Mapping urban form into local climate zones for the continental US from 1986-2020.

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people's activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling and cloud-enabled modeling - an approach that could be generalized to other countries and continents. Our model achieved good performance: 0.76 overall accuracy (0.55-0.96 class-wise F1 scores). To our knowledge, this is the first high-resolution, longitudinal LCZ map for the continental US. Our work may be useful for a variety of fields including earth system science, urban planning, and public health.

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations.

Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO2 models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO2. Our results suggest that street view imagery alone may provide sufficient information to explain NO2 variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R2 of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)─a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO2 pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data.

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R2 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R2: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.

Street-view greenspace exposure and objective sleep characteristics among children.

Greenspace may benefit sleep by enhancing physical activity, reducing stress or air pollution exposure. Studies on greenspace and children's sleep are limited, and most use satellite-derived measures that do not capture ground-level exposures that may be important for sleep. We examined associations of street view imagery (SVI)-based greenspace with sleep in Project Viva, a Massachusetts pre-birth cohort. We used deep learning algorithms to derive novel metrics of greenspace (e.g., %trees, %grass) from SVI within 250m of participant residential addresses during 2007-2010 (mid-childhood, mean age 7.9 years) and 2012-2016 (early adolescence, 13.2y) (N = 533). In early adolescence, participants completed >5 days of wrist actigraphy. Sleep duration, efficiency, and time awake after sleep onset (WASO) were derived from actigraph data. We used linear regression to examine cross-sectional and prospective associations of mid-childhood and early adolescence greenspace exposure with early adolescence sleep, adjusting for confounders. We compared associations with satellite-based greenspace (Normalized Difference Vegetation Index, NDVI). In unadjusted models, mid-childhood SVI-based total greenspace and %trees (per interquartile range) were associated with longer sleep duration at early adolescence (9.4 min/day; 95%CI:3.2,15.7; 8.1; 95%CI:1.7,14.6 respectively). However, in fully adjusted models, only the association between %grass at mid-childhood and WASO was observed (4.1; 95%CI:0.2,7.9). No associations were observed between greenspace and sleep efficiency, nor in cross-sectional early adolescence models. The association between greenspace and sleep differed by racial and socioeconomic subgroups. For example, among Black participants, higher NDVI was associated with better sleep, in neighborhoods with low socio-economic status (SES), higher %grass was associated with worse sleep, and in neighborhoods with high SES, higher total greenspace and %grass were associated with better sleep time. SVI metrics may have the potential to identify specific features of greenspace that affect sleep.

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment.

National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO2, PM2.5, O3, CO, PM10, SO2) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R2 increased 0.02-0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R2 = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.

High-Spatial-Resolution Estimates of Ultrafine Particle Concentrations across the Continental United States.

There is growing evidence that ultrafine particles (UFP; particles smaller than 100 nm) are likely more toxic than larger particles. However, the health effects of UFP remain uncertain due in part to the lack of large-scale population-based exposure assessment. We develop a national-scale empirical model of particle number concentration (PNC; a measure of UFP) using data from mobile monitoring and fixed sites across the United States and a land-use regression (LUR) modeling framework. Traffic, commercial land use, and urbanicity-related variables explain much of the spatial variability of PNC (base model R2 = 0.77, RMSE = 2400 cm-3). Model predictions are robust across a diverse set of evaluations [random 10-fold holdout cross-validation (HCV): R2 = 0.72, RMSE = 2700 cm-3; spatially defined HCV: R2 = 0.66, RMSE = 3000 cm-3; evaluation against an independent data set: R2 = 0.54, RMSE = 2600 cm-3]. We apply our model to predict PNC at ∼6 million residential census blocks in the contiguous United States. Our estimates are annual average concentrations for 2016-2017. The predicted national census-block-level mean PNC ranges between 1800 and 26 600 cm-3 (population-weighted average: 6500 cm-3), with hotspots in cities and near highways. Our national PNC model predicts large urban-rural, intra-, and inter-city contrasts. PNC and PM2.5 are moderately correlated at the city scale, but uncorrelated at the regional/national scale. Our high-spatial-resolution national PNC estimates are useful for analyzing population exposure (socioeconomic disparity, epidemiological health impact) and environmental policy and regulation.

Using Street View Imagery to Predict Street-Level Particulate Air Pollution.

Land-use regression (LUR) models are frequently applied to estimate spatial patterns of air pollution. Traditional LUR often relies on fixed-site measurements and GIS-derived variables with limited spatial resolution. We present an approach that leverages Google Street View (GSV) imagery to predict street-level particulate air pollution (i.e., black carbon [BC] and particle number [PN] concentrations). We developed empirical models based on mobile monitoring data and features extracted from ∼52 500 GSV images using a deep learning model. We tested theory- and data-driven feature selection methods as well as models using images within varying buffer sizes (50-2000 m). Compared to LUR models with traditional variables, our models achieved similar model performance using the street-level predictors while also identifying additional potential hotspots. Adjusted R2 (10-fold CV R2) with integrated feature selection was 0.57-0.64 (0.50-0.57) and 0.65-0.73 (0.61-0.66) for BC and PN models, respectively. Models using only features near the measurement locations (i.e., GSV images within 250 m) explained ∼50% of air pollution variability, indicating PN and BC are strongly affected by the street-level built environment. Our results suggest that GSV imagery, processed with computer vision techniques, is a promising data source to develop LUR models with high spatial resolution and consistent predictor variables across administrative boundaries.

Combining expert and crowd-sourced training data to map urban form and functions for the continental US.

Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.

Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression.

National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979-2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979-2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R2 conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.

Land Use Regression models for 60 volatile organic compounds: Comparing Google Point of Interest (POI) and city permit data.

Land Use Regression (LUR) models of Volatile Organic Compounds (VOC) normally focus on land use (e.g., industrial area) or transportation facilities (e.g., roadway); here, we incorporate area sources (e.g., gas stations) from city permitting data and Google Point of Interest (POI) data to compare model performance. We used measurements from 50 community-based sampling locations (2013-2015) in Minneapolis, MN, USA to develop LUR models for 60 VOCs. We used three sets of independent variables: (1) base-case models with land use and transportation variables, (2) models that add area source variables from local business permit data, and (3) models that use Google POI data for area sources. The models with Google POI data performed best; for example, the total VOC (TVOC) model has better goodness-of-fit (adj-R2: 0.56; Root Mean Square Error [RMSE]: 0.32 µg/m3) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). Area source variables were selected in over two thirds of models among the 60 VOCs at small-scale buffer sizes (e.g., 25 m-500 m). Our work suggests that VOC LUR models can be developed using community-based sampling and that models improve by including area sources as measured by business permit and Google POI data.

Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community.

Most empirical air quality models (e.g., land use regression) focus on urban areas. Mobile monitoring for model development offers the opportunity to explore smaller, rural communities - an understudied population. We use mobile monitoring to systematically sample all daylight hours (7 am to 7 pm) to develop empirical models capable of estimating hourly concentrations in Blacksburg, VA, a small town in rural Appalachia (population: 182 635). We collected ∼120 h of mobile monitoring data for particle number (PN) and black carbon (BC). We developed (1) daytime (12-h average) models that approximate long-term concentrations and (2) spatiotemporal models for estimating hourly concentrations. Model performance for the daytime models is consistent with previous fixed-site and short-term sampling studies; adjusted R2 (10-fold CV R2) was 0.80 (0.69) for the PN model and 0.67 (0.58) for the BC model. The spatiotemporal models had comparable performance (10-fold CV R2 for the PN [BC] models: 0.42 [0.25]) to previous mobile monitoring studies that isolate specific time periods. Temporal and spatial model coefficients had similar magnitudes in the spatiotemporal models suggesting both factors are important for exposure. We observed similar spatial patterns in Blacksburg (e.g., roadway gradients) as in other studies in urban areas suggesting similar exposure disparities exist in small, rural communities.

Correlates of the Built Environment and Active Travel: Evidence from 20 US Metropolitan Areas.

BACKGROUND: Walking and bicycling are health-promoting and environmentally friendly alternatives to the automobile. Previous studies that explore correlates of active travel and the built environment are for a single metropolitan statistical area (MSA) and results often vary among MSAs. OBJECTIVES: Our goal was to model the relationship between the built environment and active travel for 20 MSAs spanning the continental United States. METHODS: We sourced and processed pedestrian and bicycle traffic counts for 20 U.S. MSAs (n=4,593 count locations), with 1­17 y of data available for each count location and the earliest and latest years of data collection being 1999 and 2016, respectively. Then, we tabulated land use, transport, and sociodemographic variables at 12 buffer sizes (100­3,000 m) for each count location. We employed stepwise linear regression to develop predictive models for morning and afternoon peak-period bicycle and pedestrian traffic volumes. RESULTS: Built environment features were significant predictors of active travel across all models. Areas with easy access to water and green space, high concentration of jobs, and high rates of active commuting were associated with higher bicycle and pedestrian volumes. Bicycle facilities (e.g., bike lanes, shared lane markings, off-street trails) were correlated with higher bicycle volumes. All models demonstrated reasonable goodness-of-fit for both bicyclists (adj-R2: 0.46­0.61) and pedestrians (adj-R2: 0.42­0.72). Cross-validation results showed that the afternoon peak-period models were more reliable than morning models. CONCLUSIONS: To our knowledge, this is the first study to model multi-city trends in bicycling and walking traffic volumes with the goal of developing generalized estimates of the impact of the built environment on active travel. Our models could be used for exposure assessment (e.g., crashes, air pollution) to inform design of health-promoting cities. https://doi.org/10.1289/EHP3389.

Urban Form, Air Pollution, and Health.

PURPOSE OF REVIEW: Urban form can impact air pollution and public health. We reviewed health-related articles that assessed (1) the relationships among urban form, air pollution, and health as well as (2) aspects of the urban environment (i.e., green space, noise, physical activity) that may modify those relationships. RECENT FINDINGS: Simulation and empirical studies demonstrate an association between compact growth, improved regional air quality, and health. Most studies are cross-sectional and focus on connections between transportation emissions and land use. The physical and mental health impacts of green space, public spaces that promote physical activity, and noise are well-studied aspects of the urban environment and there is evidence that these factors may modify the relationship between air pollution and health. Urban form can support efforts to design clean, health-promoting cities. More work is needed to operationalize specific strategies and to elucidate the causal pathways connecting various aspects of health.

Population-Level Exposure to Particulate Air Pollution during Active Travel: Planning for Low-Exposure, Health-Promoting Cities.

BACKGROUND: Providing infrastructure and land uses to encourage active travel (i.e., bicycling and walking) are promising strategies for designing health-promoting cities. Population-level exposure to air pollution during active travel is understudied. OBJECTIVES: Our goals were a) to investigate population-level patterns in exposure during active travel, based on spatial estimates of bicycle traffic, pedestrian traffic, and particulate concentrations; and b) to assess how those exposure patterns are associated with the built environment. METHODS: We employed facility-demand models (active travel) and land use regression models (particulate concentrations) to estimate block-level (n = 13,604) exposure during rush-hour (1600-1800 hours) in Minneapolis, Minnesota. We used the model-derived estimates to identify land use patterns and characteristics of the street network that are health promoting. We also assessed how exposure is correlated with indicators of health disparities (e.g., household income, proportion of nonwhite residents). Our work uses population-level rates of active travel (i.e., traffic flows) rather than the probability of walking or biking (i.e., "walkability" or "bikeability") to assess exposure. RESULTS: Active travel often occurs on high-traffic streets or near activity centers where particulate concentrations are highest (i.e., 20-42% of active travel occurs on blocks with high population-level exposure). Only 2-3% of blocks (3-8% of total active travel) are "sweet spots" (i.e., high active travel, low particulate concentrations); sweet spots are located a) near but slightly removed from the city-center or b) on off-street trails. We identified 1,721 blocks (~ 20% of local roads) where shifting active travel from high-traffic roads to adjacent low-traffic roads would reduce exposure by ~ 15%. Active travel is correlated with population density, land use mix, open space, and retail area; particulate concentrations were mostly unchanged with land use. CONCLUSIONS: Public health officials and urban planners may use our findings to promote healthy transportation choices. When designing health-promoting cities, benefits (physical activity) as well as hazards (air pollution) should be evaluated.

Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, 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 [PM2.5], 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 R(2): ∼0.5 [PN], ∼0.4 [PM2.5], 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 (n = 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-R(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.

Applicability of a noise-based model to estimate in-traffic exposure to black carbon and particle number concentrations in different cultures.

Several studies show that a significant portion of daily air pollution exposure, in particular black carbon (BC), occurs during transport. In a previous work, a model for the in-traffic exposure of bicyclists to BC was proposed based on spectral evaluation of mobile noise measurements and validated with BC measurements in Ghent, Belgium. In this paper, applicability of this model in a different cultural context with a totally different traffic and mobility situation is presented. In addition, a similar modeling approach is tested for particle number (PN) concentration. Indirectly assessing BC and PN exposure through a model based on noise measurements is advantageous because of the availability of very affordable noise monitoring devices. Our previous work showed that a model including specific spectral components of the noise that relate to engine and rolling emission and basic meteorological data, could be quite accurate. Moreover, including a background concentration adjustment improved the model considerably. To explore whether this model could also be used in a different context, with or without tuning of the model parameters, a study was conducted in Bangalore, India. Noise measurement equipment, data storage, data processing, continent, country, measurement operators, vehicle fleet, driving behavior, biking facilities, background concentration, and meteorology are all very different from the first measurement campaign in Belgium. More than 24h of combined in-traffic noise, BC, and PN measurements were collected. It was shown that the noise-based BC exposure model gives good predictions in Bangalore and that the same approach is also successful for PN. Cross validation of the model parameters was used to compare factors that impact exposure across study sites. A pooled model (combining the measurements of the two locations) results in a correlation of 0.84 when fitting the total trip exposure in Bangalore. Estimating particulate matter exposure with traffic noise measurements was thus shown to be a valid approach across countries and cultures.

Health impacts of the built environment: within-urban variability in physical inactivity, air pollution, and ischemic heart disease mortality.

BACKGROUND: Physical inactivity and exposure to air pollution are important risk factors for death and disease globally. The built environment may influence exposures to these risk factors in different ways and thus differentially affect the health of urban populations. OBJECTIVE: We investigated the built environment's association with air pollution and physical inactivity, and estimated attributable health risks. METHODS: We used a regional travel survey to estimate within-urban variability in physical inactivity and home-based air pollution exposure [particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5), nitrogen oxides (NOx), and ozone (O3)] for 30,007 individuals in southern California. We then estimated the resulting risk for ischemic heart disease (IHD) using literature-derived dose-response values. Using a cross-sectional approach, we compared estimated IHD mortality risks among neighborhoods based on "walkability" scores. RESULTS: The proportion of physically active individuals was higher in high- versus low-walkability neighborhoods (24.9% vs. 12.5%); however, only a small proportion of the population was physically active, and between-neighborhood variability in estimated IHD mortality attributable to physical inactivity was modest (7 fewer IHD deaths/100,000/year in high- vs. low-walkability neighborhoods). Between-neighborhood differences in estimated IHD mortality from air pollution were comparable in magnitude (9 more IHD deaths/100,000/year for PM2.5 and 3 fewer IHD deaths for O3 in high- vs. low-walkability neighborhoods), suggesting that population health benefits from increased physical activity in high-walkability neighborhoods may be offset by adverse effects of air pollution exposure. POLICY IMPLICATIONS: Currently, planning efforts mainly focus on increasing physical activity through neighborhood design. Our results suggest that differences in population health impacts among neighborhoods are similar in magnitude for air pollution and physical activity. Thus, physical activity and exposure to air pollution are critical aspects of planning for cleaner, health-promoting cities.

Reducing motor vehicle greenhouse gas emissions in a non-California state: a case study of Minnesota.

Approaches for reducing greenhouse gas (GHG) emissions from motor vehicles include more-efficient vehicles, lower-carbon fuels, and reducing vehicle-kilometers traveled (VKT). Many U.S. states are considering steps to reduce emissions through actions in one or more of these areas. We model several technology and policy options for reducing GHGs from motor vehicles in Minnesota. Considerable analysis of transportation GHGs has been done for California, which has a large population and vehicle fleet and can enact unique emissions regulations; Minnesota represents a more typical state with respect to many demographic and transportation parameters. We conclude that Minnesota has a viable approach to meeting its stated GHG reduction targets (15% by 2015 and 30% by 2025, relative to year 2005) only if advancements are made in all three areas-vehicle efficiency, carbon content of fuels, and VKT. If policies focus on only one or two areas, potential improvements may be negated by backsliding in another area (e.g., increasing VKT offsetting improvements in vehicle efficiency).