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Light pollution, often an overlooked environmental challenge, is rapidly becoming a significant concern for Indian urban cities due to the accelerating pace of urbanization and infrastructural growth. The excessive and inefficient use of artificial light at night has profound implications for ecosystems, human health, and energy sustainability. This paper uses satellite-based data from the National Oceanic and Atmospheric Administration’s (NOAA, US) Visible Infrared Imaging Radiometer Suite (VIIRS) in Ahmedabad, India, to analyse and forecast changes in light pollution from 2014 to 2022. According to the proposed the seasonal autoregressive integrated moving average with exogenous regressor forecast for Ahmedabad, India, the radiance levels keep rising, and in the absence of mitigating measures, worsening the negative impacts on energy use, human health, and biodiversity. This study highlights the utility of remote sensing and time-series forecasting techniques in understanding and addressing urban environmental challenges.

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An Alarming Threat of Light Pollution in Ahmedabad City: Efficient Time-Series Forecasting Framework

  • 会议论文

  • 发表日期：

    2026年

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  • Saikat Mondal,
  • Pragna Labani Sikdar,
  • Parag Kumar Guha Thakurta

摘要

Light pollution, often an overlooked environmental challenge, is rapidly becoming a significant concern for Indian urban cities due to the accelerating pace of urbanization and infrastructural growth. The excessive and inefficient use of artificial light at night has profound implications for ecosystems, human health, and energy sustainability. This paper uses satellite-based data from the National Oceanic and Atmospheric Administration’s (NOAA, US) Visible Infrared Imaging Radiometer Suite (VIIRS) in Ahmedabad, India, to analyse and forecast changes in light pollution from 2014 to 2022. According to the proposed the seasonal autoregressive integrated moving average with exogenous regressor forecast for Ahmedabad, India, the radiance levels keep rising, and in the absence of mitigating measures, worsening the negative impacts on energy use, human health, and biodiversity. This study highlights the utility of remote sensing and time-series forecasting techniques in understanding and addressing urban environmental challenges.