Forests cover a considerable proportion of the land area and function importantly to regulate the global climate system as carbon (C) sinks. Rapid urbanization process may be changing the C sink function of forests worldwide, but its potential effects on the accumulation of soil organic C (SOC) remain inconclusive. In this study, we collected litter and soil samples in the surface layer of six urban and suburban forests to fractionate SOC by combining physical, chemical and biological methods and quantify the contents of these SOC fractions. The results showed that urban forests contained significantly lower contents of labile and non-labile SOC fractions, therefore contributing to significantly lower total SOC content in urban than in suburban forests. Interestingly, we observed that the SOC content was significantly correlated with the litter C stock in urban forests but not in suburban forests, while it was significantly correlated with the soil microbial biomass in suburban forests but not in urban forests. These scenarios suggest that urbanization could have substantially regulated the SOC accumulation mode in the surface soil of subtropical forests, e.g., from microbial filtering pathways to abiotic ones, therefore influencing the stabilization of SOC in these subtropical forests. 

Urbanization induced changes in the accumulation mode of organic carbon in the surface soil of subtropical forests

This study aimed to develop the Addis Ababa Driving Cycle (DC) using real-time data from passenger vehicles in Addis Ababa based on a neural network (NN) and principal component analysis (PCA) approach. Addis Ababa has no local DC for automobile emissions tests and standard DCs do not reflect the current scenario. During the DC’s development, the researchers determined the DC duration based on their experience and the literature. A k-means clustering method was also applied to cluster the dimensionally reduced data without identifying the best clustering method. First, a shape-preserving cubic interpolation technique was applied to remove outliers, followed by the Bayes wavelet signal denoising technique to smooth the data. Rules were then set for the extraction of trips and trip indicators before PCA was applied, and the machine learning classification was applied to identify the best clustering method. Finally, after training the NN using Bayesian regularization with a back propagation, the velocity for each route section was predicted and its performance had an overall R-value of 0.99. Compared with target data, the DCs developed by the NN and micro trip methods have a relative difference of 0.056 and 0.111, respectively, and resolve the issue of the DC duration decision in the micro trip method.

/pdf/a-neural-network-and-principal-component-analysis-approach-mmh29sdu.pdf

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

Earth observation has long been used to map and monitor urban expansion. However, urban development within built-up areas is hardly studied, mostly because these changes do not yield a change in the predominant land cover. This lack of land cover change makes it nearly impossible to detect such changes using medium-resolution imagery. Here we aim to analyze the importance of changes withing urban areas, using a manual interpretation of very-high-resolution imagery from selected areas in Nairobi. For this area we map changes at the building level between 2009 and 2021. Buildings are categorized as "persistent", "newly developed", "replaced", "improved", or "removed". While persistence and newly built are overall the most common trajectories, we find multiple tiles with > 25% of the building area replaced, improved, and removed, potentially reflecting changes in land use as well as social conditions. These findings suggest the need to move beyond analysis of built-up area expansion towards the detection of inner-urban development. 

Mapping intra-urban development trajectories in Nairobi, Kenya

Analysing and characterizing the swing in solar insolation across space and time require a technique known as spatio-temporal and hyper-temporal variability analysis. Multiple measurements of a quantity provide different values. Spatial variability. The range measures spatial variability. Identifying regional fluctuations in solar insolation and their impacts on energy production may be helpful in specialized energy management systems. It's the study of unpredictability in science. In this portion, we looked at the techniques a researcher uses to examine a topic, including variability and reasoning. The researcher must know research and analytical methodologies. Researchers must know how to undertake variability analysis and employ research techniques. They must also know which techniques or processes are significant, what they mean and why. Researchers must grasp the assumptions behind certain approaches and the criteria for determining whether certain techniques and procedures are acceptable. 

Variability of Energy Resources at a Diverse Scale

Quantifying and analyzing the impact assessment on land use change of urban growth using a timeline

Urban patterns have shown that shifts in the social, economic, and geographical features of an area result in metropolitan growth, planned or otherwise. Rapid urbanization takes a toll on the ecological makeup of the environment and the wellbeing of humans at multiple levels. Thus, this study was drafted to quantify urban growth rates, types, and changes, with regards to urbanization patterns from 1990 to 2020. It tries to evaluate whether Addis Ababa’s urban expansion conforms to the diffusion-coalescence theory using remote sensing data. The Spatio-temporal pattern and changing aspects of the built-up land were examined using urban growth types, spatial metrics, and a gradient method. The last decade evidenced the most pronounced growth within a buffer distance of 10 to 22 km from the city center. Edge expansion was the most dominant form of growth across all three decades with more than 61% of new development each. Infill and outlying growth showed decadal variability with an alternating dominance. The Spatio-temporal investigation confirmed that the urban class and growth types remained consistent. The team concluded that diffusion and coalescence are two simultaneously occurring phases of urban growth rather than two dichotomous successive phases. Spatio-temporal patterns and the dynamic behavior of spatial metrics are instrumental in comprehending the urban growth process and cycle. Moreover, the results can aid in assessing the land use planning policy as well as to guide future land use planning.

Quantifying spatial patterns of urbanization: growth types, rates, and changes in Addis Ababa City from 1990 to 2020

Climate change has emerged as a very important threat to economic development, atmosphere, and public health. One of the driving factors behind global climate change is road transportation. Therefore, the sector needs to take on the responsibility of addressing its negative impacts on the environment. This study seeks to find ways of mitigating the impacts of climate change. It pays particular attention to greenhouse gas emissions and other selected air pollutants based on a new navigation concept called eco-route, a 3D eco-routing transportation planning method that can help reduce the environmental footprints of Road transports in Addis Ababa city for distribution vehicles. The model applied in this study considered the road gradient, varying velocity or speed of vehicles, and weight of vehicles to evaluate gradient effects on consumption of fuel, CO2 and also other air pollutants emission. The model is applied in three scenarios within different vehicle weight ranges and three different cases in Addis Ababa. The finds of the study imply the eco-routes emission reduction potential from fuel and CO2 in the tested scenarios is up to 39.81% while other air pollutants account for 25.65%. The results prove that eco routes have the ability of reducing Fuel consumption, CO2 and other air pollutant emission rate. Eco routes have the potential to provide sustainable transportation opportunities for Addis Ababa city. This study recommends that eco-routes should be used instead of shortest and fastest routes where significant road gradients exist.

/pdf/applying-3d-eco-routing-model-to-reduce-environmental-mneaoaj26o.pdf

Seifu Woldemichael Busho

Papers

Quantifying spatial patterns of urbanization: growth types, rates, and changes in Addis Ababa City from 1990 to 2020

Applying 3D-eco routing model to reduce environmental footprint of road transports in Addis Ababa City