{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:09:22Z","timestamp":1783570162761,"version":"3.55.0"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"IntAirSense: Intelligent Air Pollution Monitoring for Smart Cities using Low-Cost Sensors"},{"name":"Dept. of Higher Education, Science & Technology and Biotechnology (DHESTBT), Government of West Bengal, India"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this article propose a framework named\n            <jats:italic>AQuaMoHo<\/jats:italic>\n            that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core,\n            <jats:italic>AQuaMoHo<\/jats:italic>\n            exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that\n            <jats:italic>AQuaMoHo<\/jats:italic>\n            can significantly help annotate the air quality data on a personal scale.\n          <\/jats:p>","DOI":"10.1145\/3580279","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T12:05:46Z","timestamp":1673957146000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["<i>AQuaMoHo<\/i>\n            : Localized Low-cost Outdoor Air Quality Sensing over a Thermo-hygrometer"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5656-6340","authenticated-orcid":false,"given":"Prithviraj","family":"Pramanik","sequence":"first","affiliation":[{"name":"NIT Durgapur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7345-1406","authenticated-orcid":false,"given":"Prasenjit","family":"Karmakar","sequence":"additional","affiliation":[{"name":"IIT Kharagpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0706-1610","authenticated-orcid":false,"given":"Praveen Kumar","family":"Sharma","sequence":"additional","affiliation":[{"name":"ITER SOA Bhubaneswar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5604-2267","authenticated-orcid":false,"given":"Soumyajit","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"IIT Kharagpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6921-5630","authenticated-orcid":false,"given":"Abhijit","family":"Roy","sequence":"additional","affiliation":[{"name":"NIT Durgapur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5222-1150","authenticated-orcid":false,"given":"Santanu","family":"Mandal","sequence":"additional","affiliation":[{"name":"NIT Durgapur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8743-4770","authenticated-orcid":false,"given":"Subrata","family":"Nandi","sequence":"additional","affiliation":[{"name":"NIT Durgapur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3531-968X","authenticated-orcid":false,"given":"Sandip","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"IIT Kharagpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4432-6307","authenticated-orcid":false,"given":"Mousumi","family":"Saha","sequence":"additional","affiliation":[{"name":"NIT Durgapur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4483-5758","authenticated-orcid":false,"given":"Sujoy","family":"Saha","sequence":"additional","affiliation":[{"name":"NIT Durgapur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,3]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2002. Measurement of PM10 particles. Retrieved from https:\/\/projects.nilu.no\/ccc\/manual\/documents\/03_15-Measurementofpm10particles.htm."},{"key":"e_1_3_2_3_2","unstructured":"2018. How air pollution is destroying our health. Retrieved from https:\/\/www.who.int\/news-room\/spotlight\/how-air-pollution-is-destroying-our-health."},{"key":"e_1_3_2_4_2","unstructured":"2021. Aeroqual: Air Quality Monitoring Equipment. Retrieved from https:\/\/www.aeroqual.com\/."},{"key":"e_1_3_2_5_2","unstructured":"2021. AirBeam: Share & Improve Your Air. Retrieved from https:\/\/www.kickstarter.com\/projects\/741031201\/airbeam-share-and-improve-your-air."},{"key":"e_1_3_2_6_2","unstructured":"2021. Measure PM and CO2 Temp Humidity with Airveda Monitors: Breathe Well. Retrieved from http:\/\/www.airveda.com\/."},{"key":"e_1_3_2_7_2","unstructured":"2021. Plume Labs: Be Empowered Against Air Pollution. Retrieved from https:\/\/flow.plumelabs.com\/."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2016.11.004"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3274167"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1145\/3063386.3063771","volume-title":"2nd International Workshop on Science of Smart City Operations and Platforms Engineering","author":"Catlett Charles E.","year":"2017","unstructured":"Charles E. Catlett, Peter H. Beckman, Rajesh Sankaran, and Kate Kusiak Galvin. 2017. Array of things: A scientific research instrument in the public way: Platform design and early lessons learned. In 2nd International Workshop on Science of Smart City Operations and Platforms Engineering. 26\u201333."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18092843"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2723919"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2016.02.003"},{"key":"e_1_3_2_15_2","article-title":"Attention-based models for speech recognition","volume":"28","author":"Chorowski Jan K.","year":"2015","unstructured":"Jan K. Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio. 2015. Attention-based models for speech recognition. Adv. Neural Inf. Process. Syst. 28 (2015).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-79064-w"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446005"},{"key":"e_1_3_2_18_2","first-page":"1","article-title":"Calibrating networks of low-cost air quality sensors","author":"deSouza Priyanka","year":"2022","unstructured":"Priyanka deSouza, Ralph Kahn, Tehya Stockman, William Obermann, Ben Crawford, An Wang, James Crooks, Jing Li, and Patrick Kinney. 2022. Calibrating networks of low-cost air quality sensors. Atmos. Measur. Techniq. Discuss. 15, 21 (2022), 1\u201334.","journal-title":"Atmos. Measur. Techniq. Discuss."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2018.00133"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2793950"},{"issue":"20","key":"e_1_3_2_21_2","first-page":"11545","article-title":"Intracity variability of particulate matter exposure is driven by carbonaceous sources and correlated with land-use variables","volume":"52","author":"Gu Peishi","year":"2018","unstructured":"Peishi Gu, Hugh Z. Li, Qing Ye, Ellis S. Robinson, Joshua S. Apte, Allen L. Robinson, and Albert A. Presto. 2018. Intracity variability of particulate matter exposure is driven by carbonaceous sources and correlated with land-use variables. Environ. Sci. Technol. 52, 20 (2018), 11545\u201311554.","journal-title":"Environ. Sci. Technol."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2020.06.016"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3267694"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2008.05.057"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/11748625_6"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2022.108008"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-60102-6"},{"key":"e_1_3_2_28_2","article-title":"Modeling PM2.5 urban pollution using machine learning and selected meteorological parameters","volume":"2017","author":"Deters Jan Kleine","year":"2017","unstructured":"Jan Kleine Deters, Rasa Zalakeviciute, Mario Gonzalez, and Yves Rybarczyk. 2017. Modeling PM2.5 urban pollution using machine learning and selected meteorological parameters. J. Electric. Comput. Eng. 2017 (2017).","journal-title":"J. Electric. Comput. Eng."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8258144"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0025101"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2008.917477"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2019.05.032"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3274895.3274907"},{"issue":"1","key":"e_1_3_2_34_2","first-page":"1","article-title":"Third-eye: A mobilephone-enabled crowdsensing system for air quality monitoring","volume":"2","author":"Liu Liang","year":"2018","unstructured":"Liang Liu, Wu Liu, Yu Zheng, Huadong Ma, and Cheng Zhang. 2018. Third-eye: A mobilephone-enabled crowdsensing system for air quality monitoring. ACM Interact. Mob. Wear. Ubiq. Technol. 2, 1 (2018), 1\u201326.","journal-title":"ACM Interact. Mob. Wear. Ubiq. Technol."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2020.109438"},{"issue":"1","key":"e_1_3_2_36_2","first-page":"1","article-title":"Exploring the relationship between air pollution and meteorological conditions in China under environmental governance","volume":"10","author":"Liu Yansui","year":"2020","unstructured":"Yansui Liu, Yang Zhou, and Jiaxin Lu. 2020. Exploring the relationship between air pollution and meteorological conditions in China under environmental governance. Sci. Rep. 10, 1 (2020), 1\u201311.","journal-title":"Sci. Rep."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3549548"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450268.3453535"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1038\/jes.2012.126"},{"key":"e_1_3_2_40_2","unstructured":"Richard E. Peltier N\u00faria Castell Andrea L. Clements Tim Dye Christoph H\u00fcglin Jesse H. Kroll Shih-Chun Candice Lung Zhi Ning Matthew Parsons Michele Penza F. Reisen and E. von Schneidemesser. 2021. An Update on Low-cost Sensors for the Measurement of Atmospheric Composition December 2020 (WMO; 1215) Geneva: World Meteorological Organization (WMO) 90. https:\/\/library.wmo.int\/doc_num.php?explnum_id=10620."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3382120"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2018.00221"},{"key":"e_1_3_2_43_2","unstructured":"PurpleAir Inc.2015. PurpleAir | Real Time Air Quality Monitoring. Retrieved from https:\/\/www2.purpleair.com\/."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2823740"},{"key":"e_1_3_2_45_2","volume-title":"Mathematical Statistics and Data Analysis","author":"Rice John A.","year":"2006","unstructured":"John A. Rice. 2006. Mathematical Statistics and Data Analysis. Cengage Learning."},{"key":"e_1_3_2_46_2","first-page":"91","volume-title":"8th ACM International Conference on Systems for Energy-efficient Buildings, Cities, and Transportation","author":"Sharma Praveen Kumar","year":"2021","unstructured":"Praveen Kumar Sharma, Prasenjit Karmakar, Soumyajit Chatterjee, Abhijit Roy, Santanu Mandal, Sandip Chakraborty, Subrata Nandi, and Sujoy Saha. 2021. Can I go for a roof walk today? Know your housing\u2019s air quality from a thermo-hygrometer. In 8th ACM International Conference on Systems for Energy-efficient Buildings, Cities, and Transportation. 91\u2013100."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1080\/09593330.2019.1640290"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2019.101997"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1021\/es5034074"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2019.105161"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005574"},{"key":"e_1_3_2_52_2","article-title":"Multitask air-quality prediction based on LSTM-autoencoder model","author":"Xu Xinghan","year":"2019","unstructured":"Xinghan Xu and Minoru Yoneda. 2019. Multitask air-quality prediction based on LSTM-autoencoder model. IEEE Trans. Cyber. 51, 5 (2019).","journal-title":"IEEE Trans. Cyber."},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219822"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01199"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3010316"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2488188"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788573"}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580279","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580279","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:19Z","timestamp":1750182559000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":56,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,8,31]]}},"alternative-id":["10.1145\/3580279"],"URL":"https:\/\/doi.org\/10.1145\/3580279","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3]]},"assertion":[{"value":"2022-04-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-15","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}