Indoor PM2 Research Articles

Investigation of source and infiltration of toxic metals in indoor PM2.5 using Pb isotopes during a season of high pollution in an urban area.

Lead (Pb) isotope ratio has been applied in source investigation for particulate matter in size < 2.5μm. However, arsenic (As) and cadmium (Cd) are carcinogenic to human and their isotope analysis is difficult. This study investigated whether the Pb isotope ratio was a useful indicator in identifying the sources of As and Cd indoors and investigating its influencing factors. This study also calculated the infiltration factor (Finf) for metals to assess the influences of indoor- and outdoor-generated metals to indoor air. The As and Cd concentrations in indoor air were 0.87 ± 0.69 and 0.19 ± 0.15ng/m3, respectively; the corresponding values for outdoor air were 1.44 ± 0.80 and 0.33 ± 0.19ng/m3. The Finf of As and Cd were 0.60 ± 0.37 and 0.58 ± 0.39, and outdoor was a predominant contributor to indoor As and Cd. The Pb isotopes ratio indicated that traffic-related emission was a major contributor to Pb. The Pb concentration was associated with those of As and Cd in indoor or outdoor air, as was the 208Pb/207Pb ratio in outdoor air. Significant correlations between indoor 208Pb/207Pb values and As and Cd concentrations in indoor air were found only in study houses with air change rate > 1.5h-1. These findings suggested that traffic-related emission was identified as a major source of As and Cr. The 208Pb/207Pb is a useful indicator in investigating the source of As and Cd; however, the air change rate influences the applicability of this approach on source identification.

Characterisation of polycyclic aromatic hydrocarbons associated with indoor PM0.1 and PM2.5 in Hanoi and implications for health risks

Polycyclic aromatic hydrocarbons (PAHs) associated with indoor PM pose a high risk to human health because of their toxicity. A total of 160 daily samples of indoor PM2.5 and PM0.1 were collected in Hanoi and analysed for 15 PAHs. In general, the concentrations of carcinogenic PAHs (car-PAHs) accounted for 21% ± 2%, 19.1% ± 2%, and 26% ± 3% of the concentrations of 15 PAHs in PM2.5, PM0.1-2.5, and PM0.1, respectively. Higher percentages of car-PAHs were found in smaller fractions (PM0.1), which can be easily deposited deep in the pulmonary regions of the human respiratory tract. The concentrations of 15 PAHs were higher in winter than in summer. The most abundant PAH species were naphthalene and phenanthrene, accounting for 11%–21% and 19%–23%, respectively. The PAH content in PM0.1 was almost twice as high as those in PM2.5 and PM0.1-2.5. Principal component analysis found that vehicle emissions and the combustion of biomass and coal were the main outdoor sources of PAHs, whereas indoor sources included cooking activities, the combustion of incense, scented candles, and domestic uses in houses. According to the results, 60%–90% of the PM0.1-bound BaP(eq) was deposited in the alveoli region, whereas 63%–75% of the PM2.5-bound BaP(eq) was deposited in head airways (HA), implying that most of the particles deposited in the HA region were PM0.1-2.5. The contributions of dibenz[a,h]anthracene and benzo[a]pyrene were dominant and contributed from 36% to 51% and 31%–50%, respectively, to the carcinogenic potential, whereas benzo[a]pyrene contributed from 30% to 49% to the mutagenic potential for both size fractions. The incremental lifetime cancer risk, simulated by Monte Carlo simulation, was within the limits set by the US EPA, indicating an acceptable risk for the occupants. These results provide an additional scientific basis for protecting human health from exposure to indoor PAHs.

Evaluation of indoor secondary organic aerosol concentrations and contributions in Chinese residences: Insights from field testing

Indoor secondary organic aerosols (SOAs) generated from reactive organic gases (ROGs) reacting with O3 have a substantial impact on indoor PM2.5 levels. China's recent decrease in atmospheric PM2.5 and increase in atmospheric O3 have resulted in correspondingly lower levels of indoor PM2.5 and higher levels of indoor O3, which will further affect indoor SOA concentration. Thus, the aims of this study were to evaluate indoor SOA concentrations and their contribution to indoor PM2.5 and analyze the impact of the air exchange rate, window-opening status, proportion of indoor organic particles, and O3 concentrations on indoor SOA generation. To this end, measurements were conducted in Beijing, Shanghai, and Shenzhen, China by synchronously collecting samples of indoor ROGs and O3, along with indoor and outdoor PM2.5 concentrations. We found a maximum indoor SOA concentration of 35.23 μg/m3, contributing over 50% to indoor PM2.5, although most samples had an average concentration of 1.65 ± 4.89 μg/m3. Shanghai had the lowest average SOA concentration and contribution, whereas Shenzhen had the highest. Terpenoid compounds were identified as crucial precursors for indoor SOA generation, with the indoor O3 concentration being an important influencing factor. Compared to the measured benchmark scenario, when the O3 concentration reached 156 ppb, the average indoor SOA concentration increased approximately 20-fold while its contribution to indoor PM2.5 increased by approximately 7-fold. Our results provide a meticulous look at indoor SOA pollution and its determinants, the knowledge of which is pivotal for the implementation of effective strategies to mitigate indoor SOA pollution.

Indoor and Personal PM2.5 Samples Differ in Chemical Composition and Alter Zebrafish Behavior Based on Primary Fuel Source.

Fine particulate matter (PM2.5) exposure has been linked to diverse human health impacts. Little is known about the potential heterogeneous impacts of PM2.5 generated from different indoor fuel sources and how exposure differs between personal and indoor environments. Therefore, we used PM2.5 collected by one stationary sampler in a kitchen and personal samplers (female and male participants), in homes (n = 24) in Kheri, India, that used either biomass or liquified petroleum gas (LPG) as primary fuel sources. PM2.5 samples (pooled by fuel type and monitor placement) were analyzed for oxidative potential and chemical composition, including elements and 125 organic compounds. Zebrafish (Danio rerio) embryos were acutely exposed to varying concentrations of PM2.5 and behavioral analyses were conducted. We found relatively high PM2.5 concentrations (5-15 times above World Health Organization daily exposure guidelines) and varied human health-related chemical composition based on fuel type and monitor placement (up to 15% carcinogenic polycyclic aromatic hydrocarbon composition). Altered biological responses, including changes to mortality, morphology, and behavior, were elicited by exposure to all sample types. These findings reveal that although LPG is generally ranked the least harmful compared to biomass fuels, chemical characteristics and biological impacts were still present, highlighting the need for further research in determining the safety of indoor fuel sources.

Indoor and Outdoor Air Quality Concentration Levels in Selected Hospital Environments in Kigali, Rwanda

BackgroundExposure to polluted air is a significant cause of negative health effects. Air quality is crucial in hospital environments as patients and healthcare workers spend more time in such settings for treatment where they experience prolonged and repetitive exposure; however, comprehensive studies on air quality in hospital environments in Rwanda are scarce.ObjectiveThis study aimed to determine the indoor and outdoor air quality concentration levels in selected hospitals and investigate potential sources of air pollution.MethodsThis descriptive cross-sectional study was conducted in two public and two private hospitals in Kigali that were randomly selected using a simple random sampling technique. Real-time particulate matter (PM), PM2.5 and PM10 were measured using calibrated Purple Air PA-II sensors. An observation checklist was used to identify potential sources of air pollution. One way ANOVA and t-tests were performed.ResultsAir quality levels in selected hospitals exceeded acceptable limits. The daily average indoor PM2.5 concentration ranged from 23.52 μg/m³ to 121.60 μg/m³, and the PM10 levels varied from 25.98 μg/m³ to 131.17 μg/m³. In all hospitals, the difference in average indoor and outdoor PM2.5 and PM10 concentrations were not statistically significant. ConclusionAll recorded concentrations exceeded the WHO air quality guidelines. The study calls for strategies to improve air quality in hospitals.Rwanda J Med Health Sci 2023;6(3):389-397

Assessing residential PM2.5 concentrations and infiltration factors with high spatiotemporal resolution using crowdsourced sensors.

Building conditions, outdoor climate, and human behavior influence residential concentrations of fine particulate matter (PM2.5). To study PM2.5 spatiotemporal variability in residences, we acquired paired indoor and outdoor PM2.5 measurements at 3,977 residences across the United States totaling >10,000 monitor-years of time-resolved data (10-min resolution) from the PurpleAir network. Time-series analysis and statistical modeling apportioned residential PM2.5 concentrations to outdoor sources (median residential contribution = 52% of total, coefficient of variation = 69%), episodic indoor emission events such as cooking (28%, CV = 210%) and persistent indoor sources (20%, CV = 112%). Residences in the temperate marine climate zone experienced higher infiltration factors, consistent with expectations for more time with open windows in milder climates. Likewise, for all climate zones, infiltration factors were highest in summer and lowest in winter, decreasing by approximately half in most climate zones. Large outdoor-indoor temperature differences were associated with lower infiltration factors, suggesting particle losses from active filtration occurred during heating and cooling. Absolute contributions from both outdoor and indoor sources increased during wildfire events. Infiltration factors decreased during periods of high outdoor PM2.5, such as during wildfires, reducing potential exposures from outdoor-origin particles but increasing potential exposures to indoor-origin particles. Time-of-day analysis reveals that episodic emission events are most frequent during mealtimes as well as on holidays (Thanksgiving and Christmas), indicating that cooking-related activities are a strong episodic emission source of indoor PM2.5 in monitored residences.

Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing

With the decreasing regional-transported levels, the health risk assessment derived from fine particulate matter (PM2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM2.5 concentration with population distribution to provide the personal daily PM2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM2.5 assimilating method was developed fusing multiple auxiliary predictors, achieving higher accuracy (R2 = 0.78–0.82) than the chemical transport model outputs without any post-simulation data-oriented enhancement (R2 = 0.31–0.64). Weekly difference was identified from hourly mobile signaling data in 30-m resolution population distribution. The population-weighted ambient PM2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM2.5 concentration was 26.5 μg/m3. The internal dose based on the assimilated indoor/outdoor PM2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model.

Temporal Variation of Indoor (a residential space) and Outdoor PM2.5 in Male’, Maldives

This study aims to assess the Indoor Air Quality (IAQ) inside a one room apartment of about 240 square feet in the Henveiru district in Male’, Maldives. Concentrations of Particulate Matter (PM2.5), one of the most detrimental air pollutants, were measured every 15 minutes from 10-09-2021 to 18-12-2021 using low-cost PM BlueSky sensors inside the building (indoor) and simultaneously outdoor, about 300m away from the indoor sensor. The BlueSky monitors were co-located and harmonized before starting the monitoring. The mean concentrations observed indoor and outdoor for 93 days were 10.1±13.1 µg/m3 and 6.5±4.9 µg/m3, respectively. Both results surpass the annual average of 5 µg/m3 guideline of World Health Organization (WHO). The number of days that surpassed the 24-hour WHO limit was 9 for indoor and 7 for outdoors. The diurnal pattern of PM2.5 shows a drastic accumulation of the pollutant inside the building when the only available ventilation (the window) was closed, which was identified at night and also during the daytime. The recorded PM2.5 levels also aligned with household activities like cooking, cleaning, and dusting. There was a significant moderate correlation (r= 0.48) between outdoor and indoor PM2.5. The I/O ratio was 1.95±3.01 indicating the presence of more indoor sources. This study serves as a preliminary assessment of indoor air quality in Male’, Maldives. Based on its findings, efforts should be directed towards ensuring proper ventilation in residential spaces and continuously monitoring air quality in more location for longer periods to ensure healthy IAQ.

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.

Indoor residential and outdoor sources of PM2.5 and PM10 in Nicosia, Cyprus

Cyprus is a typical eastern Mediterranean country that suffers from local emissions, transported anthropogenic pollution, and dust storms all year round. Therefore, exposures to PM in ambient and residential micro-environments are of great public health concern. Our study collected indoor and outdoor PM2.5 and PM10 samples simultaneously in 22 houses in Nicosia, Cyprus, during warm seasons and cold seasons from February 2019 to May 2021. Samples were analyzed for mass and constituents’ concentrations. To determine indoor and outdoor sources of PM in residential environments, we used the EPA positive matrix factorization (PMF) model to conduct source apportionment analyses for both indoor and outdoor PM2.5 and PM10 particles. Generally, six types of residential-level PM sources were resolved: biomass burning, traffic, local or regional secondary sulfate pollution, Ca-rich particles, sea salt, and soil dust. In the source apportionment of PM2.5, the main contribution to outdoor levels (33.1%) was associated with sulfate-rich transported pollution. The predominant contribution to indoor levels (48.0%) was attributed to secondary sulfate pollution as a mixture of local- and regional-scale pollutants. Biomass burning and traffic sources constituted the main outdoor sources of indoor PM2.5, while the Ca-rich particles were identified to almost originate from indoors. By contrast, the largest fraction (29.3%) of the ambient PM10 and a smaller proportion (10.2%) of indoor PM10 were attributed to Ca-rich particles. Indoor PM10 was associated mainly with outdoor sources, except for the soil dust which originated from indoor activities.

High levels of polycyclic aromatic compounds in outdoor and indoor PM10 of an urban residential environment during a winter pollution event in Strasbourg, France

High levels of polycyclic aromatic compounds in outdoor and indoor PM10 of an urban residential environment during a winter pollution event in Strasbourg, France

Large-scale spatiotemporal deep learning predicting urban residential indoor PM2.5 concentration

Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of daily average urban residential PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 μg/m3, root-mean-square error of 13.3 μg/m3, and 95 % prediction interval coverage of 85 %. To demonstrate the application process, this model was applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 μg/m3, far exceeding the WHO standard. The validity of the model at the population level can be further bolstered, making it valuable for assessing and managing indoor air pollution-related health risks.

Fine Particulate Matter Exposure and Health Impacts from Indoor Activities

Exposure to fine particulate matter (PM2.5) is an important contributor to global human disease burden, particularly indoors where people spend the majority of their time and exposure is highest. We propose a framework linking indoor PM2.5 emissions from human activities to exposure and health impacts, expressed in Disability-Adjusted Life Years (DALY). Derived dynamic indoor PM2.5 concentrations—capturing temporal variations through different window-opening scenarios and air renewal rates—are used to estimate uncertainty for a parametric model (up to a factor of 114). Intake fractions (fraction of emitted substance taken in (μgintake/μgemitted)), effect factors (μDALY/μgintake), related impact characterisation factors (health impact per unit mass emitted (μDALY/μgemitted)), and impact scores (health impact per hour activity (μDALY/hactivity)) are provided for 19 one-hour indoor activities and can be flexibly scaled to real activity durations. Indoor concentrations exceeded recommended World Health Organization (WHO) limits for all activities at low ventilation rates. Per person, 98 to 119 μDALY/hactivity (52 to 63 minuteslost/hactivity) was associated with traditional fuel cook stoves, with high air renewal rates (3 and 14 h-1). The burning of candles, at low air renewal rates of 0.2 to 0.6 h-1, results in 7 to 11 μDALY/hactivity (4 to 11 minuteslost/hactivity). Derived impact scores and characterisation factors serve as a starting point for integrating indoor PM2.5 emissions and exposure into life cycle impact and public health assessments.

Strategies for Effective Management of Indoor Air Quality in a Kindergarten: CO2 and Fine Particulate Matter Concentrations.

The educational and play-related activities of children proceed mainly indoors in a kindergarten. High concentrations of indoor PM2.5 and CO2 have been linked to various harmful effects on children, considerably impacting their educational outcomes in kindergarten. In this study, we explore different scenarios involving the operation of mechanical ventilation systems and air purifiers in kindergartens. Using numerical models to analyze indoor CO2 and PM2.5 concentration, we aim to optimize strategies that effectively reduce these harmful pollutants. We found that the amount of ventilation required to maintain good air quality, per child, was approximately 20.4 m3/h. However, we also found that as the amount of ventilation increased, so did the concentration of indoor PM2.5; we found that this issue can be resolved using a high-grade filter (i.e., a MERV 13 grade filter with a collection efficiency of 75%). This study provides a scientific basis for reducing PM2.5 concentrations in kindergartens, while keeping CO2 levels low.

The Effectiveness of a Mechanical Ventilation System for Indoor PM2.5 in Residential Houses.

The mechanical ventilation systems used in houses are designed to reduce carbon dioxide emissions while minimizing the energy loss resulting from ventilation. However, the increase in indoor fine particulate (PM2.5) concentration because of external PM2.5 influx through the ventilation system poses a problem. Here, we analyzed the changes in indoor PM2.5 concentration, distinguishing between cases of high and low outdoor PM2.5 concentrations and considering the efficiency of the filters used in residential mechanical ventilation systems. When using filters with the minimum efficiency reporting value (MERV) of 10 in the ventilation system, the outdoor PM2.5 concentration was 5 μg/m³; compared to the initial concentration, the indoor PM2.5 concentration after 60 min decreased to 73%. When the outdoor PM2.5 concentration was 30-40 μg/m³, the indoor PM2.5 concentration reached 91%. However, when MERV 13 filters were used, the indoor PM2.5 concentration consistently dropped to 73-76%, regardless of the outdoor PM2.5 concentration. Furthermore, by comparing the established equation with the mass balance model, the error was confirmed to be within 5%, indicating a good fit. This allows for the prediction of indoor PM2.5 under various conditions when using mechanical ventilation systems, enabling the formulation of strategies for maintaining indoor PM2.5, as recommended by the World Health Organization.

The disease burden related to time-weighted PM2.5 exposure in China and the potential health benefits of the national standards for indoor air quality: A modeling study

PM2.5 pollution is a major environmental risk factor, causing substantial disease burden and economic loss. The Indoor Air Quality Standard (GB/T 18,883–2022) with the targeted daily average indoor PM2.5 concentration (50 μg/m3) has just been issued in China. To determine whether this new guideline can provide sufficient protection, we evaluate the health and economic burden attributable to the time-weighted average PM2.5 exposure under the three scenarios related to this guideline, using the Global Burden of Disease (GBD) and Willingness-to-pay (WTP) method based on the disease mortality rate, exposure-response relationship and socio economic data. The annual mean time-weighted average PM2.5 (TWPM) concentration in 2020 under the Unchanged, Indoor Air Policy (IAP), Indoor and Outdoor Air Policy (IOAP) scenario were 22.4, 21.7 and 17.5 μg/m3 in China, respectively. Compared with the Unchanged scenario, in which the TWPM-related premature deaths and economic burden were estimated to be 1.22 million and 3.35 trillion RMB Yuan, 0.03 million premature deaths and 8.74 billion RMB Yuan were estimated to be avoided in the IAP scenario; 0.18 million premature deaths and 58.58 billion RMB Yuan in the IOAP scenario. The greater TWPM-related mortality occurred in the heavily polluted and densely populated region, such as the Beijing-Tianjin-Hebei, Fenwei Plain and Sichuan and Chongqing region. The TWPM-related disease burden in China remained obvious even if China complies with the new standard. In comparison to simply improving indoor air quality, boosting both indoor and outdoor air quality may achieve more significant health and economic benefits. Therefore, with the goal to improve indoor air quality, it is still necessary to strengthen outdoor ambient air quality improvement actions.

Towards healthy and energy-efficient buildings in the context of Egypt: Modelling demand-controlled ventilation to improve the indoor air quality in a generic office space in Cairo

Cairo is characterised by high concentrations of ambient air pollutants, especially air particulate matter of diameter less than 2.5 micrometre (PM2.5). Many studies have emphasized the impact of PM2.5 on people’s health and wellbeing including a World Bank report that has attributed 12% of the total annual deaths in Cairo to the exposure to ambient PM2.5. On one hand. improving the energy efficiency of buildings may involve implementing energy efficiency measures that aim to achieve indoor thermal comfort by maximizing the use of natural ventilation and minimizing mechanical air-conditioning. However, while natural ventilation can help reduce CO2 levels, it can also potentially lead to an increase in indoor PM2.5 levels. This study aims to investigate the impact of multiple air filtration scenarios on the energy consumption and the indoor air quality for a shoebox model that aims to represent generic offices Cairo. The study uses EnergyPlus simulations that leverage an Energy Management System script to model the demand-controlled ventilation, apply air filters when required, and simulate the increase in energy use due to the relevant pressure drops in the air system. The results for the scenarios investigated in the study highlighted that air filters can reduce the average indoor PM2.5 levels by nearly 40% during occupancy hours while causing an estimated increase of around 2-7% in the total operational energy. Given data and assumptions relevant to the study context, it was found that filtering the recirculated air while minimizing the introduction outdoor fresh air can be sufficient to minimize indoor PM2.5 levels.

Study on peak hours, ventilation, and resident activities towards indoor air quality on PM2.5 in Surabaya

Several parameters contribute to the standard level of indoor air quality (IAQ), such as PM2.5 concentration, which is the primary parameter in this study. Several variables influence IAQ, such as infiltration and resident activities of outside air pollution through ventilation. Peak-hour traffic congestion can significantly raise outdoor air pollution. This study was aimed at finding out more about the influence of peak hours, the relationship between ventilation, resident activities, and indoor air quality (IAQ) in houses near the main road, as well as suggestions for improving IAQ. AirVisual Pro was the tool used to record PM2.5 concentration, temperature, and humidity; Kestrel 5500 was used to assess wind direction; and questionnaires were completed by residents. SPSS software is utilized for data analysis including the Pearson correlation coefficient, multicollinearity, autocorrelation, and multiple linear regression (MLR). The average indoor and outdoor PM2.5 concentrations in all units during peak and non-peak hours meet the quality standards on weekday and weekend. According to the correlation analysis, indoor and outdoor PM2.5 concentrations had no significant correlation regardless of whether the window is open or closed. Temperature (-1.473), humidity (-0.033), the number of furniture (3.660), fan usage (-8.005), cooking activities (3.755), cleaning activities (14.940), smoking behavior (4.545), and peak hours (6.896) associated with PM2.5 concentration, according to the MLR analysis. Increase fan use, no smoking inside the house, and less furniture are advised to improve IAQ.

기계식 공기청정기 가동에 의한 실내 미세먼지 제거율 비교

기계식 공기청정기 가동에 의한 실내 미세먼지 제거율 비교

Assessing indoor PM2.5 microbial activity in a university campus environments in Beijing

Most people spend 90 % of their time inside, indoor bioaerosols significantly impact human health. This study aimed to investigate the substantial impact of indoor bioaerosols, particularly the harmful small particle-size components within Particulate Matter 2.5 (PM2.5), on human health in a crowded campus setting. Conducted during the summer at a Beijing university, the research assesses microbial activity within PM2.5 in offices (occupied by both teachers and students) and student dormitories, encompassing bedrooms and common areas. The optimized Fluorescein Diacetate (FDA) hydrolysis method was employed for assessment. Indoor microbial activity ranged from 1.18 to 176.56 ng m−3 sodium fluorescein while outdoor activity ranged from 6.14 to 90.15 ng m−3 sodium fluorescein. Meteorological factors, including heavy rain and strong winds, influenced the correlation between indoor and outdoor microbial activity, contrasting with the effect of air pollution levels. Offices with high occupancy and outdoor environments with strong winds or heavy rain often resulted in an I/O ratio >2, and a negative correlation was observed between the I/O ratio and indoor microbial activity. The study underscores the significance of ventilation type (air conditioning vs. natural ventilation), emphasising the importance of maintenance and clean living habits in reducing indoor microbial loads. Furthermore, it identifies population density as a pivotal factor affecting microbial activity in office air. This study contributes to assessing Indoor Air Quality (IAQ) and controlling bioaerosol pollution, and provides an effective measure for rapidly evaluating biological air pollution in crowded indoor environments using the PM-based microbial activity.