Ground-level Ozone Concentrations Research Articles

Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes

Abstract. The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic and stomatal) responses to varying meteorological conditions and concentrations of CO2 and ground-level ozone (O3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere–atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R2>0.85 and R2>0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr−1. Application of Monin–Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting an increase of +7 % in global GPP. Present-day O3 concentrations simulated by GEOS-Chem and an O3 damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions of up to 15 % in eastern China and the eastern USA. Regional correlations are generally unchanged when O3 is present and biases are reduced, especially for regions with high O3 damage. An increase in atmospheric CO2 concentration of 20 ppmv from the level in 2000 to the level in 2010 modestly decreases O3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.

Spatiotemporal ozone concentration variations and their influencing factors in Zhejiang, China, 2018–2022: A geographically and temporally weighted regression

In this study, we applied an extended geographically and temporally weighted regression model (GTWR) to explore the spatiotemporal non-stationarity of ground-level ozone (O3) concentrations and the influencing factors in four geographic zones of Zhejiang Province from 2018 to 2022. Our results showed that meteorological conditions and emissions were both important for O3 concentration during this period, and their influences showed significant spatial heterogeneity. Relative humidity (Rh) had negative associations with O3 concentration in all the 90 counties in our study period, and there was a strong positive correlation between maximum air temperature (MaxT) and O3 concentration. The impacts of precipitation on O3 were partly positive in Hangzhou Bay and mostly negative in the other three zones due to the precipitation time. The MaxT had the most significant influence in Hangzhou Bay due to the lowest NO2/VOCs ratio, and NO2 positively influenced O3 except in Hangzhou Bay. The high ground-level O3 occurrence in 2022 resulted from meteorological factors and emissions combined.

Impact of aerosol actinic radiative effect on ozone during haze pollution in the Pearl River Delta region

Large amounts of aerosol in the atmosphere can cause significant attenuation of photochemical radiation and have a substantial impact on ozone and other secondary pollutants involved in photochemical reactions. This study examines the impact of aerosol actinic radiative effect on ozone and secondary pollutants during typical haze processes in the Pearl River Delta (PRD) region. The investigation employed lidar data and Weather Research Forecast–Community Multiscale Air Quality (WRF-CMAQ) modeling system. The results revealed that the aerosol caused a significant decrease in ground-level photolysis rates of NO2 and ozone by 22% and 29%, respectively, when haze pollution was severe. The actinic radiative effect of aerosol caused an average reduction of more than 10 ppb in ground level ozone concentration in the PRD, with the core area experiencing a maximum reduction of 30 ppb. Additionally, the core area witnessed a maximum reduction of 2 ppb in peroxyacetyl nitrate (PAN) and 2 μg/m3 in PM2.5. Due to reduced photolysis rates, nitrogen oxides increased by more than 5 ppb on average, reaching up to 10 ppb, while volatile organic compounds (VOCs) increased by a maximum of 10 ppb in the core region. Temporally, the aerosol actinic radiative effect was most pronounced in the morning and less significant in the afternoon.

Summertime tropospheric ozone source apportionment study in the Madrid region (Spain)

Abstract. The design of emission abatement measures to effectively reduce high ground-level ozone (O3) concentrations in urban areas is very complex. In addition to the strongly non-linear chemistry of this secondary pollutant, precursors can be released by a variety of sources in different regions, and locally produced O3 is mixed with that transported from the regional or continental scales. All of these processes depend also on the specific meteorological conditions and topography of the study area. Consequently, high-resolution comprehensive modeling tools are needed to understand the drivers of photochemical pollution and to assess the potential of local strategies to reduce adverse impacts from high tropospheric O3 levels. In this study, we apply the Integrated Source Apportionment Method (ISAM) implemented in the Community Multiscale Air Quality (CMAQ v5.3.2) model to investigate the origin of summertime O3 in the Madrid region (Spain). Consistent with previous studies, our results confirm that O3 levels are dominated by non-local contributions, representing around 70 % of mean values across the region. Nonetheless, precursors emitted by local sources, mainly road traffic, play a more important role during O3 peaks, with contributions as high as 25 ppb. The potential impact of local measures is higher under unfavorable meteorological conditions associated with regional accumulation patterns. These findings suggest that this modeling system may be used in the future to simulate the potential outcomes of specific emission abatement measures to prevent high-O3 episodes in the Madrid metropolitan area.

Determinants of honeybee hive survival and its implications for urban biodiversity in Toronto and Montreal: A Canadian case study

Cities are shown to provide favourable conditions for western honeybees (Apis mellifera) by protecting them from agricultural pesticides and offering a greater diversity of flora. Nevertheless, while current research primarily focuses on the causes of pollinator – and particularly honeybee – decline, including pesticide exposure, climate change, and habitat fragmentation, little attention is dedicated to urban apiculture and factors associated with the survival of honeybees within cities. Here, we analyse data from 3,697 honeybee hives in Montreal and Toronto to assess the effect of urban and environmental factors on hive survival. We find ground-level ozone concentrations, the height at which hives are located, and the number of surrounding hives to be negatively associated with hive survival, which may point towards an issue of hive saturation. Conversely, vegetation density and the COVID-19 pandemic are positively associated with the likelihood of hive survival, emphasizing the effect of urban air pollution. These findings highlight the need for urban planners to consider neighborhood-scale environmental factors to identify potential venues for enhancing honeybee survival within cities beyond existing urban beekeeping initiatives.

Estimating ground-level high-resolution ozone concentration across China using a stacked machine-learning method

Accurate estimation of ambient ozone concentrations is key to evaluating its detrimental impacts on human health and the ecological environment. While China has made strides in modeling ambient ozone exposure, nationwide modeling performance and spatial resolution need improvement. To address this, we developed an advanced three-level method, integrating multi-source data as predictors to estimate daily full-coverage ozone concentrations across China, with a 5-km spatial resolution. This modeling method employs subregional division, machine-learning models tailored to each subregion, and a stacking approach for estimate integration. This approach achieved substantial daily scale model performance, yielding R2 [root mean square error] values of 0.94, 0.83, and 0.74 [9.63 μg/m³, 16.59 μg/m³, 20.97 μg/m³] for sample-based, city-based, and temporal 10-fold cross-validation, respectively. Our modeling process identified six subregions for ground-level ozone modeling: Northeast (NE), North China Plain (NCP), Lower and Middle Reaches Plain of the Yangtze River (LRPYR), Southeast (SE), Southwest (SW), and Middle-West (MW). Among multi-source predictors, temperature and humidity are the most important modeling predictors in all subregions. Validation and comparative analyses highlighted the enhancement of overall model performance through subregional division and stacking machine-learning models, particularly for improving predictive accuracy on days without ground-level ozone data. The resultant estimates showed that high ozone exposure in 2020 was concentrated in the spring and summer. Our method and findings hold potential for improving ground-level ozone estimation and advancing ozone-related epidemiological research and environmental management.

Associations of ambient ozone exposure and CD4+ T cell levels with mortality among people living with HIV: An eight-year longitudinal study

There has been a consistent upward trend in ground-level ozone (O3) concentration in China. People living with HIV (PLWH) may be more vulnerable to the health impacts of O3 exposure due to their immunosuppressed state. This study aims to investigate the association between ambient O3 exposure and mortality among PLWH, as well as the potential exacerbating effects of a decreased CD4+ T cell level. Daily maximum 8-hour O3 concentrations were assigned to 7270 PLWH at a county level in Guangxi, China. Every 10-unit increase in ambient O3 concentration was associated with a significant rise in all-cause mortality ranging from 7.3 % to 28.7 % and a significant rise in AIDS-related mortality ranging from 8.4 % to 14.5 %. When PLWH had a higher CD4+ count (≥350 cells/μL), elevated O3 concentration was associated with increased blood CD4+ count at lag0 [percent change with 95 % confidence interval, 0.20(0.00, 0.40)], lag1 [0.26(0.06, 0.47)], and lag2 [0.23(0.03, 0.44)]; however, an opposite association was observed when CD4+ count was <350 cells/μL for half-year average [−2.45(−4.71, −0.14)] and yearly average [−3.42(−5.51, −1.29)] of O3 exposure. The association of O3 exposure with all-cause and AIDS-related mortality was more prominent among those with higher CD4+ count. Exploratory analysis revealed possible associations between O3 exposure and respiratory infections and clinical symptoms. These findings suggest potential synergistic effects between a compromised immune status and elevated O3 exposure levels on mortality risk among PLWH. Ambient O3 exposure should be considered as an emerging mortality risk factor for PLWH in the era of antiretroviral therapy, requiring further attention from researchers and healthcare professionals.

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models

Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite data products, especially the lack of ground-level ozone characterization, and the discontinuity of ground observations, there is a pressing need for high-precision models to simulate ground-level ozone to assess surface ozone pollution. In this study, we have compared several widely utilized ensemble learning and deep learning methods for ground-level ozone simulation. Furthermore, we have thoroughly contrasted the temporal and spatial generalization performances of the ensemble learning and deep learning models. The 3-Dimensional Convolutional Neural Network (3-D CNN) model has emerged as the optimal choice for evaluating the daily maximum 8-h average ozone in Yunnan Province. The model has good performance: a spatial resolution of 0.05° × 0.05° and strong predictive power, as indicated by a Coefficient of Determination (R2) of 0.83 and a Root Mean Square Error (RMSE) of 12.54 μg/m³ in sample-based 5-fold cross-validation (CV).In the final stage of our study, we applied the 3-D CNN model to generate a comprehensive daily maximum 8-h average ozone dataset for Yunnan Province for the year 2021. This application has furnished us with a crucial high-resolution and highly accurate dataset for further in-depth studies on the issue of ozone pollution in Yunnan Province.

Research on satellite data-driven algorithm for ground-level ozone concentration inversion: case of Yunnan, China

Research on satellite data-driven algorithm for ground-level ozone concentration inversion: case of Yunnan, China

High-Spatiotemporal-Resolution Estimation of Ground-Level Ozone in China Based on Machine Learning

High concentrations of ground-level ozone (O3) pose a significant threat to human health. Obtaining high-spatiotemporal-resolution information about ground-level O3 is of paramount importance for O3 pollution control. However, the current monitoring methods have a lot of limitations. Ground-based monitoring falls short in providing extensive coverage, and remote sensing based on satellites is constrained by specific spectral bands, lacking sensitivity to ground-level O3. To address this issue, we combined brightness temperature data from the Himawari-8 satellite with meteorological data and ground-based station data to train four machine learning models to obtain high-spatiotemporal-resolution information about ground-level O3, including Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Among these, the CatBoost model exhibited superior performance, achieving a ten-fold cross-validation R2 of 0.8534, an RMSE of 17.735 μg/m3, and an MAE of 12.6594 μg/m3. Furthermore, all the selected feature variables in our study positively influenced the model. Subsequently, we employed the CatBoost model to estimate averaged hourly ground-level O3 concentrations at a 2 km resolution. The estimation results indicate a close relationship between ground-level O3 concentrations and human activities and solar radiation.

Ground-level ozone concentration and emergency conditions in patients with bronchial asthma on southern coast of Crimea: retrospective study

OBJECTIVE. To evaluate the link of emergency conditions in patients with bronchial asthma to the ground-level ozone concentration in different seasons of the year on the southern coast of Crimea.&#x0D; MATERIALS AND METHODS. Epidemiological study of frequency of visits concerning emergency conditions in patients with bronchial asthma in Yalta according to the ambulance station from April 2010 to March 2011; permanent recording of the ground-level ozone concentration at the background environmental monitoring station (BEMS) by the optical method using automatic gas analyzer APOA 370 (HORIBA). Analysis of the collated data dependence via the Spearman non-parametric correlation analysis.&#x0D; RESULTS: Correlation analysis of daily, average monthly and seasonal data on ambulance call with regard to severe bronchial asthma and average daily, maximum, minimum ground-level concentration (GLC) during the astronomical year (April 2010- March 2011) has revealed single-type dependence in nature on the amplitude of ozone changes: statistically significant in November and at the level of trends – in September 2010 that does not provide a sufficient basis to talk about a negative ozone impact on the condition of patients with bronchial asthma on the territory of the southern coast of Crimea. The possible causes of the identified weak dependence are analyzed, though the daily average of ozone was typically higher than MPV (maximum permissible value) throughout the follow-up period, and in the summer – higher than MPV with 8-hour exposure.&#x0D; DISCUSSION. However, considering data of numerous foreign studies on the quantification of ozone negative impact on the human body, it is necessary to analyze more long-term series of observations in view of related weather factors of the Southern coast of Crimea that can significantly modify response of the body to GLC. This will determine ozone levels along with other weather conditions, safe for patients with bronchial asthma, including those who undergo rehabilitation in sanatorium-resort institutions of Crimea.

Insights into soil NO emissions and the contribution to surface ozone formation in China

Abstract. Elevated ground-level ozone concentrations have emerged as a major environmental issue in China. Nitrogen oxide (NOx) is a key precursor to ozone formation. Although control strategies aimed at reducing NOx emissions from conventional combustion sources are widely recognized, soil NOx emissions (mainly as NO) due to microbial processes have received little attention. The impact of soil NO emissions on ground-level ozone concentration is yet to be evaluated. This study estimated soil NO emissions in China using the Berkeley–Dalhousie Soil NOx Parameterization (BDSNP) algorithm. A typical modeling approach was used to quantify the contribution of soil NO emissions to surface ozone concentration. The brute-force method (BFM) and the Ozone Source Apportionment Technology (OSAT) implemented in the Comprehensive Air Quality Model with Extensions (CAMx) were used. The total soil NO emissions in China for 2018 were estimated to be 1157.9 Gg N, with an uncertainty range of 715.7–1902.6 Gg N. Spatially, soil NO emissions are mainly concentrated in Central China, North China, Northeast China, the northern Yangtze River Delta (YRD), and the eastern Sichuan Basin, with distinct diurnal and monthly variations that are mainly affected by the temperature and timing of fertilizer application. Both the BFM and OSAT results indicate a substantial contribution of soil NO emissions to the maximum daily 8 h (MDA8) ozone concentrations by 8.0–12.5 µg m−3 on average for June 2018, with the OSAT results being consistently higher than the BFM results. The results also showed that soil NO emissions led to a relative increase in ozone exceedance days by 10.5 %–43.5 % for selected regions. Reducing the soil NO emissions resulted in a general decrease in monthly MDA8 ozone concentrations, and the magnitude of ozone reduction became more pronounced as reductions increased. However, even with complete reductions in soil NO emissions, approximately 450.3 million people are still exposed to unhealthy ozone levels, necessitating multiple control policies at the same time. This study highlights the importance of soil NO emissions for ground-level ozone concentrations and the potential for reducing NO emissions as a future control strategy for ozone mitigation in China.

Stochastic modelling of daily air pollution in Burgas, Bulgaria

Exceeding the norms and limits of atmospheric air pollution causes enormous damage to the population’s health and the environment. Determining the factors affecting air quality is a current task in a local, regional, and global scale. In this study, we use daily time series data for the main air pollutants in Burgas, Bulgaria – O3, NO, NO2, CO, SO2, and PM10, to analyze, model, and forecast these levels depending on meteorological factors. For this purpose, the stochastic ARIMA method and ARIMA with transfer functions are applied. Results are obtained for univariate and multivariate time series. Particular attention is paid to the concentrations of the secondary pollutant ground-level ozone (O3), which are modelled as a function of all variables considered. Results were evaluated using root mean square error, mean absolute percentage errors, and the coefficient of determination. Short-term forecasts have been obtained for seven days ahead. Model accuracy up to 84% has been established.

Variation trend prediction of ground-level ozone concentrations with high-resolution using landscape pattern data.

Scientifically configuring landscape patterns based on their relationship with ground-level ozone concentrations (GOCs) is an effective way to prevent and control ground-level ozone pollution. In this paper, a GOC variation trend prediction model (hybrid model) combining a generalized linear model (GLM) and a logistic regression model (LRM) was established to analyze the spatiotemporal variation patterns in GOCs as well as their responses to landscape patterns. The model exhibited satisfactory performance, with percent of samples correctly predicted (PCP) value of 82.33% and area under receiver operating characteristics curve (AUC) value of 0.70. Using the hybrid model, the per-pixel rise probability of annual average GOCs at a spatial resolution of 1 km in Shenzhen were generated. The results showed that (1) annual average GOCs were increasing in Shenzhen from 2015 to 2020, and had obvious spatial differences, with a higher value in the west and a lower value in the east; (2) variation trend in GOCs was significant positively correlated with landscape heterogeneity (HET), while significant negatively correlated with dominance (DMG) and contagion (CON); (3) GOCs in Shenzhen has a great risk of rising, especially in GuangMing, PingShan, LongGang, LuoHu and BaoAn. The results provide not only a preliminary index for estimating the GOC variation trend in the absence of air quality monitoring data but also guidance for landscape optimizing design from the perspective of controlling ground-level ozone pollution.

Wheat yield response to elevated O3 concentrations differs between the world's major producing regions

Ground-level ozone (O3) concentration is rising in Asia, which accommodates the world's top-two wheat producers (China and India). Because wheat is among the species of high O3 sensitivity, yield loss due to rising O3 in Asia is a major threat to global wheat supply. We estimated the relationships between O3 dose on AOT40 (accumulated daytime O3 concentrations above 40 ppb for 90 days) and relative wheat yield for four wheat producing regions: China, India, Europe and North America using results of O3 elevation experiments conducted therein. When compared on the same AOT40, the estimated yield loss was greatest in China followed by India, Europe, and North America in this order. In China, Europe and North America, the yield loss was primarily due to the reduction of single grain weight, whereas in India reduction of the number of grains contributed more to the yield loss than single grain weight. The greater response of the number of grains to O3 in India can be explained by the earlier start of O3 elevation, but the seasonal change in O3 concentrations cannot explain the lower yield loss in North America than China and India. Referring to the past reports of lower yield sensitivity to O3 in older cultivars, we compared the year of release of cultivars between the regions. In North America, they used cultivars released in 1980s or earlier, whereas in China they used cultivars released in 2000s. In Europe and India, most cultivars were released between those in North America and China. The difference in cultivars could therefore be a cause the differential yield response among the regions. We argue that the O3-induced yield loss should be quantified using the dose-response relationships for each region accounting for the effects of seasonal change in O3 concentrations, cultivars and climate on the yield response.

Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m−3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

Photochemistry in the urban agglomeration along the coastline of southeastern China: Pollution mechanism and control implication

The concentrations of ground-level ozone (O3) in China have undergone a rapid increase in recent years, resulting in adverse impacts on the air quality and climate change. However, limited research has been conducted on the coastal urban agglomerations with increasingly serious O3 pollution. Therefore, in order to better understand in situ photochemistry, comprehensive field observations of O3 and its precursors, coupled with the model simulation, were conducted in autumn of 2019 at six sites in an urban agglomeration along the coastline of southeastern China. Results indicated that O3 pollution in the southern part of the urban agglomeration was more severe than that in the northern part, due to higher levels of O3 precursors and stronger atmospheric oxidation capacity (AOC) in the southern regions. Oxygenated volatile organic compounds (OVOCs), NO2, and CO dominated the total OH reactivity, and the site-average daytime Ox (O3 + NO2) increments correlated well (R2 = 0.94) with the total OH reactivity of CO and VOCs at these sites except for Quanzhou, where industrial emissions (35.1 %) and solvent usages (33.7 %) dominated the VOC sources. However, vehicle exhausts (31.1 %) were the most predominant contributors to the VOC sources at other sites. The results of model simulations showed that net O3 formation rates were larger at the southern sites. Furthermore, O3 production was mainly controlled by VOCs at most sites, but co-limited by VOCs and NOx at Quanzhou. The most significant VOC groups contributing to O3 formation were aromatics and alkenes, with m/p-xylene, toluene, propene, and ethene being the main contributors at these sites. This study offers a more comprehensive understanding of the characteristics and formation of photochemical pollutions on the scale of the urban areas, indicating the critical need to reduce VOC emissions as a means of mitigating their photochemical effects.

Ambient ozone at a rural Central European site and its vertical concentration gradient close to the ground.

The representativeness of ambient air quality of an in situ measurement is key in the use and correct interpretation of the measured concentration values. Though the horizontal representativeness aspect is generally not neglected in air pollution studies, a detailed, high-resolution vertical distribution of ambient air pollutant concentrations is rarely addressed. The aim of this study is twofold: (i) to explore the vertical distribution of ground-level ozone (O3) concentrations measured at four heights above the ground-namely at 2, 8, 50, and 230m-and (ii) to examine in detail the vertical O3 concentration gradient in air columns between 2 and 8, 8 and 50, and 50 and 230m above the ground. We use the daily mean O3 concentrations measured continuously at the Košetice station, representing the rural Central European background ambient air quality observed during 2015-2021. We use the semiparametric GAM (generalised additive model) approach (with complexity or roughness-penalised splines implementation) to analyse the data with sufficient flexibility. Our models for both O3 concentrations and O3 gradients use (additive) decomposition into annual trend and seasonality (plus an overall intercept). The seasonal and year-to-year patterns of the modelled O3 concentrations look very similar at first glance. Nevertheless, a more detailed look through O3 gradients shows that they differ substantially with respect to their seasonal and long-term dynamics. The vertical O3 concentration gradient in 2-230m is not uniform but changes substantially with increasing height and shows by far the highest dynamics near the ground between 2 and 8m, differing in both the seasonal and annual aspects for all the air columns inspected. We speculate that non-linear changes of both seasonal and annual components of vertical O3 gradients are due to atmospheric-terrestrial interactions and to meteorological factors, which we will explore in a future study.

The Stratosphere-to-Troposphere Transport Related to Rossby Wave Breaking and Its Impact on Summertime Ground-Level Ozone in Eastern China

In summertime, eastern China experiences severe ozone pollution. Stratosphere-to-troposphere transport (STT), as the primary natural source of tropospheric ozone, may have a non-negligible contribution to ground-level ozone. Rossby wave breaking (RWB) is a leading mechanism that triggers STT, which can be categorized as anticyclonic wave breakings (AWBs) and cyclonic wave breakings (CWBs). This study uses an objective method to diagnose AWBs and CWBs and to investigate their influence on the surface ozone in eastern China using ground-based ozone observations, satellite ozone data from AIRS, a stratospheric ozone tracer simulated by CAM-chem, and meteorological fields from MERRA-2. The results indicate that AWBs occur mainly and frequently over northeast China, while CWBs occur mostly over the northern Sea of Japan. STTs triggered by AWBs mainly have sinking areas over the North China Plain, increasing the ground-level ozone concentrations by 5–10 ppbv in eastern China. The downwelling zones in the CWBs extend from Mongolia to the East China Sea, potentially causing an elevation of 5–10 ppbv of ozone in both central and eastern China. This study gives an overview of the impacts of AWBs and CWBs on surface ozone in eastern China and helps to improve our understanding of summertime ozone pollution in eastern China.

Machine learning model to predict vehicle electrification impacts on urban air quality and related human health effects

Air pollution is a prevailing environmental problem in cities worldwide. The future vehicle electrification (VE), which in Europe will be importantly fostered by the ban of thermal engines from 2035, is expected to have an important effect on urban air quality. Machine learning models represent an optimal tool for predicting changes in air pollutants concentrations in the context of future VE. For the city of Valencia (Spain), a XGBoost (eXtreme Gradient Boosting package) model was used in combination with SHAP (SHapley Additive exPlanations) analysis, both to investigate the importance of different factors explaining air pollution concentrations and predicting the effect of different levels of VE. The model was trained with 5 years of data including the COVID-19 lockdown period in 2020, in which mobility was strongly reduced resulting in unprecedent changes in air pollution concentrations. The interannual meteorological variability of 10 years was also considered in the analyses. For a 70% VE, the model predicted: 1) improvements in nitrogen dioxide pollution (−34% to −55% change in annual mean concentrations, for the different air quality stations), 2) a very limited effect on particulate matter concentrations (−1 to −4% change in annual means of PM2.5 and PM10), 3) heterogeneous responses in ground-level ozone concentrations (−2% to +12% change in the annual means of the daily maximum 8-h average concentrations). Even at a high VE increase of 70%, the 2021 World Health Organization Air Quality Guidelines will be exceeded for all pollutants in some stations. VE has a potentially important impact in terms of reducing NO2-associated premature mortality, but complementary strategies for reducing traffic and controlling all different air pollution sources should also be implemented to protect human health.