Predicting ozone levels from climatic parameters and leaf traits of Bel-W3 tobacco variety

Käffer, Márcia Isabel; Domingos, Marisa; Lieske, Isadora; Vargas, Vera Maria Ferrão

doi:10.1016/j.envpol.2019.01.130

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…The initiation, propagation, and sequestration processes that determine the formation of ozone lesions after stomatal control, apoplastic antioxidants, and ROS perception can be described as a self-amplifying loop called the oxidative cell death cycle [44]. Yet, although the molecular biological mechanisms causing a plant's visible damage from O 3 are not fully understood, physiological studies suggest that chronic elevations of O 3 reduce productivity primarily by decreasing photosynthesis [45].…”

Section: Discussionmentioning

confidence: 99%

“…The decomposition of O 3 and subsequent accumulation of ROS in the extracellular space activates some signaling due to a finely balanced network of ROS-mediated cellular signaling [47]. Gaseous air pollutants penetrate leaf stomata and reach deep into cell membranes, so stomatal movements are key feedback loops, which is the first defense mechanism via regulating gas fluxes [45,48]. Conversely, environmental conditions that cause higher stomatal conductance than necessary for CO 2 absorption can exacerbate damage from O 3 absorption in forests.…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of the Importance of Some East Asian Tree Species for Refinement of Air Quality by Estimating Air Pollution Tolerance Index, Anticipated Performance Index, and Air Pollutant Uptake

et al. 2020

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Potentials of tree species as biofilters depend on appropriate selection based on their tolerance to air pollution, which is usually evaluated by the air pollution tolerance index (APTI) and anticipated performance index (API). Thus, these index values need as a means of scientific understanding to assess the role of urban trees for better greenspace planning/management to mitigate impacts of gaseous air pollution such as ozone (O3) and sulfur dioxide (SO2). O3 exposure to Chionanthus retusus, Pinus densiflora, and Ginkgo biloba showed higher stomatal O3 flux than the others, finally resulting in both favoring stomatal movement and maintaining carbon fixation. In contrast, despite the whole tree enhanced SO2 uptake under excess SO2 exposure, the carbon assimilation capacity was only found in Taxus cuspidata and Zelkova serrata as a consequence of no stomatal sluggishness. On the basis of API, P. densiflora and Prunus × yedoensis were good performers for developing greenspace, while Z. serrata and G. biloba were moderate performers; however, C. retusus and T. cuspidata were estimated to be poor and very poor performers, respectively, for reducing the air quality injury caused by air pollutants. The present study suggests that an integration of both APTI and API based on stomatal absorption flux is needed for selecting sound tree-species in greenspace planning/construction to control gaseous air pollutions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluation of the Importance of Some East Asian Tree Species for Refinement of Air Quality by Estimating Air Pollution Tolerance Index, Anticipated Performance Index, and Air Pollutant Uptake

et al. 2020

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“…Moreover, in Charqueadas high O 3 levels were influenced by the meteorological conditions and the atmospheric pollutants emitted from local sources. The passage of high thermal pressure systems accompanied by polar air masses, resulted in consecutive days of clear sky and mild winds, favoring the formation of O 3 peaks that typically occur in heat waves combined with elevated global solar radiation and dry air in cities [31].…”

Section: Discussionmentioning

confidence: 99%

Monthly-Term Associations Between Air Pollutants and Respiratory Morbidity in South Brazil 2013–2016: A Multi-City, Time-Series Analysis

Agudelo-Castañeda

Teixeira

Alves

et al. 2019

IJERPH

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Most air pollution research conducted in Brazil has focused on assessing the daily-term effects of pollutants, but little is known about the health effects of air pollutants at an intermediate time term. The objective of this study was to determine the monthly-term association between air pollution and respiratory morbidity in five cities in South Brazil. An ecological time-series study was performed using the municipality as the unit of observation in five cities in South Brazil (Gravataí, Triunfo, Esteio, Canoas, and Charqueadas) between 2013 and 2016. Data for hospital admissions was obtained from the records of the Hospital Information Service. Air pollution data, including PM10, SO2, CO, NO2, and O3 (µg/m3) were obtained from the environmental government agency in Rio Grande do Sul State. Panel multivariable Poisson regression models were adjusted for monthly counts of respiratory hospitalizations. An increase of 10 μg/m3 in the monthly average concentration of PM10 was associated with an increase of respiratory hospitalizations in all age groups, with the maximum effect on the population aged between 16 and 59 years (IRR: Incidence rate ratio 2.04 (95% CI: Confidence interval = 1.97–2.12)). For NO2 and SO2, stronger intermediate-term effects were found in children aged between 6 and 15 years, while for O3 higher effects were found in children under 1 year. This is the first multi-city study conducted in South Brazil to account for intermediate-term effects of air pollutants on respiratory health.

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“…In addition to causing heart and lung diseases, tropospheric ozone is a general throat and lung irritant, causing coughing, wheezing, and difficult breathing [2]. Ozone also damages plant leaves and can harm agricultural crops [3].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Juarez¹,

Petersen

2021

Atmosphere

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Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

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Predicting ozone levels from climatic parameters and leaf traits of Bel-W3 tobacco variety

Cited by 8 publications

References 43 publications

Evaluation of the Importance of Some East Asian Tree Species for Refinement of Air Quality by Estimating Air Pollution Tolerance Index, Anticipated Performance Index, and Air Pollutant Uptake

Evaluation of the Importance of Some East Asian Tree Species for Refinement of Air Quality by Estimating Air Pollution Tolerance Index, Anticipated Performance Index, and Air Pollutant Uptake

Monthly-Term Associations Between Air Pollutants and Respiratory Morbidity in South Brazil 2013–2016: A Multi-City, Time-Series Analysis

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

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