Air pollution monitoring with <i>Tradescantia</i> hybrid and optical sensors in Curitiba and Araucária, Brazil

Rodrigues, Leatrice Talita; Mercuri, Emílio Graciliano Ferreira; Noe, Steffen Manfred

doi:10.2478/fsmu-2023-0005

Cited by 2 publications

(9 citation statements)

References 19 publications

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“… Tradescantia clone 4430 plant from the experiment on air pollution and particulate matter in Curitiba, Brazil (Rodrigues et al, 2023 ). …”

Section: Methodsmentioning

confidence: 99%

“…The experiments that generated the images were related to the biomonitoring of air quality in Curitiba and Araucária-PR, Brazil, from 2020 to 2021 (Rodrigues et al, 2023 ), and the biomonitoring of soil quality after contamination by diesel oil in different concentrations, and this last experiment was carried out in the Federal University of Paraná (UFPR) laboratory from 2022 to 2023 (Goeldner, 2023 ). Rodrigues et al ( 2023 ) deployed eight monitoring points and a control to compare Tradescantia clone 4430 bio-monitoring with particulate matter measurements using the SDS011 optical sensor. Each monitoring point contained five pots of Tradescantia plants that were acclimatized for 2 months.…”

Section: Methodsmentioning

confidence: 99%

“…The preparation procedures for the Trad-SHM bioassay (Goeldner, 2023 ; Rodrigues et al, 2023 ) followed those described in the study by Underbrink et al ( 1973 ) with some adaptations. The flowers were always collected in the morning, as they close up and wilt in the afternoon, and placed in pots labeled with their origin.…”

Section: Methodsmentioning

confidence: 99%

“…Plant cells can change color due to environmental stress factors such as pH, temperature, light intensity, and nutrient availability (Chapin et al, 1987 ; Młodzińska et al, 2009 ; Hu, 2013 ), which affect the presence of pigments such as chlorophyll (Vogelmann and Evans, 2002 ), carotenoids (Langi et al, 2018 ), and anthocyanins (Nassour et al, 2020 ). Tradescantia pallida via Trad-MCN bioassay (Carreras et al, 2006 ; Prajapati and Tripathi, 2008 ) and Tradescantia clone 4430 via Trad-SHM bioassay (Rodrigues et al, 2023 ) revealed sensibility to air quality, and these studies show that plants exposed to sites with highest traffic volumes had higher frequencies of micronuclei and color change in stamen hair compared with the control area. Tradescantia demonstrated genotoxicity of ambient air due to ionizing radiations (Ichikawa et al, 1969 ; Ichikawa, 1992 ; Panek et al, 2011 ), Caldwell et al ( 1974 ) performed field and laboratory experiments showing response of Tradescantia plants to elevated intensities of global UV-B radiation.…”

Section: Introductionmentioning

confidence: 92%

“…Figure5represents the results from the study byRodrigues et al (2023) with data from four sampling points (Jardim Botânico, Jardim das Américas, Mercês, and Orleans) measured between…”

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confidence: 99%

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Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification

Rodrigues,

Goeldner,

Mercuri

et al. 2024

Front. Big Data

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Tradescantia plant is a complex system that is sensible to environmental factors such as water supply, pH, temperature, light, radiation, impurities, and nutrient availability. It can be used as a biomonitor for environmental changes; however, the bioassays are time-consuming and have a strong human interference factor that might change the result depending on who is performing the analysis. We have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells, which can be stressed due to air pollution and soil contamination. The study introduces a novel dataset, Trad-204, comprising single-cell images from Tradescantia clone 4430, captured during the Tradescantia stamen-hair mutation bioassay (Trad-SHM). The dataset contain images from two experiments, one focusing on air pollution by particulate matter and another based on soil contaminated by diesel oil. Both experiments were carried out in Curitiba, Brazil, between 2020 and 2023. The images represent single cells with different shapes, sizes, and colors, reflecting the plant's responses to environmental stressors. An automatic classification task was developed to distinguishing between blue and pink cells, and the study explores both a baseline model and three artificial neural network (ANN) architectures, namely, TinyVGG, VGG-16, and ResNet34. Tradescantia revealed sensibility to both air particulate matter concentration and diesel oil in soil. The results indicate that Residual Network architecture outperforms the other models in terms of accuracy on both training and testing sets. The dataset and findings contribute to the understanding of plant cell responses to environmental stress and provide valuable resources for further research in automated image analysis of plant cells. Discussion highlights the impact of turgor pressure on cell shape and the potential implications for plant physiology. The comparison between ANN architectures aligns with previous research, emphasizing the superior performance of ResNet models in image classification tasks. Artificial intelligence identification of pink cells improves the counting accuracy, thus avoiding human errors due to different color perceptions, fatigue, or inattention, in addition to facilitating and speeding up the analysis process. Overall, the study offers insights into plant cell dynamics and provides a foundation for future investigations like cells morphology change. This research corroborates that biomonitoring should be considered as an important tool for political actions, being a relevant issue in risk assessment and the development of new public policies relating to the environment.

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“… Tradescantia clone 4430 plant from the experiment on air pollution and particulate matter in Curitiba, Brazil (Rodrigues et al, 2023 ). …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

“…Figure5represents the results from the study byRodrigues et al (2023) with data from four sampling points (Jardim Botânico, Jardim das Américas, Mercês, and Orleans) measured between…”

mentioning

confidence: 99%

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Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification

Rodrigues,

Goeldner,

Mercuri

et al. 2024

Front. Big Data

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Particulate matter forecast and prediction in Curitiba using machine learning

Chaves,

da Silva,

Mercuri

et al. 2024

Front. Big Data

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IntroductionAir quality is directly affected by pollutant emission from vehicles, especially in large cities and metropolitan areas or when there is no compliance check for vehicle emission standards. Particulate Matter (PM) is one of the pollutants emitted from fuel burning in internal combustion engines and remains suspended in the atmosphere, causing respiratory and cardiovascular health problems to the population. In this study, we analyzed the interaction between vehicular emissions, meteorological variables, and particulate matter concentrations in the lower atmosphere, presenting methods for predicting and forecasting PM2.5.MethodsMeteorological and vehicle flow data from the city of Curitiba, Brazil, and particulate matter concentration data from optical sensors installed in the city between 2020 and 2022 were organized in hourly and daily averages. Prediction and forecasting were based on two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM) neural network. The baseline model for prediction was chosen as the Multiple Linear Regression (MLR) model, and for forecast, we used the naive estimation as baseline.ResultsRF showed that on hourly and daily prediction scales, the planetary boundary layer height was the most important variable, followed by wind gust and wind velocity in hourly or daily cases, respectively. The highest PM prediction accuracy (99.37%) was found using the RF model on a daily scale. For forecasting, the highest accuracy was 99.71% using the LSTM model for 1-h forecast horizon with 5 h of previous data used as input variables.DiscussionThe RF and LSTM models were able to improve prediction and forecasting compared with MLR and Naive, respectively. The LSTM was trained with data corresponding to the period of the COVID-19 pandemic (2020 and 2021) and was able to forecast the concentration of PM2.5 in 2022, in which the data show that there was greater circulation of vehicles and higher peaks in the concentration of PM2.5. Our results can help the physical understanding of factors influencing pollutant dispersion from vehicle emissions at the lower atmosphere in urban environment. This study supports the formulation of new government policies to mitigate the impact of vehicle emissions in large cities.

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Air pollution monitoring with Tradescantia hybrid and optical sensors in Curitiba and Araucária, Brazil

Cited by 2 publications

References 19 publications

Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification

Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification

Particulate matter forecast and prediction in Curitiba using machine learning

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