Maximum Surface Ozone Concentration Research Articles

Evaluation of isoprene light response curves for bryophyte-dominated ecosystems and implications for atmospheric composition

Isoprene is emitted from numerous plant species in response to light and temperature and parameterisations of these relationships, based on observations from a few vascular plant species, have been shown to be broadly applicable to many different vegetation types. Here, we investigate their performance when applied to an ecosystem dominated by bryophytes. Over a six-week period, emissions of isoprene were measured above a Scottish peat bog. The light response derived on the basis of both canopy-scale flux and whole-plant enclosure measurements, deviated from the classical response, showing no sign of saturation within the observed range. We attribute this response to the canopy architecture of moss hummocks, which may attenuate light differently compared to a grass canopy. Both existing big-leaf and canopy-level emission algorithms, developed for vascular plants but commonly used for moorland vegetation, failed to replicate the observed fluxes, overestimating at low light intensities (<1000 μmol m−2 s−1 photosynthetically active radiation) and underestimating during daytime clear sky conditions. The light response was optimised for bryophyte-dominated ecosystems using measured fluxes and incorporated into the EMEP4UK chemical transport model and applied exclusively to moorland. The revised parameterisation resulted in a small reduction in the average annual isoprene emissions in the northern latitudes (5%), but peak isoprene emissions and concentrations increased by up to a factor of two. Yet, no significant change in average or maximum surface ozone concentrations was observed, reflecting that the northern latitudes are in a chemical regime that is strongly NOx limited, in part due to the spatial segregation with the urban sources of NO x . We conclude that, the anticipated increase in isoprene emissions from the northern latitudes in response to climate change is unlikely to contribute towards ozone-related air quality issues, as long as NO x pollution does not increase. However, the non-saturating light response may be equally applicable to non-vascular plants elsewhere, including in the tropics.

Forecasting of Surface Ozone Concentration by Using Artificial Neural Networks in Rural and Urban Areas in Central Poland

This paper presents the development of artificial neural network models for the prediction of the daily maximum hourly mean of surface ozone concentration for the next day at rural and urban locations in central Poland. The models were generated with six input variables: forecasted basic meteorological parameters and the maximum O3 concentration recorded on the previous day and number of the month. The training data set covered the period from April 2015 to September 2015. An analogous data set of input variables, for the 2014 year, not used during the process of training the networks, was used as test data to examine the quality of these models. From the results of simulations for the year 2014, the average relative error values were equal to 15.3% and 15.7% for Belsk and Warsaw stations, respectively. The mean error (ME) value indicates a tendency to overestimate the predicted values by 4.8 µg/m3 for Belsk station and to underestimate the predicted values by 0.9 µg/m3 for Warsaw station. The analysis of days when the relative error value was &gt;50% revealed that all predictions with extremely high relative error value were associated with relatively low daily maximum surface ozone concentration values that occurred suddenly due to a sharp drop in day-to-day ozone concentration values.

Spatio-Temporal Characteristics of Tropospheric Ozone and Its Precursors in Guangxi, South China

The temporal and spatial distributions of tropospheric ozone and its precursors (NO2, CO, HCHO) are analyzed over Guangxi (GX) in South China. We used tropospheric column ozone (TCO) from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) onboard the Aura satellite (OMI/MLS), NO2 and HCHO from OMI and CO from the Measurements of Pollution in the Troposphere (MOPITT) instrument in the period 2005–2016. The TCO shows strong seasonality, with the highest value in spring and the lowest value observed in the monsoon season. The seasonal variation of HCHO is similar to that of TCO, while NO2 and CO show slightly different patterns with higher values in spring and winter compared to lower values in autumn and summer. The surface ozone, NO2 and CO observed by national air quality monitoring network sites are also compared with satellite-observed TCO, NO2 and CO, showing good agreement for NO2 and CO but a different seasonal pattern for ozone. Unlike TCO, surface ozone has the highest value in autumn and the lowest value in winter. To reveal the difference, the vertical profiles of ozone and CO from the measurement of ozone and water vapor by airbus in-service aircraft (MOZAIC) observations over South China are also examined. The seasonal averaged vertical profiles of ozone and CO show obvious enhancements at 2–6 km altitudes in spring. Furthermore, we investigate the dependence of TCO and surface ozone on meteorology and transport in detail along with the ECMWF reanalysis data, Tropical Rainfall Measuring Mission (TRMM) 3BV42 dataset, OMI ultraviolet index (UV index) dataset, MODIS Fire Radiative Power (FRP) and back trajectory. Our results show that the wind pattern at 800 hPa plays a significant role in determining the seasonality of TCO over GX, especially for the highest value in spring. Trajectory analysis, combined with MODIS FRP suggests that the air masses that passed through the biomass burning (BB) region of Southeast Asia (SEA) induced the enhancement of TCO and CO in the upper-middle troposphere in spring. However, the seasonal cycle of surface ozone is associated with wind patterns at 950 hPa, and the contribution of the photochemical effect is offset by the strong summer monsoon, which results in the maximum surface ozone concentration in post-monsoon September. The variations in the meteorological conditions at different levels and the influence of transport from SEA can account for the vertical distribution of ozone and CO. We conclude that the seasonal distribution of TCO results from the combined impact of meteorology and long-term transport.

Prognosis of maximum daily surface ozone concentration within the greater Athens urban area, Greece

&lt;p&gt;In recent decades, there has been an increasing interest in the prognosis of &amp;nbsp;maximum surface ozone concentrations due to the adverse effects on human health, animal population, agricultural productivity and forestry. The present study deals with the development and application of Artificial Neural Network (ANN) models in predicting the maximum daily surface ozone concentration in several locations within the greater Athens area (GAA), 24-hours in advance. Meteorological and air pollution data during the period 2001 to 2005 were provided by the network of the Hellenic Ministry of the Environment, Energy and Climate Change. Hourly values of barometric pressure and total solar irradiance for the same period have been recorded by the National Observatory of Athens. A training data set for the ANN prognostic model was generated by employing the superposed epoch analysis.&lt;/p&gt;&#x0D; &lt;div&gt;&#x0D; &lt;p&gt;The evaluation of the performance of the developed model, using appropriate statistical indices, clearly indicates that the risk of surface ozone values exceeding the European Union (EU) threshold for human health protection can be successfully predicted. This suggests that the proposed ANN model can be used to issue warnings for the general public and especially certain sensitive groups of the population.&amp;nbsp;&lt;/p&gt;&#x0D; &lt;/div&gt;&#x0D; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&#x0D;

Application of Multiple Linear Regression Models and Artificial Neural Networks on the Surface Ozone Forecast in the Greater Athens Area, Greece

An attempt is made to forecast the daily maximum surface ozone concentration for the next 24 hours, within the greater Athens area (GAA). For this purpose, we applied Multiple Linear Regression (MLR) models against a forecasting model based on Artificial Neural Network (ANN) approach. The availability of basic meteorological parameters is of great importance in order to forecast the ozone’s concentration levels. Modelling was based on recorded meteorological and air pollution data from thirteen monitoring sites within the GAA (network of the Hellenic Ministry of the Environment, Energy and Climate Change) over five years from 2001 to 2005. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that in every aspect, the prognostic model by far is the ANN model. This suggests that the ANN model can be used to issue warnings for the general population and mainly sensitive groups.

Trends of surface ozone concentrations in germany and their connections with changes in meteorological variables

Changes in daily mean and daily maximum surface ozone concentrations, temperature, and relative humidity are evaluated based on the data of long-term observations (from 11 to 16 years) carried out at eight German stations. For all stations the trends of daily mean surface ozone concentration are statistically significant and positive. The trend values are different and generally range from 0 to 10% per 10 years. The trends of the maximum daily concentration are, on average, approximately 1.5 times less. Noticeable part of trends of the surface ozone concentration is connected with the trends of meteorological variables: temperature, relative humidity, and air mass transport direction. After the account of the influence of the trends of meteorological variables, the trends of the surface ozone concentration at most stations substantially decrease. The highest trend values of daily mean and daily maximum surface ozone concentrations are observed in a cold season; in a warm season, the trend values are much lower, at some stations they are statistically insignificant. A conclusion is made that for a correct revealing of reasons of long-term changes in the surface ozone concentration one should take into account changes in meteorological factors influencing its formation.

Statistical modeling of daily maximum surface ozone concentrations

A statistical model of the daily maximum surface ozone concentrations is suggested based on correlations with its predictors. Among the predictors are the temperature; relative humidity; mean wind speed in the planetary boundary layer; concentrations of other trace gases; and the “meteorological pollution potential,” which can characterize adverse (for atmospheric dispersion) meteorological conditions. The statistical model is suitable for surface ozone forecasting; it uses current meteorological parameters, as well as their forecasted values. The most significant predictors of the surface ozone in the Moscow region are the meteorological pollution potential and anomalies (deviations from the norms) of the temperature, relative humidity, and surface ozone on the previous day. The model was tested using the data obtained for the Moscow region and some German stations. Such a model is better than the “climate” and “inertial” models and can ensure a determination coefficient of the surface ozone anomalies of about 50%.

Statistical forecast of surface ozone concentration in Moscow

Two synoptic-statistical methods for forecasting daily maximum surface ozone concentrations are proposed based on the relations between surface ozone and meteorological variables in the Moscow region. The methods use current ozone measurements and forecasts of meteorological variables and of synoptic situation. Statistically, the methods provide better forecast results than climatic and persistence methods. Compared with the persistence forecast, the above methods reduce the variance of the forecast error from 1.5 to 2 times. The most significant predictors for forecasting daily maximum surface ozone concentration with lead times of one to three days for Moscow are the forecast time (Julian day of the year), prognostic temperature, relative humidity, indices of the meteorological pollution potential of the atmosphere (MPP), and surface ozone concentration observed on the previous day. The forecast efficiency is demonstrated using the 2006 observational data from the stations of the Institute of Atmospheric Physics of the Russian Academy of Sciences-Moscow State University and Mosecomonitoring State Nature Protection Organization.

A summer simulation of biogenic contributions to ground‐level ozone over the continental United States

The role of biogenic emissions in the production of ground‐level ozone has been the subject of considerable scientific investigation. However, because existing studies generally draw their conclusions from simulations of episodes lasting days to a week, there is a need to evaluate the biogenic impact over a relatively long timescale. Moreover, the magnitude of synergistic interaction between anthropogenic and biogenic emissions should be carefully quantified, and this issue is not accounted for in most previous investigations. In this study, we performed a summer seasonal (June to August 1995) model evaluation of surface ozone across the continental United States. A three‐dimensional regional climate, emissions, and air quality modeling system was used to do the simulations. The factor separation (FS) technique was applied to quantify the contributions from biogenic emissions alone and those from the synergy between anthropogenic and biogenic emissions. In the first step of this study, U.S. Environmental Protection Agency's National Emission Trends (NET) 1996 and 2020 “control case” raw anthropogenic emissions inventories were processed through Sparse Matrix Operator Kernel Emissions (SMOKE), an emissions model, to generate the speciated, gridded, and hourly emissions data needed for the air quality model. Next, six air quality simulations were carried out assuming zero emissions, biogenic only emissions, 1995 anthropogenic only emissions, biogenic plus 1995 anthropogenic emissions, 2020 anthropogenic only emissions, and biogenic plus 2020 anthropogenic emissions. The model results show that ground‐level ozone concentrations decrease moderately under the EPA's 2020 emissions scenario for many areas within the continental United States, with large reductions in vast areas of the eastern United States. They also show that the 1995 summer average “total biogenic contribution” to daily maximum surface ozone concentrations can reach 34 ppb. Biogenic emissions are associated with at least 20% of surface ozone concentrations for the most areas of the continental United States, with the peaks reaching more than 40% in California coastal areas, the southeastern states, and northeastern areas. A sizable portion of this “total biogenic contribution,” however, (up to 80% in some areas) is due to the synergy between anthropogenic and biogenic emissions and would thus be influenced by controls on anthropogenic source emissions.

Artificial Neural Network-Derived Trends in Daily Maximum Surface Ozone Concentrations

ABSTRACT Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emissions. In this paper, a technique is described that attempts to maximize the removal of meteorological variability from a daily maximum ozone time series, thereby revealing longer term changes in ozone concentrations with increased confidence. The technique employs artificial neural network [multilayer perceptron (MLP)] models, and is shown to remove more of the meteorological variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.

Meteorologically adjusted trends in UK daily maximum surface ozone concentrations

A methodology to meteorologically adjust daily UK surface ozone data is presented which reveals the impact of longer-term variations in precursor emissions more clearly. Based on this approach a general site-dependant decline in meteorologically adjusted summer daily maximum ozone concentrations has occurred between 1994 and 1998 and is between 0.7 and 2.3 ppb yr −1.

Observations of Transport Processes for Ozone and Ozone Precursors during the 1991 Lake Michigan Ozone Study

The Lake Michigan Air Quality Region (LMAQR) continues to experience ozone concentrations in urban and rural areas above the federal standard of 125 ppb. During the summer of 1991, the LMAQR states sponsored the Lake Michigan Ozone Study, which included a comprehensive field measurement program to gather data to understand the complex meteorology and air quality of that region and to verify predictions from air quality models. Air quality and meteorological data were collected by aircraft, surface monitors, rawinsondes, and radar wind profilers to characterize pollutant distributions and understand transport regimes. The unique meteorology of the LMAQR and the stable conditions over the lake produce several transport regimes. This paper presents observations of these transport regimes on two days with maximum surface ozone concentrations above 160 ppb in Wisconsin and Michigan. Transport of air with the highest ozone concentrations occurred in a conduction layer, which is a shallow layer of cool air over Lake Michigan. The conduction layer provided a stable environment where morning emissions remained concentrated and allowed reaction at high precursor concentrations. Southerly and southwesterly winds transported pollutants in this layer northward. Eventually this air flowed onshore and produced many nearshore ozone exceedances along the Wisconsin and Michigan shorelines. To successfully model ozone in the LMAQR, meteorological and air quality models must simulate these transport processes and stable conditions over the lake.