Analysis of spatio-temporal distribution characteristics and socioeconomic drivers of urban air quality in China

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data

Background

Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors.

Methods

This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods.

Results

Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety.

Conclusion

Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

Synergistic effect of combating air pollutants and carbon emissions in the Yangtze River Delta of China: spatial and temporal divergence analysis and key influencing factors

Synergizing the reduction of air pollutants and carbon emissions (APCE) has become a critical tactic alternative to address the issue of climate change. Taking the Yangtze River Delta (YRD) region of China as a case study, this paper explores the spatial and temporal distribution pattern of the coupling coordination degree (CCD) of combating APCE from 2011 to 2022, analyzes the dynamic change in CCD using the convergence test, and investigates the key factors affecting CCD via the Tobit regression model. The results show that (1) from 2011 to 2022, the air pollutants (AP) and CO2 emission (CE) in the YRD region decrease at the annual rate of 10.32% and 0.85%, respectively; (2) the CCD of reducing APCE in the YRD presents a W-shaped fluctuation before 2016 and then steps into a steady increase status after 2016; (3) the order of CCD in four provincial-level units by 2022 is Shanghai > Zhejiang > Jiangsu > Anhui. The proportion of cities where the CCD of reducing APCE enters the high-coordination period has reached 87.8%; and (4) the Tobit regression results affirm that economic growth, industrial structure, and green technological innovation exacerbate the CCD of combating APCE, while opening-up level mitigates it. The findings offer policymakers valuable insights into the importance of pursuing collaborative governance over APCE and ensuring sustainable development.

Coping with extremes: Alternations in diet, gut microbiota, and hepatic metabolic functions in a highland passerine

In wild animals, diet and gut microbiota interactions are critical moderators of metabolic functions and are highly contingent on habitat conditions. Challenged by the extreme conditions of high-altitude environments, the strategies implemented by highland animals to adjust their diet and gut microbial composition and modulate their metabolic substrates remain largely unexplored. By employing a typical human commensal species, the Eurasian tree sparrow (Passer montanus, ETS), as a model species, we studied the differences in diet, digestive tract morphology and enzyme activity, gut microbiota, and metabolic energy profiling between highland (the Qinghai-Tibet Plateau, QTP; 3230 m) and lowland (Shijiazhuang, Hebei; 80 m) populations. Our results showed that highland ETSs had enlarged digestive organs and longer small intestinal villi, while no differences in key digestive enzyme activities were observed between the two populations. The 18S rRNA sequencing results revealed that the dietary composition of highland ETSs were more animal-based and less plant-based than those of the lowland ones. Furthermore, 16S rRNA sequencing results suggested that the intestinal microbial communities were structurally segregated between populations. PICRUSt metagenome predictions further indicated that the expression patterns of microbial genes involved in material and energy metabolism, immune system and infection, and xenobiotic biodegradation were strikingly different between the two populations. Analysis of liver metabolomics revealed significant metabolic differences between highland and lowland ETSs in terms of substrate utilization, as well as distinct sex-specific alterations in glycerophospholipids. Furthermore, the interplay between diet, liver metabolism, and gut microbiota suggests a dietary shift resulting in corresponding changes in gut microbiota and metabolic functions. Our findings indicate that highland ETSs have evolved to optimize digestion and absorption, rely on more protein-rich foods, and possess gut microbiota tailored to their dietary composition, likely adaptive physiological and ecological strategies adopted to cope with extreme highland environments.

Dynamic evolutionary characteristics and influence mechanisms of carbon emission intensity in counties of the Yangtze River Delta, China

Clarifying the intrinsic mechanism of county carbon emission intensity (CEI) is essential for guiding the realization of a low-carbon economy as well as for the strategic goals of carbon peaking and carbon neutrality. However, at present, scholars mostly focus on provincial and city scales, with the identification of influencing factors and spatial effect mechanisms of CEI rarely included in the analysis framework. Herein, with the help of three spatial weight matrices, the spatial autocorrelation, the "F + S" influence factor identification method, and the spatial panel econometric model were used to analyze the evolutionary paths and influencing factors of CEI for 209 counties in the Yangtze River Delta (YRD) from 2007 to 2020. The results show that (1) the CEI of the YRD decreased from 1.998t/104 RMB to 0.858t/104 RMB. Furthermore, the spatial pattern was low in the southeast and high in the northwest, with high-value areas concentrated in municipal districts and resource-based counties. (2) Moran's I spatial autocorrelation index indicated significant spatial clustering of county CEI. (3) Financial science and technology expenditure, industrial structure, share of urban built-up land, and the urban-rural income gap affected the change in CEI and its spatial effect, whereas total imports and exports had a significant negative effect on local CEI. Therefore, to achieve China's "double carbon" goal, it is necessary to consider the five development concepts as the core, strengthen inter-county exchanges and collaboration, as well as promote collaborative management of the ecological environment.

Satellite-Based Long-Term Spatiotemporal Trends in Ambient NO2 Concentrations and Attributable Health Burdens in China From 2005 to 2020

Despite the recent development of using satellite remote sensing to predict surface NO₂ levels in China, methods for estimating reliable historical NO₂ exposure, especially before the establishment of NO₂ monitoring network in 2013, are still rare. A gap-filling model was first adopted to impute the missing NO₂ column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO₂ concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO₂ associated mortality burdens in China. The coverage of satellite NO₂ column densities increased from 46.9% to 100% after gap-filling. The ensemble model predictions had good agreement with observations, and the sample-based, temporal and spatial cross-validation (CV) R ² were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO₂ concentrations, with both by-year CV R ² and external separate year validation R ² achieving 0.80. The estimated national NO₂ levels showed a increasing trend during 2005-2011, then decreased gradually until 2020, especially in 2012-2015. The estimated annual mortality burden attributable to long-term NO₂ exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite-based ensemble model could provide reliable long-term NO₂ predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO₂ and call for more targeted policies to reduce the emission of nitrogen oxides in China.

Public Concern about Air Pollution and Related Health Outcomes on Social Media in China: An Analysis of Data from Sina Weibo (Chinese Twitter) and Air Monitoring Stations

To understand the temporal variation, spatial distribution and factors influencing the public's sensitivity to air pollution in China, this study collected air pollution data from 2210 air pollution monitoring sites from around China and used keyword-based filtering to identify individual messages related to air pollution and health on Sina Weibo during 2017-2021. By analyzing correlations between concentrations of air pollutants (PM_2.5, PM₁₀, CO, NO₂, O₃ and SO₂) and related microblogs (air-pollution-related and health-related), it was found that the public is most sensitive to changes in PM_2.5 concentration from the perspectives of both China as a whole and individual provinces. Correlations between air pollution and related microblogs were also stronger when and where air quality was worse, and they were also affected by socioeconomic factors such as population, economic conditions and education. Based on the results of these correlation analyses, scientists can survey public concern about air pollution and related health outcomes on social media in real time across the country and the government can formulate air quality management measures that are aligned to public sensitivities.

Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology

BACKGROUND

The spatiotemporal variation of observed trace gases (NO₂, SO₂, O₃) and particulate matter (PM_2.5, PM₁₀) were investigated over cities of Yangtze River Delta (YRD) region including Nanjing, Hefei, Shanghai and Hangzhou. Furthermore, the characteristics of different pollution episodes, i.e., haze events (visibility < 7 km, relative humidity < 80%, and PM_2.5 > 40 µg/m³) and complex pollution episodes (PM_2.5 > 35 µg/m³ and O₃ > 160 µg/m³) were studied over the cities of the YRD region. The impact of China clean air action plan on concentration of aerosols and trace gases is examined. The impacts of trans-boundary pollution and different meteorological conditions were also examined.

RESULTS

The highest annual mean concentrations of PM_2.5, PM₁₀, NO₂ and O₃ were found for 2019 over all the cities. The annual mean concentrations of PM_2.5, PM₁₀, and NO₂ showed continuous declines from 2019 to 2021 due to emission control measures and implementation of the Clean Air Action plan over all the cities of the YRD region. The annual mean O₃ levels showed a decline in 2020 over all the cities of YRD region, which is unprecedented since the beginning of the China's National environmental monitoring program since 2013. However, a slight increase in annual O₃ was observed in 2021. The highest overall means of PM_2.5, PM₁₀, SO₂, and NO₂ were observed over Hefei, whereas the highest O₃ levels were found in Nanjing. Despite the strict control measures, PM_2.5 and PM₁₀ concentrations exceeded the Grade-1 National Ambient Air Quality Standards (NAAQS) and WHO (World Health Organization) guidelines over all the cities of the YRD region. The number of haze days was higher in Hefei and Nanjing, whereas the complex pollution episodes or concurrent occurrence of O₃ and PM_2.5 pollution days were higher in Hangzhou and Shanghai.The in situ data for SO₂ and NO₂ showed strong correlation with Tropospheric Monitoring Instrument (TROPOMI) satellite data.

CONCLUSIONS

Despite the observed reductions in primary pollutants concentrations, the secondary pollutants formation is still a concern for major metropolises. The increase in temperature and lower relative humidity favors the accumulation of O₃, while low temperature, low wind speeds and lower relative humidity favor the accumulation of primary pollutants. This study depicts different air pollution problems for different cities inside a region. Therefore, there is a dire need to continuous monitoring and analysis of air quality parameters and design city-specific policies and action plans to effectively deal with the metropolitan pollution.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-022-00668-2.

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis

Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O₃, NO₂, SO₂, PM_2.5 and PM₁₀ of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM_2.5, PM₁₀ and CO are the main pollutants affecting air quality and fluctuation, followed by SO₂, NO₂ and O₃.

Study on Spatial–Temporal Patterns and Factors Influencing Human Settlement Quality in Beijing

Human settlements lay the basis for urban sustainable development and embody comprehensive urban competitiveness. Based on data from the period 2010–2019, the entropy value method, global spatial correlation, and local spatial correlation are adopted to systematically analyze the overall level and spatial–temporal pattern of human settlement quality in Beijing. In particular, this study sought to uncover the factors that influence human settlement quality in Beijing by using the panel data model. The results show that the quality of human settlements in Beijing has generally followed an upward trend, with slow growth and a slight decline since 2017. Despite significant spatial positive correlations and stable local spatial self-correlation, the spatial difference is still evident, and regional correlation needs further improvement. Medical resources, economic development, public services, governance investment, and infrastructure are significantly and positively correlated with human settlement quality, while population growth is significantly and negatively correlated with it. Based on this study, specific recommendations are proposed which can be used as a reference for Beijing and other cities’ human settlement construction and its improvement.