Implementation and initial calibration of carbon-13 soil organic matter decomposition in Yasso model

Soil carbon sequestration has gained traction as a mean to mitigate rising atmospheric carbon dioxide concentrations. Verification of different methods’ efficiency to increase soil carbon sink requires, in addition to good quality measurements, reliable models capable of simulating the effect of the sequestration practices. One way to get insight of the methods’ effects on carbon cycling processes is to analyse different carbon isotope concentrations in soil organic matter. In this paper we introduce a carbon-13 isotope specific soil organic matter decomposition add-on into the Yasso soil carbon model and assess its functionality. The new 13C-dedicated decomposition is straightforward to implement and depends linearly on the default Yasso model parameters and the relative carbon isotope (13C/12C) concentration. Despite of their simplicity, the modifications considerably improve the model behaviour in a 50-year long simulation.

NEURAL NETWORK CALCULATION OF CARBON DIOXIDE CONCENTRATIONS

Снижение углеродного следа в настоящее время является одной из приоритетных задач мировой экономики. Для достижения этой цели необходимо с одной стороны снижать выбросы парниковых газов, с другой стороны развивать методы мониторинга парниковых газов в атмосферном воздухе для обеспечения контроля эффективности принимаемых решений.Учитывая сложность процессов рассеивания газов в атмосферном воздухе, значительными преимуществами в вопросах определения концентраций атмосферных примесей обладают нейросетевые методы моделирования. В данной статье представлен метод расчета концентраций углекислого газа в атмосферном воздухе с помощью спроектированной и обученной каскадной нейросетевой модели, позволяющей при расчете концентраций учитывать сложное влияние метеорологических факторов и локальных условий рассеивания. Первым уровнем модели является расчет концентрации оксида углерода по известным параметрам источников выбросов этого вещества с использованием регламентированной методики расчета рассеивания примесей в атмосфере в Унифицированной программе расчета рассеивания «Эколог-Город». Вторым уровнем является нейронная сеть, которая корректирует рассчитанную на первом шаге концентрацию по заданным метеорологическим параметрам для увеличения точности моделирования. Третьим уровнем является нейронная сеть, позволяющая по полученной на предыдущем шаге концентрации оксида углерода, а также измеренным значениям коэффициента химической трансформации и концентрации атмосферного озона производить расчет концентрации углекислого газа.Полученная каскадная модель апробирована на территории г. Нижнекамск. Достигнутая точность расчета концентрации углекислого составила более 95%. Таким образом, представленная технология позволяет расширить возможности локальной системы мониторинга в условиях недостаточного количества измерений диоксида углерода. Reducing the carbon footprint is currently one of the priorities for the world economy. To do this, it is necessary to reduce greenhouse gas emissions, as well as to develop methods for monitoring greenhouse gases in the atmospheric air to ensure control over the effectiveness of decisions taken.Considering the complexity of the processes of dispersion of gases in the atmospheric air, neural network modeling methods have significant advantages in determining the concentrations of atmospheric impurities. This article presents a method for calculating the concentration of carbon dioxide in the atmospheric air using a designed and trained cascade neural network model, which makes it possible to take into account the complex influence of meteorological factors and local dispersion conditions when calculating concentrations. The first level of the model is the calculation of the concentration of carbon monoxide according to the known parameters of the emission sources of this substance using the regulated method for calculating the dispersion of impurities in the atmosphere in the Unified program for calculating dispersion "Ecolog-City". The second level is a neural network, which corrects the concentration calculated at the first step according to the specified meteorological parameters to increase the modeling accuracy. The third level is a neural network that allows calculating the concentration of carbon dioxide based on the concentration of carbon monoxide obtained at the previous step, as well as the measured values of the coefficient of chemical transformation and concentration of atmospheric ozone.The resulting cascade model was tested on the territory of Nizhnekamsk. The achieved accuracy of calculating the concentration of carbon dioxide was more than 95%. Thus, the presented technology makes it possible to expand the capabilities of the local monitoring system in conditions of an insufficient number of measurements of carbon dioxide.

Investigating Increased CO2 concentration on the pH of various plant species

The concept of bioremediation is quickly becoming the norm in the resolution of environmental issues. The steady increase in carbon dioxide levels, as documented by NASA, inspired scientists to engineer plants to absorb excess carbon dioxide from the atmosphere. Here, we have explored the consequences of the uptake of excess carbon dioxide by select plants. Carbon dioxide dissolves in water to produce carbonic acid, which dissociates to yield H+ ions. We hypothesized that increased carbon dioxide absorption results in decrease in pH of plant sap. Three plants (Byophyllum pinnatum, Romaine Lettuce and Nevada Lettuce), exposed to increased carbon dioxide concentrations (15%), demonstrated a consistent increase in pH towards alkalinity compared to control plants. Based on the outcome being opposite of what we have hypothesized, our results suggest Byophyllum pinnatum, Romaine lettuce and Nevada lettuce, all have a unique homeostatic system to prevent over-absorption of carbon dioxide in a carbon dioxide-rich environment.

Nature-Based Resilience: A Multi-Type Evaluation of Productive Green Infrastructure in Agricultural Settings in Ontario, Canada

Nature-based solutions such as green infrastructure present an opportunity to reduce air pollutant concentrations and greenhouse gas emissions. This paper presents new findings from a controlled field study in Ontario, Canada, evaluating the impact of productive applications of green infrastructure on air pollution and carbon dioxide concentrations across different agricultural morphologies compared to other non-productive applications. This study demonstrates that productive green infrastructure applications are as beneficial as non-productive applications in reducing ozone, nitrogen dioxide, and carbon dioxide concentrations. Nature-based solutions present an opportunity to build climate resilience into agricultural systems through supply-side mitigation and adaptation. The implementation of productive green infrastructure could be a viable agricultural practice to address multiple climate change impacts.

Towards Healthy Levels of Carbon Dioxide in Schools of the National Oil Company of Abu Dhabi

In their annual indoor air quality assessment for ADNOC Schools, the Abu Dhabi Education Council has reported hazardous levels (∼3000 ppm) of carbon dioxide in fifteen classrooms. Exposure of 5,090 students attending the school for ∼eight hours (typical school day) to such high levels of carbon dioxide would induce adverse health conditions like headaches, drowsiness, and lack of concentration on the short term and serious diseases like asthma and sick building syndrome on the long term. The Health, Safety, and Environment committee of the school has identified clogged air intake vents and dirty AC filters as the main cause of the high carbon dioxide concentrations reported. The outdoor (ambient) carbon dioxide level is measured and has an eight-hour average value of 419 ppm. After cleaning thoroughly, the indoor levels of carbon dioxide, temperature, and relative humidity were monitored simultaneously in each classroom and have average values of ∼1117 ppm, ∼24°C, and ∼37%, respectively. In addition, the average indoor-to-outdoor ratio of carbon dioxide has been improved from 3000 / 419 ≈ 7.2 before cleaning the AC filters to an average ratio of ( 1,117 / 419 ≈ 2.7 ) after cleaning. Thus, ventilation rates in the classrooms monitored in this project are adequate and the corrective actions taken were effective.

Huber Estimation for Uncertain Autoregressive Model

Traditional time analysis deals with observations in chronological order assuming the observations are precise numbers under the framework of probability theory, whereas data are imprecisely collected in many cases. This paper characterizes the imprecisely observed data as uncertain variables and estimates the unknown parameters in the uncertain autoregressive model using Huber loss function, which is more flexible compared with other robust estimations for a pre-given [Formula: see text] that regulates the amount of robustness. Then prediction value and prediction interval of the future value are given. What is more, a method to choose [Formula: see text] by cross-validation is proposed. At last, numerical examples show our methods in detail and illustrate the robustness of Huber estimation by comparing it with the least square estimation. black Our methods are also applied to a set of real data with carbon dioxide concentrations.

Experimental Study of Fuel Consumption and Exhaust Gas Composition of a Diesel Engine Powered by Biodiesel from Waste of Animal Origin

The use of biofuel is one method for limiting the harmful impact of diesel engines on the environment. It is also a way of gradually becoming less dependent on the depleting petroleum resources. New resources for producing biodiesel are currently being sought. The authors produced esters from animal fat waste, obtaining a fuel that can power diesel engines and identifying a way to utilise unnecessary waste. The animal fat methyl ester (AME) was produced using a reactor constructed for non-industrial ester production. The aim underlying this paper was to determine whether a diesel engine can be fuelled with AME biodiesel and to test this fuel’s impact on exhaust gas composition and fuel consumption. Fuelling a Perkins 1104D-44TA engine with AME biodiesel led to a reduction in the smoke opacity of the exhaust gas as well as in carbohydrate, particulate matter, and carbon monoxide concentrations. The carbon dioxide concentrations were similar for biodiesel and diesel fuel. Slight increases in nitrogen oxides concentrations and brake-specific fuel consumption were found for AMEs. An engine can be fuelled with AME biodiesel, but it is necessary to improve its low-temperature properties.