Research on adaption to air pollution in Chinese cities: Evidence from social media-based health sensing

Ambient Air Pollution and Hospitalizations for Schizophrenia in China

Importance

Schizophrenia episodes may be triggered by short-term environmental stimuli. Short-term increases in ambient air pollution levels may elevate the risk of schizophrenia episodes, yet few epidemiologic studies have examined this association.

Objective

To investigate whether short-term increases in air pollution levels are associated with an additional risk of schizophrenia episodes, independent of absolute air pollution concentrations, and whether sustained increases in air pollution levels for several days are associated with more pronounced risks of schizophrenia episodes.

Design, Setting, and Participants

This nationwide, population-based, time-stratified case-crossover study was performed based on hospitalization records for schizophrenia across 295 administrative divisions of prefecture-level or above cities in China. Records were extracted from 2 major health insurance systems from January 1, 2013, to December 31, 2017. Thirty-six cities with a small number of schizophrenia hospitalizations (n < 50) were excluded. Data analysis for this study was performed from January to March 2024.

Exposure

Daily absolute concentrations of fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide were collected. Air pollution increases between neighboring days (APINs) were generated as the differences in absolute air pollution concentrations on the current day minus that on the previous day. Sustained increases (APIN ≥5 μg/m3 for PM2.5 and PM10, APIN ≥1 μg/m3 for nitrogen dioxide and sulfur dioxide, and APIN ≥0.05 mg/m3 for carbon monoxide) lasting for 1 or more to 4 or more days were defined for different air pollutants.

Main Outcome and Measure

Patients with schizophrenia episodes were identified by principal discharge diagnoses of schizophrenia. A conditional logistic regression model was used to capture the associations of absolute concentrations, APINs, and sustained increase events for different air pollutants with risks of schizophrenia hospitalizations.

Results

The study included 817 296 hospitalization records for schizophrenia across 259 Chinese cities (30.6% aged 0-39 years, 56.4% aged 40-64 years, and 13.0% aged ≥65 years; 55.04% male). After adjusting for the absolute concentrations of respective air pollutants, per-IQR increases in 6-day moving average (lag0-5) APINs of PM2.5, PM10, nitrogen dioxide, sulfur dioxide, and carbon monoxide were associated with increases of 2.37% (95% CI, 0.88%-3.88%), 2.95% (95% CI, 1.46%-4.47%), 4.61% (95% CI, 2.93%-6.32%), 2.16% (95% CI, 0.59%-3.76%), and 2.02% (95% CI, 0.39%-3.68%) in schizophrenia hospitalizations, respectively. Greater risks of schizophrenia hospitalizations were associated with sustained increases in air pollutants lasting for longer durations up to 4 or more days.

Conclusions and Relevance

This case-crossover study of the association between ambient air pollution increases and schizophrenia hospitalizations provides novel evidence that short-term increases in ambient air pollution levels were positively associated with an elevated risk of schizophrenia episodes. Future schizophrenia prevention practices should pay additional attention to APINs, especially sustained increases in air pollution levels for longer durations, besides the absolute air pollution concentrations.

Paris air quality monitoring for the 2024 Olympics and Paralympics: focus on air pollutants and pollen

BACKGROUND

Exposure to air pollution can affect the health of individuals with respiratory disease, but may also impede the health and performance of athletes. This is potentially relevant for people travelling to and competing in the Olympic and Paralympic Games (OPG) in Paris. We describe anticipated air quality in Paris based on historical monitoring data and describe the impact of the process on the development of monitoring strategies for future international sporting events.

METHODS

Air pollutant data for July to September 2020-2023 and pollen data for 2015-2022 were provided by Airparif (particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3)) and RNSA stations in the Paris region. Airparif's street-level numerical modelling provided spatial data for the OPG venues.

RESULTS

The maximum daily mean PM2.5 was 11±6 µg/m3 at traffic stations, below the WHO recommended daily air quality threshold (AQT). Daily NO2 concentrations ranged from 5±3 µg/m3 in rural areas to 17±14 µgm3 in urban areas. Near traffic stations, this rose to 40±24 µg/m3 exceeding the WHO AQT. Both peaked around 06:00 and 20:00 UTC (coordinated universal time). The ambient O3 level exceeded the AQT on 20 days per month and peaked at 14:00 UTC. The main allergenic taxa from June to September was Poaceae (ie, grass pollen variety).

CONCLUSION

Air pollutant levels are expected to be within accepted air quality thresholds at the Paris OPG. However, O3 concentrations may be significantly raised in very hot and clear conditions and grass pollen levels will be high, prompting a need to consider and manage this risk in susceptible individuals.

Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public.

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, 'The Global Burden of Disease,' reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021's WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well. During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated-but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI prediction based on satellite images. Our hybrid model consists of two parts as follows:•The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation.•The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI. From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R2 (R2 > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them. Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.

The impact of air pollution on regional innovation: empirical evidence based on 267 cities in China

Based on the spatially correlated effects of air pollution on regional innovation, theoretical hypotheses are proposed, and this paper employs a spatial Durbin model to conduct empirical tests using panel data from 267 Chinese cities from 2003 to 2019, and investigates the mediating effect of human capital. Research has shown that (1) air pollution significantly reduces regional innovation output and has a negative spatial spillover effect significantly in the short term; (2) in the process of regional innovation impacted by air pollution, human capital acts as a mediator role; and (3) analysis of heterogeneity reveals that, from the regional perspective, air pollution has significantly damaged regional innovation in eastern and middle cities, but not significantly influences western cities, and in terms of innovation types, there is a stronger detrimental effect on invention patents exerted by air pollution compared to non-innovation patents. The study's findings provide theoretical and empirical evidence to strengthen environmental governance, enhance regional innovation and promote the coordinated development of regional innovation.

China's innovation and research contribution to combating neglected diseases: a secondary analysis of China's public research data

BACKGROUND

Many emerging and developing economies, such as China, have played the important roles in combating global neglected diseases (NDs). This study aims to explore China's public landscape of research projects and funding of NDs and to provide empirical evidence on promoting China's participation in addressing global health priorities that disproportionately affect developing countries.

METHODS

We systematically sourced China's public funding information from the National Natural Science Foundation of China and provincial science and technology agency websites up to August 16, 2019. Following the G-FINDER R&D scope, we screened projects of NDs for analysis. National-funded projects were reviewed on an annual basis for exploring the trends and distribution of funding flows. Information on provincial-funded projects was compared with national projects by disease, research type, and geographical distribution.

RESULTS

A total of 1266 projects were included for analysis and categorized by year, funding source, recipient, disease, research type, region, and province. China's national public funding for ND research reached a historical peak of USD 16.22 million in 2018. But the proportion of ND research to all public-funded projects was less than 0.5%, and over half of the ND projects were allocated to "the big three," i.e., tuberculosis, HIV/AIDS, and malaria. About 58% of national and provincial ND projects focus on basic research. Economically developed regions and municipalities play dominant roles in leading national ND research, such as Beijing, Shanghai, and Guangdong. Provincial ND projects are primarily driven by endemic regions.

CONCLUSIONS

As a new emerging high-tech innovator, China has gradually increased public input to ND-related innovation and research. But there is still a large funding gap among NDs that requires China's increased support and participation. National development plans and cooperative health needs should be taken into account for China's participation in promoting global research and development (R&D) for combating NDs.

Does the built environment of settlements affect our sentiments? A multi-level and non-linear analysis of Xiamen, China, using social media data

Introduction

Humans spend most of their time in settlements, and the built environment of settlements may affect the residents' sentiments. Research in this field is interdisciplinary, integrating urban planning and public health. However, it has been limited by the difficulty of quantifying subjective sentiments and the small sample size.

Methods

This study uses 147,613 Weibo text check-ins in Xiamen from 2017 to quantify residents' sentiments in 1,096 neighborhoods in the city. A multilevel regression model and gradient boosting decision tree (GBDT) model are used to investigate the multilevel and nonlinear effects of the built environment of neighborhoods and subdistricts on residents' sentiments.

Results

The results show the following: (1) The multilevel regression model indicates that at the neighborhood level, a high land value, low plot ratio, low population density, and neighborhoods close to water are more likely to improve the residents' sentiments. At the subdistrict level, more green space and commercial land, less industry, higher building density and road density, and a smaller migrant population are more likely to promote positive sentiments. Approximately 19% of the total variance in the sentiments occurred among subdistricts. (2) The proportion of green space and commercial land, and the density of buildings and roads are linearly correlated with residents' sentiments. The land value is a basic need and exhibits a nonlinear correlation with sentiments. The plot ratio, population density, and the proportions of industrial land and the migrant population are advanced needs and are nonlinearly correlated with sentiments.

Discussion

The quantitative analysis of sentiments enables setting a threshold of the influence of the built environment on residents' sentiments in neighborhoods and surrounding areas. Our results provide data support for urban planning and implementing targeted measures to improve the living environment of residents.

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 (PM2.5, 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 PM2.5 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.

Influence and prediction of PM2.5 through multiple environmental variables in China

Fine particulate matter (PM2.5) is an important indicator to measure the degree of air pollution. With the pursuit of sustainable development of China's economy and society, air pollution has been paid more and more attention. The spatial distribution of PM2.5 is affected by multiple factors. In this study, we selected Normalized Difference Vegetation Index (NDVI), precipitation, temperature, wind speed and elevation data to analyze the impact of each variable on PM2.5 in different regions of China. The results show that the high-value areas of PM2.5 were mainly concentrated in the North China Plain, the middle and lower reaches of the Yangtze River Plain, the Sichuan Basin, and the Tarim Basin. PM2.5 showed an upward trend in North China, Northeast China and Northwest China, while in most of South China, especially the Sichuan Basin, PM2.5 showed a downward trend. Therefore, the northern region of China needs to take measures to curb the growth of PM2.5. In Northwest China, wind speed and temperature had a greater impact on PM2.5. In North China, wind speed had a greater impact on PM2.5. In southern China, temperature and NDVI had a greater impact on PM2.5. The deep learning model can better simulate the spatial distribution of PM2.5 based on the selected variables. The clustering effect of single variable is better than multivariate spatial information clustering based on principal component analysis (PCA). It is difficult to explain which variable has the greatest impact on PCA clustering. This study can provide an important reference for PM2.5 prevention and control in different regions of China.

Association of ambient air pollution exposure and its variability with subjective sleep quality in China: A multilevel modeling analysis

Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM_2.5) and ≤10 μm (PM₁₀) led to 0.4% (95% confidence interval (CI): 1.002-1.006) and 0.3% (95% CI: 1.001-1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM_2.5 and PM₁₀ were 2.2% (95% CI: 1.012-1.032) and 2.2% (95% CI: 1.012-1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.

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.

Air pollution and innovation performance of Chinese cities: human capital and labour cost perspective

Environmental protection and innovation performance are key issues that affect the sustainable development and value growth of cities. Using data of 272 prefecture-level cities during 2002-2016 and 2,169 listed companies, and the air ventilation coefficient and government environmental regulations, as the instrumental variables for PM_2.5 concentrations, this paper applies two-stage OLS (2SLS) to investigate how air pollution affects China's technological innovation and its realization mechanism. The results indicate that the rise in air pollution significantly inhibits the technological innovation level of regions as a whole as well as individual enterprises. When considering the spatial effect of the spread of PM_2.5 concentrations, due to positive spillover effects on innovation activities, the spread of air pollution has negative impacts on technological innovation activities in surrounding cities. Human capital and labour costs are important channels through which air pollution influences China's technological innovation. The implementation of pilot carbon trading policies can effectively reduce air pollution and then contribute to the achievement of the goals of the green growth strategy.

How does new-type urbanization affect air pollution? Empirical evidence based on spatial spillover effect and spatial Durbin model

To alleviate the ecological and environmental problems caused by rapid urban development, China has formulated and implemented the new-type urbanization strategy. However, there is insufficient empirical research on the specific relationship between new-type urbanization and air pollution. Therefore, based on the panel data of 30 provinces in China from 2005 to 2018, this paper constructs a comprehensive evaluation index system of new-type urbanization using the five dimensions of population, economy, space, society, and green. The spatial Durbin model and the spatial mediating model are used to discuss the spatial effect, transmission mechanism, and regional heterogeneity of new-type urbanization on air pollution. The results show that China's air pollution mainly presents a spatial pattern of high-high agglomeration and low-low agglomeration, and there are spatial fluctuations. The construction of new-type urbanization significantly reduces local air pollution, and the industrial structure optimization, technological innovation, and energy structure adjustment are considered as important transmission mechanisms. However, under the fiscal decentralization and political tournament system in China, the policy implementation deviation may weaken the emission reduction effect of new-type urbanization, which is not conducive to regional environmental governance. From the sub-regional level, the impact of new-type urbanization on air pollution has regional heterogeneity. A robustness test confirms the reliability of our research conclusions. This study also proposes some policy suggestions that the government can utilize in grasping the policy focus of new-type urbanization construction to discover effective ways of controlling air pollution.

Spatio-Temporal Patterns of Fitness Behavior in Beijing Based on Social Media Data

Fitness is an important way to ensure the health of the population, and it is important to actively understand fitness behavior. Although social media Weibo data (the Chinese Tweeter) can provide multidimensional information in terms of objectivity and generalizability, there is still more latent potential to tap. Based on Sina Weibo social media data in the year 2017, this study was conducted to explore the spatial and temporal patterns of urban residents’ different fitness behaviors and related influencing factors within the Fifth Ring Road of Beijing. FastAI, LDA, geodetector technology, and GIS spatial analysis methods were employed in this study. It was found that fitness behaviors in the study area could be categorized into four types. Residents can obtain better fitness experiences in sports venues. Different fitness types have different polycentric spatial distribution patterns. The residents’ fitness frequency shows an obvious periodic distribution (weekly and 24 h). The spatial distribution of the fitness behavior of residents is mainly affected by factors, such as catering services, education and culture, companies, and public facilities. This research could help to promote the development of urban residents’ fitness in Beijing.