IAQMS-street v2.0: a two-way coupled regional-urban–street-network model system for Beijing, China
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.
Tao Wang et al.
Status: open (until 25 Jun 2023)
- RC1: 'Comment on gmd-2023-5', Anonymous Referee #1, 23 Apr 2023 reply
- RC2: 'Comment on gmd-2023-5', Anonymous Referee #2, 29 May 2023 reply
Tao Wang et al.
Tao Wang et al.
Viewed (geographical distribution)
This study presents a new version of the regional urban road network air quality modelling system IAQMS-street v2.0 to simulate urban background and road network pollution. The manuscript is generally well organised, at this stage the reviewer has a positive attitude towards its publication. However, there are still some points regarding the numerical conditions that should be further explained and modified. The detailed comments of the reviewer are as follows
Line 163 “The fixed time step for interfacing between NAQPMS and MUNICH was 20 min, i.e., the same as that of the regional model.” Is the time step 20 min also for MUNICH? As this study focuses on pollutant diffusion and chemical reaction at the street scale (100 m). The reviewer thinks that 20 min is too long. For example, the previous study on MUNICH with chemical reactions used a time step of 100 s. https://doi.org/10.5194/gmd-15-7371-2022
Please justify that the simulation results based on 20 min can achieve similar simulation accuracy with a smaller time step (e.g. 5 min). In addition, a sensitivity test should also be carried out on the time step for the interface between NAQPMS and MUNICH (e.g. 5 min).
Figure 1. How high is the bottom layer in this study? Please provide a reasonable explanation that the simulation results are not dependent on the height of the bottom layer.
Figure 7. Why is the number of NO observations less than NO2 and O3? Also, it seems that only the daily averaged data are shown. The hourly averaged data should be shown as the prediction accuracy of the peak concentration is important.
IAQMS-street v2.0 could use more CPU time than IAQMS-street v1.0. It is helpful for the potential user of IAQMS-street v2.0 to know the detailed CPU time in this study for different scenarios.