Dockery DW, Pope CA, Xu X, Spengler JD, Ware JH, Fay ME, et al. An association between air pollution and mortality in six U.S. cities. N Engl J Med. 1993;329:1753–9.

Article  CAS  PubMed  Google Scholar 

Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, et al. Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med. 2007;356:447–58.

Article  CAS  PubMed  Google Scholar 

Kirwa K, Szpiro AA, Sheppard L, Sampson PD, Wang M, Keller JP, et al. Fine-scale air pollution models for epidemiologic research: insights from approaches developed in the multi-ethnic study of atherosclerosis and air pollution (MESA Air). Curr Envir Health Rep. 2021;8:113–26.

Article  Google Scholar 

Lindström J, Szpiro AA, Sampson PD, Oron AP, Richards M, Larson TV, et al. A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates. Environ Ecol Stat. 2014;21:411–33.

Article  PubMed  Google Scholar 

Sampson PD, Szpiro AA, Sheppard L, Lindström J, Kaufman JD. Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data. Atmos Environ. 2011;45:6593–606.

Article  CAS  Google Scholar 

Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar SD, Kaufman J. Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics. 2010;21:606–31.

Article  CAS  Google Scholar 

Keller J, Olives C, Kim SY, Sheppard L, Sampson P, Szpiro A, et al. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution. Environ Health Perspect. 2015;123:301–9.

Article  PubMed  Google Scholar 

Jerrett M, Arain MA, Kanaroglou P, Beckerman B, Crouse D, Gilbert NL, et al. Modeling the intraurban variability of ambient traffic pollution in Toronto, Canada. J Toxicol Environ Health A. 2007;70:200–12.

Article  CAS  PubMed  Google Scholar 

Young MT, Bechle MJ, Sampson PD, Szpiro AA, Marshall JD, Sheppard L, et al. Satellite-based NO2 and model validation in a national prediction model based on universal kriging and land-use regression. Environ Sci Technol. 2016;50:3686–94.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ryan PH, LeMasters GK. A review of land-use regression models for characterizing intraurban air pollution exposure. Inhal Toxicol. 2007;19:127–33.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Briggs DJ, de Hoogh C, Gulliver J, Wills J, Elliott P, Kingham S, et al. A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments. Sci Total Environ. 2000;253:151–67.

Article  CAS  PubMed  Google Scholar 

Briggs DJ, Collins S, Elliott P, Fischer P, Kingham S, Lebret E, et al. Mapping urban air pollution using GIS: a regression-based approach. Int J Geogr Inf Sci. 1997;11:699–718.

Article  Google Scholar 

Ross Z, English PB, Scalf R, Gunier R, Smorodinsky S, Wall S, et al. Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses. J Expo Sci Environ Epidemiol. 2006;16:106–14.

Article  CAS  PubMed  Google Scholar 

Gilbert NL, Goldberg MS, Beckerman B, Brook JR, Jerrett M. Assessing spatial variability of ambient nitrogen dioxide in montréal, canada, with a land-use regression model. J Air Waste Manag Assoc. 2005;55:1059–63.

Article  CAS  PubMed  Google Scholar 

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 

English PB, Olmedo L, Bejarano E, Lugo H, Murillo E, Seto E, et al. The Imperial County community air monitoring network: a model for community-based environmental monitoring for public health action. Environ Health Perspect. 2017;125:074501.

Article  PubMed  PubMed Central  Google Scholar 

Gao M, Cao J, Seto E. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an, China. Environ Pollut. 2015;199:56–65.

Article  CAS  PubMed  Google Scholar 

Heimann I, Bright VB, McLeod MW, Mead MI, Popoola OAM, Stewart GB, et al. Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors. Atmos Environ. 2015;113:10–9.

Article  CAS  Google Scholar 

Ikram J, Tahir A, Kazmi H, Khan Z, Javed R, Masood U. View: implementing low cost air quality monitoring solution for urban areas. Environ Syst Res. 2012;1:10.

Article  Google Scholar 

Jiang Q, Kresin F, Bregt AK, Kooistra L, Pareschi E, van Putten E, et al. Citizen sensing for improved urban environmental monitoring. J Sens. 2016;2016:1–9.

CAS  Google Scholar 

Jiao W, Hagler G, Williams R, Sharpe R, Brown R, Garver D, et al. Community Air Sensor Network (CAIRSENSE) project: evaluation of low-costsensor performance in a suburban environment in the southeastern United States. Atmos Meas Tech. 2016;9:5281–92.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Malings C, Tanzer R, Hauryliuk A, Kumar SPN, Zimmerman N, Kara LB, et al. Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring. Atmos Meas Tech. 2019;12:903–20.

Article  Google Scholar 

Mead MI, Popoola OAM, Stewart GB, Landshoff P, Calleja M, Hayes M, et al. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos Environ. 2013;70:186–203.

Article  CAS  Google Scholar 

Moltchanov S, Levy I, Etzion Y, Lerner U, Broday DM, Fishbain B. On the feasibility of measuring urban air pollution by wireless distributed sensor networks. Sci Total Environ. 2015;502:537–47.

Article  CAS  PubMed  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 

Kumar P, Morawska L, Martani C, Biskos G, Neophytou M, Di Sabatino S, et al. The rise of low-cost sensing for managing air pollution in cities. Environ Int. 2015;75:199–205.

Article  PubMed  Google Scholar 

Jerrett M, Donaire-Gonzalez D, Popoola O, Jones R, Cohen RC, Almanza E, et al. Validating novel air pollution sensors to improve exposure estimates for epidemiological analyses and citizen science. Environ Res. 2017;158:286–94.

Article  CAS  PubMed  Google Scholar 

Bi J, Wildani A, Chang HH, Liu Y. Incorporating low-cost sensor measurements into high-resolution PM2.5 modeling at a large spatial scale. Environ Sci Technol. 2020;54:2152–62.

Article  CAS  PubMed  Google Scholar 

Bi J, Carmona N, Blanco MN, Gassett AJ, Seto E, Szpiro AA, et al. Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection. Environ Int. 2022;158:106897.

Article  CAS  PubMed  Google Scholar 

Bi J, Stowell J, Seto EYW, English PB, Al-Hamdan MZ, Kinney PL, et al. Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: a case study in Imperial County, California, USA. Environ Res. 2020;180:108810.

Article  CAS  PubMed  Google Scholar 

Bi J, Zuidema C, Clausen D, Kirwa K, Young MT, Gassett AJ, et al. Within-city variation in ambient carbon monoxide concentrations: leveraging low-cost monitors in a spatiotemporal modeling framework. Environ Health Perspect. 2022;130:097008.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gilbert NL, Woodhouse S, Stieb DM, Brook JR. Ambient nitrogen dioxide and distance from a major highway. Sci Total Environ. 2003;312:43–6.

Article  CAS  PubMed  Google Scholar 

PSCAA. 2019 Air quality data summary. Puget Sound Clean Air Agency;2020 https://pscleanair.gov/DocumentCenter/View/4164/Air-Quality-Data-Summary-2019.

US EPA. Integrated Science Assessment (ISA) for Oxides of Nitrogen – Health Criteria (Final Report, Jan 2016) [Internet]. Washington, DC: US Environmental Protection Agency; 2016. Report No.: EPA/600/R-15/068. Available from: https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=310879.

ACT-AP. Adult Changes in Thought Air Pollution Study (ACT-AP). 2021]. Available from: https://deohs.washington.edu/air-pollution-aging-brain-and-alzheimers-disease.

Kukull WA, Higdon R, Bowen JD, McCormick WC, Teri L, Schellenberg GD, et al. Dementia and Alzheimer disease incidence: a prospective cohort study. Arch Neurol. 2002;59:1737–46.

Article  PubMed  Google Scholar 

Schulte JK, Fox JR, Oron AP, Larson TV, Simpson CD, Paulsen M, et al. Neighborhood-scale spatial models of diesel exhaust concentration profile using 1-nitropyrene and other nitroarenes. Environ Sci Technol. 2015;49:13422–30.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liu LJS, Box M, Kalman D, Kaufman J, Koenig J, Larson T, et al. Exposure assessment of particulate matter for susceptible populations in Seattle. Environ Health Perspect. 2003;111:909–18.

Article  PubMed  PubMed Central  Google Scholar 

Wilton D, Szpiro A, Gould T, Larson T. Improving spatial concentration estimates for nitrogen oxides using a hybrid meteorological dispersion/land use regression model in Los Angeles, CA and Seattle, WA. Sci Total Environ. 2010;408:1120–30.

Article  CAS  PubMed