Air Quality Monitoring Research Articles

Clinical Study on the Induction of Psoriasis Flare-Ups by PM2.5 Air Pollutants via Immune Barrier Dysfunction.

This study explores the clinical correlation between the air pollutant PM2.5 and the induction of psoriasis flare-ups through the disruption of the immune barrier. Air quality data, the average mass concentration of the primary atmospheric pollutant PM2.5, and meteorological data spanning from November 2023 to March 2024 were gathered from 9 air quality monitoring stations situated within our city, courtesy of the local Meteorological Information Center. Psoriasis cases were sourced from our hospital's dermatology department, encompassing patients diagnosed and treated from November 2023 to March 2024 and residing within the city. From January 2023 to May 2024, aberrant expression of Th1 cells and Th2 cells was observed in psoriasis flare-up patients. Spearman correlation coefficient analysis revealed a positive correlation between PM2.5, PM10, and Th1 cells, and a negative correlation with Th2 cells, exhibiting significant differences (P < .05). Supplemental Figure 1 illustrates an increase in psoriasis flare-ups on the fifth day following a unit increase in PM2.5 concentration, with an excess risk (ER) value of 0.046 [95% confidence interval (CI): 0.137-0.893]. Conversely, after an increase of 1 unit of PM10, there was a decrease on the third and fourth days, followed by increases on the fifth, sixth, and seventh days, with ER values of 0.038 (95% CI: 0.013-0.067), 0.045 (95% CI: 0.019-0.073), 0.051 (95% CI: 0.034-0.078), 0.057 (95% CI: 0.045-0.083), and 0.061 (95% CI: 0.051-0.087), respectively. Air pollutant PM2.5 could potentially exacerbate psoriasis flare-ups by compromising the immune barrier, suggesting a plausible mechanism linked to the onset of this condition.

Multiyear high-temporal-resolution measurements of submicron aerosols at 13 French urban sites: data processing and chemical composition

Abstract. This paper presents a first comprehensive analysis of long-term measurements of atmospheric aerosol components from aerosol chemical speciation monitor (ACSM) and multiwavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA (Chemical Characterization of Particles) program. The datasets contain the mass concentrations of major chemical species within submicron aerosols (PM1), namely organic aerosols (OAs), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl−), and equivalent black carbon (eBC). Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both guidance from the French Reference Laboratory for Air Quality Monitoring (LCSQA) and the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) standard operating procedures. Key findings include geographical differences in the aerosol chemical composition, seasonal variations, and diel patterns, which are influenced by meteorological conditions, anthropogenic activities, and proximity to emission sources. Overall, OA dominates PM1 at each site (43 %–60 % of total mass), showing distinct seasonality with higher concentrations (i) in winter, due to enhanced residential heating emissions, and (ii) in summer, due to increased photochemistry favoring secondary aerosol formation. NO3 is the second most important contributor to PM1 (15 %–30 %), peaking in late winter and early spring, especially in northern France, and playing a significant role during pollution episodes. SO4 (8 %–14 %) and eBC (5 %–11 %) complement the major fine-aerosol species, with their relative contributions strongly influenced by the origin of air masses and the stability of meteorological conditions, respectively. A comparison with the 3D chemical transport model (CTM) CHIMERE shows high correlations between simulations and measurements, albeit with an OA concentration underestimation of 46 %–76 %. Regional discrepancies in NO3 concentration levels emphasize the importance of these datasets with respect to validating air quality models and tailoring air pollution mitigation strategies. The datasets can be found at https://doi.org/10.5281/zenodo.13318298 (Chebaicheb et al., 2024).

Air Quality Legislation in Australia and Canada—A Review

Air pollution is a pervasive global issue affecting human health, ecosystems, and the environment. This paper reviews the evolution and implementation of air quality legislation in Australia and Canada, providing a comparative analysis of their approaches to managing air pollution. Both countries have established robust legal frameworks to address air quality issues, involving governmental policies, regulatory mechanisms, and collaborative efforts among stakeholders. Australia began its air quality regulation at the state level in the 1950s, evolving into a national framework with the National Environment Protection Measure for Ambient Air Quality in 1998. In contrast, Canada centralized its efforts with the Clean Air Act in 1971, complemented by the National Air Pollution Surveillance program. Key findings reveal that, while both nations have achieved significant improvements in air quality, challenges remain in addressing the broader impacts of air pollution, such as climate change and health-related economic burdens. Australia’s air quality standards are generally more stringent than Canada’s for particulate matter but more relaxed for sulfur dioxide, nitrogen dioxide, and ozone. Both countries employ advanced air quality monitoring and reporting systems, with Australia’s Air Quality Index and Canada’s Air Quality Health Index providing critical public health information. The study highlights the need for continuous improvement and a more integrated approach to air quality management. By examining the legislative and regulatory landscapes of Australia and Canada, this paper offers valuable insights for other countries striving to enhance their air quality governance and mitigate the adverse effects of air pollution on a global scale.

Varying Performance of Low-Cost Sensors During Seasonal Smog Events in Moravian-Silesian Region

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and NO2 concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in PM10 readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating PM10 levels due to the prevalence of coarse particles. Calibration adjustments, based on the PM1/PM10 ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions.

Air quality monitoring system based on low power wide area network technology at public transport stops

&lt;span&gt;Mass migration from rural areas to urban areas has caused problems of traffic congestion, high industrial concentration and inequity in the distribution of housing in the world's capitals, generating a significant threat to sustainable development and public health due to air pollution air. In the Peruvian context, the importance of real-time monitoring of air quality is highlighted according to the standards established by the government. Several studies propose real-time environmental monitoring systems using internet of thing (IoT) technologies, electrochemical and optical sensors to measure pollutants, highlighting the need for data analysis. The objective of the paper is to show the implementation of IoT devices called sensor nodes, with long range wide area network (LoRaWAN) transmission technology for continuous monitoring of polluting gas concentrations. In addition, they are integrated into a central node called gateway to perform real-time monitoring through a web application. As an initial result, IoT devices demonstrated their effectiveness for real-time monitoring. Despite being a prototype-level result, the next stage involves its deployment at public transport stops in Lima. Overcoming the limitations of the solution, this paper establishes the foundation for future research on pollution and public health.&lt;/span&gt;

Visualization of particulate matters in air using lensless microscopy

Abstract. Air quality is crucial for health, yet monitoring airborne particulate matter (PM), particularly PM2.5, remains challenging due to pollution and widespread distribution. PM2.5, with diameters less than 2.5 micrometers, are known to have significant health implications. Traditional methods of PMs detection and analysis are often time-consuming or require complex optical systems. This study explores the use of lensless microscopy as an innovative and cost-effective alternative for the visualization and measurement of PMs. Utilizing computational imaging techniques, lensless microscopy captures high-resolution images without the need for traditional lenses, facilitating rapid and accurate analysis of air quality. We describe the implementation of lensless imaging to detect and quantify PM2.5 particles in the air, highlighting the use of the single light wavelength and the computational reconstruction method. Our experiments demonstrated that this approach could effectively capture fine details of PMs, achieving resolutions comparable to traditional microscopy. This validates the potential of lensless microscopy as a practical tool for real-time air quality monitoring. By adopting lensless microscopy, we offer a more efficient and economical method for the continuous monitoring of PMs, which can significantly contribute to environmental health studies and pollution control efforts. According to specific needs, simply replacing the flow rate of the air pump can adapt to the PM measurement speed requirements in different environments. This research introduces a novel approach to air quality assessment, providing a promising solution for the rapid and detailed analysis of airborne particulate matter while at an affordable cost.

A Comprehensive Analysis of Santiago's Air Quality Forecasts Using SARIMA Model

Abstract. This study analyses Santiagos air quality by forecasting key pollutant concentrations- PM2.5, PM10, NO, and O-over June 2024 to May 2025 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Historical data from 2016 to 2024 shows seasonal peaks, particularly in winter, driven by residential heating and atmospheric conditions. A notable decrease in annual peak NO level, from 71 g/m in 2019 to 23 g/m in 2020, likely due to the COVID-19 pandemic and air quality initiatives. The SARIMA forecasts indicate that PM2.5 levels could reach up to 80 g/m, NO around 28 g/m, O around 47ppb and PM10 around 45g/m during peak periods, highlighting ongoing seasonal pollution challenges. To address these, policymakers should enforce stricter emissions regulations, promote cleaner heating, expand public transportation, and improve air quality monitoring. Additional strategies like traffic management, urban greening, and preparedness for high-pollution days are also recommended. This study demonstrates the SARIMA models effectiveness in forecasting air quality trends, offering valuable insights for policy development to protect public health in Santiago.

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

Review of IoT Systems for Air Quality Measurements Based on LTE/4G and LoRa Communications

The issue of air pollution has recently come to light due to rapid urbanization and population growth globally. Due to its impact on human health, such as causing lung and heart diseases, air quality monitoring is one of the main concerns. Improved air pollution forecasting techniques and systems are needed to minimize the human health impact. Systems that fall under the Internet of Things (IoT) topology have been developed to assess and track numerous air quality metrics. This paper presents a review of IoT systems for air quality measurements, where the emphasis is placed on systems with LTE/4G and LoRa communication capabilities. Firstly, an overview of the IoT monitoring system is provided with recent technologies in the market. A critical review is provided of IoT systems regarding air quality using LTE/4G and LoRa communications systems. Lastly, this paper presents a market analysis of commercial IoT devices in terms of the costs, availability of the device, particulate matter each device can measure, etc. A comparative study of these devices is also presented on LTE/4G and possibly LoRa communications systems.

Indoor Air Quality Monitoring System

The awareness of the risks associated with contaminated indoor air quality is often lacking among individuals. This study employs a prototyping method, utilizing an Arduino Uno integrated with various sensors to monitor air quality. A sound buzzer alerts users when the sensor readings indicate hazardous conditions. Additionally, a Wi-Fi module connects the Arduino Uno to the internet, facilitating data uploads to the IoT platform ThingSpeak.

Wireless Sensing Network for Implementation of Air Quality Monitoring System and Indoor Air Quality Index Application

Indoor air quality significantly impacts respiratory health and mental activity. This study utilizes a wireless sensing network (WSN) based on the Internet of Things (IoT) to monitor indoor air quality, referred to as an indoor air quality monitoring system. The system was installed and applied on campus at the University of Baghdad. The present study aims to monitor air quality parameters continuously within laboratories. Carbon monoxide, sulfur dioxide, nitrogen dioxide, ammonia, and particulate objects are the pollutants chosen to be monitored by the installed system in this study. These pollutants were selected because they affect indoor facilities' comfort, health, and working conditions. Colored coded data was employed in the monitoring system; defined ranges for each pollutant were also integrated. Sensor nodes, wireless modules that connect to the IoT server, and user applications are the main components of the IAQMS system. LCDs, mobile applications, the ThingSpeak web server, and the LabView platform are examples of techniques used to present data collected by the system. Additionally, the system includes a notification function that alerts students and lab personnel when indoor air quality index IAQI values signal unhealthy indoor air quality. The proactive approach ensures a regulated standard for indoor air quality and pollutants.

Health Risk Assessment of Ambient Air Benzene Among Primary School Children in Urban and Rural Areas in Johor, Malaysia

Introduction: It is a fact that children are vulnerable, and are at risk from benzene, a volatile, carcinogenic organic compound. The aim of our study is to determine the levels of ambient air benzene and examine the non-carcinogenic and carcinogenic risks involved Methods: A cross-sectional study was conducted in two urban and two rural primary schools in Johor, Malaysia. Benzene concentrations were measured using BUCK Libra L-4 pumps and analyzed in the gas chromatography-mass spectrometry (GC-MS). Data were collected from 334 10-12 year old children, to calculate the exposure levels based on their body weights and heights. The hazard quotient (HQ) served to evaluate the non-carcinogenic risks, whereas the lifetime cancer risk (LCR) was determined with the aid of the United States Environmental Protection Agency (USEPA). Results and Discussion: Benzene concentrations were higher in rural than urban areas, surpassing the European Union (EU) standard of 5 μg/m³. It was also established that the highest average levels recorded were 6.89 ± 6.68 μg/m³. The HQ values, nonetheless, had indicated no immediate non-carcinogenic risk, while LCR estimates were found to be within a tolerable range across all sites. Findings showed that although the immediate risk from benzene exposure is low, long-term exposure still poses a significant cancer risk to children; even low levels of chronic exposure can heighten the likelihood of children to develop cancers. Conclusion: This study has produced a revelation that there are elevated benzene levels in rural areas in Johor. Despite the low, immediate non-carcinogenic risks, further investigation on the potential for long-term cancer risks is warranted. These risks can be addressed by conducting stricter air quality monitoring, enhancing vehicle emission standards, and introducing educational programs that can raise awareness about benzene exposure.

Evaluation of Machine Learning Application on the Prediction of Particulate Matter Concentrations in Small/Medium-Sized City

Objectives:In this study, Machine Learning (ML) algorithms were evaluated to predict the concentration of particulate matter (PM10 and PM2.5) using air quality and meteorological data in small/medium-sized city.Methods:ML models, including Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), were used to predict PM10 and PM2.5 concentrations. Five air quality variables, including NO2, SO2, CO, PM10 and PM2.5, and seven meteorological variables, including temperature, humidity, vapor pressure, wind speed, precipitation, local atmospheric pressure, and sea-level atmosphere pressure, were collected from three air quality monitoring stations and one meteorological observatory from 2017 to 2022. A total of 52,583 sets of data were used for ML. The prediction accuracies of the applied ML models were evaluated using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2).Results and Discussion:Higher ML performance was obtained when using the data including PM10 and PM2.5 compared to the data excluding these variables. Among five different ML models, the XGB model showed the highest accuracy in predicting PM10 and PM2.5 one hour in the future. However, poorer performance was obtained as the predicted period increased from one hour to 72 hours.Conclusion:The application of ML algorithms for the short-term prediction of PM10 and PM2.5 was successful in this study. However, more input variables and deep learning algorithms are needed for long-term prediction of PM10 and PM2.5.

Flexible and highly selective NO2 gas sensor based on direct-ink-writing of eco-friendly graphene oxide for smart wearable application

Nitrogen dioxide (NO2) is a major cause of respiratory disorders in outdoor and indoor environments. Real-time NO2 monitoring using nonintrusive wearable devices can save lives and provide valuable health data. This study reports a room-temperature, wearable, and flexible smart NO2 gas sensor fabricated via cost-effective printing technology on a polyimide substrate. The sensor uses alkali lignin with edge-oxidised graphene oxide (EGO-AL) ink, demonstrating a sensitivity of 1.70% ppm⁻1 and a detection limit of 12.70 ppb, with excellent selectivity towards NO2. The high sensing properties are attributed to labile oxygen functional groups from GO and alkali lignin, offering abundant interacting sites for NO2 adsorption and electron transfer. The sensor fully recovers to the baseline after heat treatment at 150 °C, indicating its reusability. Integration into lab coats showcased its wearable application, utilising a flexible printed circuit board to wirelessly alert the wearer via cell phone to harmful NO2 levels (>3 ppm) in the environment. This smart sensing application underscores the potential for practical, real-time air quality monitoring, personal safety enhancement, and health management.

Air quality in Bulgaria—Monitoring data, perspectives, scientific research

&lt;p class="1"&gt;This paper addresses air quality in Bulgaria, a pressing environmental issue with monitoring data indicating persistent challenges in urban areas due to pollutants like PM10 and NO&lt;sub&gt;2&lt;/sub&gt;. Bulgaria’s government operates a comprehensive air quality monitoring network, offering real-time data to track pollutants across multiple regions. Scientific research in this area focuses on identifying pollution sources, assessing public health impacts, and developing models for pollution control. Efforts to improve air quality include implementing stricter regulations, investing in green technologies, and conducting public awareness campaigns. Future research emphasizes sustainable urban planning, the adoption of renewable energy, and advanced monitoring technologies to strengthen air quality management in Bulgaria.&lt;/p&gt;

Indoor Air Quality System: A Design of Monitoring CO and CO2 Substances for Elderly Home Using IoT

The use of an IoT-based monitoring system for CO and CO2 gases in elderly homes is a necessary innovation for remotely monitoring air quality. This work was successful in establishing a remote monitoring system that can measure CO and CO2 gases utilizing IoT technology combined with Blynk software. The MQ-7 sensors detect CO gas, while the MQ-135 sensors detect CO2. Data from these sensors is delivered to a central server, which then sends it to the I2C 16X2 LCD panel and Blynk software, allowing data visualization via an internet connection. The study's findings indicate that this system can detect and monitor CO and CO2 gas levels with a high degree of precision. This device can also monitor air quality in real time using the Blynk software. As a result, users may accurately monitor air quality (CO and CO2 gases) in elderly homes.

Air quality modeling intercomparison and multiscale ensemble chain for Latin America

Abstract. A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO2 exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO2, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers.

Extreme Saharan dust events expand northward over the Atlantic and Europe, prompting record-breaking PM10 and PM2.5 episodes

Abstract. Unprecedented extreme Saharan dust (duxt) events have recently expanded northward from subtropical NW Africa to the Atlantic and Europe, with severe impacts on the Canary Islands, mainland Spain and continental Portugal. These six historic duxt episodes occurred on 3–5 and 22–29 February 2020, 15–21 February 2021, 14–17 January 2022, 29 January–1 February 2022, and 14–20 March 2022. We analyzed data of 341 governmental air quality monitoring stations (AQMSs) in Spain (330) and Portugal (11), where PM10 and PM2.5 are measured with European norm (EN) standards, and found that during duxt events PM10 concentrations are underestimated due to technical limitations of some PM10 monitors meaning that they can not properly measure extremely high concentrations. We assessed the consistency of PM10 and PM2.5 data and reconstructed 1690 PM10 (1 h average) data points of 48 and 7 AQMSs in Spain and Portugal, respectively, by using our novel “duxt-r” method. During duxt events, 1 h average PM10 and PM2.5 concentrations were within the range 1000–6000 µg m−3 and 400–1200 µg m−3, respectively. The intense winds leading to massive dust plumes occurred within meteorological dipoles formed by a blocking anticyclone over western Europe and a cutoff low located to the southwest, near the Canary Islands and Cape Verde, or into the Sahara. These cyclones reached this region via two main paths: by deviating southward from the Atlantic mid-latitude westerly circulation or northward from the tropical belt. The analysis of the 2000–2022 PM10 and PM2.5 time series shows that these events have no precedent in this region. The 22–29 February 2020 event led to (24 h average) PM10 and PM2.5 concentrations within the range 600–1840 and 200–404 µg m−3, respectively, being the most intense dust episode ever recorded on the Canary Islands. The 14–20 March 2022 event led to (24 h average) PM10 and PM2.5 values within the range 500–3070 and 100–690 µg m−3 in southeastern Spain, 200–1000 and 60–260 µg m−3 in central Spain, 150–500 and 75–130 µg m−3 in the northern regions of mainland Spain, and within the ranges 200–650 and 30–70 µg m−3 in continental Portugal, respectively, being the most intense dust episode ever recorded in these regions. All duxt events occurred during meteorological anomalies in the Northern Hemisphere characterized by subtropical anticyclones shifting to higher latitudes, anomalous low pressure expanding beyond the tropical belt and amplified mid-latitude Rossby waves. New studies have reported on recent record-breaking PM10 and PM2.5 episodes linked to dipole-induced extreme dust events from North Africa and Asia in a paradoxical context of a multidecadal decrease in dust emissions, a topic that requires further investigation.

Spatial and temporal patterns of urban air pollution in tehran with a focus on PM2.5 and associated pollutants

Understanding the spatial and temporal dynamics of air pollutants is crucial for effective urban air pollution management. This study focuses on the temporal dynamics of air quality monitoring stations (AQMSs) and the association among air pollutants, particularly PM2.5, in Tehran, Iran. Using time series clustering and the Copula model, we analyzed data from 2019 to 2022. We found that the levels and dynamics of O3 and SO2 were similar across most AQMSs and unrelated to geographical positions. CO levels and dynamics were consistent among urban and border AQMSs, with higher concentrations in urban stations. NO2 levels and dynamics varied significantly among northern AQMSs with no relationship geographical positions. PM10 levels and dynamics had a relationship with geographical positions, with western clusters having the highest and northern clusters the lowest concentrations. The dynamics of PM2.5 showed significant relationship among AQMSs in the eastern, southern, and western regions, but not in the north. We also observed that PM10 and O3 levels were higher in warm seasons, whereas CO, SO2, NO2, and PM2.5 levels were higher in cold seasons. Most pollutants, except O3, peaked during traffic hours. Notably, the significant increase in PM2.5 since spring 2021 was primarily due to PM10. Policymakers should focus on these spatial and temporal variations to improve urban air quality and public health outcomes through targeted interventions.

Chemoresistive Gas Sensors Based on Electrospun 1D Nanostructures: Synergizing Morphology and Performance Optimization

Gas sensors are essential for safety and quality of life, with broad applications in industry, healthcare, and environmental monitoring. As urbanization and industrial activities intensify, the need for advanced air quality monitoring becomes critical, driving the demand for more sensitive, selective, and reliable sensors. Recent advances in nanotechnology, particularly 1D nanostructures like nanofibers and nanowires, have garnered significant interest due to their high surface area and improved charge transfer properties. Electrospinning stands out as a promising technique for fabricating these nanomaterials, enabling precise control over their morphology and leading to sensors with exceptional attributes, including high sensitivity, rapid response, and excellent stability in harsh conditions. This review examines the current research on chemoresistive gas sensors based on 1D nanostructures produced by electrospinning. It focuses on how the morphology and composition of these nanomaterials influence key sensor characteristics—sensitivity, selectivity, and stability. The review highlights recent advancements in sensors incorporating metal oxides, carbon nanomaterials, and conducting polymers, along with their modifications to enhance performance. It also explores the use of fiber-based composite materials for detecting oxidizing, reducing, and volatile organic compounds. These composites leverage the properties of various materials to achieve high sensitivity and selectivity, allowing for the detection of a wide range of gases in diverse conditions. The review further addresses challenges in scaling up production and suggests future research directions to overcome technological limitations and improve sensor performance for both industrial and domestic air quality monitoring applications.