Chronic Exposure to Ambient Ozone and Lung Function in Young Adults

Tager, Ira B.; Balmes, John R.; Lurmann, Frederick; Ngo, Long; Alcorn, Siana; Künzli, Nino

doi:10.1097/01.ede.0000183166.68809.b0

Cited by 173 publications

(143 citation statements)

References 42 publications

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“…Findings of previous studies have shown that prolonged inhalation of O 3 gas damages the respiratory system (6)(7)(8)(9)(10)(11)(12) and extrapulmonary organs (13,14). A series of meta-analyses and evaluation of geographic and seasonal O 3 gas-related risk has provided evidence for the association between O 3 gas and mortality (15)(16)(17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

Anti-inflammatory effects of ozonated water in an experimental mouse model

Aso¹,

Mori²,

Kawamoto³

et al. 2014

Biomedical Reports

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Abstract. Previous studies have suggested that ozonated water is safe and possesses antibacterial effects for treatment of experimental peritonitis rats. In this study, we evaluated the anti-inflammatory effects of ozonated water that was intraperitoneally injected into an experimental inflammatory mouse model. The concentrations of dissolved ozone decreased constantly and lineally, while the half-life of dissolved ozone was 36.8±2.7 min (27˚C). The 10-ppm ozonated water was injected intraperitoneally into mice with lipopolysaccharide (LPS)-induced acute inflammation. The results showed that the intraperitoneal injection of ozonated water decreased the levels of tumor necrosis factor-α (TNF-α) and increased the activity of superoxide dismutase (SOD). The results suggest that ozonated water has anti-inflammatory properties and is a potential therapeutic option for acute inflammation.

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Section: Introductionmentioning

confidence: 99%

Anti-inflammatory effects of ozonated water in an experimental mouse model

Aso¹,

Mori²,

Kawamoto³

et al. 2014

Biomedical Reports

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“…Moreover, O 3 is well known to have deleterious effects on materials, human health and plants (PORG, 1997). Indeed, O 3 is responsible for damages on polymeric materials such as rubbers, but also on textiles, dyes, surface coatings, metals and buildings materials (Lee et al, 1996;Massey, 1999;Ahmad et al, 2000;Almeida et al, 2000;Boyce et al, 2001) and causes deleterious impacts to human health, including lung inflammation, reduction in lung function, respiratory diseases, and mortality (Rastogi et al, 1991;Uysal and Schapira, 2003;Bell et al, 2005;Ito et al, 2005;Levy et al, 2005;Targer et al, 2005;Hazucha and Lefohn, 2007). On vegetation, O 3 slows-down the stomatal closing, decreases the photosynthetic capacity and alters plant biomass and leaf area, although these effects depend on species, canopy structure and age of tissues (Paoletti, 2005;Paoletti and Grulke, 2005;Ainsworth, 2008;Wittig et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Predicting and partitioning ozone fluxes to maize crops from sowing to harvest: the Surfatm-O<sub>3</sub> model

et al. 2011

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Abstract. Terrestrial ecosystems represent a major sink for ozone (O 3 ) and also a critical control of tropospheric O 3 budget. However, due to its deleterious effects, plant functioning is affected by the ozone absorbed. It is thus necessary to both predict total ozone deposition to ecosystems and partition the fluxes in stomatal and non-stomatal pathways. The Surfatm-O 3 model was developed to predict ozone deposition to agroecosystems from sowing to harvest, taking into account each deposition pathways during bare soil, growth, maturity, and senescence periods. An additional sink was added during senescence: stomatal deposition for yellow leaves, not able to photosynthesise but transpiring. The model was confronted to measurements performed over three maize crops in different regions of France. Modelled and measured fluxes agreed well for one dataset for any phenological stage, with only 4 % difference over the whole cropping season. A larger discrepancy was found for the two other sites, 15 % and 18 % over the entire study period, especially during bare soil, early growth and senescence. This was attributed to site-specific soil resistance to ozone and possible chemical reactions between ozone and volatile organic compounds emitted during late senescence. Considering both night-time and daytime conditions, non-stomatal deposition was the major ozone sink, from 100 % during bare soil period to 70-80 % on average during maturity. However, considering only daytime conditions, especially under optimal climatic conditions for plant functioning, stomatal flux could represent 75 % of total ozone flux. This model could improve estimates of crop yield losses and projections of tropospheric ozone budget.

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“…The health effects related to short-term ozone exposure include irritation of eyes and throat, adverse effects on pulmonary function, aggravation of respiratory symptoms, and increase in medication usage, hospital admissions and mortality. The long-term exposure can cause reduction in lung function development [6,7]. Children, adults who are active outdoors [8], and people with preexisting respiratory illnesses are more sensitive to ozone than others.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

İnal

2010

CLEAN Soil Air Water

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Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, TurkeyTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, crossvalidation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d 2 ) for the testing data were 0.90, 8.78 mg/m 3 , 11.15 mg/m 3 , and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM 10 , maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d 2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. IntroductionOzone (O 3 ) is a secondary pollutant and formed in the lower atmosphere (troposphere) by the complex reactions of nitrogen oxides (NO x ) and volatile organic compounds (VOCs) in the presence of solar radiation. Ozone formation in the troposphere is a rapid photochemical cycle and a non-linear process depending on the concentrations of precursors, meteorological parameters, and the sunlight intensity and spectral distribution [1,2]. Briefly, it involves the photolysis of nitrogen dioxide (NO 2 ) by solar radiation to form nitric oxide (NO) and a ground-state oxygen atom (O).The major reaction forming O 3 in the troposphere includes the reaction of oxygen atom with oxygen molecule (O 2 ). Collision of recently formed O 3 with a third body (i.e., a molecule from the surrounding air) removes the excess energy of ozone and allows it to stabilize.O 3 is removed by the reaction with NO to reform NO 2 ;However, reactions between NO and reactive radical species that are formed by the oxidation of reactive VOCs can also oxidize NO to NO 2 without the involvement of ozone, NO ÀÀÀÀÀÀÀ!

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Chronic Exposure to Ambient Ozone and Lung Function in Young Adults

Cited by 173 publications

References 42 publications

Anti-inflammatory effects of ozonated water in an experimental mouse model

Anti-inflammatory effects of ozonated water in an experimental mouse model

Predicting and partitioning ozone fluxes to maize crops from sowing to harvest: the Surfatm-O<sub>3</sub> model

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

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Chronic Exposure to Ambient Ozone and Lung Function in Young Adults

Cited by 173 publications

References 42 publications

Anti-inflammatory effects of ozonated water in an experimental mouse model

Anti-inflammatory effects of ozonated water in an experimental mouse model

Predicting and partitioning ozone fluxes to maize crops from sowing to harvest: the Surfatm-O&lt;sub&gt;3&lt;/sub&gt; model

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

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Predicting and partitioning ozone fluxes to maize crops from sowing to harvest: the Surfatm-O<sub>3</sub> model