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

Observation-based method for O3 formation sensitivity research is an important tool to analyze the causes of ground-level O3 pollution, which has broad application potentials in determining the O3 pollution formation mechanism and developing prevention and control strategies. This paper outlined the development history of research on O3 formation sensitivity based on observational methods, described the principle and applicability of the methodology, summarized the relative application results in China and provided recommendations on the prevention and control of O3 pollution in China based on relevant study results, and finally pointed out the shortcomings and future development prospects in this field in China. The overview study showed that the O3 formation sensitivity in some urban areas in China in recent years presented a gradual shifting tendency from the VOC-limited regime to the transition regime or the NOx-limited regime due to the implementation of the O3 precursors emission reduction policies; O3 pollution control strategies and precursor control countermeasures should be formulated based on local conditions and the dynamic control capability of O3 pollution control measures should be improved. There are still some current deficiencies in the study field in China. Therefore, it is recommended that a stereoscopic monitoring network for atmospheric photochemical components should be further constructed and improved; the atmospheric chemical mechanisms should be vigorously developed, and standardized methods for determining the O3 formation sensitivity should be established in China in the near future.

Spatiotemporal characteristics of ozone and the formation sensitivity over the Fenwei Plain

High surface ozone (O₃) levels affect human and environmental health. The Fenwei Plain (FWP), one of the critical regions for China's "Blue Sky Protection Campaign", has reported severe O₃ pollution. This study investigates the spatiotemporal properties and the causes of O₃ pollution over the FWP using high-resolution data from the TROPOspheric Monitoring Instrument (TROPOMI) from 2019 to 2021. This study characterizes spatial and temporal variations in O₃ concentration by linking O₃ columns and surface monitoring using a trained deep forest machine learning model. O₃ concentrations in summer were 2-3 times higher than those found in winter due to higher temperatures and greater solar irradiation. The spatial distributions of O₃ correlate with the solar radiation showing decreased trends from the northeastern to the southwestern FWP, with the highest O₃ values in Shanxi Province and the lowest in Shaanxi Province. For urban areas, croplands and grasslands, the O₃ photochemistry in summer is NO_x-limited or in the transitional regime, while it is VOC-limited in winter and other seasons. Reducing NO_x emissions would be effective for decreasing O₃ levels in summer, while VOC reductions are necessary for winter. The annual cycle in vegetated areas included both NO_x-limited and transitional regimes, indicating the importance of NO_x controls to protect ecosystems. The O₃ response to limiting precursors shown here is of importance for optimizing control strategies and is illustrated by emission changes during the 2020 COVID-19 outbreak.

Research on the ozone formation sensitivity indicator of four urban agglomerations of China using Ozone Monitoring Instrument (OMI) satellite data and ground-based measurements

Near surface ozone is a typical secondary pollutant, and is mostly generated by a series of complex photochemical reactions of volatile organic compounds (VOCs) and nitrogen oxides (NO_x) in the air under sunlight. At present, a large number of studies have applied FNR (a ratio of formaldehyde (HCHO) to nitrogen dioxide (NO₂) retrieved by satellite) indicator to study the ozone formation sensitivity (OFS). OFS analysis is critical for taking targeted ozone pollution prevention and control measures. Regional OFS can be more accurately diagnosed by utilizing localized FNR threshold. In this study, localized FNR thresholds were established for four severe ozone polluted urban agglomerations in China (Beijing-Tianjin-Hebei (BTH) region, Yangtze River Delta (YRD) region, Pearl River Delta (PRD) region, and Chengdu-Chongqing (CY) region), based on the statistical analysis between FNR (obtained from OMI observation, with daily transit time of approximately 13:45 local standard time) and ΔO₃/ΔNO₂ (the ratio of ozone change to nitrogen dioxide change between two consecutive months, obtained from ground measurements) from 2014 to 2016. And these thresholds were verified by the statistical analysis between FNR and ΔO₃/O₃ (ozone change rate between two consecutive months), and between FNR and O₃ concentration during the OFS significant shift months. Furthermore, the results were also compared and verified with the method proposed by previous studies. The results indicate that there are significant regional dependences in the FNR threshold, and the lower-upper limits for the four urban agglomerations are as follows: 0.65-1.21 for BTH, 0.64-1.48 for the YRD, 1.25-2.39 for the PRD, and 1.44-3.69 for CY (FNR < lower limit indicates VOCs-limited regime; lower limit < FNR < upper limit indicates transitional regime; FNR > upper limit indicates NO_x-limited regime). This method eliminates the problems associated with the undifferentiated use of FNR thresholds in different regions and significantly reduces the deviations for OFS.

A numerical study of reducing the concentration of O3 and PM2.5 simultaneously in Taiwan

Since the 24-hr PM_2.5 (particle aerodynamic diameter less than 2.5 μm) concentration standard was regulated in Taiwan in 2012, the PM_2.5 concentration has been decreasing year by year, but the ozone (O₃) concentration remains almost the same. In particular, the daily maximum 8-hr average O₃ (MDA8 O₃) concentration frequently exceeds the standard. The goal of this study is to find a solution for reducing PM_2.5 and O₃ simultaneously by numerical modeling. After the Volatile Organic Compounds (VOC_S)-limited and nitrogen oxides (NO_X)-limited areas were defined in Taiwan, then, in total, 50 scenarios are simulated in this study. In terms of the average in Taiwan, the effect of VOC_S emission reduction is better than that of NO_X on the decrease in PM_2.5 concentration, when the same reduction proportion (20%, 40%) is implemented. While the effect of further NO_X emission reduction (60%) will exceed that of VOC_S. The decrease in PM_2.5 is proportional to the reduction in precursor emissions such as NO_X, VOC_S, sulfur dioxides (SO₂), and ammonia (NH₃). The lower reduction of NO_X emission for whole Taiwan caused O₃ increases on average but higher reduction can ease the increase, which suggests the implement of NO_X emission reductions must be cautious. When comparing administrative jurisdictions in terms of grids, districts/towns, and cities/counties, it was found that controlling NO_X and VOC_S at a finer spatial resolution of control units did not benefit the decrease in PM_2.5 but did benefit the decrease in O₃. The enhanced O₃ control strategies obviously cause a higher decrease of O₃ throughout Taiwan due to NO_X and VOC_S emission changes when they are implemented in the right places. Finally, three sets of short-term and long-term goals of controlling PM_2.5 and O₃ simultaneously are drawn from the comprehensive rankings for all simulated scenarios, depending on whether PM_2.5 or O₃ control is more urgent. In principle, the short-term scenarios could be ordinary or enhanced version of O₃ decrease with lower NO_X/VOC_S emissions, while the long-term scenario is enhanced version of O₃ decrease plus high emission reductions for all precursors.

A review on methodology in O3-NOx-VOC sensitivity study

Gaining insight into the response of surface ozone (O₃) formation to its precursors plays an important role in the policy-making of O₃ pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O₃ and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O₃ and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O₃ prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O₃ and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O₃ concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O₃ formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O₃ formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O₃ and its precursors could be used to test and improve the simulation performance of modern models.

Evaluation of Regional Air Quality Models over Sydney, Australia: Part 2, Comparison of PM2.5 and Ozone

Accurate air quality modelling is an essential tool, both for strategic assessment (regulation development for emission controls) and for short-term forecasting (enabling warnings to be issued to protect vulnerable members of society when the pollution levels are predicted to be high). Model intercomparison studies are a valuable support to this work, being useful for identifying any issues with air quality models, and benchmarking their performance against international standards, thereby increasing confidence in their predictions. This paper presents the results of a comparison study of six chemical transport models which have been used to simulate short-term hourly to 24 hourly concentrations of fine particulate matter less than and equal to 2.5 µm in diameter (PM2.5) and ozone (O3) for Sydney, Australia. Model performance was evaluated by comparison to air quality measurements made at 16 locations for O3 and 5 locations for PM2.5, during three time periods that coincided with major atmospheric composition measurement campaigns in the region. These major campaigns included daytime measurements of PM2.5 composition, and so model performance for particulate sulfate (SO42−), nitrate (NO3−), ammonium (NH4+) and elemental carbon (EC) was evaluated at one site per modelling period. Domain-wide performance of the models for hourly O3 was good, with models meeting benchmark criteria and reproducing the observed O3 production regime (based on the O3/NOx indicator) at 80% or more of the sites. Nevertheless, model performance was worse at high (and low) O3 percentiles. Domain-wide model performance for 24 h average PM2.5 was more variable, with a general tendency for the models to under-predict PM2.5 concentrations during the summer and over-predict PM2.5 concentrations in the autumn. The modelling intercomparison exercise has led to improvements in the implementation of these models for Sydney and has increased confidence in their skill at reproducing observed atmospheric composition.

Attribution of Tropospheric Ozone to NO x and VOC Emissions: Considering Ozone Formation in the Transition Regime

An improved three-regime (3R) O₃ attribution technique for O₃ source apportionment in regional chemical transport models is developed to divide the entire range of VOC-NO _x-O₃ formation sensitivity to VOC-limited, transition, and NO _x-limited regimes based on the value of a regime indicator R. The threshold R values to mark the start ( R_ts) and end ( R_te) of the transition regime are defined at the point where O₃-NO _x sensitivity turns from negative to positive and where O₃-NO _x sensitivity is ten times higher than O₃-VOC sensitivity, respectively. R_ts and R_te are determined using NO _x and VOC sensitivity simulations in a box model with a modified SAPRC-11 mechanism. For the widely used indicator ration R = ( P_H2O2 + P_ROOH)/ P_HNO3, which is based on the production rates of H₂O₂, HNO₃ and organic hydroperoxides (ROOH), the recommended R_ts and R_te values are 0.047 and 5.142, respectively. Parameterized attribution functions, depending only on the values of R, are developed to apportion modeled in situ O₃ formation in the transition regime to NO _x and VOCs. The new 3R and the traditional two-regime (2R) schemes are incorporated into the Community Multiscale Air Quality (CMAQ) model to quantify NO _x and VOC contributions to regional O₃ concentrations in China in August 2013. The 3R approach predicts approximately 5-10 ppb and up to 15 ppb higher NO _x contributions to 8 h O₃ in in the North China Plain, the Yangtze River Delta and the Pearl River Delta than the 2R approach. The big differences in O₃ attribution between 2R and 3R can have significant policy implications for air pollution emission controls.

Development and application of observable response indicators for design of an effective ozone and fine particle pollution control strategy in China

Designing effective control policies requires efficient quantification of the nonlinear response of air pollution to emissions. However, neither the current observable indicators nor the current indicators based on response-surface modeling (RSM) can fulfill this requirement. Therefore, this study developed new observable RSM-based indicators and applied them to ambient fine particle (PM_2.5) and ozone (O₃) pollution control in China. The performance of these observable indicators in predicting O₃ and PM_2.5 chemistry was compared with that of the current RSM-based indicators. H₂O₂×HCHO/NO₂ and total ammonia ratio, which exhibited the best performance among indicators, were proposed as new observable O₃- and PM_2.5-chemistry indicators, respectively. Strong correlations between RSM-based and traditional observable indicators suggested that a combination of ambient concentrations of certain chemical species can serve as an indicator to approximately quantify the response of O₃ and PM_2.5 to changes in precursor emissions. The observable RSM-based indicator for O₃ (observable peak ratio) effectively captured the strong NO_x-saturated regime in January and the NO_x-limited regime in July, as well as the strong NO_x-saturated regime in northern and eastern China and their key regions, including the Yangtze River Delta and Pearl River Delta. The observable RSM-based indicator for PM_2.5 (observable flex ratio) also captured strong NH₃-poor condition in January and NH₃-rich condition in April and July, as well as NH₃-rich in northern and eastern China and the Sichuan Basin. Moreover, analysis of these newly developed observable response indicators suggested that the simultaneous control of NH₃ and NO_x emissions produces greater benefits in provinces with higher PM_2.5 exposure by up to 1.2 μg m^-3 PM_2.5 per 10 % NH₃ reduction compared with NO_x control only. Control of volatile organic compound (VOC) emissions by as much as 40 % of NO_x controls is necessary to obtain the cobenefits of reducing both O₃ and PM_2.5 exposure at the national level when controlling NO_x emissions. However, the VOC-to-NO_x ratio required to maintain benefits varies significantly from 0 to 1.2 in different provinces, suggesting that a more localized control strategy should be designed for each province.

Combining analytical frameworks to assess livelihood vulnerability to climate change and analyse adaptation options

Experts working on behalf of international development organisations need better tools to assist land managers in developing countries maintain their livelihoods, as climate change puts pressure on the ecosystem services that they depend upon. However, current understanding of livelihood vulnerability to climate change is based on a fractured and disparate set of theories and methods. This review therefore combines theoretical insights from sustainable livelihoods analysis with other analytical frameworks (including the ecosystem services framework, diffusion theory, social learning, adaptive management and transitions management) to assess the vulnerability of rural livelihoods to climate change. This integrated analytical framework helps diagnose vulnerability to climate change, whilst identifying and comparing adaptation options that could reduce vulnerability, following four broad steps: i) determine likely level of exposure to climate change, and how climate change might interact with existing stresses and other future drivers of change; ii) determine the sensitivity of stocks of capital assets and flows of ecosystem services to climate change; iii) identify factors influencing decisions to develop and/or adopt different adaptation strategies, based on innovation or the use/substitution of existing assets; and iv) identify and evaluate potential trade-offs between adaptation options. The paper concludes by identifying interdisciplinary research needs for assessing the vulnerability of livelihoods to climate change.

The use of experimental data and their uncertainty for assessing ozone photochemistry in the Eastern Iberian Peninsula

Observation-based methods are useful tools to explore the sensitivity of ozone concentrations to precursor controls. With the aim of assessing the ozone precursor sensitivity in two locations: Paterna (suburban) and Villar del Arzobispo (rural) of the Turia river basin in the east of Spain, the photochemical indicator O(3)/NO(y) and the Extent-of-Reaction (EOR) parameter have been calculated from field measurements. In Paterna, the O(3)/NO(y) ratio varied from 0 to 13 with an average value of 5.1 (SD 3.2), whereas the averaged value for the EOR was 0.43 (SD 0.14). In Villar del Arzobispo, the O(3)/NO(y) ratio changed from 5 to 30 with a mean value of 13.6 (SD 4.7) and the EOR gave an averaged value of 0.72 (SD 0.11). The results show two different patterns of ozone production as a function of the location. The suburban area shows a VOC-sensitive regime whereas the rural one shows a transition regime close to NO(x)-sensitive conditions. No seasonal differences in these regimes are observed along the monitoring campaigns. Finally, an analysis of the influence of the measurement quality of NO(y), NO(x) and O(3) on the uncertainty of the O(3)/NO(y) ratio and the EOR was performed showing that the uncertainty of O(3)/NO(y) is not dependent on either its value or the individual values of O(3) and NO(y) but just on the quality of O(3) and NO(y) measurements. The maximum uncertainty is 26% as long as the combined uncertainties of O(3) and NO(y) remain below the 7.5%. The case of the EOR is different and its uncertainty depends on both the value of the EOR parameter and the individual concentration values of NO(y) and NO(x). The uncertainty of the EOR estimation can be very high (>200%) if the combined uncertainties of both NO(y) and NO(x) are high (>7.5%), or especially, if u(NO(y)) and u(NO(x)) differ considerably from each other (>3.5%).

Global tropospheric ozone dynamics. Part II: Numerical modelling of tropospheric ozone variability

Various causes of tropospheric changes have been considered in Part I in connection with the analysis of observation data. It is clear however, that the principal instrument for understanding numerous and often interacting causes of ozone changes is numerical modelling. A review of the current status of the numerical modelling has been made for the variability of the ozone concentration in the troposphere. Observation data on tropospheric ozone and relevant numerical modelling results show that a necessity exists to get more adequate global observational data.

METHODS FOR ATTRIBUTING AMBIENT AIR POLLUTANTS TO EMISSION SOURCES

▪ Abstract  Six methods for attributing ambient pollutants to emission sources are reviewed: emissions analysis, trend analysis, tracer studies, trajectory analysis, receptor modeling, and dispersion modeling. The ranges of applicability, types of information provided, limitations, performance capabilities, and areas of active research of the different methods are compared. For primary, nonreactive pollutants whose effects of concern occur on a global scale, an accounting of emissions rates by source type and location largely characterizes source contributions. For other pollutants or smaller spatial scales, accurate estimates of emissions are needed for identifying the emissions reduction potentials of possible control measures and as inputs to dispersion models. Emission levels are frequently known with factor-of-two accuracy or worse, and improved estimates are needed for dispersion modeling. The analysis of regional or urban-scale trends in emissions and ambient pollutant concentrations can provide qualitative information on source contributions, but quantitative results are limited by the confounding influence of variations in meteorology and uncertainties in the areas over which emissions affect concentrations. Tracer studies are useful for quantifying dispersion characteristics of plumes, qualitatively characterizing transport directions, and providing empirical data for evaluating trajectory and dispersion models. Data are usually temporally limited to a short study period, typically do not provide information on vertical pollutant distributions, and are most applicable to the transport of primary, nonreactive pollutants. Trajectory analyses are routinely used to estimate atmospheric transport directions. Trajectory errors of about 20% of travel distance are considered typical of the better models and data sets. Receptor models use measurements of ambient pollutant concentrations to quantify the contributions of different source types to primary particulate matter or volatile organic compounds, or to characterize source-region contributions to a single pollutant. Accuracy rates of ∼30% are often achieved when quantifying the contributions from different types of emission sources. Dispersion models are well-suited for estimating quantitative source-receptor relationships, as the effects of individual emission sources or source regions can be studied. Lagrangian and Gaussian dispersion models are computationally efficient and can simulate the transport of nonreactive primary or linear secondary species. Eulerian models are computationally intensive but lend themselves to the simulation of nonlinear chemistry. Careful evaluation of modeling accuracy is needed for a model application to fulfill its potential for source attribution. Accuracy can be evaluated through a combination of performance evaluation, sensitivity analysis, diagnostic evaluation, and corroborating analyses.