Research on ozone formation sensitivity based on observational methods: Development history, methodology, and application and prospects in China

Impact of agricultural straw open-field burning on concentrations of six criteria air pollutants in China

Agricultural straw open-field burning (ASOB) is a major source of fine particles and carbonaceous aerosols, particularly in China, India, and Southeast Asia. However, the exposure-lag-response relationship between straw burning and urban air pollution in China remains insufficiently investigated. This study compiled satellite-based ASOB data along with daily meteorological and air pollution monitoring data for 156 Chinese cities from 2015 to 2020. The ASOB points detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) were identified as exposure events, and their exposure-lag-response relationships with daily pollutant levels were elucidated using distributed lag nonlinear models. The nation-level estimate of the impact of ASOB points on urban air quality was obtained by a meta-analysis. The results revealed significant short-term elevation in the daily concentrations of six pollutants. Each increase of 10 straw burning points is associated with an increase of 8.89, 8.52, 8.17, 2.43, and 0.84 μg/m3 in PM10, O3, PM2.5, NO2, and SO2, respectively, and an increase of 0.048 mg/m3 in CO with a lag of 0-3 days. Regional and seasonal ASOB variations and their effects were observed, revealing a pronounced effect in East China, particularly from October to December. ASOB contributed 4.54 % of O3 and 2.72 % of PM2.5 concentrations in air pollution waves in the high-intensity ASOB burning seasons. This study highlights the adverse impact of open-field straw burning on air quality, even under China's strict ASOB ban, providing scientific evidence for future assessments of the cost-effectiveness of straw-burning bans and policy refinements.

Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis

Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, traditional approaches, such as sensitivity and uncertainty analyses, are computationally intensive and inefficient, as they require numerous model runs. In this study, we explored the use of machine learning, specifically XGBoost combined with SHAP analysis, as an efficient diagnostic tool for analyzing simulation biases, focusing on ozone modeling in Guangdong Province as a case study. We used the bias of model inputs as features and excluded a dataset that was more susceptible to observational uncertainties to better target bias sources. Results reveal that biases in concentrations of NO2, NO and PM2.5, temperature and biogenic emissions are important sources that lead to O3 simulation biases. Notably, NOx emissions were identified as the primary cause, particularly in VOC-limited regimes during autumn and winter. Additionally, underestimated NOx emissions caused the model to misrepresent the NO2-O3 relationship, leading to an underestimation of the spatial extent of VOC-limited regimes in the PRD. This study demonstrates that enhancing NOx emission estimates reduces O3 simulation biases in the PRD by 34% and enhances the representation of the NO2-O3 relationship.

BVOCs' role in dynamic shifts of summer ozone formation regimes across China and policy implications

Biogenic volatile organic compounds (BVOCs) are crucial players in atmospheric chemistry, significantly impacting the formation of tropospheric ozone (O₃). While China has made substantial strides in reducing anthropogenic VOC (AVOCs) emissions, O₃ levels persist, highlighting the complex interplay between biogenic and anthropogenic sources. A critical knowledge gap exists in understanding how BVOC emissions influence ozone formation regimes (OFRs) and how this knowledge can inform effective air quality policies. This study employs the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 3.2 and the Community Multiscale Air Quality Modeling System (CMAQ) version 5.3.3 models, combined with process analysis (PA) and the Integrated Source Apportionment Method (ISAM), to evaluate the impact of BVOC emissions on OFRs in China. The models simulate BVOC emissions and their effects on OFRs across various regions during July 2019. The findings highlight that BVOCs play a pivotal role in shifting OFRs, with significant implications for ozone mitigation strategies in China. The study suggests that effective ozone control measures must consider the dual impact of BVOCs and AVOCs, with tailored strategies for different regions and times of day. The study also proposes potential challenges in mitigating BVOC emissions and outlines future research directions for interdisciplinary collaboration to address the complexities of ozone pollution management. This research advances the understanding of BVOCs' roles in ozone formation dynamics and provides a foundation for developing more effective air quality management policies in China, especially as global greening and climate change continue to influence BVOC emissions.

Characterization and Source Apportionment Analysis of PM2.5 and Ozone Pollution over Fenwei Plain, China: Insights from PM2.5 Component and VOC Observations

PM2.5 and volatile organic compounds (VOCs) have been identified as the primary air pollutants affecting the Fenwei Plain (FWP), necessitating urgent measures to improve its air quality. To gain a deeper understanding of the formation mechanisms of these pollutants, this study employed various methods such as HYSPLIT, PCT, and PMF for analysis. Our results indicate that the FWP is primarily impacted by PM2.5 from the southern Shaanxi air mass and the northwestern air mass during winter. In contrast, during summer, it is mainly influenced by O3 originating from the southern air mass. Specifically, high-pressure fronts are the dominant weather pattern affecting PM2.5 pollution in the FWP, while high-pressure backs predominately O3 pollution. Regarding the sources of PM2.5, secondary nitrates, vehicle exhausts, and secondary sulfates are major contributors. As for volatile organic compounds, liquefied petroleum gas sources, vehicle exhausts, solvent usage, and industrial emissions are the primary sources. This study holds crucial scientific significance in enhancing the regional joint prevention and control mechanism for PM2.5 and O3 pollution, and it provides scientific support for formulating effective strategies for air pollution prevention and control.

Discrepant Global Surface Ozone Responses to Emission- and Heatwave-Induced Regime Shifts

Heatwaves have substantial but poorly quantified impacts on surface ozone photochemical regimes. As heatwaves of increasing severity occur, communities face more serious exposure to ozone, necessitating a more comprehensive understanding of the impact of heatwaves on the nonlinear response of ozone to its precursors for guiding policies in emission reductions. Here we estimate the spatiotemporal evolution of global ozone chemistry based on machine learning and in situ observations and show that emission changes and heatwaves alter ozone photochemical regimes, leading to diverse ozone changes across regions. Sustained emission reductions in East Asia decreased the ozone formation sensitivity to formaldehyde (HCHO) and fine particulate matter (PM2.5), counteracting the adverse high-temperature effect. Quantified results reveal that heatwaves increased the sensitivity of ozone to HCHO and PM2.5, enhancing their positive contributions and causing increased ozone trends across most regions, with a global average anomaly of 9.4 μg/m3. Meanwhile, heatwave-induced PM2.5 anomalies concentrated in wildfire-risk zones, coupled with increased HCHO, elevated downwind ozone levels. Specifically, the effects in wildfire-endangered western Canada and heatwave-exposed southeastern United States contributed to a chemically driven ozone increase of 0.18 μg/m3/month in Northern America. Our results demonstrate that more targeted and substantial regulation of volatile organic compounds will be beneficial in mitigating future intensifying climate penalty effects.

Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China

Ground-level ozone (O3) has been an emerging concern in China. Due to its complicated formation mechanisms, understanding the effects of influencing factors is critical for making effective efforts on the pollution control. This study aims to present and demonstrate the practicality of a data-driven technique that applies a machine learning (ML) model coupled with the SHapley Additive exPlanations (SHAP) approach in O3 simulation and sensitivity analysis. Based on hourly measured concentrations of O3 and its major precursors, as well as meteorological factors in a northern area of Nanjing, China, a Light Gradient Boosting Machine (LightGBM) model was established to simulate O3 concentrations in different seasons, and the SHAP approach was applied to conduct in-depth analysis on the impacts of influencing factors on O3 formation. The results indicated a reliable performance of the ML model in simulating O3 concentrations, with the coefficient of determination (R2) between the measured and simulated larger than 0.80, and the impacts of influencing factors were reasonably evaluated by the SHAP approach on both seasonal and diurnal time scales. It was found that although volatile organic compounds (VOCs) and nitrogen oxides (NOx), as well as temperature and relative humidity, were generally the main influencing factors, their sensitivities to O3 formation varied significantly in different seasons and with time of the day. This study suggests that the data-driven ML model is a practicable technique and may act as an alternative way to perform mechanism analysis to some extent, and has immense potential to be applied in both problem research and decision-making for air pollution control.

PM2.5 pollution modulates the response of ozone formation to VOC emitted from various sources: Insights from machine learning

Numerous studies have linked ozone (O3) production to its precursors and fine particulate matter (PM2.5), while the complex interaction effects of PM2.5 and volatile organic compounds (VOCs) on O3 remain poorly understood. A systematic approach based on an interpretable machine learning (ML) model was utilized to evaluate the primary driving factors that impact O3 and to elucidate how changes in PM2.5, VOCs from different sources, NOx, and meteorological conditions either promote or inhibit O3 formation through their individual and synergistic effects in a tropical coastal city, Haikou, from 2019 to 2020. The results suggest that under low PM2.5 levels, alongside the linear O3-PM2.5 relationship observed, O3 formation is suppressed by PM2.5 with higher proportions of traffic-derived aerosol. Vehicle VOC emissions contributed maximally to O3 formation at midday, despite the lowest concentration. VOCs from fossil fuel combustion and industry emissions, which have opposing effects on O3, act as inhibitors and promoters by inducing diverse photochemical regimes. As PM2.5 pollution escalates, the impact of these VOCs reverses, becoming more pronounced in shaping O3 variation. Sensitivity analysis reveals that the O3 formation regime is VOC-limited, and effective regional O3 mitigation requires prioritizing substantial VOC reductions to offset enhanced VOC sensitivity induced by the co-reduction in PM2.5, with a focus on industrial and vehicular emissions, and subsequently, fossil fuel combustion once PM2.5 is effectively controlled. This study underscores the potential of the SHAP-based ML approach to decode the intricate O3-NOx-VOCs-PM2.5 interplay, considering both meteorological and atmospheric compositional variations.

Comparison of the ozone formation mechanisms and VOCs apportionment in different ozone pollution episodes in urban Beijing in 2019 and 2020: Insights for ozone pollution control strategies

Ground-level ozone (O3) pollution has been a tough issue in urban areas of China in the past decade. Clarifying the formation mechanisms of O3 and the sources of its precursors is necessary for the effective prevention of O3 pollution. In this study, a comparative analysis of O3 formation mechanisms and VOCs apportionment for five O3 pollution episodes was carried out at two urban sites (CRAES and CGZ) in Beijing in 2019 and 2020 by applying an observation-based modeling approach in order to obtain insights into O3 pollution control strategies. Results indicated that O3 pollution levels were generally more severe in 2019 than in 2020 during the observation periods. O3 formation at the two sites was both VOCs-limited on O3 polluted days and non-O3 polluted days. Stronger atmospheric oxidation capacity and ROx radicals cycling processes were found on O3 polluted days which could accelerate the local production of O3, and local photochemical production dominated the observed O3 concentrations at the two sites even on non-O3 polluted days. Emission reduction of VOCs should be a priority for mitigating O3 pollution, and alkenes and biogenic VOCs was the priority species at the CRAES and CGZ sites, respectively. Additionally, the reduction of oxygenated VOCs should also be important for the ozone control. Gasoline exhaust at the CRAES site, and solvent utilization and fuel evaporation at the CGZ site were main anthropogenic sources of VOCs. Therefore, local control measures should be further strengthened and differentiated control strategies of VOCs in the aspects of area, time, sources and species should be adopted in urban Beijing in the future. Overall, the findings of this study could provide a scientific understanding of the causes of O3 pollution and significant guidelines for formulating O3 control strategies from the perspective of different ozone pollution episodes in urban Beijing.

Evolution of Ozone Formation Sensitivity during a Persistent Regional Ozone Episode in Northeastern China and Its Implication for a Control Strategy

In recent years, the magnitude and frequency of regional ozone (O3) episodes have increased in China. We combined ground-based measurements, observation-based model (OBM), and the Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model to analyze a typical persistent O3 episode that occurred across 88 cities in northeastern China during June 19-30, 2021. The meteorological conditions, particularly the wind convergence centers, played crucial roles in the evolution of O3 pollution. Daily analysis of the O3 formation sensitivity showed that O3 formation was in the volatile organic compound (VOC)-limited or transitional regime at the onset of the pollution episode in 92% of the cities. Conversely, it tended to be or eventually became a NOx-limited regime as the episode progressed in the most polluted cities. Based on the emission-reduction scenario simulations, mitigation of the regional O3 pollution was found to be most effective through a phased control strategy, namely, reduction of a high ratio of VOCs to NOx at the onset of the pollution and lower ratio during evolution of the O3 episode. This study presents a new possibility for regional O3 pollution abatement in China based on a reasonable combination of OBM and the WRF-CMAQ model.