Low-cost clamp for the measurement of vegetation spectral signatures.

Spectral signatures allow the characterization of a surface from the reflected or emitted energy along the electromagnetic spectrum. This type of measurement has several potential applications in precision agriculture. However, capturing the spectral signatures of plants requires specialized instruments, either in the field or the laboratory. The cost of these instruments is high, so their incorporation in crop monitoring tasks is not massive, given the low investment in agricultural technology. This paper presents a low-cost clamp to capture spectral leaf signatures in the laboratory and the field. The clamp can be 3D printed using PLA (polylactic acid); it allows the connection of 2 optical fibers: one for a spectrometer and one for a light source. It is designed for ease of use and holds a leave firmly without causing damage, allowing data to be collected with less disturbance. The article compares signatures captured directly using a fiber and the proposed clamp; noise reduction across the spectrum is achieved with the clamp.

Introducing PLEMS: the application of a low-cost, portable monitoring system in environmental walks.

The application of innovative systems using low-cost microcontrollers in human biometeorology studies is a promising alternative to conventional monitoring devices, which are usually cost-intensive and provide measurements at specific points, as in stationary meteorological stations. A Portable Low-cost Environmental Monitoring System (PLEMS) aimed at the pedestrian scale is introduced. The backpack-type equipment consists of a microcontroller with attached sensors that assess environmental conditions in a broad sense, integrating measurements of air quality, lighting and noise levels alongside variables typically measured at meteorological stations. The application of the system took place in altogether 12 environmental walks carried out with questionnaire-surveys with concurrent environmental monitoring with the PLEMS in Curitiba, Brazil, a subtropical location characterized by a Cfb climate type. Results allowed us to test the equipment and method of data gathering within a limited period (approximately 50 min) and for a short walking circuit of 800 m. The equipment was successfully able to capture even slightest differences in environmental conditions among points of interest, whereas subjective responses (n= 3843 responses to a total of 11 questions) showed consistency with measured data. From a multi-domain perspective, relevant insights could be obtained for the measured variables.

A low-cost and open-hardware portable 3-electrode sleep monitoring device.

To continue sleep research activities during the lockdown resulting from the COVID-19 pandemic, experiments that were previously conducted in laboratories were shifted to the homes of volunteers. Furthermore, for extensive data collection, it is necessary to use a large number of portable devices. Hence, to achieve these objectives, we developed a low-cost and open-source portable monitor (PM) device capable of acquiring electroencephalographic (EEG) signals using the popular ESP32 microcontroller. The device operates based on instrumentation amplifiers. It also has a connectivity microcontroller with Wi-Fi and Bluetooth that can be used to stream EEG signals. This portable single-channel 3-electrode EEG device allowed us to record short naps and score different sleep stages, such as wakefulness, non rapid eye movement sleep (NREM), stage 1 (S1), stage 2 (S2), stage 3 (S3) and stage 4 (S4). We validated the device by comparing the obtained signals to those generated by a research-grade counterpart. The results showed a high level of accurate similarity between both devices, demonstrating the feasibility of using this approach for extensive and low-cost data collection of EEG sleep recordings.

Single-arm, first-in-human feasibility study results for an ultra-low-cost insulin pump.

BACKGROUND: Use of Continuous Subcutaneous Insulin Infusion (CSII) has been shown to improve glycemic outcomes in Type 1 Diabetes (T1D), but high costs limit accessibility. To address this issue, an inter-operable, open-source Ultra-Low-Cost Insulin Pump (ULCIP) was developed and previously shown to demonstrate comparable delivery accuracy to commercial models in standardised laboratory tests. This study aims to evaluate the updated ULCIP in-vivo, assessing its viability as an affordable alternative for those who cannot afford commercially available devices. METHODS: This first-in-human feasibility study recruited six participants with T1D. During a nine-hour inpatient stay, participants used the ULCIP under clinical supervision. Venous glucose, insulin, and ß-Hydroxybutyrate were monitored to assess device performance. RESULTS: Participants displayed expected blood glucose and blood insulin levels in response to programmed basal and bolus insulin dosing. One participant developed mild ketosis, which was treated and did not recur when a new pump reservoir was placed. All other participants maintained ß-Hydroxybutyrate < 0.6 mmol/L throughout. CONCLUSION: The ULCIP safely delivered insulin therapy to users in a supervised inpatient environment. Future work should focus on correcting a pump hardware issue identified in this trial and extending device capabilities for use in closed loop control. Longer-term outpatient studies are warranted. TRIAL REGISTRATION: The trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12623001288617) on the 11 December 2023.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements.

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Smartphone-based hyperspectral imaging for ice sheet and proglacial applications in South-West Greenland.

Hyperspectral imaging is a valuable analytical technique with significant benefits for environmental monitoring. However, the application of these technologies remains limited, largely by the cost and bulk associated with available instrumentation. This results in a lack of high-resolution data from more challenging and extreme environmental settings, limiting our knowledge and understanding of the effects of climate change in these regions. In this article we challenge these limitations through the application of a low-cost, smartphone-based hyperspectral imaging instrument to measurement and monitoring activities at the Greenland Ice Sheet. Datasets are captured across a variety of supraglacial and proglacial locations covering visible and near infrared wavelengths. Our results are comparable to the existing literature, despite being captured with instrumentation costing over an order of magnitude less than currently available commercial technologies. Practicalities for field deployment are also explored, demonstrating our approach to be a valuable addition to the research field with the potential to improve the availability of datasets from across the cryosphere, unlocking a wealth of data collection opportunities that were hitherto infeasible.

A novel latex patch model enables cost-effective hands-on teaching in vascular surgery.

Objectives: We developed a new simulator for hands-on teaching of vascular surgical skills, the Leipzig Latex Patch Model (LPM). This study aimed to quantify the effectiveness and acceptance of the LPM evaluated by students, as well as evaluation of the results by experienced vascular surgeons. Methods: A prospective, single-center, single-blinded, randomized study was conducted. Fifty 5th-year medical students were randomized into two groups, first performing a patch suture on the LPM (study group) or established synthetic tissue model (control), then on porcine aorta. The second suture was videotaped and scored by two surgeons using a modified Objective Structured Assessment of Technical Skill (OSATS) score. We measured the time required for suturing; the participants completed questionnaires. Results: Participants required significantly less time for the second suture than the first (median: LPM 30 min vs. control 28.5 min, p = 0.0026). There was no significant difference in suture time between the groups (median: 28 min vs. 30 min, p = 0.2958). There was an increase in confidence from 28 % of participants before to 58 % after the course (p < 0.0001). The cost of materials per participant was 1.05€ (LPM) vs. 8.68€ (control). The OSATS-scores of the LPM group did not differ significantly from those of the control (median: 20.5 points vs. 23.0 points, p = 0.2041). Conclusions: This pilot study demonstrated an increase in technical skills and confidence through simulator-based teaching. Our data suggests comparable results of the LPM compared to the conventional model, as assessed by the OSATS-score. This low-cost, low-threshold training model for vascular suturing skills should make hands-on training more accessible to students and surgical residents. Key message: We developed and validated a low-cost, low-threshold training model for vascular suturing skills. This should make hands-on training more accessible to medical students and surgical residents in the future.

Assessing bias in personal exposure estimates when indoor air quality is ignored: A comparison between GPS-enabled mobile air sensor data and stationary outdoor sensor data.

Neglecting indoor air quality in exposure assessments may lead to biased exposure estimates and erroneous conclusions about the health impacts of exposure and environmental health disparities. This study assessed these biases by comparing two types of personal exposure estimates for 100 individuals: one derived from real-time particulate matter (PM2.5) measurements collected both indoors and outdoors using a low-cost portable air monitor (GeoAir2.0) and the other from PurpleAir sensor network data collected exclusively outdoors. The PurpleAir measurement data were used to create smooth air pollution surfaces using geostatistical methods. To obtain mobility-based exposure estimates, both sets of air pollution data were combined with the individuals' GPS tracking data. Paired-sample t-tests were then performed to examine the differences between these two estimates. This study also investigated whether GeoAir2.0- and PurpleAir-based estimates yielded consistent conclusions about gender and economic disparities in exposure by performing Welch's t-tests and ANOVAs and comparing their t-values and F-values. The study revealed significant discrepancies between GeoAir2.0- and PurpleAir-based estimates, with PurpleAir data consistently overestimating exposure (t = 5.94; p < 0.001). It also found that females displayed a higher average exposure than males (15.65 versus. 8.55 µg/m3) according to GeoAir2.0 data (t = 4.654; p = 0.055), potentially due to greater time spent indoors engaging in pollution-generating activities traditionally associated with females, such as cooking. This contrasted with the PurpleAir data, which indicated higher exposure for males (43.78 versus. 46.26 µg/m3) (t = 3.793; p = 0.821). Additionally, GeoAir2.0 data revealed significant economic disparities (F = 7.512; p < 0.002), with lower-income groups experiencing higher exposure-a disparity not captured by PurpleAir data (F = 0.756; p < 0.474). These findings highlight the importance of considering both indoor and outdoor air quality to reduce bias in exposure estimates and more accurately represent environmental disparities.

Low-cost, reliable, and highly efficient removal of COD and total nitrogen from sewage using a sponge-filled trickling filter.

Development of low-cost and reliable reactors demanding minimal supervision is a need-of-the-hour for sewage treatment in rural areas. This study explores the performance of a multi-stage sponge-filled trickling filter (SPTF) for sewage treatment, employing polyethylene (PE) and polyurethane (PU) media. Chemical oxygen demand (COD) and nitrogen transformation were evaluated at hydraulic loading rates (HLRs) ranging from 2 to 6 m/d using synthetic sewage as influent. At influent COD of ∼350 mg/L, PU-SPTF and PE-SPTF achieved a COD removal of 97% across all HLRs with most of the removal occurring in the first segments. Operation of PE-SPTF at an HLR of 6 m/d caused substantial wash-out of biomass, while PU-SPTF retained biomass and achieved effluent COD < 10 mg/L even at HLR of 8-10 m/d. The maximum Total Nitrogen removal by PE-SPTF and PU-SPTF reactors was 93.56 ± 1.36 and 92.24 ± 0.66%, respectively, at an HLR of 6 m/d. Simultaneous removal of ammonia and nitrate was observed at all the HLRs in the first segment of both SPTFs indicating ANAMMOX activity. COD removal data, media depth, and HLRs were fitted (R2 > 0.99) to a first-order kinetic relationship. For a comparable COD removal, CO2 emission by PU-SPTF was 3.5% of that of an activated sludge system.

Insight mechanism of magnetic activated catalyst derived from recycled steel residue for black liquor degradation.

The present work deals with developing a method for revalorizing steel residues to create sunlight-active photocatalysts based on iron oxides. Commercial-grade steel leftovers are oxidized under different combinations of pH and temperature (50-90 °C and 3 ≥ pH ≤ 5) in a low energy-intensive setup. The material with the highest production efficiency (yield > 12%) and magnetic susceptibility (χm = 387 × 10-6 m3/kg) was further explored and modified by diffusion of M2+ (Zn and Co) ions within the structure of the oxide using a hydrothermal method to create ZnFe2O4, CoFe2O4 and combined Co-Zn ferrite. (Co-Zn)Fe2O4 displayed a bandgap of 2.02 eV and can be activated under sunlight irradiation. Electron microscopy studies show that (Co-Zn)Fe2O4 consists of particles with diameters between 400 and 700 nm, homogeneous size, even distribution, and good dispersibility. Application of the developed materials in the sunlight catalysis of black liquors from cellulose extraction resulted in a reduction of the Chemical Oxygen Demand (- 15% on average) and an enhancement in biodegradability (> 0.57 BOD/COD) after 180 min of reaction. Since the presented process employs direct solar light, it opens the possibility to large-scale water treatment and chemical upgrading applications.

Establishing an itaconic acid production process with Ustilago species on the low-cost substrate starch.

Ustilago maydis and Ustilago cynodontis are natural producers of a broad range of valuable molecules including itaconate, malate, glycolipids, and triacylglycerols. Both Ustilago species are insensitive toward medium impurities, and have previously been engineered for efficient itaconate production and stabilized yeast-like growth. Due to these features, these strains were already successfully used for the production of itaconate from different alternative feedstocks such as molasses, thick juice, and crude glycerol. Here, we analyzed the amylolytic capabilities of Ustilago species for metabolization of starch, a highly abundant and low-cost polymeric carbohydrate widely utilized as a substrate in several biotechnological processes. Ustilago cynodontis was found to utilize gelatinized potato starch for both growth and itaconate production, confirming the presence of extracellular amylolytic enzymes in Ustilago species. Starch was rapidly degraded by U. cynodontis, even though no α-amylase was detected. Further experiments indicate that starch hydrolysis is caused by the synergistic action of glucoamylase and α-glucosidase enzymes. The enzymes showed a maximum activity of around 0.5 U ml-1 at the fifth day after inoculation, and also released glucose from additional substrates, highlighting potential broader applications. In contrast to U. cynodontis, U. maydis showed no growth on starch accompanied with no detectable amylolytic activity.

Assessment of PM2.5 Concentration at University Transit Bus Stops Using Low-Cost Aerosol Monitors by Student Commuters.

Particulate matter of 2.5 µm and smaller (PM2.5) is known to cause many respiratory health problems, such as asthma and heart disease. A primary source of PM2.5 is emissions from cars, trucks, and buses. Emissions from university transit bus systems could create zones of high PM2.5 concentration at their bus stops. This work recruited seven university students who regularly utilized the transit system to use a low-cost personal aerosol monitor (AirBeam) each time they arrived at a campus bus stop. Each participant measured PM2.5 concentrations every time they were at a transit-served bus stop over four weeks. PM2.5 concentration data from the AirBeam were compared with an ADR-1500 high-cost monitor and EPA PM2.5 reference measurements. This methodology allowed for identifying higher-than-average concentration zones at the transit bus stops compared to average measurements for the county. By increasing access to microenvironmental data, this project can contribute to public health efforts of personal protection and prevention by allowing individuals to measure and understand their exposure to PM2.5 at the bus stop. This work can also aid commuters, especially those with pre-existing conditions who use public transportation, in making more informed health decisions and better protecting themselves against new or worsening respiratory conditions.

Effect of low-cost kitchen with improved cookstove on birthweight of neonates in Shahjadpur, Bangladesh: a cluster-randomised controlled trial.

Background: Smoke from biomass fuels used for cooking in traditional cookstoves contains a variety of health-damaging pollutants. Inhalation of these pollutants by pregnant women has been linked to abnormal foetal development and adverse pregnancy outcomes, including low birthweight (LBW). There is a dearth of data on environmental interventions that have the potential to reduce exposure to biomass fuel during pregnancy and improve birth outcomes. International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) therefore, designed a low-cost kitchen with an improved cookstove and examined the impact of this intervention on the birthweight of neonates. Methods: icddr,b conducted a cluster-randomised controlled trial of a 'low-cost kitchen with improved cookstove' intervention among 1,267 pregnant women who used traditional cookstoves in a rural sub-district of Bangladesh. All participants were enrolled during the first trimester of pregnancy among 104 randomly selected clusters after obtaining informed consent. The model kitchens were installed in 628 participants' households of the intervention group, and 639 participants continued to use traditional cookstoves as the control group. The primary outcome was the proportion of LBW neonates between the intervention and control groups. The study also examined if the intervention would reduce CO exposure, measured by the differences in maternal blood carbon monoxide saturation (SpCO) levels and prevalence of LBW in neonates. We performed a generalized structural equation model for jointly assessing the simultaneous relationships of biomass fuel exposure to LBW of neonates and the relationships of LBW of neonates to maternal blood SpCO level. This trial was registered with ClinicalTrials.gov (NCT02923882). Findings: We found that in the intervention group using 'low-cost kitchen with improved cookstove', the risk of LBW reduced by 37% (adjusted risk ratio: 0.63, 95% CI [0.45, 0.89]). Between the second and third trimester, the mean maternal blood SpCO level was significantly reduced from 10.4% to 8.9% (p-value <0.01) in the intervention group but remained unchanged in the control group (11.6% and 11.5%). Of the total effects of the intervention on the risk of LBW, 48.3% was mediated through maternal blood SpCO level. Interpretation: The risk of LBW among rural neonates was reduced in the intervention group using 'low-cost kitchen with improved cookstove', which may be attributed to the reduction in maternal blood SpCO level. Additional research is needed to identify other mechanisms through which biomass fuel exposure might lead to adverse pregnancy outcomes. Funding: Grand Challenges Canada: Rising Stars in Global Health Programme.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

Low-Cost Hourly Ambient Black Carbon Measurements at Multiple Cities in Africa.

There is a notable lack of continuous monitoring of air pollutants in the Global South, especially for measuring chemical composition, due to the high cost of regulatory monitors. Using our previously developed low-cost method to quantify black carbon (BC) in fine particulate matter (PM2.5) by analyzing reflected red light from ambient particle deposits on glass fiber filters, we estimated hourly ambient BC concentrations with filter tapes from beta attenuation monitors (BAMs). BC measurements obtained through this method were validated against a reference aethalometer between August 2 and 23, 2023 in Addis Ababa, Ethiopia, demonstrating a very strong agreement (R2 = 0.95 and slope = 0.97). We present hourly BC for three cities in sub-Saharan Africa (SSA) and one in North America: Abidjan (Côte d'Ivoire), Accra (Ghana), Addis Ababa (Ethiopia), and Pittsburgh (USA). The average BC concentrations for the measurement period at the Abidjan, Accra, Addis Ababa Central summer, Addis Ababa Central winter, Addis Ababa Jacros winter, and Pittsburgh sites were 3.85 µg/m3, 5.33 µg/m3, 5.63 µg/m3, 3.89 µg/m3, 9.14 µg/m3, and 0.52 µg/m3, respectively. BC made up 14-20% of PM2.5 mass in the SSA cities compared to only 5.6% in Pittsburgh. The hourly BC data at all sites (SSA and North America) show a pronounced diurnal pattern with prominent peaks during the morning and evening rush hours on workdays. A comparison between our measurements and the Goddard Earth Observing System Composition Forecast (GEOS-CF) estimates shows that the model performs well in predicting PM2.5 for most sites but struggles to predict BC at an hourly resolution. Adding more ground measurements could help evaluate and improve the performance of chemical transport models. Our method can potentially use existing BAM networks, such as BAMs at U.S. Embassies around the globe, to measure hourly BC concentrations. The PM2.5 composition data, thus acquired, can be crucial in identifying emission sources and help in effective policymaking in SSA.

Evaluation of biocementation of slope soil for erosion control with low-cost materials.

In this study, biocementation of slope soil was performed using low-cost, commercially available materials to create a nutrient solution with native Cytobacillus hornekea. The high cost of laboratory-grade materials and microbes for biocementation is one of the main obstacles to its popularity. However, the cost of biocementation has been reduced significantly without reducing the strength when low-cost materials were used instead of laboratory-grade materials in this study. Direct shear test results and SEM also proved the suitability of the low-cost biocementation. Artificial rainfall with an intensity of 50-60 mm/h resulted in soil erosion of around 10% and 2% without and with biocementation, respectively. The amount of produced calcium carbonate was around 3.9% while using the low-cost materials with native microbes which is quite comparable with the laboratory-grade materials (3.4%).

Biodegradable and Ultra-High Expansion Ratio PPC-P Foams Achieved by Microcellular Foaming Using CO2 as Blowing Agent.

Poly(propylene carbonate-co-phthalate) (PPC-P) is an amorphous copolymer of aliphatic polycarbonate and aromatic polyester; it possesses good biodegradability, superior mechanical performances, high thermal properties, and excellent affinity with CO2. Hence, we fabricate PPC-P foams in an autoclave by using subcritical CO2 as a physical blowing agent. Both saturation pressure and foaming temperature affect the foaming behaviors of PPC-P, including CO2 adsorption and desorption performance, foaming ratio, cell size, porosity, cell density, and nucleation density, which are investigated in this research. Moreover, the low-cost PPC-P/nano-CaCO3 and PPC-P/starch composites are prepared and foamed using the same procedure. The obtained PPC-P-based foams show ultra-high expansion ratio and refined microcellular structures simultaneously. Besides, nano-CaCO3 can effectively improve PPC-P's rheological properties and foamability. In addition, the introduction of starch into PPC-P can lead to a large number of open cells. Beyond all doubt, this work can certainly provide both a kind of new biodegradable PPC-P-based foam materials and an economic methodology to make biodegradable plastic foams. These foams are potentially applicable in the packaging, transportation, and food industry.

Structural Engineering of Prussian Blue Analogues Enabling All-Climate and Ultralong Cycling Sodium-Ion Batteries.

The development of cost-efficient, long-lifespan, and all-climate sodium-ion batteries is of great importance for advancing large-scale energy storage but is plagued by the lack of suitable cathode materials. Here, we report low-cost Na-rich Mn-based Prussian blue analogues with superior rate capability and ultralong cycling stability over 10,000 cycles via structural optimization with electrochemically inert Ni atoms. Their thermal stability, all-climate properties, and potential in full cells are investigated in detail. Multiple in situ characterizations reveal that the outstanding performances benefit from their highly reversible three-phase transformations and trimetal (Mn-Ni-Fe) synergistic effects. In addition, a high sodium diffusion coefficient and a low volume distortion of 2.3% are observed through in situ transmission electron microscopy and first-principles calculations. Our results provide insights into the structural engineering of Prussian blue analogues for advanced sodium-ion batteries in large-scale energy storage applications.

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.

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.