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doi.org/10.26434/chemrxiv.12780482.v1
Primary Prevention of Outdoor Lead (Pb) Exposure on ResidentialProperties in Rochester, NY, and Potential of a Sustainable RemediationSolution Involving the Reuse of Drinking Water Treatment Residual(WTR), a Waste Generated Daily by the CityPadmini Das, Stephanie Zamule, Deanna R. Bolduc, Michelle J. Patton, Meghan L. Mendola, Julia Penoyer,Benjamin Lyon, Hanna M. Chittenden, Alexander C. Hoyt, Cassandra V. Dupre, Jack J. Wessel, Ivan Gergi,Charlotte V. Buechi, Jane A. Shebert, Mandeep Chauhan, David Giacherio, Jacob C. Phouthavong-Murphy
Submitted date: 08/08/2020 • Posted date: 10/08/2020Licence: CC BY-NC-ND 4.0Citation information: Das, Padmini; Zamule, Stephanie; Bolduc, Deanna R.; Patton, Michelle J.; Mendola,Meghan L.; Penoyer, Julia; et al. (2020): Primary Prevention of Outdoor Lead (Pb) Exposure on ResidentialProperties in Rochester, NY, and Potential of a Sustainable Remediation Solution Involving the Reuse ofDrinking Water Treatment Residual (WTR), a Waste Generated Daily by the City. ChemRxiv. Preprint.https://doi.org/10.26434/chemrxiv.12780482.v1
Prevention of lead (Pb) poisoning is imperative for public health and environmental justice. This studypresents both assessment and affordable mitigation of Pb exposure risks at individual yard scales, which arecritically important to homeowners to attain primary prevention, but not yet explored thoroughly.
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1
Title 1
Primary prevention of outdoor lead (Pb) exposure on residential properties in Rochester, NY, 2
and potential of a sustainable remediation solution involving the reuse of drinking water 3
treatment residual (WTR) waste generated daily by the city. 4
The Authors 5
Padmini Das1, Ph.D.; Stephanie M. Zamule1, Ph.D.; Deanna R. Bolduc1; Michelle J. Patton1; 6
Meghan L. Mendola1; Julia M. Penoyer1, B.S.; Benjamin E. Lyon1; Hanna M. Chittenden1, B.S.; 7
Alexander C. Hoyt1 B.S.; Cassandra E. Dupre1, B.S.; Jack L. Wessel1, B.S.; Ivan Gergi1, B.S.; 8
Charlotte V. Buechi1, Jane A. Shebert1, M.S.; Mandeep Chauhan2, M.S.; David Giacherio1, 9
Ph.D.; and Jacob C. Phouthavong-Murphy3, OMS-III. 10
Affiliations 11
1Nazareth College of Rochester. 4245 East Ave, Rochester, NY 14618, USA; 2Northeastern 12
University, 360 Huntington Ave, Boston, MA 02115, USA; 3Touro College of Osteopathic 13
Medicine 230 West 125th Street, New York, NY 10027 14
15
Corresponding Author 16
Padmini Das, PhD. Email: [email protected], phone: 5855000424; 585-389-2552, fax: 585-389-17
2672; Address: 4245 East Ave, Rochester, NY 14618, USA. 18
Competing Interests: 19
The authors declare they have no actual or potential competing financial interests. 20
21
Keywords: Lead (Pb), primary prevention, in-situ risk assessment, XRF, soil, dust, urban 22
gardens, remediation, chemical immobilization, water treatment residual, WTR, Al-WTR 23
24
25
2
Abstract 26
Background 27
Prevention of lead (Pb) poisoning is imperative for public health and environmental justice. This 28
study presents both assessment and affordable mitigation of Pb exposure risks at individual yard 29
scales, which are critically important to homeowners to attain primary prevention, but not yet 30
explored thoroughly. 31
Objectives and Methods 32
In phase I, the potential risk of Pb exposure from soil, surface-dust, and vegetables grown in yard-33
gardens were measured along with secondary estimation of predicted blood Pb levels in children 34
in seven urban and six suburban residential properties in and around Rochester, NY, U.S. In phase 35
II, aluminum-based water treatment residuals (Al-WTR), a waste sludge generated by the city’s 36
drinking water treatment plant, was investigated for its potential to immobilize Pb as a remedial 37
measure. 38
Results 39
All tested properties with pre-1978 built structures were found to contain Pb in exterior paint at 40
varying depth, soil, surface dust, and garden vegetables. Soil Pb levels (SLL) were heterogeneous 41
(11 to 173,500 mg/kg) and higher in urban than suburban properties (mean 3,178 and 461 mg/kg 42
respectively), underscoring the inequitable risk. 71% of vegetables collected across four property 43
gardens exceeded the European Union’s health-based Pb guidelines and the median Pb in plants 44
was 72 times higher than the U.S. market-basket values. Very strong sorption (>90%), minimal 45
desorption (<2%), effective prevention (>85%) of Pb leaching from high Pb-containing exterior 46
paint chips and estimated Freundlich and Langmuir parameters suggest practically irreversible Pb-47
WTR binding. 48
Discussion 49
Primary prevention tools were developed to communicate risk with homeowners through GIS-50
maps that illustrate risk-categories and prediction models to calculate SLL and predicted Pb in 51
vegetables using distance from the house. Al-WTR showed very high Pb-immobilizing ability, 52
3
suggesting the implementation of this no-cost, zero-waste sorbent as a soil amendment would 53
provide sustainable primary prevention for low-income urban communities where Pb exposure 54
prevails. 55
1. Introduction 56
Lead (Pb) exposure continues to be the single most critical environmental health issue affecting 57
American children (CDC 2002), despite the ban on lead-based paint (LBP) and the phasing out of 58
leaded gasoline that took place four decades ago (USEPA 2011; Wagner and Langley-Turnbaugh 59
2007). Lead is a potent neurotoxicant particularly detrimental to developing fetuses and children 60
under the age of six, as it causes cognitive and behavioral impairments, learning disabilities, 61
attention deficit, juvenile delinquency, and violent or even criminal behavior (CDC 2002). The 62
number of children with elevated blood lead levels (EBLL) has been reduced due to the 63
implementation of secondary interventions such as testing BLL in children followed by tracking 64
and mitigating the hazard source. However, the problem remains unaddressed in two critical areas. 65
Firstly, it is increasingly evident from recent research that although EBLL-based secondary 66
prevention has been effective in reducing exposure to some extent, it comes at a cost to children 67
who have already been exposed, and who pay a steep price in irreparable systemic damage through 68
cumulative exposure (Crinnion and Pizzorno 2019). 80% of the total body Pb in children, and 90% 69
in adults, is stored in bone; and while the half-life of Pb is only thirty-five days in blood, it is eight 70
to forty years in bone (Hu et al. 2007). Pregnant and postmenopausal women undergo increased 71
bone turnover, causing Pb poisoning of the fetus and Pb-related age-associated problems in adult 72
women (Gulson et al. 2007; Nash et al. 2004). Cumulative exposure through bone-Pb turnover 73
continues throughout life causing neuroinflammatory diseases like Alzheimer’s and Parkinsonism 74
at variable BLL (Bakulski et al. 2012; Weisskopf et al. 2010); increased cardiovascular mortality 75
in BLL as low as 3.62 µg/dL (Menke et al. 2006); and anxiety and depressive disorders in young 76
adults with BLL as low as 1.7 µg/dL (Bouchard et al. 2009). The BLL action limit for children has 77
recently been reduced from 10 µg/dL to 5 µg/dL by U.S. Centers for Disease Control and 78
Prevention, despite their acknowledgment that there is no safe limit for BLL in children; however, 79
only 18 states (not including NY) have adopted this standard thus far (CDC 2018). Moreover, 80
USEPA regulatory limits for residential soil Pb levels (SLL) (400 and 1200 mg/kg for bare soil in 81
4
children’s play areas and the remainder of the yard, respectively; USEPA 2001) are associated 82
with BLL higher than 5 µg/dL according to two prediction models that independently showed a 83
steep increase in BLL at lower soil Pb levels (Mielke et al. 2007; Johnson and Bretsch 2002). 84
These guidelines, which are substantially higher than the soil Pb standards set by some European 85
nations (40 to 150 mg/kg) and Canada (140 mg/kg), have remained unchanged for decades, despite 86
recommendations by researchers to lower them to safer limits (Mielke et al. 2008; Reagan and 87
Silderbard 1989; Bell et al. 2010). 88
Secondly, the current state of residential Pb exposure poses critical questions related to equality 89
and social justice. Despite a substantial drop in the national average, EBLL still prevails in densely 90
populated urban communities, meaning that children from lower-income minority families suffer 91
most from Pb poisoning (Mielke 1999; Geltman et al. 2001). Fillippelli et al. 2005 reported that 92
15% of children living in urban communities exhibit BLL’s above 10 µg/dL, compared to only 93
2.2% of children nationally. Similar trends were observed in western New York state cities, like 94
Rochester and Buffalo, where most frequent Pb exposure was found to occur in low-income urban 95
neighborhoods, where people of color are highly concentrated (Magavern 2018). The established 96
correlation between Pb exposure and crime in teens and young adults in low-income urban 97
communities (Nevin et al. 2007) underscores the socioeconomic injustice of this issue and the 98
importance of developing a sustainable intervention that will address the environmental, social, 99
and economic aspects of this problem equitably. 100
Primary prevention, which directly measures and corrects hazards before an exposure happens 101
(CDC 2005), is the only process that can provide an equitable solution to the Pb exposure issue 102
without vulnerable populations being disproportionately affected as collateral damage. Sustainable 103
measures of primary prevention are not easy to attain, as they involve a number of multifaceted 104
tasks and challenges. A complete assessment of SLL distribution, outdoor surface dust, and 105
property garden vegetables at the individual property scale is critically important for homeowners 106
to understand the contamination pattern, associated health risks, and potential remedial strategies 107
(Clark and Knudsen 2013). Primary contributors of EBLL in children are Pb in soil and house dust 108
that consists of 20-80% resuspended Pb from the soil, along with other dietary sources. (Mielke et 109
al. 2007; Wu et al. 2008; Bugadalski et al. 2013). One-third of U.S. residents grow at least one 110
vegetable in their yard for intended consumption (National Gardening Association 2011), 111
5
heightening the risk of Pb exposure from contaminated yard soils (Clark and Knudsen 2013). This 112
percentage is expected to increase due to the recent COVID-19 pandemic given the disrupted 113
access to fresh and nutritious foods (Lal 2020). Further, an effective and affordable remediation 114
solution must be provided to the families of lower socioeconomic standing to prevent Pb exposure 115
at identified hot spots. Surface soils act as a sink for accumulating Pb over years and can remain 116
so for centuries in the absence of proper remediation and intervention (LaBelle et al. 1987; Datko-117
Williams et al. 2014). Although the excavation of the surface soil with high Pb is the best practice 118
to remove the risk (Mielke et al. 2011), this process is not economically feasible for homeowners 119
in lower socioeconomic urban neighborhoods. The lack of access to risk assessment measures and 120
economically-feasible alternatives for Pb removal contributes to the ongoing inequitable Pb 121
poisoning in children. 122
To address primary prevention, we investigated thirteen residential properties in and around 123
Rochester, NY, United States, utilizing three unique approaches that differ from earlier studies. As 124
opposed to evaluating commercial and residential locations on a city-wide scale, we focused on a 125
site-specific scale of individual properties with structures built prior to 1978 as the principal 126
outdoor Pb sources. Furthermore, we determined the spatial distribution of SLL, dividing every 127
property into broader risk categories such as: safe areas with minimal to no risk; low-risk areas 128
safer for children to play, but not safe enough to grow edible produce; and high-risk areas with Pb 129
hot spots, which should be avoided and considered for remedial actions. Using this data, primary 130
prevention tools including mathematical prediction models and Geographical Information System 131
(GIS) maps were developed to provide homeowners with a thorough understanding of the risk 132
distribution on their properties. Finally, to address the need for a cost-effective remediation 133
strategy to reduce exposure risk from the Pb hot spots, we explored the potential reuse of a waste 134
sludge as a soil amendment to chemically immobilize Pb. This water treatment residual (WTR) is 135
generated as a waste product on a continual basis by the city’s largest drinking water treatment 136
plants. Successful implementation of this technology will provide the city with a sustainable 137
solution for reducing costs of waste management as well as provide homeowners with a low-cost 138
solution to reduce the risk of Pb exposure from high-risk property hot spots. To the best of our 139
knowledge, this study is the first to focus on both aspects of primary prevention by directly 140
measuring the outdoor Pb levels at individual yard scales in urban and suburban residential 141
6
properties and proposing a low-cost, zero-waste solution for the homeowners by reusing a waste 142
that the city generates every day. 143
2. Methods 144
2.1. Phase I: Assessment 145
2.1.1. Field Site Descriptions 146
Seven urban and six suburban residential properties were selected based on varying characteristics 147
including the age of the property’s structures; socioeconomic categorizations of the homeowners 148
such as race, education, and household income; and the willingness of the homeowners to 149
participate in this study. Tableau data visualization software (version 2020.1) was used to create 150
site location maps with respect to the number of houses built prior to 1978 and the socioeconomic 151
standing of the corresponding areas of the sub-county (figure 1). The software was used to develop 152
a geographical map of Monroe and Ontario counties utilizing latitude and longitude coordinates 153
for each sub-county boundary. An urban location was defined by a geographical location within 154
the city limits of Rochester, NY, United States. A suburban location was defined as a geographical 155
location outside the city limits and within the county limits of Monroe or Ontario counties. The 156
socioeconomic categorization data was collected from the Census Bureau for each respective sub-157
county. 158
2.1.2. In-situ measurement of soil Pb using XRF 159
Soil lead concentrations were measured on-site using a USHUD certified Thermo ScientificTM 160
NitonTM XLp 300 series x-ray fluorescence analyzer (XRF). The XRF was set to “standard bulk” 161
mode while an uncovered soil surface was chosen to conduct the measurement in each location (n 162
= 20). The soil surface locations included: i) alongside structures including corners and beneath 163
doors, front stairs, and windows; ii) along a perpendicular line starting from a high soil Pb point 164
adjacent to the structure towards the property line; iii) adjacent to sidewalks and near structures of 165
neighboring properties. Additional parameters including variation in slopes and presence of loose 166
paint chips were noted if observed. Once recorded, each measurement was marked with flags with 167
different colors (figure S.1), a process used to communicate with the homeowners about the 168
distribution of soil Pb on their property and potential risk associated with it. 169
7
2.1.3. Measurement of Pb in paint and outdoor dust 170
Exterior paint was measured in-situ using the “paint mode” of the XRF on outdoor surfaces such 171
as siding, doors, steps, window sills, pillars, and old housing materials stored outside and inside a 172
barn, (site E only). Calibration of the XRF was performed according to the HUD calibration 173
protocol. Levels of Pb in the paint on a given surface area and a depth index indicating how close 174
the Pb was to the surface were obtained as part of each measurement. 175
Outdoor dust collection was completed using Fisher ScientificTM dust wipes (catalog number: 176
NC0309992) on any surface which registered an XRF lead reading. To ensure maximum 177
collection, the dust wipes were unfolded for the initial collection, then folded in half and used 178
within the same surface area. Pb levels on each dust wipe were measured in the laboratory using 179
the “dust wipe” mode of the XRF by placing each wipe in a Niton Portable Test Stand and scanning 180
for 80 seconds total (20 seconds for each position of the holder). 181
2.1.4. Collection of produce/plant samples and preparations 182
Four assessed properties (A, H, C, M) had home gardens. Produce and plant samples were 183
collected with the permission of the homeowners. Plant samples were cleaned and weighed using 184
a Fisher Science Education analytical balance, dried in a ThermoScientific Heratherm OGS100 185
oven at 105°C for 24 hours (or until completely dry), and then weighed again to calculate moisture 186
content. A dry weight (maximum of 0.5 g or based on the available mass of produce) of plant 187
sample was digested in Fisher Scientific Trace Metal Grade HNO3 following the US EPA 3051 188
protocol in a CEM MARS6 digestion microwave unit. Once digested, samples were filtered 189
through Thermo scientific 0.45 µm PTFE-L Luer-Lock syringe filters, diluted to 50 mL with RO 190
water from a Thermofisher Scientific Barnstead Nanopure purifier, and centrifuged at room 191
temperature in an Eppendorf 5424R for 15 minutes at 4,000 rpm. 192
2.2. Phase II: Remediation 193
2.2.1. WTR Collection, Characterization, and TCLP 194
Two batches of WTR were collected from two sludge lagoons of Monroe County Water 195
Authority’s drinking water treatment plant in Greece, NY, U.S. in 2017 and 2018. These lagoons 196
8
contained the post-flocculation waste sludge from Lake Ontario source water after being treated 197
with alum (aluminum sulfate). WTR samples were then thoroughly mixed, air-dried, ground into 198
powder, and passed through a 2 mm sieve. Triplicate samples collected from each batch of WTR 199
were used for determining physicochemical properties, including pH, EC, moisture content, and 200
organic matter content. Toxicity characteristic leaching procedures (TCLP) were performed using 201
EPA SW-846 methods 1311. 202
2.2.2. Sorption and Desorption of Pb by WTR 203
Kinetic and equilibrium sorption and desorption experiments were carried out in triplicate using a 204
1:20 solid to solution (m/v) ratio (SSR) of WTR to a lead-spiked solution made from lead nitrate 205
and 0.01M KCl as a background electrolyte. Kinetic sorption/desorption experiments were 206
conducted at four initial Pb concentrations (0, 25, 200, and 1000 mg/L) for 24h with six 207
intermediate sample collections after 0, 1, 2, 5, 10, and 24 h during end-over-end mixing on an 208
Analytical Testing Model DC-20 Rotary Agitator shaker at 250 rpm. Similarly, equilibrium 209
sorption experiments were conducted at six initial Pb concentrations (0, 200, 400, 600, 800, and 210
1000 mg/L) for 10 h using the same sampling method. Three subsequent desorption cycles (24h 211
each) were carried out to determine the extent of the potential release of pre-adsorbed Pb from 212
WTR. In addition to shaking time, the WTR remained in contact with the Pb solution for 213
approximately 8 min (0.13 h) during the sample preparation. This time in combination with the 214
shaking time was represented as the contact time for kinetic data. After the respective sorption or 215
desorption processes, samples were centrifuged using an Eppendorf 5804R for 15 minutes at 4000 216
rpm, filtered through Thermo scientific 0.45 µm PTFE-L Luer-Lock syringe filters, and the 217
supernatants were analyzed for Pb and Al. 218
2.2.3. Optimizing WTR to prevent paint chip Pb leaching 219
Loose paint chips were collected from the yard of site A both before and after the home was 220
renovated, in order to conduct two experiments. First, a TCLP test was conducted to estimate the 221
Pb leaching potential from these scattered paint chips in simulated landfill conditions following 222
the EPA SW-846 methods 1311. Second, a batch desorption experiment in the presence of varying 223
levels of WTR was performed to determine the optimal amount of WTR necessary to effectively 224
prevent Pb leaching. Four WTR-to-paint chips (m/m) ratios (10, 20, 50, and 100) were added to a 225
9
0.01 M KCl solution while maintaining a 1:20 SSR. Samples were equilibrated for 7 d with two 226
desorption cycles (1 and 6 d) using end-over-end mixing. The samples were centrifuged at 4000 227
rpm for 15 mins, filtered through a Thermo scientific 0.45 µm PTFE-L Luer-Lock syringe filter, 228
and the supernatants were analyzed for Pb. 229
2.3. Pb Analysis 230
In phase I, Plant samples were analyzed for lead content in triplicate using an Agilent Technologies 231
4210 MP-AES. The MP-AES was calibrated with standards ranging from 2500 to 10000 μg/L 232
prepared from a stock solution of Agilent Technologies 1000 μg/mL Pb in 5% HNO3. Method 233
analysis settings were as follows: pump speed of 25 rpm, sample uptake time of 15 seconds, 234
stabilization time of 15 seconds, read time of 30 seconds, and nebulizer flow of 0.8 L/min. 235
In phase II, Pb in 5% of in-situ soil samples (following USEPA method 6200), Pb and Al from the 236
batch sorption/desorption, and Pb from leaching experiments were measured using a Perkin Elmer 237
400 graphite flame Atomic Absorption Spectrophotometer (AAS). After TCLP, the eight RCRA 238
metals (Arsenic, Barium, Cadmium, Chromium, Lead, Mercury, Selenium, and Silver) were 239
measured using a Perkin Elmer Optima 8000 inductively coupled plasma optical emission 240
spectroscopy (ICP-OES) to attain data at lower detection limits. Calibration coefficient limits were 241
set to 0.995, with an allowed calibration error of 10%, and quality control samples were accepted 242
within +5% error margins. 243
2.4. Data analyses, Modeling, and Mapping 244
Statistical analyses were carried out using JMP IN version 13.0 (Sall et al. 2005). One-way and 245
Two-way ANOVA were performed as required, followed by mean comparisons using the Tukey–246
Kramer honest significant difference (HSD) test. Multivariate correlation analyses of Pb in surface 247
dust were conducted with Pb in paint and depth index; bivariate correlation analyses of Pb in edible 248
produce or plant parts were conducted with the adjacent soil Pb levels. Two types of prediction 249
models were developed using the relationship between i) soil Pb over distance from the structure(s) 250
at each site and ii) Pb in produce/plants grown in the yard gardens with the adjacent soil Pb 251
concentrations. Sorption data were fit to both Freundlich and Langmuir isotherm models to gain 252
insight on possible mechanisms of sorption between Pb and WTR and the reversibility of this 253
10
binding. XRF measurements of SLL were taken along with the latitude and longitude of each 254
sampling location and the collected readings were imported into ArcGIS Pro 2.5.2 and portrayed 255
with proportional symbols, color categories, and heat maps. 256
3. Results 257
3.1. Outdoor Pb-exposure in urban and suburban residential properties 258
As described in fig. 1 and table S.1, seven urban and six suburban residential properties built 259
between 1830 and 2003 were tested for outdoor Pb exposure sources. Among those, the house at 260
site F was the only structure built after 1978. As expected, there was no Pb found in exterior paint, 261
dust, or natural soil at this property. Interestingly, low soil Pb levels (27.8 + 0.98 mg/kg) were 262
found at two sites in the garden area, both of which were covered with store-bought potting soil 263
and mulch, suggesting that either may have contributed Pb to the garden. Two suburban sites (I 264
and J) were found to have Pb in exterior paint and soil; however, the SLL in these two properties 265
were below 400 mg/kg. All seven urban (A, B, C, G, H, K, M) sites and one suburban (L) site were 266
found to have SLL higher than 400 mg/kg. Table S.1 shows the Pb in paint, its depth index, and 267
the maximum concentration of Pb in soil and dust. 268
3.1.1. Distribution of Pb in soils 269
The distribution of SLL was extremely heterogeneous in all properties assessed and thus was 270
explored further in eight properties that surpassed the federal limit. Three factors were found to be 271
controlling this distribution; i) distance from the most prominent source of LBP, ii) slopes and iii) 272
the presence of a secondary source at close proximity. Among these, the distance from the primary 273
source of LBP was found to be the dominant factor controlling the distribution of SLL. The 274
maximum SLL were found alongside the source structures at all sites, indicating the most likely 275
primary source was LBP that leached into the soil over the years (figure 2). The data shows a 276
continual decrease of soil Pb over distance, dropping below 100 mg/kg in all properties at variable 277
distances from the houses. However, a few exceptions were noted in A2, L1, G1, G2, and K. These 278
variations in SLL against the effect of distance were observed due to the presence of loose paint 279
chips at close proximity to the measured locations in A2 and slopes in L1. Both sites G and K were 280
11
inner-city residential properties with smaller yards where the structures of the neighboring 281
property influenced the distribution of SLL by contributing as secondary sources of Pb (figure 3). 282
In each yard, SLL showed a biphasic pattern: initially steep and then a gradual decrease over 283
distance from the source structures. This trend was utilized to create site-specific and overall 284
regression models. Site-specific models based on polynomial cubic regression equations are 285
presented for seven yards in table 1, which exhibited both significance as well as strength (R2 286
ranging from 0.84 to 0.99). In contrast, the overall bivariate regression models across all sites were 287
significant for linear (p = 0.0011) as well as nonlinear polynomial (n = 2 to 6) fit (table S.2), but 288
neither were acceptable to be used, as the regression coefficients were too low (no more than 0.24). 289
3.1.2. Pb in outdoor surface dust 290
Figure 4 shows the multivariate correlation of surface dust collected from different surfaces with 291
Pb in paint and depth index. All surfaces showed a significant (p < 0.05) correlation with both Pb 292
in paint and depth index. Interestingly, Pb was found in all of these surfaces at a low depth index, 293
suggesting the presence of Pb closer to the surface that impacted its loading in the dust. Figure S.3 294
shows a similar multivariate correlation for surfaces with a higher depth index and the surface dust 295
only showed a significant correlation with Pb in paint but not with depth index. Surface dust 296
measurements in all urban properties assessed (with the exception of site C) exceeded HUD’s 297
action level of 800 µg/ft2 for exterior concrete. In contrast, surface dust measurements were within 298
this limit in all suburban properties tested, with the exception of a barn at site E which showed a 299
very high risk of potential Pb exposure through inhalation of dust. The assessment of Pb was 300
conducted inside the barn built in 1830; however, the barn is considered an outdoor source as it 301
was not located adjacent to the house built in 1966. The barn is currently used to store materials 302
from a previously demolished structure and seldom used as a play area for visiting grandchildren 303
of the current property owners. All 19 objects tested in this barn (including the wall, doors, wall 304
decor, and various old materials) contained Pb in the paint with an average depth index of 1.66 + 305
0.62, showing most of the Pb is on the surface. All 19 dust samples collected from this site were 306
found to contain Pb averaging 1,551 µg/ft2, almost twice the level of HUD’s action level. The 307
highest concentration of Pb-containing surface dust reported in this study (site E) was 8,612 µg/ft2, 308
12
which is 11 times higher than the federal guideline, showing the extent of the hidden risk of 309
potential Pb exposure through inhalation of dust. 310
3.1.3. Pb in produce/plants in outdoor home gardens 311
Table 2 shows the plant Pb concentrations (mg/kg) and the Pb levels of the adjacent soil in four 312
yard-gardens at sites A, C, H, and M. Tomatoes collected from the garden of Nazareth College of 313
Rochester, NY, which had adjacent SLL below detection limits, were used to calculate the 314
background plant Pb levels of plants from the property sites. Pb concentrations in tomatoes from 315
the Nazareth College garden were subtracted from the Pb levels found in plants from the properties 316
tested to focus on the effect of SLL on the Pb uptake and accumulation in plant tissues. Tomato 317
plants were found in three gardens (sites A, C, and M), and the lead accumulation in tomatoes 318
significantly (p<0.0001; F ratio=750.7) increased in a linear manner (R2 = 0.99) with increasing 319
SLL. Site A had several garden beds filled with potting soil which contained soil Pb, but at much 320
lower levels than 400 mg/kg. Currently, there is no existing guideline defining the permissible 321
level of Pb in the soil for gardening. All plants collected from property garden beds, including 322
arugula, lettuce, radish, garlic, tomato, potato, and broccoli, were found to contain high 323
concentrations of Pb, ranging from 1.25 mg/kg in garlic to 4.67 mg/kg in radish (by dry weight). 324
Sites H and M had gardens in locations with higher SLL levels compared to other locations in their 325
respective properties. Consequently, the tomatoes from site M and elephant foot yam from site H 326
exhibited a very high accumulation of Pb. Overall, as shown in figure S.4, the increase in 327
accumulated Pb in plants collected from all four yard-gardens over adjacent SLL followed a 328
polynomial square fit. Equations 1 and 2 present two prediction models that were derived based 329
on the strong correlation between the soil Pb and Pb in plant tissues per dry weight (d.w.) and fresh 330
weight (f.w.) respectively. 331
332
𝑦 = 2.331 − 0.005 ∗ 𝑥 + 5.74𝑒−7 ∗ (𝑥 − 812.1)2 --------Equation 1 333
𝑦 = 2.338 − 0.0008 ∗ 𝑥 + 6.99 𝑒−8 ∗ (𝑥 − 812.1)2 --------Equation 2 334
13
Where y is Pb concentration in (mg/kg) in plant tissues and x is Pb concentration (mg/kg) in the 335
adjacent soils. Both models were significant (p < 0.0001; F ratio = 120.5 and 47.7 respectively) 336
with strong regression coefficients (R2 = 0.85 and 0.89 respectively). 337
3.2. Chemical immobilization of Pb by WTR 338
3.2.1. Physico-chemical and TCLP characteristics of WTR 339
Table 3 presents the relative physicochemical properties and the results of the TCLP of the two 340
batches of WTR. Both exhibited almost neutral pH, high moisture content, and low organic matter 341
content. TCLP levels of the RCRA metals revealed a complete absence of lead and mercury and 342
the presence of other metals in substantially lower concentrations than the criteria set by the EPA. 343
Both physicochemical properties and TCLP data for these two batches of WTR were statistically 344
similar, which was evident in p > 0.1 and low F ratio. 345
3.2.2. Pb kinetic and equilibrium sorption/desorption by WTR 346
Kinetic sorption experiments showed that the binding of Pb by WTR was rapid and varied with 347
the initial Pb concentrations in solution, while the Pb levels in the control solutions with no WTR 348
remained unchanged (fig. 5). Equilibrium times could not be determined in lower Pb 349
concentrations because of WTR’s extremely rapid and strong sorption for Pb, as the sorption 350
reached 100% instantaneously (within 8 minutes of contact time with no shaking) for 25 mg/L and 351
within 5.13 h contact time in 200 mg/L initial Pb concentrations. At 1000 mg/L initial Pb in 352
solution, the Pb sorption by WTR reached equilibrium within 5.13 h contact time and did not 353
exhibit any significant change in sorbed Pb up to 24.13 h contact time. Absolutely no desorption 354
occurred in any of these initial concentrations within 24 h. 355
The extent of equilibrium sorption of Pb by WTR increased with increasing Pb concentration in 356
solution (fig. 6a), but the percent sorption showed a reverse trend. However, very high sorption 357
affinities (> 90%) were exhibited in all initial loads within 10h (fig. 6d). The data followed an L-358
type curve (fig. 6a) and showed strong fit to the linearized Freundlich (Eqn. 2; fig. 6b; R2 = 0.99) 359
as well as (Eqn. 3; fig. 6c; R2 = 0.98) Langmuir equations and the associated parameters were 360
calculated accordingly. 361
14
𝑙𝑜𝑔 𝑄 = 𝑙𝑜𝑔 𝐾𝐹 + (1 / 𝑛) 𝑙𝑜𝑔 𝐶𝑒𝑞-------Equation 3 362
Where KF (mg/kg) and n are the Freundlich equilibrium constants representing the sorption 363
capacity and adsorption intensity respectively; Q (mg/kg) is the adsorbed amount of Pb by WTR 364
at equilibrium; Ceq (mg/L) is the equilibrium concentration of Pb in solution. The value of KF was 365
determined as 1.6 mg/g, which exhibited a very high sorption capacity of Pb by WTR. The n value 366
determined in this study was above unity (1.82), indicating favorable adsorption of Pb onto WTR 367
through physical processes (McKay 1980; Özer and Pirinççi 2006). 368
(Ce / Q) = (1 / 𝐾𝐿𝑄𝑚) + (Ce / 𝑄𝑚) --------Equation 4 369
Where Qm (mg/kg) is the maximum adsorption capacity and KL (L/mg) represents the Langmuir 370
constant related to the energy of adsorption. A good fit to the Langmuir isotherm indicates the 371
monolayer coverage and homogeneous distribution of Pb on the active binding sites of the WTR 372
surfaces with an adsorption maximum of 25 mg/g, that exhibits a very high sorption capacity. 373
Additionally, another Langmuir constant named separation factor (RL) was calculated following 374
equation 4. 375
𝑅𝐿 = 1 / 1 + 𝐾𝐿𝐶0--------Equation 5 376
Where C0 (mg/L) is the initial Pb concentration. The RL values indicates the nature and the 377
feasibility of the adsorption process, such as unfavorable sorption if RL > 1, linear sorption if RL = 378
1, favorable sorption if 0 < RL <1, or irreversible sorption if RL = 0 (Meroufel et al. 2013). The 379
value of RL calculated for all tested initial concentrations of Pb in this study ranged from 0.03 to 380
0.15, which were between zero to unity, suggesting favorable sorption of Pb on WTR. In addition, 381
these values were much closer to zero than one, indicating more irreversibility in adsorption of Pb 382
on WTR. This was evident in the equilibrium desorption of Pb from WTR. Figure 6e shows the 383
total desorbed Pb after three desorption cycles (24 h each). No desorption was noted in 72 h up to 384
600 mg/L initial Pb concentration, indicating complete hysteresis or complete irreversible binding 385
of Pb by WTR at these initial loads. Although with the further increase of initial Pb, the extent of 386
desorption increased; it remained restricted to < 2% of the sorbed Pb in WTR even after 72 h of 387
shaking. 388
389
15
3.2.3. WTR concentration effects on paint chip Pb leaching 390
As evident from figure S.1, the numerous loose paint chips found in the yard at site A both before 391
and after the renovation of the structure resulted in an extremely high SLL. Locations with buried 392
paint chips exhibited an average of 12 and 5 times higher SLL as compared to the surrounding soil 393
with no paint chips before and after renovation, respectively. The surface soil with both loose and 394
buried paint chips posed a very high risk of Pb exposure through dust loading as well as leaching 395
into the stormwater runoff. A TCLP assessment of these paint chips collected from the yard 396
revealed that they released 47.9 + 1.71 mg/L (n=6) of Pb in solution, which is 9.6 times higher 397
than the EPA permissible Pb levels for TCLP. This data highlights the need for determining 398
potential Pb leaching from these paint chips in a stormwater solution and provided us with an ideal 399
opportunity to test the effectiveness of WTR to prevent the mobilization of Pb from the paint chips 400
with extremely high Pb-leaching potential. 401
The results were highly promising as WTR concentrations significantly (p<0.0001 and F ratio = 402
90.7) reduced Pb leaching from loose paint chips. 43, 61, 84, and 93% prevention of Pb leaching 403
was achieved by 10, 20, 50, and 100% WTR to paint chips (m/m) application after two desorption 404
cycles of 1 and 6 d. Effect of time was masked (p=0.2) by WTR’s very strong affinity for Pb; no 405
significant difference between the higher WTR treatments suggests the application of WTR in a 1 406
to 2 ratio to paint chips would be enough to achieve the optimum prevention. As these paint chips 407
exhibited the maximum Pb concentrations and acted as the major contributor of SLL in site A, a 408
lower dose of WTR would be enough for achieving optimum prevention of Pb leaching in soils at 409
lower SSR. 410
4. Discussion 411
4.1 Field Assessment 412
Although the City of Rochester succeeded in reducing the number of children with EBLL 413
(compared to neighboring cities like Buffalo and Syracuse) through its adoption of an aggressive 414
secondary intervention approach, the city continues to face significant issues related to Pb 415
exposure (Magavern 2018). This is consistent with our findings showing that the overall SLL 416
medians across all tested urban and suburban properties with structures built prior to 1978 were 417
16
413 and 155 mg/kg, respectively, with mean SLL as high as 3,178 and 461 mg/kg, respectively. 418
This study also documented the highest maximum residential SLL (173,500 mg/kg), as compared 419
to those reported over the last two decades in urban and suburban areas of thirty-one U.S. cities 420
(data compared with 36 research articles; not shown). Risk of Pb exposure through soil Pb, 421
potential dust loading, and produce grown in property gardens was found to be much higher in the 422
urban residential properties where residents already face significant socioeconomic challenges. 423
According to the 2010 Rochester census data, urban neighborhoods in the City of Rochester have 424
the highest percentage of residents living below the poverty line, the lowest percentage of those 425
with above-average income, and the broadest distribution of race with the highest percentage of 426
African American residents when compared to suburban sub-counties. The unjust burden of Pb 427
exposure in urban neighborhoods in which many low-income minority families reside has been 428
documented in twenty studies conducted over the last four decades using data from sixteen U.S. 429
cities (Datko-Williams et al. 2014). 430
The prevalence and correlation of SLL and BLL are well established in the literature (Bickel 2010; 431
Zahran et al. 2010; Wu et al. 2010). The overall median SLL in urban properties exceeding the 432
federal standard clearly underscores this risk. However, the lower median SLL of suburban 433
properties does not represent a risk-free condition either. Adverse health effects of Pb are 434
associated with BLL as low as 2 mg/dL, which is associated with SLL < 100 mg/kg (Canfield et 435
al. 2003; Schwarz et al. 2012). Two existing prediction models by Mielke et al. (2007) and Johnson 436
and Bretsch (2002) independently showed a steep increase in BLL at SLL <100 mg/kg and a 437
gradual rise at SLL >300 mg/kg. To assess the exposure risk more specifically, we used both of 438
these models (equations 5 and 6) to predict the potential BLL if residents (especially children) are 439
being exposed at tested residential properties. 440
𝐵𝐿𝐿 = 2.038 + 0.172 ∗ (𝑆𝐿𝐿 𝑚𝑒𝑑𝑖𝑎𝑛)0.5 -------Equation 6 (Mielke et al. 2007) 441
𝐵𝐿𝐿 = 2.097 ∗ 𝐿𝑛(𝑆𝐿𝐿 𝑚𝑒𝑑𝑖𝑎𝑛) − 3.6026 -------Equation 7 (Johnson and Bretsch 2002) 442
SLL in all but one suburban property was predicted to result in BLL > 5 µg/dL according to 443
equation 7, whereas five out of the seven urban properties and one out of the five suburban 444
properties were predicted to result in BLL > 5 µg/dL according to equation 6 (table 4). We 445
observed that the predicted BLL based on the median SLL of the entire property underestimates 446
17
the predicted risk for properties with high variation in SLL throughout the property. For instance, 447
as discussed earlier, site A showed a high variation in SLL throughout the property, both before 448
and after renovation. The predicted BLL of the front yard after the renovation was as high as 20.9 449
µg/dL (equation 6) and 16.1 µg/dL (equation 7), both of which are much higher than the predicted 450
BLL based on the median SLL of the entire property. In such cases, yard-specific predictions 451
would be a more appropriate measure of risk assessment. Racial and socioeconomic distribution 452
in the City of Rochester, along with the SLL and estimated BLL documented in this study, echo 453
the findings of Mielke et al. (1999) and Geltman et al. (2001), who reported that higher SLL in 454
urban soils result in inequitable EBLL in children of lower-income families, minorities, and recent 455
immigrants. This is also evident from the data shown by Korfmacher (2007) that low-income 456
families in the crescent area in urban Rochester characterized by high poverty, crime, and pre-457
1950 rental housing correlated with 23.6% of children under 6 years of age having blood lead 458
levels greater than 10 µg/dL. 459
In accordance with this study, extreme heterogeneous distribution of soil Pb has been documented 460
by other researchers, and has been observed to decrease with increasing distance from three Pb 461
source categories: i) industrial sites or brownfields with residues of historic Pb use (Marshall and 462
Thornton 1993; Wu et al. 2010), ii) areas near highways or roads with intense traffic density 463
(Mielke et al. 2008; Laidlaw 2001; Filippelli et.al. 2005), and iii) aged structures with exterior 464
LBP (USEPA 2000; Litt et al. 2002; and Clark and Knudson 2013). Some studies have reported 465
that the combustion of leaded gasoline is the primary source of Pb in larger urban settings, as noted 466
in Baltimore (Mielke et al. 1983), New Orleans (Mielke et al. 2008), and Indianapolis (Laidlaw 467
2001; Filippelli et.al. 2005). Clark and Knudson (2013) argue that exterior LBP dominates the 468
distribution of soil Pb in smaller urban communities like Appleton, WI. Our study showed that 469
even in a medium-sized urban community like Rochester, with a history of past industrialized use 470
of heavy metals, the primary source of Pb in all tested sites was exterior LBP from structures built 471
prior to 1978, as none of these sites are located in neighborhoods with high traffic density or at 472
close proximity of any brownfield or superfund site. The building structures can also act as barriers 473
and facilitate atmospheric deposition of Pb (Mielke et al. 1983), leaving a pattern of higher Pb 474
trapping in the street side of the front yard as compared to the back or side yard. A combined effect 475
of preferential trapping and LBP results in a U-shaped pattern of higher SLL near the house and 476
the road, and lower SLL in the middle of the yard (Olszowy et al. 1995). Lack of these types of 477
18
spatial patterns negates the effect of preferential trapping on properties we assessed. Based on the 478
findings of our current study, we argue that even in medium urban communities, leaded exterior 479
paint can be the dominant source of soil Pb if the property is located at a certain distance from the 480
industrial sites, freeways, or dense traffic area. The location and age of the building structure are 481
the two most important parameters that influence which source will dominate the distribution of 482
soil Pb and to what extent in any given community, irrespective of its size. 483
Nationwide studies conducted by U.S. Housing and Urban Development report that 76% of homes 484
built prior to 1960 have Pb in exterior paint and 83% of pre-1980 housing stock contains Pb in the 485
paint (USHUD 1990, 1995). This study found Pb in exterior paint at varying depths for all tested 486
urban and suburban properties built prior to 1978, supporting the observation of Clark and 487
Knudson (2013) that all communities of similar age and site throughout the US show a similar 488
trend of contamination. SLL can exist around all sides of the structures and extend far into the 489
yard (Clark and Knudson 2013; USEPA 2000, Litt et al. 2002). In all properties, the SLL was 490
observed to be very high alongside the structures and decreased with increasing distance from the 491
homes. In addition, we also noted that in inner-city properties like site A, G, and K, the neighboring 492
structures, adjacent to the tested yards, also acted as prominent sources of Pb. Buried or scattered 493
paint chips in the soil of site A increased the SLL in some locations against the influence of 494
distance. 495
Pb contamination in residential soil is so widespread that it can be found in unapparent locations, 496
increasing the threat of continual risk of exposure. In accordance with previous studies, we have 497
observed three such unforeseen scenarios: i) high soil Pb in a suburban house with a well-498
maintained exterior (Clark and Knudson 2013) was observed in site L, which exhibited SLL as 499
high as 3000 mg/kg; ii) high soil Pb in a property with an unpainted exterior was noted in site B, 500
where the SLL surpassed the federal guideline only beneath the windows, a trend also observed 501
by Linton et al. (1980); and iii) increase in SLL after renovation and repainting of a house (Clark 502
and Knudson 2013), which is duly noted in site A. Table S.3 presents a comparative SLL, estimated 503
BLL, and potential dust loading values in the three yards of site A that illustrates the detrimental 504
effects of renovation activity on soil Pb. 505
19
Dust is a major source of concern for children, as inhalation is a major route of Pb exposure. The 506
respiratory tract is responsible for the absorption of lead particles into the systemic circulation, and 507
respiration and metabolic rates are faster in children, resulting in increased absorption of lead via 508
dust (Menezes-Filho 2018). Two tools were used in this study to estimate the risk of Pb exposure 509
from outdoor dust: i) direct measurement of accumulated dust on the outdoor surfaces, and ii) 510
secondary estimation of potential lead on play surfaces (PLOPS). Overall, accumulated Pb in dust 511
showed significant (p<0.001) positive correlation with Pb in paint and negative correlation with 512
depth index, suggesting flaking LBP on surfaces such as window sills, doors, steps, and old 513
building materials can potentially contribute Pb to dust. 514
Lead loading on exterior surfaces has been found to be a continuous process, primarily due to 515
resuspension of soil Pb (Caravanos et al. 2006; Mielke et al. 2006). Researchers identified the 516
resuspension of soil Pb during dry seasons as the major driving force behind the seasonally EBLL 517
in children in New Orleans, Syracuse, and Indianapolis (Laidlaw et al. 2005). Mielke et al. (2006) 518
developed a new tool for measuring the potential Pb surface loading per area (mg/ft2) of the soil. 519
Potential Pb on play surfaces, a measure of the Pb concentration of a soil surface per surface area 520
as a source of Pb dust for children who would play on that surface, was calculated based on a 521
prediction model created by Mielke et al. (2006) (equation 8). 522
𝑃𝐿𝑂𝑃𝑆 = −43.74 + 24.85 ∗ (𝑆𝐿𝐿 𝑚𝑒𝑑𝑖𝑎𝑛)0.69 -------Equation 8 523
The PLOPS values for all urban and suburban properties are presented in table 5 to provide insight 524
into the overall potential of Pb exposure from these surfaces, both directly through hand-to-mouth 525
activity, or indirectly through resuspension of Pb dust. Mielke et al. (2006) also showed a large 526
drop in PLOPS in 136 properties and vacant lots in New Orleans after the contaminated surfaces 527
were treated with a soil cover. Similar measures should be considered for the properties assessed 528
in our study, as the large PLOPS values indicate a high potential risk of Pb exposure to children if 529
they play in these areas. 530
The relationship between Pb in produce and the adjacent soil Pb is equivocal in the literature; with 531
some studies reporting maximum Pb levels in vegetables associated with the highest SLL 532
(Bielinska 2009; Huang et al. 2012; Moir and Thornton 1989) and other studies finding only weak 533
correlations between crop Pb levels and the adjacent soil Pb, due to unverified reasons (Hough et 534
20
al. 2004; Murray et al. 2011; McBride et al. 2014). Our study found strong and significant (r = 535
0.92 based on fresh weight and r = 0.85 based on dry weight; p < 0.001) correlations between the 536
plant-accumulated Pb and SLL. Ten out of fourteen edibles tested exceeded the health-based 537
guidance values set by the European Union (EU) (EC 2006). There are no such standards currently 538
in the U. S., but the median Pb in edible plants found in this study was 72 times higher than the 539
median market basket concentrations of Pb reported by the U. S. Food and Drug Administration’s 540
(FDA) Total Diet Study. (USFDA 2010). The FDA recommended the maximum ingestion lead 541
level for a child is 3 µg/day; eating between four and five grams of contaminated produce would 542
reach that limit (Frank 2019). Interestingly, Pb levels exceeding the EU guideline standards were 543
found in plants grown in both indigenous soils (sites H and M) of these properties as well as potting 544
soils (site C and side yard of site A). The median Pb in plants grown in indigenous soils (1.05 545
mg/kg) was higher than those grown in the potting soil (0.21 mg/kg) and showed a stronger 546
correlation with the Pb concentrations in adjacent soils (r = 0.91 and 0.69 for indigenous and 547
potting soils respectively). However, it is deeply concerning that the edible plants grown in potting 548
soils containing Pb lower than 60 mg/kg were found to contain Pb levels exceeding the health 549
guidelines. The federal standard of 400 mg/kg soil Pb provides a misconception of safety for soils 550
containing lower Pb concentrations; there are no current levels set by either U.S. Environmental 551
Protection Agency (EPA) or HUD for urban gardening. Moreover, each species of produce has 552
different biological properties that determine their uptake, absorption, and bioavailability of Pb, 553
demonstrated by the varying lead concentrations in them (USFDA 2010). Differential uptake of 554
Pb by different vegetables at similar SLL was noted in the current study (site A) in Rochester as 555
well as by McBride et al. (2014) in New York City and Buffalo, NY; and Misenheimer et al. 556
(2018) in Puerto Rico. These findings strongly support the development of new plant-specific 557
federal guidelines for soil Pb to promote safer gardening practices. 558
4.2 Safer use of yards by primary prevention tools 559
560
Based on results of the field assessment, we developed three primary prevention tools to help 561
homeowners understand the Pb exposure risks throughout their properties as the first step of 562
primary prevention: i) a prediction model to calculate soil Pb using the measured distance from 563
the structure; ii) a prediction model to estimate Pb in edible produce grown in yard gardens from 564
the adjacent soil Pb that can be calculated from the distance from the built structure, iii) GIS maps 565
21
showing Pb hot spots with high risk, low risk, and safe areas in the yards. Prediction models and 566
GIS maps presented by prior research, covering a larger city area, are crucial for bringing necessary 567
changes in the policy (Wu et al. 2010), but this is an ongoing, long-term process. Considering the 568
continual Pb exposure and elevated BLL in families with lower socioeconomic standing, it is of 569
utmost importance to develop an easier process for the homeowners to understand the risk to be 570
able to avoid it. To the best of our knowledge, the present study is the first attempt to develop site-571
specific prediction models and precise GIS maps illustrating the potential risk through mapping 572
the SLL in the exact latitude and longitude of the sampling locations on individual residential 573
properties. The GIS maps also mark the safer locations to create gardens based on the two 574
prediction models we developed. These measures would provide the homeowners with an 575
opportunity to predict soil Pb levels to plan safer uses of their yards. 576
577
City-wide larger models to calculate soil Pb based on distance from urban centers of big cities or 578
roads with high traffic density considered the atmospheric deposition of Pb as the dominant source 579
(Yaylah-Abanuz 2019; Brown et al. 2008; Zhang et al. 2015; Wu et al. 2010). However, residential 580
soil Pb with exterior LBP as the major source varies widely from one property to another, 581
depending on the age of the structure(s), presence and types of siding, past land use, and renovation 582
processes. Site-specific prediction models would provide homeowners with an opportunity to 583
predict soil Pb levels using the distance from a structure to utilize their yards more safely. For 584
instance, a play area should be planned in soil that contains less than 400 mg/kg Pb, according to 585
federal guidelines. Based on our site-specific models, SLL drops from 400 mg/kg to < 100 mg/kg 586
if the play area is located 2 m further from the house. Considering the steep increase of BLL 587
between 100 to 300 mg/kg SLL, simply moving a play area a few meters further from the structure 588
would increase the chance of primary prevention at no cost. Although we highly recommend at 589
least a one-time assessment of soil Pb for each property, it is likely that one prediction model 590
would be valid for one block or neighborhood if the homes were built at a similar time; however, 591
further investigation of this assumption is necessary. 592
593
This study also presents prediction models exhibiting a strong correlation (r = 0.85 and 0.92) 594
between Pb accumulation (per d.w. and f.w.) in produce/plant parts and the Pb levels in the 595
adjacent soil. One limitation of these prediction models is that they will not be able to provide any 596
22
insight into the plant-specific differential Pb uptake, as the pronounced effect of soil Pb may have 597
masked the effects of plant-specific uptake in the current study. However, utilization of these 598
equations would be able to provide a rough estimate of the potential Pb accumulation into 599
vegetables from the adjacent SLL to estimate a safer location for a garden. For instance, the 600
vegetable gardens in sites M and H were located in the areas with the highest SLL, resulting in 601
median Pb levels in edible parts of 1.58 and 0.56 mg/kg, respectively, which exceed the EU 602
guideline by 15.8 and 5.6 folds, respectively (EC 2006). Both of these properties have areas with 603
much lower soil Pb further into the yard. Simply moving these gardens to such locations would be 604
a useful measure of intervention to reduce the Pb exposure risk through the consumption of Pb-605
contaminated vegetables from the property gardens. We estimated an SLL value associated with 606
the EU guideline of 0.1 mg/kg Pb per plant f.w. using a reverse fit of equation 2 (R2 = 0.97, 607
p<0.0001) which associates with a SLL of 64.7 mg/kg; a slightly lower value of 0.09 mg/kg Pb 608
per plant f.w. associates with an SLL of 58.2 mg/kg. Using the reverse fit of the site-specific 609
models, we calculated the associated distance with 58.2 mg/kg soil Pb in site M and site H, and 610
these distances are found to be 6.44 and 3.85 m respectively. Thus, our combined models indicate 611
that to attain a minimum requirement for the primary prevention, a home garden should be set up 612
at least 6.44 m away in site M and 3.85 m away in site H from their respective structures. Similarly, 613
a safer distance can be calculated for any property garden to avoid consumption of Pb through 614
homegrown vegetables. 615
616
Figure 8 presents two reference GIS maps of site A showing the Pb exposure risk associated with 617
soil Pb distribution in three areas of the property before and after the renovation of the home. To 618
simply and clearly communicate risk levels to homeowners, we developed a visual tool utilizing a 619
graded color scheme of green to black representing risk levels. Table S.4 presents a guideline 620
created for the homeowners to explain each color category, associated risk, and the recommended 621
use of that location on their properties. In addition to using the federal standards for assigning 622
colors to the range of Pb concentrations, we have also used our prediction models to mark areas 623
of the property where it would be safer to create a garden based on the EU health guidelines for 624
edible produce. The areas marked in red to black colors represent hotspots with very high to 625
extreme risk of potential Pb exposure and thus must be avoided and should be considered for 626
remedial actions. 627
23
4.3 Sustainable Remediation 628
629
The second and final step of primary prevention is to correct the hazards through remedial actions, 630
which led us to investigate an affordable process that would provide substantial risk reduction 631
without economically burdening the homeowners. WTR collected from Rochester’s largest 632
drinking water treatment plant not only exhibited a very high Pb-binding ability but was also found 633
to be safe when used as a soil amendment according to the federal TCLP guidelines. Past research 634
documented varying effectiveness of different sorbents to immobilize lead, including activated 635
carbon (Moyo et al. 2013), natural and engineered clay-like zeolite (Wingenfelder et al. 2005), 636
mordenite (Turkyilmaz et al. 2014), montmorillonite (Zhang and Hou 2008), bentonite (Inglezakis 637
et al. 2007), palygorskite, and sepiolite (Shirvani et al. 2006). Although these technologies provide 638
in-situ alternatives to the current expensive ex-situ practices, the cost of these natural or engineered 639
sorbents in bulk could become cost-prohibitive to low-income urban families. WTR is a no-cost 640
sorbent which would provide these homeowners with an affordable remediation alternative to be 641
utilized as soil cover or amendment in the most concerning areas of their properties. The only 642
required cost is associated with its implementation, which could be further reduced by conducting 643
a proper assessment to mark the risk hotspots (as shown in the GIS maps for site A), where the 644
remediation process is absolutely needed. Very few studies have investigated the effectiveness of 645
Pb adsorption by Fe and Al-based WTR collected from different parts of the world. Pb sorption 646
by Al-WTR showed a similar pattern of L type sorption isotherm (Castaldi et al. 2015; Zhou et al. 647
2011). Adsorption maximum was reported to be 17.55, 15.66, and 15.13 mg/g by WTRs collected 648
from Miyamachi and Nishino in Japan and Italy, respectively, as compared to 24.4 mg/g found in 649
this study (Putra and Tanaka 2011). Furthermore, Soleimanifer et al. (2019) reported the unique 650
approach of reducing metal contamination in urban stormwater with WTR coated mulch; a similar 651
approach could be further investigated for its potential to reduce plant-Pb uptake if used in home 652
gardens. The sorption desorption data along with the calculated Freundlich and Langmuir isotherm 653
parameters suggests very strong, favorable, and more irreversible adsorption of Pb on WTR 654
through a physical process, promising an effective option for reusing this waste byproduct that the 655
city generates every day to immobilize Pb in the high-risk soils of many contaminated residential 656
properties. 657
658
24
Using WTR as a soil amendment will prevent the runoff and leaching of soil Pb and also decrease 659
the Pb available to be accumulated in garden vegetables. However, the implementation of only 660
WTR will not prevent the resuspension of Pb into dust. Our goal is to implement a dual-process 661
remediation system using WTR as a soil amendment with the addition of a plant cover to decrease 662
the dust loading of Pb. Several studies have shown the phytoremediation potential of Pb (Jagetiya 663
and Kumar 2020), but it is of utmost importance to use a non-edible plant in the Pb hot spot areas 664
to prevent its further entry into the food chain. Vetiver, a perennial non-edible and non-invasive 665
grass has been shown to be highly effective for Pb removal (Andra et al. 2009); however, the harsh 666
winters of western NY could be a serious impediment for its implementation. Our group has tested 667
17 native inedible plant species for their potential tolerance of high Pb concentration, but all plants 668
showed severe phytotoxic effects of Pb in hydroponic media, where 100% Pb was available for 669
plant uptake (data not shown). This further underscores the utility of WTR for Pb removal, which 670
according to the results of this study would retain more than 90% of total soil Pb, making only a 671
small fraction available for the plants to tolerate. The ongoing studies in our greenhouse are 672
characterizing this dual process technique using WTR as an amendment to the soils from the 673
backyard of site A, with an SLL median of 2900 mg/kg, and switchgrass (Panicum virgatum), high 674
biomass, perennial, fast-growing, inedible grass, which is also native to western NY and which 675
was previously studied for its ability to remove other environmental contaminants (Phouthavong-676
Murphy 2020). This combined remediation process would provide the homeowners with an 677
effective and affordable primary prevention alternative to intervene with the Pb exposure from the 678
risk hotspots of their property. 679
680
Acknowledgements 681
682
The authors acknowledge Mr. Larry Peckham and Mrs. Nancy Peckham for funding the High 683
School Summer Research Program in 2018 and Nazareth College Summer Opportunities for 684
Activities in Research and Sponsorship (SOARS) in 2018 for partially funding this project. 685
686
We acknowledge Mr. John C. Kelly Jr. and other officials from Monroe County Water Authority 687
(MCWA) for their help in WTR collection; our community partners and homeowners for their 688
communication and participation; Dr. Callie Babbitt from Rochester Institute of Technology for 689
25
conducting the ICP-OES analyses of TCLP samples; Mrs. Sharon Luxmore for assistance with 690
ordering materials; and Ms. Aelis Spiller, Ms. Ariel Struzyk, Mr. LaMar Jackson, Mr. Sean Dillon, Ms. 691
Maria Vargas Morales, and Ms. Bailey Groth for assistance in sample preparation and field measurements; 692
and Mr. Shyamal K. Mitra and Dr. Ananta Paine for providing feedback about this manuscript. 693
694
Author contributions 695
P.D. designed the research, field assessment, and experiments. P.D., D.R.B., M.J.P, B.E.L., A.C.T., 696
J.M.P., H.M.C., J.L.W., I.G., and D.G. conducted the field assessment; B.E.L. and A.C.T analyzed Pb in 697
surface dust, J. S., C.V.B., M.L.M., D. R. B., M.J.P., C.E.D. analyzed Pb in plants with the supervision of 698
S.M.Z. and P.D.; J.L.W., I.G., J.P.M., J.M.P., H.M.C., A.C.T. conducted phase II experiments with the 699
supervision of S.M.Z. and P.D; P.D. conducted the secondary analyses, isotherm modeling, statistical data 700
analyses, and developed the prediction models. M.C. created the GIS maps and D.R.B. created the 701
Tableau maps. P.D. wrote the manuscript with S.M.Z. and J.P.M. with contributions and feedback from 702
all other authors. 703
704
Abbreviations: 705
Pb: lead; WTR: drinking water treatment residual; XRF: x-ray Fluorescence Analyzer; Al-WTR: 706
aluminum-based water treatment residual; SLL: soil lead level; mg/kg: milligram per kilogram; 707
WTR: water treatment residual; GIS: geographical information systems; LBP: lead based paint; 708
EBLL: elevated blood lead level; BLL: blood lead level; µ𝑔/𝑑𝐿: micrograms per deciliter; 709
USEPA: United States Environmental Protection Agency; USHUD: United States Department of 710
Housing and Urban Development; n: number of samples; HUD: Department of Housing and Urban 711
Development; TCLP: toxicity characteristic leaching procedures; SSR: solid to solution ratio; KCl: 712
potassium chloride; mg/L: milligrams per liter; HNO3: nitric acid; AAS: atomic absorption 713
spectrophotometer; RCRA metals: 8 metals (Arsenic, Barium, Cadmium, Chromium, Lead, 714
Mercury, Selenium, Silver) listed under Resource Conservation and Recovery Act; ICP-OES: 715
individually coupled plasma optical emission spectroscopy; Tukey-Kramer HSD: Tukey Kramer 716
honest significant difference. 717
718
719
26
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35
Table 1: Polynomial cubic fit of lead concentrations (mg/kg) in residential soil by distance from potential source of Pb based paint.
The nonlinear correlation fit, regression equation, and parameter estimates were conducted using JMP (version 15). Parameters of the
regression equations were estimated and the significance levels of the parameters were expressed as * (p < 0.05), ** (p < 0.01), *** (p <
0.001), **** (p < 0.0001).
Site R2 F
ratio p value Regression Equations
Significant
Parameters
A1 0.9994 632.9 0.0292 𝑦 = 3684 – 575.9 ∗ x + 89.45 ∗ (x − 4.321)2 – 2.277 ∗ (x − 4.321)3 Intercept**, x2**
A3 0.9366 14.78 0.0266 𝑦 = 2141 – 1307 ∗ x + 8370 ∗ (x − 1.045)2 – 5519 ∗ (x − 1.045)3 x2*
A4 0.9868 50.02 0.0197 𝑦 = 2226 – 282.6 ∗ x + 126.5 ∗ (x − 3.569)2 – 28.10 ∗ (x − 3.569)3 x2*
L1 0.9567 22.13 0.0151 𝑦 = 388.3 – 249.5 ∗ x + 497.1 ∗ (x − 2.199)2 – 108.1 ∗ (x − 2.199)3 x2*, x3*
L2 0.8593 18.32 0.0004 𝑦 = 420.3 – 170.1 ∗ x + 277.5 ∗ (x − 2.110)2 – 61.83 ∗ (x − 2.110)3 x2***, x3*
M 0.8402 21.03 <0.0001 𝑦 = 394.3 – 68.31 ∗ x + 135.8 ∗ (x − 3.029)2 – 28.03 ∗ (x − 3.029)3 x2***, x3**
H 0.9820 218.5 <0.0001 𝑦 = 752.9 – 220.8 ∗ x + 127.8 ∗ (x − 2.286)2 – 22.73 ∗ (x − 2.286)3 Intercept****,
x****, x2****, x3*
C1 0.9607 32.63 0.0029 𝑦 = 232.8 – 183.5 ∗ x + 486.1 ∗ (x − 1.181)2 – 246.8 ∗ (x − 1.181)3 x2**
C2 0.9868 150.4 <0.0001 𝑦 = 153.3 – 71.01 ∗ x + 362.7 ∗ (x − 1.371)2 – 268.6 ∗ (x − 1.371)3 x2****, x3**
B 0.9865 97.77 0.0003 𝑦 = 1175 – 713.6 ∗ x + 105.9 ∗ (x − 1.124)2 – 150.5 ∗ (x − 1.124)3 Intercept***, x***,
x3*
G1 0.8887 13.31 0.0081 𝑦 = 361.8 – 71.36 ∗ x + 97.78 ∗ (x − 1.439)2 – 41.25 ∗ (x − 1.439)3 Intercept*, x2*
G2 0.9848 86.41 0.0004 𝑦 = 508.9 – 71.63 ∗ x + 13.23 ∗ (x − 3.458)2 – 0.4656 ∗ (x − 3.458)3 Intercept***, x**,
x2**
Table 2. Pb concentrations in produce or parts of plants (mg/kg) grown in the gardens of the tested residential properties. Data are
expressed as mean (n=3) + one standard deviation. Mean comparison is conducted by one-way ANOVA (F ratio = 121.4; p <0.0001)
followed by Tukey-Kramer honest significant difference test. Mean comparison letters are expressed as superscript and levels not
connected by same letters are significantly different from one another.
36
Produce/Plants Parts of plants
analyzed Site
Pb
concentrati
on in soil
(mg/kg)
Pb
concentration
in plants by dry
weight (mg/kg)
Pb concentration
in plants by
fresh weight
(mg/kg)
Guidance Value
(EC, 2006)
Tomato
(Solanum lycopersicum) Tomato (fruit) Garden bed in C 8.5 0 + 0G 0.02 + 0.009I 0.1
Arugula
(Eruca vesicaria) Leaves Garden bed 1 in A 11.2 3.52 + 0.22 DE 0.19 + 0.016H 0.3
Lettuce
(Lactuca sativa)
Leaves Garden bed 8 in A 28.3 2.98 + 0.10 DE 0.01 + 0.005I 0.3
Radish
(Raphanus sativus) Radish (root) Garden bed 8 in A 28.3 4.67 + 0.33 DE 0.08 + 0.014I 0.1
Garlic
(Allium sativum) Garlic (bulb) Garden bed 7 in A 30.4 1.25 + 0.02 F 0.34 + 0.004G 0.3
Tomato
(Solanum lycopersicum) Stem Garden bed 7 in A 30.4 1.97 + 0.04 F 0.37 + 0.006G 0.3
Potato
(Solanum tuberosum) Root Garden bed 2 in A 32.6 1.88 + 0.07 F 0.23 + 0.008H 0.1
Broccoli
(Brassica oleracea var. italica) Leaves Garden bed 5 in A 52.8 1.79 + 0.07 F 0.48 + 0.012EF 0.3
Ground Ivy
(Glechoma hederacea) Whole plant Front yard in A 7400 16.07 + 0.37 A 3.35 + 0.59A N/A
Elephant Foot Yam
(Amorphophallus paeoniifolius) Whole plant Front yard garden in H 358.7 3.42 + 0.13 E 0.34 + 0.013G 0.1
Elephant Foot Yam
(Amorphophallus paeoniifolius) Whole plant Front yard garden in H 511.4 4.42 + 0.01 DE 1.05 + 0.003D 0.1
Elephant Foot Yam
(Amorphophallus paeoniifolius) Whole plant Front yard garden in H 663.3 4.89 + 0.08D 0.55 + 0.008E 0.1
Elephant Foot Yam
(Amorphophallus paeoniifolius) Whole plant Front yard garden in H 998.9 4.54 + 0.14DE 0.40 + 0.013FG 0.1
Elephant Foot Yam
(Amorphophallus paeoniifolius) Whole plant Front yard garden in H 1042 10.89 + 0.56B 1.22 + 0.054C 0.1
Tomato
(Solanum lycopersicum) Tomato (fruit) Back yard garden in M 985.1 8.40 + 0.30C 1.59 + 0.056B 0.1
37
Table 3. Physicochemical characteristics and TCLP of WTR collected from lagoon 1 in 2017 and lagoon 2 in 2018. Data are
expressed as mean (n = 3) + one standard deviation. Mean comparison is conducted between WTR collected from different space and
time.
TCLP RCRA Metals (mg/L)
Moisture
Content
(%)
pH
Electrical
Conductivit
y (𝞵s/cm)
Organic
matter
content
(%)
Ba Ag As Cr Pb Cd Se Hg
WTR from
Lagoon 1
collected in
2017
48.32 +
1.997
6.99 +
0.064
371.8 +
38.09
2.195 +
0.166
1.157 +
0.0102
0.001 +
0.000
0.086 +
0.023
0.033 +
0.006
0 +
0
0.003 +
0.001
0.036 +
0.006 0 + 0
WTR from
Lagoon 2
collected in
2018
47.99 +
1.962
6.98 +
0.035
450.3 +
52.50
2.259 +
0.157
1.047 +
0.021
0.001 +
0.000
0.098 +
0.035
0.04 +
0.001
0 +
0
0.002 +
0.001
0.036 +
0.006 0 + 0
EPA
permissible
Limit
100 5 5 5 5 1 1 0.2
F ratio 0.042 0.155 4.398 0.235 3.381 NA 0.273 3.811 NA 0.500 0.018 NA
p value 0.848 0.714 0.104 0.653 0.139 NA 0.629 0.123 NA 0.519 0.899 NA
38
Table 4. Percentile, mean, standard deviation (SD) of soil lead level (SLL) (mg/kg) and potential blood lead level (BLL) (g/dL) in
children and potential lead on play surfaces (PLOPS) (g /ft2) using three prediction models developed by Mielke et al., (2007),
Mielke et al., (2006), and Johnson and Bretsch (2002) respectively.
Percentile SLL (mg/kg) SLL Statistics
(mg/kg) Potential BLL (g/dL)
PLOPS
(g /ft2)
Max 75% Median 25% Min Mean SD N Equation 6 Equation 7 Equation 8
Urban
A after
Renovation 173500 7346 1665 455 57 9619 25045 79 9.1 12 4107
A before
Renovation 50000 6811 963 257 82 6857 11768 42 7.4 10.8 2800
C 1553 992 578 144 11 619 469 58 6.2 9.7 1956
M 3743 990 442 253 58 707 759 36 5.7 9.2 1617
H 2378 530 357 181 112 502 490 45 5.3 8.7 1389
G 1200 427 276 198 75 328 206 54 4.9 8.2 1156
B 1077 504 187 131 65 329 326 16 4.4 7.4 874
K 847.7 248 109 82 32 190 180 43 3.8 6.2 589
Suburban
L 3088 1099 558 201 34 753 720 45 6.1 9.7 1909
I 1295 150 116 80 65 202 301 16 3.9 6.4 615
J 115.7 97 82 47 37 76 28 8 3.6 5.6 478
E 248.4 63 45 35 25 66 61 15 3.2 4.4 301
Overall
Urban 173500 1073 413 185 11 3178 12768 373 5.5 9.0 1542
Suburban 3088 711 155 66 25 461 627 84 4.2 7.0 763
39
Figure 1. Field assessment site maps with respect to number of pre-1978 built structures (a), racial diversity (b), education (c), and
annual household income (d) of the sub-counties. These maps were created using Tableau software, which developed a geographical
map of Monroe and Ontario County utilizing latitude and longitude coordinates for each sub-county boundary. In subfigure 1a, the
size of the bubble depicts the number of pre-1978 houses found within that sub-county, and the color of the bubble denotes the
respective sub-county. In subfigure 1b, 1c, and 1d, the distribution of racial diversity, education, and annual income data in their
respective categories was represented by overlaying pie-charts into each sub-county.
Figure 2. Effect of distance (m) from potential source of lead-based paint on the lead concentrations (mg/kg) of residential soils in the
urban or suburban neighborhoods of Rochester, NY; Data are expressed as mean (n = 20) + one standard deviation. Each subfigure
expressed data for one residential property except site A, which was expressed in two subfigures (a, b). The subfigures are arranged
left to right in rows based on their decreasing Y axis values, indicating the magnitude of soil Pb, of which the maximum was recorded
in the front yard of site A after renovation (a) followed by the back yard of site A (b) before and after renovation; site L (c); site M (d);
site H (e); site C (f), site B (g); site G (h); and site K (i).
Figure 3. SLL measurements in the backyard of site G showing two assessment lines; G1 (pink line), which was measured from the
corner of the garage at site G, showed a decrease in soil Pb with distance except on one location (X), which was found to be
horizontally aligned to the corner of the neighbor’s house, indicating it to be an additional source of LBP (fig. S.2). The line G2 was
measured from a point adjacent to the property line between these two houses and was horizontally aligned to the corner of the
neighbor’s house. G2 also showed a continual decrease with distance in the soil Pb concentration, except the increase at the last
measured location point, which is the closest to the main house in site G, suggesting an additional source of LBP. The photograph is
included in the manuscript with the permission of the homeowner.
Figure 4. Multivariate correlation among Pb in outdoor dust, paint and the depth index indicating how deep Pb is in the paint from a
window (a), door (b), and front stairs (c) of site G; discarded old house materials from site A (d); pillars from the front porch in site H
(e); and discarded old house materials left in a barn at site E (f).
40
Figure 5. Kinetic sorption (%) of Pb in presence or absence of WTR. Data are expressed as mean (n = 3) + one standard deviation.
Mean comparison is conducted by two-way ANOVA followed by Tukey-Kramer honest significant difference test. It is conducted
separately for each initial Pb concentration. Levels not connected by same letters are significantly different.
Figure 6. Equilibrium sorption and desorption of Pb in 5% WTR. Linear (a), Freundlich (b), and Langmuir (C) isotherms of Pb-
sorption by WTR in 24h. Data are expressed as mean (n = 3) + one standard deviation. % sorption (d) and % total desorption (e) of Pb
by WTR in three desorption cycles of 24h each. Desorption of Al (mg/kg) from WTR during sorption of Pb (f). Mean comparison is
conducted by one-way ANOVA followed by Tukey-Kramer honest significant difference test. Levels not connected by same letters
are significantly different.
Figure 7. Effect of WTR concentrations on the leaching of lead (µg/L) from the paint chips collected from site A. Data are expressed
as mean (n = 3). Mean comparison is conducted by two-way ANOVA followed by Tukey-Kramer honest significant difference test.
Levels not connected by same letters are significantly different.
Figure 8: GIS maps showing distribution of SLL (mg/kg) in site A before (8a) and after (8b) the restoration/repainting of the built
structure. These maps were created using ArcGIS Pro 2.5.2 by plotting the SLL (mg/kg) measurements into their respective latitude
and longitude. An approximate property-line (dotted line) was overlaid to show the boundaries of the property segregating it from the
side walk in south and west and three built structures of neighboring property in north and east. Based on different guidelines for
safety, 7 sizes and color categories were used, each representing a range of SLL.
41
Figure 1:
Figure 2:
a b
c d
42
0
5000
10000
15000
20000
25000
30000
35000
40000
0 0.5 1 1.5 2 2.5
Le
ad
Co
nc
en
tra
tio
n in
so
il
(mg
/kg
)
Distance from potential source of Pb based Paint (m)
A5 (after renovation)
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
0 2 4 6 8 10Le
ad
Co
nc
en
tra
tio
n in
so
il
(mg
/kg
)
Distance from potential source of Pb based Paint (m)
A1 (beforerenovation)A2 (afterrenovation)A3 (afterrenovation)A4 (afterrenovation)
0
500
1000
1500
2000
2500
3000
3500
0 2 4 6
Le
ad
Co
nc
en
tra
tio
n in
so
il
(mg
/kg
)
Distance from potential source of Pb based Paint (m)
L1 L2
0
500
1000
1500
2000
2500
3000
3500
0 2 4 6 8
Le
ad
Co
nc
en
tra
tio
n in
s
oil
(m
g/k
g)
Distance from potential source of Pb based paint (m)
M
0200400600800100012001400160018002000
0 2 4Le
ad
Co
nc
en
tra
tio
n in
s
oil
(m
g/k
g)
Distance from potential source of Pb based Paint (m)
H
0
200
400
600
800
1000
1200
1400
1600
1800
0 1 2 3
Le
ad
Co
nc
en
tra
tio
n in
s
oil
(m
g/k
g)
Distance from potential source of Pb based Paint (m)
C1 C2
0
200
400
600
800
1000
1200
0 1 2 3Le
ad
Co
nc
en
tra
tio
n in
s
oil
(m
g/k
g)
Distance from potential source of Pb based Paint (m)
B
0
100
200
300
400
500
600
700
800
900
0 2 4 6 8
Le
ad
Co
nc
en
tra
tio
n in
s
oil
(m
g/k
g)
Distance from potential source of Pb based Paint (m)
G1 G2
0
100
200
300
400
500
600
700
0 5
Le
ad
Co
nc
en
tra
tio
n in
s
oil
(m
g/k
g)
Distance from potential source of Pb based Paint (m)
K
a b
c
d
D e f
g h i
43
Figure 3:
44
Figure 4:
500
750
1000
1250
1500
1750
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1
1.2
1.4
1.6
1.8
Pb in dust
r=0.8202
r=-0.6845
500 1000 1500
r=0.8202
Pb in paint
r=-0.9757
0.1 0.3 0.5 0.7
r=-0.6845
r=-0.9757
Depth index
1 1.2 1.4 1.6 1.8
50
100
150
200
250
300
350
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1.5
2
2.5
3
Pb in dust
r=-0.6914
r=-0.7013
50 150 250 350
r=-0.6914
Pb in paint
r=0.7785
0.15 0.25 0.35 0.45
r=-0.7013
r=0.7785
Depth index
1.5 2 2.5 3
30
35
40
45
0.15
0.2
0.25
0.3
0.35
0.4
0.45
2
2.5
3
Pb in dust
r=0.8736
r=0.7256
30 35 40 45
r=0.8736
Pb in paint
r=0.9368
0.15 0.25 0.35 0.45
r=0.7256
r=0.9368
Depth index
2 2.5 3
0
500
1000
1500
2000
2500
3000
3500
0
5
10
15
20
1
1.5
2
2.5
Pb in dust
r=0.8894
r=-0.2848
0 1000 2000 3000
r=0.8894
Pb in paint
r=-0.3090
0 5 10 15 20
r=-0.2848
r=-0.3090
Depth index
1 1.5 2 2.5
0
1000
2000
3000
4000
5000
6000
0
5
10
15
0.9
0.95
1
1.05
1.1
Pb in dust
r=0.9128
r=0.0000
0 2000 4000 6000
r=0.9128
Pb in paint
r=0.0000
0 5 10 15
r=0.0000
r=0.0000
Depth index
0.9 0.95 1 1.05 1.1
a b c
d e
f
45
Figure 5.
0
20
40
60
80
100
120
0 5 10 15 20 25
% S
orp
tio
n o
f P
b
Contact Time (h)
0 mg/L Pb 5% WTR 25 mg/L Pb 5%WTR
200 mg/L Pb 5%WTR 1000 mg/L Pb 5%WTR
0 mg/L Pb 0%WTR 25 mg/L Pb 0%WTR
200 mg/L Pb 0%WTR 1000 mg/LPb 0% WTR
Tukey-Kramer HSD
% WTR*Contact time (h)
Initial
Pb
(mg/L)
Contact
time
(h)
WTR
(%)
Mean
Comparison
Letters
25 0.13 5 A
25 1.13 5 A
25 2.13 5 A
25 5.13 5 A
25 10.13 5 A
25 24.13 5 A
25 0.13 0 B
25 1.13 0 B
25 2.13 0 B
25 5.13 0 B
25 10.13 0 B
25 24.13 0 B
220 0.13 5 D
220 1.13 5 C
220 2.13 5 B
220 5.13 5 A
220 10.13 5 A
220 24.13 5 A
220 0.13 0 E
220 1.13 0 E
220 2.13 0 E
220 5.13 0 E
220 10.13 0 E
220 24.13 0 E
1025 0.13 5 D
1025 1.13 5 C
1025 2.13 5 B
1025 5.13 5 A
1025 10.13 5 A
1025 24.13 5 A
1025 0.13 0 FG
1025 1.13 0 G
1025 2.13 0 EFG
1025 5.13 0 E
1025 10.13 0 EFG
1025 24.13 0 EF
46
Figure 6.
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
0 20 40 60 80 100
So
rbe
d P
b b
y W
TR
Q (
mg
/kg
)
Equilibrium Pb Concentration in
Solution Ceq (mg/L)
y = 0.5481x + 3.2092
R² = 0.99743.5
3.6
3.7
3.8
3.9
4.0
4.1
4.2
4.3
4.4
0 0.5 1 1.5 2 2.5
log
Q
log Ceq
y = 4E-05x + 0.0014
R² = 0.9808
0.0000
0.0010
0.0020
0.0030
0.0040
0.0050
0.0060
0 50 100
Ce
q/Q
Ceq
a b c
d e f
47
Figure 7.
0
1000
2000
3000
4000
5000
6000
7000
8000
010
2050
100
Le
ac
he
d L
ea
d in
so
luti
on
(µ
g/L
)
% WTR
1 Day 7 Days
Tukey-Kramer HSD
(WTR % * Time in days)
WTR
%
Time
(days)
Mean
Comparison
Letters
0 1 A
10 1 AB
20 1 BC
50 1 DE
100 1 DE
0 7 A
10 7 CD
20 7 CDE
50 7 E
100 7 E
48
Figure 8:
Figure 8 Footnote: Table S.4 includes a guideline created for the homeowners to explain each color category used in these maps,
associated risk, and the recommended use of that location in their yards.
a b
49
Supplementary Information
Table S.1: Overview of outdoor Pb exposure in the urban and suburban residential properties in Rochester, NY.
Residential
Properties Neighborhood Year
Pb in Paint
(mg/cm2) Depth Index
Maximum
Pb in soil
(mg/kg)
Maximum Pb in
outdoor dust
(g/ft2)
A (Before
Renovation) Urban Unknown 33.7 1.0 50000 Not Measured
A (After
Renovation) Urban Unknown 20.3 6.3 173500 3635
B Urban 1923 2.3 1.0 1077 Not Measured
C Urban 1930 5.9 10 1553 43.39
E Suburban barn 1830 14.1 1.6 NA 8612
F Suburban 2001 0 1.0 0 0
G Urban 1910 6.4 4.4 1200 1969
H Urban 1883 27.7 5.5 2378 1479
1I Suburban 1900 23.0 1.9 390.5 290.2
J Suburban 1954 8.4 6.5 115.7 83.86
K Urban 1930 19.4 9.0 847.7 156.4
L Suburban 1923 33.8 10.0 3088 80.42
M Urban 1910 38.2 4.7 3743 Not Measured
50
Table S.2: Parameters of linear and nonlinear bivariate regression fit of Pb concentrations (mg/kg) in residential soil by distance (m) from
potential source of Pb based paint using data collected from all properties.
Degrees of Fit R2 F ratio p value
1 0.074 11.13 0.0011
2 0.138 11.04 <0.0001
3 0.179 9.992 <0.0001
4 0.209 9.017 <0.0001
5 0.229 8.016 <0.0001
6 0.238 6.986 <0.0001
51
Table S.3: Distribution of soil Pb and associated risks in three yards of site A. Percentile, mean, standard deviation (SD) of soil lead level (SLL)
(mg/kg) and potential blood lead level (BLL) (g/dL) in children and potential lead on play surfaces (PLOPS) (g /ft2) using three prediction
models developed by Mielke et al., (2007), Johnson and Bretsch (2002), and Mielke et al., (2006), respectively.
Percentile SLL
(mg/kg)
SLL Statistics
(mg/kg) Potential BLL (g/dL) PLOPS
(g/ft2)
Equation 8 Max 75% Median 25% Min Mean Std Dev N Equation 6 Equation 7
Front
yard
Before
Renovation 4130 3516 854 392 211 1667 1568 7 7.1 10.6 2575
After
Renovation 173500 26350 12050 3660 373 26167 39086 26 20.9 16.1 16220
Side yard
Before
Renovation 1044 621.3 345.4 201 82 410 286 11 5.2 8.7 1358
After
Renovation 740.6 550.95 367.5 121 61 362 237 9 5.3 8.8 1420
Back yard
Before
Renovation 50000 19625 2899.5 645 232 11326 14045 24 11.3 13.1 6042
After
Renovation 19000 2534.75 1644.5 469 68 2340 3211 42 9.0 11.9 4071
52
Table S.4: A guideline created for the homeowners to explain each color category used in the GIS map (figure 8), associated risk, and the
recommended use of that location in their yards.
Color
categories Based on SLL
Risk
Categories Recommended use
Dark Green
58 mg/kg, which associates with the European Union’s
advisory limit for Pb in vegetables, calculated according to
the prediction models developed in this study.
Minimal to
No Risk
Safer locations to set up yard
gardens
Light Green 100 mg/kg, which is Minnesota’s current soil Pb advisory
and recommended federal standard by scientists
Very Low
Risk
Safer area for the children to
play
Not safe for home gardens
Yellow 400 mg/kg, current federal limit for children’s play area Low Risk
Low exposure risk for the
children to play
Not safe for home gardens
Orange 1200 mg/kg, current federal limit for remaining yard Moderate
Risk
Not safe for the children to
play
Not safe for home gardens
Red Three extents of high concentrations exceeding all
regulatory threshold Risk Hotspots
Should be completely avoided.
Remedial actions are required. Maroon
Black
53
Figure S.1: In situ measurements of soil Pb by XRF and marking the assessment with colored flags, a tool that was used to communicate with the
homeowners to show them the distribution of soil Pb and risk hotspots instantly. Red, green, and blue flags were used to mark SLL > 400, 80 –
400, and < 80 mg/kg, respectively. The photograph is be included in the manuscript with the permission of the homeowner. Photo courtesy:
Michael Fisher.
54
Figure S.2: Lead concentration (mg/kg) in soil at Location A before (a) and after (b) renovation in 2018 and 2019 respectively. Data are expressed
as mean (n=20) and + one standard deviation.
1
2
3
4
5
Pb concentration of soil (mg/kg)
Bulk Soil
Soil with burried paintchips
1
2
3
4
5
6
7
8
9
Pb concentration of soil (mg/kg)
Bulk Soil
Soil with burried paintchips
a b
55
Figure S.3: Multivariate correlation among Pb in outdoor dust, paint and the depth index on discarded painted pieces of wood from site A (a), a
window in site G (b), and a window in site H (c).
10
20
30
40
50
60
70
80
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1.5
2
2.5
Pb in dust
r=0.1586
r=0.0527
10 30 50 70
r=0.1586
Pb in paint
r=0.9484
0.5 1.5 2.5 3.5 4.5
r=0.0527
r=0.9484
Depth index
1.5 2 2.5
56
Figure S.4: Effect of soil Pb (mg/kg) on Pb concentration in plants (mg/kg) collected from all four home gardens of the tested residential
properties and its bivariate regression with a polynomial square fit.
download fileview on ChemRxivChemRxiv Final Pb Manuscript_Das.pdf (3.43 MiB)
Title
Primary prevention of outdoor lead (Pb) exposure on residential properties in Rochester, NY, and
potential of a sustainable remediation solution involving the reuse of drinking water treatment
residual (WTR) waste generated daily by the city.
The Authors
Padmini Das1, Ph.D.; Stephanie M. Zamule1, Ph.D.; Deanna R. Bolduc1; Michelle J. Patton1;
Meghan L. Mendola1; Julia M. Penoyer1, B.S.; Benjamin E. Lyon1; Hanna M. Chittenden1, B.S.;
Alexander C. Hoyt1 B.S.; Cassandra E. Dupre1, B.S.; Jack L. Wessel1, B.S.; Ivan Gergi1, B.S.;
Charlotte V. Buechi1, Jane A. Shebert1, M.S.; Mandeep Chauhan2, M.S.; David Giacherio1, Ph.D.;
and Jacob C. Phouthavong-Murphy3, OMS-III.
Affiliations
1Nazareth College of Rochester. 4245 East Ave, Rochester, NY 14618, USA; 2Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA; 3Touro College of Osteopathic Medicine 230 West 125th Street, New York, NY 10027
Corresponding Author
Padmini Das, PhD. Email: [email protected], phone: 5855000424; 585-389-2552, fax: 585-389-
2672; Address: 4245 East Ave, Rochester, NY 14618, USA.
Competing Interests:
The authors declare they have no actual or potential competing financial interests.
Keywords: Lead (Pb), primary prevention, in-situ risk assessment, XRF, soil, dust, urban
gardens, remediation, chemical immobilization, water treatment residual, WTR, Al-WTR
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Abstract
Background
Prevention of lead (Pb) poisoning is imperative for public health and environmental justice. This
study presents both assessment and affordable mitigation of Pb exposure risks at individual yard
scales, which are critically important to homeowners to attain primary prevention, but not yet
explored thoroughly.
Objectives and Methods
In phase I, the potential risk of Pb exposure from soil, surface-dust, and vegetables grown in
yard-gardens were measured along with secondary estimation of predicted blood Pb levels in
children in seven urban and six suburban residential properties in and around Rochester, NY,
U.S. In phase II, aluminum-based water treatment residuals (Al-WTR), a waste sludge generated
by the city’s drinking water treatment plant, was investigated for its potential to immobilize Pb as
a remedial measure.
Results
All tested properties with pre-1978 built structures were found to contain Pb in exterior paint at
varying depth, soil, surface dust, and garden vegetables. Soil Pb levels (SLL) were
heterogeneous (11 to 173,500 mg/kg) and higher in urban than suburban properties (mean 3,178
and 461 mg/kg respectively), underscoring the inequitable risk. 71% of vegetables collected
across four property gardens exceeded the European Union’s health-based Pb guidelines and the
median Pb in plants was 72 times higher than the U.S. market-basket values. Very strong
sorption (>90%), minimal desorption (<2%), effective prevention (>85%) of Pb leaching from
high Pb-containing exterior paint chips and estimated Freundlich and Langmuir parameters
suggest practically irreversible Pb-WTR binding.
Discussion
Primary prevention tools were developed to communicate risk with homeowners through GIS-
maps that illustrate risk-categories and prediction models to calculate SLL and predicted Pb in
vegetables using distance from the house. Al-WTR showed very high Pb-immobilizing ability,
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suggesting the implementation of this no-cost, zero-waste sorbent as a soil amendment would
provide sustainable primary prevention for low-income urban communities where Pb exposure
prevails.
1. Introduction
Lead (Pb) exposure continues to be the single most critical environmental health issue affecting
American children (CDC 2002), despite the ban on lead-based paint (LBP) and the phasing out
of leaded gasoline that took place four decades ago (USEPA 2011; Wagner and Langley-
Turnbaugh 2007). Lead is a potent neurotoxicant particularly detrimental to developing fetuses
and children under the age of six, as it causes cognitive and behavioral impairments, learning
disabilities, attention deficit, juvenile delinquency, and violent or even criminal behavior (CDC
2002). The number of children with elevated blood lead levels (EBLL) has been reduced due to
the implementation of secondary interventions such as testing BLL in children followed by
tracking and mitigating the hazard source. However, the problem remains unaddressed in two
critical areas.
Firstly, it is increasingly evident from recent research that although EBLL-based secondary
prevention has been effective in reducing exposure to some extent, it comes at a cost to children
who have already been exposed, and who pay a steep price in irreparable systemic damage
through cumulative exposure (Crinnion and Pizzorno 2019). 80% of the total body Pb in
children, and 90% in adults, is stored in bone; and while the half-life of Pb is only thirty-five
days in blood, it is eight to forty years in bone (Hu et al. 2007). Pregnant and postmenopausal
women undergo increased bone turnover, causing Pb poisoning of the fetus and Pb-related age-
associated problems in adult women (Gulson et al. 2007; Nash et al. 2004). Cumulative exposure
through bone-Pb turnover continues throughout life causing neuroinflammatory diseases like
Alzheimer’s and Parkinsonism at variable BLL (Bakulski et al. 2012; Weisskopf et al. 2010);
increased cardiovascular mortality in BLL as low as 3.62 µg/dL (Menke et al. 2006); and anxiety
and depressive disorders in young adults with BLL as low as 1.7 µg/dL (Bouchard et al. 2009).
The BLL action limit for children has recently been reduced from 10 µg/dL to 5 µg/dL by U.S.
Centers for Disease Control and Prevention, despite their acknowledgment that there is no safe
limit for BLL in children; however, only 18 states (not including NY) have adopted this standard
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thus far (CDC 2018). Moreover, USEPA regulatory limits for residential soil Pb levels (SLL)
(400 and 1200 mg/kg for bare soil in children’s play areas and the remainder of the yard,
respectively; USEPA 2001) are associated with BLL higher than 5 µg/dL according to two
prediction models that independently showed a steep increase in BLL at lower soil Pb levels
(Mielke et al. 2007; Johnson and Bretsch 2002). These guidelines, which are substantially higher
than the soil Pb standards set by some European nations (40 to 150 mg/kg) and Canada (140
mg/kg), have remained unchanged for decades, despite recommendations by researchers to lower
them to safer limits (Mielke et al. 2008; Reagan and Silderbard 1989; Bell et al. 2010).
Secondly, the current state of residential Pb exposure poses critical questions related to equality
and social justice. Despite a substantial drop in the national average, EBLL still prevails in
densely populated urban communities, meaning that children from lower-income minority
families suffer most from Pb poisoning (Mielke 1999; Geltman et al. 2001). Fillippelli et al.
2005 reported that 15% of children living in urban communities exhibit BLL’s above 10 µg/dL,
compared to only 2.2% of children nationally. Similar trends were observed in western New
York state cities, like Rochester and Buffalo, where most frequent Pb exposure was found to
occur in low-income urban neighborhoods, where people of color are highly concentrated
(Magavern 2018). The established correlation between Pb exposure and crime in teens and
young adults in low-income urban communities (Nevin et al. 2007) underscores the
socioeconomic injustice of this issue and the importance of developing a sustainable intervention
that will address the environmental, social, and economic aspects of this problem equitably.
Primary prevention, which directly measures and corrects hazards before an exposure happens
(CDC 2005), is the only process that can provide an equitable solution to the Pb exposure issue
without vulnerable populations being disproportionately affected as collateral damage.
Sustainable measures of primary prevention are not easy to attain, as they involve a number of
multifaceted tasks and challenges. A complete assessment of SLL distribution, outdoor surface
dust, and property garden vegetables at the individual property scale is critically important for
homeowners to understand the contamination pattern, associated health risks, and potential
remedial strategies (Clark and Knudsen 2013). Primary contributors of EBLL in children are Pb
in soil and house dust that consists of 20-80% resuspended Pb from the soil, along with other
dietary sources. (Mielke et al. 2007; Wu et al. 2008; Bugadalski et al. 2013). One-third of U.S.
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residents grow at least one vegetable in their yard for intended consumption (National Gardening
Association 2011), heightening the risk of Pb exposure from contaminated yard soils (Clark and
Knudsen 2013). This percentage is expected to increase due to the recent COVID-19 pandemic
given the disrupted access to fresh and nutritious foods (Lal 2020). Further, an effective and
affordable remediation solution must be provided to the families of lower socioeconomic
standing to prevent Pb exposure at identified hot spots. Surface soils act as a sink for
accumulating Pb over years and can remain so for centuries in the absence of proper remediation
and intervention (LaBelle et al. 1987; Datko-Williams et al. 2014). Although the excavation of
the surface soil with high Pb is the best practice to remove the risk (Mielke et al. 2011), this
process is not economically feasible for homeowners in lower socioeconomic urban
neighborhoods. The lack of access to risk assessment measures and economically-feasible
alternatives for Pb removal contributes to the ongoing inequitable Pb poisoning in children.
To address primary prevention, we investigated thirteen residential properties in and around
Rochester, NY, United States, utilizing three unique approaches that differ from earlier studies.
As opposed to evaluating commercial and residential locations on a city-wide scale, we focused
on a site-specific scale of individual properties with structures built prior to 1978 as the principal
outdoor Pb sources. Furthermore, we determined the spatial distribution of SLL, dividing every
property into broader risk categories such as: safe areas with minimal to no risk; low-risk areas
safer for children to play, but not safe enough to grow edible produce; and high-risk areas with
Pb hot spots, which should be avoided and considered for remedial actions. Using this data,
primary prevention tools including mathematical prediction models and Geographical
Information System (GIS) maps were developed to provide homeowners with a thorough
understanding of the risk distribution on their properties. Finally, to address the need for a cost-
effective remediation strategy to reduce exposure risk from the Pb hot spots, we explored the
potential reuse of a waste sludge as a soil amendment to chemically immobilize Pb. This water
treatment residual (WTR) is generated as a waste product on a continual basis by the city’s
largest drinking water treatment plants. Successful implementation of this technology will
provide the city with a sustainable solution for reducing costs of waste management as well as
provide homeowners with a low-cost solution to reduce the risk of Pb exposure from high-risk
property hot spots. To the best of our knowledge, this study is the first to focus on both aspects
of primary prevention by directly measuring the outdoor Pb levels at individual yard scales in
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urban and suburban residential properties and proposing a low-cost, zero-waste solution for the
homeowners by reusing a waste that the city generates every day.
2. Methods
2.1. Phase I: Assessment
2.1.1. Field Site Descriptions
Seven urban and six suburban residential properties were selected based on varying
characteristics including the age of the property’s structures; socioeconomic categorizations of
the homeowners such as race, education, and household income; and the willingness of the
homeowners to participate in this study. Tableau data visualization software (version 2020.1)
was used to create site location maps with respect to the number of houses built prior to 1978 and
the socioeconomic standing of the corresponding areas of the sub-county (figure 1). The
software was used to develop a geographical map of Monroe and Ontario counties utilizing
latitude and longitude coordinates for each sub-county boundary. An urban location was defined
by a geographical location within the city limits of Rochester, NY, United States. A suburban
location was defined as a geographical location outside the city limits and within the county
limits of Monroe or Ontario counties. The socioeconomic categorization data was collected from
the Census Bureau for each respective sub-county.
2.1.2. In-situ measurement of soil Pb using XRF
Soil lead concentrations were measured on-site using a USHUD certified Thermo ScientificTM
NitonTM XLp 300 series x-ray fluorescence analyzer (XRF). The XRF was set to “standard bulk”
mode while an uncovered soil surface was chosen to conduct the measurement in each location
(n = 20). The soil surface locations included: i) alongside structures including corners and
beneath doors, front stairs, and windows; ii) along a perpendicular line starting from a high soil
Pb point adjacent to the structure towards the property line; iii) adjacent to sidewalks and near
structures of neighboring properties. Additional parameters including variation in slopes and
presence of loose paint chips were noted if observed. Once recorded, each measurement was
marked with flags with different colors (figure S.1), a process used to communicate with the
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homeowners about the distribution of soil Pb on their property and potential risk associated with
it.
2.1.3. Measurement of Pb in paint and outdoor dust
Exterior paint was measured in-situ using the “paint mode” of the XRF on outdoor surfaces such
as siding, doors, steps, window sills, pillars, and old housing materials stored outside and inside a
barn, (site E only). Calibration of the XRF was performed according to the HUD calibration
protocol. Levels of Pb in the paint on a given surface area and a depth index indicating how close
the Pb was to the surface were obtained as part of each measurement.
Outdoor dust collection was completed using Fisher ScientificTM dust wipes (catalog number:
NC0309992) on any surface which registered an XRF lead reading. To ensure maximum
collection, the dust wipes were unfolded for the initial collection, then folded in half and used
within the same surface area. Pb levels on each dust wipe were measured in the laboratory using
the “dust wipe” mode of the XRF by placing each wipe in a Niton Portable Test Stand and
scanning for 80 seconds total (20 seconds for each position of the holder).
2.1.4. Collection of produce/plant samples and preparations
Four assessed properties (A, H, C, M) had home gardens. Produce and plant samples were
collected with the permission of the homeowners. Plant samples were cleaned and weighed using
a Fisher Science Education analytical balance, dried in a ThermoScientific Heratherm OGS100
oven at 105°C for 24 hours (or until completely dry), and then weighed again to calculate
moisture content. A dry weight (maximum of 0.5 g or based on the available mass of produce) of
plant sample was digested in Fisher Scientific Trace Metal Grade HNO3 following the US EPA
3051 protocol in a CEM MARS6 digestion microwave unit. Once digested, samples were filtered
through Thermo scientific 0.45 µm PTFE-L Luer-Lock syringe filters, diluted to 50 mL with RO
water from a Thermofisher Scientific Barnstead Nanopure purifier, and centrifuged at room
temperature in an Eppendorf 5424R for 15 minutes at 4,000 rpm.
2.2. Phase II: Remediation
2.2.1. WTR Collection, Characterization, and TCLP
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Two batches of WTR were collected from two sludge lagoons of Monroe County Water
Authority’s drinking water treatment plant in Greece, NY, U.S. in 2017 and 2018. These lagoons
contained the post-flocculation waste sludge from Lake Ontario source water after being treated
with alum (aluminum sulfate). WTR samples were then thoroughly mixed, air-dried, ground into
powder, and passed through a 2 mm sieve. Triplicate samples collected from each batch of WTR
were used for determining physicochemical properties, including pH, EC, moisture content, and
organic matter content. Toxicity characteristic leaching procedures (TCLP) were performed
using EPA SW-846 methods 1311.
2.2.2. Sorption and Desorption of Pb by WTR
Kinetic and equilibrium sorption and desorption experiments were carried out in triplicate using
a 1:20 solid to solution (m/v) ratio (SSR) of WTR to a lead-spiked solution made from lead
nitrate and 0.01M KCl as a background electrolyte. Kinetic sorption/desorption experiments
were conducted at four initial Pb concentrations (0, 25, 200, and 1000 mg/L) for 24h with six
intermediate sample collections after 0, 1, 2, 5, 10, and 24 h during end-over-end mixing on an
Analytical Testing Model DC-20 Rotary Agitator shaker at 250 rpm. Similarly, equilibrium
sorption experiments were conducted at six initial Pb concentrations (0, 200, 400, 600, 800, and
1000 mg/L) for 10 h using the same sampling method. Three subsequent desorption cycles (24h
each) were carried out to determine the extent of the potential release of pre-adsorbed Pb from
WTR. In addition to shaking time, the WTR remained in contact with the Pb solution for
approximately 8 min (0.13 h) during the sample preparation. This time in combination with the
shaking time was represented as the contact time for kinetic data. After the respective sorption or
desorption processes, samples were centrifuged using an Eppendorf 5804R for 15 minutes at
4000 rpm, filtered through Thermo scientific 0.45 µm PTFE-L Luer-Lock syringe filters, and the
supernatants were analyzed for Pb and Al.
2.2.3. Optimizing WTR to prevent paint chip Pb leaching
Loose paint chips were collected from the yard of site A both before and after the home was
renovated, in order to conduct two experiments. First, a TCLP test was conducted to estimate the
Pb leaching potential from these scattered paint chips in simulated landfill conditions following
the EPA SW-846 methods 1311. Second, a batch desorption experiment in the presence of
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varying levels of WTR was performed to determine the optimal amount of WTR necessary to
effectively prevent Pb leaching. Four WTR-to-paint chips (m/m) ratios (10, 20, 50, and 100)
were added to a 0.01 M KCl solution while maintaining a 1:20 SSR. Samples were equilibrated
for 7 d with two desorption cycles (1 and 6 d) using end-over-end mixing. The samples were
centrifuged at 4000 rpm for 15 mins, filtered through a Thermo scientific 0.45 µm PTFE-L Luer-
Lock syringe filter, and the supernatants were analyzed for Pb.
2.3. Pb Analysis
In phase I, Plant samples were analyzed for lead content in triplicate using an Agilent
Technologies 4210 MP-AES. The MP-AES was calibrated with standards ranging from 2500 to
10000 μg/L prepared from a stock solution of Agilent Technologies 1000 μg/mL Pb in 5%
HNO3. Method analysis settings were as follows: pump speed of 25 rpm, sample uptake time of
15 seconds, stabilization time of 15 seconds, read time of 30 seconds, and nebulizer flow of 0.8
L/min.
In phase II, Pb in 5% of in-situ soil samples (following USEPA method 6200), Pb and Al from
the batch sorption/desorption, and Pb from leaching experiments were measured using a Perkin
Elmer 400 graphite flame Atomic Absorption Spectrophotometer (AAS). After TCLP, the eight
RCRA metals (Arsenic, Barium, Cadmium, Chromium, Lead, Mercury, Selenium, and Silver)
were measured using a Perkin Elmer Optima 8000 inductively coupled plasma optical emission
spectroscopy (ICP-OES) to attain data at lower detection limits. Calibration coefficient limits
were set to 0.995, with an allowed calibration error of 10%, and quality control samples were
accepted within +5% error margins.
2.4. Data analyses, Modeling, and Mapping
Statistical analyses were carried out using JMP IN version 13.0 (Sall et al. 2005). One-way and
Two-way ANOVA were performed as required, followed by mean comparisons using the Tukey–
Kramer honest significant difference (HSD) test. Multivariate correlation analyses of Pb in
surface dust were conducted with Pb in paint and depth index; bivariate correlation analyses of
Pb in edible produce or plant parts were conducted with the adjacent soil Pb levels. Two types of
prediction models were developed using the relationship between i) soil Pb over distance from
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the structure(s) at each site and ii) Pb in produce/plants grown in the yard gardens with the
adjacent soil Pb concentrations. Sorption data were fit to both Freundlich and Langmuir isotherm
models to gain insight on possible mechanisms of sorption between Pb and WTR and the
reversibility of this binding. XRF measurements of SLL were taken along with the latitude and
longitude of each sampling location and the collected readings were imported into ArcGIS Pro
2.5.2 and portrayed with proportional symbols, color categories, and heat maps.
3. Results
3.1. Outdoor Pb-exposure in urban and suburban residential properties
As described in fig. 1 and table S.1, seven urban and six suburban residential properties built
between 1830 and 2003 were tested for outdoor Pb exposure sources. Among those, the house at
site F was the only structure built after 1978. As expected, there was no Pb found in exterior
paint, dust, or natural soil at this property. Interestingly, low soil Pb levels (27.8 + 0.98 mg/kg)
were found at two sites in the garden area, both of which were covered with store-bought potting
soil and mulch, suggesting that either may have contributed Pb to the garden. Two suburban sites
(I and J) were found to have Pb in exterior paint and soil; however, the SLL in these two
properties were below 400 mg/kg. All seven urban (A, B, C, G, H, K, M) sites and one suburban
(L) site were found to have SLL higher than 400 mg/kg. Table S.1 shows the Pb in paint, its
depth index, and the maximum concentration of Pb in soil and dust.
3.1.1. Distribution of Pb in soils
The distribution of SLL was extremely heterogeneous in all properties assessed and thus was
explored further in eight properties that surpassed the federal limit. Three factors were found to
be controlling this distribution; i) distance from the most prominent source of LBP, ii) slopes and
iii) the presence of a secondary source at close proximity. Among these, the distance from the
primary source of LBP was found to be the dominant factor controlling the distribution of SLL.
The maximum SLL were found alongside the source structures at all sites, indicating the most
likely primary source was LBP that leached into the soil over the years (figure 2). The data
shows a continual decrease of soil Pb over distance, dropping below 100 mg/kg in all properties
at variable distances from the houses. However, a few exceptions were noted in A2, L1, G1, G2,
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and K. These variations in SLL against the effect of distance were observed due to the presence
of loose paint chips at close proximity to the measured locations in A2 and slopes in L1. Both
sites G and K were inner-city residential properties with smaller yards where the structures of the
neighboring property influenced the distribution of SLL by contributing as secondary sources of
Pb (figure 3).
In each yard, SLL showed a biphasic pattern: initially steep and then a gradual decrease over
distance from the source structures. This trend was utilized to create site-specific and overall
regression models. Site-specific models based on polynomial cubic regression equations are
presented for seven yards in table 1, which exhibited both significance as well as strength (R2
ranging from 0.84 to 0.99). In contrast, the overall bivariate regression models across all sites
were significant for linear (p = 0.0011) as well as nonlinear polynomial (n = 2 to 6) fit (table
S.2), but neither were acceptable to be used, as the regression coefficients were too low (no more
than 0.24).
3.1.2. Pb in outdoor surface dust
Figure 4 shows the multivariate correlation of surface dust collected from different surfaces with
Pb in paint and depth index. All surfaces showed a significant (p < 0.05) correlation with both Pb
in paint and depth index. Interestingly, Pb was found in all of these surfaces at a low depth index,
suggesting the presence of Pb closer to the surface that impacted its loading in the dust. Figure
S.3 shows a similar multivariate correlation for surfaces with a higher depth index and the
surface dust only showed a significant correlation with Pb in paint but not with depth index.
Surface dust measurements in all urban properties assessed (with the exception of site C)
exceeded HUD’s action level of 800 µg/ft2 for exterior concrete. In contrast, surface dust
measurements were within this limit in all suburban properties tested, with the exception of a
barn at site E which showed a very high risk of potential Pb exposure through inhalation of dust.
The assessment of Pb was conducted inside the barn built in 1830; however, the barn is
considered an outdoor source as it was not located adjacent to the house built in 1966. The barn
is currently used to store materials from a previously demolished structure and seldom used as a
play area for visiting grandchildren of the current property owners. All 19 objects tested in this
barn (including the wall, doors, wall decor, and various old materials) contained Pb in the paint
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with an average depth index of 1.66 + 0.62, showing most of the Pb is on the surface. All 19 dust
samples collected from this site were found to contain Pb averaging 1,551 µg/ft2, almost twice
the level of HUD’s action level. The highest concentration of Pb-containing surface dust reported
in this study (site E) was 8,612 µg/ft2, which is 11 times higher than the federal guideline,
showing the extent of the hidden risk of potential Pb exposure through inhalation of dust.
3.1.3. Pb in produce/plants in outdoor home gardens
Table 2 shows the plant Pb concentrations (mg/kg) and the Pb levels of the adjacent soil in four
yard-gardens at sites A, C, H, and M. Tomatoes collected from the garden of Nazareth College of
Rochester, NY, which had adjacent SLL below detection limits, were used to calculate the
background plant Pb levels of plants from the property sites. Pb concentrations in tomatoes from
the Nazareth College garden were subtracted from the Pb levels found in plants from the
properties tested to focus on the effect of SLL on the Pb uptake and accumulation in plant
tissues. Tomato plants were found in three gardens (sites A, C, and M), and the lead
accumulation in tomatoes significantly (p<0.0001; F ratio=750.7) increased in a linear manner
(R2 = 0.99) with increasing SLL. Site A had several garden beds filled with potting soil which
contained soil Pb, but at much lower levels than 400 mg/kg. Currently, there is no existing
guideline defining the permissible level of Pb in the soil for gardening. All plants collected from
property garden beds, including arugula, lettuce, radish, garlic, tomato, potato, and broccoli,
were found to contain high concentrations of Pb, ranging from 1.25 mg/kg in garlic to 4.67
mg/kg in radish (by dry weight). Sites H and M had gardens in locations with higher SLL levels
compared to other locations in their respective properties. Consequently, the tomatoes from site
M and elephant foot yam from site H exhibited a very high accumulation of Pb. Overall, as
shown in figure S.4, the increase in accumulated Pb in plants collected from all four yard-
gardens over adjacent SLL followed a polynomial square fit. Equations 1 and 2 present two
prediction models that were derived based on the strong correlation between the soil Pb and Pb
in plant tissues per dry weight (d.w.) and fresh weight (f.w.) respectively.
x−812.1¿2
y=2.331−0.005∗x+5.74e−7∗¿ --------Equation 1
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x−812.1¿2
y=2.338−0.0008∗x+6.99e−8∗¿ --------Equation 2
Where y is Pb concentration in (mg/kg) in plant tissues and x is Pb concentration (mg/kg) in the
adjacent soils. Both models were significant (p < 0.0001; F ratio = 120.5 and 47.7 respectively)
with strong regression coefficients (R2 = 0.85 and 0.89 respectively).
3.2. Chemical immobilization of Pb by WTR
3.2.1. Physico-chemical and TCLP characteristics of WTR
Table 3 presents the relative physicochemical properties and the results of the TCLP of the two
batches of WTR. Both exhibited almost neutral pH, high moisture content, and low organic
matter content. TCLP levels of the RCRA metals revealed a complete absence of lead and
mercury and the presence of other metals in substantially lower concentrations than the criteria
set by the EPA. Both physicochemical properties and TCLP data for these two batches of WTR
were statistically similar, which was evident in p > 0.1 and low F ratio.
3.2.2. Pb kinetic and equilibrium sorption/desorption by WTR
Kinetic sorption experiments showed that the binding of Pb by WTR was rapid and varied with
the initial Pb concentrations in solution, while the Pb levels in the control solutions with no WTR
remained unchanged (fig. 5). Equilibrium times could not be determined in lower Pb
concentrations because of WTR’s extremely rapid and strong sorption for Pb, as the sorption
reached 100% instantaneously (within 8 minutes of contact time with no shaking) for 25 mg/L
and within 5.13 h contact time in 200 mg/L initial Pb concentrations. At 1000 mg/L initial Pb in
solution, the Pb sorption by WTR reached equilibrium within 5.13 h contact time and did not
exhibit any significant change in sorbed Pb up to 24.13 h contact time. Absolutely no desorption
occurred in any of these initial concentrations within 24 h.
The extent of equilibrium sorption of Pb by WTR increased with increasing Pb concentration in
solution (fig. 6a), but the percent sorption showed a reverse trend. However, very high sorption
affinities (> 90%) were exhibited in all initial loads within 10h (fig. 6d). The data followed an L-
type curve (fig. 6a) and showed strong fit to the linearized Freundlich (Eqn. 2; fig. 6b; R2 = 0.99)
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as well as (Eqn. 3; fig. 6c; R2 = 0.98) Langmuir equations and the associated parameters were
calculated accordingly.
log Q=log K F+(1/n) logC eq -------Equation 3
Where KF (mg/kg) and n are the Freundlich equilibrium constants representing the sorption
capacity and adsorption intensity respectively; Q (mg/kg) is the adsorbed amount of Pb by WTR
at equilibrium; Ceq (mg/L) is the equilibrium concentration of Pb in solution. The value of KF
was determined as 1.6 mg/g, which exhibited a very high sorption capacity of Pb by WTR. The n
value determined in this study was above unity (1.82), indicating favorable adsorption of Pb onto
WTR through physical processes (McKay 1980; Özer and Pirinççi 2006).
(Ce/Q)=(1 /K L Qm)+(Ce /Qm) --------Equation 4
Where Qm (mg/kg) is the maximum adsorption capacity and KL (L/mg) represents the Langmuir
constant related to the energy of adsorption. A good fit to the Langmuir isotherm indicates the
monolayer coverage and homogeneous distribution of Pb on the active binding sites of the WTR
surfaces with an adsorption maximum of 25 mg/g, that exhibits a very high sorption capacity.
Additionally, another Langmuir constant named separation factor (RL) was calculated following
equation 4.
RL=1/1+K LC0 --------Equation 5
Where C0 (mg/L) is the initial Pb concentration. The RL values indicates the nature and the
feasibility of the adsorption process, such as unfavorable sorption if RL > 1, linear sorption if RL
= 1, favorable sorption if 0 < RL <1, or irreversible sorption if RL = 0 (Meroufel et al. 2013). The
value of RL calculated for all tested initial concentrations of Pb in this study ranged from 0.03 to
0.15, which were between zero to unity, suggesting favorable sorption of Pb on WTR. In
addition, these values were much closer to zero than one, indicating more irreversibility in
adsorption of Pb on WTR. This was evident in the equilibrium desorption of Pb from WTR.
Figure 6e shows the total desorbed Pb after three desorption cycles (24 h each). No desorption
was noted in 72 h up to 600 mg/L initial Pb concentration, indicating complete hysteresis or
complete irreversible binding of Pb by WTR at these initial loads. Although with the further
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increase of initial Pb, the extent of desorption increased; it remained restricted to < 2% of the
sorbed Pb in WTR even after 72 h of shaking.
3.2.3. WTR concentration effects on paint chip Pb leaching
As evident from figure S.1, the numerous loose paint chips found in the yard at site A both before
and after the renovation of the structure resulted in an extremely high SLL. Locations with
buried paint chips exhibited an average of 12 and 5 times higher SLL as compared to the
surrounding soil with no paint chips before and after renovation, respectively. The surface soil
with both loose and buried paint chips posed a very high risk of Pb exposure through dust
loading as well as leaching into the stormwater runoff. A TCLP assessment of these paint chips
collected from the yard revealed that they released 47.9 + 1.71 mg/L (n=6) of Pb in solution,
which is 9.6 times higher than the EPA permissible Pb levels for TCLP. This data highlights the
need for determining potential Pb leaching from these paint chips in a stormwater solution and
provided us with an ideal opportunity to test the effectiveness of WTR to prevent the
mobilization of Pb from the paint chips with extremely high Pb-leaching potential.
The results were highly promising as WTR concentrations significantly (p<0.0001 and F ratio =
90.7) reduced Pb leaching from loose paint chips. 43, 61, 84, and 93% prevention of Pb leaching
was achieved by 10, 20, 50, and 100% WTR to paint chips (m/m) application after two
desorption cycles of 1 and 6 d. Effect of time was masked (p=0.2) by WTR’s very strong affinity
for Pb; no significant difference between the higher WTR treatments suggests the application of
WTR in a 1 to 2 ratio to paint chips would be enough to achieve the optimum prevention. As
these paint chips exhibited the maximum Pb concentrations and acted as the major contributor of
SLL in site A, a lower dose of WTR would be enough for achieving optimum prevention of Pb
leaching in soils at lower SSR.
4. Discussion
4.1 Field Assessment
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Although the City of Rochester succeeded in reducing the number of children with EBLL
(compared to neighboring cities like Buffalo and Syracuse) through its adoption of an aggressive
secondary intervention approach, the city continues to face significant issues related to Pb
exposure (Magavern 2018). This is consistent with our findings showing that the overall SLL
medians across all tested urban and suburban properties with structures built prior to 1978 were
413 and 155 mg/kg, respectively, with mean SLL as high as 3,178 and 461 mg/kg, respectively.
This study also documented the highest maximum residential SLL (173,500 mg/kg), as
compared to those reported over the last two decades in urban and suburban areas of thirty-one
U.S. cities (data compared with 36 research articles; not shown). Risk of Pb exposure through
soil Pb, potential dust loading, and produce grown in property gardens was found to be much
higher in the urban residential properties where residents already face significant socioeconomic
challenges. According to the 2010 Rochester census data, urban neighborhoods in the City of
Rochester have the highest percentage of residents living below the poverty line, the lowest
percentage of those with above-average income, and the broadest distribution of race with the
highest percentage of African American residents when compared to suburban sub-counties. The
unjust burden of Pb exposure in urban neighborhoods in which many low-income minority
families reside has been documented in twenty studies conducted over the last four decades using
data from sixteen U.S. cities (Datko-Williams et al. 2014).
The prevalence and correlation of SLL and BLL are well established in the literature (Bickel
2010; Zahran et al. 2010; Wu et al. 2010). The overall median SLL in urban properties exceeding
the federal standard clearly underscores this risk. However, the lower median SLL of suburban
properties does not represent a risk-free condition either. Adverse health effects of Pb are
associated with BLL as low as 2 mg/dL, which is associated with SLL < 100 mg/kg (Canfield et
al. 2003; Schwarz et al. 2012). Two existing prediction models by Mielke et al. (2007) and
Johnson and Bretsch (2002) independently showed a steep increase in BLL at SLL <100 mg/kg
and a gradual rise at SLL >300 mg/kg. To assess the exposure risk more specifically, we used
both of these models (equations 5 and 6) to predict the potential BLL if residents (especially
children) are being exposed at tested residential properties.
SLL median¿0.5
BLL=2.038+0.172∗¿ -------Equation 6 (Mielke et al. 2007)
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BLL=2.097∗ln(SLL median)−3.6026 -------Equation 7 (Johnson and Bretsch 2002)
SLL in all but one suburban property was predicted to result in BLL > 5 µg/dL according to
equation 7, whereas five out of the seven urban properties and one out of the five suburban
properties were predicted to result in BLL > 5 µg/dL according to equation 6 (table 4). We
observed that the predicted BLL based on the median SLL of the entire property underestimates
the predicted risk for properties with high variation in SLL throughout the property. For instance,
as discussed earlier, site A showed a high variation in SLL throughout the property, both before
and after renovation. The predicted BLL of the front yard after the renovation was as high as
20.9 µg/dL (equation 6) and 16.1 µg/dL (equation 7), both of which are much higher than the
predicted BLL based on the median SLL of the entire property. In such cases, yard-specific
predictions would be a more appropriate measure of risk assessment. Racial and socioeconomic
distribution in the City of Rochester, along with the SLL and estimated BLL documented in this
study, echo the findings of Mielke et al. (1999) and Geltman et al. (2001), who reported that
higher SLL in urban soils result in inequitable EBLL in children of lower-income families,
minorities, and recent immigrants. This is also evident from the data shown by Korfmacher
(2007) that low-income families in the crescent area in urban Rochester characterized by high
poverty, crime, and pre-1950 rental housing correlated with 23.6% of children under 6 years of
age having blood lead levels greater than 10 µg/dL.
In accordance with this study, extreme heterogeneous distribution of soil Pb has been
documented by other researchers, and has been observed to decrease with increasing distance
from three Pb source categories: i) industrial sites or brownfields with residues of historic Pb use
(Marshall and Thornton 1993; Wu et al. 2010), ii) areas near highways or roads with intense
traffic density (Mielke et al. 2008; Laidlaw 2001; Filippelli et.al. 2005), and iii) aged structures
with exterior LBP (USEPA 2000; Litt et al. 2002; and Clark and Knudson 2013). Some studies
have reported that the combustion of leaded gasoline is the primary source of Pb in larger urban
settings, as noted in Baltimore (Mielke et al. 1983), New Orleans (Mielke et al. 2008), and
Indianapolis (Laidlaw 2001; Filippelli et.al. 2005). Clark and Knudson (2013) argue that exterior
LBP dominates the distribution of soil Pb in smaller urban communities like Appleton, WI. Our
study showed that even in a medium-sized urban community like Rochester, with a history of
past industrialized use of heavy metals, the primary source of Pb in all tested sites was exterior
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LBP from structures built prior to 1978, as none of these sites are located in neighborhoods with
high traffic density or at close proximity of any brownfield or superfund site. The building
structures can also act as barriers and facilitate atmospheric deposition of Pb (Mielke et al.
1983), leaving a pattern of higher Pb trapping in the street side of the front yard as compared to
the back or side yard. A combined effect of preferential trapping and LBP results in a U-shaped
pattern of higher SLL near the house and the road, and lower SLL in the middle of the yard
(Olszowy et al. 1995). Lack of these types of spatial patterns negates the effect of preferential
trapping on properties we assessed. Based on the findings of our current study, we argue that
even in medium urban communities, leaded exterior paint can be the dominant source of soil Pb
if the property is located at a certain distance from the industrial sites, freeways, or dense traffic
area. The location and age of the building structure are the two most important parameters that
influence which source will dominate the distribution of soil Pb and to what extent in any given
community, irrespective of its size.
Nationwide studies conducted by U.S. Housing and Urban Development report that 76% of
homes built prior to 1960 have Pb in exterior paint and 83% of pre-1980 housing stock contains
Pb in the paint (USHUD 1990, 1995). This study found Pb in exterior paint at varying depths for
all tested urban and suburban properties built prior to 1978, supporting the observation of Clark
and Knudson (2013) that all communities of similar age and site throughout the US show a
similar trend of contamination. SLL can exist around all sides of the structures and extend far
into the yard (Clark and Knudson 2013; USEPA 2000, Litt et al. 2002). In all properties, the SLL
was observed to be very high alongside the structures and decreased with increasing distance
from the homes. In addition, we also noted that in inner-city properties like site A, G, and K, the
neighboring structures, adjacent to the tested yards, also acted as prominent sources of Pb.
Buried or scattered paint chips in the soil of site A increased the SLL in some locations against
the influence of distance.
Pb contamination in residential soil is so widespread that it can be found in unapparent locations,
increasing the threat of continual risk of exposure. In accordance with previous studies, we have
observed three such unforeseen scenarios: i) high soil Pb in a suburban house with a well-
maintained exterior (Clark and Knudson 2013) was observed in site L, which exhibited SLL as
high as 3000 mg/kg; ii) high soil Pb in a property with an unpainted exterior was noted in site B,
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where the SLL surpassed the federal guideline only beneath the windows, a trend also observed
by Linton et al. (1980); and iii) increase in SLL after renovation and repainting of a house
(Clark and Knudson 2013), which is duly noted in site A. Table S.3 presents a comparative SLL,
estimated BLL, and potential dust loading values in the three yards of site A that illustrates the
detrimental effects of renovation activity on soil Pb.
Dust is a major source of concern for children, as inhalation is a major route of Pb exposure. The
respiratory tract is responsible for the absorption of lead particles into the systemic circulation,
and respiration and metabolic rates are faster in children, resulting in increased absorption of
lead via dust (Menezes-Filho 2018). Two tools were used in this study to estimate the risk of Pb
exposure from outdoor dust: i) direct measurement of accumulated dust on the outdoor surfaces,
and ii) secondary estimation of potential lead on play surfaces (PLOPS). Overall, accumulated
Pb in dust showed significant (p<0.001) positive correlation with Pb in paint and negative
correlation with depth index, suggesting flaking LBP on surfaces such as window sills, doors,
steps, and old building materials can potentially contribute Pb to dust.
Lead loading on exterior surfaces has been found to be a continuous process, primarily due to
resuspension of soil Pb (Caravanos et al. 2006; Mielke et al. 2006). Researchers identified the
resuspension of soil Pb during dry seasons as the major driving force behind the seasonally
EBLL in children in New Orleans, Syracuse, and Indianapolis (Laidlaw et al. 2005). Mielke et
al. (2006) developed a new tool for measuring the potential Pb surface loading per area (mg/ft2)
of the soil. Potential Pb on play surfaces, a measure of the Pb concentration of a soil surface per
surface area as a source of Pb dust for children who would play on that surface, was calculated
based on a prediction model created by Mielke et al. (2006) (equation 8).
SLL median¿0.69
PLOPS=−43.74+24.85∗¿ -------Equation 8
The PLOPS values for all urban and suburban properties are presented in table 5 to provide
insight into the overall potential of Pb exposure from these surfaces, both directly through hand-
to-mouth activity, or indirectly through resuspension of Pb dust. Mielke et al. (2006) also showed
a large drop in PLOPS in 136 properties and vacant lots in New Orleans after the contaminated
surfaces were treated with a soil cover. Similar measures should be considered for the properties
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assessed in our study, as the large PLOPS values indicate a high potential risk of Pb exposure to
children if they play in these areas.
The relationship between Pb in produce and the adjacent soil Pb is equivocal in the literature;
with some studies reporting maximum Pb levels in vegetables associated with the highest SLL
(Bielinska 2009; Huang et al. 2012; Moir and Thornton 1989) and other studies finding only
weak correlations between crop Pb levels and the adjacent soil Pb, due to unverified reasons
(Hough et al. 2004; Murray et al. 2011; McBride et al. 2014). Our study found strong and
significant (r = 0.92 based on fresh weight and r = 0.85 based on dry weight; p < 0.001)
correlations between the plant-accumulated Pb and SLL. Ten out of fourteen edibles tested
exceeded the health-based guidance values set by the European Union (EU) (EC 2006). There
are no such standards currently in the U. S., but the median Pb in edible plants found in this
study was 72 times higher than the median market basket concentrations of Pb reported by the U.
S. Food and Drug Administration’s (FDA) Total Diet Study. (USFDA 2010). The FDA
recommended the maximum ingestion lead level for a child is 3 µg/day; eating between four and
five grams of contaminated produce would reach that limit (Frank 2019). Interestingly, Pb levels
exceeding the EU guideline standards were found in plants grown in both indigenous soils (sites
H and M) of these properties as well as potting soils (site C and side yard of site A). The median
Pb in plants grown in indigenous soils (1.05 mg/kg) was higher than those grown in the potting
soil (0.21 mg/kg) and showed a stronger correlation with the Pb concentrations in adjacent soils
(r = 0.91 and 0.69 for indigenous and potting soils respectively). However, it is deeply
concerning that the edible plants grown in potting soils containing Pb lower than 60 mg/kg were
found to contain Pb levels exceeding the health guidelines. The federal standard of 400 mg/kg
soil Pb provides a misconception of safety for soils containing lower Pb concentrations; there are
no current levels set by either U.S. Environmental Protection Agency (EPA) or HUD for urban
gardening. Moreover, each species of produce has different biological properties that determine
their uptake, absorption, and bioavailability of Pb, demonstrated by the varying lead
concentrations in them (USFDA 2010). Differential uptake of Pb by different vegetables at
similar SLL was noted in the current study (site A) in Rochester as well as by McBride et al.
(2014) in New York City and Buffalo, NY; and Misenheimer et al. (2018) in Puerto Rico. These
findings strongly support the development of new plant-specific federal guidelines for soil Pb to
promote safer gardening practices.
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4.2 Safer use of yards by primary prevention tools
Based on results of the field assessment, we developed three primary prevention tools to help
homeowners understand the Pb exposure risks throughout their properties as the first step of
primary prevention: i) a prediction model to calculate soil Pb using the measured distance from
the structure; ii) a prediction model to estimate Pb in edible produce grown in yard gardens from
the adjacent soil Pb that can be calculated from the distance from the built structure, iii) GIS
maps showing Pb hot spots with high risk, low risk, and safe areas in the yards. Prediction
models and GIS maps presented by prior research, covering a larger city area, are crucial for
bringing necessary changes in the policy (Wu et al. 2010), but this is an ongoing, long-term
process. Considering the continual Pb exposure and elevated BLL in families with lower
socioeconomic standing, it is of utmost importance to develop an easier process for the
homeowners to understand the risk to be able to avoid it. To the best of our knowledge, the
present study is the first attempt to develop site-specific prediction models and precise GIS maps
illustrating the potential risk through mapping the SLL in the exact latitude and longitude of the
sampling locations on individual residential properties. The GIS maps also mark the safer
locations to create gardens based on the two prediction models we developed. These measures
would provide the homeowners with an opportunity to predict soil Pb levels to plan safer uses of
their yards.
City-wide larger models to calculate soil Pb based on distance from urban centers of big cities or
roads with high traffic density considered the atmospheric deposition of Pb as the dominant
source (Yaylah-Abanuz 2019; Brown et al. 2008; Zhang et al. 2015; Wu et al. 2010). However,
residential soil Pb with exterior LBP as the major source varies widely from one property to
another, depending on the age of the structure(s), presence and types of siding, past land use, and
renovation processes. Site-specific prediction models would provide homeowners with an
opportunity to predict soil Pb levels using the distance from a structure to utilize their yards more
safely. For instance, a play area should be planned in soil that contains less than 400 mg/kg Pb,
according to federal guidelines. Based on our site-specific models, SLL drops from 400 mg/kg to
< 100 mg/kg if the play area is located 2 m further from the house. Considering the steep
increase of BLL between 100 to 300 mg/kg SLL, simply moving a play area a few meters further
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from the structure would increase the chance of primary prevention at no cost. Although we
highly recommend at least a one-time assessment of soil Pb for each property, it is likely that one
prediction model would be valid for one block or neighborhood if the homes were built at a
similar time; however, further investigation of this assumption is necessary.
This study also presents prediction models exhibiting a strong correlation (r = 0.85 and 0.92)
between Pb accumulation (per d.w. and f.w.) in produce/plant parts and the Pb levels in the
adjacent soil. One limitation of these prediction models is that they will not be able to provide
any insight into the plant-specific differential Pb uptake, as the pronounced effect of soil Pb may
have masked the effects of plant-specific uptake in the current study. However, utilization of
these equations would be able to provide a rough estimate of the potential Pb accumulation into
vegetables from the adjacent SLL to estimate a safer location for a garden. For instance, the
vegetable gardens in sites M and H were located in the areas with the highest SLL, resulting in
median Pb levels in edible parts of 1.58 and 0.56 mg/kg, respectively, which exceed the EU
guideline by 15.8 and 5.6 folds, respectively (EC 2006). Both of these properties have areas with
much lower soil Pb further into the yard. Simply moving these gardens to such locations would
be a useful measure of intervention to reduce the Pb exposure risk through the consumption of
Pb-contaminated vegetables from the property gardens. We estimated an SLL value associated
with the EU guideline of 0.1 mg/kg Pb per plant f.w. using a reverse fit of equation 2 (R2 = 0.97,
p<0.0001) which associates with a SLL of 64.7 mg/kg; a slightly lower value of 0.09 mg/kg Pb
per plant f.w. associates with an SLL of 58.2 mg/kg. Using the reverse fit of the site-specific
models, we calculated the associated distance with 58.2 mg/kg soil Pb in site M and site H, and
these distances are found to be 6.44 and 3.85 m respectively. Thus, our combined models
indicate that to attain a minimum requirement for the primary prevention, a home garden should
be set up at least 6.44 m away in site M and 3.85 m away in site H from their respective
structures. Similarly, a safer distance can be calculated for any property garden to avoid
consumption of Pb through homegrown vegetables.
Figure 8 presents two reference GIS maps of site A showing the Pb exposure risk associated with
soil Pb distribution in three areas of the property before and after the renovation of the home. To
simply and clearly communicate risk levels to homeowners, we developed a visual tool utilizing
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a graded color scheme of green to black representing risk levels. Table S.4 presents a guideline
created for the homeowners to explain each color category, associated risk, and the
recommended use of that location on their properties. In addition to using the federal standards
for assigning colors to the range of Pb concentrations, we have also used our prediction models
to mark areas of the property where it would be safer to create a garden based on the EU health
guidelines for edible produce. The areas marked in red to black colors represent hotspots with
very high to extreme risk of potential Pb exposure and thus must be avoided and should be
considered for remedial actions.
4.3 Sustainable Remediation
The second and final step of primary prevention is to correct the hazards through remedial
actions, which led us to investigate an affordable process that would provide substantial risk
reduction without economically burdening the homeowners. WTR collected from Rochester’s
largest drinking water treatment plant not only exhibited a very high Pb-binding ability but was
also found to be safe when used as a soil amendment according to the federal TCLP guidelines.
Past research documented varying effectiveness of different sorbents to immobilize lead,
including activated carbon (Moyo et al. 2013), natural and engineered clay-like zeolite
(Wingenfelder et al. 2005), mordenite (Turkyilmaz et al. 2014), montmorillonite (Zhang and Hou
2008), bentonite (Inglezakis et al. 2007), palygorskite, and sepiolite (Shirvani et al. 2006).
Although these technologies provide in-situ alternatives to the current expensive ex-situ
practices, the cost of these natural or engineered sorbents in bulk could become cost-prohibitive
to low-income urban families. WTR is a no-cost sorbent which would provide these homeowners
with an affordable remediation alternative to be utilized as soil cover or amendment in the most
concerning areas of their properties. The only required cost is associated with its implementation,
which could be further reduced by conducting a proper assessment to mark the risk hotspots (as
shown in the GIS maps for site A), where the remediation process is absolutely needed. Very few
studies have investigated the effectiveness of Pb adsorption by Fe and Al-based WTR collected
from different parts of the world. Pb sorption by Al-WTR showed a similar pattern of L type
sorption isotherm (Castaldi et al. 2015; Zhou et al. 2011). Adsorption maximum was reported to
be 17.55, 15.66, and 15.13 mg/g by WTRs collected from Miyamachi and Nishino in Japan and
Italy, respectively, as compared to 24.4 mg/g found in this study (Putra and Tanaka 2011).
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Furthermore, Soleimanifer et al. (2019) reported the unique approach of reducing metal
contamination in urban stormwater with WTR coated mulch; a similar approach could be further
investigated for its potential to reduce plant-Pb uptake if used in home gardens. The sorption
desorption data along with the calculated Freundlich and Langmuir isotherm parameters suggests
very strong, favorable, and more irreversible adsorption of Pb on WTR through a physical
process, promising an effective option for reusing this waste byproduct that the city generates
every day to immobilize Pb in the high-risk soils of many contaminated residential properties.
Using WTR as a soil amendment will prevent the runoff and leaching of soil Pb and also
decrease the Pb available to be accumulated in garden vegetables. However, the implementation
of only WTR will not prevent the resuspension of Pb into dust. Our goal is to implement a dual-
process remediation system using WTR as a soil amendment with the addition of a plant cover to
decrease the dust loading of Pb. Several studies have shown the phytoremediation potential of Pb
(Jagetiya and Kumar 2020), but it is of utmost importance to use a non-edible plant in the Pb hot
spot areas to prevent its further entry into the food chain. Vetiver, a perennial non-edible and
non-invasive grass has been shown to be highly effective for Pb removal (Andra et al. 2009);
however, the harsh winters of western NY could be a serious impediment for its implementation.
Our group has tested 17 native inedible plant species for their potential tolerance of high Pb
concentration, but all plants showed severe phytotoxic effects of Pb in hydroponic media, where
100% Pb was available for plant uptake (data not shown). This further underscores the utility of
WTR for Pb removal, which according to the results of this study would retain more than 90% of
total soil Pb, making only a small fraction available for the plants to tolerate. The ongoing
studies in our greenhouse are characterizing this dual process technique using WTR as an
amendment to the soils from the backyard of site A, with an SLL median of 2900 mg/kg, and
switchgrass (Panicum virgatum), high biomass, perennial, fast-growing, inedible grass, which is
also native to western NY and which was previously studied for its ability to remove other
environmental contaminants (Phouthavong-Murphy 2020). This combined remediation process
would provide the homeowners with an effective and affordable primary prevention alternative
to intervene with the Pb exposure from the risk hotspots of their property.
Acknowledgements
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The authors acknowledge Mr. Larry Peckham and Mrs. Nancy Peckham for funding the High
School Summer Research Program in 2018 and Nazareth College Summer Opportunities for
Activities in Research and Sponsorship (SOARS) in 2018 for partially funding this project.
We acknowledge Mr. John C. Kelly Jr. and other officials from Monroe County Water Authority
(MCWA) for their help in WTR collection; our community partners and homeowners for their
communication and participation; Dr. Callie Babbitt from Rochester Institute of Technology for
conducting the ICP-OES analyses of TCLP samples; Mrs. Sharon Luxmore for assistance with
ordering materials; and Ms. Aelis Spiller, Ms. Ariel Struzyk, Mr. LaMar Jackson, Mr. Sean Dillon, Ms.
Maria Vargas Morales, and Ms. Bailey Groth for assistance in sample preparation and field
measurements; and Mr. Shyamal K. Mitra and Dr. Ananta Paine for providing feedback about this
manuscript.
Author contributions
P.D. designed the research, field assessment, and experiments. P.D., D.R.B., M.J.P, B.E.L., A.C.T., J.M.P.,
H.M.C., J.L.W., I.G., and D.G. conducted the field assessment; B.E.L. and A.C.T analyzed Pb in surface
dust, J. S., C.V.B., M.L.M., D. R. B., M.J.P., C.E.D. analyzed Pb in plants with the supervision of S.M.Z.
and P.D.; J.L.W., I.G., J.P.M., J.M.P., H.M.C., A.C.T. conducted phase II experiments with the supervision
of S.M.Z. and P.D; P.D. conducted the secondary analyses, isotherm modeling, statistical data analyses,
and developed the prediction models. M.C. created the GIS maps and D.R.B. created the Tableau maps.
P.D. wrote the manuscript with S.M.Z. and J.P.M. with contributions and feedback from all other authors.
Abbreviations:
Pb: lead; WTR: drinking water treatment residual; XRF: x-ray Fluorescence Analyzer; Al-WTR:
aluminum-based water treatment residual; SLL: soil lead level; mg/kg: milligram per kilogram;
WTR: water treatment residual; GIS: geographical information systems; LBP: lead based paint;
EBLL: elevated blood lead level; BLL: blood lead level; µg /dL : micrograms per deciliter;
USEPA: United States Environmental Protection Agency; USHUD: United States Department of
Housing and Urban Development; n: number of samples; HUD: Department of Housing and
Urban Development; TCLP: toxicity characteristic leaching procedures; SSR: solid to solution
25
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
25
ratio; KCl: potassium chloride; mg/L: milligrams per liter; HNO3: nitric acid; AAS: atomic
absorption spectrophotometer; RCRA metals: 8 metals (Arsenic, Barium, Cadmium, Chromium,
Lead, Mercury, Selenium, Silver) listed under Resource Conservation and Recovery Act; ICP-
OES: individually coupled plasma optical emission spectroscopy; Tukey-Kramer HSD: Tukey
Kramer honest significant difference.
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Table 1: Polynomial cubic fit of lead concentrations (mg/kg) in residential soil by distance from potential source of Pb based paint. The nonlinear correlation fit, regression equation, and parameter estimates were conducted using JMP (version 15). Parameters of the regression equations were estimated and the significance levels of the parameters were expressed as * (p < 0.05), ** (p < 0.01), *** (p < 0.001), **** (p < 0.0001).
Site R2 Fratio
p value Regression EquationsSignificantParameters
A1 0.9994 632.9 0.0292 y=3684 – 575.9∗x+89.45∗(x−4.321)2 –2.277∗(x−4.321)3 Intercept**, x2**
A3 0.9366 14.78 0.0266 y=2141 –1307∗x+8370∗(x−1.045)2 – 5519∗(x−1.045)3 x2*
A4 0.9868 50.02 0.0197 y=2226 – 282.6∗x+126.5∗( x−3.569 )2 – 28.10∗(x−3.569)3 x2*
L1 0.9567 22.13 0.0151 y=388 .3 –249.5∗x+497.1∗(x−2.199)2 – 108.1∗( x−2.199)3 x2*, x3*
L2 0.8593 18.32 0.0004 y=420.3 – 170.1∗x+277.5∗(x−2.110)2 – 61.83∗(x−2.110 )3 x2***, x3*
M 0.8402 21.03 <0.0001 y=394.3 –68.31∗x+135.8∗(x−3.029)2 –28.03∗(x−3.029)3 x2***, x3**
H 0.9820 218.5 <0.0001 y=752.9 –220.8∗x+127.8∗(x−2.286)2 – 22.73∗(x−2.286)3 Intercept****, x****,x2****, x3*
C1 0.9607 32.63 0.0029 y=232.8 –183.5∗x+486.1∗(x−1.181)2 – 246.8∗(x−1.181)3 x2**
C2 0.9868 150.4 <0.0001 y=153.3 –71.01∗x+362.7∗(x−1.371)2 –268.6∗(x−1.371)3 x2****, x3**
B 0.9865 97.77 0.0003 y=1175 – 713.6∗x+105.9∗(x−1.124 )2 –150.5∗(x−1.124)3 Intercept***, x***,x3*
G1 0.8887 13.31 0.0081 y=361.8 –71.36∗x+97.78∗(x−1.439)2 – 41.25∗(x−1.439)3 Intercept*, x2*
G2 0.9848 86.41 0.0004 y=508.9 – 71.63∗x+13.23∗( x−3.458 )2 – 0.4656∗(x−3.458)3 Intercept***, x**,x2**
Table 2. Pb concentrations in produce or parts of plants (mg/kg) grown in the gardens of the tested residential properties. Data are expressed as mean (n=3) + one standard deviation. Mean comparison is conducted by one-way ANOVA (F ratio = 121.4; p <0.0001) followed by Tukey-Kramer honest significant difference test. Mean comparison letters are expressed as superscript and levels not connected by same letters are significantly different from one another.
35
10751076
1077
1078
10791080
10811082
1083
1084
1085
1086
35
Produce/PlantsParts of plants
analyzedSite
Pbconcentration in soil(mg/kg)
Pbconcentration
in plants by dryweight (mg/kg)
Pb concentrationin plants byfresh weight
(mg/kg)
Guidance Value(EC, 2006)
Tomato(Solanum lycopersicum)
Tomato (fruit) Garden bed in C 8.5 0 + 0G 0.02 + 0.009I 0.1
Arugula(Eruca vesicaria)
Leaves Garden bed 1 in A 11.2 3.52 + 0.22 DE 0.19 + 0.016H 0.3
Lettuce(Lactuca sativa) Leaves Garden bed 8 in A 28.3 2.98 + 0.10 DE 0.01 + 0.005I 0.3
Radish(Raphanus sativus)
Radish (root) Garden bed 8 in A 28.3 4.67 + 0.33 DE 0.08 + 0.014I 0.1
Garlic(Allium sativum)
Garlic (bulb) Garden bed 7 in A 30.4 1.25 + 0.02 F 0.34 + 0.004G 0.3
Tomato(Solanum lycopersicum)
Stem Garden bed 7 in A 30.4 1.97 + 0.04 F 0.37 + 0.006G 0.3
Potato(Solanum tuberosum)
Root Garden bed 2 in A 32.6 1.88 + 0.07 F 0.23 + 0.008H 0.1
Broccoli(Brassica oleracea var. italica)
Leaves Garden bed 5 in A 52.8 1.79 + 0.07 F 0.48 + 0.012EF 0.3
Ground Ivy(Glechoma hederacea)
Whole plant Front yard in A 7400 16.07 + 0.37 A 3.35 + 0.59A N/A
Elephant Foot Yam(Amorphophallus paeoniifolius)
Whole plant Front yard garden in H 358.7 3.42 + 0.13 E 0.34 + 0.013G 0.1
Elephant Foot Yam(Amorphophallus paeoniifolius)
Whole plant Front yard garden in H 511.4 4.42 + 0.01 DE 1.05 + 0.003D 0.1
Elephant Foot Yam(Amorphophallus paeoniifolius)
Whole plant Front yard garden in H 663.3 4.89 + 0.08D 0.55 + 0.008E 0.1
Elephant Foot Yam(Amorphophallus paeoniifolius)
Whole plant Front yard garden in H 998.9 4.54 + 0.14DE 0.40 + 0.013FG 0.1
Elephant Foot Yam(Amorphophallus paeoniifolius)
Whole plant Front yard garden in H 1042 10.89 + 0.56B 1.22 + 0.054C 0.1
Tomato(Solanum lycopersicum)
Tomato (fruit) Back yard garden in M 985.1 8.40 + 0.30C 1.59 + 0.056B 0.1
36
10871088
36
Table 3. Physicochemical characteristics and TCLP of WTR collected from lagoon 1 in 2017 and lagoon 2 in 2018. Data are expressed as mean (n = 3) + one standard deviation. Mean comparison is conducted between WTR collected from different space and time.
TCLP RCRA Metals (mg/L)
MoistureContent
(%)pH
ElectricalConductivity (� s/cm)
Organicmattercontent
(%)
Ba Ag As Cr Pb Cd Se Hg
WTR fromLagoon 1
collected in2017
48.32 +1.997
6.99 +0.064
371.8 +38.09
2.195 +0.166
1.157 +0.0102
0.001 +0.000
0.086 +0.023
0.033 +0.006
0 +0
0.003 +0.001
0.036 +0.006
0 + 0
WTR fromLagoon 2
collected in2018
47.99 +1.962
6.98 +0.035
450.3 +52.50
2.259 +0.157
1.047 +0.021
0.001 +0.000
0.098 +0.035
0.04 +0.001
0 +0
0.002 +0.001
0.036 +0.006
0 + 0
EPApermissible
Limit100 5 5 5 5 1 1 0.2
F ratio 0.042 0.155 4.398 0.235 3.381 NA 0.273 3.811 NA 0.500 0.018 NA
p value 0.848 0.714 0.104 0.653 0.139 NA 0.629 0.123 NA 0.519 0.899 NA
Table 4. Percentile, mean, standard deviation (SD) of soil lead level (SLL) (mg/kg) and potential blood lead level (BLL) (g/dL) in
children and potential lead on play surfaces (PLOPS) (g /ft2) using three prediction models developed by Mielke et al., (2007),
Mielke et al., (2006), and Johnson and Bretsch (2002) respectively.
37
1089
1090
109110921093
109410951096
1097
1098
1099
37
Percentile SLL (mg/kg)SLL Statistics
(mg/kg)Potential BLL (g/dL)
PLOPS(g /ft2)
Max 75% Median 25% Min Mean SD N Equation 6 Equation 7 Equation 8
UrbanA after
Renovation173500 7346 1665 455 57 9619 25045 79 9.1 12 4107
A beforeRenovation
50000 6811 963 257 82 6857 11768 42 7.4 10.8 2800
C 1553 992 578 144 11 619 469 58 6.2 9.7 1956
M 3743 990 442 253 58 707 759 36 5.7 9.2 1617
H 2378 530 357 181 112 502 490 45 5.3 8.7 1389
G 1200 427 276 198 75 328 206 54 4.9 8.2 1156
B 1077 504 187 131 65 329 326 16 4.4 7.4 874
K 847.7 248 109 82 32 190 180 43 3.8 6.2 589
Suburban
L 3088 1099 558 201 34 753 720 45 6.1 9.7 1909
I 1295 150 116 80 65 202 301 16 3.9 6.4 615
J 115.7 97 82 47 37 76 28 8 3.6 5.6 478
E 248.4 63 45 35 25 66 61 15 3.2 4.4 301
Overall
Urban 173500 1073 413 185 11 3178 12768 373 5.5 9.0 1542
Suburban 3088 711 155 66 25 461 627 84 4.2 7.0 763
38
1100
1101
1102110311041105
38
Figure 1. Field assessment site maps with respect to number of pre-1978 built structures (a), racial diversity (b), education (c), and annual household income (d) of the sub-counties. These maps were created using Tableau software, which developed a geographical map of Monroe and Ontario County utilizing latitude and longitude coordinates for each sub-county boundary. In subfigure 1a, the size of the bubble depicts the number of pre-1978 houses found within that sub-county, and the color of the bubble denotes the respective sub-county. In subfigure 1b, 1c, and 1d, the distribution of racial diversity, education, and annual income data in their respective categories was represented by overlaying pie-charts into each sub-county.
Figure 2. Effect of distance (m) from potential source of lead-based paint on the lead concentrations (mg/kg) of residential soils in the urban or suburban neighborhoods of Rochester, NY; Data are expressed as mean (n = 20) + one standard deviation. Each subfigure expressed data for one residential property except site A, which was expressed in two subfigures (a, b). The subfigures are arranged left to right in rows based on their decreasing Y axis values, indicating the magnitude of soil Pb, of which the maximum was recorded in the front yard of site A after renovation (a) followed by the back yard of site A (b) before and after renovation; site L (c); site M (d); site H (e); site C (f), site B (g); site G (h); and site K (i).
Figure 3. SLL measurements in the backyard of site G showing two assessment lines; G1 (pink line), which was measured from the corner of the garage at site G, showed a decrease in soil Pb with distance except on one location (X), which was found to be horizontally aligned to the corner of the neighbor’s house, indicating it to be an additional source of LBP (fig. S.2). The line G2 was measured from a point adjacent to the property line between these two houses and was horizontally aligned to the corner of the neighbor’s house. G2 also showed a continual decrease with distance in the soil Pb concentration, except the increase at the last measured location point, which is the closest to the main house in site G, suggesting an additional source of LBP. The photograph is included in the manuscript with the permission of the homeowner.
Figure 4. Multivariate correlation among Pb in outdoor dust, paint and the depth index indicating how deep Pb is in the paint from a window (a), door (b), and front stairs (c) of site G; discarded old house materials from site A (d); pillars from the front porch in site H (e); and discarded old house materials left in a barn at site E (f).
Figure 5. Kinetic sorption (%) of Pb in presence or absence of WTR. Data are expressed as mean (n = 3) + one standard deviation. Mean comparison is conducted by two-way ANOVA followed by Tukey-Kramer honest significant difference test. It is conducted separately for each initial Pb concentration. Levels not connected by same letters are significantly different.
39
1106
1107
1108
1109
1110
11111112
1113
1114
1115
1116
1117
11181119
1120
1121
1122
1123
1124
1125
11261127
1128
1129
11301131
1132
1133
1134
39
Figure 6. Equilibrium sorption and desorption of Pb in 5% WTR. Linear (a), Freundlich (b), and Langmuir (C) isotherms of Pb-sorption by WTR in 24h. Data are expressed as mean (n = 3) + one standard deviation. % sorption (d) and % total desorption (e) of Pb by WTR in three desorption cycles of 24h each. Desorption of Al (mg/kg) from WTR during sorption of Pb (f). Mean comparison is conducted by one-way ANOVA followed by Tukey-Kramer honest significant difference test. Levels not connected by same letters aresignificantly different.
Figure 7. Effect of WTR concentrations on the leaching of lead (µg/L) from the paint chips collected from site A. Data are expressed as mean (n = 3). Mean comparison is conducted by two-way ANOVA followed by Tukey-Kramer honest significant difference test. Levels not connected by same letters are significantly different.
Figure 8: GIS maps showing distribution of SLL (mg/kg) in site A before (8a) and after (8b) the restoration/repainting of the built structure. These maps were created using ArcGIS Pro 2.5.2 by plotting the SLL (mg/kg) measurements into their respective latitude and longitude. An approximate property-line (dotted line) was overlaid to show the boundaries of the property segregating it from the side walk in south and west and three built structures of neighboring property in north and east. Based on different guidelines for safety, 7 sizes and color categories were used, each representing a range of SLL.
Figure 1:
40
1135
1136
1137
1138
1139
11401141
1142
1143
11441145
1146
1147
1148
1149
115011511152115311541155115611571158115911601161116211631164
40
Figure 2:
41
a b
c d
1165
11661167116811691170
41
0 0.5 1 1.5 2 2.50
5000
10000
15000
20000
25000
30000
35000
40000A5 (after renovation)
Distance from potential source of Pb based Paint (m)
Le
ad
Co
nc
en
tra
tio
n in
so
il (m
g/k
g)
0 1 2 3 4 5 6 7 8 9 100
2000400060008000
100001200014000160001800020000 A1
(before renovation)A2 (after renovation)
Distance from potential source of Pb based Paint (m)
Le
ad
Co
nc
en
tra
tio
n in
so
il (m
g/k
g)
0 1 2 3 4 5 60
500
1000
1500
2000
2500
3000L1 L2
Distance from potential source of Pb based Paint (m )
Lead C
oncentr
ation in
soil
(mg/k
g)
0 1 2 3 4 5 6 7 80
500
1000
1500
2000
2500
3000
3500 M
Distance from potential source of Pb based paint (m)
Le
ad
Co
nc
en
tra
tio
n in
so
il (m
g/k
g)
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
500
1000
1500
2000H
Distance from potential source of Pb based Paint (m)
Le
ad
Co
nc
en
tra
tio
n in
so
il (m
g/k
g)
0 0.5 1 1.5 2 2.5 30
200400600800
10001200140016001800
C1
Distance from potential source of Pb based Paint (m )
Le
ad
Co
nc
en
tra
tio
n in
so
il (m
g/k
g)
42
ab
c
dD
e f
g h i
1171
1172
42
0 0.5 1 1.5 2 2.5 30
200
400
600
800
1000
1200B
Distance from potential source of Pb based Paint (m)
Le
ad
Co
nc
en
tra
tio
n in
so
il (m
g/k
g)
0 1 2 3 4 5 6 7 8
G1 G2
Distance from potential source of Pb based Paint (m)
Le
ad
Co
nc
en
tra
tio
n in
so
il (m
g/k
g)
0 1 2 3 4 5 6 7 80
100
200
300
400
500
600K
Distance from potential source of Pb based Paint (m )
Le
ad
Co
nc
en
tra
tio
n in
so
il (m
g/k
g)
43
dD e f
11731174
43
Figure 3:
44
G1
G2
X
11751176117711781179118011811182118311841185118611871188118911901191119211931194119511961197119811991200120112021203120412051206
44
Figure 4:
45
500
750
1000
1250
1500
1750
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1
1.2
1.4
1.6
1.8
Pb in dust
r=0.8202
r=-0.6845
500 1000 1500
r=0.8202
Pb in paint
r=-0.9757
0.1 0.3 0.5 0.7
r=-0.6845
r=-0.9757
Depth index
1 1.2 1.4 1.6 1.8
50
100
150
200
250
300
350
0.15
0.20.25
0.3
0.35
0.4
0.45
0.5
1.5
2
2.5
3
Pb in dust
r=-0.6914
r=-0.7013
50 150 250 350
r=-0.6914
Pb in paint
r=0.7785
0.15 0.25 0.35 0.45
r=-0.7013
r=0.7785
Depth index
1.5 2 2.5 3
30
35
40
45
0.15
0.2
0.25
0.3
0.35
0.4
0.45
2
2.5
3
Pb in dust
r=0.8736
r=0.7256
30 35 40 45
r=0.8736
Pb in paint
r=0.9368
0.15 0.25 0.35 0.45
r=0.7256
r=0.9368
Depth index
2 2.5 3
0
5001000
1500
20002500
30003500
0
5
10
15
20
1
1.5
2
2.5
Pb in dust
r=0.8894
r=-0.2848
0 1000 2000 3000
r=0.8894
Pb in paint
r=-0.3090
0 5 10 15 20
r=-0.2848
r=-0.3090
Depth index
1 1.5 2 2.5
0
1000
2000
3000
4000
5000
6000
0
5
10
15
0.9
0.95
1
1.05
1.1
Pb in dust
r=0.9128
r=0.0000
0 2000 4000 6000
r=0.9128
Pb in paint
r=0.0000
0 5 10 15
r=0.0000
r=0.0000
Depth index
0.9 0.95 1 1.05 1.1
a b c
de
f
12071208120912101211121212131214121512161217121812191220122112221223122412251226122712281229123012311232123312341235123612371238
45
Figure 5.
0 5 10 15 20 250
20
40
60
80
100
120
0 mg/L Pb 5% WTR25 mg/L Pb 5%WTR200 mg/L Pb 5%WTR1000 mg/L Pb 5%WTR0 mg/L Pb 0%WTR25 mg/L Pb 0%WTR
Contact Time (h)
% S
orp
tio
n o
f P
b
46
Tukey-Kramer HSD % WTR*Contact time (h)
InitialPb
(mg/L)
Contacttime (h)
WTR(%)
MeanComparison
Letters25 0.13 5 A25 1.13 5 A25 2.13 5 A25 5.13 5 A25 10.13 5 A25 24.13 5 A25 0.13 0 B25 1.13 0 B25 2.13 0 B25 5.13 0 B25 10.13 0 B25 24.13 0 B
220 0.13 5 D220 1.13 5 C220 2.13 5 B220 5.13 5 A220 10.13 5 A220 24.13 5 A220 0.13 0 E220 1.13 0 E220 2.13 0 E220 5.13 0 E220 10.13 0 E220 24.13 0 E
1025 0.13 5 D1025 1.13 5 C1025 2.13 5 B1025 5.13 5 A1025 10.13 5 A1025 24.13 5 A1025 0.13 0 FG1025 1.13 0 G1025 2.13 0 EFG1025 5.13 0 E1025 10.13 0 EFG1025 24.13 0 EF
123912401241
1242
12431244124512461247
46
Figure 6.
0 10 20 30 40 50 60 70 80 90 100
02000400060008000
100001200014000160001800020000
Equilibrium Pb Concentration in Solution Ceq (mg/L)
Sorb
ed P
b b
y W
TR
Q (
mg/k
g)
0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2
3.2
3.4
3.6
3.8
4.0
4.2
4.4
f(x) = 0.55x + 3.21R² = 1
log Ceq
log
Q
0 10 20 30 40 50 60 70 80 90 100
0.0000
0.0010
0.0020
0.0030
0.0040
0.0050
0.0060
f(x) = 0x + 0R² = 0.98
Ceq
Ce
q/Q
47
a b c
d e f
12481249125012511252
1253125412551256125712581259126012611262126312641265126612671268
47
Figure 7.
48
Tukey-Kramer HSD(WTR % * Time in
days)
WTR%
Time
(days)
MeanCompari
sonLetters
0 1 A10 1 AB20 1 BC50 1 DE100 1 DE0 7 A10 7 CD20 7 CDE50 7 E100 7 E
1269127012711272
48
010
2050
100
0
1000
2000
3000
4000
5000
6000
7000
8000
7 Days 1 Day
% WTR
Le
ac
he
d L
ea
d in
so
luti
on
(µ
g/L
)
49
127412751276
49
Figure 8:
Figure 8 Footnote: Table S.4 includes a guideline created for the homeowners to explain each color category used in these maps, associated risk, and the recommended use of that location in their yards.
50
ba
127712781279
12801281
1282
1283
1284
1285
1286
1287
1288
1289
1290
50
Supplementary Information
Table S.1: Overview of outdoor Pb exposure in the urban and suburban residential properties in Rochester, NY.
ResidentialProperties
Neighborhood YearPb in Paint(mg/cm2)
Depth IndexMaximumPb in soil(mg/kg)
Maximum Pb inoutdoor dust
(g/ft2)A (Before
Renovation)Urban Unknown 33.7 1.0 50000 Not Measured
A (AfterRenovation)
Urban Unknown 20.3 6.3 173500 3635
B Urban 1923 2.3 1.0 1077 Not MeasuredC Urban 1930 5.9 10 1553 43.39E Suburban barn 1830 14.1 1.6 NA 8612F Suburban 2001 0 1.0 0 0G Urban 1910 6.4 4.4 1200 1969H Urban 1883 27.7 5.5 2378 14791I Suburban 1900 23.0 1.9 390.5 290.2J Suburban 1954 8.4 6.5 115.7 83.86K Urban 1930 19.4 9.0 847.7 156.4L Suburban 1923 33.8 10.0 3088 80.42M Urban 1910 38.2 4.7 3743 Not Measured
Table S.2: Parameters of linear and nonlinear bivariate regression fit of Pb concentrations (mg/kg) in residential soil by distance (m) from potential source of Pb based paint using data collected from all properties.
51
12911292
12931294
1295129612971298129913001301130213031304
51
Degrees of Fit R2 F ratio p value
1 0.074 11.13 0.00112 0.138 11.04 <0.00013 0.179 9.992 <0.00014 0.209 9.017 <0.00015 0.229 8.016 <0.00016 0.238 6.986 <0.0001
Table S.3: Distribution of soil Pb and associated risks in three yards of site A. Percentile, mean, standard deviation (SD) of soil lead level (SLL) (mg/kg) and potential blood lead level (BLL) (g/dL) in children and potential lead on play surfaces (PLOPS) (g /ft2) using three prediction
models developed by Mielke et al., (2007), Johnson and Bretsch (2002), and Mielke et al., (2006), respectively.
52
1305
130613071308130913101311131213131314131513161317131813191320132113221323132413251326
132713281329
52
Percentile SLL (mg/kg)
SLL Statistics(mg/kg)
Potential BLL (g/dL) PLOPS(g/ft2)
Equation 8Max 75% Median 25% Min Mean Std Dev N Equation 6 Equation 7
Frontyard
BeforeRenovation
4130 3516 854 392 211 1667 1568 7 7.1 10.6 2575
AfterRenovation
173500 26350 12050 3660 373 26167 39086 26 20.9 16.1 16220
Side yard
BeforeRenovation
1044 621.3 345.4 201 82 410 286 11 5.2 8.7 1358
AfterRenovation
740.6 550.95 367.5 121 61 362 237 9 5.3 8.8 1420
Back yard
BeforeRenovation
50000 19625 2899.5 645 232 11326 14045 24 11.3 13.1 6042
AfterRenovation
19000 2534.75 1644.5 469 68 2340 3211 42 9.0 11.9 4071
Table S.4: A guideline created for the homeowners to explain each color category used in the GIS map (figure 8), associated risk, and the recommended use of that location in their yards.
53
1330133113321333133413351336133713381339134013411342134313441345
53
Figure S.1: In situ measurements of soil Pb by XRF and marking the assessment with colored flags, a tool that was used to communicate with the homeowners to show them the distribution of soil Pb and risk hotspots instantly. Red, green, and blue flags were used to mark SLL > 400, 80 –
54
Colorcategories
Based on SLLRisk
CategoriesRecommended use
Dark Green58 mg/kg, which associates with the European Union’s
advisory limit for Pb in vegetables, calculated according tothe prediction models developed in this study.
Minimal toNo Risk
Safer locations to set up yardgardens
Light Green100 mg/kg, which is Minnesota’s current soil Pb advisory
and recommended federal standard by scientistsVery Low
Risk
Safer area for the children toplay
Not safe for home gardens
Yellow 400 mg/kg, current federal limit for children’s play area Low RiskLow exposure risk for the
children to playNot safe for home gardens
Orange 1200 mg/kg, current federal limit for remaining yardModerate
Risk
Not safe for the children toplay
Not safe for home gardensRed
Three extents of high concentrations exceeding allregulatory threshold
Risk HotspotsShould be completely avoided.Remedial actions are required.
MaroonBlack
13461347134813491350135113521353135413551356135713581359136013611362136313641365136613671368136913701371137213731374137513761377
54
400, and < 80 mg/kg, respectively. The photograph is be included in the manuscript with the permission of the homeowner. Photo courtesy: Michael Fisher.
55
13781379138013811382
13831384138513861387138813891390
55
Figure S.2: Lead concentration (mg/kg) in soil at Location A before (a) and after (b) renovation in 2018 and 2019 respectively. Data are expressed as mean (n=20) and + one standard deviation.
1
2
3
4
5
0 50000 100000 150000 200000 250000
Soil with burried paint chips
Bulk Soil
Pb concentration of soil (mg/kg)
1
2
3
4
5
6
7
8
9
0 50000 100000 150000 200000 250000
Soil with burried paint chips
Bulk Soil
Pb concentration of soil (mg/kg)
56
ba
1391139213931394
13951396139713981399140014011402140314041405140614071408
56
Figure S.3: Multivariate correlation among Pb in outdoor dust, paint and the depth index on discarded painted pieces of wood from site A (a), a window in site G (b), and a window in site H (c).
57
10
20
30
40
50
60
70
80
0.51
1.52
2.53
3.54
4.5
1.5
2
2.5
Pb in dust
r=0.1586
r=0.0527
10 30 50 70
r=0.1586
Pb in paint
r=0.9484
0.5 1.5 2.5 3.5 4.5
r=0.0527
r=0.9484
Depth index
1.5 2 2.5
1409141014111412141314141415141614171418141914201421142214231424142514261427142814291430143114321433
57
Figure S.4: Effect of soil Pb (mg/kg) on Pb concentration in plants (mg/kg) collected from all four home gardens of the tested residential properties and its bivariate regression with a polynomial square fit.
58
143414351436
143714381439
1440
1441
1442
58
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