Urban Air Pollution Control Research Articles

Spatiotemporal Variation Characteristics of Ozone and Identification of Key Influencing Factors Based on Random Forest Model： A Case Study of Chuzhou City

The cause of ozone pollution is a complex scientific problem. Studying the spatiotemporal variation characteristics of O3 at different time scales and analyzing the key influencing factors of O3 concentration is of great significance for the precise formulation of urban air pollution control measures and the improvement of urban air quality. Based on the analysis of the spatiotemporal variation characteristics of O3 concentration in Chuzhou City, we studied the 12 ozone-influencing factors of meteorology and pollutants at multiple time scales using Spearman correlation analysis and a random forest model. The results showed that： ① The O3 pollution level of Chuzhou City showed an aggravating trend, and the O3 concentration distribution showed a spatial pattern of "high in the southeast and low in the northwest." ② From February to May, SO2 concentration had a strong impact on the increase in O3 concentration. From June to September, PM2.5 and PM10 were significantly positively correlated with ozone and had a greater impact. ③ Relative humidity, temperature, and wind speed had a significant impact on O3, whereas barometric pressure and hourly rainfall had a weak impact. ④ The O3 pollution mechanism in Chuzhou City changed from "pollutant-controlled" to "meteorology-controlled." ⑤ Among meteorological and pollutant factors, the three influencing factors that had the greatest influence on O3 concentration were temperature, wind speed, and relative humidity, with PM10 concentration, PM2.5 concentration, and SO2 concentration also contributing. All of the above six influencing factors had a significant nonlinear relationship with the O3 concentration.

Effect of urban street canyon aspect ratio on fire-induced air pollutants dispersion under cross wind

The incidence of vehicle fires in urban street canyons has continuously increased with the process of urban modernization. The dispersion and accumulation of fire-induced air pollutants seriously affect residents’ safety. The effects of the street canyon aspect ratio and fire source location on fire-induced air pollutants dispersion under cross wind were numerically investigated in the paper. The results show that the street canyon aspect ratio can affect flow patterns and vortex structure and then lead to different accumulation states of air pollutants. Smoke development can be divided into four different regimes according to cross wind velocity. The re-entrainment phenomenon occurs when the fire source is close to the windward building or in the center of the street, but it does not occur when the fire source is close to the leeward building. Smoke tends to accumulate in street canyons when the aspect ratio is smaller and the fire source is closer to the leeward building. The variation of critical velocity with the aspect ratio can be divided into three regions, i.e., initial linear growth region (0.5 ≤ W/H < 1), constant region (1 ≤ W/H ≤ 1.5), and second linear growth region (1.5 < W/H ≤ 2). The dimensionless critical velocity and critical Froude number are used to quantify the re-entrainment phenomenon in different fire scenarios, and prediction models are finally proposed. The main findings can provide a more detailed reference for urban layout and air pollution control to reduce fire-induced air pollutants.

Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM1 pollution and the landscape pattern, urban sustainable planning and air pollution control.

A Study on the Leaf Retention Capacity and Mechanism of Nine Greening Tree Species in Central Tropical Asia Regarding Various Atmospheric Particulate Matter Values

With the rapid advancement of the global economy, there has been a noticeable escalation in the level of inhalable particulate matter (PM) pollution in the atmosphere. The utilization of plants has been recognized as an effective means to mitigate the escalation in the atmospheric PM concentration through the capture and retention of this particulate matter on their leaves. This research focuses on investigating the PM retention capacity of nine commonly found greening plant species in Changsha, China, located in the country’s mid-subtropical region. In this study, we employed an air aerosol generator (QRJZFSQ-II) and a portable leaf area meter (LI-3000C) to systematically evaluate the PM retention in unit leaf area for different PM values. In addition, the leaf surface structure was observed via scanning electron microscopy, and the relationship between the leaf microstructure and the retained particles was quantitatively analyzed. The results showed that (1) there were significant differences in the retention of TSP, PM10, and PM2.5 per unit leaf area among the nine greening tree species analyzed. Rosa saturata was found to have the best retention effect regarding TSP and PM2.5, and Rhododendron simsii was found to have the best retention effect regarding PM10. (2) There were significant differences in the contents of TSP and PM2.5 per leaf area among the different tree species with different life forms (p &lt; 0.05), with the order of retention being shrub &gt; arbor (needle leaves) &gt; arbor (broad leaves). (3) Coniferous plants have a deep leaf surface texture, which is conducive to capturing more particles on their leaf surface, and (4) the long stomata diameter was significantly negatively correlated with PM retention, and the stomata density was significantly positively correlated with PM retention. However, the short diameter and small area of stomata demonstrated no significant correlation with PM retention (p &lt; 0.05). Considering the selection of suitable tree species for greening in urban air pollution control, we suggest that Osmanthus fragrans, Pseudolarix amabilis, Rosa saturata, and Rhododendron simsii be used more frequently in urban areas affected by severe air pollution.

Greenery planning for urban air pollution control based on biomonitoring potential: Explicit emphasis on foliar accumulation of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs)

In this study, efficiencies of eight indigenous plants of Baishnabghata Patuli Township (BPT), southeast Kolkata, India, were explored as green barrier species and potentials of plant leaves were exploited for biomonitoring of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs). The present work focused on studying PM capturing abilities (539.32−2766.27 μg cm−2) of plants (T. divaricata, N. oleander and B. acuminata being the most efficient species in retaining PM) along with the estimation of foliar contents of PM adhered to leaf surfaces (total sPM (large + coarse): 526.59−2731.76 μg cm−2) and embedded within waxes (total wPM (large + coarse): 8.73−34.51 μg cm−2). SEM imaging used to analyse leaf surfaces affirmed the presence of innate corrugated microstructures as main drivers for particle capture. Accumulation capacities of PAHs of vehicular origin (total index, TI > 4) were compared among the species based on measured concentrations (159.92−393.01 μg g−1) which indicated T. divaricata, P. alba and N. cadamba as highest PAHs accumulators. Specific leaf area (SLA) of plants (71.01−376.79 cm2 g−1), a measure of canopy−atmosphere interface, had great relevance in PAHs diffusion. Relative contribution (>90%) of 4–6 ring PAHs to total carcinogenic equivalent and potential as well as 5−6 ring PAHs to total mutagenic equivalent and potential had also been viewed with respect to benzo[a]pyrene. In−depth analysis of foliar traits and adoption of plant−based ranking strategies (air pollution tolerance index (APTI) and anticipated performance index (API)) provided a rationale for green belting. Each of the naturally selected plant species showed evidences of adaptations during abiotic stress to maximize survival and filtering effects for reductive elimination of ambient PM and PAHs, allowing holistic management of green spaces.

Urban 2D and 3D morphology and the pattern of ozone pollution: a 68-city study in China

ContextAir pollution significantly impacts urban sustainable development and public health. Urban ozone pollution (UOP) is currently one of the most challenging tasks for urban air pollution control, and is possibly linked to urban morphology. However, the effect of urban two-dimensional (2D) (coverage or density, etc.) and three-dimensional (3D) (density + height, etc.) morphology on the UOP concentration remains unclear.ObjectivesThe objective of this study was to explore the influence of urban morphology on UOP concentration and provide useful information to control urban air pollutants.MethodsFirst, based on building height and remotely sensed UOP data from 68 Chinese cities, the general spatial pattern of urban 3D morphology and UOP was detected across different climate zones in China. Then, this study used variance decomposition to investigate the contribution of 2D and 3D urban morphology to UOP in China.ResultsThe study showed that China's urban morphology was dominated by Medium Rise & Medium Density (MRMD). Large cities had higher UOP levels in summer, especially for the urban morphology with Low Rise & High Density (LRHD). Further, UOP concentrations were substantially higher in the southern temperate zone than in other climatic zones. Anthropogenic factors (rather than natural factors) were always the dominant factors influencing UOP across different seasons; specifically, urban 2D and 3D morphology can explain 40% of UOP variation. The effects of urban 3D and 2D morphologies on UOP concentrations varied seasonally. Urban 2D morphology dominated in spring, whereas 3D morphology dominated in winter.ConclusionsOur study elucidates the effect of urban morphology on UOP and provides insights for sustainable urban development.

IoT-Enabled Smart Anti-Smog Towers: A Novel Approach to Urban Air Pollution Control

IoT-Enabled Smart Anti-Smog Towers: A Novel Approach to Urban Air Pollution Control

Spatiotemporal Distribution Characteristics of Air Pollution and Health Risks in Key Cities of China

Based on the monitoring data of five pollutants in 168 key cities under air pollution prevention and control in China from 2015 to 2020, using the MAKESENS model and the aggregate risk index(ARI), this study quantitatively analyzed the spatial and temporal distribution characteristics of air pollution and health risks in China and the six urban agglomerations. The results showed that:① PM2.5 pollution was the most serious pollution in Chinese key cities. Only 15% of the cities' six-year average concentrations of PM2.5 reached the National Secondary Standard, followed by that of NO2; 77% of the cities' six-year average concentrations of NO2 reached the National Secondary Standard. The urban agglomerations of Beijing-Tianjin-Hebei and Fenwei plain had the most serious air pollution, and the six-year average concentrations of PM2.5, SO2, CO, and NO2 were higher than those of other urban agglomerations. ② The concentrations of PM2.5, SO2, CO, and NO2 in key cities of China showed a decreasing trend, whereas the concentration of O3 in other urban agglomerations showed an increasing trend, except in the Chengdu-Chongqing urban agglomeration. The concentration of SO2 in the urban agglomerations of Beijing-Tianjin-Hebei and Fenwei plain changed the most significantly. ③ The health risk of air pollution in the key cities of China generally showed a decreasing trend, with a sharp decline from 2017 to 2018, and the population exposed to extremely high risks dropped from 160 million to 32.54 million. The urban agglomeration in the middle reaches of the Yangtze River had the most significant decline in health risks, whereas the key cities in China faced higher health risks in spring and winter seasons. ④ The Beijing-Tianjin-Hebei and Fenwei plain urban agglomerations had the highest health risks, and the urban agglomeration in the middle reaches of the Yangtze River had the lowest; O3 gradually replaced PM2.5 as the main pollutant affecting the health risk. These results can provide a reference for evaluating the effectiveness of urban air pollution control in China during the 13th Five-Year Plan period.

Studying the Regional Transmission of Air Pollution Based on Spatiotemporal Multivariable Data

Imported air pollution has a significant impact on urban air quality. Relevant studies have shown that many urban air pollution events are not resourced by local emissions but are imported by air pollution from surrounding areas transported across regions. The prevention and control of air pollution is very necessary. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which are extremely limited in time and space, and cannot satisfy continuous time-space air pollution research. Therefore, aiming at the problem of urban air pollution control, this paper used MERRA-2 reanalysis data and ground monitoring data to establish a “Time-Longitude-Latitude” three-dimensional pollution curve, and then a genetic algorithm was used to optimize its fitting. This study finally reconstructed the imported air pollution transmission route. This paper takes an air pollution event that occurred in Xuzhou City, China, on 12 January 2020, as an example. Through the analysis of aerosol optical depth (AOD), particulate matter (PM), wind speed, and other factors, we found the source, transmission route, and impact time of this pollution. We have verified the correctness and accuracy of the reconstructed contamination transport paths. It is proved that the method is universal and it can quickly and accurately restore the air pollution transmission route and identify the urban imported air pollution transmission entrance. This method will also provide strong data support for the division of responsibilities of environmental protection departments in various regions for severe air pollution transmission events and provide effective governance ideas for the prevention and control of imported air pollution in recipient cities.

Hour-by-Hour Prediction Model of Air Pollutant Concentration Based on EIDW-Informer—A Case Study of Taiyuan

Prediction of air pollutant concentrations is currently one of the most important methods for the prevention and control of urban air pollution in most countries, and accurate and timely prediction of pollutant concentrations is of great significance for urban pollution control. Using Taiyuan, China, as a case study, this study examines how to predict hourly air pollutant concentrations over longer periods of time while ensuring their accuracy. In this paper, an air pollutant concentration prediction method based on improved inverse distance interpolation and Informer model (EIDW-Informer), and hour-by-hour prediction of PM2.5, NO2, and O3 concentrations in Taiyuan, China is carried out. In this study, historical data from seven environmental monitoring stations in Taiyuan City were used to build multidimensional environmental vectors and calculate the similarity between sample points. Then, the missing values in the dataset were interpolated according to the similarity and distance weights, and the long series prediction was performed by Informer. The experimental results show that the EIDW-Informer method has advantages in hour-by-hour prediction compared to LSTM, CNN-LSTM, and Attention-LSTM models, which improves by 20%, 27%, and 43% on 1 h, 8 h, and 72 h time scales, respectively.

Foliar particulate matter retention and toxic trace element accumulation of six roadside plant species in a subtropical city

As a major source of air pollution, particulate matter (PM) and associated toxic trace elements pose potentially serious threats to human health and environmental safety. As is known that plants can reduce air PM pollution. However, the relationship between PM of different sizes and toxic trace elements in foliar PM is still unclear. This study was performed to explore the association between PM of different sizes (PM2.5, PM10, PM>10) and toxic trace elements (As, Al, Cu, Zn, Cd, Fe, Pb) as well as the correlation among toxic trace elements of six roadside plant species (Cinnamomum camphora, Osmanthus fragrans, Magnolia grandiflora, Podocarpus macrophyllus, Loropetalum chinense var. rubrum and Pittosporum tobira) in Changsha, Hunan Province, China. Results showed that P. macrophyllus had the highest ability to retain PM, and C. camphora excelled in retaining PM2.5. The combination of P. macrophyllus and C. camphora was highly recommended to be planted in the subtropical city to effectively reduce PM. The toxic trace elements accumulated in foliar PM varied with plant species and PM size. Two-way ANOVA showed that most of the toxic trace elements were significantly influenced by plant species, PM size, and their interactions (P < 0.05). Additionally, linear regression and correlation analyses further demonstrated the homology of most toxic trace elements in foliar PM, i.e., confirming plants as predictors of PM sources as well as environmental monitoring. These findings contribute to urban air pollution control and landscape configuration optimization.

Dry deposition effect of urban green spaces on ambient particulate matter pollution in China

Particulate matter (PM) is a major source of urban air pollution that poses a serious threat to the environment and human health. This study quantified the dry deposition effect of PM2.5 and PM10 on vegetation using a mathematical model to overcome the limitations of traditional site-scale research. Additionally, multi-source satellite remote sensing products were combined to form a raster dataset to estimate the effect of dry deposition on PM2.5 and PM10 in China's urban green spaces from 2000 to 2020. The spatial and temporal changes in the long-term series were analyzed, and the influence of environmental factors on dry deposition was analyzed in combination with wavelet changes. The experimental results showed that: 1) from 2000 to 2020, the dry deposition effect of PM2.5 and PM10 on vegetation showed an initial increasing and then decreasing trend caused by the sudden drop in atmospheric pollutant particle concentration driven by local policies; 2) broad-leaved forests provided the main dry deposition effects in urban spaces, accounting for 89.22 %, indicating a need to increase the density of these forest types in urban development planning to improve air quality; and 3) PM2.5, PM10, and environmental impact factors have time-frequency scale coherences, and the coherence between PM2.5 reduction and these factors is more complex than that of PM10, with precipitation being the best variable to explain the change in PM2.5 and PM10. These findings are important for the prevention and control of urban air pollution, regional planning of green spaces, and sustainable development of cities.

Numerical simulation studies of the flow field and pollutant diffusion around street canyons under different thermal stratifications

In previous studies of pollutant dispersion around street canyons, researchers have found that pollutants do not dissipate easily under stable conditions, but they have tended to study only under weak stability conditions and relatively little research has been done on strong stability conditions. In this study, we mainly used 3-D computational fluid dynamics (CFD) technology to investigate the influence of various Richardson numbers (Rib) on the flow field structure and pollutant dispersion around an ideal street canyon under stable stratification. The results show that under stable stratification, different Rib values have significant effects on the flow field, turbulent field, and concentration field around the street canyon. The main effects are that as Rib increases, the vortices inside the street canyon gradually become larger and move downward, the length of the attachment point behind the buildings first increases and then gradually decreases, and the normalized turbulent kinetic energy (TKE/uH2) gradually decreases. The vertical diffusion range of the plume decreases, the horizontal transport distance increases, and the maximum concentration value at the center of the plume gradually increases. This research result may provide new scientific basis for the prediction and control of urban air pollution.

Identification of heavily polluted areas based on a novel grey integrated incidence model: A case study of the Yangtze River Delta, China

Identifying the severity of regional pollution is an important test for the government to develop precise air pollution management policies. By investigating the temporal patterns of six air pollutants, it is found that air pollution in Yangtze River Delta (YRD) is caused by high pollutant concentrations in individual cities. Therefore, a novel grey projection tetrahedron integrated incidence model (GTIIM) is proposed to identify the administrative cities in need of priority control. First, GTIIM model is constructed and its mechanism and properties are discussed. The GTIIM addresses the problem of separating the shape similarity and distance similarity between two time series in existing grey comprehensive incidence models. Then, the GTIIM model allows cities to be ranked according to the severity of pollution, a process that focuses on the numerical and temporal trend characteristics of air pollution time series. Results suggest that (1) the spatial distribution of heavily polluted cities differs for each pollutant. (2) Cities located in the northern part of the YRD contribute more to air pollution, with pollution levels gradually increasing from south to north. (3) The air pollution of ChangZhou, ChuZhou and YangZhou are relatively serious and need to be prioritized for urban air pollution control. Our work provides new insights into recognizing spatial pattern of heavily polluted administrative cities based on time series correlation analysis.

The underlying mechanisms of PM2.5 and O3 synergistic pollution in East China: Photochemical and heterogeneous interactions

The rapid development of Chinese cities is accompanied by air pollution. Although the implementation of air pollution control strategies in recent years has alleviated PM2.5 pollution, O3 pollution and the synergistic pollution of PM2.5 and O3 have become more serious. To understand the underlying chemical interaction mechanisms between PM2.5 and O3, we applied the modified Weather Research and Forecasting model with Chemistry (WRF-Chem) to study the effects of aerosol-photolysis feedback and heterogeneous reactions on the two pollutants and revealed the contribution of different mechanisms in different seasons and regions in Yangtze River Delta (YRD) in eastern China. We found that, through the aerosol-photolysis feedback, PM2.5 decreased the surface photolysis rates JNO2 and JO1D, resulting in a decrease in O3 concentration in the VOC-sensitive area and a slight increase in the NOx-sensitive area. The heterogeneous reactions reduced O3 concentration in the YRD in spring, autumn and winter by consuming HxOy. While in summer, the heterogeneous absorption of NOx decreased O3 in the NOx-sensitive areas and increased O3 in the VOC-sensitive areas. Heterogeneous reactions also promoted the secondary formation of fine sulfate and nitrate aerosols, especially in winter. Through the combined effect of two chemical processes, PM2.5 can lead to a decrease in O3 concentration of −3.3 ppb (−7.6 %), −2.2 ppb (−4.0 %), −2.9 ppb (−6.3 %), and − 5.9 ppb (−18.7 %), in spring, summer, autumn and winter in YRD. Therefore, if the PM2.5 concentration decreases, the weakening effect of PM2.5 on the ozone concentration will be reduced, resulting in the aggravation of ozone pollution. This study is important for understanding the synergistic pollution mechanism and provides a scientific basis for the coordinated control of urban air pollution.

Correlation analysis of urban building form and PM2.5 pollution based on satellite and ground observations

Fine particulate matter (PM2.5) pollution is a key issue affecting the health of urban residents. To explore the impact of urban building form on PM2.5 pollution, this study focused on Wuhan, a Chinese megacity. Based on the urban building data, various building form indices were first calculated in grids to quantitatively present the spatial distribution of urban buildings. The city-scale PM2.5 distribution was obtained with satellite remote sensing and ground air pollution monitoring data. The impact of urban building forms on PM2.5 pollution distribution was then analyzed. The results show that the changes in PM2.5 concentration in Wuhan in the north–south direction have a relatively obvious correlation with the windward area ratio of buildings. The dense north–south buildings can slow the spread of near-surface particulate pollution. This finding demonstrates that the building blocking effect of PM2.5 diffusion in Wuhan is significant. The results of this study can provide a reference for urban planning, architectural design, and air pollution control strategies.

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

In 2019, South Korea’s Framework Act on The Management of Disasters and Safety was revised to include respirable particulate matter as a social disaster. Urban air pollution, especially particulate matter pollution, has been a serious threat to socioeconomic development and public health. In order to address this problem, strong climate crisis response strategies and policies to improve urban air quality are necessary. Therefore, it is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of this study is to develop consistent methodologies for the construction of an index system and for assessing the influencing factors of urban particulate matter pollution based on population, social welfare, land use, environmental, transportation, and economic governance considerations. We applied the local indicators of spatial association and geographical detector methods, and 35 influencing factors were selected to assess their influence on urban air pollution occurrence in 229 cities and counties in South Korea. The results indicated the spatial pattern of the particulate matter concentration in these locations showed strong spatial correlation, and it was confirmed that there was a difference in distribution according to the season. As a result of the analysis of influencing factors, it was found that environment and land use characteristics were the main influencing factors for PM10 and PM2.5. The explanatory power between the two influencing factors of particulate matter was greater than that of a single influencing factor. In addition, most influencing factors resulted in both positive and negative effects on urban fine particulate matter pollution. The interaction relationship of all factors showed a strong action effect in the case of both PM10 and PM2.5, so it was confirmed that all influencing factors were interdependent. In particular, the findings proved that combining the two factors would have a more pronounced effect on particulate matter than when they were independent. We confirmed the significant results for the factors affecting particulate matter. This study offers suggestions on reducing urban air pollution occurrence that can be used to provide a basis and reference for the government to form policies on urban air pollution control in cities and counties.

Air pollution risk assessment related to fossil fuel-driven vehicles in megacities in China by employing the Bayesian network coupled with the Fault Tree method

Vehicle emissions have become one of the key pollution sources affecting air quality and human health in China's megacities. How to curb excess vehicle emissions has become a key pain point of urban air pollution prevention and control. This work tries to explore an effective integrated approach/framework to quantitatively assess the risk factors of excess vehicle emissions (EVE) and their impact on air quality for China's typical megacities. Bayesian Network is employed as the assessment tool by coupling with the Fault Tree method to curb the above problem for the first time. Four megacities (Beijing, Tianjin, Hangzhou, and Guangzhou) in China are selected as case studies, and the risk factors leading to EVE are identified to construct the Bayesian Network model. At the same time, some accurate quantisation algorithms of the occurrence probability of root nodes were proposed for the target megacities from 2014 to 2019. The analysis results show that the variation trend of the probability of EVE has a good positive correlation with the variation trend of air quality in some megacities. From 2014 to 2019, the no-occurrence probability of EVE in Beijing, Tianjin, and Hangzhou increased from 0.4972, 0.4973, and 0.6314 to 0.6491, 0.6846, and 0.7564; the good air quality rate increased from 47.1%, 47.9%, and 59.2%–65.8%, 60%, and 78.6%. Based on the developing trend of the historical data/information and considering the impact of new energy vehicles, the no-occurrence probability of EVE in Beijing and Tianjin is predicted to be increased from 0.6888 to 0.7929 in 2020 to 0.8561 and 0.8645 in 2025. This work may provide a novel approach and perspective that can realise accurate traceability of key risk factors, quantitative risk assessment and prediction function of urban vehicle emissions for sustainable development of China's megacities.

A multiscale air quality and health risk modelling system: Design and application over a local traffic management case study

Air pollution is nowadays a serious public health problem worldwide, especially in urban areas, due to high population density and intense anthropogenic activity. This paper aims to present the development of a modelling tool suitable for simulating multiscale air quality and health impacts - the modair4health system, and its application to an urban case study. The modair4health system includes the online model WRF-Chem, which provides meteorological and air quality fields from regional to urban scales, and the computational fluid dynamics model VADIS, which uses the urban WRF-Chem outputs to simulate the flow and pollutant dispersion in urban built-up areas. A health module based on World Health Organization (WHO) methodologies was also integrated into the system to quantify physical and economic health impacts resulting from air quality changes.The system was applied over a local case study, which represents one of the busiest road traffic areas of the city of Coimbra in Portugal, to assess its operationality in estimating NO2 concentrations and health impacts, by testing two traffic management scenarios. This scenario analysis considered a 4-domain nesting approach, with the finer resolution (4 m) domain focusing on the local case study and on two simulation periods, for which short-term health impacts were estimated. Spatially, the air quality and health greatest benefits were simulated around roads, where higher emission reductions were estimated, but they were also strongly influenced by the urban structure, local weather and population affected.The modair4health system has revealed to be an important multiscale modelling tool for integrated air quality and health assessment, able to support decision makers by facilitating the choice of cost-effective air quality and health management strategies and decisions. Moreover, its user-friendly interface allows to quickly test other urban air pollution control policies and the easy adaptation and application to other case studies considering regional to local atmospheric influences.

Combined model of air quality index forecasting based on the combination of complementary empirical mode decomposition and sequence reconstruction

One of the most important issues that cities face is air pollution. In this study, a novel integrated forecasting model of the air quality index (AQI) is suggested to carry out reliable prediction, providing useful references for urban air pollution control, public health construction, and residents' travel planning. Firstly, the original data is decomposed by the method of complementary set empirical mode decomposition (CEEMD), and the subsequences of different frequencies are formed. Secondly, the fuzzy entropy (FE) algorithm is used to reconstruct the subsequence. Then, the combined forecasting model is established, and different prediction methods are selected for different frequency subsequences. The new high-frequency sequences, low-frequency sequences, and trend sequences are predicted by the whale algorithm optimized long short term neural network (WOA-LSTM) and the extreme learning machine (ELM), respectively. Empirical analysis are carried out with the example of Beijing and Chengdu. The results indicated that: (1) The proposed CEEMD—FE—WOA-LSTM—ELM model effectively integrates the characteristics of the original sequence and has the highest prediction accuracy among all the comparison models. (2) It is necessary to preprocess the data, which can effectively extract data features. Taking Beijing as an example, compared with the non-decomposition model, after adding the decomposition algorithm, the prediction accuracy rate (PA) is increased by 8.55% on average, the RMSE is decreased by 10.36 on average, and the MAPE is decreased by 6.11% on average. (3) The overall prediction level and prediction accuracy can be effectively increased by applying various prediction methods for recombination sequences with various frequency. The research results can provide references for urban air quality prediction.