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1. Identifying optimal co-location calibration periods for low-cost sensors

   https://doi.org/10.5194/amt-16-169-2023  

   Levy Zamora, Misti; Buehler, Colby; Datta, Abhirup; Gentner, Drew R.; Koehler, Kirsten (January 2023, Atmospheric Measurement Techniques)

   Abstract. Low-cost sensors are often co-located with reference instruments to assess their performance and establish calibration equations, but limiteddiscussion has focused on whether the duration of this calibration period can be optimized. We placed a multipollutant monitor that containedsensors that measured particulate matter smaller than 2.5 µm (PM2.5), carbon monoxide (CO), nitrogendioxide (NO2), ozone (O3), and nitric oxide (NO) at a reference field site for 1 year. We developed calibration equationsusing randomly selected co-location subsets spanning 1 to 180 consecutive days out of the 1-year period and compared the potential root-mean-square error (RMSE) and Pearson correlation coefficient (r) values. The co-located calibration period required to obtain consistent results varied bysensor type, and several factors increased the co-location duration required for accurate calibration, including the response of a sensor toenvironmental factors, such as temperature or relative humidity (RH), or cross-sensitivities to other pollutants. Using measurements fromBaltimore, MD, where a broad range of environmental conditions may be observed over a given year, we found diminishing improvements in the medianRMSE for calibration periods longer than about 6 weeks for all the sensors. The best performing calibration periods were the ones that contained arange of environmental conditions similar to those encountered during the evaluation period (i.e., all other days of the year not used in thecalibration). With optimal, varying conditions it was possible to obtain an accurate calibration in as little as 1 week for all sensors, suggestingthat co-location can be minimized if the period is strategically selected and monitored so that the calibration period is representative of thedesired measurement setting. 

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2. Fixed wavelength interferometer sensors for low-cost chem-bio sensing applications

   https://doi.org/10.1117/12.3003036  

   Shen, Jianhao; Gauna, Juan C.; Rai, Arushi; McClimans, Liam; Lawandi, Roseanna; Perera, Asela; Kango-Singh, Madhuri; Chakravarty, Swapnajit (March 2024, Proceedings of the SPIE)

   Miller, Benjamin L.; Weiss, Sharon M.; Danielli, Amos (Ed.)

   We experimentally demonstrated slow wave enhanced phase and spectral sensitivity in asymmetric Michelson interferometer sensors with a phase sensitivity 277,750 rad/RIU-cm and theoretical phase sensitivity as high as 461,810 rad/RIU-cm. In the context of low-cost chip integrated photonic packaged sensors, in this paper we will experimentally demonstrate a method for active tuning of interferometer fringes using phase change materials that will potentially overcome fabrication induced variation of interference fringe wavelengths, thus allowing sensor chip packaging with a fixed wavelength laser and available integrated photodetectors. 

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3. Calibration of low-cost particulate matter sensors for coal dust monitoring

   https://doi.org/10.1016/j.scitotenv.2022.160336  

   Amoah, Nana A.; Xu, Guang; Kumar, Ashish Ranjan; Wang, Yang (February 2023, Science of The Total Environment)
4. Low-Cost Portable Readout System Design for Inductively Coupled Resonant Sensors

   https://doi.org/10.1109/TIM.2022.3173277  

   Roy, Subhanwit; Chan, Yee Jher; Reuel, Nigel F.; Neihart, Nathan M. (January 2022, IEEE Transactions on Instrumentation and Measurement)
5. Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

   https://doi.org/10.1038/s41612-022-00293-z  

   Iyer, Shiva R.; Balashankar, Ananth; Aeberhard, William H.; Bhattacharyya, Sujoy; Rusconi, Giuditta; Jose, Lejo; Soans, Nita; Sudarshan, Anant; Pande, Rohini; Subramanian, Lakshminarayanan (October 2022, npj Climate and Atmospheric Science)

   Abstract The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments’ ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities. 

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