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The number of layers present in a two-dimensional (2D) nanomaterial plays a critical role in applications that involve surface interaction, for example, gas sensing. This paper reports the synthesis of 2D WS2 nanoflakes using the facile liquid exfoliation technique. The nanoflakes were exfoliated using bath sonication (BS-WS2) and probe sonication (PS-WS2). The thickness of the BS-WS2 was found to range between 70 and 200 nm, and that of PS-WS2 varied from 0.6 to 80 nm, indicating the presence of single to few layers of WS2 when characterized using atomic force microscope. All the WS2 samples were thoroughly characterized using electron microscopes, x-ray diffractometer, Raman spectroscopy, UV–Visible spectroscopy, Fourier transform infrared spectroscope, and thermogravimetric analyser. Both the nanostructured samples were exposed to 2 ppm of NO2 at room temperature. Interestingly, BS-WS2 which comprises of a greater number of WS2 layers exhibited −14.2% response as against −3.4% response of PS-WS2, the atomically thin sample. The BS-WS2 sample was found to be highly selective towards NO2 but was slower (with incomplete recovery) as compared to PS-WS2. The PS-WS2 sample was observed to exhibit −11.9% to −27.4% response to 2–10 ppm of CO and −3.4%–35.2% response to 2–10 ppm of NO2 at room temperature, thereby exhibiting the potential to detect two gases simultaneously. These gases could be accurately predicted and quantified if the response times of the PS-WS2 sample were considered. The atomically thin WS2-based sensor exhibited a limit of detection of 131 and 81 ppb for CO and NO2, respectively. [ABSTRACT FROM AUTHOR]

A sustainable city is a smart city with a minimal impact on the environment, by incorporating technologies to reduce pollution. Traffic congestion which is a major concern contributes to global warming and climate change. Traffic forecasting projects future traffic patterns, using historical and current data to enhance traffic flow management. We propose a whole novel approach for predicting traffic congestion rate based on air quality data. We developed a new ensemble voting model based on Long Short Term Memory (LSTM) and Polynomial Regression (PolReg) models that use a new voting thresholded algorithm instead of the existing voting ones. The hyperparameters were optimized with the Genetic Agorithm, to overcome the non-stationarity of time series. A comparative study with the literature confirmed that our framework outperforms existing researches by keeping an absolute effectiveness according to learning curves, with Mean Absolute Error of 0.04, R-Squared of 0.93, and Root Mean Square Error (RMSE) of 0.05. [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]

Low-cost sensors are becoming an important component of air quality monitoring. They are used in various outdoor areas beyond the protective enclosure of air quality monitoring stations. Therefore, they are exposed to a wide range of environmental conditions. In this study, we investigated the operation status and performance of an air quality monitor consisting of low-cost sensors for measuring PM2.5 and CO under extreme air pollutant concentrations. Over deployment periods of three haze episodes in Beijing, China, and two bushfire episodes in the Blue Mountains, Australia, the monitors collected air quality data for community and research users. During the extreme air pollution episodes, the operation of the monitors was not affected by the extremely high PM2.5 and CO concentrations. They showed the greatest overestimation of PM2.5 measurements in comparison with the reference instruments during the Beijing summer haze period. The regression slopes between the monitors and reference instruments were 2.24 for the summer haze period, 1.58 for the spring haze period, and 1.54 for the summer haze period. Under the extreme air pollution conditions in the Blue Mountains, monitors overestimated PM2.5 in the winter bushfire period but underestimated it in the summer bushfire period. The absolute values of the PM2.5 and CO concentrations from the monitors may vary from the reference instruments; however, they captured the trends and rapid elevation of air pollutants under extreme air pollution conditions. The results of this study demonstrate that low-cost air quality monitors can be used under extreme conditions in different areas for specified purposes such as community, citizen applications, air pollution management, and scientific research. [ABSTRACT FROM AUTHOR]

Our research addresses the imperative need for an efficient air quality monitoring and forecasting system to mitigate the significant health risks of air pollution. Departing from conventional binary data collection methods, we employ image‐based techniques to overcome inherent limitations. A pioneering aspect of our work involves the development of a novel model capable of predicting six air pollutants (PM2.5, PM10, O3, CO, SO2, NO2) along with Air Quality Index for Bengaluru, Delhi, and Tamil Nadu, achieving a commendable mean absolute error of 0.1432 on the test set. The efficacy of our approach is validated through a meticulously curated dataset comprising approximately 5455 images. We emphasize the significance of normalization by presenting outputs before and after, shedding light on the impact of parameters with varying ranges and strategies employed to mitigate such discrepancies. A detailed analysis of our model's best and worst outputs provides valuable insights into its strengths and limitations. To enhance user accessibility, we introduce an innovative image‐based, real‐time, user‐friendly dashboard that allows users to conveniently assess a location's air pollution levels by uploading an image. This holistic approach offers a promising avenue for accurate air quality prediction and real‐time monitoring. [ABSTRACT FROM AUTHOR]

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Negative Air Ions (NAIs), essential for environmental and human health, facilitate air purification and offer antimicrobial benefits. Our study achieves hourly estimations of NAIs using a machine learning framework, developed from a multi‐layer selection pipeline of over 200 variables, to identify the key determinants critical for adapting to high‐resolution NAIs dynamics. Addressing site sparsity and NAIs volatility, we introduced a hybrid stacking incorporating pseudo sites generated from Kriging Spatiotemporal Augmentation (KSTA) to mitigate spatial overfitting. Our approach, validated in Zhejiang, China, demonstrates exceptional accuracy, achieving R2 values of 0.90 (sample‐based), 0.85 (temporal‐based), and 0.79 (site‐based). This work not only sheds light on NAIs behavior in relation to diurnal shifts, land use, and environmental events, but also integrates a health grading system, enhancing public health strategies through precise air quality assessment. Plain Language Summary: Our study advances the environmental science by developing a machine‐learning model capable of estimation the hourly concentrations of negative air ions (NAIs) across Zhejiang Province, China. NAIs, vital for air purification and human health, are significantly related to environmental conditions. Taking advantage of a novel approach that reduces errors commonly seen in similar models, we provide a highly accurate, hour‐by‐hour analysis of air quality. Our research goes beyond mere prediction; it investigates how various factors like vegetation, soil type, and weather events influence NAI levels. The insights gained offer a comprehensive view of how NAI distribution changes over time and across different landscapes, informing environmental policy and public health strategies with precise air quality assessments. This study contributes significantly to the field by introducing an essential tool for continuous environmental monitoring, thereby facilitating more effective decision‐making in environmental and public health sectors. Key Points: A hybrid stacking machine learning framework shows superior accuracy (R2 = 0.90), combing a filter pipeline dealing with more than 200 variablesKriging Spatiotemporal Augmentation (KSTA) as a pseudo‐label strategy to resolve spatial overfitting due to sparse sitesAn hourly estimate of NAIs reveals NAIs dynamics correlations with environmental factors and land use, supporting public health policy [ABSTRACT FROM AUTHOR]

The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first ultraviolet–visible instrument for air quality monitoring in geostationary orbit. Since its launch in 2020, GEMS has provided hourly daytime air quality information over Asia. However, to date, validation and applications of these data are lacking. Here we evaluate the effectiveness of the first 1.5-year GEMS aerosol optical depth (AOD) data in estimating ground-level particulate matter (PM) concentrations at an hourly scale. To do so, we employ random forest models and use GEMS AOD data and meteorological variables as input features to estimate PM10 and PM2.5 concentrations, respectively, in South Korea. The model-estimated PM concentrations are strongly correlated with ground measurements, but they exhibit negative biases, particularly during high aerosol loading months. Our results indicate that GEMS AOD values represent underestimates compared to ground-measured AOD values, possibly leading to negative biases in the final PM estimates. Further, we demonstrate that more training data could significantly improve random forest model performance, thus indicating the potential of GEMS for high-resolution surface PM prediction when sufficient data are accumulated over the coming years. Our results will serve as a reference to aid the evaluation of future GEMS AOD retrieval algorithm improvements and also provide initial guidance for data users. [ABSTRACT FROM AUTHOR]

Urbanization and industrialization have led to a significant increase in air pollution, posing a severe environmental and public health threat. Accurate forecasting of air quality is crucial for policymakers to implement effective interventions. This study presents a novel AIoT platform specifically designed for PM2.5 monitoring in Southwestern Morocco. The platform utilizes low-cost sensors to collect air quality data, transmitted via WiFi/3G for analysis and prediction on a central server. We focused on identifying optimal features for PM2.5 prediction using Minimum Redundancy Maximum Relevance (mRMR) and LightGBM Recursive Feature Elimination (LightGBM-RFE) techniques. Furthermore, Bayesian optimization was employed to fine-tune hyperparameters of popular machine learning models for the most accurate PM2.5 concentration forecasts. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). Our results demonstrate that the LightGBM model achieved superior performance in PM2.5 prediction, with a significant reduction in RMSE compared to other evaluated models. This study highlights the potential of AIoT platforms coupled with advanced feature selection and hyperparameter optimization for effective air quality monitoring and forecasting. [ABSTRACT FROM AUTHOR]

Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies. [ABSTRACT FROM AUTHOR]

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rapid expansion of low-cost sensor networks for air quality monitoring necessitates rigorous calibration to ensure data accuracy. Despite numerous published field calibration studies, a universal and comprehensive assessment of factors affecting sensor calibration remains elusive, leading to potential discrepancies in data quality across different networks. To address these challenges, this study deployed eight sensor-based monitors equipped with electrochemical sensors for NO2, NO, CO, and O3 measurement in strategically chosen locations within Hong Kong, Macau, and Shanghai, covering a wide range of climatic conditions: Hong Kong's subtropical climate, Macau's similar yet distinct urban environment, and Shanghai's more variable climate. This strategic deployment ensured that the sensors' performance and calibration processes were tested across diverse atmospheric conditions. Each monitor employed a patented dynamic baseline tracking method for the gas sensors, which isolates the concentration signals from temperature and humidity effects, enhancing the sensors' accuracy and reliability. The tests, which involved evaluating the validation performance by analyzing randomly selected calibration sample subsets ranging from 1 to 15 days, indicated that the length of the calibration period, pollutant concentration range, and time averaging period are pivotal for sensor calibration quality. We determined that a 5–7 days calibration period minimizes calibration coefficient errors, and a wider concentration range improves the validation R2 values for all sensors, suggesting the necessity of setting specific concentration range thresholds. Moreover, a time averaging period of at least 5 minutes for data with 1-minute resolution was recommended to enable optimal calibration in field operation. This study emphasizes the need for a comprehensive calibration assessment and the importance of considering environmental variability in sensor calibration condition. These findings offer methodological guidance for the calibration of other sensor types, providing a reference for future research in the field of sensor calibration. [ABSTRACT FROM AUTHOR]

Sensor fault detection is an important phase for process surveillance. Indeed, successful execution of process tasks depends on the state of the available data. In industrial applications, systems have an uncertain behavior, so methods based on interval principles are useful in this context, such as interval kernel principal component analysis (IKPCA) and Interval-Kernel-Partial-Least Square (IKPLS). These techniques do not always achieve efficiency since they are based on an invariant time. To overcome this restriction, we used a sliding windows principle to develop these methods in dynamic uncertain systems. In addition, a small size of the sliding window can improve the computation time and adapt to the rapidly changing process dynamics. It is for this reason that we propose a second method; the reduced rank moving window IKPCA method (MW-RRIKPCA), In order to improve the performance of the suggested methods based on sliding windows, compared to classical ones, we carried out a comparative study using Tennessee Eastman process (TEP) data and the air quality monitoring network (AIRLOR). [ABSTRACT FROM AUTHOR]

The relationship between green and grey urban infrastructure, local meteorological conditions, and traffic-related air pollution is complex and dynamic. This case study examined the effect of evolving morphologies around a city square park in Dublin and explores the twin impacts of local urban development (grey) and maturing parks (green) on particulate matter (PM) pollution. A fixed air quality monitoring campaign and computational fluid dynamic modelling (ENVI-met) were used to assess current (baseline) and future scenarios. The baseline results presented the distribution of PM in the study area, with bimodal (PM2.5) and unimodal (PM10) diurnal profiles. The optimal vegetation height for air quality within the park also differed by wind direction with 21 m vegetation optimal for parallel winds (10.45% reduction) and 7 m vegetation optimal for perpendicular winds (30.36% reduction). Increased building heights led to higher PM2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM2.5 concentrations by up to 30.9% under parallel winds. The findings from this study provide evidence of the importance of more in-depth analysis of green and grey urban infrastructure in the urban planning decision-making process to avoid deteriorating air quality conditions around city square parks. [ABSTRACT FROM AUTHOR]

Knowledge of the chemical composition of particulate matter (PM) is essential for understanding its source distribution, identifying potential health impacts of toxic elements and to develop efficient air pollution abatement strategies. Traditional methods for analysing PM composition, such as collection on filter substrates and subsequent offline analysis with e.g., inductively coupled plasma mass spectrometry (ICP-MS), are time-consuming and prone to measurement errors due to multiple preparation steps. Emerging near-real time techniques based on non-destructive Energy Dispersive X-ray Fluorescence (EDXRF) offer advantages for continuous monitoring and source apportionment. This study characterises the Horiba PX-375 EDXRF monitor by applying a straightforward performance evaluation including (a) limit of detection (LoD), (b) identification and quantification of uncertainty sources, and (c) investigating and comparing measurement results from three contrasting sites in Luxembourg (urban, semi-urban, rural). We used multi-element reference materials (ME-RMs) from UC Davis for calibration and performed measurements during spring and summer 2023. The LoDs for toxic elements like Ni, Cu, Zn, and Pb were below 3 ng m-3 at one-hour time resolution. Higher LoDs were observed for lighter elements (e.g., Al, Si, S, K, Ca). Expanded uncertainties ranged between 5 and 25 % for elemental concentrations above 20 ng m-3 and were maximal for concentrations below 10 ng m-3, reaching 60–85 %. Elemental analysis revealed S and mineral elements (Fe, Si, Ca, Al) as dominant contributors to PM10. Toxic elements (As, Ni, Pb) were often below the LoD, suggesting minimal exposure risk in the sampled areas. Our results explained on average 51–74 % of the gravimetric PM10 mass at the three sites. The study highlights the suitability and importance of the continuous PX-375 particle monitor for future air quality monitoring and source apportionment studies, particularly under changing emission scenarios and air pollution abatement strategies. [ABSTRACT FROM AUTHOR]

The American Inflation Reduction Act (IRA) of 2022 contains climate-related provisions that may have noteworthy implications for cancer control and prevention. This commentary assesses the potential co-benefits of the IRA for cancer control efforts, specifically policies and programs to reduce carcinogen exposure via air quality monitoring and air pollution reduction. Allocations through the IRA for air quality improvement, paired with its environmental justice provisions, hold promise for advancing cancer prevention by targeting resources to communities most susceptible to environmental hazards. Moreover, climate resilience measures dictated by the IRA are crucial for oncology professionals grappling with the dual challenges of climate change and cancer care. Climate-driven extreme weather events can exacerbate carcinogen exposure and disrupt access to cancer care, underscoring the need for resilient health-care infrastructure. The IRA's provisions for clean energy incentives and infrastructure upgrades offer oncology care institutions opportunities to mitigate emissions and bolster resilience against climate-related disruptions, ultimately improving cancer outcomes. Climate-related initiatives funded by the IRA present a unique and timely avenue to advance equitable cancer control efforts. This commentary underscores the critical intersection between climate resilience policy and oncology care, highlighting the potential to promote a healthier and more resilient future for all. [ABSTRACT FROM AUTHOR]

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment. [ABSTRACT FROM AUTHOR]

This article focuses on the influence of occupants on the concentration of contaminants (radon Rn-222 and CO2) as well as the thermal comfort parameters. A series of sensors were placed to measure the concentration of the contaminants, temperature, and relative humidity in the test room at the Institute of Nuclear Physics PAN in Krakow (IFJ PAN), Poland. The test room is an office that is typical of the offices used in the facility. The occupants that used the space kept a detailed diary of their entry, exit, and number of people entering. The results showed that the accumulation of contaminants in such spaces may be severe and risks the health and safety of occupants. The accumulation of CO2 was extremely noticeable and did not diminish to the background level between the re-entry of the occupants to the office. The same was consistent for the radon concentration. The study shows how ventilation methods and small changes in occupant work strategy may influence the contaminant concentration within a test zone. [ABSTRACT FROM AUTHOR]

Particulate matter is one of the most dangerous air pollutants, especially in urban areas, due to its significant adverse health effects. Traditionally, air quality monitoring relies on fixed reference stations, which often have a low temporal and spatial resolution. To address this limitation, a low-cost, portable air quality monitoring device with a rapid measurement response was used to assess particulate matter concentration levels in the afternoon hours in central Thessaloniki, Greece. This approach enabled the identification of local hotspots directly related to human activities. Statistical analysis and spatial mapping were employed, and data collected were categorized using k-means clustering. The findings of the study suggest that data acquired via portable low-cost sensors can describe the local variability of PM2.5 concentrations. The results indicate that local activities, such as increased human accumulation, traffic congestion at traffic lights, market working hours, together with meteorological parameters, can significantly impact air quality in specific urban locations. They also highlight the differences between data recorded in colder and warmer periods, with the concentrations of PM2.5 in the first period being 3.7 μg/m3 greater on average than in the second. These differences are also identified via the k-means clustering method, which suggest that higher concentrations appear mostly during the colder period of the study. [ABSTRACT FROM AUTHOR]

Surface ozone (O3) and nitrogen oxides (NOx = NO + NO2) measured at the rural station in Belsk (51.83° N, 20.79° E), Poland, over the period of 1995–2023, were examined for long-term variability of O3 and its relationship to changes in the air temperature and NOx. Negative and positive trends were found for the 95th and 5th percentile, respectively, in the O3 data. A weak positive correlation (statistically significant) of 0.33 was calculated between O3 and the temperature averaged from sunrise to sunset during the photoactive part of the year (April–September). Recently, O3 maxima have become less sensitive to temperature changes, reducing the incidence of photochemical smog. The ozone–climate penalty factor decreased from 4.4 µg/m3/°C in the 1995–2004 period to 3.9 µg/m3/°C in the 2015–2023 period. The relationship between Ox (O3 + NO2) and NOx concentrations averaged from sunrise to sunset determined the local and regional contribution to Ox variability. The seasonal local and regional contributions remained unchanged in the period of 1995–2023, stabilizing the average O3 level at Belsk. "NOx-limited" and "VOC-limited" photochemical regimes prevailed in the summer and autumn, respectively. For many winter and spring seasons between 1995 and 2023, the type of photochemical regime could not be accurately determined, making it difficult to build an effective O3 mitigation policy. [ABSTRACT FROM AUTHOR]

The high concentrations of particulate matter (PM) in the atmosphere have adverse effects on both the environment and human health, as well as on urban and faunal biodiversity. Short-term events, such as the burning of fireworks, attributable to human activity, result in a significant and rapid increase in PM concentration levels within the atmosphere. We present findings derived from observations made using low-cost PM sensors deployed as part of the Environmental Monitoring Network in Querétaro, Mexico. These sensors utilize the Environmental Geo Monitoring Stations (EMGA) device, developed by the Institute of Geosciences at UNAM. The observation period spanned from 10 December 2023 to 10 January 2024, encompassed the Christmas and New Year holidays, and focused on PM2.5 concentrations, and a comparison with a similar period from 10 February 2024 to 10 March 2024 considered a typical activity period in the area. The results reveal two substantial increases in PM2.5 concentrations (from less than 50 µg/m3 to more than 340 µg/m3) over time and spatial distribution during this period, with notable increases observed during the festive season coincident with the Christmas and New Year celebrations. Specifically, prolonged periods exceeding up to 6 h were noted on festivity days. This research offers insight into the effects, trends, and spatial–temporal distribution of pollutants within the city of Queretaro, which may be used as a reference for other cities around the world. [ABSTRACT FROM AUTHOR]

Air quality forecasting is crucial for public health and urban planning. However, traditional machine learning models face challenges with centralized data collection, raising privacy and security concerns. Federated learning (FL) offers a promising solution by enabling model training across decentralized data sources while preserving data privacy. This study presents an FL framework for predicting the Air Quality Index (AQI) using data from many Internet of Things (IoT) sensors deployed in urban areas. The proposed FL framework facilitates model training using diverse sensor data while maintaining data privacy at each source. Local computational resources at the sensor level are used for initial data processing and model training, with only model updates shared centrally, reducing data transmission requirements. The FL model achieved comparable accuracy to centralized approaches while enhancing data privacy. This work represents a significant advancement for smart city initiatives and environmental monitoring, offering a scalable, real-time, and privacy-aware framework for air quality monitoring systems that leverage IoT technology. [ABSTRACT FROM AUTHOR]

In urban environments, various pollutants generated by road traffic, human, and industrial activities degrade outdoor and indoor air quality. Among these pollutants, nitrogen oxides (NOx) are subject to air quality regulations designed to protect human health and the environment. It is therefore crucial to keep their concentration as low as possible. Advanced oxidation processes are a practical choice for the degradation of NOx; among them, heterogeneous photocatalysis has proven to be a viable route. However, while the efficiency of this process has been widely demonstrated on a laboratory scale, it is still the subject of debate for real-life applications. The purpose of this study was to present a new field experiment on the application of a photocatalytic coating to outdoor walls. Air quality monitoring stations were used to evaluate the NOx concentration reduction instead of the chemiluminescent analyzer, in order to increase the number of sampling points. Statistical analysis was carried out to interpret the results. Density probability functions were plotted and showed a positive impact of the coating, leading to lower NOx concentrations. This work was completed by a laboratory-scale assessment of the coating's durability using abrasion, QUV, and immersion/drying tests. The air depollution capacity of the chosen coating was significantly reduced after QUV testing. [ABSTRACT FROM AUTHOR]

Recently, a new class of two-dimensional (2D) materials known as MXenes, such as Ti3C2Tx, have received significant attention due to their exceptional structural and physiochemical properties. MXenes are widely used in a variety of applications, including sensors, due to their excellent charge transport, high catalytic, and conducive properties, making them superior materials for sensing applications. Sensing technology has attracted significant interest from the scientific community due to its wide range of applications. In particular, gas sensing technology is essential in today's world due to its vital role in detecting various gases. Gas sensors have an essential role in real-time environmental monitoring health assessment, and the demand for air quality monitoring is driving the gas sensor market forward. Similarly, optical sensors are a related technology that can rapidly detect toxic substances and biomaterials using optical absorption spectroscopy. MXenes are highly desirable for gas and optical sensing applications due to their abundant active sites, metallic conductivity, optical properties, customizable surface chemistry, and exceptional stability. In this review article, we compile recent advancements in the development of gas sensors and optical sensors using MXenes and their composite materials. This review article would be beneficial for researchers working on the development of MXenes-based gas sensors and optical sensors. [ABSTRACT FROM AUTHOR]

The article examines the intersection of science, policy, and law in delivering clean air, highlighting the complexity and urgency of improving air quality standards. Topics discussed include the need for robust legal frameworks to enforce air quality standards, the challenges of setting scientifically informed and feasible standards, and the importance of public engagement and transparency in air quality governance.

Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ± 0.03 and ± 0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of - 0.225 km and 55.29 % (71.70 %) of data points falling within ± 1 km (1.5 km). [ABSTRACT FROM AUTHOR]

Indoor air quality (IAQ) in the built environment is significantly influenced by particulate matter, volatile organic compounds, and air temperature. Recently, the Internet of Things (IoT) has been integrated to improve IAQ and safeguard human health, comfort, and productivity. This review seeks to highlight the potential of IoT integration for monitoring IAQ. Additionally, the paper details progress by researchers in developing IoT/mobile applications for IAQ monitoring, and their transformative impact in smart building, healthcare, predictive maintenance, and real-time data analysis systems. It also outlines the persistent challenges (e.g., data privacy, security, and user acceptability), hampering effective IoT implementation for IAQ monitoring. Lastly, the global developments and research landscape on IoT for IAQ monitoring were examined through bibliometric analysis (BA) of 106 publications indexed in Web of Science from 2015 to 2022. BA revealed the most significant contributing countries are India and Portugal, while the top productive institutions and researchers are Instituto Politecnico da Guarda (10.37% of TP) and Marques Goncalo (15.09% of TP), respectively. Keyword analysis revealed four major research themes: IoT, pollution, monitoring, and health. Overall, this paper provides significant insights for identifying prospective collaborators, benchmark publications, strategic funding, and institutions for future IoT-IAQ researchers. [ABSTRACT FROM AUTHOR]

As recent geopolitical conflicts and climate change escalate, the effects of war on the atmosphere remain uncertain, in particular in the context of the recent large-scale war between Russia and Ukraine. We use satellite remote sensing techniques to establish the effects that reduced human activities in urban centers of Ukraine (Kharkiv, Donetsk, and Mariupol) have on Land Surface Temperatures (LST), Urban Heat Islands (UHI), emissions, and nighttime light. A variety of climate indicators, such as hot spots, changes in the intensity and area of the UHI, and changes in LST thresholds during 2022, are differentiated with pre-war conditions as a reference period (i.e., 2012–2022). Findings show that nighttime hot spots in 2022 for all three cities cover a smaller area than during the reference period, with a maximum decrease of 3.9% recorded for Donetsk. The largest areal decrease of nighttime UHI is recorded for Kharkiv (− 12.86%). Our results for air quality changes show a significant decrease in carbon monoxide (− 2.7%, based on the average for the three cities investigated) and an increase in Absorbing Aerosol Index (27.2%, based on the average for the three cities investigated) during the war (2022), compared to the years before the war (2019–2021). The 27.2% reduction in nighttime urban light during the first year of the war, compared to the years before the war, provides another measure of conflict-impact in the socio-economic urban environment. This study demonstrates the innovative application of satellite remote sensing to provide unique insights into the local-scale atmospheric consequences of human-related disasters, such as war. The use of high-resolution satellite data allows for the detection of subtle changes in urban climates and air quality, which are crucial for understanding the broader environmental impacts of geopolitical conflicts. Our approach not only enhances the understanding of war-related impacts on urban environments but also underscores the importance of continuous monitoring and assessment to inform policy and mitigation strategies. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer's calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Poás Volcano made a magmatic eruption in April 2017. The volcanic outburst resulted in an ash and vapor column towering over three kilometers high. Since that time, there has been a continual release of gases, aerosols, and more recently, ash, posing potential issues for visitors and park rangers. In this work, the potential for exposure to acid gases and aerosols faced by park rangers, officials, and visitors to the Poás Volcano National Park was evaluated, and the concentrations found were compared with the exposure limits established by the Occupational Safety and Health Administration (OSHA). The study was conducted between October 2021 and November 2022. the concentrations of HCl(g), HNO3(ac), HF(g), and H2SO4(ac) were determined at three strategic points: the ranger station, the visitor center, and the main crater viewpoint. The maximum concentrations obtained were (7.0 ± 1.6) ppb for HCl(g), (6.2 ± 2.8) ppb for HNO3(ac), and (0.029 ± 0.044) ppm for H2SO4(ac). There were no concentration values above the detection limit (0.94 μg/m3) for HF(g). By comparing the data obtained with similar studies, it is concluded that the measured values in Poás Volcano National Park are low and only show similarities to the results found in volcanoes within the national territory. The exposure limit established by OSHA (0.02 ppm) was only surpassed by H2SO4(ac), and could be the cause of health effects experienced over the years by park rangers. To minimize these risks, the use of personal protective equipment and air quality monitoring is essential. [ABSTRACT FROM AUTHOR]

Air quality assessments often require source apportioning of the air pollutants observed at the receptor site. Conventional source apportionment models are subject to high uncertainties due to the lack of accurate emission profiles of all the contributing sources and a limited number of measurements at the receptor sites. Recent advances in the development and application of low-cost PM2.5 sensors have facilitated the formation of a more robust database with greater numbers of measurements per location and time. The main objective of this study is to combine a large database of PM2.5 concentration records to records from low-cost sensors in Denver, Colorado, during January 2021. Using wind speed and wind direction at the receptors, we developed a visualization tool for source tracing of PM2.5 with resulting statistical analyses and back-trajectory modeling. For this purpose, a combination of in-house and existing packages of R scripts along with National Oceanic and Atmospheric Administration (NOAA)'s trajectory model and climate and weather toolkits were used. In general, the results show that the PM2.5 measurements obtained from such a network of PM2.5 sensors incorporated with hourly wind field data, which are publicly available, can provide a powerful screening tool to discover the transport pathways of PM2.5 before requiring costly source apportionment approaches. The fraction of PM2.5 concentration detected by each sensor in regard to wind direction and speed bins were quantified using this method. The results of cluster analysis identified the area groups in respect to wind speed and wind direction bins, which shines a light on how far and in which direction polluting sources are. Finally, the back-trajectory modeling outputs illustrated the exact travel path of the PM2.5-laden air parcels of each day to each sensor. [ABSTRACT FROM AUTHOR]

A couple of air quality (AQ) parameters were monitored with two types of low-cost sensors (LCSs) before, during and after the garden fence rebuilding of a dwelling house, located at the junction of a main road and a side street in a suburban area of Budapest, Hungary. The AQ variables, recorded concurrently indoors and outdoors, were particulate matter (PM1, PM2.5, PM10) and some gaseous trace pollutants, such as CO2, formaldehyde (HCHO) and volatile organic compounds (VOCs). Medium-size aerosol (PM2.5-1), coarse particulate (PM10-2.5) and indoor-to-outdoor (I/O) ratios were calculated. The I/O ratios showed that indoor fine and medium-size PM was mostly of outdoor origin; its increased levels were observed during the renovation. The related pollution events were characterized by peaks as high as 100, 95 and 37 µg/m3 for PM1, PM2.5-1 and PM10-2.5, respectively. Besides the renovation, some indoor sources (e.g., gas-stove cooking) also contributed to the in-house PM1, PM2.5-1 and PM10-2.5 levels, which peaked as high as 160, 255 and 220 µg/m3, respectively. In addition, these sources enhanced the indoor levels of CO2, HCHO and, rarely, VOCs. Increased and highly fluctuating VOC levels were observed in the outdoor air (average: 0.012 mg/m3), mainly due to the use of paints and thinners during the reconstruction, though the use of a nearby wood stove for heating was an occasional contributing factor. The acquired results show the influence of the fence renovation-related activities on the indoor air quality in terms of aerosols and gaseous components, though to a low extent. The utilization of high-resolution LCS-assisted monitoring of gases and PMx helped to reveal the changes in several AQ parameters and to assign some dominant emission sources. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze the air quality in Hangzhou and its surrounding areas during the 2023 Asian Games and assess the impact of safeguard measures on air quality. By comparing air quality monitoring data from Hangzhou and nearby cities between 23 September 2023, and 8 October 2023, with the same period in 2022, the analysis focused on the changes in air quality and the impact of PM2.5 and O3 on human health. The results show that during the 2023 Asian Games in Hangzhou, compared to the same period in 2022, the concentrations of pollutants showed the following changes: PM10 and NO2 concentrations decreased by 16.1% and 34.1%, respectively. PM2.5, CO, and O3 concentrations increased by 0.9%, 9.5%, and 14.4%, respectively. Spatially, compared with the same period in 2022, the PM2.5 concentration in Quzhou City decreased the most, reaching 23%, while the PM2.5 concentration in Huzhou City increased the most, reaching 27.1%. The largest decrease in PM10 concentration is in Wenzhou City, which is up to 28.2%, while the largest increase is in Zhoushan City, which is 9.2%. The concentration of NO2 generally decreases in all regions, with the biggest decrease in Zhoushan City, which reaches 36.2%. The concentration of CO only decreases in Shaoxing, and the highest increase is in Huzhou City. In terms of O3, the largest proportion increase is 23% in Ningbo. During the 2023 Asian Games in Hangzhou, the number of deaths from all-cause and cardiovascular diseases caused by short-term exposure to PM2.5 was 1780 and 795, respectively, which was 8.2% lower than the same period in 2022. During the Asian Games, the number of deaths from all-cause and cardiovascular diseases due to short-term exposure to O3 was 7332 and 3177, respectively, an increase of 2.8% compared with the same period in 2022. [ABSTRACT FROM AUTHOR]

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

The issue of promoting peri-urban agriculture seems to be an important one in response to the various current concerns about the future of cities in the face of climate change. At first glance, these urban and peri-urban agricultural areas, which in the past served as a shelter for cities, are now threatened and increasingly losing their function of protecting urban areas from climatic hazards. This question is a priority when considering the future of agriculture in the context of urban expansion. We are therefore raising a number of concerns linked to multiple issues: significant reduction of land available for local agriculture, accelerated degradation of the city's natural environment, fragmentation of ecosystems, and deterioration of air and water quality. Around these questions, this research is structured to provide theoretical and practical knowledge that will enable us to place urban and peri-urban agriculture at the heart of urban concerns, in a perspective of reaction to climate change. The interest of this work is reinforced in the case of peri-urban areas surrounding the capital of Algeria, Algiers, such as the commune of Delly Brahim: a concrete example of a territory concerned by the subject. On the one hand, this periphery boasts important agricultural production zones in close geographical proximity to the capital Algiers, and on the other, it plays a fundamental economic, environmental, and landscape role in relation to its region. Between its dense urban residential areas, agricultural lands, and natural areas rich in resources, this territory is today confronted with numerous environmental vulnerabilities. The aim is therefore to examine the issue of preserving the agricultural sector and its role in mitigating climate change. This study is based on data collected in 2020 to analyze the current and future dynamics of these territories. [ABSTRACT FROM AUTHOR]

Background: Environmental exposures have been postulated to play an important role in the pathophysiology of chronic rhinosinusitis (CRS). Particulate matter (PM) is one of the most widely studied ambient air pollutants, but its peri-operative impact on CRS is unknown. Objective: To determine the effect of acute, peri-operative PM exposure on outcomes after endoscopic sinus surgery (ESS). Methods: Participants with CRS who self-selected ESS were prospectively enrolled. The 22-item SinoNasal Outcome Test (SNOT-22) and Medical Outcomes Study Questionnaire Short-Form 6-D (SF-6D) health utility values scores were recorded. Using residence zip codes, a secondary analysis of patient exposure to PM <2.5 μm and <10 μm (PM2.5 and PM10, respectively) was performed for the month of surgery utilizing data from Environmental Protection Agency air quality monitors. Spearman's correlation coefficients (ρ), 95% confidence intervals (CIs), and effect estimates (β) were used to determine the magnitudes of association. Simple, multivariate regression analysis was also completed. Results: One hundred and seven patients from four geographically unique institutions across the US were enrolled with a follow-up of 6 months. Patients with higher peri-operative PM2.5 exposure had less improvement in their SNOT-22 scores after ESS compared to those with less exposure using both univariate analysis (ρ = 0.26, 95% CI: 0.08, 0.43; P =.01) and after covariate adjustment with multivariate analysis (B = 1.06, 95% CI: 0.001, 2.14, P =.05). Similar associations were not found with SF-6D outcomes or with PM10 as an exposure of interest. No significant correlations were found between peri-operative PM levels and Lund-Kennedy endoscopy scores post-operatively. Conclusion: Preliminary data from this pilot study reveal that PM exposure at the time of ESS may negatively associate with post-operative improvement in sinonasal quality-of-life. Larger, population-based studies with more standardized PM exposure windows are needed to confirm the clinical significance of the present findings. [ABSTRACT FROM AUTHOR]

The concern about indoor air quality in various micro-environments has been on the rise as buildings are vulnerable to an array of contaminants generated from various sources. The study aims to investigate and highlight the pollutant sources and composition in middle-income residential spaces through a case study approach. With large migration from small towns and saturation of megacities, the middle-income population in tier-2 cities in India is on the rise, raising concern about the well-being of the dense urban population. It involves a careful selection of typical MIG housing to identify major pollution sources from outdoor and indoor environments. The focus lies in the findings stemming from continuous air quality monitoring of PM10, PM2.5, SO2, NO2, HCHO, TVOC, CO and C6H6 and an in-depth physio-chemical analysis facilitated by Fourier transform infrared (FTIR), Analytical Scanning Electron Microscope (A-SEM) and energy-dispersive X-ray (EDX) spectroscopy of settled household dust. Analysis unveiled indoor PM2.5 concentrations of 21.93 µg/m3, exceeding WHO standards, whereas, TVOC concentrations were higher indoors than outdoors. Stark disparities between indoor and outdoor dust compositions and sources were also observed. Despite limitations, it provides a portrait of middle-income housing conditions, offering insights for interventions and future research on indoor air quality challenges. [ABSTRACT FROM AUTHOR]

An optimal solution for monitoring air pollution, the Internet of Things (IoT)-enabled system delivers real-time data and insights on the air quality within a specific location. Air pollution poses a substantial risk to human health worldwide, with pollutants like nitrogen dioxide, particulate matter, ozone, and sulfur dioxide contributing to a range of cardiovascular and respiratory ailments. Monitoring air pollution levels is critical to understand the effect on public health and the environment. Air Pollution Monitoring includes the systematic analysis and measurement of pollutant concentration in the air, through a network of monitoring stations equipped with instruments and sensors. This station provides real-time data on air quality, allowing authorities to evaluate issue warnings, and pollution levels, and implement strategies to alleviate its negative impact. Machine learning (ML) approaches are becoming more integrated into air pollution monitoring systems for enhancing efficiency and accuracy. By analyzing vast quantities of information gathered from satellite imagery, monitoring stations, and other sources, ML approaches could detect patterns, forecast pollution levels, and pinpoint sources of pollution. This study introduces Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning (APMP-AVOAML) model in IoT environment. The drive of the APMP-AVOAML methodology is to recognize and classify the air quality levels in the IoT environment. In the APMP-AVOAML technique, a four stage process is encompassed. Firstly, min-max normalization is applied for scaling the input data. Secondly, a harmony search algorithm (HSA) based feature selection process is executed. Thirdly, the extreme gradient boosting (XGBoost) model is utilized for air pollution prediction. Finally, AVOA based parameter selection process is exploited for the XGBoost model. To illustrate the performance of the APMP-AVOAML algorithm, a brief experimental study is made. The resultant outcomes inferred that the APMP-AVOAML methodology has resulted in effectual outcome. [ABSTRACT FROM AUTHOR]

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time. [ABSTRACT FROM AUTHOR]

Air pollution is a significant health concern in India. As a developing country, India has numerous issues such as air pollution and other issues. The aim of this study is to design and develop a system that is used to monitor and predict the air quality index (AQI) of the area using machine learning and IoT (Internet of Things). The system will generate AQI values and a line plot graph for future forecasting values. The proposed system will make it simple, convenient, and convenient for users to monitor air quality. It will also be used to forecast the Air Quality Index of the region using the Zhongli F1-AQI. The performance of the proposed system is evaluated using 5 different methods with and without imputation. [ABSTRACT FROM AUTHOR]

This study proposes a novel air pollution exposure index (APEI) metric, drawing from traditional elements in rhythmanalysis and public-life studies to help understand how people are exposed to air pollution in their urban environment and when the risks are greatest. It expands on the notion of rhythm as a socially constructed natural phenomenon connected to urban life and spaces. Air quality monitoring data, as well as bus times and in situ traffic and pedestrian counts, from Cable Street and Dalton Square in Lancaster were applied to define the APEI, which combines an air pollution index (NO2 and PM10), a surrogate for ambient air pollution level, with a population index, a surrogate for population flow. The index values were subsequently ranked in numeric order, i.e., a higher ranking shows increased exposure risk and vice versa, to determine total exposure on an individual level. This metric proves valuable in defining air pollution exposure status and recognizing factors associated with high pollution and population levels. Similarly, by comparing APEI values, one could evaluate exposure levels in different locations and seasons to verify when the APEI has increased at a given location and the different rhythms and flows responsible for an individual's exposure. Hence, it has potential use for urban planners and the city council's policymakers who are involved in Lancaster Air Quality Management. [ABSTRACT FROM AUTHOR]

Relative humidity is a crucial parameter in several fields, such as air quality monitoring, food storage, or control of industrial processes. In this study, we propose a wireless humidity sensor based on magnetoelastic resonance sensors functionalized with Metal–Organic Frameworks (MOFs). Different MOF water harvesters were tested as sensor active coatings: MOF-801, MOF-808, UiO-66-NH2, Al-Fumarate, and CAU-23. Their water absorption capacity and overall performance, when integrated into the sensors, were evaluated. As expected, the selected MOFs showed promising water harvesting capacity, enabling a successful sensor response to humidity in a wide range of relative humidity: 3%–85%. These humidity sensors showed a great sensitivity, stability, and selectivity to water molecules. The response time of the devices was around 15 s, with stabilization and recovery times in adsorption and desorption processes of less than 1 min for certain ranges of operation. In addition to the satisfactory behavior as humidity sensors, magnetoelastic resonators have emerged as a promising tool for the characterization of the dynamic adsorption capacity of MOF materials, as they easily provide a quantitative measure of the water mass adsorbed by the material. [ABSTRACT FROM AUTHOR]

Monitoring of IAQ is one of the foundations of the preventative actions prompted by the worldwide recognition of COVID-19 transmission. The measurement of CO2 has emerged as one of the most popular, dependable, and easy ways to indirectly evaluate the state of indoor air renewal. Reducing the risk of respiratory diseases transmitted by aerosols is attainable through implementing and validating prevention measures made possible by CO2 control. Isolation centers are like health facilities in that they are linked to IAQ, and the presence of natural ventilation can significantly improve the circulation of fresh air, which speeds up the removal of contaminants. This is true even though healthcare facilities are among the environments with the highest rate of COVID-19 propagation. Our investigation revealed, however, that no substantial critical data on air quality in Tanzanian isolation centers is presently available. The process of metabolic CO₂ creation and accumulation within health isolation center cubicles was investigated in this study. Crucially, we suggest comparing settings under various conditions using the indicator ppm/patient. In this research, we experimentally assessed the value of changing a few HVAC system characteristics. We looked at the data to see how well the filtration system worked concerning the submicron particle concentration. Study recommendations for CO2 detectors and ways to reduce infection risk in shared isolation center cubicles are provided. We also show the correlation between particle size and CO2 concentration, the correlation between CO2 concentration derivatives and air volume presented per patient in isolation cubicles, the correlation between patient occupancy and CO2 concentration levels in isolation cubicles, and how to improve air quality by adjusting the patient's bed position. The study also found that for exposure lengths of two to three hours, a typical hospital cubicle with fifty to one hundred people should have an average interior CO₂ value of less than 900 ppm. Carers' length of stay in the hospital substantially impacted the permissible CO2 concentration. By establishing a connection between indoor air monitoring and healthcare goals, this study will aid in determining the feasibility of establishing regulations for interior CO2 content depending on occupancy settings, strengthening preventive efforts against COVID-19. In the post-pandemic era, it will be essential to find ways to make health facilities air cleaner so that infectious diseases cannot spread in the future. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science. [ABSTRACT FROM AUTHOR]

Odors existing in natural environment are typically mixtures of a large variety of chemical compounds in specific proportions. It is a challenging task for an electronic nose to recognize the gas mixtures. Most current research is based on the overall response of sensors and uses relatively simple datasets, which cannot be used for complex mixtures or rapid monitoring scenarios. In this study, a novel electronic nose (E-nose) using a spiking neural network (SNN) model was proposed for the detection and recognition of gas mixtures. The electronic nose integrates six commercial metal oxide sensors for automated gas acquisition. SNN with a simple three-layer structure was introduced to extract transient dynamic information and estimate concentration rapidly. Then, a dataset of mixed gases with different orders of magnitude was established by the E-nose to verify the model's performance. Additionally, random forests and the decision tree regression model were used for comparison with the SNN-based model. Results show that the model utilizes the dynamic characteristics of the sensors, achieving smaller mean squared error (MSE < 0.01) and mean absolute error (MAE) with less data compared to random forest and decision tree algorithms. In conclusion, the electronic nose system combined with the bionic model shows a high performance in identifying gas mixtures, which has a great potential to be used for indoor air quality monitoring in practical applications. [ABSTRACT FROM AUTHOR]

(1) Background: Haemorrhagic strokes (HS), including intracerebral (ICH) and subarachnoid haemorrhages (SAH), account for approximately 10–15% of strokes worldwide but are associated with worse functional outcomes and higher rates of mortality, and financial burden than ischemic stroke. There is evidence that confirmed poor air quality may increase the incidence of haemorrhagic strokes. The aim of our study was to evaluate the association between individual ambient air pollutants and the risk of haemorrhagic stroke in an urban environment without high levels of air pollution. (2) Methods: A time-series cross-sectional study design was used. A daily air pollution concentration (Agency of Regional Air Quality Monitoring in the Gdansk Metropolitan Area) and incidence of haemorrhagic strokes (National Health Fund) were obtained and covered the time period from 1 January 2014 to 31 December 2018. A generalised additive model with Poisson regression was used to estimate the associations between 24-h mean concentrations of SO2, NO, NO2, NOx, CO, PM10, PM2.5, and O3 and a daily number of haemorrhagic strokes. (3) Results: The single-day lag model results showed that NO2, NO and NOx exposure was associated with increased risk of ICH (88% events) with RR of 1.059 (95% CI: 1.015–1.105 for lag0), 1.033 (95% CI: 1.007–1.060 for lag0) and 1.031 (95% CI: 1.005–1.056 for lag0), but not for SAH (12% events). Exposure to CO was related to a substantial and statistically significant increase in incidence for 1.031 (95% CI: 1.002–1.061 for lag0) but not for SAH. Higher SO2, PM10, PM2.5, and O3 exposures were not significantly related to both ISC and SAH. (4) Conclusions: In this time-series cross-sectional study, we found strong evidence that supports the hypothesis that transient elevations in ambient NO2, NO and CO are associated with a higher relative risk of intracerebral but not subarachnoid haemorrhage. [ABSTRACT FROM AUTHOR]