A systematic global review of fixed air quality monitoring stations: Spatial distribution, typologies, measured pollutants, technologies, regulatory standards, and research gaps
-
Elahe Noruzzade
,
Nezam Mirzaei
,
Sima Janghorbanian
,
Mahdieh Hassanvand
,
Nava Nikkhoo
,
Mohammad Khanizadeh
Abstract
Air pollution is a global threat that significantly affects human and environmental health, and fixed Air Quality Monitoring Stations (AQMS), play a pivotal role in assessing ambient air conditions and informing regulatory policies. This systematic review provides a global overview of fixed air pollution monitoring stations, focusing on the geographical distribution of stations, classification, pollutants measured at each station, measurement techniques for each pollutant, monitoring frameworks, and implementation challenges. A comprehensive search of PubMed, Scopus, Web of Science, and grey literature identified 17 eligible studies covering diverse regions across Europe, Asia, Africa, and the Americas. This assessment uncovers Critical disparities in air quality monitoring architectures, revealing: (i) non-uniform station distribution patterns, (ii) technology adoption gaps, and (iii) pollutant coverage imbalances that collectively hinder comparable air quality assessments across regions and While high-income countries operate and maintain sophisticated networks and advanced, reference-grade analyzers, low- and middle-income countries use low-resolution, short-term, or inexpensive sensors that provide limited and fragmented data. This review, synthesizing global evidence, highlights the urgent need for equitable, reliable, and policy-driven monitoring systems worldwide.
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6. Barrero M, Orza J, Cabello M, Cantón L. Categorisation of air quality monitoring stations by evaluation of PM10 variability. Science of The Total Environment. 2015;524:225–36. https://doi.org/10.1016/j.scitotenv.2015.03.138
7. Yatkin S, Gerboles M, Belis C, Karagulian F, Lagler F, Barbiere M, et al. Representativeness of an air quality monitoring station for PM2. 5 and source apportionment over a small urban domain. Atmospheric pollution research. 2020;11(2):225–33. https://doi.org/10.1016/j.apr.2019.10.004
8. Shukla K, Aggarwal SG. A technical overview on beta-attenuation method for the monitoring of particulate matter in ambient air. Aerosol and Air Quality Research. 2022;22(12):220195. https://doi.org/10.1007/s12647-013-0073-x
9. Dinh T-V, Park B-G, Lee S-W, Park J-H, Baek D-H, Choi I-Y, et al. Comparison of PM2. 5 monitoring data using light scattering and beta attenuation methods: a case study in Seoul Metro Subway. Asian Journal of Atmospheric Environment. 2022;16(4):2022116. https://doi.org/10.5572/ajae.2022.116
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17. Huang Y, Mok W-c, Yam Y-s, Zhou JL, Surawski NC, Organ B, et al. Evaluating in-use vehicle emissions using air quality monitoring stations and on-road remote sensing systems. Science of The Total Environment. 2020;740:139868. https://doi.org/10.1016/j.scitotenv.2020.139868
18. Nthusi V. Nairobi Air Quality Monitoring Sensor Network Report-April 2017. Nairobi: Researchnet. 2017. http://dx.doi.org/10.13140/RG.2.2.10240.64009
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21. Tabinda AB, Munir S, Yasar A, Ilyas A. Seasonal and temporal variations of criteria air pollutants and the influence of meteorological parameters on the concentration of pollutants in ambient air in Lahore, Pakistan. Pakistan Journal of Scientific & Industrial Research Series a: Physical Sciences. 2016;59(1):34–42. https://dx.doi.org/10.52763/pjsir.phys.sci.59.1.2016.34.42
22. Sharma P, Sharma P, Jain S, Kumar P. An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity Delhi. Atmospheric Environment. 2013;70:7–17. http://dx.doi.org/10.1016/j.atmosenv.2013.01.004
23. Azmi SZ, Latif MT, Ismail AS, Juneng L, Jemain AA. Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia. Air Quality, Atmosphere & Health. 2010;3:53–64. https://doi.org/10.1007/s11869-009-0051-1
24. Sajani SZ, Scotto F, Lauriola P, Galassi F, Montanari A. Urban air pollution monitoring and correlation properties between fixed-site stations. Journal of the Air & Waste Management Association. 2004;54(10):1236–41. https://doi.org/10.1080/10473289.2004.10470993
25. Chen S-P, Chang C-C, Liu J-J, Chou CC-K, Chang JS, Wang J-L. Recent improvement in air quality as evidenced by the island-wide monitoring network in Taiwan. Atmospheric environment. 2014;96:70–7. https://doi.org/10.1016/j.atmosenv.2014.06.060
26. Huang J, Pan X, Guo X, Li G. Health impact of China's Air Pollution Prevention and Control Action Plan: an analysis of national air quality monitoring and mortality data. The Lancet Planetary Health. 2018;2(7):e313–e23. https://doi.org/10.1016/s2542-5196(18)30141-4
27. Zhao S, Yu Y, Yin D, He J, Liu N, Qu J, et al. Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in situ air quality monitoring data from China National Environmental Monitoring Center. Environment international. 2016;86:92–106. https://doi.org/10.1016/j.envint.2015.11.003
28. Adams MD, Kanaroglou PS. Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models. Journal of environmental management. 2016;168:133–41. https://doi.org/10.1016/j.jenvman.2015.12.012
29. Davuliene L, Jasineviciene D, Garbariene I, Andriejauskiene J, Ulevicius V, Bycenkiene S. Long-term air pollution trend analysis in the South-eastern Baltic region, 1981–2017. Atmospheric Research. 2021;247:105191. https://doi.org/10.1016/j.atmosres.2020.105191
30. Choi S-D. Evaluation of Urban Air Quality Monitoring Stations in Ulsan using Geographic Information System. Journal of Environmental Analysis, Health and Toxicology. 2022. https://doi.org/10.36278/jeaht.25.3.99
31. Collado E, Harish G, Sáez Y. Design of a solar-powered air pollution monitoring system under tropical climate environments. 2022 IEEE 40th Central America and Panama Convention (CONCAPAN). 2022:1–6. https://doi.org/10.1109/CONCAPAN48024.2022.9997667
32. Haskell-Craig Z, Josey KP, Kinney PL, deSouza P. Equity in the Distribution of Regulatory PM2. 5 Monitors. arXiv preprint arXiv:241018692. 2024. https://doi.org/10.48550/arXiv.2410.18692
33. Gulia S, Khanna I, Shukla K, Khare M. Ambient air pollutant monitoring and analysis protocol for low and middle income countries: An element of comprehensive urban air quality management framework. Atmospheric Environment. 2020;222:117120. https://doi.org/10.1016/j.atmosenv.2019.117120
34. Lee H, Park S, Mayer H. Statistical Characteristics of Air Quality Index DAQx*-Specific Air Pollutants Differentiated by Types of Air Quality Monitoring Stations: A Case Study of Seoul, Republic of Korea. Sustainability. 2023. https://doi.org/10.3390/su15118599
35. Whitehill AR, Lunden M, LaFranchi B, Kaushik S, Solomon PA. Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment. Atmospheric Measurement Techniques. 2024;17(9):2991–3009. https://doi.org/10.5194/amt-17-2991-2024, 2024
36. Hellan SP, Lucas CG, Goddard NH, editors. Bayesian optimisation for active monitoring of air pollution. Proceedings of the AAAI Conference on Artificial Intelligence; 2022. https://doi.org/10.1609/aaai.v36i11.21448
37. GYAN M, Amankwah A, Mensah-Bonsu J, Nana Ama B-K. Prospecting for Air Pollution Hot Spots in the Atmosphere using Lapse Rate. Journal of Environmental Studies. 2024;35(1):23–31. https://doi.org/10.21608/jesj.2024.256265.1062
38. Choi M-S, Kim Y-H. A Study on Air Pollutants Emitted from Yeosu National Industrial Complex Affecting Air Quality in Yeosu, Republic of Korea. Journal of Korean Society for Atmospheric Environment. 2023. https://ui.adsabs.harvard.edu/link_gateway/2023JKSAE..39...77C/doi:10.5572/KOSAE.2023.39.1.77
39. Baca-López K, Fresno C, Espinal-Enríquez J, Martínez-García M, Camacho-López MA, Flores-Merino MV, et al. Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health. Front Public Health. 2020;8:536174. https://doi.org/10.3389/fpubh.2020.536174
40. Liu Q, Cui B, Liu Z. Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling. Atmosphere. 2024. https://doi.org/10.3390/atmos15050553
41. Considine EM, Braun D, Kamareddine L, Nethery RC, deSouza P. Investigating use of low-cost sensors to increase accuracy and equity of real-time air quality information. Environmental science & technology. 2023;57(3):1391–402. https://pubs.acs.org/doi/10.1021/acs.est.2c06626?goto=supporting-info
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43. Azeraf E, Wagner A, Bialic E, Mellah S, Lelandais L. Real-time Pollutant Identification through Optical PM Micro-Sensor. arXiv preprint arXiv:250310724. 2025. https://doi.org/10.48550/arXiv.2503.10724
44. Bencs L, Plósz B, Mmari AG, Szoboszlai N. Comparative study on the use of some low-cost optical particulate sensors for rapid assessment of local air quality changes. Atmosphere. 2022;13(8):1218. http://dx.doi.org/10.3390/atmos13081218
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2. Martins C. Air pollution and health – the importance of air monitoring and burden of disease for attaining SDGs. European Journal of Public Health. 2022;32(Supplement_3). DOI: https://doi.org/10.1093/eurpub/ckac129.018
3. Almeida-Silva M, Wolterbeek HT, Almeida S. Elderly exposure to indoor air pollutants. Atmospheric Environment. 2014;85:54–63. http://dx.doi.org/10.1016/j.atmosenv.2013.11.061
4. Połednik B, Piotrowicz A, Pawłowski L, Guz Ł. Traffic-related particle emissions and exposure on an urban road. Archives of Environmental Protection. 2018;44(2):83–93. http://dx.doi.org/10.24425/119706
5. Ding J, Ren C, Wang J, Feng Z, Cao S-J. Spatial and temporal urban air pollution patterns based on limited data of monitoring stations. Journal of Cleaner Production. 2024;434:140359. https://doi.org/10.1016/j.jclepro.2023.140359
6. Barrero M, Orza J, Cabello M, Cantón L. Categorisation of air quality monitoring stations by evaluation of PM10 variability. Science of The Total Environment. 2015;524:225–36. https://doi.org/10.1016/j.scitotenv.2015.03.138
7. Yatkin S, Gerboles M, Belis C, Karagulian F, Lagler F, Barbiere M, et al. Representativeness of an air quality monitoring station for PM2. 5 and source apportionment over a small urban domain. Atmospheric pollution research. 2020;11(2):225–33. https://doi.org/10.1016/j.apr.2019.10.004
8. Shukla K, Aggarwal SG. A technical overview on beta-attenuation method for the monitoring of particulate matter in ambient air. Aerosol and Air Quality Research. 2022;22(12):220195. https://doi.org/10.1007/s12647-013-0073-x
9. Dinh T-V, Park B-G, Lee S-W, Park J-H, Baek D-H, Choi I-Y, et al. Comparison of PM2. 5 monitoring data using light scattering and beta attenuation methods: a case study in Seoul Metro Subway. Asian Journal of Atmospheric Environment. 2022;16(4):2022116. https://doi.org/10.5572/ajae.2022.116
10. Madruga DG. Importance of Air Quality Networks in Controlling Exposure. Environmental Emissions. 2021:37. http://dx.doi.org/10.5772/intechopen.92335
11. Ung A, Wald L, Ranchin T, Weber C, Hirsch J, Perron G, et al. Satellite data for air pollution mapping over a city–Virtual stations. Observing our environment from space-new solutions for a new millennium: CRC Press; 2021. p. 147–51. https://www.researchgate.net/publication/42433064_Satellite_data_for_the_air_pollution_mapping_over_a_city_-_The_use_of_virtual_stations
12. Zeitouni K, Abboud M, Taher Y, editors. Enriching fixed stations air pollution monitoring with opportunistic mobile monitoring. Workshop on Big Mobility Data Analytics (BMDA) co-located with EDBT/ICDT 2023 Joint Conference; 2023. https://hal.science/hal-04278244
13. Samad A, Garuda S, Vogt U, Yang B. Air pollution prediction using machine learning techniques–an approach to replace existing monitoring stations with virtual monitoring stations. Atmospheric Environment. 2023;310:119987. http://dx.doi.org/10.1016/j.atmosenv.2023.119987
14. Galán-Madruga D, García-Cambero JP. An optimized approach for estimating benzene in ambient air within an air quality monitoring network. Journal of Environmental Sciences. 2022;111:164–74. https://doi.org/10.1016/j.jes.2021.03.005
15. Galán-Madruga D. A methodological framework for improving air quality monitoring network layout. Applications to environment management. Journal of Environmental Sciences. 2021;102:138–47. https://doi.org/10.1016/j.jes.2020.09.009
16. Lorenzo-Sáez E, Oliver-Villanueva J-V, Lemus-Zúñiga L-G, Coll-Aliaga E, Castillo CP, Lavalle C. Assessment of an air quality surveillance network through passive pollution measurement with mobile sensors. Environmental Research Letters. 2021;16(5):054072. DOI: 10.1088/1748-9326/abe435
17. Huang Y, Mok W-c, Yam Y-s, Zhou JL, Surawski NC, Organ B, et al. Evaluating in-use vehicle emissions using air quality monitoring stations and on-road remote sensing systems. Science of The Total Environment. 2020;740:139868. https://doi.org/10.1016/j.scitotenv.2020.139868
18. Nthusi V. Nairobi Air Quality Monitoring Sensor Network Report-April 2017. Nairobi: Researchnet. 2017. http://dx.doi.org/10.13140/RG.2.2.10240.64009
19. Astudillo GD, Garza-Castanon LE, Avila LIM. Design and evaluation of a reliable low-cost atmospheric pollution station in urban environment. IEEE Access. 2020;8:51129–44. https://doi.org/10.1109/ACCESS.2020.2980736
20. Munir S, Mayfield M, Coca D, Jubb SA. Structuring an integrated air quality monitoring network in large urban areas–Discussing the purpose, criteria and deployment strategy. Atmospheric Environment: X. 2019;2:100027. https://doi.org/10.1016/j.aeaoa.2019.100027
21. Tabinda AB, Munir S, Yasar A, Ilyas A. Seasonal and temporal variations of criteria air pollutants and the influence of meteorological parameters on the concentration of pollutants in ambient air in Lahore, Pakistan. Pakistan Journal of Scientific & Industrial Research Series a: Physical Sciences. 2016;59(1):34–42. https://dx.doi.org/10.52763/pjsir.phys.sci.59.1.2016.34.42
22. Sharma P, Sharma P, Jain S, Kumar P. An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity Delhi. Atmospheric Environment. 2013;70:7–17. http://dx.doi.org/10.1016/j.atmosenv.2013.01.004
23. Azmi SZ, Latif MT, Ismail AS, Juneng L, Jemain AA. Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia. Air Quality, Atmosphere & Health. 2010;3:53–64. https://doi.org/10.1007/s11869-009-0051-1
24. Sajani SZ, Scotto F, Lauriola P, Galassi F, Montanari A. Urban air pollution monitoring and correlation properties between fixed-site stations. Journal of the Air & Waste Management Association. 2004;54(10):1236–41. https://doi.org/10.1080/10473289.2004.10470993
25. Chen S-P, Chang C-C, Liu J-J, Chou CC-K, Chang JS, Wang J-L. Recent improvement in air quality as evidenced by the island-wide monitoring network in Taiwan. Atmospheric environment. 2014;96:70–7. https://doi.org/10.1016/j.atmosenv.2014.06.060
26. Huang J, Pan X, Guo X, Li G. Health impact of China's Air Pollution Prevention and Control Action Plan: an analysis of national air quality monitoring and mortality data. The Lancet Planetary Health. 2018;2(7):e313–e23. https://doi.org/10.1016/s2542-5196(18)30141-4
27. Zhao S, Yu Y, Yin D, He J, Liu N, Qu J, et al. Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in situ air quality monitoring data from China National Environmental Monitoring Center. Environment international. 2016;86:92–106. https://doi.org/10.1016/j.envint.2015.11.003
28. Adams MD, Kanaroglou PS. Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models. Journal of environmental management. 2016;168:133–41. https://doi.org/10.1016/j.jenvman.2015.12.012
29. Davuliene L, Jasineviciene D, Garbariene I, Andriejauskiene J, Ulevicius V, Bycenkiene S. Long-term air pollution trend analysis in the South-eastern Baltic region, 1981–2017. Atmospheric Research. 2021;247:105191. https://doi.org/10.1016/j.atmosres.2020.105191
30. Choi S-D. Evaluation of Urban Air Quality Monitoring Stations in Ulsan using Geographic Information System. Journal of Environmental Analysis, Health and Toxicology. 2022. https://doi.org/10.36278/jeaht.25.3.99
31. Collado E, Harish G, Sáez Y. Design of a solar-powered air pollution monitoring system under tropical climate environments. 2022 IEEE 40th Central America and Panama Convention (CONCAPAN). 2022:1–6. https://doi.org/10.1109/CONCAPAN48024.2022.9997667
32. Haskell-Craig Z, Josey KP, Kinney PL, deSouza P. Equity in the Distribution of Regulatory PM2. 5 Monitors. arXiv preprint arXiv:241018692. 2024. https://doi.org/10.48550/arXiv.2410.18692
33. Gulia S, Khanna I, Shukla K, Khare M. Ambient air pollutant monitoring and analysis protocol for low and middle income countries: An element of comprehensive urban air quality management framework. Atmospheric Environment. 2020;222:117120. https://doi.org/10.1016/j.atmosenv.2019.117120
34. Lee H, Park S, Mayer H. Statistical Characteristics of Air Quality Index DAQx*-Specific Air Pollutants Differentiated by Types of Air Quality Monitoring Stations: A Case Study of Seoul, Republic of Korea. Sustainability. 2023. https://doi.org/10.3390/su15118599
35. Whitehill AR, Lunden M, LaFranchi B, Kaushik S, Solomon PA. Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment. Atmospheric Measurement Techniques. 2024;17(9):2991–3009. https://doi.org/10.5194/amt-17-2991-2024, 2024
36. Hellan SP, Lucas CG, Goddard NH, editors. Bayesian optimisation for active monitoring of air pollution. Proceedings of the AAAI Conference on Artificial Intelligence; 2022. https://doi.org/10.1609/aaai.v36i11.21448
37. GYAN M, Amankwah A, Mensah-Bonsu J, Nana Ama B-K. Prospecting for Air Pollution Hot Spots in the Atmosphere using Lapse Rate. Journal of Environmental Studies. 2024;35(1):23–31. https://doi.org/10.21608/jesj.2024.256265.1062
38. Choi M-S, Kim Y-H. A Study on Air Pollutants Emitted from Yeosu National Industrial Complex Affecting Air Quality in Yeosu, Republic of Korea. Journal of Korean Society for Atmospheric Environment. 2023. https://ui.adsabs.harvard.edu/link_gateway/2023JKSAE..39...77C/doi:10.5572/KOSAE.2023.39.1.77
39. Baca-López K, Fresno C, Espinal-Enríquez J, Martínez-García M, Camacho-López MA, Flores-Merino MV, et al. Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health. Front Public Health. 2020;8:536174. https://doi.org/10.3389/fpubh.2020.536174
40. Liu Q, Cui B, Liu Z. Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling. Atmosphere. 2024. https://doi.org/10.3390/atmos15050553
41. Considine EM, Braun D, Kamareddine L, Nethery RC, deSouza P. Investigating use of low-cost sensors to increase accuracy and equity of real-time air quality information. Environmental science & technology. 2023;57(3):1391–402. https://pubs.acs.org/doi/10.1021/acs.est.2c06626?goto=supporting-info
42. Gangwar A, Singh S, Mishra R, Prakash S. The state-of-the-art in air pollution monitoring and forecasting systems using IoT, big data, and machine learning. Wireless Personal Communications. 2023;130(3):1699–729. https://doi.org/10.1007/s11277-023-10351-1
43. Azeraf E, Wagner A, Bialic E, Mellah S, Lelandais L. Real-time Pollutant Identification through Optical PM Micro-Sensor. arXiv preprint arXiv:250310724. 2025. https://doi.org/10.48550/arXiv.2503.10724
44. Bencs L, Plósz B, Mmari AG, Szoboszlai N. Comparative study on the use of some low-cost optical particulate sensors for rapid assessment of local air quality changes. Atmosphere. 2022;13(8):1218. http://dx.doi.org/10.3390/atmos13081218
45. Seinfeld JH, Pandis SN. Atmospheric chemistry and physics: from air pollution to climate change: John Wiley & Sons; 2016. https://www.amazon.com/Atmospheric-Chemistry-Physics-Pollution-Climate/dp/1118947401
46. Hashmy Y, Khan Z, Hafiz R, Younis U, Tauqeer T. MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutants. arXiv preprint arXiv:210412594. 2021. https://doi.org/10.48550/arXiv.2104.12594
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| Files | ||
| Issue | Vol 10 No 3 (2025): Summer 2025 | |
| Section | Review Article(s) | |
| DOI | https://doi.org/10.18502/japh.v10i3.19602 | |
| Keywords | ||
| Air quality monitoring stations (AQMS); Ambient air pollution; Fixed stations | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Noruzzade E, Mirzaei N, Janghorbanian S, Hassanvand M, Nikkhoo N, Khanizadeh M. A systematic global review of fixed air quality monitoring stations: Spatial distribution, typologies, measured pollutants, technologies, regulatory standards, and research gaps. JAPH. 2025;10(3):445-468.
