PM Sensors Research Articles

Differentiating local and regional drivers of exceedances of WHO PM2.5 standards with a low-cost sensor network in the greater male’ region (GMR)

Although pollution is visible at certain times in the capital Greater Male’ Region (GMR), Maldives, only limited quantitative measures of the pollution concentrations are available. Hence, this study aims to understand the spatiotemporal patterns of Particulate Matter 2.5 (PM2.5) and its contribution by both local and regional sources. Data was collected using harmonized and calibrated low-cost PM sensors from October 2021 to September 2023 across 8 locations in GMR. Results showed that the annual average PM2.5 concentration in both years surpassed the WHO guideline. Higher levels of PM2.5 were measured in the dry season and the 24-h WHO-recommended level was surpassed in 67% of the days in the dry season. This is the effect of pollutants transported from the subcontinental regions. PM2.5 concentrations in at least 93% of the days in the wet season (Apr–Oct) remained within the daily limit. Similar levels and patterns of PM2.5 concentrations were noticed across all locations, despite higher concentrations and more frequent exceedances were observed in certain locations in GMR. Besides vehicular emission, electricity generation, biomass burning, sea transport and construction activities are the likely local sources of PM2.5. The diurnal trend revealed significant peaks on weekend evenings in some hotspots due to increased outdoor activities. Hence to reduce regional drivers of pollutants, corporations from countries in the region must take action and municipalities need to implement plans to moderate local emissions.

In chamber calibration and performance evaluation of air quality low-cost sensors

Assessing individual exposure to PM2.5 (particulate matter of aerodynamic diameter lesser than 2.5 μm) requires precise monitoring of PM2.5 concentrations at specific geographical and temporal scales. This demand is met globally by low-cost particulate matter sensors, although calibrating them is difficult. In this study, four low-cost PM sensors, Sharp GP2Y1010AU0F, Honeywell HPMA115S0-XXX, Plantower PMSA003-A, and Sensirion SPS30, were calibrated and tested using various aerosols. The calibration method has three steps: individual (considering each sensor independently to a single aerosol type; n = 1), combined (all sensors for a specific model together for a specific aerosol type; n = 4), and generic (all sensors for a given model together to all aerosols; n = 16). Sensor responses are processed using linear, quadratic, power-law, and artificial neural network (ANN) algorithms in each calibration stage. Performance metrics, including coefficient of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage coefficient of variation (% CV), were utilized for assessment. Amongst all the four tested sensors, the Sensirion SPS30 sensors gave the best performance with a minimum R2 value of 0.911 when calibrated with a generic ANN calibration algorithm. Also, the MAPE was less than 10 %, and the RMSE was less than 7 % when exposed to different particles. Sensirion SPS30 showed the lowest inter-sensor variability with % CV less than 6 %. Sensors identified monodisperse polystyrene latex (PSL) particle size in the investigation. Regardless of exposure to 0.3, 0.46, 0.60, or 1.0 μm PSL, the reported number size distribution for the PMSA003 sensor remained consistent and did not align with the results from Grimm. As the PSL size rose, the SPS30 size distribution changed towards larger particle sizes, although it did not always match Grimm data. As the PSL size increased, the sensor's PM1, PM2.5, and PM10 mass proportions altered.

Improving Data Quality of Low-Cost Light-Scattering PM Sensors: Toward Automatic Air Quality Monitoring in Urban Environments

Improving Data Quality of Low-Cost Light-Scattering PM Sensors: Toward Automatic Air Quality Monitoring in Urban Environments

Spatial and temporal variation of cooking-emitted particles in distinct zones using scanning mobility particle sizer and a network of low-cost sensors

Exposure to ambient and household fine-particulate matter is identified as a substantial contributor to premature mortality in India, according to the Global Burden of Disease Studies. This study examines the impacts of typical Indian cooking practices on indoor air quality characteristics by monitoring the evolution of fine and ultrafine particle (UFP) concentration in the dining facility of a residential educational institute in India. The monitoring area was spread across the kitchen (zone 1) and the dining hall (zone 2). A combination of validated low-cost PM sensors (LCS), DustTrak8433, and Scanning Mobility Particle Sizer (SMPS) was utilized for real-time data acquisition while using Liquefied Petroleum Gas (LPG) as the cooking fuel. PM2.5 and UFP concentrations were monitored at 1.3 m and 1.8 m from the floor to assess the vertical variation of pollutants during cooking activities, including breakfast, lunch, and dinner, and processes such as preheating, reheating, stir-frying, and deep-frying. It was found that the prolonged cooking durations involved in high-heat cooking methods like stir-frying and deep-frying resulted in a rise in coarser UFP (300–550 nm) and PM2.5, causing a higher exposure to PM and UFP concentration. PM2.5 levels are higher at upper heights during typical cooking processes because of temperature-driven convection currents and hygroscopic growth of particles due to high humidity levels. Air exchange rates (AER) considerably varied by using chimneys and were low during the controlled (closed doors) compared to mixed ventilation (opened doors) conditions. The maximum AER was obtained during lunch (4.3–9.9 h−1) compared to breakfast (-7.8–6.8 h−1) and dinner (0.55–7.9 h−1). The decrement rate of PM2.5 inside zone 1 was highest during lunch (126 µgm−3 h−1), coinciding with the highest AER during mixed ventilation. It is recommended that improving ventilation and better design of the kitchen can reduce the exposure of PM and UFP in commercial and rural area kitchens.

Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania.

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.

MitH: A framework for Mitigating Hygroscopicity in low-cost PM sensors

Air quality estimation using low-cost sensors is a pressing issue, with meteorological factors often causing measurement discrepancies. Hygroscopicity, arising from humidity’s interaction with particulates, leads to inaccurate PM concentration readings in laser-scattering low-cost PM sensors. Common remedies involve data removal during high relative humidity or reference station calibration, but these solutions are not always practical or accurate due to the localized nature of hygroscopicity. In this paper, the authors present an adaptive correction framework that dynamically models hygroscopicity effectively mitigating humidity’s impact on particle measurements. The framework exploits historical sensors’ data, providing real-time adaptability in any context without relying on reference data, thus improving air quality estimations from low-cost sensors.

Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment

In terms of particle number distribution, the agreement ratio between a low-cost sensor and a research-grade instrument spans several orders of magnitude. The particle number can be constrained as a function of the reported particle number.

Measurements of traffic-related air pollution at a U.S.–Mexico port of entry and its impacts on nearby community

Traffic-related air pollutants are especially prevalent in border cities with multiple ports of entry (POEs). Excessive emissions due to long delays of idling commercial and passenger vehicles at the POEs could exacerbate various types of respiratory health conditions for the POE users and workers. We conducted a 1-month in-traffic air monitoring campaign at a POE in El Paso, Texas using three continuous FEM instruments for three criteria pollutants (PM, O3, and NO2). Concurrent monitoring of PM in the nearby community was performed using a number of low-cost PM sensors. The 1-month average PM2.5 concentration at the POE was found below 11.6 µg/m3, while the 5-min average of in-traffic PM2.5 concentration could be as high as 450 µg/m3. The in-traffic PM2.5 concentrations were comparable to the data concurrently measured in a state-operated community monitoring site approximately 0.4 km away from the POE. Ozone (O3) concentrations remained low in the region. The in-traffic 1-h NO2 concentrations were below the 1-h NAAQS of 100 ppb, except for only two 5-min intervals. Our results suggest that traffic emissions do not result in elevated levels of pollutant concentrations at this POE compared to the concentration levels observed in the nearby community. In addition, the performance and accuracy of the low-cost sensors appear to be less reliable during our study, although the devices were capable of detecting the trends and variability in pollutant concentrations in real time.

Research and Development of Environmental Monitoring Alarm and Automatic Flag Control System for Barracks

This paper proposes a real-time flag alarm system that can monitor air quality and automatically plant colored flags to inform the people in the barracks. This system automatically measures the local PM 2.5 concentrations with PM sensors; and automatically measures the temperature and humidity with temperature and humidity sensors, then converts the measured values into the grades of danger coefficients and the grades of AQI to plant or replace flags by automatic control. The danger coefficient grades are represented by four colored flags, namely, green, blue, yellow, and red; meanwhile, the AQI grades are represented by six colored flags, namely, green, yellow, orange, red, purple, and maroon. Moreover, this system displays all measured data and related information with electronic billboards to provide a reference for people participating in outdoor activities.

Polyborosiloxane-Encapsulated Carbon Nanotube/Polyurethane Foams for Multifunctional Pressure Memory and Pain Bionic Sensors

Electronic skin (e-skin) is a bionic human skin material that is widely used in artificial intelligence devices. Pressure sensors, as the main component of e-skin, can perceive active pressure spatial distribution in real time. However, in addition to perceiving touch and pressure, human skin also has the ability to sense pain after being impacted or heavily pressed. Therefore, it is hoped that e-skin has the ability to sense the occurrence and disappearance of pain. Here, a pressure memory sensor (PM sensor) based on pressure memory foam (PM foam) was fabricated by a simple and easy-to-scale preparation process. PM foam exhibited excellent electrical conductivity due to the dense three-dimensional conductive network formed by carbon nanotubes (CNTs). PM sensors can achieve pressure memory and spontaneous recovery performance due to self-adhesion between the cell walls provided by polyborosiloxane (PBS). We investigated the effects of CNT and PBS contents on PM sensor sensitivity and memory time and realized the adjustment of sensitivity (0.012–0.099 kPa–1) and memory time (54–325 s). The sensor can bionically sense pain under heavy pressure or impact. In addition, PM foam exhibited excellent electromagnetic interference shielding performance and achieved a shielding effectiveness of more than 30 dB. PM foam realized the intelligent regulation of electromagnetic wave reflection and electromagnetic wave absorption through the conversion of a foam state and solid state. PM foam had excellent Joule heating performance to resist extreme cold environments. Multifunctional pressure memory and pain bionic sensors have great significance and application prospects in e-skin.

An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring.

In recent years, the availability on the market of low-cost sensors (LCSs) and low-cost monitors (LCMs) for air quality monitoring has attracted the interest of scientists, communities, and professionals. Although the scientific community has raised concerns about their data quality, they are still considered a possible alternative to regulatory monitoring stations due to their cheapness, compactness, and lack of maintenance costs. Several studies have performed independent evaluations to investigate their performance, but a comparison of the results is difficult due to the different test conditions and metrics adopted. The U.S. Environmental Protection Agency (EPA) tried to provide a tool for assessing the possible uses of LCSs or LCMs by publishing guidelines to assign suitable application areas for each of them on the basis of the mean normalized bias (MNB) and coefficient of variance (CV) indicators. Until today, very few studies have analyzed LCS performance by referring to the EPA guidelines. This research aimed to understand the performance and the possible application areas of two PM sensor models (PMS5003 and SPS30) on the basis of the EPA guidelines. We computed the R2, RMSE, MAE, MNB, CV, and other performance indicators and found that the coefficient of determination (R2) ranged from 0.55 to 0.61, while the root mean squared error (RMSE) ranged from 11.02 µg/m3 to 12.09 µg/m3. Moreover, the application of a correction factor to include the humidity effect produced an improvement in the performance of the PMS5003 sensor models. We also found that, based on the MNB and CV values, the EPA guidelines assigned the SPS30 sensors to the "informal information about the presence of the pollutant" application area (Tier I), while PMS5003 sensors were assigned to the "supplemental monitoring of regulatory networks" area (Tier III). Although the usefulness of the EPA guidelines is acknowledged, it appears that improvements are necessary to increase their effectiveness.

Identifying Particulate Matter Variances Based on Environmental Contexts: Installing and Surveying Real-Time Measuring Sensors

Previous research suggests that there should be environmental solutions for the emerging health threats caused by poor air quality, such as particulate matters (PM, including PM2.5 and PM10). Research related to air quality (measured by PM) using land-use regression and geographically weighted regression shows some patterns among different environmental contexts which could reduce the threats from such elements; however, there is little concrete evidence for such threats. To fill this research gap, this study installed real-time PM sensors at human breathing heights at five locations in Seoul, South Korea, and recorded the PM values collected between November 2021 and January 2023. Three-phase time-series analyses were conducted on the collected data. Lower levels of PM concentration were found in more enclosed spaces. In particular, when a space was surrounded by vegetation, the air quality significantly increased. The purpose of this study is to explore variations in air quality, particularly PMs densities, in different types of land use within urban areas such as Seoul. Greater metropolitan areas such as Seoul have a great number of health problems caused by air quality. This study’s results contribute to policy and decision-making in urban design to tackle such problems and to provide spatial guidelines for public health and welfare.

Evaluating the Performance of Low-cost PM Sensors over Multiple COALESCE Network Sites

Air quality is a global concern, with particulate matter receiving considerable attention due to its impact on human health and climate change. Recent advances in low-cost sensors allow their deployment in large number to measure spatio-temporal and real-time air quality data. Low-cost sensors need careful evaluation with both regulatory approved methods and other data sets to understand their efficacy. In this work, PM concentrations measured by deploying low-cost sensors at four regional sites are evaluated through comparison with satellite-based model MERRA-2 and the SASS reference instrument. Daily PM2.5 mass concentration variation was analyzed at four regional sites of India from January 2020 to July 2020, including pre-lockdown and six different lockdown periods. Higher PM2.5 concentration was observed at Rohtak (119 µg/m3) compared to Mahabaleshwar (33 µg/m3), Bhopal (45 µg/m3) and Kashmir sites during the pre-lock down period. During the lockdown period, the PM2.5 mass concentration was reduced significantly compared to the pre-lockdown period at every location, although the PM2.5 concentration was different at each location. The air quality trend was quite similar in both the measurements, however, MERRA-2 reconstructed PM2.5 was significantly lower in the pre-lockdown period compared to the lockdown periods. Significant differences were observed between low-cost sensor measurements and MERRA-2 reanalysis data. These are attributed to the MERRA-2 modelling analysis that measures less PM2.5 concentration as compared to ground-based measurements, whereas low-cost sensor are ground-based measurements but needs corrections as it is subject to the calibration dependencies and biases.

CFD analysis of a multi-rotor flying robot for air pollution inspection

The aim of the work was to create a CFD model of the flow generated around the drone to estimate the impact of field parameters on the results of actual measurements from PM sensors that are positioned differently in relation to the propellers. The model created with the use of the ANSYS Fluent software allowed one to determine the criterion of their sufficient distance. The robots with four, six and eight rotors were analyzed. For these, the turbulence intensity, velocity and pressure distributions were determined. The paper also presents the results of PM measurements carried out under field conditions using two sensors mounted on the hexacopter robot.

Establishing Correlation between Cruise Ship Activities and Ambient PM Concentrations in the Kotor Bay Area Using a Low-Cost Sensor Network

The analysis of cruise ships is focusing on port areas where they may represent a significant source of anthropogenic emissions. In order to determine the correlation between cruise ship activities (hoteling and maneuvering) in ports with the ambient concentration of pollutants associated with marine diesel fuel combustion, the low-cost sensors are finding their market share due to lower prices compared to the referent ones. In this study, a network of four low-cost PM sensors was used to determine the correlation between ambient PM2.5 and PM10 mass concentrations with cruise ship activities in the Kotor Bay area during 27 days in the peak summer season, with a 10-min resolution. Recorded data and the Openair model were used to investigate the potential relationship between cruise ship operations and temporal fluctuations in PM concentrations in the ambient air. Additionally, an Tier 3 methodology developed through the European Monitoring and Evaluation Programme of the European Environmental Agency (EMEP/EEA) was applied in order to estimate the total cruise ship PM emissions. The study has shown that weather conditions play a significant role in local PM concentrations, so that, with predominant ENE wind directions, the west side of the Bay experienced on average higher concentrations of both PM2.5 and PM10. Rain precipitation and higher winds tend to decrease rapidly ambient PM concentrations. Higher PM levels are associated mainly with lower wind speeds and the inflows from neighboring berths/anchorages. During the maneuvering (arrival and departure) of cruise ships, higher spikes in PM values were detected, being more visible for PM10 than PM2.5. A significant correlation between daily average PM concentrations and cruise ships’ daily estimated PM emission was not found. As a result, higher temporal resolution demonstrated a stronger correlation.

Air Pollution Exposure and Public Transport in Addis Ababa: Vulnerability Scoping Study

The effects of air pollution on human health are well documented in a range of epidemiological studies; exposure increases the risk of lung cancer, heart disease, bronchitis and other cardiorespiratory conditions. Whilst, air pollution is considered to impact on all groups, particularly when exposed over prolonged periods of time, some groups are considered to be more susceptible than others. This scoping study explores the vulnerability of commuters, bus drivers and street vendors who are exposed to poor air quality on buses or at bus stations via the deployment of low-cost PM sensors in both indoor and outdoor settings. The study also probed perceptions of commuters, street vendors and bus drivers regarding air pollution. Findings underscore the vulnerability of these groups to air pollution and suggest that those from low socioeconomic backgrounds are likely to face a triple burden of exposure both at home, in work or during commutes. The air pollution levels at both bus station (outdoor) and bus (indoor) settings regularly exceed World Health Organization (WHO) guideline amounts. Bus station PM2.5 were measured to be on average 113 ± 99 µg/m3 and peaked at 323 ± 203 µg/m31. Further to this, air pollution levels tend to peak during early morning and late evening hours, consistently reaching unhealthy levels throughout the day. Measured levels of PM2.5 on buses ranged between PM2.5 49 ± 19 µg/m3 and 105 ± 45 µg/m3 on journeys measured. Survey findings suggest that survey respondents are relatively cognizant of their inherent vulnerability to air pollution, perceiving their vulnerability on a scale of 1-5 at an average of 3.87/5 (five indicating extreme levels of vulnerability). Respondents demonstrated a mixed understanding of the potential sources of air pollution, though this is to be expected given the relative lack of knowledge regarding air pollution in many societies.

Applying machine learning for large scale field calibration of low‐cost PM2.5and PM10air pollution sensors

AbstractLow‐cost air quality monitoring networks can potentially increase the availability of high‐resolution monitoring to inform analytic and evidence‐informed approaches to better manage air quality. This is particularly relevant in low and middle‐income settings where access to traditional reference‐grade monitoring networks remains a challenge. However, low‐cost air quality sensors are impacted by ambient conditions which could lead to over‐ or underestimation of pollution concentrations and thus require field calibration to improve their accuracy and reliability. In this paper, we demonstrate the feasibility of using machine learning methods for large‐scale calibration of AirQo sensors, low‐cost PM sensors custom‐designed for and deployed in Sub‐Saharan urban settings. The performance of various machine learning methods is assessed by comparing model corrected PM usingk‐nearest neighbours, support vector regression, multivariate linear regression, ridge regression, lasso regression, elastic net regression, XGBoost, multilayer perceptron, random forest and gradient boosting with collocated reference PM concentrations from a Beta Attenuation Monitor (BAM). To this end, random forest and lasso regression models were superior for PM2.5and PM10calibration, respectively. Employing the random forest model decreased RMSE of raw data from 18.6 μg/m3to 7.2 μg/m3with an average BAM PM2.5concentration of 37.8 μg/m3while the lasso regression model decreased RMSE from 13.4 μg/m3to 7.9 μg/m3with an average BAM PM10concentration of 51.1 μg/m3. We validate our models through cross‐unit and cross‐site validation, allowing analysis of AirQo devices' consistency. The resulting calibration models were deployed to the entire large‐scale air quality monitoring network consisting of over 120 AirQo devices, which demonstrates the use of machine learning systems to address practical challenges in a developing world setting.

Performance assessment of NOVA SDS011 low-cost PM sensor in various microenvironments

Over the last 10years, as a possible alternative to the conventional approach to air quality monitoring, real-time monitoring systems that use low-cost sensors and sensor platforms have been frequently applied. Generally, the long-term characteristics of low-cost PM sensors and monitoring have not been thoroughly documented except for a few widely used sensors and monitors. This article addresses the laboratory and field validation of three low-cost PM monitors of the same type that use the NOVA SDS011 PM sensor module over a 1-year period. In outdoor environments, we co-located low-cost PM monitors with GRIMM EDM180 monitors at the National Air Quality Monitoring stations. In indoor environments, we co-located them with a Turnkey Osiris PM monitor. Several performance aspects of the PM monitors were examined: operational data coverage, linearity of response, accuracy, precision, and inter-sensor variability. The obtained results show that inter-monitor R values were typically higher than 0.95 regardless of the environment. The tested monitors demonstrate high linearity in comparison with PM10 and PM2.5 concentrations measured in outdoor air with reference-equivalent instrumentation with R2 values ranging from 0.52 up to 0.83. In addition, very good agreement (R2 values ranging from 0.93 up to 0.97) with the gravimetric PM10 and PM2.5 method is obtained in the indoor environment (30 < RH < 70%). High RH (over 70%) negatively affected the PM monitors' response, especially in the case of PM10 concentrations (high overestimation).

Urban Particulate Matter Hazard Mapping and Monitoring Site Selection in Nablus, Palestine

Few air pollution studies have been applied in the State of Palestine and all showed an increase in particulate matter concentrations above WHO guidelines. However, there is no clear methodology for selecting monitoring locations. In this study, a methodology based on GIS and locally calibrated low-cost sensors was tested. A GIS-based weighted overlay summation process for the potential sources of air pollution (factories, quarries, and traffic), taking into account the influence of altitude and climate, was used to obtain an air pollution hazard map for Nablus, Palestine. To test the methodology, eight locally calibrated PM sensors (AirUs) were deployed to measure PM2.5 concentrations for 55 days from 7 January to 2 March 2022. The results of the hazard map showed that 82% of Nablus is exposed to a high and medium risk of PM pollution. Sensors’ readings showed a good match between the hazard intensity and PM concentrations. It also shows an elevated PM2.5 concentrations above WHO guidelines in all areas. In summary, the overall average for PM2.5 in the Nablus was 48 µg/m3. This may indicate the effectiveness of mapping methodology and the use of low-cost, locally calibrated sensors in characterizing air quality status to identify the potential remediation options.

Indoor and Ambient Air Pollution in Chennai, India during COVID-19 Lockdown: An Affordable Sensors Study

The Tamil Nadu Air Pollution and Health Effects study (TAPHE-2) aims to evaluate the relationship between air pollution and birth outcome in a rural-urban cohort of 300 pregnant women. Due to COVID-19 related lockdowns, some TAPHE-2 activities were delayed; however, continuous indoor and outdoor air quality data were collected in and around Chennai, India. We report here the impact of graded COVID-19 lockdown on indoor particulate matter (PM2.5 and PM10) levels based on calibrated data from affordable real-time PM sensors called atmos™ and ambient PM levels from publicly available regulatory monitors. The study period was between 11 March and 30 June 2020 (i.e., 100 days of continuous monitoring), which coincided with four phases of a nationwide graded lockdown. Field calibration coefficients for the atmos PM were derived by collocating them with reference-grade PM monitors. The normalized root mean square error (NRMSE) of the atmos hourly PM2.5 (PM10) improved from 41% to 15% (33% to 18%) after applying the field calibration coefficients. Lockdowns resulted in significant reductions in indoor and ambient PM levels, with the highest reduction observed during lockdown phase 2 (L2) and phase 3 (L3). Reductions as high as 70%, 91%, and 62% were observed in ambient PM2.5, indoor PM2.5, and indoor PM10 relative to pre-lockdown levels (PL), respectively. The indoor PM2.5/PM10 ratio decreased during the lockdown, suggesting a decline in the fine mode dominance in PM10. The indoor-to-outdoor (I/O) ratios in PM2.5 marginally increased during L1, L2, and L3 phases compared to that of PL levels, suggesting an uneven reduction in indoor and ambient PM2.5 levels during the lockdown.