Hourly Surface Ozone Research Articles

Performance evaluation of UKESM1 for surface ozone across the pan-tropics

Abstract. Surface ozone monitoring sites in the tropics are limited, despite the risk that surface ozone poses to human health, tropical forest and crop productivity. Atmospheric chemistry models allow us to assess ozone exposure in unmonitored locations and evaluate the potential influence of changing policies and climate on air quality, human health and ecosystem integrity. Here, we utilise in situ ozone measurements from ground-based stations in the pan-tropics to evaluate ozone from the UK Earth system model, UKESM1, with a focus on remote sites. The study includes ozone data from areas with limited previous data, notably tropical South America, central Africa and tropical northern Australia. Evaluating UKESM1 against observations beginning in 1987 onwards, we show that UKESM1 is able to capture changes in surface ozone concentration at different temporal resolutions, albeit with a systematic high bias of 18.1 nmol mol−1 on average. We use the diurnal ozone range (DOR) as a metric for evaluation and find that UKESM1 captures the observed DOR (mean bias of 2.7 nmol mol−1 and RMSE of 7.1 nmol mol−1) and the trend in DOR with location and season. Results from this study reveal that hourly ozone concentrations from UKESM1 require bias correction before use for impact assessments based on human and ecosystem health. Indeed, hourly surface ozone data have been crucial to this study, and we encourage other modelling groups to include hourly surface ozone output as a default.

Parsimonious estimation of hourly surface ozone concentration across China during 2015–2020

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015–2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations

This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012–2021). The LESO ensemble was generated by establishing the relationship between surface O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 μg/m3. The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

Spatial and temporal variation of O3, NO and NO2 concentrations at rural and urban sites in Marmara Region of Turkey

The first continuous measurements of the hourly surface ozone (O3), nitric oxide (NO) and nitrogen dioxide (NO2) concentrations at 7 rural and 15 urban sites in Marmara Region of Turkey during the period from March 2013 to April 2016. The results indicate that surface O3 is an extensive problem throughout the Marmara Region. O3 levels have similar seasonal pattern in rural and urban sites by following the order of summer, spring, fall, and winter. The seasonal results showed an opposite behaviour among O3 and NOx (NO + NO2) in both rural and urban sites, marked by maximums (minimum NOx) of O3 in summer and minimums (maximum NOx) in fall and winter. The summer concentrations vary between 91.5 and 123.6 μg/m3 and 74.6–112.4 μg/m3 at rural and urban sites respectively. Furthermore, O3 peak concentration time in rural areas in O3 seasons (April–September) occurred at around 17:00–18:00 LST while in urban areas at around 15:00–16:00 LST, respectively. Air quality standards for O3 concentrations in Sile and Yalova rural sites were significantly exceeded during O3 season. This exceedance made a linkage between back trajectories and air masses with respect to the O3 levels. 72-hr back trajectories at an altitude of 500 m agl were computed by HYSPLIT model to assess the type of air masses which reach the area. AOT40 (accumulated exposure over a threshold of 40 ppb) index was performed for both vegetation and forestry standards and EU Directive were significantly exceeded at all rural stations.

Use of North American and European air quality networks to evaluate global chemistry–climate modeling of surface ozone

Abstract. We test the current generation of global chemistry–climate models in their ability to simulate observed, present-day surface ozone. Models are evaluated against hourly surface ozone from 4217 stations in North America and Europe that are averaged over 1° × 1° grid cells, allowing commensurate model–measurement comparison. Models are generally biased high during all hours of the day and in all regions. Most models simulate the shape of regional summertime diurnal and annual cycles well, correctly matching the timing of hourly (~ 15:00 local time (LT)) and monthly (mid-June) peak surface ozone abundance. The amplitude of these cycles is less successfully matched. The observed summertime diurnal range (~ 25 ppb) is underestimated in all regions by about 7 ppb, and the observed seasonal range (~ 21 ppb) is underestimated by about 5 ppb except in the most polluted regions, where it is overestimated by about 5 ppb. The models generally match the pattern of the observed summertime ozone enhancement, but they overestimate its magnitude in most regions. Most models capture the observed distribution of extreme episode sizes, correctly showing that about 80 % of individual extreme events occur in large-scale, multi-day episodes of more than 100 grid cells. The models also match the observed linear relationship between episode size and a measure of episode intensity, which shows increases in ozone abundance by up to 6 ppb for larger-sized episodes. We conclude that the skill of the models evaluated here provides confidence in their projections of future surface ozone.

Enhancements in nocturnal surface ozone at urban sites in the UK.

Analysis of diurnal patterns of surface ozone (O3) at multiple urban sites in the UK shows the occurrence of prominent nocturnal enhancements during the winter months (November-March). Whilst nocturnal surface ozone (NSO) enhancement events have been observed at other locations, this is the first time that such features have been demonstrated to occur in the UK and the second location globally. The observed NSO enhancement events in the UK were found to be so prevalent that they are clearly discernible in monthly diurnal cycles averaged over several years of data. Long-term (2000-2010) analysis of hourly surface ozone data from 18 urban background stations shows a bimodal diurnal variation during the winter months with a secondary nighttime peak around 0300 hours along with the primary daytime peak. For all but one site, the daily maxima NSO concentrations during the winter months exceeded 60 μg/m(3) on >20 % of the nights. The highest NSO value recorded was 118 μg/m(3). During the months of November, December, and January, the monthly averaged O3 concentrations observed at night (0300 h) even exceeded those observed in the daytime (1300 h). The analysis also shows that these NSO enhancements can last for several hours and were regional in scale, extending across several stations simultaneously. Interestingly, the urban sites in the north of the UK exhibited higher NSO than the sites in the south of the UK, despite their daily maxima being similar. In part, this seems to be related to the sites in the north typically having lower concentrations of nitrogen oxides.

Variation of surface ozone exceedance around Klang Valley, Malaysia

The total hourly surface ozone (O3) exceedance from the 100ppbv hourly O3 standard set by the Department of Environment Malaysia (DOE) was analysed, as elevated O3 concentrations pose health risks to humans and harms vegetation. Air quality data from 2008 to 2010 were obtained from a total of seven stations located around the west coast of Peninsular Malaysia. Cheras and Shah Alam monitoring stations consistently showed a high frequency of noncompliance to the DOE standards. Hierarchical Agglomerative Cluster Analysis (HACA) was performed on the daily maximum O3 concentration to analyse the spatial variability. Three distinct clusters were obtained from HACA runs on the daily maximum O3 and the results reflected O3 exceedance pattern among the stations. Analysis of the monthly average O3, nitrogen oxide (NO), and nitrogen dioxide (NO2) concentrations indicated a strong localised influence on the O3 exceedance patterns. It can be concluded that the O3 exceedance pattern in the Klang Valley area is strongly influenced by local pollutant emission and dispersion characteristics.

Assessment of surface ozone variability in an urban coastal area at the Eastern Mediterranean

The surface ozone is a pollutant of major concern due to its impact on receptors, at currently occurring ambient levels in many regions of the world. The aim of this work is to present the results derived from an analysis of hourly surface ozone concentrations, measured at the urban station of Volos, a coastal medium-sized city at the eastern seaboard of Central Greece, during the 8-year period 2001-2008. The regional climate that is characterized by hot and dry summers with intense sunshine plays an important role in the observed exceedances of the air quality ozone limits. The analysis showed that, ozone diurnal patterns depict daytime photochemical ozone built up, during the sunlight hours of the day. It is remarkable that the maximum daily 8-h averages often exceeded the standard value that is assigned by the EU Directive for human health protection, during almost the warm period of the year, mainly at noon and afternoon hours.

Nocturnal surface ozone enhancement and trend over urban and suburban sites in Portugal

Long term (2000–2010) analysis of hourly surface ozone data from 6 out of 9 background air pollution monitoring sites in Portugal shows prominent secondary/nocturnal peak in monthly averaged diurnal variation during winter months (Nov–Feb). Of 9 sites: 3 are urban, 5 suburban and 1 rural. The bimodal diurnal variation of surface ozone shows a major peak around 15 h and a minor peak around 03–05 h with minima around 08 h and 20 h in the months of November–February. During winter months, more than 50% of observations in Porto and Lisbon region had nocturnal surface ozone concentrations higher than 40 μg m−3 compared to monthly averaged early morning/late evening minima (20±5 μg m−3). Analysis also shows an increasing trend in nocturnal surface ozone daily maxima over 5 urban/suburban sites and over another site (Alfragide) which exhibits the same trend but only in the last 7 years of the studied period. Also the non-parametric Mann–Kendall test confirms that the trends are statistically significant. The enhancement of nocturnal surface ozone is most likely associated with transport process and prevailing meteorological conditions since photochemical production of ozone ceases after sunset.

Ozone in ambient air at a tropical megacity, Delhi: characteristics, trends and cumulative ozone exposure indices

Seven year data of hourly surface ozone concentration is analyzed to study diurnal cycle, trends, excess of ozone levels above threshold value and cumulative ozone exposure indices at a tropical megacity, Delhi. The ozone levels clearly exhibit a diurnal cycle, similar to what has been found in other urban places. A sharp increase in the ozone levels during forenoon and a sharp decrease in the early afternoon can be observed. The average rate of increase in ozone concentration between 09 and 12 h has been observed to be 7.1 ppb h−1. We find that the daily maximum and daytime 8-h (10–17 h) ozone levels are increasing at a rate of about 1.7 (± 0.7) and 1.3 (± 0.56) ppb y−1, respectively. The directives on ozone pollution in ambient air provided by United Nations Economic Commission for Europe and World Health Organization for vegetation (AOT40) and human health protection were used to assess the air quality. The present surface ozone levels in the city are high enough to exceed “Critical Levels” which are considered to be safe for human health, vegetation and forest. The human health threshold was exceeded for up to ~45 days per year. The AOT40 (Accumulated exposure Over a Threshold of 40 ppb) threshold was exceeded significantly during winter (D-J-F) and pre-monsoon (M-A-M) (Rabi crop growing season) season in India. Translating AOT40 exceedances during pre-monsoon into relative yield loss we estimate yield loss of 22.7%, 22.5%, 16.3% and 5.5% for wheat, cotton, soybean and rice, respectively.

Prediction of tropospheric ozone concentrations by using the design system approach

Data on the concentrations of non-methane hydrocarbons (NMHC), nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO), and meteorological parameters (air temperature and solar radiation) were used to predict the concentration of tropospheric ozone using the Design-Ease software. These data were collected on hourly basis over a 12-month period. Sampling of the data was conducted automatically. The effect of the NMHC, NO, NO2,CO, temperature and solar radiation variables in predicting ozone concentrations was examined under two scenarios: (i) when NO is included with the absence of NO2; and (ii) when NO2 is addressed with the absence of NO. The results of these two scenarios were validated against ozone actual data. The predicted concentration of ozone in the second scenario (i.e., when NO2 is addressed) was in better agreement with the real observations. In addition, the paper indicated that statistical models of hourly surface ozone concentrations require interactions and non-linear relationships between predictor variables in order to accurately capture the ozone behavior.

Effective 1-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks

The aim of this research was to develop pure predictive models in order to provide 24 h advance forecasts of the hourly ozone concentration for the rural site of Carcagente (Valencia, Spain) and the urban sites of Paterna (Valencia, Spain) and Alcoy (Alicante, Spain) over 4 years from 1996 to 1999. The peculiarity of the model presented here is that it uses past and previously predicted information of inputs exclusively, thus being this is the first genuine 24 h advance O 3 predictive model with neural networks. We used autoregressive-moving average with exogenous inputs (ARMAX), multilayer perceptrons and FIR neural networks. Five performance measures yield reasonably good results in the three sampling sites. The results indicate that the models developed predict the O 3 time series more effectively compared with previous procedures based on dynamical system theory. The neural network's models yield better results than linear models when exogenous inputs are included. The prediction accuracy of these models enables, for the first time, an effective warning to be made in cases where EU public information threshold values are exceeded.

Statistical surface ozone models: an improved methodology to account for non-linear behaviour

Using UK data as a case study, this paper demonstrates that statistical models of hourly surface ozone concentrations require interactions and non-linear relationships between predictor variables in order to accurately capture the ozone behaviour. Comparisons between linear regression, regression tree and multilayer perceptron neural network models of hourly surface ozone concentrations quantify these effects. Although multilayer perceptron models are shown to more accurately capture the underlying relationship between both the meteorological and temporal predictor variables and hourly ozone concentrations, the regression tree models are seen to be more readily physically interpretable.