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<p>India has set an ambitious target of achieving net-zero carbon emissions by 2070. Road transport, contributing to 12% of India’s CO<sub>2</sub> emissions, plays a significant role in exacerbating urban air pollution. Given India’s swift urbanization, CO<sub>2</sub> emissions from this sector could potentially double by 2050, jeopardizing climate goals. We present CHETNA-Road, a comprehensive 500-meter gridded city traffic emissions dataset for 15 Indian cities derived from street-level floating car data (FCD) based on GPS position and speed of individual vehicles. We applied statistical and machine-learning techniques to improve data quality and extrapolated data to cover all city traffic instead of only the vehicles equipped with GPS using fuel consumption data. We estimated daily CO<sub>2</sub> and ten major pollutant emissions using the COPERT model, which includes speed and vehicle-type dependent emission factors. Finally, we evaluated our dataset against global coarser resolution emission datasets, including Carbon-Monitor cities, EDGAR, and CAMS. Our dataset provides critical insights into India’s road traffic emissions and serves as a foundation for targeting congestion and pollution reduction strategies.</p>

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High-resolution gridded CO2 and pollutant emission data from road traffic in Indian cities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月12日

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  • Rohith Teja Mittakola,
  • Philippe Ciais,
  • Marc Barthelemy,
  • Qinren Shi,
  • Xavier Bonnemaizon,
  • Nicolas Megel,
  • Harish C. Phuleria,
  • Kounik De Sarkar,
  • Chuanlong Zhou

摘要

India has set an ambitious target of achieving net-zero carbon emissions by 2070. Road transport, contributing to 12% of India’s CO2 emissions, plays a significant role in exacerbating urban air pollution. Given India’s swift urbanization, CO2 emissions from this sector could potentially double by 2050, jeopardizing climate goals. We present CHETNA-Road, a comprehensive 500-meter gridded city traffic emissions dataset for 15 Indian cities derived from street-level floating car data (FCD) based on GPS position and speed of individual vehicles. We applied statistical and machine-learning techniques to improve data quality and extrapolated data to cover all city traffic instead of only the vehicles equipped with GPS using fuel consumption data. We estimated daily CO2 and ten major pollutant emissions using the COPERT model, which includes speed and vehicle-type dependent emission factors. Finally, we evaluated our dataset against global coarser resolution emission datasets, including Carbon-Monitor cities, EDGAR, and CAMS. Our dataset provides critical insights into India’s road traffic emissions and serves as a foundation for targeting congestion and pollution reduction strategies.