Aerosol Science and Engineering最新文献

{"title":"Next-Generation Air Quality Management: Unveiling Advanced Techniques for Monitoring and Controlling Pollution","authors":"Sheetal Kumari,&nbsp;Alakto Choudhury,&nbsp;Preeti Karki,&nbsp;Monika Simon,&nbsp;Jyoti Chowdhry,&nbsp;Anshdha Nandra,&nbsp;Pinki Sharma,&nbsp;Abhishek Sengupta,&nbsp;Anamika Yadav,&nbsp;M. P. Raju,&nbsp;Juhi Gupta,&nbsp;Manoj Chandra Garg","doi":"10.1007/s41810-024-00281-1","DOIUrl":"10.1007/s41810-024-00281-1","url":null,"abstract":"<div><p>Air quality management has become critical due to the growing concerns about air pollution resulting from industrialization, urbanization, and anthropogenic activities. This paper explores advanced techniques for monitoring and controlling pollution, with a focus on next-generation technologies that can address dynamic environmental challenges. We examine the evolution of air quality monitoring, from basic visual methods to sophisticated technological advancements like remote sensing and the Internet of Things (IoT). Innovations in sensor technologies, such as nanosensors, offer compact, affordable, and highly responsive solutions, making them suitable for widespread deployment in air quality monitoring networks. The paper delves into innovative sensor technologies and optical sensors for real-time air quality analysis. These technologies enable accurate detection and measurement of particulate matter (PM) and volatile organic compounds (VOCs), providing crucial data for pollution management. We discuss emerging applications, such as nanosensors, optical sensors, and chemical sensors, and highlight their advantages in capturing detailed air quality data. Big data analytics play a central role in air quality management, allowing for comprehensive analysis and prediction of pollution levels. Machine learning models, including artificial neural networks (ANN), support vector machines (SVM), and decision trees, are used to forecast air quality and identify pollution patterns. Data fusion strategies, combining sensor networks with chemistry-transport models (CTMs), offer enhanced air quality assessments and forecasting capabilities. The paper also discusses smart control systems for emission reduction, emphasizing adaptive control algorithms for real-time emission management. The integration of IoT-enabled smart city components provides real-time monitoring and adaptive responses to pollution events, leading to more effective pollution control. Renewable energy sources, such as wind, solar, and biomass, are explored for their potential to reduce air pollution and support sustainable energy transition. Challenges and future directions are addressed, with a focus on regulatory frameworks and executive actions to support pollution control measures.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 1","pages":"5 - 26"},"PeriodicalIF":2.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying the Causes of Air Pollution in the Tehran Metropolis-Iran and Policy Recommendations for Sustainability","authors":"Amir Ghahremanlou,&nbsp;Davoud Ghahremanlou","doi":"10.1007/s41810-025-00303-6","DOIUrl":"10.1007/s41810-025-00303-6","url":null,"abstract":"<div><p>The high rate of urbanization, vegetation destruction, and high vehicle congestion in Tehran Metropolitan Area, Iran are speeding up the accumulation rate of air pollutants. Although several studies on air pollution, a comprehensive study on daily temporal and clustering pattern in urban districts’ pollutants for sustainability remains missing. We aim to fill this research gap by analyzing pollutants in place and time in the urban districts of Tehran. This study’s novelty lies in its approach to understanding of the air pollution dynamics using Sentinel-5P satellite data and the application of advanced spatial statistical techniques. Using coding in the GEE environment, concentrations of 2022 pollutants such as CO, NO₂, SO₂, O₃, and AI (aerosol index) were extracted from the satellite. For the first time, the daily temporal patterns of the main pollutants in Tehran were examined to identify the causes of one of the most polluted years. High values of CO and NO₂ prevail in the first and last quarters of the year due to increased traffic as schools reopen. In addition, Moran’s I associated with global autocorrelation analysis from all pollutant values was above 0.9, indicating a cluster-like spatial distribution pattern. The LISA analysis identified the locations of clustering pollutants at urban area level. Of all the pollutants studied in Tehran, particulate matter had the highest number of clusters, 282 HH clusters, amounting to a total of 483. A knowledge of these hotspots of pollution and the potential sources of emissions will capable urban planners in taking appropriate actions.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"370 - 384"},"PeriodicalIF":2.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bibliometric Analysis of the Artificial Intelligence Application in Air Pollution (2007–2023): Evolution of Hotspots and Research Trends","authors":"Jinyao Shi,&nbsp;Hao Yuan,&nbsp;Jie Guan,&nbsp;Zhanchen Wang,&nbsp;Liang Shang","doi":"10.1007/s41810-025-00300-9","DOIUrl":"10.1007/s41810-025-00300-9","url":null,"abstract":"<div><p>Air pollution significantly threatens human health, leading to extensive research on its various parameters. In this study, we conducted a bibliometric analysis to assess the progress of artificial intelligence (AI) applications in air pollution research from 2007 to 2023. We utilized a comprehensive dataset from the Web of Science (WoS) core database and applied CiteSpace, bibliometrix of R package, VOSviewer, Alluvial Diagram Generator and Scimago Graphica to analyze countries, institutions, journals, authors, co-cited articles, keywords, and the dual-map overlay of journal categories. The results indicate a significant increase in publications since 2018, with China (979 publications) and the USA (459 publications) at the forefront. The Chinese Academy of Sciences tops the list with 106 papers and 3366 citations, and a centrality score of 0.22. Two authoritative journals, <i>Atmospheric Environment</i> and <i>Science of the Total Environment</i>, have the most citations with 3916 and 3501. However, research teams remain dispersed, with no large-scale collaborations formed to date. Keywords encompass \"deep learning,\" \"artificial neural network,\" \"indoor air quality,\" \"aerosol optical depth,\" \"random forests,\" and \"hospital admissions.\" The transformation of academic categories over the past 17 years, from seven initial groups in 2007 to the formation of ten new interdisciplinary groups between 2022 and 2023, underscores the broad impact of AI technologies and their expanding applications across various fields. The dual-map overlay analysis uncovers surprising connections between veterinary medicine and atmospheric research, highlighting the potential impact of air pollution on livestock and human health, emerging as a potential hotspot for future studies. Moreover, AI is demonstrating its interdisciplinary reach within the agricultural sector.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"317 - 330"},"PeriodicalIF":2.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Distribution Characteristics of Secondary PM2.5 in Beijing-Tianjin-Hebei Region Based on Time Series Decomposition","authors":"Qing Yao,&nbsp;Jing Ding,&nbsp;Zi-rui Liu,&nbsp;Xu Yang,&nbsp;Ying-xiao Tang,&nbsp;Zi-yin Cai,&nbsp;Su-qin Han","doi":"10.1007/s41810-025-00293-5","DOIUrl":"10.1007/s41810-025-00293-5","url":null,"abstract":"<div><p>Secondary aerosol is a major contributor to fine particulate matter (PM_2.5) air pollution in Beijing-Tianjin-Hebei (BTH) region, and the spatial and temporal variations of which in BTH was still not well known. In this study, the concentrations of Primary PM_2.5 (PPM) and Secondary PM_2.5 (SPM) in 13 cities of the BTH regions were divided and qualified using the approximate envelope method (AEM) from March 2017 to February 2023, and the long-term trends and drivers of PPM and SPM were discussed. Over the six-year period, PM_2.5 concentrations in the BTH region show an overall decreasing trend, but the decreasing trend has leveled off in recent years. Specifically, PPM showed a continuously and rapidly downward trend in the BTH region, with an annual reduction rate of 2.57 µg·m^− 3/year, while lower decrease trend was found for the SPM (1.96 µg·m^− 3/year). After removing the effects of seasonal and short-term meteorological factors, the results obtained using the time series decomposition better reflect the evolutionary trends of PPM and SPM, which suggested that the decline of PPM and SPM are both mainly contributed by the primary emission reduction from the south-central region of BTH during the autumn and winter seasons, although the SPM seems to also affected by the special topography that favor for the regional transport and secondary transformation of primary pollutants, thus partly explains the slowing down of the decreasing trend of PM_2.5 in recent years. This work provides an observation-based assessment to better understand the rapid changes in primary and secondary aerosol distributions in the BTH region.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"222 - 234"},"PeriodicalIF":2.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationships Among Aerosol Particles, Airborne Bacteria, and Microclimatic Factors in an Enclosed Dairy Milking Barn","authors":"Md. Aminul Islam,&nbsp;Atsuo Ikeguchi,&nbsp;Takanori Naide","doi":"10.1007/s41810-025-00298-0","DOIUrl":"10.1007/s41810-025-00298-0","url":null,"abstract":"<div><p>Dairy barns are a crucial source of aerosol and airborne bacteria emissions. An investigation was conducted to report the indoor microclimatic factors, aerosols, and airborne bacteria in an enclosed type of dairy barn in Tochigi, Japan, from April 2017 to May 2018. A mean higher airflow (1.90 m/sec), temperature (23.86 °C), and relative humidity (RH) (88.69%) were found during the summer in the barn. Significant differences in airflow, temperature, and RH were found between the summer and winter in the barn. The concentrations of 0.3–2.0 μm aerosols were greater during all the seasons, and the concentration of 2.0–10.0 μm aerosols during winter was greater in the barn. Higher concentrations of aerobic bacteria, <i>Staphylococcus aureus</i> (<i>S. aureus</i>), and <i>Escherichia coli</i> (<i>E. coli</i>) were observed during the winter in the barn. A total of 11 significant relationships were observed among aerosols, airborne bacteria, and microclimatic factors. A significant positive relationship (<i>R</i> = 0.96, <i>p</i> = 0.01) was observed between the RH and 0.5–1.0 μm aerosols during the autumn. A significant negative relationship (<i>R</i> = 0.96, <i>p</i> = 0.00) between 2.0–5.0 μm aerosols and airborne <i>S. aureus</i> was observed during the summer. These findings will be crucial in mitigating aerosols and airborne bacteria in dairy barns. Clinical trial registration. Not applicable.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"331 - 342"},"PeriodicalIF":2.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amino Acid and Amine Compounds in Fine Aerosol Particles: A Review of Determination Methods, Abundances, Origins, and Atmospheric Processes","authors":"Yu Xu,&nbsp;Hong-Wei Xiao,&nbsp;Hao Xiao,&nbsp;Hua-Yun Xiao","doi":"10.1007/s41810-025-00299-z","DOIUrl":"10.1007/s41810-025-00299-z","url":null,"abstract":"<div><p>Amino acid and amine compounds in aerosols are typical reduced nitrogen-containing organic compounds. These species originate from diverse natural and anthropogenic sources, including terrestrial and marine emissions and agricultural and industrial activities. Moreover, aerosol amino acid and amine compounds can also be produced through secondary processes in the atmosphere. Their chemical diversity and reactivity significantly impact the formation of aerosols, the physicochemical properties of aerosols, cloud formation, nutrient cycling, climate dynamics, and public health. Advanced analytical techniques, such as high-resolution mass spectrometry and isotopic analysis, have enabled detailed characterization and improved our understanding of their compositions, abundances, origins, atmospheric transformations, and environmental impacts. However, the evolution of chemical analytical methodologies has also led to the generation of data that is no longer directly comparable. This review discusses various detection methods of the amino acid and amine compounds in fine aerosol particles, attempting to provide scientific references for future unified optimal detection methods. Subsequently, the compositions and abundances in different atmospheric environments (i.e., urban, suburban, rural, and background environments) were summarized and compared. Further discussion on the potential origins and atmospheric chemical processes of the amino acid and amine compounds indicates that their diverse sources and atmospheric transformation are the key factors leading to fluctuations in their composition and abundance. The overall review is expected to provide a framework or data reference for future research in this rapidly evolving field.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"301 - 316"},"PeriodicalIF":2.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Fine Aerosol in Two Different Climate Regions of India Using MERRA-2 Products, Ground-based Measurements, and Machine Learning","authors":"Dilna Anand M,&nbsp;Anurag Sahu,&nbsp;Jai Prakash","doi":"10.1007/s41810-024-00279-9","DOIUrl":"10.1007/s41810-024-00279-9","url":null,"abstract":"<div><p>Satellite-derived aerosol data from Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2), have received a lot of attention and offer substantial information for estimating aerosols, as well as for air quality, climate, and health assessments.This study proposes an integration of multiple measurement approaches, including empirical modeling of MERRA-2 aerosol components, namely black carbon (BC), organic carbon (OC), dust, sea-salt, and sulfate (SO₄²⁻), ground-based PM_2.5, and the feasibility of machine learning to simulate PM_2.5 concentrations and develop accurate and robust models in two distinct climatic regions in India—Rajasthan (arid/semi-arid) and Kerala (humid/semi-humid). The 42-year MERRA-2 aerosol datasets (1980–2022) were obtained at the daily and monthly resolution, while hourly ground-based PM_2.5 datasets were collected from fourteen (14) continuous ambient air quality monitoring stations (CAAQM_S) in Rajasthan and Kerala. Firstly, we examined long-term trends of empirical-based PM_2.5 [MERRA2-EE-PM_2.5] from 1980 to 2022, and results indicated a significant rise in PM_2.5 levels in both Rajasthan and Kerala post-2000, largely attributed to commercialization and industrialization. A detailed correlation analysis of MERRA-2 aerosol components with PM_2.5 highlighted that dust, BC, OC, and SO₄^2- were the main contributors in the arid and semi-arid regions of Rajasthan. In contrast, Kerala’s humid regions showed differences among these components, highlighting the complexity of regional aerosol impacts. Further, MERRA2-EE-PM_2.5 underestimates, implying the need for accurate weighting of each aerosol component and advanced models with meteorological parameters. Therefore, we ensembled three machine learning (ML) models: random forest (RF), gradient boosting (GB), and k-nearest neighbour (k-NN) to estimate modelled-PM_2.5 using MERRA-2 aerosol components and meteorological parameters. The results showed an R² value increase from 0.42 to 0.62 after accounting for meteorological parameters in the ML models. These findings highlight the ineptness of conventional MERRA2-EE-PM_2.5 and emphasize the need for customized regional models that assign appropriate weights to each aerosol component according to specific climate conditions. Our findings support the potential of machine learning as a tool to refine PM_2.5 prediction, facilitating more accurate health risk assessments and policy-making for air quality management in future studies.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 1","pages":"27 - 50"},"PeriodicalIF":2.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Air Pollution Prediction With Random Forest Algorithm","authors":"Sukhendra Singh,&nbsp;Manoj Kumar,&nbsp;Birendra Kumar Verma,&nbsp;Sushil Kumar","doi":"10.1007/s41810-025-00292-6","DOIUrl":"10.1007/s41810-025-00292-6","url":null,"abstract":"<div><p>Air pollution is a significant environmental concern, especially in urban areas with dense populations. Long-term exposure to air pollution can cause serious health problems, such as respiratory diseases, cardiac problems, and even premature mortality. Reducing air pollution is essential for protecting human health and the environment. This paper aims to predict air quality in National Capital Territory (NCT) of Delhi, India using machine learning algorithms, specifically focusing on the Random Forest model. Air pollution is a significant environmental concern, particularly in densely populated urban areas like NCT Delhi India. It involves the contamination of the atmosphere with hazardous substances such as Particulate Matter (PM_2.5, PM₁₀), Nitrogen Dioxide (NO₂), and Sulfur Dioxide (SO₂). In our research, we proposed a machine-learning framework using random forest algorithms to predict air quality. We used hourly measurements of key pollutants from January 2017 to December 2018 in the NCT Delhi India region, with predictions evaluated through k-fold cross-validation and grid search techniques. Our findings revealed that the Random Forest regression model outperforms simpler models, achieving an R² value of 98.89, highlighting its efficacy in capturing complex relationships among air quality parameters. The random forest model can be utilized by policymakers to make informed decisions regarding the management and regulation of air quality.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"208 - 221"},"PeriodicalIF":2.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Assessment of Air Quality During Diwali Celebrations in Himalayan Foothill Cities","authors":"Ankur Kansal,&nbsp;Parag Madhukar Dhakate,&nbsp;Amar Deep,&nbsp;Alok Sagar Gautam,&nbsp;Sneha Gautam","doi":"10.1007/s41810-025-00295-3","DOIUrl":"10.1007/s41810-025-00295-3","url":null,"abstract":"<div><p>Monitoring of atmospheric air pollutants was conducted across multiple sites in Uttarakhand, India, during the Diwali campaigns from 2021 to 2023, encompassing pre-Diwali, Diwali day, post-Diwali, and control periods. Gaseous pollutants (NOx, SOx), particulate matter (PM₁₀, PM_2.5), metal concentrations (Al, Ni, Fe), and Air Quality Index (AQI) variations were analyzed to assess the impact of fire-related activities on air quality. Peak concentrations of NOx and SOx during Diwali days in 2021, 2022, and 2023 exceeded normal levels, indicating severe pollution episodes in urban areas such as Dehradun and Haridwar. PM₁₀ and PM_2.5 levels surged significantly during Diwali, with concentrations often surpassing national standards, highlighting acute health risks associated with respiratory and cardiovascular diseases. Elevated metal concentrations, particularly aluminum and iron, were traced to firework emissions, underscoring the environmental impact of cultural festivities. AQI data consistently categorized air quality as “severe” during Diwali periods, reflecting widespread pollution impacts across monitored sites. Post-Diwali, AQI levels showed gradual recovery, influenced by meteorological conditions that exacerbated pollutant retention in the atmosphere. Comparative analysis with global studies reveals parallels in air quality degradation during festive seasons worldwide, emphasizing the universal challenges of mitigating pollution amid cultural celebrations. Efforts to promote eco-friendly firecrackers in 2023 yielded initial reductions in pollutant levels but underscored ongoing global imperatives for sustainable environmental practices. The highest PM_2.5 concentration observed during the study period was 183.4 µg/m³ on November 12, 2023, in Dehradun (NH). The highest NOx concentration during the study period occurred on October 24, 2022, at Rishikul Ayurvedic College, Haridwar (55.93 µg/m³). These findings underscore the critical need for stringent pollution control measures and international collaborations to mitigate the environmental and public health impacts of cultural festivals globally.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"260 - 271"},"PeriodicalIF":2.0,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Portable and Low-Cost Sensor System for Air Pollution Measurement","authors":"Zike Wang,&nbsp;Linqiang Ma,&nbsp;Ruixuan Yang,&nbsp;Jianhuai Ye","doi":"10.1007/s41810-025-00284-6","DOIUrl":"10.1007/s41810-025-00284-6","url":null,"abstract":"<div><p>Air pollution poses a significant threat to human health, yet traditional monitoring platforms and reference-grade instruments are costly and offer limited spatial coverage. This study developed a custom-designed sensor system that integrates electrochemical sensors with a microcontroller for data collection and processing. Various algorithms were tested for sensor data calibration, with the random forest algorithm delivering the highest accuracy and lowest error. Further analysis emphasized the importance of temperature and relative humidity in improving the predictive accuracy of the models, particularly for the calibration of CO and O_<i>x</i> sensors. Model performance was significantly enhanced by using larger training datasets, requiring 28 days of data for optimal calibration of CO and NO sensors. In contrast, the NO₂ and O_<i>x</i> sensors performed well with smaller datasets (7 days or more). The sensor system was further employed to monitor variations in particulate matter concentrations and examine the relationships among different pollutants. Results show that reducing NO_<i>x</i> and O₃ concentrations to 50% of their original levels led to a 25% and 5% decrease in PM, respectively, but their simultaneous reduction resulted in only a 15% decrease, suggesting a nonlinear interaction between these pollutants and PM. The system was also used to monitor air quality inside and outside subway stations, providing real-world validation of the performance of the sensor system and demonstrating its versatility. Overall, this study demonstrates the feasibility and effectiveness of the custom-designed sensor system for air quality monitoring, emphasizing its potential for high-resolution sensor network deployment and its capability to generate valuable data to inform urban air quality policy decisions.</p><h3>Graphical abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 1","pages":"90 - 101"},"PeriodicalIF":2.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147643185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}