ACT-AP Mobile Monitoring

<h1>1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; About This Document</h1> <p>This directory contains air pollution concentration data from the Adult Changes in Thought &ndash; Traffic-Related Air Pollution (ACT-TRAP) mobile monitoring study (Blanco et al., 2022).</p> <p>&nbsp;</p> <h1>2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Data Source</h1> <p>The ACT-TRAP mobile monitoring study was an air pollution monitoring campaign that was conducted between March 2019 and March 2020 in the greater Seattle area. Briefly, the campaign collected 2-minute pollutant concentrations at 309 stop locations throughout the greater Seattle area. Pollutants were simultaneously measured with high temporal resolution (measurements every 1-60 sec). These included particle number concentration (PNC, an indicator of ultrafine particulates or UFP) from four different instruments, black carbon (BC), nitrogen dioxide (NO₂), carbon dioxide (CO₂), and fine particulate matter (PM_2.5). Each stop location was visited approximately 29 times during all seasons and days of the week between the hours of approximately 5 AM and 11 PM.</p> <p>&nbsp;</p> <h1>3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Data</h1> <p>We summarized high-resolution instrument data into median (and mean) stop concentrations, and these were used to calculate annual average concentrations for each of the 309 mobile monitoring sites. Using universal kriging-partial least squares (UK-PLS) regression models with hundreds of geographic covariate predictors, we generated out-of-sample TRAP predictions for monitoring locations, cohort locations (for epidemiologic purposes), census block centroids, and a grid (for visual purposes).</p> <p>&nbsp;</p> <p>The relevant data files are:</p> <p>&nbsp;</p> <p>1.&nbsp;&nbsp;&nbsp;&nbsp; Stop-level data (stop_data.csv)</p> <p>2.&nbsp;&nbsp;&nbsp;&nbsp; Annual average estimates and predictions for the mobile monitoring sites (N=309) (monitoring_location_estimates_and_predictions.csv)</p> <p>3.&nbsp;&nbsp;&nbsp;&nbsp; Annual average model predictions for:</p> <p>a.&nbsp;&nbsp;&nbsp;&nbsp; a grid in the region (grid_predictions.csv)</p> <p>b.&nbsp;&nbsp;&nbsp;&nbsp; 2010 Census block centroids for Washington state (census_block_predictions.csv)</p> <p>4.&nbsp;&nbsp;&nbsp;&nbsp; The geographic covariates available for modeling for the: &nbsp;</p> <p>a.&nbsp;&nbsp;&nbsp;&nbsp; monitoring sites (dr0311_mobile_covars.csv)</p> <p>b.&nbsp;&nbsp;&nbsp;&nbsp; grid (dr0311_grid_covars.csv)</p> <p>c.&nbsp;&nbsp;&nbsp;&nbsp; 2010 census block covariates (block10_intpts_wa.csv)</p> <p>5.&nbsp;&nbsp;&nbsp;&nbsp; UK-PLS model performances for annual average TRAP from the mobile monitoring campaign (model_performances.csv). &nbsp;</p> <p>6.&nbsp;&nbsp;&nbsp;&nbsp; Spatial files for</p> <p>a.&nbsp;&nbsp;&nbsp;&nbsp; A polygon encompassing all of the monitoring sites. There are three versions:</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; i.&nbsp;&nbsp;&nbsp;&nbsp; The first includes all land and water areas (monitoring_area.rda) and is the simplest.</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ii.&nbsp;&nbsp;&nbsp;&nbsp; Same as above but excludes major water areas (monitoring_land.rda). This is useful for mapping, for example.</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; iii.&nbsp;&nbsp;&nbsp;&nbsp; Same as above but excludes all water areas (monitoring_land_zero_water.rda). This is the file used to make predictions to ensure that no predictions are made on bodies of water.</p> <p>&nbsp;</p> <p>On-road data were also collected while the vehicle was in motion. Please inquire if you are interested in these data.</p> <p>&nbsp;</p> <h1>4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Data Dictionaries</h1> <p>Data dictionaries for all the campaign data can be found in data_dictionaries.docx.</p> <p>&nbsp;</p> <p>Geographic covariates come from the MESA Air geodatabase. Details on these covariates can be found in MESAAirDOOP_20190501.pdf and here: <a href="https://deohs.washington.edu/sites/default/files/MESAAirDOOP_Rev12.pdf">https://deohs.washington.edu/sites/default/files/MESAAirDOOP_Rev12.pdf</a>.</p> <p>&nbsp;</p> <h1>5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Reference</h1> <p>Blanco MN, Gassett A, Gould T, Doubleday A, Slager DL, Austin E, Seto E, Larson TV, Marshall JD, Sheppard L. Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign. Environ Sci Technol. 2022 Aug 16;56(16):11460-11472. doi: 10.1021/acs.est.2c01077. Epub 2022 Aug 2. PMID: 35917479; PMCID: PMC9396693.</p> <p>&nbsp;</p>