Development of exhaust emission factors for vessels: A review and meta-analysis of available data

This research quantifies current sources of non-exhaust particulate matter traffic emissions in London using simultaneous, highly time-resolved, atmospheric particulate matter mass and chemical composition measurements. The measurement campaign ran at Marylebone Road (roadside) and Honor Oak Park (background) urban monitoring sites over a 12-month period between 1 September 2019 and 31 August 2020. The measurement data were used to determine the traffic increment (roadside–background) and covered a range of meteorological conditions, seasons, and driving styles, as well as the influence of the COVID-19 “lockdown” on non-exhaust concentrations. Non-exhaust particulate matter (PM)10 concentrations were calculated using chemical tracer scaling factors for brake wear (barium), tyre wear (zinc), and resuspension (silicon) and as average vehicle fleet non-exhaust emission factors, using a CO2 “dilution approach”. The effect of lockdown, which saw a 32% reduction in traffic volume and a 15% increase in average speed on Marylebone Road, resulted in lower PM10 and PM2.5 traffic increments and brake wear concentrations but similar tyre and resuspension concentrations, confirming that factors that determine non-exhaust emissions are complex. Brake wear was found to be the highest average non-exhaust emission source. In addition, results indicate that non-exhaust emission factors were dependent upon speed and road surface wetness conditions. Further statistical analysis incorporating a wider variability in vehicle mix, speeds, and meteorological conditions, as well as advanced source apportionment of the PM measurement data, were undertaken to enhance our understanding of these important vehicle sources.

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A Study on Characteristic Emission Factors of Exhaust Gas from Diesel Locomotives

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17113788 ◽

2020 ◽

Vol 17 (11) ◽

pp. 3788 ◽

Cited By ~ 1

Author(s):

Min-Kyeong Kim ◽

Duckshin Park ◽

Minjeong Kim ◽

Jaeseok Heo ◽

Sechan Park ◽

...

Keyword(s):

Fuel Consumption ◽

Environmental Protection Agency ◽

High Speed ◽

Emission Factors ◽

Exhaust Emission ◽

Emission Measurement ◽

Exhaust Gas ◽

Diesel Locomotive ◽

Diesel Vehicles ◽

Engine Power

Use of diesel locomotives in transport is gradually decreasing due to electrification and the introduction of high-speed electric rail. However, in Korea, up to 30% of the transportation of passengers and cargo still uses diesel locomotives and diesel vehicles. Many studies have shown that exhaust gas from diesel locomotives poses a threat to human health. This study examined the characteristics of particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons in diesel locomotive engine exhaust. Emission concentrations were evaluated and compared with the existing regulations. In the case of PM and NOx, emission concentrations increased as engine output increased. High concentrations of CO were detected at engine start and acceleration, while hydrocarbons showed weakly increased concentrations regardless of engine power. Based on fuel consumption and engine power, the emission patterns of PM and gaseous substances observed in this study were slightly higher than the U.S. Environmental Protection Agency Tier standard and the Korean emission standard. Continuous monitoring and management of emissions from diesel locomotives are required to comply with emission standards. The findings of this study revealed that emission factors varied based on fuel consumption, engine power, and actual driving patterns. For the first time, a portable emission measurement system (PEMS), normally used to measure exhaust gas from diesel vehicles, was used to measure exhaust gas from diesel locomotives, and the data acquired were compared with previous results. This study is meaningful as the first example of measuring the exhaust gas concentration by connecting a PEMS to a diesel locomotive, and in the future, a study to measure driving characteristics and exhaust gas using a PEMS should be conducted.

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A new method to determine exhaust emission factors for heavy duty vehicles

The Science of The Total Environment ◽

10.1016/0048-9697(95)04650-p ◽

1995 ◽

Vol 169 (1-3) ◽

pp. 213-217 ◽

Cited By ~ 5

Author(s):

P. Jost ◽

D. Hassel ◽

K.S. Sonnborn

Keyword(s):

Emission Factors ◽

New Method ◽

Exhaust Emission ◽

Heavy Duty ◽

Heavy Duty Vehicles

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Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence

Sustainability ◽

10.3390/su13169058 ◽

2021 ◽

Vol 13 (16) ◽

pp. 9058

Author(s):

Gi-Young Chae ◽

Seung-Hyun An ◽

Chul-Yong Lee

Keyword(s):

Artificial Intelligence ◽

Natural Gas ◽

Meta Analysis ◽

Demand Forecasting ◽

Premature Death ◽

Exhaust Emission ◽

Demand Prediction ◽

Global Demand ◽

Meta Regression Analysis ◽

Change Concerns

Ship exhaust emission is the main cause of coastal air pollution, leading to premature death from cardiovascular cancer and lung cancer. In light of public health and climate change concerns, the International Maritime Organization (IMO) and several governments are reinforcing policies to use clean ship fuels. In January 2020, the IMO reduced the acceptable sulfur content in ship fuel to 0.5% m/m (mass/mass) for sustainability. The use of liquified natural gas (LNG) as a ship fuel is currently the most likely measure to meet this regulation, and LNG bunkering infrastructure investment and network planning are underway worldwide. Therefore, the aim of this study is to predict the LNG bunkering demand for investment and planning. So far, however, there has been little quantitative analysis of LNG bunkering demand prediction. In this study, first, the global LNG bunkering demand was predicted using meta-regression analysis. Global demand for LNG bunkering is forecast to increase from 16.6 million tons in 2025 to 53.2 million tons in 2040. Second, LNG bunkering prediction by country and region was performed through analogy and artificial intelligence methods. The information and insights gained from this study may facilitate policy implementation and investments.

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Methane Emission Factors from Vietnamese Rice Production: Pooling Data of 36 Field Sites for Meta-Analysis

Climate ◽

10.3390/cli8060074 ◽

2020 ◽

Vol 8 (6) ◽

pp. 74

Author(s):

Thi Bach Thuong Vo ◽

Reiner Wassmann ◽

Van Trinh Mai ◽

Duong Quynh Vu ◽

Thi Phuong Loan Bui ◽

...

Keyword(s):

Meta Analysis ◽

Ghg Emissions ◽

Field Measurements ◽

Emission Factors ◽

Rice Production ◽

Intergovernmental Panel ◽

Closed Chamber ◽

National Budget ◽

South Vietnam ◽

Cropping Seasons

Rice production is a significant source of greenhouse gas (GHG) emissions in the national budget of many Asian countries, but the extent of emissions varies strongly across agro-environmental zones. It is important to understand these differences in order to improve the national GHG inventory and effectively target mitigation options. This study presents a meta-analysis of CH4 database emission factors (EFs) from 36 field sites across the rice growing areas of Vietnam and covering 73 cropping seasons. The EFs were developed from field measurements using the closed chamber technique. The analysis for calculating baseline EFs in North, Central and South Vietnam in line with the Intergovernmental Panel on Climate Change (IPCC) Tier 2 methodology was specified for the three cropping seasons being early-(E), mid-(M) and late-year (L) seasons. Calculated average CH4 EFs are given in kg ha−1 d−1 and reflect the distinct seasons in North (E: 2.21; L: 3.89), Central (E: 2.84; M+L: 3.13) and South Vietnam (E: 1.72; M: 2.80; L: 3.58). Derived from the available data of the edapho-hydrological zones of the Mekong River Delta, season-based EFs are more useful than zone-based EFs. In totality, these average EFs indicate an enormous variability of GHG emissions in Vietnamese rice production and represent much higher values than the IPCC default. Seasonal EFs from Vietnam exceeded IPCC defaults given for Southeast Asia corresponding to 160% (E), 240% (M) and 290% (L) of the medium value, respectively.

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