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<p>Assessing bacterial contamination in coastal waters is essential for protecting public health and aquatic resources, yet field-based assessments are often constrained by cost and logistical challenges. This study presents a remote sensing–based spatial modeling approach to evaluate bacterial contamination in the northern coastal Nile Delta of Egypt. Multispectral Landsat imagery was processed to derive land use/cover, water quality parameters (TP, TN, DO, SS, and BOD), and key environmental variables including chlorophyll (NDVI) and water temperature (LST). These variables were incorporated into a novel cartographic model designed to map bacterial contamination risk. Model outputs were compared with a previously published algorithm to assess relative performance. An observed fluctuation was reported in water quality parameters; TP (0.029 to 367&#xa0;mg/L), TN (0.000302 to 0.0488&#xa0;mg/L), BOD (0 to 15.7&#xa0;mg/L), SS (16.6 to 759&#xa0;mg/L), DO (3.48 to 6.62&#xa0;mg/L), temperature (13.2 to 34.5&#xa0;°C) and Chlorophyll index (-1 to 0.858). The developed model showed similar spatial pattern to the previous empirical algorithm since the high bacterial contamination was located at the southern part of the study area particularly at Manzala and Malahaat Lakes due to the extensive wastewater discharge into these sites. These findings demonstrate that integrating remote sensing with spatial modeling offers an effective and transferable early-warning tool for monitoring bacterial contamination, especially in data-limited and inaccessible regions.</p>

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Environmental variables-based spatial model for locating bacterial contamination in the Nile coastal region

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Ahmed El-Zeiny,
  • Engy Hassan,
  • Mona Kaamoush

摘要

Assessing bacterial contamination in coastal waters is essential for protecting public health and aquatic resources, yet field-based assessments are often constrained by cost and logistical challenges. This study presents a remote sensing–based spatial modeling approach to evaluate bacterial contamination in the northern coastal Nile Delta of Egypt. Multispectral Landsat imagery was processed to derive land use/cover, water quality parameters (TP, TN, DO, SS, and BOD), and key environmental variables including chlorophyll (NDVI) and water temperature (LST). These variables were incorporated into a novel cartographic model designed to map bacterial contamination risk. Model outputs were compared with a previously published algorithm to assess relative performance. An observed fluctuation was reported in water quality parameters; TP (0.029 to 367 mg/L), TN (0.000302 to 0.0488 mg/L), BOD (0 to 15.7 mg/L), SS (16.6 to 759 mg/L), DO (3.48 to 6.62 mg/L), temperature (13.2 to 34.5 °C) and Chlorophyll index (-1 to 0.858). The developed model showed similar spatial pattern to the previous empirical algorithm since the high bacterial contamination was located at the southern part of the study area particularly at Manzala and Malahaat Lakes due to the extensive wastewater discharge into these sites. These findings demonstrate that integrating remote sensing with spatial modeling offers an effective and transferable early-warning tool for monitoring bacterial contamination, especially in data-limited and inaccessible regions.