scholarly journals Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

2020 ◽  
Vol 13 (1) ◽  
pp. 23-40
Author(s):  
Luolin Wu ◽  
Ming Chang ◽  
Xuemei Wang ◽  
Jian Hang ◽  
Jinpu Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Air Quality ◽  
Real Time ◽  
Urban Areas ◽  
Vehicle Emissions ◽  
Heavy Duty ◽  
Spatiotemporal Resolution ◽  
The Real ◽  
The Impact ◽  
Road Emission

Abstract. Rapid urbanization in China has led to heavy traffic flows in street networks within cities, especially in eastern China, the economically developed region. This has increased the risk of exposure to vehicle-related pollutants. To evaluate the impact of vehicle emissions and provide an on-road emission inventory with higher spatiotemporal resolution for street-network air quality models, in this study, we developed the Real-time On-road Emission (ROE v1.0) model to calculate street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode Map navigation application. This Python-based model obtains street-scale traffic data from the map application programming interface (API), which are open-access and updated every minute for each road segment. The results of application of the model to Guangzhou, one of the three major cities in China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HCs), PM2.5, and PM10 to be 35.22×104, 12.05×104, 4.10×104, 0.49×104, and 0.55×104 Mg yr−1, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive areas and suburban town centers. Emission contribution shows that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) in urban areas and LDVs and heavy-duty trucks (HDTs) in suburban areas, indicating that the traffic control policies regarding trucks in urban areas are effective. In this study, the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) was applied to investigate the impact of traffic volume change on street-scale photochemistry in the urban areas by using the on-road emission results from the ROE model. The modeling results indicate that the daytime NOx concentrations on national holidays are 26.5 % and 9.1 % lower than those on normal weekdays and normal weekends, respectively. Conversely, the national holiday O3 concentrations exceed normal weekday and normal weekend amounts by 13.9 % and 10.6 %, respectively, owing to changes in the ratio of emission of volatile organic compounds (VOCs) and NOx. Thus, not only the on-road emissions but also other emissions should be controlled in order to improve the air quality in Guangzhou. More significantly, the newly developed ROE model may provide promising and effective methodologies for analyzing real-time street-level traffic emissions and high-resolution air quality assessment for more typical cities or urban districts.

scholarly journals Development of a real-time on-road emission (ROE v1.0)model for street-scale air quality modeling based on dynamic traffic big data

2019 ◽  
Author(s):  
Luolin Wu ◽  
Ming Chang ◽  
Xuemei Wang ◽  
Jian Hang ◽  
Jinpu Zhang
Keyword(s):  
Big Data ◽  
Air Quality ◽  
Real Time ◽  
Urban Areas ◽  
Vehicle Emissions ◽  
Eastern China ◽  
Heavy Duty ◽  
Rapid Urbanization ◽  
The Impact ◽  
Road Emission

Abstract. Rapid urbanization in China has led to heavy traffic flows in street networks within cities, especially in eastern China, the economically developed region. This has increased the risk of exposure to vehicle-related pollutants. To evaluate the impact of vehicle emissions and provide an on-road emission inventory with higher spatial–temporal resolution for street-network air quality models, in this study, we developed the Real-time On-road Emission (ROE v1.0) model to calculate street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode map navigation application. This Python-based model obtains street-scale traffic data from the map application programming interface (API), which are open-access and updated every minute for each road segment. The results of application of the model to Guangzhou, one of the three major cities in China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HC), PM10, and PM2.5 to be 35.22 × 104 Mg/a, 12.05 × 104 Mg/a, 4.10 × 104 Mg/a, 0.49 × 104 Mg/a, and 0.55 × 104 Mg/a, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive area and suburban town centers. Emission contributions show that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles in urban areas and LDVs and heavy-duty trucks in suburban areas, indicating that the traffic control policies regarding duty trucks in urban areas are effective. In this study, the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) was applied to investigate the impact of traffic volume change on street-scale photochemistry in the urban area by using the on-road emission results from the ROE model. The modeling results indicate that the daytime NOx concentrations on national holidays are 26.5 % and 9.1 % lower than those on normal weekdays and normal weekends, respectively. Conversely, the national holiday O3 concentrations exceed normal weekday and normal weekend amounts by 13.9 % and 10.6 %, respectively, owing to changes in the ratio of emission of VOCs and NOx. Thus, not only the on-road emission, but other emissions should be controlled in order to improve the air quality in Guangzhou. More significantly, the newly developed ROE model may provide promising and effective methodologies for analyzing real-time street-level traffic emissions and high-resolution air quality assessment for more typical cities or urban districts.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

scholarly journals Modelling the impact of megacities on local, regional and global tropospheric ozone and the deposition of nitrogen species

2013 ◽  
Vol 13 (24) ◽  
pp. 12215-12231 ◽  
Author(s):  
Z. S. Stock ◽  
M. R. Russo ◽  
T. M. Butler ◽  
A. T. Archibald ◽  
M. G. Lawrence ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Climate Model ◽  
Surface Ozone ◽  
Global Scale ◽  
Chemical Environment ◽  
Ozone Production ◽  
The Uk ◽  
The Impact ◽  
Local Emissions

Abstract. We examine the effects of ozone precursor emissions from megacities on present-day air quality using the global chemistry–climate model UM-UKCA (UK Met Office Unified Model coupled to the UK Chemistry and Aerosols model). The sensitivity of megacity and regional ozone to local emissions, both from within the megacity and from surrounding regions, is important for determining air quality across many scales, which in turn is key for reducing human exposure to high levels of pollutants. We use two methods, perturbation and tagging, to quantify the impact of megacity emissions on global ozone. We also completely redistribute the anthropogenic emissions from megacities, to compare changes in local air quality going from centralised, densely populated megacities to decentralised, lower density urban areas. Focus is placed not only on how changes to megacity emissions affect regional and global NOx and O3, but also on changes to NOy deposition and to local chemical environments which are perturbed by the emission changes. The perturbation and tagging methods show broadly similar megacity impacts on total ozone, with the perturbation method underestimating the contribution partially because it perturbs the background chemical environment. The total redistribution of megacity emissions locally shifts the chemical environment towards more NOx-limited conditions in the megacities, which is more conducive to ozone production, and monthly mean surface ozone is found to increase up to 30% in megacities, depending on latitude and season. However, the displacement of emissions has little effect on the global annual ozone burden (0.12% change). Globally, megacity emissions are shown to contribute ~3% of total NOy deposition. The changes in O3, NOx and NOy deposition described here are useful for quantifying megacity impacts and for understanding the sensitivity of megacity regions to local emissions. The small global effects of the 100% redistribution carried out in this study suggest that the distribution of emissions on the local scale is unlikely to have large implications for chemistry–climate processes on the global scale.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

scholarly journals Numerical simulation of urban thermal plume merging

2021 ◽  
Author(s):  
Shuojun Mei ◽  
Chao Yuan ◽  
Wenhui He ◽  
Tanya Talwar
Keyword(s):  
Air Quality ◽  
Thermal Comfort ◽  
Urban Areas ◽  
Turbulence Models ◽  
Wave Radiation ◽  
Thermal Plume ◽  
Water Tank ◽  
Surface Cooling ◽  
Thermal Plumes ◽  
The Impact

<p>Densely packed urban buildings trap outgoing long-wave radiation, leading to reduced surface cooling and increased building surface temperature. In calm conditions, poor natural ventilation causes both thermal comfort and air quality issue. The buoyancy flow generated by heated urban surfaces is the main driving of the urban flow and pollutant dispersion. A 3D numerical modelling is conducted to investigate the thermal plumes merging and buoyancy-driven airflow in urban areas. The performances of four different turbulence models, i.e., two URANS (Unsteady Reynolds-averaged Navier–Stokes equations) models and two LES (Large-Eddy Simulation) models are evaluated by comparing the velocity field with previous water tank measurements. Validation results show that all four turbulence models can capture the bending of thermal plumes toward the centre, and LES models provide a better prediction on the vertical velocity profiles, while both URANS models show underestimation. The plume merging mechanism is analysed with the high accuracy LES results. Both pressure difference and swaying motion caused by mean flow and turbulence are important for plume merging. The turbulence coherent structure of plume merging is analysed by a quadrant analysis, which shows ejection and sweep events could significantly change with the building density. A case study with complex urban geometry is conducted to show the impact of thermal plumes merging in the real high-density urban areas. The convergence airflow at the pedestrian level is estimated to 2 m/s under a surface-air temperature difference of 5 °C, which is comparable to wind-driven ventilation and beneficial to thermal comfort and air quality.</p>

scholarly journals Emission of fine particles (PM2.5) from residential biomass combustion in Croatia and how to reduce it

2020 ◽  
Vol 7 (2) ◽  
pp. 84-94
Author(s):  
Mirela Poljanac
Keyword(s):  
Air Quality ◽  
Human Health ◽  
Urban Areas ◽  
Fine Particles ◽  
Biomass Combustion ◽  
Rural And Urban Areas ◽  
Wood Burning ◽  
Rural And Urban ◽  
The Impact ◽  
Pm2.5 Emissions

Wood burning in residential appliances is very represented in the Republic of Croatia. It is a main or an additional form of heating for many households in rural and urban areas and is therefore an important source of air pollution. The choice of energy and the combustion appliance used in home have a significant impact on PM2.5 emissions. The paper informs the reader about PM2.5 emissions, their main sources and impacts on human health, environment, climate, air quality, and the reason why PM2.5 emissions from residential wood burning are harmful. Paper also gives an overview of spatial PM2.5 emission distribution in Croatia, their five air quality zones and four agglomerations. The paper analyses the sources and their contribution to PM2.5 emissions with the relevance of PM2.5 emissions from residential plants, the use of fuels in residential plants and their contribution to PM2.5 emissions and PM2.5 emissions by fuel combustion technologies in residential sector. Appropriate strategies, policies, and actions to reduce the impact of residential biomass (wood) burning on the environment, air quality and human health are considered.

scholarly journals Applying a Model for Trip Assignment and Dynamic Routing of Automated Taxis with Congestion: System Performance in the City of Delft, The Netherlands

2018 ◽  
Vol 2672 (8) ◽  
pp. 588-598 ◽  
Author(s):  
Xiao Liang ◽  
Gonçalo Homem de Almeida Correia ◽  
Bart van Arem
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Traffic Congestion ◽  
Programming Model ◽  
Profit Maximization ◽  
The Real ◽  
Real Size ◽  
The Impact ◽  
The City

This paper proposes a method of assigning trips to automated taxis (ATs) and designing the routes of those vehicles in an urban road network, and also considering the traffic congestion caused by this dynamic responsive service. The system is envisioned to provide a seamless door-to-door service within a city area for all passenger origins and destinations. An integer programming model is proposed to define the routing of the vehicles according to a profit maximization function, depending on the dynamic travel times, which varies with the ATs’ flow. This will be especially important when the number of automated vehicles (AVs) circulating on the roads is high enough that their routing will cause delays. This system should be able to serve not only the reserved travel requests, but also some real-time requests. A rolling horizon scheme is used to divide one day into several periods in which both the real-time and the booked demand will be considered together. The model was applied to the real size case study city of Delft, the Netherlands. The results allow assessing of the impact of the ATs movements on traffic congestion and the profitability of the system. From this case-study, it is possible to conclude that taking into account the effect of the vehicle flows on travel time leads to changes in the system profit, the satisfied percentage and the driving distance of the vehicles, which highlights the importance of this type of model in the assessment of the operational effects of ATs in the future.

scholarly journals Impacts of Current and Projected Oil Palm Plantation Expansion on Air Quality Over Southeast Asia

2016 ◽  
Author(s):  
Sam J. Silva ◽  
Colette L. Heald ◽  
Jeffrey A. Geddes ◽  
Kemen G. Austin ◽  
Prasad S. Kasibhatla ◽  
...  
Keyword(s):  
Air Quality ◽  
Southeast Asia ◽  
Oil Palm ◽  
Urban Areas ◽  
Transport Model ◽  
Chemical Transport Model ◽  
Oil Palm Plantation ◽  
Deposition Velocities ◽  
Current Constellation ◽  
The Impact

Abstract. Over recent decades oil palm plantations have rapidly expanded across Southeast Asia (SEA). According to the United Nations, oil palm production in SEA increased by a factor of 3 from 1995 to 2010. We investigate the impacts of current (2010) and future (2020) oil palm expansion in SEA on surface-atmosphere exchange and the resulting air quality in the region. For this purpose, we use satellite data, high-resolution land maps, and the chemical transport model GEOS-Chem. Relative to a no oil palm plantation scenario (~ 1990), overall simulated isoprene emissions in the region increase by 13 % due to oil palm plantations in 2010 and a further 11 % by 2020. In addition, the expansion of palm plantations leads to local increases in ozone deposition velocities of up to 20 %. The net result of these changes is that oil palm expansion in SEA increases surface O3 by up to 3.5 ppbv over dense urban regions, and could rise more than 4.5 ppbv above baseline levels by 2020. Biogenic secondary organic aerosol loadings also increase by up to 1 μg m−3 due to oil palm expansion, and could increase a further 2.5 μg m−3 by 2020. Our analysis indicates that while the impact of recent oil palm expansion on air quality in the region has been significant, the retrieval error and sensitivity of the current constellation of satellite measurements limit our ability to observe these impacts from space. Oil palm expansion is likely to continue to degrade air quality in the region in the coming decade and hinder efforts to achieve air quality regulations in major urban areas such as Kuala Lumpur and Singapore.

scholarly journals Urban Trees and Their Impact on Local Ozone Concentration—A Microclimate Modeling Study

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 154 ◽  
Author(s):  
Helge Simon ◽  
Joachim Fallmann ◽  
Tim Kropp ◽  
Holger Tost ◽  
Michael Bruse
Keyword(s):  
Air Quality ◽  
Urban Planning ◽  
Ozone Concentration ◽  
Green Infrastructure ◽  
Urban Areas ◽  
Urban Trees ◽  
Box Model ◽  
Dominant Factor ◽  
Isoprene Emission ◽  
The Impact

Climate sensitive urban planning involves the implementation of green infrastructure as one measure to mitigate excessive heat in urban areas. Depending on thermal conditions, certain trees tend to emit more biogenic volatile organic compounds, which act as precursors for ozone formation, thus hampering air quality. Combining a theoretical approach from a box model analysis and microscale modeling from the microclimate model ENVI-met, we analyze this relationship for a selected region in Germany and provide the link to air quality prediction and climate sensitive urban planning. A box model study was conducted, indicating higher ozone levels with higher isoprene concentration, especially in NO-saturated atmospheres. ENVI-met sensitivity studies showed that different urban layouts strongly determine local isoprene emissions of vegetation, with leaf temperature, rather than photosynthetic active radiation, being the dominant factor. The impact of isoprene emission on the ozone in complex urban environments was simulated for an urban area for a hot summer day with and without isoprene. A large isoprene-induced relative ozone increase was found over the whole model area. On selected hot spots we find a clear relationship between urban layout, proximity to NOx emitters, tree-species-dependent isoprene emission capacity, and increases in ozone concentration, rising up to 500% locally.

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