Characterizing spatiotemporal patterns of elevated PM2.5 exposures in a megacity of China using combined mobile and stationary measurements

Exposure to PM2.5 (particles with an aerodynamic diameter equal to or less than 2.5 μm) is associated with a variety of negative health outcomes. Measurements from sparsely situated air quality monitoring stations (AQMSs) may be inappropriate to represent real PM2.5 exposures, particularly in traffic-related environments. In this study, efforts were made to characterize spatiotemporal variation of PM2.5 pollutions over Shenzhen, China from July 2019 to June 2020 using combined mobile (on-road PM2.5) and stationary (AQMS PM2.5) measurements. Monthly-average concentrations of on-road PM2.5 ranged from 10.4 ± 6.1 to 47.3 ± 23.9 μg/m3, and showed consistent trend with AQMS PM2.5 concentrations which ranged from 8.3 ± 3.1 to 37.2 ± 12.9 μg/m3. On-road PM2.5 and AQMS PM2.5 concentrations dropped by 54.6% and 30.2% in February 2020, probably due to the low anthropogenic emissions during the period of Spring Festival and COVID-19 lockdown. Weekend effect on both on-road and AQMS PM2.5 concentrations was not noticeable. Relative high on-road PM2.5 concentrations were observed during morning and evening rush hours. An "elevated concentration” concept was applied to estimate the influence of emissions on PM2.5 exposures. Elevated concentrations showed strong diurnal and spatial variation, and was about 5.0 μg/m3 on-average. Mappings of on-road PM2.5 and elevated concentrations confirmed the heterogeneity of spatial distribution of PM2.5 exposures in Shenzhen where PM2.5 pollutions were more severe in western and northern areas. Our results highlight the elevated PM2.5 exposures in traffic-related environments, and the inequity in urban exposure levels and health.

Effect of Transportation Operation on Air Quality in China Based on MODIS AOD during the Epidemic

With the rapid growth of automobile numbers and the increased traffic congestion, traffic has increasingly significant effects on regional air quality and regional sustainable development in China. This study tried to quantify the effect of transportation operation on regional air quality based on MODIS AOD. This paper analyzed the space-time characteristics of air quality and traffic during the epidemic by series analysis and kernel density analysis, and quantified the relationship between air quality and traffic through a Geographically Weighted Regression (GWR) model. The main research conclusions are as follows: The epidemic has a great impact on traffic and regional air quality. PM2.5 and NO2 had the same trend with traffic congestion delay index (CDI), but they were not as obvious as CDI. Both cities with traffic congestion and cities with the worst air quality showed strong spatial dependence. The concentration areas of high AOD value in the east areas of the Hu line were consistent with the two gathering centers formed by cities with traffic congestion in space, and also consistent with the gathering center of cities with poor air quality. The concentration area of AOD decline was consistent with the gathering center formed by cities with the worst air quality. AOD had a strong positive correlation with road network density, and its GWR correlation coefficient was 0.68, then These provinces suitable for GWR or not suitable were divided. This study has a great significance for the transportation planning, regional planning, air quality control strategies and regional sustainable development, etc.

Satellite-based estimates of daily NO2 exposure in urban agglomerations of China and application to spatio-temporal characteristics of hotspots

The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd

Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China.

Studying the spatiotemporal evolution of carbon emissions from the perspective of major function-oriented zones (MFOZs) is crucial for making a carbon reduction policy. However, most previous research has ignored the spatial characteristics and MFOZ influence. Using statistical and spatial analysis tools, we explored the spatiotemporal characteristics of carbon emissions in Guangdong Province from 2001 to 2021. The following results were obtained: (1) Carbon emissions fluctuated from 2020 to 2021 because of COVID-19. (2) Over the last 20 years, the proportion of carbon emissions from urbanization development zones (UDZs) has gradually decreased, whereas those of the main agricultural production zones (MAPZs) and key ecological function zones (KEFZs) have increased. (3) Carbon emissions efficiency differed significantly among the three MFOZs. (4) Carbon emissions from coastal UDZs were increasingly apparent; however, the directional characteristics of MAPZ and KEFZ emissions were not remarkable. (5) Carbon transfer existed among the three kinds of MFOZs, resulting in the economy and carbon emissions being considerably misaligned across Guangdong Province. These results indicated that the MFOZ is noteworthy in revealing how carbon emissions evolved. Furthermore, spatiotemporal characteristics, especially spatial characteristics, can help formulate carbon reduction policies for realizing carbon peak and neutrality goals in Guangdong Province.

Spatial-temporal Evolution and Influencing Factors of Digital Financial Inclusion: County-level Evidence from China.

The vigorous development of information and communications technology has accelerated reshaping of the financial industry. The COVID-19 pandemic has further catalyzed the demand for digital financial services. Digital financial inclusion relies on information technology to overcome spatial limitations. In this case, the research question is whether it adheres to the spatial laws governing conventional financial activities. This study uses exploratory spatial data analysis and a geographical detector to elucidate the spatiotemporal characteristics and factors influencing digital financial inclusion at the county level in China (Data don't include that of Hong Kong, Macao and Taiwan of China) from 2014 to 2020. The research findings indicate: first, China's county-level digital financial inclusion is generally increasing and exhibits significant spatial autocorrelation. Second, population density, level of traditional financial development, government regulation, and education level are key determinants of China's county-level digital financial inclusion. Third, policies should be differentiated by region to narrow the spatial gap in digital financial inclusion. The results provide a reference for other developing countries on using digital technology to develop financial inclusion.

Spatiotemporal evolution of online attention to vaccines since 2011: An empirical study in China.

Since the outbreak of Coronavirus Disease 2019 (COVID-19), the Chinese government has taken a number of measures to effectively control the pandemic. By the end of 2021, China achieved a full vaccination rate higher than 85%. The Chinese Plan provides an important model for the global fight against COVID-19. Internet search reflects the public's attention toward and potential demand for a particular thing. Research on the spatiotemporal characteristics of online attention to vaccines can determine the spatiotemporal distribution of vaccine demand in China and provides a basis for global public health policy making. This study analyzes the spatiotemporal characteristics of online attention to vaccines and their influencing factors in 31 provinces/municipalities in mainland China with Baidu Index as the data source by using geographic concentration index, coefficient of variation, GeoDetector, and other methods. The following findings are presented. First, online attention to vaccines showed an overall upward trend in China since 2011, especially after 2016. Significant seasonal differences and an unbalanced monthly distribution were observed. Second, there was an obvious geographical imbalance in online attention to vaccines among the provinces/municipalities, generally exhibiting a spatial pattern of "high in the east and low in the west." Low aggregation and obvious spatial dispersion among the provinces/municipalities were also observed. The geographic distribution of hot and cold spots of online attention to vaccines has clear boundaries. The hot spots are mainly distributed in the central-eastern provinces and the cold spots are in the western provinces. Third, the spatiotemporal differences in online attention to vaccines are the combined result of socioeconomic level, socio-demographic characteristics, and disease control level.

Spatiotemporal Characteristics and Influencing Factors of Tourism-Urbanization-Technology-Ecological Environment on the Yunnan-Guizhou-Sichuan Region: An Uncoordinated Coupling Perspective.

The tourism, urbanization, technology, and the ecological environment both promote and restrict each other. Coordinating the relationship between the four is of great significance to the realization of high-quality sustainable regional development. Taking the Yunnan-Guizhou-Sichuan region as an example, this paper constructs an uncoordinated coupling model for the tourism-urbanization-technology-ecological environment system. Using exploratory spatial analysis and geographic information systems, this paper reveals the temporal and spatial evolution law affecting the uncoordinated coupling relationship between tourism, urbanization, technology and the ecological environment in the Yunnan-Guizhou-Sichuan region from 2010 to 2020, before establishing a panel Tobit model that is used to explore the factors affecting the four systems. The research shows the following: (1) The level of comprehensive development for tourism, urbanization, technology, and the ecological environment in Yunnan, Guizhou, and Sichuan has increased rapidly. Of all these, the tourism industry was the most affected by COVID-19 in 2020, while the level of urbanization, technology, and ecological environment developments in the three provinces has become similar over time. (2) Uncoordinated development between cities is a prominent problem; while the uncoordinated coupling spatial agglomeration in various regions is relatively stable, the proportion of cities with no significant agglomeration form amounts to more than 70%, with mostly low-low (L-L) and high-high (H-H) agglomeration types. (3) The degree to which uncoordinated coupling exists among the four systems in the Yunnan-Guizhou-Sichuan region is affected by many factors. Only eco-environmental pressure has a significant positive correlation with the degree of uncoordinated coupling, while the tourism scale, economic urbanization, eco-environmental response, and investment in technology have a significant negative correlation. These results provide a theoretical basis and practical references for strengthening the government's macro-control and promoting collaborative regional development.

Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics.

COVID-19 blocked Wuhan in China, which was sealed off on Chinese New Year's Eve. During this period, the research on the relevant topics of COVID-19 and emotional expressions published on social media can provide decision support for the management and control of large-scale public health events. The research assisted the analysis of microblog text topics with the help of the LDA model, and obtained 8 topics ("origin", "host", "organization", "quarantine measures", "role models", "education", "economic", "rumor") and 28 interactive topics. Obtain data through crawler tools, with the help of big data technology, social media topics and emotional change characteristics are analyzed from spatiotemporal perspectives. The results show that: (1) "Double peaks" feature appears in the epidemic topic search curve. Weibo on the topic of the epidemic gradually reduced after January 24. However, the proportion of epidemic topic searches has gradually increased, and a "double peaks" phenomenon appeared within a week; (2) The topic changes with time and the fluctuation of the topic discussion rate gradually weakens. The number of texts on different topics and interactive topics changes with time. At the same time, the discussion rate of epidemic topics gradually weakens; (3) The political and economic center is an area where social media is highly concerned. The areas formed by Beijing, Shanghai, Guangdong, Sichuan and Hubei have published more microblog texts. The spatial division of the number of Weibo social media texts has a high correlation with the economic zone division; (4) The existence of the topic of "rumor" will enable people to have more communication and discussion. The interactive topics of "rumors" always have higher topic popularity and low emotion text expressions. Through the analysis of media information, it helps relevant decision makers to grasp social media topics from spatiotemporal characteristics, so that relevant departments can accurately grasp the public's subjective ideas and emotional expressions, and provide decision support for macro-control response strategies and measures and risk communication.