Trees and Streets as Drivers of Urban Stormwater Nutrient Pollution

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Trees and Streets as Drivers of Urban Stormwater Nutrient Pollution Benjamin D. Janke, Jacques C Finlay, and Sarah E. Hobbie Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02225 • Publication Date (Web): 30 Jul 2017 Downloaded from http://pubs.acs.org on July 30, 2017

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Environmental Science & Technology

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Title: Trees and Streets as Drivers of Urban Stormwater Nutrient Pollution Authors: Benjamin D. Janke*, Jacques C. Finlay, Sarah E. Hobbie Affiliation (all authors): University of Minnesota Department of Ecology, Evolution and Behavior 140 Gortner Laboratory 1479 Gortner Ave St. Paul, MN, USA 55108 *Corresponding Author: Phone: 612-716-6012 Email: [email protected]

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benefits provided to people, and potentially to water quality through reduction of

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stormwater volume by interception. However, few studies have addressed the full range

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of potential impacts of trees on urban runoff, which includes deposition of nutrient-rich

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leaf litter onto streets connected to storm drains. We analyzed the influence of trees on

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stormwater nitrogen and phosphorus export across 19 urban watersheds in Minneapolis-

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St. Paul, MN, USA, and at the scale of individual streets within one residential

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watershed. Stormwater nutrient concentrations were highly variable across watersheds

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and strongly related to tree canopy over streets, especially for phosphorus. Stormwater

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nutrient loads were primarily related to road density, the dominant control over runoff

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volume. Street canopy exerted opposing effects on loading, where elevated nutrient

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concentrations from trees near roads outweighed the weak influence of trees on runoff

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reduction. These results demonstrate that vegetation near streets contributes substantially

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to stormwater nutrient pollution, and therefore to eutrophication of urban surface waters.

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Urban landscape design and management that account for trees as nutrient pollution

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sources could improve water quality outcomes, while allowing cities to enjoy the myriad

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benefits of urban forests.

Abstract: Expansion of tree cover is a major management goal in cities because of the substantial

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TOC Graphic:

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Introduction

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Urban ecosystems are characterized by high levels of nutrient inputs associated with

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humans123 and by amplified hydrologic transport due to extensive impervious surfaces

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and storm drains. Aquatic ecosystems within and downstream of cities are subject to

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excessive stormwater loading from the landscape, leading to flooding, loss of ecosystem

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function, and degradation of habitat4–7. The most pervasive effect of excessive

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stormwater nutrient loading to lakes, streams, and coastal waters is eutrophication, which

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results in abundant algal growth including harmful cyanobacterial blooms, as well as low

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oxygen, fish kills, and noxious odor, leading to degradation of aquatic habitat, recreation,

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and water supply8.

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Efforts to improve water quality of urban lakes and streams have focused heavily on the

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reduction and treatment of stormwater runoff, typically through installation of end-of-

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pipe management structures such as detention ponds and infiltration trenches. However,

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widespread improvement of urban water quality has not been achieved, despite the

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substantial resources invested in stormwater management9. Therefore, there is increasing

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interest in strategies both for reducing non-point source nutrient pollution within

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watersheds and for restoring more natural hydrologic regimes10–13. Particular emphasis is

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placed on the expansion of “green” infrastructure14, often defined as engineered

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structures such as bioswales and vegetated rooftops, but also including urban vegetation

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in lawns, parks, and boulevards. Green infrastructure is appealing for stormwater

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management because it provides reduction of runoff volume and peak flows via

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interception of rainfall, infiltration of stormwater, and evapotranspiration, which



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potentially decrease associated runoff nutrient loads12,14,15. Green infrastructure, and trees

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in particular, also have co-benefits, improving flood control, air quality, mental health,

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recreational opportunities, property and aesthetic values, and climate change resiliency16–

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.

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Trees are a crucial component of green infrastructure, and the expansion of tree cover has

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been widely promoted in cities23,24. Trees potentially improve water quality by decreasing

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nutrient export when used in bioswales and planter boxes25–27, and by reducing

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stormwater volumes and peak flows (and presumably nutrient export) at watershed scales

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28–31

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waters of expanded tree cover. While trees and other vegetated areas near streets promote

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nutrient uptake27, large pools of nutrients in plant biomass and soils could serve as

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sources of nitrogen (N) and phosphorus (P) transported to stormwater systems via

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erosion, litterfall, and leaching32–35. If trees, as an integral part of green infrastructure,

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contribute nutrients to stormwater, then disentangling the opposing influences of runoff

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volume reduction and increases in stormwater nutrient concentrations is essential.

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Furthermore, incomplete understanding of nutrient sources to streets and storm drains,

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including vegetation as well as atmospheric deposition36,37, pet waste3, and fertilizer and

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erosion from lawns38,39, is a major impediment to development of effective nutrient

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pollution management strategies11, and to understanding the potential water quality

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consequences of increasingly “green” urban environments.

. However, few studies have quantified a nutrient reduction benefit to downstream

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In this study, we assessed the role of vegetation, and trees adjacent to streets in particular,

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on urban stormwater runoff quality by analyzing factors that control stormwater nutrient

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levels across a large urban area, the Minneapolis-St. Paul metropolitan area, Minnesota,

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USA (TCMA). We used extensive stormwater monitoring datasets based on over 2,300

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measurements taken from 2005 to 2014 in 19 watersheds to compare nutrient

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concentrations and loading across gradients of tree, vegetation, and impervious cover

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typical of urban residential watersheds. We used these robust datasets to address the

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following questions: (1) How does the cover of vegetation, and especially trees adjacent

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to streets, affect nutrient loads and concentrations in stormwater? (2) Does the volume

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reduction provided by street trees offset the potential enhanced nutrient inputs to streets

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from leaf litter (e.g. leaves, seeds, pollen, flowers)? and (3) How important are “green”

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nutrient sources relative to other factors associated with nutrient inputs to urban areas,

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such as atmospheric deposition?

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Background and Methods

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Study Sites, Data Acquisition and Collection

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We focused on understanding nutrient sources at two spatial scales dominated by urban

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land use in the Twin Cities Metropolitan Area of Minneapolis-St. Paul (TCMA),

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Minnesota, USA (Fig. 1). We used an extensive, multi-year dataset for 2,362 stormwater

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runoff events across 19 urban sub-watersheds of the Mississippi River (Table S1) along

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with high-resolution land cover data to assess the influence of urban vegetation and other

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potential drivers of nutrient pollution (Tables 1, S2). We complemented these analyses

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with investigations at the scale of individual streets with varying street tree canopy cover



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within a single residential watershed. Study watersheds were small, ranging in size from

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4 ha to 3,170 ha, with generally mixed urban land use dominated by low-density

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residential. In some watersheds, remnant surface water features (lakes, ponds) were

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present. Development age across sites ranged from roughly 20 years old in the outer

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suburbs, where street tree canopy tended to be minimal due to development in former

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agricultural lands, to 100 years or older at sites in the urban core, where tree canopy was

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older and denser. Drainage infrastructure was on average older in the urban core than in

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areas of younger development; however, storm drain systems in the study watersheds

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have been completely separated from sanitary sewers since 1996, and both storm and

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sanitary systems are tested for leaks and maintained by municipalities and watershed

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managers. These features, along with the lack of evidence for gross contamination of

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sewage at sites with baseflow40 suggest that leaking sanitary sewers did not influence our

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study sites. Use of P in lawn fertilizer has been restricted for individual household use

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since 2004, while N fertilizer use is not regulated.

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Stormwater nutrient chemistry and hydrology data from five watershed management

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organizations were integrated into our analyses (Fig. 1; Table S1). Data were collected as

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part of regional stormwater monitoring programs initiated as early as 2001, but more

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typically since 2005. Monitoring was usually conducted during the April to November

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warm period of each year. Cross-site comparisons used only the data collected from 2005

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– 2014, restricted to the warm season (April 1 – October 31) when the majority of annual

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precipitation occurs (79% on average from 1981 - 2010)41. Monitoring protocols,

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including sample collection, chemical analyses, and quality control procedures, were


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similar among organizations (Table S1). Nine sites did not have baseflow. For most of

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the other 10 sites, the influence of baseflow on stormwater was small since runoff rates

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were generally larger during storms than during baseflow by an order of magnitude or

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more40; for sites with appreciable baseflow (MS1, MS2), sliding-interval baseflow

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separation was applied to hydrologic data42.

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Stormwater samples were primarily composite samples (n = 1895), combined from sub-

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samples within an event to provide a single, volume-weighted composite. Roughly 17%

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of the samples (n = 330) were grab samples; however, the potential bias of including grab

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samples was minimal, as the significance of regressions (see below) were unchanged

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when grab samples were excluded from the data set. Samples were analyzed for

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concentrations of total phosphorus (TP), total dissolved phosphorus (TDP), nitrate- plus

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nitrite-nitrogen (hereafter NOx-N), ammonium nitrogen (NH4-N), and total Kjeldahl

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nitrogen (TKN). Total nitrogen (TN) was estimated as the sum of TKN + NOx-N, and

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total organic nitrogen (TON) as TKN – NH4-N. The majority of samples were analyzed

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by the Metropolitan Council Environmental Services Laboratory (St. Paul, MN), using

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standard U.S. E.P.A. protocols43. Soluble reactive phosphorus (SRP) was not consistently

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measured at most sites, so TDP was used in the data analysis. For the CRWD sites, for

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which SRP was generally measured instead of TDP, we estimated TDP from SRP using a

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linear regression applied to a subset of 641 runoff samples that had been measured for

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both forms (TDP [mg/L] = 1.20*SRP [mg/L] + 0.012, R2 = 0.91; unpublished data).

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Stormwater event mean concentrations (EMC) observed in this study for N and P (Table

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2) were typical of urban runoff44, and similar to previous observations in the TCMA45. TP

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and TN greatly exceeded that measured in precipitation in the study area, including in

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rainfall at the AHUG watershed during 2011-2013 (0.03 mg/L for TP, 1.05 mg/L for TN,

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n = 27 samples; unpublished data), and in wet deposition measurements of TP across the

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TCMA in a 1980 study46 (TP = 0.06 mg/L, n = 5 sites). Stormwater NOx-N (0.45 mg/L)

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was similar to mean wet deposition at AHUG (0.25 mg/L) and in Payne et al.46 (0.46

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mg/L), while NH4-N (0.24 mg/L) was much lower than observations in the two

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precipitation data sets (0.69 mg/L at AHUG and 0.92 mg/L in Payne et al.46).

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Continuous flow was recorded at all sites but quality-controlled data for stormwater

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runoff volumes were available only for a subset of 12 sites. Nutrient yields (kg/km2) were

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estimated for each sampled event at these sites by multiplying the observed volume by

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the observed concentration (typically from a volume-weighted composite, but sometimes

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represented by a grab sample), and normalized by watershed area3,40,42.

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We also investigated the street scale in a small (17 ha) residential watershed in St. Paul,

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MN (AHUG; Table S1). During several late spring (post leaf-out) and fall (post leaf-

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drop) events from fall 2012 through spring 2015, we sampled street gutter runoff from 9

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blocks within the watershed that varied in street canopy coverage due to differences in

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tree species and age. Runoff was sampled using a 1-L plastic bottle by collecting water as

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it entered the catch basins at the end of each major block. Water samples were analyzed

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for major nutrient forms including TP, SRP, NOx-N, NH4-N, total dissolved N (TDN),


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and particulate N (PN) at the University of Minnesota (UMN) using similar laboratory

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methods as MCES40. For these samples, TN was estimated as TDN + PN, and TON as

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TN – NOx-N – NH4-N.

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Data Analysis and Model Selection Approach

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Land cover, land use, and hydrologic connectivity

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In cities, primary new sources of N and P to the landscape include fertilizer, pet waste,

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and atmospheric deposition from automobiles and industrial activities3, all of which may

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be exported to stormwater during runoff events. Much of the N and P from these sources

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may also be assimilated by plants and microbes, and bound to soil, where it can later

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become a source of nutrients to runoff through leaching of vegetation and surface soils,

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leaf (and other) litter and grass clippings that fall or are washed or blown into streets, and

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eroded soils. Urban stormwater hydrology, which influences the magnitude of nutrient

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loading, is primarily controlled by the extent and configuration of impervious

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surfaces47,48, which also serve as accumulation areas for atmospheric deposition.

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Although we did not have direct information to trace these sources, to gain insights into

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the importance of potential nutrient sources to stormwater and the factors that influence

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stormwater runoff volume, we analyzed relationships between stormwater nutrient (and

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water) export and watershed characteristics related to streets, impervious cover, traffic,

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population, housing density, and vegetation cover (Tables 1, S2). The variables used in

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analyses, and the potential sources of nutrients and runoff that they represent, are

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summarized in Table 1 and described briefly below (see SI for details on data sources and

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calculation of characteristics). All spatial analyses were completed in ESRI ArcGIS 10.1.



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Land cover and land use attributes that potentially influence stormwater N and P included

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vegetation and factors associated with human activities such as traffic volume (average

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annual daily traffic), population density (people/km2), and low-density residential parcel

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area (as a fraction of total watershed area). Vegetation was described by total vegetation

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(open lawn + tree) cover, total tree cover, and tree canopy over the street as well as tree

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canopy over and within 1.52 m and 6.10 m of the street (Table S2). Limitations of the

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spatial data prevented the estimation of total or near-street turf grass cover (see SI).

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Traffic density is related to the potential input of local inorganic N by deposition from

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combustion by vehicles, and is concentrated near roadways36. Population density

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(people/km2) is associated with nutrient inputs from pets and vehicles, and potentially

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food waste or trash. Low-density (three families or fewer) residential parcel area is

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closely associated with lawn area and with household nutrient inputs such as lawn

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fertilizer or pet waste. Without explicit numbers on pet ownership or lawn fertilizer

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application rates in the study watersheds, we acknowledge that residential parcel area

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integrates the potential effect of both nutrient sources. A recent study3 found that the

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largest new inputs of N and P to our study watersheds were fertilizer and pet waste,

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respectively. However, past fertilization may have accrued in soils, which complicates

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source tracing of P.

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Drainage intensity, which exerts a dominant influence on stormwater runoff volumes,

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was characterized by total impervious area, total street area, and street density (length per

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unit area watershed). Street density was assumed to represent the most directly-connected


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impervious areas, as a true effective impervious area (EIA) could not be determined for

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all watersheds due to limitations of spatial and hydrologic data. Additionally, incomplete

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storm drain maps for many watersheds prevented the characterization of the extent of

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storm sewer connectivity of the drainage areas.

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Statistical Analysis

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The influence of near-street tree canopy on stormwater nutrient concentrations, and its

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importance relative to other human and landscape factors in the urban study area, was

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assessed using two sets of analyses. First, the across-site relationships of stormwater

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volumes and nutrient concentrations to individual watershed characteristics (Table 1)

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were investigated with simple linear regression (SLR). Event mean nutrient

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concentrations (EMC) by site were used in the regressions, with data restricted to the

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typical monitoring season (i.e. April 1 – October 31) since not all sites were monitored

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year-round. Mean event runoff and nutrient yields by site were used in the regressions for

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the subset of sites with event hydrology data (n = 12; Table S1). Statistical significance is

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reported at p < 0.05 and p < 0.001.

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Multiple linear regression analysis (MLR) was used to assess the influence of street tree

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canopy relative to the other watershed factors on nutrient concentrations and yields.

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Candidate factors were assembled separately for each nutrient form by first selecting

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those variables hypothesized to influence stormwater nutrients that also had high

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correlation coefficients from SLR. For sets of highly collinear factors (Pearson |r| > 0.7),

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such as street density and street area, the factors with the lowest correlation to the nutrient



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of interest were excluded. The full model for each nutrient was then tested exhaustively

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for every combination of candidate factors (main effects only; no interactions), with sub-

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models ranked by sample size-adjusted Akaike Information Criterion (AICc). Models for

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which constituent factors exceeded a variance inflation factor (VIF) of 5.0 were rejected.

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Adjusted R2 was then computed for all models within AICc 2.0 of the best model49. Best

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model selection, including estimates of coefficients, significance, and effect size (as

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provided by η2), is shown in the SI along with model fits to observations. R was used for

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all statistical analyses (MLR and SLR).

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Analyses of the net influence of trees on stormwater nutrient yields via effects on runoff

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reduction and on stormwater EMC were complicated by our relatively small data subset

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for nutrient loads (n=12 sites), and by covariance of street canopy cover with street

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density (and with runoff volume) among these 12 sites (R2= 0.40; Table S2). To examine

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the influence of tree canopy on nutrient loading via effects on both concentration and

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runoff, we constructed a nutrient yield model from the MLR analyses for water yield and

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for EMC of TP and TN (see Results and SI). Nutrient yields were estimated as a product

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of predicted EMC and predicted water yield for hypothetical watershed configurations

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(combinations of street canopy and street density within ranges present in our dataset).

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Results and Discussion

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Urban trees as a major source of nutrients to stormwater

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Our results indicate that trees adjacent to streets were a dominant factor in determining N

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and P concentrations in stormwater during the warm weather period (April – October),


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when typically 60-80% of annual runoff and nutrient loading from stormwater occurs in

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our study sites (n=7 sites3). Analyses of stormwater concentration data provided strong

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evidence for this conclusion; variation in event mean concentration (EMC) of TP across

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sites was explained significantly in simple linear regression (SLR) by tree canopy over (r

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= 0.84, p < 0.001) and near the street (Table S3), and TP in the watersheds with the

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greatest street canopy cover was up to three-fold higher than in those with negligible

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street canopy (Fig. 2). Street canopy was also the dominant influence on TP when

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considered with other factors in multiple linear regression (MLR; Table 3), as all

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candidate models within 2.0 AICc units (n=5) included street canopy. Similarly for

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nitrogen, EMC of TN was strongly related to street canopy (r = 0.68, p < 0.05; Fig. 2,

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Table S3). N was primarily delivered as organic N (71% of TN on average across sites),

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which was even more strongly influenced by street canopy (r = 0.71, p < 0.001). Street

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canopy, along with total impervious area (TIA) and residential area, were the dominant

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influences on TN when all variables were considered (Adj. R2 = 0.69; Table 3). TON was

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most closely associated with street canopy (present in all 3 models within 2.0 AICc of the

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best model; Table 3), which along with residential area comprised the best model by

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AICc (Adj. R2 = 0.55).

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Concentrations of N and P in gutter runoff in the AHUG watershed showed strong

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positive (but seasonally variable) relationships with street canopy (Fig. 3), confirming the

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influence of street canopy on nutrient concentrations observed at the watershed scale

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(Table S3). In fall, the influence of street canopy on stormwater N and P concentration

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was especially strong (r = 0.95, p < 0.001 for TP; r = 0.96, p < 0.001 for SRP; r = 0.77, p



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< 0.05 for TN; r = 0.81, p < 0.05 for TON). For TP and SRP, the relationship was not

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significant in late spring (leaf-out); however, TN and TON were positively related to

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street canopy during this period (r = 0.75, p < 0.05 for TN; r = 0.73, p < 0.05 for TON).

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Seasonal patterns in stormwater P and N concentrations at the watershed scale further

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indicated trees and vegetation as major sources of nutrients to stormwater. These seasonal

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patterns mirrored the phenology of urban vegetation with seasonal peaks in means of P

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and N that coincided with autumn leaf drop and with spring leaf-out and flowering (Fig.

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S1), and were strongly related to presence of street trees in the study watersheds. For

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example, elevated spring TP and TN concentrations across sites (characterized by mean

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May-minus-September difference in concentration) were significantly related to street

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canopy (R2 = 0.38, p < 0.05 for TP; R2 = 0.26, p < 0.05 for TN). Less variable and

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decreasing concentrations of TP and TN over summer (Fig. S1) are consistent with

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establishment of lawns and trees during the growing season, accompanied by low rates of

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litterfall34. The subsequent increase of mean event TP and TDP concentrations from

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September to October were significantly correlated with street canopy (R2 = 0.30, p

Trees and Streets as Drivers of Urban Stormwater Nutrient Pollution

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