## INTRODUCTION

### Lead Smelting in the United States

As the second leading producer of the recycled lead after China, the United States produced about 19% of the world’s supply in 2012, with the metal reclaimed primarily from recycled lead-acid batteries [5]. The industry has rapidly consolidated the production into a handful of communities in the United States, and while large numbers of spent batteries are now sending to China and Mexico, the tonnage of lead-acid batteries recycled in the United States has more than doubled in the last 40 years [5]. To reclaim the lead for use in new batteries, the old batteries are cracked, washed, pulverized and melted in a furnace at facilities known as smelters. During the process, some lead is released into the surrounding environment. Lead does not degrade, may persist indefinitely in soil, and is frequently detected in dangerous concentrations in both soil and ambient air near smelters that remove lead from mined ore or recycled batteries [4, 6].

Because batteries are heavy and expensive to transport, smelters are usually located near the largest sources of scrap (i.e., in large cities). As a result, urban communities in the United States bear the majority of the pollution from this industry. As early as the 1970s, research demonstrated extremely high rates of childhood lead poisoning among those living near U.S. lead smelters as well as adverse health effects to smelter workers [710]. For example, one study in El Paso, Texas, estimated that 2,700 residents living near the Asarco smelter between 1 and 19 years of age had a lead level over 40 μg/dL in blood [9]. Despite the marked reductions in the use of lead in gasoline additives and the closure of primary smelters in the United States, ambient air emissions from secondary lead smelting operations remain high in host communities.

### Health Risks of Lead Exposure

Inorganic lead is one of the most ubiquitous toxic substances, yet it is also an extremely potent neurotoxin. At high levels of exposure, lead causes damage to almost all organs and organ systems [11]. Cognitive deficits, neurodevelopmental delays and psychological impairments are associated with even low levels of lead exposure [12, 13]. Although lead has been the target of environmental regulations in the United States for decades and the problems of gross lead poisoning have largely receded, chronic exposure to low levels of lead is still a significant public health issue, particularly among people of color and other marginalized communities. These communities are more likely to live in older housing stock where lead is often found in paint or pipes, as well as near industrial sources [11, 12].

The reference level for elevated blood lead has been revised in the downward trend over the years due to the compelling evidence that any exposure to lead can have deleterious effects. The blood lead level of 10 μg/dL had been widely accepted as a “level of concern” in the United States since 1991 [14]. In 2012, the Centers for Disease Control and Prevention (CDC) declared that there is no “safe” level of lead and lowered the elevated blood lead level of concern from 10 to 5 μg/dL [15, 16]. The CDC estimates that approximately 2.6% of children between the ages of 1 and 5 in the United States have blood lead levels >5 μg/dL [15]. This updated blood lead reference level (5 μg/dL) shifted the public health efforts from responses of toxicity to primary prevention [17, 18].

Lead is rapidly absorbed into the bloodstream and bio-accumulates in the bones and kidneys. Children are particularly vulnerable to the effects of these contaminants, as their bodies are still developing. Many state and county health departments in the United States have programs designed to identify children exposed to lead-based paint, however, much less attention has been given to non-paint lead sources (e.g., industrial emissions or soil contamination) and their importance is often underestimated. Nationally, approximately 30% of childhood lead poisoning cases (>10 μg/dL) are due to exposure to sources other than lead-based paint [12].

### Soil as the Major Pathway of Lead Exposure to Some Communities

On the other hand, the soil is the major source of atmospheric lead aerosols in the urban environment [1922]. Lead from paint sources after depositing nearby is mostly concentrated in very fine soil particles, resuspends in the air, particularly during dry, hot summer and fall months and redeposits on soil surfaces [25, 26]. The (re)distribution of lead in the soil is therefore affected by wind patterns (e.g., wind direction) [27]. Because of the resuspension and redeposition process, lead-contaminated urban soil continues to be the major pathway of lead exposure to young children, particularly those in low-income neighborhoods [28]. A study estimates that approximately 87% of the total lead in the blood of children living near a smelter were from soil and dust [29].

Since lead-contaminated soil poses long-term health risks to communities, it is essential to evaluate the distribution of soil lead to aid the soil cleanup decision making. Geographic information systems (GIS) has been used to evaluate the spatial distributions of heavy metals in soils [3234]. In this study, we examine soil lead levels in the residential communities near a former acid-battery recycling smelter Exide in southeastern Los Angeles. Our goal is to understand the distribution of soil lead risk levels using GIS, particularly a hot spot analysis, and discuss how the result might provide insights for a more effective cleanup process.

## CASE EXAMINATION: SOIL LEAD CONTAMINATION NEAR THE FORMER SMELTER FACILITY IN SOUTHEASTERN LOS ANGELES COUNTY

### The Contamination History of the Exide Smelter

The site of the former lead-acid battery smelter Exide Technologies (and its predecessor GNB) is located in the City of Vernon, California, about seven miles southeast of downtown Los Angeles. The facility has been in operation at this site since 1922. Operating for more than 30 years, the Exide facility crushed, washed and processed up to 40,000 lead-acid vehicle batteries per day. Although the City of Vernon calls itself “exclusively industrial” and has fewer than 120 residents, just beyond the Vernon municipal boundaries are densely populated, urban low-income communities, predominantly Latinos and immigrants [35].

Despite more than 100 documented environmental concerns from inspectors on lead and acid leaks, an overflowing pond of toxic sludge, cracks in the floor and high levels of lead in the soil, the state agency allowed the facility to operate for decades without a full permit. Ongoing concerns had been raised by the surrounding community about the emissions from Exide and their potential health hazards. In May 2015, the U.S. Department of Justice intervened and struck a bargain with Exide “to immediately and forever close a battery recycling facility” and “to pay US$50 million to clean-up the site and surrounding neighborhoods” instead of criminal prosecution [36]. Exide officials admitted to four felonies: the illegal disposal, storage, shipment and transportation of hazardous waste. In 2016, California’s Governor Jerry Brown declared Exide-related contamination an “environmental disaster” and allocated US$176.6 million for investigation and remediation[37].

### Sampling and Remediation of Soil Lead in the Surrounding Communities

Led by Department of Toxic Substances Control (DTSC), a part of the California Environmental Protection Agency, the subsequent soil investigation and remediation project focuses on the residential properties, schools, parks, daycare and childcare facilities in an approximately 1.7-mile radius area surrounding the Exide facility. This area, known as preliminary investigation area (PIA), encompasses various city boundaries, including Cities of Vernon, Huntington Park, Commerce, Bell, Maywood and the Boyle Heights neighborhood in City of Los Angeles [38]. The prevailing wind directions based on the closest National Weather Station KCQT (Los Angeles Downtown/USC Campus at 34.02355N 118.29122W, approximately 5 miles west-northwest of Exide) are westerlies during most of the year, although localized wind patterns may be slightly different. The areas immediately adjacent to the Exide facility are industrial or commercial and are not included in the investigation (Figure 1). The investigation assessed the levels of toxic metals, including lead, in the soil with the ultimate aim to assess the need for soil remediation in the residential communities.

FIGURE 1.

The PIA of soil lead contamination near the Exide smelter; the wind rose shows the average hourly wind patterns in 2014–2018 at the closest weather station KCQT (Source: Midwestern Regional Climate Center cli-MATE).

FIGURE 1.

The PIA of soil lead contamination near the Exide smelter; the wind rose shows the average hourly wind patterns in 2014–2018 at the closest weather station KCQT (Source: Midwestern Regional Climate Center cli-MATE).

Residential soil sampling was conducted by various contractors overseen by DTSC within the PIA between March 2014 and January 2018. Of the approximately 10,000 residential properties within the PIA, 8,521 properties were sampled. A median number of 11 soil samples were taken per property at different locations in the yard. Soil lead levels [milligram per kilogram (mg/kg) or parts per million (ppm)] along with other heavy metals (e.g., arsenic, cadmium, zinc and antimony) were measured using a handheld X-ray fluorescent analyzer (XRF) or in the laboratory. The soil sampling data were partially released in March 2018 by DTSC and have been periodically updated [39]. We used the latest release, dated October 28, 2018, at the time when the case was examined, which included samples from 8,385 properties. Only results from the surface soil samples at 0–3 inches deep were used in this case study since the surface soil is most relevant for human exposure.

A 95% upper confidence limit (if more than eight samples) or maximum values (if less than eight samples) were calculated by DTSC from all surface soil lead samples and were used in this study as the representative soil lead level for each property. While the California Human Health Screening Level for the lead on residential property is 80 ppm [40], DTSC adopted 400 ppm as the minimum representative soil lead level requirement (or one sample >1,000 ppm) to be eligible for remediation [38]. In other words, only properties with the representative soil lead levels equal to or greater than 400 ppm are qualified for soil cleanup.

With soil lead contamination being persistent on-site, soil replacement was chosen to be the remediation practice in the DTSC project [38]. Soil replacement involves physically removing soils to a certain depth and importing lead-free soil, which costs around US\$40,000 per parcel. The current allocation from the state of California allows approximately 2,500 parcels to receive the soil remediation.

### Exploratory Spatial Data Analysis of Soil Lead

The threshold of 400 ppm to qualify for a soil lead cleanup may not provide sufficient protection for residents from the adverse soil lead impacts. We explored the data distributions both descriptively and spatially for such potential impacts. Table 1 shows the number and percentage of the soil lead levels in the sampled properties. Almost all of the samples (96.9%) exceeded the state threshold of 80 ppm, but only 25.6% of the sampled properties are qualified for the DTSC soil cleanup. Among the 2,144 properties that meet this threshold, 297 properties, also called parcels, were already remediated (as of February 22, 2019) [41].

TABLE 1.

The number and percentage of sampled properties exceeded California health-based residential soil lead standard (80 ppm) vs current cleanup standard (400 ppm)

<80 ppmBetween 80 and 400 ppm≥400 ppmTotal
No. of sampled properties 261 5,980 2,144 8,385
% sampled properties 3.1 71.3 25.6 100
% of all eligible properties* in PIA 2.5 58.8 21.1 82.4
<80 ppmBetween 80 and 400 ppm≥400 ppmTotal
No. of sampled properties 261 5,980 2,144 8,385
% sampled properties 3.1 71.3 25.6 100
% of all eligible properties* in PIA 2.5 58.8 21.1 82.4

*The total number of the properties within PIA eligible to receive soil sampling (N = 10,173).

Next, using a GIS software ESRI ArcGIS Pro 2.2, we mapped the spatial distribution of the soil lead samples on a parcel basis. Figure 2 shows that the parcel locations with high (≥80 ppm) vs very high (≥400 ppm) soil lead levels were spatially blended. No clear spatial trend of the high soil lead locations can be identified quickly in this map.

FIGURE 2.

The representative soil lead (Pb) levels on the parcel basis. Most parcels exceeded California health screening standard (80 ppm) but were not eligible for cleanup under the DTSC current eligibility for remediation (>400 ppm).

FIGURE 2.

The representative soil lead (Pb) levels on the parcel basis. Most parcels exceeded California health screening standard (80 ppm) but were not eligible for cleanup under the DTSC current eligibility for remediation (>400 ppm).

To assess local spatial clustering of high soil lead levels, we used a hot spot analysis based on a Getis-Ord Gi* statistic to identify statistically significant clusters [42]. To map hot spots, we first constructed a spatial weights matrix to inform the spatial structure of the soil lead samples (e.g., samples within a defined distance band are considered to be adjacent and thus influential to each other). An Incremental Spatial Autocorrelation tool in ArcGIS Pro was used to find the appropriate distance bandwidth (which identified the first peak of z-score by running Moran’s I with various distance bands). Due to the inconsistent sample density (no residential housing is immediately next to the Exide facilities), the sample points were geographically split into two groups (North and South) and the smallest peak of z-score at the radius of 250 m between two groups was found. In addition to the 250-m radius distance band, we used a minimum adjacent sample size of three for any soil sample locations to build the Spatial Weights Matrix.

The result of the hot spot analysis shows spatial clusters of high (hot spots) and low (cold spots) soil lead values with the certainty (i.e., 99, 95, or 90% confidence levels) (Figure 3). The hot spots with 99% confidence level, meaning that we can be 99% certain about the locations being spatially clustered with high soil lead level samples, were mainly concentrated in the Boyle Height neighborhood (east of S Indiana Street and north of Whittier Boulevard between Interstate Hwy 5 and Olympic Boulevard near Spence Street), East Los Angeles (west of S Indiana Street near Whittier Boulevard and the north tip of the study area near Hwy 60 and Downey Road) and City of Maywood (east of Atlantic Boulevard on E 57th Street between Loma Vista Avenue and Carmelita Avenue). The next group of hotspots with a 95% confidence level was generally near the ones with 99% confidence ones.

FIGURE 3.

The hot spots of soil lead (Pb) in the Exide study area. Hot spots are statistically significant spatial clusters of high soil lead sample locations.

FIGURE 3.

The hot spots of soil lead (Pb) in the Exide study area. Hot spots are statistically significant spatial clusters of high soil lead sample locations.

## CONCLUSION

As a ubiquitous and persistent pollutant in urban soil, lead poses a significant threat to the health of young children from low-income communities of color who live in urban areas. In this case study, we described the nuances of a lead-contaminated soil cleanup and used a hot spot analysis to inform the soil lead contamination distributions in the residential community near the former Exide smelter in southeastern Los Angeles. Our case examination showed some spatial clustering of high soil lead in Boyle Heights, East Los Angeles and City of Maywood, suggesting a high cleanup priority should be considered in these hot spot locations to ensure safety in these residential communities.

Our results also suggest the potential for high soil lead levels in unsampled household locations near hot spots. Remediation efforts should consider neighborhood-scale, rather than parcel-scale, cleanup strategies and prioritize unsampled properties in hot spot areas.

## CASE STUDY QUESTIONS

1. As most of soil lead levels of the sampled properties exceeded California’s screening standard (80 ppm) and re-suspension of soil lead is a known issue, is parcel-level soil cleanup sufficient for the safety measure?

2. What did the hot spot analysis tell us about the pattern of soil lead contamination? More specifically, what might be the drivers of this spatial pattern and what would you do differently in the cleanup decision/process based on this result?

3. What additional information do you think can help to make recommendations of the soil clean-up process in this case study?

4. The current soil cleanup requires owners’ permissions, yet most residents in this low-income, predominately Latino community, are renters. How would the program encourage more homeowners to participate?

## AUTHOR CONTRIBUTIONS

A-MW—Literature review, method development, data analysis, writing and project coordination. JJ—Literature review, writing, editing and proofreading.

We thank Monica Finnstrom (Spatial Sciences Institute, University of Southern California) for assistance with initial data handling and geocoding, and we thank Wendy Gutschow (Department of Preventive Medicine, University of Southern California) for the valuable comments on the manuscript. This work was supported in part by NIEHS #5P30ES007048.

## COMPETING INTERESTS

The authors have declared that no competing interests exist.

## REFERENCES

REFERENCES
1.
Sullivan M.
Reducing lead in air and preventing childhood exposure near lead smelters: Learning from the US experience
.
New Solut
.
2015
;
25
(
1
):
78
101
.
2.
Brandvold LA, Popp BR, Swartz SJ.
Lead content of plants and soils from three abandoned smelter areas in and near Socorro, New Mexico
.
Environ Geochem Health
.
1996
;
18
(
1
):
1
4
.
3.
Environmental impacts of metal ore mining and processing: a review
.
J Environ Qual
.
1997
;
26
(
3
):
590
602
.
4.
Eckel WP, Rabinowitz MB, Foster GD.
Discovering unrecognized lead-smelting sites by historical methods
.
Am J Public Health
.
2001
;
91
(
4
):
625
.
5.
Secretariat of the Commission for Environmental Cooperation
2013
.
6.
Wang JD, Soong WT, Chao KY, Hwang YH, Jang CS.
Occupational and environmental lead poisoning: case study of a battery recycling smelter in Taiwan
.
J Toxicol Sci
.
1998
;
23
(
Suppl II
):
241
245
.
7.
Morse DL, Landrigan PJ, Rosenblum BF, Hubert JS, Housworth J.
El Paso revisited: epidemiologic follow-up of an environmental lead problem
.
JAMA
.
1979
;
242
(
8
):
739
741
.
8.
Landrigan PJ, Baker EL.
Exposure of children to heavy metals from smelters: epidemiology and toxic consequences
.
Environ Res
.
1981
;
25
(
1
):
204
224
.
9.
Centers for Disease Control and Prevention (CDC)
.
.
MMWR Morb Mortal Wkly Rep
.
1997
;
46
(
37
):
871
.
10.
Winegar DA, Levy BS, Andrews JJ, Landrigan PJ, Scruton WH, Krause MJ.
Chronic occupational exposure to lead: an evaluation of the health of smelter workers
.
J Occup Med
.
1977
;
19
(
9
):
603
606
.
11.
Tong S, Schirnding YE, Prapamontol T.
Environmental lead exposure: a public health problem of global dimensions
.
Bull World Health Organ
.
2000
;
78
:
1068
1077
.
12.
Levin R, Brown MJ, Kashtock ME et al.
Lead exposures in US children, 2008: implications for prevention
.
Environ Health Perspect
.
2008
;
116
(
10
):
1285
1293
.
13.
Jusko TA, Henderson CR Jr., Lanphear BP, Cory-Slechta DA, Parsons PJ, Canfield RL.
Blood lead concentrations < 10 μg/dL and child intelligence at 6 years of age
.
Environ Health Perspect
.
2008
;
116
(
2
):
243
248
.
14.
Ettinger AS, Leonard ML, Mason J.
CDC’s Lead Poisoning Prevention Program: a long-standing responsibility and commitment to protect children from lead exposure
.
J Public Health Manage Pract
.
2019
;
25
(
1 Suppl
):
S5
S12
.
15.
Lanphear BP, Hornung R, Khoury J et al.
Low-level environmental lead exposure and children’s intellectual function: an international pooled analysis
.
Environ Health Perspect
.
2005
;
113
(
7
):
894
899
.
16.
Dignam T, Kaufmann RB, LeStourgeon L, Brown MJ.
Control of lead sources in the United States, 1970-2017: public health progress and current challenges to eliminating lead exposure
.
J Public Health Manage Pract
.
2019
;
25
(
1 Suppl
):
S13
S22
.
17.
Centers for Disease Control and Prevention (CDC)
.
CDC Response to Advisory Committee on Childhood Lead Poisoning Prevention Recommendations in “Low Level Lead Exposure Harms Children: A Renewed Call of Primary Prevention” [Internet]
. Atlanta, GA. US Department of Health & Human Services;
2012
18.
Wheeler W, Brown MJ.
Blood lead levels in children aged 1–5 years – United States, 1999–2010
.
MMWR
.
2013
;
62
(
13
):
245
.
19.
Mielke HW, Reagan PL.
Soil is an important pathway of human lead exposure
.
Environ Health Perspect
.
1998
;
106
(
Suppl 1
):
217
229
.
20.
Laidlaw MA, Zahran S, Mielke HW, Taylor MP, Filippelli GM.
Re-suspension of lead contaminated urban soil as a dominant source of atmospheric lead in Birmingham, Chicago, Detroit and Pittsburgh, USA
.
Atmos Environ
.
2012
;
49
:
302
310
.
21.
Laidlaw MA, Filippelli GM.
Resuspension of urban soils as a persistent source of lead poisoning in children: a review and new directions
.
Appl Geochem
.
2008
;
23
(
8
):
2021
2039
.
22.
Young TM, Heeraman DA, Sirin G, Ashbaugh LL.
Resuspension of soil as a source of airborne lead near industrial facilities and highways
.
Environ Sci Technol
.
2002
;
36
(
11
):
2484
2490
.
23.
Stehouwer R, Macneal K.
Lead in Residential Soils: Sources, Testing, and Reducing Exposure [Internet]
.
University Park, PA
:
The Pennsylvania State University
;
2010
[cited 17 July 2019]. Available: https://extension.psu.edu/lead-in-residential-soils-sources-testing-and-reducing-exposure.
24.
Jacobs DE, Clickner RP, Zhou JY et al.
The prevalence of lead-based paint hazards in US housing
.
Environ Health Perspect
.
2002
;
110
(
10
):
A599
A606
.
25.
Laidlaw MA, Mielke HW, Filippelli GM, Johnson DL, Gonzales CR.
Seasonality and children’s blood lead levels: developing a predictive model using climatic variables and blood lead data from Indianapolis, Indiana, Syracuse, New York, and New Orleans, Louisiana (USA)
.
Environ Health Perspect
.
2005
;
113
(
6
):
793
800
.
26.
Sabin LD, Lim JH, Venezia MT, Winer AM, Schiff KC, Stolzenbach KD.
Dry deposition and resuspension of particle-associated metals near a freeway in Los Angeles
.
Atmos Environ
.
2006
;
40
(
39
):
7528
7538
.
27.
Hafen MR, Brinkmann R.
Analysis of lead in soils adjacent to an interstate highway in Tampa, Florida
.
Environ Geochem Health
.
1996
;
18
(
4
):
171
179
.
28.
Filippelli GM, Laidlaw MA.
The elephant in the playground: confronting lead-contaminated soils as an important source of lead burdens to urban populations
.
Perspect Biol Med
.
2010
;
53
(
1
):
31
45
.
29.
Carrizales L, Razo I, Tellez-Hernandez JI et al.
Exposure to arsenic and lead of children living near a copper-smelter in San Luis Potosi, Mexico: Importance of soil contamination for exposure of children
.
Environ Res
.
2006
;
101
(
1
):
1
10
.
30.
Centers for Disease Control and Prevention (CDC)
.
Pica Behavior and Contaminated Soil [Internet]
;
2019
[cited 29 May 2019]. Available: https://www.cdc.gov/healthcommunication/toolstemplates/entertainmented/tips/Pica.html.
31.
White BM, Bonilha HS, Ellis C.
Racial/ethnic differences in childhood blood lead levels among children< 72 months of age in the United States: a systematic review of the literature
.
J Racial Ethn Health Disparities
.
2016
;
3
(
1
):
145
153
.
32.
Markus J, McBratney AB.
A review of the contamination of soil with lead: II. Spatial distribution and risk assessment of soil lead
.
Environ Int
.
2001
;
27
(
5
):
399
411
.
33.
McGrath D, Zhang C, Carton OT.
Geostatistical analyses and hazard assessment on soil lead in Silvermines area, Ireland
.
Environ Pollut
.
2004
;
127
(
2
):
239
248
.
34.
Schwarz K, Weathers KC, Pickett ST, Lathrop RG, Pouyat RV, Cadenasso ML.
A comparison of three empirically based, spatially explicit predictive models of residential soil Pb concentrations in Baltimore, Maryland, USA: understanding the variability within cities
.
Environ Geochem Health
.
2013
;
35
(
4
):
495
510
.
35.
Johnston JE, Hricko A.
Industrial lead poisoning in Los Angeles: anatomy of a public health failure
.
Environ Justice
.
2017
;
10
(
5
):
162
167
.
36.
The U.S. Attorney Office, Central District of California
.
Exide Technologies Admits Role in Major Hazardous Waste Case and Agrees to Permanently Close Battery Recycling Facility in Vernon [Internet]
. United States Department of Justice; 12 Mar 2015 [cited 22 February 2019]. Available: https://www.justice.gov/usao-cdca/pr/exide-technologies-admits-role-major-hazardous-waste-case-and-agrees-permanently-close.
37.
California Legislative Counsel Bureau
. Senate Bill No. 93 Chapter 9 [Internet]. State of California. 2016; 94. [cited 20 January 2019]. Available: http://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201520160SB93
38.
California Department of Toxic Substances Control
.
Executive Summary and Introduction. Chapter 1, Exide Residential Cleanup Plan and Environmental Impact Report
;
2017
. pp.
1
42
. Available: https://www.envirostor.dtsc.ca.gov/public/community_involvement/2532932877/EIR%20Executive%20Summary%20(English).pdf.
39.
California Department of Toxic Substances Control
.
Soil Sampling Data for the Exide Preliminary Investigation Area [Internet]
. CA.Gov;
2019
[cited 16 December 2018]. Available: https://dtsc.ca.gov/soil-sampling-data-for-the-exide-preliminary-investigation-area/.
40.
Carlisle, J.
Revised California Human Health Screening Levels for Lead [Internet]
.
Sacramento
:
Office of Environmental Health Hazard Assessment, California Environmental Protection Agency
; 17 Sep 2009 [cited 29 May 2019]. Available: http://leginfo.legislature.ca.gov/faces/codes_displaySection.xhtml?sectionNum=57008.&lawCode=HSC.
41.
California Department of Toxic Substances Control
.
Residential Cleanup [Internet]
. CA.Gov.
2019
[cited December 16 2018]. Available: https://dtsc.ca.gov/residential-cleanup/.
42.
Getis A, Ord JK.
The analysis of spatial association by use of distance statistics
.
Geog Anal
.
1992
;
24
(
3
):
189
206
.