Low-cost Particulate Matter Sensors Research Articles

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment

Atmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure, highlighting the crucial need for monitoring PM levels to develop and implement effective risk mitigation measures. Notwithstanding, accurate measurement of PM concentration relies on expensive and cumbersome equipment. Despite the rising popularity of low-cost alternatives, their reliability remains questionable, given their sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. This article proposes a novel approach to efficient correction of low-cost PM sensors. The primary calibration model is a feedforward artificial neural network (ANN), which directly renders predicted output of the corrected sensor based on environmental variables such as temperature, humidity, and atmospheric pressure. The ANN hyper-parameters are identified by aligning time series of prior reference and low-cost sensor readings, which enables the network to learn typical temporal changes of the sensor outcome as a function of the aforementioned parameters as well as operational relationships between the sensor and the reference device. The architecture of the ANN is optimized in terms of the number of neurons in each layer to enhance its generalization capability. Our methodology has been demonstrated using a custom-designed portable monitoring platform and reference data acquired from public stations in Gdansk. The results are indicative of excellent calibration reliability. The achieved correlation coefficients w.r.t. the reference readings are 0.86, 0.88, and 0.72 for PM1, PM10, and PM2.5, respectively, whereas RMSE values are only 3.0, 3.9, and 5.4 µg/m³.

Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction

Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM1 and PM2.5, and 0.76 PM10 (particles categorized as having diameter equal to or less than 1 μm, 2.5 μm, and 10 μm, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 µg/m3.

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM2.5), a significant portion of ambient PM2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM2.5, are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM2.5 both temporally and spatially. Monthly network-average BC was 3 × higher in winter than late spring, while PM2.5 was only 10% lower. In both seasons, average PM2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM2.5, and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM2.5. Thus, including BC in addition to PM2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

Indication Variability of the Particulate Matter Sensors Dependent on Their Location.

Particulate matter (PM) suspended in the air significantly impacts human health. Those of anthropogenic origin are particularly hazardous. Poland is one of the countries where the air quality during the heating season is the worst in Europe. Air quality in small towns and villages far from state monitoring stations is often much worse than in larger cities where they are located. Their residents inhale the air containing smoke produced mainly by coal-fired stoves. In the frame of this project, an air quality monitoring network was built. It comprises low-cost PMS7003 PM sensors and ESP8266 microcontrollers with integrated Wi-Fi communication modules. This article presents research results on the influence of the PM sensor location on their indications. It has been shown that the indications from sensors several dozen meters away from each other can differ by up to tenfold, depending on weather conditions and the source of smoke. Therefore, measurements performed by a network of sensors, even of worse quality, are much more representative than those conducted in one spot. The results also indicated the method of detecting a sudden increase in air pollutants. In the case of smokiness, the difference between the mean and median indications of the PM sensor increases even up to 400 µg/m3 over a 5 min time window. Information from this comparison suggests a sudden deterioration in air quality and can allow for quick intervention to protect people's health. This method can be used in protection systems where fast detection of anomalies is necessary.

Towards a hygroscopic growth calibration for low-cost PM2.5 sensors

Abstract. Low-cost particulate matter (PM) sensors continue to grow in popularity, but issues such as aerosol-size-dependent sensitivity drive the need for effective calibration schemes. Here we devise a time-evolving calibration method for the Plantower PMS5003 PM2.5 mass concentration measurements. We use 2 years of measurements from the Berkeley Environmental Air-quality and CO2 Network sensors deployed in San Francisco and Los Angeles in our analysis. The calibration uses a hygroscopic growth correction factor derived from κ-Köhler theory, where the calibration parameters are determined empirically using US Environmental Protection Agency Air Quality System (EPA AQS) reference data at co-location sites during the period from 2021–2022. The parameters are found to vary cyclically through the seasons, and the seasonal cycles match changes in sulfate and elemental carbon PM composition fractions throughout the year. In both regions, the seasonal RH dependence calibration performs better than the uncalibrated data and data calibrated with the EPA's national Plantower calibration algorithm. In the San Francisco Bay Area, the seasonal RH dependence calibration reduces the root mean square error (RMSE) by ∼40 % from the uncalibrated data and maintains a mean bias much smaller than the EPA national calibration scheme (−0.90 vs −2.73 µg m−3). We also find that calibration parameters forecasted beyond those fit with the EPA reference data continue to outperform the uncalibrated data and EPA calibration data, enabling real-time application of the calibration scheme even in the absence of reference data. While the correction greatly improves the data accuracy, non-Gaussian distribution of the residuals suggests that other processes besides hygroscopic growth can be parameterized for future improvement of this calibration.

Field testing of low-cost particulate matter sensors for Digital Twin applications in nanomanufacturing processes

The EU-project ASINA is testing Low-Cost Particulate Matter Sensors (LCPMS) for industrial monitoring of the concentration of airborne particles, with the purpose of integrating this sensor technology within the data collection layer of Digital Twins (DTs) for manufacturing.This paper shows the results of field performance evaluations carried out with five LCPMS from different manufacturers (Alphasense OPC-N3, Plantower 9003, Sensirion SPS30, Sensirion SEN55 and Tera Sensor NetxPM), during several field sampling campaigns, conducted in four pre-commercial and commercial pilot lines (PLs) that manufacture nano-enabled products, belonging to the ASINA and OASIS H2020 EU-projects [2,28]. Field tests consisted of deploying LCPMS in manufacturing process, measuring in parallel with collocated reference and informative instruments (OPS TSI 3330/CPC TSI 3007), to enable intercomparison.The results show the complexity and differential response of the LCPMS depending on the characteristics of the monitored scenario (PL). Overall, they exhibit uneven precision and linearity and significant bias, so their use in industrial digital systems without proper calibration can lead to uncertain and biased measurements. In this sense, simple linear models are not able to capture the complexity of the problem (non-linear systems) and advanced calibration schemes (e.g. based on machine learning), applied “scenario by scenario” and in operating conditions as close as possible to the final application, are suggested to achieve reliable measurements with the LCPMS.

Evaluation and calibration of low-cost particulate matter sensors for respirable coal mine dust monitoring

Evaluation and calibration of low-cost particulate matter sensors for respirable coal mine dust monitoring

Development of a physics-based method for calibration of low-cost particulate matter sensors and comparison with machine learning models

Low cost particulate matter sensors are receiving significant attention as they can be used in large number for spatial and temporal measurement of PM mass and number concentration. However, the data reliability is questionable as these sensors are affected by numerous parameters such as temperature, relative humidity, and hygroscopicity of particles. To ensure accurate and reliable measurement of particulate matter concentrations, performance evaluation and calibration of LCS co-located with reference instrument at site is essential. In this study, the performance of the low-cost sensor (APT Maxima) was evaluated with the reference instrument SASS (Speciation Air Sampling System) sampler for developing suitable calibration factor that include impact of the meteorological parameter such as relative humidity and temperature, and hygroscopicity. In this study, we have demonstrated a systematic physics-based method for calibration of LCS based on κ-Köhler theory and Mie theory. For comparison with statistical models, linear regression and machine learning algorithm were also applied. In physics-based model, calculated total light scattered intensity shows good linearity with reference PM2.5 measurements. Physics based model performed better for both the sites as compared to MLR, kNN, RF, and GB ML algorithms with R2, RMSE, and MAE values of 0.72,18.21, and 13.36 for Bhopal site, and 0.91, 7.84, and 5.76 for Kashmir site, respectively. Study indicates that physics-based approach for LCS calibration is suitable and can be transferable to different sites.

Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning.

Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021-2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM2.5) in the garden of one participant's house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM2.5 measurements at daily resolution (bias/RMSE: 9.45/11.65 μg m-3). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model's RMSE, MAE, and bias by 44-56, 38-48, and 41-52%, respectively.

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution-Part B-Particle Number Concentrations.

Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors' to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity.

Performance Evaluation of Low-Cost Particulate Matter Sensors for PM2.5 and PM10 Measurement

This paper presents the performance evaluation of low-cost particulate matter sensors (LCPMS) using light scattering technique. By creating a device for controlling sensors and measuring particulate matter with diameter of less than 2.5 microns (PM2.5) and 10 microns (PM10) for high- and low-concentration environments in a test chamber and for comparing with the average PM2.5 and PM10 of 1-hour and 24-hour continuous of Pollution Control Department (BAM1020) in February to June 2022. From the PM2.5 and PM10 results, it was found that LCPMS and BAM1020 are very strong correlation in the perfect positive. Correlation coefficient square (R2) and measurement error (EM) are decreased by decreasing sampler time of LCPMS detection. The average PM2.5 of 24-hour continuous of LCPMS_5 is very strong correlation with BAM1020 by R2 = 0.7786 and EM = 20.35. The average PM10 of 24-hour continuous of LCPMS_1 is very strong correlation with BAM1020 by R2 = 0.8662 and EM = 13.53.

Technical note: Identifying a performance change in the Plantower PMS 5003 particulate matter sensor

Communities, researchers, and citizen scientists have increasingly been deploying low-cost particulate matter (PM) sensors to understand pollution levels and personal pollution exposure. Currently, tens of thousands of these low-cost sensors have been deployed throughout the world. One of the most commonly used sensors is the Plantower PMS 5003. In 2021, PurpleAir (one of the largest sensor networks) noted a change in the performance of their PM sensors that could be identified by the ratio of PM counts in two size bins (≥0.3 μm and ≥0.5 μm). This study confirmed that in June 2021, an updated version of the Plantower PMS 5003 (PMS-U) was deployed between approximately June 2021 and January 2022. Our laboratory study revealed that the PMS-Us PM2.5 concentrations were biased low for raw concentrations (CF = 1) below 16 μg/m3. Our field study also suggests that this bias translates into an approximately 3 μg/m3 lower PM2.5 concentrations (raw) over the long term, and between March to May 2023, more than 10% of outdoor sensors in the PurpleAir network are PMS-Us. This level of bias would not affect some uses of low-cost sensor networks, such as during wildfires. However, this bias could be important for health or environmental justice studies. This study also developed and evaluated a correction method for PMS-U sensors to reduce the observed bias in PMS-U sensors.

Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data

Low-cost particulate matter (PM) sensors have been widely used following recent sensor-technology advancements; however, inherent limitations of low-cost monitors (LCMs), which operate based on light scattering without an air-conditioning function, still restrict their applicability. We propose a regional calibration of LCMs using a multivariate Tobit model with historical weather and air quality data to improve the accuracy of ambient air monitoring, which is highly dependent on meteorological conditions, local climate, and regional PM properties. Weather observations and PM2.5 (fine inhalable particles with diameters ≤ 2.5 μm) concentrations from two regions in Korea, Incheon and Jeju, and one in Singapore were used as training data to build a visibility-based calibration model. To validate the model, field measurements were conducted by an LCM in Jeju and Singapore, where R2 and the error after applying the model in Jeju improved (from 0.85 to 0.88) and reduced by 44% (from 8.4 to 4.7 μg m−3), respectively. The results demonstrated that regional calibration involving air temperature, relative humidity, and other local climate parameters can efficiently correct the bias of the sensor. Our findings suggest that the proposed post-processing using the Tobit model with regional weather and air quality data enhances the applicability of LCMs.

Simulation of the influence of residential biomass burning on air quality in an urban area

Residential biomass burning is often one of the dominant local sources of air pollution in urban areas during the winter months. However, the corresponding particle emissions are quite uncertain and chemical transport models (CTMs) often have difficulties reproducing the observed winter particulate matter (PM) concentrations. In this work, we combine measurements from a low-cost PM sensor network and the CTM PMCAMx (Particulate Matter Comprehensive Air quality Modeling with extensions) to estimate the spatial and temporal distribution of biomass burning emissions in an urban area.An estimate of 8100 kg d−1 (40 g d−1 per person) PM2.5 emissions from residential biomass burning was calculated for the city of Patras including organic compounds with effective saturation concentrations C* up to 104 μg m−3. The spatial distribution of the emissions was based on the density of fireplaces in the city. The temporal distribution of emissions was based on measurements of the biomass burning organic aerosol (bbOA) and are higher from 18:00 to 22:00 LT peaking at 21:00 LT. The nighttime (18:00-22:00 LT) bbOA emissions were 4 times higher than the corresponding morning (8:00-13:00 LT) ones. Estimated biomass burning emissions vary from day to day based on ambient temperature, with higher emissions during the colder days. The Volatility Basis Set (VBS) was used to simulate bbOA that is treated as semi-volatile and chemically reactive.PMCAMx predicts that bbOA concentration reaches on average 15 μg m−3 in the high-density bbOA emission area during the simulated period, while the corresponding peak hourly concentrations are higher than 40 μg m−3 most of the nights. The average predicted bbOA concentration in the city center is 3–6 μg m−3 and is lower than 1 μg m−3 at the suburbs. The model predicts that bbOA concentrations peak at 9:00 LT in the morning and at 21:00 LT during the nighttime, reproducing the bbOA measurements.

121 Applying Low-Cost Particulate Matter Sensors for Characterizing Occupational Exposure; Findings from Field Studies in Different Industries

Abstract Low-cost particulate matter sensors are a up-and-coming technology for monitoring occupational exposure to hazardous substances in high spatial and temporal resolutions. Low costs, small sizes and high measurement frequencies offer the potential to continuously, automatically and remotely measure dust exposure and emission in the workplace. However, much remains unknown about how these devices can be successfully applied in occupational settings. TNO (the Netherlands) has, in collaboration with HSE (UK) and NIOSH (US), performed field studies at seven companies in the bakery, woodworking and welding industry to explore the use of sensors. A total of 145 full / half-shift measurements were collected for 37 workers and 15 static positions. Measurement data was obtained using four types of sensor and conventional respirable gravimetric and high-resolution reference devices. High-resolution contextual information was collected using additional sensors, including an ultrasound-based indoor positioning system, to investigate how these can further characterize exposure situations. Sensor accuracy compared to reference measurements ranged up to R2-values of 0.92, but large variations were observed within and between industries. Overall, stationary measurements showed less variation in performance compared to personal measurements. Different types of sensor seemed more accurate for specific industries. Contextual measurements provided additional information on when and where exposure occurred, for example by dynamic hazard maps based on indoor location and personal sensor measurements. Although performance of sensors remains lower than conventional methods, they may provide accurate measurements in specific situations. Additionally, sensors can provide new types of information that cannot be captured by time-integrated measurements.

Enhancing Machine Learning with Low-Cost P M2.5 Air Quality Sensor Calibration using Image Processing

Low-cost particulate matter sensors, due to their increased mobility compared to reference monitors, are transforming air quality monitoring. Calibrating these sensors requires training data from reference monitors, which is traditionally done through conventional procedures or by using machine learning techniques. The latter outperforms traditional methods, but still requires deployment of a reference monitor and significant amounts of training data from the target sensor. In this study, we present a cutting-edge machine learning-based transfer learning technique for rapid sensor calibration with Co-deployment with reference monitors is kept to a minimum. This approach integrates data from a small number of sensors, including the target sensor, reducing the dependence on a reference monitor. Our studies reveal that In recent research, a transfer learning method using a meta-agnostic model has been proposed, and the results proved to be much more effective than the previous method. In trials, calibration errors were successfully reduced by up to 32\% and 15\% compared to the best raw and baseline observations. This shows the great potential of transfer learning methods to increase the effectiveness of learning in the long term. These results highlight the potential of this innovative transfer learning technique for rapidly and accurately calibrating low-cost particulate matter sensors using machine learning.

Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance

Particulate Matter (PM) low-cost sensors (LCS) present a cost-effective opportunity to improve the spatiotemporal resolution of airborne PM data. Previous studies focused on PM-LCS-reported hourly data and identified, without fully addressing, their limitations. However, PM-LCS provide measurements at finer temporal resolutions. Furthermore, government bodies have developed certifications to accompany new uses of these sensors, but these certifications have shortcomings. To address these knowledge gaps, PM-LCS of two models, 8 Sensirion SPS30 and 8 Plantower PMS5003, were collocated for one year with a Fidas 200S, MCERTS-certified PM monitor and were characterised at 2 min resolution, enabling replication of certification processes, and highlighting their limitations and improvements. Robust linear models using sensor-reported particle number concentrations and relative humidity, coupled with 2-week biannual calibration campaigns, achieved reference-grade performance, at median PM2.5 background concentration of 5.5 μg/m3, demonstrating that, with careful calibration, PM-LCS may cost-effectively supplement reference equipment in multi-nodes networks with fine spatiotemporality.

Laboratory evaluation of the Alphasense OPC-N3, and the Plantower PMS5003 and PMS6003 sensors

Ambient levels of particulate matter (PM) are linked to numerous adverse health effects. However, fewer studies have evaluated the effects of coarse PM (larger than 2.5 μm in diameter), and ambient measurements of coarse PM are particularly sparse. Although low-cost PM sensors have been used to complement regulatory measurements of PM2.5, many of these sensors, such as the Plantower PMS5003, are ineffective in measuring coarse PM. The Alphasense OPCs have shown promise in detecting coarse PM and have been used in the field to measure PM10 concentrations. These field evaluations have identified inter-sensor variability. Although field evaluation is critical for understanding sensor performance under environmentally relevant conditions, it provides limited information about sensor response characteristics, which are essential for determining the factors that may affect sensor measurements and contribute to inter-sensor variability. This study aims to understand these factors by conducting a size-selectivity study using monodisperse particles, and evaluating the effect of instrument-specific properties, like flow rate and laser strength, on the sensor-reported sizes and number counts. This study also evaluates a common low-cost sensor, the Plantower PMS5003, and a newer version, the PMS6003, for size selectivity. Monodisperse dioctyl sebacate particles of various diameters (2, 3, 5, 6, 9, and 10 μm) were generated using a flow-focusing monodisperse aerosol generator, and the performance of nine different OPCs was evaluated. For all sizes tested, the nine OPC-N3s detected the particles, showed a peak near the target diameter, and exhibited some inter-sensor variability. The four PMS5003s and four PMS6003s detected all particle sizes but assigned all particles to the smallest size bin i.e., 0.35 μm–1 μm. With an aerodynamic particle sizer (APS) as a reference, the OPCs showed a positive bias for mean particle diameter and a coefficient of variance (CV) of less than 10%. For number concentration, the OPCs showed a negative bias, compared to the APS, and inter-sensor variability increased with the particle diameter. The laser wavelength of the OPC-N3s varied between 600 and 650 nm and appeared to have some effect on inter-sensor variability of the mean size. The flow rate reported by the OPC appeared to affect the inter-sensor variability in the number concentration.

Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors

Air quality is one of the most important factors in public health. While outdoor air quality is widely studied, the indoor environment has been less scrutinised, even though time spent indoors is typically much greater than outdoors. The emergence of low-cost sensors can help assess indoor air quality. This study provides a new methodology, utilizing low-cost sensors and source apportionment techniques, to understand the relative importance of indoor and outdoor air pollution sources upon indoor air quality. The methodology is tested with three sensors placed in different rooms inside an exemplar house (bedroom, kitchen and office) and one outdoors. When the family was present, the bedroom had the highest average concentrations for PM2.5 and PM10 (3.9 ± 6.8 ug/m3 and 9.6 ± 12.7 μg/m3 respectively), due to the activities undertaken there and the presence of softer furniture and carpeting. The kitchen, while presenting the lowest PM concentrations for both size ranges (2.8 ± 5.9 ug/m3 and 4.2 ± 6.9 μg/m3 respectively), presented the highest PM spikes, especially during cooking times. Increased ventilation in the office resulted in the highest PM1 concentration (1.6 ± 1.9 μg/m3), highlighting the strong effect of infiltration of outdoor air for the smallest particles. Source apportionment, via positive matrix factorisation (PMF), showed that up to 95 % of the PM1 was found to be of outdoor sources in all the rooms. This effect was reduced as particle size increased, with outdoor sources contributing >65 % of the PM2.5, and up to 50 % of the PM10, depending on the room studied. The new approach to elucidate the contributions of different sources to total indoor air pollution exposure, described in this paper, is easily scalable and translatable to different indoor locations.