Probabilistic total PM2.5 emissions from vehicular sources in Australian perspective

Iqbal, Asif; Afroze, Shirina; Rahman, M.F.

doi:10.1007/s10661-021-09352-z

Cited by 6 publications

(2 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The on-road vehicle emissions were calculated by EF and activity data at the city level. In the present study, the vehicle emission inventories of CO, NOx, SO 2 , NH 3 , VOCs, PM 2.5 , PM 10 , BC, and OC were calculated using the following Equation (1) [ 7 , 27 , 28 , 29 , 30 ], which was fit for evaporative emissions and operational emissions:

where E is the annual emissions (t/a) of CO, NOx, SO 2 , NH 3 , VOCs, PM 2.5 , PM 10 , BC, and OC; P is vehicle population; EF is the emission factor, in grams of pollutant per unit distance for each single vehicle in g/km; VKT is the annual vehicle kilometres travelled, obtained from the GEI [ 26 ]; and j represents the national vehicular emission limit standard (Pre-China 1, China 1, China 2, China 2, China 3, China 4, and China 5 emission standards). i is the vehicle type, including 12 diesel vehicles (heavy-duty DTs, medium-duty DTs, light-duty DTs, mini diesel trucks (MDTs), DTCs, low-speed DTs, heavy-duty DVs, medium-duty DVs, medium-duty passenger vehicles (MDPV), small DVs, diesel taxis, and diesel buses), 12 gasoline vehicles (heavy-duty GTs, medium-duty GTs, light-duty GTs, mini GTs, heavy-duty GVs, medium-duty GVs, small GVs, mini GVs, gasoline taxis, gasoline buses, normal gasoline motorcycles, and light gasoline motorcycles), and nine OF type vehicles, (OF buses, OF taxis, OF HDTs, OF medium-duty trucks, OF light-duty trucks, OF heavy-duty vehicles, OF medium-duty vehicles, OF small passenger vehicles, and OF mini passenger vehicles).…”

Section: Methodsmentioning

confidence: 99%

A Comprehensive 2018-Based Vehicle Emission Inventory and Its Spatial–Temporal Characteristics in the Central Liaoning Urban Agglomeration, China

Liu

Zhao

Wang

et al. 2022

IJERPH

View full text Add to dashboard Cite

Rapid economic expansion and urbanisation have seriously affected the atmospheric environmental quality of the Central Liaoning Urban Agglomeration (CLUA). This study aimed to establish a detailed vehicle emission inventory of the CLUA with a 3 km × 3 km gridded spatiotemporal distribution. A top-down methodology using vehicle kilometres travelled annually, emission factors, and activity data of each city was established. Carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), ammonia (NH3), volatile organic compounds (VOCs), particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), particulate matter with an aerodynamic diameter less than 10 μm (PM10), Black Carbon (BC), and organic carbon (OC) emissions were 291.0, 221.8, 3.6, 2.2, 42.8, 9.3, 10.3, 5.2, and 1.6 Gg in 2018, respectively. The contribution of diesel heavy-duty trucks to NOx, SO2, PM2.5, PM10, BC, and OC emissions was greater than 54.5%, the largest contribution of all vehicles. Gasoline small passenger vehicles were the primary contributor to CO, VOC, and NH3 emissions, contributing 37.3%, 39.5%, and 75.3% of total emissions, respectively. For emission standards, Pre-China 1 vehicles were the largest contributor to CO and VOC emissions and China 3 vehicles contributed the largest amount of NOx, SO2, PM2.5, PM10, BC, and OC emissions. The spatial distribution of pollutants showed “obvious lines” and grids with high emissions were concentrated in expressways, national highways, and provincial highways. The temporal variation showed morning–evening peaks during diurnal variations, which was consistent with resident behaviour. This work can help us understand vehicular emission characteristics of the CLUA and provide basic data for air quality modelling. Future research should investigate traffic flow by vehicle types and emission factors at a local level, which will be helpful for transport management planning.

show abstract

Section: Methodsmentioning

confidence: 99%

A Comprehensive 2018-Based Vehicle Emission Inventory and Its Spatial–Temporal Characteristics in the Central Liaoning Urban Agglomeration, China

Liu

Zhao

Wang

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Air pollution on the NCP has been a research hotspot due to frequent haze pollution, characterized by the accumulation of PM 2.5 . The formation of PM 2.5 results from direct emission from primary sources and efficient secondary transformation of aerosols from precursor gases [44][45][46][47][48][49]. The main gaseous pollutants include NO 2 , SO 2 , CO, and O 3 [50].…”

Section: Introductionmentioning

confidence: 99%

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Liu

Yang

Duan

et al. 2022

IJERPH

View full text Add to dashboard Cite

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017–2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017–2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

show abstract

Air pollution trends and exceedances: ozone and particulate matter outlook in Brazilian highly urbanized zones

Boari,

Pedruzzi,

Vieira-Filho

2023

Environ Monit Assess

View full text Add to dashboard Cite

Probabilistic total PM2.5 emissions from vehicular sources in Australian perspective

Cited by 6 publications

References 57 publications

A Comprehensive 2018-Based Vehicle Emission Inventory and Its Spatial–Temporal Characteristics in the Central Liaoning Urban Agglomeration, China

A Comprehensive 2018-Based Vehicle Emission Inventory and Its Spatial–Temporal Characteristics in the Central Liaoning Urban Agglomeration, China

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Air pollution trends and exceedances: ozone and particulate matter outlook in Brazilian highly urbanized zones

Contact Info

Product

Resources

About