Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM_2.5 during wildfires. We estimated PM_2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R² of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM_2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM_2.5 concentrations during a major wildfire event

A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM_2.5 in the Contiguous United States

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM_2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM_2.5, land use and traffic indicators. Normalized cross-validated <i>R</i>² values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated <i>R</i>² were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM_2.5 at multiple scales over the contiguous U.S

A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM_2.5 in the Contiguous United States

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM_2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM_2.5, land use and traffic indicators. Normalized cross-validated <i>R</i>² values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated <i>R</i>² were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM_2.5 at multiple scales over the contiguous U.S

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA

<p>The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The <i>R</i>² for converted hourly averaged Dylos mass measurements versus a PM_2.5 BAM was 0.79 and that versus a PM₁₀ BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM_2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (<i>R</i>² = 0.35–0.81).</p> <p><i>Implications</i>: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.</p

Development of Land Use Regression Models for PM_2.5, PM_2.5 Absorbance, PM₁₀ and PM_coarse in 20 European Study Areas; Results of the ESCAPE Project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM_2.5, PM_2.5 absorbance, PM₁₀, and PM_coarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (<i>R</i>²) was 71% for PM_2.5 (range across study areas 35–94%). Model <i>R</i>² was higher for PM_2.5 absorbance (median 89%, range 56–97%) and lower for PM_coarse (median 68%, range 32– 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower <i>R</i>² was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation <i>R</i>² results were on average 8–11% lower than model <i>R</i>². Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE

Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO₂), nitrogen oxides (NO_<i>x</i>) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM₁₀ and PM_2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM₁₀ and PM_2.5) explaining on average between 67 and 79% of the concentration variance (<i>R</i>²) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM₁₀ and PM_2.5 fractions and Si, Ni, and K in the PM₁₀ fraction performed moderately with <i>R</i>² ranging from 50 to 61%. Si, NI, and K models for PM_2.5 performed poorest with <i>R</i>² under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE