Laboratory evaluation of low-cost air quality monitors and single sensors for monitoring typical indoor emission events in Dutch daycare centers

Understanding particulate matter emissions from cooking meals, health impacts and policy path in Ecuador

Cooking is a major source of indoor air pollution, but little is known about its emissions or health impacts in Ecuadorian households. This study quantified PM₂.₅ and PM₁₀ emissions from six common menus (three fried, three stewed) cooked in a real-life kitchen in Guayaquil lacking natural or mechanical ventilation. Each menu was replicated 30 times, yielding 180 PM concentration profiles. After quality control, 120 profiles were retained for analysis. Median PM₂.₅ and PM₁₀ 24 h concentrations were 16 μg/m3 and 21 μg/m3, respectively-exceeding WHO 24-hour guidelines 16 % for PM₂.₅. Using Disability-Adjusted Life Years (DALYs), the harm from exposure was estimated at 990 DALYs per 100,000 person-years for the analyzed cooking scenarios. These levels indicate quantifiable chronic health risks despite emissions being lower than in other Low Middle Income Countries studies. Findings support the need for indoor air quality guidelines, ventilation strategies, and public health policies tailored to urban Latin American households.

Blockchain and IoT integration for secure short-term and long-term air quality monitoring system using optimized neural network

Accurate air pollution prediction is vital for residents' well-being. This research introduces a secure air quality monitoring system using neural networks and blockchain for robust analysis, precise predictions, and early pollution detection. Blockchain guarantees data integrity, security, and transparency. Goals include real-time air quality data, secure blockchain recording, and enhanced safety through informed decisions. The research integrates blockchain and IoT for short- and long-term air quality monitoring, utilizing an optimized neural network. IoT sensors collect PM2.5, PM10, CO, NO2, and SO2, processed through noise removal and normalization, with feature extraction using N-tuple contrastive learning. Predictions utilize Graph attention-based deep Residual shrinkage Network and Bidirectional long short Term Memory (GRNBTM) categorized into five levels. An adaptive bowerbird algorithm optimizes parameters, reducing computational complexity. Blockchain integration ensures secure, tamper-proof data storage with a lightweight consensus-based algorithm. The GRNBTM model's air quality monitoring performance is extensively simulated and analyzed at 30-min, 2-h, 1-day, and 1-month intervals, demonstrating superior performance over existing techniques.

Species profile of volatile organic compounds emission and health risk assessment from typical indoor events in daycare centers

Daycare centers (DCCs) play an instrumental role in early childhood development, making them a significant indoor environment for a large number of children globally. Amidst routine DCC activities, young children are exposed to a myriad of volatile organic compounds (VOCs), potentially impacting their health. Therefore, this study aims to investigate the VOC emissions during typical DCCs activities and evaluate respective health risk assessments. Employing a full-scale experimental setup within a well-controlled climate chamber, research was conducted into VOC emissions during three typical DCC events: arts-and-crafts (painting, gluing, modeling), cleaning, and sleeping activities tied to mattresses. The research identified 96 distinct VOCs, grouped into twelve categories, from 20 different events examined. Each event exhibited a unique VOC fingerprint, pinpointing potential source tracers. Also, significant variations in VOC emissions from different events were demonstrated. For instance, under cool & dry conditions, acrylic painting recorded high total VOC concentrations of 808 μg/m3, whereas poster painting showed only 58 μg/m3. Given these disparities, the study emphasizes the critical need for carefully selecting arts-and-crafts materials and cleaning agents in DCCs to effectively reduce VOC exposure. It suggests ventilating new mattresses before use and regular mattress check-ups to mitigate VOCs exposure during naps. Importantly, it revealed that certain events resulted in VOC levels exceeding the 10-5 cancer risk thresholds for younger children. Specifically, tetrachloroethylene and styrene from used mattresses in cool & dry conditions, ethylene oxide from new mattresses in warm & humid conditions, and styrene, during sand modeling in both conditions, were the key compounds contributing to this risk. These findings highlight the critical need for age-specific health risk assessments in DCCs. This study highlights the significance of understanding the profiles of VOC emissions from indoor events in DCCs, emphasizing potential health implications and laying a solid foundation for future investigations in this field.

Application of artificial intelligence algorithms and low-cost sensors to estimate respirable dust in the workplace

The Internet of Things (IoT) and low-cost sensor technology have become common tools for environmental exposure monitoring; however, their application in measuring respirable dust (RD) in the workplace remains limited. This study aimed to develop a predictive model for RD using artificial intelligence (AI) algorithms and low-cost sensors and subsequently assess its validity using a standard sampling approach. Various low-cost sensors were combined into an RD sensor module and mounted on a portable aerosol monitor (GRIMM 11-D) for two weeks. AI algorithms were used to capture data per minute over 14 days to establish predictive RD models. The best-fitting model was validated using an aluminum cyclone equipped with an air pump and polytetrafluoroethylene filters to sample the 8-hour RD for 5 days at an aircraft manufacturing company. This module was continuously monitored for two weeks to evaluate its stability. The RD concentration measured by GRIMM 11-D in a general outdoor environment over two weeks was 28.1 ± 16.1 μg/m3 (range: 2.4-85.3 μg/m3). Among the various established models, random forest regression was observed to have the best prediction capacity (R2 = 0.97 and root mean square error = 2.82 μg/m3) in comparison to the other 19 methods. Field-based validation revealed that the predicted RD concentration (35.9 ± 4.1 μg/m3, range: 32.7-42.9 μg/m3) closely approximated the results obtained by the traditional method (38.1 ± 8.9 μg/m3, range: 28.1-52.5 μg/m3), and a strong positive Spearman correlation was observed between the two (rs = 0.70). The average bias was -2.2 μg/m3 and the precision was 5.8 μg/m3, resulting in an accuracy of 6.2 μg/m3 (94.2 %). Data completeness was 99.7 % during the continuous two-week monitoring period. The developed sensor module of RD exhibited excellent predictive performance and good data stability that can be applied to exposure assessments in occupational epidemiological studies.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.