Dynamic assessment of PM2.5 exposure and health risk using remote sensing and geo-spatial big data

Environmental health risks and impacts of PM2.5 exposure on human health in residential areas, Bantul, Yogyakarta, Indonesia

Air pollution, particularly PM2.5, significantly impacts public health in developing areas. This study evaluates PM2.5 exposure among residents and conducts a health risk assessment within the human community in Bantul Regency, Indonesia, utilizing a high-volume air sampler (HVAS) over 24 h in a residential area and interviewing 36 respondents. The findings of this study show that PM2.5 concentrations varied from 50.7 to 61.9 μg/m³, exceeding the national ambient air quality standards (NAAQS) of 35 μg/m³. The risk hazard quotient (RQ) values of PM2.5 were greater than 1, signifying considerable health risk. Epidemiological statistical analysis indicates a significant correlation (p-value < 0.05) between PM2.5 exposure, health complaints, and respondent characteristics. Residents report health issues including cough, headache, eye irritation, breathlessness, and wheezing. The findings emphasize the imperative for more rigorous air quality standards and regulations, enhanced public awareness and education regarding preventive practices, and urban planning development strategies incorporating green infrastructure. These measures are crucial for alleviating health hazards and enhancing air quality in impacted areas.

Inequality in infrastructure access and its association with health disparities

Economic, social and environmental infrastructure forms a fundamental pillar of societal development. Ensuring equitable access to infrastructure for all residents is crucial for achieving the Sustainable Development Goals, yet knowledge gaps remain in infrastructure accessibility and inequality and their associations with human health. Here we generate gridded maps of economic, social and environmental infrastructure distribution and apply population-weighted exposure models and mixed-effects regressions to investigate differences in population access to infrastructure and their health implications across 166 countries. The results reveal contrasting inequalities in infrastructure access across regions and infrastructure types. Global South countries experience only 50-80% of the infrastructure access of Global North countries, whereas their associated inequality levels are 9-44% higher. Both infrastructure access and inequality are linked to health outcomes, with this relationship being especially pronounced in economic infrastructure. These findings underscore the necessity of informed decision-making to rectify infrastructure disparities for promoting human well-being.

A brief review of methods for determining time-activity patterns in California

Air pollution exposure has been found to be linked with numerous adverse human health effects. Because both air pollution concentrations and the location of human individuals change spatiotemporally, understanding the time-activity patterns (TAPs) is of utmost importance for the mitigation of adverse exposures and to improve the accuracy of air pollution and health analyses. "Time-activity patterns" outlined here broadly refer to the spatiotemporal positions of individuals. In this review paper, we briefly review past efforts on collecting individual TAP information for air pollution and health studies, with a specific focus on California efforts. We also critically summarize emerging technologies and approaches for collecting TAP data. Specifically, we critically reviewed five types of emerging TAP data sources, including call detail record, social media location data, Google Location History, iPhone Significant Location, and crowd-sourced location data. This review provides a comprehensive summary and critique of different methods to collect TAP information and offers recommendations for use in retrospective air pollution and health studies.Implications: In this review paper, we provide a comprehensive overview of approaches for collecting time-activity pattern (TAP) data from individuals, a crucial component in understanding human behavior and its implications across various fields such as urban planning, environmental science, and, particularly, public health in relation to air pollution exposures.Furthermore, our paper introduces and critically evaluates several emerging methods for TAP data collection. These novel approaches, including but not limited to Google Location History, iPhone Significant Locations, and crowdsourced smartphone location data, offer unprecedented granularity in tracking human activities. By showcasing these methodologies and their often not well-recognized weaknesses, we highlight both the potential and limitations of these tools to advance our understanding of human behavior patterns, especially in terms of how individuals interact with their environments. This discussion not only showcases the originality of our work but also sets the stage for future research directions that can benefit from these innovative data collection strategies.

High-resolution monthly assessment of population exposure to PM2.5 and its relationship with socioeconomic activities using multisource geospatial data

Understanding the spatiotemporal dynamics of population exposure to PM2.5 (PEP) and its relationship with socioeconomic activity (SEA) is crucial to reduce exposure risks and health dangers. However, few studies have investigated the dynamic variations of PEP within large regions at high spatiotemporal resolution; further, the impact mechanism between PEP and SEA remains largely unclear. Therefore, we estimated highly accurate PM2.5 concentrations in the Hunan province, China, using the Boruta and random forest (RF) algorithms and evaluated high-spatiotemporal-resolution PEP based on the estimated PM2.5 and obtained population data. Nighttime light data were used as a proxy of SEA to analyze the relationship between PEP and SEA. The results revealed that the Boruta-RF model predicted PM2.5 with fewer errors than the RF and stepwise multiple linear regression models, with the mean root-mean-square error reduced by 6.18% and 11.15%, respectively. The monthly PM2.5 concentrations in 2015 showed a U-shaped curve, with the entire provincial population exposed to monthly mean concentrations > 15 μg/m3. Heavier PM2.5 pollution tended to occur in densely populated areas, particularly in winter months. Using both fine-scale PM2.5 and population data improved the reliability of monthly PEP assessments and avoided over- and under-responses. Moreover, the PEP risk exhibited a unimodal structure, with a peak in January, at which point the urban-rural difference in PEP was the greatest. Further, PEP was positively influenced by SEA, with clear spatial spillover effects. SEA had an active impact on PEP during festivals and holidays, with the greatest consistency between the two occurring in November. These findings provide crucial insights for managing PM2.5 pollution.

Evaluating the feasibility of estimating particulate mass emissions of older-model diesel vehicle using smoke opacity measurements

Real-world emissions of particulate matter (PM) and smoke opacity were studied for an older-model diesel pickup truck during four types of driving tests, namely fixed-point test, snap-acceleration test, road test, and hill road test (uphill/downhill). A portable emissions measurement system (PEMS) and an opacimeter were used to measure real-time concentrations of PM and smoke opacity, respectively, and simultaneously. Correlation analysis showed a significant positive association between PM and opacity, suggesting the feasibility of using an opacimeter to estimate PM mass emissions from diesel vehicles. Additionally, regression analyses were performed to evaluate the relationship between opacity and PM mass concentration. The results of this study indicate that PM emission concentrations from older-model diesel vehicles can be estimated with a reasonable accuracy by using a smoke opacimeter, which is a relatively simple and cost-effective method of emission testing, as an alternative to sophisticated PM measurement instruments.

Assessing the spatiotemporal evolution and socioeconomic determinants of PM2.5-related premature deaths in China from 2000 to 2021

China's swift socioeconomic development has led to extremely severe ambient PM2.5 levels, the associated negative health outcomes of which include premature death. However, a comprehensive explanation of the socioeconomic mechanism contributing to PM2.5-related premature deaths has not yet to be fully elucidated through long-term spatial panel data. Here, we employed a global exposure mortality model (GEMM) and the system generalized method of moments (Sys-GMM) to examine the primary determinants contributing to premature deaths in Chinese provinces from 2000 to 2021. We found that in the research period, premature deaths in China increased by 46 %, reaching 1.87 million, a figure that decreased somewhat after the COVID-19 outbreak. 62 thousand premature deaths were avoided in 2020 and 2021 compared to 2019, primarily due to the decline in PM2.5 concentrations. Premature deaths have increased across all provinces, particularly in North China, and a discernible spatial agglomeration effect was observed, highlighting effects on nearby provinces. The findings also underscored the significance of determinants such as urbanization, import and export trade, and energy consumption in exacerbating premature deaths, while energy intensity exerted a mitigating influence. Importantly, a U-shaped relationship between premature deaths and economic development was unveiled for the first time, implying the need for vigilance regarding potential health impact deterioration and the implementation of countermeasures as the per capita GDP increases in China. Our findings deserve attention from policymakers as they shed fresh insights into atmospheric control and Health China action.

Association between outdoor jogging behavior and PM2.5 exposure: Evidence from massive GPS trajectory data in Beijing

Outdoor jogging is one of the most popular practised exercises worldwide, providing various benefits for health and wellbeing. However, PM2.5 exposure risks of jogging behaviors were rarely explored. This study aims to investigate the association between jogging behavior and PM2.5 exposure with big data. PM2.5 exposure concentration and dose inhalation of individuals were calculated by integrating hourly PM2.5 concentration data and jogging GPS trajectory recorded by a sports app during 2015 in Beijing, after which relationships between jogging behaviors and PM2.5 exposure were unpacked using statistics analysis and structural equation modelling. Experimental results on massive jogging trajectories show that: (1) the average jogging PM2.5 exposure concentration is 60.43 μg/m3, and female joggers inhaled significantly less air pollution dose (19.70 μg) than men (24.91 μg). (2) There exist significant spatiotemporal disparities in jogging exposure to PM2.5. Joggings in the city center, in the morning, on weekdays and in autumn and winter seasons were exposed to higher pollution concentrations. (3) Jogging behavior characteristics, especially distance, activity space size, duration and rotation, were systematically associated with PM2.5 exposure across space and time. (4) The role of gender directly shaped joggers' dose inhalation of PM2.5 pollution and indirectly via duration, timing choice and distance. (5) The effects of weather conditions on joggers' exposure to PM2.5 are mainly via direct effects, whereas the direct impacts of precipitation and wind speed are mitigated by indirect effects stemming from jogging behavior patterns. Our findings provide insights for personal guidance and policy intervention for the sake of promoting physical activity and reducing PM2.5 exposure.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data

BACKGROUND

Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability.

METHODS

This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers' exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers' activities.

RESULTS

The taxi drivers' weekday and weekend 24-h PM2.5 exposure was 83.60 μg/m3 and 55.62 μg/m3 respectively, 3.4 and 2.2 times than the WHO's recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the "Inner Ring Road", while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the "Third Ring Road".

CONCLUSION

These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

The impact of long-term PM1 exposure on all-cause mortality and its interaction with BMI: A nationwide prospective cohort study in China

BACKGROUND

China has a serious air pollution problem and a high prevalence of obesity. The interaction between the two and its impact on all-cause mortality is a public health issue of great concern.

OBJECTIVES

This study aimed to investigate the association between long-term exposure to particulate matter with aerodynamic diameter ≤ 1 μm (PM1) and all-cause mortality, as well as the interaction effect of body mass index (BMI) in the association.

METHODS

A total of 33,087 participants from 162 counties in 25 provinces in China were included, with annual average PM1 exposure being estimated based on the county address. The PM1-mortality relation was evaluated using the time-varying Cox proportional hazards models, with the dose-response relationship being fitted using the penalized splines. Besides, the potential interaction effect of BMI in the PM1-mortality relation was evaluated.

RESULTS

The incidence of all-cause deaths was 76.99 per 10,000 person-years over a median of 8.2 years of follow-up. After controlling for potential confounders, the PM1-mortality relation was approximately J-shaped. The full-adjustment analysis observed the hazard ratio (HR) of all-cause mortality was 1.114 [95 % confidence interval (CI): 1.017-1.220] corresponding to a 10 μg/m3 rise in PM1 concentration. Further stratified analyses suggested the adverse effects of PM1 might be more pronounced among the underweight.

DISCUSSION

Higher PM1 concentrations were associated with an increase in all-cause mortality. The BMI might further alter the relation, and the underweight population was the sensitive subgroup of the population that needed to be protected.

Effects of Fireworks Burning on Air Quality during the Chinese Spring Festival-Evidence from Zhengzhou, China

Fireworks burning significantly degrades air quality over a short duration. The prohibition of fireworks burning (POFB) policy of 2016 and the restricted-hours fireworks burning (RHFB) policy of 2023 in Zhengzhou City provide an ideal opportunity to investigate the effects of such policies and of fireworks burning on air quality during the Spring Festival period. Based on air quality ground-based monitoring data and meteorological data for Zhengzhou City, the article analyzes the impact of the POFB policy and the RHFB policy on air quality. The results show that: (1) The ban on fireworks burning significantly affects Spring Festival air quality, with a decrease of 16.0% in the Air Quality Index (AQI) value in 2016 compared to 2015 and a 74.9% increase in 2023 compared to 2022. (2) From 2016 to 2022, the Spring Festival period witnessed a substantial decrease in average concentration of main pollutants, along with a delayed occurrence of peak concentrations, indicating a noticeable "peak-shaving" effect. However, in 2023, there was an increase in pollutant concentrations, volatility, and a significant surge in hourly concentration. (3) The POFB policy and RHFB policy notably impacted PM2.5 and PM10, with a decrease of 16.1% and 23.6% in PM2.5 and PM10 concentrations, respectively, in 2016 compared to 2015, but an increase of 74.5% and 79.2%, respectively, in 2023 compared to 2022. (4) The contribution of fireworks burning to PM2.5 concentrations significantly decreased during the fireworks burning period (FBP) in 2016 after the POFB policy and increased significantly in 2023 during FBP after the implementation of the RHFB policy. Unfavorable meteorological conditions will undoubtedly exacerbate air quality pollution caused by fireworks burning.

Spatio-Temporal Distribution Characteristics and Drivers of PM2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic

PM_2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM_2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM_2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM_2.5 concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM_2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM_2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.

The Impact of Campus Outdoor Space Features on Students' Emotions Based on the Emotion Map

To explore the influence of campus public space characteristics on students' emotions, we investigated the association mechanism between public space characteristics and students' emotions concerning the distribution of students' emotions in public spaces. The present study used photographs of facial expressions taken over two consecutive weeks as a source of data regarding the students' affective reactions. The collected facial expression images were analyzed using facial expression recognition. Values were assigned to the expression data, combined with geographic coordinates to create an emotion map of the campus public space using GIS software. Then, spatial feature data via emotion marker points were collected. We used smart wearable devices to combine the ECG data with spatial characteristics and took SDNN and RMSSD as ECG indicators to assess mood changes. We analyzed the correlation between these spatial features and heart rate variability and developed regression models for the ECG data. The findings show that sky visibility, space D/H, green visibility, skyline change index, and boundary permeability can engage students' positive emotions in a meaningful way. On the other hand, paving visibility and road linearity tends to induce negative emotions in students' minds.

Evaluating Urban Vitality of Street Blocks Based on Multi-Source Geographic Big Data: A Case Study of Shenzhen

Urban vitality is the comprehensive form of regional development quality, sustainability, and attractiveness. Urban vitality of various regions within the cities has difference, and the quantitative evaluation of urban vitality within the cities can help guide to future city constructions. Evaluation of urban vitality needs the combination of multi-source data. Existing studies have developed index method and estimation models mainly based on geographic big data to evaluate urban vitality. This study aims to combine remote sensing data with geographic big data to evaluate urban vitality of Shenzhen at street block scale and build the estimation model by random forest method. Indexes and random forest model were built, and some further analyses were conducted. The results were: (1) urban vitality in Shenzhen was high in the coastal areas, business areas, and new towns; (2) compared to indexes, the estimation model had advantages of more accurate results, combination of various data, and the ability to analyze feature contributions; and (3) taxi trajectory, nighttime light, and housing rental data had the strongest influence on urban vitality.

Air Pollution, Environmental Protection Tax and Well-Being

The effective control of air pollution to advance human health and improve well-being has risen to the forefront of discussion in recent years. Based on China's 2019 environmental protection tax data and China Social Survey (CSS) data, this paper studies the effects of subjective air pollution and the environmental protection tax on residents' well-being using an econometric mediation effect model. The research conclusions are as follows: (1) Subjective air pollution can significantly reduce residents' well-being, (2) an environmental protection tax can significantly improve residents' well-being and it can eliminate some of the negative influence of subjective air pollution on residents' well-being, and (3) the grouping test of residents' income, regional distribution, urban and rural structure, age structure, gender structure, and other variables shows that the effects of subjective air pollution on residents' well-being are heterogeneous among different populations. After further endogeneity testing with the instrumental variables method, adjusting the primary variables, and altering the research procedures, the results are still robust. Based on these findings, we should vigorously promote the development of ecological civilization and good air quality and support reforming the environmental protection tax system to enhance well-being. It is also necessary to shift from a crude development model to a green industry and business model. While emphasizing social equity and production efficiency, we should ensure the synchronous development of cities and villages. Additionally, tangible steps should be implemented to raise people's incomes, expand young people's work options, and enhance their satisfaction. The article focuses on the impact of subjective air pollution on residents' well-being, adding air pollution to the factors affecting well-being. Furthermore, the article finds that the environmental protection tax has two advantages: it can govern air pollution and promote green development, and, at the same time, it can enhance social harmony and improve residents' well-being.

Personal PM2.5 Exposure Monitoring of Informal Cooking Vendors at Indoor and Outdoor Markets in Johannesburg, South Africa

Air pollutants of concern include particulate matter (PM) in fine size fractions. Thus far, a few studies have been conducted to study the adverse health effects of environmental and occupational air pollutants among informal vendors in big cities in South Africa. Informal vendors in these cities may experience higher exposure to road dust, cooking fumes, and air pollution. This exposure assessment was part of a health risk assessment study of vendors. The objective of this exposure assessment was to determine the differences between outdoor and indoor informal vendors' personal PM_2.5 exposures during trading hours. A walkthrough survey was conducted to map the homogeneous exposure groups (HEGs) at vendor markets for sampling purposes, and one market was selected from each of the three identified HEGs. Twenty-five informal cooked food vendors from both indoor (inside buildings) and outdoor (street or roadside vendors) markets in the inner city of Johannesburg, South Africa, participated in the study. HEG-1 were vendors from indoor stalls who used electricity and gas for cooking (10 vendors), HEG-2 was composed of informal outdoor vendors at a fenced site market who used open fire for cooking (10 vendors), and HEG-3 (5 vendors) were roadside vendors who used gas for cooking. Cooking vendors from outdoor markets recorded higher TWA concentrations than indoor market vendors. The vendors' PM_2.5 concentrations ranged from <0.01 mg/m³ to 0.77 mg/m³. The mean concentrations of PM_2.5 were found to be 0.12 mg/m³, and 0.18 mg/m³ for HEG-2, and HEG-3, respectively. HEG-2 recorded the highest PM_2.5 TWA concentrations, followed by HEG-3 and HEG-1. All concentrations were below the South African occupational exposure limit. The findings point to the need for further research into the health risks associated with outdoor cooking vendors, particularly those who utilize open fires.

PM2.5 concentration assessment based on geographical and temporal weighted regression model and MCD19A2 from 2015 to 2020 in Xinjiang, China

PM2.5 is closely linked to both air quality and public health. Many studies have used models combined with remote sensing and auxiliary data to inverse a large range of PM2.5 concentrations. However, the data's spatial resolution is limited. and better results might have been obtained if higher resolution data had been used. Therefore, this paper establishes a geographical and temporal weighted regression model (GTWR) and estimates the PM2.5 concentration in Xinjiang from 2015 to 2020 using 1 km resolution MCD19A2 (MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006) data and 9 auxiliary variables. The findings indicate that the GTWR model performs better than the simple linear regression (SLR) and geographically weighted regression (GWR) models in terms of accuracy and feasibility in retrieving PM2.5 concentrations in Xinjiang. Simultaneously, by combining the GTWR model with MCD19A2 data, a spatial distribution map of PM2.5 with better spatial resolution can be obtained. Next, the regional distribution of annual PM2.5 concentrations in Xinjiang is consistent with the terrain from 2015 to 2020. The low value area is primarily found in the mountainous area with higher terrain, while the high value area is primarily in the basin with lower terrain. Overall, the southwest is high and the northeast is low. In terms of time change, the six-year PM2.5 shows a single peak distribution with 2016 as the inflection point. Lastly, from 2015 to 2020, the seasonal average PM2.5 concentration in Xinjiang has a significant difference, thereby showing winter (66.15μg/m3)>spring (52.28μg/m3)>autumn (40.51μg/m3)>summer (38.63μg/m3). The research shows that the combination of MCD19A2 data and GTWR model has good applicability in retrieving PM2.5 concentration.

Effect of Acute PM2.5 Exposure on Lung Function in Children: A Systematic Review and Meta-Analysis

Objective

The objective of this study was to conduct a systematic review and meta-analysis to identify the adverse effects of acute PM2.5 exposure on lung function in children.

Design

Systematic review and meta-analysis. Setting, participants and measures: Eligible studies analyzing PM2.5 level and lung function in children were screened out. Effect estimates of PM2.5 measurements were quantified using random effect models. Heterogeneity was investigated with Q-test and I² statistics. We also conducted meta-regression and sensitivity analysis to explore the sources of heterogeneity, such as different countries and asthmatic status. Subgroup analyses were conducted to determine the effects of acute PM2.5 exposure on children of different asthmatic status and in different countries.

Results

A total of 11 studies with 4314 participants from Brazil, China and Japan were included finally. A 10 μg/m³ increase of PM2.5 was associated with a 1.74L/min (95% CI: -2.68, -0.90) decrease in peak expiratory flow (PEF). Since the asthmatic status and country could partly explain the heterogeneity, we conducted the subgroup analysis. Children with severe asthma were more susceptible to PM2.5 exposure (-3.11 L/min per 10 μg/m³ increase, 95% CI -4.54, -1.67) than healthy children (-1.61 L/min per 10 μg/m³ increase, 95% CI -2.34, -0.91). In the children of China, PEF decreased by 1.54 L/min (95% CI -2.33, -0.75) with a 10 μg/m³ increase in PM2.5 exposure. In the children of Japan, PEF decreased by 2.65 L/min (95% CI -3.82, -1.48) with a 10 μg/m³ increase of PM2.5 exposure. In contrast, no statistic association was found between every 10 μg/m³ increase of PM2.5 and lung function in children of Brazil (-0.38 L/min, 95% CI -0.91, 0.15).

Conclusion

Our results demonstrated that the acute PM2.5 exposure exerted adverse impacts on children's lung function, and children with severe asthma were more susceptible to the increase of PM2.5 exposure. The impacts of acute PM2.5 exposure varied across different countries.

Impact of the Digital Economy on PM2.5: Experience from the Middle and Lower Reaches of the Yellow River Basin

The development of the digital economy holds great significance for alleviating haze pollution. To estimate the impact of the digital economy on haze pollution, this paper explores the spatiotemporal evolutionary characteristics of the digital economy and PM2.5 concentration in the middle and lower reaches of the Yellow River Basin from 2011 to 2019 and conducts regression analysis by combining a fixed effect (FE) model and the spatial Durbin model (SDM). Moreover, this study divides the mitigation effect of haze pollution into a direct effect and a spatial spillover effect, and it further analyzes the mechanism from the perspectives of technological innovation and the industrial structure. The empirical results show that the development level of the digital economy increases year by year and that the concentration of PM2.5 decreases year by year. The digital economy level and PM2.5 concentration in the downstream region are higher than those in the middle region, and the digital economy is negatively correlated with haze pollution. Similarly, the spatial spillover effect of the digital economy is conducive to curbing haze pollution. The robustness test also supports this conclusion. In addition, there is regional heterogeneity in the impact of the digital economy on haze pollution. The direct effect and spatial spillover effect of the digital economy on haze pollution in the downstream region are greater than those in the middle region. This study suggests that to realize air pollution prevention and control, it is necessary to strengthen the construction of digital infrastructure and create a good digital economy development environment based on local conditions. Encouraging the development of digital technological innovation and promoting industrial digital transformation hold great significance for alleviating haze pollution.

Impact of the COVID-19 pandemic on air pollution from jet engines at airports in central eastern China

Aircraft engine emissions (AEEs) generated during landing and takeoff (LTO) cycles are important air pollutant sources that directly impact the air quality at airports. Although the COVID-19 pandemic triggered an unprecedented collapse in the civil aviation industry, it also relieved some environmental pressure on airports. To quantify the impact of COVID-19 on AEEs, the amounts of three typical air pollutants (i.e., HC, CO, and NO_x) from LTO cycles at airports in central eastern China were estimated before and after the pandemic. The study also explored the temporal variation and the spatial autocorrelation of both the emission quantity and the emission intensity, as well as their spatial associations with other socioeconomic factors. The results illustrated that the spatiotemporal distribution pattern of AEEs was significantly influenced by the policies implemented and the severity of COVID-19. The variations of AEEs at airports with similar characteristics and functional positions generally followed similar patterns. The results also showed that the studied air pollutants present positive spatial autocorrelation, and a positive spatial dependence was found between the AEEs and other external socioeconomic factors. Based on the findings, some possible policy directions for building a more sustainable and environment-friendly airport group in the post-pandemic era were proposed. This study provides practical guidance on continuous monitoring of the AEEs from LTO cycles and studying the impact of COVID-19 on the airport environment for other regions or countries.

Tracking short-term health impacts attributed to ambient PM2.5 and ozone pollution in Chinese cities: an assessment integrates daily population

Joint and synergistic control of PM_2.5 and ozone pollution is an urgent need in China and a global-widely concerned issue. Health impact assessment could provide a comprehensive perspective for PM_2.5-ozone coordinated control strategies. For a detailed understanding of the seasonality and regionality of the health impacts attributed to PM_2.5 and ozone in China, this study extended the classic health impact function by daily population and assessed the short-term (daily) health impacts in 335 Chinese cities in 2021. Population migration indexes from Baidu were introduced to estimate the cities' daily population. Using this method, we quantitatively investigated the influence of population on short-term health impact assessment and identified which was significant in the Pearl River Delta (PRD) region and other populous cities. Although the annual sums of PM_2.5- and ozone-related daily health impacts were close for all Chinese cities, the PM_2.5-related health impact was equivalent to 333.96% and 32.07% of that ozone-related, during the cold and warm periods. The correlation and local spatial association analysis found significant city-specific and city-cluster associations of daily health impacts during the warm period and in Beijing-Tianjin-Hebei and surrounding regions (BTHS) and the Yangtze River Delta (YRD). Policymakers could promote period- and pollutant-targeted control actions for the major city groups, especially the BTHS, YRD, and PRD. Our methods and findings investigated the various influences of the population on short-term health impact assessment and proposed the PM_2.5-ozone collaborative control idea for key regions and city groups.

Exposure and Inequality of PM2.5 Pollution to Chinese Population: A Case Study of 31 Provincial Capital Cities from 2000 to 2016

Fine particulate matter (PM_2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM_2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM_2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM_2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM_2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM_2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m^-3 per year from 2000 to 2016. The proportion of the population exposed to high PM_2.5 (above 35 μg m^-3) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM_2.5.

Air Pollution Characteristics during the 2022 Beijing Winter Olympics

Using air pollution monitoring data from 31 January to 31 March 2022, we evaluated air quality trends in Beijing and Zhangjiakou before and after the 2022 Winter Olympics and compared them with the conditions during the same period in 2021. The objective was to define the air quality during the 2022 Winter Olympics. The results indicated that: (1) the average concentrations of PM_2.5, PM₁₀, NO₂, CO, and SO₂ in Zhangjiakou during the 2022 Winter Olympics were 28.15, 29.16, 34.96, 9.06, and 16.41%, respectively, lower than those before the 2022 Winter Olympics; (2) the five pollutant concentrations in Beijing showed the following pattern: during the 2022 Winter Olympics (DWO) < before the 2022 Winter Olympics < after 2022 Winter Paralympics < during 2022 Winter Paralympics; (3) on the opening day (4 February), the concentrations of the five pollutants in both cities were low. PM_2.5 and PM₁₀ concentrations varied widely without substantial peaks and the daily average maximum values were 15.17 and 8.67 µg/m³, respectively, which were 65.56 and 69.79% lower than those of DWO, respectively; (4) the PM_2.5 clean days in Beijing and Zhangjiakou DWO accounted for 94.12 and 76.47% of the total days, respectively, which were 11.76 and 41.18% higher than those during the same period in 2021; (5) during each phase of the 2022 Winter Olympics in Beijing and Zhangjiakou, the NO₂/SO₂ and PM_2.5/SO₂ trends exhibited a decrease followed by an increase. The PM_2.5/PM₁₀ ratios in Beijing and Zhangjiakou were 0.65 and 0.67, respectively, indicating that fine particulate matter was the main contributor to air pollution DWO.

PM2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model

Compiling fine-resolution geospatial PM_2.5 concentrations data is essential for precisely assessing the health risks of PM_2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM_2.5 is captured by the inclusion of multi-scale covariate effects, while the modelling of genuine scale-dependent variabilities pertaining to the spatial random process of PM_2.5 has not yet been much studied. Consequently, this work proposed a multi-scale spatial random effect model (MSSREM), based a recently developed fixed-rank Kriging method, to capture both the scale-dependent variabilities and the spatial dependence effect simultaneously. Furthermore, a small-scale Monte Carlo simulation experiment was conducted to assess the performance of MSSREM against classic geospatial Kriging models. The key results indicated that when the multiple-scale property of local spatial variabilities were exhibited, the MSSREM had greater ability to recover local- or fine-scale variations hidden in a real spatial process. The methodology was applied to the PM_2.5 concentrations modelling in North China, a region with the worst air quality in the country. The MSSREM provided high prediction accuracy, 0.917 R-squared, and 3.777 root mean square error (RMSE). In addition, the spatial correlations in PM_2.5 concentrations were properly captured by the model as indicated by a statistically insignificant Moran's I statistic (a value of 0.136 with p-value > 0.2). Overall, this study offers another spatial statistical model for investigating and predicting PM_2.5 concentration, which would be beneficial for precise health risk assessment of PM_2.5 pollution exposure.

Can Economic Growth and Environmental Protection Achieve a "Win-Win" Situation? Empirical Evidence from China

Achieving a "win-win" situation regarding economic growth and environmental protection has become a common goal for sustainable development in all countries around the world. As the world's largest developing country and the second largest economy, China has been striving to maintain economic growth while improving environmental quality to achieve its sustainable development goals. Applying the decoupling approach, a model widely used to quantify the relationship between the environment and the economy, this study analyzed the relationship between the economy and the environment, examining the decoupling performance of economic growth and environmental impacts in 30 Chinese provinces, autonomous regions, and municipalities to investigate whether economic growth and environmental protection have achieved a "win-win" situation. Nighttime light (NTL) data were used to measure the performance of economic growth. In addition, an environmental pressure index (EPI) assessment framework covering 6 primary and 11 secondary indicators was constructed to measure the environmental quality of China over time. First, NTL data proved to be a valid data source for assessing decoupling performance; second, environmental pressure at both the national and provincial levels significantly decreased during the study period; third, the relationship between the economy and the environment has been further improved, and economic growth and environmental protection have achieved a "win-win" situation. These findings offer an in-depth analysis of the decoupling of the economy and the environment in China and serve as a guide for future implementation strategies for sustainable development in various regions.

Turbulent transport dissimilarities of particles, momentum, and heat

The turbulent transport of particles is normally assumed to be similar to the momentum (or heat) transport, both in observations and simulations. However, observations from the boundary layer reinforcement experiment conducted at the Pingyuan County Meteorological Bureau, Shandong Province, China, showed dissimilar turbulent transports for momentum, heat, and particles. Our results reveal the prevalence of ejection and sweep motions in the transport of momentum and heat but not in that of particles. For momentum transport, sweep motion is more efficient, and the contribution of ejection (sweeps) motion is higher during the day (night) for heat transport. Momentum transport may be affected by pollutants during heavy pollution episodes (HPEs), whereas heat transport is affected by pollutants at night during HPEs. The sink/source differences lead to differences in particle transport for different HPEs. Furthermore, the momentum motion does not transport heat and particles in the same manner, particularly during HPEs. Compared to heat and momentum transport, the transport of particles is not significantly affected by stability. The turbulent transport of momentum is often smaller than that of particles and heat. Therefore, certain dissimilarities exist in the turbulent transport of momentum, heat, and particles. Overall, these findings found by the observations shed some light on the turbulent transport of particles in mesoscale models, and the turbulent transport dissimilarities between momentum, heat, and particles have an important impact on correcting and obtaining an accurate particle flux.

Prenatal and Postnatal Exposure to Ambient Air Pollution and Preschool Asthma in Neonatal Jaundice Infants

Purpose

Both air pollutant exposure and neonatal jaundice (NJ) have known effects on childhood asthma, but a higher total serum bilirubin (TSB) level has been associated with lung protection. This study aimed to assess whether prenatal/postnatal exposure to ambient air pollutants is related to the development of asthma in infants with NJ.

Patients and Methods

A nested case–control retrospective study was performed using the data of infants with NJ in the Kaohsiung Medical University Hospital Research Database. Data on average ambient air pollution concentrations within six months, the first year and second year after birth, and in the first, second and third prenatal trimesters were collected. NJ was defined as TSB levels ≥ 2 mg/dl with the diagnosis less than one-month-old. Asthma was defined as a diagnosis with medication use. We constructed conditional logistic regression models to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs).

Results

Exposure to NO and SO₂ at all six time points in the study was significantly associated with an increased risk of preschool asthma in infants with NJ. The overall peak OR (95% CI) of SO₂, PM_2.5, PM₁₀, NO, NO₂, and NO_X were 1.277 (1.129–1.444), 1.057 (1.023–1.092), 1.035 (1.011–1.059), 1.272 (1.111–1.455), 1.168 (1.083–1.259) and 1.104 (1.051–1.161), respectively. Fetuses in the first and second trimester were most vulnerable to ambient air pollutant exposure such as SO₂ PM_2.5, NO, NO₂ and NO_X during the prenatal period. Exposure to all six ambient air pollutants during the first and second years after birth significantly affected preschool asthma in NJ infants.

Conclusion

In different time windows, prenatal and postnatal exposure to SO₂, PM_2.5, PM₁₀, NO, NO₂, and NO_X were associated with preschool asthma in NJ infants. The relatively high impact of NO and SO₂ exposure in infants with NJ requires further studies and prevention measures.

Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China

The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.

Exploring Employment Spatial Structure Based on Mobile Phone Signaling Data: The Case of Shenzhen, China

Debate on the shift from a monocentric to polycentric urban structure has been extensive. Polycentricity generally refers to the co-existence of several centers in a city. Existing studies regarding China have mainly focused on the morphological characteristics of urban centers, but few recent studies have focused on functional dimensions of urban centers. Emerging big data sources provide new opportunities to explore the morphological and functional perspectives of urban spatial structure. This study uses mobile phone signaling data and develops a new methodology to measure urban centers’ functional centrality. The study area focuses on Shenzhen City, which has rapidly transformed from a village into a metropolitan city in the past few decades. As the first economic special zone in China, Shenzhen has adopted a polycentric urban plan since the beginning of the urbanization process. This study explores the spatial employment structure of the city from the morphological and function dimensions. Based on the findings, this study discusses the role of urban planning in forming an urban spatial structure and provides implications for future planning.

Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health

Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms ‘big’ geospatial data and geospatial ‘big data’ have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies.

Estimation of Regional Ground-Level PM2.5 Concentrations Directly from Satellite Top-of-Atmosphere Reflectance Using A Hybrid Learning Model

The accurate prediction of PM2.5 concentrations is important for environmental protection. The accuracy of the commonly used prediction methods is not high; so, this paper proposes a PM2.5 concentration prediction method based on a hybrid learning model. The Top-of-Atmosphere Reflectance (TOAR), PM2.5 data decomposed by wavelets, and meteorological data were used as input features to build an integrated prediction model using random forest and LightGBM, which was applied to PM2.5 concentration prediction in the Beijing–Tianjin–Hebei region. The practical application showed that the proposed method using TOAR, incorporating wavelet decomposition with meteorological element data, had an improvement of 0.06 in the R2 of the model accuracy and a reduction of 2.93 and 1.14 in the root mean square error (RMSE) and mean absolute error (MAE), respectively, over the model using Aerosol Optical Depth (AOD). Our model had a prediction accuracy of R2 of 0.91, which was better than the other models. We used this model to estimate and analyze the variation in PM2.5 concentrations in the Beijing–Tianjin–Hebei region, and the results were the same as the actual PM2.5 concentration distribution trend. Obviously, the proposed model has a high prediction accuracy and can avoid the errors caused by the limitations of the AOD inversion method.

Measuring PM2.5 Concentrations from a Single Smartphone Photograph

PM2.5 participates in light scattering, leading to degraded outdoor views, which forms the basis for estimating PM2.5 from photographs. This paper devises an algorithm to estimate PM2.5 concentrations by extracting visual cues and atmospheric indices from a single photograph. While air quality measurements in the context of complex urban scenes are particularly challenging, when only a single atmospheric index or cue is given, each one can reinforce others to yield a more robust estimator. Therefore, we selected an appropriate atmospheric index in various outdoor scenes to identify reasonable cue combinations for measuring PM2.5. A PM2.5 dataset (PhotoPM-daytime) was built and used to evaluate performance and validate efficacy of cue combinations. Furthermore, a city-wide experiment was conducted using photographs crawled from the Internet to demonstrate the applicability of the algorithm in large-area PM2.5 monitoring. Results show that smartphones equipped with the developed method could potentially be used as PM2.5 sensors.

Simulating PM2.5 Concentrations during New Year in Cuenca, Ecuador: Effects of Advancing the Time of Burning Activities

Fine particulate matter (PM2.5) is dangerous to human health. At midnight on 31 December, in Ecuadorian cities, people burn puppets and fireworks, emitting high amounts of PM2.5. On 1 January 2022, concentrations between 27.3 and 40.6 µg m-3 (maximum mean over 24 h) were measured in Cuenca, an Andean city located in southern Ecuador; these are higher than 15 µg m-3, the current World Health Organization guideline. We estimated the corresponding PM2.5 emissions and used them as an input to the Weather Research and Forecasting with Chemistry (WRF-Chem 3.2) model to simulate the change in PM2.5 concentrations, assuming these emissions started at 18:00 LT or 21:00 LT on 31 December 2021. On average, PM2.5 concentrations decreased by 51.4% and 33.2%. Similar modeling exercises were completed for 2016 to 2021, providing mean decreases between 21.4% and 61.0% if emissions started at 18:00 LT. Lower mean reductions, between 2.3% and 40.7%, or even local increases, were computed for emissions beginning at 21:00 LT. Reductions occurred through better atmospheric conditions to disperse PM2.5 compared to midnight. Advancing the burning time can help reduce the health effects of PM2.5 emissions on 31 December.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Assessing personal travel exposure to on-road PM2.5 using cellphone positioning data and mobile sensors

PM_2.5 pollution imposes substantial health risks on urban residents. Previous studies mainly focused on assessing peoples' exposures at static locations, such as homes or workplaces. There has been a scarcity of research that quantifies the dynamic PM_2.5 exposures of people when they travel in cities. To address this gap, we use cellphone positioning data and PM_2.5 concentration data collected from smart sensors along roads in Guangzhou, China, to assess personal travel exposure to on-road PM_2.5. First, we extract the trips of cellphone users from their trajectories and use the shortest path algorithm to calculate their travel routes on the road network. Second, the travel exposure of each user is estimated by associating their movement patterns with PM_2.5 concentrations on roads. The result shows that most users' average travel exposures per hour fall within the range of 20 ug/m³ to 75 ug/m³. Travel exposure varies across users, and 54.0% of users experience low travel exposure throughout the day, 25.5% of users experience high travel exposure in the evening, and 20.5% of users experience high travel exposure in the afternoon. Furthermore, the impacts of on-road PM_2.5 on urban populations are uneven across roads. More attention should be given to roads with high PM_2.5 concentrations and traffic flows in each period, such as Huan Shi Middle Road in the morning, Inner Ring Road in the afternoon, and Xinjiao Middle Road in the evening. The findings in this study can contribute to a more in-depth understanding of the relationship between air pollution and the travel activities of urban populations.

Associations between Daily Ambient Air Pollution and Pulmonary Function, Asthma Symptom Occurrence, and Quick-Relief Inhaler Use among Asthma Patients

Particulate matter (PM) is harmful to human health, especially for people with asthma. The goal of this study was to enhance the knowledge about the short-term effects of daily air concentrations of PM on health outcomes among asthma patients. The novelty of this study was the inclusion of a homogeneous group of patients (N = 300) with diagnosed and partly controlled asthma. Patients recorded their symptoms, asthma quick-relief inhaler use, and peak expiratory flow (PEF) measurements in a diary for two weeks. Data on particulate air pollution were obtained from stationary monitoring stations. We have shown that particulate pollutants (PM₁₀ and PM_2.5) are associated with significant deterioration of PEF and an increase in the frequency of early asthma symptoms, as well as asthma quick-relief inhaler use. These effects are observed not only on the day of exposure, but also on the following day. For public health practice, these results support the rationale for using peak-flow meters as necessary devices for proper asthma self-management and control, especially in locations where the air is polluted with particles. This may decrease the number of asthma patients seeking medical help.

An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors

Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations.

Estimating High-Resolution PM2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM2.5) concentrations. However, existing approaches to estimate PM2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R2 values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available.

Air Pollution, Environmental Violation Risk, and the Cost of Debt: Evidence from China

Although a firm’s exposure to air pollution-related risk has become an important factor that creditors cannot ignore in the procedure of lending decision making with the aggravation of air pollution, empirical evidence on whether and how air pollution affects the cost of debt has been relatively scarce. Employing a series of Chinese listed firms from the main board of the Shanghai and Shenzhen Stock Exchanges covering 2014 to 2018, our research responds to this research gap by exploring how air pollution-induced environmental violation risk affects the cost of debt by constructing an assessment system of firms’ environmental violation risk. The results shed light on an issue that firms exposed to higher concentrations of air pollution may suffer a higher environmental violation risk, resulting in a higher debt cost. In addition, a further analysis shows that environmental regulatory pressure and heavily polluting firms enhance the influence of air pollution on the cost of debt, while state-owned firms and firms’ economic contributions weaken the influence of air pollution on the cost of debt. Our research is conducive to highlighting not only the importance of environmental governance for mitigating the cost of debt to the firms exposed to air pollution, but also its importance to creditors exposed to their clients’ environmental violation risk and default risk.

What drives long-term PM2.5-attributable premature mortality change? A case study in central China using high-resolution satellite data from 2003 to 2018

Ambient PM_2.5 was reported to be related to numerous negative health outcomes, leading to adverse public health impacts in many countries such as China. Despite the apparent reduction in PM_2.5 levels over China due to its emission control policies in recent years, the health burdens were not reduced as much as expected. This calls for a comprehensive analysis to explain the reasons behind to provide a useful reference for formulating effective emission control strategies. Taking central China as an example due to its large population and high levels of PM_2.5, this study quantified the spatiotemporal dynamics of premature mortality associated with PM_2.5 pollution in central China for each year during 2003-2018 and applied a decomposition analysis to dissect the contribution of various driving factors including ambient PM_2.5 level, demographic distribution and baseline incidence rate of four diseases related to air pollution. Results show significant spatiotemporal variations in PM_2.5-attributed health impact in central China, including Henan, Hubei, and Hunan provinces. Five Henan cities had the largest PM_2.5-attributable premature mortality (∼8-12 K premature mortalities), while three Hubei cities and one Hebei city had the least chronic PM_2.5-related all-cause mortality numbers (<1 K mortalities). Throughout the study period, the PM_2.5-caused premature mortality decreased by 54 K, in which changes in PM_2.5 levels and baseline incidence rates of stroke and chronic obstructive pulmonary disease contributed to the positive effect, whereas demographic changes and baseline incidence rate change of ischemic heart disease and lung cancer brought a countervailing effect. Our findings suggest more dynamic and comprehensive policies and measures that take into account spatiotemporal variations of health burden for effective alleviation of the health impact of PM_2.5 pollution in the country.

Can Changes in Urban Form Affect PM2.5 Concentration? A Comparative Analysis from 286 Prefecture-Level Cities in China

It is crucial to the sustainable development of cities that we understand how urban form affects the concentration of fine particulate matter (PM2.5) from a spatial–temporal perspective. This study explored the influence of urban form on PM2.5 concentration in 286 prefecture-level Chinese cities and compared them from national and regional perspectives. The analysis, which explored the influence of urban form on PM2.5 concentration, was based on two types of urban form indicators (socioeconomic urban index and urban landscape index). The results revealed that cities with high PM2.5 concentrations tended to be clustered. From the national perspective, urban built-up area (UA) and road density (RD) have a significant correlation with PM2.5 concentration for all cities. There was a significant negative correlation between the number of patches (NP) and the average concentration of PM2.5 in small and medium-sized cities. Moreover, urban fragmentation had a stronger impact on PM2.5 concentrations in small cities. From a sub-regional perspective, there was no significant correlation between urban form and PM2.5 concentration in the eastern and central regions. On the other hand, the influence of population density on PM2.5 concentration in northeastern China and northwestern China showed a significant positive correlation. In large- and medium-sized cities, the number of patches (NP), the largest patch index (LPI), and the contagion index (CONTAG) were also positively correlated with PM2.5 concentration, while the LPI in small cities was significantly negatively correlated with PM2.5 concentration. This shows that, for more developed areas, planning agencies should encourage moderately decentralized and polycentric urban development. For underdeveloped cities and shrinking cities, the development of a single center should be encouraged.

Association between Maternal Exposure to Ambient Air Pollution and the Risk of Preterm Birth: A Birth Cohort Study in Chongqing, China, 2015-2020

Recent study results on the association between maternal exposure to ambient air pollution with preterm birth have been inconsistent. The sensitive window of exposure and influence level of air pollutants varied greatly. We aimed to explore the association between maternal exposure to ambient air pollutants and the risk of preterm birth, and to estimate the sensitive exposure time window. A total of 572,116 mother–newborn pairs, daily concentrations of air pollutants from nearest monitoring stations were used to estimate exposures for each participant during 2015–2020 in Chongqing, China. We applied a generalized additive model and estimated RRs and 95% CIs for preterm birth in each trimester and the entire pregnancy period. In the single-pollutant model, we observed that each 10 μg/m³ increase in PM_2.5 had a statistically significant effect on the third trimester and entire pregnancy, with RR = 1.036 (95% CI: 1.021, 1.051) and RR = 1.101 (95% CI: 1.075, 1.128), respectively. Similarly, for each 10 μg/m³ increase in PM₁₀, there were 2.7% (RR = 1.027, 95% CI: 1.016, 1.038) increase for PTB on the third trimester, and 3.8% (RR = 1.038, 95% CI: 1.020, 1.057) increase during the whole pregnancy. We found that for each 10 mg/m³ CO increases, the relative risk of PTB increased on the first trimester (RR = 1.081, 95% CI: 1.007, 1.162), second trimester (RR = 1.116, 95% CI: 1.035, 1.204), third trimester (RR = 1.167, 95% CI: 1.090, 1.250) and whole pregnancy (RR = 1.098, 95% CI: 1.011, 1.192). No statistically significant RR was found for SO₂ and NO₂ on each trimester of pregnancy. Our study indicates that maternal exposure to high levels of PM_2.5 and PM₁₀ during pregnancy may increase the risk for preterm birth, especially for women at the late stage of pregnancy. Statistically increased risks of preterm birth were associated with CO exposure during each trimester and entire pregnancy. Reducing exposure to ambient air pollutants for pregnant women is clearly necessary to improve the health of infants.

Anthropogenic Sources Dominate Foliar Chromium Dust Deposition in a Mining-Based Urban Region of South Africa

Dust pollution can be severe in urban centers near mines and smelters. Identification of dust sources and assessing dust capturing plant morphological traits may help address the problem. A chromium (Cr) mining and ferrochrome smelting region in Sekhukhuneland, South Africa, was investigated to identify the sources of Cr in soil and plant leaf surfaces and to evaluate the association between Cr sources and plant morphology. Combinations of bi- and multivariate statistical analysis techniques were applied. Non-significant relation between Cr quantities in surface soil and on leaf surfaces suggested negligible Cr dust contribution from soil to leaves. Association among Cr, Fe, Mg, Al, and Si levels on leaf surfaces confirmed their shared origin, possibly from chromite containing dust dispersed by mines, smelters, roads, and tailings. Both plant morphology and Cr sources (number and proximity to mines and roads) conjointly determined Cr dust deposition on leaf surfaces. Air mass movement patterns further identified local polluters, i.e., mines, ferrochrome smelters, and roads, as dominant dust sources in the region. Common plant species showed Cr dust adhesion favouring traits (plant tallness, larger leaf area, dense epicuticular wax structures, and larger stomata) and projected dust mitigation prospects for Sekhukhuneland.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China

Understanding the spatiotemporal characteristics of PM2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing-Tianjin-Hebei region during 2014-2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m-3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.

Consumption-based PM2.5-related premature mortality in the Beijing-Tianjin-Hebei region

The Beijing-Tianjin-Hebei (BTH) region, which has a resource-dependent economy dominated by clusters of heavy industries, has long borne the highest PM_2.5 pollution levels in China, prompting serious concerns about the region's disease burden. Pollution-intensive industries in the BTH region not only meet local consumer demands but also those of other places via the supply chain. In the present study, we combined a multi-region input-output model with adjoint source sensitivity modeling technique at a high resolution (0.25° × 0.3125°) to apportion PM_2.5-related mortality risks in the BTH to consuming areas and sectors. The model predicted that exposure to ambient PM_2.5 caused 0.12 million premature deaths (95% confidence interval: 0.08-0.15) in the BTH region in 2013. The adjoint source sensitivity results showed that only 46% of the total premature deaths were attributable to local consumption. The top contributors of local consumption were rural households and the agricultural sector in Hebei, and service sector in Beijing. Consumption of other domestic regions and international export accounted for 25% of the total premature deaths in the BTH, mainly to support consumption of manufacturing and construction products of these outer regions. Atmospheric transport of pollutants, mainly from the surrounding areas, accounted for the remaining 29% of total deaths in BTH. Our findings underline the consumption-based driven force of BTH's pollution and associated health impacts, which may facilitate the joint control actions among the BTH region and its surrounding areas from a comprehensive perspective.

Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data

Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data.

Long-term ozone exposure and mortality from neurological diseases in Canada

BACKGROUND

There is increasing interest in the health effects of air pollution. However, the relationships between ozone exposure and mortality attributable to neurological diseases remain unclear.

OBJECTIVES

To assess associations of long-term exposure to ozone with death from Parkinson's disease, dementia, stroke, and multiple sclerosis.

METHODS

Our analyses were based on the 2001 Canadian Census Health and Environment Cohort. Census participants were linked with vital statistics records through 2016, resulting in a cohort of 3.5 million adults/51,045,700 person-years, with 8,500/51,300/43,300/1,300 deaths from Parkinson's/dementia/stroke/multiple sclerosis, respectively. Ten-year average ozone concentrations estimated by chemical transport models and adjusted by ground measurements were assigned to subjects based on postal codes. Cox proportional hazards models were used to calculate hazard ratios (HRs) for deaths from the four neurological diseases, adjusting for eight common demographic and socioeconomic factors, seven environmental indexes, and six contextual covariates.

RESULTS

The fully adjusted HRs for Parkinson's, dementia, stroke, and multiple sclerosis mortalities related to one interquartile range increase in ozone (10.1 ppb), were 1.09 (95% confidence interval 1.04-1.14), 1.08 (1.06-1.10), 1.06 (1.04-1.09), and 1.35 (1.20-1.51), respectively. The covariates did not influence significance of the ozone-mortality associations, except airshed (i.e., broad region of Canada). During the period of 2001-2016, 5.66%/5.01%/ 3.77%/19.11% of deaths from Parkinson's/dementia/stroke/multiple sclerosis, respectively, were attributable to ozone exposure.

CONCLUSIONS

We found positive associations between ozone exposure and mortality due to Parkinson's, dementia, stroke, and multiple sclerosis.

Observed inequality in urban greenspace exposure in China

Given the important role of green environments playing in healthy cities, the inequality in urban greenspace exposure has aroused growing attentions. However, few comparative studies are available to quantify this phenomenon for cities with different population sizes across a country, especially for those in the developing world. Besides, commonly used inequality measures are always hindered by the conceptual simplification without accounting for human mobility in greenspace exposure assessments. To fill this knowledge gap, we leverage multi-source geospatial big data and a modified assessment framework to evaluate the inequality in urban greenspace exposure for 303 cities in China. Our findings reveal that the majority of Chinese cities are facing high inequality in greenspace exposure, with 207 cities having a Gini index larger than 0.6. Driven by the spatiotemporal variability of human distribution, the magnitude of inequality varies over different times of the day. We also find that exposure inequality is correlated with low greenspace provision with a statistical significance (p-value < 0.05). The inadequate provision may result from various factors, such as dry cold climate and urbanization patterns. Our study provides evidence and insights for central and local governments in China to implement more effective and sustainable greening programs adjusted to different local circumstances and incorporate the public participatory engagement to achieve a real balance between greenspace supply and demand for developing healthy cities.

Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey-related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no-sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey-derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco-health achievement.