Ground Ozone Levels Research Articles

Effect of tropospheric ozone and its protectants on gas exchange parameters, antioxidant enzymes and quality of Garlic (Allium sativum. L).

An experimental study was conducted to assess the detrimental effect of ground-level ozone (O3) on garlic physiology and to find out appropriate control measures against ground-level O3, at TNAU-Horticultural Research farm, Udhagamandalam. Elevated ground ozone levels significantly decreased garlic leaf chlorophyll, photosynthetic rate, stomatal conductance, total soluble solids and pungency. The garlic chlorophyll content was highest in ambient ozone level and lowest in elevated ozone@200ppb, highest stomatal conductance was recorded in ambient ozone with foliar spray of 3%Panchagavya, and the lowest was observed in elevated ozone@200ppb. Since the elevated O3 had reduced in garlic photosynthetic rate significantly the lowest was observed in elevated O3@200ppb and the highest photosynthetic rate was observed in ambient Ozone with foliar spray 3% of panchagavya after a week. The antioxidant enzymes of garlic were increased with increased concentration of tropospheric ozone. The highest catalase (60.97µg of H2O2/g of leaf) and peroxidase (9.13 ΔA/min/g of leaf) concentration was observed at 200ppb elevated ozone level. Garlic pungency content was highest in ambient ozone with foliar spray of 0.1% ascorbic acid and the lowest was observed under elevated O3@200ppb. Highest total soluble solids were observed in ambient ozone with foliar spray of 3%Panchagavya and the lowest observed in elevated ozone@200ppb. Thus, tropospheric ozone has a detrimental impact on the physiology of crops, which reduced crop growth and yield. Under elevated O3 levels, ascorbic acid performed well followed by panchagavya and neem oil. The antioxidant such as catalase and peroxidase had positive correlation among themselves and had negative correlation with chlorophyll content, stomatal conductance, photosynthetic rate, pungency and TSS. The photosynthetic rate has high positive correlation with chlorophyll content, pungency and TSS. Correlation analysis confirmed the negative effects of tropospheric ozone and garlic gas exchange parameters and clove quality. The ozone protectants will reduce stomatal opening by which the entry of O3 in to the cell will be restricted and other hand they also will alleviate ROS and allied stresses.

Ground Ozone Level Prediction Using Machine Learning

Because of the increasing attention on environmental issues, especially air pollution, predicting whether a day is polluted or not is necessary to people’s health. In order to solve this problem, this research is classifying ground ozone level based on big data and machine learning models, where polluted ozone day has class 1 and non-ozone day has class 0. The dataset used in this research was derived from the UCI Website, containing various environmental factors in Houston, Galveston and Brazoria area that could possibly affect the occurrence of ozone pollution [1]. This dataset is first filled up for further process, next standardized to ensure every feature has the same weight, and then split into training set and testing set. After this, five different machine learning models are used in the prediction of ground ozone level and their final accuracy scores are compared. In conclusion, among Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine (SVM), the last one has the highest test score of 0.949. This research utilizes relatively simple methods of forecasting and calculates the first accuracy scores in predicting ground ozone level; it can thus be a reference for environmentalists. Moreover, the direct comparison among five different models provides machine learning field an insight to determine the most accurate model. In the future, Neural Network can also be utilized to predict air pollution, and its test scores can be compared with the previous five methods to conclude the accuracy of Neuron Network.

Assessment of Ground Ozone Level under the Physiological Strain Conditions

Assessment of Ground Ozone Level under the Physiological Strain Conditions

Nonlinear dynamical analysis of ground level ozone concentrations at different temporal scales

The nonlinear dynamical analysis of ground level ozone concentration is carried out by using correlation integral method to examine its scale invariance property. The dynamics of the time series is often studied at one temporal scale. It is assumed that if the time series is determined to be chaotic at one temporal scale, its behavior at another scale can be determined as the scale shifts are allowed due to scale invariance property. The actual dynamics at other scales is however not yet analyzed. The assumption of scale invariance of the time series at different time scales is tested in this study. The analysis is carried out for ground ozone levels observed during 2006 at two sites of different land use characteristics, as traffic and mixed-use in Delhi at four temporal scales as 1 h, 4 h, 8 h and 24 h. The chaotic nature is observed for the ozone concentration with 1 h and 4 h frequency, whereas at 8 h and 24 h time scale, the ozone concentration shows random behavior. As expected, a decrease in the variability is observed in the ozone levels with increase in the scales from 1 h to 24 h. The results indicated the temporal scale shifts are allowed from 1 h to 4 h resolution and vice versa. The ozone time series at 8 h and 24 h scalings however, should be dealt separately. Further analysis for corresponding NO 2 concentration at two sites suggested finite d 2 for 1 h, 4 h and 8 h scalings with higher value at traffic site than that at mixed-use site. The analysis also indicated same degrees of freedom for ozone and NO 2 concentration at traffic site whereas at mixed-use site the number of variables governing the NO 2 pollution are less than the ozone concentration.

Modeling Spatiotemporal Forest Health Monitoring Data

Forest health monitoring schemes were set up across Europe in the 1980s in response to concerns about air pollution-related forest dieback (Waldsterben) and have continued since then. Recent threats to forest health are climatic extremes likely due to global climate change and increased ground ozone levels and nitrogen deposition. We model yearly data on tree crown defoliation, an indicator of tree health, from a monitoring survey carried out in Baden-Württemberg, Germany since 1983. On a changing irregular grid, defoliation and other site-specific variables are recorded. In Baden-Württemberg, the temporal trend of defoliation differs among areas because of site characteristics and pollution levels, making it necessary to allow for space–time interaction in the model. For this purpose, we propose using generalized additive mixed models (GAMMs) incorporating scale-invariant tensor product smooths of the space–time dimensions. The space–time smoother allows separate smoothing parameters and penalties for the space and time dimensions and thus avoids the need to make arbitrary or ad hoc choices about the relative scaling of space and time. The approach of using a space–time smoother has intuitive appeal, making it easy to explain and interpret when communicating the results to nonstatisticians, such as environmental policy makers. The model incorporates a nonlinear effect for mean tree age, the most important predictor, allowing the separation of trends in time, which may be pollution-related, from trends that relate purely to the aging of the survey population. In addition to a temporal trend due to site characteristics and other conditions modeled with the space–time smooth, we account for random temporal correlation at site level by an autoregressive moving average (ARMA) process. Model selection is carried out using the Bayes information criterion (BIC), and the adequacy of the assumed spatial and temporal error structure is investigated with the empirical semivariogram and the empirical autocorrelation function.

Near-ground ozone source attributions and outflow in central eastern China during MTX2006

Abstract. A 3-D regional chemical transport model, the Nested Air Quality Prediction Model System (NAQPMS), with an on-line tracer tagging module was used to study the source of the near-ground (<1.5 km above ground level) ozone at Mt. Tai (36.25° N, 117.10° E, 1534 m a.s.l.) in Central Eastern China (CEC) during the Mount Tai eXperiment 2006 (MTX2006). The model reproduced the temporal and spatial variations of near-ground ozone and other pollutants, and it captured highly polluted and clean cases well. The simulated near-ground ozone level over CEC was 60–85 ppbv (parts per billion by volume), which was higher than values in Japan and over the North Pacific (20–50 ppbv). The simulated tagged tracer data indicated that the regional-scale transport of chemically produced ozone over other areas in CEC contributed to the greatest fraction (49%) of the near-ground mean ozone at Mt. Tai in June; in situ photochemistry contributed only 12%. Due to high anthropogenic and biomass burning emissions that occurred in the southern part of the CEC, the contribution to ground ozone levels from this area played the most important role (32.4 ppbv, 37.9% of total ozone) in the monthly mean ozone concentration at Mt. Tai; values reached 59 ppbv (62%) on 6–7 June 2006. The monthly mean horizontal distribution of chemically produced ozone from various ozone production regions indicated that photochemical reactions controlled the spatial distribution of O3 over CEC. The regional-scale transport of pollutants also played an important role in the spatial and temporal distribution of ozone over CEC. Chemically produced ozone from the southern part of the study region can be transported northeastwardly to the northern rim of CEC; the mean contribution was 5–10 ppbv, and it reached 25 ppbv during high ozone events. Studies of the outflow of CEC ozone and its precursors, as well as their influences and contributions to the ozone level over adjacent regions/countries, revealed that the contribution of CEC ozone to mean ozone mixing ratios over the Korean Peninsula and Japan was 5–15 ppbv, of which about half was due to the direct transport of ozone from CEC and half was produced locally by ozone precursors transported from CEC.

Prévision des maxima journaliers d'ozone. Approche multimodèle

The paper deals of the forecasting of daily maximum ground-ozone level in an urban area. As the phenomenon of ozone production is nonlinear, a nonlinear modelling based on the multiple model approach has been pursued. It consists of representing a system as a combination of locally valid affine models. The identification tasks of such a model include the determination of the number and the structure of the local models, the definition of the operating regimes associated to the local models and the parameter estimation. The application of the proposed procedure to ozone level forecasting is reported: results on learning and validation data show the performances of the model. The test of the model in real situation is presented.

Prévisions de concentrations d'ozone. Comparaison de différentes méthodes statistiques de type « boîte noire »

The paper investigates the application of black box modelling to the prediction of the daily maxima of ground-ozone level. The main interest of these modelling approaches is their genericity as they are solely based on the available data provided by the Associations of air quality monitoring and they can be transposed from a geographical area to another one. The paper realises a comparative study of four statistical learning approaches, the decisions trees (, the neural networks, the least-angle regression and the support vector regression, to the ozone level prediction. Before concluding, the obtained results and their comments are presented.

Prediction of extreme ozone levels in Barcelona, Spain

Barcelona is one of the most polluted cities in Western Europe, although our levels of air pollution are within the World Health Organisation air quality guidelines. However, high concentrations of air pollution have not been studied yet. Ground ozone levels is a topic of considerable environmental concern, since excessive level of ozone are taken as indicative of high pollution. In terms of the air quality guidelines ozone levels higher than 100 microg m(-3) can start to be health-hazards for human health. Our objective is to report a detailed analysis of ozone data exceeding the thresholds established by the air quality guidelines. Data analysed were collected in two measurement stations in Barcelona, for the reference period 1991--1996. Applying statistical techniques commonly used in the analysis of extreme values, mainly the Peak Over Threshold method was used for in this study. The analysis reveal that the ozone threshold values for the protection of human health has exceeded many times in both stations. The estimated return values for 3, 10, and 40 yr exceed the threshold value for information to the public of almost once in both stations, also it seems to be unlikely that the threshold value for warning to the public will be exceeded in 40 yr.

Prediction of maximum daily ozone level using combined neural network and statistical characteristics

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. As one of major pollutants, ozone, especially ground level ozone, is responsible for various adverse effects on both human being and foliage. Therefore, prediction of ambient ozone levels in certain environment, especially the ground ozone level in densely urban areas, is of great importance to urban air quality and city image. To date, though several ozone prediction models have been established, there is still a need for more accurate models to develop effective warning strategies. The development of such models is difficult because the meteorological variables and the photochemical reactions involved in ozone formation are very complex. The present work aims to develop an improved neural network model, which combines the adaptive radial basis function (ARBF) network with statistical characteristics of ozone in selected specific areas, and is used to predict the daily maximum ozone concentration level. The improved method is trained and testified by hourly time series data collected at three air pollutant-monitoring stations in Hong Kong during 1999 and 2000. The simulation results demonstrate the effectiveness and the reliability of the proposed method.

An intervention analysis of air quality data at Santiago, Chile

Air quality data at Santiago, Chile (PM 10, PM 2.5 and ozone) from 1989 to 1998 are analyzed with the goal of estimating trends in and impacts of public policies on air quality levels. Those policies, in effect since the late 1980s, have been essentially aimed at PM 10 pollution abatement. The analyses show that fall and winter air quality has been improving consistently, specially the PM 2.5 levels. The estimated trends for the monthly averages of PM 10 concentrations range from −1.5 to −3.3% per annum, whereas the trends for monthly averages of PM 2.5 concentrations range from −5 to −7% per annum. The monthly averages of ground ozone daily maxima do not have a significant trend for two of the downtown monitor sites; at the other three monitoring sites (including the one with the highest impacts) there is a clear downward trend between −5 and −3% per annum. The seasonal averages of a declimatized ozone production rate show a downward trend from 1988 through 1995, and no additional improvements have occurred thereafter. These mixed results for ground ozone levels are ascribed to a shift in the magnitude and spatial distribution of emissions in the city, and so there is a need for additional ozone abatement policies and further research on air pollution abatement options.

99/03385 Impacts from a fossil fuel power plant on ozone levels in Memphis, Tennessee

The Tennessee Valley Authority (TVA) Allen power plant is located on the Mississippi River in the southwest corner of Memphis, Tennessee. Allen has three coal-fired cyclone boilers with a rated capacity of 272 MW each. It is a Phase 2 plant under Title IV of the Clean Air Act and is the largest single source of NO{sub x} in the Memphis area. TVA plans to reduce Allen NOx emissions through a combination of burning low-sulfur coal (which has the benefit of reducing NO{sub x} emissions while also reducing SO{sub 2} emissions) and installing gas re-burn technology. A modeling study using the SAI, Inc., UAM-V photochemical model was conducted to examine the potential impacts of NO{sub x} reductions on ozone levels in the Memphis area. A series of four model simulations were made in which different Allen emissions scenarios were examined. The focus period of the photochemical modeling was 11--14 July 1995 when measurements in and near Memphis indicated peak hourly ozone levels of 135--140 ppb. This analysis primarily examined computed impacts within 50 km of Memphis. Allen was computed to contribute as much as 20--30 ppb to ground ozone levels 20-50 km downwind using its NO{sub x} emission rate beforemore » Title IV compliance. After compliance it was computed to contribute only about 10--20 ppb. At the same time, maximum daily ozone reductions due to Allen NO{sub x} titration of ozone were between 30 and 60 ppb. These benefits will be reduced by 30--50% after Title IV compliance, and are expected to occur within 30 km of the plant. More model grid cells indicated dis-benefits (net ground-level ozone increases) than benefits on three of the four episode days using the Title IV compliance emission rate. Significant ozone dis-benefits were expected because of the well-documented NO titration of ozone within plumes having a high ratio of NO to volatile organic compounds.« less