Assessing the impact of the National Clean Air Programme in Uttar Pradesh's non-attainment cities: a prophet model time series analysis

• Published in: The Lancet regional health. Southeast Asia Vol. 30; p. 100486
• Main Authors: Bera, Om Prakash, Venkatesh, U., Pal, Gopal Krushna, Shastri, Siddhant, Chakraborty, Sayantan, Grover, Ashoo, Joshi, Hari Shanker
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.11.2024; Elsevier
• Subjects: Air pollution; Air quality index (AQI); Facebook prophet forecasting; National clean air programme; Non-attainment city; Particulate matter; PM10; Particulate matter; Air pollution; PM10; Air quality index (AQI); Facebook prophet forecasting; Non-attainment city; National clean air programme
• ISSN: 2772-3682; 2772-3682
• DOI: 10.1016/j.lansea.2024.100486

Uttar Pradesh, India's largest state, faces critical pollution levels, necessitating urgent action. The National Clean Air Programme (NCAP) targets a 40% reduction in particulate pollution by 2026. This study assesses the impact of NCAP on 15 non-attainment cities in Uttar Pradesh using the Prophet forecasting model. Monthly data on AQI and PM10 concentrations from 2016 to 2023 were sourced from the Uttar Pradesh Pollution Control Board. Significant changes in mean AQI and PM10 levels from 2017 to 2023 were evaluated using the Friedman test. Prophet models forecast PM10 concentrations for 2025–26, with relative percentage changes calculated and model evaluation metrics assessed. Most cities exhibited unhealthy air quality. Jhansi had the lowest AQI (72.73) in 2023, classified as ‘moderate’ by WHO standards. Gorakhpur consistently showed ‘poor’ AQI levels, peaking at 249.31 in 2019. Western Uttar Pradesh cities such as Ghaziabad, Noida, and Moradabad had significant pollution burdens. Predictions showed Bareilly with over a 70% reduction in PM10 levels, Raebareli 58%, Moradabad 55%, Ghaziabad 48%, Agra around 41%, and Varanasi 40%, meeting NCAP targets. However, Gorakhpur and Prayagraj predicted increases in PM10 levels by 50% and 32%, respectively. Moradabad's model showed the best performance with an R2 of 0.81, MAE of 17.27 μg/m3, and MAPE of 0.10. Forecasting PM10 concentrations in Uttar Pradesh's non-attainment cities offers policymakers substantial evidence to enhance current efforts. While existing measures are in place, our findings suggest that intensified provisions may be necessary for cities predicted to fall short of meeting program targets. The Prophet model's forecasts can pinpoint these at-risk areas, allowing for targeted interventions and regional adjustments to strategies. This approach will help promote sustainable development customized to each city's specific needs. No funding was issued for this research.