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.

Mapping pollution exposure and chemistry during an extreme air quality event (the 2018 Kilauea eruption) using a low-cost sensor network.

Extreme air quality episodes represent a major threat to human health worldwide but are highly dynamic and exceedingly challenging to monitor. The 2018 Kilauea Lower East Rift Zone eruption (May to August 2018) blanketed much of Hawai'i Island in "vog" (volcanic smog), a mixture of primary volcanic sulfur dioxide (SO2) gas and secondary particulate matter (PM). This episode was captured by several monitoring platforms, including a low-cost sensor (LCS) network consisting of 30 nodes designed and deployed specifically to monitor PM and SO2 during the event. Downwind of the eruption, network stations measured peak hourly PM2.5 and SO2 concentrations that exceeded 75 µg m-3 and 1,200 parts per billion (ppb), respectively. The LCS network's high spatial density enabled highly granular estimates of human exposure to both pollutants during the eruption, which was not possible using preexisting air quality measurements. Because of overlaps in population distribution and plume dynamics, a much larger proportion of the island's population was exposed to elevated levels of fine PM than to SO2 Additionally, the spatially distributed network was able to resolve the volcanic plume's chemical evolution downwind of the eruption. Measurements find a mean SO2 conversion time of ∼36 h, demonstrating the ability of distributed LCS networks to observe reaction kinetics and quantify chemical transformations of air pollutants in a real-world setting. This work also highlights the utility of LCS networks for emergency response during extreme episodes to complement existing air quality monitoring approaches.

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors.

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.

Monitoring and Ensuring Worker Health in Controlled Environments Using Economical Particle Sensors.

Nowadays, indoor air quality monitoring has become an issue of great importance, especially in industrial spaces and laboratories where materials are handled that may release particles into the air that are harmful to health. This study focuses on the monitoring of air quality and particle concentration using low-cost sensors (LCSs). To carry out this work, particulate matter (PM) monitoring sensors were used, in controlled conditions, specifically focusing on particle classifications with PM2.5 and PM10 diameters: the Nova SDS011, the Sensirion SEN54, the DFRobot SEN0460, and the Sensirion SPS30, for which an adapted environmental chamber was built, and gaged using the Temtop M2000 2nd as a reference sensor (SRef). The main objective was to preliminarily assess the performance of the sensors, to select the most suitable ones for future research and their possible use in different work environments. The monitoring of PM2.5 and PM10 particles is essential to ensure the health of workers and avoid possible illnesses. This study is based on the comparison of the selected LCS with the SRef and the results of the comparison based on statistics. The results showed variations in the precision and accuracy of the LCS as opposed to the SRef. Additionally, it was found that the Sensirion SEN54 was the most suitable and valuable tool to be used to maintain a safe working environment and would contribute significantly to the protection of the workers' health.

Developing a Cloud-Based Air Quality Monitoring Platform Using Low-Cost Sensors.

Conventional air quality monitoring has been traditionally carried out in a few fixed places with expensive measuring equipment. This results in sparse spatial air quality data, which do not represent the real air quality of an entire area, e.g., when hot spots are missing. To obtain air quality data with higher spatial and temporal resolution, this research focused on developing a low-cost network of cloud-based air quality measurement platforms. These platforms should be able to measure air quality parameters including particulate matter (PM10, PM2.5, PM1) as well as gases like NO, NO2, O3, and CO, air temperature, and relative humidity. These parameters were measured every second and transmitted to a cloud server every minute on average. The platform developed during this research used one main computer to read the sensor data, process it, and store it in the cloud. Three prototypes were tested in the field: two of them at a busy traffic site in Stuttgart, Marienplatz and one at a remote site, Ötisheim, where measurements were performed near busy railroad tracks. The developed platform had around 1500 € in materials costs for one Air Quality Sensor Node and proved to be robust during the measurement phase. The notion of employing a Proportional-Integral-Derivative (PID) controller for the efficient working of a dryer that is used to reduce the negative effect of meteorological parameters such as air temperature and relative humidity on the measurement results was also pursued. This is seen as one way to improve the quality of data captured by low-cost sensors.

Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor.

This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.

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.

Gas Detection and Classification Using Multimodal Data Based on Federated Learning.

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long-short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client's data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches.

Feasibility Study on the Use of NO2 and PM2.5 Sensors for Exposure Assessment and Indoor Source Apportionment at Fixed Locations.

Recent advances in sensor technology for air pollution monitoring open new possibilities in the field of environmental epidemiology. The low spatial resolution of fixed outdoor measurement stations and modelling uncertainties currently limit the understanding of personal exposure. In this context, air quality sensor systems (AQSSs) offer significant potential to enhance personal exposure assessment. A pilot study was conducted to investigate the feasibility of the NO2 sensor model B43F and the particulate matter (PM) sensor model OPC-R1, both from Alphasense (UK), for use in epidemiological studies. Seven patients with chronic obstructive pulmonary disease (COPD) or asthma had built-for-purpose sensor systems placed inside and outside of their homes at fixed locations for one month. Participants documented their indoor activities, presence in the house, window status, and symptom severity and performed a peak expiratory flow test. The potential inhaled doses of PM2.5 and NO2 were calculated using different data sources such as outdoor data from air quality monitoring stations, indoor data from AQSSs, and generic inhalation rates (IR) or activity-specific IR. Moreover, the relation between indoor and outdoor air quality obtained with AQSSs, an indoor source apportionment study, and an evaluation of the suitability of the AQSS data for studying the relationship between air quality and health were investigated. The results highlight the value of the sensor data and the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. The use of AQSSs at fixed locations shows promise for larger-scale and/or long-term epidemiological studies.

Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring.

The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users' health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system.

Easy-to-Use MOX-Based VOC Sensors for Efficient Indoor Air Quality Monitoring.

This research paper presents a case study on the application of Metal Oxide Semiconductor (MOX)-based VOC/TVOC sensors for indoor air quality (IAQ) monitoring. This study focuses on the ease of use and the practical benefits of these sensors, drawing insights from measurements conducted in a university laboratory setting. The investigation showcases the straightforward integration of MOX-based sensors into existing IAQ monitoring systems, highlighting their user-friendly features and the ability to provide precise and real-time information on volatile organic compound concentrations. Emphasizing ease of installation, minimal maintenance, and immediate data accessibility, this paper demonstrates the practicality of incorporating MOX-based sensors for efficient IAQ management. The findings contribute to the broader understanding of MOX sensor capabilities, providing valuable insights for those seeking straightforward and effective solutions for indoor air quality monitoring. This case study outlines the feasibility and benefits of utilizing MOX-based sensors in various environments, offering a promising avenue for the widespread adoption of user-friendly technologies in IAQ management.

Effect of Three-Dimensional-Printed Thermoplastics Used in Sensor Housings on Common Atmospheric Trace Gasses.

Low-cost air quality sensors (LCSs) are becoming more ubiquitous as individuals and communities seek to reduce their exposure to poor air quality. Compact, efficient, and aesthetically designed sensor housings that do not interfere with the target air quality measurements are a necessary component of a low-cost sensing system. The selection of appropriate housing material can be an important factor in air quality applications employing LCSs. Three-dimensional printing, specifically fused deposition modeling (FDM), is a standard for prototyping and small-scale custom plastics production because of its low cost and ability for rapid iteration. However, little information exists about whether FDM-printed thermoplastics affect measurements of trace atmospheric gasses. This study investigates how five different FDM-printed thermoplastics (ABS, PETG, PLA, PC, and PVDF) affect the concentration of five common atmospheric trace gasses (CO, CO2, NO, NO2, and VOCs). The laboratory results show that the thermoplastics, except for PVDF, exhibit VOC off-gassing. The results also indicate no to limited interaction between all of the thermoplastics and CO and CO2 and a small interaction between all of the thermoplastics and NO and NO2.

Low-Cost Sensor System for Air Purification Process Evaluation.

With the development of civilisation, the awareness of the impact of versatile aerosol particles on human health and the environment is growing. New advanced materials and techniques are needed to purify the air to reduce this impact. This brings the necessity of fast and low-cost devices to evaluate the air quality from particulate and gaseous impurities, especially in a place where gas chromatography (GC) techniques are unavailable. Small portable and low-cost systems may work separately or be incorporated into devices responsible for air-cleaning processes, such as filters, smoke adsorbers, or plasma air cleaners. Given the above, this study proposes utilising a self-assembled low-cost system to evaluate air quality, which can be used in many outdoor and indoor applications. ESP32 boards with the wireless communication protocol ESP-NOW were used as the framework of the system. The concentration of aerosol particles was measured using Alphasense sensors. The concentrations of the following gases were measured: NO2, SO2, O3, CO, CO2, and H2S. The system was used to evaluate the quality of air containing tobacco smoke after passing through an actual DBD plasma reactor where the purification occurred. A high amount of reduction in aerosol particles and a reduction in the SO2 concentration were detected. An increase in the NO2 concentration was seen as an undesirable effect. The aerosol particle measurements were compared with those using a professional device (GRIMM, Hamburg, Germany), which showed the same trends in aerosol particle behaviour. The obtained results are auspicious and are a step towards producing a low-cost, efficient system for evaluating air quality as well as indoor and outdoor conditions.

Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania.

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment.

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring.

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent.

The Relationship between Indoor and Outdoor Fine Particulate Matter in a High-Rise Building in Chicago Monitored by PurpleAir Sensors.

In urban areas like Chicago, daily life extends above ground level due to the prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between 8 April and 7 May 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 m away. The results show that outdoor PM2.5 concentrations peak at 14 m height, and then decline by 0.11 µg/m3 per meter elevation, especially noticeable from midnight to 8 a.m. under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 µg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

Advancing air quality monitoring: A low-cost sensor network in motion - Part I.

In urban areas, high levels of air pollution pose significant risks to human health, emphasising the need for detailed air quality (AQ) monitoring. However, traditional AQ monitoring relies on the data from Reference Monitoring Stations, which are sparsely distributed and provide only hourly or daily data, failing to capture the spatial and temporal variability of air pollutant concentrations. Addressing this challenge, we introduce in this article the ExpoLIS system, an all-weather mobile AQ monitoring system that integrates various AQ low-cost sensors (LCSs), providing high spatio-temporal resolution data. This study demonstrates that the inclusion of an extended sampling device may mitigate the effect of the meteorological parameters and other disturbances on readings. At the same time, it did not reduce the quality of the data, both in static conditions and in motion, as we were able to maintain a certain level of agreement between the LCSs. In conclusion, the ExpoLIS system proves its versatility by enabling the collection of large quantities of accurate data, allowing a deeper understanding of the AQ dynamics in urban environments.

Spatialized PM2.5 during COVID-19 pandemic in Brazil's most populous southern city: implications for post-pandemic era.

Brazil has experienced one of the highest COVID-19 fatality rates globally. While numerous studies have explored the potential connection between air pollution, specifically fine particulate matter (PM2.5), and the exacerbation of SARS-CoV-2 infection, the majority of this research has been conducted in foreign regions-Europe, the United States, and China-correlating generalized pollution levels with health-related scopes. In this study, our objective is to investigate the localized connection between exposure to air pollution exposure and its health implications within a specific Brazilian municipality, focusing on COVID-19 susceptibility. Our investigation involves assessing pollution levels through spatial interpolation of in situ PM2.5 measurements. A network of affordable sensors collected data across 9 regions in Curitiba, as well as its metropolitan counterpart, Araucaria. Our findings distinctly reveal a significant positive correlation (with r-values reaching up to 0.36, p-value < 0.01) between regions characterized by higher levels of pollution, particularly during the winter months (with r-values peaking at 0.40, p-value < 0.05), with both COVID-19 mortality and incidence rates. This correlation gains added significance due to the intricate interplay between urban atmospheric pollution and regional human development indices. Notably, heightened pollution aligns with industrial hubs and intensified vehicular activity. The spatial analysis performed in this study assumes a pivotal role by identifying priority regions that require targeted action post-COVID. By comprehending the localized dynamics between air pollution and its health repercussions, tailored strategies can be implemented to alleviate these effects and ensure the well-being of the public.

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from - 0.33 to 33.77%, and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.