A Mobile Sensing Based Stochastic Model to Forecast AQI Variation of Pollution Hotspots on Urban Neighborhoods


Ena Jain
Debopam Acharaya


Due to massive population migration, most Indian cities have experienced fast urbanization, resulting in a significant increase in construction activity, traffic pollution, and uncontrolled expansion. Some of these cities also have a high concentration of polluting industries, significantly worsening air quality. Pollution hotspots exist in certain cities, with levels well surpassing the authorized mark. Air pollution is generally classified as extremely hyper-local, which signifies that the pollution index decreases as we travel away from hotspots. Since the pollution data collected from traditional sources is occasionally inadequate, the extended consequences of such hotspots on neighboring communities remain unidentified. If the flux in pollution values in neighboring locales is efficiently mapped for locations encountered travelling further from identified hotspots, AQI levels for these areas can be forecasted and projected. Knowledge from monitoring these levels will aid the city administrations and government in drafting suitable proposals for susceptible establishments like hospitals and schools. In this research work, the Air Quality Index (AQI) data was accurately gathered at an identified pollution hotspot and its immediate neighborhood over a defined period along a specific route and a mathematical model was developed to forecast how AQI varies with distance for best results. Stochastic models such as ARMA and ARIMA were used to create the predicted model. Its reliability and performance were measured using various forecasting error calculation methods such as MPE (Mean Percentage Error), MAP (Mean Absolute Percentage), MAD (Mean Absolute Deviation), RMSE (Root Mean Square Error), and MSE (Mean Square Error).



How to Cite
Ena Jain, & Debopam Acharaya. (2023). A Mobile Sensing Based Stochastic Model to Forecast AQI Variation of Pollution Hotspots on Urban Neighborhoods. International Journal of Next-Generation Computing, 14(2). https://doi.org/10.47164/ijngc.v14i2.1195


  1. A, T.R., ABOU-SHLEEL, S.M., EL-MOHANDES, M.A., AND EL-SHIRBENY, M.A. 2020. Assessing Open Rice Straw Burning Impacts on Air Quality of Great Cairo Based on Dispersion Models. J. Biol. Chem. Environ. Sci 15, 1, 1–20.
  2. AKIMOTO, H. 2003. Global Air Quality and Pollution. Science 302, 5651, 1716–1719. DOI: https://doi.org/10.1126/science.1092666
  3. ALI, H., SOE, J.K., AND WELLER, S.R. 2015. A real-time ambient air quality monitoring wireless sensor network for schools in smart cities. 2015 IEEE 1st International Smart Cities Conference, ISC2 2015, 3–8. DOI: https://doi.org/10.1109/ISC2.2015.7366163
  4. ALKABBANI, H., RAMADAN, A., ZHU, Q., AND ELKAMEL, A. 2022. An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach. Atmosphere 13, 7. DOI: https://doi.org/10.3390/atmos13071144
  5. ARAM, S., TROIANO, A., PASERO, E., ELETTRONICA, D., AND TORINO, P. 2012. Environment Sensing using Smartphone. 1–4. DOI: https://doi.org/10.1109/SAS.2012.6166275
  6. BARTHWAL, A. AND ACHARYA, D. 2018. An Internet of Things System for Sensing , Analysis & Forecasting Urban Air Quality. 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 1–6. DOI: https://doi.org/10.1109/CONECCT.2018.8482397
  7. BARTHWAL, A. AND ACHARYA, D. 2019. Extreme Value Analysis of Urban Air Quality using Internet of Things. International Journal of Next-Generation Computing 10, 1, 19–35.
  8. BARTHWAL, A., ACHARYA, D., AND LOHANI, D. 2019. IoT System based Forecasting and Modeling Exceedance Probability and Return Period of Air Quality using Extreme Value Distribution. 2019 IEEE Sensors Applications Symposium (SAS), 1–6. DOI: https://doi.org/10.1109/SAS.2019.8706035
  9. BURKE, J., ESTRIN, D., HANSEN, M., ET AL. 2006. Participatory Sensing. 1–5.
  10. CPCB. 2014. National Air Quality Index. Central Pollution Control Board (CPCB) January, 1–44.
  11. DOERING, M. High-Resolution Large-Scale Air Pollution Monitoring : Approaches and Challenges. 5–9.
  12. DONZELLI, G., CIONI, L., CANCELLIERI, M., LLOPIS‐MORALES, A., AND MORALES‐SUÁREZ‐VARELA, M. 2021. Relations between air quality and covid‐19 lockdown measures in valencia, spain. International Journal of Environmental Research and Public Health 18, 5, 1–11. DOI: https://doi.org/10.3390/ijerph18052296
  14. GILBERT, P., COX, L.P., AND WETHERALL, D. 2010. Toward Trustworthy Mobile Sensing. DOI: https://doi.org/10.1145/1734583.1734592
  15. GOLDMAN, J., SHILTON, K., BURKE, J., ET AL. 2009. the patterns that shape our world. May.
  16. HEDGECOCK, W., VÖLGYESI, P., LEDECZI, A., KOUTSOUKOS, X., AND ALDROUBI, A. Mobile Air Pollution Monitoring Network. 2, 795–796.
  17. JO, J., JO, B., KIM, J., KIM, S., AND HAN, W. 2020. Development of an IoT-Based indoor air quality monitoring platform. Journal of Sensors 2020, 13–15. DOI: https://doi.org/10.1155/2020/8749764
  18. KIM, Y. 2013. Mobile Observatory : an Exploratory Study of Mobile Air Quality Monitoring Application. 733–736. DOI: https://doi.org/10.1145/2494091.2495997
  19. KUMAR, S. AND JAIN, M.K. 2021. Exposure to Particulate Matter and CO2 in indoor conditions at IIT(ISM) Dhanbad. Materials Today: Proceedings xxxx. DOI: https://doi.org/10.1016/j.matpr.2021.04.496
  20. LANE, N.D., MILUZZO, E., LU, H., ET AL. 2010. A D H OC AND S ENSOR N ETWORKS A Survey of Mobile Phone Sensing. September, 140–150. DOI: https://doi.org/10.1109/MCOM.2010.5560598
  21. LEPEULE, J., LADEN, F., DOCKERY, D., AND SCHWARTZ, J. 2012. Chronic exposure to fine particles and mortality: An extended follow-up of the Harvard six cities study from 1974 to 2009. Environmental Health Perspectives 120, 7, 965–970. DOI: https://doi.org/10.1289/ehp.1104660
  22. LIU, B., ZHAO, Q., JIN, Y., SHEN, J., AND LI, C. 2021a. Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector. Scientific Reports 11, 1, 1–12. DOI: https://doi.org/10.1038/s41598-021-82871-4
  23. LIU, G., DONG, X., KONG, Z., AND DONG, K. 2021b. Does national air quality monitoring reduce local air pollution? The case of PM2.5 for China. Journal of Environmental Management 296, July, 113232. DOI: https://doi.org/10.1016/j.jenvman.2021.113232
  24. MIN, K.T., FORYS, A., AND SCHMID, T. 2014. Demonstration abstract: AirFeed — Indoor real time interactive air quality monitoring system. IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, 325–326. DOI: https://doi.org/10.1109/IPSN.2014.6846785
  25. MUN, M., REDDY, S., SHILTON, K., ET AL. 2009. PEIR , the Personal Environmental Impact Report , as a Platform for Participatory Sensing Systems Research. 55–68. DOI: https://doi.org/10.1145/1555816.1555823
  26. NIKZAD, N., VERMA, N., ZIFTCI, C., ET AL. 2010. CitiSense : Improving Geospatial Environmental Assessment of Air Quality Using a Wireless Personal Exposure Monitoring System.
  27. PREDIĆ, B., YAN, Z., EBERLE, J., STOJANOVIC, D., AND ABERER, K. 2013. ExposureSense: Integrating daily activities with air quality using mobile participatory sensing. 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 303–305. DOI: https://doi.org/10.1109/PerComW.2013.6529500
  28. QIU, H., YU, I.T.S., WANG, X., TIAN, L., TSE, L.A., AND WONG, T.W. 2013. Differential effects of fine and coarse particles on daily emergency cardiovascular hospitalizations in Hong Kong. Atmospheric Environment 64, 296–302. DOI: https://doi.org/10.1016/j.atmosenv.2012.09.060
  29. SHIN, Y., KWIATKOWSKI, D., SCHMIDT, P., AND PHILLIPS, P.C.B. 1992. Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Are Nonstationary? Journal of Econometrics 54, 1–3, 159–178. DOI: https://doi.org/10.1016/0304-4076(92)90104-Y
  30. SUN, W., LI, Q., AND THAM, C.-K. 2014. Wireless deployed and participatory sensing system for environmental monitoring. 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 158–160. DOI: https://doi.org/10.1109/SAHCN.2014.6990342
  31. TANEJA, K., AHMAD, S., AHMAD, K., AND ATTRI, S.D. 2016. Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach. Atmospheric Pollution Research 7, 4, 585–596. DOI: https://doi.org/10.1016/j.apr.2016.02.004
  32. TILAK, S. 2013. Real-World Deployments of Participatory Sensing Applications : 2013. DOI: https://doi.org/10.1155/2013/583165
  33. VAGNOLI, C., MARTELLI, F., DE FILIPPIS, T., ET AL. 2014. The SensorWebBike for air quality monitoring in a smart city. IET Seminar Digest 2014, 15564, 4–7. DOI: https://doi.org/10.1049/ic.2014.0043
  34. VÖLGYESI, P., NÁDAS, A., KOUTSOUKOS, X., AND LÉDECZI, Á. 2008. Air Quality Monitoring with SensorMap. 529–530. DOI: https://doi.org/10.1109/IPSN.2008.50
  35. WU, L. AND WANG, Y. 2009. Modelling DGM(1,1) under the Criterion of the Minimization of Mean Absolute Percentage Error. 2009 Second International Symposium on Knowledge Acquisition and Modeling, 123–126. DOI: https://doi.org/10.1109/KAM.2009.175
  36. YU, R., WU, W., XIA, N., GENG, H., AND LIU, M. 2011. Real-time Carbon Dioxide Emission Monitoring System Based on Participatory Sensing Technology. 230–235. DOI: https://doi.org/10.1109/IWACI.2011.6160008