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Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing.The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB.Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PMIn the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 μg/mOur findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.

Limited studies investigated the association of prenatal exposure to PMA total of 4319 eligible pregnant women in Peking University Birth Cohort in Tongzhou (PKUBC-T) were included in the study. Based on mothers' residential addresses, we estimated prenatal PMPrenatal exposure to PMThe study identified that prenatal exposure to PM

Recent studies in specific population subgroups (e.g., pregnant women) have suggested PM exposure increases the risk of hyperuricemia. However, no studies have examined this in the general population. Furthermore, the underlying mechanism through which PM impacts hyperuricemia risk is poorly understood.To assess the association between long-term exposure to PM and risk of hyperuricemia and whether this association is mediated by lipid profile.We included 5939 participants in Southwest China from the China Multi-Ethnic Cohort (baseline 2018-2019, follow-up 2020-2021). Long-term PM pollutants (PMA total of 837 participants were newly diagnosed with hyperuricemia. The odds ratios of hyperuricemia associated with an interquartile range (IQR) increase in PMHigher particulate matter exposure was associated with higher hyperuricemia incidence. The decline in HDL-C and rise in LDL-C partially mediated this association. These findings were conducive to scientific research about the underlying mechanism of PM on hyperuricemia.

The effects of fine particulate matter (PM) on de novo hypertensive disorders of pregnancy (HDP) were inconsistent during the first and second trimesters. This study aimed to assess the trimester-specific effects of PM

Lack of evidence exists on whether air pollution exposure may affect ovarian reserve, especially for Chinese women.To explore the association between exposure to various air pollutants and anti-Müllerian hormone (AMH), a predictor of ovarian reserve, over different exposure windows in Shandong Province, China.We enrolled 18,878 women who had AMH measurements in the Center for Reproductive Medicine, Shandong University during 2010-2019. Daily average concentrations of ambient particulate matter with diameters ≤1 μm/2.5 μm/10 μm (PMThe level of AMH changed by -8.8% (95% confidence interval (CI): -12.1%, -5.3%), -2.1% (95% CI: -3.5%, -0.6%), -1.9% (95% CI: -3.3%, -0.5%), and -4.5% (95% CI: -7.1%, -1.9%) per 10 μg/mWe found that ovarian reserve was negatively associated with air pollution exposure for women, particularly from the primary to secondary follicle stage. The effect estimate increased by the reduction in the diameter of PMs, which also varied across population sub-groups.

Long-term exposure to air pollutants is likely to be associated with mental disorders, but relevant studies remain limited and inconsistent, and evidence to assess causality is particularly lacking, especially in developing countries. In addition, there are few studies on the role of physical activity in this relationship. We investigated the causal relationship between air pollutant exposure and mental health among Chinese adults and whether physical activity could play a positive role in this relationship. Using the balanced panel data for 2014 and 2016 from the China Family Panel Study, a representative Chinese national cohort study, we selected and validated appropriate instrumental variable to explore the causal relationship between air pollution and mental health and explored the moderating effect of physical activity using an instrumental variable fixed effects model (IVFE) in a counterfactual causal inference framework. PM

Long-term exposure to ambient ozone (OA total of 6211 individuals were recruited in this study. Random forest model was applied to predict the daily average concentrations of OLong-term exposure to OThe results suggested that long-term exposure to high levels of O

Few studies examined the association of prenatal exposure to green spaces with children's body mass index (BMI) Z-score, and no study evaluated the joint effect of prenatal green spaces and PMThe study was based on a birth cohort in Beijing, China, in which 13,253 mothers (LMP from 2014 to 2017) and their children were included. We estimated prenatal green spaces exposure by calculating average normalized difference vegetation index with 500 m buffers (NDVI-500), prenatal PMA 0.1 increase in prenatal NDVI-500 exposure, a 10 μg/mThe study suggested the beneficial effect of prenatal exposure to green spaces on child growth and its interaction with PM

Many studies have illustrated adverse effects of short-term exposure to air pollution on human health, which usually assumes a linear exposure-response (E-R) function in the delineation of health effects due to air pollution. However, nonlinearity may exist in the association between air pollutant concentrations and health outcomes such as adult pneumonia hospital visits, and there is a research gap in understanding the nonlinearity. Here, we utilized both the distributed lag model (DLM) and nonlinear model (DLNM) to compare the linear and nonlinear impacts of air pollution on adult pneumonia hospital visits in the coastal city of Qingdao, China. While both models show adverse effects of air pollutants on adult pneumonia hospital visits, the DLNM shows an attenuation of E-R curves at high concentrations. Moreover, the DLNM may reveal delayed health effects that may be missed in the DLM, e.g., ozone exposure and pneumonia hospital visits. With the stratified analysis of air pollutants on adult pneumonia hospital visits, both models consistently reveal that the influence of air pollutants is higher during the cold season than during the warm season. Nevertheless, they may behave differently in terms of other subgroups, such as age, gender and visit types. For instance, while no significant impact due to PM

Previous studies assessing adverse health have traditionally focused on a single environmental exposure, failing to reflect the reality of various exposures present simultaneously. Air pollution, ambient temperature and greenness have been proposed as critical environmental factors associated with metabolic syndrome (MetS). However, evidence exploring their joint relationships with MetS is needed for identifying interactive factors and developing more targeted public health interventions. The baseline data was obtained from China Multi-Ethnic Cohort (CMEC). Environmental data of air pollutants (PM

Human beings and society are experiencing substantial consequences caused by non-optimum temperatures. However, limited studies have assessed the economic burden of premature deaths attributable to non-optimum temperatures.To characterize the association between daily mean temperature and the economic burden of premature deaths.A total of 3 228 098 deaths were identified from a national mortality dataset in Italy during 2015 and 2019. We used the value of statistical life to quantify the economic losses of premature death. A two-stage time-series analysis was performed to evaluate the economic losses of premature deaths associated with non-optimum temperatures. Attributable burden for non-optimum temperatures compared with minimum risk temperature were estimated. Potential effect modifiers were further explored.From 2015 to 2019, the economic loss of premature deaths due to non-optimum temperatures was $525.52 billion (95% CI: $461.84-$580.80 billion), with the attributable fraction of 5.74% (95% CI: 5.04%-6.34%). Attributable economic burden was largely due to moderate cold temperatures ($309.54 billion, 95% CI: $249.49-$357.34 billion). A higher economic burden was observed for people above the age of 65, accounting for 75.97% ($452.42, 95%CI: $406.97-$488.76 billion) of the total economic burden. In particular, higher fractions attributable to heat temperatures were observed for provinces with the lowest level of GDP per capita but the highest level of urbanization.This study shows a considerable economic burden of premature deaths attributed to non-optimum temperatures. These figures can help inform tailored prevention to tackle the large economic burden imposed by non-optimum temperatures.

Purpose We summarized the evidence on the effects of heat and cold exposures on mortality in China. We included studies published on this topic in both Chinese and English, thereby filling a gap in knowledge using data from a country that consists of one-fifth of the world's population. Methods We conducted a systematic search of peer-reviewed studies on the association between daily mean temperature and mortality published from 2001 up to July 2018. We searched one Chinese database (China National Knowledge infrastructure, http://www.cnki.net ) and three English databases (PubMed, Scopus, Web of Science). We converted the effect estimates of heat/cold to rate ratios (RRs) associated with 1° increase/decrease beyond the heat/cold reference temperatures. For studies that provided lag-specific estimates, we used both the maximum and minimum of RR estimates. We calculated summary effect estimates for all-cause and cause-specific mortalities, as well as RRs stratified by sex, age, and socioeconomic status. We also investigated patterns of heat and cold adaptation at different latitudes, and at different reference temperatures. Results In total, 45 articles were included in this systematic review. For every 1° temperature increase/decrease beyond reference points, the rate of non-accidental mortality increased by 2% (RR, 1.02; 95% confidence interval (95% CI [1.01–1.02]) for heat and 4% (RR, 1.04; 95% CI [1.03–1.04]) for cold, respectively; the rate of cardiovascular mortality increased 3% (RR, 1.03; 95% CI [1.03–1.04]) for heat and 6% (RR, 1.06; 95% CI [1.04–1.07]) for cold; the rate of respiratory mortality increased 2% (RR, 1.02; 95% CI [1.01–1.03]) for heat and 2% (RR, 1.02; 95% CI [1.00–1.04]) for cold; the rate of cerebrovascular mortality increased 2% (RR, 1.02; 95% CI [1.02–1.03]) for heat and 3% (RR, 1.03; 95% CI [1.02–1.04]) for cold. We identified a variation in optimal temperature range related to latitude of the residential area, and differences in people's capability to adapt to heat versus cold. Conclusion We found consistent evidence of the association between temperature and mortality, as well as evidence of patterns in human adaptation, and we discussed the implications of our findings.

Epidemiological studies increasingly provide evidence about the adverse health effects of temperature variability (TV), which reflects short-term intra- and inter-day temperature change. However, calculation of TV only considers the temporal variability and lacks spatial variability. This study intends to investigate whether the lack of spatial variability in TV calculations has biased the health effect estimates. We collected daily data from the fine-gridded hourly temperatures and more than 2 million all-cause mortality counts in Zhejiang province in China from 2009 to 2015. A spatiotemporal TV index was developed by calculating the standard deviation of the hourly temperatures based on records from multiple sites. This new index could be compared to the two typical temporal TV indices that are calculated based on the hourly temperatures from one-site and area-average records. The three types of TV are compared using a three-stage analytical approach: district-specific time series Poisson regression, meta-analysis, and calculation of attributable mortality fraction. We observe that both spatiotemporal and temporal TVs produce very similar TV–mortality associations, attributable mortality fractions, and model fits at the district level. For example, the mortality increase associated for every increase of 1 °C during 0–7 exposure days is 1.53% (95% CI: 1.31, 1.73) in spatiotemporal TV, whereas it is 1.48% (95% CI: 1.27, 1.68) and 1.45% (95% CI: 1.24, 1.67) in the one-site and area-average temporal TV, respectively. Thus, time series models using temporal TV index are equally good at estimating the associations between TV and mortality as spatiotemporal TV at the district level in population-based epidemiological studies in China. Epidemiological studies using temperature from one site or the averages of multiple sites in TV calculation will not bias the effect estimates of TV. Our study could provide an important guidance method for future TV-related research in China and even in other countries.

No evidence exists concerning the association between blood pressure and ambient particles with aerodynamic diameter

Obesity and ambient air pollution are independent risk factors of type 2 diabetes mellitus (T2DM), however, the evidence regarding their joint associations on T2DM was sparsely studied in low-middle income countries.A total of 38,841 participants were selected from Henan Rural Cohort study which was carried out during 2015-2017. Obesity was identified by body mass index (BMI), WC (waist circumstance), WHR (waist-to-hip ratio), WHtR (waist-to-height ratio), BFP (body fat percent), and VFI (visceral fat index). Three-year averaged-concentrations of NOPositive associations of six obesity measures and four air pollutants with FBG levels and prevalent T2DM were observed. Obese participants measured by BMI plus high exposure to NOThe synergistic associations of obesity and air pollutants on FBG levels and prevalent T2DM were observed, indicating that obese participants were at high risk for prevalent T2DM in highly polluted rural regions.

Background Greenness exposure may lower blood pressure. However, few studies of this relationship have been conducted with children and adolescents, especially in low and middle-income countries. Objectives To evaluate associations between greenness around schools and blood pressure among children and adolescents across China. Methods We recruited 61,229 Chinese citizens aged 6–18 years from 94 schools in a nationwide cross-sectional study in seven Chinese provinces/province-level municipalities. Participants' blood pressures and hypertension were assessed with standardized protocols. Greenness levels within 500 m and 1,000 m of each school were estimated with three satellite-based indices: vegetation continuous fields (VCF), normalized difference vegetation index (NDVI), and soil adjusted vegetation index (SAVI). Generalized linear mixed models were used to evaluate associations between greenness and blood pressure, greenness and prevalent hypertension, using coefficient and odds ratio respectively. Stratified analyses and mediation analyses were also performed. Results One interquartile range increase in greenness was associated with a 17%–20% reduced prevalence of hypertension for all measures of greenness (odds ratios for VCF500m = 20% (95% CI:18%, 23%); for NDVI500m = 17% (95% CI:13%, 21%); and for SAVI500m = 17% (95% CI: 13%, 20%). Increases in greenness were also associated with reductions in systolic blood pressure (0.48–0.58 mmHg) and diastolic blood pressure (0.26–0.52 mmHg). Older participants, boys, and urban dwellers showed stronger associations than their counterparts. No evidence of mediation was observed for air pollution (i.e., NO2 and PM2.5) and body mass index. Conclusion Higher greenness around schools may lower blood pressure levels and prevalent hypertension among Chinese children and adolescents, particularly in older subjects, boys, and those living in urban districts. Further studies, preferably longitudinal, are needed to examine causality.

Background Living in greener areas may reduce adiposity, but epidemiological evidence on this topic is still inconsistence and limited, especially in rural areas. Methods We performed a cross-sectional study among 4651 Uyghur adults in rural areas in Xinjiang province, northwestern China, from May to September 2016. We measured residential greenness levels using satellite-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) in 100 m, 300 m, 500 m, and 1000 m buffers around each home address. Body height, weight, and waist circumference were assessed according to recommended guidelines. Data on baseline characteristics and confounders were collected using a questionnaire. We used generalized linear mixed models to estimate the associations of residential greenness with overweight/obesity prevalence and obesity-related anthropometric indices. Results Higher residential greenness levels were associated with lower waist circumference and body mass index levels, as well as with a lower odds ratio of peripheral overweight/obesity prevalence. No significant association was found for greenness and central obesity prevalence. The associations persisted in magnitude and direction across several sensitivity analyses we performed. Stratified analysis suggested that the associations were generally stronger in older adults than those in younger adults. Additionally, neither air pollutants nor physical activity significantly mediated the associations between greenness and obesity. Conclusions Our results suggest that higher residential greenness were associated with lower odds of overweight/obesity and lower obesity-related anthropometric indices among rural Uyghur adults in China, especially for older adults.

Background The widely used Air Quality Index (AQI) has been criticized due to its inaccuracy, leading to the development of the air quality health index (AQHI), an improvement on the AQI. However, there is currently no consensus on the most appropriate construction strategy for the AQHI. Objectives In this study, we aimed to evaluate the utility of AQHIs constructed by different models and health outcomes, and determine a better strategy. Methods Based on the daily time-series outpatient visits and hospital admissions from 299 hospitals (January 2016–December 2018), and mortality (January 2017–December 2019) in Guangzhou, China, we utilized cumulative risk index (CRI) method, Bayesian multi-pollutant weighted (BMW) model and standard method to construct AQHIs for different health outcomes. The effectiveness of AQHIs constructed by different strategies was evaluated by a two-stage validation analysis and examined their exposure-response relationships with the cause-specific morbidity and mortality. Results Validation by different models showed that AQHI constructed with the BMW model (BMW-AQHI) had the strongest association with the health outcome either in the total population or subpopulation among air quality indexes, followed by AQHI constructed with the CRI method (CRI-AQHI), then common AQHI and AQI. Further validation by different health outcomes showed that AQHI constructed with the risk of outpatient visits generally exhibited the highest utility in presenting mortality and morbidity, followed by AQHI constructed with the risk of hospitalizations, then mortality-based AQHI and AQI. The contributions of NO2 and O3 to the final AQHI were prominent, while the contribution of SO2 and PM2.5 were relatively small. Conclusions The BMW model is likely to be more effective for AQHI construction than CRI and standard methods. Based on the BMW model, the AQHI constructed with the outpatient data may be more effective in presenting short-term health risks associated with the co-exposure to air pollutants than the mortality-based AQHI and existing AQIs.

Background The association of airborne particulate matter of 1 μm or less (PM1) with fetal growth hasn't been studied. We aimed to investigate the association of PM1 with fetal growth parameters measured via ultrasonography and birth weight. Methods The birth cohort included 18,669 pregnant women who were pregnant between 2014 and 2017 in Tongzhou Maternal and Child Health Hospital of Beijing, China. The predicted PM1 concentration was matched with the residential addresses of each woman. The fetal abdominal circumference (AC), head circumference (HC), femur length (FL) and estimated fetal weight (EFW) were evaluated via ultrasonography, while birth weight was measured at birth. The fetal parameters and birth weight were standardized as gestational-age- and gender-adjusted Z-score. We defined undergrowth of fetal parameters, low birth weight (LBW) and small-for-gestational-age (SGA) as categorized outcomes. Generalized estimating equations and generalized linear regression were used to examine the associations of PM1 with quantitative and categorized outcomes, respectively. Results A 10 μg/m3 increase in PM1 was associated with decrement in the Z-scores of AC [-0.027, 95% confidence intervals (CI): -0.047~ -0.07]EFW (-0.055, 95%CI: -0.075~-0.035). These results remained robust after adjusting nitrogen dioxide and sulphur dioxide. We didn't observe significant results regarding the analyses of undergrowth of all fetal parameters and the analyses of birth weight outcomes. Conclusion This study identified the negative associations between PM1 and fetal parameters in utero. The findings provided robust evidence that strategies for reducing PM1 exposure can prevent early-life health.

The large amount of missing values has challenged the application of satellite-retrieved aerosol optical depth (AOD) in mapping surface PM

Background Previous studies have indicated that exposure to green space may benefit human health. However, the available evidence concerning the effects of greenness, especially school-based greenness, on pediatric obesity is scarce. Objective To explore the association between school-based greenness and adiposity in children and adolescents in China. Method We conducted a nationwide cross-sectional study of 56,620 children and adolescents (aged 6–18 years) in seven provinces/municipalities across China. School-based greenness was assessed using satellite-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) within 100-, 500-, and 1000-m circular buffers around each school's address. Generalized linear mixed regression models were used to estimate associations of greenness with BMI z-scores (zBMI), waist circumference, and prevalent overweight/obesity. We also explored the potential mediating role of ambient air pollution and physical activity in the greenness-adiposity associations. Result In the adjusted model, an IQR increase in NDVI-1000m was associated with lower zBMI (β: -0.11, 95% confidence interval[CI]: -0.13,-0.09) and waist circumference (β: -0.64, 95%CI: -0.78,-0.50). Consistently, an IQR increase in NDVI-100m, NDVI-500m, NDVI-1000m was associated with 7–20% lower odds of overweight/obesity in the adjusted models. Air pollutants mediated 6.5–29.1% of the association between greenness and zBMI. No significant mediation effect was observed for physical activity. Conclusion Higher school-based greenness levels were associated with lower zBMI, waist circumference, and lower odds of overweight and obesity in children and adolescents. Ambient air pollutants may partially mediate the greenness-adiposity associations.

For six weeks from February 9, 2014, smoke and ash from a fire in the Morwell open cut brown coal mine adjacent to the Hazelwood power station covered parts of the Latrobe Valley, in south eastern Australia.To evaluate the health impact of the mine fire, daily counts of ambulance attendances from July 2010 to March 2015 were analysed.Time series models were used to evaluate the relative risk of ambulance attendances during the mine fire, in comparison with the remainder of the analysis period, and to also assess the risk of ambulance attendances associated with lagged effects of exposure to mine fire-related PMA 10 μg/mExposure to smoke and ash from a fire in an open cut brown coal mine was associated with increased ambulance attendances, particularly for respiratory conditions. These findings guide the development and implementation of effective and timely strategies and health service planning to respond and mitigate health risks that arise in affected communities during future major air pollution episodes.

Little evidence exists about the effects of long-term exposure to ambient air pollution on metabolic syndrome (MetS). This study aimed to determine the association between long-term ambient air pollution and MetS in China. A total of 15,477 adults who participated in the 33 Communities Chinese Health Study (33CCHS) in 2009 were evaluated. MetS was defined based on the recommendation by the Joint Interim Societies. Exposure to air pollutants was assessed using data from monitoring stations and a spatial statistical model (including particles with diameters ≤ 1.0 µm (PM1), ≤ 2.5 µm (PM2.5), and ≤ 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)). Two-level logistic regression analyses were utilized to assess the associations between air pollutants and MetS. The prevalence of MetS was 30.37%. The adjusted odds ratio of MetS per 10 µg/m3 increase in PM1, PM2.5, PM10, SO2, NO2, and O3 were 1.12 (95% CI = 1.00–1.24), 1.09 (95% CI = 1.00–1.18), 1.13 (95% CI = 1.08–1.19), 1.10 (95% CI = 1.02–1.18), 1.33 (95% CI = 1.12–1.57), and 1.10 (95% CI = 1.01–1.18), respectively. Stratified analyses indicated that the above associations were stronger in participants with the demographic variables of males

Background Limited evidence is known about whether long-term exposures to air borne particulate matters of 2.5 μm or less (PM2.5) impact human hematologic index for women preparing for pregnancy. No study assessed the effect of PM1, which is small enough to reach the blood circulation. Objective To evaluate whether exposure to PM1 and PM2.5 is associated with blood cell count of woman preparing for pregnancy. Method Based on the baseline data of a national birth cohort in China, we analysed the white blood cell (WBC), red blood cells (RBC) and thrombocyte counts of 1,203,565 women who are aged 18–45 years, being Han ethnicity, had no chronic disease and preparing for pregnancy. We matched their home addresses and examination date with daily concentrations of PM1 and PM2.5 which were estimated by a machine learning method with remote sensing, meteorological and land use information. Generalized additive mixed model to examine the associations between exposure to one-year average exposure to PMs prior to the health examination and the blood cells counts, after adjustment for potential individual variables. Results A 10 μg/m3 PM1 increment was associated with −1.49% (95%CI: 1.56%, −1.42%) difference in WBC count; with 0.33% (95%CI: 0.30%, 0.36%) difference of RBC count; and with 1.08% (95%CI: 1.01%, 1.15%) difference of thrombocyte count. For PM2.5, the corresponding difference was −0.47% (95%CI: 0.54%, −0.39%) for WBC; was 0.06% (95%CI: 0.03%, 0.09%) for RBC; and was 1.10% (95%CI: 1.02%, 1.18%) for thrombocyte. Women working as workers, being overweight and with tobacco smoking exposure had higher associations between PMs and hematologic index than their counterparts (p Conclusion Long-term exposure to PMs were associated with decrement in WBC, as well as increment in RBC and thrombocytes among Han Chinese women preparing for pregnancy. Measures such as using air purifiers and wearing a mask in polluted areas should be improved to prevent women from the impact of PMs.

Background Dyslipidemia is a crucial risk factor for cardiovascular diseases. Previous studies have suggested that air pollution is associated with blood lipids. However, little evidence exists in low- and middle-income regions. We aimed to investigate the association between air pollution and blood lipids in southwestern China. Methods We included 67,305 participants aged 30–79 years from the baseline data of the China Multi-Ethnic Cohort (CMEC) study. Three-year average concentrations of particles with diameters ≤1 μm (PM1), particles with diameters ≤ 2.5 μm (PM2.5), particles with diameters ≤ 10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) were estimated using satellite-based spatiotemporal models. Individual serum lipids, including cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), were measured. Linear, logistic, and quantile regression models were used to evaluate the association between ambient air pollution and blood lipids. Results All five air pollutants in our study were associated with lipid levels. Increased air pollution exposure was associated with a high risk of dyslipidemia. Each 10 μg/m3 increase in PM2.5 was associated with 0.92% (95% confidence interval (CI): 0.64%, 1.20%), 2.23% (95% CI: 1.44%, 3.02%), and 3.04% (95% CI: 2.61%, 3.47%) increases in TC, TG, and LDL-C levels, respectively, and a 2.03% (95% CI: 1.69%, 2.37%) decrease in HDL-C levels, and high risks of dyslipidemia (OR = 1.14, 95% CI: 1.10, 1.18). Stronger associations of air pollution with blood lipids were found in participants with high lipid levels than in those with low lipid levels. Conclusion Long-term exposure to air pollutants was associated with blood lipid levels and the risk of dyslipidemia. People with high lipid levels were more susceptible to air pollution. Therefore, air pollution prevention and control may help reduce the incidence of dyslipidemia and the burden of CVDs.

Background For six weeks from February 9, 2014, smoke and ash from a fire in the Morwell open cut brown coal mine adjacent to the Hazelwood power station covered parts of the Latrobe Valley, in south eastern Australia. Aims To evaluate the health impact of the mine fire, daily counts of ambulance attendances from July 2010 to March 2015 were analysed. Methods Time series models were used to evaluate the relative risk of ambulance attendances during the mine fire, in comparison with the remainder of the analysis period, and to also assess the risk of ambulance attendances associated with lagged effects of exposure to mine fire-related PM2.5 levels. The models controlled for factors likely to influence ambulance attendances including seasonality, long-term temporal trends, day of the week, daily maximum temperature and public holidays. Results A 10 μg/m3 increase in fire-related PM2.5 was found to be associated with a 42% (95%CI: 14–76%) increase in ambulance attendances for respiratory conditions and a 7% (0–14%) increase in all ambulance attendances over a 20-day lag period. A smaller effect associated with exposure to fire-related PM2.5 was identified when assuming shorter lag effects. Similar results were identified when assessing whether ambulance attendances increased during the 30-day mine fire period. There was a 15% (8–21%) increased risk of ambulance attendances for all conditions and a 47% (19–81%) increased risk for respiratory conditions during the mine fire period. Conclusions Exposure to smoke and ash from a fire in an open cut brown coal mine was associated with increased ambulance attendances, particularly for respiratory conditions. These findings guide the development and implementation of effective and timely strategies and health service planning to respond and mitigate health risks that arise in affected communities during future major air pollution episodes.

The large amount of missing values has challenged the application of satellite-retrieved aerosol optical depth (AOD) in mapping surface PM2.5 concentrations. In this study, we developed a non-AOD random forest model to estimate daily concentrations of PM2.5 in Guangdong Province, China, where more than 80% of AOD data were missing. The predictive ability of the non-AOD model was compared with that of a AOD-based model. Daily ground-based measurements of PM2.5 were obtained from 148 ground-monitoring sites in Guangdong with a 60 km rectangle buffer from January 2016 to December 2018. Daily MODIS MAIAC AOD were downloaded from NASA at a resolution of approximately 1 km. Two random forest models were developed to predict ground-level PM2.5 with one included AOD as a predictor and the other one without AOD. The two random forest models were developed using the same dataset and their predictive abilities were compared. The results of 10-fold cross validation (CV) showed that the non-AOD and AOD-based random forest models yielded similar performance. The CV R2 (RMSE) for the non-AOD model in 2016–2018 were 0.82 (8.4 μg/m3), 0.81 (9.5 μg/m3) and 0.78 (9.4 μg/m3), and those for AOD-based model were 0.83 (8.2 μg/m3), 0.82 (9.2 μg/m3) and 0.80 (9.0 μg/m3), respectively. Higher consistency of estimated PM2.5 were observed in areas close to monitoring sites than those far away from sites, and in southern coastal than northern areas. As the non-AOD random forest model is not affected by AOD missingness, it can be used for epidemiological studies to estimate individual-level exposure to PM2.5 at a high resolution without spatial or temporal gaps.

Socio-economic status (SES) and air pollutants are thought to play an important role in human obesity. The evidence of interactive effect between SES and long-term exposure to mixture of air pollutants on obesity is limited, thus, this study is aimed to investigate their interactive effects on obesity among a rural Chinese population.A total of 38,817 individuals were selected from the Henan Rural Cohort Study. Structural equation modeling (SEM) was applied to construct the latent variables of low SES (educational level, marital status, family yearly income, and number of family members), air pollution (particulate matter with aerodynamics diameters ≤ 1.0 μm, ≤ 2.5 μm or ≤ 10 μm, and nitrogen dioxide) and obesity (body mass index, waist circumference, waist-to-hip ratio, waist-to-height ratio, body fat percentage and visceral fat index). Generalized linear regression models were used to assess associations between the constructed latent variables. Interaction plots were applied to describe interactive effect of air pollution and low SES on obesity and biological interaction indicators (the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP) and synergy index (S)) were also calculated.Increased latent variables of low SES and mixture of air pollution were associated with a higher odds of latent variable of obesity (odds ratios (OR) (95% confidence interval (CI)) were 1.055 (1.049, 1.060) and 1.050 (1.045, 1.055)). The association of the mixture of air pollutants on obesity was aggravated by increased values of the latent variable of low SES (P 0.001). Furthermore, the values of RERI, AP and S were 0.073 (0.051, 0.094), 0.057 (0.040, 0.073) and 1.340 (1.214, 1.479), respectively, indicating an additive effect of estimated latent variable of low SES and air pollution on obesity.These findings suggested that low SES aggravated the negative effect of mixture of air pollutants on obesity, implying that individuals with low SES may be more susceptible to exposure to high levels of mixture of air pollutants related to increased risk of prevalent obesity.

Background Prolonged sleep latency is associated with far-reaching public health consequences. Although evidence about the effect of air pollution on sleep problem has been shown, the effect on sleep latency remained unknown. The study aimed to analyze the association between long-term exposure to air pollution and prolonged sleep latency in rural China. Methods In all, 27935 participants were included in the study from Henan Rural Cohort Study. A satellite-based spatiotemporal model was used to evaluate the 3-year average concentration of air pollutants at the home address of participants before the baseline survey. Air pollutants included NO2 (nitrogen dioxide), PM1 (particulate matter with aerodynamic diameters ≤1 μm), PM2.5 (particulate matter with aerodynamic diameters ≤ 2.5 μm), and PM10 (particulate matter with aerodynamic diametes ≤ 10 μm). A logistic regression model was conducted to assess the odds ratio (OR) and 95% confidence interval (95% CI) between air pollutants and prolonged sleep latency. Results There were 5825 (20.85%) participants with prolonged sleep latency. The average concentration of NO2, PM1, PM2.5, and PM10 were 38.22 (2.54) μg/m3, 56.29 (1.75) μg/m3, 72.30 (1.87) μg/m3, and 130.01 (4.58) μg/m3. The odds ratio (95%CI) of prolonged sleep latency with an IQR increase of NO2, PM1, PM2.5, and PM10 were 1.59 (1.33–1.90), 1.23 (1.13–1.33), 1.28 (1.13–1.45) and 1.43 (1.22–1.67). The stratified analysis showed the effect of air pollutants was stronger among those with stroke. Conclusion Long-term exposure to NO2, PM1, PM2.5 and PM10 were associated with prolonged sleep latency. The adverse impact of air pollution should be considered when treating sleep problems.

Background The association between ambient air pollutants exposure and suicidal ideation (SI) has rarely been explored, especially in Chinese population. Therefore, we aimed to investigate the long-term effects of ambient air pollutants on SI among a Chinese rural population. Method We recruited 29997 participants from the Henan Rural Cohort study in 2016–2017. SI was evaluated by using the ninth item of the Patient Health Questionnaire-9 (PHQ-9) in the baseline survey. We adopted a satellite-based spatiotemporal model to estimate participants’ exposure to particulate matters (PMs) (PM with an aerodynamic diameter ≤1 μm (PM1), ≤2.5 μm (PM2.5) or ≤10 μm (PM10), respectively) and nitrogen dioxide (NO2), and then calculated the 3-year average concentrations of the four pollutants. We used logistic regression models to explore the association between long-term exposure to ambient air pollutants and SI. In addition, we conducted several stratified analyses to examine effect modification of selected factors. Results The odds ratios (95% confidence intervals [CI]) of SI in response to each 1 μg/m3 increase in PM1, PM2.5, PM10 and NO2 concentrations were 1.08 (1.01, 1.15), 1.10 (1.02, 1.19), 1.05 (1.01, 1.09) and 1.12 (1.04, 1.21), respectively. Individuals exposed to PM1, PM2.5, PM10 or NO2 concentrations in the fourth quartile had a 1.36-fold (95%CI: 1.08, 1.72), 1.69-fold (95%CI: 1.05, 2.72), 1.49-fold (95%CI: 1.09, 2.05) or 1.71-fold (95%CI: 1.15, 2.85) risks of SI, compared to the ones with corresponding air pollutants in the first quartile. Besides, the risks of SI increased with the quartiles of air pollutants (PM1: Ptrend = 0.002, PM2.5: Ptrend = 0.003, PM10: Ptrend = 0.010, NO2: Ptrend = 0.010). Stratified analyses suggested that males, highly educated participants, ever-drinkers and people aged range 36–64 years were more vulnerable to the adverse effects of air pollutants. Conclusions This study provided evidence for the long-term effects of ambient PMs and NO2 on SI in rural Chinese adults, particularly for males, highly educated participants, ever-drinkers and people aged range 36–64 years.

No evidence exists concerning the effect of airborne particulate matter of 1 μm or less (PM1) on blood pressure of young adults planning for pregnancy. We collected health examination information of about 1.2 million couples (aged 18–45 years) from a national birth cohort in China from Jan 1, 2013 to Oct 1, 2014 and matched their home address to daily PM1 and PM2.5 concentrations, which were predicted by remote sensing information. Generalized additive mixed models were used to analyze associations between long-term exposure to PM and blood pressure, after controlling for individual factors. A 10 μg/m3 increase in PM1 was associated with increased systolic blood pressure (SBP) for 0.26 (95%CI: 0.24, 0.29) mmHg in females and 0.29 (95%CI: 0.26, 0.31) mmHg in males, respectively. PM1 was also associated with increased DBP for 0.22 (95%CI: 0.20, 0.23) mmHg in females and 0.17 (95%CI: 0.15, 0.19) mmHg in males, respectively. Similar effects on blood pressure were found for PM2.5, meanwhile, the effect of PM2.5 on SBP increased with the scale of PM1 included in PM2.5 (p for interaction term

Objectives The associations of long-term exposure to air pollution with osteoporosis are rarely reported, especially in rural China. This study aimed to explore the association among rural Chinese population. Methods A total of 8033 participants (18–79 years) derived from the Henan Rural Cohort Study (n = 39,259) were included in this cross-sectional study. Exposure to air pollutants was estimated using machine learning algorithms with satellite remote sensing, land use information, and meteorological data [including particulate matter with aerodynamic diameters ≤1.0 μm (PM1), ≤2.5 μm (PM2.5), and ≤10 μm (PM10), and nitrogen dioxide (NO2)]. The bone mineral density of each individual was measured by using ultrasonic bone density apparatus and osteoporosis was defined based on the T-score ≤ −2.5. Multiple logistic regression models were used to examine the association of air pollution and osteoporosis prevalence. Results We observed that per 1 μg/m3 increase in PM1, PM2.5, PM10 and NO2 were associated with a 14.9%, 14.6%, 7.3%, and 16.5% elevated risk of osteoporosis. Compared with individuals in the first quartile, individuals in the fourth quartile had higher odds ratio (OR) of osteoporosis (P-trend Conclusions Long-term exposure to air pollutants were positively associated with high-risk of osteoporosis, indicated that improving air quality may be beneficial to improve rural residents health.

Background Studies on the hypertensive effect of long-term air pollution exposure were inconclusive and showed scarce evidence from rural areas in developing countries. In this context, we examined the associations of air pollution exposure with hypertension and blood pressure, and their effect modifiers in rural Chinese adults. Methods We studied 39,259 participants from a cohort established in five rural regions of central China. Individual exposures to PM2.5 and PM10 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm and 10 μm) and nitrogen dioxide (NO2) was evaluated using satellite-based spatiotemporal models. Mixed-effect regression models were applied to examine the associations of long-term exposure to air pollution with hypertension and four blood pressure component measurements, including systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP). Several potential effect modifiers related to demographic and behavioral factors were also examined. Results The results showed that for each 1 μg/m3 increase in PM2.5, PM10 and NO2, the adjusted odds ratio of hypertension was 1.029 (95%CI: 1.001,1.057), 1.015 (95%CI: 1.001, 1.029) and 1.069 (95%CI: 1.038, 1.100), respectively. These three air pollutants were also associated with increased SBP (except for PM10), DBP and MAP. The hypertensive effects of air pollution were more pronounced among males, smokers, drinkers, individuals with a high-fat diet, and those with high-level physical activity. Conclusion Long-term exposure to PM2.5, PM10 and NO2 was associated with increased blood pressure and hypertension in rural Chinese adults, and the associations were modified by several behavioral factors., Graphical abstract Image 1, Highlights • The study aimed at rural population in developing countries. • The effect estimates of NO2 was observed to be larger than PM2.5 and PM10. • Pulse pressure was negatively associated with air pollution exposure. • Several behavioral factors can modify the hypertensive effect of air pollution.

Background Heatwave impact on morbidity of people in rural areas has rarely been assessed in prior studies, and recently published literature has documented heatwave impact on a wide spectrum of diseases, for example, ear and eye diseases. Objectives To examine the associations between heatwaves and cause-specific emergency department visits (EDVs) across eight communities in both urban and rural regions throughout Queensland, Australia. Methods Daily data on EDVs, air pollution and climatic conditions during the 1st January 2013 to the 31st December 2015 were obtained from relevant government agencies. Heatwave was defined as ≥ 95th percentile of the mean temperature for three or more consecutive days in each community. A quasi-Poisson generalized additive model with a distributed lag non-linear model was used to assess the heatwave impacts on EDVs. Random effect meta-analysis was performed to investigate the effects of heatwaves on cause-specific EDVs across the urban and rural regions as well as the whole Queensland. The causes of EDVs investigated in this study were infectious and parasitic diseases (ICD code: A00–B99), endocrine, nutritional and metabolic diseases (E00–E90), mental and behavioural disorders (F00–F99), diseases of the nervous system (G00–G99), diseases of the ear and mastoid process (H60–H95), diseases of the circulatory system (I00–I99), diseases of the respiratory system (J00–J99), diseases of the skin and subcutaneous tissue (L00–L99), diseases of the musculoskeletal system and connective tissue (M00–M99), diseases of the genitourinary system (N00–N99), and injury, poisoning and certain other consequences of external causes (S00–T98). Results The meta-analysis results showed that there were significant effects of heatwaves on total EDVs and a wide-spectrum of cause-specific EDVs. For example, EDVs for endocrine, nutritional and metabolic diseases (RR: 1.18, 95% CI: 1.04 – 1.34), diseases of the nervous system (RR: 1.09, 95% CI: 1.02 – 1.17), and diseases of the genitourinary system (RR: 1.05, 95% CI: 1.00 – 1.09) increased substantially during heatwave days. The effect of heatwaves on total EDVs was similar for rural (RR: 1.04, 95% CI: 1.01 – 1.07) and urban regions (RR: 1.04, 95% CI: 1.00 – 1.07). Conclusions A wide range of diseases were sensitive to heatwave impacts. Residents in urban and rural areas were all vulnerable to heatwave impacts, calling for heat adaptation measures to be undertaken in Queensland, Australia.

Little evidence exists about the effects of long-term exposure to ambient air pollution on metabolic syndrome (MetS). This study aimed to determine the association between long-term ambient air pollution and MetS in China. A total of 15,477 adults who participated in the 33 Communities Chinese Health Study (33CCHS) in 2009 were evaluated. MetS was defined based on the recommendation by the Joint Interim Societies. Exposure to air pollutants was assessed using data from monitoring stations and a spatial statistical model (including particles with diameters ≤ 1.0 µm (PM

Introduction The proportion of imported malaria cases in China has increased over recent years, and has presented challenges for the malaria elimination program in China. However, little is known about the geographic distribution and environmental suitability for malaria transmission under projected climate change scenarios. Methods Using the MaxEnt model based on malaria presence-only records, we produced environmental suitability maps and examined the relative contribution of topographic, demographic, and environmental risk factors for P. vivax and P. falciparum malaria in China. Results The MaxEnt model estimated that environmental suitability areas (ESAs) for malaria cover the central, south, southwest, east and northern regions, with a slightly wider range of ESAs extending to the northeast region for P. falciparum. There was spatial agreement between the location of imported cases and area environmentally suitable for malaria transmission. The ESAs of P. vivax and P. falciparum are projected to increase in some parts of southwest, south, central, north and northeast regions in the 2030 s, 2050 s, and 2080 s, by a greater amount for P. falciparum under the RCP8.5 scenario. Temperature and NDVI values were the most influential in defining the ESAs for P. vivax, and temperature and precipitation the most influential for P. falciparum malaria. Conclusion This study estimated that the ESA for malaria transmission in China will increase with climate change and highlights the potential establishment of further local transmission. This model should be used to support malaria control by targeting areas where interventions on malaria transmission need to be enhanced.

Objectives To examine the short-term effects of ambient temperature on respiratory symptoms for school children with asthma across Australia. Methods A panel of 270 children (7–12 years) with asthma was recruited from six Australian cities. They were asked to record their respiratory symptoms every day in the morning (for night-time symptoms) and evening (for daytime symptoms) for four weeks. Daily ambient temperature, relative humidity and air pollution data were obtained from fixed monitors nearby. A mixed logistic regression model was used to examine the effects of ambient temperature on respiratory symptoms adjusted for children׳s sex, age, standing height, weight and air pollution. Subjects were specified as random effects. Results The relationships between ambient temperature and respiratory symptoms were linear. Increasing temperatures induced the risks of children׳s asthmatic symptoms, especially for “wheeze/chest tightness” and to a lesser extent for “cough/phlegm”. The effects were acute and lasted for four days (lag 0–3) in general. With increasing ambient temperature, boys were more at risk than girls. Conclusions High ambient temperature is a risk factor for respiratory symptoms in children with asthma. As ambient temperature increases, policies and strategies for rising temperatures will be necessary to protect asthmatic children.

Background China's rapid economic development has resulted in severe particulate matter (PM) air pollution and the control and prevention of infectious disease is an ongoing priority. This study examined the relationships between short-term exposure to ambient particles with aerodynamic diameter ≤2.5 µm (PM 2.5 ) and measles incidence in China. Methods Data on daily numbers of new measles cases and concentrations of ambient PM 2.5 were collected from 21 cities in China during Oct 2013 and Dec 2014. Poisson regression was used to examine city-specific associations of PM 2.5 and measles, with a constrained distributed lag model, after adjusting for seasonality, day of the week, and weather conditions. Then, the effects at the national scale were pooled with a random-effect meta-analysis. Results A 10 µg/m 3 increase in PM 2.5 at lag 1 day, lag 2 day and lag 3 day was significantly associated with increased measles incidence [relative risk (RR) and 95% confidence interval (CI) were 1.010 (1.003, 1.018), 1.010 (1.003, 1.016) and 1.006 (1.000, 1.012), respectively]. The cumulative relative risk of measles associated with PM 2.5 at lag 1–3 days was 1.029 (95% CI: 1.010, 1.048). Stratified analyses by meteorological factors showed that the PM 2.5 and measles associations were stronger on days with high temperature, low humidity, and high wind speed. Conclusions We provide new evidence that measles incidence is associated with exposure to ambient PM 2.5 in China. Effective policies to reduce air pollution may also reduce measles incidence.

Background China is experiencing more and more days of serious air pollution recently, and has the highest lung cancer burden in the world. Objectives To examine the associations between lung cancer incidence and fine particles (PM2.5) and ozone in China. Methods We used 75 communities’ data of lung cancer incidence from the National Cancer Registration of China from 1990 to 2009. The annual concentrations of fine particles (PM2.5) and ozone at 0.1°×0.1° spatial resolution were generated by combing remote sensing, global chemical transport models, and improvements in coverage of surface measurements. A spatial age-period-cohort model was used to examine the relative risks of lung cancer incidence associated with the air pollutants, after adjusting for impacts of age, period, and birth cohort, sex, and community type (rural and urban) as well as the spatial variation on lung cancer incidence. Results The relative risks of lung cancer incidence related to a 10 µg/m3 increase in 2-year average PM2.5 were 1.055 (95% confidence interval (CI): 1.038, 1.072) for men, 1.149 (1.120, 1.178) for women, 1.060 (1.044, 1.075) for an urban communities, 1.037 (0.998, 1.078) for a rural population, 1.074 (1.052, 1.096) for people aged 30–65 years, and 1.111 (1.077, 1.146) for those aged over 75 years. Ozone also had a significant association with lung cancer incidence. Conclusions The increased risks of lung cancer incidence were associated with PM2.5 and ozone air pollution. Control measures to reduce air pollution would likely lower the future incidence of lung cancer.

Background - As heatwaves are expected to be more frequent, longer, and more intense in the future, it is imperative to understand how heatwaves affect health. However, it is intensely debated about how a heatwave should be defined. Objectives - This study explored the possibility of developing a health risk-based definition for heatwave, and assessed the heat-related mortality in the three largest Australian cities. Methods - Daily data on climatic variables and non-accidental deaths for Brisbane, Melbourne and Sydney during the period 1988–2009 were obtained from relevant government agencies. Several local heatwave definitions were tested by using percentiles (e.g., from the 75th to 99th centile) of mean temperature with duration ≥2 days across these cities. We examined the relative risks of mortality associated with heatwaves in each city using Poisson generalised additive model, after controlling for long-term trend, within-season variation, day of the week, and relative humidity. Then, Bayesian hierarchical model with segment–spline was used to examine the threshold for the heatwave-related impacts. Results - A consistent and significant increase in mortality during heatwaves was observed in all three cities. The pooled data show that the relative risk of mortality started to increase around the 95th centile of temperature, increased sharply at the 97th centile and rose alarmingly at the 99th centile. Based on research findings, we proposed tiered health risk-based metrics to define a heatwave. Conclusions - Our findings provide supportive evidence for developing health risk-based metrics to assess the impacts of heatwave. These findings may have important implications for assessing and reducing the burden of heat-related mortality.

Children are vulnerable to temperature extremes. This paper aimed to review the literature regarding the relationship between ambient temperature and children's health and to propose future research directions. A literature search was conducted in February 2012 using the databases including PubMed, ProQuest, ScienceDirect, Scopus and Web of Science. Empirical studies regarding the impact of ambient temperature on children's mortality and morbidity were included. The existing literature indicates that very young children, especially children under one year of age, are particularly vulnerable to heat-related deaths. Hot and cold temperatures mainly affect cases of infectious diseases among children, including gastrointestinal diseases, malaria, hand, foot and mouse disease, and respiratory diseases. Pediatric allergic diseases, like eczema, are also sensitive to temperature extremes. During heat waves, the incidences of renal disease, fever and electrolyte imbalance among children increase significantly. Future research is needed to examine the balance between hot- and cold-temperature related mortality and morbidity among children; evaluate the impacts of cold spells on cause-specific mortality in children; identify the most sensitive temperature exposure and health outcomes to quantify the impact of temperature extremes on children; elucidate the possible modifiers of the temperature and children's health relationship; and project children's disease burden under different climate change scenarios.

Most studies examining the temperature-mortality association in a city used temperatures from one site or the average from a network of sites. This may cause measurement error as temperature varies across a city due to effects such as urban heat islands. We examined whether spatiotemporal models using spatially resolved temperatures produced different associations between temperature and mortality compared with time series models that used non-spatial temperatures. We obtained daily mortality data in 163 areas across Brisbane city, Australia from 2000 to 2004. We used ordinary kriging to interpolate spatial temperature variation across the city based on 19 monitoring sites. We used a spatiotemporal model to examine the impact of spatially resolved temperatures on mortality. Also, we used a time series model to examine non-spatial temperatures using a single site and the average temperature from three sites. We used squared Pearson scaled residuals to compare model fit. We found that kriged temperatures were consistent with observed temperatures. Spatiotemporal models using kriged temperature data yielded slightly better model fit than time series models using a single site or the average of three sites' data. Despite this better fit, spatiotemporal and time series models produced similar associations between temperature and mortality. In conclusion, time series models using non-spatial temperatures were equally good at estimating the city-wide association between temperature and mortality as spatiotemporal models.