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The spatiotemporal pattern of surface ozone and its impact on agricultural productivity in China.

Xiaoguang ChenJing GaoLuoye ChenMadhu KhannaBinlei GongMaximilian Auffhammer
Published in: PNAS nexus (2023)
The slowing of agricultural productivity growth globally over the past two decades has brought a new urgency to detect its drivers and potential solutions. We show that air pollution, particularly surface ozone (O 3 ), is strongly associated with declining agricultural total factor productivity (TFP) in China. We employ machine learning algorithms to generate estimates of high-resolution surface O 3 concentrations from 2002 to 2019. Results indicate that China's O 3 pollution has intensified over this 18-year period. We coupled these O 3 estimates with a statistical model to show that rising O 3 pollution during nonwinter seasons has reduced agricultural TFP by 18% over the 2002-2015 period. Agricultural TFP is projected to increase by 60% if surface O 3 concentrations were reduced to meet the WHO air quality standards. This productivity gain has the potential to counter expected productivity losses from 2°C warming.
Keyphrases
  • climate change
  • human health
  • heavy metals
  • particulate matter
  • machine learning
  • risk assessment
  • air pollution
  • high resolution
  • health risk assessment
  • hydrogen peroxide
  • cystic fibrosis