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[Display omitted] The rapid, precise identification and quantification of specific biomarkers, toxins, or pathogens is currently a key strategy for achieving more efficient diagnoses. Herein a dopamine-biotin monomer was synthetized and oxidized in the presence of hexamethylenediamine, to obtain adhesive coatings based on polydopamine-biotin (PDA-BT) on different materials to be used in targeted molecular therapy. Insight into the structure of the PDA-BT coating was obtained by solid-state 13C NMR spectroscopy acquired, for the first time, directly onto the coating, deposited on alumina spheres. The receptor binding capacity of the PDA-BT coating toward 4-hydroxyazobenzene-2-carboxylic acid/Avidin complex was verified by means of UV– vis spectroscopy. Different deposition cycles of avidin onto the PDA-BT coating by layer-by-layer assembly showed that the film retains its receptor binding capacity for at least eight consecutive cycles. Finally, the feasibility of PDA-BT coating to recognize cell lines with different grade of overexpression of biotin receptors (BR) was investigated by tumor cell capture experiments by using MCF-7 (BR+) and HL-60 (BR−) cell lines. The results show that the developed system can selectively capture MCF-7 cells indicating that it could represent a first approach for the development of future more sophisticated biosensors easily accessible, low cost and recyclable with the dual and rapid detection of both proteins and cells. [ABSTRACT FROM AUTHOR]

The widely used low-cost particulate matter (PM) sensors in Thailand, such as the DustBoy, require performance improvements to ensure their data align with the established standards set by the US Environmental Protection Agency (US EPA). This study evaluates the accuracy and reliability of the DustBoy, a commonly used PM2.5 monitoring device in Thailand. A comparative analysis was conducted between the DustBoy and the US EPA's Federal Reference Method (FRM) and Federal Equivalent Method (FEM). The research involved both laboratory and field testing, where the DustBoy's performance was analyzed at various particulate matter concentration levels and environmental conditions. The study demonstrated that the DustBoy readings diverged from those of standard monitors at higher PM2.5 concentrations; however, a positive correlation between the devices remained evident. Below 100 µg/m3, the DustBoy overestimated PM concentrations compared to the FRM devices but underestimated them compared to the FEM devices. At higher concentrations, the DustBoy showed a significant overestimation, although the data trends aligned with those of standard devices. The sensor performance was also affected by factors such as the sensor age and device model. Corrections were developed to adjust the DustBoy readings to match the reference devices more closely, enhancing the accuracy post-adjustment. These corrections will refine the DustBoy's public data reporting and serve as guidelines for other low-cost sensors in Thailand. [ABSTRACT FROM AUTHOR]

The combustion efficiency of wood pellets is partly affected by their average length. The ISO 17829 standard defines the methodology for assessing the average length of sample pellets, but the method does not always lead to representative data. Furthermore, a standard analysis is time-consuming as it requires manual measurement of the pellets using a caliper. This paper, whilst evaluating the effect of pellet length on combustion efficiency, proposes a pending-patented dimensional image processing method (DIP) for assessing pellet length. DIP allows the dimensional data of grouped and stacked pellets to be obtained by exploiting the shadows produced by pellets when exposed to a light source, assuming that different-sized pellets produce different shadows. Thus, the proposed method allows for the extraction of dimensional information from non-distinct objects, overcoming the reliance of classical image processing methods on object distance for effective segmentation. Combustion tests, carried out using pellets varying only in length, confirmed the influence of length on combustion efficiency. Shorter pellets, compared to longer ones, significantly reduced CO emissions by up to 94% (mg/MJ). However, they exhibited a higher fuel mass consumption rate (kg/h), with an increase of up to 22.8% compared to the longest sample. In addition, longer pellets produced fewer but larger shadows than shorter ones. Further studies are needed to correlate the number and size of shadows with samples' average length so that DIP could be implemented in stoves and programmed to communicate with the control unit and automatically optimize the setting in order to improve combustion efficiency. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) is one of the major airborne pollutants in urban environments and is associated with severe health impacts. In this study, a dense network of low-cost sensor (LCS) is used to cover large spatial area and detect ambient PM2.5 concentration in Guwahati city. The measurements were conducted at multiple sites in different seasons between July 2022 and June 2023. Seasonal variability significantly influences regional meteorology, aerosol optical depth (AOD), and PM2.5 concentration. The seasonal average PM2.5 concentration was highest during winter (113.05 µg m −3), followed by post-monsoon (56.11 µg m−3), then pre-monsoon (46.60 µg m−3), and least for monsoon (32.36 µg m−3) season. The elevated PM2.5 concentrations may be attributed to environmental conditions (low ambient temperature, calm wind, and low planetary boundary layer height) that resulted in the least dispersion of PM2.5. The concentration-weighted trajectory (CWT) analysis identifies the effect of regional (Indo-Gangetic Plain and northeast region) and transboundary (Bay of Bengal, Bangladesh, and northwest Asian countries) transported air masses on urban air quality. Post-monsoon and winter season has a high influence on long-range transported aerosols, whereas the monsoon and pre-monsoon seasons are affected by ocean and land air masses. Changes in surrounding activities and meteorology influence spatial distribution of PM2.5 particles. Elevated PM2.5 concentrations were recorded at in-city and outskirt sites because of the nearby activities (industry and traffic) and build-up area. In meteorology, wind significantly affects spatial dispersion of PM2.5 concentration to the sites located in upwind and downwind directions. [ABSTRACT FROM AUTHOR]

Data uncertainty affects the accuracy of pollution source detection (PSD), particularly in the background of low‐cost water quality sensing and low‐accuracy data challenge. This study aims to develop a novel PSD method to use low‐accuracy sensor data, namely, the method of coupled forward data Assimilation and inverse Optimization in PSD (A&O‐PSD). This approach primarily employs filtering strategies to handle observation errors and extract hidden trend information during forward water quality data assimilation, and then optimal estimation of pollution source information through inverse optimization with enhanced trend information matching, avoiding the non‐Gaussian distribution challenge of pollution source information. Both real‐world pollution events and semi‐synthetic cases were used to evaluate the methodology and compare its performance with the traditional optimization approach (T‐PSD). The results indicated that T‐PSD is significantly affected by observational and parameter noise, engendering noticeable biases in PSD under the low‐accuracy sensor conditions. In contrast, the A&O‐PSD could accomplish the estimation task of PSD in real‐world pollution events, with improved robustness against noise interference. Furthermore, A&O‐PSD achieved an accuracy improvement of over 10% compared to T‐PSD in estimating pollution source locations within the typical noise distribution range of most low‐accuracy sensors currently available, making it possible to use low‐accuracy data that would otherwise be unusable in T‐PSD. Overall, the A&O‐PSD method, combined with low‐cost low‐accuracy water quality sensing, offers an effective solution for watershed environmental management. Plain Language Summary: Human activities significantly contribute to water pollution, such as illegal dumping or sudden pollution incidents. These events depend on the decision‐making behavior of polluting enterprises and socio‐economic factors, leading to concealed and uncorrelated pollution discharges with unpredictable outlet distributions. The challenges in PSD also lie in identifying discharges (location, time, and release mass) from limited and inaccurate water quality monitoring. Since the field sampling and laboratory chemical analysis for water quality observations are associated with high labor, financial, and time costs; while in in situ monitoring, the sensor‐based measurement is prone by significant errors, especially in complex, long‐term natural environments. To address the challenges of unknown distributions of pollution source information and low‐accuracy data, we propose the method of coupled forward data Assimilation and inverse Optimization (A&O‐PSD), which can extract credible trend information from inaccurate sensor data and optimally estimate pollution source information. A&O‐PSD has proven effective in both real‐world and semi‐synthetic pollution events, demonstrating robustness against data and parameter uncertainty. It offers a flexible framework for utilizing low‐cost low‐accuracy water quality sensing, with potential implications for sensor hardware development to improve accuracy. Key Points: A pollution source detection (PSD) paradigm of coupled forward assimilation and inverse optimization is proposedThe accuracy of PSD in low accuracy sensor data is improved by trend‐match strategyA&O‐PSD with better robustness tolerates inaccurate sensor data and model parameters [ABSTRACT FROM AUTHOR]

Indoor air quality (IAQ) poses a significant public health concern, and exposures to high levels of fine particulate matter (PM2.5) and carbon dioxide (CO2) could have detrimental health impacts. This study focused on assessing the indoor air pollutants in a residential house located in the town of Mission, Hidalgo County, South Texas, USA. The PM2.5 and CO2 were monitored indoors: the kitchen and the bedroom. This investigation also aimed to elucidate the effects of household activities such as cooking and human occupancy on these pollutants. Low-cost sensors (LCSs) from TSI AirAssure™ were used in this study. They were deployed within the breathing zone at approximately 1.5 m above the ground. Calibration of the low-cost sensors against Federal Equivalent Method (FEM) instruments was undertaken using a multiple linear regression method (MLR) model to improve the data accuracy. The indoor PM2.5 levels were significantly influenced by cooking activities, with the peak PM2.5 concentrations reaching up to 118.45 μg/m3. The CO2 levels in the bedroom increased during the occupant's sleeping period, reaching as high as 1149.73 ppm. The health risk assessment was assessed through toxicity potential (TP) calculations for the PM2.5 concentrations. TP values of 0.21 and 0.20 were obtained in the kitchen and bedroom, respectively. The TP values were below the health hazard threshold (i.e., TP < 1). These low TP values could be attributed to the use of electric stoves and efficient ventilation systems. This research highlights the effectiveness of low-cost sensors for continuous IAQ monitoring and helps promote better awareness of and necessary interventions for salubrious indoor microenvironments. [ABSTRACT FROM AUTHOR]

Indonesia accounts for more than one third of the world's tropical peatlands. Much of the peatland in Indonesia has been deforested and drained, meaning it is more susceptible to fires, especially during drought and El Niño events. Fires are most common in Riau (Sumatra) and Central Kalimantan (Borneo) and lead to poor regional air quality. Measurements of air pollutant concentrations are sparse in both regions contributing to large uncertainties in both fire emissions and air quality degradation. We deployed a network of 13 low‐cost PM2.5 sensors across urban and rural locations in Central Kalimantan and measured indoor and outdoor PM2.5 concentrations during the onset of an El Niño dry season in 2023. During the dry season (September 1st to October 31st), mean outdoor PM2.5 concentrations were 136 μg m−3, with fires contributing 90 μg m−3 to concentrations. Median indoor/outdoor (I/O) ratios were 1.01 in rural areas, considerably higher than those reported during wildfires in other regions of the world (e.g., USA), indicating housing stock in the region provides little protection from outdoor PM2.5. We combined WRF‐Chem simulated PM2.5 concentrations with the median fire‐derived I/O ratio and questionnaire results pertaining to participants' time spent I/O to estimate 1.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous air quality (>55.4 μg m−3) during the dry season. Our work provides new information on the exposure of people in Central Kalimantan to smoke from fires and highlights the need for action to help reduce peatland fires. Plain Language Summary: More than one third of the world's tropical peatlands are in Indonesia. Much of the peatland in Indonesia has been deforested and drained, meaning it is more susceptible to fires, especially during drought. Fires are most common in Riau (Sumatra) and Central Kalimantan (Borneo) and lead to poor regional air quality. There are not many measurements of air pollution in either region, and this means the air quality impacts of fires are not well understood. We deployed a network of air quality (AQ) sensors across urban and rural Central Kalimantan. The sensors measured the concentration of fine particulate matter (PM2.5), a major component of air pollution that is directly emitted by fires. The AQ sensors were deployed sensors inside and outside of people's homes during the onset of a dry season, when drought occurred (in 2023). Indoor and outdoor PM2.5 concentrations were very similar, indicating housing in the region provides little protection from outdoor PM2.5. We estimate 1.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous AQ in 2023. Our work provides new information on the exposure of people in Central Kalimantan to fire PM2.5 and highlights the need for action to reduce peatland fires. Key Points: Mean outdoor PM2.5 concentrations during the dry season were 136 μg m−3 and fires contributed ∼90 μg m−3 to PM2.5 concentrationsI/O ratios indicate that housing stock provides little protection from outdoor PM2.51.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous air quality (>55.4 μg m−3) during the dry season [ABSTRACT FROM AUTHOR]

The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine-, and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino's built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant. [ABSTRACT FROM AUTHOR]

With increasing concerns about the impact of outdoor air quality on public health, demand rises for cost-effective, scalable air monitoring solutions. Low-cost sensors offer promise for monitoring outdoor air quality, with potential for widespread deployment. This article presents a comprehensive evaluation methodology for these sensors, focusing on real-world outdoor performance and end-user perspectives. Relevant methodologies for evaluating wireless air monitor with low-cost sensors were sourced from databases. The study outlines rigorous field-testing methodology, addressing sensor accuracy and stability tested in diverse environmental conditions under various climatic and geographical scenarios. This study explores end-user perspectives ensuring data relevance. This research contributes to discourse on low-cost sensor use, emphasizing a comprehensive evaluation framework encompassing technical performance, usability testing, and user perspectives. Insights gained can guide reliable, user-centric air monitoring solutions, enhancing our ability to mitigate health risks from outdoor air pollution. [ABSTRACT FROM AUTHOR]

Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%. [ABSTRACT FROM AUTHOR]

Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology's complexity, costs, fabrication and implementation challenges often hinder engineers' exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication. [ABSTRACT FROM AUTHOR]

• A quad-core Nova SDS029 optical particle sizer is introduced and evaluated. • The low-cost SDS029 rivals the reference devices in field applications. • Networking low-cost particle sizers for high spatial-temporal measurement is imperative. Low-cost sensors are widely used to collect high-spatial-resolution particulate matter data that traditional reference monitoring devices cannot. In addition to the mass concentration, the number concentration and size distribution are also fundamental in determining the origin and hazard level of particulate pollution. Therefore, low-cost optical sensors have been improved to establish optical particle sizers (OPSs). In this study, a low-cost OPS, the Nova SDS029, is introduced, and it is evaluated in comparison to two reference instruments—the GRIMM 11-D and the TSI 3330. We first tested the sizing accuracy using polystyrene latex spheres. Then, we assessed the mass and number size distribution accuracy in three application scenarios: indoor smoking, ambient air quality, and mobile monitoring. The evaluations suggest that the low-cost SDS029 rivals research-grade optical sizers in many aspects. For example, (1) the particle diameters obtained with the SDS029 are close to the reference instruments (usually < 10%) in the 0.3–5 µm range; (2) the number of particles and mass concentration are highly correlated (r ≥ 0.99) with the values obtained with the reference instruments; and (3) the SDS029 slightly underestimates the number concentration, but the derived PM 2.5 values are closer to monitoring station than the reference instruments. The successful application of the SDS029 in multiple scenarios suggests that a plausible particle size distribution can be obtained in an easy and cost-efficient way. We believe that low-cost OPSs will increasingly be used to map the sources and risk levels of particles at the city scale. [Display omitted] [ABSTRACT FROM AUTHOR]

The global challenge of air pollution's adverse health effects, particularly highlighted in Dhaka, Bangladesh, underscores the significant impact of particulate matter (PM) exposure. This study aims to assess the current sources of PM2.5 emissions in different microenvironments around Dhaka and explore potential risk factors to assess individual 24 h exposure to PM2.5. A commercially available low-cost sensor was utilized for collecting data for 15 days under various environmental conditions. The average concentrations for PM1.0, PM2.5, and PM10 were 37.05 ± 24.36 µg/m3, 57.22 ± 40.75 µg/m3, and 69.22 ± 48.46 µg/m3, respectively. The highest PM2.5 concentrations were found (78.87 ± 53.69 μg/m3) in restaurants and residences (62.35 ± 41.70 μg/m3), while air-conditioned shopping malls exhibited the lowest concentrations (20.08 ± 15.57 μg/m3). Driving with windows closed and utilizing air conditioning resulted in a 33–52% reduction in PM2.5 concentrations inside the car. The Hazard Quotient (HQ) for PM2.5 varied by location, with a low level observed in the air-conditioned locations and a moderate level observed in restaurants and non-air-conditioned shopping malls. The significance of this study lies in its potential to inform public health strategies and urban planning initiatives aimed at reducing air pollution exposure in highly populated cities like Dhaka. [ABSTRACT FROM AUTHOR]

The spatial–temporal variations of fine particulate matter (PM2.5) in Akure, a city in southwestern Nigeria, are examined based on multi-year continuous observations using low-cost PM2.5 sensors at six different sites. The average annual concentration of PM2.5 across these sites was measured at 41.0 µg/m3, which surpassed both the Nigerian national air quality standard and the World Health Organization air quality guideline level. PM2.5 levels were significantly higher during the dry season (November–March), often exceeding hazardous levels (over 350 µg/m3), than during the wet season. The analyses of trends in air mass trajectories and satellite data on fire occurrences imply that the transport of dust and accumulation of PM2.5 originating from local/regional open burning activities played crucial roles in increased PM2.5 concentrations during the dry season. Further, site-to-site variations in the PM2.5 levels were observed, with relatively high concentrations at less urbanized sites, likely due to high local emissions from solid fuel combustion, waste burning, and unpaved road dust. Diurnal patterns showed morning and evening peaks at less urbanized sites, accounting for an estimated 51–77% of local emissions. These results highlight the importance of local emission sources in driving spatial–temporal PM2.5 variations within the city and the need for targeted mitigation strategies to address the significant air pollution challenges in Akure and similar regional cities in West Africa. [ABSTRACT FROM AUTHOR]

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full "heating season" (15 October 2022–15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = −4.8 and −2.9 μg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 μg/m3) and slightly under-estimated PM10 concentrations (MB = −0.4 μg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68–0.75 vs. 0.59–0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs. [ABSTRACT FROM AUTHOR]

Abstract Indonesia accounts for more than one third of the world's tropical peatlands. Much of the peatland in Indonesia has been deforested and drained, meaning it is more susceptible to fires, especially during drought and El Niño events. Fires are most common in Riau (Sumatra) and Central Kalimantan (Borneo) and lead to poor regional air quality. Measurements of air pollutant concentrations are sparse in both regions contributing to large uncertainties in both fire emissions and air quality degradation. We deployed a network of 13 low‐cost PM2.5 sensors across urban and rural locations in Central Kalimantan and measured indoor and outdoor PM2.5 concentrations during the onset of an El Niño dry season in 2023. During the dry season (September 1st to October 31st), mean outdoor PM2.5 concentrations were 136 μg m−3, with fires contributing 90 μg m−3 to concentrations. Median indoor/outdoor (I/O) ratios were 1.01 in rural areas, considerably higher than those reported during wildfires in other regions of the world (e.g., USA), indicating housing stock in the region provides little protection from outdoor PM2.5. We combined WRF‐Chem simulated PM2.5 concentrations with the median fire‐derived I/O ratio and questionnaire results pertaining to participants' time spent I/O to estimate 1.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous air quality (>55.4 μg m−3) during the dry season. Our work provides new information on the exposure of people in Central Kalimantan to smoke from fires and highlights the need for action to help reduce peatland fires.

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

In this article, the problem of real‐time estimation of the fermentative ethanol process is tackled. The considered observer is a model‐based technique that is robust regarding the model parameter uncertainties and inline noisy measurements. An unstructured kinetic model was used to describe the production of ethanol in a batch bioreactor for Saccharomyces cerevisiae. The biomass concentration was selected as the measured bioreactor´s output via an inline device, where the estimate variables were the substrate and ethanol concentrations. An experimental prototype was constructed to demonstrate the observer's real‐time performance. The experimental results show that the robust, smooth sliding mode observer performs better than the standard proportional sliding mode observer. [ABSTRACT FROM AUTHOR]

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory. [ABSTRACT FROM AUTHOR]

CO2 monitoring is important for carbon emission evaluation. Low-cost and medium-precision sensors (LCSs) have become an exploratory direction for CO2 observation under complex emission conditions in cities. Here, we used a calibration method that improved the accuracy of SenseAir K30 CO2 sensors from ±30 ppm to 0.7–4.0 ppm for a CO2-monitoring instrument named the SENSE-IAP, which has been used in several cities, such as in Beijing, Jinan, Fuzhou, Hangzhou, and Wuhan, in China since 2017. We conducted monthly to yearly synchronous observations using the SENSE-IAP along with reference instruments (Picarro) and standard gas to evaluate the performance of the LCSs for indoor use with relatively stable environments. The results show that the precision and accuracy of the SENSE-IAP compared to the standard gases were rather good in relatively stable indoor environments, with the short-term (daily scale) biases ranging from −0.9 to 0.2 ppm, the root mean square errors (RMSE) ranging from 0.7 to 1.6 ppm, the long-term (monthly scale) bias ranging from −1.6 to 0.5 ppm, and the RMSE ranging from 1.3 to 3.2 ppm. The accuracy of the synchronous observations with Picarro was in the same magnitude, with an RMSE of 2.0–3.0 ppm. According to our evaluation, standard instruments or reliable standard gases can be used as a reference to improve the accuracy of the SENSE-IAP. If calibrated daily using standard gases, the bias of the SENSE-IAP can be maintained within 1.0 ppm. If the standard gases are hard to access frequently, we recommend a calibration frequency of at least three months to maintain an accuracy within 3 ppm. [ABSTRACT FROM AUTHOR]

The PurpleAir PA-II sensor provides low-cost in situ measurements of meteorological variables including temperature and relative humidity (RH), as well as fine particulate matter (PM2.5) in real time. The sensors have been used in several studies investigating intracity differences in temperature and PM2.5. While the adoption and use of low-cost sensors has many benefits, care must be taken to ensure proper calibration and testing. This is true not only for PM2.5 measurements but also for temperature and RH given the synergistic health impacts from extreme heat and air pollution exposure. Here, we compare continuous temperature and RH measurements from a PA-II sensor to measurements from a Campbell Scientific 107 temperature probe and Vaisala HMP45C RH probe. All three instruments were co-located from December 2021 to June 2023 in Asheville, North Carolina. We found that the PA-II has an overall high temperature bias of 2.6 °C and root mean square error (RMSE) of 2.8 °C. Applying a linear regression correction reduces RMSE to 1.0 °C, while applying the constant 4.4 °C correction suggested by PurpleAir reduces RMSE to only 2.2 °C. Our PA-II RH measurements have a low bias of −17.4% and uncorrected RMSE of 18.5%. A linear regression correction improves the RH RMSE to 4.5%. Applying the constant 4% RH correction suggested by PurpleAir reduces RMSE to only 14.8%. We present new correction factors that differ from those suggested by PurpleAir, which overcorrect the high temperature bias and undercorrect the low RH bias. We also show that our correction factors improve estimates of dewpoint temperature (RMSE of 0.6 °C and 0.9 °C) compared to the corrections suggested by PurpleAir. [ABSTRACT FROM AUTHOR]

This study evaluates low-cost sensors (LCSs) for measuring coarse and fine particulate matter (PM) to clarify and measure air pollution. LCSs monitored PM10, PM2.5 (fine particulates), and PM1.0 concentrations at four sites in Samutprakarn, Thailand from December 2021 to April 2022. Average daily PM10, PM2.5, and PM1.0 concentrations at the monitoring locations were 53–79, 34–45, and 31–43 μg/m3, respectively. In December 2021, the monitoring station had a daily PM2.5 value above 100 μg/m3, indicating haze occurrences. However, the monitoring site's daily PM10 and PM1.0 concentrations did not surpass Thailand's ambient air quality threshold. We also measured and calibrated comparative particulate matter concentrations from LCSs and a tapered element oscillating microbalance (TEOM) monitor (Pollution Control Department (PCD) standard analytical method). PM2.5 concentrations from the LCSs were lower than TEOM, but the difference was not statistically significant. The PM2.5 monitoring station provided near-real-time air quality data for health risk reduction, especially when PM levels were high. Based on this study, authorities and local agencies may consider improving air quality regulation in Samutprakan, focusing on suburban PM2.5 air pollution. [ABSTRACT FROM AUTHOR]

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Portable Sensor Nodes (PSNs) can supplement geographically sparse government-run static air quality monitoring stations (AQMSs). A PSN typically consists of several low-cost pollution sensors for different air pollutants, which must be calibrated to improve the accuracy of measurements. These sensors can be co-located with the high accuracy monitoring equipment (HAME) at AQMSs for calibration. Existing studies have suggested that different pollution sensors may favor different calibration models; even the same pollution sensors in different PSNs may favor different models. However, it is impractical to co-locate each PSN with HAME due to limited access to AQMSs, making large-scale sensor calibration difficult. This study proposes UNI-CAL for calibrating different pollutants, including nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5 and PM10), based on a novel AI-driven model with residual blocks capturing the complex non-linear interactions of raw measurements plus citywide domain-specific information, including meteorology, background pollution, and temporal characteristics. UNI-CAL further allows transfer calibration, i.e., the calibration of sensors from calibrated ones. UNI-CAL has improved the performance of direct calibration by 3.143% on average compared to the best baseline across all pollutants and PSNs on all evaluation metrics. Moreover, domain-specific information has significantly improved the direct calibration performance of UNI-CAL by 4.852% on average. Furthermore, UNI-CAL has demonstrated a strong capability in transfer calibration and achieved the best performance in most scenarios after incorporating domain-specific information. In the future, one can collect more data covering different environmental conditions and explore advanced semi-supervised learning techniques to improve the consistency, robustness, generalizability, and transferability of the proposed calibration framework.

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. Recent advances in air quality monitoring have produced portable, easy-to-use, low-cost, sensor-based monitors which have given a new dimension to air pollutant monitoring and have democratized the air quality monitoring process by making monitors and results directly available at the community level. This study used PurpleAir © sensors for PM2.5 assessment in California, USA. The evaluation of PM2.5 from sensors included Quality Assurance and quality control (QA/QC) procedures, assessment concerning reference-monitored PM2.5 concentrations, and the formulation of a decision support system integrating these observations using geostatistical techniques. The hourly and daily average observed PM2.5 concentrations from PurpleAir monitors followed the trends of observed PM2.5 at regulatory monitors. PurpleAir monitors also captured the peak PM2.5 concentrations due to incidents such as forest fires. In comparison with reference-monitored PM2.5 levels, it was found that PurpleAir PM2.5 concentrations were mostly higher. The most important reason for PurpleAir’s higher PM2.5 concentrations was the inclusion of moisture or water vapor as an aerosol in contrast to measurements of PM2.5 excluding water content in FEM/FRM and non-FEM/FRM monitors. Long-term assessment (2016–2023) revealed that R2 values were between 0.54 and 0.86 for selected collocated PurpleAir sensors and regulatory monitors for hourly PM2.5 concentrations. Past research studies that were conducted for mostly shorter periods resulted in higher R2 values between 0.80 and 0.98. This study aims to provide reasonable estimations of PM2.5 concentrations with high spatiotemporal resolutions based on statistical models using PurpleAir measurements. The methods of Kriging and IDW, geostatistical interpolation techniques, showed similar spatio-temporal patterns. Overall, this study revealed that low-cost, sensor-based PurpleAir sensors could be effective and reliable tools for episodic and long-term ambient air quality monitoring and developing mitigation strategies.

A rapid increase in the population of Togo, and in particular that of the capital city of Lomé, has led to an increase in urban sprawl, anthropogenic activities such as traffic and combustion, and air pollution. To measure and identify trends in concentrations of fine particulate matter (PM2.5) in the city of Lomé in Togo, a PurpleAir PA-II-SD monitor is placed in the rapidly expanding peripheral district of Agoè-Minamadou for three years. A correction factor, based on a colocation with a ThermoFischer TEOM reference monitor at the University of Lomé, is presented and applied to the PurpleAir data. We demonstrate improvement in PM2.5 estimates using this locally-built correction factor over a previous correction factor based on a colocation in nearby Accra, Ghana. Daily mean corrected PM2.5 concentrations were 21.5 µg m-3. Concentrations exceeded the WHO daily recommended thresholds (15 µg/m3) on 68.2% of days measured during the study. Over three years of measurement, air quality in Lomé shows very little improvement. [ABSTRACT FROM AUTHOR]

The development of low-cost structural and environmental sensors has sparked a transformation across numerous fields, offering cost-effective solutions for monitoring infrastructures and buildings. However, the affordability of these solutions often comes at the expense of accuracy. To enhance precision, the LARA (Low-cost Adaptable Reliable Anglemeter) system averaged the measurements of a set of five different accelerometers working as inclinometers. However, it is worth noting that LARA's sensitivity still falls considerably short of that achieved by other high-accuracy commercial solutions. There are no works presented in the literature to enhance the accuracy, precision, and resolution of low-cost inclinometers using artificial intelligence (AI) tools for measuring structural deformation. To fill these gaps, artificial intelligence (AI) techniques are used to elevate the precision of the LARA system working as an inclinometer. The proposed AI-driven tool uses Multilayer Perceptron (MLP) to glean insight from high-accuracy devices' responses. The efficacy and practicality of the proposed tools are substantiated through the structural and environmental monitoring of a real steel frame located in Cuenca, Spain. [ABSTRACT FROM AUTHOR]

Individuals who spend most of their time indoors are especially sensitive to indoor air quality (IAQ), which significantly impacts their general well-being and health. Traditional IAQ measurement techniques, however, are frequently pricy, complicated, and labor-intensive. In this study, we used a low-cost, simple-to-use, and handy sensor system to track the levels of carbon dioxide (CO2), nitrogen dioxide (NO2), ozone (O3), particulate matter (PM1.0, PM2.5, and PM10), temperature, and relative humidity (RH) in a laboratory at the Bamidele Olomilua University of Education, Science, and Technology in Ikere-Ekiti for a month. We contrasted the outcomes with other benchmarks and WHO recommendations. However, the NO2 levels (144.00–303.00 ppb) exceeded the suggested levels (National Institute for Occupational Safety and Health (NIOSH)—70 ppb; National Ambient Air Quality Standards (NAAQS)—100 ppb; National Environmental Standards and Regulations Enforcement Agency (NESREA)—120 ppb; and World Health Organization (WHO)—25 ppb), suggesting a possible cause of indoor contaminants. We also noticed that the temperature and humidity varied considerably throughout the day, which impacted the inhabitants' thermal comfort and ventilation. The principal component analysis (PCA) findings indicate that particulate matter, the weather, photochemical reactions, and combustion processes are the key contributors to fluctuation in the air quality measurements. Based on their quantities and relationships, these elements can have a variety of effects on both the natural environment as well as well-being. Our monitoring device can give immediate information and warnings, assisting in locating and reducing indoor airborne pollutant sources and enhancing indoor air quality (IAQ). This work shows that adopting a low-cost sensor system for IAQ measurement in underdeveloped nations, where such data are sparse and frequently erroneous, is both feasible and beneficial. [ABSTRACT FROM AUTHOR]

Air pollution is a critical public health problem that has increased during the past decades. High levels of air pollution have affected natural environments and people's health, causing significant problems and, in severe cases, premature death. A growing trend called "Personal air monitoring" has become important for prevention of and reduction in exposure to air pollutants. The development of personal particulate matter sensors is still a topic of study among the scientific community. Some important identified challenges are improving the sample rate, precision, stability, dimensions and costs, making personal monitoring of air quality affordable. This work proposes the development of a low-cost particulate matter optical sensor to count the number of particles in real time using the Arduino platform and wireless transmission. Our results demonstrated that using a digital input of the microcontroller instead of the analog–digital converter, after conditioning the sensor signal, allows a very high max particle count, which can be compared to that of expensive sensors. In addition, particulate matter (PM) measurements were compared with a GP2Y1014AU0F dust sensor to validate the accuracy of the sensor. [ABSTRACT FROM AUTHOR]

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. Recent advances in air quality monitoring have produced portable, easy-to-use, low-cost, sensor-based monitors which have given a new dimension to air pollutant monitoring and have democratized the air quality monitoring process by making monitors and results directly available at the community level. This study used PurpleAir © sensors for PM2.5 assessment in California, USA. The evaluation of PM2.5 from sensors included Quality Assurance and quality control (QA/QC) procedures, assessment concerning reference-monitored PM2.5 concentrations, and the formulation of a decision support system integrating these observations using geostatistical techniques. The hourly and daily average observed PM2.5 concentrations from PurpleAir monitors followed the trends of observed PM2.5 at regulatory monitors. PurpleAir monitors also captured the peak PM2.5 concentrations due to incidents such as forest fires. In comparison with reference-monitored PM2.5 levels, it was found that PurpleAir PM2.5 concentrations were mostly higher. The most important reason for PurpleAir's higher PM2.5 concentrations was the inclusion of moisture or water vapor as an aerosol in contrast to measurements of PM2.5 excluding water content in FEM/FRM and non-FEM/FRM monitors. Long-term assessment (2016–2023) revealed that R2 values were between 0.54 and 0.86 for selected collocated PurpleAir sensors and regulatory monitors for hourly PM2.5 concentrations. Past research studies that were conducted for mostly shorter periods resulted in higher R2 values between 0.80 and 0.98. This study aims to provide reasonable estimations of PM2.5 concentrations with high spatiotemporal resolutions based on statistical models using PurpleAir measurements. The methods of Kriging and IDW, geostatistical interpolation techniques, showed similar spatio-temporal patterns. Overall, this study revealed that low-cost, sensor-based PurpleAir sensors could be effective and reliable tools for episodic and long-term ambient air quality monitoring and developing mitigation strategies. [ABSTRACT FROM AUTHOR]