Proposal

ENVB 530 Term Project Proposal

Identification of locations for Bio-infiltration Cells in Hochelaga, Montreal

Chelsea Scheske

1.0 Introduction & Background

The development of urban areas effects natural cycles and processes. Two cycles in particular, the hydrological cycle and the carbon cycle, are affected by the removal of vegetation that occurs during urban development. The hydrological cycle is directly impacted by the changes in groundcover that come with urban development, such as the conversion of vegetative cover to parking lots, roads, and rooftops (EPA, 2015). In a natural, undeveloped watershed, rainfall soaks into vegetation covered soils and is filtered and cleaned through natural processes. In contrast, in a conventional urban setting rainfall is typically unable to infiltrate through pavement or cannot reach the ground due to rooftops. This results in an altered hydrological cycle with the effect of increased flooding and runoff. In addition, water passing over these surfaces picks up pollutants that are then carried into nearby waterbodies, or into the ground water. The removal of vegetation during development also impacts the carbon cycle. Trees and other vegetation naturally uptake carbon dioxide (CO₂) and produce oxygen (O₂) necessary for life on earth to continue (Bonan, 2008). They remediate pollution from air, water, and soil through their life cycle, contribute to reduction of the heat island effect, improve storm water management, provide habitats for animals, and deliver myriad other ecosystem services (Mitchell, 2012).

As our population continues to increase, urban development puts increasing pressure on the remaining natural terrain. But is there a way to minimize the negative effects? Low Impact Development (LID) is a development paradigm that seeks to mimic the natural functions of a watershed, interrupting hydrological and carbon cycles as little as possible through maintenance of vegetation and the retention of water, encouraging it to infiltrate into the ground rather than allowing it to run off. The benefits of LID are social, environmental, and economic, and include improved water quality, reduction of flooding events, restoration of aquatic habitats, improved groundwater recharge, and enhancement of the urban landscape (EPA, 2015). There are many methods employed to this end. The following proposal describes a project focused on two of these – the maximization of permeable surfaces, and the support of healthy vegetative growth.

Though most urban plans include trees, the urban setting is not ideal for most large plant life. A natural tree growth pattern is shown in Figure 1. This figure demonstrates that a large portion of tree root growth is shallow and lateral. In addition, for healthy growth, a typical tree requires 120 ft³ of soil when the crown has a 10ft diameter, 500 ft³ when the crown is 21ft diameter, and 1000 ft³ with a 30 ft crown diameter, as illustrated in Figure 2 (Mitchell, 2012).

Lateral root growth and the required soil volume are not supported by a conventional tree pit, shown in Figures 3-6. In a conventional tree pit, the roots have a small radius in which they can freely penetrate the soil. However, the surrounding soil tends to be so compacted that roots are not able to move through the soil, so the tree cannot anchor itself or access enough water and nutrients to grow to its full height. This often contributes to unhealthy growth, early death, or trees collapsing under wind force due to insufficient anchoring. It can also lead trees to seek space by growing roots upwards, leading to costly infrastructure damage. Research indicates that a tree with a diameter at breast height (DBH) of 30 inches removes 70 times the pollution as a tree with a 3-inch diameter (Nowak, 1995). In addition, a mature tree can increase the life of the pavement under its canopy by more than 10 years (Stolte, 2016). This suggests that directing resources into ensuring that urban trees reach maturity is a worthwhile cause.

The current project focuses on a specific LID method designed specifically to support the growth of mature trees in an urban setting: the bio-infiltration tree pit. This technology supports healthy tree growth, while providing passive filtration for rainfall that would otherwise contribute to urban runoff. In these systems, shown in Figure 7, pavement is suspended on top of interlocking cells which are filled with quality soil. Tree roots are able to freely pass between the pores in the cells, spreading out in a way that mimics unfettered root growth in nature. Many municipalities are recognizing the value of these systems as a way to support healthy tree growth and the myriad accompanying ecological services (Stolte, 2016).

2.0 Project Objectives and Questions

Dr. Grant Clark’s Ecological Engineering Lab at Macdonald Campus is engaged in a LID project, in which suspended pavement tree-pits are being built in Montreal’s Hochelaga neighborhood. The lab wishes to identify potential locations for bio-infiltration tree-pits in the neighborhood, based on a GIS analysis. The analysis will include a slope analysis along with an estimation of infiltration rates based on soil data for areas identified as potential tree-pit locations. If feasible, an assessment of the available sunlight during the summer and winter at the suitable areas will be completed using 3D models of the buildings and the shading estimate tool. The sunlight assessment will be based on an existing study by Norris (2014), “Made in the shade: using GIS to model pedestrian shade in Austin, Texas”.

The three main questions to be answered through the project are presented below:

1. Which areas in Hochelaga are suitable for the construction of suspended pavement tree-pits?

2. What infiltration rates can be expected, based on the soil types under the identified suitable areas?

3. How much sunlight can be expected in suitable areas? Can this information be employed to choose the best tree species for each area?

3.0 Data

1. Location: Hochelaga, Montreal, QC. Coordinates: 45.57525°N 73.53325°W

The study takes place in the Hochelaga neighborhood of Montreal. The neighborhood was named for an Iroquoian village, as historians believed an ancient village may have been located there (Trigger, 1976). This is an urban neighborhood in the east of the city, part of the Mercier-Hochelaga-Maisonneuve borough. Hochelaga is one of the five most populated boroughs in Montreal. Approximately 129,000 Montrealers, 7.1% of Montreal’s population, live in the region; a mix of working-class Quebecois, students, and recent immigrants (Hochelaga.ca, 2019). Economically, the region paints a complicated picture. In the past the region was an industrial hub. Periods of booms and busts resulted in a neighborhood characterized by one of the most low-income populations in Montreal, while more recent gentrification is changing the landscape. Currently, Hochelaga is considered a lively and up-and-coming neighborhood, and is a good candidate for enhancement with LID development installations (Hochelaga.ca, 2019).

Data List:

1. Montreal Boroughs: https://www.arcgis.com/home/item.html?id=7422af60f3534943bf28b0a12962ce92

2. Digital Elevation Models (DEMs)

Two sources for DEMs of Hochelaga have been identified: Earth Engine, and OpenCanada.ca. Figure 9 shows the DEM acquired from Earth Engine. The quality of each of these sources will be compared, and the most suitable selected. DEM will be used with other layers to determine areas with a slope suitable for tree pit installation. The critical slope is to be determined with further research.

Data List:

1. Earth Engine: https://developers.google.com/earth-engine/datasets/catalog/NRCan_CDEM
2. OpenCanada.ca: https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333

3. Montreal Sidewalk Networks

New tree pits will be installed in sidewalks, or existing tree pits will be retrofitted. To identify suitable locations for installations, a map of the sidewalks in Hochelaga is required. Likely, this data will be taken from Earth Engine or Google myMaps and exported for use in ArcMap. An exploration of available LiDAR data will be performed in an effort to identify existing tree pits, if no such map exists (data has not yet been found).

Data List:

1. Earth Engine: https://developers.google.com/earth-engine/datasets/ (for realistic topographic view)
2. Lab 2 Data - Montreal-NRN-FullIsland-MTM1b
3. Existing Tree Pits – LiDAR Canopy data (https://canadiangis.com/data.php#Quebec)

4. Soil Data Montreal

In order to estimate infiltration rates in the chosen installation sites, soil data will be used along with infiltration formulas. The developed model can be validated on site, and potentially applied to similar projects in other areas. Soil data for the study region has been requested from the McGill GIC.

Data List:

1. Soil Data Hochelaga – requested from the McGill GIC (11 March 2019) - https://www.mcgill.ca/library/find/maps/irda

5. Building Footprints

To carry out the shade analysis, building footprints and LiDAR images of the study region can be used to develop 3D models of the buildings. There are several tools in the ESRI toolbox that may be applied to complete a shade analysis, including the Sun Shadow Volume tool, and the Shadow Impact Analysis tool. Further research will determine which of these is the most appropriate. Note that this part of the project is the most ambitious and may be out of scope. An attempt will be made to complete it, but I am aware that it may be too challenging.

Data List:

1. Building footprints: http://donnees.ville.montreal.qc.ca/dataset/cartographie-de-base/resource/696c50d7-5482-4669-8377-ea943d656d21 or http://donnees.ville.montreal.qc.ca/dataset/maquette-numerique-batiments-citygml-lod2-avec-textures
2. LiDAR data: https://canadiangis.com/data.php#Quebec

6. Other Data

If the shade analysis can be completed, it may be possible to provide recommendations on the most suitable tree species to be planted in different zones, based on the amount of available sunlight and soil type. In this even, information on the requirements of the available species will be required.

4.0 Methodology

The goals of the project will be carried out primarily through the development of an ArcGIS Model Builder Model. Certain datasets may be developed in Earth Engine and Google myMaps and exported to ArcMap. ArcScene will be employed to complete the 3D shade analysis portion of the project. If it appears that it will simplify the model for the end user, the Model Builder script will be exported into Python and streamlined where possible. Potential applications for python include writing analysis data to a file.

The project can be divided into three main portions: Suitability Analysis, Soil Infiltration Estimation and Shade Estimate Analysis. The initial methodology is as follows:

Suitability Analysis:

1. Collect relevant data layers (listed in 3.0 Data)

2. Clip or Extract by Mask -> all data to Hochelaga extents

3. Calculate Slope using DEM

4. Identify suitable areas for tree-pits using project constraints (to be determined, currently in conversation with Dr. Clark to this end) and the Suitability Analysis process.

Soil Infiltration Estimation:

5. Using the soil data layer, identify soil type under the suitable areas and use Raster Calculator to estimate the infiltration rate for each. Use Python to write data to file.

Shade Estimate Analysis:

6. Using the building footprint and LiDAR data, produce 3D models of the buildings near the Suitable Areas

7. Use Sun Shadow Volume Calculator or Shadow Impact Analysis tools to estimate the amount of shade falling across the suitable sites.

8. Extract the data that intersects the suitable tree pit areas and the sun shadows. Calculate sun available for the trees in the identified suitable areas. Write data to file.

9. Produce feature class that visually identifies the areas out of the suitable areas that are MOST suitable based on the sun requirements of the species and identify which areas are optimalfor which species.

*Note that this methodology is subject to change as the project develops.

References