Urban Air Research Articles

Design industrial 5.1 air quality monitoring system and develop smart city infrastructure

In order to meet the requirements of urban construction and further urbanization of the country, the author proposes an industrial 5.1 air quality monitoring system to develop smart city infrastructure. The author utilizes a wireless network of lighting nodes to solve the cost and positioning accuracy issues of perception nodes covering a large area, achieving real-time alarm of urban air quality status and location of pollution occurrence. The author also adopted the latest design concept of monitoring systems combined with cloud platform interfaces, breaking the closed design of traditional IoT systems and enabling better utilization of air quality data. The test results indicate that: The communication distance of CC2530 can be maintained at around 70m under normal power, while the spacing between urban street lights is approximately 30m, which fully meets the project requirements. After two days of testing, the system alarm function and various functions are running normally. ConclusionThe key parts of the system have been tested and simulated, and ideal results have been obtained.

Analysing urban local cold air dynamics and climate functional zones using interpretable machine learning: A case study of Tianhe district, Guangzhou

Deterioration of the thermal environment in built-up areas poses a serious threat to human health, comfort, and urban infrastructure, while also increasing energy consumption and carbon emissions. This underscores the need to optimize wind environments as a key mitigation strategy for urban areas. This paper analyzed the effects of human activities and natural factors on local cold air in Tianhe District, Guangzhou, from the perspective of local ventilation systems. The KLAM_21 (Kaltluft Abfluss Modell) was used to simulate local cold air flow and delineate climate functional zones. A random forest model, interpreted with the SHapley Additive exPlanation (SHAP) method, assessed the impact of various factors on local cold air dynamics. The study found that: (1) The northern mountainous area is a crucial cold source; (2) Some open spaces in the built environment fail to function as effective local cold air corridors; (3) High-intensity urban development hinders local cold air transmission; (4) Water bodies are more effective than green spaces in collecting and transmitting local cold air. This study provided technical methods for identifying climate functional zones and understanding local cold air dynamics, as well as theoretical support for the construction of local ventilation systems in urban areas.

Air pollution prediction using blind source separation with Greylag Goose Optimization algorithm

Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies.

Collaboration between High-Speed Rail and Urban Transportation to Improve Urban Air Quality in Green Travel

Collaboration between High-Speed Rail and Urban Transportation to Improve Urban Air Quality in Green Travel

Evaluating Indoor Air Quality in Schools: Is the Indoor Environment a Haven during High Pollution Episodes?

Pollution data were collected at five schools in Hong Kong using low-cost, sensor-based monitors both indoors and outdoors during two consecutive high pollution episodes. The pollutants monitored included NO2, O3, PM2.5, and PM10, which were also used as input to a health risk communication protocol known as Air Quality Health Index (AQHI). CO2 was also measured simultaneously. The study aimed to assess the relationship between indoor pollutant concentrations and AQHI levels with those outdoors and to evaluate the efficacy of building operating practices in protecting students from pollution exposure. The results indicate that the regular air quality monitoring stations and outdoor pollutant levels at schools exhibit similar patterns. School AQHI levels indoors were generally lower than those outdoors, with PM10 levels showing a larger proportional contribution to the calculated values indoors. NO2 levels in one school were in excess of outdoor values. CO2 monitored in classrooms commonly exceeded indoor guidelines, suggesting poor ventilation. One school that employed air filtration had lower indoor PM concentrations compared to other schools; however, they were still similar to those outdoors. O3 levels indoors were consistently lower than those outdoors. This study underscores the utility of on-site, sensor-based monitoring for assessing the health impacts of indoor and community exposure to urban air pollutants. The findings suggest a need for improved ventilation and more strategic air intake placement to enhance indoor air quality.

Numerical simulation of the transition flight aerodynamics of cross-shaped quad-tiltrotor UAV

In order to enhance the stability of the tilt transition process, a new configuration of Quad-Tiltrotor UAV was presented in this paper. Firstly, numerical simulation was used to calculate and analyze the aerodynamic interaction between the front rotor/fuselage/rear rotor during the transition state mode. The calculation model of the isolated rotor, front-rear rotor, front rotor-fuselage, and front rotor-rear rotor-fuselage combination states are established. Besides, the effects of pitch, roll, and yaw moment on the fuselage at different tilt angles are analyzed. It is concluded that the front rotor is the leading factor in the aerodynamic interference of the whole UAV in the different combination states. The research results can provide a reference for the optimization design of the overall layout, structure, and flight control strategy of the cross-shaped quad-tiltrotor UAV, and can also provide solutions for the logistics application of urban air traffic.

Chemical characterization and influencing factors of gaseous and particulate components in cooking oil fume (COF) from traditional Chinese dishes: Insights from high-resolution mass spectrometry

Cooking oil fumes (COF) are known to emit a wide range of organic compounds with significant impacts on human health and urban air quality. This study used HPLC-QToF-MS and Vocus PTR-TOF to explore the chemical constituents and influencing factors of the COF generated from eight typical Chinese dishes representing different areas in a laboratory kitchen. The results revealed that both CHO and CHON compounds exhibited strong reducibility and saturability, with CHO compounds being the dominant and CHON compounds showing greater diversity. 24 among 168 CHO compounds were identical with those generated from heating soybean oil, representing 72.4%–92.3% in abundance and 22.2%–29.2% in quantity. That was 5 among 113 CHON compounds, accounting for 7.8%–10% in abundance and 4.7%–6.7% in quantity. These findings suggest that the major CHO compounds from heating soybean oil continued to dominate the abundances in dishes. The diversity of CHO compounds and the presence of CHON compounds were influenced by the food ingredients. The VOC analysis indicated that oxygen-containing organics were the major components. 6 identical VOC species between cooking dishes and heating soybean oil were identified, comprising 36.02%–67.84% of the total VOCs mass. Notably, poor ventilation could result in even higher COF concentrations in the connected room compared to the kitchen itself.

Variations in the leaf economics spectrum, anatomical, ultrastructural, and stomatal traits of five tree species in the urban-rural air pollution environment

Rapid urbanization has contributed to global increases in air pollution derived from urban areas. Unlike natural forests, urban forests are exposed to higher concentrations of airborne pollutants due to the strong urban-suburban-rural pollutant emission gradients. However, there remains a pressing lack of available information pertaining to the urban air pollution-related effects on the leaf economics spectrum, anatomical, ultrastructural, and stomatal traits of tree species along an urban-rural gradient. Here, the degree to which urban air pollution impacts the adaption of greening tree species and associated service functions was assessed by sampling five common tree species (Acer pictum, Fraxinus chinensis, Koelreuteria paniculata, Salix babylonica, Sophora japonica) along urban-rural-natural forests in the Beijing metropolitan region of China. These analyses revealed a significant reduction in leaf mass per unit area (–13.4 %), leaf thickness (–16.7 %), and stomatal area (–27.5 %) with increasing proximity to areas of greater air pollution that coincide with significant increases in leaf tissue density (+12.6 %), leaf nitrogen content (+10.1 %), relative chlorophyll content (+2.7 %), and stomatal density (+11.9 %). Higher air pollution levels were associated with organelle changes including gradual disintegration of chloroplasts, larger intercellular spaces and apparent starch and plastoglobuli deposition. Air pollution was conducive to the strengthening of the trade-off potential and adaptation strategies of trees in urban ecosystems, which are associated with trees with a rapid investment return strategy associated with thick leaves and strong photosynthetic capacity. These results provide strong empirical evidence of the profound air pollution-induced changes in leaf functional traits and adaption ability of urban forest tree species.

Polycyclic aromatic hydrocarbons exposure associated with increased risk of psoriasis.

Psoriasis is considered to be multifactorial, with both genetic and environmental factors contributing to its development. Polycyclic aromatic hydrocarbons (PAHs) are widespread in the environment, originating from sources such as cigarette smoke, exhaust emissions, grilled foods, smoked foods and urban air. Researchs have established a link between PAHsexposure and autoimmune disorders; however, specific effects of PAHs on psoriasis remain underexplored. This study aims to evaluate the correlation between PAHs exposure and susceptibility to psoriasis. We analysed eight monohydroxy PAHs (1-Hydroxynaphthalene (1-NAP), 2-Hydroxynaphthalene (2-NAP), 3-Hydroxyfluorene (3-FLU), 2-Hydroxyfluorene (2-FLU), 1-Hydroxyphenanthrene (1-PHE), 1-Hydroxypyrene (1-PYR), 2-Hydroxyphenanthrene (2-PHE) and 3-Hydroxyphenanthrene (3-PHE)) in 5996 participants from the National Health and Nutrition Examination Survey (NHANES). We employed multivariate logistic regression, trend analysis, weighted quantile sum (WQS) regression and restricted cubic spline (RCS) analysis to investigate the relationship between PAHs exposure and psoriasis risk. Multivariate logistic regression and trend analysis revealed that monohydroxy PAHs, including 2-NAP, 3-FLU, 2-FLU and the mixture of 2-PHE and 3-PHE, are associated with an increased risk of psoriasis. Additionally, WQS regression showed a significant positive correlation between combined exposure to monohydroxy PAHs and psoriasis risk, with the mixture of 2-PHE and 3-PHE (47.3%) being the most influential factor. RCS regression further corroborated these findings. Specifically, 2-FLU can increase the expression of psoriasis-related inflammatory factors in HaCaT cells. In conclusion, PAHs exposure increases the risk of developing psoriasis. Efforts to reduce PAHs levels in the environment and minimise exposure are crucial for public health strategies aimed at preventing psoriasis.

COVID-19 lockdown impact on air quality and associated health benefit in two contrasting urban cities in Eastern Indo Gangetic Plain

A key challenge in controlling deteriorating urban air quality is a lack of clear understanding of the regional emissions characteristics and their impact on human health. COVID-19 lockdown provided an opportunity to enhance understanding of background air quality. Towards this, we studied the effect of the lockdown on air pollutants level and associated health benefits in two contrasting urban cities of Eastern IGP, Asansol (industrial) and Kolkata (metropolitan), by analyzing data from 2019 to 2021. The outcomes revealed that the level of exceedance of air pollutants is usually higher in Asansol but significantly decreased in both cities during the lockdown period. Particle concentrations were reduced by 50–70 % compared to Pre-Lockdown and by 20–35 % against the same period in 2019. Kolkata witnessed a higher reduction in PM levels than Asansol. Diurnal variation comparison showed a higher reduction of particle levels during lockdown in the morning at Asansol while in the evening at Kolkata. The health benefits associated with the reduction in PM2.5 concentration were quantified using the BenMAP-CE model, which revealed that improving air quality, like during the lockdown period, would save annually 0.46 and 2.91 deaths per 100,000 persons in Asansol and Kolkata, respectively. Altogether, this study's outcomes provide essential insights to policymakers for regional factors associated to varying air quality and health benefits associated to improvement in air quality.

Green Plant Landscape Design for Urban Air Quality Purification with Computer Image Processing in Cloud, Grid, and Cluster Computing

Green Plant Landscape Design for Urban Air Quality Purification with Computer Image Processing in Cloud, Grid, and Cluster Computing

Impact of Outdoor Air Pollutants Exposure on the Severity and Outcomes of Community-Acquired Pneumonia in Gabes Region, Tunisia.

Background Acute community-acquired pneumonia (CAP) is considered the leading cause of infectious death worldwide. Air pollution and prolonged exposure to airborne contaminants have been implicated in various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). However, the specific impact of air pollution on pneumonia, particularly CAP, remains underexplored. Given the rising levels of urban air pollution and its potential health ramifications, our study aimed to examine the association between exposure to outdoor air pollution and severity as well as the outcomes of pneumonia cases requiring hospitalization. Methodology A cohort analytical study with retrospective data collection was carried out in the pulmonology department of the Gabès University Hospital between January and October 2022. We compared levels of particulate matter less than or equal to 10µm in aerodynamic diameter (PM10),sulfur dioxide (SO2),ozone (O3), moisture and ambient temperature with severity and outcomes ofpneumonia requiring hospitalization. The choice of these specific pollutants and environmental factors was based on their established impact on respiratory health and their prevalence in the study region. Results Increased sulfur dioxide (SO2) levels were associated with increased use of non-invasive ventilation (NIV) (r = 0.400). Higher levels of particulate matter (PM10) were significantly associated with the development of lung abscesses. Similarly, increased humidity and ambient temperature were strongly correlated with the development of lung abscesses. Increased air SO2 levels were correlated with a higher CURB65 score (r = 0.299). High outdoor SO2 levels and increasing moisture content were associated with increased Pneumonia Severity Index (PSI) score (r = 0.303 and = 0.310, respectively). Higher levels of PM10 were associated with an increased risk of pleural effusion, a serious complication of pneumonia. Finally, higher ambient temperatures were correlated with more extensive opacities on chest X-rays (r = 0.706), suggesting the severity of pneumonia. Conclusion This study highlights the significant associations between environmental factors and various clinical parameters in pneumonia patients. The findings underscore the importance of considering environmental exposures, such as air quality and weather conditions, in understanding and managing the severity of pneumonia.

Spatial and temporal analysis of scooter-induced traffic patterns and their environmental implications

Scooters are a popular means of transportation in urban areas. However, studies examining their spatial and temporal features are lacking. This study examined traffic patterns in New Taipei City from September 2021 to June 2023 using real-time surveillance camera images and the YOLOv4 algorithm. The focus of the study was to investigate the role of scooters in urban transportation and their effect on air quality. The findings revealed that sedans and scooters accounted for approximately 90% of the total vehicles, and their usage exhibited significant spatial and temporal variations across the city. Sedans were more prevalent in rural areas, whereas scooters were predominant in urban and suburban regions. An examination of diurnal patterns revealed that peak traffic occurred during early morning and evening rush hours, with distinct usage patterns between weekdays and weekends. Through hierarchical clustering, the city's stations were categorized into three types based on the dominant vehicle usage: sedan-dominant, sedan-prevailing, and scooter-dominant. The analysis also established a correlation between the number of vehicles and air pollution, highlighting the significant role of sedans and scooters as primary sources of emissions, particularly in areas where scooters were dominant. This suggests that the current emission inventories may underestimate the impact of scooters and buses on air quality while overestimating the trucks' contribution. Consequently, this study emphasizes the significance of scooters in determining urban air quality and advocates for improved monitoring and image identification technologies to accurately assess the numbers and speeds of scooters. These measures are essential for improving the accuracy of emissions inventories and forecasts, which are crucial for effective urban air quality management.

A new attention-based CNN_GRU model for spatial-temporal PM2.5 prediction.

Accurately predicting thespatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection.

Impact of fine particulate pollution exposures on respiratory health in a megacity of Pakistan

Air pollution poses a substantial barrier to global environmental sustainability and citizen well-being. However, there is a lack of research that specifically examines the effects of short-term exposure to PM2.5 and its components on health outcomes in developing nations in Asia, regions often cited as having some of the most severe urban air quality issues globally. The present study evaluated the associations between PM2.5 components and hospital admissions (HA) and emergency room visits (ERV) for respiratory diseases in the megacity of Karachi, Pakistan. We measured PM2.5 constituents, including black carbon (BC) and ionic species (sulfate - SO42−; nitrate - NO3−; and ammonium - NH4+) at two sites (August 2008 to August 2009) in Karachi. The HA and ERV, respiratory disease outcome data, were collected from three local, large tertiary care hospitals. We assessed the lag structure of excess risk (ER) of the pollutants-outcomes association (0–6 single and cumulative lag days) using time-series quasi-Poisson models after adjusting for temperature, humidity, and day of the week. Among a total 13,827 patients, the highest ERs for all respiratory urgent care use (UCU) were observed for PM2.5 (10.3, 95% CI: 2.59%-18.59), NH4+ (9.58%, 95% CI: 1.50%–18.30%), air quality index (9.11%, 95%CI: 2.54%–16.09%) and SO42− (7.26%, 95% CI: 1.03%–13.87%) within 0–4 lag days. Additionally, patients with chronic obstructive pulmonary disease (COPD), tuberculosis (TB), or other pulmonary diseases and older or male patients were more vulnerable to these pollutants. This first study in Pakistan found that PM2.5 and its constituents were associated with respiratory HAs and ERVs for the inhabitants of the megacity of Karachi. These associations varied by different PM constituents, disease subtypes, age, and gender. Our results provide important information to policymakers for developing regulations for improving air quality and public health. Further studies are urgently needed in other developing countries to disentangle the air pollution health effects.

Aeroacoustic investigation of side-by-side urban air mobility aircraft in full configuration with ground effect

Aeroacoustic investigation of side-by-side urban air mobility aircraft in full configuration with ground effect

A study on air quality prediction with multiple features based on GCN-LSTM

Abstract Air pollution is closely intertwined with public health. However, air quality forecasting is a very challenging project, and the influencing factors are intertwined. This study focuses on analyzing and forecasting urban air quality from a spatiotemporal perspective, based on various factors that affect air quality. In this paper, a diverse dataset impacting air quality was collected, including pollutant concentrations, meteorological conditions, and traffic flow data. A GCN-LSTM model was developed to thoroughly explore the spatial relationships between different sites within the study area. This paper also conducted forecasts over different time lengths. The experiments revealed that weather data and traffic data can help improve air quality prediction results. The forecasting accuracy for RMSE and MAE were improved by an average of 8.7% and 8.5% in PM2.5, and 12.8% and 11.9% in NO2, respectively. Within the model, the GCN-LSTM model, which considers spatiotemporal features, outperformed single models and proved to be more advantageous in long-term forecasting. The findings of this study contribute to the improvement of air quality prediction effectiveness.

Optimizing EV charging stations: a simulation-based approach to performance and grid integration

This study addresses the optimization of electric vehicle (EV) charging stations, focusing on enhancing performance and grid integration through a comprehensive simulation approach. By employing advanced simulation tools in Simulink® and MATLAB®, alongside electrical installation planning with SIMARIS®, we meticulously analyze the charging process, infrastructure requirements, and their implications on the power grid. Our results demonstrate significant improvements in charging station efficiency and reliability, highlighting the effectiveness of our proposed control strategies and harmonic mitigation techniques. Notably, the integration of renewable energy sources emerges as a pivotal factor in reducing operational costs and carbon emissions, furthering the sustainability of EV charging solutions. The research delineates the environmental benefits, emphasizing the reduction of greenhouse gas emissions and enhancement of urban air quality, pivotal in the global shift towards cleaner transportation modes. This work contributes valuable insights into the design and grid integration of EV charging stations, offering a scalable model for future infrastructure development. It serves as a critical resource for engineers, policymakers, and stakeholders in the realm of electric mobility, advocating for a strategic transition to EVs supported by robust and efficient charging infrastructure.

HOUSING PRICES AS PAYMENT FOR ECOSYSTEM SERVICES OF URBAN GREEN SPACES: CASE STUDY IN KYIV

The purpose of the study is to analyze the impact of the availability of urban green spaces (UGS) and air quality on the cost of residential real estate in Kyiv. Additionally, it seeks to identify the level of awareness among city residents regarding the advantages of living close to UGSs in areas with relatively clean air and their willingness to pay a higher price for such housing. The QGIS software was employed to assess the pedestrian and time accessibility levels of recreational green spaces, and the results are depicted on the corresponding map. The least accessible for everyday recreation is the UGS in the central districts of the city, which traditionally have the highest population density and the most expensive residential real estate. The article analyzes the priority criteria for determining the housing cost, depending on the spatial localization relative to the city center and the comfort class of real estate. The distribution of housing across different tiers and the price range for the apartments in new constructions in Kyiv during 2015–2021 were also examined. To identify the correlation between the cost of residential real estate in the capital’s new buildings and the availability of green spaces nearby, we investigated the spatial distribution of prices in the largest residential complexes in the city. The results of spatial visualization obtained through Voronoi diagrams or Delaunay triangulation indicate significant disparities in the cost of housing located in the center of Kyiv or on the periphery. Logically, residential complexes with the highest apartment prices are located in the central districts of Kyiv and near the metro stations. However, most of these locations lack accessible green areas suitable for everyday recreation. Therefore, there is no positive correlation between housing prices and the presence of UGSs nearby. To achieve the research goal, we also conducted a sociological survey among Kyiv residents in various districts of the city. The survey results indicate that most Kyiv residents do not have the desire or are not currently willing to pay a higher price for the opportunity to live near a park or forest and breathe clean air, i.e., to enjoy the ecosystem services provided by UGSs fully. The scientific novelty of the research lies in determining the pedestrian and time accessibility of recreational green spaces in Kyiv, identifying trends in spatial clustering of the most expensive residential real estate in central city areas, and demonstrating the lack of a positive impact of the availability of green areas on the per square meter cost of housing in new developments. Additionally, the study reveals a remarkably low willingness of the city residents to pay a higher price for the opportunity to receive ecosystem services from available UGSs.

Ecological benefits of roof gardens and the difference of roof gardens in different regions

As a result of the rapid pace of urbanization today, environmental destruction is outpacing regeneration, and many "urban diseases" are occurring frequently. Therefore, there is a need for a facility that can protect the environment without destroying the original city - roof gardens. This paper focuses on the ecological benefits of rooftop gardens and their application in practical situations. It is found that rooftop gardens can improve air quality, store rainwater, mitigate the heat island effect and protect biodiversity. These effects have well alleviated the problems of urban air pollution, water resource stress, urban heat island effect and destruction of ecological environment. And research has found that there are already many excellent examples of the application of rooftop gardens at home and abroad, helping to confirm that rooftop gardens do indeed have the ecological benefits mentioned above, adding to the feasibility. Rooftop gardens are indispensable in the construction of future cities for the well-being of urban residents.