Analysis reveals rising ozone levels correlate with forest yield losses and economic impacts in Southwestern China, suggesting urgent ecological interventions.
As a key tropospheric photochemical pollutant, ground-level ozone (O3) poses significant threats to ecosystems through its strong oxidative capacity. With China’s rapid industrialization and urbanization, worsening O3 pollution has emerged as a critical environmental concern. This study examines O3’s impacts on forest ecosystems in Southwestern China (Yunnan, Guizhou, Sichuan, and Chongqing), which harbors crucial forest resources. We analyzed high-resolution monitoring data from over 200 stations (2019–2023), employing spatial interpolation to derive the regional maximum daily 8 h average O3 (MDA8-O3, ppb) and accumulated O3 exposure over 40 ppb (AOT40) metrics. Through AOT40-based exposure–response modeling, we quantified the forest relative yield losses (RYL), economic losses (ECL) and ECL/GDP (GDP: gross domestic product) ratios in this region. Our findings reveal alarming O3 increases across the region, with a mean annual MDA8-O3 anomaly trend of 2.4% year−1 (p < 0.05). Provincial MDA8-O3 anomaly trends varied from 1.4% year−1 (Yunnan, p = 0.059) to 4.3% year−1 (Guizhou, p < 0.001). Strong correlations (r > 0.85) between annual RYL and annual MDA8-O3 anomalies demonstrate the detrimental effects of O3 on forest biomass. The RYL trajectory showed an initial decline during 2019–2020 and accelerated losses during 2020–2023, peaking at 13.8 ± 6.4% in 2023. Provincial variations showed a 5-year averaged RYL ranging from 7.10% (Chongqing) to 15.85% (Yunnan). O3 exposure caused annual ECL/GDP averaging 4.44% for Southwestern China, with Yunnan suffering the most severe consequences (ECL/GDP averaging 8.20%, ECL averaging CNY 29.8 billion). These results suggest that O3-driven forest degradation may intensify, potentially undermining the regional carbon sequestration capacity, highlighting the urgent need for policy interventions. We recommend enhanced monitoring networks and stricter control methods to address these challenges.
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Xia et al. (2025) studied this question.
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