scholarly journals Road Traffic Emission Inventory in an Urban Zone of West Africa: Case of Yopougon City (Abidjan, Côte d’Ivoire)

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1111
Author(s):  
Madina Doumbia ◽  
Adjon A. Kouassi ◽  
Siélé Silué ◽  
Véronique Yoboué ◽  
Cathy Liousse ◽  
...  
Keyword(s):  
Public Transportation ◽  
Road Traffic ◽  
Field Measurements ◽  
Mitigation Strategies ◽  
Pollutant Emissions ◽  
Volume Data ◽  
Traffic Emission ◽  
High Emission ◽  
Vehicle Fleet ◽  
Reduce Emissions

Road traffic emission inventories based on bottom-up methodology, are calculated for each road segment from fuel consumption and traffic volume data obtained during field measurements in Yopougon. High emissions of black carbon (BC) from vehicles are observed at major road intersections, in areas surrounding industrial zones and on highways. Highest emission values from road traffic are observed for carbon monoxide (CO) (14.8 t/d) and nitrogen oxides (NOx) (7.9 t/d), usually considered as the major traffic pollution tracers. Furthermore, peak values of CO emissions due to personal cars (PCs) are mainly linked to the old age of the vehicle fleet with high emission factors. The highest emitting type of vehicle for BC on the highway is PC (70.2%), followed by inter-communal taxis (TAs) (13.1%), heavy vehicles (HVs) (9.8%), minibuses (GBs) (6.4%) and intra-communal taxis (WRs) (0.4%). While for organic carbon (OC) emissions on the main roads, PCs represent 46.7%, followed by 20.3% for WRs, 14.9% for TAs, 11.4% for GB and 6.7% for HVs. This work provides new key information on local pollutant emissions and may be useful to guide mitigation strategies such as modernizing the vehicle fleet and reorganizing public transportation, to reduce emissions and improve public health.

scholarly journals Greenhouse gas emissions profiles of neighbourhoods in Durban, South Africa – an initial investigation

2017 ◽  
Vol 30 (1) ◽  
pp. 191-214 ◽  
Author(s):  
Meryl Jagarnath ◽  
Tirusha Thambiran
Keyword(s):  
Greenhouse Gas ◽  
Ghg Emissions ◽  
Mitigation Strategies ◽  
Low Carbon ◽  
Economic Activities ◽  
Emissions Inventory ◽  
High Emission ◽  
Development Goals ◽  
Reduce Emissions ◽  
The City

Because current emissions accounting approaches focus on an entire city, cities are often considered to be large emitters of greenhouse gas (GHG) emissions, with no attention to the variation within them. This makes it more difficult to identify climate change mitigation strategies that can simultaneously reduce emissions and address place-specific development challenges. In response to this gap, a bottom-up emissions inventory study was undertaken to identify high emission zones and development goals for the Durban metropolitan area (eThekwini Municipality). The study is the first attempt at creating a spatially disaggregated emissions inventory for key sectors in Durban. The results indicate that particular groups and economic activities are responsible for more emissions, and socio-spatial development and emission inequalities are found both within the city and within the high emission zone. This is valuable information for the municipality in tailoring mitigation efforts to reduce emissions and address development gaps for low-carbon spatial planning whilst contributing to objectives for social justice.

scholarly journals Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 53
Author(s):  
Giovanni De Nunzio ◽  
Mohamed Laraki ◽  
Laurent Thibault
Keyword(s):  
Air Quality ◽  
Road Traffic ◽  
Estimation Error ◽  
Driving Behavior ◽  
Pollutant Emissions ◽  
Road Segment ◽  
Traffic Information ◽  
Volume Data ◽  
Modeling Framework ◽  
Boundary Parameter

Air pollution poses a major threat to health and climate, yet cities lack simple tools to quantify the costs and effects of their measures and assess those that are most effective in improving air quality. In this work, a complete modeling framework to estimate road traffic microscopic pollutant emissions from common macroscopic road and traffic information is proposed. A machine learning model to estimate driving behavior as a function of traffic conditions and road infrastructure is coupled with a physics-based microscopic emissions model. The up-scaling of the individual vehicle emissions to the traffic-level contribution is simply performed via a meta-model using both statistical vehicles fleet composition and traffic volume data. Validation results with real-world driving data show that: the driving behavior model is able to maintain an estimation error below 10% for relevant boundary parameter of the speed profiles (i.e., mean, initial, and final speed) on any road segment; the traffic microscopic emissions model is able to reduce the estimation error by more than 50% with respect to reference macroscopic models for major pollutants such as NOx and CO2. Such a high-resolution road traffic emissions model at the scale of every road segment in the network proves to be highly beneficial as a source for air quality models and as a monitoring tool for cities.

scholarly journals Carpooling as an Immediate Strategy to Post-Lockdown Mobility: A Case Study in University Campuses

2021 ◽  
Vol 13 (10) ◽  
pp. 5512
Author(s):  
Ricardo Tomás ◽  
Paulo Fernandes ◽  
Joaquim Macedo ◽  
Margarida Cabrita Coelho
Keyword(s):  
Public Transportation ◽  
Road Traffic ◽  
Pollutant Emissions ◽  
University Campus ◽  
Microscopic Traffic Simulation ◽  
University Campuses ◽  
The Road ◽  
Traffic Performance ◽  
Campus Networks ◽  
The University

Carpooling is a mobility concept that has been showing promising results in reducing single occupancy use of private cars, which prompted many institutions, namely universities, to implement carpooling platforms to improve their networks sustainability. Nowadays, currently under a pandemic crisis, public transportation must be used with limitations regarding the number of occupants to prevent the spread of the virus and commuters are turning even more to private cars to perform their daily trips. Carpooling under a set of precaution rules is a potential solution to help commuters perform their daily trips while respecting COVID-19 safety recommendations. This research aimed to develop an analysis of the road traffic and emission impacts of implementing carpooling, with social distancing measures, in three university campus networks through microscopic traffic simulation modeling and microscopic vehicular exhaust emissions estimation. Results indicate that employing carpooling for groups of up to three people to safely commute from their residence area to the university campus has the potential to significantly reduce pollutant emissions (reductions of 5% and 7% in carbon dioxide and nitrogen oxides can be obtained, respectively) within the network while significantly improving road traffic performance (average speed increased by 7% and travel time reduced by 8%).

scholarly journals The Potential Impacts of Electric Vehicles on Urban Air Quality in Shanghai City

2021 ◽  
Vol 13 (2) ◽  
pp. 496
Author(s):  
Xiaojian Hu ◽  
Nuo Chen ◽  
Nan Wu ◽  
Bicheng Yin
Keyword(s):  
Air Quality ◽  
Electric Vehicles ◽  
Public Transportation ◽  
Road Traffic ◽  
Urban Air Quality ◽  
Urban Air ◽  
Vehicular Emissions ◽  
Potential Reduction ◽  
Efficient Measure ◽  
Shanghai City

The Shanghai government has outlined plans for the new vehicles used for the public transportation, rental, sanitation, postal, and intra-city freight to be completely powered by electricity by 2020. This paper analyzed the characteristics of vehicle emissions in Shanghai in the past five years. The potential reduction in road traffic related emissions due to the promotion and application of electric vehicle in Shanghai was evaluated. The potential reduction was quantified by vehicular emissions. The vehicular emissions inventories are calculated by the COPERT IV model under the different scenarios, of which the results indicate that promoting electric vehicles is the efficient measure to control all road traffic related emissions and improve urban air quality. The results also provided basis and support for making policies to promote and manage electric vehicles.

scholarly journals Traffic noise feedback in agent-based Integrated Land-Use/Transport Models

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nico Kuehnel ◽  
Dominik Ziemke ◽  
Rolf Moeckel
Keyword(s):  
Land Use ◽  
Low Income ◽  
Noise Exposure ◽  
Road Traffic ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Common Source ◽  
Environmental Implications ◽  
Transport Models ◽  
Agent Based

Road traffic is a common source of negative environmental externalities such as noise and air pollution. While existing transport models are capable of accurately representing environmental stressors of road traffic, this is less true for integrated land-use/transport models. So-called land-use-transport-environment models aim to integrate environmental impacts. However, the environmental implications are often analyzed as an output of the model only, even though research suggests that the environment itself can have an impact on land use. The few existing models that actually introduce a feedback between land-use and environment fall back on aggregated zonal values. This paper presents a proof of concept for an integrated, microscopic and agent-based approach for a feedback loop between transport-related noise emissions and land-use. The results show that the microscopic link between the submodels is operational and fine-grained analysis by different types of agents is possible. It is shown that high-income households react differently to noise exposure when compared low-income households. The presented approach opens new possibilities for analyzing and understanding noise abatement policies as well as issues of environmental equity. The methodology can be transferred to include air pollutant emissions in the future.

scholarly journals Importance of Meteorology and Chemistry in Determining Air Pollutant Levels During COVID-19 Lockdown in Indian Cities

2021 ◽  
Author(s):  
Leigh Crilley ◽  
Yashar Iranpour ◽  
Cora J. Young
Keyword(s):  
Atmospheric Chemistry ◽  
Field Measurements ◽  
Air Pollutant ◽  
Mitigation Strategies ◽  
Ozone Production ◽  
Linear Relationships ◽  
Regional Sources ◽  
Volatile Organic ◽  
Emission Changes ◽  
Indian Cities

To accurately quantify impact of short-term interventions (such as COVID-19 lockdown) on air pollutant levels, meteorology and atmospheric chemistry need to be considered in addition to emission changes. We demonstrate that regional sources have a significant influence on PM<sub>2.5 </sub>levels in Delhi and Hyderabad due to the small reduction calculated post-lockdown after weather-normalization, indicating that future PM<sub>2.5</sub> mitigation strategies should focus on national-scale, as well as local sources. Furthermore, we demonstrate with field measurements that ozone production in Delhi is likely volatile organic compound (VOC)-limited, in agreement with previous modelling predictions, indicating that ozone mitigation should focus on dominant VOC sources. This work highlights the complexity in developing mitigation strategies for air pollution due to its non-linear relationships with emissions, chemistry and meteorology.

scholarly journals Self-Reported Mask Wearing Greatly Exceeds Directly Observed Use: Urgent Need for Policy Intervention in Kenya

2021 ◽  
Author(s):  
Aleksandra Jakubowski ◽  
Dennis Egger ◽  
Carolyne Nekesa ◽  
Layna Lowe ◽  
Michael Walker ◽  
...  
Keyword(s):  
Low Income ◽  
Public Transportation ◽  
Behavioral Interventions ◽  
Economic Consequences ◽  
Mitigation Strategies ◽  
Sub Saharan Africa ◽  
Rural Setting ◽  
Direct Observations ◽  
Improve Compliance ◽  
Sub Saharan

AbstractBackgroundMany countries in sub-Saharan Africa have so far avoided large outbreaks of COVID-19, perhaps due to the strict lockdown measures that were imposed early in the pandemic. Yet the harsh socio-economic consequences of the lockdowns have led many governments to ease the restrictions in favor of less stringent mitigation strategies. In the absence of concrete plans for widespread vaccination, masks remain one of the few tools available to low-income populations to avoid the spread of SARS-CoV-2 for the foreseeable future.MethodsWe compare mask use data collected through self-reports from phone surveys and direct observations in public spaces from population-representative samples in Ugunja subcounty, a rural setting in Western Kenya. We examine mask use in different situations and compare mask use by gender, age, location, and the riskiness of the activityFindingsWe assess mask use data from 1,960 phone survey respondents and 9,549 direct observations. While only 12% of people admitted in phone interviews to not wearing a mask in public, 90% of people we observed did not have a mask visible (77.7% difference, 95% CI 0.742, 0.802). Self-reported mask use was significantly higher than observed mask use in all scenarios (i.e. in the village, in the market, on public transportation).InterpretationWe find limited compliance with the national government mask mandate in Kenya using directly observed data, but high rates of self-reported mask use. This vast gap suggests that people are aware that mask use is socially desirable, but in practice they do not adopt this behavior.Focusing public policy efforts on improving adoption of mask use via education and behavioral interventions may be needed to improve compliance.FundingWeiss Family Foundation, International Growth Centre

Performance Comparison of Bus Travel Time Prediction Models across Indian Cities

2018 ◽  
Vol 2672 (31) ◽  
pp. 87-98 ◽  
Author(s):  
Jairam R ◽  
B. Anil Kumar ◽  
Shriniwas S. Arkatkar ◽  
Lelitha Vanajakshi
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Public Transportation ◽  
Prediction Models ◽  
Road Traffic ◽  
Performance Comparison ◽  
Heterogeneous Traffic ◽  
Transport Systems ◽  
Intelligent Transport System ◽  
Travel Time Prediction

Road traffic congestion has become a global worry in recent years. In many countries congestion is a major factor, causing noticeable loss to both economy and time. The rapid increase in vehicle ownership accompanied by slow growth of infrastructure has resulted in space constraints in almost all major cities in India. To mitigate this issue, authorities have shifted to more sustainable management solutions like Intelligent Transport System (ITS). Advanced Public Transportation System (APTS) is an important area in ITS which could considerably offset the growing ownership of private vehicles as public transport holds a noticeable mode share in several major cities in India. Getting access to real-time information about public transport would certainly attract more users. In this regard, this work aims at developing a reliable structure for predicting arrival/travel time of various public transport systems under heterogeneous traffic conditions existing in India. The data used for the study is collected from three cities—Surat, Mysore, and Chennai. The data is analyzed across space and time to extract patterns which are further utilized in prediction models. The models examined in this paper are k-NN classifier, Kalman Filter and Auto-Regressive Integrated Moving Average (ARIMA) techniques. The performance of each model is evaluated and compared to understand which methods are suitable for different cities with varying characteristics.

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