Abstract
Air quality management is a public health priority at the global scale. Accurate air quality monitoring along with understanding the sources of air pollution is the first step to adequate air quality management. Apart from sampler-assisted ground-based monitoring of air pollutants, the use of geospatial technologies and the deployment of field sensors have surfaced as a new hope for strengthening the air quality monitoring network. This review provides information on the types, characteristics, and robustness of field sensors and geospatial technologies that are used for air quality monitoring and management. The technology used in sensors and the methodology for geospatial technologies have been discussed. We conclude that the evolving network of field sensors and cutting-edge geospatial technologies will certainly lead to better air quality management in India. The efforts in this direction will not only provide a sustainable solution to the current crisis of air pollution but also lead to the collection of highly time-resolved data from even remote and least studied hard areas where ground-based sampling is a limitation. The airshed approach in this context offers a sustainable solution by targeting and synergising air pollution management across administrative boundaries. The synergy between ground-based stations, geospatial technologies, and field sensors will lead to a hub of data resources that will help policymakers frame policies for air quality management. Additionally, this will be an asset to researchers working in the field of atmospheric chemistry and pollutant dynamics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbots, H. (2014). Air assessment for the Nantahala and Pisgah NFs (pp. 1–41). https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/stelprd3795477.pdf
Agrawal, G., Mohan, D., & Rahman, H. (2021). Ambient air pollution in selected small cities in India: Observed trends and future challenges. IATSS Research, 45(1), 19–30. https://doi.org/10.1016/j.iatssr.2021.03.004
Aleixandre, M., & Gerboles, M. (2012). Review of small commercial sensors for indicative monitoring of ambient gas. Chemical Engineering Transactions, 30(x), 169–174. https://doi.org/10.3303/CET1230029
Arabia, S. (2019). Monitoring air pollution using satellite data, x, 772–780.
Balakrishnan, K., Ganguli, B., Ghosh, S., Sambandam, S., Roy, S. S., & Chatterjee, A. (2013). A spatially disaggregated time-series analysis of the short-term effects of particulate matter exposure on mortality in Chennai, India. Air Quality, Atmosphere and Health, 6, 111–121. https://doi.org/10.1007/s11869-011-0151-6
Balakrishnan, K., Cohen, A., & Smith, K. R. (2014). Perspectives | Editorial addressing the burden of disease attributable to air pollution in India: The need to integrate across household. Environmental Health Perspectives, 1, 6–7. https://doi.org/10.1289/ehp.1002517.U.S
Beig, G., Chate, D. M., Sahu, S. K., Parkhi, N. S., Srinivas, R., Ali, K., Ghude, S., Yadav, S., & Trimbake, H. K. (2015). GAW Report No. 217 System of Air Quality Forecasting and Research (SAFAR—India) (Vol. 41, Issue 217).
Brauer, M., Guttikunda, S. K., Nishad, K. A., Dey, S., Tripathi, S. N., Weagle, C., & Martin, R. V. (2019). Examination of monitoring approaches for ambient air pollution: A case study for India. Atmospheric Environment, 216(December 2018), 116940. https://doi.org/10.1016/j.atmosenv.2019.116940
Chauhan, A., Singh, R. P., Matsumi, Y., Hayashida, S., Nakayama, T., Gupta, S. K., & Shukla, D. P. (2022). Variability of the particulate matter concentration in the northern parts of India using low-cost sensors. In IGARSS 2022–2022 IEEE international geoscience and remote sensing symposium (pp. 6686–6689). IEEE.
CPCB. (2003). Guidelines for ambient air quality monitoring guidelines for ambient air quality monitoring.
CPCB. (2011). Guidelines for the measurement of ambient air pollutants guidelines for real time sampling guidelines for real time sampling.
CPCB. (2014). National air quality index.
Dey, S., Di Girolamo, L., van Donkelaar, A., Tripathi, S. N., Gupta, T., & Mohan, M. (2012). Variability of outdoor fine particulate (PM2.5) concentration in the Indian Subcontinent: A remote sensing approach. Remote Sensing of Environment, 127, 153–161. https://doi.org/10.1016/j.rse.2012.08.021
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E., Kahn, R. A., Martonchik, J. V., Ackerman, T. P., Davies, R., Gerstl, S. A. W., Gordon, H. R., Muller, J., Myneni, R. B., Sellers, P. J., Pinty, B., & Verstraete, M. M. (1998). Multiangle Imaging Spectro Radiometer (MISR) instrument description and experiment overview. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1072–1087. https://doi.org/10.1109/36.700992
Feng, F., & Wang, K. (2019). Does the modern-era retrospective analysis for research and applications-2 aerosol reanalysis introduce an improvement in the simulation of surface solar radiation over China? International Journal of Climatology, 39(3), 1305–1318. https://doi.org/10.1002/joc.5881
Gaines Wilson, J., & Zawar-Reza, P. (2006). Intraurban-scale dispersion modelling of particulate matter concentrations: Applications for exposure estimates in cohort studies. Atmospheric Environment, 40(6), 1053–1063. https://doi.org/10.1016/j.atmosenv.2005.11.026
Ganguly, T., Selvaraj, K. L., & Guttikunda, S. K. (2020). National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans. Atmospheric Environment: X, 8, 100096. https://doi.org/10.1016/j.aeaoa.2020.100096
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., et al. (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1
Gordon, T., Balakrishnan, K., Dey, S., Rajagopalan, S., Thornburg, J., Thurston, G., Agrawal, A., Collman, G., Guleria, R., & Limaye, S. (2018). Air pollution health research priorities for India: Perspectives of the Indo-U.S. Communities of Researchers. Environment International, 119(March), 100–108. https://doi.org/10.1016/j.envint.2018.06.013
Greene, D. S. (2005). Comparison between Tapered Element Microbalance (TEOM) and Federal Reference Method (FRM) for PM 2.5 measurement in East Tennessee. TRACE: Tennessee Research and Creative Exchange.
Guttikunda, S. K., Nishadh, K. A., & Jawahar, P. (2019). Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Climate, 27(August 2018), 124–141. https://doi.org/10.1016/j.uclim.2018.11.005
Guttikunda, S., Ka, N., Ganguly, T., & Jawahar, P. (2023). Plugging the ambient air monitoring gaps in India’s national clean air programme (NCAP) airsheds. Atmospheric Environment, 301(March 2022), 119712. https://doi.org/10.1016/j.atmosenv.2023.119712
Hadjimitsis, D. G., Themistocleous, K., & Nisantzi, A. (2012). Air pollution monitoring using earth observation & GIS. In Air pollution—Monitoring, modelling and health. IntechOpen, May 2014. https://doi.org/10.5772/33939
Han, Y., van Paul, D., Quanhua, L., Fuzhong, W., Yan, B., Treadon, R., & John, D. (2006, August). NOAA technical report NESDIS 122 JCSDA Community Radiative Transfer Model (CRTM)—Version 1. NOAA Tech Report. http://docs.lib.noaa.gov/noaa_documents/NESDIS/TR_NESDIS/TR_NESDIS_122.pdf
Jia, Q. (2019). Urban air quality assessment method based on gis technology. Applied Ecology and Environmental Research, 17(4), 9367–9375. https://doi.org/10.15666/aeer/1704_93679375
Juginović, A., Vuković, M., Aranza, I., & Biloš, V. (2021). Health impacts of air pollution exposure from 1990 to 2019 in 43 European countries. Scientific Reports, 0123456789, 1–15. https://doi.org/10.1038/s41598-021-01802-5
Jung, Y. J., Lee, Y. K., Lee, D. G., Ryu, K. H., & Nittel, S. (2008). Air pollution monitoring system based on geosensor network. In IGARSS 2008—2008 IEEE International Geoscience and Remote Sensing Symposium (pp. III-1370–III-1373). https://doi.org/10.1109/IGARSS.2008.4779615
Kahn, R. A., Gaitley, B. J., Garay, M. J., Diner, D. J., Eck, T. F., Smirnov, A., & Holben, B. N. (2010). Multiangle Imaging Spectro Radiometer global aerosol product assessment by comparison with the Aerosol Robotic Network. Journal of Geophysical Research, 115(D23), D23209. https://doi.org/10.1029/2010JD014601
Kar, J., Deeter, M. N., Fishman, J., Liu, Z., Omar, A., Creilson, J. K., Trepte, C. R., Vaughan, M. A., & Winker, D. M. (2010). Wintertime pollution over the Eastern Indo-Gangetic Plains as observed from MOPITT, CALIPSO and tropospheric ozone residual data. Atmospheric Chemistry and Physics, 10(24), 12273–12283. https://doi.org/10.5194/acp-10-12273-2010
Kaur, K., & Kelly, K. E. (2022). Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah. Atmospheric Measurement Techniques, 16(December), 1–27.
Kumar, A., Singh, I. P., & Sud, S. K. (2011). Energy efficient and low-cost indoor environment monitoring system based on the IEEE 1451 standard. IEEE Sensors Journal, 11(10), 2598–2610. https://doi.org/10.1109/JSEN.2011.2148171
Kumar, A., Kim, H., & Hancke, G. P. (2013). Environmental monitoring systems: A review. IEEE Sensors Journal, 13(4), 1329–1339. https://doi.org/10.1109/JSEN.2012.2233469
Kumar, V., Senarathna, S. D., Gurajala, S., Olsen, W., Sur, S., Mondal, S., & Dhaniyala, S. (2023). Understanding the source components captured by the Purple Air Network. ChemRxiv, 1–39. https://doi.org/10.26434/chemrxiv-2023-7wtxs
Kushwaha, M., Sumi, M., Arora, P., Dye, T., & Matte, T. (2022). Integrated use of low-cost sensors to strengthen air quality management in Indian cities.
Li, Q. F., Wang-Li, L., Liu, Z., & Heber, A. J. (2012). Field evaluation of particulate matter measurements using tapered element oscillating microbalance in a layer house. Journal of the Air and Waste Management Association, 62(3), 322–335. https://doi.org/10.1080/10473289.2011.650316
Liu, Z., Liu, D., Huang, J., Vaughan, M., Uno, I., Sugimoto, N., Kittaka, C., Trepte, C., Wang, Z., Hostetler, C., & Winker, D. (2008). Airborne dust distributions over the Tibetan Plateau and surrounding areas derived from the first year of CALIPSO lidar observations. Atmospheric Chemistry and Physics, 8(16), 5045–5060. https://doi.org/10.5194/acp-8-5045-2008
Malings, C., Tanzer, R., Hauryliuk, A., Kumar, S. P. N., Zimmerman, N., Kara, L. B., & Presto, A. A. (2019). Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring. Atmospheric Measurement Techniques, 12(2), 903–920. https://doi.org/10.5194/amt-12-903-2019
Mishra, S. S., & Parasar, D. (2021). Application of Geospatial Artificial Intelligence in mapping of air pollutants in urban cities. Engineering and Technology Journal for Research and Innovation (ETJRI), III(Ii), 21–27.
MOEF & CC. (2019). National clean air programme. Press Information Bureau, GoI, pp. 1–8. https://pib.gov.in/PressReleseDetail.aspx?PRID=1559384
Mohan, M., & Kandya, A. (2015). Impact of urbanisation and land-use/land-cover change on diurnal temperature range: A case study of tropical urban airshed of India using remote sensing data. Science of the Total Environment, 506–507, 453–465. https://doi.org/10.1016/j.scitotenv.2014.11.006
Nagendra, S. M. S., Yasa, P. R., Narayana, M. V., Khadirnaikar, S., & Rani, P. (2018). Mobile monitoring of air pollution using low cost sensors to visualise spatio-temporal variation of pollutants at urban hotspots. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2018.10.006
Nandakumar, V., Bishop, D., Alonas, E., LaBelle, J., Joshi, L., & Alford, T. L. (2011). A low-cost electrochemical biosensor for rapid bacterial detection. IEEE Sensors Journal, 11(1), 210–216. https://doi.org/10.1109/JSEN.2010.2055847
Pande, P., Dey, S., Chowdhury, S., Choudhary, P., Ghosh, S., Srivastava, P., & Sengupta, B. (2018). Seasonal transition in PM 10 exposure and associated all-cause mortality risks in India [Research-article]. Environmental Science & Technology, 52, 8756–8763. https://doi.org/10.1021/acs.est.8b00318
Pant, P., Lal, R. M., Guttikunda, S. K., Russell, A. G., Nagpure, A. S., Ramaswami, A., & Peltier, R. E. (2019). Monitoring particulate matter in India: Recent trends and future outlook. Air Quality, Atmosphere and Health, 12(1), 45–58. https://doi.org/10.1007/s11869-018-0629-6
Prakash, J., Choudhary, S., Raliya, R., Chadha, T., & Fang, J. (2022). PM sensors as an indicator of overall air quality: Pre-COVID and COVID periods. Atmospheric Pollution Research, 13(11), 101594. https://doi.org/10.1016/j.apr.2022.101594
Rai, A. C., Kumar, P., Pilla, F., Skouloudis, A. N., Di, S., Ratti, C., Yasar, A., & Rickerby, D. (2017). End-user perspective of low-cost sensors for outdoor air pollution monitoring. Science of the Total Environment, 607–608, 691–705. https://doi.org/10.1016/j.scitotenv.2017.06.266
Report, R. A. (2020). RIHN annual report. https://digitalcommons.macalester.edu/tapestries/vol10/iss1/2/
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., et al. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14), 3624–3648. https://doi.org/10.1175/JCLI-D-11-00015.1
Rohayu Haron Narashid, & Wan Mohd Naim Wan Mohd. (2010). Air quality monitoring using remote sensing and GIS technologies. In 2010 international conference on science and social research (CSSR 2010), Cssr (pp. 1186–1191). https://doi.org/10.1109/CSSR.2010.5773713
Sahu, R., Dixit, K. K., Mishra, S., Kumar, P., Shukla, A. K., Sutaria, R., Tiwari, S., & Tripathi, S. N. (2020). Validation of low-cost sensors in measuring PM10 concentrations at two sites in Delhi National Capital Region. Sensors, 20(January), 1347.
Sahu, R., Nagal, A., Dixit, K. K., Unnibhavi, H., Mantravadi, S., & Nair, S. (2021). Robust statistical calibration and characterisation of portable low-cost air quality monitoring sensors to quantify real-time O3 and NO2 concentrations in diverse environments. Atmospheric Measurement Techniques, 2, 37–52.
Shukla, K., & Aggarwal, S. G. (2022). A technical overview on beta-attenuation method for the monitoring of particulate matter in ambient air. Aerosol and Air Quality Research, 22(12), 220195. https://doi.org/10.4209/aaqr.220195
Singh, R. P. (2016). Asian atmospheric pollution sources, characteristics and impacts. Elsevier.
Singh, D., Dahiya, M., Kumar, R., & Nanda, C. (2021). Sensors and systems for air quality assessment monitoring and management: A review. Journal of Environmental Management, 289(November 2020), 112510. https://doi.org/10.1016/j.jenvman.2021.112510
Singh, D., Dahiya, M., & Nanda, C. (2022). Geospatial view of air pollution and health risk over North Indian region in COVID-19 scenario. Journal of the Indian Society of Remote Sensing, 50(6), 1145–1162. https://doi.org/10.1007/s12524-022-01520-z
Snider, G., Weagle, C. L., Martin, R. V., Van Donkelaar, A., Conrad, K., Cunningham, D., & Gordon, C. (2015). SPARTAN: A global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health. Atmospheric Measurement Techniques, 8(June 2014), 505–521. https://doi.org/10.5194/amt-8-505-2015
Snider, G., Weagle, C. L., Murdymootoo, K. K., Ring, A., Ritchie, Y., Stone, E., Walsh, A., Akoshile, C., Anh, N. X., Balasubramanian, R., Brook, J., Qonitan, F. D., Dong, J., Griffith, D., He, K., Holben, B. N., Kahn, R., & Lagrosas, N. (2016). Variation in global chemical composition of PM 2.5: Emerging results from SPARTAN. Atmospheric Chemistry and Physics, 16(15), 9629–9653. https://doi.org/10.5194/acp-16-9629-2016
Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R. W., Hagler, G. S. W., Shelow, D., Hindin, D. A., Kilaru, V. J., & Preuss, P. W. (2013). The changing paradigm of air pollution monitoring. Environmental Science & Technology, 47(20), 11369–11377. https://doi.org/10.1021/es4022602
Taloor, A. K., Singh, A. K., Kumar, P., Kumar, A., Tripathi, J. N., Kumari, M., Kotlia, B. S., Kothyari, G. C., Tiwari, S. P., & Johnson, B. A. (2022). Geospatial technology-based analysis of air quality in India during the COVID-19 pandemic. Remote Sensing, 14(18), 4650. https://doi.org/10.3390/rs14184650
Tripathi, S., Jain, V., Mukherjee, A., Banerjee, S., & Rai, P. (2023). Predicting PM 2.5 based on microsatellite imagery and low-cost sensor network using CNN-RT-RF Joint Model.
Venkataraman, C., Brauer, M., Tibrewal, K., Sadavarte, P., Ma, Q., & Cohen, A. (2018). Source influence on emission pathways and ambient PM 2.5 pollution over India (2015–2050). Atmospheric Chemistry and Physics, 18(11), 8017–8039.
World Health. (2006). Air quality guidelines. Air Quality Guidelines, 91, 1–496.
Yi, W., Lo, K., Mak, T., Leung, K., Leung, Y., & Meng, M. (2015). A survey of wireless sensor network based air pollution monitoring systems. Sensors, 15(12), 31392–31427. https://doi.org/10.3390/s151229859
Zawar-Reza, P., & Sturman, A. (2008). Application of airshed modelling to the implementation of the New Zealand National Environmental Standards for air quality. Atmospheric Environment, 42(38), 8785–8794. https://doi.org/10.1016/j.atmosenv.2008.07.045
Zheng, T., Bergin, M. H., Johnson, K. K., Tripathi, S. N., Shirodkar, S., Landis, M. S., Sutaria, R., & Carlson, D. E. (2018). Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments. Atmospheric Measurement Techniques, 11, 4823–4846.
Zheng, T., Bergin, M. H., Sutaria, R., Tripathi, S. N., & Caldow, R. (2019). Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi. Atmospheric Measurement Techniques, 12(9), 5161–5181.
Acknowledgements
KS acknowledges the University Grants Commission (UGC), India, for financial assistance in the form of Junior Research Fellowship (JRF).
Conflict of Interest
The authors confirm that there are no apparent financial conflicts of interest or personal affiliations that may have potentially impacted the work presented in this chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sharma, K., Yadav, S. (2023). Air Quality Monitoring Using Geospatial Technology and Field Sensors. In: Mushtaq, F., Farooq, M., Mukherjee, A.B., Ghosh Nee Lala, M. (eds) Geospatial Analytics for Environmental Pollution Modeling. Springer, Cham. https://doi.org/10.1007/978-3-031-45300-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-45300-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45299-4
Online ISBN: 978-3-031-45300-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)