# Performance and applicability of low-cost PM sensors to assess global pollution variability through machine learning techniques

Rajat Sharma, Andry Razakamanantsoa, Ashutosh Kumar, Thaseem Thajudeen, Agnès Jullien

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Rajat Sharma, Andry Razakamanantsoa, Ashutosh Kumar, Thaseem Thajudeen, Agnès Jullien. Performance and applicability of low-cost PM sensors to assess global pollution variability through machine learning techniques. Atmospheric environment: X, 2025, 26, 10.1016/j.aeaoa.2025.100331. hal-05103243

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# Performance and applicability of low-cost PM sensors to assess global pollution variability through machine learning techniques

Rajat Sharma<sup>a,*</sup>, Andry Razakamanantsoa<sup>a</sup>, Ashutosh Kumar<sup>b</sup>, Thaseem Thajudeen<sup>c</sup>, Agnès Jullien<sup>a</sup>

## ARTICLE INFO

### Keywords:

- Low-cost PM sensors

- Air quality monitoring

- Performance index

- Data reliability

- Machine learning

- Performance parameters

## ABSTRACT

Air quality monitoring and analyses became easy and affordable due to emergence of low-cost sensors. Recently, the efforts to improve the monitoring and understanding of region-specific air pollution events attracted immense global attention. Nevertheless, the applicability issues were observed due to data reliability and inconsistency, caused by reserve testing of performance parameters for better accuracy, selection and deployment of sensors without considering their fitness for the purpose, and area-specific requirements. This paper analyses and evaluates low-cost sensor deployments across lower, middle, and higher income group of countries, emphasizing variations in pollutant sources, performance parameters, and machine learning approaches for local source categorization. The performance parameters were analyzed using three Key parameters: (1) the Performance Index, (2) Sector Sensitivity Ratio, and (3) Data Reliability Indicator, that provide a comprehensive understanding of sensor efficiency in diverse environments. Our findings reveal distinct trends among income group countries. Higher income group countries exhibited the highest performance Index (0.35), followed by middle (0.33) and lower income group countries (0.27). However, the lower income group countries showed the highest data reliability indicator for maximum sector contribution (14.26), surpassing the higher (11.74) and middle income group (10.71) countries. Sector wise, transport (higher income), industry (middle income), and power (low income) demonstrated the highest data reliability based on its indicator. Additionally, it was observed that advanced machine learning algorithms helped to improve performance parameters, particularly in middle and lower income group countries where pollution variability is higher. These findings underscored the disparities in sensor performance and data reliability across diverse income groups.

## 1. Introduction

In the era of relentless growth and technological advancement, nations spanning every income level low, middle, and high strive to pave the path towards a sustainable future and to achieve the targets outlined in the Sustainable Development Goals (SDGs), viz., SDG 3, 11, and 12 (United Nations, 2015) by 2030. Among these SDGs, achieving substantial reduction in air pollution related deaths (SDG 3) and improving urban air quality (SDG 7), are the prominent to cater air quality. Nevertheless, air pollution caused economic impact of more than $ 5

trillion in lost welfare and $ 225 billion in lost income which could have been minimized (WHO, 2020).

In order to reduce the impact of air pollution, a progressive approach towards a cleaner future was envisioned which led to the launching of several air quality monitoring campaigns across the globe at both governmental and private levels. The United Nations Environment Programme (UNEP) and UN Habitat along with IQAir, a Swiss air quality technology company, launched one of the largest and successful air quality monitoring campaigns in 2020 which monitors air quality in five continents with over 80000 sensors (UNEP, 2020; IQAIR, 2023). In view

**Abbreviations:** PM, Particulate Matter; LCS, Low-cost sensor; RF, Random Forest; GBDT, Gradient Boosting Decision Trees; ANN, Artificial Neural Network; SVM, Support Vector Machine; LR, Linear Regression; MLR, Multiple Linear Regression; SVR, Support Vector Regression; GPR, Gaussian Process Regression; NN, Neural Network; GAM, Generalized Additive Model; ML, Machine Learning.

* Corresponding author.

E-mail address: rajat.sharma@univ-eiffel.fr (R. Sharma).

<footnote><sup>a</sup> Université Gustave Eiffel, Département GERS-GIE, 44344, Bouguenais, France</footnote>

<footnote><sup>b</sup> Department of Energy & Environment, Thapar Institute of Engineering & Technology, Patiala, 147004, Punjab, India</footnote>

<footnote><sup>c</sup> School of Mechanical Sciences, Indian Institute of Technology Goa, 403401, India</footnote>
of such monitoring campaigns, factors, such as increase in the affordability, type of low-cost sensors, and marketing strategies became more consumer centric which led to the surge in installation of low-cost sensors (LCSs) (Snyder et al., 2013; Kumar et al., 2015). However, the reliability of data was found to be a major challenge related to LCSs in such air quality monitoring campaigns (Miskell et al., 2018; Qiao et al., 2021).

To overcome the challenge of LCSs data reliability, United States Environmental Protection Agency (US EPA) took a significant step in 2021 by formulating the guidelines for evaluating the performance parameters and testing protocols of LCSs for enhancing the reliability of data. The performance metrics improvement was established by setting up the targeted values for precision, bias, linearity, and error for monitoring of air quality through LCSs (Duvall et al., 2021). In this regard, numerous review articles were published, exploring the cutting edge development of LCSs, their technological aspects, and diverse applications (Alfano et al., 2020; Stavroulas et al., 2020; Liang, 2021; Kang et al., 2022). However, the persistent issue of data quality across various sources and performance of LCSs are yet to be fully established.

A comprehensive summary of the published review articles in this area is presented, focusing on evaluation of performance parameters and application of machine learning (ML) models. The review conducted by (Kang et al., 2022) focused on the evaluation of performance of LCSs in which key performance indicators were R² (Coefficient of Determination), RMSE (Root mean square error), MNB (Mean Normalized Bias), and CV (Coefficient of Variation). The study covered a wide range of topics, including environmental conditions, reference instruments and regression models. In this study, the issues with performance indicators were addressed as per the compliance with US EPA guidelines for review in the area of LCSs. Liang (2021) reviewed articles dealing with mainstream calibration methods and performance parameters. This investigation also delved into the responsiveness of sensor calibration to different location variables. Karagulian et al. (2019) undertook the task of estimating the agreement between LCS data and reference measurements to facilitate the selection of suitable LCS. Moreover, Alfano et al. (2020) conducted a review of approximately 50 LCS models, assessing their characteristics on the basis of R² metrics and their suitability.

## 2. Setting the scene from international practices and scope

Table 1 provides the overview of reviewed articles which are considered for setting up the scene in global context. In case of lower, middle and higher income group countries, a notable variations in emission sources and their impacts were found. However, in the absence of a systematic parametric approach, these variations could not be addressed. It must be noted that the complexity of pollutant concentration in the ambient air arose from diverse sources, influenced by the economic value and cultural practices of a specific region (Yang et al., 2019; Dhiman et al., 2024). Low income countries were found to be more dependent on fossil fuels than their higher income counterparts (Köhler et al., 2019; Goforth and Nock, 2022), prompting a consideration of the applicability of uniform method and calibration technique across different areas.

There are studies reporting only performance metrics (such as R², RMSE, MAE) and those that actually implement a calibration model. Performance parameters is the measure of sensor performance relative to a reference instrument but do not necessarily involve any correction to the raw data provided by the sensor. These studies are common in laboratory or initial field testing. However, calibration techniques actively modify the sensor output through various models, categorized in laboratory calibration, field co-location, correction factors, statistical regression models, ML models, remote calibration (ML based models), spatial temporal calibration, inverse modelling, historical reference models (models trained on past co-locations) and ensemble-based models employed across the globe. Thus, while performance parameters are essential for evaluating sensor, they alone do not constitute a

<caption>**Table 1**</caption>

Comparison of various ML Models, performance parameters, and their region of application (*) RF: Random Forest/GBDT: Gradient Boosting Decision Trees/ ANN: Artificial Neural Network/SVM: Support Vector Machine/LR: Linear Regression/MLR: Multiple Linear Regression/SVR: Support Vector Regression/ GPR: Gaussian Process Regression/NN: Neural Network/GAM: Generalized Additive Model.

<table><thead><tr><th rowspan="2">Study Area<br>-References</th><th colspan="2">Focus of Study</th><th rowspan="2">ML Models<br>(*)</th></tr><tr><th>Theoretical Framework</th><th>Performance<br>Parameters Analyzed</th></tr></thead><tbody><tr><td>Mostly higher income countries along with some middle income countries. Liang, 2021</td><td>Difference between Regulatory monitors and LCS. Principles of LCS Overview of calibration methods Machine learning techniques theoretical descriptions LCS response in hotspot and factors</td><td>Coefficient of determination (R²)</td><td>RF<br>GBDT<br>ANN<br>SVM</td></tr><tr><td>Higher Income Countries Karagulian et al. (2019)</td><td>Review for both Gas and PM Sensors Cost and performance analysis LCSs Classifications.</td><td>Coefficient of determination (R²)<br>Slope Intercept Also measure R² for different sensor systems at different averaging time (1 h, and 24 h) PM measurements Delved into several studies that utilize metrics, such as the Coefficient of Determination (R²), MNB, MNE, and various Machine Learning methods to analyze performance</td><td>ANN<br>LR<br>MLR<br>SVR<br>Kholer</td></tr><tr><td>Higher Income Countries Alfano et al. (2020)</td><td>Review of 50 LCPMS (Low-Cost Particulate Matter Sensors) Discussion on Particulate Matter Basics, Measurement Parameters, common sources of PM Principles of LCPMS Detailed review of laboratory setup characterizations. Field studies and calibration models are also discussed.</td><td>Statistical analysis approaches for the comparison at different levels: a) environmental setting, b) pollutant attribute, and c) sensor original equipment manufacturer (OEM) specification, reference instrument, and regression model. Comparison based on EPA performance goals for five application areas (Tier I–Tier V) Based on MNB R² MNE MAE RMSE</td><td>LR<br>MLR<br>NN<br>RF<br>Raw Data</td></tr><tr><td>Higher Income Countries Kang et al. (2022)</td><td>One of the most focused paper on performance analysis Focused on sensor performance for different pollutant attributes, reference monitors and in environmental settings Discussion on Regression models for calibration</td><td>MNB R² MNE MAE RMSE MAPE</td><td>SVR<br>RF<br>LR<br>MLR<br>NN<br>GAM</td></tr><tr><td>Higher Income Countries Concas et al. (2021)</td><td>Advantages and disadvantages of PM and gas sensors Data collection and performance characterization</td><td>MNB R² MNE MAE RMSE MAPE</td><td>SVR<br>RF<br>LR<br>MLR<br>NN<br>GAM</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th rowspan="2">Study Area<br>-References</th><th colspan="2">Focus of Study</th><th rowspan="2">ML<br>Models<br>(*)</th></tr><tr><th>Theoretical Framework</th><th>Performance<br>Parameters Analyzed</th></tr></thead><tbody><tr><td rowspan="2">Mostly Higher<br>Income<br>Countries<br>Along with some<br>middle income<br>countries<br>Narayana et al.<br>(2022)</td><td>discussion.<br>ML based calibration,<br>issues and techniques<br>discussion.</td><td>MSE<br>MRE</td><td></td></tr><tr><td>Sensor principles for PM<br>and gas sensors.<br>Advantages and<br>disadvantages of FRM<br>and FEM methods.<br>Hardware setup analysis<br>for sensors.<br>Discussion on Sensor<br>integration with<br>microcontroller and<br>communication<br>techniques.</td><td>For calibration<br>discussion on<br>laboratory setup.<br>Discussion regarding<br>which study shows<br>the performance<br>variations.</td><td>GAM<br>LR<br>MLR<br>GBDT<br>SVM<br>ANN</td></tr></tbody></table>

calibration methodology unless accompanied by a correction process. These models differ significantly in their design and applicability. Laboratory calibration establishes sensor performance under controlled conditions (Budde et al., 2018; Kuula et al., 2020; Bozilov et al., 2022; Bathory et al., 2022; He et al., 2020; Kaur and Kelly, 2023) whereas field co-location captures performance in real-world environments through direct comparison with regulatory monitors (Sayahi et al., 2019; Kostopoulos et al., 2020; Nothhelfer et al., 2023). Correction factors and regression models offer adjustments limited to specific deployments, while ML models allow complex, nonlinear correction and in case of remote calibration without physical co-location (Tang et al., 2025; Zhao et al., 2025). Spatiotemporal models integrate geographic and temporal trends to improve prediction and calibration jointly (Seng et al., 2021; Iyer et al., 2022). Inverse modeling aims to decode the optical response function of sensors, while historical reference models rely on past co-location data to transfer calibration across sites. Ensemble-based models combine multiple techniques (e.g., ML + kriging). However, there was a limited clarity on the suitable model under specific conditions based on sources of emissions. By focusing on the most widely used ML algorithm for calibration of LCSs for particulate matter (PM), this study is classified for lower, middle and higher income group countries. In this regard, the most result oriented and famous models were explored, while evaluating the global view towards testing of performance parameters.

To meet the regulatory limits applicable to the outdoor pollutants, a monitoring station must rigorously follow the global standards for calibration, operation, and data quality. The LCS's calibration was found to be relying on the established benchmarks, such as Federal Reference Method and Federal Equivalent Method for accuracy and compliance (EPA, 2023). These methods encompassed traditional methods in the instruments, such as Beta Attenuation Monitoring (BAM), in stack Particulate Filtration, Very Sharp Cut Cyclone, Tapered Element Oscillating Microbalance, and Laser Aerosol Spectrometry (Gilliam and Hall, 2016). The intricate relationship between emissions and environment was profoundly influenced by various meteorological and physicochemical factors, exhibiting regional variations (Zhao et al., 2020; Sharma and Kumar, 2023).

In order to detect spatiotemporal variations, LCSs were found to be the most easy and affordable way for stationary and mobile monitoring of air pollutants across an area. The sensors' application was within the areas, aligning the EPA goals, spanning from education and information dissemination to hotspot identification, characterization, supplemental monitoring, personal exposure assessment, and regulatory compliance. However, the LCS was not approved for monitoring of air quality data for regulatory purposes (Williams et al., 2014). The percentage of commercially available LCSs using light scattering, was found to be

quite high, however, the level of data uncertainty was also found to be higher (Rai et al., 2017). Various studies showed that a nonlinear relationship is most dominant with various environmental factors and with various locations (Kelly et al., 2017; Nowack et al., 2021). The pre-deployment of LCSs or its field colocation for calibration was found to be the most common approach for calibration by collocating sensors with reference instruments for a particular time period.

To address sensor data reliability and performance in various varying emission sources, we employed a methodology that incorporates a broader range of metrics, including linearity, bias, error, and precision, to generate a comprehensive Performance Index. Additionally, the emissions from various countries exhibit significant variation, yet their impact on the performance of LCSs has been underexplored. Therefore, this study focuses on Sector Contribution Ratio for specific countries and the various contributing sectors. Finally, a Data Reliability Indicator is derived from the selected studies to provide an analytical view of global sensor performance.

A global overview of sensors' manufacturers and origin of monitors are presented in Fig. 1. It is noted that monitors developed by different industries used similar sensors. Notably, PMS 5003 is found to be a widely used sensor which is prevalent in lower income group countries. This sensor is incorporated into majority of devices, including Purpleair, Airly, AirQo, Sentinair. Similarly, PMS7003, NOVA SDS011, and Alphasense (OPC-N3,OPC-N2). However, algorithm of these sensor monitors is found to be varying which is provided by manufacturers. Holder et al. (2020; Wallace, 2022 discussed how provided algorithms are modified to stop the overestimation. The algorithm is called as proprietary algorithm which is usually not disclosed by the manufacturers. In PMS 5003, users have a choice between two algorithms, CF_1 and CF_ATM, described as suitable for 'factory' and 'atmospheric' applications, respectively. However, these terminologies are not clearly defined and most of the data was evaluated in post processing step using ML models. By recognizing the global significance of LCSs in air quality monitoring and prominent role of performance parameters, we aim to.

1. Evaluating the methodologies employed in selected studies from various income group countries in reference to performance and reliability of LCSs for PM monitoring.

2. Evaluating the Performance Index, Sector Contribution Ratio and Data Reliability Indicator using a structured methodological approach to assess and compare performance parameters.

3. Analyze and compare ML algorithms and emerging trends for field calibration of LCSs.

### 3. Global sectors contributing to PM<sub>2.5</sub> emissions

Following the analysis of PM<sub>2.5</sub> in the selected papers, a refined analysis of their findings and measured values is provided in Fig. (2a-2d). Hence Fig. 2a, shows source profiling of PM<sub>2.5</sub> in four continents based on sectorial division, such as agriculture, industry, power, residential and transport. As noticed, the PM<sub>2.5</sub> emissions directly relates to industrial, economical, urbanization and population factors with varied regions having different causes of emission but attributed to a major sector (McDuffie et al., 2021; Crippa et al., 2019; Tian et al., 2018; Zhang et al., 2020). Data from these studies are used to establish sources in the region of our selected studies location (Table S1). PM<sub>2.5</sub> contributions through different sources around the world has been thoroughly explained and divided by (Lim et al., 2020; Dasadhikari et al., 2019). A more refined spatial coverage through network of sensors is required to cover the macroscopic aspect of a region. The dust cause major contribution in the peaks the PM<sub>2.5</sub> concentrations around the world we summarized to make it clear from Fig. (2b-2d).

Source apportionment of PM<sub>2.5</sub> is regionally diverse, and this variability shows the importance of selecting sensor types and calibration strategies to improve performance. For instance, in African regions (Fig. 2b), dust from the Sahara is a significant PM<sub>2.5</sub> contributor, with
<figure><img src="image_4.png" /><figcaption>Fig. 1. International overview of sensors' manufacturers and origin of monitors.</figcaption></figure>

(Evan et al., 2016) highlighting how terrain driven wind patterns shape the emission profile. This means that the connection between dust and different climate events affect wind patterns. However, nations like Malawi, Congo, South Africa, and Botswana see the power sector as the primary PM₂.₅ source, while Uganda is dominated by residential emissions. These differences suggest that sensors deployed in dusty regions must be sensitive to coarse PM, whereas those in power-dominated regions may require different strategy.

Similarly, in the United States (Fig. 2c), dust contributes far less compared to the power and residential sectors, raising questions about whether sensor configurations optimized for these conditions remain effective in African or Asian contexts. In Europe, the transportation

sector leads overall PM₂.₅ emissions, but national profiles differ: agriculture dominates in the UK, France, and Italy, while Norway sees the highest contributions from dust (McDuffie et al., 2021; Crippa et al., 2019).

Asia (Fig. 2d) presents the most complex scenario, with residential, industrial, power, and dust sources varying widely by country (Lim et al., 2020; Dasadhikari et al., 2019). India and Maldives are dominated by the residential sector, closely followed by industrial and power sectors, as depicted in Fig. 2d, China mirrors this mix, with dust contribution leading the way. Malaysia, Taiwan, and Thailand showcase the power sector at the forefront, while the Republic of Korea witnesses industrial PM₂.₅ contributions at its peak. When combined with the
<figure><img src="image_5.png" /><figcaption>Fig. 2. Global Overview of PM<sub>2.5</sub> Emissions and Concentrations. (a) Sectoral contributions (%) to PM<sub>2.5</sub> emissions across continents. (b–d) Annual average PM<sub>2.5</sub> concentrations (µg/m<sup>3</sup>) for (b) Africa, (c) Europe and the US, and (d) Asia, computed from raw data in Table S2.</figcaption></figure>

regional profile of pollution sources in a specific area, this allows to strategically select sensors based on their strengths and weaknesses, aiding in deployment planning based on dominant source.

The above considerations and studies covering various regions of the world can inform for sensor selection, when conducting an emission inventory check and analyzing satellite data to identify areas with both low and high concentrations of pollutants. The type of aerosol sensor is being exposed to has direct impact on the performance of low-cost PM sensors. This has direct implications for post-deployment data analysis, particularly when employing ML models.

# 4. Low-cost PM sensors: principles, applications, and challenges

## 4.1. Principles and design considerations of LCSs

Table 2 summarizes overview of LCSs for PM predominantly function based on the principle of light scattering, offering an efficient method to assess particle density through changes in air characteristics (Wang et al., 2015; Cho and Baek, 2021). The key components outlined in Table 2, including light emitting diodes (laser/infrared), fans, pumps, and phototransistors, enable compact and cost effective designs that have gained widespread adoption over alternative methods (Giordano et al., 2021). These components allow the sensor to draw ambient air into a detection chamber where the light source scatters upon encountering aerosols. The scattered light is detected by a photodiode, which converts it into an electrical signal proportional to the particle size and density (Williams et al., 2014; Wang et al., 2015).

The mechanisms of light scattering detailed in Table 2, Rayleigh, Mie, and geometric scattering are central to understanding the

interaction between particles and light. While Rayleigh and geometric scattering exhibit simpler relationships between particle size and scattered light, Mie scattering introduces complexities due to its dependency on particle size, shape, and refractive index (Wang et al., 2015). These factors, especially relevant when using infrared diodes, pose challenges for accurate measurements in diverse environmental settings. Despite their advantages, LCSs face limitations, as indicated in Table 2, such as restricted size resolution and interference from background particles (Kuula et al., 2020; Molina et al., 2023). This is primarily due to the reliance on collective scattering rather than individual particle measurement, which, while cost effective, sacrifices precision. However, design optimization, such as improving the geometry and configuration of modules can enhance performance and mitigate these issues (Morawska et al., 2018). The theoretical assumptions listed in Table 2, including particle shape (spherical/irregular), independence from motion, and neglect of inter particle scattering, underline the sensor's reliance on simplifications that can impact accuracy (Kuula et al., 2020; Molina et al., 2023). Thus, LCSs offer an appealing balance of affordability and functionality, continued refinement in design and calibration is essential to address these limitations for improving their application in real world air quality monitoring.

## 4.2. Applications, potential, and limitations of LCSs

The highest and lowest concentrations of an area and variation in the sources of emission drastically affect the performance of sensors. The comparative performance of air quality sensors used in international papers, as summarized in Table 3, reveals significant challenges related to sensor deployment in diverse environments, data reliability issues,
<table><caption>**Table 2**
Overview of principle and design considerations of PM LCSs.</caption><thead><tr><th>Aspect</th><th>Details</th><th>References</th></tr></thead><tbody><tr><td>Working Principle</td><td><ul><li>Light scattering, utilizing the interaction of light with particles to determine their size and concentration.</li><li>Efficient and adaptable for varying conditions.</li></ul></td><td>Wang et al. (2015); Cho and Baek (2021)</td></tr><tr><td>Mechanisms of Light Scattering</td><td><ul><li>Rayleigh Scattering: For particles much smaller than the light wavelength. Mie Scattering: For particles near the light wavelength, complex due to refractive index and shape. Geometric Scattering: For particles much larger than the wavelength.</li></ul></td><td>Wang et al. (2015)</td></tr><tr><td>Key Design Components</td><td><ul><li>Light-emitting diodes (laser/ infrared), fans, pumps, phototransistors, detection chamber, lenses, sensor position, and air inlet for continuous flow.</li></ul></td><td>Wang et al. (2015); Giordano et al. (2021)</td></tr><tr><td>Functioning</td><td><ul><li>Air is drawn into the detection chamber using a fan/pump. A light source emits a beam that scatters upon encountering aerosols. Scattered light is detected by a photodiode, which converts the light signal to an electrical signal proportional to particle size and density.</li></ul></td><td>Williams et al. (2014); Wang et al. (2015)</td></tr><tr><td>Environmental Sensitivity</td><td><ul><li>Sensor performance is influenced by humidity, temperature, and particle composition.</li></ul></td><td>Giordano et al. (2021)</td></tr><tr><td>Advantages of LCSs</td><td><ul><li>Compact, cost-effective, real-time measurements. Accessible for low-resource settings and large-scale deployment.</li></ul></td><td>Wang et al. (2015); Cho and Baek (2021)</td></tr><tr><td>Limitations</td><td><ul><li>Restricted size resolution (unable to detect individual particles). Challenges with background particle interference (e.g., high ambient PM levels).</li></ul></td><td>Kuula et al. (2020); Molina et al., 2023</td></tr><tr><td>Mitigation Measures</td><td><ul><li>Optimization in design geometry and configuration. Use of adaptive algorithms and machine learning for real-time calibration and error correction.</li></ul></td><td>Morawska et al. (2018)</td></tr><tr><td>Theoretical Assumptions</td><td><ul><li>Assumes spherical/irregular shapes. Neglects inter-particle scattering. Independence of scattering from motion.</li></ul></td><td>Kuula et al. (2020); Molina et al., 2023</td></tr></tbody></table>

and performance variability. These findings are based on reviewed studies evaluating sensors like Nova SDS011, PMS5003, PMS7003, SPS30, OPC-N3, and PPD42 shown in Fig. 1.

LCSs may perform inconsistently across different areas, primarily due to variations in source emissions and environmental conditions. Nova SDS011, although capable of capturing fine dust particle dynamics in laboratory settings (Budde et al., 2018), it fails to provide accurate PM2.5 measurements in real-world environments. Nova SDS011 overestimates is reaching 66 % with PM2.5 no useable readings for soot due to strong absorption and low light scattering (Bozilov et al., 2022). This limitation makes it unsuitable for regions dominated by biomass burning or industrial soot emissions. PMS5003 and PMS7003, widely integrated into global air quality monitoring systems like Airly and PurpleAir (Table 3), these sensors exhibit poor performance for particles <300 nm or soot heavy areas (Sayahi et al., 2019), found biases in mass concentration measurements and low linearity (R² < 0.5), while (Bathory et al., 2022) observed systematic overestimation of PM2.5 due to multiplicative bias. SPS30, as highlighted by (Kuula et al., 2020), this sensor performs well within its valid detection range (<0.9 µm), achieving near regulatory grade accuracy (Table 3). However,

overlapping detection bins introduce errors when applied in environments with a diverse particle size distribution, such as urban and industrial settings. These observations from field and laboratory settings emphasize the importance of aligning sensor capabilities with the emission profiles and environmental conditions of specific world regions. Remote calibration has enabled scalable corrections, especially for large-scale sensor networks (Tang et al., 2025). introduces a remote calibration method using an interpretable semi-supervised learning model. The model leverages both labelled data (Reference grade) and unlabeled data (from LCSs) to learn calibration mappings (Table 3). (Zhao et al., 2025) provides STXGBoost model which effectively captured the spatiotemporal variability of PM2.5 concentrations, leading to improved prediction accuracy by utilized existing sensor network data without additional deployments. The spatial transferability of calibration model for a network of PMS5003 sensors was tested by (Malyan et al., 2024) while collocating with BAM, they found that spatial transferability of the calibration models was influenced by the distance between sites, source composition, and particle size distribution.

### 4.2.1. Data reliability

The reliability of sensor data can be compromised by environmental factors, inter sensor variability, and inadequate calibration practices. Relative humidity and temperature adjustments, Nova SDS011 and PMS sensors show significant inaccuracies without corrections for RH and temperature (Table 3). For example (Nothhelfer et al., 2023), found that PM2.5 concentrations were overestimated by up to 38 times when aerosols were not properly dried, and (Bozilov et al., 2022) emphasized the importance of RH corrections in Serbian microenvironments. The variability between sensor units is evident in studies like (Kaur and Kelly, 2023), where OPC-N3 performed consistently for particles near the target diameter, but PMS5003 and PMS6003 frequently misclassified particles, assigning all sizes to the smallest bin (0.35–1 µm) (Table 3). Such inconsistencies can significantly affect data reliability in networked deployments. The overlapping bin ranges in SPS30 suggest a generalized factory calibration using a test aerosol, as noted by (Kuula et al., 2020) (Table 3). This calibration method reduces reliability in detecting specific size fractions and interpreting field data accurately.

### 4.2.2. Performance variability

Sensor performance varies significantly due to differences in particle sources, detection ranges, and design limitations. Sensors like Nova SDS011, PMS5003, and PMS7003 exhibit poor performance in detecting soot and particles <300 nm (Table 3). As noted by Sayahi et al. (2019), PMS5003 has low scattering efficiency for small light absorbing particles, and (He et al., 2020) found significant misclassification of particle sizes in LCSs in liquid particle testing experiment. Kaur and Kelly (2023) demonstrated that OPC-N3 had better performance for particles near the target diameter compared to PMS sensors, which struggled with bin assignments (Table 3). This inconsistency is particularly problematic in areas with high variability in particle size distribution, such as urban settings. The SPS30 sensor achieves high accuracy for PM1 measurements within its operational range (<0.9 µm), but its overlapping detection bins create risks of misinterpretation when used outside this range (Kuula et al., 2020).

### 4.3. Machine learning techniques for post processing sensor data

The comparative evaluation of LCS reveals that several ML techniques are being leveraged for calibration to improve sensor performance, as noted in Table 3. The use of ML methods helps to enhance performance parameters from complex dataset with environmental covariates by recalibrating measurements and enhance sensor adaptability in different environments. Many studies have explored the use of supervised learning techniques for LCS calibration, a category that remains consistent across global studies (Table 1, Table 3).
<table><caption>**Table 3**
Air quality sensors' performance in different studies.</caption><thead><tr><th>Name of the Sensors</th><th>Deployment Description</th><th>Calibration Method(s)</th><th>Key Findings</th><th>Issues Identified</th><th>Recommendations</th><th>References</th></tr></thead><tbody><tr><td rowspan="3"><b>Nova SDS011</b><br>Lifetek PMS,<br>PAQMON 1.0<br>PM, SensuAir</td><td>Laboratory setup</td><td>Reference co-location measurements based statistical performance evaluation.</td><td>High temporal resolution for fine dust dynamics.</td><td>Misrepresentation of PM<sub>2.5</sub> concentration (66% capture); no soot data due to strong absorption and low light scattering.</td><td>Network-based calibration models; corrections for RH and soot.</td><td>Budde et al. (2018)</td></tr><tr><td>Laboratory and field setup</td><td>Laboratory, field reference co-location measurements with correction factor based statistical performance evaluation.</td><td>Overestimation of PM<sub>2.5</sub> in Serbian microenvironments.</td><td>Lack of corrections for RH and temperature; failure to analyze soot data due to optical limitations.</td><td>Adjust for RH, temperature; improve soot particle calibration.</td><td>Bozilov et al. (2022)</td></tr><tr><td>Field monitoring setup</td><td>Field co-location calibration with empirical correction factor derived from co-located data</td><td>PM<sub>2.5</sub> levels overestimated (up to 38x) when aerosols not dried; underestimation at RH &lt;45 %.</td><td>Significant biases in diverse conditions; unsuitable for deployment in default state.</td><td>Aerosol preconditioning and local calibration essential for accuracy.</td><td>Nothhelfer et al. (2023)</td></tr><tr><td rowspan="2"><b>PM5003</b><br>Airly, AIRQo,<br>SentinAir,<br>PurpleAir,<br>assembled<br>PAir</td><td>Field monitoring setup</td><td>Field reference co-location with Beta Attenuation Monitor; linear statistical calibration model</td><td>Highly source-dependent accuracy; poor linearity (R<sup>2</sup> &lt; 0.5) for particles &lt;300 nm; difficulty with soot detection</td><td>Bias for small and light-absorbing particles; scattering inefficiency for particles &lt;300 nm.</td><td>Source-specific calibration for finer particles; redesign detection methods for better linearity.</td><td>Sayahi et al. (2019)</td></tr><tr><td>Laboratory setup; Controlled + co-location setups with multiple aerosol types</td><td>Tikhonov Regularization (inverse modeling)</td><td>Detected particles smaller than listed detection range (&lt;0.3 µm); misassignment in particle size bins.</td><td>Size misclassification, especially for liquid particles.</td><td>Improve bin alignment algorithms; emphasize calibration for diverse particle types.</td><td>He et al. (2020)</td></tr><tr><td rowspan="2"><b>PM5003</b><br>Atmos, AIRQ<br>laboratory,<br>Atmotrack</td><td>Field deployment with calibration method uses remote calibration principles.</td><td>Linear regression correction for PM1 and PM2.5; exclusions of high coarse PM (e.g., Sahara dust)</td><td>Sensors showed reasonable correlation with reference instruments for fine PM (R<sup>2</sup> = 0.82 for PM1) but significant bias for coarse PM.</td><td>High bias and low correlation for coarse PM (PM2.5-10); performance varies with dust events</td><td>Use linear regression for fine PM correction; exclude high coarse PM data for better results</td><td>Kosmopoulos et al. (2020)</td></tr><tr><td>Laboratory setup</td><td>Combined laboratory and field co-location calibration, with correction factors, and humidity-controlled pre-conditioning (via aerosol dryer).</td><td>Individual calibration reduces errors (relative error 11-14 %); overestimation of PM<sub>2.5</sub> due to bias.</td><td>Positive and multiplicative bias affecting network consistency.</td><td>Protocols for individual calibration within sensor networks.</td><td>Bathory et al. (2022)</td></tr><tr><td><b>OPC-N3,<br>PM5003,<br>PM5003</b></td><td>Laboratory setup</td><td>Laboratory calibration with size-resolved aerosol response analysis using monodisperse and polydisperse aerosols in controlled conditions.</td><td>OPC-N3 detected particles around target diameters with a consistent peak; PM5003 and PM5003 misclassified sizes (all to smallest bin: 0.35-1 µm); variability in CV (19.5 % for 2 µm to 34.2 % for 10 µm).</td><td>PMS sensors unreliable for liquid particles and particle size bins; OPC-N3 susceptible to variability for larger particles.</td><td>Inter-sensor calibration critical; improve size classification algorithms for PM5003 and PM5003.</td><td>Kaur and Kelly (2023)</td></tr><tr><td><b>SPS30</b></td><td>Laboratory setup</td><td>Laboratory calibration focusing on particle-size selectivity using monodisperse aerosols</td><td>Minimal cross-unit variability (1-2.4 % for PM1, PM2.5, PM10); overlapping detection ranges for size bins (e.g., 0.7-1.3 µm in multiple bins). Valid detection ranges vary widely across units (e.g., 1.0-2.1 µm, &gt;5.9 µm); R<sup>2</sup> values range 0.36-0.75 for PM<sub>2.5</sub> measurements.</td><td>Risks of misinterpretation outside valid operational range; overlapping bin issues.</td><td>Calibrate bins individually; align usage with operational detection range.</td><td>Kuula et al. (2020)</td></tr><tr><td><b>PPD42</b></td><td>-</td><td>-</td><td>Valid detection ranges vary widely across units (e.g., 1.0-2.1 µm, &gt;5.9 µm); R<sup>2</sup> values range 0.36-0.75 for PM<sub>2.5</sub> measurements.</td><td>Detection efficiency varies significantly; poor performance for particles &lt;1.0 µm.</td><td>Tailored calibration for specific detection ranges; improve sensor design for better consistency.</td><td>Multiple above Studies</td></tr><tr><td><b>XHAQSN-808</b></td><td>Used existing datasets from: LCS and Regulatory-grade monitors</td><td>Remote calibration using interpretable semi-supervised ML. Data-driven; generalizable model; no field campaign conducted</td><td>RMSD reduction up to 10.8 %. Reduce spatial misalignment by using Aerosol Optical Depth (AOD) and environmental covariates. PM<sub>2.5</sub> predictive generalizability is enhanced with R<sup>2</sup> increased by 11.8 %.</td><td>Dependence on metadata quality. No real-world deployment validation</td><td>Works without co-location Designed for low-data regions Aims at exposure misclassification in underserved communities Integrated framework improves accuracy + resolution Enables high-res urban PM<sub>2.5</sub> maps for decision support</td><td>Tang et al. (2025)</td></tr><tr><td><b>SCAD-300</b></td><td>Remote calibration using statistical periods + standard</td><td>Calibrated on low-emission period stats STXGBoost = XGBoost + Kriging + spatiotemporal features</td><td>LCS R<sup>2</sup> improved from 0.60-0.85 (post-calibration) STXGBoost R<sup>2</sup>: 0.96 (1h), 0.92 (4h), 0.89 (48h)</td><td>Dependence on “Low Emission Periods”. The remote calibration step relies on data from periods with reduced anthropogenic activity for calibration.</td><td>Zhao et al. (2025)</td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 3 (continued)**</caption><thead><tr><td>Name of the Sensors</td><td>Deployment Description</td><td>Calibration Method(s)</td><td>Key Findings</td><td>Issues Identified</td><td>Recommendations</td><td>References</td></tr></thead><tbody><tr><td></td><td>instrument reference</td><td></td><td></td><td>Calibration appears to be pre-trained, not dynamically updated in real-time, so drift over time might degrade accuracy</td><td></td><td></td></tr></tbody></table>

Supervised learning algorithms are trained on labelled datasets, which refer to instances where each point has input and corresponding output. The input is a set of features or attributes, ((x1, y1), ..., (xn, yn)), denoted as xi, and the output label is denoted as yi. The goal is to extract general patterns or trends from the labelled examples, allowing the algorithm to create hypotheses about the underlying relationships between input features and output labels. Unsupervised learning works with unlabeled data, focusing on discovering hidden patterns or structures. Common tasks include clustering similar data points and reducing the number of features while keeping important information.

It is important to recognize that not all ML models are appropriate for every sensor calibration task. As seen in Table 4, various algorithms come with their own set of strengths and limitations. For instance, MLR is effective for data with linear relationships but tends to underperform with complex datasets, showing poor generalization for non linear patterns and being sensitive to multicollinearity (Chan et al., 2022). On the other hand, SVR (derived from SVM) can handle high dimensional data and model non linear relationships but is computationally expensive and may overfit smaller datasets. However, a non-parametric methods, such as kNN, RF, and XGB show much better performance in complex sensor calibration tasks. kNN is simple and can model intricate relationships but its prediction phase is sensitive to noisy data. RF and XGB models, meanwhile, have shown remarkable success in LCS calibration, particularly when working with diverse environmental data. For example (Kumar and Sahu, 2021), demonstrated that kNN, RF, and GB provided impressive results for calibrating LCS sensors, with R² values ranging from 0.75 to 0.97 and RMSE values from 72.24 to 0.31. These results suggest that non-parametric models excel in handling real world sensor data, providing greater reliability and consistency. On the other hand, models, such as MLR and SVR showed more variability in performance, as observed in (Patra et al., 2021), where R² values ranged from 0.65 to 0.91 and RMSE from 50.1 to 1.32 for PM₂.₅ calibration. Similarly (Vajs et al., 2021), the linear models, such as LR performed poorly for LCS calibration with R² values between 0.50 and 0.82 and RMSE values ranging from 30.5 to 12.1, indicating significant calibration challenges due to environmental influences like temperature and humidity. Apos-tolopoulos et al. (2024) applied RF to improve the calibration and inter-unit consistency of electrochemical sensors. The results show that ML significantly reduces measurement errors with an R² increase from 0.65 to 0.82 for CO sensors, suggesting that similar model variations can be applied to both PM and gaseous sensors for enhanced calibration and reduced variability. Table 5 shows the classification of models and terminology.

In terms of real-world application (Wang et al., 2023), explored the performance of several supervised ML models, including GAM, RF, OLS, Elastic Net, and SVR, in stationary environments. They found that linear regression models, such as OLS and Elastic Net performed reasonably well with R² values between 0.6 and 0.8, indicating good correlation with reference data. Notably, Elastic Net achieved particularly low RMSE (<0.5 µg/m³) for calibration under stationary conditions, proving effective in controlled environments. However, when these models were applied in mobile settings, their performance deteriorated drastically with R² values dropping to between 0.1 and 0.3. This highlights the difficulty of transferring models trained in stationary conditions to dynamic, mobile environments where environmental factors fluctuate more widely. In contrast, RF and GAM models demonstrated superior

transferability, maintaining better performance across both stationary and mobile settings.

Sensor's performance using SLR, MLR, XGBoost, and a feedforward NN was tested by (Si et al., 2020). Before calibration, the sensor showed overestimations and an RMSE of 9.93. After calibration, NN achieved the lowest RMSE (3.91), outperforming other models. Post-calibration showed no significant variance between NN, XGBoost, and the reference data. As these studies underscore the varying performance of different ML models across environments, it is clear that the choice of calibration model depends not only on the complexity of the data but also on the environmental conditions in which the sensors are deployed. This has significant implications for how LCS calibration is approached across different regions, especially when considering the varying resources and conditions of countries with different income levels.

## 5. Study selection process for evaluation across income groups

The selection process was undertaken through a systematic three step approach which is explained below.

### 5.1. Defining keywords for literature search

Initially, keywords were defined, including “LCS”, “Low Cost”, “Sensors”, “Monitoring”, “Calibration”, “Machine Learning”, “Particulate Matter”, and “Performance Parameters”. These keywords were combined using ‘AND’ as a Boolean operator. The search was classified globally based on UN economic classifications of countries, allowing differentiation between lower to higher income group economies. The aim was to identify the studies of LCSs monitoring focusing on sensor performance parameters.

### 5.2. Selection of databases for review

To comprehensively capture relevant literature, a web based search was conducted, utilizing Google Scholar and Scopus database. These databases were chosen to access peer reviewed articles aligning with the keywords.

### 5.3. Literature selection criteria

Subsequently, utilizing the defined search query, searches were conducted across the selected database. The scope was limited to articles featuring at least one of the predefined keywords in their title, abstract, or indexed keywords. Furthermore, a publication year limitation was imposed, focusing primarily on articles published after 2018. This temporal boundary aimed to emphasize recent advancements in performance parameters and calibration.

### 5.4. Filtration process and finalized studies

Fig. 3 explains the methodology for selecting diverse studies from across the globe evaluating LCSs performance. Among these studies (24 in total), 6 are from lower income group countries, 8 are from middle income group countries and 10 are from higher income group countries. This shows the diverse area across the globe, dealing with all evaluation criteria, including performance parameters, calibration and colocation
<table><caption>**Table 4**
Machine learning models for sensor calibration.</caption><thead><tr><th>Model/Method</th><th>Working Mechanism</th><th>Advantages</th><th>Limitations</th><th>References</th></tr></thead><tbody><tr><td>Supervised learning<br>Decision Trees (DT)</td><td><ul><li>The dataset is homogeneously divided based on conditional checks that determine the outcome.</li><li>The model iteratively splits the dataset into different regions using predictors to reduce the error.</li><li>This splitting process begins at the root node and proceeds through branches until reaching leaf nodes.</li><li>Each tree consists of nodes and branches, where:<ul><li>Nodes represent attributes (or features).</li><li>Branches represent values or outcomes of the conditional checks.</li></ul></li></ul></td><td>Simple implementation, interpretable, suitable for smaller datasets.</td><td>Less predictive performance if the relationship between predictors and response variable is not well defined. Overfitting of data and high variance are common limitations.</td><td>Chen and Guestrin (2016)</td></tr><tr><td>Multiple Linear<br>Regression (MLR)</td><td><ul><li>Fits a linear equation to predict a continuous outcome based on multiple input variables</li><li>Combines multiple decision trees for classification.</li><li>Utilizes majority voting from all trees to determine the final prediction.</li><li>Enhances overall accuracy and performance.</li><li>Known for speed and reliability, hence widely adopted.</li><li>Becomes harder to interpret results as dataset size increases.</li><li>Larger datasets can negatively affect both performance and running time.</li></ul></td><td>Simple, interpretable, fast</td><td>Assumes linearity, sensitive to multicollinearity, poor with complex relationships</td><td>(Ray, 2019)</td></tr><tr><td>Random Forest (RF)</td><td><ul><li>Combines multiple decision trees for classification.</li><li>Utilizes majority voting from all trees to determine the final prediction.</li><li>Enhances overall accuracy and performance.</li><li>Known for speed and reliability, hence widely adopted.</li><li>Becomes harder to interpret results as dataset size increases.</li><li>Larger datasets can negatively affect both performance and running time.</li></ul></td><td>Reduces overfitting, high accuracy, robust to noise, handles non-linear relationships well.</td><td>Increased complexity; difficult interpretation with large datasets, computationally expensive.</td><td>Chen and Guestrin (2016)</td></tr><tr><td>Support Vector<br>Machine (SVM)</td><td><ul><li>Excels in efficiently performing classification and regression using non-linear kernel techniques.</li><li>Establishes specific margins between classes to minimize error.</li><li>Well-suited for capturing non-linear relationships between input features and output variables.</li></ul></td><td>Suitable for non-linear relationships, fast implementation.</td><td>Requires ample training data; sensitive to kernel parameters, relying on the assumption of independently and identically distributed data.</td><td>Balabin and Lomakina (2011)</td></tr><tr><td>Artificial Neural<br>Network (ANN)</td><td><ul><li>Mimics the human brain's functionality through interconnected layers of nodes.</li><li>Adapts to changing input values, producing corresponding outputs.</li><li>Shallow neural networks are commonly used for low-cost sensor calibration.</li><li>Layers employ activation functions, such as sigmoid, hyperbolic tangent, and linear units.</li><li>Weighted regression forms a significant part of ANN parameterization.</li></ul></td><td>Highly flexible, captures complex relationships, can adapt dynamically to changes in input.</td><td>Computationally intensive; requires large datasets, prone to overfitting without regularization.</td><td>(Mahajan & Kumar, 2020; Yusaf et al., 2011)</td></tr><tr><td>Xtreme/Gradient<br>Boosting Decision<br>Tree (XGB/GBDT)</td><td><ul><li>Built upon decision tree models, it serves as an improved and efficient technique.</li><li>Combines weaker learners to form a strong model by refining predictions using gradient information from the loss function.</li><li>Key steps include sampling, gradient calculation, optimal partitioning, and node splitting.</li></ul></td><td>High accuracy, reduces both bias and variance, robust against overfitting with appropriate tuning.</td><td>Computationally demanding, sensitive to hyperparameter settings, risk of overfitting on small data.</td><td>(Ke et al., 2017; Zhang et al., 2021)</td></tr><tr><td>Generalized Additive<br>Model (GAM)</td><td><ul><li>Extends generalized linear models (GLMs) to capture non-linear relationships.</li><li>Uses smooth functions (often spline-based) to model complex patterns while applying penalties to prevent overfitting.</li><li>Incorporates predictors, such as time vectors (inter-annual, monthly, and weekday variations) and meteorological variables.</li></ul></td><td>Captures intricate non-linear patterns; interpretable results, mitigates overfitting via penalization.</td><td>Complex implementation; sensitive to data quality and selection of smoothness penalties.</td><td>(Bishop and Nasrabadi, 2006; Wood, 2017)</td></tr><tr><td>Unsupervised learning<br>K-Nearest Neighbors<br>(KNN)</td><td><ul><li>Predicts output based on the average (or weighted average) of the k-nearest neighbors in feature space.</li></ul></td><td>Computationally more efficient than hierarchical clustering when variables are huge. Tighter clusters than hierarchical clustering.</td><td>Slow at prediction, affected by feature scaling. Prediction of K value is hard. Performance suffers when clusters are globular. Different initial partitions result in different final clusters impacts performance.</td><td>Ray (2019)</td></tr></tbody></table>
<table><caption>**Table 5**

Classification of various models and nomenclature of various terminologies.</caption><thead><tr><th rowspan="2">Category</th><th colspan="2">Terminologies</th></tr><tr><th>Full Form</th><th>Abbreviation</th></tr></thead><tbody><tr><td rowspan="3">Raw Data</td><td>Raw Data</td><td>RD</td></tr><tr><td>Raw Data Non Heating Season</td><td>RDNHS</td></tr><tr><td>Raw Data Heating Season</td><td>RDHS</td></tr><tr><td rowspan="7">Regression Models</td><td>Raw Data Non Heating Season</td><td>RDNHS</td></tr><tr><td>Raw Data Heating Season</td><td>RDHS</td></tr><tr><td>Multiple Linear Regression</td><td>MLR</td></tr><tr><td>Ordinary Least Squares</td><td>OLS</td></tr><tr><td>Linear Regression</td><td>LR</td></tr><tr><td>Ridge Regression</td><td>Ridge</td></tr><tr><td>Least Absolute Shrinkage and Selection Operator</td><td>Lasso</td></tr><tr><td rowspan="2">Environmental Corrections</td><td>Elastic Net</td><td>Elastic Net</td></tr><tr><td>Bayesian Ridge</td><td>Bayesian Ridge</td></tr><tr><td rowspan="2">Machine Learning Models</td><td>Univariate Linear Regression Mode</td><td>ULR</td></tr><tr><td>Koehler</td><td>K</td></tr><tr><td rowspan="2">MLR</td><td>MLR RH</td><td>MLR RH</td></tr><tr><td>Robust Linear Model</td><td>RLM</td></tr><tr><td rowspan="3">Advanced Calibration Models</td><td>Robust Linear Model RH</td><td>RLM + RH</td></tr><tr><td>RH Correction Factor</td><td>RH-CF</td></tr><tr><td>Random Forest</td><td>RF</td></tr><tr><td rowspan="10">Non-linear Models</td><td>Support Vector Machine</td><td>SVM</td></tr><tr><td>K-Nearest Neighbors</td><td>KNN</td></tr><tr><td>XGBoost</td><td>XGB</td></tr><tr><td>Gradient Boosting</td><td>GB</td></tr><tr><td>Multilayer Perceptron</td><td>MLP</td></tr><tr><td>AdaBoost</td><td>AdaBoost</td></tr><tr><td>Gradient Boosting Regression Tree</td><td>GBRT</td></tr><tr><td>Error Augmented Two-Phase Calibration</td><td>EEATC</td></tr><tr><td>Constrained Two-Stage Multivariate Linear Regression</td><td>CTMLR</td></tr><tr><td>Non-linear</td><td>Non-linear</td></tr><tr><td rowspan="3">Mixed Effects Models Specific Use-Case Models</td><td>Generalized Additive Model One Variable</td><td>GAM-1Var</td></tr><tr><td>Generalized Additive Model Four Variables</td><td>GAM-4Var</td></tr><tr><td>Linear Mixed-Effects Model</td><td>LME</td></tr><tr><td rowspan="2">PMD Measurement Case I</td><td>PMD Measurement Case I</td><td>PMD-CI</td></tr><tr><td>PMD Measurement, Temperature, Humidity Case II</td><td>PMD-CII</td></tr></tbody></table>

measures, monitoring period and providing detailed analysis of raw data along with model evaluation through ML. A comprehensive analysis and detailed data for all selected 24 studies, including a breakdown of results based on performance parameters are present in supplementary file Table S2. As evident from Table S2, more reviews are focused on higher income group countries which is probably due to a greater number of studies.

# 6. Sensor performance parameters from reviewed studies

## 6.1. Performance characteristics and parameters

Evaluating the performance and reliability of low-cost PM sensors is crucial for determining their suitability for specific applications, particularly in diverse environmental context. Low-cost PM sensors underwent a comprehensive assessment in the studies selected for review, focusing on parameters, such as linearity, precision, error and bias (Table S2). Fig. 4 describes the categories of ML models used per country, separating the 3 groups of countries 4a) lower, 4b) middle and 4c) higher income. Calibration matrices were analyzed and compared using key metrics recommended by the Environmental Protection Agency (EPA), including the R², coefficient of variation, standard deviation, slope, intercept, RMSE, NRMSE, MAE, MBE, and MNE. These

metrics provide a comprehensive understanding of the sensor performance. The calibration matrices are directly influenced by attributes, such as environmental variability, pollutant composition, and operational conditions, which vary in accordance with the pollution sources dominant in a region as discussed in Section 3.

## 6.2. Evaluation of sensor performance parameters across income group countries

### 6.2.1. Sensor performance parameters for lower income group countries

From the literature survey, lower income group countries were found to be having the least number of reference monitors per million people (Martin et al., 2019). In view to this fact, the reference stations in these countries were chosen at a particular location for air quality studies. In most of the cases of African region, BAM was located at the US embassy which was referred for data collocation and calibration of sensors. Being one of the most polluted region and reliability on fossil fuels for fulfilling the energy demands, the characteristics, particle type and particle size usually varied from region to region. Out of 24 studies, six studies were found to be from the lower income group countries, especially from the African region. Among these studies, only two studies were focusing on the calibration methods for improvement in performance parameters through ML method. McFarlane et al. (2021a,b) reported one of the first ambient PM2.5 measurement in Kinshasa, Democratic Republic of Congo and Brazzaville. The Purple Air sensors were used in this study with collocation at the U.S. Embassy in Kampala. Adong et al. (2022) deployed one of the biggest networks of sensors with AirQo sensors, following a common assumption of (McFarlane et al., 2021a,b) regarding the similarity in climate and emissions, and applying the collocated field correction factors for a collated location. From Table S2, it can be seen that R² for the factory calibrated sensors for AirQo device is around 0.52 but for purpleAir device its 0.81. This variation is direct indication towards the proprietary algorithms used by the companies as they both use similar sensors PMS5003. As discussed earlier these algorithms are not public and how the particle size conversion has taken place is unknown with a standard idea about the basics. Furthermore, the substantial variation in MAE between McFarlane (14.8) and Adong (4.6) underscores the existence of notable errors in predicting specific PM2.5 values. The subsequent enhancement, as depicted in Fig. 4a, resulting from the application of different ML calibration models, emphasizes the ongoing efforts to address and improve the accuracy of PM2.5 measurements. Lassman et al. (2020) deployed an Aerosol Mass and Optical Depth device with LCSs for the measurement of aerosol concentration but sensor performance parameters again are not tested before deployment. Dingemanse and Tademe (2023) deployed SPS30 Sensirion sensors for more than 412 days with R² of 0.92 for linear regression and slope of 1.08 but there was no other analysis was performed using other methods or performance testing.

### 6.2.2. Sensor performance parameters for middle income group countries

Middle income group countries include mostly the developing nations with high pollution sources from industrial, transport and residential sectors. There are 8 studies included which follows our selection criteria in middle income group countries. This group is the most diverse as it includes studies from Asia, Africa and Europe.

From Fig. 2 in the Asian region, it is clear that there is too much concentration from local sources especially in from power, transport and industrial sector. Dejchanchaiwong et al. (2023) used PMS7003, Plan tower to understand the key factors affecting performance, including

emission sources, relative humidity, temperature and PM2.5 concentration in Thailand. From Figs. 4b and 5b, it can be seen that Multiple regression models were used in this study. The congested areas were found to have sensor data underestimation. Unadjusted PM2.5 LCS performance varied with location, showing correlations ranging from 0.29 to 0.91 with FEM instruments. Adjusted PM2.5 LCS performance at certain locations with high traffic emissions in both seasons was not
<figure><img src="image_6.png" /><figcaption>Fig. 3. Literature selection criteria of this study.</figcaption></figure>

acceptable. This issue was attributed to very small particle size from emission sources, low concentrations, and RH levels. The study provides info regarding the linearity, bias, precision and error. Hua et al. (2021a, b) for 180 days study in China use calibration linear model and a generalized additive model (GAM) categorized by relative humidity for PM<sub>2.5</sub> PMS5003 sensors. The study indicates that under conditions of relative humidity below 75%, a linear calibration of LCS PM<sub>2.5</sub> observations yields PM<sub>2.5</sub> mass concentrations closely aligned with the reference method, demonstrating a notable correlation coefficient (R) of 0.86. Conversely, when relative humidity exceeds 75%, the GAM calibration model surpasses the linear model, exhibiting a higher R<sup>2</sup> of approximately 0.83. In summary, the linear model proved effective in dry conditions, while the GAM model excels in capturing PM<sub>2.5</sub> variations in humid conditions. It is evident that LCSs for PM<sub>2.5</sub> are influenced by relative humidity, emphasizing the necessity for condition specific calibration methods to enhance data quality and align with reference measurements. The source apportionment studies and then adjusted ML patterns based on network analysis of sensors can be quite useful in these scenarios for future research. Kulkarni et al. (2022) provides and inter and intracity variations in performance of low-cost sensors. Atmos sensors was used in this study equipped with PMS7003 sensors. The Atmos PM<sub>2.5</sub> readings tended to overestimate PM<sub>2.5</sub> values in Indo Gangetic Plain cities (Chandigarh and New Delhi) but underestimated them in western India (Mumbai). However, measurements in south Indian cities (Bengaluru and Chennai) were generally accurate. GAM outperformed other calibration models in improving accuracy, with up to a 70% enhancement. Cross validation confirmed the effectiveness of the correction models. The bias range in intracity (Mumbai) Atmos PM<sub>2.5</sub> readings was comparable to intercity variations. Overall, the GAM excelled in correcting raw LCS PM<sub>2.5</sub> measurements across study locations. Studies conducted by (Rabuan et al., 2023; Khaleel et al., 2023) utilized LCSs for measurement and monitoring purposes. However, it is worth noting that these studies did not thoroughly test regression models for the evaluation of sensor performance.

There is a noticeable pattern in research studies conducted on the African continent, regarding the usage of LCSs deployment and performance testing. It appears that studies primarily employ such methods for measurement purposes, overlooking the critical aspect of data quality when considering performance parameters especially for lower income

group countries but for middle income group countries, for instance, in the study conducted by (Aniyikaiye et al., 2023), the Dylos DC1700 was deployed for measuring air quality around brick kilns. The researchers utilized empirical formulas to derive PM<sub>2.5</sub> from particle number concentration, incorporating correction factors. While this represents a noteworthy advancement, especially for low income group countries, there remains a dearth of information concerning crucial metrics, such as bias, RMSE, and other parameters essential for comprehending pollutant variations. McFarlane et al. (2021) takes use of ML models further by comparing Raw data, with ML models like GMR, MLR and RF increasing the R<sup>2</sup> by 60%. Error and bias were also provided for the comparison. The study shows an ideal case for the improvement of bias and error through ML approaches over raw data for purple Air Sensors. The studies reviewed indicate that simply relying on the measurement of high quality data is insufficient for both research purposes and the reliability of LCS. Raw sensor data show underestimation or overestimation of PM<sub>2.5</sub> concentrations without proper calibration. Research conducted across Asia, Africa, and Europe reveals that LCSs often require adjustment through ML models to correct bias and improve precision. The impact of environmental factors is particularly notable, with linear models being effective in dry conditions and GAM models having better results in more humid environments. Uncalibrated LCS produce results with significant error margins, however, relying on one performance measure, like R<sup>2</sup>, can give a false sense of accuracy. A sensor might show a strong correlation but still have high errors in actual values. This highlights the importance of using multiple performance checks like error and bias and setting clear thresholds to judge reliability. Even if some data appear high quality, without proper, context-specific calibration and a complete performance assessment, the results can be misleading. While some progress has been made through correction factors, there is still a lack of comprehensive quality analysis based on performance parameters. This underscores that high quality measurements alone are insufficient; robust, context specific calibration approaches remain essential for reliable sensor deployment in different environments.

### 6.2.3. *Sensor performance parameters for higher income group countries*

The higher income group countries have well established monitoring systems, with growing focus towards the low-cost sensor studies Figs. 4c
Lower Income Countries

<figure><img src="image_7.png" /><figcaption>(a)</figcaption></figure>

Middle Income Countries

<figure><img src="image_8.png" /><figcaption>(b)</figcaption></figure>

Higher Income Countries

<figure><img src="image_9.png" /><figcaption>(c)</figcaption></figure>

**Fig. 4.** Machine learning approaches and performance parameters for results evaluation in (a) lower-income group countries, (b) middle income group countries, and (c) higher income group countries.
<figure><img src="image_10.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(c)</figcaption></figure>

Fig. 5. Model performance metrics in (a) Lower-income group countries, (b) Middle income group countries, and (c) Higher income group countries.
and 5c. Bulot et al. (2023) used LR and OLS, MLR on PM mass including RH and on PM number including RH, κ-koehler correction on PM mass, Laulainen correction on PM mass, RLM on PM mass including RH and on PM number including RH. SVM, GBM on PM mass with or without RH and κ koehler correction on the particle number concentration. Study evaluates the performance of Sensirion SPS30 and Plantower PMS5003 sensors before and after calibration. The intersensory variability between randomly chosen sensors was high. The study provides recommendation to test at least two sensors before deployment. For PMS5003, hourly data showed lower uncertainty than daily data, possibility was considered as greater number of data points. The study suggests certifying sensors at finer temporal resolutions, proposing RMSE as an alternative metric. After a two week calibration, sensors met MCERTS standards, but the suggested calibration frequency is twice a year, challenging the yearly requirement. The study proposes the adoption of RMSE as a less sensitive alternative metric, showcasing notable performance distinctions among tested averaging windows. To accommodate this metric, there is a call for the development of new performance standards. The recommendation, based on sensor calibration scenarios, suggests a benchmark value of 3 µg/m³. Kosmopoulos et al. (2023) findings regarding particles with diameters ranging from 0.3 to 10 µm indicate a notable influence of particle size on the response sensors. It was observed that LCSs exhibited high accuracy in detecting number concentrations for particles smaller than 1 µm at both urban (R² = 0.9) and background sites (R² = 0.92), with a moderate correlation observed in semi arid areas (R² = 0.69) compared to reference instruments. However, their performance significantly decreased for coarser aerosol fractions across all sites, with R² values indicating poor correlation (R² < 0.31). Particularly during periods dominated by coarse particles, such as dust events, PMS 5003 sensors demonstrated inadequate reporting of accurate number distributions (R² values < 0.47), consistently underestimating particle number concentrations which is similar issue as discussed in section 4 (Suriano and Prato, 2023a). analyzed PMS5003 and SPS30, in accordance with performance parameters. R² was found to range from 0.55 to 0.61, and RMSE ranged from 11.02 to 12.09 µg/m³. Notably, the introduction of a correction factor to account for humidity effects led to an enhancement in the performance of PMS5003 sensor models. Additionally, based on MNB and CV values, the EPA guidelines categorized SPS30 sensors for application area. This makes a good case for the study in this domain.

# 7. Results and analysis

## 7.1. Quantitative assessment: sensor performance, sectoral contributions, and data reliability (including formulae and results derived from selected studies)

The calibration matrices are significantly influenced by key attributes related to accuracy, precision, and reliability which requires a systematic evaluation of sensor performance. To achieve this, three metrics, Performance Index, Sector Contribution Ratio, and Data Reliability Indicator were developed to quantify sensor efficacy. These metrics are designed to support cross study comparisons and facilitate the evaluation of advanced ML algorithms. The methodology developed below evaluates the overall performance and effectiveness of the sensors in different environments where predominant sector varies and so does the performance parameters which directly impact data reliability of sensors in different income group countries. Finally, the Data Reliability Indicator assess sensor adaptability and consistency under varying conditions.

### 7.1.1. Performance Index

The Performance Index serves as a measure of overall sensor accuracy and sensitivity. The Performance Index quantifies the overall effectiveness of sensors by integrating four critical performance parameters: linearity, bias, precision, and error. Each study included in the

review was carefully analyzed, and the metrics used were graded based on their availability and threshold values referred from US EPA (Duvall et al., 2021). For linearity, studies reporting the coefficient of determination (R²) were awarded a point if more than or equal to threshold value. Bias was evaluated using three key indicators: MNB, slope, and intercept. Precision, which highlights the consistency of the sensor's measurements, was assessed using two metrics: CV and SD. Lastly, error was assessed through five widely recognized indicators: MAE, MNB, RMSE, NRMSE, and MBE with each indicator contributing one point. This method assigns equal weight to key performance parameters. This approach reduces the risk of biased interpretations due to emphasis on a single parameter evaluation and promotes a more reliable understanding of sensor performance.

The Performance Index is computed as the average of the scores for linearity, bias, precision, and error, resulting in an assessment of the sensor's performance.

$$PI = \frac{S_{Linearity} + S_{Bias} + S_{Precision} + S_{Error}}{4} \qquad (1)$$

Where, PI is the Performance Index, SLinearity, SBias, SPrecision, SError are the scores for Linearity, Bias, Precision and Error, respectively.

The Performance Index (Fig. 6a) for the lower income group countries turns out to be the lowest (0.27), followed by middle income group countries (0.33) and higher income group countries (0.35).

### 7.1.2. Sector contribution ratio

In countries across different income groups, the factors influencing emission concentrations vary significantly. Each income group countries experiences distinct emission profiles, with sectors contributing differently to the overall particulate matter levels (Fig. 2). To quantify these differences, we introduce the Sector Contribution Ratio which compares the highest and lowest contributions from specific sectors within each income group countries. This study focuses on five key sectors: Agriculture, Industry, Power, Residential, and Transport. These sectors were selected because they offer reliable and well documented data on PM₂.₅ emissions (Table S2). However other sectors like dust concentrations, remaining sources, and the shipping sector were excluded intentionally. The shipping sector was omitted due to its tendency to bias results given its specialized nature, while remaining sources sector is too uncertain to quantify accurately. Dust concentrations were also left out because PM sensor performance can be unreliable, especially in regions prone to dust storms (Mei et al., 2020; Báthory et al., 2022). Dust is more prevalent in semi arid and low income regions, often influenced by natural factors like dust storms. Including dust concentrations in our analysis could skew the overall evaluation, especially in regions where dust storms are a dominant pollution source. These natural events can overwhelm the contributions of anthropogenic sources, potentially distorting the attribution of PM₂.₅ levels. Dust concentrations (Fig. 2) are indeed a significant contributor to PM₂.₅ in many countries but they are from a natural origin while this study aims to focus on anthropic sources of dust. while we do consider multiple sectors, such as Agriculture, Industry, Power, Residential, and Transport, the focus on these anthropogenic sources is intentional. These sectors are directly linked to human activities that can be controlled or mitigated, unlike natural dust sources. We thus decided to exclude dust from this study for key reasons. By concentrating on these five sectors, the Sector Contribution Ratio provides clear insight into which sectors are the most dominant and least dominant contributors to particulate matter emissions. The approach is founded on the assumption that the most dominant sector is likely the primary contributor to pollution in the study region, aligning performance expectations with the sector exerting the greatest influence on air quality:

$$R_{SCRmax} = \frac{R_{SMax}}{\Sigma(R_{SAgri} + R_{SInd} + R_{SPow} + R_{SRes} + R_{STrans})} \qquad (2)$$
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 6.** Evaluation of a) Performance Index, b) Data Reliability indicator (Max vs Min Sector) c) data reliability Indicators across various income group countries d) Maximum Sector Contribution Ratio, and e) Minimum Sector Contribution Ratio.</figcaption></figure>
$$R_{\text{SCRmin}} = \frac{R_{\text{SMin}}}{\Sigma(R_{\text{SAgri}} + R_{\text{SInd}} + R_{\text{SPow}} + R_{\text{SRes}} + R_{\text{STrans}})} \quad (3)$$

Where, RSCRmax is the Sector Contribution Ratio of sector contributing maximum and RSCRmin is the sector contributing least to PM2.5 emissions. RSMAX and RSMIN are the rate of emission from maximum and minimum contributing sector. RSAgri, RSInd, RSPow, RSRes and RSTrans are the rate of emissions from Agriculture, Industry, Power, Residential and Transport sectors.

For the maximum sector contribution (Fig. 6d), low income group countries show a better Sector Contribution Ratio (0.51) compared to higher income (0.34) and middle income group countries (0.32). For the minimum sector contribution ratio (Fig. 6e), higher income group countries have a better value (4.18), followed by middle income (2.49) and low income group countries (0.89).

### 7.1.3. Data reliability indicator

Data reliability is one of the major concerns for LCS. Reliability issues occur because of the reason that same sensors with similar calibration models are being used in areas with varied emission sectors. The post processing of the data is not reliable because as seen from Fig. 5 the usage of ML methods to improve accuracy based on performance metrics are very limited. To address this, we calculate data reliability indicator that factors in the Sector Contribution Ratios for maximum and minimum sector of all income group countries as well as within individual sectors.

$$DRI = PI * R_{\text{SCR}(x)} \quad (4)$$

Where, DRI is Data Reliability Indicator, PI is Performance Index and RSCR(x) is the rate of Sector Contribution Ratio where (x) can be RSMAX, RSMIN, RSAgri, RSInd, RSPow, RSRes and RSTrans.

Equation (4) provides a method for calculating sector specific data reliability factors by combining the emission contribution of each sector with the sensor's demonstrated performance under dominant sectoral conditions. This means that reliability is not solely about how much a sector emits, but how well the sensor captures emissions from that specific sector.

The Data Reliability Indicator values (Fig. 6b) for low income group countries exhibit a higher data reliability score for their maximum contributing sectors (14.26) compared to the score of higher income (11.74) and middle income group countries (10.71). From the (Fig. 6c), higher income group countries exhibit relatively balanced data reliability across all sectors, with the highest reliability in the power sector (9.13) and agriculture (8.04), while power and transport have the lowest values at 5.07 and 5.66, respectively. In contrast, lower income countries display an imbalance, with power sector data reliability (13.67) standing out as an extreme outlier with the residential sector (8.26) showing a relatively higher value. This may indicate a concentrated effort on specific areas while neglecting sectors like agriculture (0.9), industry (1.97), and transport (1.74). Middle income group countries present a more uniform distribution compared to lower income. The highest reliability indicators are found in the power (9.06) residential sector (8.67), and industry (8.53) sectors while agriculture (3.52) and transport (3.28) are significantly lower.

## 7.2. Discussion of findings and implications

As discussed in Section 7.1, the analysis and results for Performance Index, Sector Contribution Ratio for the maximum and minimum contributing sectors, and Data Reliability Indicator across different income group countries. The Performance Index (Fig. 6a) follows a general trend where higher income countries demonstrate better systematic monitoring infrastructure, leading to improved sensor performance and utilization of performance parameters. However, when compared with the Data Reliability Indicator (Fig. 6b), low income countries exhibit

better reliability for the maximum contributing sector, highlighting potential differences in data consistency across economic groups. The Sector Contribution Ratio for the maximum contributing sector (Fig. 6d) indicates that low income countries perform better in this regard, whereas higher income countries have an advantage in the minimum sector contribution ratio (Fig. 6e). This suggests that while lower-income regions may have stronger representation in key contributing sectors, higher income regions ensure better distribution across all sectors. Data reliability across sectors (Fig. 6c) varies depending on the sector. Higher income countries show better reliability in the agriculture and transport sectors, whereas middle income countries lead in industrial and residential sectors. For the power sector, low income countries exhibit the highest reliability. These variations highlight the differing strengths of monitoring infrastructure and sectoral representation among income groups when using low-cost sensor data.

The low income group countries with the lowest Performance Index reflects systemic inefficiencies and limited resources for addressing complex pollution challenges. However, the group's high Sector Contribution Ratio underscores the overwhelming presence of diverse and often unregulated pollutant sources, which complicates capturing accurate data. The data reliability indicator shows lowest values for agriculture, industrial and transport sector however power sector shows highest reliability, also data reliability for residential sector is better than higher income group countries. Despite lower overall performance, low income group countries exhibit higher data reliability for their maximum contributing sectors due to focused resource allocation. This reflects that even strategic monitoring even with limited resources can yield high quality data. A key reason low income regions can show a lower data reliability indicator for most sectors than higher and middle income group countries is that the data often comes from a small number of inconsistent sources. For instance, low income group countries sometimes depend on reference monitors set up by external agencies, and these monitors tend to be operated under standardized protocols. Because there are fewer local monitoring networks to compare against and fewer conflicting methodologies or sensors data from these sources can appear more "uniform and lesser evaluated," raising the better reliability score for strategic deployment. Studies conducted in low income group countries lack comprehensive performance parameter testing. Fig. 5a explains the evaluation of the model performance metrics as different ML models are used but only in certain studies. There is a critical necessity for a fit for purpose analysis that can assess both higher and lower concentration zones within a given area. Reference monitors for a particular area is not easily accessible but post processing of data by taking into consideration the environmental factors with different ML methods is important. Additionally, it is imperative to employ a standardized approach by incorporating emission inventory and satellite data based analyses prior to deploying sensors. This ensures a robust foundation for obtaining high quality and reliable data. Invest in infrastructure and deploy robust ML algorithms (e.g., RF, XGB) to mitigate high variability and improve data reliability. Enhance monitoring systems to address environmental stressors contributing to better sensitivity. The regions with higher sensitivity, which indicate higher pollutant variability or noise in the data, tend to show more reliable data when advanced algorithms are applied. High Sector Contribution Ratio, as seen in these countries, represents substantial pollutant variability from a mix of unknown sources. Advanced algorithms like ML, RF and XGB excel in these scenarios, achieving high Data Reliability Indicator values by effectively capturing complex and noisy data. These models are particularly suitable for low income regions, where diverse pollutants require robust methods to ensure reliable data outcomes. Conversely, simpler models, such as OLS and LR underperform under high sensitivity conditions, as they lack the flexibility to adapt to pollutant diversity.

The middle income group countries with second highest Performance Index signifies capability for efficient functionality in the most diverse industrial environments. Around the world many of these
nations also rank among the world's most polluted due to rapid industrial expansion, which results in considerable variability in pollution levels. The data reliability along the industrial, power and residential sector show overall better but similar patterns (Fig. 5b). This consistency suggests that data from middle income group countries are moderately responsive to changes within these sectors. There is a lack of strategic deployment to identify properly the sectorial influences. With a highest Data Reliability Indicator in industrial sector middle income group countries, benefits from advanced models like Elastic Net, GAM and AdaBoost, showing moderate improvement in data reliability as sensitivity increases. Middle income regions, grappling with rapid development and diverse pollutants, require hybrid approaches that balance interpretability and adaptability. Low income regions, with simpler governance structures, can prioritize robust algorithms to manage pollutant variability while gradually improving alignment.

The higher income group presents a contrasting profile, with the highest overall Performance Index across sectors. In terms of data reliability, the power sector performs best, followed by agriculture, industry, transport, and residential sectors, as reflected in Fig. 6c. Higher income group countries have a better infrastructure already in place but the data evaluation and sensor performance are not well regulated as reliability scores are better but very low when compared to highest score of 100 which is similar for each income group country. The higher income group countries usually have numerous monitoring stations equipped with varying technologies, calibration practices, and data collection methods. This broader scope can introduce inconsistencies, ultimately lowering the overall reliability when compared to middle income group countries. The results of these studies showed new findings while maintaining a decorum for the performance evaluation of the sensors. Higher income group countries have lower concentration thresholds of PM levels when compared to middle and low income group countries. The usage of ML models and approaches of other physical models are more in these countries (Fig. 5c) and they need to move towards a wholesome approach based on source apportionment and emission inventory inclusion for calibration purposes. Narayana et al. (2023) makes a great case for using already established sensor and data by creating a novel estimated error augmented two phase calibration (EEATC) method based on USEPA Dataset for Colorado and comparing the model with ML models. There model EEATC performed better than the established ML models. In the implementation of EEATC, the initial phase involves employing MLR to capture linear relationships within the data, followed by the second phase utilizing RF to capture nonlinear patterns. However, study only deal with linear and nonlinear models, MLR and RF and study beyond these are important. Focus on refining existing systems for incremental improvements, with emphasis on optimizing sensor calibration and adopting advanced predictive models. Tailoring algorithm choice based on regional characteristics is crucial. Low income regions require noise resilient algorithms, while higher income group countries can leverage less computationally intensive models for efficiency. The high sector variation led to lower Data Reliability Indicator. This suggests that while pollutant profiles are narrower in these regions than in others, the specialization of studies may lead to inconsistencies in broader data reliability.

## 8. Conclusions

LCSs have emerged as an essential tool for air quality measurement, offering significant spatial coverage advantages over expensive reference grade monitors. However, their performance, remains a challenge due to the lack of regulatory compliance regarding measurement methods and the undisclosed nature of algorithms for mass concentration conversion. This study examined the deployment, monitoring, and performance outcomes of LCS networks across high, middle, and low income group countries focusing on variations in emission sources, performance parameters, and use of ML approaches for local source categorization. To quantify sensor performance, a methodology was

developed; it calculates Performance Index based on accuracy, precision, bias, and error. Additionally, the Sector Contribution Ratio identified pollution sources across income group countries and from these insights a Data Reliability Indicator was analyzed. The comparative analysis of the Performance Index, Sector Contribution Ratio, Data Reliability Indicator reveals that advanced monitoring systems without proper calibration methods do not necessarily ensure consistent data reliability. In addition, the study identifies key differences in pollution sources and monitoring effectiveness across regions. Countries with higher incomes show the best sensor performance (Performance Index: 0.35), supported by advanced monitoring networks. However, inconsistent technologies and calibration methods reduce data reliability, especially in sectors with lower emissions (reliability score: 4.18). Middle income countries achieve a slightly lower Performance Index (0.33) but show consistent data quality across major sectors like industry, power, and residential. This consistency, however, points to a lack of targeted strategies for sector-specific monitoring. Low income countries, despite having the lowest Performance Index (0.27), record the highest reliability scores (14.26) in their major pollution sectors. This is due to their reliance on standardized reference monitors, often installed by international agencies, which offer uniform but limited coverage. These findings highlight that strong infrastructure alone does not ensure better monitoring outcomes. ML models, viz., Random Forest (RF), AdaBoost (ADA), and Extreme Gradient Boosting (XGB) are particularly effective in capturing complex pollution patterns from diverse emission sources, however traditional methods, viz., Linear Regression (LR) and Multiple Linear Regression (MLR) often struggle under such conditions due to their limited capacity to handle nonlinearity and variability. High income countries can benefit from adopting these advanced models by refining calibration methods and expanding the scope of pollutant monitoring to enhance data reliability. In low and middle income countries where sectoral variability is higher and collocated measurements are limited, the challenge is also greater. However, models, viz., Gradient Boosting Decision Trees (GBDT), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) are well-suited for dealing with incomplete datasets and can adapt to diverse local conditions, thereby improving the overall Data Reliability Indicator. To further enhance accuracy, models, viz., Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Generalized Additive Models (GAM) provide additional flexibility, making them valuable tools. To enhance the global utility of LCSs, several actions are recommended. For Low Income Regions: Develop robust ML models capable of managing high pollutant variability and Sector Contribution Ratio specific to the region, deploy reference monitors and expand local monitoring networks to improve overall performance, invest in infrastructure and establish regulatory frameworks. For Middle Income Regions: Expand LCS deployment with enhanced spatial coverage and model refinements to minimize errors and address misalignment challenges, develop monitoring strategies and use of ML models that address the industrial and urban dynamics, ensuring that sector pollutant variations are more accurately captured. For Higher Income Regions: Optimize existing algorithms through improved calibration techniques and integrate satellite imagery and historical datasets to define robust limits for autocalibration.

By combining LCS data with satellite imagery, emission inventories, and older datasets, it is possible to establish comprehensive baselines for calibration and autocalibration. This would enhance data reliability and reduce the dependence on costly reference grade monitors.

## CRediT authorship contribution statement

**Rajat Sharma:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization.
**Andry Razakamanantsoa:** Writing – review & editing, Supervision, Formal analysis, Conceptualization.
**Ashutosh Kumar:** Writing – review & editing, Validation, Investigation.
**Thaseem Thajudeen:** Writing –
review & editing, Validation, Investigation. **Agnès Jullien:** Writing – review & editing, Validation, Supervision, Conceptualization.

## Funding

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie COFUND grant agreement No 101034248.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.aeaoa.2025.100331.

## Data availability

Data is provided in the Supplementary file

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