Anthropogenic CO2 in the Dakar (Senegal) Urban Area Deduced from 14C Concentration in Tree Leaves

Radiocarbon ◽  
2017 ◽  
Vol 59 (3) ◽  
pp. 1009-1019 ◽  
Author(s):  
Maurice Ndeye ◽  
Matar Sène ◽  
Doudou Diop ◽  
Jean-François Saliège
Keyword(s):  
Mathematical Model ◽  
Urban Area ◽  
20Th Century ◽  
Urban Areas ◽  
Simple Mathematical Model ◽  
Tree Leaves ◽  
Suess Effect ◽  
Fossil Carbon ◽  
Nuclear Bomb ◽  
Clean Air

AbstractRadiocarbon (14C) in atmospheric CO2 for the Dakar (Senegal) urban area was measured using tree leaves collected by botanists from 1900 to 2003. The aim of our study was to compare the local Suess effect in Dakar to the global one during the 20th century. The ∆14C of atmospheric CO2 in this region decreased from 1900 to 1958 during the pre-bomb era (–2±5‰ to –22±4‰). From 1958 to 1964, nuclear bomb tests injected a large amount of artificial 14C into the atmosphere, reflected in the rise of ∆14C values. In the Dakar region, the atmospheric ∆14C reached 773±8‰ in 1964, but subsequently decreased to 80±5‰ by 2003, which is consistent with the global exponential decreasing trend. The ∆14C record presented here remains slightly lower than the global record. This result is attributed to the input of anthropogenic fossil carbon into the atmosphere. The amount of carbon input can be evaluated by comparing urban areas to those of clean air sites. The calculation of anthropogenic fossil carbon is deduced from a simple mathematical model.

scholarly journals Regional Suess Effect in the Upper Silesia Urban Area

Radiocarbon ◽  
1986 ◽  
Vol 28 (2A) ◽  
pp. 655-660 ◽  
Author(s):  
Romuald Awsiuk ◽  
Mieczysław F Pazdur
Keyword(s):  
Urban Area ◽  
Wind Direction ◽  
Upper Silesia ◽  
Industrial Region ◽  
Suess Effect ◽  
Vertical Stability ◽  
Clean Air ◽  
Concentration Measurements

The study of a regional Suess effect is based on three sets of samples of atmospheric CO2: 1) a series of samples collected at the same site in Gliwice from 1980 to 1984, 2) samples collected simultaneously at different sites within the limits of an urban and industrial region of Upper Silesia, and 3) samples collected simultaneously outside this region along an eastern direction. Results of 14C concentration measurements show systematic decrease of Δ14C with the rate close to the corresponding value for clean air. Depletion of 14C concentration was found to be virtually the same in the whole urban area. Analysis of regional synoptic data reveals correlation of individual Δ14C values with wind direction, frequency of calm, and vertical stability of the atmosphere.

The Status of Livelihood Vulnerability and Its CopingCapacities of Urban Squatters in Kathmandu Metropolitan City, Nepal

Patan Pragya ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 19-32
Author(s):  
Chhabi Ram Baral
Keyword(s):  
Data Collection ◽  
Urban Area ◽  
Urban Areas ◽  
Quantitative Methods ◽  
Urban Poverty ◽  
Rural Poverty ◽  
Poor People ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
The Status

Urban poverty is one of multidimensional issue in Nepal. Increasing immigration from the outer parts of Kathmandu due to rural poverty, unemployment and weak security of the lives and the properties are core causes pushing people into urban areas. In this context how squatter urban area people sustain their livelihoods is major concern. The objectives of the study are to find out livelihood assets and capacities squatters coping with their livelihood vulnerability in adverse situation. Both qualitative and quantitative methods are applied for data collection. It is found that squatters social security is weak, victimized by severe health problems earning is not regular with lack of physical facilities and overall livelihood is critical. This study helps to understand what the changes that have occurred in livelihood patterns and how poor people survive in urban area.

scholarly journals The Wood Quality of Small-Leaved Lime (Tilia cordata Mill.) Trees in an Urban Area: A Pilot Study

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 420
Author(s):  
Benas Šilinskas ◽  
Aistė Povilaitienė ◽  
Gintautas Urbaitis ◽  
Marius Aleinikovas ◽  
Iveta Varnagirytė-Kabašinskienė
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Wood Quality ◽  
Wood Products ◽  
Urban Trees ◽  
Measuring System ◽  
Forest Site ◽  
Tilia Cordata ◽  
Mean Values ◽  
Wood Development

This study performed a pilot evaluation of the wood quality—defined by a single parameter: dynamic modulus of elasticity (MOEdyn, N mm−2)—of small-leaved lime (Tilia cordata Mill.) trees in urban areas. A search of the literature revealed few studies which examined the specifics of tree wood development in urban areas. Little is known about the potential of wood from urban trees wood of their suitability for the timber industry. In this study, an acoustic velocity measuring system was used for wood quality assessment of small-leaved lime trees. The MOEdyn parameter was evaluated for small-leaved lime trees growing in two urban locations (along the streets, and in an urban park), with an additional sample of forest sites taken as the control. MOEdyn was also assessed for small-leaved lime trees visually assigned to different health classes. The obtained mean values of MOEdyn of 90–120-year old small-leaved lime trees in urban areas ranged between 2492.2 and 2715.8 N mm−2. For younger trees, the values of MOEdyn were lower in the urban areas than in the forest site. Otherwise, the results of the study showed that the small-leaved lime wood samples were of relatively good quality, even if the tree was classified as moderately damaged (which could cause a potential risk to the community). Two alternatives for urban tree management can be envisaged: (1) old trees could be left to grow to maintain the sustainability of an urban area until their natural death, or (2) the wood from selected moderately damaged trees could be used to create wood products, ensuring long-term carbon retention.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Cost-efficient factors in local public spending: Detecting relationships between local environments, population size and urban area category

2021 ◽  
pp. 239980832110038
Author(s):  
Hiroki Baba ◽  
Yasushi Asami
Keyword(s):  
Population Size ◽  
Urban Area ◽  
Urban Areas ◽  
Local Governments ◽  
Statistical Significance ◽  
Population Decline ◽  
Public Spending ◽  
Local Environment ◽  
Environment Factors ◽  
Major Factors

This study examines regional differences in local environment factors to better understand the sustainability of local governments indexed by per capita public spending. Under the condition of heterogeneous population size, we examine how factor characteristics differ depending on the spatial context represented by the urban area category. By employing a Cobb–Douglas cost function with congestion effects on public service provision, the estimated factors enable us to articulate major factors and differences in cost-efficiency between urban area categories. We found that statistical significance and even the signatures of local environment factors differ depending on the urban employment area category. Regarding factors such as the ratios of employees in secondary and tertiary industries, these did not tend to be statistically significant in small-sized urban areas, while small-sized cities in large-sized urban areas were likely to gain confidence intervals. Moreover, we did not observe any statistical significance for the ratio of elderly people due to the balance of spending between national and local governments. These findings could contribute to sustainable management of cities in the advent of population decline.

scholarly journals InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia

2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Luca Pulvirenti ◽  
Marco Chini ◽  
Nazzareno Pierdicca
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
The Other ◽  
Spatial Average ◽  
Calibration Data ◽  
Suburban Areas ◽  
Persistent Scatterers ◽  
Optical Images ◽  
Interferometric Phase ◽  
Multitemporal Data

A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. The approach assumes the availability of a map of persistent scatterers (PSs) inside the urban settlement and is based on the analysis of the temporal trend of the InSAR coherence and the spatial average of the exponential of the InSAR phase in each PS. Both interferometric products are expected to have high and stable values in the PSs; therefore, anomalous decreases may indicate that floodwater is present in an urban area. The stack of Sentinel-1 data has been divided into two subsets. The first one has been used as a calibration set to identify the PSs and determine, for each PS, reference values of the coherence and the spatial average of the exponential of the interferometric phase under standard non-flooded conditions. The other subset has been used for validation purposes. Flood maps produced by UNOSAT, analyzing very-high-resolution optical images of the floods that occurred in Beletweyne in April–May 2018, October–November 2019, and April–May 2020, have been used as reference data. In particular, the map of the April–May 2018 flood has been used for training purposes together with the subset of Sentinel-1 calibration data, whilst the other two maps have been used to validate the products generated by applying the proposed method. The main product is a binary map of flooded PSs that complements the floodwater map of rural/suburban areas produced by applying a well-consolidated algorithm based on intensity data. In addition, a flood severity map that labels the different districts of Beletweyne, as not, partially, or totally flooded has been generated to consolidate the validation. The results have confirmed the effectiveness of the proposed method.

scholarly journals GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

2021 ◽  
Vol 13 (4) ◽  
pp. 544
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Gnss Receivers ◽  
Multipath Signal ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

scholarly journals Estimating Environmental Contamination and Element Deposition at an Urban Area of Central Italy

Urban Science ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 76 ◽  
Author(s):  
Stefano Loppi ◽  
Adelmo Corsini ◽  
Luca Paoli
Keyword(s):  
Urban Area ◽  
Environmental Contamination ◽  
Urban Areas ◽  
Central Italy ◽  
Protective Role ◽  
Evernia Prunastri ◽  
Physico Chemical ◽  
Living Organisms ◽  
Study Sites ◽  
Economic Constraints

Air quality monitoring in many urban areas is based on sophisticated and costly equipment to check for the respect of environmental quality standards, but capillary monitoring is often not feasible due to economic constraints. In such cases, the use of living organisms may be very useful to complement the sparse data obtained by physico-chemical measurements. In this study, the bioaccumulation of selected trace elements (Al, As, Cd, Ce, Cr, Cu, Fe, Ni, Pb, S, Sb, Zn) in lichen samples (Evernia prunastri) transplanted for three months at an urban area of Central Italy was investigated to assess the main environmental contaminants, their sources, and the fluxes of element depositions. The results pinpointed Cu and Sb as the main contaminants and suggested a common origin for these two elements from non-exhaust sources of vehicular traffic, such as brake abrasion. Most study sites were, however, found to be subjected to low or moderate environmental contamination, and the lowest contamination corresponded to the main green areas, confirming the important protective role of urban forests against air pollution. Ranges of estimated mean annual element deposition rates in the study area were similar or lower than those reported for other urban areas.

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