Spatial drivers of atmospheric NO₂ and NH₃ in China: Disentangling source- versus process-driven controls

Area of Science:

• Atmospheric chemistry and air pollution research.
• Environmental science and management.
• Machine learning applications in environmental monitoring.

Background:

• Effective air quality management necessitates differentiating drivers of nitrogen dioxide (NO2) and ammonia (NH3).
• Nationwide air quality dynamics in China require detailed characterization for targeted interventions.
• Previous studies often lack granular attribution of factors influencing NO2 and NH3 concentrations.

Purpose of the Study:

• To characterize the nationwide dynamics of NO2 and NH3 concentrations in China from 2015 to 2023.
• To identify and differentiate the primary drivers and influencing factors for NO2 and NH3.
• To provide a scientific basis for developing tailored nitrogen mitigation strategies.

Main Methods:

• Utilized an interpretable machine-learning framework integrating satellite observations.
• Employed Random Forest modeling and SHapley Additive exPlanations (SHAP) for attribution analysis.
• Analyzed nationwide data from 2015 to 2023 to assess pollutant dynamics and drivers.

Main Results:

• NO2 concentrations significantly declined nationwide (6.20 to 4.90 μg/m³), while NH3 levels remained stable.
• NO2 hotspots were observed in urban-industrial areas, driven primarily by anthropogenic factors (35.1%).
• NH3 enhancements occurred over agricultural regions, driven by ecological and agricultural factors (30.9%), including fertilizer use.

Conclusions:

• Divergent drivers for NO2 (source-driven) and NH3 (process-driven) necessitate distinct management approaches.
• Emission control policies have been effective for NO2, but NH3 requires integrated strategies focusing on agricultural best practices and ecosystem adaptation.
• This study provides crucial insights for developing region-specific nitrogen mitigation strategies in China.