Ambient temperature enhanced acute cardiovascular-respiratory mortality effects of PM2.5 in Beijing, China

The synergistic effect of high temperature and relative humidity on non-accidental deaths at different urbanization levels

Numerous studies have examined the impact of temperature on mortality, yet research on the combined effect of temperature and humidity on non-accidental deaths remains limited. This study investigates the synergistic impact of high temperature and humidity on non-accidental deaths in China, assessing the influence of urban development and urbanization level. Utilizing the distributed lag nonlinear model (DLNM) of quasi-Poisson regression, we analyzed the relationship between Wet Bulb Globe Temperature (WBGT) and non-accidental deaths in 30 Chinese cities from 2010 to 2016, including Guangzhou during 2012-2016. We stratified temperature and humidity across these cities to evaluate the influence of varying humidity levels on deaths under high temperatures. Then, we graded the duration of heat and humidity in these cities to assess the impact of deaths with different durations. Additionally, the cities were categorized based on gross domestic product (GDP), and a vulnerability index was calculated to examine the impact of urban development and urbanization level on non-accidental deaths. Our findings reveal a pronounced synergistic effect of high temperature and humidity on non-accidental deaths, particularly at elevated humidity levels. The synergies of high temperature and humidity are extremely complex. Moreover, the longer the duration of high temperature and humidity, the higher the risk of non-accidental death. Furthermore, areas with higher urbanization exhibited lower relative risks (RR) associated with the synergistic effects of heat and humidity. Consequently, it is imperative to focus on damp-heat related mortality among vulnerable populations in less developed regions.

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

Relationships between long-term exposure to major PM2.5 constituents and outpatient visits and hospitalizations in Guangdong, China

Ambient fine particulate matter (PM2.5) has attracted considerable attention due to its crucial role in the rising global disease burden. Evidence of health risks associated with exposure to PM2.5 and its major constituents is important for advancing hazard assessments and air pollution emission policies. We investigated the relationship between exposure to major constituents of PM2.5 and outpatient visits as well as hospitalizations in Guangdong Province, China, where 127 million residents live in a severe PM2.5 pollution environment. An approach that integrates the generalized weighted quantile sum (gWQS) regression with the difference-in-differences (DID) approach was used to assess the overall mixture effects and relative contributions of each constituent. We observed significant associations between long-term exposure to the mixture of PM2.5 constituents (WQS index) and outpatient visits (IR%, percentage increases in risk per unit WQS index increase:1.73, 95%CI: 1.72, 1.74) as well as hospitalizations (IR%:5.15, 95%CI: 5.11, 5.20). Black carbon (weight: 0.34) and nitrate (weight: 0.60) respectively exhibited the highest contributions to outpatient visits and hospitalizations. The overall mixture effects on outpatient visits and hospitalizations were higher with increased summer air temperatures (IR%: 7.54, 95%CI: 7.33, 7.74 and IR%: 9.55, 95%CI: 8.36, 10.75, respectively) or decreased winter air temperatures (IR%: 1.88, 95%CI: 1.68, 2.08 and IR%: 4.87, 95%CI: 3.73, 6.02, respectively). Furthermore, the overall mixture effects on outpatient visits and hospitalizations were significantly higher in populations with higher socioeconomic status (P < 0.01). It's crucial to address the primary sources of nitrate precursor substances and black carbon (mainly traffic-related and industrial-related air pollutants) and consider the complex interaction effects between air temperature and PM2.5 in the context of climate change. Of particular concern is the need to prioritize healthcare demands in economically disadvantaged regions and to address the health inequalities stemming from the uneven distribution of healthcare resources and PM2.5 pollution.

Associations between PM2.5 and O3 exposures and new onset type 2 diabetes in regional and national samples in the United States

BACKGROUND

Exposure to particulate matter ≤2.5 μm in diameter (PM2.5) and ozone (O3) has been linked to numerous harmful health outcomes. While epidemiologic evidence has suggested a positive association with type 2 diabetes (T2D), there is heterogeneity in findings. We evaluated exposures to PM2.5 and O3 across three large samples in the US using a harmonized approach for exposure assignment and covariate adjustment.

METHODS

Data were obtained from the Veterans Administration Diabetes Risk (VADR) cohort (electronic health records [EHRs]), the Reasons for Geographic and Racial Disparities in Stroke (REGARDS) cohort (primary data collection), and the Geisinger health system (EHRs), and reflect the years 2003-2016 (REGARDS) and 2008-2016 (VADR and Geisinger). New onset T2D was ascertained using EHR information on medication orders, laboratory results, and T2D diagnoses (VADR and Geisinger) or report of T2D medication or diagnosis and/or elevated blood glucose levels (REGARDS). Exposure was assigned using pollutant annual averages from the Downscaler model. Models stratified by community type (higher density urban, lower density urban, suburban/small town, or rural census tracts) evaluated likelihood of new onset T2D in each study sample in single- and two-pollutant models of PM2.5 and O3.

RESULTS

In two pollutant models, associations of PM2.5, and new onset T2D were null in the REGARDS cohort except for in suburban/small town community types in models that also adjusted for NSEE, with an odds ratio (95% CI) of 1.51 (1.01, 2.25) per 5 μg/m3 of PM2.5. Results in the Geisinger sample were null. VADR sample results evidenced nonlinear associations for both pollutants; the shape of the association was dependent on community type.

CONCLUSIONS

Associations between PM2.5, O3 and new onset T2D differed across three large study samples in the US. None of the results from any of the three study populations found strong and clear positive associations.

Hybrid unorganized machines to estimate the number of hospital admissions caused by PM[Formula: see text] concentration

Air pollution levels exceeding the recommended limit can be the main cause of illnesses that affect human health, mainly diseases of the respiratory system. Consequently, this high exposure can impact public health management, given the increase in hospital admissions. One of the most influential air pollution parameters related to respiratory diseases is particulate matter (PM) concentrations. Thus, this paper proposes to estimate hospital admissions due to respiratory diseases caused by PM concentration with an aerodynamic diameter less than 10 [Formula: see text]m (PM[Formula: see text]), using artificial neural networks. Three hybrid neural network models are developed by combining two architectures denoted unorganized machines: extreme learning machines and echo state networks. These models also comprise extension strategies that seek to improve the generalization capability and the variation in the nonlinear outputs. Case studies explore three cities' datasets from São Paulo state, Brazil: Cubatão, Campinas, and São Paulo, to assess the quality of the hospital admissions estimations obtained by applying the proposed models. Results demonstrate that the hybrid models outperform the previously developed standard approaches in several scenarios. An overall analysis shows that the hybrid models can be a suitable strategy considering the instance particularities, especially in large datasets.

The interactive effects of extreme temperatures and PM2.5 pollution on mortalities in Jiangsu Province, China

Exposure to extreme temperatures or fine particles is associated with adverse health outcomes but their interactive effects remain unclear. We aimed to explore the interactions of extreme temperatures and PM2.5 pollution on mortalities. Based on the daily mortality data collected during 2015-2019 in Jiangsu Province, China, we conducted generalized linear models with distributed lag non-linear model to estimate the regional-level effects of cold/hot extremes and PM2.5 pollution. The relative excess risk due to interaction (RERI) was evaluated to represent the interaction. The relative risks (RRs) and cumulative relative risks (CRRs) of total and cause-specific mortalities associated with hot extremes were significantly stronger (p < 0.05) than those related to cold extremes across Jiangsu. We identified significantly higher interactions between hot extremes and PM2.5 pollution, with the RERI range of 0.00-1.15. The interactions peaked on ischaemic heart disease (RERI = 1.13 [95%CI: 0.85, 1.41]) in middle Jiangsu. For respiratory mortality, RERIs were higher in females and the less educated. The interaction pattern remained consistent when defining the extremes/pollution with different thresholds. This study provides a comprehensive picture of the interactions between extreme temperatures and PM2.5 pollution on total and cause-specific mortalities. The projected interactions call for public health actions to face the twin challenges, especially the co-appearance of hot extremes and PM pollution.

An integrated analysis of air pollution and meteorological conditions in Jakarta

Air pollution and climate change are general problems for society. This paper proposes an integrated analysis of the Air Quality Index (AQI) and meteorological conditions in Jakarta. The column-based data integration model is applied to create integrated data of the Air Quality Index and meteorological conditions. The integrated data is then used to generate a causal graph using the PC algorithm. The causal graph reveals that there exist causal relationships between pollutants and meteorological conditions, e.g, humidity, rainfall, wind speed, and duration of sunshine affect particulate matter 10 (PM[Formula: see text]); wind speed affects sulfur dioxide (SO[Formula: see text]); temperature affects ozone (O[Formula: see text]). The historical data records that the average wind speed is decreased and the number of unhealthy days has risen. Ozone and particulate matter are two pollutants that mainly influence poor air quality in Jakarta. The integrated data is also used to train Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting. Experimental results show that LSTM using integrated data produces smaller errors for forecasting AQI and meteorological conditions.

Effect of long-term exposure to PM2.5 on the risk of type 2 diabetes and arthritis in type 2 diabetes patients: Evidence from a national cohort in China

BACKGROUND

It remains unclear whether type 2 diabetes and the complication of arthritis are causally related to the PM_2.5 pollutant. Therefore, we aimed to investigate the associations of long-term PM_2.5 exposure with type 2 diabetes and with arthritis in type 2 diabetes patients.

MATERIALS AND METHODS

This study used data from the China Health and Retirement Longitudinal Survey (CHARLS) implemented during 2011-2018. The associations were analyzed by Cox proportional hazards regression models, and the population-attributable fraction (PAF) was calculated to assess the burden of type 2 diabetes and arthritis-attributable to PM_2.5.

RESULTS

A total of 21,075 participants were finally included, with 19,121 analyzed for PM_2.5 and type 2 diabetes risk and 12,427 analyzed for PM_2.5 and arthritis risk, of which 1,382 with newly-diagnosed type 2 diabetes and 1,328 with arthritis during the follow-up. Overall, each 10 μg/m³ increment in PM_2.5 concentration was significantly associated with an increase in the risk of type 2 diabetes (HR = 1.26, 95 %CI1.22 to 1.31), and the PAF of type 2 diabetes attributable to PM_2.5 was 13.54 %. In type 2 diabetes patients, each 10 μg/m³ increment in PM_2.5 exposure was associated with an increase in arthritis (HR = 1.42, 95 %CI: 1.28 to 1.57), and the association was significantly greater than that (H = 1.23, 95 %CI: 1.19 to 1.28) in adults without type 2 diabetes. The PAFs of arthritis-attributable to PM_2.5 in participants with and without type 2 diabetes were 18.54 % and 10.69 %, respectively.

CONCLUSION

Long-term exposure to PM_2.5 may increase the risk of type 2 diabetes and make type 2 diabetes patients susceptible to arthritis.

Identification of Health Effects of Complex Air Pollution in China

After the Chinese government introduced a series of policies to strengthen the control of air pollution, the concentration of particulate matter has decreased, but the concentration of ozone has increased, and the problem of complex air pollution still exists, posing a serious threat to public health. Therefore, disentangling the health effect of multi-pollutants has been a long-discussed challenge in China. To evaluate the adverse effects of complex air pollution, a generalized additive model was used to assess the health risks of different pollution types in eight metropolises in different climates in China from 2013 to 2016. Instead of directly introducing multiple pollutant concentrations, we integrated the concentration levels of PM_2.5, NO₂, and O₃ into a set of predictors by grouping methods and divided air pollution into three high single-pollutant types and four high multi-pollutant types to calculate mortality risk in different types. The comprehensive results showed that the impact of high multi-pollutant types on mortality risk was greater than that of high single-pollutant types. Throughout the study period, the high multi-pollutant type with high PM_2.5, NO₂, and O₃ and the high multi-pollutant type with high PM_2.5 and NO₂ were more associated with death, and the highest RRs were 1.129 (1.080, 1.181) and 1.089 (1.066, 1.113), respectively. In addition, the pollution types that most threaten people are different in different cities. These differences may be related to different pollution conditions, pollutant composition, and indoor-outdoor activity patterns in different cities. Seasonally, the risk of complex air pollution is greater in most cities in the warm season than in the cold season. This may be caused by the modifying effects of high temperature on pollutants in addition to different indoor-outdoor activity patterns in different seasons. The results also show that calculating the effect of individual air pollutants separately and adding them together may lead to an overestimation of the combined effect. It further highlights the urgency and need for air pollution health research to move towards a multi-pollutant approach that considers air pollution as a whole in the context of atmospheric abatement and global warming.

Interactive short-term effects of meteorological factors and air pollution on hospital admissions for cardiovascular diseases

A substantial number of studies have demonstrated the association between air pollution and adverse health effects. However, few studies have explored the potential interactive effects between meteorological factors and air pollution. This study attempted to evaluate the interactive effects between meteorological factors (temperature and relative humidity) and air pollution ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) on cardiovascular diseases (CVDs). Next, the high-risk population susceptible to air pollution was identified. We collected daily counts of CVD hospitalizations, air pollution, and weather data in Nanning from January 1, 2014, to December 31, 2015. Generalized additive models (GAMs) with interaction terms were adopted to estimate the interactive effects of air pollution and meteorological factors on CVD after controlling for seasonality, day of the week, and public holidays. On low-temperature days, an increase of [Formula: see text] in [Formula: see text], [Formula: see text], and [Formula: see text] was associated with increases of 4.31% (2.39%, 6.26%) at lag 2; 2.74% (1.65%, 3.84%) at lag 0-2; and 0.13% (0.02%, 0.23%) at lag 0-3 in CVD hospitalizations, respectively. During low relative humidity days, a [Formula: see text] increment of lag 0-3 exposure was associated with increases of 3.43% (4.61%, 2.67%) and 0.10% (0.04%, 0.15%) for [Formula: see text] and [Formula: see text], respectively. On high relative humidity days, an increase of [Formula: see text] in [Formula: see text] was associated with an increase of 5.86% (1.82%, 10.07%) at lag 0-2 in CVD hospitalizations. Moreover, elderly (≥ 65 years) and female patients were vulnerable to the effects of air pollution. There were interactive effects between air pollutants and meteorological factors on CVD hospitalizations. The risk that [Formula: see text], [Formula: see text], and [Formula: see text] posed to CVD hospitalizations could be significantly enhanced by low temperatures. For [Formula: see text] and [Formula: see text], CVD hospitalization risk increased in low relative humidity. The effects of [Formula: see text] were enhanced at high relative humidity.

Acute effects of black carbon on mortality in nine megacities of China, 2008-2016: a time-stratified case-crossover study

Black carbon (BC) may have more adverse effects on human health than other constituents of PM_2.5. The daily mean concentrations of BC in China are much higher than those in developed countries and are estimated to account for more than a quarter of global anthropogenic BC emissions. However, reports on the health effects of BC in China have been limited. Thus, a time-stratified case-crossover study was conducted to evaluate the impacts of BC on daily mortality risk in nine Chinese megacities from 2008-2016. Our results show that for all-cause mortality, when compared to the interquartile range (IQR) of BC concentration increased, odds ratios (ORs) were in the range of 1.01-1.06 (95% CIs: 0.99-1.10). For cardiovascular mortality, ORs were in the range of 1.02-1.07 (95% CIs: 1.003-1.12), and for respiratory mortality, ORs were in the range of 1.01-1.15 (95% CIs: 1.00-1.18). The effects of BC in the nine cities were robust after adjusting for PM_2.5, or even became more prominent. Furthermore, BC had stronger effects in spring and winter in northern cities, whereas in mid-latitude cities, BC had stronger effects in the warm seasons. In southern cities, BC had stronger effects in the cool and dry seasons. Our findings support an association between residential exposure to BC and mortality and thus provide further evidence that BC negatively impacts human health and is helpful for decision-making.

Interactive Effects Between Temperature and PM2.5 on Mortality: A Study of Varying Coefficient Distributed Lag Model - Guangzhou, Guangdong Province, China, 2013-2020

Introduction

There is a large body of epidemiological evidence showing significantly increased mortality risks from air pollution and temperature. However, findings on the modification of the association between air pollution and mortality by temperature are mixed.

Methods

We used a varying coefficient distributed lag model to assess the complex interplay between air temperature and PM_2.5 on daily mortality in Guangzhou City from 2013 to 2020, with the aim of establishing the PM_2.5-mortality association at different temperatures and exploring synergetic mortality risks from PM_2.5 and temperature on vulnerable populations.

Results

We observed near-linear concentration-response associations between PM_2.5 and mortality across different temperature levels. Each 10 μg/m³ increase of PM_2.5 in low, medium, and high temperature strata was associated with increments of 0.73% [95% confidence interval (CI): 0.38%, 1.09%], 0.12% (95% CI: −0.27%, 0.52%), and 0.46% (95% CI: 0.11%, 0.81%) in non-accidental mortality, with a statistically significant difference between low and medium temperatures (P=0.02). There were significant modification effects of PM_2.5 by low temperature for cardiovascular mortality and among individuals 75 years or older.

Conclusions

Low temperatures may exacerbate physiological responses to short-term PM_2.5 exposure in Guangzhou, China.

Contribution of heavy metals in PM2.5 to cardiovascular disease mortality risk, a case study in Guangzhou, China

Heavy metals play an important role in inducing fine particulate matter (PM_2.5) related cardiovascular disease (CVD). However, most of the past researches concerned the associations between CVD mortality and the PM_2.5 mass, which may not reveal the CVD mortality risk contributed by heavy metals in PM_2.5. This study explored the correlations between individual heavy metals in PM_2.5 and CVD mortality, identified the heavy metals that significantly contribute to PM_2.5-related CVD, heart disease (HD), and cerebrovascular disease (CEV) mortality, and attempted to establish corresponding source control measures. Over a 2-year study period, PM_2.5 was sampled daily in Guangzhou, China and analyzed for heavy metals. The airborne pollution and weather data, along with CVD, HD, and CEV mortality, were obtained at the same time. The excess risk (ER) of mortality was linked to the individual heavy metals using a distributed lag non-linear model. PM_2.5 and most heavy metals showed significant correlations with the CVD, HD, and CEV mortality; the largest cumulative ER (LCER) values of CVD mortality associated with an interquartile range increase in the levels of lead, cadmium, arsenic, selenium, antimony, nickel, thallium, aluminum, iron, and PM_2.5 were 2.43%, 2.23%, 1.66%, 2.39%, 1.19%, 1.21%, 2.69%, 3.29%, 1.74%, and 2.40%, respectively. Most heavy metals showed comparable LCER values of HD and CEV mortality. Heavy metals with the addition of PM_2.5 were divided into three groups following their LCER values; lead, cadmium, arsenic, antimony, thallium, zinc, aluminum, and iron, whose contributions were greater than or equal to the average effect of the PM_2.5 components, should be limited on a priority basis. These findings indicated that heavy metals play roles in the CVD, HD, and CEV mortality risk of PM_2.5, and specific control measures which aimed at the emission sources should be taken to reduce the CVD mortality risk of PM_2.5.

The effect of air pollution when modified by temperature on respiratory health outcomes: A systematic review and meta-analysis

BACKGROUND

Respiratory diseases are a leading cause of mortality and morbidity, and are exacerbated by air pollution and temperature.

AIM

To assess published literature on the effect of air pollution modified by temperature on respiratory mortality and hospital admissions.

METHODS

We identified 26,656 papers in PubMed and Web of Science, up to March 2021, and selected for analysis; inclusion criteria included observational studies, short-term air pollution, and temperature exposure. Air pollutants considered were particulate matter with a diameter of 2.5 μg/m³, and 10 μg/m³ (PM_2.5, and PM₁₀), ozone (O₃), and nitrogen dioxide (NO₂). A random-effects model was used for our meta-analysis.

RESULTS

For respiratory mortality we found that when the effect PM₁₀ is modified by high temperatures there is an increased pooled Odds Ratio [OR, 95% Confidence Interval (CI)] of 1.021 (1.008 to 1.034) and for the effect of O₃ the pooled OR is 1.006 (1.001-1.012) during the warm season. For hospital admissions, the effects of PM₁₀ and O₃ respectively, during the warm season found an increased pooled OR of 1.011 (0.999-1.024), and 1.015 (0.995-1.036). In our analysis for low temperatures, results were inconsistent.

CONCLUSIONS

Exposure to air pollution when modified by high temperature is likely to increase the odds of respiratory mortality and hospital admissions. Analysis on the interaction effect of air pollution and temperature on health outcomes is a relatively new research field and results are largely inconsistent; therefore, further research is encouraged to establish a more conclusive conclusion on the strength and direction of this effect.

Observed causative impact of fine particulate matter on acute upper respiratory disease: a comparative study in two typical cities in China

Association between fine particulate matter (PM_2.5) and respiratory health has attracted great concern in China. Substantial epidemiological evidences confirm the correlational relationship between PM_2.5 and respiratory disease in many Chinese cities. However, the causative impact of PM_2.5 on respiratory disease remains uncertain and comparative analysis is limited. This study aims to explore and compare the correlational relationship as well as the causal connection between PM_2.5 and upper respiratory tract infection (URTI) in two typical cities (Beijing, Shenzhen) with rather different ambient air environment conditions. The distributed lag nonlinear model (DLNM) was used to detect the correlational relationship between PM_2.5 and URTI by revealing the lag effect pattern of PM_2.5 on URTI. The convergent cross mapping (CCM) method was applied to explore the causal connection between PM_2.5 and URTI. The results from DLNM indicate that an increase of 10 μg/m³ in PM_2.5 concentration is associated with an increase of 1.86% (95% confidence interval: 0.74%-2.99%) in URTI at a lag of 13 days in Beijing, compared with 2.68% (95% confidence interval: 0.99-4.39%) at a lag of 1 day in Shenzhen. The causality detection with CCM quantitatively demonstrates the significant causative influence of PM_2.5 on URTI in both two cities. Findings from the two methods consistently show that people living in low-concentration areas (Shenzhen) are less tolerant to PM_2.5 exposure than those in high-concentration areas (Beijing). In general, our study highlights the adverse health effects of PM_2.5 pollution on the general public in cities with various PM_2.5 levels and emphasizes the needs for the government to provide appropriate solutions to control PM_2.5 pollution, even in cities with low PM_2.5 concentration.

Global Burden of Respiratory Diseases Attributable to Ambient Particulate Matter Pollution: Findings From the Global Burden of Disease Study 2019

Background: Exposure to ambient particulate matter pollution (APMP) is a global health issue that directly affects the human respiratory system. Thus, we estimated the spatiotemporal trends in the burden of APMP-related respiratory diseases from 1990 to 2019.

Methods: Based on the Global Burden of Disease Study 2019, data on the burden of APMP-related respiratory diseases were analyzed by age, sex, cause, and location. Joinpoint regression analysis was used to analyze the temporal trends in the burden of different respiratory diseases over the 30 years.

Results: Globally, in 2019, APMP contributed the most to chronic obstructive pulmonary disease (COPD), with 695.1 thousand deaths and 15.4 million disability-adjusted life years (DALYs); however, the corresponding age-standardized death and DALY rates declined from 1990 to 2019. Similarly, although age-standardized death and DALY rates since 1990 decreased by 24% and 40%, respectively, lower respiratory infections (LRIs) still had the second highest number of deaths and DALYs attributable to APMP. This was followed by tracheal, bronchus, and lung (TBL) cancer, which showed increased age-standardized death and DALY rates during the past 30 years and reached 3.78 deaths per 100,000 persons and 84.22 DALYs per 100,000 persons in 2019. Among children aged < 5 years, LRIs had a huge burden attributable to APMP, whereas for older people, COPD was the leading cause of death and DALYs attributable to APMP. The APMP-related burdens of LRIs and COPD were relatively higher among countries with low and low-middle socio-demographic index (SDI), while countries with high-middle SDI showed the highest burden of TBL cancer attributable to APMP.

Conclusions: APMP contributed substantially to the global burden of respiratory diseases, posing a significant threat to human health. Effective actions aimed at air pollution can potentially avoid an increase in the PM_2.5-associated disease burden, especially in highly polluted areas.

Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration

The particulate matter PM10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models’ development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models’ performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models’ superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models’ efficiency to assist the hospital admissions management during high air pollution episodes.

Particulate matter pollution and emergency room visits for respiratory diseases in a valley Basin city of Northwest China

Epidemiological studies have suggested that particulate matter (PM) pollution seriously affects human health, particularly it is closely associated with respiratory diseases. The aim of this study is to quantitatively evaluate the effect of PMs (PM₁₀ and PM_2.5) on emergency room (ER) visits for respiratory diseases in Lanzhou, a valley basin city in northwest China. Based on the data of the ER visits, daily concentration of particulate matters and daily meteorological elements from January 1, 2013, to July 31, 2017, we used a generalized additive model (GAM) of time series to evaluate the exposure-response relationship between PMs and respiratory ER visits. Seasonal modified effects of PM_2.5 and PM₁₀ on different age and gender groups were also performed. Results showed that the highest incidence of respiratory diseases occurred in winter. Respiratory ER visits for the total were significantly associated with PM_2.5 (at lag 0 day) and PM₁₀ (at lag 3 days), with relative risks (RRs) of 1.042 (95%CI: 1.036 -1.047) and 1.013 (95%CI: 1.011-1.016), respectively. Effects of PM pollutants on respiratory diseases are different among different age and gender groups. Children under 15 years and the elders over 60 years were the most sensitive to PM pollution, and males were more sensitive than females. The results obtained in the current study would provide a scientific evidence for local government to make policy decision for prevention of respiratory diseases.

Effects of extreme temperature on respiratory diseases in Lanzhou, a temperate climate city of China

Under the global climate warming, extreme weather events occur more and more frequently. Epidemiological studies have proved that extreme temperature is strongly correlated with respiratory diseases. We evaluated extreme-temperature effect on respiratory emergency room (ER) visits for 5 years in Lanzhou, a northwest temperate climate city of China from January 1st, 2013, to August 31st, 2017. We built a distributed lag non-linear model (DLNM) to evaluate the lag effect up to 30 days. Results showed the relative risk (RR) of respiratory disease always reached the maximum at lag 0 day and decreased to 1.0 at lag 5 days. Extremely low temperature showed the lag effect of 22 days and the maximum RR was 1.415 (95% CI 1.295-1.546) at lag 0 day. Extremely high temperature showed the lag effect of 7 days and the maximum RR was 1.091 (95% CI 1.069-1.114) at lag 0 day. The elders (age > 65 years) were at the greatest risk to extreme temperatures and the response were very acute. Children (age ≤ 15 years) were at the lowest risk but the lag effect lasted the longest lag days than other subgroups. Males showed longer-term lag effect and higher RR than females. Our study indicated that the extremely low temperature has a significantly greater effect on respiratory diseases than extremely high temperature.

The Impact of the Synergistic Effect of Temperature and Air Pollutants on Chronic Lung Diseases in Subtropical Taiwan

Previous studies have suggested an association between air pollution and lung disease. However, few studies have explored the relationship between chronic lung diseases classified by lung function and environmental parameters. This study aimed to comprehensively investigate the relationship between chronic lung diseases, air pollution, meteorological factors, and anthropometric indices. We conducted a cross-sectional study using the Taiwan Biobank and the Taiwan Air Quality Monitoring Database. A total of 2889 participants were included. We found a V/U-shaped relationship between temperature and air pollutants, with significant effects at both high and low temperatures. In addition, at lower temperatures (<24.6 °C), air pollutants including carbon monoxide (CO) (adjusted OR (aOR):1.78/Log 1 ppb, 95% CI 0.98–3.25; aOR:5.35/Log 1 ppb, 95% CI 2.88–9.94), nitrogen monoxide (NO) (aOR:1.05/ppm, 95% CI 1.01–1.09; aOR:1.11/ppm, 95% CI 1.07–1.15), nitrogen oxides (NO_x) (aOR:1.02/ppm, 95% CI 1.00–1.05; aOR:1.06/ppm, 95% CI 1.04–1.08), and sulfur dioxide (SO₂) (aOR:1.29/ppm, 95% CI 1.01–1.65; aOR:1.77/ppm, 95% CI 1.36–2.30) were associated with restrictive and mixed lung diseases, respectively. Exposure to CO, NO, NO₂, NO_x and SO₂ significantly affected obstructive and mixed lung disease in southern Taiwan. In conclusion, temperature and air pollution should be considered together when evaluating the impact on chronic lung diseases.

Acute effects of particulate matter with different sizes on respiratory mortality in Shenzhen, China

There are relatively few studies that focus on the health effects of exposure to size-specific particles on respiratory mortality in China. We aimed to examine the association between different particle sizes and mortality from cause-specific respiratory diseases. We used a time series model with a quasi-Poisson link to investigate the relationship between different particle sizes and mortality from respiratory diseases, chronic obstructive pulmonary diseases (COPD), pneumonia, and asthma in Shenzhen during 2014-2017. A total of 3716 mortalities due to respiratory diseases were collected. Both PM₁ and PM_2.5 were associated with mortality of overall respiratory diseases, COPD, and pneumonia. An interquartile range (IQR) increase in PM₁ at lag03 was associated with a 12.21% (95% CI: 2.59%, 22.75%) increase in respiratory mortality, and each IQR increase in PM_2.5 at lag03 corresponded to a 12.09% (95% CI: 2.52%, 22.56%) increase in respiratory mortality. PM_1-2.5 was not associated with mortality from all-cause or cause-specific respiratory diseases. This study suggests that both PM₁ and PM_2.5 may increase the risk of mortality due to respiratory diseases in Shenzhen, China.

Climatic modification effects on the association between PM1 and lung cancer incidence in China

Background

Nationwide studies that examine climatic modification effects on the association between air pollution and health outcome are limited in developing countries. Moreover, few studies focus on PM1 pollution despite its greater health effect.

Objectives

This study aims to determine the modification effects of climatic factors on the associations between PM1 and the incidence rates of lung cancer for males and females in China.

Methods

We conducted a nationwide analysis in 345 Chinese counties (districts) from 2014 to 2015. Mean air temperature and relative humidity over the study period were used as the proxies of climatic conditions. In terms of the multivariable linear regression model, we examined climatic modification effects in the stratified and combined datasets according to the three-category and binary divisions of climatic factors. Moreover, we performed three sensitivity analyses to test the robustness of climatic modification effects.

Results

We found a stronger association between PM1 and the incidence rate of male lung cancer in counties with high levels of air temperature or relative humidity. If there is a 10 μg/m³ shift in PM1, then the change in male incidence rate relative to its mean was higher by 4.39% (95% CI: 2.19, 6.58%) and 8.37% (95% CI: 5.18, 11.56%) in the middle and high temperature groups than in the low temperature group, respectively. The findings of climatic modification effects were robust in the three sensitivity analyses. No significant modification effect was discovered for female incidence rate.

Conclusions

Male residents in high temperature or humidity counties suffer from a larger effect of PM1 on the incidence rate of lung cancer in China. Future research on air pollution-related health impact assessment should consider the differential air pollution effects across different climatic conditions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10912-8.

Long-term exposure to ambient PM2.5 and stroke mortality among urban residents in northern China

Evidence is still limited for the role of long-term PM_2.5 exposure in cerebrovascular diseases among residents in high pollution regions. The study is aimed to investigate the long-term effects of PM_2.5 exposure on stroke mortality, and further explore the effect modification of temperature variation on the PM_2.5-mortality association in northern China. Based on a cohort data with an average follow-up of 9.8 years among 38,435 urban adults, high-resolution estimates of PM_2.5 derived from a satellite-based model were assigned to each participant. A Cox regression model with time-varying exposures and strata of geographic regions was employed to assess the risks of stroke mortality associated with PM_2.5, after adjusting for individual risk factors. The cross-product term of PM_2.5 exposure and annual temperature range was further added into the regression model to test whether the long-term temperature variation would modify the association of PM_2.5 with stroke mortality. Among the study participants, the annual mean level of PM_2.5 concentration was 66.3 μg/m³ ranging from 39.0 μg/m³ to 100.6 μg/m³. For each 10 μg/m³ increment in PM_2.5, the hazard ratio (HR) was 1.31 (95% CI: 1.04-1.65) for stroke mortality after multivariable adjustment. In addition, the HRs of PM_2.5 decreased gradually as the increase of annual temperature range with the HRs of 1.95 (95% CI: 1.36-2.81), 1.53 (95% CI: 1.06-2.22), and 1.11 (95% CI: 0.75-1.63) in the low, middle, and high group of annual temperature range, respectively. The findings provided further evidence of long-term PM_2.5 exposure on stroke mortality in high-exposure settings such as northern China, and also highlighted the view that assessing the adverse health effects of air pollution might not ignore the role of temperature variations in the context of climate change.

Study on the Characteristics of Size-Segregated Particulate Water-Soluble Inorganic Ions and Potentially Toxic Metals during Wintertime in a High Population Residential Area in Beijing, China

Airborne particulate matter (PM) pollution often occurs in the wintertime in northern China, posing a potential threat to human health. To date, there are limited studies about the metals and inorganic ions to link source apportionments and health risk assessments in the different size-segregated PM samples. In this study, our samples were collected by a high-volume air sampler from 26 December 2018 to 11 January 2019 in a high population residential area (Beijing). Water-soluble inorganic ions, metal elements in the different size-segregated PM samples were determined for health risk assessments by inhalation of PM. During the sampling period in Beijing, the average concentrations of PM1.1 and PM1.1–2.0 were 39.67 ± 10.66 μg m−3 and 32.25 ± 6.78 μg m−3. Distinct distribution profiles characterized the different elements. The markers of coal combustion Pb, As, and Se had >52% of their mass concentration in PM1.1. The average mass ratios of (NO3− + NO2−)/SO42−, Cl−/Na+, Cl−/K+, and Cl−/(NO3− + NO2−) were 1.68, 6.58, 6.18, and 0.57, which showed that coal combustion and vehicle emissions were the main anthropogenic sources of PM in Beijing in winter. PM1.1 was the major contributor of Pb, Cd, and As for carcinogenic risks (CR) and hazard quotient (HQ). It was indicated that PM1.1 is more harmful than coarse PM. The toxic elements of Cr (VI) (1.12 × 10−6), V (0.69 × 10−6), and As (0.41 × 10−6) caused higher CR for children than Ni, Cd, Co, and Pb. Meanwhile, Pb (35.30 × 10−6) and Ni (21.07 × 10−6) caused higher CR for adults than As, Cr (VI), V, Co, and Cd, especially PM1.1. This study provides detailed composition data and the first report on human health in a high population residential area in Beijing.

A review of the impact of outdoor and indoor environmental factors on human health in China

The Intergovernmental Panel on Climate Change (IPCC) reported that global climate change has led to the increased occurrence of extreme weather events. In the context of global climate change, more evidence indicates that abnormal meteorological conditions could increase the risk of epidemiological mortality and morbidity. In this study, using a systematic review, we evaluated a total of 175 studies (including 158 studies on outdoor environment and 17 studies on indoor environment) to summarize the impact of outdoor and indoor environment on human health in China using the database of PubMed, Web of Science, the Cochrane Library, and Embase. In particular, we focused on studies about cardiovascular and respiratory mortality and morbidity, the prevalence of digestive system diseases, infectious diseases, and preterm birth. Most of the studies we reviewed were conducted in three of the metropolises of China, including Beijing, Guangzhou, and Shanghai. For the outdoor environment, we summarized the effects of climate change-related phenomena on health, including ambient air temperature, diurnal temperature range (DTR), temperature extremes, and so on. Studies on the associations between temperature and human health accounted for 79.7% of the total studies reviewed. We also screened out 19 articles to explore the effect of air temperature on cardiovascular diseases in different cities in the final meta-analysis. Besides, modern lifestyle involves a large amount of time spent indoors; therefore, indoor environment also plays an important role in human health. Nevertheless, studies on the impact of indoor environment on human health are rarely reported in China. According to the limited reports, adverse indoor environment could impose a high health risk on children.

Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics

Studies in air pollution epidemiology are of paramount importance in diagnosing and improve life quality. To explore new methods or modify existing ones is critical to obtain better results. Most air pollution epidemiology studies use the Generalized Linear Model, especially the default version of R, Splus, SAS, and Stata softwares, which use maximum likelihood estimators in parameter optimization. Also, a smooth time function (usually spline) is generally used as a pre-processing step to consider seasonal and long-term tendencies. This investigation introduces a new approach to GLM, proposing the estimation of the free coefficients through bio-inspired metaheuristics - Particle Swarm Optimization (PSO), Genetic Algorithms, and Differential Evolution, as well as the replacement of the spline function by a simple normalization procedure. The considered case studies comprise three important cities of São Paulo state, Brazil with distinct characteristics: São Paulo, Campinas, and Cubatão. We considered the impact of particles with an aerodynamic diameter less than 10 μm (PM₁₀), ambient temperature, and relative humidity in the number of hospital admissions for respiratory diseases (ICD-10, J00 to J99). The results showed that the new approach (especially PSO) brings performance gains compared to the default version of statistical software like R.

Particulate matter and emergency visits for asthma: a time-series study of their association in the presence and absence of wildfire smoke in Reno, Nevada, 2013-2018

BACKGROUND

Health risks due to particulate matter (PM) from wildfires may differ from risk due to PM from other sources. In places frequently subjected to wildfire smoke, such as Reno, Nevada, it is critical to determine whether wildfire PM poses unique risks. Our goal was to quantify the difference in the association of adverse asthma events with PM on days when wildfire smoke was present versus days when wildfire smoke was not present.

METHODS

We obtained counts of visits for asthma at emergency departments and urgent care centers from a large regional healthcare system in Reno for the years 2013-2018. We also obtained dates when wildfire smoke was present from the Washoe County Health District Air Quality Management Division. We then examined whether the presence of wildfire smoke modified the association of PM_2.5, PM_10-2.5, and PM₁₀ with asthma visits using generalized additive models. We improved on previous studies by excluding wildfire-smoke days where the PM concentration exceeded the maximum PM concentration on other days, thus accounting for possible nonlinearity in the association between PM concentration and asthma visits.

RESULTS

Air quality was affected by wildfire smoke on 188 days between 2013 and 2018. We found that the presence of wildfire smoke increased the association of a 5 μg/m³ increase in daily and three-day averages of PM_2.5 with asthma visits by 6.1% (95% confidence interval (CI): 2.1-10.3%) and 6.8% (CI: 1.2-12.7%), respectively. Similarly, the presence of wildfire smoke increased the association of a 5 μg/m³ increase in daily and three-day averages of PM₁₀ with asthma visits by 5.5% (CI: 2.5-8.6%) and 7.2% (CI: 2.6-12.0%), respectively. We did not observe any significant increases in association for PM_10-2.5 or for seven-day averages of PM_2.5 and PM₁₀.

CONCLUSIONS

Since we found significantly stronger associations of PM_2.5 and PM₁₀ with asthma visits when wildfire smoke was present, our results suggest that wildfire PM is more hazardous than non-wildfire PM for patients with asthma.

Temperature modulation of the adverse consequences on human mortality due to exposure to fine particulates: A study of multiple cities in China

Exposure to particulate matter of smaller than 2.5 μm in diameter (PM_2.5) is linked to increased human mortality, and could be further complicated by concurrent ambient air temperatures. Published reports indicate that the association between ambient temperatures and mortality due to PM_2.5 exposure is dissimilar across different geographic areas. Thus, it is unclear how ambient temperatures at different geographic locations can together modulate the influence of PM_2.5 on mortality. In this paper, we examined how temperature modulated the association between mortality and PM_2.5 exposure in 15 Chinese cities during 2014-2016. For analysis, First, Poisson generalized additive models under different temperature stratifications (<10th, 10-90th, and >90th temperature percentiles) was used to estimate PM_2.5 associations to mortality, which were specific to different cities. Second, we used a meta-analysis to combine the effects at each temperature stratum and region (southern and northern China). Results revealed that high temperatures (daily mean temperature >90th percentile) robustly amplified observed associations of mortality and PM_2.5 exposure, and the modifications were heterogeneous geographically. In the northern regions, a 10 μg/m³ increment in PM_2.5 was associated with 0.18%, 0.28%, and 1.54% increase in non-accidental mortalities and 0.33%, 0.39%, and 1.32% increase in cardiovascular mortalities at low, moderate, and high temperature levels, respectively. In the southern regions, a 10 μg/m³ increment in PM_2.5 was associated with 0.52%, 0.62%, and 1.90% increase in non-accidental mortalities and 0.55%, 0.98%, and 2.25% increase in cardiovascular mortalities at low, moderate, and high temperature levels, respectively. It is concluded that temperature altered PM_2.5-mortality associations in southern and northern China synergistically, but the effect was more pronounced in the south. Therefore, geography and temperature need to be considered when studying how PM_2.5 affects health.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

The unidirectional causality influence of factors on PM2.5 in Shenyang city of China

Air quality issue such as particulate matter pollution (PM_2.5 and PM₁₀) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of particles and analyze the causality factors. The results demonstrated that variation trend of particles was almost similar, the annual, monthly and daily distribution had their own characteristics. Particles decreased gradually from south to north, from west to east. Correlation analysis showed that wind speed was the most important factor affecting particles, and temperature, air pressure and relative humidity were key factors in some seasons. Path analysis showed that there was complex unidirectional causal relationship between particles and individual or combined effects, and NO₂ and CO were key factors affecting PM_2.5. The hot and cold areas changed little with the seasons. All the above results suggests that planning the industrial layout, adjusting industrial structure, joint prevention and control were necessary measure to reduce particles concentration.

PM2.5 induces apoptosis, oxidative stress injury and melanin metabolic disorder in human melanocytes

Recent growing evidence suggested that particulate matter 2.5 (PM2.5) has strong toxic effects on skin systems. However, the possible effects and the mechanisms of PM2.5 on vitiligo remain poorly understood. Therefore, the present study aimed to further investigate the effects and possible mechanisms of PM2.5 on vitiligo. Human keratinocytes (HaCaT cells) and human melanocytes (PIG1 cells and PIG3V cells) were exposed to PM2.5 (0-200 µg/ml) for 24 h. The cell viability of the three cell lines was measured by a Cell Counting Kit-8 assay. The secretions of stem cell factor (SCF) and basic fibroblast growth factor (bFGF) in HaCaT cells were evaluated by ELISA. The melanin contents, cellular tyrosinase activity, apoptosis, cell migration, malondialdehyde (MDA) contents, superoxide dismutase (SOD) levels, glutathione peroxidase (GSH-Px) levels and related protein expressions in PIG1 cells and PIG3V cells were evaluated by a NaOH assay, DOPA assay, Annexin V-FITC/Propidium Iodide staining, MDA assay, SOD assay, GSH-Px assay and western blotting, respectively. It was demonstrated that PM2.5 exposure inhibited cell viability of all three cell lines (HaCaT, PIG1 and PIG3V cells). PM2.5 exposure attenuated the secretions of SCF and bFGF in HaCaT cells. Moreover, PM2.5 exposure attenuated the activation of tyrosinase and melanogenesis, inhibited cell migration, and induced apoptosis and oxidative stress injury in PIG1 cells and PIG3V cells. In addition, PM2.5 exposure caused upregulated cytosolic cytochrome C and activated caspase-3 in PIG1 cells and PIG3V cells. Furthermore, PM2.5 exposure activated the nuclear factor erythroid 2-related factor 2 and heme oxygenase-1 signaling pathway. The present results suggested that PM2.5 exposure could inhibit the secretions of SCF and bFGF in keratinocytes, and cause oxidative stress injury and melanin metabolic disorder in melanocytes. Therefore, PM2.5 could be a new risk factor for vitiligo.

Characteristics of indoor and outdoor fine particles in heating period at urban, suburban, and rural sites in Harbin, China

Concurrent indoor-outdoor fine particulate matter (PM_2.5) measurements were conducted at urban, suburban, and rural sites in Harbin, a megacity in the northeast of China. Chemical constituents of indoor-outdoor PM_2.5 were determined. Infiltration factors (F_INF) of all sites were calculated according to the indoor to outdoor (I/O) ratios of PM_2.5 based on the regression analysis. Linear discriminant analysis (LDA) is applied to determine the indoor-outdoor relationship. Secondary organic carbon (SOC) was calculated on the basis of organic carbon to elemental carbon (OC/EC) ratios. The mean concentrations of indoor and outdoor PM_2.5 were 166.4 ± 32.5 μg/m³ and 228.4 ± 83.7 μg/m³, respectively, during the heating period. OC/EC and potassium ion to elemental carbon (K⁺/EC) ratios verified that biomass was an important source in Harbin especially for rural sites. The nitrate to sulfate (NO₃^-/SO₄^2-) ratio indicates the higher contribution of traffic emissions in urban sites. Cr was the only species that exceeded the guidelines of WHO 2002, which was mainly emitted from coal and oil combustion. SOC/OC and NO₃^-/SO₄^2- ratios, and ion-balanced acidity (the ratio of cation to anion, R_+/-) showed a large urban-rural and indoor-outdoor difference. The highest SOC/OC ratio was found at urban sites, up to 38.3% for indoors. SOC/OC ratios and R_+/- values of indoor environments were higher, which is attributed to the conducive condition of forming the secondary pollutants during the heating period. The results of LDA indicated that the distributions of the chemical components of PM_2.5 at three sites were statistically dissimilar. Graphical abstract.

Short-term effects of ambient temperature on non-external and cardiovascular mortality among older adults of metropolitan areas of Mexico

Multi-city studies assessing the association between acute exposure to temperature and mortality in Latin American are limited. To analyze the short-term effect of changes in temperature (increase and decrease) on daily non-external and cardiovascular mortality from 1998 to 2014, in people 65 years old and over living in 10 metropolitan areas of Mexico. Analyses were performed through Poisson regression models with distributed lag non-linear models. Statistical comparison of minimum mortality temperature (MMT) and city-specific cutoffs of 24-h temperature mean values (5th/95th and 1st/99th percentiles) were used to obtain the mortality relative Risk (RR) for cold/hot and extreme cold/extreme hot, respectively, for the same day and lags of 0-3, 0-7, and 0-21 days. A meta-analysis was conducted to synthesize the estimates (RR_pooled). Significant non-linear associations of temperature-mortality relation were found in U or inverted J shape. The best predictors of mortality associations with cold and heat were daily temperatures at lag 0-7 and lag 0-3, respectively. RR_pooled of non-external causes was 6.3% (95%CI 2.7, 10.0) for cold and 10.2% (95%CI 4.4, 16.2) for hot temperatures. The RR_pooled for cardiovascular mortality was 7.1% (95%CI 0.01, 14.7) for cold and 7.1% (95%CI 0.6, 14.0) for hot temperatures. Results suggest that, starting from the MMT, the changes in temperature are associated with an increased risk of non-external and specific causes of mortality in elderly people. Generally, heat effects on non-external and specific causes of mortality occur immediately, while cold effects occur within a few days and last longer.

The short-term effects of air pollutants on influenza-like illness in Jinan, China

Background

There is valid evidence that air pollution is associated with respiratory disease. However, few studies have quantified the short-term effects of six air pollutants on influenza-like illness (ILI). This study explores the potential relationship between air pollutants and ILI in Jinan, China.

Methods

Daily data on the concentration of particulate matters < 2.5 μm (PM 2.5), particulate matters < 10 μm (PM10), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) and ILI counts from 2016 to 2017 were retrieved. The wavelet coherence analysis and generalized poisson additive regression model were employed to qualify the relationship between air pollutants and ILI risk. The effects of air pollutants on different age groups were investigated.

Results

A total of 81,459 ILI counts were collected, and the average concentrations of PM2.5, PM10, O₃, CO, SO₂ and NO₂ were 67.8 μg/m³, 131.76 μg/ m³, 109.85 μg/ m³, 1133 μg/ m³, 33.06 μg/ m³ and 44.38 μg/ m³, respectively. A 10 μg/ m³ increase in concentration of PM2.5, PM10, CO at lag0 and SO₂ at lag01, was positively associated with a 1.0137 (95% confidence interval (CI): 1.0083–1.0192), 1.0074 (95% CI: 1.0041–1.0107), 1.0288 (95% CI: 1.0127–1.0451), and 1.0008 (95% CI: 1.0003–1.0012) of the relative risk (RR) of ILI, respectively. While, O3 (lag5) was negatively associated with ILI (RR 0.9863; 95%CI: 0.9787–0.9939), and no significant association was observed with NO₂, which can increase the incidence of ILI in the two-pollutant model. A short-term delayed impact of PM2.5, PM10, SO2 at lag02 and CO, O3 at lag05 was also observed. People aged 25–59, 5–14 and 0–4 were found to be significantly susceptible to PM2.5, PM10, CO; and all age groups were significantly susceptible to SO₂; People aged ≥60 year, 5–14 and 0–4 were found to be significantly negative associations with O₃.

Conclusion

Air pollutants, especially PM2.5, PM10, CO and SO₂, can increase the risk of ILI in Jinan. The government should create regulatory policies to reduce the level of air pollutants and remind people to practice preventative and control measures to decrease the incidence of ILI on pollution days.

Association between outdoor PM2.5 and prevalence of COPD: a systematic review and meta-analysis

There were conflictions and differences among the results of cross-sectional studies association between PM_2.5 and COPD prevalence. We aimed to explore the real association between outdoor PM_2.5 and COPD prevalence, analyze the possible cause to the differences and conflictions in previous cross-sectional studies. Cross-sectional literatures about the association between outdoor PM_2.5 and COPD prevalence were selected up to 12 September 2018. Subgroup analysis was performed to explore the source of the heterogeneity. Publication bias was tested via funnel plot. Leave-one-out method was used to conduct influential analysis. Variance analysis was used to analyze the influence of concentration, literature quality and age (over 60 or not) on the ln (aOR) values. The initial search revealed 230 studies, of which 8 were selected. The heterogeneity in this study was significant (I²=62, P<0.01), and random effects model was used. The pooled OR for the association between PM_2.5 and COPD prevalence is 2.32(95%CI, 1.91-2.82). There was no evidence of publication bias. Subgroup analysis showed the subgroup of age seemed to be the source of heterogeneity (P=0.0143, residual I²=0%). Variance analysis showed that the differences of ln (aOR) among each concentration group(p=0.0075) were statistically significant, the same as age groups(P=0.0234). This meta-analysis study demonstrated a conclusive association between PM_2.5 and prevalence of COPD (OR: 2.32, 95%CI 1.91-2.82). The significant heterogeneity among selected studies was mainly caused by age (over 60 or not). High PM_2.5 concentration should be needed in further research of the relationship between PM_2.5 and chronic diseases.

Acute and Cumulative Effects of Haze Fine Particles on Mortality and the Seasonal Characteristics in Beijing, China, 2005-2013: A Time-Stratified Case-Crossover Study

We observed significant effects of particulate matter (PM_2.5) on cause-specific mortality by applying a time-stratified case-crossover and lag-structure designs in Beijing over a nine-year study period (2005–2013). The year-round odds ratio (OR) was 1.005 on the current day with a 10 μg/m³ increase in PM_2.5 for all-cause mortality. For cardiovascular mortality and stroke, the ORs were 1.007 and 1.008 on the current day, respectively. Meanwhile, during a lag of six days, the cumulative effects of haze on relative risk of mortality, respiratory mortality and all-cause mortality was in the range of 2~11%. Moreover, we found a significant seasonal pattern in the associations for respiratory mortality: significant associations were observed in spring and fall, while for all-cause mortality, cardiovascular mortality, cardiac and stroke, significant associations were observed in winter. Moreover, increasing temperature would decrease risks of mortalities in winter taking fall as the reference season. We concluded that in summer, temperature acted as a direct enhancer of air pollutants; while in winter and spring, it was an index of the diameter distribution and composition of fine particles.

Effect and threshold of PM2.5 on population mortality in a highly polluted area: a study on applicability of standards

For assessing the effect and threshold of PM_2.5 on mortality in highly polluted areas and further studying the standard applicability, daily data on meteorological factors, air pollutants, and mortality were obtained in Jinan, China, from 2011 to 2017. A generalized additive model (GAM) and a distributed lag non-linear model (DLNM) were employed to assess the nonlinearity and the hysteresis of associations. We further explored the breakpoints to evaluate the existence of the threshold. The correlation between mortality and PM_2.5 was nonlinear. The impact of average PM_2.5 on non-accidental mortality (RR = 1.11; 95% CI = 1.06, 1.16), cardiovascular disease (CVD) mortality (RR = 1.17; 95% CI = 1.10, 1.24), and respiratory disease (RD) mortality (RR = 1.17; 95% CI = 1.10, 1.24) reached the highest in the current day (lag 0). The excess risks of PM_2.5 at secondary standard level to non-accidental, CVD, and RD mortality are 8.79% (95% CI = 3.84, 13.98), 14.41% (95% CI = 7.79, 21.43), 15.35% (95% CI = 1.76, 30.74), respectively. The saturation points exist in highly polluted areas. Above the saturation points of 247 μg/m³ for non-accidental mortality, 245 μg/m³ for CVD mortality, and 250 μg/m³ for RD mortality, the model of all three relationships presented a harvesting effect. This study underscores the necessity of the ongoing efforts of reducing particulate air pollution and the adjustment of the standards in seriously polluted areas to adapt to regional conditions. At the same time, for highly polluted areas, it is advocated to strengthen personal protection to decrease the saturation point and control the concentration of pollutants as much as possible, which will substantially save more cost that benefits the public.

Impact of heat on mortality and morbidity in low and middle income countries: A review of the epidemiological evidence and considerations for future research

Heat waves and high air temperature are associated with increased morbidity and mortality. However, the majority of research conducted on this topic is focused on high income areas of the world. Although heat waves have the most severe impacts on vulnerable populations, relatively few studies have studied their impacts in low and middle income countries (LMICs). The aim of this paper is to review the existing evidence in the literature on the impact of heat on human health in LMICs. We identified peer-reviewed epidemiologic studies published in English between January 1980 and August 2018 investigating potential associations between high ambient temperature or heat waves and mortality or morbidity. We selected studies according to the following criteria: quantitative studies that used primary and/or secondary data and report effect estimates where ambient temperature or heat waves are the main exposure of interest in relation to human morbidity or mortality within LMICs. Of the total 146 studies selected, eighty-two were conducted in China, nine in other countries of East Asia and the Pacific, twelve in South Asia, ten in Sub-Saharan Africa, eight in the Middle East and North Africa, and seven in each of Latin America and Europe. The majority of studies (92.9%) found positive associations between heat and human morbidity/mortality. Additionally, while outcome variables and study design differed greatly, most utilized a time-series study design and examined overall heath related morbidity/mortality impacts in an entire population, although it is notable that the selected studies generally found that the elderly, women, and individuals within the low socioeconomic brackets were the most vulnerable to the effects of high temperature. By highlighting the existing evidence on the impact of extreme heat on health in LMICs, we hope to determine data needs and help direct future studies in addressing this knowledge gap. The focus on LMICs is justified by the lack of studies and data studying the health burden of higher temperatures in these regions even though LMICs have a lower capacity to adapt to high temperatures and thus an increased risk.

Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013-2017 by geostatistics

Fine particulate matter (PM_2.5) is a typical air pollutant and has adverse health effects across the world, especially in the rapidly developing China due to significant air pollution. The PM_2.5 pollution varies with time and space, and is dominated by the locations owing to the differences in geographical conditions including topography and meteorology, the land use and the characteristics of urbanization and industrialization, all of which control the pollution formation by influencing the various sources and transport of PM_2.5. To characterize these parameters and mechanisms, the 5-year PM_2.5 pollution patterns of Jiangsu province in eastern China with high-resolution was investigated. The Kriging interpolation method of geostatistical analysis (GIS) and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were conducted to study the spatial and temporal distribution of air pollution at 110 sites from national air quality monitoring network covering 13 cities. The PM_2.5 pollution of the studied region was obvious, although the annual average concentration decreased from previous 72 to recent 50 μg m⁻³. Evident temporal variations showed high PM_2.5 level in winter and low in summer. Spatially, PM_2.5 level was higher in northern (inland, heavy industry) than that in eastern (costal, plain) regions. Industrial sources contributed highest to the air pollution. Backward trajectory clustering and potential source contribution factor (PSCF) analysis indicated that the typical monsoon climate played an important role in the aerosol transport. In summer, the air mass in Jiangsu was mainly affected by the updraft from near region, which accounted for about 60% of the total number of trajectories, while in winter, the long-distance transport from the northwest had a significant impact on air pollution.

Satellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, China

Severe and persistent haze accompanied by high concentrations of fine particulate matter (PM_2.5) has become a great public health concern in urban China. However, research on the health effects of PM_2.5 in China has been hindered by the lack of high-quality exposure estimates. In this study, we assessed the excess mortality associated with both short- and long-term exposure to ambient PM_2.5 simultaneously using satellite-derived exposure data at a high spatiotemporal resolution. Adult registries of non-accidental, respiratory and cardiovascular deaths in urban Beijing in 2013 were collected. Exposure levels were estimated from daily satellite-based PM_2.5 concentrations at 1 km spatial resolution from 2004 to 2013. Mixed Poisson regression models were fitted to estimate the cause-specific mortality in association with PM_2.5 exposures. With the mutual adjustment of short- and long-term exposure of PM_2.5, the percent increases associated with every 10 μg/m³ increase in short-term PM_2.5 exposure were 0.09% (95% CI: -0.14%, 0.33%; lag 01), 1.02% (95% CI: 0.08%, 1.97%; lag 04) and 0.09% (95% CI: -0.23%, 0.42%; lag 01) for non-accidental, respiratory and cardiovascular mortality, respectively; those attributable to every 10 μg/m³ increase in long-term PM_2.5 exposure (9-year moving average) were 16.78% (95% CI: 10.58%, 23.33%), 44.14% (95% CI: 20.73%, 72.10%) and 3.72% (95% CI: -3.75%, 11.77%), respectively. Both associations of short- and long-term exposure with the cause-specific mortality decreased after they were mutually adjusted. Associations between short-term exposure to satellite-based PM_2.5 and cause-specific mortality were larger than those estimated using fixed measurements. Satellite-based PM_2.5 predictions help to improve the spatiotemporal resolution of exposure assessments and the mutual adjustment model provide better estimation of PM_2.5 associated health effects. Effects attributable to long-term exposure of PM_2.5 were larger than those of short-term exposure, which should be more concerned for public health.

The association between short and long-term exposure to PM2.5 and temperature and hospital admissions in New England and the synergistic effect of the short-term exposures

BACKGROUND

Particulate matter < 2.5 μm in diameter (PM2.5) and heat are strong predictors of morbidity, yet few studies have examined the effects of long-term exposures on non-fatal events, or assessed the short and long-term effect on health simultaneously.

OBJECTIVE

We jointly investigated the association of short and long-term exposures to PM2.5 and temperature with hospital admissions, and explored the modification of the associations with the short-term exposures by one another and by temperature variability.

METHODS

Daily ZIP code counts of respiratory, cardiac and stroke admissions of adults ≥65 (N = 2,015,660) were constructed across New-England (2001-2011). Daily PM2.5 and temperature exposure estimates were obtained from satellite-based spatio-temporally resolved models. For each admission cause, a Poisson regression was fit on short and long-term exposures, with a random intercept for ZIP code. Modifications of the short-term effects were tested by adding interaction terms with temperature, PM2.5 and temperature variability.

RESULTS

Associations between short and long-term exposures were observed for all of the outcomes, with stronger effects of long-term exposures to PM2.5. For respiratory admissions, the short-term PM2.5 effect (percent increase per IQR) was larger on warmer days (1.12% versus -0.53%) and in months of higher temperature variability (1.63% versus -0.45%). The short-term temperature effect was higher in months of higher temperature variability as well. For cardiac admissions, the PM2.5 effect was larger on colder days (0.56% versus -0.30%) and in months of higher temperature variability (0.99% versus -0.56%).

CONCLUSIONS

We observed synergistic effects of short-term exposures to PM2.5, temperature and temperature variability. Long-term exposures to PM2.5 were associated with larger effects compared to short-term exposures.

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM_2.5 can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM_2.5, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM_2.5 levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008-2011 and 2014-2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM_2.5 concentrations varied from 0.98 to 54.2 μg m^-3, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM_2.5, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM_2.5 exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

Temporal trends in respiratory mortality and short-term effects of air pollutants in Shenyang, China

Short-term exposures to air pollution are associated with acute effects on respiratory health. This study aimed to describe 10-year temporal trends in respiratory mortality in the urban areas of Shenyang, China, according to gender and age and estimate the effects of air pollution on respiratory diseases (ICD-10J00-J99) and lung cancer (ICD-10 C33-C34) using a case-crossover design. During the study period 2013-2015, the exposure-response relationship between ambient air pollutants and mortality data was fitted by a quasi-Poisson model. Age-standardized mortality rates for a combined number of respiratory diseases and for lung cancer declined in Shenyang; however, death counts increased with aging. Deaths from respiratory diseases increased by 4.7% (95% CI, 0.00-9.9), and lung cancer mortality increased by 6.5% (95% CI, 1.2-12.0), both associated with a 10 μg/m³ increase in exposure to particulate matter < 2.5 μg in diameter (PM2.5). Moreover, males in Shenyang's urban areas were more susceptible to the acute effects of PM2.5 and SO₂ exposure; people aged ≥ 65 years had a high susceptibility to ozone, and those aged < 65 years were more susceptible to other air pollutants. These results provided an updated estimate of the short-term effects of air pollution in Shenyang. Since population aging is also associated with increasing mortality from respiratory diseases and lung cancer, reinforcing air quality control measures and health-promoting behaviors is urgent and necessary in Shenyang.

Temporal-spatial characteristics and source apportionment of PM2.5 as well as its associated chemical species in the Beijing-Tianjin-Hebei region of China

PM_2.5 and its major chemical compositions were sampled and analyzed in January, April, July and October of 2014 at Beijing (BJ), Tianjin (TJ), Langfang (LF) and Baoding (BD) in order to probe the temporal and spatial characteristics as well as source apportionment of PM_2.5 in the Beijing-Tianjin-Hebei (BTH) region. The results showed that PM_2.5 pollution was severe in the BTH region. The average annual concentrations of PM_2.5 at four sampling sites were in the range of 126-180 μg/m³, with more than 95% of sampling days exceeding 35 μg/m³, the limit ceiling of average annual concentration of PM_2.5 regulated in the Chinese National Ambient Air Quality Standards (GB3095-2012). Additionally, concentrations of PM_2.5 and its major chemical species were seasonally dependent and demonstrated spatially similar variation characteristics in the BTH region. Concentration of toxic heavy metals, such as As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, Se, and Zn, were higher in winter and autumn. Secondary inorganic ions (SO₄^2-, NO₃^-, and NH₄⁺) were the three-major water-soluble inorganic ions (WSIIs) of PM_2.5 and their mass ratios to PM_2.5 were higher in summer and autumn. The organic carbon (OC) and elemental carbon (EC) concentrations were lower in spring and summer than in autumn and winter. Five factors were selected in Positive Matrix Factorization (PMF) model analysis, and the results showed that PM_2.5 pollution was dominated by vehicle emissions in Beijing, combustion emissions including coal burning and biomass combustion in Langfang and Baoding, and soil and construction dust emissions in Tianjin, respectively. The air mass that were derived from the south and southeast local areas around BTH regions reflected the features of short-distant and small-scale air transport. Shandong, Henan, and Hebei were identified the major potential sources-areas of secondary aerosol emissions to PM_2.5.

Cardiorespiratory health effects of particulate ambient air pollution exposure in low-income and middle-income countries: a systematic review and meta-analysis

BACKGROUND

Most prospective studies on the health effects of particulate ambient air pollution exposure have focused on high-income countries, which have much lower pollutant concentrations than low-income and middle-income countries (LMICs) and different sources of pollution. We aimed to investigate the cardiorespiratory health effects of particulate ambient air pollution exposure in LMICs exclusively.

METHODS

For this systematic review and meta-analysis, we searched PubMed, Web of Science, Embase, LILACS, Global Health, and Proquest for studies published between database inception and Nov 28, 2016, investigating the cardiorespiratory health effects of particulate ambient air pollution exposure in LMICs. Data were extracted from published studies by one author, and then checked and verified by all authors independently. We pooled estimates by pollutant type (particulate matter with a diameter of <2·5 μm [PM_2·5] or 2·5-10 μm [PM₁₀]), lag, and outcome, and presented them as excess relative risk per 10 μg/m³ increase in particulate ambient air pollution. We used a random-effects model to derive overall excess risk. The study protocol is registered with PROSPERO, number CRD42016051733.

FINDINGS

Of 1553 studies identified, 91 met the full eligibility criteria. Only four long-term exposure studies from China were identified and not included in the meta-analysis. A 10 μg/m³ increase in same-day PM_2·5 was associated with a 0·47% (95% CI 0·34-0·61) increase in cardiovascular mortality and a 0·57% (0·28-0·86) increase in respiratory mortality. A 10 μg/m³ increase in same-day PM₁₀ was associated with a 0·27% (0·11-0·44) increase in cardiovascular mortality and a 0·56% (0·24-0·87) increase in respiratory mortality.

INTERPRETATION

Short-term exposure to particulate ambient air pollution is associated with increases in cardiorespiratory morbidity and mortality in LMIC's, with apparent regional-specific variations.

FUNDING

None.

Case-crossover analysis of heat-coded deaths and vulnerable subpopulations: Oklahoma, 1990-2011

The extent of the association between temperature and heat-coded deaths, for which heat is the primary cause of death, remains largely unknown. We explored the association between temperature and heat-coded deaths and potential interactions with various demographic and environmental factors. A total of 335 heat-coded deaths that occurred in Oklahoma from 1990 through 2011 were identified using heat-related International Classification of Diseases codes, cause-of-death nomenclature, and narrative descriptions. Conditional logistic regression models examined the association between temperature and heat index on heat-coded deaths. Interaction by demographic factors (age, sex, marital status, living alone, outdoor/heavy labor occupations) and environmental factors (ozone, PM₁₀, PM_2.5) was also explored. Temperatures ≥99 °F (the median value) were associated with approximately five times higher odds of a heat-coded death as compared to temperatures <99 °F (adjusted OR = 4.9, 95% CI 3.3, 7.2). The effect estimates were attenuated when exposure to heat was characterized by heat index. The interaction results suggest that effect of temperature on heat-coded deaths may depend on sex and occupation. For example, the odds of a heat-coded death among outdoor/heavy labor workers exposed to temperatures ≥99 °F was greater than expected based on the sum of the individual effects (observed OR = 14.0, 95% CI 2.7, 72.0; expected OR = 4.1 [2.8 + 2.3-1.0]). Our results highlight the extent of the association between temperature and heat-coded deaths and emphasize the need for a comprehensive, multisource definition of heat-coded deaths. Furthermore, based on the interaction results, we recommend that states implement or expand heat safety programs to protect vulnerable subpopulations, such as outdoor workers.

Concentration-Response Relationship between PM2.5 and Daily Respiratory Deaths in China: A Systematic Review and Metaregression Analysis of Time-Series Studies

The association between the particulate matters with aerodynamic diameter ≤ 2.5 μm (PM_2.5) and daily respiratory deaths, particularly the concentration-response pattern, has not been fully examined and established in China. We conducted a systematic review of time-series studies to compile information on the associations between PM_2.5 concentration and respiratory deaths and used metaregression to assess the concentration-response relationship. Out of 1,957 studies screened, eleven articles in English and two articles in Chinese met the eligibility criteria. For single-day lags, per 10 μg/m³ increase in PM_2.5 concentration was associated with 0.30 [95% confidence interval (CI): 0.10, 0.50] percent increase in daily respiratory deaths; for multiday lags, the corresponding increase in respiratory deaths was 0.69 (95% CI: 0.55, 0.83) percent. Difference in the effects was observed between the northern cities and the south cities in China. No statistically significant concentration-response relationship between PM_2.5 concentrations and their effects was found. With increasingly wider location coverage for PM_2.5 data, it is crucial to further investigate the concentration-response pattern of PM_2.5 effects on respiratory and other cause-specific mortality for the refinement and adaptation of global and national air quality guidelines and targets.

Ambient particulate air pollution (PM2.5) is associated with the ratio of type 2 diabetes to obesity

We used county level data for T2D prevalence across the mainland USA and matched this to county level ambient PM2.5. Multiple linear regression was used to determine the relation between prevalence of T2D with PM2.5 after adjustment for confounding factors. PM2.5 explained 6.3% of the spatial variation in obesity, and 17.9% of the spatial variation in T2D. After correcting the T2D prevalence for obesity, race, poverty, education and temperature, PM2.5 still explained 8.3% of the residual variation in males (P < 0.0001) and 11.5% in females (P < 0.0001). The effect on obesity prevalence corrected for poverty, race education and temperature was much lower and hence the ratio of T2D to obesity prevalence was significantly associated with PM2.5 in males (R2 = 11.1%, P < 0.0001) and females (R2 = 16.8%, P < 0.0001). This association was repeated across non-African countries (R2 = 14.9%, P < 0.0001). High levels of PM2.5 probably contribute to increased T2D prevalence in the USA, but have a more minor effect on the obesity. Exposure to high environmental levels of PM2.5 (relative to the USA) may explain the disproportional risk of T2D in relation to obesity in Asian populations.

Acute effects of ambient temperature and particulate air pollution on fractional exhaled nitric oxide: A panel study among diabetic patients in Shanghai, China

Background

Epidemiological studies have shown the associations of ambient temperature and particulate matter (PM) air pollution with respiratory morbidity and mortality. However, the underlying mechanisms have not been well characterized. The aim of this study is to investigate the associations of temperature and fine and coarse PM with fractional exhaled nitric oxide (FeNO), a well-established biomarker of respiratory inflammation.

Methods

We conducted a longitudinal panel study involving six repeated FeNO tests among 33 type 2 diabetes mellitus patients from April to June 2013 in Shanghai, China. Hourly temperature and PM concentrations were obtained from a nearby fixed-site monitoring station. We then explored the associations between temperature, PM, and FeNO using linear mixed-effect models incorporated with distributed lag nonlinear models for the lagged and nonlinear associations. The interactions between temperature and PM were evaluated using stratification analyses.

Results

We found that both low and high temperature, as well as increased fine and coarse PM, were significantly associated with FeNO. The cumulative relative risk of FeNO was 1.75% (95% confidence interval [CI], 1.04–2.94) comparing 15 °C to the referent temperature (24 °C) over lags 0–9 days. A 10 μg/m³ increase in fine and coarse PM concentrations were associated with 1.18% (95% CI, 0.18–2.20) and 1.85% (95% CI, 0.62–3.09) FeNO in lag 0–1 days, respectively. PM had stronger effects on cool days than on warm days.

Conclusions

This study suggested low ambient temperature, fine PM, and coarse PM might elevate the levels of respiratory inflammation. Our findings may help understand the epidemiological evidence linking temperature, particulate air pollution, and respiratory health.

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Both low and high temperatures were significantly associated with FeNO.

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The increases of fine and coarse PM concentrations were associated with FeNO.

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Both fine and coarse PM had stronger effects in cool days.

The Threshold Temperature and Lag Effects on Daily Excess Mortality in Harbin, China: A Time Series Analysis

Background:

A large number of studies have reported the relationship between ambient temperature and mortality. However, few studies have focused on the effects of high temperatures on cardio-cerebrovascular diseases mortality (CCVDM) and their acute events (ACCVDM).

Objective:

To assess the threshold temperature and time lag effects on daily excess mortality in Harbin, China.

Methods:

A generalized additive model (GAM) with a Poisson distribution was used to investigate the relative risk of mortality for each 1 °C increase above the threshold temperature and their time lag effects in Harbin, China.

Results:

High temperature threshold was 26 °C in Harbin. Heat effects were immediate and lasted for 0–6 and 0–4 days for CCVDM and ACCVDM, respectively. The acute cardiovascular disease mortality (ACVDM) seemed to be more sensitive to temperature than cardiovascular disease mortality (CVDM) with higher death risk and shorter time lag effects. The lag effects lasted longer for cerebrovascular disease mortality (CBDM) than CVDM; so did ACBDM compared to ACVDM.

Conclusion:

Hot temperatures increased CCVDM and ACCVDM in Harbin, China. Public health intervention strategies for hot temperatures adaptation should be concerned.