Tropospheric Ozone Data Research Articles

Long-Term Tropospheric Ozone Data Analysis 1997–2019 at Giordan Lighthouse, Gozo, Malta

Long-term data analysis of the hourly ozone volume fractions in the middle of the Mediterranean Seawas carried out covering a period of 22 years. It was noticed that the amount of ozone during this period very rarely exceeded the recommended upper limit value of 80 ppb and that the amount of tropospheric ozone in the area is rather low. Fourier data analysis shows the presence of only a seasonal cycle in ozone concentrations. Statistical analysis of the data is showing a slightly negative trend in ozone concentrations of −0.46 ± 0.08 ppb/year for average values and a slightly higher negative trend of −0.54 ± 0.11 ppb/year for the 95th percentile values. These results obtained through simple linear regression were confirmed using the more appropriate Mann–Kendall test. The possible quadratic trend was not observed for the whole series of data. Air mass trajectories were calculated for those days in the year with the highest pollution, indicating that during those days horizontal air transfer, in most cases, brings the air mass from the North and from Sicily in Southern Italy.

Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data.

Gaps in the measurement series of atmospheric pollutants can impede the reliable assessment of their impacts and trends. We propose a new method for missing data imputation of the air pollutant tropospheric ozone by using the graph machine learning algorithm "correct and smooth". This algorithm uses auxiliary data that characterize the measurement location and, in addition, ozone observations at neighboring sites to improve the imputations of simple statistical and machine learning models. We apply our method to data from 278 stations of the year 2011 of the German Environment Agency (Umweltbundesamt - UBA) monitoring network. The preliminary version of these data exhibits three gap patterns: shorter gaps in the range of hours, longer gaps of up to several months in length, and gaps occurring at multiple stations at once. For short gaps of up to 5 h, linear interpolation is most accurate. Longer gaps at single stations are most effectively imputed by a random forest in connection with the correct and smooth. For longer gaps at multiple stations, the correct and smooth algorithm improved the random forest despite a lack of data in the neighborhood of the missing values. We therefore suggest a hybrid of linear interpolation and graph machine learning for the imputation of tropospheric ozone time series.

Tropospheric ozone retrieval by a combination of TROPOMI/S5P measurements with BASCOE assimilated data

Abstract. We present a new tropospheric ozone dataset based on TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P) total ozone measurements combined with stratospheric ozone data from the Belgian Assimilation System for Chemical ObsErvations (BASCOE) constrained by assimilating ozone observations from the Microwave Limb Sounder (MLS). The BASCOE stratospheric data are interpolated to the S5P observations and subtracted from the TROPOMI total ozone data. The difference is equal to the tropospheric ozone residual column from the surface up to the tropopause. The tropospheric ozone columns are retrieved at the full spatial resolution of the TROPOMI sensor (5.5×3.5 km2) with daily global coverage. Compared to the Ozone Mapping and Profiler Suite Modern-Era Retrospective analysis for Research and Applications 2 (OMPS-MERRA-2) data, a global mean positive bias of 3.3 DU is found for the analysed period April 2018 to June 2020. A small negative bias of about −0.91 DU is observed in the tropics relative to the operational TROPOMI tropical tropospheric data based on the convective cloud differential (CCD) algorithm throughout the same period. The new tropospheric ozone data (S5P-BASCOE) are compared to a set of globally distributed ozonesonde data integrated up to the tropopause level. We found 2254 comparisons with cloud-free TROPOMI observations within 25 km of the stations. In the global mean, S5P-BASCOE deviates by 2.6 DU from the integrated ozonesondes. Depending on the latitude the S5P-BASCOE deviate from the sondes and between −4.8 and 7.9 DU, indicating a good agreement. However, some exceptional larger positive deviations up to 12 DU are found, especially in the northern polar regions (north of 70∘). The monthly mean tropospheric column and time series for selected areas showed the expected spatial and temporal pattern, such as the wave one structure in the tropics or the seasonal cycle, including a summer maximum, in the mid-latitudes.

Spatio-temporal characterization of tropospheric ozone and its precursor pollutants NO2 and HCHO over South Asia

In recent decades, South Asia has experienced declining air quality, with much of the attention being focused on extremely high levels of particulate matter. Here, we analyze tropospheric ozone (O3), formaldehyde (HCHO), and nitrogen dioxide (NO2) to assess other measures of air quality across South Asia from 2008 to 2018. The IASI-Forli retrieved tropospheric ozone data was validated with ozonesonde, reanalysis (ERA5), satellite (TES), and model simulation products (GEOS-Chem and TOMCAT/SLIMCAT). Space-based observations of these three trace gases were used to conduct a spatio temporal analysis over South Asia using trend analysis (Theil-Sen and linear regression), change-point detection (Pettitt's test), and hotspot identification (Getis-Ord Gi*). We used the formaldehyde-nitrogen dioxide ratio (FNR) to identify NOx limited, VOC limited, and transitional regimes in South Asia. Counter to previous studies, a statistically significant decrease of HCHO (−0.0041 DU yr−1) and O3 (−0.064 DU yr−1) was detected for South Asia; however, NO2 is increasing the 0.001 DU yr−1 over South Asia during 2008–18. The Indo-Gangetic Plains emerged as being critically affected by the three trace gases. Certain parts of southern and south-eastern India are gradually emerging as NO2 and HCHO hotpots. No significant O3 hotspots were discernible, though coldspots existed along the Himalaya belt of India, Nepal, and Bhutan and mountainous tracts of Pakistan. FNR indicates the reduction of NOx in NOx-limited regime of the Indo-Gangetic Plains reduced the formation of tropospheric O3 over South Asia.

Multimodel System Identification Based on New Fuzzy Partitioning Similarity Measure

The problem of identifying unstructured nonlinear systems is generally addressed on the basis of multi-model representations involving several linear local models. In the present work, local models are combined to get a global representation using incremental fuzzy clustering. The main contribution is a novel vector similarity measure defined in the System Working Space (SWS) that combines the angular deviation and the usual Euclidean distance. Such a combination makes the new metric highly discriminating leading to a better partitioning of the operating space providing, thereby, a higher accuracy of the model. The developed partitioning method is first evaluated by performing linear local model (LLM) based identification of a academic benchmark multivariable nonlinear system. Then, the performances of the identification method are evaluated using experimental tropospheric ozone data. These evaluations illustrate the supremacy of the new method over the standard Euclidian-distance based partitioning approach.

Harmonisation and trends of 20-year tropical tropospheric ozone data

Abstract. Using a convective-cloud differential (CCD) method, developed in-house and applied to retrievals of total ozone and cloud data from three European satellite instruments (viz. GOME/ERS-2, 1995–2003; SCIAMACHY/Envisat, 2002–2012 and GOME-2/MetOp-A, 2007–2015), monthly mean tropical tropospheric columns of ozone (TTCO) have been retrieved, which are in good agreement with ozonesondes (biases less than 6 DU). As small differences in TTCO between the individual instruments were evident, it was necessary to develop a scheme to harmonise the three datasets into one consistent time series starting from 1996 until 2015. Correction offsets (biases) between the instruments using SCIAMACHY as intermediate reference have been calculated and six different harmonisation or merging scenarios have been evaluated. Depending on the merging approach, the magnitude, pattern and uncertainty in the trends strongly vary. The harmonisation or merging represents an additional source of uncertainty in the trends (2 DU decade−1 on average, in most of the cases exceeding the uncertainty from the regression). For studying further details on tropospheric ozone trends on various spatial scales in the tropics, we stick with one preferred merged dataset that shows best agreement with ozonesondes. In this merged dataset, no correction was applied for GOME, and mean biases with respect to SCIAMACHY in the overlapping period (2007–2012) were calculated and applied for GOME-2 in each grid box (2.5° × 5°). In contrast with other studies we found that the tropospheric trend averaged over the tropics (−15° S to 15° N) is not statistically significant. The mean tropospheric ozone trend equals −0.2 ± 0.6 DU decade−1 (2σ). Regionally, tropospheric ozone has a statistically significant increase of ∼ 3 DU decade−1 over southern Africa ( ∼ 1.5 % yr−1), the southern tropical Atlantic ( ∼ 1.5 % yr−1), southeastern tropical Pacific Ocean ( ∼ 1 % yr−1), and central Oceania ( ∼ 2 % yr−1) and by ∼ 2 DU decade−1 over central Africa (2–2.5 % yr−1) and south India ( ∼ 1.5 % yr−1). On the other hand, tropospheric O3 decreases by ∼ 3 DU decade−1 over the Caribbean Sea and parts of the North Pacific Ocean ( ∼ 2 % yr−1), and by less than 2 DU decade−1 over some regions of the southern Pacific and Indian oceans ( ∼ 0.5–1 % yr−1).

Spatio-temporal assessment and seasonal variation of tropospheric ozone in Pakistan during the last decade

This study uses the tropospheric ozone data derived from combined observations of Ozone Monitoring Instrument/Microwave Limb Sounder instruments by using the tropospheric ozone residual method. The main objective was to study the spatial distribution and temporal evolution in the troposphere ozone columns over Pakistan during the time period of 2004 to 2014. Results showed an overall increase of 3.2 ± 1.1DU in tropospheric ozone columns over Pakistan. Spatial distribution showed enhanced ozone columns in the Punjab and southern Sindh consistent to high population, urbanization, and extensive anthropogenic activities, and exhibited statistically significant temporal increase. Seasonal variations in tropospheric ozone columns are driven by various factors such as seasonality in UV-B fluxes, seasonality in ozone precursor gases such as NOx and volatile organic compounds (caused by temperature dependent biogenic emission) and agricultural fire activities in Pakistan. A strong correlation of 96% (r = 0.96) was found between fire events and tropospheric ozone columns in Pakistan.

OPTIMAL EXTRACTION OF TROPOSPHERIC OZONE COLUMN BY SIMULTANEOUS USE OF OMI AND TES DATA AND THE SURFACE TEMPERATURE

Abstract. This article aims to increase the accuracy of Ozone data from tropospheric column (TOC) of the OMI and TES satellite instruments. To validate the estimated amount of satellite data, Ozonesonde data is used. The vertical resolution in both instruments in the tropospheric atmosphere decreases so that the degree of freedom signals (DOFS) on the average for TES is reduced to 2 and for OMI is reduced to1. But this decline in accuracy in estimation of tropospheric ozone is more obvious in urban areas so that estimated ozone in both instruments alone in non-urban areas show a high correlation with Ozonesonde. But in urban areas this correlation is significantly reduced, due to the ozone pre-structures and consequently an increase on surface-level ozone in urban areas. In order to improve the accuracy of satellite data, the average tropospheric ozone data from the two instruments were used. The aim is to increase the vertical resolution of ozone profile and the results clearly indicate an increase in correlations, but nevertheless the satellite data have a positive bias towards the earth data. To reduce the bias, with the solar flux and nitrogen dioxide values and surface temperatures are calculated as factors of ozone production on the earth’s surface and formation of mathematical equations based on coefficients for each of the mentioned values and multiplication of these coefficients by satellite data and repeated comparison with the values of Ozonesonde, the results showed that bias in urban areas is greatly reduced.

Differences in the Same OMI/MLS Aura Tropospheric Ozone Data Set Published Before and After January 2013

Fil: Cionco, Rodolfo Gustavo. Universidad Tecnologica Nacional; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina

Tropical tropospheric ozone column retrieval for GOME-2

Abstract. This paper presents the operational retrieval of tropical tropospheric ozone columns (TOCs) from the Second Global Ozone Monitoring Experiment (GOME-2) instruments using the convective-cloud-differential (CCD) method. The retrieval is based on total ozone and cloud property data provided by the GOME Data Processor (GDP) 4.7, and uses above-cloud and clear-sky ozone column measurements to derive a monthly mean TOC between 20° N and 20° S. Validation of the GOME-2 TOC with several tropical ozonesonde sites shows good agreement, with a high correlation between the GOME-2 and sonde measurements, and small biases within ~ 3 DU. The TOC data have been used in combination with tropospheric NO2 measurements from GOME-2 to analyse the effect of the 2009–2010 El Niño–Southern Oscillation (ENSO) on the tropospheric ozone distribution in the tropics. El Niño induced dry conditions in September–October 2009 resulted in relatively high tropospheric ozone columns over the southern Indian Ocean and northern Australia, while La Niña conditions in September–October 2010 resulted in a strong increase in tropospheric NO2 in South America, and enhanced ozone in the eastern Pacific and South America. Comparisons of the GOME-2 tropospheric ozone data with simulations of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model for 2009 El Niño conditions illustrate the usefulness of the GOME-2 TOC measurements in evaluating chemistry climate models (CCMs). Evaluation of CCMs with appropriate satellite observations helps to identify strengths and weaknesses of the model systems, providing a better understanding of driving mechanisms and adequate relations and feedbacks in the Earth atmosphere, and finally leading to improved models.

A practical approach for assessing the effect of grouping in hierarchical spatio-temporal models

Hierarchical spatio-temporal models allow for the consideration and estimation of many sources of variability. A general spatio-temporal model can be written as the sum of a spatio-temporal trend and a spatio-temporal random effect. When spatial locations are considered to be homogeneous with respect to some exogenous features, the groups of locations may share a common spatial domain. Differences between groups can be highlighted both in the large-scale, spatio-temporal component and in the spatio-temporal dependence structure. When these differences are not included in the model specification, model performance and spatio-temporal predictions may be weak. This paper proposes a method for evaluating and comparing models that progressively include group differences. Hierarchical modeling under a Bayesian perspective is followed, allowing flexible models and the statistical assessment of results based on posterior predictive distributions. This procedure is applied to tropospheric ozone data in the Italian Emilia–Romagna region for 2001, where 30 monitoring sites are classified according to environmental laws into two groups by their relative position with respect to traffic emissions.

A simple non-separable, non-stationary spatiotemporal model for ozone

The past two decades have witnessed an increasing interest in the use of space-time models for a wide range of environmental problems. The fundamental tool used to embody both the temporal and spatial components of the phenomenon in question is the covariance model. The empirical estimation of space-time covariance models can prove highly complex if simplifying assumptions are not employed. For this reason, many studies assume both spatiotemporal stationarity, and the separability of spatial and temporal components. This second assumption is often unrealistic from the empirical point of view. This paper proposes the use of a model in which non-separability arises from temporal non-stationarity. The model is used to analyze tropospheric ozone data from the Emilia-Romagna Region of Italy.

Ozone variability in the midlatitude upper troposphere and lower stratosphere diagnosed from a monthly SAGE II climatology relative to the tropopause

A midlatitude (25°–65°) monthly zonal median ozone climatology in the upper troposphere and lower stratosphere (UTLS), from 8 to 20 km with a 0.5‐km vertical resolution and a 5° latitudinal resolution, is developed on the basis of version 6.2 (V6.2) ozone profile retrievals from the Stratospheric Aerosol and Gas Experiment (SAGE) II measurements from October 1984 to August 2005. To avoid mixing of the tropospheric ozone data with stratospheric values, the thermal tropopause height is used as a base altitude for developing the climatology (the monthly mean tropopause height has been added back to the climatological profile). This feature of the developed ozone climatology, together with the near global SAGE II data coverage, complements the existing ozone climatologies in the midlatitude UTLS. In addition to using this climatology to describe hemispheric differences in the UTLS ozone (the primary purpose of this paper), the database can also be used to initialize atmospheric chemistry‐transport models or for satellite data retrieval. The specific new findings include (1) the differences in the vertical structure of monthly ozone evolution across the tropopause between the NH and the SH, (2) all year bimodal probability distribution functions (PDFs) of the tropopause ozone, and (3) the annual cycle of the tropopause ozone PDF with increasing (decreasing) presence of ozone‐rich air leading to tropopause ozone enhancements (reductions) during spring and early summer (fall and winter). The derived climatology is shown to be consistent with the ozonesonde climatologies of Logan (1985, 1999a) in many respects, including ozone seasonal cycle at the tropopause and in the UT, the broad summer ozone maximum in the northern UT, and non‐Gaussian ozone PDFs at the tropopause. This consistency strengthens the confidence in SAGE II satellite ozone remote sensing in the UTLS. The derived SAGE II midlatitude ozone climatology is compared to ozonesonde measurements at Hohenpeissenberg (47.4°N, 11°E), Germany, and Lauder (45°S, 169.7°E), New Zealand. The monthly ozone climatology data are provided as auxiliary material to this report.

Meteorological normalisation and non-parametric smoothing for quality assessment and trend analysis of tropospheric ozone data

Despite extensive efforts to ensure that sampling and installation and maintenance of instruments are as efficient as possible when monitoring air pollution data, there is still an indisputable need for statistical post processing (quality assessment). We examined data on tropospheric ozone and found that meteorological normalisation can reveal (i) errors that have not been eliminated by established procedures for quality assurance and control of collected data, as well as (ii) inaccuracies that may have a detrimental effect on the results of statistical tests for temporal trends. Moreover, we observed that the quality assessment of collected data could be further strengthened by combining meteorological normalisation with non-parametric smoothing techniques for seasonal adjustment and detection of sudden shifts in level. Closer examination of apparent trends in tropospheric ozone records from EMEP (European Monitoring and Evaluation Programme) sites in Finland showed that, even if potential raw data errors were taken into account, there was strong evidence of upward trends during winter and early spring.

Observing tropospheric trace gases from space: recent advances and future capabilities

We present a brief overview of the methodology developed over the past decade to derive tropospheric ozone fields from satellite measurements and will highlight some of the recent progress that has resulted in the ability to formulate its depiction on a daily basis. For the first time, we present a climatology of tropospheric ozone derived from TOMS and SBUV observations using the empirically corrected tropospheric ozone residual methodology described in Fishman and Balok (1999) and Balok and Fishman (2001). When compared to an earlier published tropospheric ozone data set that had been derived from TOMS and SAGE measurements, much better agreement is found than when uncorrected SBUV data had been used. The availability of such a data set will allow for much better temporal resolution than had been previously available. Despite the recent advances made through the technique presented here as well as the satellite capabilities that are currently just over the horizon, it is suggested that a quantum leap in measurement capability can be attained through the use instruments on geostationary platforms.

Detecting trends and biases in time series of ozonesonde data

In this paper, we discuss the complexities associated with the analysis and interpretation of trends in time series of ozonesonde data, focussing on approaches to deal with the special constraints imposed by the irregular nature of the ozonesonde data set. To improve upon earlier studies which have used monthly mean values and parametric techniques on ozonesonde data, a non-parametric statistical method is introduced to enable us to work with data from individual flights rather than with monthly mean values. To this end, ozone time series data are separated into their long-term, seasonal and short-term components to properly characterize the various scales of motion (climatic, annual and synoptic scale) embedded in the data set. We show that the statistical method used here meets the requirements for a reliable analysis of ozonesonde data. It is shown further that this approach enables us to estimate trends in the ozonesonde data with a very high degree of confidence. We then introduce a non-parametric technique for discerning sudden changes in time series data and discuss its usefulness in detecting potential biases in ozonesonde time series data, introduced by changes in instrumentation, flight time, preflight preparation and data reduction procedures. The results show that the method is able to detect discontinuities in the ozonesonde data which are supported by station histories. It is shown that the long-term trend estimates can be significantly affected by the presence of biases in the data. Although further research is necessary to adequately account for artificial breaks in the data at all heights and stations, there is an indication that the estimated upward trend in the raw tropospheric ozone data at Payerne, Hohenpeissenberg and Edmonton might be attributable, in part, to the presence of biases in the data.

Extreme value theory for a class of nonstationary time series with applications

Consider a class of nonstationary time series with the form $Y_t = \mu_t + \xi_t$ where ${\xi_t}$ is a sequence of infinite moving averages of independent random variables with regularly varying tail probabilities and different scale parameters. In this article, the extreme value theory of ${Y_t}$ is studied. Under mild conditions, convergence results for a point process based on the moving averages are proved, and extremal properties of the nonstationary time series, including the convergence of maxima to extremal processes and the limit point process of exceedances, are derived. The results are applied to the analysis of tropospheric ozone data in the Chicago area. Probabilities of monthly maximum ozone concentrations exceeding some specific levels are estimated, and the mean rate of exceedances of daily maximum ozone over the national standard 120 ppb is also assessed.

Analysis of the distribution of ozone over the southern Atlantic region

Tropospheric ozone data measured by ozonesondes during the Transport and Atmospheric Chemistry Near the Equator‐Atlantic (TRACE A) field mission and the multiyear pre‐TRACE A program are analyzed jointly with tropospheric ozone amounts derived from remote satellite data (“residuals”). We present here the first detailed analysis of the entire Ascension Island pre‐TRACE A data set. Data from the three pre‐TRACE A ozonesonde sites are used to establish a coherent spatial and temporal climatology of ozone in the southern tropical Atlantic region. This analysis shows a significant ozone seasonality over the Atlantic region, with a period of maximum values that extends from the austral winter through at least October at Natal, Brazil, and Ascension Island. Concentrations begin to decline somewhat earlier at Brazzaville, Congo, especially at lower altitudes. Although Natal exhibits a significantly lower annual average than Ascension Island or Brazzaville by about 4 Dobson Units (DU), the magnitude of the seasonal amplitude at Natal is the largest of the three stations. Additionally, more of the seasonal amplitude at Natal is due to a contribution from ozone in the middle and upper troposphere than at either Ascension Island or Brazzaville. Amplitudes as large as 15 DU are measured at individual sites, and the residuals show an average amplitude over the southern tropical Atlantic region of 10–12 DU. Statistical comparison of the residuals to the ozonesonde climatology show that while the residuals tend to underpredict both the means and the seasonal amplitudes compared to the in situ data, they provide a good representation of the variance of ozone in this region and predict the local annual and seasonal means to within better than 10% and seasonal amplitudes to within 15%.

Identification of Widespread Pollution in the Southern Hemisphere Deduced from Satellite Analyses

Vertical profiles of ozone obtained from ozonesondes in Brazzaville, Congo (4 degrees S, 15 degrees E), and Ascension Island (8 degrees S, 15 degrees W) show that large quantities of tropospheric ozone are present over southern Africa and the adjacent eastern tropical South Atlantic Ocean. The origin of this pollution is widespread biomass burning in Africa. These measurements support satellite-derived tropospheric ozone data that demonstrate that ozone originating from this region is transported throughout most of the Southern Hemisphere. Seasonally high levels of carbon monoxide and methane observed at middle- and high-latitude stations in Africa, Australia, and Antarctica likely reflect the effects of this distant biomass burning. These data suggest that even the most remote regions on this planet may be significantly more polluted than previously believed.

Comparison of satellite total ozone measurements with the distribution of tropospheric ozone obtained by an airborne UV-DIAL system over the Amazon Basin

During July and August 1985, an extensive field program was conducted throughout the Amazon Basin in Brazil to characterize the chemical composition of the atmosphere in this region of the world. Using the NASA Electra aircraft, a comprehensive picture of the distribution of tropospheric ozone was obtained during this 3-week experiment. The data indicate that substantial gradients of tropospheric ozone can be present on a horizontal scale of 100 km and a vertical scale of less than 1 km. Throughout this period. the integrated amount of ozone in the lowest 3-4 km of the atmosphere more than doubled because of the photochemical reactions associated with the polutants emitted to the atmosphere as a result of widespread biomass burning that was present during the latter portions of the field experiment. This study compares this comprehensive tropospheric ozone data set with a set of satellite total ozone measurements. These comparisons indicate that the spatial and temporal variability in the total ozone satellite data is closely related to the variability in the observed distributions of tropospheric ozone, even though the troposphere contains less than 15% of the total ozone in the atmosphere. Additional data obtained from a series of ozonesonde launches and other satellite measurements confirm that the increase in total ozone amounts over this period was a result of ozone increases in the troposphere. DOI: 10.1111/j.1600-0889.1988.tb00112.x