World Health Organization. 9 out of 10 people worldwide breathe polluted air, but more countries are taking action. 2018.

Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet. 2017;389:1907–18.

Article  PubMed  PubMed Central  Google Scholar 

International Agency for Research on Cancer. Outdoor Air Pollution (Vol. 109). Lyon; 2016.

Environmental Protection Agency. NAAQS Table. 2010. Available from: https://www.epa.gov/criteria-air-pollutants/naaqs-table

Apte J, Messier K, Gani S, Brauer M, Kirchstetter T, Lunden M, et al. High-resolution air pollution mapping with Google Street View cars: exploiting big data (Supplemental Material). Environ Sci Technol. 2017;51:6999–7008.

Article  CAS  PubMed  Google Scholar 

Maryland Department of the Environment. Ambient Air Monitoring Network Plan for Calendar Year 2019. Baltimore; 2018.

Ye Q, Li HZ, Gu P, Robinson ES, Apte JS, Sullivan RC, et al. Moving beyond fine particle mass: High-spatial resolution exposure to source-resolved atmospheric particle number and chemical mixing state. Environ Health Perspect. 2020;128.

Saha PK, Sengupta S, Adams P, Robinson AL, Presto AA. Spatial correlation of ultrafine particle number and fine particle mass at urban scales: implications for health assessment. Environ Sci Technol. 2020;54:9295–304.

Article  CAS  PubMed  Google Scholar 

Snyder EG, Watkins TH, Solomon PA, Thoma ED, Williams RW, Hagler GSW, et al. The changing paradigm of air pollution monitoring. Environ Sci Technol. 2013;47:11369–77.

Article  CAS  PubMed  Google Scholar 

Piedrahita R, Xiang Y, Masson N, Ortega J, Collier A, Jiang Y, et al. The next generation of low-cost personal air quality sensors for quantitative exposure monitoring. Atmos Meas Tech. 2014;7:3325–36.

Article  Google Scholar 

Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar SD, Kaufman JD. Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics. 2009;21:n/a–n/a.

Article  Google Scholar 

Buehler C, Xiong F, Levy Zamora M, Skog K, Kohrman-Glaser J, Colton S, et al. Stationary and portable multipollutant monitors for high spatiotemporal resolution air quality studies including online calibration. Atmos Measurement Tech. 2020;in review.

Datta A, Saha A, Zamora ML, Buehler C, Hao L, Xiong F, et al. Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore. Atmos Environ. 2020;242:117761.

Article  CAS  Google Scholar 

Morawska L, Thai PK, Liu X, Asumadu-Sakyi A, Ayoko G, Bartonova A, et al. Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: how far have they gone? Vol. 116, Environment International. Elsevier Ltd; 2018. 286–99.

Levy Zamora M, Xiong F, Gentner D, Kerkez B, Kohrman-Glaser J, Koehler K. Field and laboratory evaluations of the low-cost plantower particulate matter sensor. Environ Sci Technol. 2019;53:838–49.

Article  CAS  PubMed  Google Scholar 

Borrego C, Ginja J, Coutinho M, Ribeiro C, Karatzas K, Sioumis T, et al. Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II. Atmos Environ. 2018;193:127–42.

Article  CAS  Google Scholar 

Brokamp C, Jandarov R, Rao MB, LeMasters G, Ryan P. Exposure assessment models for elemental components of particulate matter in an urban environment: a comparison of regression and random forest approaches. Atmos Environ. 2017;151:1–11.

Article  CAS  Google Scholar 

Loh BG, Choi GH. Calibration of portable particulate matter–monitoring device using web query and machine learning. Saf Health Work 2019;10:452–60.

Article  PubMed  PubMed Central  Google Scholar 

Lim CC, Kim H, Vilcassim MJR, Thurston GD, Gordon T, Chen LC, et al. Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea. Environ Int. 2019;131:105022.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zimmerman N, Presto AA, Kumar SPN, Gu J, Hauryliuk A, Robinson ES, et al. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmos Meas Tech. 2018;11:291–313.

Article  Google Scholar 

EPA. Risk Assessment Forum White Paper: Probabilistic Risk Assessment Methods and Case Studies. 2014. Available from: https://www.epa.gov/sites/production/files/2014-12/documents/raf-pra-white-paper-final.pdf

NIOSH. How NIOSH Conducts Risk Assessments. 2017. Available from: https://www.cdc.gov/niosh/topics/riskassessment/how.html

Daniels R, Gilbert S, Kuppusamy S, Kuempel E, Park R, Pandalai S, et al. Current Intelligence Bulletin 69 - NIOSH Practices in Occupational Risk Assessment. 2020.

Patton AN, Medvedovsky K, Zuidema C, Peters TM, Koehler K. Probabilistic machine learning with low-cost sensor networks for occupational exposure assessment and industrial hygiene decision making. Ann Work Exposures Health. 2022;66:580–90.

Article  Google Scholar 

Buehler C, Xiong F, Zamora ML, Skog KM, Kohrman-Glaser J, Colton S, et al. Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration. Atmos Meas Tech. 2021;14:995–1013.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Duan T, Avati A, Ding DY, Thai KK, Basu S, Ng AY, et al. NGBoost: Natural Gradient Boosting for Probabilistic Prediction. 2019.

Gneiting T, Raftery AE. Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc. 2007;102:359–78.

Article  CAS  Google Scholar 

Heffernan C, Peng R, Gentner DR, Koehler K, Datta A. Gaussian Process filtering for calibration of low-cost air-pollution sensor network data. arXiv. 2022 [cited 2022 Jun 7]. Report No.: arXiv:2203.14775. Available from: http://arxiv.org/abs/2203.14775

Baltimore City Department of Health. Neighborhood Health Profiles - Frequently Asked Questions | Baltimore City Health Department. 2017 [cited 2020 Sep 30]. Available from: https://health.baltimorecity.gov/node/231

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.

Chang FJ, Chang LC, Kang CC, Wang YS, Huang A. Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques. Sci Total Environ. 2020;736:139656.

Article  CAS  PubMed  Google Scholar 

Huang K, Xiao Q, Meng X, Geng G, Wang Y, Lyapustin A, et al. Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain. Environ Pollut. 2018;242:675–83.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhan Y, Luo Y, Deng X, Grieneisen ML, Zhang M, Di B. Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ Pollut. 2018;233:464–73.

Article  CAS  PubMed  Google Scholar 

Zhao Z, Qin J, He Z, Li H, Yang Y, Zhang R. Combining forward with recurrent neural networks for hourly air quality prediction in Northwest of China. Environ Sci Pollut Res. 2020;1–18.