Geostatistical Prediction of Microbial Water ... - ACS Publications

Subscriber access provided by Kaohsiung Medical University

Environmental Modeling

Geostatistical prediction of microbial water quality throughout a stream network using meteorology, land cover, and spatiotemporal autocorrelation David Andrew Holcomb, Kyle P Messier, Marc L Serre, Jakob G Rowny, and Jill R Stewart Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01178 • Publication Date (Web): 11 Jun 2018 Downloaded from http://pubs.acs.org on June 11, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 36

Environmental Science & Technology

1

Geostatistical prediction of microbial water quality

2

throughout a stream network using meteorology,

3

land cover, and spatiotemporal autocorrelation

4

David A. Holcomb,† Kyle P. Messier,‡ Marc L. Serre,† Jakob Rowny,† Jill R. Stewart*,†

5

†Department of Environmental Sciences and Engineering, Gillings School of Global Public

6

Health, University of North Carolina, Chapel Hill, North Carolina 27599-7431, United States

7

‡Department of Civil, Architectural, and Environmental Engineering, University of Texas,

8

Austin, Texas 78712, United States

9

ACS Paragon Plus Environment

1


Page 2 of 36

10

TOC Graphic

11

ABSTRACT. Predictive modeling is promising as an inexpensive tool to assess water quality.

12

We developed geostatistical predictive models of microbial water quality that empirically

13

modelled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations

14

to improve prediction. We compared five geostatistical models featuring different

15

autocorrelation structures, fit to 676 observations from 19 locations in North Carolina’s Jordan

16

Lake watershed using meteorological and land cover predictor variables. Though stream distance

17

metrics (with and without flow-weighting) failed to improve prediction over the Euclidean

18

distance metric, incorporating temporal autocorrelation substantially improved prediction over

19

the space-only models. We predicted FC throughout the stream network daily for one year,

20

designating locations “impaired”, “unimpaired”, or “unassessed” if the probability of exceeding

21

the state standard was >90%, 10% but 0.1 but < 0.9 were considered unassessed. Additionally,

235

any points with > 0.5 probability of exceeding the standard were considered more likely than not

236

to be impaired.

237

Results and Discussion

238

Geostatistical model comparison. We fit an OLS model, assuming uncorrelated error, and four

239

geostatistical models, distinguished by four different covariance models for the error function, to

240

FC concentration data. Table 1 compares the performance of the five models, which were

241

assessed foremost by minimizing AIC, followed by RMSE and corrected prediction R2. The

242

OLS model and the three spatial models performed similarly, with the OLS model faring slightly

243

worse by all three metrics and the Euclidean space-only model performing slightly better by

244

AIC. The three spatial models all estimated the same proportion of spatial autocorrelation (3% of

245

the total variance) regardless of covariance model structure. Incorporating temporal

246

autocorrelation greatly improved performance, substantially reducing AIC and RMSE and

247

allowing the space/time model to explain over 70% of the variance in FC concentration. The

248

mean function explained the greatest variance portion (59%) in all four geostatistical models; the

249

OLS model also explained 59% of the total variance. The correlated error component of the

250

space/time model accounted for 34% of the total variance, far exceeding the 3% correlated error

251

in the spatial models. RSME and R2 values for space/time model predictions at individual

252

sampling sites ranged from 0.61 – 1.48 ln(cfu)/100 mL and 0.42 – 0.88, respectively (Table S4).


14

Page 15 of 36


253

Table 1. Comparison of model performance and proportion of variance explained by geostatistical

254

model components.

Performance metrics

Model

Proportion of variance explained Error function

Covariance structure AIC

RMSE*

𝐑𝟐 *

Mean function

[ln(cfu)]

255

Correlated error proportion

Unexplained variance (nugget) proportion

OLS

Nugget

2214

1.21

0.59

0.59

-

0.41

Space-only

Euclidean + nugget

2187

1.19

0.61

0.59

0.03

0.38

Stream distance

Stream distance + Euclidean + nugget

2191

1.19

0.61

0.59

0.03

0.38

Flowweighted

Flowweighted stream distance + Euclidean + nugget

2191

1.19

0.61

0.59

0.03

0.38

Space/time

Euclidean + nugget

2090

1.02

0.71

0.59

0.34

0.07

*Calculated from LOOCV prediction residuals

256

Effects of stream distance and spatiotemporal autocorrelation: error function models.

257

Compared with the OLS model assuming uncorrelated error, we observed a strong improvement

258

in model performance by considering temporal autocorrelation but little improvement when

259

modeling the error function using only spatial autocorrelation (Table 2). Incorporating a stream

260

distance metric had no effect on model performance, as Euclidean distance-based autocorrelation


15


Page 16 of 36

261

dominated the spatially-correlated error. The longest spatial range was associated with the flow-

262

weighted stream distance component, but the small partial sill suggests limited impact of stream

263

processes in driving FC concentrations—further reflected in the negligible partial sill and

264

reduced spatial range of the stream distance model without flow-weighting.

265

Scale may be an important factor in the relative importance of land and stream-based

266

processes driving microbial contamination; over-land processes appear to dominate in-stream

267

processes in our small watershed, with sampling locations at most 20 km apart. A recent study

268

conducted on a similarly small watershed likewise realized only minor performance gains by

269

modeling correlated error using a flow-weighted stream distance metric, despite observing

270

substantial spatial autocorrelation.59 By contrast, other studies conducted on whole river basins

271

found the stream distance metric (with various flow-weighting schemes) improved FIB

272

estimation, suggesting that in-stream effects may grow dominant at larger scales.30,39

273

Considering larger spatial scales generally increases the number of flow-connected sampling

274

locations analyzed, which may lead to performance gains from flow-weighted and stream

275

distance metrics.60 Several studies predicting other physiochemical characteristics in streams

276

obtained mixed results regarding the relative performance of Euclidean versus (flow-weighted)

277

stream distance metrics.35,57,61–63 The appropriate distance metric for modeling a particular water

278

quality characteristic is dependent on several interacting factors, including the underlying

279

processes driving the characteristic, the spatial scale of the analysis, and the distribution of

280

sampling locations on the network.64


16

Page 17 of 36

281


Table 2. Error function parameter estimates. Unexplained variance (nugget)

Stream distance covariance

Euclidean covariance

Model Partial sill [ln(cfu)2]

Partial sill [ln(cfu)2]

Spatial range [km]

Temporal range [days]

Partial sill [ln(cfu)2]

Spatial range [km]

OLS

1.48

-

-

-

-

-

Space-only

1.36

0.112

8.8

-

-

-

Stream distance

1.36

0.112

8.9

-

3.4 × 10−4

10.4

Flowweighted

1.36

0.098

9.3

-

0.012

22.7

Space/time

0.24

1.23

12.8

2.1

-

-

282 283

Including time substantially decreased the amount of uncorrelated error, though temporal

284

autocorrelation fully decayed in two days. In contrast to the spatial models, the space/time model

285

only estimates spatial autocorrelation for observations made within this short temporal range,

286

revealing much stronger spatial patterns. The short temporal range, increased spatial range, and

287

large partial sill suggest that localized microbial water quality is dependent on broader

288

environmental conditions that are subject to substantial temporal variability. This is in keeping

289

with previous research at freshwater beaches, in which the day of sampling explained the largest

290

portion of FIB concentration variance between multiple beaches.19 As such, the space/time

291

model may be useful for predicting water quality at unmonitored locations when microbial

292

concentrations are known elsewhere, but is limited for prediction at unmonitored times.

293

Effects of land cover and meteorology: mean function models. We used the standardized

294

predictor variables to specify a full mean function for the flow-weighted model (Table S3). The


17


Page 18 of 36

295

model with the lowest AIC following backwards elimination retained the following predictors:

296

percent forested upstream watershed area, percent agricultural upstream watershed area, hourly

297

average surface temperature, precipitation in the preceding two days, precipitation in the

298

preceding seven days (excluding the previous two days), and the interaction between the two

299

precipitation terms. The other four models were fit to the same set of predictors. The parameter

300

estimates differed slightly between the five models due to the influence of covariance structure

301

(Table S2), but the mean function for all five models accounted for the same proportion of the

302

total variance in the data (Table 1).

303

The direction of effect estimates (Figure 2) largely coincided with our expectations.

304

Increasing forest cover was protective against microbial pollution,11,14,65,66 while antecedent

305

rainfall and warmer temperatures were associated with increased microbial

306

concentrations.11,13,16,23,25,67 Precipitation in the preceding two days was associated with the

307

largest increase in FC concentration. Agricultural land use also exhibited a strong positive

308

relationship with FC, though it was the least precise parameter estimate and previous studies

309

report conflicting effects of agriculture on FIB.11,66,68 This uncertainty could relate to the

310

geographic distribution of agricultural land on the periphery of the study area: although many

311

sites had agricultural lands upstream, the watersheds generally transitioned to other land uses

312

nearer the sites.

313

Despite previous studies suggesting urbanization increases microbial pollution, we did

314

not observe a significant association between increasing impervious surfaces and FC

315

concentration.11,14,66,69 Though urbanized land was common, most watersheds had low

316

impervious surface percentages, reflecting low-intensity development with potentially diffuse

317

impacts on FC loading. We also failed to detect a significant effect of solar radiation, which is


18

Page 19 of 36


318

known to inactivate microbes in the environment.65,67,70–72 This may be due in part to the spatial

319

coarseness of the solar radiation measurement, for which a single value measured at the NOAA

320

Durham station was used for all samples collected at a given time. Site-specific topography and

321

canopy cover could have substantially altered the amount of solar radiation reaching the water.

322

To address these limitations, future applications may consider model-based estimation of solar

323

radiation, which has been used effectively in mechanistic FIB models.28,73–75

324

The models presented here provide nuanced estimates of the overall effects of

325

precipitation by considering the interaction between the two precipitation timeframes. More

326

recent precipitation has a stronger effect on microbial concentration than precipitation further

327

removed in time, but the negative interaction estimate suggests that past precipitation mitigates

328

the impact of more recent precipitation (SI). In systems where FC sources accumulate on land

329

before precipitation flushes contamination into water bodies, it follows that past precipitation

330

reduces the stock of contamination available for subsequent rain to wash into the stream.

331

Precipitation intensity may also act as an important driver of contamination in some systems,

332

although it did not appreciably affect the predictive performance of the models presented in this

333

paper (data not shown).

334

Although likely similar to many other mixed-use watersheds, the associations between

335

FC and the predictors considered in this study should be generalized with caution. The study area

336

comprised a small watershed with limited LULC composition, the effects of which were derived

337

essentially from 19 observations for each LULC class (corresponding to the 19 sampling sites).

338

As with the covariance components, scale likely also affects the magnitude of predictor effects.

339

The pronounced sensitivity to precipitation might be tempered in larger systems featuring

340

spatially heterogenous meteorological conditions and a wider range of LULC characteristics.


19


Page 20 of 36

341

Furthermore, we might expect associations with meteorology to differ in systems dominated by

342

sources with distinctive loading dynamics, such as agricultural watersheds receiving intermittent

343

manure applications.

344

Figure 2. Mean function GLS parameter estimates (points) and 95% confidence intervals (bars)

345

for the space/time model. Estimates are for standardized (mean-centered, standard deviation-

346

scaled) input variables, and represent the expected change in ln(FC) concentration for a 1 standard

347

deviation increase in the input variable with all other variables held constant. Note that the three

348

rain terms are related through the interaction and cannot be interpreted in isolation (SI). The

349

intercept corresponds to the expected ln(FC) concentration when all other predictors equal their

350

respective means.

351

Space/time prediction of microbial water quality. The space/time geostatistical model was

352

chosen to predict FC concentrations at unmonitored locations and times. We predicted universal

353

kriging means and standard errors at each prediction location at 11 a.m. for each of the 365 days

354

between April 1, 2010 and March 31, 2011 and calculated the probability each prediction

355

exceeded the NC standard of 200 cfu/100 mL, from which a corresponding impairment status

356

was assigned. Figure 3 illustrates the spatial and temporal trends in prediction mean, standard


20

Page 21 of 36


357

error, and impairment status. Prediction error was lower on days FC were measured (Figure 3B),

358

but the short temporal range largely prevented observational data from affecting predictions on

359

consecutive days (Figure 3E). Predictions outside the temporal range were made entirely on the

360

basis of the mean function, which can be observed in the flattening of localized prediction means

361

between days with and without sample data (Figures 3A and 3D).

362

Mean FC concentrations exceeding the standard were predicted on every day of the

363

study; on 36 days (10% of study period), more than 90% of stream miles had predicted FC

364

means above the standard. Assigning impairment status only to predictions with a high

365

probability (< 90%) of being above or below the state standard, some portion of the watershed

366

was considered impaired on 209 days (57%), while unimpaired stream miles were predicted on

367

190 days (52%). We observed no strong spatial patterns of impairment; rather, impairment

368

tended to be event driven, wherein large portions of the watershed briefly became impaired

369

before a relatively rapid return to a generally unassessed state (Figure 3G). While unimpairment

370

likewise displayed no strong spatial pattern, the temporal trends for unimpaired stream miles

371

appeared more seasonally driven, with notable background levels of unimpaired stream miles

372

during the winter months. Conversely, relatively few stream miles were considered unimpaired

373

during months in which impairment events are more common, even when no impairment event

374

was ongoing; in such cases, the majority of the watershed was considered unassessed.


21


Page 22 of 36

375

Figure 3. Predicted spatial and temporal trends in microbial water quality. The top two rows

376

(panels A-F) map the prediction means (panels A and D), standard errors (panels B and E), and

377

impairment status (panels C and F) for the whole study area on two consecutive days. The bottom

378

row (panel G) is the percentage of stream miles assigned each impairment status by day for the

379

entire one-year study period, using the same scale as the impairment status maps; the dashed line

380

represents the percentage of stream miles more likely impaired than not. Water samples were

381

collected for the first day, which is reflected in the lower standard errors (panel B) compared with


22

Page 23 of 36


382

the following day (panel E), for which no sample data are available. Areas assigned an impaired

383

status on the first day (panel C) largely correspond to the areas of reduced standard error arising

384

from the observational data (panel B).

385

Role of predictive models and observational data in assessing watershed impairment. Our

386

space/time model predicted that the Jordan Lake near-upstream watershed frequently suffered

387

from unsafe levels of microbial contamination. However, despite 33% of all prediction means

388

exceeding the state standard, only 5% of all predictions could be confidently considered

389

impaired, and only 6% were confidently assessed as unimpaired. The other 89% of predictions

390

were unassessed, meaning we could not determine whether FC concentrations fell above or

391

below the NC standard after accounting for prediction uncertainty. Access to water sample data

392

on the day of prediction was strongly associated with an increased ability to assign impairment

393

status. Under a simple linear regression, any FC observations on a given day was associated with

394

a 20% reduction (p < 0.001) in unassessed stream miles.

395

Because impairment appears largely event-driven, increases in related meteorological

396

variables, particularly precipitation, produced elevated mean FC predictions. However, the high

397

temporal variability and low temporal autocorrelation range of FC concentrations limited the

398

ability of models alone to predict impairment status with high-confidence. Access to recent

399

monitoring data, however, reduced prediction uncertainty sufficiently to confidently assign

400

impairment status to larger portions of the stream network. While a recent study in a similar

401

watershed observed clear spatial patterns in FIB, they did not translate into the substantial

402

reductions in prediction error necessary to confidently assess impairment status. 59 That their

403

samples were collected monthly, without intentional storm sampling or considering temporal

404

autocorrelation, suggests spatial relationships may impact background FIB concentrations, only


23


Page 24 of 36

405

to be drowned out by events driving brief, widespread impairment. This dynamic suggests

406

frequent, sample-based monitoring remains necessary to confidently assess the safety and legal

407

status of a water body. Implementing spatiotemporal predictive models may reduce the number

408

of concurrent sampling locations required to adequately assess water quality throughout a stream

409

network, providing the means to increase sampling frequency without increasing monitoring

410

expenses.

411

In developing geostatistical models that consider the spatiotemporal patterns and

412

environmental drivers of microbial water quality impairment, we identified clearly dominant

413

factors in each structural model component that together largely explain observed FC

414

concentrations and enable their prediction. Precipitation dominated in the model component that

415

predicts FC directly, while temporal patterns dominated spatial patterns in the component that

416

contributes indirectly to prediction by considering autocorrelation in the outcome. Rather than

417

orienting monitoring activities around specific locations of heightened concern, the dominance of

418

precipitation and temporal effects suggests a monitoring strategy that targets sampling according

419

to temporal criteria, employing frequent sampling to better capture diverse meteorological

420

conditions driving impairment events. Because we constructed the model using remotely sensed

421

and continuously reported predictors and we jointly estimated the different model component

422

parameters using likelihood-based methods, others may apply the model to geographically sparse

423

monitoring data to assess microbial water quality impairment throughout stream networks

424

without the need for additional specialized measurements or knowledge. Environmental

425

managers, regulators, and researchers can implement our model to inform management

426

decisions, target remediation efforts, and issue timely warnings for the protection of public

427

health.


24

Page 25 of 36


428

AUTHOR INFORMATION

429

Corresponding Author

430

*Email: [email protected]; phone: (919) 966-7553

431

Notes

432

The authors declare no competing financial interest.

433

ACKNOWLEDGMENTS

434

This work was supported in part by the Water Resources Research Institute of the University of

435

North Carolina, project number 70252. We are grateful to Prahlad Jat for assisting with the

436

stream network analysis and display.

437

Supporting Information Available. Profiling for maximum likelihood estimation; universal

438

kriging prediction error approximation; additive function values for flow-weighting; exploratory

439

analysis of input variables; visual interpretation of error function; mean function parameter

440

estimates; derivation of precipitation interaction effects; prediction at individual sampling sites;

441

animated maps of daily watershed predictions. This information is available free of charge via

442

the Internet at http://pubs.acs.org.

443

REFERENCES

444 445 446

(1)

Dadswell, J. V. Microbiological Quality of Coastal Waters and Its Health Effects. Int. J.

Environ. Health Res. 1993, 3 (1), 32–46, 10.1080/09603129309356762. (2)

Byappanahalli, M. N.; Nevers, M. B.; Korajkic, A.; Staley, Z. R.; Harwood, V. J.

447

Enterococci in the Environment. Microbiol. Mol. Biol. Rev. 2012, 76 (4), 685–706,

448

10.1128/MMBR.00023-12.


25


449

(3)

Dorevitch, S.; Pratap, P.; Wroblewski, M.; Hryhorczuk, D. O.; Li, H.; Liu, L. C.; Scheff,

450

P. A. Health Risks of Limited-Contact Water Recreation. Environ. Health Perspect. 2011, 120

451

(2), 192–197, 10.1289/ehp.1103934.

452

(4)

Soller, J. A.; Schoen, M. E.; Bartrand, T.; Ravenscroft, J. E.; Ashbolt, N. J. Estimated

453

Human Health Risks from Exposure to Recreational Waters Impacted by Human and Non-

454

Human Sources of Faecal Contamination. Water Res. 2010, 44 (16), 4674–4691,

455

10.1016/j.watres.2010.06.049.

456

(5)

Marion, J. W.; Lee, J.; Lemeshow, S.; Buckley, T. J. Association of Gastrointestinal

457

Illness and Recreational Water Exposure at an Inland U.S. Beach. Water Res. 2010, 44 (16),

458

4796–4804, 10.1016/j.watres.2010.07.065.

459

(6)

Brown, J. M.; Proum, S.; Sobsey, M. D. Escherichia Coli in Household Drinking Water

460

and Diarrheal Disease Risk: Evidence from Cambodia. Water Sci. Technol. 2008, 58 (4), 757,

461

10.2166/wst.2008.439.

462

(7)

Wade, T. J.; Pai, N.; Eisenberg, J. N. S.; Colford, J. M. Do U.S. Environmental

463

Protection Agency Water Quality Guidelines for Recreational Waters Prevent Gastrointestinal

464

Illness? A Systematic Review and Meta-Analysis. Environ. Health Perspect. 2003, 111 (8),

465

1102–1109, 10.1289/ehp.6241.

466

(8)

Page 26 of 36

Colford, J. M.; Wade, T. J.; Schiff, K. C.; Wright, C. C.; Griffith, J. F.; Sandhu, S. K.;

467

Burns, S.; Sobsey, M.; Lovelace, G.; Weisberg, S. B. Water Quality Indicators and the Risk of

468

Illness at Beaches With Nonpoint Sources of Fecal Contamination. Epidemiology 2007, 18 (1),

469

27–35, 10.1097/01.ede.0000249425.32990.b9.


26

Page 27 of 36

470 471 472


(9)

Federal Water Pollution Control Act. 33 U.S.C. §§1251-1387; 1972;

http://www.epw.senate.gov/water.pdf. (10) USEPA. National Summary of State Information. United States Environmental

473

Protection Agency http://iaspub.epa.gov/waters10/attains_nation_cy.control (accessed Sep 25,

474

2015).

475

(11) Rowny, J. G.; Stewart, J. R. Characterization of Nonpoint Source Microbial

476

Contamination in an Urbanizing Watershed Serving as a Municipal Water Supply. Water Res.

477

2012, 46 (18), 6143–6153, 10.1016/j.watres.2012.09.009.

478

(12) Mallin, M. A.; Johnson, V. L.; Ensign, S. H. Comparative Impacts of Stormwater Runoff

479

on Water Quality of an Urban, a Suburban, and a Rural Stream. Environ. Monit. Assess. 2009,

480

159 (1–4), 475–491, 10.1007/s10661-008-0644-4.

481

(13) Coulliette, A. D.; Noble, R. T. Impacts of Rainfall on the Water Quality of the Newport

482

River Estuary (Eastern North Carolina, USA). J. Water Health 2008, 06 (4), 473,

483

10.2166/wh.2008.136.

484

(14) DiDonato, G. T.; Stewart, J. R.; Sanger, D. M.; Robinson, B. J.; Thompson, B. C.;

485

Holland, A. F.; Van Dolah, R. F. Effects of Changing Land Use on the Microbial Water Quality

486

of Tidal Creeks. Mar. Pollut. Bull. 2009, 58 (1), 97–106, 10.1016/j.marpolbul.2008.08.019.

487

(15) Stumpf, C. H.; Piehler, M. F.; Thompson, S.; Noble, R. T. Loading of Fecal Indicator

488

Bacteria in North Carolina Tidal Creek Headwaters: Hydrographic Patterns and Terrestrial

489

Runoff Relationships. Water Res. 2010, 44 (16), 4704–4715, 10.1016/j.watres.2010.07.004.


27


Page 28 of 36

490

(16) Liao, H.; Krometis, L.-A. H.; Cully Hession, W.; Benitez, R.; Sawyer, R.; Schaberg, E.;

491

von Wagoner, E.; Badgley, B. D. Storm Loads of Culturable and Molecular Fecal Indicators in

492

an Inland Urban Stream. Sci. Total Environ. 2015, 530–531, 347–356,

493

10.1016/j.scitotenv.2015.05.098.

494 495 496

(17) Leecaster, M. K.; Weisberg, S. B. Effect of Sampling Frequency on Shoreline Microbial Assessments. Mar. Pollut. Bull. 2001, 42 (11), 1150–1154. (18) Whitman, R. L.; Nevers, M. B. Escherichia Coli Sampling Reliability at a Frequently

497

Closed Chicago Beach: Monitoring and Management Implications. Environ. Sci. Technol. 2004,

498

38 (16), 4241–4246, 10.1021/es034978i.

499

(19) Whitman, R. L.; Nevers, M. B. Summer E. Coli Patterns and Responses along 23

500

Chicago Beaches. Environ. Sci. Technol. 2008, 42 (24), 9217–9224, 10.1021/es8019758.

501

(20) USEPA. Predictive Tools for Beach Notification., EPA-823-R-10-003; 2010;

502

http://water.epa.gov/scitech/swguidance/standards/criteria/health/recreation/upload/P26-Report-

503

Volume-I-Final_508.pdf.

504

(21) Nevers, M. B.; Whitman, R. L. Efficacy of Monitoring and Empirical Predictive

505

Modeling at Improving Public Health Protection at Chicago Beaches. Water Res. 2011, 45 (4),

506

1659–1668, 10.1016/j.watres.2010.12.010.

507

(22) Avila, R.; Horn, B.; Moriarty, E.; Hodson, R.; Moltchanova, E. Evaluating Statistical

508

Model Performance in Water Quality Prediction. J. Environ. Manage. 2018, 206, 910–919,

509

10.1016/j.jenvman.2017.11.049.


28

Page 29 of 36


510

(23) Gonzalez, R. a; Conn, K. E.; Crosswell, J. R.; Noble, R. T. Application of Empirical

511

Predictive Modeling Using Conventional and Alternative Fecal Indicator Bacteria in Eastern

512

North Carolina Waters. Water Res. 2012, 46 (18), 5871–5882, 10.1016/j.watres.2012.07.050.

513

(24) Farnham, D. J.; Lall, U. Predictive Statistical Models Linking Antecedent Meteorological

514

Conditions and Waterway Bacterial Contamination in Urban Waterways. Water Res. 2015, 76,

515

143–159, 10.1016/j.watres.2015.02.040.

516

(25) Coulliette, A. D.; Money, E. S.; Serre, M. L.; Noble, R. T. Space/Time Analysis of Fecal

517

Pollution and Rainfall in an Eastern North Carolina Estuary. Environ. Sci. Technol. 2009, 43

518

(10), 3728–3735, 10.1021/es803183f.

519

(26) Nevers, M. B.; Whitman, R. L. Nowcast Modeling of Escherichia Coli Concentrations at

520

Multiple Urban Beaches of Southern Lake Michigan. Water Res. 2005, 39 (20), 5250–5260,

521

10.1016/j.watres.2005.10.012.

522

(27) Eleria, A.; Vogel, R. M. Predicting Fecal Coliform Bacteria Levels in the Charles River,

523

Massachusetts, USA. J. Am. Water Resour. Assoc. 2005, 41 (5), 1195–1209, 10.1111/j.1752-

524

1688.2005.tb03794.x.

525

(28) Safaie, A.; Wendzel, A.; Ge, Z.; Nevers, M. B.; Whitman, R. L.; Corsi, S. R.;

526

Phanikumar, M. S. Comparative Evaluation of Statistical and Mechanistic Models of Escherichia

527

Coli at Beaches in Southern Lake Michigan. Environ. Sci. Technol. 2016, 50 (5), 2442–2449,

528

10.1021/acs.est.5b05378.


29


529

(29) Zhang, Z.; Deng, Z.; Rusch, K. A.; Walker, N. D. Modeling System for Predicting

530

Enterococci Levels at Holly Beach. Mar. Environ. Res. 2015, 109, 140–147,

531

10.1016/j.marenvres.2015.07.003.

532

(30) Money, E. S.; Carter, G. P.; Serre, M. L. Modern Space/Time Geostatistics Using River

533

Distances: Data Integration of Turbidity and E. Coli Measurements to Assess Fecal

534

Contamination Along the Raritan River in New Jersey. Environ. Sci. Technol. 2009, 43 (10),

535

3736–3742, 10.1021/es803236j.

536

Page 30 of 36

(31) Akita, Y.; Carter, G.; Serre, M. L. Spatiotemporal Nonattainment Assessment of Surface

537

Water Tetrachloroethylene in New Jersey. J. Environ. Qual. 2007, 36 (2), 508,

538

10.2134/jeq2005.0426.

539

(32) Messier, K. P.; Kane, E.; Bolich, R.; Serre, M. L. Nitrate Variability in Groundwater of

540

North Carolina Using Monitoring and Private Well Data Models. Environ. Sci. Technol. 2014, 48

541

(18), 10804–10812, 10.1021/es502725f.

542

(33) Messier, K. P.; Campbell, T.; Bradley, P. J.; Serre, M. L. Estimation of Groundwater

543

Radon in North Carolina Using Land Use Regression and Bayesian Maximum Entropy. Environ.

544

Sci. Technol. 2015, 49 (16), 9817–9825, 10.1021/acs.est.5b01503.

545

(34) Reyes, J. M.; Serre, M. L. An LUR/BME Framework to Estimate PM2.5 Explained by on

546

Road Mobile and Stationary Sources. Environ. Sci. Technol. 2014, 48 (3), 1736–1744,

547

10.1021/es4040528.


30

Page 31 of 36

548


(35) Money, E.; Carter, G. P.; Serre, M. L. Using River Distances in the Space/Time

549

Estimation of Dissolved Oxygen along Two Impaired River Networks in New Jersey. Water Res.

550

2009, 43 (7), 1948–1958, 10.1016/j.watres.2009.01.034.

551

(36) Money, E. S.; Sackett, D. K.; Aday, D. D.; Serre, M. L. Using River Distance and

552

Existing Hydrography Data Can Improve the Geostatistical Estimation of Fish Tissue Mercury at

553

Unsampled Locations. Environ. Sci. Technol. 2011, 45 (18), 7746–7753, 10.1021/es2003827.

554

(37) Ver Hoef, J. M.; Peterson, E. E. A Moving Average Approach for Spatial Statistical

555

Models of Stream Networks. J. Am. Stat. Assoc. 2010, 105 (489), 6–18,

556

10.1198/jasa.2009.ap08248.

557 558 559

(38) Cressie, N.; Frey, J.; Harch, B.; Smith, M. Spatial Prediction on a River Network. J. Agric. Biol. Environ. Stat. 2006, 11 (2), 127–150, 10.1198/108571106X110649. (39) Jat, P.; Serre, M. L. A Novel Geostatistical Approach Combining Euclidean and Gradual-

560

Flow Covariance Models to Estimate Fecal Coliform along the Haw and Deep Rivers in North

561

Carolina. Stoch. Environ. Res. Risk Assess. 2018, 10.1007/s00477-018-1512-6.

562

(40) USACE. B. Everett Jordan Dam and Lake Master Plan Update., United States Army

563

Corps of Engineers; 2007;

564

ftp://ftp.chathamnc.org/Chatham_ConservationPlan_GIS/Plans_Policies_Ordinances/USACoE_J

565

ordan Lake Master Plan Update.pdf.

566 567

(41) NCDENR. Jordan Lake Current Allocations. North Carolina Department of Environment and Natural Resources http://www.ncwater.org/?page=313 (accessed Sep 18, 2015).


31


568

(42) Hodges-Copple, J. An Overview of the Research Triangle Region of North Carolina.,

569

Triangle J Council of Governments; 2011; http://www.campo-nc.us/TRM/Expert-

570

Panel/Presentations/PDFs/Triangle-Overview-2011.pdf.

571

Page 32 of 36

(43) APHA; AWWA; WEF. Standard Methods for the Examination of Water and

572

Wastewater, 20th ed.; American Public Health Association, American Waterworks Association,

573

Water Environment Federation: Washington, D.C., 1998.

574 575 576

(44) USEPA. STORET Data Warehouse. United States Environmental Protection Agency http://www.epa.gov/storet/dbtop.html (accessed Sep 10, 2013). (45) NCEI. United States Climate Reference Network. National Centers for Environmental

577

Information, National Oceanic and Atmospheric Administration

578

ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/hourly02/ (accessed May 1, 2014).

579 580

(46) NCDOT. Contour and Elevation Data. North Carolina Department of Transportation https://connect.ncdot.gov/resources/gis/pages/cont-elev_v2.aspx (accessed Jun 15, 2014).

581

(47) Peterson, E. E.; Hoef, J. M. Ver. STARS: An ArcGIS Toolset Used to Calculate the

582

Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data. J. Stat.

583

Softw. 2014, 56 (2), 1–17, 10.18637/jss.v056.i02.

584

(48) Hoef, J. M. Ver; Peterson, E. E.; Clifford, D.; Shah, R. SSN: An R Package for Spatial

585

Statistical Modeling on Stream Networks. J. Stat. Softw. 2014, 56 (3), 1–45,

586

10.18637/jss.v056.i03.

587 588

(49) Schwanghart, W.; Kuhn, N. J. TopoToolbox: A Set of Matlab Functions for Topographic Analysis. Environ. Model. Softw. 2010, 25 (6), 770–781, 10.1016/j.envsoft.2009.12.002.


32

Page 33 of 36

589


(50) Homer, C. G.; Dewitz, J. A.; Yang, L.; Jin, S.; Danielson, P.; Xian, G.; Coulston, J.;

590

Herold, N. D.; Wickham, J. D.; Megown, K. Completion of the 2011 National Land Cover

591

Database for the Conterminous United States-Representing a Decade of Land Cover Change

592

Information. Photogramm. Eng. Remote Sensing 2015, 81 (5), 345–354.

593 594

(51) R Core Team. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing; Vienna, Austria, 2017.

595

(52) Zimmerman, D. L.; Stein, M. Classical Geostatistical Methods. In Handbook of Spatial

596

Statistics; Gelfand, A., Diggle, P., Fuentes, M., Guttorp, P., Eds.; CRC Press: Boca Raton, FL,

597

2010; pp 29–44.

598

(53) Zimmerman, D. L. Likelihood-Based Methods. In Handbook of Spatial Statistics;

599

Gelfand, A. E., Diggle, P. J., Fuentes, M., Guttorp, P., Eds.; cr: Boca Raton, FL, 2010; pp 45–56.

600

(54) Schielzeth, H. Simple Means to Improve the Interpretability of Regression Coefficients.

601 602 603 604 605

Methods Ecol. Evol. 2010, 1 (2), 103–113, 10.1111/j.2041-210X.2010.00012.x. (55) Beale, C. M.; Lennon, J. J.; Yearsley, J. M.; Brewer, M. J.; Elston, D. A. Regression Analysis of Spatial Data. Ecol. Lett. 2010, 13 (2), 246–264, 10.1111/j.1461-0248.2009.01422.x. (56) Genton, M. G. Separable Approximations of Space-Time Covariance Matrices. Environmetrics 2007, 18 (7), 681–695, 10.1002/env.854.

606

(57) Jat, P.; Serre, M. L. Bayesian Maximum Entropy Space/Time Estimation of Surface

607

Water Chloride in Maryland Using River Distances. Environ. Pollut. 2016, 219, 1148–1155,

608

10.1016/j.envpol.2016.09.020.


33


609

(58) NCDENR. Surface Waters and Wetland Standards, North Carolina Department of

610

Environment and Natural Resources Division of Water Quality; North Carolina, USA, 2007;

611

http://portal.ncdenr.org/c/document_library/get_file?uuid=f12e8078-b128-44cc-b55b-

612

fc5e7d876f3c&groupId=38364.

613

(59) Niell, A. J.; Tetzlaff, D.; Strachan, N. J. C.; Hough, R. L.; Avery, L. M.; Watson, H.;

614

Soulsby, C. Using Spatial-Stream-Network Models and Long-Term Data to Understand and

615

Predict Dynamics of Faecal Contamination in A... Sci. Total Environ. 2017, No. August,

616

10.1016/j.scitotenv.2017.08.151.

Page 34 of 36

617

(60) Peterson, E. E.; Ver Hoef, J. M.; Isaak, D. J.; Falke, J. a.; Fortin, M.-J.; Jordan, C. E.;

618

McNyset, K.; Monestiez, P.; Ruesch, A. S.; Sengupta, A.; et al. Modelling Dendritic Ecological

619

Networks in Space: An Integrated Network Perspective. Ecol. Lett. 2013, 16 (5), 707–719,

620

10.1111/ele.12084.

621

(61) Peterson, E. E.; Urquhart, N. S. Predicting Water Quality Impaired Stream Segments

622

Using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland.

623

Environ. Monit. Assess. 2006, 121 (1–3), 615–638, 10.1007/s10661-005-9163-8.

624

(62) Peterson, E. E.; Merton, A. A.; Theobald, D. M.; Urquhart, N. S. Patterns of Spatial

625

Autocorrelation in Stream Water Chemistry. Environ. Monit. Assess. 2006, 121 (1–3), 571–596,

626

10.1007/s10661-005-9156-7.

627

(63) Isaak, D. J.; Peterson, E. E.; Ver Hoef, J. M.; Wenger, S. J.; Falke, J. A.; Torgersen, C.

628

E.; Sowder, C.; Steel, E. A.; Fortin, M.-J.; Jordan, C. E.; et al. Applications of Spatial Statistical

629

Network Models to Stream Data. Wiley Interdiscip. Rev. Water 2014, 1 (3), 277–294,

630

10.1002/wat2.1023.


34

Page 35 of 36


631

(64) Som, N. a.; Monestiez, P.; Ver Hoef, J. M.; Zimmerman, D. L.; Peterson, E. E. Spatial

632

Sampling on Streams: Principles for Inference on Aquatic Networks. Environmetrics 2014, 25

633

(5), 306–323, 10.1002/env.2284.

634

(65) Cho, K. H.; Cha, S. M.; Kang, J.-H.; Lee, S. W.; Park, Y.; Kim, J.-W.; Kim, J. H.

635

Meteorological Effects on the Levels of Fecal Indicator Bacteria in an Urban Stream: A

636

Modeling Approach. Water Res. 2010, 44 (7), 2189–2202, 10.1016/j.watres.2009.12.051.

637

(66) Nash, M. S.; Heggem, D. T.; Ebert, D.; Wade, T. G.; Hall, R. K. Multi-Scale Landscape

638

Factors Influencing Stream Water Quality in the State of Oregon. Environ. Monit. Assess. 2009,

639

156 (1–4), 343–360, 10.1007/s10661-008-0489-x.

640

(67) Whitman, R. L.; Przybyla-Kelly, K.; Shively, D. A.; Nevers, M. B.; Byappanahalli, M. N.

641

Sunlight, Season, Snowmelt, Storm, and Source Affect E. Coli Populations in an Artificially

642

Ponded Stream. Sci. Total Environ. 2008, 390 (2–3), 448–455, 10.1016/j.scitotenv.2007.10.014.

643

(68) Kang, J.-H.; Lee, S. W.; Cho, K. H.; Ki, S. J.; Cha, S. M.; Kim, J. H. Linking Land-Use

644

Type and Stream Water Quality Using Spatial Data of Fecal Indicator Bacteria and Heavy

645

Metals in the Yeongsan River Basin. Water Res. 2010, 44 (14), 4143–4157,

646

10.1016/j.watres.2010.05.009.

647

(69) Mallin, M. a M.; Williams, K. E. K.; Esham, E. C.; Lowe, R. P. Effect of Human

648

Development on Bacteriological Water Quality in Coastal Watersheds. Ecol. Appl. 2000, 10 (4),

649

1047–1056.

650 651

(70) Korajkic, A.; McMinn, B. R.; Shanks, O. C.; Sivaganesan, M.; Fout, G. S.; Ashbolt, N. J. Biotic Interactions and Sunlight Affect Persistence of Fecal Indicator Bacteria and Microbial


35


652

Source Tracking Genetic Markers in the Upper Mississippi River. Appl. Environ. Microbiol.

653

2014, 80 (13), 3952–3961, 10.1128/AEM.00388-14.

Page 36 of 36

654

(71) Davies-Colley, R. J.; Bell, R. G.; Donnison, A. M. Sunlight Inactivation of Enterococci

655

and Fecal Coliforms in Sewage Effluent Diluted in Seawater. Appl. Environ. Microbiol. 1994, 60

656

(6), 2049–2058.

657

(72) Whitman, R. L.; Nevers, M. B.; Korinek, G. C.; Byappanahalli, M. N. Solar and

658

Temporal Effects on Escherichia Coli Concentration at a Lake Michigan Swimming Beach Solar

659

and Temporal Effects on Escherichia Coli Concentration at a Lake Michigan Swimming Beach

660

†. Appl. Environ. Microbiol. 2004, 70 (7), 4276, 10.1128/AEM.70.7.4276.

661

(73) Thupaki, P.; Phanikumar, M. S.; Beletsky, D.; Schwab, D. J.; Nevers, M. B.; Whitman,

662

R. L. Budget Analysis of Escherichia Coli at a Southern Lake Michigan Beach. Environ. Sci.

663

Technol. 2010, 44 (3), 1010–1016, 10.1021/es902232a.

664

(74) Nguyen, T. D.; Thupaki, P.; Anderson, E. J.; Phanikumar, M. S. Summer Circulation and

665

Exchange in the Saginaw Bay-Lake Huron System. J. Geophys. Res. Ocean. 2014, 119 (4),

666

2713–2734, 10.1002/2014JC009828.

667 668

(75) Annear, R. L.; Wells, S. A. A Comparison of Five Models for Estimating Clear-Sky Solar Radiation. Water Resour. Res. 2007, 43 (10), 10.1029/2006WR005055.


36

Geostatistical Prediction of Microbial Water ... - ACS Publications

Recommend Documents