Relationship between fine particulate matter, weather condition and daily non-accidental mortality in Shanghai, China: A Bayesian approach

Association between synoptic types in Beijing and acute myocardial infarction hospitalizations: A comprehensive analysis of environmental factors

BACKGROUND

Environmental factors like air pollution and temperature can trigger acute myocardial infarction (AMI). However, the link between large-scale weather patterns (synoptic types) and AMI admissions has not been extensively studied. This research aimed to identify the different synoptic air types in Beijing and investigate their association with AMI occurrences.

METHODS

We analyzed data from Beijing between 2013 and 2019, encompassing 2556 days and 149,632 AMI cases. Using principal component analysis and hierarchical clustering, classification into distinct synoptic types was conducted based on weather and pollution measurements. To assess the impact of each type on AMI risk over 14 days, we employed a distributed lag non-linear model (DLNM), with the reference being the lowest risk type (Type 2).

RESULTS

Four synoptic types were identified: Type 1 with warm, humid weather; Type 2 with warm temperatures, low humidity, and long sunshine duration; Type 3 with cold weather and heavy air pollution; and Type 4 with cold temperatures, dryness, and high wind speed. Type 4 exhibited the greatest cumulative relative risk (CRR) of 1.241 (95%CI: 1.150, 1.339) over 14 days. Significant effects of Types 1, 3, and 4 on AMI events were observed at varying lags: 4-12 days for Type 1, 1-6 days for Type 3, and 1-11 days for Type 4. Females were more susceptible to Types 1 and 3, while individuals younger than 65 years old showed increased vulnerability to Types 3 and 4.

CONCLUSION

Among the four synoptic types identified in Beijing from 2013 to 2019, Type 4 (cold, dry, and windy) presented the highest risk for AMI hospitalizations. This risk was particularly pronounced for males and people under 65. Our findings collectively highlight the need for improved methods to identify synoptic types. Additionally, developing a warning system based on these synoptic conditions could be crucial for prevention.

Analysis of severe ozone-related human health and weather influence over China in 2019 based on a high-resolution dataset

Ozone pollution in 2019 in China is particularly severe posing a tremendous threat to the health of Chinese inhabitants. In this study, we constructed a more reliable and accurate 1-km gridded dataset for 2019 with as many sites as possible using the inverse distance weight interpolation method to analyze spatiotemporal ozone pollution characteristics and health burden attributed to ozone exposure from the perspective of different diseases and weather influence. The accuracy of this new dataset is higher than other public datasets, with the coefficient of determination of 0.84 and root-mean-square error of 8.77 ppb through the validation of 300 external sites which were never used for establishing retrieval methods by the datasets mentioned-above. The averaged MDA8 (the daily maximum 8 h average) ozone concentrations over China was 43.5 ppb, and during April-July, 83.9% of total grids occurred peak-month ozone concentrations. Overall, the highest averaged exceedance days (60 days) and population-weighted ozone concentrations (55.0 ppb) both concentrated in central-eastern China including 9 provinces (only 11.4% of the national territory); meanwhile, all-cause premature deaths attributable to ozone exposure reached up to 142,000 (54.9% of national total deaths) with higher deaths for cardiovascular and respiratory, and the provincial per capita premature mortality was 0.27~0.44‰. The six most polluted weather types in the central-eastern China are in order as follows: westerly (SW and W), cyclonic, northerly, and southerly (NW, N, and S) types, which accounts for approximately 73.2% of health burden attributed to daily ozone exposure and poses the greatest public health risk with mean daily premature deaths ranging from 466 to 610. Our findings could provide an effective support for regional ozone pollution control and public health management in China.

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China

Ambient pollutants, particularly fine particulate matter (PM2.5) and ozone (O3), pose significant risks to both public health and economic development. In recent years, PM2.5 concentration in China has decreased significantly, whereas that of O3 has increased rapidly, leading to considerable health risks. In this study, a generalized additive model was employed to establish the relationship of PM2.5 and O3 exposure with non-accidental mortality across 17 districts and counties in Jilin Province, China, over 2015-2016. The health burden and economic losses attributable to PM2.5 and O3 were assessed using high-resolution satellite and population data. According to the results, per 10 µg/m3 increase in PM2.5 and O3 concentrations related to an overall relative risk (95% confidence interval) of 1.004 (1.001-1.007) and 1.009 (1.005-1.012), respectively. In general, the spatial distribution of mortality and economic losses was uneven. Throughout the study period, a total of 23,051.274 mortalities and 27,825.015 million Chinese Yuan (CNY) in economic losses were attributed to O3 exposure, which considerably surpassing the 5,450.716 mortalities and 6,553,780 million CNY in economic losses attributed to PM2.5 exposure. The O3-related health risks and economic losses increased by 3.75% and 9.3% from 2015 to 2016, while those linked to PM2.5 decreased by 23.33% and 18.7%. Sensitivity analysis results indicated that changes in pollutant concentrations were the major factors affecting mortality rather than baseline mortality and population.

Using a novel strategy to investigate the spatially autocorrelated and clustered associations between short-term exposure to PM2.5 and mortality and the attributable burden: A case study in the Sichuan Basin, China

Due to the lack of statistical methods, few studies have investigated the spatial autocorrelated distribution in the association between short-term exposure to PM2.5 and mortality and used a statistical manner to explore the association-clustered regions, which play important roles in identifying high-sensitivity/susceptibility regions. The Sichuan Basin (SCB) is one of the most PM2.5-polluted areas, and the extreme economic imbalance may cause considerable spatial heterogeneity and clustering in PM2.5-mortality association. In this work, we used a recently proposed strategy by us to investigate the spatially autocorrelated and clustered association between daily PM2.5 and cardiorespiratory mortality from 2015 to 2019 in 130 counties of the SCB. First, generalized additive models were independently constructed to obtain the county-level association estimations. Then, an estimation-error-based spatial scan statistic was used to detect the association-clustered regions. Third, multivariate conditional meta autoregression was used to obtain the spatially autocorrelated association distribution, based on which the attributable deaths were mapped and their inequality was evaluated using the Gini coefficient and Lorenz curve. Results showed that two significantly association-clustered regions were detected. One is mainly located in the megacity Chengdu where PM2.5 presented a significantly stronger association with no threshold effect at low-level PM2.5 but a threshold at high-level PM2.5. In the other cluster, a threshold effect at low-level PM2.5 but no threshold at high-level PM2.5 were found. The mortality risk at low/middle-level PM2.5 decreased from Chengdu as the center to the surrounding areas. A total of 29,129 (2.0 %) deaths were attributable to the excess PM2.5 exposure. The attributable deaths also decreased from Chengdu as the center to the surrounding areas with Gini coefficients of 0.43 and 0.3 for absolute and relative attributable deaths, respectively. This novel strategy provided a new epidemiological perspective regarding the association and implicated that Chengdu is significantly deserving of more attention regarding PM2.5-related health loss.

Exploring Health Effects under Specific Causes of Mortality Based on 90 Definitions of PM2.5 and Cold Spell Combined Exposure in Shanghai, China

In this study, a total of 90 definitions were set up based on six air pollution definitions, five cold spell definitions, and three combined exposure scenarios. The relative risks (RRs) on all-cause, circulatory, and respiratory mortality were explored by a model combining a distributed linear lag model with quasi-Poisson regression. The definition in which daily PM_2.5 increases more than 75 μg/m³ for at least 2 days and the average temperature falls below the 10th percentile for at least 2 days produced the best model fit performance in all-cause mortality. The high peaks of the health effect were generally observed around the lag days 6-9. The cumulative relative risks (CRRs) were more significant in the simultaneous-exposure scenario and higher in respiratory mortality, where the highest CRR (12.15, 3.69-40.03) was observed in definition P1T5, in which daily PM_2.5 increases more than 75 μg/m³, and the average temperature falls below the 2.5th percentile for at least two days. For relative risk due to interaction (RERI), we found positive additive interactions (RERI > 0) between PM_2.5 pollution and cold spell, especially in respiratory mortality. Clarifying the definition of combined events can help policymakers to capture health risks and construct more effective risk warning systems.

Indoor air quality prediction using optimizers: A comparative study

Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%.

Statistical Learning of the Worst Regional Smog Extremes with Dynamic Conditional Modeling

This paper is concerned with the statistical learning of the extreme smog (PM 2.5 ) dynamics of a vast region in China. Differently from classical extreme value modeling approaches, this paper develops a dynamic model of conditional, exponentiated Weibull distribution modeling and analysis of regional smog extremes, particularly for the worst scenarios observed in each day. To gain higher modeling efficiency, weather factors will be introduced in an enhanced model. The proposed model and the enhanced model are illustrated with temporal/spatial maxima of hourly PM 2.5 observations each day from smog monitoring stations located in the Beijing–Tianjin–Hebei geographical region between 2014 and 2019. The proposed model performs more precisely on fittings compared with other previous models dealing with maxima with autoregressive parameter dynamics, and provides relatively accurate prediction as well. The findings enhance the understanding of how severe extreme smog scenarios can be and provide useful information for the central/local government to conduct coordinated PM 2.5 control and treatment. For completeness, probabilistic properties of the proposed model were investigated. Statistical estimation based on the conditional maximum likelihood principle is established. To demonstrate the estimation and inference efficiency of studies, extensive simulations were also implemented.

Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China

Background: Exposures to both ambient fine particulate matter (PM_2.5) and extreme weather conditions have been associated with cardiovascular disease (CVD) deaths in numerous epidemiologic studies. However, evidence on the associations with CVD deaths for interaction effects between PM_2.5 and weather conditions is still limited. This study aimed to investigate associations of exposures to PM_2.5 and weather conditions with cardiovascular mortality, and further to investigate the synergistic or antagonistic effects of ambient air pollutants and synoptic weather types (SWTs). Methods: Information on daily CVD deaths, air pollution, and meteorological conditions between 1 January 2012 and 31 December 2014 was obtained in Shanghai, China. Generalized additive models were used to assess the associations of daily PM_2.5 concentrations and meteorological factors with CVD deaths. A 15-day lag analysis was conducted using a polynomial distributed lag model to access the lag patterns for associations with PM_2.5. Results: During the study period, the total number of CVD deaths in Shanghai was 59,486, with a daily mean of 54.3 deaths. The average daily PM_2.5 concentration was 55.0 µg/m³. Each 10 µg/m³ increase in PM_2.5 concentration was associated with a 1.26% (95% confidence interval (CI): 0.40%, 2.12%) increase in CVD mortality. No SWT was statistically significantly associated with CVD deaths. For the interaction between PM_2.5 and SWT, statistically significant interactions were found between PM_2.5 and cold weather, with risk for PM_2.5 in cold dry SWT decreasing by 1.47% (95% CI: 0.54%, 2.39%), and in cold humid SWT the risk decreased by 1.45% (95% CI: 0.52%, 2.36%). In the lag effect analysis, statistically significant positive associations were found for PM_2.5 in the 1-3 lag days, while no statistically significant effects were found for other lag day periods. Conclusions: Exposure to PM_2.5 was associated with short-term increased risk of cardiovascular deaths with some lag effects, while the cold weather may have an antagonistic effect with PM_2.5. However, the ecological study design limited the possibility to identify a causal relationship, so prospective studies with individual level data are warranted.

Acute effect of daily fine particulate matter pollution on cerebrovascular mortality in Shanghai, China: a population-based time series study

Numerous studies have investigated the impacts of ambient fine particulate matter (PM_2.5) on human health. In this study, we examined the association of daily PM_2.5 concentrations with the number of deaths for the cerebrovascular disease on the same day, using the generalized additive model (GAM) controlling for temporal trend and meteorological variables. We used the data between 2012 and 2014 from Shanghai, China, where the adverse health effects of PM_2.5 have been of particular concern. Three different approaches (principal component analysis, shrinkage smoothers, and the least absolute shrinkage and selection operator regularization) were used in GAM to handle multicollinear meteorological variables. Our results indicate that the average daily concentration of PM_2.5 in Shanghai was high, 55 μg/m³, with an average daily death for cerebrovascular disease (CVD) of 62. There was 1.7% raised cerebrovascular disease deaths per 10 μg/m³ increase in PM_2.5 concentration in the unadjusted model. However, PM_2.5 concentration was no longer associated with CVD deaths after controlling for meteorological variables. The results were consistent in the three modelling techniques that we used. As a large number of people are exposed to air pollution, further investigation with longer time period including individual-level information is needed to examine the association.

Particulate Matter Mortality Rates and Their Modification by Spatial Synoptic Classification

Air pollution levels are highly correlated with temperature or humidity, so we investigated the relationship between PM₁₀ and the spatial synoptic classification (SSC) scheme on daily mortality, according to age group and season. Daily death data for 2000-2014 from Seoul, Korea, were acquired, and time-series analysis was applied with respect to season and to each of seven distinct SSC types: dry moderate (DM); dry polar (DP); dry tropical (DT); moist moderate (MM); moist polar (MP); moist tropical (MT); and transition (T). Modification effects were estimated for daily, non-accidental, cardiovascular, and respiratory mortality between PM₁₀ and SSC types. The following SSC-type-specific increased mortalities were observed, by cause of death: non-accidental mortality: DT (1.86%) and MT (1.86%); cardiovascular mortality: DT (2.83%) and MM (3.00%); respiratory mortality: MT (3.78%). Based on simplified weather types, increased PM₁₀ effects in non-accidental mortality rates were observed in dry (1.54%) and moist (2.32%) conditions among those aged 40-59 years and were detected regardless of conditions in other age groups: 60-74 (1.11%), 75-84 (1.55%), and 85+ (1.75%). The effects of particulate air pollution, by SSC, suggest the applicability of SSC to the comparison and understanding of acute effects of daily mortality based on weather type.

Comparison of Frequentist and Bayesian Generalized Additive Models for Assessing the Association Between Daily Exposure to Fine Particles and Respiratory Mortality: A Simulation Study

Objective: To compare the performance of frequentist and Bayesian generalized additive models (GAMs) in terms of accuracy and precision for assessing the association between daily exposure to fine particles and respiratory mortality using simulated data based on a real time-series study. Methods: In our study, we examined the estimates from a fully Bayesian GAM using simulated data based on a genuine time-series study on fine particles with a diameter of 2.5 μm or less (PM_2.5) and respiratory deaths conducted in Shanghai, China. The simulation was performed by multiplying the observed daily death with a random error. The underlying priors for Bayesian analysis are estimated using the real world time-series data. We also examined the sensitivity of Bayesian GAM to the choice of priors and to true parameter. Results: The frequentist GAM and Bayesian GAM show similar means and variances of the estimates of the parameters of interest. However, the estimates from Bayesian GAM show relatively more fluctuation, which to some extent reflects the uncertainty inherent in Bayesian estimation. Conclusions: Although computationally intensive, Bayesian GAM would be a better solution to avoid potentially over-confident inferences. With the increasing computing power of computers and statistical packages available, fully Bayesian methods for decision making may become more widely applied in the future.

Association between short-term exposure to particulate matter air pollution and cause-specific mortality in Changzhou, China

BACKGROUND

Extensive studies have linked ambient particulate matter (PM) to an increased mortality burden from a wide range of causes. However, the effects of PM on mortality rates from specific causes were unclear. This study aimed to estimate the detrimental effects of PM on cause specific deaths in Changzhou, China.

METHOD

Data representing daily mortality rates, weather conditions and particulate air pollution levels were obtained from government-controlled agencies of Changzhou, from January 1, 2015 to December 31, 2016. An inverse distance weighting method was used to assess the population exposure to PM and a time-series was performed to detect the detrimental effects of PM.

RESULTS

Positive associations were identified between PMs and daily mortality rates from non-accidental, circulatory, hypertensive, respiratory and chronic lower respiratory causes at a lag of 0-3 days. The effects of PMs were strongest on hypertensive mortality, with an increase of 5.27% (95% confidence interval (CI): 2.43-8.19%) and 3.52% (95% CI: 1.55-5.53%), per 10 μg/m³ increment in PM_2.5 and PM₁₀ respectively. The elderly exhibited a higher mortality risk with PMs exposure. Females were more vulnerable to circulatory, hypertensive and respiratory death while males were more sensitive to chronic lower respiratory and neurodegenerative mortality. The effects were stronger in warm seasons for circulatory mortality and stronger in cold seasons for respiratory mortality.

CONCLUSION

These findings indicate that PM could exert adverse influences on the outcomes of several pathological processes, especially for women and the elderly with hypertension disease.

Association between PM2.5 Exposure and All-Cause, Non-Accidental, Accidental, Different Respiratory Diseases, Sex and Age Mortality in Shenzhen, China

Background: China is at its most important stage of air pollution control. Research on the association between air pollutants and human health is very important and necessary. The purpose of this study was to evaluate the association between PM_2.5 concentrations and residents’ mortality and to compare the effect of PM_2.5 on the different diseases, accidental deaths, sex or age of residents from high polluted areas with less polluted areas. Methods: The semi-parametric generalized additive model (GAM) with Poisson distribution of time series analysis was used. The excess risk (ER) of mortality with the incremental increase of 10 µg/m³ in PM_2.5 concentration was calculated. Concentration-response relationship curves and autocorrelation between different lags of PM_2.5 were also evaluated. Results: PM_2.5 exposure was significantly associated with the mortality of residents. The strongest ERs per 10 µg/m³ increase in PM_2.5 were 0.74% (95% CI: 0.11–1.38%) for all-cause, 0.67% (95% CI: 0.01–1.33%) for non-accidental, 1.81% (95% CI: 0.22–3.42%) for accidental, 3.04% (95% CI: 0.60–5.55%) for total respiratory disease, 6.38% (95% CI: 2.78–10.11%) for chronic lower respiratory disease (CLRD), 8.24% (95% CI: 3.53–13.17%) for chronic obstructive pulmonary disease (COPD), 1.04% (95% CI: 0.25–1.84%) for male and 1.32% (95% CI: 0.46–2.19%) for elderly. Furthermore, important information on the concentration-response relationship curves was provided. Conclusions: PM_2.5 can increase the risk of residents’ mortality, even in places with less air pollution and developed economy in China.

A Review of Recent Advances in Research on PM2.5 in China

PM_2.5 pollution has become a severe problem in China due to rapid industrialization and high energy consumption. It can cause increases in the incidence of various respiratory diseases and resident mortality rates, as well as increase in the energy consumption in heating, ventilation, and air conditioning (HVAC) systems due to the need for air purification. This paper reviews and studies the sources of indoor and outdoor PM_2.5, the impact of PM_2.5 pollution on atmospheric visibility, occupational health, and occupants’ behaviors. This paper also presents current pollution status in China, the relationship between indoor and outdoor PM_2.5, and control of indoor PM_2.5, and finally presents analysis and suggestions for future research.