October 29 | 2018

A machine learning approach to estimate hourly exposure to fine particulate matter for urban, rural, and remote populations during wildfire seasons. 

Yao J, Brauer M, Raffuse SM, Henderson S.

Environ Sci Technol. 2018 Oct 24. Epub ahead of print] DOI:10.1021/acs.est.8b01921


Exposure to wildfire smoke averaged over 24-hour periods has been associated with a wide range of acute cardiopulmonary events, but little is known about the effects of sub-daily exposures immediately preceding these events. One challenge for studying sub-daily effects is the lack of spatially and temporally resolved estimates of smoke exposures. Inexpensive and globally applicable tools to reliably estimate exposure are needed. Here we describe a Random Forests machine learning approach to estimate 1-hour average population exposure to fine particulate matter during wildfire seasons from 2010 to 2015 in British Columbia, Canada, at a 5km by 5km resolution. The model uses remotely sensed fire activity, meteorology assimilated from multiple data sources, and geographic/ecological information. Compared with observations, model predictions had a correlation of 0.93, root mean squared error of 3.2 µg/m3, mean fractional bias of 15.1%, and mean fractional error of 44.7%. Spatial cross-validation indicated an overall correlation of 0.60, with an interquartile range from 0.48 to 0.70 across monitors. This model can be adapted for global use, even in locations without air quality monitoring. It is useful for epidemiologic studies on sub-daily exposure to wildfire smoke, and for informing public health actions if operationalized in near-real-time.

October 22 | 2018

Socioeconomic status and environmental noise exposure in Montreal, Canada.

Dale LM, Goudreau S, Perron S, Ragettli MS, Hatzopoulou M, Smargiassi A.

BMC Public Health. 2015 Feb 28;15:205. doi: 10.1186/s12889-015-1571-2




This study’s objective was to determine whether socioeconomically deprived populations are exposed to greater levels of environmental noise.


Indicators of socioeconomic status were correlated with LAeq24h noise levels estimated with a land-use regression model at a small geographic scale.


We found that noise exposure was associated with all socioeconomic indicators, with the strongest correlations found for median household income, proportion of people who spend over 30% of their income on housing, proportion of people below the low income boundary and with a social deprivation index combining several socio-economic variables.


Our results were inconsistent with a number of studies performed elsewhere, indicating that locally conducted studies are imperative to assessing whether this double burden of noise exposure and low socioeconomic status exists in other contexts. The primary implication of our study is that noise exposure represents an environmental injustice in Montreal, which is an issue that merits both investigation and concern.



October 15 | 2018

Association between residential self-selection and non-residential built environment exposures.

Howell NA, Farber S, Widener MJ, Allen J, Booth GL.

Health Place. 2018 Oct 1;54:149-154 DOI: 10.1016/j.healthplace.2018.08.009


Studies employing ‘activity space’ measures of the built environment do not always account for how individuals self-select into different residential and non-residential environments when testing associations with physical activity. To date, no study has examined whether preferences for walkable residential neighborhoods predict exposure to other walkable neighborhoods in non-residential activity spaces. Using a sample of 9783 university students from Toronto, Canada, we assessed how self-reported preferences for a walkable neighborhood predicted their exposure to other walkable, non-residential environments, and further whether these preferences confounded observed walkability-physical activity associations. We found that residential walkability preferences and non-residential walkability were significant associated (β = 0.42, 95% CI: (0.37, 0.47)), and further that these preferences confounded associations between non-residential walkability exposure and time spent walking (reduction in association = 10.5%). These results suggest that self-selection factors affect studies of non-residential built environment exposures.

October 9 | 2018

Who has access to urban vegetation? A spatial analysis of distributional green equity in 10 US cities. 

Lorien Nesbitt, Michael J. Meitner, Cynthia Girling, Stephen R.J. Sheppard, Yuhao Lua.

Landscape and Urban Planning Volume 181, January 2019, Pages 51-79



This research examines the distributional equity of urban vegetation in 10 US urbanized areas using very high resolution land cover data and census data. Urban vegetation is characterized three ways in the analysis (mixed vegetation, woody vegetation, and public parks), to reflect the variable ecosystem services provided by different types of urban vegetation. Data are analyzed at the block group and census tract levels using Spearman’s correlations and spatial autoregressive models. There is a strong positive correlation between urban vegetation and higher education and income across most cities. Negative correlations between racialized minority status and urban vegetation are observed but are weaker and less common in multivariate analyses that include additional variables such as education, income, and population density. Park area is more equitably distributed than mixed and woody vegetation, although inequities exist across all cities and vegetation types. The study finds that education and income are most strongly associated with urban vegetation distribution but that various other factors contribute to patterns of urban vegetation distribution, with specific patterns of inequity varying by local context. These results highlight the importance of different urban vegetation measures and suggest potential solutions to the problem of urban green inequity. Cities can use our results to inform decision making focused on improving environmental justice in urban settings.

October 1 | 2018

Capturing the spatial variability of noise levels based on a short-term monitoring campaign and comparing noise surfaces against personal exposures collected through a panel study.

Fallah-Shorshani M, Minet L, Liu R, Plante C, Goudreau S, Oiamo T, Smargiassi A, Weichenthal S, Hatzopoulou M.

Environ Res. 2018 Aug 17;167:662-672. DOI: 10.1016/j.envres.2018.08.021 



Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LAeq (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R2 (0.44-0.64), deviance (51-72%) and Akaike information criterion (AIC) (469.2-434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data.