An integrated air quality modeling system coupling regional-urban and street models in Beijing

Wang, Tao; Li, Jie; Pan, Jinxiu; Ji, Dongsheng; Kim, Youngseob; Wu, Leiming; Wang, Xuemei; Xiaole, Pan; Sun, Yele; Wang, Zifa; Yang, Weihong; Du, Huiyun

doi:10.1016/j.uclim.2022.101143

Cited by 9 publications

(7 citation statements)

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“…Code and data availability. The source codes, observation data, and model output in our work are available online via Zenodo (https://doi.org/10.5281/zenodo.7298947, Wang and Li, 2022)…”

Section: Discussionmentioning

confidence: 99%

“…An one-way integrated air-quality modeling system named IAQMS-street v1.0 have been developed in previous research (Wang et al, 2022), In IAQMS-street v1.0, the pollutants background concentrations in study period were simulated by NAQPMS, and as the input data of MUNICH, the background concentration were provided for the simulation of pollutants at street scale. MUNICH was used as a standalone model with a one-way coupling approach.…”

Section: Coupled Regional-urban-street-network Scale Modelmentioning

confidence: 99%

“…Based on traffic big data, the traffic volume was calculated by using the Underwood speed-volume calculation formula (Greenshields et al, 1961), with the proportion of different vehicles on the road being set before using the ROE. For a detailed description of ROE, we refer to Wu et al (2020); for the model configuration of traffic emissions in Beijing, we refer to Wang et al (2022).…”

Section: Traffic Emission Modelmentioning

confidence: 99%

“…MUNICH has been widely used for investigating the air quality at the street scale (Gavidia-Calderón et al, 2021;Lugon et al, 2020;Kim et al, 2018). Further studies showed that the simulations of the street-scale model are influenced by the utilized background field in each case, which may be provided by a regional model (Lv et al, 2022;Wang et al, 2022;Kim et al, 2018). Therefore, to provide a more dynamic and precise background field, it is essential to build a two-way integrated regional-urban-streetnetwork air-quality model, the feedback of street-network scale model on regional-urban background needs to be considered Many researchers have focused on the development and application of coupled regional-urban-street-network scale airquality models (Lv et al, 2022;Nuterman et al, 2021;Biggart et al, 2020;Lugon et al, 2020;Benavides et al, 2019;Kim et al, 2018;Hood et al, 2018;Isakov et al, 2007;Isakov et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…However, the contribution of traffic to the NOx emissions increased from 67.2% in 2013 to >80% in 2017. In additional, emission uncertainties, caused by the spatial mismatch between the locations of emissions and spatial proxies, can lead to additional uncertainties in air-quality simulations, especially in small-scale regions In this study, we developed a new version of dynamic urban-street scale model (IAQMS-street v2.0) using a two-way coupling between the MUNICH street-scale model and NAQPMS regional air-quality model based on previous version of IAQMS-street v1.0 (Wang et al, 2022), the hourly variation and spatial distribution of O3 and NOx concentrations were simulated in Beijing during August 2021. An upscaling module is added to transfer the pollutants from street scale model to regional model, which is not considered in IAQMS-street v1.0.…”

Section: Introductionmentioning

confidence: 99%

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IAQMS-street v2.0: a two-way coupled regional-urban–street-network model system for Beijing, China

Wang

Liu

et al. 2023

Preprint

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Abstract. Owing to the substantial traffic emissions in urban areas, especially near road areas, the concentrations of pollutants, such as ozone (O3) and its precursors, have a large gap with the regional averages and their distributions cannot be captured accurately by traditional single-scale air-quality models. In this study, a new version of a regional-urban-street-network model (IAQMS-street v2.0) is presented. An upscaling module is implemented in IAQMS-street v2.0 to calculate the impact of mass transfer to regional scale from street network. The influence of pollutants in street network is considered in the concentration calculation on regional scale, which is not considered in a previous version (IAQMS-street v1.0). In this study, the simulated results in Beijing during August 2021 by using IAQMS-street v2.0, IAQMS-street v1.0, and the regional model (NAQPMS) are compared. On-road traffic emissions in Beijing, as the key model-input data, were established using intelligent image-recognition technology and real-time traffic big data from navigation applications. The simulated results showed that the O3 and nitrogen oxides (NOx) concentrations in Beijing were reproduced by using IAQMS-street v2.0 both on regional scale and street scale. The prediction fractions within a factor of two (FAC2s) between simulations and observations of NO and NO2 increased from 0.11 and 0.34 in NAQPMS to 0.78 and 1.00 in IAQMS-street v2.0, respectively. The normalized mean biases (NMBs) of NO and NO2 decreased from 2.67 and 1.33 to -0.25 and 0.08. the concentration of NOx at street scale is higher than that at the regional scale, and the simulated distribution of pollutants on regional scale was improved in IAQMS-street v2.0 compared with that in IAQMS-street v1.0. We further used the IAQMS-street v2.0 to quantify the contribution of local on-road traffic emissions to the O3 and NOx emissions and analyze the effect of traffic-regulation policies in Beijing. Results showed that heavy-duty trucks are the major source of on-road traffic emissions of NOx. The relative contributions of local traffic emissions to NO2, NO, and O3 emissions were 53.41, 57.45, and 8.49 %, respectively. We found that traffic-regulation policies in Beijing largely decreased the concentrations of NOx and hydrocarbons (HC); however, the O3 concentration near the road increased due to the decrease consumption of O3 by NO. To decrease the O3 concentration in urban areas, controlling the local emissions of HC and NOx from other sources requires consideration.

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“…Code and data availability. The source codes, observation data, and model output in our work are available online via Zenodo (https://doi.org/10.5281/zenodo.7298947, Wang and Li, 2022)…”

Section: Discussionmentioning

confidence: 99%

Section: Coupled Regional-urban-street-network Scale Modelmentioning

confidence: 99%

Section: Traffic Emission Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IAQMS-street v2.0: a two-way coupled regional-urban–street-network model system for Beijing, China

Wang

Liu

et al. 2023

Preprint

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Recent Progress in Atmospheric Chemistry Research in China: Establishing a Theoretical Framework for the “Air Pollution Complex”

et al. 2023

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Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the “air pollution complex” was first proposed by Professor Xiaoyan TANG in 1997. For papers published in 2021 on air pollution (only papers included in the Web of Science Core Collection database were considered), more than 24 000 papers were authored or co-authored by scientists working in China. In this paper, we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years, including studies on (1) sources and emission inventories, (2) atmospheric chemical processes, (3) interactions of air pollution with meteorology, weather and climate, (4) interactions between the biosphere and atmosphere, and (5) data assimilation. The intention was not to provide a complete review of all progress made in the last few years, but rather to serve as a starting point for learning more about atmospheric chemistry research in China. The advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established, provided robust scientific support to highly successful air pollution control policies in China, and created great opportunities in education, training, and career development for many graduate students and young scientists. This paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances, whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China, to hopefully be addressed over the next few decades.

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A systematic comparison of different machine learning models for the spatial estimation of air pollution

Cerezuela-Escudero,

Montes-Sanchez,

Dominguez-Morales

et al. 2023

Appl Intell

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Air pollutants harm human health and the environment. Nowadays, deploying an air pollution monitoring network in many urban areas could provide real-time air quality assessment. However, these networks are usually sparsely distributed and the sensor calibration problems that may appear over time lead to missing and wrong measurements. There is an increasing interest in developing air quality modelling methods to minimize measurement errors, predict spatial and temporal air quality, and support more spatially-resolved health effect analysis. This research aims to evaluate the ability of three feed-forward neural network architectures for the spatial prediction of air pollutant concentrations using the measures of an air quality monitoring network. In addition to these architectures, Support Vector Machines and geostatistical methods (Inverse Distance Weighting and Ordinary Kriging) were also implemented to compare the performance of neural network models. The evaluation of the methods was performed using the historical values of seven air pollutants (Nitrogen monoxide, Nitrogen dioxide, Sulphur dioxide, Carbon monoxide, Ozone, and particulate matters with size less than or equal to 2.5 $$\upmu $$ μ m and to 10 $$\upmu $$ μ m) from an urban air quality monitoring network located at the metropolitan area of Madrid (Spain). To assess and compare the predictive ability of the models, three estimation accuracy indicators were calculated: the Root Mean Squared Error, the Mean Absolute Error, and the coefficient of determination. FFNN-based models are superior to geostatistical methods and slightly better than Support Vector Machines for fitting the spatial correlation of air pollutant measurements. Graphical abstract

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An integrated air quality modeling system coupling regional-urban and street models in Beijing

Cited by 9 publications

References 50 publications

IAQMS-street v2.0: a two-way coupled regional-urban–street-network model system for Beijing, China

IAQMS-street v2.0: a two-way coupled regional-urban–street-network model system for Beijing, China

Recent Progress in Atmospheric Chemistry Research in China: Establishing a Theoretical Framework for the “Air Pollution Complex”

A systematic comparison of different machine learning models for the spatial estimation of air pollution

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