Regionalization methods dependent on hydrological models comprise techniques for transferring calibrated parameters in instrumented watersheds (donor basins) to non-instrumented watersheds (target ...basins). There is a lack of flow regionalization studies in regions with humid subtropical and hot temperate climates, and one of the main novelties of this research is to assess the regionalization of low flows in Paraná in the south of Brazil. In addition to filling this gap, this research presents innovative artificial-intelligence techniques for transferring parameters from hydrological models. This study aims to evaluate regionalization methods for transferring GR4J parameters and predicting river flow in catchments from the south of Brazil. We created a dataset for the state of Paraná with daily hydrological time series (precipitation, evapotranspiration, and river flow) and watershed physiographic and climatological indices for 126 catchments. Rigorous quality-controlling techniques were applied to recover data from 1979 to 2020. The regionalization methods compared in this study are based on simple spatial proximity, physiographic–climatic similarity, and regression by random forest techniques. Direct regression of Q95 was calculated using random forest techniques and compared with indirect methods, i.e. using regionalization of GR4J parameters. A set of 100 basins was used to train the regionalization models, and another 26 catchments (pseudo-non-instrumented) were used to evaluate and compare the performance of regionalizations. The GR4J model showed acceptable performances for the sample of 126 catchments, with 65 % of watersheds presenting a log-transformed Nash–Sutcliffe coefficient greater than 0.70 during the validation period. According to the evaluation carried out for the sample of 26 basins, regionalization based on physiographic–climatic similarity was shown to be the most robust method for the prediction of daily and Q95 reference flow in basins from the state of Paraná. When increasing the number of donor basins, the method based on spatial proximity has comparable performance to the method based on physiographic–climatic similarity. Based on the physiographic–climatic characteristics of the basins, it was possible to classify six distinct groups of watersheds in Paraná. Each group shows similarities in forest cover, urban area, number of days with more than 150 mm of precipitation, and average duration of consecutive dry days. Although the physiographic–climatic similarity method obtained the best performance, the use of machine learning algorithms to regionalize the model parameters had good performance using climatic and physiographic indices as inputs. This research represents a proof of concept that basins without flow monitoring can have a good approximation of streamflow if physiographic–climatic information is provided.
Water and carbon balances in a hemi-boreal forest Mercuri, Emílio Graciliano Ferreira; Tamm, Toomas; Noe, Steffen Manfred
Metsanduslikud uurimused,
11/2023, Letnik:
78, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The carbon and water fluxes and their inter-relations are key aspects of ecosystem dynamics. In this study, regionalization was used in transferring parameters from the GR4J-Cemaneige model ...calibrated in Reola hydrographic basin to predict daily flows in Kalli basin; both watersheds are located in the southeast of Estonia. Evapotranspiration data was collected from the MODIS sensor of the Terra satellite and from the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR Estonia). Precipitation data was collected from Tartu–Tõravere and SMEAR Estonia stations and river flow from Reola hydrometric station. The year 2011 was used for model warm-up, model calibration was done in 2012–2017 and the 2018–2020 period was used for validation. The GR4J-Cemaneige model was calibrated at Reola Basin, with a Nash-Sutcliffe Efficiency index of 0.73. The 6 constants of Reola subbasin were transferred to Kalli subbasin for river flow simulation. Net ecosystem exchange (NEE) was measured at the 70 m SMEAR tower with the eddy covariance technique. The balances indicate that the ecosystem at Kalli watershed is slowly becoming a source of carbon and less water is available at the catchment reservoir. NEE has increased from -1.23 μmol m
s
in 2015 to -0.62 μmol m
s
in 2020, while the delta water storage decreased from 0.24 mm in 2015 to -0.05 mm in 2020. This behavior may increase soil drying and oxidation, and it will probably release more carbon in the future. This research allows a better understanding of the Järvselja hemi-boreal forest water-carbon dynamics.
A major release of methane from the Nord Stream pipelines occurred in the Baltic Sea on 26 September 2022. Elevated levels of methane were recorded at many observational sites in northern Europe. ...While it is relatively straightforward to estimate the total emitted amount from the incidents (around 330 kt of methane), the detailed vertical and temporal distributions of the releases are needed for numerical simulations of the incident. Based on information from public media and basic physical concepts, we reconstructed vertical profiles and temporal evolution of the methane releases from the broken pipes and simulated subsequent transport of the released methane in the atmosphere. The parameterization for the initial rise of the buoyant methane plume has been validated with a set of large-eddy simulations by means of the UCLALES model. The estimated emission source was used to simulate the dispersion of the gas plume with the SILAM chemistry transport model. The simulated fields of the excess methane led to a noticeable increase in concentrations at several carbon-monitoring stations in the Baltic Sea region. Comparison of the simulated and observed time series indicated an agreement within a couple of hours between the timing of the plume arrival/departure at the stations with observed methane peaks. Comparison of absolute levels was quite uncertain. At most of the stations the magnitude of the observed and modeled peaks was comparable with the natural variability of methane concentrations. The magnitude of peaks at a few stations close to the release was well above natural variability; however, the magnitude of the peaks was very sensitive to minor uncertainties in the emission vertical profile and in the meteorology used to drive SILAM. The obtained emission inventory and the simulation results can be used for further analysis of the incident and its climate impact. They can also be used as a test case for atmospheric dispersion models.
Complex mixtures of substances are in the atmosphere and they can cause diseases in humans and biological communities after acute or chronic exposition. This paper focuses on the physical measurement ...of particulate matter, a proxy for air pollution, and a biological method for mutation assessment due to plants’ exposure to air pollution. The objective of this research was to characterize the air pollution seasonality in municipalities in southern Brazil, and also to understand the relation between air pollution and the biological response of the
sp. clone 4430. The optical sensor SDS011 was used for measurements of particulate matter (PM) and the Trad-SHM bioassay was chosen to quantify the mutagenic alterations that occurred in stamen hairs during the study period, with PM data being measured every 5 seconds and the flowers being harvested approximately every two weeks for laboratory analysis. The Pearson test was applied to verify the correlation between PM and mutations in stamen hair as a result of which it was observed that there is a positive correlation between these data, with the highest value found being r = 0.61. Also, the period with the highest occurrence of pink cells was between autumn and spring, the same period in which an unusual increase in PM concentrations was also observed, a period that corresponds to a less favorable dispersion of pollutants in the atmosphere. The use of
sp. clone 4430 showed sensitivity to the environments in which it was exposed. Biomonitoring is an important tool for understanding the effects of pollutants on the ecosystem.
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
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
clone 4430, captured during the
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.
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.
Introduction Air 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. Methods Meteorological 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. Results RF 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. Discussion The 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.
•The new analytical solution of the extended Graetz problem quantifies the dynamics of particles in a tube with air flow.•The solution represents the particulate matter (PM) concentration profile ...inside the tube and the PM deposition on the wall.•The series solution can represent the PM profile in the isokinetic sampling region of the cylindrical element.•The new series solution has high accuracy compared to another existing solution and is presented using 75 eigenvalues.•The proposed solution has the potential to contribute to environmental analyses and pollution control in chimneys and pipes.
Two different analytical solutions of the extended Graetz problem are analyzed in order to quantify the dynamics of particulate matter (PM) in a cylindrical element. Advection, diffusion and deposition of particles are calculated in a symmetrical circular tube with fully developed laminar flow applying the theory of confluent hypergeometric functions. The following in silico experiments are proposed: (i) the simulation of a thin tube deposition, acting as a filtering element for PM and (ii) the modeling of isokinetic sampling in the inner region of the tube. Two mathematical formulations are compared to obtain the general analytical solution of the advection-diffusion equation in cylindrical coordinates. After applying the variable separation method, a second order ordinary differential equation (ODE) is obtained, this ODE is solved with two distinct forms: (a) applying a transformation of variables to Whittaker’s function and (b) using a new variable transformation proposition. No studies were found that compare these analytical solutions, as well as the use of this new variable transformation, although both cases are essentially different ways of applying the Frobenius method. In this work, 75 eigenvalues and their constants are presented for the first time using five decimal places. The new series solution has high accuracy compared to the previous one, it can provide the concentration profile, the deposition rate and the determination of the PM profile in the isokinetic sampling region. Our solution proved to be more stable close to the tube wall, which may improve the techniques for measuring the flow of particles in tubes. Furthermore, using more eigenvalues improved the estimation of PM2.5 and PM10 deposition. The results presented here show that the proposed new analytic solution has the potential to contribute to numerical and experimental environmental analyses.
Particulate matter (PM) is a major air pollutant that can have adverse effects on human health, especially for vulnerable populations such as children, the elderly, and those with respiratory or ...cardiovascular conditions. This study presents a method for prediction of particulate matter concentration with aerodynamic diameter smaller then 10 μm (PM10) in an urban environment. Meteorological data and vehicle flow data from an urban road in Curitiba, Brazil, were used. The air quality was analyzed in two monitoring points located 1 km apart, the sampling points are named Politécnico and Perkons, where SDS011 optical sensors were installed. The prediction was based on the machine learning algorithm Random Forest (RF). The baseline concentration was a dataset from historical records of particulate matter measurements from monitoring stations in Curitiba. Several scenarios were tested and it was concluded that the daily time scale presents the best performance in PM10 prediction, with 80.42% accuracy, using the baseline and PM10 Perkons as descriptors. The most important meteorological variables for the prediction were: air temperature (°C), wind speed (m/s), and wind gust (m/s). Throughout the day there were two peaks with large amounts of pollutants in the air, near 8:00 am and 6:00 pm, times when there are the largest flows of vehicles circulating on the road. The Random Forest algorithm proved to be a good estimator of PM concentration, which is a proxy for air pollution.
Die Vorhersage biogener Isoprenemissionen mittels eines prozessorientierten Emissionsmodells erfordert die möglichst genaue mathematische Beschreibung der zugrunde liegenden physiologischen Prozesse. ...In dieser Arbeit wurde ein mathematisches Modell der Blattphotosynthese entwickelt, das sowohl die Regulation der Blattleitfähigkeit durch Umweltfaktoren wie Licht, Temperatur und Luftfeuchte betrachtet als auch die vom Licht und der Temperatur abhängige CO2-Fixierung beinhaltet. Es wurde zunächst eine Übersicht über die möglichen Ausprägungen von Photosynthesemodellen gegeben. Dabei wurden auch die unverzichtbaren Bausteine der Photosynthesemodelle thematisiert und eine Klasseneinteilung der Modelle vorgenommen. Anschließend wurde die Blattphotosynthese auf theoretischer Grundlage beschrieben. Insbesondere führt die theoretische Beschreibung des Gaswechsels auf eine allgemeine Formulierung eines Blattphotosynthesemodells mit physikochemischen Wurzeln. Da die Photosynthese jedoch auch aus dem biochemischen Blickwinkel der CO2-Fixierung im Calvin-Zyklus betrachtet werden kann, entsteht das "ci-Dilemma" - man kann die Assimilationsrate auf zwei Wegen bestimmen, die nicht notwendigerweise denselben Wert liefern. Die Lösung dieses Dilemmas führt nun zur Formulierung eines prozessorientierten Blattphotosynthesemodells, das die zugrundeliegenden Prozesse in drei Subsystemen abbildet. (1) Die Blattleitfähigkeit, die über Umweltfaktoren (Licht, Temperatur, Luftfeuchte) gesteuert wird, (2) die CO2-Aufnahme über ein source-sink-Mechanismus des intrazellulären CO2-Speichers und (3) die biochemische CO2-Fixierung im Calvin-Zyklus und der Export von Triosephosphaten aus den Chloroplasten. Für das erste Subsystem, die Blattleitfähigkeit, wurde eine Steuerfunktion G(I, VPD) abgeleitet, die den Gleichgewichtswert der Blattleitfähigkeit bezüglich des Lichts (I) und des Wasserdampfdruckdefizits (VPD) zwischen Blatt und Umgebung charakterisiert. Ausgehend von einem nach Kirschbaum et al. (1988) modifizierten Modell mit drei Variablen wurde ein einfacheres Modell mit einer Variablen entwickelt und diese dann verglichen. Es konnte dabei kein signifikanter Unterschied zwischen den beiden Ansätzen festgestellt werden. Weiter wurde untersucht, welchen Einfluss die Verknüpfung der Steuergrößen (multiplikativ oder minimal) auf die Blattleitfähigkeit besitzt. Für das vorgestellte Modell war der minimale Ansatz für die Steuerfunktion G(I, VPD) besser geeignet die gegebenen Messdaten der Blattleitfähigkeit zu approximieren. Zur Überprüfung der Strukturgültigkeit des neu formulierten Modells der Blattphotosynthese wurde ein Skelettmodell mit drei Variablen (gs, pi und aps) untersucht. Im Vergleich mit den Messdaten liefert das Skelettmodell eine sehr gute Übereinstimmung bei der Assimilationsrate A und der Blattleitfähigkeit gs. Die intrazelluläre CO2-Konzentration wurde in den meisten Fällen leicht überschätzt und in einem Fall relativ stark unterschätzt. Die leichte Überschätzung deutet darauf hin, dass die sehr einfach gewählte Carboxylierungsreaktion des Skelettmodells nicht alle wichtigen Aspekte des realen Systems berücksichtigt. Insgesamt zeigen die simulierten Ergebnisse der fünf Tagesgänge, dass die Modellstruktur die typische Dynamik der Photosynthese im Tagesverlauf wiedergibt und somit eine gute Approximation der Blattphotosynthese ist. Ein überraschendes Ergebnis ist, dass es mit der Variation von nur drei Parametern möglich ist, alle fünf Tage zu simulieren. Die Variation der Parameter lässt sich zudem aus den klimatischen Gegebenheiten während der Messtage erklären. Das Skelettmodell wurde mit einem Modell des Calvin-Zyklus erweitert, welches als Intermediate RuBP, PGA, Triosephosphat, Pi, Ru5P, ATP/ADP und NADPH/NADP berücksichtigt. Dabei wurden die von Farquhar et al. (1980) beschriebenen Limitierungen der Assimilationsrate durch CO2 und RuBP berücksichtigt. Das Modell berücksichtigt ebenfalls die temperaturabhängige Konkurrenz der Carboxylierungsrate zur Oxygenierungsrate der Rubisco. Der nach Giersch et al. (1990) modellierte Phosphattranslokator lässt bezüglich der Isoprensynthese eine Unterscheidung zwischen den chloroplastidären und cytosolischen Triosephosphat-pools zu und ermöglicht es somit zwischen dem DOXP- und dem Mevalonatweg zu unterscheiden.
