The decay of airborne bacteria and fungi in a constant temperature and humidity test chamber

Impact of haze on potential pathogens in surface bioaerosol in urban environments

Air quality considerably affects bioaerosol dynamics within the atmosphere. Frequent haze events, with their associated alterations in bioaerosol composition, may pose potential health risks. This study investigated the microbial diversity, community structure, and factors of PM2.5 within an urban environment. We further examined the impact of haze on potentially pathogenic bacteria in bioaerosols, and analyzed the sources of haze pollution. Key findings revealed that the highest levels of microbial richness and diversity were associated with lightly polluted air conditions. While the overall bacterial community structure remained relatively consistent across different air quality levels, the relative abundance of specific bacterial taxa exhibited variations. Meteorological and environmental conditions, particularly sulfur dioxide, nitrogen dioxide, and carbon monoxide, exerted a greater influence on bacterial diversity and community structure compared to the physicochemical properties of the PM2.5 particles themselves. Notably, haze events were observed to strengthen interactions among airborne pathogens. Stable carbon isotope analysis suggested that coal combustion and automobile exhaust were likely to represent the primary source of haze during winter months. These findings indicate that adoption of clean energy alternatives such as natural gas and electricity, and the use of public transportation, is crucial to mitigate particle and harmful pollutant emissions, thereby protecting public health.

Variability in Microbial Communities Driven by Particulate Matter on Human Facial Skin

Microbial communities are known to play an important role in maintaining ecological balance and can be used as an indicator for assessing environmental pollution. Numerous studies have revealed that air pollution can alter the structure of microbial communities, which may increase health risks. Nevertheless, the relationships between microbial communities and particulate matter (PM) caused by air pollution in terms of health risk assessment are not well understood. This study aimed to validate the influences of PM chemical compositions on microbial communities and assess the associated health risks. Our results, based on similarity analysis, revealed that the stability structure of the microbial communities had a similarity greater than 73%. In addition, the altered richness and diversity of microbial communities were significantly associated with PM chemical compositions. Volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) exerted a positive influence on microbial communities in different environmental variables. Additionally, a stronger linear correlation was observed between hydroxyl radicals (·OH) and the richness of microbial communities. All estimated health risks from PM chemical compositions, calculated under different environmental variables, significantly exceeded the acceptable level by a factor of more than 49. Cr and 1,2-Dibromoethane displayed dual adverse effects of non-carcinogenic and carcinogenic risks. Overall, the study provides insights into the fundamental mechanisms of the variability in microbial communities driven by PM, which may support the crucial role of PM chemical compositions in the risk of microorganisms in the atmospheric environment.

Full-length 16S rRNA gene sequencing and machine learning reveal the bacterial composition of inhalable particles from two different breeding stages in a piggery

Bacterial loading aggravates the harm of particulate matter (PM) to public health and ecological systems, especially in operations of concentrated animal production. This study aimed to explore the characteristics and influencing factors of bacterial components of inhalable particles at a piggery. The morphology and elemental composition of coarse particles (PM₁₀, aerodynamic diameter ≤ 10 µm) and fine particles (PM_2.5, aerodynamic diameter ≤ 2.5 µm) were analyzed. Full-length 16 S rRNA sequencing technology was used to identify bacterial components according to breeding stage, particle size, and diurnal rhythm. Machine learning (ML) algorithms were used to further explore the relationship between bacteria and the environment. The results showed that the morphology of particles in the piggery differed, and the morphologies of the suspected bacterial components were elliptical deposited particles. Full-length 16 S rRNA indicated that most of the airborne bacteria in the fattening and gestation houses were bacilli. The analysis of beta diversity and difference between samples showed that the relative abundance of some bacteria in PM_2.5 was significantly higher than that in PM₁₀ at the same pig house (P < 0.01). There were significant differences in the bacterial composition of inhalable particles between the fattening and gestation houses (P < 0.01). The aggregated boosted tree (ABT) model showed that PM_2.5 had a great influence on airborne bacteria among air pollutants. Fast expectation-maximization microbial source tracking (FEAST) showed that feces was a major potential source of airborne bacteria in pig houses (contribution 52.64-80.58 %). These results will provide a scientific basis for exploring the potential risks of airborne bacteria in a piggery to human and animal health.

Characteristics and Traceability Analysis of Microbial Assemblage in Fine Particulate Matter from a Pig House

Fine particulate matter (PM2.5) can carry numerous substances and penetrate deep into the respiratory tract due to its small particle size; associated harmful microorganisms are suspected to increase health risks for humans and animals. To find out the microbial compositions of PM2.5 in piggeries, their interaction and traceability, we collected PM2.5 samples from a piggery while continuously monitoring the environmental indicators. We also identified pathogenic bacteria and allergens in the samples using high-throughput sequencing technology. We analyzed the microbial differences of PM2.5 samples at different heights and during different times of day and investigated the microbial dynamics among the PM2.5 samples. To better understand the interaction between microorganisms and environmental factors among different microbial communities, we applied the network analysis method to identify the correlation among various variables. Finally, SourceTracker, a commonly used microbial traceability tool, was used to predict the source of airborne microorganisms in the pig house. We identified 14 potential pathogenic bacteria and 5 allergens from PM2.5 in the pig houses, of which Acinetobacter was the dominant bacterium in all samples (relative abundance > 1%), which warrants attention. We found that bacteria and fungi directly affected the the microbial community. The bacterial community mainly played a positive role in the microbial community. Environmental variables mainly indirectly and positively affected microbial abundance. In the SourceTracker analysis using fecal matter and feed as sources and PM2.5 sample as sink, we found that fecal matter made the greatest contribution to both bacterial and fungal components of PM2.5. Our findings provide important insights into the potential risks of pathogens in PM2.5 to human and animal health and their main sources.

Fungal Diversity and Its Relationship with Environmental Factors in Coastal Sediments from Guangdong, China

As one core of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), Guangdong is facing some serious coastal environmental problems. Fungi are more vulnerable to changes in coastal environments than bacteria and archaea. This study investigated the fungal diversity and composition by high-throughput sequencing and detected basic parameters of seven environmental factors (temperature, dissolved oxygen, pH, salinity, total organic carbon, total nitrogen, and total phosphorus) at 11 sites. A total of 2056 fungal operational taxonomic units (OTUs) belonging to 147 genera in 6 phyla were recovered; Archaeorhizomyces (17.5%) and Aspergillus (14.19%) were the most dominant genera. Interestingly, a total of 14 genera represented the first reports of coastal fungi in this study. Furthermore, there were nine genera of fungi that were significantly correlated with environmental factors. FUNGuild analysis indicated that saprotrophs and pathogens were the two trophic types with the highest proportions. Saprotrophs were significantly correlated with total organic carbon (TOC), total nitrogen (TN), and total phosphorus (TP), while pathogens were significantly correlated with pH. This study provides new scientific data for the study of the diversity and composition of fungal communities in coastal ecosystems.

Kinetics and mechanism analysis on self-decay of airborne bacteria:biological and physical decay under different temperature

Bioaerosol as an important medium has aroused widespread concern on its potential hazards in disease transmission and environment biosafety. However, little is known about the duration and self-decay of airborne bacteria in the atmosphere environment. Further, the self-decay process is proposed to include biological-decay and physical-decay. At present, there are many reports on the bacteria apoptosis mechanism and airborne particle migration. However, few studies focus on self-decay during the physical movement of airborne bacteria. The present study investigated self-decay laws and efficiencies of airborne bacteria in the sealed reactor under room temperature (18 ± 2 °C, RT) and low temperature (3 ± 2 °C, LT). The self-decay rate constants of 0.0089, 0.0133, 0.0092, and 0.0122 min^-1 were obtained under RT-E. coli, LT-E. coli, RT-S. aureus and LT-S. aureus, respectively. There was no significant difference between the self-decay efficiency of gram-negative and gram-positive bacteria under the same conditions. Nevertheless, gram-negative bacteria were more sensitive to temperature change compared with gram-positive bacteria, where the self-decay efficiency of gram-negative under LT was 49% higher than that under RT, and the value of gram-positive was 32% at the same condition. Furthermore, the laws of biological-decay and physical-decay conformed to the first-order kinetic model by theoretical derivation. Biological-decay accounted for 59.5% at RT and 88.5% at LT among self-decay, which is mainly caused by energy absorption, environmental stress, and bacterial structure changes. Physical-decay mainly caused by gravity settlement accounting for 40% at RT and 10% at LT among self-decay, approximately. Meanwhile, the influence of environmental factors on self-decay was mainly reflected in the biological-decay process. Overall, it is of great significance for clarifying the changing laws of bioaerosol and controlling the transmission of airborne bacteria.

Effect of inlet-outlet configurations on the cross-transmission of airborne bacteria between animal production buildings

Cross-transmission of airborne pathogens between buildings facilitates the spread of both human and animal diseases. Rational spatial arrangement of buildings and air inlet-outlet design are well-established preventive measures, but the effectiveness of current configurations for mitigating pathogens cross-transmission is still under assessment. An intensive field study in a laying hen farm was conducted to elucidate the spatial distribution of airborne bacteria (AB) and the source of AB at the inlets under different wind regimes. We found higher concentrations of AB at the interspace and sidewall inlets of buildings with sidewall exhaust systems than at those with endwall exhaust systems. We observed significant differences in bacterial diversity and richness at the interspace and sidewall inlets between buildings with side exhaust systems and those with endwall exhaust systems. We further found that the AB emitted from buildings could translocate to the sidewall inlets of adjacent building to a greater extent between buildings with sidewall exhaust systems than between those with endwall exhaust systems. Our findings revealed that sidewall exhaust systems aggravate cross-transmission of AB between buildings, suggesting that endwall exhaust systems or other compensatory preventive measures combined with sidewall exhaust systems could be a better choice to suppress airborne cross-transmission.