Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Physicochemical characteristics of airborne microplastics of a typical coastal city in the Yangtze River Delta Region, China.

Airborne microplastics (MPs) are important pollutants that have been present in the environment for many years and are characterized by their universality, persistence, and potential toxicity. This study investigated the effects of terrestrial and marine transport of MPs in the atmosphere of a coastal city and compared the difference between daytime and nighttime. Laser direct infrared imaging (LDIR) and polarized light microscopy were used to characterize the physical and chemical properties of MPs, including number concentration, chemical types, shape, and size. Backward trajectories were used to distinguish the air masses from marine and terrestrial transport. Twenty chemical types were detected by LDIR, with rubber (16.7%) and phenol-formaldehyde resin (PFR; 14.8%) being major components. Three main morphological types of MPs were identified, and fragments (78.1%) are the dominant type. MPs in the atmosphere were concentrated in the small particle size segment (20-50 µm). The concentration of MPs in the air mass from marine transport was 14.7 items/m3 - lower than that from terrestrial transport (32.0 items/m3). The number concentration of airborne MPs was negatively correlated with relative humidity. MPs from terrestrial transport were mainly rubber (20.2%), while those from marine transport were mainly PFR (18%). MPs in the marine transport air mass were more aged and had a lower number concentration than those in the terrestrial transport air mass. The number concentration of airborne MPs is higher during the day than at night. These findings could contribute to the development of targeted control measures and methods to reduce MP pollution.

Early detection of critical urban events using mobile phone network data.

Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by individuals. Such information is invaluable for monitoring large urban areas and supporting the implementation of decision-making services. When analyzed in real time, NSD can enable the early detection of critical urban events, including fires, large accidents, stampedes, terrorist attacks, and sports and leisure gatherings, especially if these events significantly impact mobile phone network activity in the affected areas. This paper presents empirical evidence that advanced NSD can detect anomalies in mobile traffic service consumption, attributable to critical urban events, with fine spatial (a spatial resolution of a few decameters) and temporal (minutes) resolutions. We introduce two methodologies for real-time anomaly detection from multivariate time series extracted from large-scale NSD, utilizing a range of algorithms adapted from the state-of-the-art in unsupervised machine learning techniques for anomaly detection. Our research includes a comprehensive quantitative evaluation of these algorithms on a large-scale dataset of NSD service consumption for the Paris region. The evaluation uses an original dataset of documented critical or unusual urban events. This dataset has been built as a ground truth basis for assessing the algorithms' performance. The obtained results demonstrate that our framework can detect unusual events almost instantaneously and locate the affected areas with high precision, largely outperforming random classifiers. This efficiency and effectiveness underline the potential of NSD-based anomaly detection in significantly enhancing emergency response strategies and urban planning. By offering a proactive approach to managing urban safety and resilience, our findings highlight the transformative potential of leveraging NSD for anomaly detection in urban environments.

Perceived differences in coastal tourism image under tourist experience-IPA analysis based on UGC data of 12 coastal cities.

The destination image perceived by tourists is crucial to coastal tourism market positioning and marketing. This paper utilizes tourists' Internet-generated content from 2017-2021, adopts the jieba text analysis method to identify the cognitive, emotional, and overall image of coastal tourism, divides the constituent elements of the destination image into four main classes and 20 subclasses through the text clustering method, and explores the tourists' perception of the image of coastal tourism with the help of the IPA model. The study found that: 1) The commonality of the cognitive image of "ocean" in 12 coastal cities is outstanding, but the internal characteristics are obvious, tourists pay more attention to coastal tourism in Bohai Rim and southern coastal areas, and Shanghai, Ningbo and Hangzhou show strong correlation; 2) Tourists' emotional image of coastal tourism destinations is dominated by positive attitudes, with a high overlap of adjectives representing positive emotions, but with heat differences in different cities; 3) The overall image of coastal tourism can be divided into three circles, including "traditional core-characteristic structure-peripheral perception", and there are obvious differences in the characteristics of the social semantic network of each city; 4) Tourists are more satisfied with the components of coastal tourism image, but pay more attention to the construction of optimized coastal tourism environment. Based on this, in the process of coastal tourism development, it is necessary to focus on creating distinctive and diversified tourism values, focusing on tourists' experience needs, improving the construction of quality tourism facilities and services, and promoting the high-quality development of coastal tourism.

Spatial evolution, influencing factors and spillover effects of logistics resilience in the Yangtze River Economic Belt.

Logistics resilience is a significant representation of sustainable development ability and a necessary support for high-quality economic development. In order to explore the influencing factors and realization mechanism of the improvement of logistics resilience of the Yangtze River Economic Belt and the high-quality and sustainable development of the economy, this paper comprehensively considers factors of the supply and demand relationship of the logistics market, industrial structure and ecological environment, and evaluates the urban logistics resilience of the Yangtze River Economic Belt by using POI data and statistical data. Combined with the spatial Durbin model, the influencing factors and spatial spillover effects of inter-city logistics resilience were revealed. This study found that the urban logistics resilience in the lower reaches of the Yangtze River has been high. Except Chongqing and Shanghai, the COVID-19 epidemic happened in 2020 led to a significant decrease in logistics resilience. In the meanwhile, every 1% increase in the logistics resilience of the city will promote the logistics resilience of the adjacent cities by 0.145%. Economic condition and urban development potential have positive effects on logistics resilience of the city and its adjacent cities. The economic condition has a direct effect coefficient of 0.166 and an indirect effect coefficient of 0.181, The direct and indirect effects of urban development potential are significantly positive, and the coefficients are 0.001 and 0.006, respectively. The level of information, government support and ability of technological innovation are helpful for the improvement of urban logistics resilience while hindering the enhancement of logistics resilience in adjacent cities. The research area can be extended in the future and more influencing factors can be considered in the future.

Analysis of urban necessities reserve index and reserve quantity under emergency conditions.

While maintaining a robust reserve of daily necessities is crucial for urban safety, but there is a lack of scientific basis for determining "what to store" and "how much to store." This paper address this gap by classifying and summarizing the emergency materials of urban necessities in Shanghai, and establishing a corresponding reserve list. By constructing an index model of daily necessities reserve, this paper provides a scientific foundation for "what to store." Additionally, the reserve levels of different types of daily necessities are classified and managed, the reserve model of emergency daily necessities is constructed. This approach clarifies the scientific basis for "how much to store," overcoming the problems of subjective factors interference and the potential mismatch between the results of objective weighting method and reality. Furthermore, to better cope with emergencies, countermeasures and suggestions are put forward: optimizing the material structure of emergency reserves, managing the material reserves at different levels, scientifically and reasonably planning the amount of emergency materials, and reducing the cost of reserves and improve the efficiency of emergency reserves.

Earth Observation and Geoinformatics to Monitoring the Environmental Status of Urban Streams Inextricably Linked to People's Mental Health.

The general condition of urban watercourses is inextricably linked to ecological balance and public health (physical and mental well-being). Given the vulnerability of these systems to climate change and human pollution, this work aims to demonstrate the effective use of environmental indicators to: a) rapidly assess soil and water conditions near urban streams and b) highlight the importance of geoinformatics and earth observation supported by ground-based techniques. There is a great need for new technology and methods for spatial and temporal monitoring and further quantification of environmental quality in urban streams and the land surrounding them to sensitize policymakers and the public to the environmental degradation of these exceptional habitats and to further protect people's mental health.

The health benefits of reducing micro-heat islands: A 22-year analysis of the impact of urban temperature reduction on heat-related illnesses in California's major cities.

This study investigates the relationship between temporal changes in temperatures characterizing local urban heat islands (UHIs) and heat-related illnesses (HRIs) in seven major cities of California. UHIs, which are a phenomenon that arises in the presence of impervious surfaces or the lack of green spaces exacerbate the effects of extreme heat events, can be measured longitudinally using satellite products. The two objectives of this study were: (1) to identify temperature trends in local temperatures to characterize UHIs across zip code tabulation areas (ZCTAs) in the seven observed cities over a 22-year period and (2) to use propensity score and inverse probability weighting to achieve exchangeability between different types of ZCTAs and assess the difference in hospital admissions recorded as HRIs attributable to temporal changes in UHIs. We use monthly land surface temperature data derived from MODIS Terra imagery from the summer months (June-September) from 2000 to 2022. We categorized ZCTAs (into three groups) based on their monthly land surface temperature trends. Of the 216 ZCTAs included in this study, the summertime land surface temperature trends of 43 decreased, while 161 remained unchanged, and 12 increased. Los Angeles had the greatest number of decreased ZCTAs, San Diego and San Jose had the highest number of increased ZCTAs. To analyze the number of monthly HRI attributable to changes in UHI, we used inverse probability of treatment weighting to analyze the difference in HRI between the years of 2006 and 2017 which were two major extreme heat events over the entire State. We observed an average reduction of 3.2 (95 % CI: 0.5; 5.9) HRIs per month and per ZCTAs in decreased neighborhoods as compared to unchanged. This study emphasizes the importance of urban climate adaptation strategies to mitigate the intensity and prevalence of UHIs to reduce health risks related to heat.

Public health impacts of air pollution from the spatiotemporal heterogeneity perspective: 31 provinces and municipalities in China from 2013 to 2020.

Air pollution has long been a significant environmental health issue. Previous studies have employed diverse methodologies to investigate the impacts of air pollution on public health, yet few have thoroughly examined its spatiotemporal heterogeneity. Based on this, this study investigated the spatiotemporal heterogeneity of the impacts of air pollution on public health in 31 provinces in China from 2013 to 2020 based on the theoretical framework of multifactorial health decision-making and combined with the spatial durbin model and the geographically and temporally weighted regression model. The findings indicate that: (1) Air pollution and public health as measured by the incidence of respiratory diseases (IRD) in China exhibit significant spatial positive correlation and local spatial aggregation. (2) Air pollution demonstrates noteworthy spatial spillover effects. After controlling for economic development and living environment factors, including disposable income, population density, and urbanization rate, the direct and indirect spatial impacts of air pollution on IRD are measured at 3.552 and 2.848, correspondingly. (3) China's IRD is primarily influenced by various factors such as air pollution, economic development, living conditions, and healthcare, and the degree of its influence demonstrates an uneven spatiotemporal distribution trend. The findings of this study hold considerable practical significance for mitigating air pollution and safeguarding public health.

Does bike-sharing reduce traffic congestion? Evidence from three mega-cities in China.

This study employs a regression discontinuity design to systematically examine the governance effect of bike-sharing on urban traffic congestion, utilizing city-level data from Beijing, Shanghai, and Wuhan in China between 2016 and 2018. We discover that the introduction of bike-sharing services significantly mitigates traffic congestion in the short term. Our heterogeneity analysis reveals that the initial deployment of shared bicycles primarily alleviates urban congestion, while additional deployments have a limited impact. Further, mechanism test analysis demonstrates that bike-sharing leads to increased metro ridership in these cities, effectively explaining the reduction in road congestion. This study underscores the pivotal role of bike-sharing services in easing urban traffic congestion and provides vital policy insights for enhancing traffic management strategies in Chinese cities.

Building running-friendly cities: effects of streetscapes on running using 9.73 million fitness tracker data in Shanghai, China.

The association between built environment and physical activity has been recognized. However, how and to what extent microscale streetscapes are related to running activity remains underexplored, partly due to the lack of running data in large urban areas. Moreover, few studies have examined the interactive effects of macroscale built environment and microscale streetscapes. This study examines the main and interactive effects of the two-level environments on running intensity, using 9.73 million fitness tracker data from Keep in Shanghai, China. Results of spatial error model showed that: 1) the explanatory power of microscale streetscapes was higher than that of macroscale built environment with R2 of 0.245 and 0.240, respectively, which is different from the prior finding that R2 is greater for macroscale built environment than for microscale streetscape; 2) sky and green view indexes were positively associated with running intensity, whereas visual crowdedness had a negative effect; 3) there were negative interactions of land use Herfindahl-Hirschman index with sky and green view indexes, while a positive interaction was observed for visual crowdedness. To conclude, greener, more open and less visually crowded streetscapes, can promote running behavior and enhance the benefits of land use mix as well. The findings highlight the importance of streetscapes in promoting running behavior, instead of a supplement to macroscale built environment.

Exploring the spatial spillover effects of Yangtze River Delta ports on urban economic growth.

The Yangtze River Delta (YRD) ports are pivotal in shaping the Yangtze River Economic Belt and advancing urban economies across China. This article utilizes panel data from 20 cities with ports in the YRD area, spanning from 2011 to 2020, using the spatial Durbin model to explore how these ports influence urban economic growth. The findings indicate that: (1) The YRD ports significantly contribute to economic growth in both the port cities and their surrounding areas, with the indirect impact on neighboring cities being more substantial than the direct effect on the cities themselves; (2) The beneficial spillover effects of the YRD ports on the economic growth of nearby cities vary in intensity over different spatial ranges, marked by distinct boundary effects and geographical attenuation. The influence extends up to approximately 110km; (3) Within the various elements impacting the economic growth of cities in the YRD, financial development prominently exhibits a threshold effect on urban economic growth; (4) Upon analyzing heterogeneity, inland and coastal port cities manifest divergent spillover effects, with inland port cities predominantly exerting a positive spillover on adjacent regions. Accordingly, in order to eventually achieve the shared prosperity of the region's economy, it is recommended that a strong top-level design be established and that efforts be made to transform the YRD region into a core region a diffusion and driving effect.

Mapping Vulnerability to Potential Crisis Events in Surabaya City: A GIS-Based Approach.

Background: This study aims to develop a vulnerability map for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to assess the city's vulnerability to COVID-19. Methods: Six key factors influencing vulnerability were identified and their relative importance determined through the Analytic Hierarchy Process (AHP) pairwise comparison matrix. GIS was utilized to classify Surabaya's vulnerability into five levels: very low, low, medium, high, and very high. Results: The resulting vulnerability map provides essential insights for decision-makers, healthcare professionals, and disaster management teams. It enables strategic resource allocation, targeted interventions, and formulation of comprehensive response strategies tailored to specific needs of vulnerable districts. Conclusions: Through these measures, Surabaya can enhance its resilience and preparedness, ensuring the well-being of its residents in the face of potential emergency outbreaks.

A novel hybrid optimization model for evaluating and forecasting air quality grades.

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models.

Optimized air quality management based on air quality index prediction and air pollutants identification in representative cities in China.

With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

A framework for urban resilience measurement and enhancement strategies: A case study in Qingdao, China.

Comprehensive measurement and analysis of urban resilience is essential to ensure sustainable urban development. This paper creates a multilevel urban resilience evaluation index system based on four dimensions of economy, society, ecology, and infrastructure and the three attributes (resistance, recovery, and adaptability), then applies the framework to Qingdao, China. The results suggest that: (1) The overall level of urban resilience in Qingdao showed an upward trend, rising from a relatively high level in 2012 to a high level in 2021. Economic and social resilience maintained a high consistency, developing rapidly, while the development of ecological and infrastructure resilience fluctuated, and infrastructure resilience was slow and lagging. (2) Qingdao's overall resilience is higher than other cities in the same region, but infrastructure resilience is relatively low. Moreover, the coupling coordination degree (CCD) of the resilience of the four subsystems in Qingdao has evolved from near imbalance to good coordination. (3) Infrastructure resilience is the primary obstacle factor in the dimension layer, followed by ecological resilience. Based on the results, corresponding improvement strategies are proposed. A comprehensive multidimensional measurement of the urban resilience of Qingdao can identify the main shortcomings and provide a reference for decision-making and resource allocation in resilient cities.

Assessing allergy risk from ornamental trees in a city: Integrating open access remote sensing data with pollen measurements.

Platanus sp. pl. (plane trees) are common ornamental tree in Poland that produces a large amount of wind-transported pollen, which contains proteins that induce allergy symptoms. Allergy sufferers can limit their contact with pollen by avoiding places with high pollen concentrations, which are restricted mainly to areas close to plane trees. Their location is thus important, but creating a detailed street tree inventory is expensive and time-consuming. However, high-resolution remote sensing data provide an opportunity to detect the location of specific plants. But acquiring high-resolution spatial data of good quality also incurs costs and requires regular updates. Therefore, this study explored the potential of using open access remote sensing data to detect plane trees in the highly urbanized environment of Poznan (western Poland). Airborne light detection and ranging (LiDAR) was used to detect training treetops, which were subsequently marked as young plane trees, mature plane trees, other trees or artefacts. Spectral and spatial variables were extracted from circular buffers (r = 1 m) around the treetops to minimize the influence of shadows and crown overlap. A random forest machine learning algorithm was applied to assess the importance of variables and classify the treetops within a radius of 6.2 km around the functioning pollen monitoring station. The model performed well during 10-fold cross-validation (overall accuracy ≈ 92%). The predicted Platanus sp. pl. locations, aggregated according to 16 wind directions, were significantly correlated with the hourly pollen concentrations. Based on the correlation values, we established a threshold of prediction confidence, which allowed us to reduce the fraction of false-positive predictions. We proposed the spatially continuous index of airborne pollen exposure probability, which can be useful for allergy sufferers. The results showed that open-access geodata in Poland can be applied to recognize major local sources of plane pollen.

Wildfires impact on PM2.5 concentration in galicia Spain.

Wildfire intensity and severity have been increasing in the Iberian Peninsula in recent years, particularly in the Galicia region, due to rising temperatures and accumulating drier combustible vegetation in unmanaged lands. This leads to substantial emissions of air pollutants, notably fine particles (PM2.5), posing a risk to public health. This study aims to assess the impact of local and regional wildfires on PM2.5 levels in Galicia's main cities and their implications for air quality and public health. Over a decade (2013-2022), PM2.5 data during wildfire seasons were analyzed using statistical methods and Lagrangian tracking to monitor smoke plume evolution. The results reveal a notable increase in PM2.5 concentration during the wildfire season (June-November) in Galicia, surpassing health guidelines during extreme events and posing a significant health risk to the population. Regional wildfire analyses indicate that smoke plumes from Northern Portugal contribute to pollution in Galician cities, influencing the seasonality of heightened PM2.5 levels. During extensive wildfires, elevated PM2.5 concentration values persisted for several days, potentially exacerbating health concerns in Galicia. These findings underscore the urgency of implementing air pollution prevention and management measures in the region, including developing effective alerts for large-scale events and improved wildfire management strategies to mitigate their impact on air quality in Galician cities.

Toward sustainable environmental management: Insights from Chinese first-tier cities' consumers' response to green electricity payment policies.

China's renewable energy industry is facing the challenge of overcapacity. The environmental management literature suggests that consumers' participation in the green electricity market holds immense potential in addressing renewable energy consumption concerns. However, the question of how payment policies influence China's consumers' willingness to pay for green electricity remains unresolved. Based on 2854 valid questionnaires from a survey conducted in China's four first-tier cities in 2023, our research findings reveal: (1) While 97.9% of consumers express a willingness to use green electricity, only 63.1% are willing to pay a higher cost, indicating the existence of a "value-action" gap between environmental awareness and actual willingness to pay. (2) China's consumers' willingness to pay for green electricity is approximately 38.4 RMB per month. This figure has decreased by 5.7 RMB compared to our survey in 2019. (3) Consumers' willingness to pay will be influenced by the attitudes of those around them. (4) The voluntary payment policy positively impacts consumers' willingness to pay for green electricity. (5) Male, younger, lower education level, higher income, and larger household size consumers exhibit a higher willingness to pay. (6) Electricity price sensitivity weakens the impact of payment policies on willingness to pay.

High-resolution mapping of urban Aedes aegypti immature abundance through breeding site detection based on satellite and street view imagery.

Identification of Aedes aegypti breeding hotspots is essential for the implementation of targeted vector control strategies and thus the prevention of several mosquito-borne diseases worldwide. Training computer vision models on satellite and street view imagery in the municipality of Rio de Janeiro, we analyzed the correlation between the density of common breeding grounds and Aedes aegypti infestation measured by ovitraps on a monthly basis between 2019 and 2022. Our findings emphasized the significance (p ≤ 0.05) of micro-habitat proxies generated through object detection, allowing to explain high spatial variance in urban abundance of Aedes aegypti immatures. Water tanks, non-mounted car tires, plastic bags, potted plants, and storm drains positively correlated with Aedes aegypti egg and larva counts considering a 1000 m mosquito flight range buffer around 2700 ovitrap locations, while dumpsters, small trash bins, and large trash bins exhibited a negative association. This complementary application of satellite and street view imagery opens the pathway for high-resolution interpolation of entomological surveillance data and has the potential to optimize vector control strategies. Consequently it supports the mitigation of emerging infectious diseases transmitted by Aedes aegypti, such as dengue, chikungunya, and Zika, which cause thousands of deaths each year.