Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM_2.5, Particle Size) Using Mobile Monitoring

Land Use Regression (LUR) models typically use fixed-site monitoring; here, we employ mobile monitoring as a cost-effective alternative for LUR development. We use bicycle-based, mobile measurements (∼85 h) during rush-hour in Minneapolis, MN to build LUR models for particulate concentrations (particle number [PN], black carbon [BC], fine particulate matter [PM_2.5], particle size). We developed and examined 1224 separate LUR models by varying pollutant, time-of-day, and method of spatial and temporal smoothing of the time-series data. Our base-case LUR models had modest goodness-of-fit (adjusted <i>R</i>²: ∼0.5 [PN], ∼0.4 [PM_2.5], 0.35 [BC], ∼0.25 [particle size]), low bias (<4%) and absolute bias (2−18%), and included predictor variables that captured proximity to and density of emission sources. The spatial density of our measurements resulted in a large model-building data set (<i>n</i> = 1101 concentration estimates); ∼25% of buffer variables were selected at spatial scales of <100m, suggesting that on-road particle concentrations change on small spatial scales. LUR model-<i>R</i>² improved as sampling runs were completed, with diminishing benefits after ∼40 h of data collection. Spatial autocorrelation of model residuals indicated that models performed poorly where spatiotemporal resolution of emission sources (i.e., traffic congestion) was poor. Our findings suggest that LUR modeling from mobile measurements is possible, but that more work could usefully inform best practices

Real-Time Prediction of Size-Resolved Ultrafine Particulate Matter on Freeways

Ultrafine particulate matter (UFP; diameter <0.1 μm) concentrations are relatively high on the freeway, and time spent on freeways can contribute a significant fraction of total daily UFP exposure. We model real-time size-resolved UFP concentrations in summer time on-freeway air. Particle concentrations (32 bins, 5.5 to 600 nm) were measured on Minnesota freeways during summer 2006 and 2007 (Johnson, J. P.; Kittelson, D. B.; Watts, W. F. Environ. Sci. Technol. 2009, 43, 5358−5364). Here, we develop and apply two-way stratified multilinear regressions, using an approach analogous to mobile-monitoring land-use regression but using real-time meteorological and traffic data. Our models offer the strongest predictions in the 10–100 nm size range (adj-<i>R</i>²: 0.79–0.89, average adj-<i>R</i>²: 0.85) and acceptable but weaker predictions in the 130–200 nm range (adj-<i>R</i>²: 0.41–0.62, average adj-<i>R</i>²: 0.52). The aggregate model for total particle counts performs well (adj-<i>R</i>² = 0.77). Bootstrap resampling (<i>n</i> = 1000) indicates that the proposed models are robust to minor perturbations in input data. The proposed models are based on readily available real-time information (traffic and meteorological parameters) and can thus be exploited to offer spatiotemporally resolved prediction of UFPs on freeways within similar geographic and meteorological environments. The approach developed here provides an important step toward modeling population exposure to UFP

Relationship between Urbanization and CO₂ Emissions Depends on Income Level and Policy

We investigate empirically how national-level CO₂ emissions are affected by urbanization and environmental policy. We use statistical modeling to explore panel data on annual CO₂ emissions from 80 countries for the period 1983–2005. Random- and fixed-effects models indicate that, on the global average, the urbanization–emission elasticity value is 0.95 (i.e., a 1% increase in urbanization correlates with a 0.95% increase in emissions). Several regions display a statistically significant, positive elasticity for fixed- and random-effects models: lower-income Europe, India and the Sub-Continent, Latin America, and Africa. Using two proxies for environmental policy/outcomes (ratification status for the Kyoto Protocol; the Yale Environmental Performance Index), we find that in countries with stronger environmental policy/outcomes, urbanization has a more beneficial (or, a less negative) impact on emissions. Specifically, elasticity values are −1.1 (0.21) for higher-income (lower-income) countries with strong environmental policy, versus 0.65 (1.3) for higher-income (lower-income) countries with weak environmental policies. Our finding that the urbanization–emissions elasticity may depend on the strength of a country’s environmental policy, not just marginal increases in income, is in contrast to the idea of universal urban scaling laws that can ignore local context. Most global population growth in the coming decades is expected to occur in urban areas of lower-income countries, which underscores the importance of these findings

InMAP: A model for air pollution interventions

<div><p>Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM_2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM_2.5 concentrations with population-weighted mean fractional bias (MFB) of −17% and population-weighted <i>R</i>² = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM_2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM_2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.</p></div

Changes in concentrations resulting from one emissions scenario as calculated by (a) WRF-Chem, (b) InMAP with a 12 km resolution grid, (c) InMAP with a 1 to 48 km variable resolution grid (i.e., a typical setup for InMAP), and (d) COBRA.

<p>For ease of viewing, there is a discontinuity at the 99th percentile of concentration values. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176131#pone.0176131.s001" target="_blank">S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176131#pone.0176131.s012" target="_blank">S12</a> Figs provide similar information for the rest of the scenarios.</p

Comparison of WRF-Chem and InMAP performance in predicting annual average observed total <i>PM</i>_2.5 concentrations.

<p>The background colors in the maps represent predicted concentrations, and the colors of the circles on the maps represent the difference between modeled and measured values at measurement locations. For the comparison shown here, on average WRF-Chem overpredicts and InMAP underpredicts as compared to observations. Abbrevations: MFB = mean fractional bias; MFE = mean fractional error; MB = mean bias; ME = mean error; MR = model ratio; <i>S</i> = slope of regression line; <i>R</i>² = squared Pearson correlation coefficient.</p

Comparison of area-weighted (black dots) and population-weighted (blue triangles) annual average predictions of changes in concentrations of PM_2.5 subspecies between WRF-Chem (<i>x</i> axis) and InMAP (<i>y</i> axis) for 11 emissions scenarios.

<p>To assist in comparison between area- and population-weighted predictions, concentrations shown here are normalized so that the largest value in each comparison equals one. The gray lines represent InMAP: WRF-Chem ratios of 1 : 1, 2 : 1, and 1 : 2. The black and blue lines represent least-squares regressions. Performance statistics for each comparison are listed below the plots. Abbreviations: MFB = mean fractional bias; MFE = mean fractional error; MR = model ratio; <i>R</i>² = squared Pearson correlation coefficient; <i>S</i> = slope of regression line.</p

Comparison of total (primary plus secondary) area-weighted (black dots) and population-weighted (blue triangles) annual average predicted PM_2.5 concentration change for WRF-Chem (<i>x</i> axis) and either InMAP or COBRA (<i>y</i> axis) for 11 emissions scenarios.

<p>To assist in comparison between area- and population-weighted predictions, concentrations shown here are normalized so that the largest value in each comparison equals one. The gray lines represent 1 : 1, 2 : 1, and 1 : 2 ratios between the models, and the black and blue lines represent least-squares regressions. Performance statistics for each comparison are listed below the plots. Abbreviations: MFB = mean fractional bias; MFE = mean fractional error; MR = model ratio; <i>R</i>² = squared Pearson correlation coefficient; <i>S</i> = slope of regression line.</p

A Spatially and Temporally Explicit Life Cycle Inventory of Air Pollutants from Gasoline and Ethanol in the United States

The environmental health impacts of transportation depend in part on where and when emissions occur during fuel production and combustion. Here we describe spatially and temporally explicit life cycle inventories (LCI) of air pollutants from gasoline, ethanol derived from corn grain, and ethanol from corn stover. Previous modeling for the U.S. by Argonne National Laboratory (GREET: Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) suggested that life cycle emissions are generally higher for ethanol from corn grain or corn stover than for gasoline. Our results show that for ethanol, emissions are concentrated in the Midwestern “Corn Belt”. We find that life cycle emissions from ethanol exhibit different temporal patterns than from gasoline, reflecting seasonal aspects of farming activities. Enhanced chemical speciation beyond current GREET model capabilities is also described. Life cycle fine particulate matter emissions are higher for ethanol from corn grain than for ethanol from corn stover; for black carbon, the reverse holds. Overall, our results add to existing state-of-the-science transportation fuel LCI by providing spatial and temporal disaggregation and enhanced chemical speciation, thereby offering greater understanding of the impacts of transportation fuels on human health and opening the door to advanced air dispersion modeling of fuel life cycles

Spatial discretization of the model domain into variable resolution grid cells.

<p>The insets show the areas around the cities of Las Vegas, Los Angeles, New York, and Miami in detail. Blue shading represents urban areas as defined by the US Census.</p