A model for estimating the lifelong exposure to PM2.5 and NO2 and the application to population studies

Impact of lifetime air pollution exposure patterns on the risk of chronic disease

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM_2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM_2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .¹.

Analyzing Characteristics of Particulate Matter Pollution in Open-Pit Coal Mines: Implications for Green Mining

The particulate pollution in the open-pit coal mines of China is particularly severe in winter. The aim of this study is to understand the pollution characteristics of particulate matter (PM) in winter and provide a basis for the prevention and control of particulate pollution. We took the problem of PM concentration at the bottom of the Haerwusu Open-pit Coal Mine (HOCM) as the research object. Dust monitoring equipment at two measurement points at different heights were positioned for continuous monitoring of the PM concentration. The data for three months were gathered. Statistical analyses were performed to analyze the variation characteristics of the PM and its relationship with meteorological factors. The results show that the average PM concentration in the study area is below the average daily limit of the China National Ambient Air Quality Standard (GB 3095-2012). However, the average concentration of PM10 exceeded the national limit in December. The order of PM concentration is observed as December > January > February. The correlation of PM is found to be positive with humidity and negative with wind speed. Temperature is found to be positively correlated with PM in December, while it is negative in January. At the same time, the temperature difference in December is negatively correlated with PM concentration. Under the combined action of multiple meteorological factors, the magnitude of the impact on the PM concentration at the bottom of the pit in winter is humidity > temperature > wind speed > temperature difference (inverse temperature intensity). In conclusion, PM2.5 is found to be more sensitive to environmental factors. The results of this study are particularly useful to progress in green mining.

A visible-light-responsive TaON/CdS photocatalytic film with a ZnS passivation layer for highly extraordinary NO2 photodegradation

Recently, TaON has become a promising photoelectrode material in the photocatalytic field owing to its suitable band gap and superior charge carrier transfer ability. In this work, we prepared a TaON/CdS photocatalytic film using a CdS nanoparticle-modified TaON film by the successive ionic layer adsorption and reaction (SILAR) method. For the first time, the ZnS nanoparticles were deposited on the TaON/CdS film using the same method. We found that pure TaON had a nanoporous morphology, thus resulting in high specific surface area and better gas adsorption capacity. Furthermore, the TaON/CdS/ZnS film displayed a highly efficient NO2 photodegradation rate under visible light irradiation owing to its stronger visible light response, photocorrosion preventive capacity, and the high separation efficiency of photo-induced electrons and holes. Interestingly, the promising TaON/CdS/ZnS film also possessed remarkable recyclability for NO2 degradation. Therefore, we suggest that the TaON/CdS/ZnS photocatalytic film might be used for the photocatalytic degradation of other pollutants or in other applications. We also put forward the feasible NO2 photocatalytic degradation mechanism for the TaON/CdS/ZnS film. From the schematic diagram, we could further obtain the photo-generated carrier transport process and NO2 photodegradation principle in detail over the ternary photocatalytic film. Moreover, the trapping experiment demonstrates that ·O2 - and h+ all play significant roles in NO2 degradation under visible light irradiation.