Spatiotemporal variations and influencing factors of PM2.5 concentrations in Beijing, China

Windows of susceptibility to air pollution during and surrounding pregnancy in relation to longitudinal maternal measures of adiposity and lipid profiles

Pregnancy is a critical window for long-term metabolic programming of fetal effects stemming from airborne particulate matter ≤2.5 μm (PM2.5) exposure. Yet, little is known about long-term metabolic effects of PM2.5 exposure during and surrounding pregnancy in mothers. We assessed potential critical windows of PM2.5 exposure during and surrounding pregnancy with maternal adiposity and lipid measures later in life. We included 517 pregnant women from the PROGRESS cohort with adiposity [body mass index (BMI), waist circumference (WC), % body fat] and lipids [total cholesterol, high-density-lipoprotein (HDL), low-density-lipoprotein (LDL)] measured repeatedly at 4, 6 and 8 years post-delivery. Monthly average PM2.5 exposure was estimated at each participant's address using a validated spatiotemporal model. We employed distributed lag interaction models (DLIMs) adjusting for socio-demographics and clinical covariates. We found that a 1 μg/m3 increase in PM2.5 exposure throughout mid-/late-pregnancy was associated with higher WC at 6-years post-delivery, peaking at 6 months of gestation: 0.04 cm (95%CI: 0.01, 0.06). We also identified critical windows of PM2.5 exposure during and surrounding pregnancy associated with higher LDL and lower HDL both measured at 4 years post-delivery with peaks at pre-conception for LDL [0.17 mg/dL (95%CI: 0.00, 0.34)] and at the 11th month after conception for HDL [-0.07 mg/dL (95%CI: -0.11, -0.02)]. Stratified analyses by fetal sex indicated stronger associations with adiposity measures in mothers carrying a male, while with lipids in mothers carrying a female fetus. Stratified analyses also indicated potential stronger deleterious lagged effects in women with folic acid intake lower than 600mcg/day during pregnancy.

Pregnancy as a Susceptible Period to Ambient Air Pollution Exposure on the Maternal Postpartum Metabolome

Pregnancy is a potential critical window to air pollution exposure for long-term maternal metabolic effects. However, little is known about potential early metabolic mechanisms linking air pollution to maternal metabolic health. We included 544 pregnant Mexican women with both ambient PM2.5 levels during pregnancy and untargeted serum metabolomics to examine associations between pregnancy PM2.5 exposure (overall and monthly) and postpartum metabolites, implementing FDR-adjusted robust linear regression controlling for covariates. Pathway enrichment analyses (in Reactome and MetaboAnalyst) and effect modification by fetal sex and folic acid supplementation were also evaluated. Higher PM2.5 exposure levels throughout pregnancy were associated with higher bile acids and amino acids, dysregulated glycerophospholipids, or lower fatty acyl levels (FDR < 0.05), among other metabolites. Potential critical windows of susceptibility to monthly PM2.5 on metabolites were observed in early to midpregnancy (FDR < 0.005). Main findings were consistent by strata of fetal sex and folic acid supplementation. Metabolic pathways corresponding to positive PM2.5-metabolite associations indicated enriched bile acid, dietary lipid, and transmembrane transport metabolism, whereas for negative PM2.5-metabolite associations, we identified altered pathways involving adipogenesis, incretin peptide hormone, GLP-1, PPAR-alpha, and fatty acid receptors (FDR < 0.05). PM2.5 exposures during pregnancy, especially in early gestation, altered maternal postpartum lipids as well as amino acid metabolism.

A study on the impact of meteorological and emission factors on PM2.5 concentrations based on machine learning

PM2.5 pollution, a major environmental and health concern, is influenced by a complex interplay of emission sources and meteorological conditions. Accurately identifying these factors and their contributions is essential for effective pollution management. This study applies Positive Matrix Factorization (PMF) to identify primary sources of PM2.5 and uses the Light Gradient Boosting Machine (LightGBM) model, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) to quantitatively assess the impact of meteorological and emission factors on PM2.5 concentrations. SHAP results reveal that meteorological factors contribute 16.6% (5.3 μg/m3) to PM2.5, with humidity being the most influential, while emission sources account for 83.4% (26.8 μg/m3), with secondary particulate matter being the dominant factor. Secondary particulate matter and biomass burning significantly impacted PM2.5 in the first and fourth quarters, while dust sources became more influential in the second quarter, and coal emissions were most prominent in the second and third quarters. Two-dimensional PDP analysis indicated that in the first and fourth quarters, secondary particulate matter concentration increased with air pressure, and the atmospheric oxidation process was more pronounced under high-humidity conditions during the day. Strong transport conditions, with wind direction shifting from north to east, also influenced secondary particulate matter levels. This study demonstrates that the LightGBM model effectively captures the nonlinear relationships between PM2.5 and meteorological and emission factors, providing a reliable approach for analyzing the causes of PM2.5 pollution.

Predictive modeling of air quality in the Tehran megacity via deep learning techniques

Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O3, NO2, SO2, PM10, and PM2.5, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered. R-squared (R2), root-mean-square error (RMSE), mean absolute error (MAE), and mean-square error (MSE) were used to assess and compare the models. This research demonstrated that DL models typically outperform ML models in forecasting air pollution. Gated recurrent units (GRUs), fully connected neural networks (FCNNs), and convolutional neural networks (CNNs) recorded R2 and MSE values of 0.5971 and 42.11 for CO, 0.7873 and 171.40 for O3, and 0.4954 and 25.17 for SO2, respectively. Consequently, the FCNN and GRU presented remarkable performance in predicting NO2 (R2 = 0.6476 and MSE = 75.16), PM10 (R2 = 0.8712 and MSE = 45.11), and PM2.5 (R2 = 0.9276 and MSE = 58.12) concentrations. In terms of operational speed, the FCNN model exhibited the most efficiency, with a minimum and maximum runtime of 13 and 28 s, respectively. The feature importance analysis suggested that CO, O3 and NO2, SO2 and PM10, and PM2.5 are most affected by temperature, humidity, PM2.5, and PM10, respectively. Thus, temperature and humidity were the primary factors affecting the variability in pollutant concentrations. The conclusions confirm that the DL models achieve significant accuracy and serve as essential instruments for managing air pollution, providing practical insights for decision-makers to adopt efficient air quality control strategies.

Spatio-temporal analysis of extreme air pollution and risk assessment

Extreme air pollution poses global health and environmental threats, necessitating robust policy interventions. This study first analyses the surface mass concentration of major aerosols (such as black carbon, organic carbon, dust, sea salts, and sulphates) to estimate global PM2.5 concentrations from 1980 to 2023. The developed model-estimated PM2.5 database was validated against data from 526 cities worldwide, showing strong accuracy, with RMSE, r, and R2 values of 7.47 μg/m³, 0.87, and 0.75, respectively. The motivation arises from the need to understand whether recent pollution increases are driven by rising emissions or natural variability, given the significant impacts on life and property. To assess both short-and long-term pollution trends, magnitudes, and risks, we proposed twelve novel extreme pollution indices, which comprehensively characterize the spatial and temporal variations in pollution. The highest PM2.5 concentrations were observed in regions near the Saharan Desert, reaching up to 90,000 μg/m³. However, significant PM2.5TOT (total pollution) concentrations were also found in the Indo-Gangetic Plain (IGP) and eastern China, ranging from 20,000 to 40,000 μg/m³. Persistent pollution burdens North Africa for approximately 350 days annually, while the IGP and eastern China experience extreme pollution for over 200 days yearly. Other pollution indices highlight the intensity and frequency of pollution in regions such as North Africa, IGP, Eastern Russia, Western USA, and Eastern China, revealing critical regional air quality challenges. Our analysis identifies cities in low-income and middle-income countries, such as New Delhi, Lahore, Dhaka, and Dammam, as being at extreme risk scores above 90 out of 100. Meanwhile, cities like Ghaziabad, Chongqing, Kolkata, Mumbai, and East London fall into the high-risk category, scoring between 60 and 80. Conversely, most cities in the EU, USA, and Canada are at very low risk, a result of the effective implementation of strategic air pollution norms and policies. The study promotes a phased approach for low- and middle-income regions, emphasizing achievable air quality standards, low-cost monitoring, targeted interventions, urban greening, public awareness, and innovative financing for improvements.

Windows of Susceptibility to Air Pollution During and Surrounding Pregnancy in Relation to Longitudinal Maternal Measures of Adiposity and Lipid Profiles

Pregnancy is a critical window for long-term metabolic programming of fetal effects stemming from airborne particulate matter ≤2.5μm (PM 2.5 ) exposure. Yet, little is known about long-term metabolic effects of PM 2.5 exposure during and surrounding pregnancy in mothers. We assessed potential critical windows of PM 2.5 exposure during and surrounding pregnancy with maternal adiposity and lipid measures later in life. We included 517 pregnant women from the PROGRESS cohort with adiposity [body mass index (BMI), waist circumference (WC), % body fat] and lipids [total cholesterol, high-density-lipoprotein (HDL), low-density-lipoprotein (LDL)] measured repeatedly at 4, 6 and 8 years post-delivery. Monthly average PM 2.5 exposure was estimated at each participant's address using a validated spatiotemporal model. We employed distributed lag interaction models (DLIMs) adjusting for socio-demographics and clinical covariates. We found that a 1 μg/m 3 increase in PM 2.5 exposure throughout mid-/late-pregnancy was associated with higher WC at 6-years post-delivery, peaking at 6 months of gestation: 0.04 cm (95%CI: 0.01, 0.06). We also identified critical windows of PM 2.5 exposure during and surrounding pregnancy associated with higher LDL and lower HDL both measured at 4 years post-delivery with peaks at pre-conception for LDL [0.17 mg/dL (95%CI: 0.00, 0.34)] and at the 11 th month after conception for HDL [-0.07 mg/dL (95%CI: -0.11, -0.02)]. Stratified analyses by fetal sex indicated stronger associations with adiposity measures in mothers carrying a male, whereas stronger associations were observed with lipids in mothers carrying a female fetus. Stratified analyses also indicated potential stronger deleterious lagged effects in women with folic acid intake lower than 600mcg/day during pregnancy.

Research on characteristics and influencing factors of road dust emission in a southern city in China

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

A hybrid model for enhanced forecasting of PM2.5 spatiotemporal concentrations with high resolution and accuracy

Forecasting concentrations of PM2.5 is important due to its known impacts on public health and environment. However, PM2.5 concentrations can vary significantly over short distances and time, which can be influenced by local emissions and short-term weather patterns. This spatiotemporal variability makes accurate PM2.5 forecasting an inherently complex and challenging task. This study presented novel methodologies for short-term PM2.5 concentration forecast by combining the atmospheric chemistry transport model Community Multiscale Air Quality Modeling System (CMAQ) with data-driven machine learning methods, namely long short-term memory (LSTM) and random forest (RF) models. The combined model system forecast PM2.5 with 1 h, 1km × 1 km spatiotemporal resolution. The LSTM system forecast time-dependent PM2.5 concentrations at observation sites with a maximum root mean square error (RMSE) of 3.66 μg/m3 for 1-hr forecast and 23.75 μg/m3 for 72-hr forecast, leveraging results obtained from the atmospheric transport model with RMSE of 45.81 μg/m3. Wavelet transform in the LSTM system allowed learning and prediction of PM2.5 concentrations at different frequencies, capturing temporal variability of PM2.5 at various time scales. The RF model predicted distributions of PM2.5 concentrations by learning LSTM results and integrating crucial features such as CMAQ results, meteorological and topographical information. The feature significance of CMAQ results was the highest among the input features in RF models. Overall, the hybrid model could help with managing and mitigating the adverse effects of air pollution by enabling informed decision-making at the individual, community and policy levels.

Associations between atmospheric PM2.5 exposure and carcinogenic health risks: Surveillance data from the year of lowest recorded levels in Beijing, China

Scant research has pinpointed the year of minimum PM2.5 concentration through extensive, uninterrupted monitoring, nor has it thoroughly assessed carcinogenic risks associated with analyzing numerous components during this nadir in Beijing. This study endeavored to delineate the atmospheric PM2.5 pollution in Beijing from 2015 to 2022 and to undertake comprehensive evaluation of carcinogenic risks associated with the composition of atmospheric PM2.5 during the year exhibiting the lowest concentration. PM2.5 concentrations were monitored gradually in 9 districts of Beijing for 7 consecutive days per month from 2015 to 2022, and 32 kinds of PM2.5 components collected in the lowest PM2.5 concentration year were analyzed. This comprehensive dataset served as the basis for carcinogenic risk assessment using Monte Carlo simulation. And we applied the Positive Matrix Factorization (PMF) method to identity the sources of atmospheric PM2.5. Furthermore, we integrated this source appointment model with risk assessment model to discern the origins of these risks. The findings revealed that the annual average PM2.5 concentration in 2022 stood at 43.1 μg/m3, marking the lowest level recorded. The mean carcinogenic risks of atmospheric PM2.5 exposure calculated at 6.30E-6 (empirical 95% CI 1.09E-6 to 2.25E-5) in 2022. The PMF model suggested that secondary sources (35.4%), coal combustion (25.6%), resuspended dust (15.1%), biomass combustion (14.1%), vehicle emissions (7.1%), industrial emissions (2.0%) and others (0.7%) were the main sources of atmospheric PM2.5 in Beijing. The mixed model revealed that coal combustion (2.41E-6), vehicle emissions (1.90E-6) and industrial emissions (1.32E-6) were the main sources of carcinogenic risks with caution. Despite a continual decrease in atmospheric PM2.5 concentration in recent years, the lowest concentration levels still pose non-negligible carcinogenic risks. Notably, the carcinogenic risks associated with metals and metalloids exceeded that of PAHs. And the distribution of risk sources did not align proportionally with the distribution of PM2.5 mass concentration.

Health risks and sources of trace elements and black carbon in PM2.5 from 2019 to 2021 in Beijing

A comprehensive health risk assessment of PM2.5 is meaningful to understand the current status and directions regarding further improving air quality from the perspective of human health. In this study, we evaluated the health risks of PM2.5 as well as highly toxic inorganic components, including heavy metals (HMs) and black carbon (BC) based on long-term observations in Beijing from 2019 to 2021. Our results showed that the relative risks of chronic obstructive pulmonary disease, lung cancer, acute lower respiratory tract infection, ischemic heart disease, and stroke decreased by 4.07%-9.30% in 2020 and 2.12%-6.70% in 2021 compared with 2019. However, they were still at high levels ranging from 1.26 to 1.77, in particular, stroke showed the highest value in 2021. Mn had the highest hazard quotient (HQ, from 2.18 to 2.56) for adults from 2019 to 2021, while Ni, Cr, Pb, As, and BC showed high carcinogenic risks (CR > 1.0×10-6) for adults. The HQ values of Mn and As and the CR values of Pb and As showed constant or slight upwards trends during our observations, which is in contrast to the downward trends of other HMs and PM2.5. Mn, Cr, and BC are crucial toxicants in PM2.5. A significant shrink of southern region sourcesof HMs and BCshrank suggests the increased importance of local sources. Industry, dust, and biomass burning are the major contributors to the non-carcinogenic risks, while traffic emissions and industry are the dominant contributors to the carcinogenic risks in Beijing.

Effect of Evaluation of Various Emission Control Policies on PM2.5 Reduction

In previous work, a methodology was developed to discuss the influence of meteorological factors, policies, and surrounding cities on PM2.5 concentrations in a city. Two models were constructed using Zibo City, Shandong Province, as the target city. Initially, we improved the established PM2.5-Meteorological-Policy (PMP) model and applied it to six other target cities in Shandong Province. Concurrently, a novel model named the PM2.5-Interregional (PI) model was further developed in each city to directly express the influence of surrounding cities on the target cities. The model construction period was from January 2014 to August 2022, with the extended prediction period until November 2022. The results confirmed that disparities in the spatial distribution in seasons became smaller after the implementation of environmental policies. Moreover, two models in each city revealed good interpretation with high adjusted R2 values (>0.7) and lower MAPE and RMSE values (the lowest was 5.53% and 2.57), suggesting reasonable short-term prediction. Additionally, meteorological factors and the combined implementation of different policy types played crucial roles in reducing PM2.5 concentrations in all cities. Specifically, the temperature and wind speed were negatively correlated with PM2.5 concentrations in all models, with temperature having a stronger influence. The Law of the People's Republic of China on the Prevention and Control of Atmospheric Pollution (LAPAP), implemented in 2016, had a clear influence on reducing PM2.5 concentrations, with the highest absolute fitted coefficient in most cities (-0.166 to -0.344). On the contrary, the influence of temperature seemed to be more significant compared to policies, due to the larger standardized coefficient in each city (-0.606 to -0.864).

Influencing factors of PM2.5 concentration in the typical urban agglomerations in China based on wavelet perspective

PM2.5 is one of the most harmful air pollutants affecting sustainable economic and social development in China. The analysis of influencing factors affecting PM2.5 concentration is significant for the improvement of air quality. In this study, three typical urban agglomerations in China (Beijing‒Tianjin‒Hebei [BTH], the Yangtze River Delta [YRD], and the Pearl River Delta [PRD]) were studied using innovative trend analysis, a Bayesian statistical model, and partial wavelet and multiwavelet coherence to analyze PM2.5 concentration variations and multi-scale coupled oscillations between PM2.5 concentration and air pollutants/meteorological factors. The results showed that: (1) PM2.5 concentration time-series showed significant downward trends, which decreased as follows: BTH > YRD > PRD. The higher the pollution level, the greater the change trend. In BTH and the PRD, PM2.5 had obvious trends and seasonal change points; whereas, the PM2.5 time-series change point in the YRD was not obvious. (2) PM2.5 had significant intermittent resonance cycles with air pollutants and meteorological factors in different time domains. There were differences in the main controlling factors affecting PM2.5 among the three urban agglomerations. (3) The explanatory ability of air pollutant combinations for variations in PM2.5 was higher than that of meteorological factor combinations. However, the synergistic effect of air pollutants/meteorological factors could better explain the PM2.5 concentration variations on all time-frequency scales. The results of this study provide a reference for ecological improvement as well as collaborative governance of air pollution.

Urban and suburban decadal variations in air pollution of Beijing and its meteorological drivers

Air pollution is a major threat to human health and ecosystems. Using 10-year (2013-2022) multi-source observations for the Beijing, China, we showed that clean-air actions have significantly reduced PM2.5, PM10, CO, NO2, and SO2 pollution, with an increase in the surface maximum daily 8-h average ozone (MDA8O3) concentrations during autumn and winter, leading to a rapid diminishment of the urban-suburban gap in air pollution. Secondary sources and vehicle emissions were enhanced in both urban and suburban areas in all seasons except summer from 2013 to 2022. By combining statistical analysis with the convergent cross-mapping model, the varying relationships between air pollution and meteorological conditions in the urban and suburban areas were delineated. The results suggested that boundary layer height and relative humidity exerted strong and stable influences on all air pollutants, except for MDA8O3, whose key meteorological driver was temperature. This study showed that increasing O3 trends in autumn and winter and aggravated O3 formation in summer in urban areas in Beijing became non-negligible from 2013 to 2022, despite the declining levels of air pollutants. Meteorological observations suggested that weather patterns in Beijing, characterized by higher temperatures, sunshine hours, and boundary layer height and lower relative humidity, have become more favorable for O3 formation in autumn and winter. Future mitigation efforts should focus on reducing VOC and NOx emissions to avoid further deterioration of O3 pollution under the frequent adverse meteorological conditions predicted under the background of global warming.

The spatiotemporal variation of PM2.5-O3 association and its influencing factors across China: Dynamic Simil-Hu lines

In recent years, PM_2.5 and O₃ composite airborne pollution has become one of the most severe environment issues in China. To get a better understanding and tackle these problems, we employed multi-year data to explore the spatiotemporal variation of the PM_2.5-O₃ relationship in China and investigated its major driving factors. Firstly, interesting patterns were found that named dynamic Simil-Hu lines, which presented a combined effect of natural and anthropogenic influences, were closely related to the spatial patterns of PM_2.5-O₃ association across seasons. Furthermore, regions with lower altitudes, higher humidity, higher atmospheric pressure, higher temperature, fewer sunshine hours, more accumulated precipitation, denser population and higher GDP often show positive PM_2.5-O₃ associations, regardless of seasonal variations. Amongst these factors, humidity, temperature and precipitation were dominant factors. This research suggests that the collaborative governance of composite atmospheric pollution should be implemented dynamically, in consideration of geographical locations, meteorological conditions and socioeconomic conditions.

Quantifying the Spatiotemporal Heterogeneity of PM2.5 Pollution and Its Determinants in 273 Cities in China

Fine particulate matter (PM_2.5) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM_2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM_2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed "U"-shaped, pulse-shaped and "W"-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM_2.5 pollution; (3) The determinants showed significant heterogeneity on PM_2.5 pollution-specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM_2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM_2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

Concentration Characteristics and Correlations with Other Pollutants of Atmospheric Particulate Matter as Affected by Relevant Policies

With the increase in global environmental pollution, it is important to understand the concentration characteristics and correlations with other pollutants of atmospheric particulate matter as affected by relevant policies. The data presented in this paper were obtained at monitoring stations in Xi'an, China, in the years from 2016 to 2020, and the spatial distribution characteristics of the mass and quantity concentrations of particulate matter in the atmosphere, as well as its correlation with other pollutants, were analyzed in depth. The results showed that the annual average concentrations of PM10 and PM2.5 decreased year by year from 2016 to 2020. The annual concentrations of PM2.5 decreased by 20.3 μg/m3, and the annual concentrations of PM10 decreased by 47.3 μg/m3. The days with concentrations of PM10 exceeding the standards decreased by 82 days, with a decrease of 66.7%. The days with concentrations of PM2.5 exceeding the standards decreased by 40 days, with a decrease of 35.4%. The concentration values of PM10 and PM2.5 were roughly consistent with the monthly and daily trends. The change in monthly concentrations was U-shaped, and the change in daily concentrations showed a double-peak behavior. The highest concentrations of particulate matter appeared at about 8:00~9:00 am and 11:00 pm, and they were greatly affected by human activity. The proportion of particles of 0~1.0 μm decreased by 1.94%, and the proportion of particles of 0~2.5 μm decreased by 2.00% from 2016 to 2020. A multivariate linear regression model to calculate the concentrations of the pollutants was established. This study provides a reference for the comprehensive analysis and control of air pollutants in Xi'an and even worldwide.

The impact of monthly air pollution exposure and its interaction with individual factors: Insight from a large cohort study of comprehensive hospitalizations in Guangzhou area

Background

Although the association between short-term air pollution exposure and certain hospitalizations has been well documented, evidence on the effect of longer-term (e. g., monthly) air pollution on a comprehensive set of outcomes is still limited.

Method

A total of 68,416 people in South China were enrolled and followed up during 2019-2020. Monthly air pollution level was estimated using a validated ordinary Kriging method and assigned to individuals. Time-dependent Cox models were developed to estimate the relationship between monthly PM₁₀ and O₃ exposures and the all-cause and cause-specific hospitalizations after adjusting for confounders. The interaction between air pollution and individual factors was also investigated.

Results

Overall, each 10 μg/m³ increase in PM₁₀ concentration was associated with a 3.1% (95%CI: 1.3%-4.9%) increment in the risk of all-cause hospitalization. The estimate was even greater following O₃ exposure (6.8%, 5.5%-8.2%). Furthermore, each 10 μg/m³ increase in PM₁₀ was associated with a 2.3%-9.1% elevation in all the cause-specific hospitalizations except for those related to respiratory and digestive diseases. The same increment in O₃ was relevant to a 4.7%-22.8% elevation in the risk except for respiratory diseases. Additionally, the older individuals tended to be more vulnerable to PM₁₀ exposure (P _interaction: 0.002), while the alcohol abused and those with an abnormal BMI were more vulnerable to the impact of O₃ (P _interaction: 0.052 and 0.011). However, the heavy smokers were less vulnerable to O₃ exposure (P _interaction: 0.032).

Conclusion

We provide comprehensive evidence on the hospitalization hazard of monthly PM₁₀ and O₃ exposure and their interaction with individual factors.

The spatial-temporal evolution mechanism of PM2.5 concentration based on China's climate zoning

Increasing attention has been given to the impact of PM2.5 concentration on human health. Exploring the influential factors of PM2.5 is conducive to improving air quality. Most existing studies explore the factors that influence the PM2.5 concentration from the perspective of cities or urban agglomerations, while few studies are conducted from the perspective of climate zones. We used the standard deviation ellipse and spatial autocorrelation analysis to explore the spatial-temporal evolution of the PM2.5 concentration in different climate zones in China during 2000-2018. We used differentiated EKC to construct panel regression models to explore the differences in the influential factors of the PM2.5 concentration in three climate zones. The number of cities with PM2.5 concentration less than 35 μg/m³ increased in the different climate zones. The center of gravity of the PM2.5 concentration has remained at the junction of the temperate and subtropical monsoon climate zones. The PM2.5 concentration had a high positive spatial autocorrelation in the different climate zones. The high-high clustering areas were located in the south of the temperate monsoon climate zone and the north of the subtropical monsoon climate zone. There was an inverted "U-shaped" curve between the PM2.5 concentration and economic development in China that varied in different climate zones. Identifying the differences in the influential factors of PM2.5 concentration in different climate zones will help to accelerate the implementation of the EKC inflection point.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the "2+26" cities of northern China

Elucidating the characteristics and influencing mechanisms of PM_2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM_2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM_2.5 concentrations in the "2 + 26" cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM_2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM_2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM_2.5 concentrations present a U-shaped distribution. The PM_2.5 concentrations in the "2 + 26" cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM_2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM_2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM_2.5 concentrations in winter. The effect of anthropogenic emissions on PM_2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM_2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM_2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM_2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the "2 + 26" cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Spatial and temporal variations of PM2.5 concentrations in Chinese cities during 2015-2019

The study analyzed the current status and changing trends of PM_2.5 pollution, and used Kriging spatial interpolation, spatial autocorrelation analysis, and scan statistics to explore the spatiotemporal characteristics and identify hotspots. The results showed that PM_2.5 pollution during 2015-2019 displayed a downward trend year by year, with a pronounced seasonal difference of higher concentrations in winter and lower concentrations in summer. By 2019, there were still 110 cities (n = 194) failed to meet China's annual grade II air quality standard (35 μg/m³). The spatial distribution of PM_2.5 was characterized by marked heterogeneity, with a significant spatial dependence and clustering characteristics. The pollution hotspots of PM_2.5 were mainly concentrated in eastern and central China, especially in the Beijing-Tianjin-Hebei region and its surrounding area. The results of this study will assist Chinese authorities in developing strategies for preventing and controlling air pollution, especially in hotspot regions and during peak periods.

Dependence Analysis of PM2.5 Concentrations in 295 Chinese Cities in the Winter of 2019–2020

Considering the current severe atmospheric pollution problems in China, a comprehensive understanding of the distribution and spatial variability of PM2.5 is critically important for controlling pollution and improving the future atmospheric environment. This study first explored the distribution of PM2.5 concentrations in China, and then developed a methodology of “dependence analysis” to investigate the relationship of PM2.5 in different cities in China. The data of daily PM2.5 concentrations were collected from the environmental monitoring stations in 295 cities in China. This study also developed a set of procedures to evaluate the spatial dependence of PM2.5 among the 295 Chinese cities. The results showed that there was a total of 154 city pairs with dependence type “11”, under a significance level of 0.5%. Dependence type “11” mainly occurred between nearby cities, and the distance between 89.0% of the dependent city pairs was less than 200 km. Furthermore, the dependent pairs mainly clustered in the North China Plain, the Northeast Plain, the Middle and Lower Yangtze Plain and the Fen-Wei Plain. The geographic conditions of the Plain areas were more conducive to the spread of PM2.5 contaminants, while the mountain topography was unfavorable for the formation of PM2.5 dependencies. The dependent city couples with distances greater than 200 km were all located within the Plain areas. The high concentration of PM2.5 did not necessarily lead to PM2.5 dependences between city pairs. The methodology and models developed in this study will help explain the concentration distributions and spatial dependence of the main atmospheric pollutants in China, providing guidance for the prevention of large-scale air pollution, and the improvement of the future atmospheric environment.

Multidimensional effects of urbanization on PM2.5 concentration in China

Recently, the contradiction between urbanization and the air environment has gradually attracted attention. However, most existing studies have explored the impact of single urbanization factors, such as population, the economy, or land, on PM_2.5 and ignored the impact of multidimensional urbanization on PM_2.5 concentration. Moreover, the heterogeneity in the mechanisms responsible for the PM_2.5 concentration caused by multidimensional urbanization has not been thoroughly studied in different regions in China. Therefore, we investigate the spatial-temporal evolution characteristics of PM_2.5 concentration in China during 1998-2019 by spatial analysis and dynamic panel models based on the environmental Kuznets curve (EKC). Then, we study the effects of multidimensional urbanization on PM_2.5 concentration, and analyze the dominant factors in China's eight economic regions. During the study period, the PM_2.5 concentration in China fluctuated before 2013 and gradually decreased thereafter. The PM_2.5 concentration has significant regional differences in China. Spatially, the PM_2.5 concentration is higher in the north than in the south and higher in the east than in the west. Additionally, there is a significant spatial spillover effect. Both population urbanization and economic urbanization show an inverted U-shaped relationship with PM_2.5 concentration in China, which is consistent with the classical EKC theory. Due to other socioeconomic factors, the PM_2.5 concentration tends to decrease linearly with increasing land urbanization rate. The effects of urbanization on the PM_2.5 concentration in the eight economic regions in China show significant differences. The effect of land urbanization on the PM_2.5 concentration is dominant in the Middle Yangtze River region, that of economic urbanization is dominant in northwestern China, and that of population urbanization is dominant in the remaining regions in China.

Assessment of the inhalation exposure and incremental lifetime cancer risk of PM2.5 bounded polycyclic aromatic hydrocarbons (PAHs) by different toxic equivalent factors and occupancy probability, in the case of Xi'an

Polycyclic aromatic hydrocarbons (PAHs) are widespread toxic pollutants in the atmosphere and have attracted much attention for decades. In this study, we compared the health risks of PAHs based on different toxic equivalent factors (TEFs) in a heavily polluted area during heating and non-heating periods. We also pay attention to occupancy probability (OP) in different polluted areas. The results showed that there were big differences for calculations by different TEFs, and also by OP or not. Age groups except adults were all lower calculated by OP than not. The sensitivity analysis results on the incremental lifetime cancer risks (ILCR) for population groups by Monte Carlo simulation identified that the cancer slope factor extremely affected the health risk assessment in heating periods, followed by daily inhalation exposure levels. However, daily inhalation exposure levels have dominated the effect on the inhalation ILCR and then followed by the cancer slope factor in non-heating periods. The big differences by different calculations investigated that it is important to set up the correlations between the pollution level and health risks, especially for the longtime health assessment.

Drivers of PM2.5 in the urban agglomeration on the northern slope of the Tianshan Mountains, China

Fine particulate matter (PM_2.5) is a major source of air pollution in China. Although there have been many studies of the drivers of PM_2.5 pollution in the megacities clustered in eastern China, the behavior of PM_2.5 in the northwestern urban agglomeration is not well understood. This study used near-surface observation data for 2015-2019 obtained from the national air environmental monitoring network to examine variation in PM_2.5 in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM). Two-factor interaction provided new insights into the dominant factors of PM_2.5 in the study region. The annual average PM_2.5 concentrations over the study period was 54.3 μg/m³, with an exceedance rate of 23.3%. Wavelet analysis showed two dominant cycles of 320-370 d and 150-200 d with high pollution events occurring in winter. The generalized additive model (GAM) contained linear functions of pressure, non-linear functions of SO₂, NO₂, relative humidity, sunshine duration and temperature. The two most primary variables, NO₂ and SO₂, represent 20.65% and 19.54% of the total deviance explained, respectively, while the meteorological factors account for 36.1% of the total deviance explained. In addition, the interaction between NO₂ and other factors had the strongest effect on PM_2.5. The deviance explained in the two factor interaction model (88.5%) was higher than that in the single factor model (78.4%). Our study emphasized that interaction between meteorological factors and pollutant emissions enhanced the impact on PM_2.5 compared with individual factors, which can provide a scientific basis for developing effective emission reduction strategies in UANSTM.

What drives the spatio-temporal distribution and spillover of air quality in China’s three urban agglomerations? Evidence from a two-stage approach

Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are the most important economic hinterlands in China, offering high levels of economic development. In 2020, their proportion of China’s total GDP reached 39.28%. Over the 5 years of 2014–2018, the annual maximum air quality index (AQI) of the three major urban agglomerations was greater than 100, thus maintaining a grade III light pollution (100 &lt; AQI &lt; 200) in Chinese air standards. This research thus uses a two-stage empirical analysis method to explore the spatial-temporal dispersal physiognomies and spillover effects of air quality in these three major urban agglomerations. In the first stage, the Kriging interpolation method regionally estimates and displays the air quality monitoring sampling data. The results show that the air quality of these three major urban agglomerations is generally good from 2014 to 2018, the area of good air is gradually expanding, the AQI value is constantly decreasing, the air pollution of YRD is shifting from southeast to northwest, and the air pollution of PRD is increasing. The dyeing industry shows a trend of concentration from northwest to south-central. In the second stage, Moran’s I and Spatial Durbin Model (SDM) explore the spatial autocorrelation and spillover effects of air quality related variables. The results show that Moran’s I values in the spatial autocorrelation analysis all pass the significance test. Moreover, public transport, per capita GDP, science and technology expenditure, and the vegetation index all have a significant influence on the spatial dispersal of air quality in the three urban agglomerations, among which the direct effect of public transport and the indirect effect and total effect of the vegetation index are the most significant. Therefore, the China’s three major urban agglomerations (TMUA) ought to adjust the industrial structure, regional coordinated development, and clean technology innovation.

Significant decline in aerosols in the mixing layer in Beijing from 2015 to 2020: Effects of regional coordinated air pollution control

Beijing's air quality has improved significantly since the implementation of the Air Pollution Prevention and Control Action Plan in 2013, but the local and regional contributions to this improvement have rarely been studied. Here, the vertical profile of the atmospheric backscattering coefficient (ABC) was measured by a ceilometer in Beijing from 2015 to 2020. The results show that the ABC in Beijing decreased the most at ground level from 2015 to 2020, decreasing 51.4%. Interannual variability decreased with height, and no noticeable change was found in the height range above 600 m. The most apparent declines occurred in autumn and winter, with decreases greater than 55.0%, and the minimum decrease occurred in summer, with a reduction of only 20.0%. To analyze the reasons for the autumn and winter declines, we divided the whole day into four periods according to the evolution characteristics of the atmospheric boundary layer. The significant decrease in the backscattering coefficient near the ground during the daytime confirms the effect of local emission reductions. In contrast, the considerable decreases in the backscattering coefficient measured at different heights in the midday mixing layer demonstrate the contribution of regional transport reduction. The above research results confirm the importance of regional coordinated air pollution control.

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.

Seasonal Investigation of MAX-DOAS and In Situ Measurements of Aerosols and Trace Gases over Suburban Site of Megacity Shanghai, China

Shanghai has gained much attention in terms of air quality research owing to its importance to economic capital and its huge population. This study utilizes ground-based remote sensing instrument observations, namely by Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS), and in situ measurements from the national air quality monitoring platform for various atmospheric trace gases including Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Ozone (O3), Formaldehyde (HCHO), and Particulate Matter (PM; PM10: diameter ≤ 10 µm, and PM2.5: diameter ≤ 2.5 µm) over Shanghai from June 2020 to May 2021. The results depict definite diurnal patterns and strong seasonality in HCHO, NO2, and SO2 concentrations with maximum concentrations during winter for NO2 and SO2 and in summer for HCHO. The impact of meteorology and biogenic emissions on pollutant concentrations was also studied. HCHO emissions are positively correlated with temperature, relative humidity, and the enhanced vegetation index (EVI), while both NO2 and SO2 depicted a negative correlation to all these parameters. The results from diurnal to seasonal cycles consistently suggest the mainly anthropogenic origin of NO2 and SO2, while the secondary formation from the photo-oxidation of volatile organic compounds (VOCs) and substantial contribution of biogenic emissions for HCHO. Further, the sensitivity of O3 formation to its precursor species (NOx and VOCs) was also determined by employing HCHO and NO2 as tracers. The sensitivity analysis depicted that O3 formation in Shanghai is predominantly VOC-limited except for summer, where a significant percentage of O3 formation lies in the transition regime. It is worth mentioning that seasonal variation of O3 is also categorized by maxima in summer. The interdependence of criteria pollutants (O3, SO2, NO2, and PM) was studied by employing the Pearson’s correlation coefficient, and the results suggested complex interdependence among the pollutant species in different seasons. Lastly, potential source contribution function (PSCF) analysis was performed to have an understanding of the contribution of different source areas towards atmospheric pollution. PSCF analysis indicated a strong contribution of local sources on Shanghai’s air quality compared to regional sources. This study will help policymakers and stakeholders understand the complex interactions among the atmospheric pollutants and provide a baseline for designing effective control strategies to combat air pollution in Shanghai.

Identifying Spatiotemporal Heterogeneity of PM2.5 Concentrations and the Key Influencing Factors in the Middle and Lower Reaches of the Yellow River

Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.

Mechanism of haze pollution in summer and its difference with winter in the North China Plain

Heavy haze pollution usually occurs in winter. However, according to the enhanced atmospheric boundary layer (ABL) field experiments conducted in the North China Plain (NCP) from 17 June to 6 July 2019, heavy haze pollution may also occur in summer, although with a lower probability. Winter haze pollution is significantly affected by adverse boundary layer meteorological conditions, whereas our study shows different mechanisms of summer haze pollution from that of winter. In summer, PM_2.5 is distributed uniformly as a thick layer at a lighter pollution level; however, the PM_2.5 column content in summer exceeds that in winter, suggesting that the better air quality in summer is mainly due to improved diffusion conditions. In summer, even under haze conditions, the ABL can develop over 1000 m and has a large ventilation similar to clean periods, which indicates both favourable vertical diffusion conditions and advection capability of the summer ABL. Unlike in winter, the heavy haze pollution in summer is often caused by regional transport which is related to local circulation. To explore the influence of different scale systems on summer haze pollution, we applied the spectral analysis method to surface PM_2.5 concentrations. Strong periodicity of PM_2.5 concentrations is found in 4-9 d and 1 d, corresponding to the impacts of large-scale synoptic system changes and the ABL evolution, respectively. The influence of weather change is much stronger than that of the ABL evolution on PM_2.5 concentrations in summer. The resulting changes in PM_2.5 concentrations are approximately 45 μg/m³ and 15 μg/m³, respectively. There has been a consensus on the importance of emission control in winter. And this study shows that heavy haze pollution can also occur in summer and regional joint emission control should also be emphasized in summer.

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.

Exploring the convergence patterns of PM2.5 in Chinese cities

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

COVID-2019 lockdown in Beijing: A rare opportunity to analyze the contribution rate of road traffic to air pollutants

In Beijing, the lockdown imposed to curb the spread of COVID-2019 has led to a sharp drop in road traffic. This provides an opportunity to quantify the contribution rate of road traffic to PM2.5 and NO₂ concentrations. This paper creatively puts forward the concept of the Maximum Possible Contribution Rate (MPCR) and estimates the MPCR of road traffic to PM2.5 and NO₂ by analyzing the daily air pollution data and road traffic data in Beijing from January 24 to March 31, 2020 and the same period in 2019. The findings of this paper include: The decrease in SO₂ concentration during the lockdown indicates a reduction in pollutant emissions from industry and households. During the lockdown, road traffic in Beijing reduced by 46.9 %, while the concentrations of PM2.5 and NO₂ in the atmosphere reduced by 5.6 % and 29.2 % respectively. The MPCR of road traffic to PM2.5 and NO₂ concentrations are 11.9 % and 62.3 %, respectively. The concentration of O₃ did not increase significantly with the decrease of PM2.5 and NO₂ concentrations. The findings of this paper provide a reference for city managers to evaluate the contribution rate of Beijing's road traffic to air pollutants and to formulate reasonable emission reduction policies.

Trends and Inequalities in the Incidence of Acute Myocardial Infarction among Beijing Townships, 2007-2018

Acute myocardial infarction (AMI) poses a serious disease burden in China, but studies on small-area characteristics of AMI incidence are lacking. We therefore examined temporal trends and geographic variations in AMI incidence at the township level in Beijing. In this cross-sectional analysis, 259,830 AMI events during 2007–2018 from the Beijing Cardiovascular Disease Surveillance System were included. We estimated AMI incidence for 307 consistent townships during consecutive 3-year periods with a Bayesian spatial model. From 2007 to 2018, the median AMI incidence in townships increased from 216.3 to 231.6 per 100,000, with a greater relative increase in young and middle-aged males (35–49 years: 54.2%; 50–64 years: 33.2%). The most pronounced increases in the relative inequalities was observed among young residents (2.1 to 2.8 for males and 2.8 to 3.4 for females). Townships with high rates and larger relative increases were primarily located in Beijing’s northeastern and southwestern peri-urban areas. However, large increases among young and middle-aged males were observed throughout peri-urban areas. AMI incidence and their changes over time varied substantially at the township level in Beijing, especially among young adults. Targeted mitigation strategies are required for high-risk populations and areas to reduce health disparities across Beijing.

Personal exposure to fine particulate matter and blood pressure: Variations by particulate sources

Fine particulate matter (PM_2.5) is a complex mixture of components which has been associated with various cardiovascular effects, such as elevated blood pressure (BP). However, evidences on specific sources behind these effects remain uncertain. Based on 140 72-h personal measurements among a panel of 36 health college students in Shanghai, China, we assessed associations between source-apportioned PM_2.5 exposure and BP changes. Based on personal filter samples, PM_2.5 source apportionment was conducted using Positive Matrix Factorization (PMF) model. Linear mixed-effects models were applied to evaluate associations of source-specific PM_2.5 exposure with BP changes. Seven sources were identified in PMF analysis. Among them, secondary sulfate (41%) and nitrate (24%) sources contributed most to personal PM_2.5, followed by industrial emissions (15%), traffic-related source (10%), coal combustion (6.2%), dust (2.4%) and aged sea salt (1.1%). We found nitrate, traffic-related source and coal combustion were significantly associated with increased BP. For example, an interquartile range increase in PM_2.5 from traffic-related source was significantly associated with increase in systolic BP [1.5 (95% CI: 0.26, 2.7) mmHg], diastolic BP [1.2 (95% CI: 0.10, 2.2) mmHg] and mean arterial pressure [1.2 (95% CI: 0.15, 2.2) mmHg]. This is the first investigation linking personal PM_2.5 source profile and BP changes. This study provides evidence that several anthropogenic emissions (especially traffic-related emission) may be particularly responsible for BP increases, and highlights that the importance of development of health-oriented PM_2.5 source control strategies.

Variation in PM2.5 sources in central North China Plain during 2017-2019: Response to mitigation strategies

Central North China Plain (NCP) is one of the most important source region of air pollutants over the Beijing-Tianjin-Hebei (BTH) region. The national government has issued abatement measures to improve the air quality in this area from 2017. To examine the effects of control measures, observational analysis on PM_2.5 characteristics was performed in a city of central NCP during 2017-2019 to investigate the variation in mass concentration, chemical composition, and emission source of PM_2.5. Annual PM_2.5 concentration significantly reduced by 16% from 2017 to 2019, implying substantial improvements in air quality. PM_2.5 enriched in autumn-winter seasons was dominated by SNA (sum of sulfate, nitrate and ammonium; ~38%), followed by organic carbon matters (OM; ~24%) and fine soil (FS; ~12%). This chemical composition was different from that in a megacity in NCP (Beijing) where OM accounted for a comparable fraction to SNA. Approximately half of SNA was attributed to nitrate, indicating that SNA changed from sulfate-driven to nitrate-driven, and the considerable effects of coal combustion cutoff, in which sulfate was concentrated. Decreased mass fraction of SNA and increased OM fraction in PM_2.5 were observed in 2018-2019 partly contributed to the decrease in PM_2.5. A progressive increase in the contribution of heterogeneous formed SNA whilst a decrease in OM was observed as the pollution elevated from clean to heavily polluted. Six sources (soil dust, biomass burning, secondary emission, road traffic, coal combustion and industry) were identified by the Positive Matrix Factorization (PMF) model in both years and dominated by secondary aerosols, respectively contributing 39% and 41% to PM_2.5. The decreasing concentrations (with reductions of 17%-61%) of the secondary source, coal combustion, soil dust and biomass burning largely accounted for the reduction in PM_2.5, as a consequence of the recent abatement measures. By contrast, contributions of vehicle-related emissions, similar to the increasing contribution of vehicles at sites in NCP after 2013, should receive increased attention.

Characteristics and potential sources of wintertime air pollution in Linfen, China

Linfen in China's Shanxi Province suffers severe air pollution in winter. Understanding the characteristics of air pollution and providing scientific support to mitigate such pollution are urgent matters. This study investigated the variations of PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO in Linfen between December 1, 2019 and February 29, 2020. The mean concentrations of PM_2.5, PM₁₀, NO₂, SO₂, MDA8 (the maximum daily 8-h average) O₃, and CO were 106.2, 139.4, 47.2, 41.0, 57.0 μg m^-3, and 1.8 mg m^-3, respectively. Large amounts of pollutants emitted by coal burning, industry, vehicles, and residents contributed to air pollution. Unfavorable meteorological conditions, such as lower temperature, weaker wind, higher relative humidity, and reduced planetary boundary layer height, made the situation worse. Fireworks and firecrackers set off to celebrate traditional Chinese festivals caused the concentration of PM pollutants to spike, with the maximum daily mean concentration of PM_2.5 reached 314 μg m^-3 and the peak hourly value reached 378.0 μg m^-3. Suspensions of commercial and social activities due to COVID-19 reduced anthropogenic emissions, mainly from industry and transportation, which decreased the level of air pollutants other than O₃. Analyses involving backward trajectory cluster, the potential source contribution function, and concentration weighted trajectory demonstrated that PM_2.5 pollution mainly came from local emissions in Shanxi Province and regional transport from Inner Mongolia, Shaanxi, Hebei, Henan, and Gansu provinces. Shanxi and its surrounding provinces should adopt measures such as tightening environmental management standards, promoting the use of renewable energy, and adjusting the transportation structure to reduce regional emissions. This study will help policy-makers draft plans and policies to reduce air pollution in Linfen.

Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown

With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O₃) pollution was generally amplified in China. However, the O₃ levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016-2019, while O₃ gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O₃ concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O₃ pollution mitigation during the lockdown. A 7.84 μg/m³ drop in O₃ concentration was driven by meteorology, with other decreases (4.11 μg/m³) explained by reduced anthropogenic emissions of O₃ precursors. Taken together, the lower NO₂/SO₂ ratios (1.25-2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O₃ concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24-58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O₃ reduction in the Guangxi region of South China.

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015-2018

Fine particulate matter (PM_2.5) is closely related to the air quality and public health. Numerous models have been introduced to simulate the PM_2.5 concentrations at large scale based on remote sensing and auxiliary data. However, the data precision provided by these models are inadequate for epidemiology and pollutant exposure studies at medium or small scale. The present study aims to calibrate PM_2.5 concentrations at 1 km resolution scale across China during 2015-2018 based on monitoring station data and auxiliary data using a novel geographically and temporally weighted regression model (GTWR). The cross-validation (CV) method and the geographically weighted regression (GWR) model are conducted for validation and cross-comparison. Additionally, the spatial autocorrelation and slope analysis methods are implemented to detect the spatiotemporal dynamic of PM_2.5 concentrations. A sample-based CV of the GTWR model demonstrates an acceptable precision with a coefficient of determination equal to 0.67, a root-mean-square error of 10.32 μg/m³, and a mean prediction error of-6.56 μg/m³. This result proves that the GTWR model can simulate PM_2.5 concentrations at a higher spatial resolution and accuracy across China than some previous models. Besides, the heterogeneity and spatiotemporal dynamic of PM_2.5 concentrations are obvious, that is, the High-High (H-H) agglomeration areas with strong haze pollution were mainly concentrated in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Chengdu-Chongqing (CY), and Guanzhong Plain (GZP). In addition, the PM_2.5 concentrations are undergoing a decreasing trend in most of the study area, and the decrease in the BTH is dramatic. The results of the present study are helpful for calibrating and detecting the spatiotemporal dynamic of PM_2.5 concentrations and useful for the government to make decisions about decreasing haze pollution in urban agglomeration scale.

Evaluation of wearing comfort of dust masks

Dust masks are widely used to prevent the inhalation of particulate matter into the human respiratory organs in polluted air environments. The filter of a dust mask inherently obstructs the natural respiratory air flows, and this flow resistance is mainly responsible for the discomfort experienced when wearing a dust mask. In atmospheric conditions seriously contaminated with fine dust, it is recommended that common citizens wear a dust mask in their everyday lives, yet many people are reluctant to wear a dust mask owing to the discomfort experienced when wearing it for a long time. Understanding of physical reasons for the discomfort is thus crucial in designing a dust mask, but remains far from clear. This study presents a technique to quantify the wearing comfort of dust masks. By developing a respiration simulator to measure the pressure loss across a dust mask, we assessed the energy costs to overcome flow resistance when breathing through various types of dust masks. The energy cost for a single inhalation varies with the mask type in a range between 0 and 10 mJ. We compared the results with the survey results of 40 people about the wearing comfort of the dust masks, which revealed that the wearing comfort crucially depends on the energy cost required for air inhalation though the dust mask. Using the measured energy cost during inhalation as a parameter to quantify the wearing comfort, we present a comprehensive evaluation of the performance of dust masks in terms of not only the filtering performance but also the wearing comfort. Our study suggests some design principles for dust mask filters, auxiliary electric fans, and check valves.