Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020

Evidence for Coordinated Control of PM2.5 and O3: Long-Term Observational Study in a Typical City of Central Plains Urban Agglomeration

Fine particulate matter (PM2.5) and Ozone (O3) pollution have emerged as the primary environmental challenges in China in recent years. Following the implementation of the Air Pollution Prevention and Control Action Plan, a substantial decline in PM2.5 concentrations was observed, while O3 concentrations exhibited an increasing trend across the country. Here, we investigated the long-term trend of O3 from 2015 to 2022 in Xinxiang City, a typical city within the Central Plains urban agglomeration. Our findings indicate that the hourly average O3 increased by 3.41 μg m-3 yr-1, with the trend characterized by two distinct phases (Phase I, 2015-2018; Phase II, 2019-2022). Interestingly, the increasing rate of O3 concentration in Phase I (7.89 μg m-3) was notably higher than that in Phase II (2.89 μg m-3). The Random Forest (RF) model was employed to identify the key factors influencing O3 concentrations during the two phases. The significant dropping of PM2.5 in Phase I could be responsible for the O3 increase. In Phase II, the reductions in nitrogen dioxide (NO2) and unfavorable meteorological conditions were the major drivers of the continued increase in O3. The Observation-Based Model (OBM) was developed to further explore the role of PM2.5 in O3 formation. Our results suggest that PM2.5 can influence O3 concentrations and the chemical sensitivity regime through heterogeneous reactions and changes in photolysis rates. In addition, the relatively high concentration of PM2.5 in Xinxiang City in recent years underscores its significant role in O3 formation. Future efforts should focus on the joint control of PM2.5 and O3 to improve air quality in the Central Plains urban agglomeration.

Causal-inference machine learning reveals the drivers of China's 2022 ozone rebound

Ground-level ozone concentrations rebounded significantly across China in 2022, challenging air quality management and public health. Identifying the drivers of this rebound is crucial for designing effective mitigation strategies. Commonly used methods, such as chemical transport models and machine learning, provide valuable insights but face limitations-chemical transport models are computationally intensive, while machine learning often fails to address confounding factors or establish causality. Here we show that elevated temperatures and increased solar radiation, as primary meteorological drivers, collectively account for 57 % of the total ozone increase, based on an integrated analysis of ground-based monitoring data, satellite observations, and meteorological reanalysis information using explainable machine learning and causal inference techniques. Compared to the year 2021, 90 % of the stations reported an increase in the Formaldehyde to Nitrogen ratio, implying a growing sensitivity of ozone formation to nitrogen oxide levels. These findings highlight the significant causal role of meteorological changes in the ozone rebound, urging the adoption of targeted ozone mitigation strategies under climate warming, particularly through varied regional strategies that consider existing anthropogenic emission levels and the prospective increase in biogenic volatile organic compounds. This identification of causal relationships in air pollution dynamics can support data-driven and accurate decision-making.

Change in aerosol optical depth in Chinese urban agglomerations during the early phase of dual-carbon actions

Dual-carbon policies were implemented by Chinese government to mitigate climate warming; however, changes in aerosol optical depth (AOD) during early phases of these actions (2020-2022) remain unclear. Thus, AOD variations during this period were investigated compared to the baseline (2015-2019 mean) across seven urban agglomerations (UAs) using multi-source data. Significant temporal variations in AOD anomalies (ΔAOD) were observed at annual and seasonal scales, with varying magnitudes. On average, AOD decrease by -0.210, -0.225, -0.180, and -0.038 in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Guanzhong Plain (GZP), and Liaozhongnan (LZN), respectively. In contrast, increases of 0.041 and 0.057 were observed in Pearl River Delta (PRD) and Tianshan Mountains northern foothills (TSBP), while Lhasa metropolitan area (LS) remain stable (0.0001). In 2021, the most significant decrease in AOD was recorded in YRD, whereas the greatest declines in other urban areas were noted in 2022. The largest ΔAOD in LZN, BTH, and YRD occurred in autumn, while the peak ΔAOD in GZP was observed in spring. In contrast, the highest ΔAOD in PRD, TSBP, and LS occurred in summer. These temporal variations in ΔAOD can be attributed to dual-carbon policies, natural and meteorological anomalies, along with the impacts of stringent lockdown policies and abrupt dust events. Path analysis indicated that the effects of various drivers on ΔAOD were interconnected. When interactions among these factors were considered, their influences on ΔAOD were found to be either amplified or altered, highlighting the importance of accounting for interactions within the human-nature-meteorological system in formulation of atmospheric pollution policies. These findings contribute to a deeper understanding of ΔAOD variation mechanisms and provide valuable insights for the development of air pollution reduction strategies at the UA scale.

Elucidating contributions of meteorology and emissions to O3 variations in coastal city of China during 2019-2022: Insights from VOCs sources

Elucidating the meteorology and emissions contribution of O3 variation is a crucial issue for implementing effective measures for O3 pollution control. We quantified the impacts of meteorology and emissions on O3 variability during spring and autumn from 2019 to 2022, using multi-year continuous observations. A machine learning (ML)-based de-weathering model revealed that meteorology accounted for a greater proportion of O3 variability (71.9% in spring and 57.4% in autumn) compared to emissions (28.1% and 42.6%, respectively). In spring, relative humidity (RH, 22.8%) and wind speed (WS, 13.7%) were key drivers, contributing to O3 decreases and increases, respectively. During autumn, temperature (T, 10.8%) and surface solar radiation (SSR, 9.45%) were the dominant factors, both contributing to O3 production. We assessed the O3 formation sensitivity based on VOCs emissions sources and evaluated the importance of emission by O3 production rate (P(O3)) calculated from box model and the positive matrix factorization (PMF) model. Vehicle emissions and solvent use were identified as the major contributors to O3 formation from 2019 to 2022 and reducing them would be beneficial for O3 pollution control. This study elucidates the relative roles of meteorological conditions and anthropogenic emissions in O3 variability and key insights for formulating future O3 control policies.

Seasonal biogenic volatile organic compound emission factors in temperate tree species: Implications for emission estimation and ozone formation

Variability in biogenic volatile organic compound (BVOC) emissions across species and seasons poses challenges for accurate regional emission estimates and effective ozone (O3) control policies. To address this issue, we conducted in-situ measurements of emission factors for six dominant tree species in Beijing across four seasons. Subsequently, we developed monthly dynamic standard emission factors (SER-MDs) to model monthly BVOC emissions and their impacts on O3 formation at citywide and district levels. Our observations revealed pronounced seasonal differences in the BVOC composition and emission rates, as well as their responsiveness to monthly average temperature. By introducing the SER-MDs, we estimated BVOC emissions from the dominant tree species in Beijing to be 38.2 Gg yr-1, with monoterpenes and isoprene contributing 49% and 11%, respectively. This calculation reduced the overestimation associated with constant standard emission factors by 31%-38% at district level. The estimates also revealed regional differences in plant compositions rather than simple feedback from regional temperature and photosynthetically active radiation periods. Under these conditions, the maximum monthly BVOC-induced O3 concentration occurred in August and ranged from 4 to 17 μg m-3 across districts, with isoprene being the dominant contributor. Quercus mongolica and Populus tomentosa played significant roles in the formation of BVOC-induced O3 due to their strong isoprene emitting potential in July-August. These results indicate the necessity of introducing species-specific rhythms of BVOC emissions from dominant species in the development of urban BVOC emission inventories. This approach could inform the development of air pollution management policies that are consistent with the local vegetation composition and O3 pollution characteristics. For Beijing and other similar northern cities, reducing the use of tree species emitting substantial amounts of isoprene during periods of regional peak ambient O3 concentrations could constitute an effective nature-based solution for improving urban air quality in the future.

Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China

Megacity Hangzhou, located in eastern China, has experienced severe O3 pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O3 deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O3 formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021-2022. The EML model achieved better performance than the single model, with R2 values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O3 variability with the contribution of 57 %-60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O3 formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM2.5), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM2.5 and NOx were also important and increased with the O3 deterioration. The impact of seven emission sources on O3 formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O3 pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O3 concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O3 pollution at different event scales, supporting the formulation of control measures.