Sources In Indoor Environments Research Articles

Localizing particulate matter sources in indoor environments with weak airflow: An experimental study using swarm intelligence methods

In indoor environments with weak airflow, advective transport diminishes while turbulent dispersion prevails. The absence of usable airflow data and the unpredictability of turbulence-led pollutant dispersion pose significant challenges to mobile robots in localizing pollution sources. This study advances the field by shifting the focus from gases to particulate matter (PM) in such environments. An initial detailed examination of airflow dynamics and PM behavior under weak airflow conditions laid the foundation for extensive 3D source localization experiments using two methodologies: the Whale Optimization Algorithm (WOA_3D) and Particle Swarm Optimization (PSO_3D), executed through a specialized multi-robot system. Evaluation across 11 distinct scenarios and 165 trials showcased the methods’ adaptability to different source heights, PM sizes, and the transition from gaseous to particulate pollutants. WOA_3D notably excelled in localizing PM2.5 sources at 1.35 m heights, achieving a 73.3% success rate and proving consistently effective across various elevations. However, its efficacy waned with larger PM sizes, and it was less effective in transitioning from ethanol vapor to PM source localization. These results highlight the importance of addressing PM settling to improve PM localization strategies, effectively combining insights into PM behavior’s complexities with methodological progress.

An in-situ versatile screening method for identifying SVOC sources in indoor environments

Indoor semivolatile organic compounds (SVOCs) pose a substantial threat to human health. However, identifying the sources of these emissions has been challenging owing to the scarcity of convenient and practical on-site methodologies. Herein, a novel method for source screening was proposed using aluminum silicate sampling strips to adsorb SVOCs from the surface air of indoor materials. The adsorbed SVOC levels indicate the emission intensity of these materials into indoor environments. Additionally, compact sampling strips can be readily fixed to any vertical surface using a static sticker, facilitating the characterization of various materials in practical settings. Laboratory-simulated experiments demonstrated the capability of the proposed method to differentiate between source and non-source materials within a 10-cm distance in the same space. In practical scenarios, the primary emission sources identified via this method exhibited a consistent correlation with the contents of the corresponding materials obtained from the traditional solvent-extraction method. As the adsorbed SVOCs were directly transferred to a GC–MS through thermal desorption instead of the solvent-extraction procedure, the proposed method demonstrated several-fold improvements in analytical sensitivity and efficiency. Using this versatile screening technique, some emerging and important SVOC species were identified within specific indoor materials. Eliminating these sources has been demonstrated as an effective approach to mitigate SVOC pollution. Overall, the proposed method offers a powerful tool for managing indoor pollutants and safeguarding human health.

Source searching in unknown obstructed environments through source estimation, target determination, and path planning

Autonomous mobile robots have been gradually employed to search unknown sources in indoor environments. However, current studies have not fully addressed the source searching problem in unknown obstructed environments with limited sensing abilities. To deal with these problems, we propose an active source searching framework, in which mobile robots can avoid obstacles actively and realize the balance between exploration and exploitation in unknown obstructed environments through an iterative process: source estimation, target determination, and path planning. First, we describe the source searching problem and introduce the environment and sensor models. Then, a novel source searching algorithm based on particle filter, MEGI-taxis, and A-star is proposed. Specifically, the particle filter is used to estimate the source term parameters. The MEGI-taxis algorithm is developed to obtain a globally optimal searching target, which leverages the Gaussian Mixture Model to extract the features of probability information. Based on the heuristic rule, the A-star algorithm is employed to plan a collision-free path for the robot navigating to the target in unknown environments. When compared to state-of-the-art solutions in simulations, our method shows better performance in success rate, mean searching steps, and stability in the source searching process. Moreover, the effectiveness of the proposed framework is verified in the diffusion field generated by the computational fluid dynamics (CFD) model based on an indoor scene. The results reveal the important practicality of our proposed framework for source searching tasks in unknown obstructed environments.

Diffraction- and Reflection-Aware Multiple Sound Source Localization

In this article, we present a novel localization method for multiple sources in indoor environments. Our approach can estimate different propagation paths, including the reflection and diffraction paths of sound waves based on a backward ray tracing technique. To estimate diffraction propagation paths, we combine a ray tracing algorithm with a uniform theory of diffraction model by exploiting the diffraction properties as propagation paths bend around the wedges of obstacles. We reconstruct the 3-D environments and wedges of obstacles in the precomputation phase and utilize these outcomes to generate primary, reflection, and diffraction acoustic rays in the runtime phase. We localize multiple sources when identifying the convergence regions of these acoustic rays based on Monte Carlo localization (MCL). Our approach supports not only stationary but also moving sources of human speech and clapping sounds. Our approach can also handle nonline-of-sight (NLOS) sources and distinguish between active and inactive source states. We evaluated and analyzed our algorithm in multiple scenarios containing obstacles and NLOS sources. Our approach can localize moving sources with the average of distance errors of 0.65 and 0.74 m in single and multiple source cases, respectively, in rooms, 7 m by 7 m in size with a height of 3 m; errors are measured according to the L2 distance between the estimated and actual source positions. We observed a 130% improvement of the localization accuracy over the prior work (J.-M. Valin <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ).

Concentration, Distribution, and Potential Sources of Heavy Metals in Households Dust in Al-Fallujah, Iraq

Household dust pollution with heavy metals attracted the attention of researchers and environmental managers due to the risk of the health of these metals. The study aims are to determine heavy metals concentrations (Cd, Cr, Cu, Ni, Pb, Zn), their spatial distribution, and their potential sources in the household dust of Al-Fallujah City, Iraq. The dust was sampled from 50 houses. The heavy metals levels in the dust were measured using the atomic absorption spectrophotometry method. The mean concentration of heavy metal was ordered as following: Zn (292.85 mg/kg) &gt; Cr (289.45 mg/kg) &gt; Ni (105.72 mg/kg) &gt; Pb (75.57 mg/kg) &gt; Cu (65.03 mg/kg) &gt; Cd (14.77 mg/kg). The mean concentration of these metals exceeded the reference values. The areal distribution of the reported heavy metals showed specific and non-specific patterns indicating point and non-point pollution sources. The heavy metals potential sources in house dust in the study area were characterized using correlation, Principle components and cluster analyses. The potential sources for Cd, Cu and Pb were interior and exterior sources, while the Ni and Cr were derived from internal sources. This study provides the environmental protection managers and decision-makers with important information about heavy metals concentrations and their sources in indoor environments.

Heavy Metal Pollution and Sources in Dust from Primary Schools and Kindergartens in Ramadi City, Iraq

The aim of this study is to determine the level of pollution with heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) and their potential sources in dust samples collected from schools in Ramadi City, Iraq. The dust samples were collected from 40 primary schools and two kindergartens and analyzed by using atomic absorption spectrophotometer. The heavy metal concentrations were found to follow the order Cr &gt; Cu &gt; Pb &gt; Ni &gt; Zn &gt; Cd. The results indicated that the concentrations of Cd, Cu, and Pb exceeded the permitted background values. The pollution level was assessed using the geo-accumulation index (Igeo) and pollution load index (PLI). The classification of dust samples according to Igeo values showed that they ranged from unpolluted for Cu, Cr, Ni, and Zn, to moderately polluted for Pb, and heavily polluted for Cd. The PLI values indicated no to moderate pollution load. The results of the comparisons of heavy metal concentrations with the background values, as well as the multivariate statistical analysis, indicated three groups of heavy metals with different sources or origins: (1) Cd and Cu (anthropogenic source: vehicle emissions); (2) Pb (mixed source); and (3) Cr, Ni, and Zn (Geogenic source). This study is the first attempt in Iraq to investigate the concentrations of heavy metals in the dust of indoor environments. This study provides the environmental protection managers and decision-makers with important information about the concentrations of heavy metals and their sources in indoor environments.

An experiment-based impulse response method to characterize airborne pollutant sources in a scaled multi-zone building

Promptly locating the airborne pollutant sources in indoor environments is of great importance for improving air quality and ensuring indoor safety, especially in the context of controlling airborne pathogens. However, most existing studies focused on short-range airborne transmission in close contact and identified pollution sources based on numerical simulations. In this study, an experiment-based impulse response method was established to estimate the source term in a multi-zone building, and uncertainty analysis (UA) was conducted by combining the errors from the sensors and systematic responses. An acrylic scaled multi-zone building was built to investigate and validate the proposed method. The prior impulse response vectors between the potential sources and sensor network were obtained through impulse response experiments. Next, four sets of representative experiments were conducted with the scaled model to evaluate the source identification method. The results showed that the source release rate and source location could be identified using the impulse response method, and most of the relative errors of the source release rate were within 30%. For source location identification, the location probabilities of the constant and dynamic real sources were 44% and 100%, respectively. This study provides a novel perspective for source term estimation (STE) in multi-zone environments.

Quantificational exposure, sources, and health risks posed by heavy metals in indoor and outdoor household dust in a typical smelting area in China.

Contamination of metals in household dust remains a concern for human health. However, few studies to date have been conducted on the contribution of both indoor and outdoor environments to the health risks posed by metals. This study was carried out to assess the potential health risks from both indoor and outdoor household dust and the respective contribution to the health risks for children. The results showed that household dusts were heavily polluted by metal(loid)s, which were up to 30 times higher than the relative background level, and were attributed to smelting activity. However, there are other pollution sources in indoor environments, since the I/O ratio values of Pb, Cd, and As were significantly higher than 1. HI values of Pb and As exceeded the threshold of (1) and accounted for approximately 60% and 24% to the HIt, respectively. The HIts of Zn, Cr, Mn, Hg, and Cu were mainly attributable to indoor dust exposure, particularly for Hg (73.44%), indicating non-carcinogenic health risks could be attributed more to the indoor dust exposure. This study highlights the potential risks of metal contamination in household environment, particularly indoor environment, on the health of children who live in the vicinity of smelting activity.

Source localization in dynamic indoor environments with natural ventilation: An experimental study of a particle swarm optimization-based multi-robot olfaction method

Source localization is important for ensuring indoor air quality and indoor environmental safety. Most available studies on source localization have been conducted in steady indoor environments with mechanical ventilation, and only a very few studies have addressed the more challenging source localization problem in indoor environments with natural ventilation. To locate contaminant sources in indoor environments with natural ventilation, this study presents a multi-robot olfaction method (URPSO) based on an adapted particle swarm optimization (PSO) algorithm by adding an upwind term and a random disturbance term into the standard PSO algorithm. The effectiveness of the presented method was first validated by three robots in a natural ventilation environment created by opening a window. For two typical source locations in the downwind zone and the recirculation zone, 13 and 15 experiments out of 15 experiments were successful, with success rates of 86.7% and 100% and average steps for source localization of 28.7 and 18.4 steps, respectively. The URPSO method was further compared with the PSO and wind utilization II (WUII) methods for locating the source in an imitated natural wind environment produced by using a fan. For the URPSO, PSO and WUII methods, 14, 3 and 5 experiments out of 15 experiments were successful, with success rates of 93.3%, 20% and 33.3% and average steps of 34.1, 34.0 and 35.8 steps, respectively. The experimental results show that the presented method has a similar source localization efficiency but a much higher success rate than the compared methods.

Experimental study on three single-robot active olfaction algorithms for locating contaminant sources in indoor environments with no strong airflow

Locating the sources of contaminants or hazardous substances in indoor environments is extremely important for ensuring indoor air quality and indoor environmental safety. Existing source localization studies have focused on environments with strong airflow, and very few studies have addressed the more challenging source localization problems in environments with no strong airflow. In this study, we developed a mobile robot for source localization and used the robot to test and compare three single-robot source localization algorithms (E. coli, spiral and hex-path) that have the potential to be applied to environments with no strong airflow. Each algorithm was tested by 15 independent experiments. Considering both the success rates and average numbers of steps, the performance of the three algorithms from high to low was in the order of the hex-path algorithm, E. coli algorithm, and spiral algorithm, with the success rates of 73.3%, 66.7% and 60.0%, and the average numbers of steps of 51.43, 54.12, and 63.67, respectively. Based on the same comparison criteria, all three algorithms can significantly improve the success rate or reduce the average number of steps compared with a random algorithm that encounters the source solely by chance. This study can provide more choices for developing single-robot source localization methods for environments with no strong airflow and can provide inspiration and guidance for improving existing methods and developing new methods.

An overview of health hazards of volatile organic compounds regulated as indoor air pollutants.

Indoor air quality (IAQ) standards and guidelines for volatile organic compounds (VOCs) have been stipulated by various national and international agencies. The main purpose of this paper is to establish an overview of indoor VOCs regarding their impacts on human health. Herein, 13 VOCs were designated as indoor air pollutants (IAPs) in the IAQ standards and guidelines. They were further grouped into four types: nonchlorinated aromatic compounds, chlorinated aromatic compounds, chlorinated aliphatic compounds and aldehydes. For this purpose, the present study discusses the criteria for designating VOCs, and summarizes their main sources in indoor environments. Because the occupational exposure limit (OEL) in workplaces has often used as a preliminary basis for establishing acceptable health-based IAQ guidelines in buildings and residences, this paper thus reviews the OEL values, especially in the American Conference of Governmental Industrial Hygienists (ACGIH)-threshold limit value (TLV). In addition, this paper also reviews the information about the classification of carcinogenicity in human by the international agencies for these VOCs. It shows that human tissues, including kidney, liver, leukemia, nasal cavity, paranasal sinus, liver and bile duct, could be more involved in the development of cancers or tumors when people are exposed to these VOCs through inhalation route in buildings over a long period of time.

Using multi-robot active olfaction method to locate time-varying contaminant source in indoor environment

Prevention of indoor contaminant source leak disasters is becoming an urgent issue in the indoor environment and public safety fields. Quickly locating such a source is a prerequisite for effective implementation of source control, and a key basis for further guidance on evacuation and emergency rescue. Indoor contaminant sources have many forms because of source characteristic variation. The current research focuses on continuous release from a source with constant intensity and instantaneous release from a source with impulse intensity. There are few studies of sources with varying release intensity. The main goal of the present research study was to develop a source localization method for a time-varying source. A computational fluid dynamics model of leakage for an indoor time-varying contaminant source is established, together with a search strategy using multi-robot active olfaction. Based on the time-varying leakage source localization method, the robots are used to verify method effectiveness through indoor two-dimensional ventilation room simulation experiments. The influence of factors such as intensity change, position and obstacles on the localization is analyzed, which provides a foundation for further study of contaminant source identification in an unsteady flow field.

ElasticFusion: Real-time dense SLAM and light source estimation

We present a novel approach to real-time dense visual simultaneous localisation and mapping. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments and beyond explored using an RGB-D camera in an incremental online fashion, without pose graph optimization or any post-processing steps. This is accomplished by using dense frame-to-model camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimizations as often as possible to stay close to the mode of the map distribution, while utilizing global loop closure to recover from arbitrary drift and maintain global consistency. In the spirit of improving map quality as well as tracking accuracy and robustness, we furthermore explore a novel approach to real-time discrete light source detection. This technique is capable of detecting numerous light sources in indoor environments in real-time as a user handheld camera explores the scene. Absolutely no prior information about the scene or number of light sources is required. By making a small set of simple assumptions about the appearance properties of the scene our method can incrementally estimate both the quantity and location of multiple light sources in the environment in an online fashion. Our results demonstrate that our technique functions well in many different environments and lighting configurations. We show that this enables (a) more realistic augmented reality rendering; (b) a richer understanding of the scene beyond pure geometry and; (c) more accurate and robust photometric tracking.

Rapid identification of multiple constantly-released contaminant sources in indoor environments with unknown release time

The sudden release of airborne hazardous contaminants in an indoor environment can potentially lead to severe disasters, such as the spread of toxic gases, fire, and explosion. To prevent and mitigate these disasters it is critical to rapidly and accurately identify the characteristics of the contaminant sources. Although remarkable achievements have been made in identifying a single indoor contaminant source in recent years, the issues related to multiple contaminant sources are still challenging. This study presents a method for identifying the exact locations, emission rates, and release time of multiple indoor contaminant sources simultaneously released at constant rates, by considering sensor thresholds and measurement errors. The method uses a two-stage procedure for rapid source identification. Before the release of contaminants, only a limited number of time-consuming computational fluid dynamics (CFD) simulations need to be conducted. After the release of contaminants, the method can be executed in real-time. Through case studies in a three-dimensional office the method was numerically demonstrated and validated, and the results show that the method is effective and feasible. The effects of sensor threshold, measurement error and total sampling time on the source identification performance were analysed, and the limitations and applicability of the method were also discussed.

Measurements of formaldehyde emissions in indoor environments using a colorimetric Passive Flux Sampler

Formaldehyde (HCHO) is one of the most abundant volatile organic compounds in indoor environments. This pollutant exhibits concentrations that are several times higher than outdoor, rising questions about potential health effects from indoor exposure. Since building and furnishing materials are known to be important sources of formaldehyde, an efficient reduction of indoor concentrations could be achieved by replacing these emitters with less emissive materials. However, measurement techniques available to monitor in-situ emissions are either too cumbersome or require a delayed analysis in the laboratory, and therefore it is not an easy task to quickly identify sources of emissions. There is a need for real-time and easy-to-use analytical tools to pinpoint the strongest sources in indoor environments. In this communication, we will describe a Passive Flux Sampler (PFS) that is suitable for in-situ and real-time measurements of formaldehyde emissions from building and furnishing materials. This PFS is made of a small exposition cell containing a nanoporous sol-gel matrix doped with Fluoral-P. When exposed for a few hours onto an emissive material, Fluoral-P selectively reacts with formaldehyde, resulting in a color change of the matrix. An appropriate detector is then used to measure the matrix optical density, which is converted into an emission rate. This PFS was calibrated using building materials whose formaldehyde emissions were measured using an emission test chamber under normalized conditions (ISO 16000-9). We will also present the first deployment of this PFS in indoor environments and its potential for a robust identification of formaldehyde emission sources.

Effects of Light’s Colour Temperatures on Visual Comfort Level, Task Performances, and Alertness among Students

Introduction: Correlated colour temperatures (CCT) of the light source in indoor environment plays an imperative role in addressing both psychological and physiological functions of the occupant. As one of the determinants of lighting quality, CCT are off particular importance which affects quality of work and in classroom learning. Objective: The aim of this study is to determine the effects of warm white light (WWL) (CCT = 3,000K), cool white light (CWL) (CCT = 4,000K) and artificial daylight (DL) (CCT=6,500K) on the performances, subjective alertness level, visual comfort level and preferences of student in Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. Methodology: A laboratory controlled experiment was conducted on total of 47 undergraduate students volunteered to participate in a series of test under three coloured light sources. FrACT software was used to assess visual task performance, modified OLS questionnaire was used to evaluate subjective comfort level and preferences, typing test and KSS alertness level monitoring was conducted. Result: Significant increase was observed in subjective alertness level (p=0.041) and computer-based performances (p=0.001) under DL condition in relative to WWL condition. In terms of typing performances, respondents performed significantly better in term of typing speed under CWL than DL and WWL. Least typing errors were made under DL, followed by CWL and WWL. CWL is the most preferred (p=0.001) and most comfortable (p=0.011) CCT environment where subjects indicated the ability to perform task longer in this coloured-lit environment. Conclusion: The study concludes that the CWL and DL were more beneficial for alertness level and academically activities for both computer-based and paper-based activities.

Monitoring of volatile organic compounds in non-residential indoor environments

A weekly monitoring campaign of volatile organic compounds (VOC), with single sampling of 24 h, was carried out in non-residential indoor environments such as libraries, pharmacies, offices, gymnasiums, etc., in order to evaluate the VOC concentrations to which people are exposed. Moreover, an outdoor sample was coupled to each indoor site to point out the influence of indoor sources. They were sampled with Radiello diffusive samplers for thermal desorption and analyzed by GC-MS. As already described in other papers, the VOC levels of most of the indoor sites were higher than that observed in the corresponding outdoor sites. For example, some sites showed a level of pollution that is ten times higher than their corresponding outdoor site. The monitored environments that had higher concentrations of the investigated VOC were the pharmacies, a newspaper stand, a copy center, and the coffee shops. Analysis of the weekly average concentrations of each pollutant and the use of literature allowed pointing out some site-specific characteristics that singled out possible sources of VOC. These results were verified analyzing the indoor-outdoor ratio (I/O) too. Newspaper stands were characterized by very high concentrations of toluene and pharmacies were characterized by high concentrations of aromatic compounds. Indoor air pollution caused by volatile organic compounds (VOC) might affect human health at home as well as in public and commercial buildings. The main VOC sources in indoor environments are human activities, personal care products, smoking, house cleaning products, building products, and outside pollution. To preserve human health it is necessary to evaluate the average concentrations of VOC to which people are exposed and to identify the main sources of indoor pollution by means of suitable indoor monitoring campaigns in several environments. These investigations allow pointing out the characteristic critical situations of some indoor environments or some other types of environments.

Passive flux sampler for measurement of formaldehyde emission rates

A new passive flux sampler (PFS) was developed to measure emission rates of formaldehyde and to determine emission sources in indoor environments. The sampler consisted of a glass Petri dish containing a 2,4-dinitrophenyl hydrazine (DNPH)-impregnated sheet. At the start of sampling, the PFS was placed with the open face of the dish on each of the indoor materials under investigation, such as flooring, walls, doors, closets, desks, beds, etc. Formaldehyde emitted from a source material diffused through the inside of the PFS and was adsorbed onto the DNPH sheet. The formaldehyde emission rates could be determined from the quantities adsorbed. The lower determination limits were 9.2 and 2.3 μg m −2 h −1 for 2- and 8-h sampling periods. The recovery rate and the precision of the PFS were 82.9% and 8.26%, respectively. The emission rates measured by PFS were in good agreement with the emission rates measured by the chamber method ( R 2 =0.963). This shows that it is possible to take measurements of the formaldehyde emission rates from sources in a room and to compare them. In addition, the sampler can be used to elucidate the emission characteristics of a source by carrying out emission measurements with different air-layer thicknesses inside the PFS and at different temperatures. The dependency of the emission rate on the thickness of the air layer inside the PFS indicated whether the internal mass transfer inside the source material or the diffusion in the gas-phase boundary layer controlled the formaldehyde emission rate from a material. In addition, as a pilot study, the formaldehyde emission rates were measured, and the largest emission source of formaldehyde could be identified from among several suspected materials in a model house by using the PFS.

Methodology for identifying particle sources in indoor environments

Growing concern about airborne particles in indoor environments requires fast source identification in order to apply remedial actions. A methodology for identifying sources emitting particles larger than 0.5 microm was designed and applied. It includes: (1) visual inspection of interior surfaces in order to identify deposited particles and inspection of potential sources (equipment, materials, activities etc.) of airborne particles. (2) Technical measurements of airborne particles at different positions in a building with simultaneous logging of activities. (3) Isolating potential activities/particle sources in a test chamber, initially free from particles, for controlled characterizations of the particles generated. The methodology was applied in a study of three houses in southern Sweden. The results show that source identification is facilitated by knowledge of concentration variations between different rooms, real-time measurements together with activity reports and information on particle characteristics that are comparable with results from laboratory simulations. In the houses to which the methodology was applied, major particle emissions from textile handling were identified, which were likely due to detergent zeolite residues.

A study to characterize the suspended particulate matter in an indoor environment in Delhi, India

To investigate potential particulate matter (PM) exposure, and the possible effective measures for interventions and assessment of sources in indoor environments, a pilot study was conducted at Jawaharlal Nehru University (JNU), New Delhi. The indoor particles were collected from 5th April to 26th June 2000, using a tapered element oscillating microbalance (TEOM). The particles were analyzed by gravimetry, atomic absorption spectrometry (AAS) and scanning electron microscopy (SEM) in order to investigate the mass concentration, physico-chemical properties and morphology of the particles. The gravimetric and AAS results confirmed that the suspended particulate matter (SPM) and metal concentrations were higher than the National Ambient Air Quality Standards (NAAQS) for Delhi. The maximum contributions of SPM were observed to be due to wind-blown crustal dust and vehicular pollution. The SEM analysis of particles showed the presence of a variety of particles, but confirmed the dominance of silicon and soot particles.