Measurements Of Particulate Matter Concentrations Research Articles

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

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

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

Health risks for children exercising in an air-polluted environment can be reduced by monitoring air quality with low-cost particle sensors

A child’s body is highly sensitive to air quality, especially regarding the concentration of particulate matter (PM). Nevertheless, due to the high cost of precision instruments, measurements of PM concentrations are rarely carried out in school areas where children spend most of their daily time. This paper presents the results of PM measurements made by a validated, low-cost university air pollution measurement system operating in a rural area near schools. An assessment of children’s exposure to PM during school hours (8 a.m.–6 p.m.) at different times of the year was carried out. We show that PM10 concentrations in the air, particularly in winter, often exceeded the alert values of 50 µg m−3, posing a health risk to children, especially when children exercise outside the school building. We also calculated the rate and total PM10 deposition in the respiratory tract during various physical activities performed in clean and polluted air. Monitoring actual PM10 concentrations as presented in this paper, using a low cost sensors, offer school authorities and teachers an opportunity to reduce health risks for children. This can be achieved by adjusting the duration and exercise intensity of children’s outdoor physical activities according to the measured air quality.

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

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

Trees help reduce street-side air pollution: A focus on cyclist and pedestrian exposure risk

Trees have been reported to help reduce streetside air pollution, hence reduce public exposure risk to traffic-related air pollutants such as particulate matter (PM). This study analyzes experimental data from field measurements of PM concentration in a shallow street canyon in Xi'an, China to investigate the effect of different tree layouts on pedestrians and cyclists exposure to inhalable, thoracic and alveolic particles during their urban commuting trips. Particle concentrations for human health risk assessment were measured in the traffic lane, bicycle lane and sidewalks under four different tree layouts — open space (no trees); sparse planting spacing; medium planting spacing and dense planting spacing. Results show significant benefit of trees for reducing air pollution, particularly attenuation of inhalable particles on sidewalks. Our data shows that medium spacing (approximately equal to crown diameter) offers the highest impact in reducing air pollution on bicycle lane (attenuating inhalable, thoracic and alveolic particles by 55.85%, 50.00% and 26.81%, respectively) and sidewalk (decrease by 59.05%, 52.42% and 28.10%, respectively, in inhalable, thoracic and alveolic particle concentrations) in comparison to on-street traffic-lane measurements. Moreover, the attenuation effect of particle concentration due to trees under medium planting spacing becomes more significant under heavier traffic. In contrast, the attenuation effect of particles with sparse or dense tree spacing diminishes to varying degrees as traffic becomes heavier beyond certain point. Our findings suggest that medium tree spacing can be cost-effective for mitigating PM pollution, hence reducing public exposure risk in urban commuting environment.

Smart Wireless Particulate Matter Sensor Node for IoT-Based Strategic Monitoring Tool of Indoor COVID-19 Infection Risk via Airborne Transmission

Indoor and outdoor air pollution are associated with particulate matter concentration of minute size that deeply penetrates the human body and leads to significant problems. These particles led to serious health problems and an increased spread of infection through airborne transmission, especially during the COVID-19 pandemic. Considering the role of particulate matter during the spread of COVID-19, this paper presents a smart wireless sensor node for measuring and monitoring particulate matter concentrations indoors. Data for these concentrations were obtained and used as a risk indicator for airborne COVID-19 transmission. The sensor node was designed to consider air quality monitoring device requirements for indoor applications, such as real-time, continuous, reliable, remote, compact-sized, low-cost, low-power, and accessible. Total energy consumption of the node during measurement and monitoring of particulate matter concentration was minimized using a low-power algorithm and a cloud storage system embedded during software development. Therefore, the sensor node consumed low energy for one cycle of the particulate matter measurement process. This low-power strategy was implemented as a preliminary design for the autonomous sensor node that enables it to integrate with an energy harvester element to harvest energy from ambient (light, heat, airflow) and store energy in the supercapacitor, which extends the sensor node life. Furthermore, the measurement data can be accessed using the Internet of Things and visualized graphically and numerically on a graphical user interface. The test and measurement results showed that the developed sensor node had very small measurement error, which was promising and appropriate for indoor particulate matter concentration measurement and monitoring, while data results were utilized as strategic tools to minimize the risk of airborne COVID-19 transmission.

The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability

Abstract. Mapping Air Pollution eMissions (MAPM) is a 2-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. In this proof-of-concept study, we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future developments. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on a city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40 %–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps.

The winter 2019 air pollution (PM<sub>2.5</sub>) measurement campaign in Christchurch, New Zealand

Abstract. MAPM (Mapping Air Pollution eMissions) is a project whose goal is to develop a method to infer airborne particulate matter (PM) emissions maps from in situ PM concentration measurements. In support of MAPM, a winter field campaign was conducted in New Zealand in 2019 (June to September) to obtain the measurements required to test and validate the MAPM methodology. Two different types of instruments measuring PM were deployed: ES-642 remote dust monitors (17 instruments) and Outdoor Dust Information Nodes (ODINs; 50 instruments). The measurement campaign was bracketed by two intercomparisons where all instruments were co-located, with a permanently installed tapered element oscillating membrane (TEOM) instrument, to determine any instrument biases. Changes in biases between the pre- and post-campaign intercomparisons were used to determine instrument drift over the campaign period. Once deployed, each ES-642 was co-located with an ODIN. In addition to the PM measurements, meteorological variables (temperature, pressure, wind speed, and wind direction) were measured at three automatic weather station (AWS) sites established as part of the campaign, with additional data being sourced from 27 further AWSs operated by other agencies. Vertical profile measurements were made with 12 radiosondes during two 24 h periods and complimented measurements made with a mini micropulse lidar and ceilometer. Here we present the data collected during the campaign and discuss the correction of the measurements made by various PM instruments. We find that when compared to measurements made with a simple linear correction, a correction based on environmental conditions improves the quality of measurements retrieved from ODINs but results in over-fitting and increases the uncertainties when applied to the more sophisticated ES-642 instruments. We also compare PM2.5 and PM10 measured by ODINs which, in some cases, allows us to identify PM from natural and anthropogenic sources. The PM data collected during the campaign are publicly available from https://doi.org/10.5281/zenodo.4542559 (Dale et al., 2020b), and the data from other instruments are available from https://doi.org/10.5281/zenodo.4536640 (Dale et al., 2020a).

Size-fractionated electrochemical quantification for compact monitoring of fine particulate matter

Airborne particulate matter (PM), especially fine particles with a diameter smaller than 2.5 µm (PM2.5 classification) are a deadly air quality concern. One possible solution to reduce individual exposure to fine PM is to develop a low-cost and wearable PM monitor. This paper introduces a new approach for continuous quantification of particles that expresses a resolution suitable for fine PM measurement and a capacity for miniaturization to support wearable monitoring using a unique combination of particle size fractionation and electrochemical quantification. High efficiency size fractionation is performed through a microfluidic device utilizing an I-shape pillar based deterministic lateral displacement (DLD) method to provide particles of uniform size for downstream quantification. Measurement of PM concentration was explored using different electrochemical methods and electrode materials, and differential pulse voltammetry (DPV) with gold electrodes was found to give the best sensitivity and repeatability. Our results show that the mechanism responsible for the particle’s electrochemical response is the ionic electret effect, linking this effect to PM measurement for the first time. A compact electrochemical instrumentation system was implemented to demonstrate this detection scheme as a wearable PM monitoring platform. DPV results with polystyrene particles demonstrate a sensitivity of for 1 µm particles of 3 aA/particle. Both size fractionation and quantification results show that this platform is a promising option for portable fine PM monitoring.

Framework and Method for Measurement of Particulate Matter Concentration using Low Cost Sensors

Framework and Method for Measurement of Particulate Matter Concentration using Low Cost Sensors

Accurate real-time monitoring of high particulate matter concentration based on holographic speckles and deep learning

Accurate real-time monitoring of particulate matter (PM) has emerged as a global issue due to the hazardous effects of PM on public health and industry. However, conventional PM monitoring techniques are usually cumbersome and require expensive equipments. In this study, Holo-SpeckleNet is proposed as a fast and accurate PM concentration measurement technique with high throughput using a deep learning based holographic speckle pattern analysis. Speckle pattern datasets of PMs for a wide range of PM concentrations were acquired by using a digital in-line holography microscopy system. Deep autoencoder and regression algorithms were trained with the captured speckle pattern datasets to directly measure PM concentration from speckle pattern images without any air intake device and time-consuming post image processing. The proposed technique was applied to predict various PM concentrations using the test datasets, optimize hyperparameters, and compare its performance with a convolutional neural network (CNN) algorithm. As a result, high PM concentration values can be measured over air quality index of 150, above which human exposure is unhealthy. In addition, the proposed technique exhibits higher measurement accuracy and less overfitting than the CNN with a relative error of 7.46 ± 3.92%. It can be applied to design a compact air quality monitoring device for highly accurate and real-time measurement of PM concentrations under hazardous environment, such as factories or construction sites.

Measuring air pollution in wildfire conditions using PurpleAir monitors

In November 2018, California experienced one of the most financially costly wildfires in history. Some PurpleAir monitors were available and collecting data in nearby cities like San Francisco, recording real-time measurements of particulate matter concentrations. Using this data, air quality data was assessed to determine how frequently the PM 2.5 concentrations were exceeding the National Ambient Air Quality standards. For approximately twelve days, the air concentrations for PM 2.5 exceeded the 24-hour limit of 35 ug/m3. Throughout this period, the sensors recorded data that was remarkably comparable between each sensor with an exception of one sensor (LPH). Compared to EPA data, the PurpleAir monitors typically had noticeably higher mass concentrations prior to the 16th of November. One of the potential issues with this data is that there are not an equal number of EPA monitors solely in the city of San Francisco. Many of the sensors are spread out throughout the Bay area. Depending upon the path of the air currents, particulates could be more concentrated in one area of the metropolitan area versus another area. An additional potential issue is that the exact location of the PurpleAir monitors are unknown, therefore it would be inappropriate to speculate whether these sensors were placed correctly to ensure accurate concentrations. Further investigation should take place to see how reliable these sensors are in extreme conditions to ensure the validity of this data as it could be a useful tool for communities' advocating for clean air while using low-cost sensors.

Development and assessment of multiple regression and neural network models for prediction of respirable PM in the vicinity of a surface coal mine in India

The findings presented in this paper are based on the study conducted in a large opencast coal project in India. As a part of this study, particulate matter (PM) levels at different distances from the pit boundary were measured to understand dispersion of particles of different sizes emitted from the mine. Portable spectrometers and weather stations were deployed for measurement of PM concentration and meteorological parameters, respectively. The objectives were to quantify PM (PM10, PM2.5, and PM1) escaping from an active mine, estimate spatial variations of PM concentration in mine surrounding, evaluate role of meteorology, distance from the mine on PM concentration in the mining locality, and develop a model for prediction of PM10, PM2.5, and PM1 levels in the surroundings of a mine. A test of equality of means, correlation analysis, bivariate regression analysis, stepwise regression analysis, and the general linear model univariate procedure (analysis of covariance (ANCOVA)) were used for data analysis. At pit boundary, the average PM10, PM2.5, and PM1 concentrations were 2.75, 2.44, and 2.58 times the corresponding background level, and it reduced to 1.51, 1.79, and 1.90 times at a distance 500 m from the mine. Stepwise regression analysis revealed that between temperature and RH, which are highly correlated, RH is a better predictor of PM concentration. Although the proposed models had moderate R2 values (AdjR2: PM10 = 0.35; PM2.5 = 0.43; PM1 = 0.42), the models were reasonably good in predicting PM concentration (index of agreement = 0.80–0.89), with better prediction for fine particles (R2 = 0.48 for PM10, 0.62 for PM2.5, and 0.66 for PM1). The general linear model results revealed that distance is the largest predictor (16%) for PM10, but RH explains the highest variability for PM2.5 (28.2%) and PM1 (31.4%) concentrations. Wind speed was the least powerful determinant (4.5–9.9%). Using meteorological parameters (RH, temperature, and wind speed) and distance as input neurons, a feed-forward back-propagation artificial neural network model has been developed for prediction of particulate matter concentration.

Indoor and Outdoor Measurements of Particulate Matter Concentrations: A Case Study Košice-Sever, Slovakia

Abstract An occurrence of indoor particulate matters largely depends on outdoor pollution and its transportation indoors as well as on the presence of indoor pollution sources. Pollutants can flow from outdoor to indoor or indoor to outdoor under various conditions e.g. contribution of indoor pollutants to outdoor (in rural location with biomass fuel used for cooking) or outdoor to indoor in urban locations. This study aims to carry out an experimental investigation of particulate matter (PM) concentrations, temperature, humidity and air velocity during three days in winter season in Košice-Sever, Slovakia. Values of outdoor PM10 concentrations ranged from 12.58 μg/m3 to 6,627.51 μg/m3. Extremely high concentrations of PM10 were found near the traffic. Outdoor mean value of PM2.5 (21.82 μg/m3) did not exceed a permissible value of 25 μg/m3. Overloads by PM10 concentrations denote almost 108% and 2,270%. The indoor/outdoor (I/O) ratio was <1 for all fractions of particulate matters.

A novel light scattering method with size analysis and correction for on-line measurement of particulate matter concentration

Based on the difference in particle size sensitivity between forward and backscattering during the measurement of particulate matter (PM) mass concentration using laser angular scattering, a method was proposed to improve its on-line measurement accuracy using three fixed detectors. Experimental and theoretical calculations indicate that the PM mass concentration sensitivity (PMCS) of the particles at the 22.5° detection angle and the asymmetry factor (I45°/I135°) are linearly related to the average particle size, and both decrease as the particle size increases. The average particle size obtained from the asymmetry factor was used to correct the PMCS. Compared with the unmodified light scattering method, when the PM mass concentration varies in the range of 1–8 mg⋅m−3, the average deviation between the light scattering method with particle size correction and the reference is reduced from 191.11 % ± 9.12 % to 8.90 % ± 3.20 %, and the maximum deviation is reduced from 227.04 % to 21.54 %. The effect of particle size on the measurement is reduced by size analysis and correction, and the accuracy of the light scattering method is much improved. Finally, laboratory measurements of the fly ash from coal-fired and biomass-fired power plants were performed.

Assessment of the Accuracy of Analyzers for Automatic Determination of PM10 and PM2.5 Particulate Matter in Ambient Air

The paper presents the test results regarding the evaluation of the accuracy of the PM2.5 and PM10 particulate matter concentration measurement performed with the uRADMonitor A3 fixed air quality monitoring station produced by SC MAGNASCI SRL. The procedure involves the calculation of the accuracy elements: trueness and precision, based on the experimental data obtained by measuring the concentration of particulate matter using the tested analysers in parallel with the reference method, SR EN 12341: 2014, and analysis of data series by Pearson correlation and linear regression.

Mobile Measurement of Particulate Matter Concentrations on Urban Streets: System Development and Field Verification

Concentrations of various particulate matter (PM) in urban areas have attracted great attention, due to the increasing demand on life quality. Many studies have highlighted the spatial variability of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> in urban areas, and found that there are significant differences between residents' exposure and background levels. Different from the strategy of establishing large-scale Airbox stations or utilizing mobile stations with portable instruments to measure residents' exposures, this study develops an on-vehicle monitoring system (OVMS), which is based on the technology of Internet of Things, to increase the spatial resolution of the monitoring data in an economical way. The parameters measured by the OVMS include PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , time, location, moving speed, ambient temperature, and relative humidity. According to the experimental results, the effects of the moving speed of the OVMS on PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> measurements are negligible (r =0.024), when the moving speed is below 57 km/hr. The correlation between the dynamic measurements provided by the OVMS and a standard instrument is high (r =0.601). These results show that the OVMS can accurately monitor PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> as it moves. The data of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> obtained by the OVMS also reveal the impacts of traffic and community pollution in urban areas on residents' exposure. In addition, this study proposes a visualized map that shows real-time PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> measurements as the OVMS travels. Map users can choose a less-polluted path to get to their destinations based on the PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> information. In addition, the OVMS measurements can be integrated with the Airbox measurements, so the visualized map can provide detailed spatial interpolation results on PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> exposures. Thus, the OVMS can be a great help in evaluating the PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> levels in certain areas of urban streets where Airbox stations are not installed.

Stationary and mobile particulate matter concentration measurement in Miskolc

A stationary and mobile device based on a low-cost dust sensor (Plantower PMS7003) was used to study particulate matter (PM) concentration in Miskolc. The stationary device was placed at the automatic monitoring station of the National Air Quality Measurement Network (OLM) in Martintelep, Miskolc. The mobile device was used to walk 4 streets along a square route with a total length of 800 m. Measurements were made on two days (September 9 and 11) between 7am and 9am, which according to preliminary experience coincided with the morning rush hour. The access route includes the OLM monitoring station, a busy road, two bypasses, a parking lot, and a smaller roadway blocked by traffic. The measured PM1, PM2.5, PM10 concentrations of the mobile device were interpolated on a circuit-by-circuit basis in ArcGIS, and conclusions were drawn for the area. The concentration of stationary and mobile devices was compared.

Stationary and mobile particulate matter concentration measurement in Miskolc

Stationary and mobile particulate matter concentration measurement in Miskolc

Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models

Black carbon (BC) is an important component of particulate matter (PM) in urban environments. BC is typically emitted from gas and diesel engines, coal-fired power plants, and other sources that burn fossil fuel. In contrast to PM, BC measurements are not always available on a large scale due to the operational cost and complexity of the instrumentation. Therefore, it is advantageous to develop a mathematical model for estimating the quantity of BC in the air, termed a BC proxy, to enable widening of spatial air pollution mapping. This article presents the development of BC proxies based on a Bayesian framework using measurements of PM concentrations and size distributions from 10 to 10,000 nm from a recent mobile air pollution study across several areas of Jordan. Bayesian methods using informative priors can naturally prevent over-fitting in the modelling process and the methods generate a confidence interval around the prediction, thus the estimated BC concentration can be directly quantified and assessed. In particular, two types of models are developed based on their transparency and interpretability, referred to as white-box and black-box models. The proposed methods are tested on extensive data sets obtained from the measurement campaign in Jordan. In this study, black-box models perform slightly better due to their model complexity. Nevertheless, the results demonstrate that the performance of both models does not differ significantly. In practice, white-box models are relatively more convenient to be deployed, the methods are well understood by scientists, and the models can be used to better understand key relationships.