Diurnal trends of indoor and outdoor fluorescent biological aerosol particles in a tropical urban area

Spectroscopic detection of bioaerosols with the wibs-4+: Anthropogenic and meteorological impacts

This research builds upon a previous study that explored the potential of the modified WIBS-4+ to selectively differentiate and detect different bioaerosol classes. The current work evaluates the influence of meteorological and air quality parameters on bioaerosol concentrations, specifically pollen and fungal spore dynamics. Temperature was found to be the most influential parameter in terms of pollen production and release, showing a strong positive correlation. Wind data analysis provided insights into the potential geographic origins of pollen and fungal spore concentrations. Fungal spores were primarily shown to originate from a westerly direction, corresponding to agricultural land use, whereas pollen largely originated from a North-easterly direction, corresponding to several forests. The influence of air quality was also analysed to understand its potential impact on the WIBS fluorescent parameters investigated. Most parameters had a negative association with fungal spore concentrations, whereas several anthropogenic influences showed notable positive correlations with daily pollen concentrations. This is attributed to similar driving forces (meteorological parameters) and geographical origins. In addition, the WIBS showed a significant correlation with anthropogenic pollutants originating from combustion sources, suggesting the potential for such modified spectroscopic instruments to be utilized as air quality monitors. By combining all meteorological and pollution data along with WIBS-4+ channel data, a set of Multiple Linear Regression (MLR) analyses were completed. Successful results with R2 values ranging from 0.6 to 0.8 were recorded. The inclusion of meteorological parameters was dependent on the spore or pollen type being examined.

Particle characterization in commercial buildings: A cross-sectional study in 40 offices in Singapore

There is a knowledge gap in understanding how existing office buildings are protecting occupants from exposure to particles from both indoor and outdoor sources. We report a cross-sectional study involving weekly measurements of size-resolved indoor and outdoor particle concentrations in forty commercial building offices in Singapore. The outdoor and indoor particles size distributions were single mode with daytime peak number concentrations at 36.5 nm and 48.7 nm. Outdoor concentrations were significantly greater than indoors for all particle diameters. Indoor particle concentrations were generally low due to: 1) relatively high indoor particle removal (IPR) rates; 2) low indoor source strengths; and 3) low indoor particle of outdoor proportion (IPOP). We found that the ventilation system type had a substantial effect on indoor particle levels, IPR and IPOP. Through linear mixed model analyses, we identified dependencies of IPR rates with the use of MERV13 filters in supply air and filter maintenance frequency, IPOP with the use of MERV13 filters in the fresh air and supply air ducts and low particle source strength with regular daily cleaning presumably due to dust reservoir removal. Lastly, the contribution of outdoor sources was mainly seen for ultrafine and fine particles but less pronounced for coarse particles. This study provided detailed understanding of particle exposure in building offices and their influencing factors, facilitating future research on health impact of particle exposures.

Aeromicrobiology: A global review of the cycling and relationships of bioaerosols with the atmosphere

Airborne microorganisms and biological matter (bioaerosols) play a key role in global biogeochemical cycling, human and crop health trends, and climate patterns. Their presence in the atmosphere is controlled by three main stages: emission, transport, and deposition. Aerial survival rates of bioaerosols are increased through adaptations such as ultra-violet radiation and desiccation resistance or association with particulate matter. Current research into modern concerns such as climate change, global gene transfer, and pathogenicity often neglects to consider atmospheric involvement. This comprehensive review outlines the transpiring of bioaerosols across taxa in the atmosphere, with significant focus on their interactions with environmental elements including abiotic factors (e.g., atmospheric composition, water cycle, and pollution) and events (e.g., dust storms, hurricanes, and wildfires). The aim of this review is to increase understanding and shed light on needed research regarding the interplay between global atmospheric phenomena and the aeromicrobiome. The abundantly documented bacteria and fungi are discussed in context of their cycling and human health impacts. Gaps in knowledge regarding airborne viral community, the challenges and importance of studying their composition, concentrations and survival in the air are addressed, along with understudied plant pathogenic oomycetes, and archaea cycling. Key methodologies in sampling, collection, and processing are described to provide an up-to-date picture of ameliorations in the field. We propose optimization to microbiological methods, commonly used in soil and water analysis, that adjust them to the context of aerobiology, along with other directions towards novel and necessary advancements. This review offers new perspectives into aeromicrobiology and calls for advancements in global-scale bioremediation, insights into ecology, climate change impacts, and pathogenicity transmittance.

The association of in-utero exposure to air pollution and atherogenic index of plasma in newborns

BACKGROUND

Prenatal exposure to particulate matter (PM) and traffic was associated with the programming of cardiovascular diseases (CVDs) in early life. However, the exact underlying mechanisms are not fully understood. Therefore, we aimed to evaluate the association between in-utero exposure to PMs and traffic indicators with the atherogenic index of plasma (AIP) in newborns, which is a precise index reflecting an enhancement of lipid risk factors for CVDs.

METHODS

In this cross-sectional study, a total of 300 mother-newborn pairs were enrolled in Sabzevar, Iran. Spatiotemporal land-use regression models were used to estimate the level of PM1, PM2.5 and PM10 at the mother's residential address. The total length of streets in different buffers (100,300 and 500m) and proximity to major roads were calculated as indicators of traffic. The AIP of cord blood samples was calculated using an AIP calculator. Multiple linear regression models were used to examine the association of PM concentrations as well as traffic indicators with AIP controlled for relevant covariates.

RESULTS

PM2.5 exposure was significantly associated with higher levels of AIP in newborns. Each interquartile range (IQR) increment of PM2.5 concentration at the mothers' residential addresses was associated with a 5.3% (95% confidence interval (CI): 0.0, 10.6%, P = 0.04) increase in the AIP. Associations between PM1, PM10 and traffic indicators with cord blood level of AIP were positive but not statistically significant.

CONCLUSION

Our findings showed that in utero exposure to PM2.5 may be associated with CVDs programming through the increase of atherogenic lipids.

Effect of Air Pollution on the Basal DNA Damage of Mother-Newborn Couples of México City

Environmental pollution of megacities can cause early biological damage such as DNA strand breaks and micronuclei formation. Comet assay tail length (TL) reflects exposure in the uterus to high levels of air pollution, primarily ozone and air particles (PM10), including mothers' smoking habits during pregnancy, conditions which can lead to low birth weight. In this biomonitoring study, we evaluated basal DNA damage in the cord blood cells of newborn children from Mexico City. We found a correlation between DNA damage in mothers and their newborns, including various parameters of environmental exposure and complications during pregnancy, particularly respiratory difficulties, malformations, obstetric trauma, neuropathies, and nutritional deficiencies. Mothers living in the southern part of the city showed double DNA damage compared to those living in the northern part (TL 8.64 μm vs. 4.18 μm, p < 0.05). Additionally, mothers' DNA damage correlates with exposure to NOx (range 0.77-1.52 ppm) and PM10 (range 58.32-75.89 μg/m3), as well maternal age >29. These results highlight the sensitivity of the comet assay in identifying differential in utero exposure for newborns whose mothers were exposed during pregnancy. They also suggest the importance of antioxidants during pregnancy and the role of the placental barrier in protecting the newborn from the DNA-damaging effects of oxidative pollution.

Environmental sampling for disease surveillance: Recent advances and recommendations for best practice

The study of infectious diseases includes both the progression of the disease in its host and how it transmits between hosts. Understanding disease transmission is important for recommending effective interventions, protecting healthcare workers, and informing an effective public health response. Sampling the environment for infectious diseases is critical to public health since it can provide an understanding of the mechanisms of transmission, characterization of contamination in hospitals and other public areas, and the spread of a disease within a community. Measurements of biological aerosols, particularly those that may cause disease, have been an ongoing topic of research for decades, and so a wide variety of technological solutions exist. This wide field of possibilities can create confusion, particularly when different approaches yield different answers. Therefore, guidelines for best practice in this area are important to allow more effective use of this data in public health decisions. This review examines air, surface and water/wastewater sampling methods, with a focus on aerosol sampling, and a goal of recommending approaches to designing and implementing sampling systems that may incorporate multiple strategies. This is accomplished by developing a framework for designing and evaluating a sampling strategy, reviewing current practices and emerging technologies for sampling and analysis, and recommending guidelines for best practice in the area of aerosol sampling for infectious disease.

Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication

People generally spend most of their time indoors, making indoor air quality be of great significance to human health. Large spatiotemporal heterogeneity of indoor air pollution can be hardly captured by conventional filter-based monitoring but real-time monitoring. Real-time monitoring is conducive to change air assessment mode from static and sparse analysis to dynamic and massive analysis, and has made remarkable strides in indoor air evaluation. In this review, the state of art, strengths, challenges, and further development of real-time sensors used in indoor air evaluation are focused on. Researches using real-time sensors for indoor air evaluation have increased rapidly since 2018, and are mainly conducted in China and the USA, with the most frequently investigated air pollutants of PM_2.5. In addition to high spatiotemporal resolution, real-time sensors for indoor air evaluation have prominent advantages in 3-dimensional monitoring, pollution peak and source identification, and short-term health effect evaluation. Huge amounts of data from real-time sensors also facilitate the modeling and prediction of indoor air pollution. However, challenges still remain in extensive deployment of real-time sensors indoors, including the selection, performance, stability, as well as calibration of sensors. In future, sensors with high performance, long-term stability, low price, and low energy consumption are welcomed. Furthermore, more target air pollutants are also expected to be detected simultaneously by real-time sensors in indoor air monitoring.

Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data

Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM_2.5 and PM₁₀) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of ∼ 60 %-80 % for bioaerosols and ∼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time.

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (T_In), indoor relative humidity (RH_In), area of opening (A_O), number of occupants (O), area per person (A_P), volume per person (V_P), CO₂ concentration (CO₂), air quality index (AQI), outer wind speed (W_S), outdoor temperature (T_Out), outdoor humidity (RH_Out), fan air speed (F_S), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO₂ level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.