Forecasting of daily total atmospheric ozone in Isfahan

A neural network combined to an artificial neural network model is used to forecast daily total atmospheric ozone over Isfahan city in Iran. In this work, in order to forecast the total column ozone over Isfahan, we have examined several neural networks algorithms with different meteorological predictors based on the ozone-meteorological relationships with previous day's ozone value. The meteorological predictors consist of temperatures (dry and dew point) and geopotential heights at standard levels of 100, 50, 30, 20 and 10 hPa with their wind speed and direction. These data together with previous day total ozone forms the input matrix of the neural model that is based on the back propagation algorithm (BPA) structure. The output matrix is the daily total atmospheric ozone. The model was build based on daily data from 1997 to 2004 obtained from Isfahan ozonometric station data. After modeling these data we used 3 year (from 2001 to 2003) of daily total ozone for testing the accuracy of model. In this experiment, with the final neural network, the total ozone are fairly well predicted, with an Agreement Index 76%.     

Evaluation of the temporal scaling variability in forecasting ground-level ozone concentrations obtained from multiple linear regressions

Ozone is a highly unpredictable pollutant which severely affects living conditions in urban and surrounding areas in the Mediterranean basin. This secondary pollutant periodically reaches extremely high concentrations, damaging human health. Multiple linear regression has been widely used in previous works due to the fact that it is a simple and versatile method for forecasting ozone concentrations. However, these models usually prove their validity using fulfillment of statistical constraints, ignoring other intrinsic characteristics existing in the time series, such as the temporal scaling behavior and the data distribution over different time scales. In previous works, it has been demonstrated that observed ozone time series are of a multifractal nature, meaning that the data distribution can be described by using the multifractal spectrum. This work focuses on the capacity of a forecasting model to reproduce the scaling features existing in an observed time series when several chemical and meteorological explanatory variables are introduced following the stepwise procedure. A comparison between the observed spectrum and the simulated ones for each step is used to check which explanatory variables better reproduce the multifractal nature in real ozone time series. It has been confirmed that a model with few explanatory variables allows reproducing the multifractal nature in the simulated time series with an acceptable accuracy without compromising the values of the coefficient of determination and root-mean-squared error, which were used as performance indicators.     

Ozone Exposures and Implications for Vegetation in Rural Areas of the Central Appalachian Mountains, U.S.A.

The United States is making the transition from the 1979 1 hr maximum ozone standard to the newly adopted 8 hr ozone standard (3 yr average of the 4th highest maximum 8 hr ozone concentration). Consequently, we analyzed and compared ozone concentrations under both standards from a variety of monitoring sites throughout the central Appalachian region of Kentucky (KY), West Virginia (WV), and Virginia (VA). Data from 1988-1999 were used to determine how ozone exposure between the two metrics compared for remote sites. Most sites exceeded the 1 hr standard in 1988-1990 due to the 3 yr averaging and multiple high ozone concentrations that occurred over the region in 1988. All sites were in compliance with the 1 hr standard every year after 1991. It was much more common for the ozone exposure to exceed the 8 hr standard, particularly from 1997-1999. Many sites showed exceedences beginning in 1995; Big Meadows (VA) exceeded the 8 hr standard all years except 1994 and 1996. Response of vegetation to ozone in these areas was determined using the combination of W126 values (sigmoidally weighted exposure index), the number of hours that average concentrations > or = 0.10 ppm (N100), and the presence of moderate or more extreme droughts. In general, W126 and N100 values suggested that negative vegetation growth responses over most of the 12 yr would have been minimal for most sites, even for those exceeding ozone standards. Drought-induced stomatal closures would have overridden more extreme negative growth responses at all but the Big Meadows site in 1988.     

Vertical ozone characteristics in urban boundary layer in Beijing

Vertical ozone and meteorological parameters were measured by tethered balloon in the boundary layer in the summer of 2009 in Beijing, China. A total of 77 tethersonde soundings were taken during the 27-day campaign. The surface ozone concentrations measured by ozonesondes and TEI 49C showed good agreement, albeit with temporal difference between the two instruments. Two case studies of nocturnal secondary ozone maxima are discussed in detail. The development of the low-level jet played a critical role leading to the observed ozone peak concentrations in nocturnal boundary layer (NBL). The maximum of surface ozone was 161.7 ppbv during the campaign, which could be attributed to abundant precursors storage near surface layer at nighttime. Vertical distribution of ozone was also measured utilizing conventional continuous analyzers on 325-m meteorological observation tower. The results showed the NBL height was between 47 and 280 m, which were consistent with the balloon data. Southerly air flow could bring ozone-rich air to Beijing, and the ozone concentrations exceeded the China’s hourly ozone standard (approximately 100 ppb) above 600 m for more than 12 h.     

Multilayer perceptron neural network for flow prediction

Artificial neural networks (ANNs) have proven to be a tool for characterizing, modeling and predicting many of the non-linear hydrological processes such as rainfall-runoff, groundwater evaluation or simulation of water quality. After proper training they are able to generate satisfactory predictive results for many of these processes. In this paper they have been used to predict 1 or 2 days ahead the average and maximum daily flow of a river in a small forest headwaters in northwestern Spain. The inputs used were the flow and climate data (precipitation, temperature, relative humidity, solar radiation and wind speed) as recorded in the basin between 2003 and 2008. Climatic data have been utilized in a disaggregated form by considering each one as an input variable in ANN(1), or in an aggregated form by its use in the calculation of evapotranspiration and using this as input variable in ANN(2). Both ANN(1) and ANN(2), after being trained with the data for the period 2003-2007, have provided a good fit between estimated and observed data, with R(2) values exceeding 0.95. Subsequently, its operation has been verified making use of the data for the year 2008. The correlation coefficients obtained between the data estimated by ANNs and those observed were in all cases superior to 0.85, confirming the capacity of ANNs as a model for predicting average and maximum daily flow 1 or 2 days in advance.     

Mixed Integer Linear Programming Models for Selecting Ground-Level Ozone Control Strategies

The traditional strategy for ground-level ozone control is to apply emission reductions across the board throughout certain time periods and locations. In this paper, we study various mixed integer linear programming (MILP) models that seek to select targeted control strategies for the Dallas Fort-Worth (DFW) region to reduce emissions, in order to achieve the State Implementation Plan (SIP) requirements with minimum cost. Statistics and optimization methods are used to determine a potential set of cost-effective control strategies for reducing ozone. These targeted control strategies are specified for different types of emission sources in various time periods and locations. Three MILP models, a static model, a sequential model, and a dynamic model, are studied in this research. These different MILP models allow decision makers to study how the targeted control strategies change under different circumstances. Meanwhile, two types of auxiliary variables are considered as supplemental control strategies in the optimization if the current set of control strategies is unable to reduce ozone to comply with the 8-h ozone standard. Results from the different models can provide decision makers with information concerning how the effectiveness of the control strategies varies with daily emission patterns and meteorology.     

Case studies on summertime measurements of O3, NO2, and SO2 with a DOAS system in an urban semi-industrial region in Athens, Greece

The objective of this study is to analyze the concentrations of SO₂, NO₂, and O₃ measured by a Differential Optical Absorption Spectroscopy (DOAS) system that was operating at the campus of Technological Education Institute of Piraeus during 2008 and 2009 warm periods (July to September) in relation to the prevailing meteorological conditions. The DOAS system was operating in a particularly polluted area of the West part of Attica basin on a continuous basis, measuring the concentration levels of the main pollutants (O₃, NO₂, and SO₂) as well as aromatic hydrocarbon substances (benzene, toluene, and xylene). According to the analysis, the SO₂ concentration levels at this measuring site are rather high and this may be attributed to the characteristics of this measuring site. Proximity of roadways and local circulation are just some of the factors that can affect the concentration levels of monitoring of pollutant concentrations such as NO₂ and surface ozone. The results provide evidence for the occurrence of an atmospheric phenomenon that produces higher ozone concentrations during weekends despite lower concentrations of ozone precursors. This phenomenon is known as the weekend effect.     

Plants as indicators of urban air pollution (ozone and trace elements) in Pisa, Italy

A biennial integrated survey, based on the use of vascular plants for the bioindication of the effects of tropospheric ozone, was performed in the area of Pisa (Tuscany, Central Italy). It also investigated the distribution of selected trace elements in plants and the data were compared with those obtained from the use of passive samplers, automatic analysers of ozone and lichen biodiversity. Photochemically produced ozone proved to be present during the warm season, with maximum hourly means surpassing 100 ppb: the use of supersensitive tobacco Bel-W3 confirmed the value of detailed, cost-effective, monitoring surveys. Trials with clover clones demonstrate that sensitive plants undergo severe biomass reduction in the current ozone regime. The mean NC-S (clover clone sensitive to ozone):NC-R (resistant) biomass ratio ranged from 0.7 (in 1999) to 0.5 (in 2000). The economic impact of these reductions deserves attention. The data obtained using passive ozone samplers exceeded those obtained using an automatic analyser. The mapping of epiphytic lichen biodiversity was not related to the geographical ozone distribution as can be seen from the tobacco's response. Lettuce plants grown under standardized conditions were used positively as bioaccumulators of trace elements: Pb was abundantly recovered, but a large portion of this element was removed by washing.