Assessing greenhouse gas and related air pollutant emissions from road traffic counts: A case study for Mauritius

2014 ◽  
Vol 32 ◽  
pp. 35-47 ◽  
Author(s):  
Anand Sookun ◽  
Ravindra Boojhawon ◽  
Soonil D.D.V. Rughooputh
Keyword(s):  
Greenhouse Gas ◽  
Road Traffic ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Traffic Counts

scholarly journals Quantifying Air Pollutant Emission from Agricultural Machinery Using Surveys—A Case Study in Anhui, China

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 440
Author(s):  
Yi Ai ◽  
Yunshan Ge ◽  
Zheng Ran ◽  
Xueyao Li ◽  
Zhibing Xu ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Road Traffic ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Pollutant Emission ◽  
Agricultural Machinery ◽  
Activity Data ◽  
Road Traffic Emissions ◽  
Using Data

Diesel-powered agricultural machinery (AM) is a significant contributor to air pollutant emissions, including nitrogen oxides (NOx) and particulate matter (PM). However, the fuel consumption and pollutant emissions from AM remain poorly quantified in many countries due to a lack of accurate activity data and emissions factors. In this study, the fuel consumption and air pollutant emission from AM were estimated using a survey and emission factors from the literature. A case study was conducted using data collected in Anhui, one of the agricultural provinces of China. The annual active hours of AM in Anhui ranged 130 to 175 h. The estimated diesel fuel consumption by AM was 1.45 Tg in 2013, approximately 25% of the total diesel consumption in the province. The air pollutants emitted by AM were 57 Gg of carbon monoxide, 14 Gg of hydrocarbon, 74 Gg of NOx and 5.7 Gg of PM in 2013. The NOx and PM emissions from AM were equivalent to 17% and 22% of total on-road traffic emissions in Anhui. Among nine types of AM considered, rural vehicles are the largest contributors to fuel consumption (31%) and air emissions (33–45%).

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 Potentially Replaceable Car Trips: Assessment of Potential Modal Change towards Active Transport Modes in Vitoria-Gasteiz

2018 ◽  
Vol 10 (10) ◽  
pp. 3510 ◽  
Author(s):  
Javier Delso ◽  
Belén Martín ◽  
Emilio Ortega
Keyword(s):  
Physical Activity ◽  
Road Traffic ◽  
Air Pollutant ◽  
Perceived Barriers ◽  
Pollutant Emissions ◽  
Personal Safety ◽  
Distance Threshold ◽  
Distance Calculation ◽  
Modal Change ◽  
Threshold Approach

Road traffic is the most important contributor to noise and air pollutant emissions in cities. Its substitution by non-motorized modes therefore has great potential to improve the urban environment while increasing levels of physical activity among the population. This paper identifies car trips that could potentially be transferred to active modes such as walking and cycling, and analyses the barriers perceived by people who travel by car. We detect potentially replaceable car trips based on a mobility survey, distance calculation, and a distance threshold approach. The answers to a set of questions in the mobility survey allow us to identify the perceived barriers for use of the bicycle, applied to Vitoria-Gasteiz (Spain). The results show that between 30% and 40% of car trips could be replaced by active modes. Personal safety and distance results are the most limiting barriers perceived by car users, while physical condition and technique are the most limiting ones for bicycle users. These results provide valuable information for implementing measures to promote the replacement of motorized trips with walking and cycling.

Air pollutant emissions from straw open burning: A case study in Tianjin

2017 ◽  
Vol 171 ◽  
pp. 155-164 ◽  
Author(s):  
Yanan Guan ◽  
Guanyi Chen ◽  
Zhanjun Cheng ◽  
Beibei Yan ◽  
Li'an Hou
Keyword(s):  
Air Pollutant ◽  
Pollutant Emissions ◽  
Open Burning

scholarly journals SUSTAINABLE INNOVATIONS FOR ELECTRICITY GENERATION IN ONTARIO

2011 ◽  
Author(s):  
Yimin Zhang ◽  
Shiva Habibi ◽  
Heather L. MacLean
Keyword(s):  
Life Cycle ◽  
Greenhouse Gas ◽  
Environmental Performance ◽  
Electricity Generation ◽  
New Technologies ◽  
Sustainable Design ◽  
Biomass Gasification ◽  
Agricultural Residues ◽  
Air Pollutant ◽  
Pollutant Emissions

The electricity generation sector is far from sustainable; in Ontario, 77% of electricity consumed is generated from non-renewable sources such as coal, natural gas and nuclear. As a result, this sector contributes significantly to many environmental challenges including global warming, smog formation, and acid deposition. It is critical to improve the sustainability of electricity generation through the incorporation of sustainable design concepts. Sustainable design takes into account the environmental performance of a product or process over its entire life cycle (including design and development, raw material acquisition, production, use, and end-of-life). Innovative design has resulted in new technologies for electricity generation. Generating electricity from biomass is one of the alternative technologies which could have the potential to improve the sustainability of the electricity generation sector. In this research we examine various scenarios for displacing coal-based generation. Coal gasification is a mature technology and to replace some or all of the feedstock with biomass, a re-design of some portions of the electricity generation technology are required. The technical changes in the process depend on several issues including the physical and chemical characteristics of biomass. We evaluate the environmental performance of electricity generation from agricultural residues through conducting a life cycle inventory for three biomass-to-electricity scenarios for the Province of Ontario; 1) a 5% co-firing of agricultural residues with coal in existing coal plants, 2) a 15% co-firing of agricultural residues with coal in existing coal plants, and 3) a hypothetical power plant which produces electricity from 100% agricultural residues using biomass gasification technology. For comparison purposes, we analyze a current coal only option using plant specific data. We quantify life cycle energy use, greenhouse gas and air pollutant emissions for electricity. Our results suggest that on a life cycle basis electricity generated from biomass can achieve a reduction in greenhouse gas emissions of 4% (for the 5% biomass co-firing) to 96% (for the 100% biomass gasification) compared to the coal-only option. Similarly, reductions in air pollutant emissions (sulfur oxides, nitrogen oxides, and particulate matter) range from 4% to 98%. Our study indicates that life cycle analysis is a useful tool for assisting decision makers in the selection of more sustainable design options for future electricity generation.

scholarly journals The Use of Artificial Neural Networks to Analyze Greenhouse Gas and Air Pollutant Emissions from the Mining and Quarrying Sector in the European Union

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1925 ◽  
Author(s):  
Jarosław Brodny ◽  
Magdalena Tutak
Keyword(s):  
Neural Networks ◽  
European Union ◽  
Greenhouse Gas ◽  
Value Added ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Mining Activities ◽  
The European Union ◽  
Eu Countries ◽  
The Eu

The European Union (EU) is considered one of the most economically developed regions worldwide. It was driven by the mining industry for several decades. Despite certain changes in this area, a number of mineral and energy resources are still being mined in the EU. Nevertheless, mining activities are accompanied by many unfavorable phenomena, especially for the environment, such as greenhouse gas and air pollutant emissions. The great diversity of the EU countries in terms of the size of the “mining and quarrying” sector means that both the volume and structure of these emissions in individual countries varies. In order to assess the current state of affairs, research was conducted to look at the structure and volume of these emissions in individual EU countries. The aim of the study was to divide these countries into homogenous groups by structure and volume of studied emissions. In order to reflect both the specificity and diversity of the EU countries, this division was based on the seven most important gases (CO2, CH4, N2O, NH3, NMVOC, CO, NOx) and two types of particulate matter (PM 2.5, PM 10) emitted into the atmosphere from the sector in question. The volume of studied emissions was also compared to the number of inhabitants of each EU country and the gross value added (GVA) by the mining and quarrying sector. This approach enabled a new and broader view on the issue of gas and air pollutant emissions associated with mining activities. The artificial Kohonen’s neural networks were used for the analysis. The developed method, the analyses and the results constitute a new approach to studying such emissions in the EU. Research that looks only at the emission of harmful substances into the environment in relation to their absolute values fail to fully reflect the complexity of this problem in individual EU countries. The presented approach and the results should broaden the knowledge in the field of harmful substance emissions from the mining and quarrying sector, which should be utilized in the process of implementing the new European climate strategy referred to as “The European Green Deal”.

scholarly journals Well-to-wheels greenhouse gas and air pollutant emissions from battery electric vehicles in China

2019 ◽  
Vol 25 (3) ◽  
pp. 355-370 ◽  
Author(s):  
Yali Zheng ◽  
Xiaoyi He ◽  
Hewu Wang ◽  
Michael Wang ◽  
Shaojun Zhang ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Electric Vehicles ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Battery Electric Vehicles ◽  
Well To Wheels

NEMO project: developing a N-RSD (Noise Remote Sensing Device) to identify high noise emitters in the road traffic flow

2021 ◽  
Vol 263 (5) ◽  
pp. 1773-1783
Author(s):  
Maximilian Ertsey-Bayer ◽  
Nikolas Kirchhoff ◽  
Sonia Alves ◽  
Bert Peeters ◽  
Viggo Henriksen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Traffic Flow ◽  
Engine Speed ◽  
Road Traffic ◽  
Spectral Characteristics ◽  
Air Pollutant ◽  
Classification Model ◽  
Pollutant Emissions ◽  
Noise Levels ◽  
Driving Conditions

NEMO (Noise and Emissions MOnitoring and Radical mitigation) is a research project aiming at developing an autonomous system to detect noise and air pollutant emissions from individual vehicles within the traffic flow. The objective is to identify high emitters within the normal traffic. For noise, a high emitter is a vehicle that is either in a poor or modified condition (e.g., with an illegal or malfunctioning exhaust) or that is driven in a noisy way (fast acceleration, high engine speed in low gear, etc.). A vehicle that has been type approved, is well maintained, and is driven under normal conditions is never a high-emitter vehicle, even if it is subjectively perceived as annoying. A Noise Remote Sensing Device (N-RSD) is being developed. This device will capture, for each individual vehicle, the driving conditions (vehicle speed, acceleration, engine speed and load) and the single-event noise levels and spectral characteristics. The noise levels will be normalized to comparable driving conditions and fed into a classification model. The classification model will then be able to identify the high emitters vehicles. When finished, the NEMO system will allow cities and road authorities to reduce annoyance and health impacts from noisy and polluting vehicles, for instance by raising awareness among drivers or by restricting access to low emission zones.

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