Low-cost sensor can be key additional tools to fixed air quality monitoring stations (AQMS)for more extended AQ assessment. Sensors can be deployed at a higher density due to their lower cost. However, the data quality of sensors is still unknown and can be function of location and meteorological conditions.One of the issues that PM sensors are dealing with is the ability to measure coarse fractions of PM. It is known that some low-cost sensors calculate PM10 concentrations based on the measured concentrations of PM2.5. The main issue with the evaluation of PM sensors for PM10 in the field, is that PM10-2.5 fractions at most AQMS are relatively small, and relying on only field test for PM10 would not identify the problem, whereas the sensor would largely underestimate the PM10 concentrations when deployed at areas or specific events with high coarse fractions.Coarse particles are defined here as PM10-2.5 = PM10 – PM2.5. A laboratory test was developed for PM sensors (as part of the CEN/TS 17660-2:2024) and will evaluate the potential of the PM10 sensor system to correctly measure the coarse fraction. This presentation presents the lab test to evaluate if sensor systems can measure also coarse PM fractions and can measure PM10 rather than calculating it from the PM2.5 signal.The sensor systems under test are placed in the test chamber in close vicinity of the optical monitor (as equivalent method) and particles are generated and mixed so that sensor systems and optical monitor are exposed to the same PM concentrations and fractions.The sensor systems are exposed to two different PM fractions over two tests (‘Coarse test’ and ‘Fine test’) to evaluate their response to PM. The size fractions generated inside the test chamber will fulfil the following requirements:Coarse test: >80% PM10-2.5 in PM10 Fine test:

Ultrafine particle (UFP) emissions from aircraft engines are associated with increased UFP concentrations near airports, raising concerns for air quality and public health. This study investigated the impact of airport operations at Brussels Airport (Belgium) on local air quality during a two-month monitoring campaign in autumn 2015. Four monitoring sites, strategically located from the airport and along a transect parallel to the main runway, were used to measure particle number size distributions (PNSD), black carbon (BC), particulate matter (PM10), and nitrogen oxides (NOx). This novel approach, using high-resolution PNSD data, provided a detailed analysis of aircraft-related UFPs under varying meteorological conditions and operational phases. Results showed strong correlations between UFP concentrations, meteorological conditions, and aircraft landing and takeoff (LTO) operations, with the smallest measured particles (10-20 nm) dominating the particle number concentrations (PNC) near the airport, contributing up to 65 % of total PNC. Aircraft emissions were responsible for 15000-20000 # cm-3 of 10-20 nm particles near the airport, with peak concentrations (30-min) reaching 65000-80000 # cm-3 during high-activity periods. While airport-related UFP concentrations decreased with distance, they persisted up to 7 km. Source apportionment analysis identified taxiing and takeoff as major contributors to UFPs near the airport (50-64 % of PNC), with road traffic, biomass burning, and regional background more significant at greater distances. Despite attempts to distinguish between landing and takeoff emissions, similar particle profiles were observed for both operations. These findings provide insight into the spatial persistence, dominant operational phases, and size distribution of aircraft-related UFP emissions, and underscore the need for targeted mitigation strategies to improve air quality near airports.

Mobile monitoring has proven to be a very efficient tool to measure and feed into models of air pollution as it complements fixed air quality monitoring networks by adding spatiotemporal resolution. This paper explores best practices, opportunities and challenges related to mobile monitoring of air pollutants, focusing on three key application areas, namely source-, exposure-, and health-related use cases. Use cases are linked to users, ensuring mobile monitoring is effectively tailored to diverse research and policy needs. Tailoring mobile monitoring involves experimental design choices (platform, instrumentation, route planning and spatiotemporal coverage) and data processing choices (data-only vs modelling) optimized towards the envisaged use case. This position paper aims to guide researchers and air pollution stakeholders in generating high-quality mobile monitoring datasets. We identify best practices, discuss monitoring strategies, and highlight future research directions. Additionally, mobile monitoring supports public engagement and actionability, allowing communities to advocate for cleaner air and drive behavior change.

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68–0.9, Uexp = 16–66%, BSU = 0.1–0.7 µg/m3) and BC (R2 = 0.82–0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38–0.55, Uexp = 111–614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75–0.83, Uexp = 37–44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

Mobile monitoring is used as an additional tool to collect air quality data at a high spatial resolution and to complement data from fixed air quality stations. Citizens are interested in contributing to air quality monitoring, and while the availability of low-cost air quality sensors can create opportunities to measure the air quality at a high spatial resolution, the data are often of lower quality, and sensors that measure combustion-related aerosols (like black carbon) are not commonly available. Mobile monitoring using a mid-range instrument can fill this gap. We present the results of a mobile BC (black carbon) monitoring campaign performed by citizens in Mechelen as part of a local citizen observatory (CO), Meet Mee Mechelen, initiated as part of the European H2020 project, Ground Truth 2.0. The goal of the study was two-fold: (1) to propose and evaluate a mobile monitoring method (data collection and data processing) to construct pollution maps of BC concentrations and (2) to demonstrate how to organize community-based air quality monitoring to measure both the spatial and temporal variations in air pollution levels. Measurements were taken during peak hours in four campaigns characterized by different meteorological conditions: October–November 2017, February–March 2018, June–July 2018 and September 2018. The results show large spatial and temporal variabilities. Spatial variability is influenced by traffic volume, stop-and-go traffic and also the building environment and the distance of biking paths from road traffic. The four different campaigns show similar spatial patterns, but due to background and meteorological influences, the absolute concentrations differ between seasons. A rescaling method using data from fixed stations in the air quality monitoring network (AQMN) was presented to construct maps representative of longer periods. This paper shows that mobile measurements can be used by CO to assess the spatial variability of air quality in a city. The data can be used to evaluate mobility plans, carry out hot spot detection, evaluate the exposure of cyclists as a function of cycling infrastructure and perform model validation. However, it is important to use high-quality instruments and apply the correct measurement methodology (number of repetitions, season) to obtain meaningful data.

Studying the air quality and exposure of the inhabitants of urban agglomerations to pollution is the basis for the creation and development of more sustainable cities. Although research on black carbon (BC) has not yet reached the official acceptable levels and guidelines, the World Health Organization clearly indicates the need to measure and control the level of this pollutant. In Poland, monitoring of the level of BC concentration is not included in the air quality monitoring network. To estimate the extent of this pollutant to which pedestrians and cyclists are exposed, mobile measurements were carried out on over 26 km of bicycle paths in Wroclaw. The obtained results indicate the influence of urban greenery next to the bicycle path (especially if the cyclist is separated from the street lane by hedges or other tall plants) and the 'breathability' (i.e., associated with surrounding infrastructure) of the area on the obtained concentrations; the average concentration of BC in such places ranged from 1.3 to 2.2 mu g/m3, whereas a cyclist riding directly on bike paths adjacent to the main roads in the city center is exposed to concentrations in the range of 2.3-14 mu g/m3. The results of the measurements, also related to stationary mea-surements made at a selected point of one of the routes, clearly indicate the importance of the infrastructure surrounding the bicycle paths, their location, and the impact of urban traffic on the obtained BC concentrations. The results presented in our study are based only on short-term-field campaigns preliminary studies. To deter-mine the quantitative impact of the characteristics of the bicycle route on the concentration of pollutants, and thus the exposure of users, the systematized research should cover a greater part of the city and be representative in terms of various hours of the day.

Traditional fixed air quality monitoring networks fulfill requirements as set in the European Air Quality Directive (2008/50/EC) and provide valuable information on ambient concentrations and temporal trends of air quality at the international, national, regional and urban level. Some short-lived pollutants or constituents, like ultrafine particle (UFPs), black carbon (BC) and nitrogen oxides (NOx), exhibit a high spatial (street-level) variability, requiring a higher monitoring resolution for more accurate exposure assessments in health or epidemiological studies. Advances in sensing and Internet of Things (IoT) technologies have resulted in smaller and more affordable stationary and mobile monitoring solutions, enabling data collection at unprecedented scales. Moreover, citizens can contribute in data collection resulting in more wide-scale data collection, dissemination and resulting impact. The collected data, however, needs adequate processing and validation in order to obtain representative exposure maps (i.e., long-term averaged concentration maps) for epidemiological studies and policy assessment. RI-URBANS aims to develop and test innovative and complementary air quality monitoring approaches in different European pilot cities. This methodological work focusses on the potential of mobile and stationary sensor applications as complementary tools for traditional (low-density) monitoring networks (Figure 1). Complementary measurements can contribute to understand spatial variability of short-lived constituents of air pollution from a diversity of pollution sources. Figure 1: Mobile and fixed sensor applications, resulting data resolution and associated requirements in terms of device (devices) and monitoring strategy (setup). We identify different data users and use cases for mobile, stationary (or combined) sensor applications and their resulting implications regarding device specifications, monitoring strategy and data processing needs. By reflecting on past studies and projects, we summarize common methodological approaches and best practices to increase the spatial resolution of air quality data. Moreover, the role of citizen engagement is evaluated, both in generating more data and air quality impact (awareness raising). This work serves as methodological input for the RI-URBANS service tools that will be tested in the pilot cities and is openly available at https://riurbans.eu/wp-content/uploads/2022/10/RI-URBANS_D13_D2.5.pdf

This study aimed to examine the validity of a mobile air quality sensor fleet in improving pollution exposure assessments in urban areas. The scope of this study involved experimental setup (sensor validation and calibration), evaluation of spatiotemporal data coverage, and analysis of the representativity of the collected mobile data. The results showed that indicative sensor data quality can be achieved after NO2 co-location calibration, although particulate matter exhibited unsatisfactory performance. An extensive mobile air quality dataset was collected in Antwerp city between February and September 2021, covering 945 km of road by a total of ∼7.9 million data points, yielding an average segment coverage of 1,050 measurements per street segment (median = 62). The collected mobile data were made available in an open data repository. From the introduced area (%) and street segment (n) coverage, we can conclude that opportunistic data collection using service fleet vehicles (e.g., postal vans) is an efficient approach for covering a wide spatial area and collecting many repeated runs (∼200 measurements/segment/month). Monthly maps showed recurring pollution gradients with hotspot locations both at the suspected (e.g., busy traffic arteries) and unexpected locations, with observed increments greatly exceeding the observed inter-sensor uncertainty. The existing air quality monitoring network (five air quality monitoring stations) properly reflected the observed NO2 exposure range (temporal variability), which was documented by the sensor fleet in Antwerp. The spatial exposure variability was improved significantly by the sensor fleet with 59% of the total street length covered after 1 month of mobile deployment (February–March). We required ∼45 repeated passages (31 after post-processing) to derive representative long-term NO2 exposure data from this opportunistic dataset. Our findings suggested that opportunistic data collection using sensors on service fleet vehicles is a valid approach for pollution exposure assessments, through proper validation and calibration strategy. Temporary deployment of mobile sensors was a valuable approach for cities with a less extensive (or lack) air quality monitoring network or those who want a more fine-grained air quality mapping.

Air pollution is the fourth cause of premature mortality (HEI, 2020) and in Europe, more than 0.3 million premature deaths are due to air pollution (EEA, 2021). In urban environments, people are exposed to a complex mixture of air pollutants with a large spatial variability. However, highly spatially resolved measurement data on air pollutants is lacking. These fine-grained data is needed to correctly assess personal exposure to air pollution for epidemiolocal studies and to support air quality management scenarios.Within RI-URBANS different innovative approaches to get insights into novel air quality parameters, source contributions, exposure to air pollution and associated health effects will be developed and tested. One of the approaches relies on mobile measurements with citizens to derive spatial air pollution maps. Mobile measurements can contribute to understand spatial variability of short-living constituents of air pollution from a diversity of pollution sources.The monitoring campaign is performed with volunteers, who are all employees of DCMR or the city of Rotterdam. They are asked to measure during their daily bicycle commutes. Before the measurement campaign, a training session was organized for the volunteers. Measurements were performed in winter (November 2022 – February 2023) and will be repeated in spring 2023.Measurements are based on the airQmap approach; more information on the approach and previous studies can be found on https://www.airQmap.com. Measurements of Black Carbon (BC) are performed using a microaethalometer (microAeth®, AE51, AethLabs) and a GPS. BC is measured at 1s temporal resolution and a flow rate of 150 mL min-1. To reduce the noise in BC measurements, the ONA (Optimized Noise-reduction Averaging, Hagler et al., 2011) algorithm was used with an attenuation threshold of 0.05. The geo-tagged measurements were aggregated (trimmed mean) and attributed to fixed points 20 m apart from each other along the cycling route.The dataset will be used to test different data processing techniques (a.o. temporal aggregation, background correction approaches) to construct representative BC maps. The collected spatiotemporal BC measurements will be analysed to identify main sources of BC in the area. The pilot study will result in guidance on best practices for mobile air quality monitoring involving citizens.This paper will present the results of the winter campaign.____________________EEA, 2021. HI of Air Pollution in EUHagler, G.S., Yelverton, T.L., Vedantham, R., Hansen, A.D., Turner, J.R., 2011. Postprocessing method to reduce noise while preserving high time resolution in Aethalometer real-time black carbon data. Aerosol Air Qual. Res. 11, 539-546.HEI, 2020. State of Global Air

Air quality improved significantly over the past decades. Nevertheless, air pollution continuous to have significant health impacts worldwide. To better assess people's exposure to air pollution, there is a need for higher, more personalized monitoring granularity. IoT sensor technologies can meet these requirements and pave the way towards more fine-grained air quality monitoring, improving our understanding while creating a higher public awareness driving behavioural change. This work tested the validity of scalable PM and NO2 calibration algorithms on various types of sensors (SDS011, OPC-N3, SPS30, NO2-A43F) in five different sensor testbeds deployed at various locations in Belgium and the Netherlands. The calibration models account for sensor gain and offset, while compensating for observed sensitivities of low-cost optical and electrochemical sensors. The calibration improves sensor data considerably (accuracy, linearity and correlation) up to sensitizing and supplementary (EU Class 1) categories at hourly and daily resolutions. Thanks to its cloud implementation and openly available input data, this calibration can be provided “as a service” on top of existing sensor networks in any city, on any sensor. Although distant calibration approaches improve sensor data, the ultimate performance will still depend on the applied sensor type, unit (design of sensor box) and granularity of the available reference monitoring network.

Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression.

Humans are generally exposed to per- and polyfluoroalkyl substances (PFAS) through their diet. Whilst plenty of data are available on commercial food products, little information exists on the contribution of self-cultivated food, such as home-produced eggs (HPE), to the dietary PFAS intake in humans. The prevalence of 17 legacy and emerging PFAS in HPE (N = 70) from free-ranging laying hens was examined at 35 private gardens, situated within a 10 km radius from a fluorochemical plant in Antwerp (Belgium). Potential influences from housing conditions (feed type and flock size) and age of the chickens on the egg concentrations was examined, and possible human health risks were evaluated. Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) were detected in all samples. PFOS was the dominant compound and concentrations (range:

To control air pollution and mitigate its negative effect on health, it is of the utmost importance to have accurate real-time forecasting models. Existing deep-learning-based air quality forecasting models typically deploy temporal and-less often-spatial modules. Yet, data scarcity emerges as a real issue in this domain, a problem that can be solved by capturing the data distribution. In this work, we address data scarcity by proposing a novel conditional variational graph autoencoder. Our model is able to forecast air pollution by efficiently encoding the spatio-temporal correlations of the known data. Additionally, we leverage dynamic context data such as weather or satellite images to condition the model's behaviour. We formulate the problem as a context-aware graph-based matrix completion task and utilize street-level data from mobile stations. Experiments on real-world air quality datasets show the improved performance of our model with respect to state-of-the-art approaches.

Humans are generally exposed to per-and polyfluoroalkyl substances (PFAS) through their diet. Whilst plenty of data are available on commercial food products, little information exists on the contribution of self-cultivated food, such as home-produced eggs (HPE), to the dietary PFAS intake in humans. The prevalence of 17 legacy and emerging PFAS in HPE (N = 70) from free-ranging laying hens was examined at 35 private gardens, situated within a 10 km radius from a fluorochemical plant in Antwerp (Belgium). Potential influences from housing conditions (feed type and number of individuals) and age of the chickens on the egg concentrations was examined, and possible human health risks were evaluated. Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) were detected in all samples. PFOS was the dominant compound and concentrations (range: 0.13-241 ng/g wet weight) steeply decreased with distance from the fluorochemical plant, while there was no clear distance trend for other PFAS. Laying hens receiving an obligate diet of kitchen leftovers, exhibited higher PFOS and PFOA concentrations in their eggs than hens feeding only on commercial food, suggesting that garden produce may be a relevant exposure pathway to both chickens and humans. The age of laying hens affected egg PFAS concentrations, with younger hens exhibiting significantly higher egg PFOA concentrations. Based on a modest human consumption scenario of two eggs per week, the European health guideline was exceeded in & GE;67% of the locations for all age classes, both nearby and further away (till 10 km) from the plant site. These results indicate that PFAS exposure via HPE causes potential human health risks. Extensive analysis in other self-cultivated food items on a larger spatial scale is highly recommended, taking into account potential factors that may affect PFAS bioavailability to garden produce.

(1) Background: This work evaluated the usability of commercial "low-cost" air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (U-exp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities.

Graph neural networks (GNNs) have proven their ability in modelling graph-structured data in diverse domains, including natural language processing and computer vision.However, like other deep learning models, the lack of explainability is becoming a major drawback for GNNs, especially in health-related applications such as air pollution estimation, where a model's predictions might directly affect humans' health and habits.In this paper, we present a novel post-hoc explainability framework for GNNbased models.More concretely, we propose a novel topology-aware kernelised node selection method, which we apply over the graph structural and air pollution information.Thanks to the proposed model, we are able to effectively capture the graph topology and, for a certain graph node, infer its most relevant nodes.Additionally, we propose a novel topological node embedding for each node, capturing in a vector-shape the graph walks with respect to every other graph node.To prove the effectiveness of our explanation method, we include commonly employed evaluation metrics as well as fidelity, sparsity and contrastivity, and adapt them to evaluate explainability on a regression task.Extensive experiments on two real-world air pollution data sets demonstrate and visually show the effectiveness of the proposed method.

Recent advances in sensor and IoT technologies allow for denser and mobile air quality measurements. These measurements are still spatiotemporally sparse at city-level, but can be interpolated using data-driven techniques. This work presents validation results of two machine-learning models to infer air quality sensor data in both space and time. Temporal validation exercises are performed at available regulatory monitoring stations following the FAIRMODE protocol. Both models show scalable to different mobile datasets with comparable prediction performance for PM2.5 (R-2 = 0.68-0.75, MAE = 2.99-2.82 mu g m(- 3)) and NO2 (R-2 = 0.8-0.82, MAE = 8.81-9.83 mu gm(- 3)) in Utrecht and Antwerp. In Oakland (Atlanta), we observed a lower performance for NO2 (R-2 = 0.46-0.41, MAE = 4.06-5.07) and BC (R-2 = 0.31-0.28, MAE = 0.48-0.27), likely caused by the less representative monitoring coverage. Although comparable in terms of prediction performance, the Geographical Random Forest (GRF) model seems to achieve slightly better accuracies, while the correlations are typically higher for the Air Variational Graph Autoencoder (AVGAE) model. This work demonstrates the potential of data driven techniques for spatiotemporal air quality inference of complementary sensor data. The observed performance metrics approach current state-of-the-art chemical transport models in terms of performance while needing much lower resources, computational power, infrastructure and processing time.

Air quality monitoring in heterogeneous cities is challenging as a high resolution in both space and time is required to accurately assess population exposure. As regulatory monitoring networks are sparse due to high investment and maintenance costs, recent advances in sensor and IoT technologies have resulted in innovative sensing approaches like mobile sensing to increase the spatial monitoring resolution. An example of such an opportunistic mobile monitoring network is “Snuffelfiets”, a project where air quality data is collected from mobile sensors attached to bicycles in Utrecht (NL). The collected data results in a sparse spatiotemporal matrix of measurements which can be completed using data-driven techniques. This work reports on the potential of two machine learning approaches to infer the collected air quality measurements in both space and time; a deep learning model based on Variational Graph Autoencoders (AVGAE) and a Geographical Random Forest model (GRF). A temporal validation exercise is performed at two regulatory monitoring stations following the FAIRMODE modelling quality objectives protocol. This work demonstrates the potential of data-driven techniques for spatiotemporal air quality inference of sensor data as the considered models performed well in terms of accuracy and correlation. The model observed performance metrics approach current state-of-the-art physical models in terms of performance while needing much lower resources, computational power, infrastructure and processing time.