Effect of short-term exposure to air pollution on daily cardio- and cerebrovascular hospitalisations in areas with a low level of air pollution

A novel concern from two sample Mendelian randomization study: The effects of air pollution exposure on the cardiovascular, respiratory, and nervous system

BACKGROUND

Cardiovascular, respiratory, and nervous system diseases have high morbidity and mortality rates, but the causal relationship between air pollution and these diseases remains controversial.

METHODS

We conducted a large-scale genome-wide association (GWAS) study using Mendelian randomization (MR) to investigate the association between air pollution like Nitrogen dioxide (NO2), Nitrogen oxides (NOX), Particulate matter with diameter<2.5μm (PM2.5), Particulate matter with diameter<10μm (PM10) and cardiovascular, respiratory, and nervous system diseases, including acute myocardial infarction, heart failure, asthma, chronic obstructive pulmonary disease (COPD), pneumonia, stroke and Parkinson's disease. This study included 337,199 patients with acute myocardial infarction, 178,726 patients with heart failure, 463,010 patients with asthma, 462,933 patients with COPD, 486,484 patients with pneumonia, 484,598 patients with stroke, and 482,730 patients with Parkinson's disease. All genetic tools were identified from GWAS. The association effects of environmental pollution and these diseases were investigated using MR analysis, sensitivity analysis with heterogeneity, pleiotropy test, and leave-one-out test.

RESULTS

Our MR analysis showed the association between NOX and the development of COPD and stroke (Odds ratio (OR)=1.010, 95 % Confidence interval (CI): 1.000～1.020, P=0.046; OR=1.017, 95 %CI:1.003-1.031, P=0.019), the association between PM2.5 and the development of asthma, COPD and stroke (OR=1.013, 95 %CI:1.003-1.024, P=0.011; OR=1.010, 95 %CI:1.000-1.019, P=0.035; OR=1.019, 95 %CI:1.004-1.033, P=0.012). No significant associations were found between the rest of the air pollution exposures and diseases. Leave-one-out sensitivity analysis showed stable results.

CONCLUSIONS

The study clarifies the relationship between air pollution and cardiovascular, respiratory, and nervous system diseases, providing valuable evidence for environmental pollution prevention and population health monitoring, and provides a clear direction and evidence for the subsequent investigation of the association between air pollution and diseases.

Markov chain quasi-Monte Carlo method for forecasting fire hotspots in Sarawak, Malaysia

Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods. The proposed method can lead to more precise and stable results, particularly in problems with high-dimensional integration or complex probability distributions. The proposed method is applied to a case study of fire hotspot detection in Sarawak, Malaysia. The outcome of this study reveals that the MCQMC method is more computationally efficient, taking only 0.0746 seconds compared to MCMC's 0.0914 seconds and QMC's 0.0994 seconds. It is shown that the best option derived by the proposed method is effective in predicting fire hotspots and providing quick solutions to protect the environment and communities from forest fires.

Layer-Dependent Sensing Performance of WS2-Based Gas Sensors

Two-dimensional (2D) materials, such as tungsten disulfide (WS2), have attracted considerable attention for their potential in gas sensing applications, primarily due to their distinctive electrical properties and layer-dependent characteristics. This research explores the impact of the number of WS2 layers on the ability to detect gases by examining the layer-dependent sensing performance of WS2-based gas sensors. We fabricated gas sensors based on WS2 in both monolayer and multilayer configurations and methodically evaluated their response to various gases, including NO2, CO, NH3, and CH4 at room temperature and 50 degrees Celsius. In contrast to the monolayer counterpart, the multilayer WS2 sensor exhibits enhanced gas sensing performance at higher temperatures. Furthermore, a comprehensive gas monitoring system was constructed employing these WS2-based sensors, integrated with additional electronic components. To facilitate user access to data and receive alerts, sensor data were transmitted to a cloud-based platform for processing and storage. This investigation not only advances our understanding of 2D WS2-based gas sensors but also underscores the importance of layer engineering in tailoring their sensing capabilities for diverse applications. Additionally, the development of a gas monitoring system employing 2D WS2 within this study holds significant promise for future implementation in intelligent, efficient, and cost-effective sensor technologies.