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Benjamin Crawford

Publications and source records attributed to Benjamin Crawford.

2 recordsLinked to original sources

High-resolution multi-pollutant mapping in Denver, Colorado

Characterizing traffic-related air pollutants (TRAPs), which significantly impact health, and greenhouse gases (GHGs) can be challenging in urban environments. Mobile monitoring has the potential to capture the spatial distribution of these pollutants. We present results from a campaign using the Denver Mobile Monitoring Laboratory (DMML) in the summer of 2023 when we measured ultrafine particles (UFPs), black carbon (BC), ozone (O 3 ), methane (CH 4 ) , and carbon dioxide (CO 2 ) concentrations in Denver, CO. Despite our campaign being brief, we obtained several interesting results. We observed elevated UFP and BC concentrations on major roads. In contrast, O 3 concentrations were higher on neighborhood streets and roads and in the industrial neighborhood of Commerce City. We consistently observed elevated CH 4 concentrations (>2.5 ppm) on highway I-70, suggesting the presence of a previously unknown major source of CH 4 . The CH 4 concentrations measured in our campaign did not align with those from an overlapping aerial campaign, suggesting that mobile monitoring is crucial to capture important, potentially intermittent CH 4 hotspots in cities. We evaluated if trees mitigated pollution concentrations, as planting trees is a key policy initiative of the city of Denver. We observed significant negative associations between tree canopy coverage and UFPs, BC, and CH 4 , and a positive association with O 3 when using linear mixed-effects regression models. Our work highlights the importance of investigating the role of tree canopy coverage to mitigate TRAPs.

Colorado

Urban tree cover provides consistent mitigation of extreme heat in arid but not humid cities

Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.

Sustainable Cities and Society