the elevated sediment/bacteria/algae in the river is either caused by the first flush rain and heavy rain events in December to March or practices of land use and land cover. Therefore, utilizing sensor measurements along with rainfall and discharge data is recommended to monitor and evaluate water quality, then in turn to provide early alerts on water resources management decisions.


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  • APHA, 1998. Standard Methods for the Examination of Water and Wastewater, 20th Edition. APHA-AWWA-WEF, Washington, D.C.
    Cavicchioli, R., Ripple, W.J., Timmis, K.N., Azam, F., Bakken, L.R., Baylis, M., Behrenfeld, M.J., Boetius, A., Boyd, P.W., Classen, A.T., et al., 2019. Scientists' warning to humanity: Microorganisms and climate change. Nat. Rev. Microbiol. 17, 569-586. https://doi.org/10.1038/s41579-019-0222-5.
    Cho, K.H., Wolny, J., Kase, J.A., Unno, T., Pachepsky, Y., 2022. Interactions of E. coli with algae and aquatic vegetation in natural waters. Water Res. 209, 117952. https://doi.org/10.1016/j.watres.2021.117952.
    Curtis, T.P., Mara, D.D., Dixo, N.G.H., Silva, S.A., 1994. Light penetration in waste stabilization ponds. Water Res. 28, 1031-1038. https://doi.org/10.1016/0043-1354(94)90188-0.
    Kroer, N., 1993. Bacterial growth efficiency on natural dissolved organic matter. Limnol. Oceanogr. 38(6), 1282-1290. https://doi.org/10.4319/lo.1993.38.6.1282.
    Maes, S., Odlare, M., Jonsson, A., 2022. Fecal indicator organisms in northern oligotrophic rivers: An explorative study on Escherichia coli prevalence in a mountain region with intense tourism and reindeer herding. Environ. Monit. Assess. 194, 264. https://doi.org/10.1007/s10661-022-09865-1.
    Martin, R.M., Dearth, S.P., LeCleir, G.R., Campagna, S.R., Fozo, E.M., Zinser, E.R., Wilhelm, S.W., 2017. Microcystin-LR does not induce alterations to transcriptomic or metabolomic profiles of a model heterotrophic bacterium. PLoS One 12(2), e0189608. https://doi.org/10.1371/journal.pone.0189608.
    Munoz, R., Guieysse, B., 2006. Algal-bacterial processes for the treatment of hazardous contaminants: A review. Water Res. 40, 2799-2815. https://doi.org/10.1016/j.watres.2006.06.011.
    New Hampshire Department of Environmental Services (NHDES), 2009. Assessment of Chlorophyll-A and Phosphorus in New Hampshire Lakes for Nutrient Criteria Development. NHDES, Concord. https://www.des.nh.gov/sites/g/files/ehbemt341/files/documents/2020-01/r-wd-09-29.pdf.
    Novotny, V., 1999. Diffuse pollution from agriculture-A worldwide outlook. Water Sci. Technol. 39(3), 1-13. https://doi.org/10.2166/wst.1999.0124.
    Pandia, K., Ravindran, S., Cole, R., Kovacs, G., Giovangrandi, L., 2010. Motion artifact cancellation to obtain heart sounds from a single chest-worn accelerometer. In: Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, Dallas, pp. 590-593. https://doi.org/10.1109/ICASSP.2010.5495553.
    Qian, Q., Sun, B., Li, X., Sun, F., Lin, C.-J., Jiang, L., 2019. Water quality evaluation on an urban stormwater retention pond using wireless sensor networks and hydrodynamic modeling. J. Irrigat. Drain. Eng. 145(12), 05019011. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001434.
    Qian, Q., Sun, F., Sun, B., Zhang, J., Zhang, Y., 2022. Forecasting water quality using AI with monitoring data collected by wireless sensors. In: Proceedings of the World Environmental and Water Resources Congress 2022. ASCE, Henderson, pp. 37-49. https://doi.org/10.1061/9780784484258.004.
    Quero, G.M., Fasolato, L., Vignaroli, C., Luna, G.M., 2015. Understanding the association of Escherichia coli with diverse macroalgae in the lagoon of Venice. Sci. Rep. 5, 10969. https://doi.org/10.1038/srep10969.
    Ramanan, R., Kim, B.-H., Cho, D.-H., Oh, H.-M., Kim, H.-S., 2016. Algae-bacteria interactions: Evolution, ecology and emerging applications. Biotechnol. Adv. 34(1), 14-29. https://doi.org/10.1016/j.biotechadv.2015.12.003.
    Savitzky, A., Golay, M.J.E., 1964. Smoothing and differentiation of data by simplified least squares rrocedures. Anal. Chem. 36(8), 1627-1639. https://doi.org/10.1021/ac60214a047.
    Schulz, K., 2007. Phytoplankton measuring and culture techniques. In: Phytoplankton Ecology (Lecture). State University of New York, Syracuse. http://www.esf.edu/efb/schulz/phytotechniques.doc.
    Seo, S., 2006. Review and comparison of methods for detecting outliers in univariate data sets. Master Thesis. University of Pittsburgh, Pittsburgh. http://d-scholarship.pitt.edu/7948/.
    Slaff, J., Drane-Maury, M., 2019. NOAA Forecasts Very Large ‘Dead Zone’ for Gulf of Mexico. National Oceanic and Atmospheric Administration, Washington, D.C. https://www.noaa.gov/media-release/noaa-forecasts-very-large-dead-zone-for-gulf-of-mexico.
    Speer, B.R., 1997. Photosynthetic pigments. In: UCMP Glossary (Online). University of California, Berkeley Museum of Paleontology, Berkeley. https://ucmp.berkeley.edu/glossary/gloss3/pigments.html.
    Texas Commission on Environmental Quality (TCEQ), 2021. report2020 Texas Integrated Report - Assessment Results for Basin 6 - Neches River Basin. TCEQ, Austin. https://www.tceq.texas.gov/assets/public/waterquality/swqm/assess/20txir/2020_Basin6.pdf.
    Thomas, O., Burgess, C., 2007. UV-visible Spectrophotometry of Water and Wastewater, Techniques and Instrumentation in Analytical Chemistry. Elsevier, Amsterdam, Boston.
    USEPA, 1986. Quality Criteria for Water 1986, EPA440/5-86-001. Office of Water Regulations and Standards, USEPA, Washington, D.C. https://www.epa.gov/sites/default/files/2018-10/documents/quality-criteria-water-1986.pdf.
    Vanden Heuvel, A., McDermott, C., Pillsbury, R., Sandrin, T., Kinzelman, J., Ferguson, J., Sadowsky, M., Byappanahalli, M., Whitman, R., Kleinheinz, G.T., 2010. The green alga, Cladophora, promotes Escherichia coli growth and contamination of recreational waters in Lake Michigan. J. Environ. Qual. 39(1), 333-344. https://doi.org/10.2134/jeq2009.0152.
    Waskom, M., 2021. Seaborn: Statistical data visualization. J. Open Source Softw. 6, 3021. https://doi.org/10.21105/joss.03021.
    YSI, 2006. YSI 6131 and 6132 Blue-green algae sensors: Phycocyanin and phycoerythrin sensors. In: YSI Sensor Brochure. YSI, Yellow Springs. https://www.fondriest.com/pdf/ysi_6131_6132_spec.pdf.
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