Indoor Particulate Matter Concentrations Research Articles

Using artificial neural networks to predict indoor particulate matter and TVOC concentration in an office building: Model selection and method development

Indoor particulate matter and TVOC pose a significant threat to the health of staff working in office buildings. The accurate prediction of indoor particulate matter and TVOC concentrations is crucial for the automatic operation of ventilation and filtration system to control these indoor pollutants. Artificial neural networks have the advantages of high accuracy, real-time monitoring, and multi-source data integrations. Their performances in predicting particulate matter and TVOC concentrations in office buildings need to be further studied. In this study, on-site sampling was conducted in a typical office building at northern China. A standardized database of indoor parameters for four different offices was created with a total of 6072 datasets for each parameter. Three artificial neural network models were used in this study: the back propagation neural network (BP-ANN), the multi-layer neural network (MLNN), as well as the long-term and short-term memory neural network (LSTM). Among these three models, the performance of the MLNN model was the best in predicting PM2.5 and PM10 concentrations. It achieved a FB ranging from -0.02 to -0.01, an NMSE ranging from 0.46 to 0.49 μg/m3, and an R2 between 0.78 and 0.81. The MLNN model and the random forest (RF) classification method were further used to predict indoor TVOC concentrations. The RF model achieved a relatively better performance with a prediction accuracy of 89.2 %. In addition, the models’ generalization abilities were further evaluated by using some smaller datasets. For the MLNN model, when predicting indoor PM2.5 concentration, as the amount of training data decreased from 80 % to 20 %, its FB decreased from 0.41 to 0.03, its NMSE changed from 1.53 μg/m3 to 0.53 μg/m3, and its R2 decreased from 0.69 to 0.07. The results in this study can contribute to the use of artificial intelligence algorithm in office buildings aiming at indoor pollutants control.

Overview of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) Field Experiment.

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles.

Hybrid models of machine-learning and mechanistic models for indoor particulate matter concentration prediction

While indoor PM2.5 concentrations are generally lower than those outdoors, daily exposure indoors can significantly exceed outdoor levels, given that people spend over 90% of their time inside. This study systematically compares traditional methods with machine learning(ML)-based approaches in the context of indoor particulate matter concentration prediction and control research, with the aim of combining their strengths for more effective outcomes.One-year indoor PM concentration data from a non-interference-operating target office were used to develop ML models. Input variables, including background concentrations (outdoor and hallway), office conditions, and weather parameters, were employed with the CNN-LSTM algorithm. The ML models, enhanced with ensemble techniques, achieved a prediction accuracy of R2 = 0.943 on the test set, not considered during model development.Using predictions under step-change events in outdoor PM2.5 levels, the ML model determined the infiltration factor, PM2.5 removal rates, and particle penetration rates, hence mechanistic model coefficients. Collaborative hybrid models, integrating ML and mechanistic approaches, yielded dynamic models (R2 = 0.928) applicable under general conditions, whereas conventional mechanistic models derived from controlled experiments were limited to controlled conditions.

A Comprehensive Analysis of Risk Assessment of Particulate Matter in Five Health Centers in Akure, Nigeria

This study presents a comprehensive assessment of indoor particulate matter (PM) concentrations, focusing on PM1, PM2.5, and PM10 in five primary health centers (PHCs): Arakale (Urban), the Federal College of Agriculture (FECA) (Urban), Iju (semi-urban), Oba-Ile (semi-urban), and Owode (Rural) within Akure local government areas in Nigeria. The primary novelty of this research lies in its detailed exploration of the toxicity potential of these PM fractions, providing valuable insights into the local air quality and associated health risks. This study assesses the health implications by calculating the toxicity potential (TP), relative risk (RR), excess risk (ER), and attributable fractions (Afs) for cardiopulmonary and lung cancer mortality. The results reveal varying RR values for all-cause mortality, with Arakale showing a slight elevation (RR: 1.061), indicating potential health risks. ER values for cardiopulmonary mortality range from 14.728 ± 7.25 to 19.04 ± 0.38, emphasizing the substantial excess risk associated with long-term PM2.5 exposure. The study also uncovers Afs for cardiopulmonary mortality ranging between 11.03 ± 0.31 and 19.22 ± 0.04, underscoring the significant contribution of PM to cardiovascular and respiratory health risks. Similarly, Afs for lung cancer mortality range from 10.03 ± 0.012 to 17.13 ± 0.10, highlighting the substantial association between PM2.5 exposure and lung cancer risk. These findings underscore the urgent need for targeted air quality management strategies and public health interventions in the studied locations to mitigate the heightened health risks associated with particulate matter pollution. The calculated RR, ER, and AF values offer crucial insights into the complex relationship between PM exposure and adverse health outcomes, providing a foundation for informed decision-making and future research endeavors.

Indoor particulate matter concentrations and air cleaner intervention association with biomarkers in former smokers with COPD

BackgroundIndoor pollutants have been associated with worse clinical outcomes in chronic obstructive pulmonary disease (COPD). Elevated biomarkers are associated with ambient pollution exposure, however the association with indoor pollution remains unclear. MethodsFormer smokers with spirometry-confirmed COPD were randomized to portable air cleaner or placebo. Indoor particulate matter (PM2.5, PM10, and ultrafine particles [UFP; PM<0.1]) and biomarkers were measured longitudinally at pre-specified intervals and course PM fraction (PM10-2.5) was calculated. Biomarkers were categorized based on associations with biologic mechanisms: inflammation (white blood cell count, interleukin [IL]-6, IL-8, IL-1β, tumor necrosis factor-α, interferon-γ, serum amyloid A), platelet activation (P-selectin, CD40 ligand [CD40L], 11-dehdydro-thromboxane-B2 [11dTxB2]), endothelial dysfunction (Vascular Cell Adhesion Molecule [VCAM]-1, Intercellular Adhesion Molecule [ICAM]-1), and oxidative stress (thiobarbituric acid reactive substances [TBARS], 8-hydroxydeoxyguanosine, 8-isoprostane). Associations between PM concentrations and each biomarker were analyzed using multivariable linear mixed models. An intention-to-treat analysis was performed to evaluate the air cleaner intervention on the biomarker levels longitudinally. ResultsFifty-eight participants were randomized to each group. Finer PM was more strongly associated with higher IL-8 (mean difference per doubling: UFP 13.9% [p = 0.02], PM2.5 6.8% [p = 0.002], PM10-2.5 5.0% [p = 0.02]) while interferon-γ was associated with UFP and IL-1β with PM10-2.5. UFP and PM2.5 were associated with elevated levels of the oxidative stress biomarkers TBARS and 8-isoprostane respectively. For platelet activation markers, UFP was associated with higher 11dTxB2 while PM2.5 was associated with higher P-selectin and CD40L. Pollutants were not associated with biomarkers of endothelial dysfunction. In intention-to-treat analysis there was no association of the air cleaner intervention with any of the biomarkers. DiscussionAmong former smokers with COPD, elevated levels of indoor air pollutants, particularly ultrafine particles (PM<0.1), were associated with elevated biomarkers of inflammation, platelet activation, and oxidative stress. However, an air cleaner intervention that reduced PM did not significantly reduce biomarker levels.

Soft sensor for predicting indoor PM2.5 concentration in subway with adaptive boosting deep learning model

Public health depends on indoor air quality (IAQ), hence soft measurement techniques must be implemented in the subway environment for more precise and reliable monitoring of indoor particulate matter concentration levels. Adaptive boosting (AdaBoost), an ensemble learning technique, is simple to code and less prone to overfitting. Compared to a single model, it is better able to take into consideration the intricate elements included in air quality data. It is suggested to use an adaptive boosting of long short-term memory (AdaBoost-LSTM) model and kernel principal component analysis (KPCA) for ensemble learning. The kernel function and PCA are first coupled to create KPCA, which is a nonlinear dimensionality reduction method for IAQ. This removes the negative impacts of noise interference. The learning performance of LSTM is then enhanced using AdaBoost as an ensemble learning technique. The KPCA-AdaBoost-LSTM model can deliver higher modeling performance, according to the results. The R2 reached 0.9007 and 0.8995 when predicting PM2.5 in the hall and platform. SHapley Additive exPlanations (SHAP) analysis was used to interpret the input contributions of the model, enhancing the interpretability and transparency of the proposed soft sensor.

A review on indoor and outdoor factors affecting the level of particulate matter in classrooms of elementary schools

For the past 20 years, particulate matter (PM) pollution has been a national concern in South Korea. In particular, indoor PM concentrations may contribute significantly to human exposure. Elementary school students spend a lot of time in classrooms. Thus, PM in the classroom is one of important factors affecting the health of students. Therefore, South Korea is currently actively conducting various regulations and researches to reduce the concentration of particulate matter in elementary school classrooms. In this study, the concentration of PM and its influencing factors (such as occupants, room type, measurement height, aging of building, ventilation, presence of air purifiers, and indoor and outdoor environmental factors) in classrooms of elementary schools were investigated. Furthermore, measures and future study directions to diminish PM levels in classrooms were discussed. For this, approximately 160 peer-reviewed articles were reviewed. As a result, many studies showed that concentrations of PM in classrooms exceeded the recommended level by the World Health Organization (WHO). In particular, students living in Asia and Europe may be more exposed to PM in classrooms compared to other regions. On the other hand, the concentration of PM and PM2.5/PM10 ratio in classrooms are greatly affected by various indoor and outdoor influencing factors. Through this study, we intend to contribute to the establishment of related policies by understanding the factors that affect PM in elementary school classrooms.

Source Identification and Characterization of Indoor Particulate Matter in Potala Palace Museum

This study aims to determine the sources of indoor particulate matter at the Potala Palace Museum in Tibet, China, and evaluate the potential hazards of these pollutants for cultural relics. Long-term monitoring of indoor and outdoor suspended particulate matter concentrations was conducted, and sediment particle samples were collected. The chemical composition of the deposited particles was determined using X-ray fluorescence analysis (XRF). The outdoor suspended particulate concentration was much lower than that indoors; the indoor PM1-10 concentration was much higher than that outdoors and was less affected by outdoor sources. The sources of indoor deposited particles in the high-plateau museum can be classified into four categories: soil dust brought in by tourists from the outdoors, incense ash, pollution from human activities, and ores. Based on data analysis and discussion, proper ventilation can dilute indoor suspended particulate matter, and the installation of air conditioning systems can control temperature and humidity at 20 °C and about 45–60%, respectively, and reduce the fluctuation value, so as to promote particle deposition and better protect the museum’s cultural relics.

GPU parallelization of particulate matter concentration modeling in indoor environment with cellular automata framework

Nowadays, the CFD approaches for modeling turbulent airflow and particulate matter (PM) concentration distribution are matured despite they all suffer from heavy computational demands. Particularly, in indoor PM concentration modeling, a novel cellular automata (CA) approach in “Modeling particulate matter concentration in indoor environment with cellular automata framework” is developed to achieve almost the same accuracy with improved efficiency as the Eulerian approach. To further enhance its efficiency, this study proposes two parallelization procedures. With the mechanism parallelization, the four PM transport mechanisms (flow advection, turbulent diffusion, gravitational settling, and boundary deposition) are simulated simultaneously instead of sequentially. Besides, using the GPU-based cell parallelization by adopting OpenCL 2.1 under Nvidia CUDA, the execution of all the PM transport mechanisms is performed parallelly on GPUs instead of sequentially on the CPU. Three parallelized CA scenarios, i.e., the parallelized CA approach with only the mechanism parallelization, with only the GPU-based cell parallelization, and with both the two parallelization procedures, are evaluated through two indoor PM concentration experiments. The three parallelized CA scenarios are found to maintain the accuracy but enhance the efficiency by 174%–210%, 1780%–5730%, and 2427%–7695%, respectively. Thus, the GPU-based cell parallelization obtains more efficiency enhancement than the mechanism parallelization. Furthermore, despite the simulations are performed on an i9 PC with an Intel UHD Graphics 630 graphic card, the parallelized CA approach with both the two parallelization procedures can enhance its efficiency up to 24–77 times, proving its considerable potentials as a useful tool for real-time 3D indoor PM distribution modeling.

Energy-focused predictive control for particulate matter concentration and thermal comfort indoors in Delhi

This paper presents a novel, model-based approach for energy-focused indoor particulate matter (PM) concentration control and indoor temperature management for a residential house. A physics-based model is developed to predict PM concentration and temperature indoors as a function of outdoor PM concentration, outdoor temperature, indoor emission, indoor heat generation, and flow rates of air handling unit (AHU), portable air filter (PAF), and air conditioning (AC) systems. The optimized settings of the ventilation systems AHU, PAF, and AC are determined employing a nonlinear model predictive control (MPC) algorithm. A case study in Delhi, one of the most polluted cities in the world, is conducted. Extensive simulation studies under seasonal changes in outdoor conditions and varying indoor emissions demonstrate the ability of the MPC controller to maintain PM concentration and temperature indoors within or close to the desired levels, while optimizing energy consumption.

Investigating the impact of relative humidity and air tightness on PM sedimentation and concentration reduction

Exposure to particulate matter (PM) has a negative impact on human health. The hygroscopicity of PM varies with the chemical composition and particle size, and the sedimentation trends leading to decreased PM concentrations also vary. However, PM hygroscopicity has mostly been considered in terms of outdoor air quality such as visibility and radiation force. Thus, in this study, the relationship between PM and ambient humidity was evaluated through two experiments based on chemical composition and PM particle size analysis, which are representative PM classification methods. Firstly, six aerosols commonly found in indoor and outdoor atmospheres were selected, and the reduction rate of PM number concentrations by particle diameter and mass concentrations (PM10 and PM2.5) was evaluated at different relative humidity(R.H) values. The results indicated that the PM concentration was effectively decreased when indoor R.H was >70%, and the spread of PM was effectively prevented when indoor R.H was >50%. Secondly, measurements were obtained from four houses with different levels of airtightness to clarify the relationship between PM and R.H in actual living environments. Higher airtightness corresponded to lower PM concentrations due to higher R.H, but this effect was not observed when the air change rate was high. Therefore, increasing airtightness to increase R.H may not be a practical solution for reducing indoor PM concentrations in all cases. Our findings suggest that indoor air quality management strategies should consider the complex interactions between PM and ambient humidity in specific building contexts.

Field and laboratory evaluation of PurpleAir low-cost aerosol sensors in monitoring indoor airborne particles

Due to the adverse health effects of residential air contaminants, there have been increased efforts to monitor indoor particulate matter (PM) concentration using low-cost sensors. However, little information is available about the performance of low-cost sensors in monitoring indoor aerosols. We established a research framework to examine the performance of a widely used low-cost sensor in the U.S. (PurpleAir) along with two research-grade light scattering sensors (Grimm 11-A, Sidepak AM520) in a laboratory chamber and a full-scale residential testbed. The results show that low-cost sensors can yield relatively high intra-model consistency for mass concentrations; however, the consistency is lower when measuring particles >1 μm than research-grade sensors. Regression analysis with research-grade sensors shows higher linearity for mass concentration than number concentration. These trends of mass and number concentrations are likely attributed to the size selectivity of Plantower PMS5003 sensor in PurpleAir that constrains the number fractions of specific particle size bins. The results also show that concentration discrepancy between the low-cost sensor and research-grade sensor increases as indoor mass concentration increases, suggesting that sensor quality assurance is needed for episodic indoor emission events that lead to elevated PM2.5 concentrations (>100 μg m−3).

Associations of pre- and postnatal exposures with optic nerve status in young adults.

We aimed to explore the effect of multiple pre- and postnatal exposures on optic nerve status in young adults due to this critical period for development. We analysed peripapillary retinal nerve fibre layer (RNFL) status and macular thickness at age 18 years in the Copenhagen Prospective Studies on Asthma in Childhood 2000 (COPSAC2000 ) cohort in relation to several exposures. Of the 269 participants (median (IQR) age, 17.6 (0.6) years; 124 boys), 60 participants whose mothers had smoked during pregnancy had a thinner RNFL: adjusted mean difference -4.6 μm (95% CI -7.7; -1.5 μm, p = 0.004) compared with participants whose mothers had not smoked during pregnancy. A total of 30 participants who were exposed to tobacco smoke both during foetal life and childhood had thinner RNFL: -9.6 μm (-13.4; -5.8 μm, p < 0.001). Smoking during pregnancy was also associated with a macular thickness deficit: -4.7 μm (-9.0; -0.4 μm, p = 0.03). Higher indoor concentrations of particulate matter 2.5 (PM2.5) was associated with thinner RNFL: -3.6 μm (-5.6; -1.6 μm, p < 0.001) and a macular deficit: -2.7 μm (-5.3; -0.1 μm, p = 0.04) in the crude analyses, but not in the adjusted analyses. No difference was found among participants who smoked at age 18 years compared with non-smokers on RNFL or macular thickness. We found that exposure to smoking during early life was associated with a thinner RNFL and macula at age 18 years. The absence of an association between active smoking at 18 years suggests that the vulnerability of the optic nerve is highest during prenatal life and early childhood.

Feasibility of Multi-Zone Simulation for Estimating Contributions of Outdoor Particulate Pollution to Indoor Particulate Matter Concentration

As concerns about the health effects of particulate matter (PM) are growing, controlling indoor PM has become vital for ensuring occupants’ health. Active strategies, such as air purification and high-performance filtering, are widely implemented to control indoor PM. However, passive strategies, including air-tightness and compartmentalization, are promising alternatives, as demonstrated by recent studies. To enhance the implementation of passive strategies, an appropriate evaluation method for passive designs must be established. The objective of this study was to investigate whether a multi-zone-based method is suitable for the evaluation of passive strategies. Multi-zone simulations were performed for four seasons, and indoor/outdoor concentration (I/O) ratios were obtained for the exterior, interior, and corridor on every floor of the reference building. The I/O ratios at different locations indicated that the outdoor particle transport in the building was accurately estimated according to the airflow rate and path. Moreover, in addition to the effects of changes in the outdoor temperature on PM transport through the building envelope, the particle size is a significant factor affecting indoor PM concentrations. The results of this study indicated that the multi-zone method can effectively estimate the number of outdoor particles that penetrate the building envelope in different seasons and the indoor particle concentration at different indoor locations.

Effect of PM concentration on anemia blood indicators reduced by air purifiers

Housewives perform daily household chores, which directly expose them to indoor particulate matter (PM). Indoor PM exposure is a potential factor that increases systemic inflammation and affects hematopoietic action. This study investigated the decrease in indoor PM concentration by air purifiers on indicators of anemia, such as hemoglobin, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). We analyzed the data of 40 housewives who were subjected to an air purifier intervention in Seoul and Ulsan, Republic of Korea. Indoor exposure levels of PM2.5 and PM10 were analyzed with sensor adjusted and gravimetric measurement. We also investigated the same anemia indicators before and after the installation of an air purifier for three months (short-term: one month, long-term: three months). Statistical analysis was performed by a multiple linear model and a linear mixed model. The indoor PM exposure levels and air purifier use were related to anemia indicators (hemoglobin and MCHC). The increase in indoor PM2.5 level was associated with a decrease in MCHC (β: 0.034, P = 0.031) and hemoglobin (β: 0.013, P = 0.04). The decrease in indoor PM2.5 concentration by air purifier was associated with an increase in MCHC (β: 0.204, P = 0.087) and hemoglobin (β: 0.190, P = 0.039). In particular, after air purifier intervention, the amounts of MCHC and hemoglobin significantly increased. Our results suggest that indoor air purifier use could decrease indoor PM level and the risk of anemia to housewives. Because the effect on anemia is subacute, it was confirmed three months after air purifier operation. In conclusion, anemia indicators can be improved by PM reduction management.

Impacts of PM on Indoor Air Quality of Airport Terminal Buildings in a Core City of North China Plain

With the economic growth and globalization, a great deal of airports are being or planned to be constructed or retrofitted in China. The air quality in airport terminal buildings has not been studied as in-depth as airport ambient air quality. Due to its unique architectural and operational characteristics, the airport terminal buildings have individual performance of indoor air quality. The contribution of outdoor particulate matter (PM) to indoor environment and passengers’ exposure to indoor PM is not well understood. The indoor (i.e., terminal buildings) and outdoor PM concentrations with particle sizes from 0.25 to 32 μm of Tianjin Binhai International Airport (IATA: TSN) were monitored continuously during two cases in winter and summer 2020. Higher indoor PM concentrations occurred in winter as well as in the arrival halls of TSN. During winter case, the indoor sources contributed more to the indoor environment than the outdoor sources, whereas the opposite was found during summer case. Sharp variation of indoor PM number concentrations existed in particles within the range of 0.25–0.40 μm in size, with a peak number concentration at particle size of about 0.30 μm. PM with smaller particle sizes were more likely to enter the indoor environment from outdoor. The comprehensive exposure in the TSN terminal buildings was higher than those in the transportation microenvironments, residences and other buildings. Significant diurnal variations of Respiratory deposition dose (RDD) were observed under various exposure durations. Our results highlight the need for further monitoring and improving the air quality in the terminal buildings.

Analysis of the influence of pressure difference across building envelope on indoor particulate matter concentration

Outdoor PM2.5, penetrated by infiltration, has a continuous and important effect on indoor environments. To determine the infiltration rate, it is important to consider the airtightness level and pressure difference across an envelope. This study investigated the influence of pressure difference and airtightness on indoor PM2.5 in a residential building with natural ventilation. Field measurements were conducted in eight residential households. The indoor and outdoor PM2.5 concentrations and pressure differences across the envelope were measured simultaneously. The airtightness of each unit was determined via a blower door test. As a result, the indoor PM2.5 concentration exhibited a significant correlation with airtightness and envelope pressure difference. However, the correlation between the PM2.5 I/O ratio and the pressure difference was found to weaken as the airtightness decreased. This appears to be influenced more by other external factors in addition to pressure under loose airtightness conditions. Thus, the influence of the factors on the penetration of PM2.5 can vary depending on the airtightness of the envelope.

Calibration of Low-cost Sensors for Measurement of Indoor Particulate Matter Concentrations via Laboratory/Field Evaluation

Calibration of Low-cost Sensors for Measurement of Indoor Particulate Matter Concentrations via Laboratory/Field Evaluation

Cooking Particulate Matter: A Systematic Review on Nanoparticle Exposure in the Indoor Cooking Environment

Background: Cooking and fuel combustion in the indoor environment are major sources of respirable suspended particulate matter (RSPM), which is an excellent carrier of potentially harmful absorbed inorganic and organic compounds. Chronic exposure to RSPM can lead to acute pulmonary illness, asthma, cardiovascular disease, and lung cancer in people involved in cooking. Despite this, questions remain about the harmfulness of different particulate matter (PM) sources generated during cooking, and the factors influencing PM physico-chemical properties. The most reliable methods for sampling and analyzing cooking emissions remain only partially understood. Objectives: This review aims to comprehensively assess the risks of PM generated during cooking, considering the main sources of PM, PM chemical composition, and strategies for PM physico-chemical analysis. We present the first systematic analysis of PM sources and chemical composition related to cooking. We highlight significant differences between studies using different experimental conditions, with a lack of a standard methodology. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement rules and the Patient, Intervention, Comparison, and Outcome (PICO) strategy for scientific research, three different scientific databases (PubMed, Scopus, and Web of Science) were screened to find scientific articles that measure, collect, and analyze the chemical composition of nanometer- and micrometer-sized PM generated during cooking activities under different conditions. Data are summarized to assess risk, evaluating the main sources and factors influencing PM generation, their chemical composition, and how they have been collected and analyzed in changing experimental conditions. Results: From 2474 search results, there were 55 studies that met our criteria. Overall, the main variable sources of PM in cooking activities relate to the stove and fuel type. The concentration and chemical–physical properties of PM are also strongly influenced by the food and food additive type, food processing type, cooking duration, temperature, and utensils. The most important factor influencing indoor PM concentration is ventilation. The PM generated during cooking activities is composed mainly of elemental carbon (EC) and its derivatives, and the porous structure of PM with high surface-to-volume ratio is a perfect carrier of inorganic and organic matter. Conclusions: This review reveals a growing interest in PM exposure during cooking activities and highlights significant variability in the chemical–physical properties of particles, and thus variable exposure risks. Precise risk characterization improves possible preventive strategies to reduce the risk of indoor pollutant exposure. However, comprehensive PM analysis needs proper sampling and analysis methods which consider all factors influencing the physico-chemical properties of PM in an additive and synergistic way. Our analysis highlights the need for method standardization in PM environmental analyses, to ensure accuracy and allow deeper comparisons between future studies.

Phthalates in glass window films are associated with dormitory characteristics, occupancy activities and habits, and environmental factors.

Phthalates are environmental endocrine disruptors that enter the human body through a variety of pathways and harm human health. The study aimed to explore the associations between phthalate concentrations in glass window films with dormitory characteristics, occupancy activities and habits, and environmental factors, of university dormitories. We surveyed these associations and measured the indoor environmental parameters of 144 dormitories from 13 universities in Beijing. Based on the results, we further explored the factors affecting phthalate concentrations using multivariate logistic regression. The results showed that phthalate concentrations in glass window films were associated with dormitory type, duration of occupancy, daily ventilation duration, window cleaning frequency, indoor relative humidity, light intensity, temperature, and particulate matter (PM10) concentration. To date, there have only been a few studies on the factors that influence phthalate concentrations in glass window films; therefore, further study is needed. Our findings determined the influence of external factors on the different types of phthalates in window films, which helps understand indoor phthalate pollution and evaluate human exposure based on phthalate concentrations in glass window films.