Traffic Stations Research Articles

Paris air quality monitoring for the 2024 Olympics and Paralympics: focus on air pollutants and pollen

BackgroundExposure to air pollution can affect the health of individuals with respiratory disease, but may also impede the health and performance of athletes. This is potentially relevant for people travelling...

Local contribution of road traffic and residential biomass burning to black carbon aerosols – Modelling and validation

Black carbon (BC) is a harmful pollutant to human health and the environment. The aim of this paper is to evaluate the local contribution of road traffic and residential biomass burning to BC pollution levels at urban scale by implementing an integrated modelling approach with high spatial and temporal resolution. The traffic emission was extended to include BC emission factors considering road network as line emission sources. A new module to calculate emissions from residential biomass burning was developed based on the heating degree days methodology and local surveys. The modelling approach implemented in Coimbra, Portugal, was validated with observations collected during a six-month monitoring campaign at urban traffic and urban background stations using thermo-optical and optical methods. At the traffic station, modelling results showed that 86% of BC concentrations are related to traffic activity, which are mostly due to hot exhaust emissions (76%). Nevertheless, biomass burning has a significant contribution in the whole urban area during winter. The results of monthly average BC concentrations obtained from dispersion modelling revealed a contribution from local road traffic and residential biomass burning emissions of around 4 μg m−3 at both monitoring sites during the coldest period, but this contribution was two times higher for the hotspots identified within the urban area. Moreover, hourly values at hotspots obtained from the modelling may achieve 85 μg m−3, thus stressing the importance of spatial and temporal analysis with high resolution. The validation approach applied in this work highlight the need for more transparency in defining “black carbon” reported by modelling to ensure comparability with measurements obtained by different techniques.

A six-year measurement-based analysis of traffic-related particulate matter pollution in urban areas: the case of Warsaw, Poland (2016-2021)

This study used PM10 and PM2.5 measurements from the State Environmental Monitoring stations in Warsaw and its suburban areas. Analysis of variability characteristics at the traffic and urban background stations was carried out for 2016-2021. A six-year analysis (2016-2021) of air quality in Warsaw, Poland, focusing highlights the persistent impact of transportation on particulate matter concentrations. Comparing a city centre traffic station with urban background locations reveals consistently higher PM10 concentrations at the traffic station throughout the year, with an annual traffic-related increase of 12.6 μg/m³ (32%). PM2.5 concentrations at the traffic station are also consistently about 1.5 μg/m³ (7%) higher. For monthly averages, the highest PM10 concentrations at the traffic station were noted in March, which may be related to the resuspention of sand and salt left over from winter snow removalp rocesses. In the case of PM2.5, the typical annual cycle with maximum concentrations in winter and minimum concentrations in summer was not observed. Diurnal variability patterns show elevated PM10 concentrations at the traffic station from 8:00 a.m. to 10:00 p.m., attributed to the resuspension process. PM2.5 patterns exhibit a smaller amplitude at the traffic station, with nighttime accumulation due to inflow. This study emphasizes the lasting impact of transportation on air quality, providing insights into pollution control strategies in urban areas.

A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility

Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow. Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial–temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder–decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial–temporal dependencies and accurately predict traffic flow. We evaluate the proposed approach on multiple benchmark datasets, and the experimental results demonstrate its superior performance compared to state-of-the-art approaches in terms of various metrics.

Enhancing computational energy transportation in IoT systems with an efficient wireless tree-based routing protocol

In this era of technological advancements, our globe has witnessed the tremendous potential of wireless technology, which has revolutionized data transport. One area with a lot of promise for increasing the usage of this technology is traffic control. Real-time path planning algorithms have emerged as an important tool for reducing traffic congestion in urban areas, addressing current state-of-the-art. Real-time traffic data collection, which leverages the capabilities of Vehicle Ad-Hoc Networks (VANETs), offers opportunities to reduce travel costs and complexity. Our study offers an optimized cluster tree-based routing protocol (OCTRPL) within the framework of an advanced IoT-based computational energy transportation system to accomplish this. Vehicles serve as network nodes in this architecture, while traffic stations serve as hubs and routers. We assure the efficient implementation of a shortest-path strategy by using the OCTRPL, thereby lowering data traffic between network nodes and increasing the network's overall lifespan. The Euclidean distance facilitates the development of an ideal cluster, allowing for the selection of Cluster Heads (CHs) using a game theoretic method. Following that, criteria such as expected transmission count, queue utilization ratio, and residual energy ratio (RER) are considered in the route selection process. This set of criteria, known as ETC, assists in selecting the best path for efficient energy transit throughout the network. We illustrate the outstanding performance of our suggested approach in meeting its intended aims by a comparative study of data. The sophisticated IoT-based computational energy transportation system, in conjunction with the efficient wireless tree-based routing protocol, demonstrates promising results and opens up new options for further research and development in this sector.

Evaluating the impact of road network configuration on hotel layout based on big data, GIS and space syntax – evidence from Shanghai

ABSTRACT Understanding the impact of road network configuration, traffic stations and people flow factors on hotel layout can provide a scientific evidence for the spatial planning of hotels and other service industries. Based on multiple geographic big data, this study uses space syntax and Geographic Information System (GIS) to explore the impact of road network on the spatial layout of hotels with varying grades. Results show that low-end hotels are most affected by road network configurations; this is followed by mid-range hotels, and then by high-end hotels; high-end hotels are mostly located in the areas with high accessibility to the road network, while the mid-range and low-end hotels are mostly distributed near areas with strong traffic carrying capacity and attractiveness. Furthermore, traffic stations and people flow also have substantial impact on the spatial distribution of hotels, with the low-end hotels being most affected and the high-end hotels being least affected. Our findings provide several important implications for the spatial planning of hotels in the rapidly developing tourism industry.

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera.

Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3D information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.

Retrospective assessment of the association between urban air pollution and children’s respiratory functions in Rome: Insights for developmental environmental health

Background & aimsNaturogenic aspects of global warming are amplified by the anthropogenic effects of the persistent exponential global population growth. Urbanization without proper planning escalates serious all-time disease pathologies in vulnerable populations, such as children, through pollution of toxic gases. We illustrate these associations with a retrospective study of respiratory heath in children living in downtown Rome when this dense urban center recorded one of the highest air pollution levels in Europe. MethodsLung and respiratory function parameters were evaluated from 2006 to 2007 in a cohort of 90 children including two age subgroups (>10 years and <10 years). Additionally, historical air pollution data from 2006 to 2008 and 2020–2021, collected from urban traffic and urban background stations in Rome, was obtained to allow for a correlational analysis between air pollution and children's respiratory health. ResultsSevere impairments on the Mead Expiratory Flow Rate were revealed alongside an increase in allergenic atopics. Historical data showed no differences between measurements from urban background and traffic stations for PM10, PM2.5, NO, and NO2, and no significant time trends. However, although both declined over time, CO measurements were consistently higher from traffic than background stations. ConclusionsOur correlational study indicates that decreases in respiratory health and function may be associated with air pollution, leading to increased susceptibility to viral infections, such as COVID-19, especially in vulnerable populations. In this context, vulnerable populations’ environmental health and disease prevention, also linked with climate change, could be addressed by focusing efforts on combatting urban pollution.

PAC: Partial Area Clustering for Re-Adjusting the Layout of Traffic Stations in City’s Public Transport

Now public transportation, included bus and subway occupies a greater role in city transport, and the layout of traffic stations is the most important part of planning and design. However, some unpredicted factors for construction of traffic stations results in a low utilization rate of public transportation resources, for example, the layout of bus stops is chaotic, there is no clear layout scope, and there is a lack of integration with residents’ travel hotspots. Towards these challenges modern transport faces, we firstly analyze the distribution of bus stops and subway stations to determine the area range needs to be optimized in the traffic net from the perspective of time and space. And then, we propose an optimization method, called ’partial area clustering’ (PAC), to improve the utilization by changing and renewing the original distribution. The novel method was based on the K-means algorithm in the field of machine learning. PAC worked to search the suitable bus platforms as the center and modified the original one to the subway. Experiment has shown that the use of public transport resources has increased by 20%. The study uses a similar cluster algorithm to solve transport networks’ problems in a novel but practical term. As a result, the PAC is expected to be used extensively in the transportation system construction process.

Invulnerability of the Urban Agglomeration Integrated Passenger Transport Network under Emergency Events.

Urgent natural environmental events, such as floods, power failures, and epidemics, result in disruptions to the traffic system and heavy disturbances in public requirements. In order to strengthen the ability of the transport network to handle urgent natural environmental issues, this paper simulates the disruption situation of traffic stations in the urban agglomeration by attacking nodes, and evaluates the ability of the transport network to resist disruptions (i.e., invulnerability). Firstly, the model of the urban agglomeration integrated passenger transport network is established based on complex network theory. The highway network, railway network, and coupling network are combined into a multi-layer network space structure, and the edge weight is calibrated by travel time and cost. Secondly, the invulnerability simulation process including multiple attack modes under random and deliberate attack strategies is sorted out. By improving the traditional network efficiency indicator, the network impedance efficiency indicator is proposed to measure network performance, and the network relative impedance efficiency indicator is used to evaluate network invulnerability and identify key nodes. Finally, Chengdu-Chongqing urban agglomeration is taken as a case study. The results show that the network does not collapse quickly and it shows certain invulnerability and robustness under continuous random attacks. Network performance and invulnerability are not necessarily positively correlated. The failure of individual nodes that are small in scale but act as transit hubs may significantly degrade the network performance. The identified key nodes have significance for guiding the construction, maintenance, and optimization of the urban agglomeration passenger transport network, which is conducive to promoting public safety.

Comparison of S5P/TROPOMI Inferred NO2 Surface Concentrations with In Situ Measurements over Central Europe

The aim of this paper is to evaluate the surface concentration of nitrogen dioxide (NO2) inferred from the Sentinel-5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI) NO2 tropospheric column densities over Central Europe for two time periods, summer 2019 and winter 2019–2020. Simulations of the NO2 tropospheric vertical column densities and surface concentrations from the Long-Term Ozone Simulation–European Operational Smog (LOTOS-EUROS) chemical transport model are also applied in the methodology. More than two hundred in situ air quality monitoring stations, reporting to the European Environment Agency (EEA) air quality database, are used to carry out comparisons with the model simulations and the spaceborne inferred surface concentrations. Stations are separated into seven types (urban traffic, suburban traffic, urban background, suburban background, rural background, suburban industrial and rural industrial) in order to examine the strengths and shortcomings of the different air quality markers, namely the NO2 vertical column densities and NO2 surface concentrations. S5P/TROPOMI NO2 surface concentrations are inferred by multiplying the fraction of the satellite and model NO2 vertical column densities with the model surface concentrations. The estimated inferred TROPOMI NO2 surface concentrations are examined further with the altering of three influencing factors: the model vertical leveling scheme, the versions of the TROPOMI NO2 data and the air mass factors applied to the satellite and model NO2 vertical column densities. Overall, the inferred TROPOMI NO2 surface concentrations show a better correlation with the in situ measurements for both time periods and all station types, especially for the industrial stations (R &gt; 0.6) in winter. The calculated correlation for background stations is moderate for both periods (R~0.5 in summer and R &gt; 0.5 in winter), whereas for traffic stations it improves in the winter (from 0.20 to 0.50). After the implementation of the air mass factors from the local model, the bias is significantly reduced for most of the station types, especially in winter for the background stations, ranging from +0.49% for the urban background to +10.37% for the rural background stations. The mean relative bias in winter between the inferred S5P/TROPOMI NO2 surface concentrations and the ground-based measurements for industrial stations is about −15%, whereas for traffic urban stations it is approximately −25%. In summer, biases are generally higher for all station types, especially for the traffic stations (~−75%), ranging from −54% to −30% for the background and industrial stations.

Understanding the topology of the road network and identifying key bayonet nodes to avoid traffic congestion

Network topology is the basic for the development of traffic management and control. In a road network, bayonets with installation of surveillance facilities are key components to recognize traffic congestion from time to time. Therefore, identifying the essential bayonets in a road network becomes one of the most efficient ways to alleviate traffic congestion for traffic engineers and transport department. To do so, this paper aims to propose a novel sorting algorithm based on similarity measurements and traffic flow information to efficiently identify key bayonets in road networks. Our research results show that by analyzing the bayonet data in a fixed period of time in a medium-sized city of China, we have successfully identified the location of key bayonet points. Most of these key bayonet points are closed to residential areas and important traffic stations. The rank of these bayonet points can help the city managers better understand the topological characteristics of the road network as well as the propagation of congestion so as to make the traffic policies or control strategies for traffic congestion alleviation.

A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction

Traffic data missing issues due to unpredictable equipment failure, extreme weather, and other reasons have brought great challenges to traffic flow prediction modeling. In this paper, a novel reinforced dynamic graph convolutional network model is proposed to simultaneously conduct data imputation and network-wide traffic flow prediction. First, a multi-graph convolutional fusion network is proposed for data imputation by using the graph convolutional network to analyze the propagation law of traffic states between traffic flow detection stations in both time and space dimensions. Second, to enhance the robustness of network-wide traffic flow prediction, a dynamic graph learning method based on deep reinforcement learning is proposed to adaptively generate the graph adjacency matrix to represent the dynamic spatiotemporal dependencies between the stations. Finally, experimental results on two real-world traffic datasets show that the proposed method outperforms other baseline methods and can effectively extract the data missing features and spatiotemporal dependence features between the stations. The visualization results of the graph adjacency matrix indicate that the proposed method can effectively identify the influential traffic stations in the process of traffic flow prediction, and the extracted dependencies between the stations are interpretable. The proposed model has strong generalization in tackling network-wide traffic flow prediction tasks with different data missing rates and missing patterns, and can be extended to assist decision-makers in enhancing traffic management and mitigating traffic congestion.

An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants

Rapid industrialization and urban development are the main causes of air pollution, leading to daily air quality and health problems. To find significant pollutants and forecast their concentrations, in this study, we used a hybrid methodology, including integrated variable selection, autoregressive distributed lag, and deleted multiple collinear variables to reduce variables, and then applied six intelligent time series models to forecast the concentrations of the top three pollution sources. We collected two air quality datasets from traffic and industrial monitoring stations and weather data to analyze and compare their results. The results show that a random forest based on selected key variables has better classification metrics (accuracy, AUC, recall, precision, and F1). After deleting the collinearity of the independent variables and adding the lag periods using the autoregressive distributed lag model, the intelligent time-series support vector regression was found to have better forecasting performance (RMSE and MAE). Finally, the research results could be used as a reference by all relevant stakeholders and help respond to poor air quality.

Prevalence of Respiratory Morbidity among Traffic Police in Peshawar Khyber Pakhtunkhwa

OBJECTIVES The purpose of the study was to assess respiratory morbidity among traffic police in Peshawar, Khyber Pakhtunkhwa-Pakistan. METHODOLOGY A descriptive cross-sectional study was conducted in the Peshawar among traffic police from all their traffic stations. A sample of 302 participants was taken conveniently from the stations as a cluster sample for the purpose. Data was collected through a structured questionnaire and was analyzed through SPSS Version-17 for its presentation. RESULTS A total of 302 participants participated in the study. The mean age of the study population was 40.45 ± 6.30 years. Almost half (49%) of the study participants belonged to the age group 35-44 years. Results further reflected that majority of the respondents (90%) were head constables and 10% (31) of them were from the grade of Assistant, Sub Inspectors, (SI) and Sub Inspectors (SI). Around 49.7% of the study participants had graduation and above and the remaining 50.3% (152) had a higher secondary qualification. Furthermore; 87 % of the participants reported that they had never used a face mask and 13 % of them had used a face mask. Respiratory morbidity reflected that the 7% of participants had a frequent cough, chronic cough (6%), phlegm (15%), chronic phlegm (12%), wheeze (4%), chronic wheeze (18%), dyspnea I (9%), while 25% of them had dyspnea II and peak flow restriction with. CONCLUSION Findings of the study concluded that the Police working in traffic are very prone to get respiratory illnesses and they have a significantly higher prevalence of respiratory morbidity. Therefore periodic monitoring is encouraged to detect the problems at early stages for their treatment and better prognosis. Further Personal protective equipment needs to be used for the better prevention of the spread of respiratory disease

SDG-oriented multi-scenario sustainable land-use simulation under the background of urban expansion.

With the continuous improvement of urbanization level and the continuous expansion of city scale, there are some unreasonable land development and utilization problems, which will make the contradiction between people and land more prominent and the risk of ecological environment deterioration more serious. It hinders the sustainable land use (SLU) and then affects the realization of regional Sustainable Development Goals (SDGs). SDGs have become a programmatic document for all countries in the world to implement sustainable development. It provides a guideline and direction for the sustainable urban expansion. The sustainable urban expansion promotes the realization of SDGs worldwide. By combining the SDGs with the urban SLU, this paper obtains the optimized future land-use demand of the multi-scenario city and the urban expansion simulation scenario to the SDGs under the multi-scenario through the uncertain mathematical model (MIFCCP) and the spatial simulation model (PLUS). We find that firstly, the net profit of land use (LNB) reaches the highest value under the economic development scenario (ED), when the probability of environmental constraint violation p = 0.01 and p = 0.15, LNB = [2625.48, 3244.98] × 109 CNY, and [2646.95, 3271.51] × 109 CNY. Ecosystem service value (ESV) reached the highest value under the sustainable development scenario (SD), when p = 0.01 and p = 0.15, ESV = [75.34, 93.12] × 109 CNY, and [72.62, 95.56] × 109 CNY. The net carbon emissions from land use (LNC) reached the minimum value in SD scenario, and when p = 0.01 and p = 0.15, the LNC reached [57.46, 71.02] × 106 ton and [56.12, 76.04] × 106 ton. Secondly, the contribution degree of 15 driving factors to the change of local types is excavated, among which, the driving factors of traffic stations have the highest contribution degree to the change of construction land and cultivated land, and the third-class roads have the highest contribution degree to the change of wetland area. Furthermore, by analyzing the indicators related to SLU in SDGs, we can build an optimization model of land use quantity structure under uncertain conditions, and the optimized results can meet the targets of economic benefits, ecological benefits, and net carbon emissions of land use under different development plans. By linking SDGs and SLU, the coupled model framework can provide scientific basis for urban land expansion strategy based on ecological environment constraints and scientific support for sustainable management of land use.

Joint occurrence of heatwaves and ozone pollution and increased health risks in Beijing, China: role of synoptic weather pattern and urbanization

Abstract. Heatwaves (HWs) paired with higher ozone (O3) concentration at the surface level pose a serious threat to human health. Their combined modulation of synoptic patterns and urbanization remains unclear. Using 5 years of summertime temperature and O3 concentration observation in Beijing, this study explored potential drivers of compound HWs and O3 pollution events and their public health effects. Three favorable synoptic weather patterns were identified to dominate the compound HWs and O3 pollution events. These weather patterns contributing to enhance those conditions are characterized by sinking air motion, low boundary layer height, and high temperatures. Under the synergy of HWs and O3 pollution, the mortality risk from all non-accidental causes increased by approximately 12.31 % (95 % confidence interval: 4.66 %, 20.81 %). Urbanization caused a higher risk of HWs and O3 in urban areas than at rural stations. Particularly, due to O3 depletion caused by NO titration at traffic and urban stations, the health risks related to O3 pollution in different regions are characterized as follows: suburban stations &gt; urban stations &gt; rural stations &gt; traffic stations. In general, favorable synoptic patterns and urbanization enhanced the health risk of these compound events in Beijing by 33.09 % and 18.95 %, respectively. Our findings provide robust evidence and implications for forecasting compound HWs and O3 pollution events and their health risks in Beijing or in other urban areas all over the world that have high concentrations of O3 and high-density populations.

Meteorology-normalized variations of air quality during the COVID-19 lockdown in three Chinese megacities

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in air quality. Here, we applied a machine learning algorithm (random forest model) to eliminate meteorological effects and characterize the high-resolution variation characteristics of air quality induced by COVID-19 in Beijing, Wuhan, and Urumqi. Our RF model estimates showed that the highest decrease in deweathered PM2.5 in Wuhan (−43.6%) and Beijing (−14.0%) was at traffic stations during lockdown period (February 1- March 15, 2020), while it was at industry stations in Urumqi (−54.2%). Deweathered NO2 decreased significantly in each city (∼30%–50%), whereas accompanied by a notable increase in O3. The diurnal patterns show that the morning peaks of traffic-related NO2 and CO almost disappeared. Additionally, our results suggested that meteorological effects offset some of the reduction in pollutant concentrations. Adverse meteorological conditions played a leading role in the variation in PM2.5 concentration in Beijing, which contributed to +33.5%. The true effect of lockdown reduced the PM2.5 concentrations in Wuhan, Beijing, and Urumqi by approximately 14.6%, 17.0%, and 34.0%, respectively. In summary, lockdown is the most important driver of the decline in pollutant concentrations, but the reduction of SO2 and CO is limited and they are mainly influenced by changing trends. This study provides insights into quantifying variations in air quality due to the lockdown by considering meteorological variability, which varies greatly from city to city, and provides a reference for changes in city scale pollutant concentrations during the lockdown.

Impact of the COVID-19 Restrictive Measures on Urban Traffic-Related Air Pollution in Serbia

This study has analyzed the traffic-related change in atmospheric pollutants levels (PM2.5, PM10, CO, NO2, SO2, and O3) caused by the COVID-19 restrictive measures, based on traffic ground-based stations data in urban areas in Serbia. The possible influence of several meteorological factors (temperature, wind, pressure, and humidity), and mobility on the pollutants’ levels were also considered. The obtained results showed a positive correlation of daily NO2 concentrations with mobility and its significant reduction during restrictive measures at all selected monitoring stations. The reduction of NO2 was higher than in other countries (71.1–111.5% for measured, and 49.3–92.6% for “deweathered” data), indicating a high traffic impact on NO2 levels in Serbia. The PM, CO, and SO2 showed a weak correlation with mobility during the period with restrictive measures, which, besides traffic, indicates the significant influence of other sources of their concentration. The O3 concentrations were increased at all measuring stations and are negatively correlated to mobility. Comparison of pollutant concentrations during restriction with the equivalent period in preceding years showed reductions in NO2 and SO2 concentrations. However, compared to previous years, the concentrations of PM2.5, PM10, and CO increased in the period with restrictive measures, indicating lower sensitivity to population mobility and higher dependence on other emission sources. The findings suggest the justification for the use of traffic reduction strategies to improve air quality.

Diverse spillover effects of COVID-19 control measures on air quality improvement: evidence from typical Chinese cities.

The COVID-19 prevention and control measures are taken by China’s government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level.