Comparison of Top-Down and Bottom-Up Road Transport Emissions through High-Resolution Air Quality Modeling in a City of Complex Orography

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

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Improvement from the satellite-derived NOx emissions on air quality modeling and its effect on ozone and secondary inorganic aerosol formation in Yangtze River Delta, China

10.5194/acp-2020-751 ◽

2020 ◽

Author(s):

Yang Yang ◽

Yu Zhao ◽

Lei Zhang ◽

Jie Zhang ◽

Xin Huang ◽

...

Keyword(s):

Air Quality ◽

Model Performance ◽

Yangtze River Delta ◽

Nox Emissions ◽

Top Down ◽

Bottom Up ◽

Inorganic Aerosols ◽

Quality Modeling ◽

Emission Estimation ◽

Reduced Rate

Abstract. We developed a top-down methodology combining the inversed chemistry transport modeling and satellite-derived tropospheric vertical column of NO2, and estimated the NOx emissions of Yangtze River Delta (YRD) region at a horizontal resolution of 9 km for January, April, July and October 2016. The effect of the top-down emission estimation on air quality modeling, and the response of ambient ozone (O3) and secondary inorganic aerosols (SO42−, NO3−, and NH4+, SNA) to the changed precursor emissions were evaluated with the Community Multi-scale Air Quality (CMAQ) system. The top-down estimates of NOx emissions were smaller than those in a national emission inventory, MEIC (i.e., the bottom-up estimates), for all the four months, and the monthly mean was calculated at 260.0 Gg/month, 24 % less than the bottom-up one. The NO2 concentrations simulated with the bottom-up estimate of NOx emissions were clearly higher than the ground observation, indicating the possible overestimation in current emission inventory attributed to its insufficient consideration of recent emission control in the region. The model performance based on top-down estimate was much better, and the biggest change was found for July with the normalized mean bias (NMB) and normalized mean error (NME) reduced from 111 % to −0.4 % and from 111 % to 33 %, respectively. The results demonstrate the improvement of NOx emission estimation with the nonlinear inversed modeling and satellite observation constraint. With the smaller NOx emissions in the top-down estimate than the bottom-up one, the elevated concentrations of ambient O3 were simulated for most YRD and they were closer to observation except for July, implying the VOC (volatile organic compound)-limit regime of O3 formation. With available ground observations of SNA in the YRD, moreover, better model performance of NO3− and NH4+ were achieved for most seasons, implying the effectiveness of precursor emission estimation on the simulation of secondary inorganic aerosols. Through the sensitivity analysis of O3 formation for April 2016, the decreased O3 concentrations were found for most YRD region when only VOCs emissions were reduced or the reduced rate of VOCs emissions was two times of that of NOx, implying the crucial role of VOCs control on O3 pollution abatement. The SNA level for January 2016 was simulated to decline 12 % when 30 % of NH3 emissions were reduced, while the change was much smaller with the same reduced rate for SO2 or NOx. The result suggests that reducing NH3 emissions was the most effective way to alleviate SNA pollution for YRD in winter.

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Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH4 Emissions in China

Sustainability ◽

10.3390/su11072054 ◽

2019 ◽

Vol 11 (7) ◽

pp. 2054 ◽

Cited By ~ 6

Author(s):

Penwadee Cheewaphongphan ◽

Satoru Chatani ◽

Nobuko Saigusa

Keyword(s):

Coal Mining ◽

Rice Cultivation ◽

Emission Inventories ◽

Ch4 Emission ◽

Top Down ◽

Activity Data ◽

Bottom Up ◽

Ch4 Emissions ◽

Model Studies ◽

Livestock Sector

Bottom-up CH4 emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH4 bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH4 emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH4 emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector.

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Older Adults' Use of Retrieval Strategies in Everyday Life

Gerontology ◽

10.1159/000446277 ◽

2016 ◽

Vol 62 (6) ◽

pp. 624-635 ◽

Cited By ~ 1

Author(s):

David J. Frank ◽

Megan L. Jordano ◽

Kelly Browne ◽

Dayna R. Touron

Keyword(s):

Older Adults ◽

Everyday Life ◽

Cognitive Abilities ◽

Specific Activity ◽

Top Down ◽

Younger Adults ◽

Bottom Up ◽

Retrieval Strategies ◽

Algorithmic Strategy ◽

Memory Confidence

Background: Despite declines in cognitive abilities, older adults often perform comparable to younger adults in everyday tasks [J Am Geriatr Soc 1999;47:172-183]. Older adults may compensate for cognitive declines by using more efficient strategies. People often improve their efficiency by switching from an algorithmic strategy where information is computed or looked-up, to a strategy where the information is retrieved directly from memory [J Exp Psychol Gen 1988;117:258-275]. However, older adults are reluctant to shift from algorithmic strategies to retrieval strategies in the laboratory, and this reluctance to use retrieval is driven by both bottom-up (slower learning) and top-down influences (memory confidence, motivation to be quick/accurate) [Psychol Aging 2004;19:452-466; Mem Cognit 2004;32:298-310]. Objective: We investigated whether bottom-up and top-down factors influence younger and older adults' decisions to use retrieval-based or algorithmic strategies in everyday life. Methods: In two studies, participants completed a daily diary for 5 (study 1) or 7 (study 2) days. Participants were asked if and how they completed daily activities within several everyday task domains. They also indicated for how long and how often they completed the specific activity (bottom-up factors), as well as how confident they were in using their memory and how motivated they were to perform the specific activity quickly and accurately (top-down influences). Results: Both studies provided evidence for bottom-up and top-down influences. Additionally, study 2 found that top-down factors (memory confidence and motivation to be quick) were more important for older compared to younger adults. Conclusion: These results indicate that strategy choices influence older adults' cognitive efficiency in everyday as well as laboratory learning.

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Estimation of Emissions from Sugarcane Field Burning in Thailand Using Bottom-Up Country-Specific Activity Data

Atmosphere ◽

10.3390/atmos5030669 ◽

2014 ◽

Vol 5 (3) ◽

pp. 669-685 ◽

Cited By ~ 20

Author(s):

Wilaiwan Sornpoon ◽

Sébastien Bonnet ◽

Poonpipope Kasemsap ◽

Praphan Prasertsak ◽

Savitri Garivait

Keyword(s):

Specific Activity ◽

Activity Data ◽

Bottom Up ◽

Sugarcane Field ◽

Country Specific ◽

Field Burning

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Towards Indirect Top-Down Road Transport Emissions Estimation

10.1109/cvprw53098.2021.00120 ◽

2021 ◽

Author(s):

Ryan Mukherjee ◽

Derek Rollend ◽

Gordon Christie ◽

Armin Hadzic ◽

Sally Matson ◽

...

Keyword(s):

Road Transport ◽

Top Down ◽

Transport Emissions ◽

Road Transport Emissions

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CRITICAL SUCCESS FACTORS OF A BOTTOM UP URBAN DESIGN PROCESS TO DELIVER SUSTAINABLE URBAN DESIGNS

International Journal of Strategic Property Management ◽

10.3846/ijspm.2018.3683 ◽

2018 ◽

Vol 22 (4) ◽

pp. 265-277 ◽

Cited By ~ 1

Author(s):

Nuwan Dias ◽

Kaushal Keraminiyage ◽

Dilanthi Amaratunga ◽

Steve Curwell

Keyword(s):

Urban Design ◽

Design Process ◽

Critical Success Factors ◽

Success Factors ◽

Ground Level ◽

Urban Environments ◽

Top Down ◽

Bottom Up ◽

Critical Success ◽

The People

A sustainable urban environment caters for peoples’ need. When the needs of the people are addressed, it increases the property values and attracts investors. The current urban design process is top-down, i.e., Designers and planners play the key role and the community has less engagement. There are serious criticisms of this process as it may not touch the “ground” level requirements, and therefore, these projects will fail to create sustainable environments. Accordingly, to overcome the drawbacks of the current top-down process, researches have discussed implementing a bottom-up process in order to deliver sustainable urban designs. Based on this argument this paper discusses what are the positive and negative implications of a bottom up urban design process and what are the critical success factors which can be derived from a bottom-up urban design process in order to deliver sustainable urban environments.

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Potential of GPS-based smartphone application data for country-wide emission estimation

10.5194/egusphere-egu2020-21374 ◽

2020 ◽

Author(s):

Karol Szymankiewicz ◽

Lech Gawuc ◽

Marek Soliwoda

Keyword(s):

Air Quality ◽

Road Traffic ◽

Smartphone Application ◽

Road Transport ◽

Activity Data ◽

Road Geometry ◽

Emission Estimation ◽

Application Data ◽

Transport Emissions ◽

Transport Emission

Road transport emissions are among the primary causes of poor air quality in cities. Typically, activity data about road transport is based on point-wise automatic traffic measurements or traffic modelling environments like VISSUM. However, such methods do not provide complete spatial patterns of emissions that are needed for air quality modelling. On the other hand, modern smartphone applications, which are used by drivers to navigate and inform about road hazards, might provide a full spatial pattern of road traffic.We will present preliminary results of road transport emission estimates based on the application of GPS-based smartphone data. The datasets describe average speed and number of users for every road part in Poland, including both major and minor roads. The data is based on the Open Street Maps road geometry and includes more than 4.5 million road segments describing 840 thousand km of roads.

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