Outdoor Air Quality Research Articles

School exposure to air pollution in the UK and Ireland: Indoor-Outdoor air quality assessment

Abstract Background Globally, 93% of children under the age of 15 (1.8 billion) are exposed to fine particulate matter (PM2.5) levels that might cause respiratory/cardiovascular disorders, impaired neuro/cognitive development, and cancer. Air pollution causes 1/10 deaths in children under the age of 5. Methods The purpose of this observational study was to collect data on indoor and outdoor air quality in 24 schools over 10 school days and compare it with city-wide average air quality levels reported in rural/urban areas in the UK and Northern Ireland. Nitrogen dioxide, volatile organic compounds (VOCs) and PM10,2.5,1 were monitored inside the classrooms and school surroundings with personal pollution monitors. City-wide averages were taken from local reference air quality and ground-source monitoring stations. Data was expressed using the Plume Labs Air Quality Index (PAQI) thresholds [low (0-20), moderate (21-50), high (51-100), very high (101+) pollution level] aligned with WHO's exposure recommendations. Results Despite both AQIs being in the moderate threshold, the school's average indoor AQI was ∼44% higher than the city-wide average. Only 27% of schools’ indoor air quality indexes matched the city-wide average, 59% were higher and 14% were lower. Rural schools had a marginally higher average indoor AQI than urban schools. The primary classroom pollutants observed were VOCs, followed by PM10. The average walk-exposure AQI of 33% of total schools was higher than the city's average. The school reporting the highest average walk AQI was seven times higher than the lowest. Conclusions Poor indoor air quality and higher levels of pollutants were found inside the majority of the schools across the regions. There is a need for mitigation strategies to identify responsible factors for indoor air pollution. A regulatory framework for school ventilation and behavior interventions like nasal washes may reduce pollution exposure and its impact on children's health outcomes and mortality. Key messages • Poor indoor air quality (∼44% higher than the city-wide average) and higher levels of pollutants (volatile organic compounds and PM10) were found inside the majority of the schools across the regions. • A regulatory framework for school ventilation and behavior interventions like nasal washes may reduce air pollution exposure and its impact on children’s health outcomes and mortality.

Biotechnical approach for a continuous simultaneous increase of indoor and outdoor air quality

Phytofiltration is the most sustainable way to achieve a better quality of inlet air in buildings in a polluted environment. But they don’t take into account the biorhythms of plants and pollute the inlet air with CO2 during breathing only time. We collected and analysed data about the biorhythms of plants. As a result, a new bi-directional phytofilter was offered for cleaning and oxygenation of the inlet ventilation air, and also to protect the environment by cleaning the exhaust air from different pollutants. The device has spaces with shifted illumination rhythms and a valve system. A controller directs the inlet air to the space(s), where plants release CO2. The outlet air runs through other spaces. Literature data show that in the less favourable case, the CO2 and oxygen emissions are balanced per day without overall CO2 gain to the environment. When plants are growing, they sequestrate CO2 to catch greenhouse gas emissions. Either natural light, artificial light, or a combination of the two can be used. While the second option simply demands one plant metabolism type, the first option needs a combination of CAM metabolism and other plants

Industrial Emission Monitoring and Assessment of Air Quality in Karachi Coastal City, Pakistan

Industrialization, anthropogenic activities, the exhaust of vehicles and exponential population growth have a significant impact on the outdoor air quality of megacities across the world. Karachi is one of the largest cities in Pakistan, South Asia. The dense population, rapid economic growth and unplanned industrial activities have improved the socioeconomic status but also deteriorated the air quality of Karachi. The severe increase in air pollution has become a threat to the local population in terms of their health issues, quality of life and environment. Therefore, it is essential to quantify and monitor the spatiotemporal variation in outdoor air quality parameters. The current study aims to monitor the air quality in four major industrial zones of Karachi for three years (2020–2022). The field data was collected during the periods of post-monsoon and pre-monsoon using the HAZ-SCANNER (HIM-6000) apparatus, which measured outdoor air pollutants such as carbon monoxide (CO), nitrogen oxides (NO2), sulfur dioxide (SO2) and particulate matter (PM10, PM2.5 and TSPM). The data from 24 stations was analyzed using statistical analysis tools to estimate the parameters and Arc GIS to map the spatial variation of each parameter. The result shows that the concentration of particulate matter (TSPM, PM2.5 and PM10), SO2, NO2 and CO values at sampling sites are moderate in the post-monsoon season as compared to the pre-monsoon season due to cyclical monsoon effects and exceed the environmental quality standards. It was also noted that the North Karachi industrial area is at lower risk due to the small-scale industry. The higher levels of air pollutants have numerous health implications and may cause chronic infections. The air pollutant has a severe impact on plant growth and soil. Therefore, it is important to implement local environmental standards regarding outdoor air pollutants to mitigate the adverse impact on human health and economic activities.

Special Issue: Advances on Indoor Air Quality Systems for Healthy and Sustainable Buildings

Abstract This special issue is an outcome of a Workshop “Impact of Sustainable Buildings in Arid Environment on the Indoor and Outdoor Air Quality,” held in Doha, Qatar, on May 17–19, 2022 with the participation of more than 50 experts in the field . We reported the highlights of the workshop in the February 2023 issue of this Journal [2]. There was a great response to the call for papers and eight manuscripts were accepted and selected for this Special Issue. These articles are authored by an outstanding roster of experts in their respective fields and tackle various research gaps that impact the quality of our indoor environments. The topics of this Special Issue spanned a large area that included the use of microchannels as a viable building insulation technique [3], enhancing the energy storage capabilities for solar-powered air conditioning systems [4], the impact of outdoor environment on the indoor air quality of commercial buildings using natural ventilation [5], utilizing machine learning for optimum manufacturing processes scheduling [6], utilizing a decision support tool for HAVC filters selection [7], Decarbonization through electrification [8] improving demand-response flexibilities in pharmaceutical buildings using optimization software, [9] and methodology for predicting the energy consumption of buildings using deep neural networks [10].

Twenty-Year Review of Outdoor Air Quality in Utah, USA

Air quality is a prevalent concern due to its imposing health risks. The state of Utah, USA, at times over the last 20 years has suffered from some of the worst air quality in the nation. The propensity for the state of Utah to experience elevated concentrations of particulate matter and ozone can in part be attributed to its unique geography that features dry, mountainous topography. Valleys in Utah create ideal environments for extended cold-pool events. In this review, we summarize the research executed in Utah over the past 20 years (2002–2022) by dividing the state into six regions: Utah Valley, Summit County, Southern Utah (regions south of Utah Valley), Cache Valley, Uinta Basin, and Salt Lake Valley. We review the published literature chronologically and provide a summary of each region identifying areas where additional research is warranted. We found that the research effort is weighted towards Uinta Basin and Salt Lake Valley, with the other regions in Utah only adding up to 20% of the research effort. We identified a need for more source apportionment studies, speciated volatile organic compound (VOC) studies, and ozone isopleths. Where ozone isopleths are not able to be created, measurement of glyoxal and formaldehyde concentrations could serve as surrogates for more expensive studies to inform ozone mitigation policies.

A novel low sampling rate and cost-efficient active sampler for medium/long-term monitoring of gaseous pollutants

Active sampling is a dependable approach for gaseous pollutants monitoring, offering high accuracy and precision that is unaffected by environmental factors such as wind and temperature in comparison to passive sampling. To measure long-term average concentrations while minimizing the use of materials, a reduced sampling rate is necessary. Thus, this study aims to develop a novel low sampling rate (down to 1 mL/min) and cost-efficient active sampler (LASP) for medium/long-term monitoring of gaseous pollutants. The LASP mainly consisted of a syringe pump, a Y-shaped fitting with two one-way valves, and a control unit for intermittent operation. Results showed that LASP can obtain a sampling rate of less than 1 mL/min and sampling rate exhibited a high level of stability. Daily average concentrations measurements for nitrogen dioxide and formaldehyde by LASP had normalized mean biases of 2.8% and 5.2%, respectively. These numbers were − 5.8% and 6.1% for weekly-average samplings. This study demonstrated applications of LASP in real outdoor (daily-average) and indoor (weekly-average) air quality measurements. It worked well with low noise levels, and without interfering with occupants' daily activities. LASP can assist in improving our ability to monitor air quality and pollutants emissions, thereby supporting health research and policy development. Environmental implicationGaseous air pollution is an important hazardous factor threatening human health. Medium/long-term air quality monitoring is essential for outdoor and indoor air quality assessment and control. However, air sampler for medium/long-term sampling is lacking. This study developed a novel low sampling rate and cost-efficient active sampler and applied it to medium/long-term air sampling. The sampler can work at a sampling rate of less than 1 mL/min. This technology provides a feasible strategy for medium/long-term monitoring of gaseous air pollutants in both environments and emission hotspots.

Surveillance-image-based outdoor air quality monitoring

Air pollution threatens human health, necessitating effective and convenient air quality monitoring. Recently, there has been a growing interest in using camera images for air quality estimation. However, a major challenge has been nighttime detection due to the limited visibility of nighttime images. Here we present a hybrid deep learning model, capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images. Our model, which integrates a convolutional neural network (CNN) and long short-term memory (LSTM), adeptly captures spatial-temporal image features, enabling air quality estimation at any time of day, including PM2.5 and PM10 concentrations, as well as the air quality index (AQI). Compared to independent CNN networks that solely extract spatial features, our model demonstrates superior accuracy on self-constructed datasets with R2 = 0.94 and RMSE = 5.11 μg m−3 for PM2.5, R2 = 0.92 and RMSE = 7.30 μg m−3 for PM10, and R2 = 0.94 and RMSE = 5.38 for AQI. Furthermore, our model excels in daytime air quality estimation and enhances nighttime predictions, elevating overall accuracy. Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model, reaffirming its applicability and superiority for air quality monitoring.

Reliability Testing of a Low-Cost, Multi-Purpose Arduino-Based Data Logger Deployed in Several Applications Such as Outdoor Air Quality, Human Activity, Motion, and Exhaust Gas Monitoring.

This contribution shows the possibilities of applying a low-cost, multi-purpose data logger built around an Arduino Mega 2560 single-board computer. Most projects use this kind of hardware to develop single-purpose data loggers. In this work, a data logger with a more general hardware and software architecture was built to perform measurement campaigns in very different domains. The wide applicability of this data logger was demonstrated with short-term monitoring campaigns in relation to outdoor air quality, human activity in an office, motion of a journey on a bike, and exhaust gas monitoring of a diesel generator. In addition, an assessment process and corresponding evaluation framework are proposed to assess the credibility of low-cost scientific devices built in-house. The experiences acquired during the development of the system and the short measurement campaigns were used as inputs in the assessment process. The assessment showed that the system scores positively on most product-related targets. However, unexpected events affect the assessment over the longer term. This makes the development of low-cost scientific devices harder than expected. To assure stability and long-term performance of this type of design, continuous evaluation and regular engineering corrections are needed throughout longer testing periods.

Effects of thermal environment and air quality on outdoor thermal comfort in urban parks of Tianjin, China.

The comfort level of outdoor thermal environments is affected by several factors. Previous studies of thermal comfort have generally investigated the main microclimatic factors as dependent variables, such as the temperature, wind speed, humidity, and thermal radiation, but the influence of the air quality has rarely been explored. In this study, we acquired meteorological element observations and conducted questionnaire surveys in Peach Blossom Park, Hebei University of Technology, and Xigu Park in Tianjin. We analyzed the effects of the outdoor air quality and thermal environment on the thermal comfort in order to provide a theoretical basis for comprehensive evaluations of the outdoor environment and the mechanism. The results showed that thermal resistance of clothing and ambient temperature followed a negative step change, where people generally reduced the minimum amount of clothing when the temperature exceeded 28 °C. One unit change in the thermal sensation vote (TSV) occurred for every 11 °C rise in the physiological equivalent temperature (PET). The neutral PET was 21.68 °C, and the comfortable PET was about 23 °C. The air quality index (AQI) and air satisfaction were negatively correlated, and satisfaction decreased by 1 unit for every change of 230 AQI. The transitional season was most comfortable when the temperature felt slightly cool (TSV = -0.70). The neutral TSV was 0.507 in the summer and -0.334 in the winter. Air quality had a significant effect on the thermal comfort vote (TCV) (p = 0.0485 < 0.05). The effect of PET on TCV was highly significant (p < 0.01).

An adaptive serial cascaded autoencoder and LSTM with multivariate regression for ambient air quality prediction with improved flow direction algorithm

ABSTRACT Forecasting of air quality is an emerging process to evade the entailment of several defects for human as well as environmental resources. Because of the poor quality of air substances, air pollution occurs. Living beings and other economic resources get affected by this polluted air very frequently. Therefore, air quality prediction is a trendsetting method to maintain a healthy life and infrastructure. Though multiple existing models are implemented, managing high-level data and deploying such standard models become cumbersome. Rather than indoor air quality, the ambient (outdoor) air quality should require the prediction process as it exists in the open environment. Thus, an intelligent ambient air quality prediction model is needed, which is designed in this paper by adopting a heuristic-aided deep learning model. The original air data is initially fetched from the three diverse data sources. It is followed by the data pre-processing stage with standard techniques. Subsequently, the resultant data is given to Adaptive Serial Cascaded Autoencoder and Long Short-Term Memory (LSTM) with Multivariate Regression (ASCA-LSMR), in which some of the hyper-parameters are tuned by proposing the novel algorithm as Fitness-based Improved Flow Direction Algorithm (FIFDA) to produce the better prediction results. Finally, experimental results indicate that our method enables more accurate predictions than all the listed traditional models and performs better in predictive performance. The RMSE of the designed FIFDA-ASCA-LSMR method attains 31.9%, 33.5%, 6.66%, and 23.7% elevated than SSA-ASCA-LSMR, DHOA-ASCA-LSMR, EHO-ASCA-LSMR, and FDA-ASCA-LSMR, for dataset 2. Thus, the designed ambient air quality prediction model reveals better performance than the other baseline approaches.

The effect of facade materials and coatings on urban heat island mitigation and outdoor thermal comfort in hot semi-arid climate

High urban temperature is generally associated with negative effects on building energy consumption, outdoor thermal comfort, air quality, and human health. Then it is necessary to find strategies to reduce urban heat island and improve outdoor thermal comfort. This research investigates and classifies facade coatings and materials with regards to urban heat island effect in hot semi-arid climate of Shiraz. The surface temperature of 20 common and modern building facade materials during two periods for five days and their effect on outdoor air temperature is investigated through field measurement. Additionally, to study the effect of each material on urban heat island and thermal comfort index, Physiological Equivalent Temperature (PET) of a neighborhood in Shiraz was simulated with ENVI-met software on June 22. The results show that during the day under direct sunlight, the living wall has the lowest surface and air temperatures, and as the nearby air temperature increases during the night, it lowers the temperature fluctuations both during the day and night. Materials with high albedo increase the Mean Radiant Temperature and PET index despite their lower surface temperature. Materials with high thermal mass and low emissivity have lower temperature. The living wall has the lowest PET index, indoor and outdoor surface temperature during the day. It can also result in 2 °C reduction of air temperature.

Pollutant emissions from biomass burning: A review on emission characteristics, environmental impacts, and research perspectives

Biomass is one most abundant resource on the earth providing important energies in support of socioeconomic development in many areas. Burning of biomass fuels comprises to nearly 10% of the total energy from anthropogenic combustion processes; however, as the burning is usually incomplete, this process yields products of incomplete combustion posing consequently significant impacts on air quality, human health, and climate change. Here, we analyzed spatiotemporal characteristics in intentional and unintentional biomass burning from different sectors, discussed impacts of biomass burning emissions on indoor and outdoor air quality, and consequent influences on human health. The global total consumption amount of biomass including both natural and anthropogenic sources was approximately 7900 Tg in 2019, with significantly large regional and sectorial discrepancies among regions. Globally, anthropogenic biomass burning amounts increased gradually, but notably in some developing countries like China residential consumption of biomass fuels, as one large sector of biomass use, decreased over time. Uncommercial biomass consumption needs to be accurately quantified. There are relatively rich datasets of pollutant emission factors from biomass burning, including laboratory and field tests, but still large variations exit and contribute substantially to the uncertainty in emission inventory. Global primary PM2.5, black carbon and organic carbon emissions from biomass burning were about 51, 4.6, and 29 Tg, respectively, contributing to nearly 70%, 55%, and 90% of the total emission from all sources, and emissions from the residential sector and open fires are major sources. Brown carbon emissions from biomass burning attracts growing interests but available studies adopted different methodologies challenging the comparability of those results. Biomass burning emissions polluted not only ambient air but more severely indoor air quality, adversely affecting human health. Future studies that should be emphasized and promoted are suggested.

Assessment of Indoor Air Quality in Different Spaces of Residential and Commercial Buildings in Jeddah, Saudi Arabia

Indoor air pollution has a significant impact on human health, triggering and inducing a wide range of diseases that result in high morbidity and mortality. In this study, indoor air pollution in different spaces of residential and commercial buildings is assessed in terms of air quality index (AQI), particulate matter PM2.5, particulate matter PM10, carbon dioxide, formaldehyde (HCHO), the total volatile organic compound (TVOC), humidity, and temperature. Two residential apartment buildings are chosen for study, one is in a busy outdoor area and the other is situated in a calm outdoor area to see the effects of outdoor air quality on indoor spaces. It is found that the indoor air quality of bedrooms in residential spaces and cafés in commercial buildings is higher than the standards, and it is recommended to provide proper ventilation for better living of the occupants. Also, it is found that there is minimal effect of infiltration of outdoor air pollutants on indoor air quality and the reason is due to the provision of mechanical ventilation with air filters and effective building envelope.

Utilizing Machine Learning for Rapid Discrimination and Quantification of Volatile Organic Compounds in an Electronic Nose Sensor Array

Volatile organic compounds (VOCs) are ubiquitous in the surroundings, originating from both industrial and natural sources. VOCs directly impact the quality of both indoor and outdoor air and play a significant role in processes such as fruit ripening and the body’s metabolism. VOC monitoring has seen significant growth recently, with an emphasis on developing low-cost, portable sensors capable of both vapor discrimination and concentration measurements. VOC sensing remains challenging, mainly because these compounds are nonreactive, appear in low concentrations and share similar chemical structures that results in poor sensor selectivity. Therefore, individual gas sensors struggle to selectively detect target VOCs in the presence of interferences. Electronic noses overcome these limitations by employing machine learning for pattern recognition from arrays of gas sensors. Here, an electronic nose fabricated with four types of functionalized gold nanoparticles demonstrates rapid detection and quantification of eight types of VOCs at four concentration levels. A robust two-step machine learning pipeline is implemented for classification followed by regression analysis for concentration prediction. Random Forest and support vector machine classifiers show excellent results of 100% accuracy for VOC discrimination, independent of measured concentration levels. Each Random Forest regression analysis exhibits high R2 and low RMSE with an average of 0.999 and 0.002, respectively. These results demonstrate the ability of gold nanoparticle gas sensor arrays for rapid detection and quantification.

Atmospheric Microplastics: Perspectives on Origin, Abundances, Ecological and Health Risks.

Microplastic (MP) pollution has aroused a tremendous amount of public and scientific interest worldwide. MPs are found widely ranging from terrestrial to aquatic ecosystems primarily due to the over-exploitation of plastic products and unscientific disposal of plastic waste. There is a large availability of scientific literature on MP pollution in the terrestrial and aquatic ecosystems, especially the marine environments; however, only recently has greater scientific attention been focused on the presence of MPs in the air and its retrospective health implications. Besides, atmospheric transport has been reported to be an important pathway of transport of MPs to the pristine regions of the world. From a health perspective, existing studies suggest that airborne MPs are priority pollutant vectors, that may penetrate deep into the body through inhalation leading to adverse health impacts such as neurotoxicity, cancer, respiratory problems, cytotoxicity, and many more. However, their effects on indoor and outdoor air quality, and on human health are not yet clearly understood due to the lack of enough research studies on that and the non-availability of established scientific protocols for their characterization. This scientific review entails important information concerning the abundance of atmospheric MPs worldwide within the existing literature. A thorough comparison of existing sampling and analytical protocols has been presented. Besides, this review has unveiled the areas of scientific concern especially air quality monitoring which requires immediate attention, with the information gaps to be filled have been addressed.

Impacts of dust storms on indoor and outdoor bioaerosol concentration in the Sistan region of Iran

Monitoring indoor and outdoor bioaerosol concentrations is essential for environmental management and evaluation of the health impact of air pollution. This study assessed indoor and outdoor microbial air quality for two cities of the Sistan region in Iran, Zabol and Zahak, in summer (July and August) and autumn (October and November) of 2022. Indoor air quality was compared between two rooms with varying conditions associated with windows (living room: windows opened to the north and to the prevailing wind; bedroom: opened to the south and opposite to the prevailing wind). Bacterial concentrations were monitored using the settle plate method inside and outside household environments at 6:30 a.m., 10:30 a.m., 4:30 p.m., 7:30 p.m., and 12:30 a.m. Zahak exhibited higher PM10 and bacterial concentrations compared to Zabol. Bacterial concentrations in the outdoor environment were significantly higher than those in indoor air and also higher in summer than autumn in both cities. There was no significant difference in indoor air samples between the two rooms (p> 0.05) in Zabol, indicating that the bacteria pollution was similar in the two rooms. Diurnal and day-to-day patterns of airborne bacteria were similar in indoor and outdoor air, consistent with PM10 variations, pointing to the association between outdoor and indoor air quality. PM10 and bacterial concentrations peaked at 10:30 a.m. and were lowest at 4:30 p.m. Results of this study have implications for other dust-prone cities of the world and offer a valuable dataset for bacterial concentrations to compare with other regions of the world.

State liability for health damage caused by excessive air pollution: Constitutinal and Private Law aspects

Air pollution severely damages human health and causes premature deaths. In order to fight against it, the European Commission initiated a revision of the Ambient Air Quality Directives aiming to improve the quality of outdoor air and to reach the Zero Pollution goal. However, the CJEU is already facing requests for a preliminary ruling dealing with state liability for health damage caused by excessive air pollution. The old common law maxim &lt;The King does no wrong= according to which a citizen may not seek redress from the government for wrongs committedmby the latter has long been surpassed. The institution of state liability is thus a widely recognised concept. The paper analyses the main features of state liability for health damage caused by polluted air and its boundaries. It focuses on the recent development of EU law in this regard and the established case law of the ECtHR. Since the right to a healthy living environment is recognised by several constitutions across the world, including Slovenia, the paper deals also with the Slovenian case law on state liability for damages caused by air and noise pollution from road and rail transport.

Smart solutions for urban health risk assessment: A PM2.5 monitoring system incorporating spatiotemporal long-short term graph convolutional network

Current spatial-temporal early warning systems aim to predict outdoor air quality in urban areas either at short or long temporal horizons. These systems implemented architectures without considering the geographical distribution of each air quality monitoring station, increasing the uncertainty of the forecasting framework. This study developed an integrated spatiotemporal forecasting architecture incorporating an extensive air quality PM2.5 monitoring network and simultaneously forecasts PM2.5 concentrations at all locations, allowing the monitoring of the health risk associated with exposure to these levels. First, this study uses a graph convolutional layer to incorporate the spatial relationship of the neighboring stations at their current state with real-time measurements. Then, it is coupled to a deep learning temporal model to form the long- and short-term time-series graph convolutional network (LSTGraphNet) model, anticipating high pollutant concentration events. This work tested the proposed model with a case study of an existing ambient air quality monitoring network in South Korea. LSTGraphNet model showed prediction performances of PM2.5 at multiple monitoring stations with a mean absolute error (MAE) of 1.82 μg/m3, 4.46 μg/m3, and 4.87 μg/m3 for forecasting horizons of one, three, and 6 h ahead, respectively. Compared to conventional sequential models, this architecture was superior among the state-of-the-art baselines, where the MAE decreased to 41%, respectively. The results of the study showed that the proposed architecture was superior to conventional sequential models and could be used as a tool for decision-making in smart cities by revealing hotspots of higher and lower PM2.5 concentrations in the long term.

Microbiological Indoor and Outdoor Air Quality in Chicken Fattening Houses.

This study was conducted at one of the largest poultry companies in Kuwait during November and December 2019 to evaluate the microbiological threats of Escherichia coli (APEC), Salmonella spp., and Aspergillus fumigatus to chickens in fattening houses by counting and identifying the microorganisms by culturing and pyrosequencing analysis. During the fattening cycle, the temperature and humidity ranged between 23.6°C and 29°C and 64.1% and 87.1%, respectively. The total bacterial population and Aspergillus fumigatus measured in the indoor and outdoor air exhibited a linear relationship during the fattening cycle. The total bacterial and Aspergillus concentrations determined during the cycle ranged between 150 and 2000 CFU/m3 and 0 and 1000 CFU/m3, respectively. E. coli and Salmonella spp. concentrations determined during the cycle ranged between 1 and 220 CFU/m3 and 4 and 110 CFU/m3, respectively. Pyrosequencing analysis of the air inside the houses at the end of the cycle revealed extensive biodiversity in the microorganisms, detecting 32 bacterial genera and 14 species. The identified species belonging to the genera Corynebacterium, Haemophilus, Streptococcus, Veillonella, and Aspergillus were identified as potentially affecting human and broiler health. The emission of potentially pathogenic bacteria to the outdoor environment from chicken housing can pose a considerable risk to human health and environmental microbial pollution. This study could guide the development of integrated control devices for monitoring microbes in broiler production facilities during chicken collection for transport to slaughterhouses.

2 Low-Cost Pm Sensor Performance Testing

Abstract Low-cost particulate matters sensors (LCPMS) are widely used in indoor and outdoor air quality monitoring. Previous studies have explored the accuracy and precision of sensors, showing that LCPMS were most accurate for PM with diameters below 1 μm, and they poorly measured PM in the 2.5–5 μm range. The study aimed to investigate the aspiration and transmission efficiency of LCPMS, to set up a standard aerosol mass concentration generation system, to further comprehend the performance characteristics of LCPMS, and to examine the effect of aerosol loading. Three models of Plantower sensors were tested in this work. An ultrasonic atomizing nozzle was used to generate micrometer-sized NaCl aerosol particles. A TSI aerodynamic particle sizer was used to measure the aerosol concentrations and size distributions upstream and downstream of the LCPMS, to determine the aspiration and transmission efficiency. The mass concentration, from 20 µg/m3 to 200 mg/m3, could be varied by controlling the solution feeding rate and the solution concentration. The high mass concentration was mainly designed for aerosol loading study. The results showed that the aspiration efficiencies of the tested LCPMS were all almost 100% for particle smaller than 5 µm. The transmission efficiency of these sensors was function of particle size and strongly dependent on sampling flow. All sensors tested showed significant performance degrading when challenged with high mass concentration. The orientation of the sensor also played a role affecting the aerosol loading. These sensors performed better when mass concentration is below 100 µg/m3.