Short-term PM2 Research Articles

Underappreciated roles of soil nitrogen oxide emissions on global acute health burden

The recognized importance of ambient fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) on human health has prompted the world to enact increasingly strict regulations on anthropogenic nitrogen oxides (NOx) emissions. However, the health concerns from soil NOx, potentially driven by fertilizer input but conventionally categorized as natural sources, remain less studied. Here, we emphasize the underappreciated roles of soil NOx emissions on health burden attributable to short-term PM2.5, O3, and NO2 exposure. Globally, we quantify acute health effects using machine-learning-based daily exposure estimates and identify influences of soil NOx emissions based on chemical transport model simulations. We find that 72.3% of the globe is affected by soil NOx emissions, whose contributions to short-term PM2.5, O3, and NO2 pollution lead to 13.9 (95% Confidence Interval [CI]: 9.1-18.8), 26.0 (18.2-34.2), and 13.9 (10.3-17.5) thousand premature mortality, respectively, in 2019. With distinct variations in regions, seasons, and pollutants, soil NOx-originated air pollution poses a global health concern, particularly for developing regions and intensively agricultural areas. In response to the intensive fertilizer use, South Asia, Southern Sub-Saharan Africa, and Central Europe witness the largest soil NOx-related health burden of up to 1.6 (95% CI: 1.1-2.1) mortality per 100k population. The overall health risk peaks in May, with O3 pollution typically dominating the soil NOx-attributable health burden during warm seasons and NO2 or PM2.5 during cold months. Our study highlights the necessity of dynamically adapted agricultural strategies for health-oriented multi-pollutant control, among which the improved use of synthetic fertilizers deserves priority under the ever-changing climate.

Heart rate variability, electrodermal activity and cognition in adults: Association with short-term indoor PM2.5 exposure in a real-world intervention study

BackgroundLong-term effects of ambient fine particulate matter (PM2.5) exposure on mortality and morbidity are well established. The study aims to evaluate how short-term indoor PM2.5 exposure affects physiological responses and understand potential mechanisms mediating the cognitive outcomes in working-age adults. MethodsThis real-world randomized single-blind crossover intervention study was conducted in an urban office setting, with desk-based air purifiers used as the intervention. Participants (N = 40) were exposed to average PM2.5 levels of 18.0 μg/m3 in control and 3.7 μg/m3 in intervention conditions. Cognitive tests, heart rate variability (HRV), and electrodermal activity (EDA) measures were conducted after 5 h of exposure. Self-reported mental effort, exhaustion, and task difficulty were collected after the cognitive tests. ResultsParticipants in the intervention condition had significantly higher HRV during cognitive testing, particularly in the standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), and high-frequency power (HF) indices. Mediation analysis revealed that elevated PM2.5 exposure reduced HRV indices, which mediated the effect on two executive function-related cognitive skills out of 16 assessed skills. No significant differences were found in EDA, self-reported task difficulty, or exhaustion, but self-reported mental effort was higher in the control condition. ConclusionsLower indoor PM2.5 level was associated with reduced mental effort and higher HRV during cognitive testing. Furthermore, the association between indoor PM2.5 exposure and executive function might be mediated through cardiovagal responses. These findings provide insights on the mechanisms through which fine particle exposure adversely affects the autonomic nervous system and how this in turn affects cognition. The potential cardiovascular and cognitive health benefits of PM2.5 reduction warrants further research.

Wildland Fire-Related Smoke PM 2.5 and Cardiovascular Disease ED Visits in the Western United States.

The impact of short-term exposure to fine particulate matter (PM 2.5 ) due to wildland fire smoke on the risk of cardiovascular disease (CVD) remains unclear. We investigated the association between short-term exposure to wildfire smoke PM 2.5 and Emergency Department (ED) visits for acute CVD in the Western United States from 2007 to 2018. ED visits for primary or secondary diagnoses of atrial fibrillation (AF), acute myocardial infarction (AMI), heart failure (HF), stroke, and total CVD were obtained from hospital associations or state health departments in California, Arizona, Nevada, Oregon, and Utah. ED visits included those that were subsequently hospitalized. Daily smoke, non-smoke, and total PM 2.5 were estimated using a satellite-driven multi-stage model with a high resolution of 1 km. The data were aggregated to the zip code level and a case-crossover study design was employed. Temperature, relative humidity, and day of the year were included as covariates. We analyzed 49,759,958 ED visits for primary or secondary CVD diagnoses, which included 6,808,839 (13.7%) AFs, 1,222,053 (2.5%) AMIs, 7,194,474 (14.5%) HFs, and 808,396 (1.6%) strokes. Over the study period from 2007-01-01 to 2018-12-31, the mean smoke PM 2.5 was 1.27 (Q1: 0, Q3: 1.29) µg/m 3 . A 10 µg/m 3 increase in smoke PM 2.5 was associated with a minuscule decreased risk for AF (OR 0.994, 95% CI 0.991-0.997), HF (OR 0.995, 95% CI 0.992-0.998), and CVD (OR 0.9997, 95% CI 0.996-0.998), but not for AMI and stroke. Adjusting for non-smoke PM 2.5 did not alter these associations. A 10 µg/m 3 increase in total PM 2.5 was linked to a small increased risk for all outcomes except stroke (OR for CVD 1.006, 95% CI 1.006-1.007). Associations were similar across sex and age groups. We identified an unexpected slight lower risk of CVD ED visits associated with short-term wildfire smoke PM 2.5 exposure. Whether these findings are due to methodological issues, behavioral changes, or other factors requires further investigation.

Resuspension of inhalable particles from clothing: A manikin-based chamber study

Clothing plays a significant role in facilitating inhalation exposure to particulate matter (PM). Nevertheless, there is a lack of studies investigating PM resuspension from clothing. This study aims to quantify size-resolved PM resuspension rate (RR) from clothing while exploring the effects of factors influencing this process. In a controlled chamber, we deposited Arizona Test Dust on a seated and clothed thermal manikin. The seated manikin was then placed in a resuspension chamber to perform arm movements using a consistent test mechanism. We evaluated the size-resolved resuspension rates in the diameter range 0.3–10 μm as a function of long-sleeve clothing type (cotton shirts vs. polyacrylic sweaters), relative humidity (RH) (35 vs. 70 %), dust loading (204 vs. 321 mg/m2), and movement intensity (0.25, 0.5, and 0.75 Hz). The short-term PM10 concentration in the breathing zone was 1.25 times higher than in the bulk air during resuspension. The size-dependent RR varied between 0.01 h−1 and 0.06 h−1. Higher RR was associated with higher movement intensity and dust loading on clothing, while a small dependence was found on clothing type and RH. The results offer a valuable dataset for enhancing current inhalation exposure models related to indoor aerosols and for developing targeted interventions to reduce exposure to particles associated with clothing.

A Novel Short-Term PM2.5 Forecasting Approach Using Secondary Decomposition and a Hybrid Deep Learning Model

PM2.5 pollution poses an important threat to the atmospheric environment and human health. To precisely forecast PM2.5 concentration, this study presents an innovative combined model: EMD-SE-GWO-VMD-ZCR-CNN-LSTM. First, empirical mode decomposition (EMD) is used to decompose PM2.5, and sample entropy (SE) is used to assess the subsequence complexity. Secondly, the hyperparameters of variational mode decomposition (VMD) are optimized by Gray Wolf Optimization (GWO) algorithm, and the complex subsequences are decomposed twice. Next, the sequences are divided into high-frequency and low-frequency parts by using the zero crossing rate (ZCR); the high-frequency sequences are predicted by a convolutional neural network (CNN), and the low-frequency sequences are predicted by a long short-term memory network (LSTM). Finally, the predicted values of the high-frequency and low-frequency sequences are reconstructed to obtain the final results. The experiment was conducted based on the data of 1009A, 1010A, and 1011A from three air quality monitoring stations in the Beijing area. The results indicate that the R2 value of the designed model increased by 2.63%, 0.59%, and 1.88% on average in the three air quality monitoring stations, respectively, compared with the other single model and the mixed model, which verified the significant advantages of the proposed model.

Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives

Particulate matter with a diameter of 2.5 microns or less (PM2.5) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily PM2.5 concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term PM2.5 concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term PM2.5 forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest PM2.5 value (236.00 μg/m3) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate PM2.5 concentration levels between 50 μg/m3 and 80 μg/m3. In Peshawar, the PM2.5 concentration levels were consistently unhealthy, with the highest peak (153.00 μg/m3) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution.

Short-term effects of wildfire-specific fine particulate matter and its carbonaceous components on perinatal outcomes: A multicentre cohort study in New South Wales, Australia

BackgroundEpidemiological evidence on the association between wildfire-specific fine particulate matter (PM2.5) and its carbonaceous components with perinatal outcomes is limited.We aimed to examine the short-term effects of wildfire-specific PM2.5 and its carbonaceous components on perinatal outcomes. MethodsA multicentre cohort of 9743 singleton births during the wildfire seasons from 1 September 2009 to 31 December 2015 across six cities in New South Wales, Australia were linked with daily wildfire-specific PM2.5 and carbonaceous components (organic carbon and black carbon). Adjusted distributed lag Cox regression models with spatial clustering were performed to estimate daily and cumulative adjusted hazard ratios (aHRs) during the last four gestational weeks for preterm birth, stillbirth, nonvertex presentation, low 5-min Apgar score, special care nursery/neonatal intensive care unit (SCN/NICU) admission, and caesarean section. ResultsDaily aHRs per 10 µg/m3 PM2.5 showed nearly inverted ‘U’-shaped positive associations and daily cumulative aHRs that increased with increasing duration of the exposures. The aHRs for lag 0–6 days were 1.17 (95 % CI: 1.04, 1.32) for preterm birth, 1.40 (95 % CI: 1.11, 1.78) for stillbirth, 1.20 (95 % CI: 1.08, 1.33) for nonvertex presentation, 1.12 (95 % CI: 0.93, 1.35) for low 5-min Apgar score, 0.99 (95 % CI: 0.83, 1.19) for SNC/NICU admission, and 1.01 (95 % CI: 0.94, 1.08) for caesarean section. Organic carbon and black carbon components for lag 0–6 days showed positive associations. The highest component-specific aHRs were 1.09 (95 % CI: 1.03, 1.15) and 4.57 (95 % CI: 1.96, 10.68) for stillbirth per 1 µg/m3 organic carbon and black carbon, respectively. The subgroups identified as most vulnerable were female births, births to mothers with low socioeconomic status, and births to mothers with high biothermal exposure. ConclusionsPositive associations of short-term wildfire-specific PM2.5 exposure and its carbonaceous components with adverse perinatal outcomes suggest that policies to reduce exposure would benefit public health.

Short-term PM2.5 exposure and DNA methylation changes of circadian rhythm genes: evidence from two experimental studies

Short-term PM2.5 exposure and DNA methylation changes of circadian rhythm genes: evidence from two experimental studies

Cardiopulmonary effects of short-term ambient PM2.5 exposure: central air quality monitor data versus personal exposure models

Cardiopulmonary effects of short-term ambient PM2.5 exposure: central air quality monitor data versus personal exposure models

Fish oil supplementation protects skin from yellowness induced by fine particulate matter through anti-inflammatory mechanism: a randomized, double-blind, placebo-controlled clinical trial.

Evidence of the effects of ambient fine particulate matter (PM2.5) exposure on skin colour and its potential preventive intervention is scarce. A randomized, double-blind, placebo-controlled trial involving 65 healthy college students suggested that short-term PM2.5 exposure may lead to darkening and yellowing of skin tone. The yellowness may be modulated by inflammatory responses and alleviated by fish-oil supplementation.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research

Given the significant health and environmental risks posed by atmospheric PM2.5 pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the PM2.5 prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast PM2.5 concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves R2 by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric PM2.5 pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

Ambient air pollution and daily mortality in ten cities of India: a causal modelling study

The evidence for acute effects of air pollution on mortality in India is scarce, despite the extreme concentrations of air pollution observed. This is the first multi-city study in India that examines the association between short-term exposure to PM2·5 and daily mortality using causal methods that highlight the importance of locally generated air pollution. We applied a time-series analysis to ten cities in India between 2008 and 2019. We assessed city-wide daily PM2·5 concentrations using a novel hybrid nationwide spatiotemporal model and estimated city-specific effects of PM2·5 using a generalised additive Poisson regression model. City-specific results were then meta-analysed. We applied an instrumental variable causal approach (including planetary boundary layer height, wind speed, and atmospheric pressure) to evaluate the causal effect of locally generated air pollution on mortality. We obtained an integrated exposure-response curve through a multivariate meta-regression of the city-specific exposure-response curve and calculated the fraction of deaths attributable to air pollution concentrations exceeding the current WHO 24 h ambient PM2·5 guideline of 15 μg/m3. To explore the shape of the exposure-response curve at lower exposures, we further limited the analyses to days with concentrations lower than the current Indian standard (60 μg/m3). We observed that a 10 μg/m3 increase in 2-day moving average of PM2·5 was associated with 1·4% (95% CI 0·7-2·2) higher daily mortality. In our causal instrumental variable analyses representing the effect of locally generated air pollution, we observed a stronger association with daily mortality (3·6% [2·1-5·0]) than our overall estimate. Our integrated exposure-response curve suggested steeper slopes at lower levels of exposure and an attenuation of the slope at high exposure levels. We observed two times higher risk of death per 10 μg/m3 increase when restricting our analyses to observations below the Indian air quality standard (2·7% [1·7-3·6]). Using the integrated exposure-response curve, we observed that 7·2% (4·2%-10·1%) of all daily deaths were attributed to PM2·5 concentrations higher than the WHO guidelines. Short-term PM2·5 exposure was associated with a high risk of death in India, even at concentrations well below the current Indian PM2·5 standard. These associations were stronger for locally generated air pollutants quantified through causal modelling methods than conventional time-series analysis, further supporting a plausible causal link. Swedish Research Council for Sustainable Development.

Unappreciated healthcare inequality against PM2.5-related mortality risk

Understanding the inequality of PM2.5-related health is crucial for promoting health, building a just society, and advancing multiple Sustainable Development Goals goals. However, previous research has predominantly concentrated on PM2.5 exposure inequality, neglecting varied prompt responses and protective behaviors against it. Here, we established the relationship between short-term healthcare expenditure and PM2.5 concentration using the number and amount of healthcare transactions across all healthcare categories based on the Union Pay data. We also assessed daily city-specific PM2.5-related mortality and healthcare expenditures and evaluated their inequalities among cities according to the income inequality index, the Gini coefficient. The results show that short-term exposure to PM2.5 leads to severe physiological and health-related economic burdens on Chinese residents. From 2017 to 2019, 77.8 (34.5–121.1) thousand deaths were attributed to daily PM2.5, with healthcare expenditures reaching 93.7 (69.1–118.3) billion Chinese Yuan. Additionally, there were significant inequalities in PM2.5-related mortality and healthcare expenditures among cities. The inequality index for PM2.5-related healthcare expenditures was 0.53, while the inequality index for PM2.5-related mortality was 0.13. The greater inequality in healthcare expenditures than in mortality, implying inadequate healthcare resources amplify the health inequality related to PM2.5 exposure. 28.6% of Chinese cities lacked affordable healthcare resources to address the high physiological burden attributable to PM2.5. Our multidimensional exploration is essential for formulating effective policies addressing environmental health inequality. Focusing on these cities with disproportionate challenges is crucial for creating a more equitable and sustainable society.

Comprehensive analysis of resilience of human airway epithelial barrier against short-term PM2.5 inorganic dust exposure using invitro microfluidic chip and exvivo human airway models.

The updated World Health Organization (WHO) air quality guideline recommends an annual mean concentration of fine particulate matter (PM2.5) not exceeding 5 or 15 μg/m3 in the short-term (24 h) for no more than 3-4 days annually. However, more than 90% of the global population is currently exposed to daily concentrations surpassing these limits, especially during extreme weather conditions and due to transboundary dust transport influenced by climate change. Herein, the effect of respirable <PM2.5 inorganic silica particle exposures on epithelial barrier integrity was simultaneously evaluated within the biomimetic microfluidic platform-based airway epithelial barrier (AEB)-on-a-chip and human bronchoscopic exvivo airway tissue models, comparatively. Silica particles at an average size of 1 μm, referred to as <PM2.5, dose-dependently tested by MTT and LDH analyses. The elicited dose of 800 μg/mL was applied to human airway epithelial cells (Calu-3) seeded to the membrane at air-liquid interface in the AEB-on-a-chip platform, which is operated under static and dynamic conditions and to exvivo human bronchoscopy bronchial tissue slices for 72 h. For both models, healthy and exposed groups were comparatively investigated. Computational fluid dynamics simulations were performed to assess shear stress profiles under different flow conditions. Qualitative and quantitative analyses were carried out to evaluate the resilience of the epithelial barrier via cell survivability, morphology, barrier integrity, permeability, and inflammation. In the AEB-on-a-chip platform, short-term exposure to 800 μg/mL PM2.5 disrupted AEB integrity via increasing barrier permeability, decreasing cell adhesion-barrier markers such as ZO-1, Vinculin, ACE2, and CD31, impaired cell viability and increased the expression levels of proinflammatory markers; IFNs, IL-6, IL-1s, TNF-α, CD68, CD80, and Inos, mostly under dynamic conditions. Besides, decreased tissue viability, impaired tissue integrity via decreasing of Vinculin, ACE2, β-catenin, and E-cadherin, and also proinflammatory response with elevated CD68, IL-1α, IL-6, IFN-Ɣ, Inos, and CD80 markers, were observed after PM2.5 exposure in exvivo tissue. The duration and concentration of PM2.5 that can be exposed during extreme weather conditions and natural events aligns with our exposure model (0-800 μg/mL 72 h). At this level of exposure, the resilience of the epithelial barrier is demonstrated by both AEB-on-a-chip platform emulating dynamic forces in the body and exvivo bronchial biopsy slices. Lung-on-a-chip models will serve as reliable exposure models in this context.

Short-Term PM2.5 Exposure and DNA Methylation Changes of Circadian Rhythm Genes: Evidence from Two Experimental Studies.

The circadian rhythm regulates many crucial physiological processes, impacting human aging and aging-related outcomes. Observational evidence links circadian rhythm disturbance to PM2.5 exposure, yet the underlying DNA methylation mechanisms remain unclear due to limited PM2.5-dominated experimental settings. Therefore, we investigated the associations between short-term PM2.5 exposure and DNA methylation changes of 1188 CpG candidates across circadian genes among 32 young adults in the FDU study, with the validation in 26 individuals from the PKU study. Further mediation analyses tested whether DNA methylation of circadian genes could mediate the influence of PM2.5 on aging measured by three epigenetic ages: DNAmGrimAge, DunedinPoAm, and the mortality risk score. We identified three CpG sites associated with personal PM2.5 exposure: cg01248361 (CSNK2A2), cg17728065 (RORA), and cg22513396 (PRKAG2). Acute effects of PM2.5 on the three loci could be mediated by several circulating biomarkers, including MDA and EGF, with up to ∼30% of mediated proportions. Three loci further showed varying potentials in mediating the aging acceleration effect of PM2.5. Locus cg17728065 is the key site exhibiting a robust mediating effect (7.54-12.52%) on PM2.5-induced aging acceleration. Our findings demonstrated that PM2.5, even short-term peaks, could leave imprints on human aging via inducing aberrant temporal fluctuation in circadian homeostasis captured by DNA methylation profiles.

Cancer mortality risk from short-term PM2.5 exposure and temporal variations in Brazil

Although some studies have found that short-term PM2.5 exposure is associated with lung cancer deaths, its impact on other cancer sites is unclear. To answer this research question, this time-stratified case-crossover study used individual cancer death data between January 1, 2000, and December 31, 2019, extracted from the Brazilian mortality information system to quantify the associations between short-term PM2.5 exposure and cancer mortality from 25 common cancer sites. Daily PM2.5 concentration was aggregated at the municipality level as the key exposure. The study included a total of 34,516,120 individual death records, with the national daily mean PM2.5 exposure 15.3 (SD 4.3) μg/m3. For every 10-μg/m3 increase in three-day average PM2.5 exposure, the odds ratio (OR) for all-cancer mortality was 1.04 (95% CI 1.03–1.04). Apart from all-cancer deaths, PM2.5 exposure may impact cancers of oesophagus (1.04, 1.00–1.08), stomach (1.05, 1.02–1.08), colon-rectum (1.04, 1.01–1.06), lung (1.04, 1.02–1.06), breast (1.03, 1.00–1.06), prostate (1.07, 1.04–1.10), and leukaemia (1.05, 1.01–1.09). During the study period, acute PM2.5 exposure contributed to an estimated 1,917,994 cancer deaths, ranging from 0 to 6,054 cases in each municipality. Though there has been a consistent downward trend in PM2.5-related all-cancer mortality risks from 2000 to 2019, the impact remains significant, indicating the continued importance of cancer patients avoiding PM2.5 exposure. This nationwide study revealed a notable association between acute PM2.5 exposure and heightened overall and site-specific cancer mortality for the first time to our best knowledge. The findings suggest the importance of considering strategies to minimize such exposure in cancer care guidelines. Environmental ImplicationThe 20-year analysis of nationwide death records in Brazil revealed that heightened short-term exposure to PM2.5 is associated with increased cancer mortality at various sites, although this association has gradually decreased over time. Despite the declining impact, the research highlights the persistent adverse effects of PM2.5 on cancer mortality, emphasizing the importance of continued research and preventive measures to address the ongoing public health challenges posed by air pollution.

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

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

A panel study of exposure to fine particulate matter and modeled respiratory deposition on biomarkers of inflammation, blood coagulation, and oxidative stress in healthy young adults in Hefei, China

Evidence is scarce on the acute cardiovascular health effects of fine particulate matter (PM2.5) and its respiratory tract depositions. We performed a panel study in Hefei, China to explore the relations of PM2.5 and its depositions in 3 respiratory tract regions over different lag times with a range of cardiovascular biomarkers among 40 college students and the potential mediating effects of inflammation. We found significant effects of PM2.5 exposure on the alterations of biomarkers at specific lag days, in a concentration-response manner, even at PM2.5 level below Chinese Ambient Air Quality Standard (CAAQS) Grade I (35 μg/m3). For example, per 10 μg/m3 increase in PM2.5 concentration was associated with −9.33% increase in ADAMTS13 on lag 0-1d. The corresponding increases in D-Dimer, GM-CSF, and MCP-1 for the same change in PM2.5 concentration on lag 5-6d were 30.51%, 11.43%, and 20.05%. Besides, tracheobronchial region (TB) deposition presented more evident effects on these biomarkers than that in PM2.5. Additionally, we observed greater associations of PM2.5-biomarkers among females, those with abnormal body mass index (BMI), and those with physical activity. The results indicated that short-term PM2.5 exposure and its respiratory tract depositions were concentration-responsive associated with damaged cardiovascular health via inducing blood coagulation, oxidative stress and inflammation among healthy young adults even at concentration below CAAQS Grade I. Elevated serum levels of systemic inflammation may be partly responsible for the effects of PM2.5 and its respiratory tract depositions on coagulation function.

Assessment of census-tract level socioeconomic position as a modifier of the relationship between short-term PM2.5 exposure and cardiovascular emergency department visits in Missouri

IntroductionAmbient particulate matter ≤ 2.5 µm in aerodynamic diameter (PM2.5) exposure elevates the risk for cardiovascular disease morbidity (CVDM). The aim of this study is to characterise which area-level measures...