Long-term Study of Minerogenic Particle Optics in Cayuga Lake

Transcription

1 Limnol. Oceanogr., 59(1), 214, in press. Long-term Study of Minerogenic Particle Optics in Cayuga Lake Steven W. Effler and Feng Peng January 15, 214

2 Light Scattering by Suspended Particles reduces clarity, imparts cloudiness often measured with a Secchi disk or turbidimeter earlier efforts focused on nutrient (i.e., Phos.) control and organic particles inorganic (mineral) particles increasingly found to be important scatter light more efficiently different sources and management strategies need to resolve the contributions of these two particle populations

3 Particle Scattering and Clarity: Study Approach phytoplankton biomass ([Chl-a]) poor predictor of SD SD 1 particle scattering widely documented (Davies-Colley et al. 23) Onondaga Lake example; c(66) theoretical representation: scattering coefficient (b p ; m 1 ) transmissometer, ac-s meter f (composition, size distribution, number conc. of particles) analytical support to provide particle optical attributes SD 1 (m 1 ) SD 1 (m 1 ) Cayuga Lake 5 1 [Chl-a] (mg m 3 ) 15 R 2 = c(66) (m 1 ) 8 1

4 UFI s Contributions in Advancing Particle Optics Studies with SAX Individual Particle Analysis by Scanning electron microscopy interfaced with Automated image and X-ray analyses ( ) Detailed characterizations for individual mineral particles morphology (size), composition (clay, calcite) input for theoretical calculations Success documented in multiple publications X-ray Counts Al Si energy (kev) K

5 Partitioning bp into Contributing Components two-component (organics and mineral) approach bp = bo + bm Clay phytoplankton Calcite mineral particles empirical, mechanistic, Mie theory bio-optical model based on results of SAX bo =.34[Chl-a].77

6 Modeling Mineral Particle Scattering: SAX Mie Approach single-particle optics to bulk properties b m N = 1 ( Qb, i PAi ) V i= 1 V: sampling volume N: num. of mineral particles in sampled volume Q b,i : scattering efficiency factor f (size, composition, λ); Mie PA i : projected area of a particle (PAV m projected area conc.) Stramski et al. (27) basic laboratory studies of particle types with a detailed characterizations of particle populations with the use of individual particle analysis, must be pressed further

7 Cayuga Lake Particle Optics Study: Monitoring Site , Apr Oct, biweekly Site 3 (pelagic) & Site 2 (nearshore, shelf ) SeaBirdc(66): light-attenuation coefficient at 66 nm (to estimate b p ) Secchi depth (SD) Laboratory measurements chlorophyll-a concentration, [Chl-a] particle analysis by SAX Site 2 Fall Creek

8 Cayuga Lake Particle Optics Study: Objectives Site 3 Site 2 document optical variability pelagic vs. near-shore tempo-spatial patterns of b m test the credibility of b m estimates and model closure (b p = b m + b o ) resolve the relative roles of b o and b m in affecting SD dynamics supports Cayuga Lake TMDL modeling (submodel for SD) Fall Creek

9 Performance of the Two-component Model compare (b m + b o ) to bulk measured b p b p : (b m + b o ) n = 199, mean = 1.3, median = 1.4 b p : (b m + b o ) a Site 2 Site 3 average by site Site 2: 1.9 Site 3: level of model closure generally satisfactory provides support for b p components and SD analyses

10 Detailed Temporal Dynamics (Year 23) [Chl a] (mg m 3 ) SD ( m) Site 2 Site 3 spatial differences not significant higher clarity at Site 3 (paired observations) 3 Site 2 mea. b p mod. b p b p (m 1 ) 2 1 b m b p patterns roughly inverse of those of SD b p (m 1 ) 2 1 Site 3 A M J J A S O N two-component model performed well b m dynamics driving b p

11 Detailed Temporal Dynamics (Year 23) b m composition b m (m 1 ) b m (m 1 ) Site 2 Site 3 Ca-rich Clay Other Quartz higher levels at Site 2 than at Site 3 (mean.6 vs..3 m 1 ) Clay peaks reflected runoff events clay minerals dominating (>65% on average) summer whiting (CaCO 3 ), especially at Site 3 A M J J A S O N

12 Particle Size Distribution (PSD) Size Dependency of Scattering F(d) (particles L 1 µm 1 ) Cumulative Cumulative b m (66) PAV or m PAV (%) m (%) 5x Jun 26 Site 2 (high flow) Aug Site 3 (whiting) 22 Apr Site 3 ( Clay dominating) 1.1 d (µm) 8 Particle size (µm) d Particle size (µm) distinctive curvature higher conc. of particles during runoff event(s) plateau feature for whiting event sample resolve particle size class contributions to PAV m (1 1 µm) b m PAV m median size of scattering, d 5

13 Relative Importance of b o vs. b m in Regulating Bulk b p [Chl-a] only [Chl-a] &b m Bulk b p (m 1 ) 2 1 n = 92 R 2 = n = 194 R 2 = y =.93x +.13 n = 194 R 2 = b o (m 1 ) b o important when b p and b m levels were low b o (m 1 ) not so much for full range of b p b o + b m (m 1 ) good closure with overall b p

14 What Regulated Secchi Depth Dynamics? SD 1 (m 1 ).4 b m <.25 m full range of b m n = 65 R 2 = n = n = 186 SD 1 (m 1 ) b o (m 1 ) n = 186 R 2 =.8 b o a predictor of SD only when b m levels low b m (m 1 ) R 2 =.47 b m more important in regulating SD. R 2 = b m + b o (m 1 ) limited benefit in adding b o

15 Scenario Analyses for Secchi Dynamics SD 1 =.16 b p +.4 Occurrence (%) 15 mean 4.7 m Site 3 long-term observations; n = 113 Occurrence (%) 15 mean 7.3 m mean 3.3 m 15 Occurrence (%) if [Chl-a] decreases by half if [Chl-a] doubles

16 Long-term Particle Optics Study Summary the most comprehensive test of SAX Mie and twocomponent modeling approaches reasonable degree of optical closure detailed particle dynamics (conc., composition, size) clarity (Secchi depth) regulated by particle scattering (b p ) scattering by algal particles (b o ) poor predictor of SD mineral particle scattering (b m ) more important to SD dynamics b p SD relationship provides insights and expectations for management strategies nutrient vs. erosion control cost vs. effect

17 Implications for TMDL Modeling need to resolve contributions of phytoplankton and mineorgenic particles to scattering and SD particle size and composition classes of PAV m may be appropriate state variables Cumulative PAV m (%) Particle size (µm) contribution of particles in 1 2 µm size range to PAV m (b m ) low flow conditions high flow conditions

18 Acknowledgments UFI s field crew, including B. Wagner, A. Prestigiacomo, A. Effler, and M. Spada, collected water samples and conducted field measurements. UFI s laboratory staff members, including G. Kehoe, measured chlorophyll-a concentrations. D. Matthews organized SeaBird and Secchi depth data.

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20 Calcite Nuclei: Differential IPA