High-resolution gridded CO(2) and pollutant emission data from road traffic in Indian cities.

Mittakola, RT; Ciais P; Barthelemy M; Shi Q; Bonnemaizon X; Megel N; Phuleria, HC; De Sarkar K; Zhou C · 2025 · PubMed Central

DOI: 10.1038/s41597-025-06287-9

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Summary

This paper introduces CHETNA-Road, a high-resolution dataset of daily gridded CO2 and pollutant emissions from road traffic in 15 Indian cities. The research addresses the urgent need for granular emission data to support India’s net-zero carbon target by 2070 and mitigate urban air pollution. Road transport accounts for 12% of India’s energy-related CO2 emissions, a figure projected to double by 2050 due to rapid urbanization and increased vehicle ownership. Existing global datasets lack the spatial and temporal resolution required for city-level policy interventions, such as congestion pricing or targeted emission reduction strategies. The authors developed a bottom-up framework using street-level floating car data (FCD) derived from GPS-equipped vehicles for the year 2021. Because FCD captures only a fraction of total traffic (approximately 35% of fuel consumption on average), the study employed statistical and machine-learning techniques to extrapolate data to all vehicles. Missing data gaps were filled using statistical imputation for short intervals and a Light Gradient-Boosting Machine (LightGBM) model for longer gaps, achieving R² scores generally above 0.60. To account for untracked vehicles, including two- and three-wheelers, the authors scaled vehicle counts using city-level fuel consumption data from the Petroleum Planning & Analysis Cell. Emissions were then estimated using the COPERT model, which calculates speed-dependent emission factors for CO2 and ten major pollutants, including NOx, PM2.5, and black carbon. The final dataset provides daily emissions at a 500-meter grid resolution. The results demonstrate that the CHETNA-Road dataset effectively captures spatiotemporal emission patterns, identifying specific hotspots and temporal variations such as weekday versus weekend differences. The machine learning model showed robust predictive power, particularly for major roads, with spatial patterns contributing significantly to prediction accuracy. The dataset was validated against coarser global datasets like EDGAR, CAMS, and Carbon-Monitor Cities, highlighting its superior granularity. The analysis reveals distinct emission profiles across cities, influenced by local vehicle density and road infrastructure. The significance of this work lies in providing the first comprehensive, open-source, high-resolution road transport emission inventory for Indian cities. By bridging the data gap for urban-scale analysis, CHETNA-Road enables policymakers to design targeted interventions, such as congestion relief zones and dynamic pricing, to reduce gridlocks and improve air quality. The dataset supports evidence-based strategies to align urban mobility with India’s long-term climate and environmental sustainability goals.

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