Metal-organic frameworks based on pyrazolates for the selective and efficient capture of formaldehyde

Indoor air pollution is one of the major threads in developed countries, notably due to high concentrations of formaldehyde, a harmful molecule difficult to eliminate. Addressing this purification challenge while adhering to the principles of sustainable development requires the use of innovative, advanced sustainable materials. Here we show that by combining state-of-the-art spectroscopic techniques with density-functional theory molecular simulations, we have developed an advantageous mild chemisorption synergistic mechanism using porous metal (III or IV) pyrazole- di-carboxylate based metal-organic framework (MOF) to trap formaldehyde in a reversible manner, without incurring significant energy penalties for regeneration. A straightforward, environmentally friendly, and scalable synthesis protocol was established for the porous, water-stable aluminum pyrazole dicarboxylate known as Al-3.5-PDA or MOF-303, capable of functioning as a highly efficient and reusable filter. It demonstrates selectivity and high storage capacity for formaldehyde under conditions typical of severe indoor use, such as in housing or vehicle cockpits, including varying VOC mixtures and concentrations, humidity, and temperature, without any accidental release. Furthermore, we have successfully regenerated this sorbent using a simple domestic protocol, ensuring the material reusability for at least 10 cycles.

Effect of formaldehyde exposure on bacterial communities in simulating indoor environments

Indoor formaldehyde (CH₂O) exceeding the recommended level is a severe threat to human health. Few studies have investigated its effect on indoor surface bacterial communities, affecting habitants' health. This study used 20-L glass containers to mimic the indoor environment with bacterial inputs from human oral respiration. The behavior of bacterial communities responding to CH₂O varied among the different CH₂O levels. The bacterial community structure significantly changed over time in the 0.054 mg·m^-3 CH₂O group, which varied from the 0.1 mg·m^-3 and 0.25 mg·m^-3 CH₂O groups. The Chao1 and Shannon index significantly increased in the 0.054 mg·m^-3 CH₂O group at 6 week, while they remained unchanged in the 0.25 mg·m^-3 CH₂O group. At 12 week, the Chao1 significantly increased in the 0.25 mg·m^-3 CH₂O group, while it remained unchanged in the 0.054 mg·m^-3 CH₂O group. Only a few Operational Taxonomic Units (OTUs) significantly correlated with the CH₂O concentration. CH₂O-induced OTUs mainly belong to the Proteobacteria and Firmicutes. Furthermore, bacterial communities formed at 6 or 12 weeks differed significantly among different CH₂O levels. Functional analysis of bacterial communities showed that inferred genes related to chemical degradation and diseases were the highest in the 0.25 mg·m^-3 CH₂O group at 12 weeks. The development of nematodes fed with bacteria collected at 12 weeks was applied to evaluate the bacterial community's hazards. This showed significantly impaired growth in the 0.1 mg·m^-3 and 0.25 mg·m^-3 CH₂O groups. These findings confirmed that CH₂O concentration and exposure time could affect the indoor bacterial community and formed bacterial communities with a possibly more significant hazard to human health after long-term exposure to high CH₂O levels.

Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence

Due to recent advancements in industrialization, climate change and overpopulation, air pollution has become an issue of global concern and air quality is being highlighted as a social issue. Public interest and concern over respiratory health are increasing in terms of a high reliability of a healthy life or the social sustainability of human beings. Air pollution can have various adverse or deleterious effects on human health. Respiratory diseases such as asthma, the subject of this study, are especially regarded as ‘directly affected’ by air pollution. Since such pollution is derived from the combined effects of atmospheric pollutants and meteorological environmental factors, and it is not easy to estimate its influence on feasible respiratory diseases in various atmospheric environments. Previous studies have used clinical and cohort data based on relatively a small number of samples to determine how atmospheric pollutants affect diseases such as asthma. This has significant limitations in that each sample of the collections is likely to produce inconsistent results and it is difficult to attempt the experiments and studies other than by those in the medical profession. This study mainly focuses on predicting the actual asthmatic occurrence while utilizing and analyzing the data on both the atmospheric and meteorological environment officially released by the government. We used one of the advanced analytic models, often referred to as the vector autoregressive model (VAR), which traditionally has an advantage in multivariate time-series analysis to verify that each variable has a significant causal effect on the asthmatic occurrence. Next, the VAR model was applied to a deep learning algorithm to find a prediction model optimized for the prediction of asthmatic occurrence. The average error rate of the hybrid deep neural network (DNN) model was numerically verified to be about 8.17%, indicating better performance than other time-series algorithms. The proposed model can help streamline the national health and medical insurance system and health budget management in South Korea much more effectively. It can also provide efficiency in the deployment and management of the supply and demand of medical personnel in hospitals. In addition, it can contribute to the promotion of national health, enabling advance alerts of the risk of outbreaks by the atmospheric environment for chronic asthma patients. Furthermore, the theoretical methodologies, experimental results and implications of this study will be able to contribute to our current issues of global change and development in that the meteorological and environmental data-driven, deep-learning prediction model proposed hereby would put forward a macroscopic directionality which leads to sustainable public health and sustainability science.