Analysis of spatial factors, time-activity and infiltration on outdoor generated PM2.5 exposures of school children in five European cities

Empowering lung cancer treatment: Harnessing the potential of natural phytoconstituent-loaded nanoparticles

Lung cancer, the second leading cause of cancer-related deaths, accounts for a substantial portion, representing 18.4% of all cancer fatalities. Despite advances in treatment modalities such as chemotherapy, surgery, and immunotherapy, significant challenges persist, including chemoresistance, non-specific targeting, and adverse effects. Consequently, there is an urgent need for innovative therapeutic approaches to overcome these limitations. Natural compounds, particularly phytoconstituents, have emerged as promising candidates due to their potent anticancer properties and relatively low incidence of adverse effects compared to conventional treatments. However, inherent challenges such as poor solubility, rapid metabolism, and enzymatic degradation hinder their clinical utility. To address these obstacles, researchers have increasingly turned to nanotechnology-based drug delivery systems (DDS). Nanocarriers offer several advantages, including enhanced drug stability, prolonged circulation time, and targeted delivery to tumor sites, thereby minimizing off-target effects. By encapsulating phytoconstituents within nanocarriers, researchers aim to optimize their bioavailability and therapeutic efficacy while reducing systemic toxicity. Moreover, the integration of nanotechnology with phytoconstituents allows for a nuanced understanding of the intricate molecular pathways involved in lung cancer pathogenesis. This integrated approach holds promise for modulating key cellular processes implicated in tumor growth and progression. Additionally, by leveraging the synergistic effects of phytoconstituents and nanocarriers, researchers seek to develop tailored therapeutic strategies that maximize efficacy while minimizing adverse effects. In conclusion, the integration of phytoconstituents with nanocarriers represents a promising avenue for advancing lung cancer treatment. This synergistic approach has the potential to revolutionize current therapeutic paradigms by offering targeted, efficient, and minimally toxic interventions. Continued research in this field holds the promise of improving patient outcomes and addressing unmet clinical needs in lung cancer management.

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.

An overview of atmospheric aerosol and their effects on human health

Epidemiologic investigations have previously been published in more than 200 papers, and several studies have examined the impacts of particle air pollution on health. The main conclusions now being made about the epidemiological evidence of particle pollution-induced health impacts are discussed in this article. Although there is no universal agreement, most reviewers conclude that particulate air pollution, particularly excellent combustion-cause contamination prevalent in many municipal and manufacturing environments, is a significant risk for cardiopulmonary sickness and mortality. Most epidemiological research has concentrated on the impacts of acute exposure, although the total public health implications of chronic acquaintance's outcome may be more extraordinarily significant. According to some reviewers, prolonged, repeated exposure raises the risk of cardiorespiratory death and chronic respiratory illness. A more general (but still universal) agreement is that short-term particle pollution exposure has been shown to aggravate pre-existing pulmonary and cardiovascular diseases and increase the number of community members who become sick, require medical treatment, or die. Several in-depth studies conducted in the global and Indian regions are addressed.

Air Pollution and Lung Cancer: A Review by International Association for the Study of Lung Cancer Early Detection and Screening Committee

INTRODUCTION

The second leading cause of lung cancer is air pollution. Air pollution and smoking are synergistic. Air pollution can worsen lung cancer survival.

METHODS

The Early Detection and Screening Committee of the International Association for the Study of Lung Cancer formed a working group to better understand issues in air pollution and lung cancer. These included identification of air pollutants, their measurement, and proposed mechanisms of carcinogenesis. The burden of disease and the underlying epidemiologic evidence linking air pollution to lung cancer in individuals who never and ever smoked were summarized to quantify the problem, assess risk prediction models, and develop recommended actions.

RESULTS

The number of estimated attributable lung cancer deaths has increased by nearly 30% since 2007 as smoking has decreased and air pollution has increased. In 2013, the International Agency for Research on Cancer classified outdoor air pollution and particulate matter with aerodynamic diameter less than 2.5 microns in outdoor air pollution as carcinogenic to humans (International Agency for Research on Cancer group 1) and as a cause of lung cancer. Lung cancer risk models reviewed do not include air pollution. Estimation of cumulative exposure to air pollution exposure is complex which poses major challenges with accurately collecting long-term exposure to ambient air pollution for incorporation into risk prediction models in clinical practice.

CONCLUSIONS

Worldwide air pollution levels vary widely, and the exposed populations also differ. Advocacy to lower sources of exposure is important. Health care can lower its environmental footprint, becoming more sustainable and resilient. The International Association for the Study of Lung Cancer community can engage broadly on this topic.

Trace elements in indoor dust-Children's health risk considering overall daily exposure

Indoor dust presents an important source of daily exposure to toxic elements. The present study reports for the first time the levels of Al, As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sb, Sn, Se, Sr, Tl, V, and Zn measured in dust samples collected from 10 kindergartens and 21 cars from Zagreb, Croatia. Based on the obtained data, we assessed the health risks from overall daily exposure to trace elements for children aged 2-6 years taking into account three pathways of dust intake-ingestion, dermal absorption, and inhalation. The median concentration of most elements was significantly higher in dust obtained from cars compared to kindergartens, especially in the cases of Co (11.62 vs. 3.60 mg kg^-1 ), Cr (73.55 vs. 39.89 mg kg^-1 ), Cu (186.33 vs. 26.01 mg kg^-1 ), Mo (8.599 vs. 0.559 mg kg^-1 ), Ni (37.05 vs. 17.38 mg kg^-1 ), and Sn (9.238 vs. 1.159 mg kg^-1 ). Oral intake was identified as the most important exposure pathway, except for Cr, Ni, and Sb where dermal contact was the main route of exposure. Health risk assessment indicated that no adverse effects are expected from overall exposure to trace elements. Although the cases of high exposure to toxic elements are not common in areas with no significant environmental pollutants, due to the health threat they may present even at low levels, their status should be carefully monitored.

Estimating risk factor attributable burden - challenges and potential solutions when using the comparative risk assessment methodology

Background

Burden of disease analyses quantify population health and provide comprehensive overviews of the health status of countries or specific population groups. The comparative risk assessment (CRA) methodology is commonly used to estimate the share of the burden attributable to risk factors. The aim of this paper is to identify and address some selected important challenges associated with CRA, illustrated by examples, and to discuss ways to handle them. Further, the main challenges are addressed and finally, similarities and differences between CRA and health impact assessments (HIA) are discussed, as these concepts are sometimes referred to synonymously but have distinctly different applications.

Results

CRAs are very data demanding. One key element is the exposure-response relationship described e.g. by a mathematical function. Combining estimates to arrive at coherent functions is challenging due to the large variability in risk exposure definitions and data quality. Also, the uncertainty attached to this data is difficult to account for. Another key issue along the CRA-steps is to define a theoretical minimal risk exposure level for each risk factor. In some cases, this level is evident and self-explanatory (e.g., zero smoking), but often more difficult to define and justify (e.g., ideal consumption of whole grains). CRA combine all relevant information and allow to estimate population attributable fractions (PAFs) quantifying the proportion of disease burden attributable to exposure. Among many available formulae for PAFs, it is important to use the one that allows consistency between definitions, units of the exposure data, and the exposure response functions. When combined effects of different risk factors are of interest, the non-additive nature of PAFs and possible mediation effects need to be reflected. Further, as attributable burden is typically calculated based on current exposure and current health outcomes, the time dimensions of risk and outcomes may become inconsistent. Finally, the evidence of the association between exposure and outcome can be heterogeneous which needs to be considered when interpreting CRA results.

Conclusions

The methodological challenges make transparent reporting of input and process data in CRA a necessary prerequisite. The evidence for causality between included risk-outcome pairs has to be well established to inform public health practice.

Estimations of infiltration factors of diurnal PM2.5 and heavy metals in children's bedrooms

Children are the sensitive population to fine particulate matter (PM_2.5 ) exposure and spend most of their time in bedroom. Infiltration factor (F_inf ) can be used to calculate the fraction of total indoor PM_2.5 with outdoor origin to increase the accuracy of exposure assessment. However, studies have ignored the diurnal variations of PM_2.5 F_inf values, and a few studies have estimated F_inf values for heavy metals in PM_2.5 in children's bedrooms. To calculate the PM_2.5 F_inf , real-time indoor and outdoor PM_2.5 concentrations and occupants' activities were collected in 56 study bedrooms. At 22 of the 56 study bedrooms, PM_2.5 samples were also collected for heavy metals analysis. We noted the PM_2.5 F_inf was higher during the daytime (0.70 ± 0.23) than nighttime (0.54 ± 0.27) during the hot season, and the time of air conditioner use was longer at nighttime. The largest F_inf value of heavy metal was V (0.88 ± 0.25), followed by Pb (0.85 ± 0.28), Mn (0.72 ± 0.26), Cr (0.69 ± 0.35), and Zn (0.61 ± 0.32), with a larger variation. Our findings suggest that the estimations of diurnal PM_2.5 and heavy metals F_inf values are necessary to increase the accuracy of exposure assessment.

Contribution of Aerosol Sources to Health Impacts

Atmospheric aerosol is one of the major leading environmental risk factors for human health worldwide, potentially causing several million premature deaths per year [...]