Short-term exposure to PM2.5 or O-3 can increase mortality risk; however, limited studies have evaluated their interaction. A multicity time series study was conducted to investigate the synergistic effect of PM2.5 and O-3 on mortality in China, using mortality data and high-resolution pollutant predictions from 272 cities in 2013-2015. Generalized additive models were applied to estimate associations of PM2.5 and O-3 with mortality. Modification and interaction effects were explored by stratified analyses and synergistic indexes. Deaths attributable to PM2.5 and O-3 were evaluated with or without modification of the other pollutant. The risk of total nonaccidental mortality increased by 0.70% for each 10 mu g/m(3) increase in PM2.5 when O-3 levels were high, compared to 0.12% at low O-3 levels. The effect of O-3 on total nonaccidental mortality at high PM2.5 levels (1.26%) was also significantly higher than that at low PM2.5 levels (0.59%). Similar patterns were observed for cardiovascular or respiratory diseases. The relative excess risk of interaction and synergy index of PM2.5 and O-3 on nonaccidental mortality were 0.69% and 1.31 with statistical significance, respectively. Nonaccidental deaths attributable to short-term exposure of PM2.5 or O-3 when considering modification of the other pollutant were 28% and 31% higher than those without considering modification, respectively. Our results found synergistic effects of short-term coexposure to PM2.5 and O-3 on mortality and suggested underestimations of attributable risks without considering their synergistic effects.

In Great Britain, limited studies have employed machine learning methods to predict air pollution especially ozone (O3) with high spatiotemporal resolution. This study aimed to address this gap by developing random forest models for four key pollutants (fine and inhalable particulate matter [PM2.5 and PM10], nitrogen dioxide [NO2] and O3) by integrating multiple-source predictors at a daily level and 1-km resolution. The out-of-bag R2 (root mean squared error, RMSE) between predictions from models and measurements from monitoring stations in 2006-2013 was 0.85 (3.63 mu g/m3) for PM2.5, 0.77 (6.00 mu g/m3) for PM10, 0.85 (9.71 mu g/m3) for NO2, and 0.85 (9.39 mu g/m3) for maximum daily 8-h average (MDA8) O3 at daily level, and the predicting accuracy was higher at monthly and annual level. The high-resolution predictions captured characterized spatiotemporal patterns of the four pollutants. Higher concentrations of PM2.5, PM10, and NO2 were distributed in densely populated southern regions of Great Britain while O3 showed an inverse spatial pattern in general, which could not be fully depicted by monitoring stations. Therefore, predictions produced in this study could improve exposure assessment with less exposure misclassification , flexible exposure windows for future epidemiological studies to investigate the impact of air pollution across Great Britain.

Large-scale epidemiological studies on the association of blood lead levels with blood pressure and hypertension prevalence are still limited, particularly among lead-exposed workers. The evidence is even more scarce on the interaction of blood lead levels with occupational variables and ambient air pollution levels. We developed mixed-effect models based on a large group of lead-exposed workers (N = 22,002). The results were also stratified by multiple groupings. Compared to participants with blood lead < 20 μg/L, those with levels > 20 μg/L had a 26–37% higher prevalence of hypertension, as well as a 0.65–13.7 mmHg higher systolic and diastolic blood pressure. Workers exposed to high PM10 levels had a 21–28% higher risk. Workers exposed to high temperatures had a 0.41–2.46 mmHg greater increase in blood pressure, and those not exposed to dust had a 1.29–1.65 mmHg greater blood pressure increase. Our findings suggested the negative impact of blood lead on blood pressure and the prevalence of hypertension, with workers exposed to high PM10 concentrations, those exposed to occupational high temperature, and those without dust exposure being more vulnerable.

Background: The connections between fine particulate matter (PM2.5) and coarse particulate matter (PM2.5-10) and daily mortality of viral pneumonia and bacterial pneumonia were unclear. Objectives: To distinguish the connections between PM2.5 and PM2.5-10 and daily mortality due to viral pneumonia and bacterial pneumonia. Methods: Using a comprehensive national death registry encompassing all areas of mainland China, we conducted a case -crossover investigation from 2013 to 2019 at an individual level. Residential daily particle concentrations were evaluated using satellite -based models with a spatial resolution of 1 km. To analyze the data, we employed the conditional logistic regression model in conjunction with polynomial distributed lag models. Results: We included 221,507 pneumonia deaths in China. Every interquartile range (IQR) elevation in concentrations of PM2.5 (lag 0-2 d, 37.6 mu g/m3) was associated with higher magnitude of mortality for viral pneumonia (3.03%) than bacterial pneumonia (2.14%), whereas the difference was not significant (p -value for difference = 0.38). An IQR increase in concentrations of PM2.5-10 (lag 0-2 d, 28.4 mu g/m3) was also linked to higher magnitude of mortality from viral pneumonia (3.06%) compared to bacterial pneumonia (2.31%), whereas the difference was not significant (p -value for difference = 0.52). After controlling for gaseous pollutants, their effects were all stable; however, with mutual adjustment, the associations of PM2.5 remained, and those of PM2.5-10 were no longer statistically significant. Greater magnitude of associations was noted in individuals aged 75 years and above, as well as during the cold season. Conclusion: This nationwide study presents compelling evidence that both PM2.5 and PM2.5-10 exposures could increase pneumonia mortality of viral and bacterial causes, highlighting the more robust effects of PM2.5 and somewhat higher sensitivity of viral pneumonia.

BACKGROUND:The new round of WHO/ILO Joint Estimates of the Work-related Burden of Disease assessment requires futher research to provide more evidence, especially on the health impact of ambient air pollution around the workplace. However, the evidence linking obstructive ventilatory dysfunction (OVD) to fine particulate matter (PM2.5) and its chemical components in workers is very limited. Evidence is even more scarce on the interactive effects between occupational factors and particle exposures. We aimed to fill these gaps based on a large ventilatory function examination of workers in southern China. METHODS:We conducted a cross-sectional study among 363,788 workers in southern China in 2020. The annual average concentration of PM2.5 and its components were evaluated around the workplace through validated spatiotemporal models. We used mixed-effect models to evaluate the risk of OVD related to PM2.5 and its components. Results were further stratified by basic characteristics and occupational factors. FINDINGS:Among the 305,022 workers, 119,936 were observed with OVD. We found for each interquartile range (IQR) increase in PM2.5 concentration, the risk of OVD increased by 27.8 (95 % confidence interval (CI): 26.5-29.2 %). The estimates were 10.9 % (95 %CI: 9.7-12.1 %), 15.8 % (95 %CI: 14.5-17.2 %), 2.6 % (95 %CI: 1.4-3.8 %), 17.1 % (95 %CI: 15.9-18.4 %), and 11 % (95 %CI: 9.9-12.2 %), respectively, for each IQR increment in sulfate, nitrate, ammonium salt, organic matter and black carbon. We observed greater effect estimates among females, younger workers, workers with a length of service of 24-45 months, and professional skill workers. Furthermore, it is particularly noteworthy that the noise-exposed workers, high-temperature-exposed workers, and less-dust-exposed workers were at a 5.7-68.2 % greater risk than others. INTERPRETATION:PM2.5 and its components were significantly associated with an increased risk of OVD, with stronger links among certain vulnerable subgroups.

It remains unclear how ambient air pollution may affect the prevalence of obstructive ventilatory dysfunction (OVD) among workers. We aim to assess the association of a comprehensive set of ambient air pollutants with OVD prevalence in workers and to explore the potential interactive effects of the occupational factors. This is a population-based cross-sectional study among 305,022 participants from the Guangdong Province, China. Mixed-effects models were used to obtain differences in the OVD risk associated with a 10 μg/m3 increase in ambient air pollution. We found that for each 10 μg/m3 increase in PM2.5, PM10, PM coarse, O3, and NO2 concentrations, the odds ratio (OR) for OVD in workers is 1.324 (95 % confidence interval (CI), 1.282-1.367), 1.292 (95 % CI, 1.268-1.315),1.666 (95 % CI, 1.614-1.719), 1.153 (95 % CI, 1.142-1.165), and 1.023 (95 % CI, 1.012-1.033). We observed that young participants (18-38 years old), women, participants with longer years of service (>48 months), participants working in large enterprises, professional skills workers, and production and manufacturing workers have higher estimated effects. In addition, we also found that workers exposed to high temperatures have higher estimated effects under air pollutants exposure, while workers exposed to noise have higher estimated effects under PM2.5, PM10, NO2, and O3 exposure. Workers exposed to dust have a lower risk of developing OVD under exposure to ambient air pollutants compared to those not exposed. Our results indicate that ambient air pollution increases the risk of OVD in workers. Moreover, air pollutants exhibit a greater estimated effect among workers exposed to high temperatures or noise. Our research findings highlight the importance of fully considering the impact of ambient air pollution on protecting the respiratory health of workers.

Ultraviolet (UV) radiation is closely related to health; however, limited measurements have hindered further investigation of its health effects in China. Machine learning algorithms have been widely used to predict environmental factors with high accuracy, but a limited number of studies have implemented it for UV radiation. The main aim of this study is to develop a UV radiation prediction model using the random forest approach and predict the UV radiation with a daily and 10 km resolution in mainland China from 2005 to 2020. The model was developed with multiple predictors, such as UV radiation data from satellites as independent variables and ground UV radiation measurements from monitoring stations as the dependent variable. Missing satellite-based UV radiation data were obtained using the 3 d moving average method. The model performance was evaluated using multiple cross-validation (CV) methods. The overall R-2 and root mean square error between measured and predicted UV radiation from model development and model 10-fold CV were 0.97 and 15.64 W m(-2) and 0.83 and 37.44 W m(-2) at the daily level, respectively. The model that incorporated erythemal daily dose (EDD) retrieved from the Ozone Monitoring Instrument (OMI) had a higher prediction accuracy than that without it. Based on predictions of UV radiation at the daily level, 10 km spatial resolution, and nearly 100 % spatiotemporal coverage, we found that UV radiation increased by 4.20 %, PM2.5 levels decreased by 48.51 %, and O-3 levels increased by 22.70 % from 2013-2020, suggesting a potential correlation among these environmental factors. The uneven spatial distribution of UV radiation was associated with factors such as latitude, elevation, meteorological factors, and season. The eastern areas of China pose a higher risk due to both high population density and high UV radiation intensity. Using a machine learning algorithm, this study generated a gridded UV radiation dataset with extensive spatiotemporal coverage, which can be utilized for future health-related research. This dataset is freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024).

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 μg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 μg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Background: A large gap exists between the latest Global Air Quality Guidelines (AQG 2021) and Chinese air quality standards for NO2. Assessing whether and to what extent air quality standards for NO2 should be tightened in China requires a comprehensive understanding of the spatiotemporal characteristics of population exposure to ambient NO2 and related health risks, which have not been studied to date.Objective: We predicted ground NO2 concentrations with high resolution in mainland China, explored exposure characteristics to NO2 pollution, and assessed the mortality burden attributable to NO2 exposure.Methods: Daily NO2 concentrations in 2019 were predicted at 1-km spatial resolution in mainland China using random forest models incorporating multiple predictors. From these high-resolution predictions, we explored the spatiotemporal distribution of NO2, population and area percentages with NO2 exposure exceeding criterion levels, and premature deaths attributable to long- and short-term NO2 exposure in China. Results: The cross-validation R2 and root mean squared error of the NO2 predicting model were 0.80 and 7.78 mu g/ m3, respectively, at the daily level in 2019. The percentage of people (population number) with annual NO2 exposure over 40 mu g/m3 in mainland China in 2019 was 10.40 % (145,605,200), and it reached 99.68 % (1,395,569,840) with the AQG guideline value of 10 mu g/m3. NO2 levels and population exposure risk were elevated in urban areas than in rural. Long- and short-term exposures to NO2 were associated with 285,036 and 121,263 non-accidental deaths, respectively, in China in 2019. Tightening standards in steps gradually would increase the potential health benefit.Conclusion: In China, NO2 pollution is associated with significant mortality burden. Spatial disparities exist in NO2 pollution and exposure risks. China's current air quality standards may no longer objectively reflect the severity of NO2 pollution and exposure risk. Tightening the national standards for NO2 is needed and will lead to significant health benefits.

The associations between air pollution and diabetes mortality of different subtypes and complications were largely unclear. We performed an individual-level, time-stratified case-crossover study among over 0.9 million diabetes deaths from all administrative regions of Chinese mainland during 2013-2019. Daily concentrations of fine particles (PM2.5), coarse particles (PM2.5-10), nitrogen dioxide (NO2) and ozone (O3) were obtained for each decedent using high-resolution prediction models. Conditional logistic regression models were utilized to analyze the data. Each interquartile range increment in PM2.5, PM2.5-10, NO2 and O3 concentrations on lag 0-2 d increased the risks of overall diabetes mortality by 2.81 %, 1.92 %, 3.96 % and 2.15 %, respectively. Type 2 diabetes had stronger associations with air pollution than type 1 diabetes. Air pollutants were associated with diabetic ketoacidosis and diabetic nephropathy, but not other complications. The exposure-response curves were approximately linear with a plateau at higher concentrations of PM2.5, PM2.5-10, and NO2, while the associations for O3 appear to be statistically significant beyond 60 mu g/m3. This nationwide study reinforces the evidence ofhigher risks of acute diabetic events following short-term air pollution exposure. We identified differential effects of air pollutants on various subtypes and complications of diabetes, which require further mechanistic investigations.

Background The high under-five mortality rate (U5MR) in Africa is a significant public health concern. Previous studies have found that satellite retrieved light at night (LAN) data with long-term and global coverage can be used as a proxy for socio-economic development and urbanization. Currently, few studies on the effects of LAN on child mortality have been conducted in Africa, a region with varying levels of urbanization between countries. Objective To quantify the correlation between risk of child mortality and LAN as an indicator of urbanization and economic development in Africa. Methods Using data from the Demographic and Health Survey (DHS) database conducted in 15 African countries out of 46 countries from 2005 to 2013, this study estimated LAN levels for children based on their year of birth and residential addresses. This study used Cox proportional hazards models to assess the association between LAN and the risk of child mortality in Africa. Results The mean U5MR was 95 per 1,000 livebirths among the 15 African countries during 2005–2013. After adjusting for covariates, each 10-unit increment in LAN was associated with a 5.3% reduction in the risk of U5MR. The effect estimates were more pronounced in areas with lower LAN. Conclusion In Africa, the risk of U5MR decreased with increasing LAN, especially in areas with lower LAN. The results suggest that the development of urbanization and socio-economic conditions may be beneficial to child health, especially in regions with low LAN. The use of LAN as a proxy may offer an intriguing approach for identifying areas requiring targeted development in urbanization and socio-economic conditions.

BACKGROUND:The urban ambient air quality has been largely improved in the past decade. It is unknown whether childhood asthma prevalence is still increasing in ever top-ranking city of Shanghai, whether the improved air quality is beneficial for children's asthma and what time window of exposure plays critical roles. METHODS:Using a repeat cross-sectional design, we analyzed the association between early life exposure to particles and wheezing/asthma in each individual and combined surveys in 2011 and 2019, respectively, in 11,825 preschool children in Shanghai. RESULTS:A significantly lower prevalence of doctor-diagnosed asthma (DDA) (6.6% vs. 10.5%, p < 0.001) and wheezing (10.5% vs. 23.2%, p < 0.001) was observed in 2019 compared to 2011. Exposure to fine particulate matter (PM2.5), coarse particles (PM2.5-10) and inhalable particles (PM10) was decreased in 2019 by 6.3%, 35.4%, and 44.7% in uterus and 24.3%, 20.2%, and 31.8% in infancy, respectively. Multilevel log-binomial regression analysis showed exposure in infancy had independent association with wheezing/DDA adjusting for exposure in uterus. For each interquartile range (IQR) increase of infancy PM2.5, PM2.5-10 and PM10 exposure, the odds ratios were 1.39 (95% confidence interval (CI): 1.24-1.56), 1.51 (95% CI:1.15-1.98) and 1.53 (95% CI:1.27-1.85) for DDA, respectively. The distributed lag non-linear model showed the sensitive exposure window (SEW) was 5.5-11 months after birth. Stratified analysis showed the SEWs were at or shortly after weaning, but only in those with <6 months of exclusive breastfeeding. CONCLUSIONS:Improved ambient PM benefits in decreasing childhood asthma prevalence. We firstly reported the finding of SEW to PM at or closely after weaning on childhood asthma.

The spatial disparity of air pollutants is one of the key influential factors for environmental inequality. We quantitatively evaluated the evolution of PM2.5 spatial disparity in China during 2013-2020, and investigated the associations between PM2.5 spatial disparity and economic indicators. Differences in PM2.5 between more- and less-polluted cities declined over time, suggesting decreased absolute disparity. However, the more polluted cities in 2013 remained so in 2017 and 2020, and vice versa, indicating persistent relative disparity. PM2.5 pollution levels increased with higher GDP per capita in less-developed areas of China, but such negative effects weakened over time, while economic development tended to promote cleaner air in developed areas of China. Therefore, policies to improve air quality and promote economic development simultaneously are needed in China to reduce the disparity of air pollution and promote all people to enjoy environmental equality.

Including data of different time intervals during model development influences the predicting accuracy of PM(2.5 )but has not been widely discussed. Therefore, we included modeling data with multiple time windows to identify optimized modeling time windows for capturing the long-term variation of PM2.5 in China during 2005-2019. In general, we incorporated PM2.5 measurements, aerosol optical depth (AOD), meteorological parameters, land use data, and other predictors to train random forest models. The study period was separated into two phases (2013-2019 and 2005-2012) according to the availability of PM2.5 measurements. First, we trained models with two strategies of choosing time windows to compare model performance in predicting PM2.5 from 2013 to 2019, when measurements were available. Strategy 1a (ST1a) refers to training one model with all available data, and Strategy 1b (ST1b) refers to training multiple models each with one-year data. Second, we trained models with additional ten strategies (ST2a-ST2j) based on data from different time windows during 2013-2019 to compare the accuracy in predicting PM2.5 be-fore 2013, when measurements were unavailable. The internal and external cross-validation (CV) indicated that the model performance of ST1b was better than ST1a. Predictions based on ST1a tended to underestimate PM2.5 levels in 2013 and 2014 when PM2.5 concentrations were high, and overestimate after 2017 when PM2.5 dropped dramatically. The external CV of predicting historical PM2.5 was the most robust in ST2i (averaged predictions from two models developed by 2013 and 2014 data, respectively). Models with data closer to historical years and PM2.5 levels performed better in predicting historical PM2.5 concentrations. Our results suggested that training models with data of current-years performed better during 2013-2019, and with data of 2013 and 2014 performed better in predicting historical PM2.5 before 2013 in China. The comparison provided evidence for choosing optimized time windows when predicting long-term PM2.5 concentrations in China.