Quantile Regression Illuminates the Heterogeneous Effect of Water Quality on Phytoplankton in Lake Taihu, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Statistical Method
3. Results
3.1. Univariate Analysis of Phytoplankton and Water Quality
3.2. Heterogeneous Effects of Water Quality on Phytoplankton under Different Quantiles
3.3. Heterogeneous Effects of Water Quality on Phytoplankton under Different Seasons
4. Discussion
4.1. Basic Characteristics of Phytoplankton and Water Quality in Lake Taihu
4.2. Heterogeneous Effects of Water Quality on Phytoplankton in Lake Taihu
4.3. Seasonal Variations in the Impact of Water Quality on Phytoplankton in Lake Taihu
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
COD | chemical oxygen demand |
TP | total phosphorus |
TN | total nitrogen |
WT | water temperature |
DO | dissolved oxygen |
pH | pondus hydrogenii |
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Variable | Min | Max | Mean | SD | Skewness | Kurtosis |
---|---|---|---|---|---|---|
Phytoplankton (ind./L) | 3.69 × 104 | 9.93 × 108 | 9.33 × 107 | 1.54 × 108 | 2.65 | 7.90 |
Transparency (m) | 0.03 | 2.35 | 0.39 | 0.26 | 2.49 | 8.70 |
DO (mg/L) | 2.47 | 14.27 | 8.70 | 1.98 | −0.16 | −0.14 |
pH | 7.34 | 9.60 | 8.23 | 0.25 | 1.02 | 3.50 |
COD (mg/L) | 2.38 | 24.19 | 4.54 | 1.79 | 4.02 | 29.46 |
TP (mg/L) | 0.02 | 0.96 | 0.11 | 0.09 | 3.30 | 18.94 |
TN (mg/L) | 0.33 | 11.31 | 2.09 | 1.31 | 1.85 | 5.86 |
WT (°C) | 0.40 | 34.80 | 18.24 | 9.09 | −0.15 | −1.23 |
Conductivity (μs/cm) | 120.00 | 860.00 | 414.00 | 107.13 | 0.42 | −0.13 |
Model | Outcome | (Intercept) | Transparency | DO | pH | COD | TP | TN | WT | Conductivity |
---|---|---|---|---|---|---|---|---|---|---|
Linear | estimation | 5.458 | −0.167 | 0.276 | 5.078 | 2.268 | 0.633 | −0.596 | 0.482 | −0.449 |
Std. Error | 3.327 | 0.090 | 0.220 | 1.524 | 0.239 | 0.136 | 0.097 | 0.089 | 0.192 | |
t-value | 1.640 | −1.857 | 1.252 | 3.333 | 9.500 | 4.643 | −6.166 | 5.419 | −2.335 | |
p-value | 0.101 | 0.064 | 0.211 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.020 | |
estimation | −2.086 | −0.198 | −0.013 | 10.170 | 1.260 | 0.817 | −0.506 | 0.457 | −0.881 | |
Std. Error | 3.695 | 0.155 | 0.149 | 1.628 | 0.391 | 0.223 | 0.150 | 0.126 | 0.157 | |
t-value | −0.564 | −1.272 | −0.090 | 6.246 | 3.227 | 3.665 | −3.380 | 3.615 | −5.618 | |
p-value | 0.573 | 0.204 | 0.928 | 0.000 | 0.001 | 0.000 | 0.001 | 0.000 | 0.000 | |
estimation | 3.451 | −0.168 | 0.135 | 6.751 | 2.036 | 0.696 | −0.682 | 0.300 | −0.686 | |
Std. Error | 4.265 | 0.124 | 0.349 | 2.103 | 0.342 | 0.177 | 0.134 | 0.138 | 0.121 | |
t-value | 0.809 | −1.356 | 0.385 | 3.211 | 5.959 | 3.938 | −5.076 | 2.179 | −5.685 | |
p-value | 0.419 | 0.175 | 0.700 | 0.001 | 0.000 | 0.000 | 0.000 | 0.030 | 0.000 | |
estimation | 5.079 | −0.067 | 0.302 | 5.328 | 2.144 | 0.913 | −0.831 | 0.380 | −0.370 | |
Std. Error | 3.977 | 0.118 | 0.293 | 1.963 | 0.339 | 0.168 | 0.121 | 0.102 | 0.156 | |
t-value | 1.277 | −0.570 | 1.032 | 2.714 | 6.325 | 5.428 | −6.868 | 3.735 | −2.366 | |
p-value | 0.202 | 0.569 | 0.302 | 0.007 | 0.000 | 0.000 | 0.000 | 0.000 | 0.018 | |
estimation | 8.758 | −0.131 | 0.348 | 3.037 | 2.545 | 0.867 | −0.903 | 0.362 | −0.239 | |
Std. Error | 4.332 | 0.110 | 0.273 | 1.951 | 0.318 | 0.172 | 0.131 | 0.104 | 0.253 | |
t-value | 2.022 | −1.185 | 1.273 | 1.557 | 8.013 | 5.043 | −6.917 | 3.472 | −0.946 | |
p-value | 0.043 | 0.236 | 0.203 | 0.120 | 0.000 | 0.000 | 0.000 | 0.001 | 0.344 | |
estimation | 11.455 | −0.104 | 0.400 | 1.708 | 2.629 | 0.758 | −0.760 | 0.512 | −0.319 | |
Std. Error | 4.299 | 0.109 | 0.273 | 1.942 | 0.303 | 0.178 | 0.121 | 0.107 | 0.259 | |
t-value | 2.665 | −0.952 | 1.467 | 0.880 | 8.685 | 4.263 | −6.262 | 4.797 | −1.230 | |
p-value | 0.008 | 0.341 | 0.143 | 0.379 | 0.000 | 0.000 | 0.000 | 0.000 | 0.219 | |
estimation | 4.321 | −0.081 | 0.596 | 4.585 | 2.602 | 0.704 | −0.715 | 0.607 | −0.200 | |
Std. Error | 4.579 | 0.113 | 0.289 | 2.103 | 0.316 | 0.177 | 0.127 | 0.109 | 0.252 | |
t-value | 0.944 | −0.715 | 2.061 | 2.180 | 8.227 | 3.972 | −5.613 | 5.543 | −0.794 | |
p-value | 0.346 | 0.475 | 0.040 | 0.029 | 0.000 | 0.000 | 0.000 | 0.000 | 0.427 | |
estimation | 7.244 | −0.134 | 0.397 | 3.131 | 3.030 | 0.493 | −0.652 | 0.603 | −0.241 | |
Std. Error | 4.032 | 0.103 | 0.263 | 1.801 | 0.268 | 0.141 | 0.110 | 0.116 | 0.224 | |
t-value | 1.797 | −1.308 | 1.507 | 1.738 | 11.306 | 3.504 | −5.937 | 5.215 | −1.080 | |
p-value | 0.073 | 0.191 | 0.132 | 0.082 | 0.000 | 0.000 | 0.000 | 0.000 | 0.280 | |
estimation | 5.043 | −0.247 | 0.276 | 4.724 | 3.084 | 0.277 | −0.411 | 0.516 | −0.428 | |
Std. Error | 3.326 | 0.106 | 0.208 | 1.453 | 0.236 | 0.144 | 0.094 | 0.106 | 0.229 | |
t-value | 1.516 | −2.323 | 1.327 | 3.250 | 13.088 | 1.927 | −4.355 | 4.859 | −1.870 | |
p-value | 0.130 | 0.020 | 0.185 | 0.001 | 0.000 | 0.054 | 0.000 | 0.000 | 0.062 | |
estimation | 4.939 | −0.290 | 0.651 | 4.222 | 3.189 | 0.074 | −0.334 | 0.463 | −0.385 | |
Std. Error | 2.393 | 0.076 | 0.095 | 1.159 | 0.213 | 0.117 | 0.061 | 0.100 | 0.143 | |
t-value | 2.064 | −3.804 | 6.866 | 3.641 | 14.946 | 0.636 | −5.477 | 4.607 | −2.683 | |
p-value | 0.039 | 0.000 | 0.000 | 0.000 | 0.000 | 0.525 | 0.000 | 0.000 | 0.007 |
Variable | Spring | Summer | Autumn | Winter | F-Value | p-Value |
---|---|---|---|---|---|---|
Phytoplankton (ind./L) | 16.77 ± 1.99 | 18.01 ± 1.48 | 17.24 ± 1.85 | 16.08 ± 1.39 | 104.60 | 0.00 |
Transparency (m) | −1.14 ± 0.48 | −1.21 ± 0.57 | −1.07 ± 0.50 | −1.08 ± 0.66 | 0.11 | 0.74 |
DO (mg/L) | 2.10 ± 0.18 | 1.93 ± 0.27 | 2.13 ± 0.17 | 2.38 ± 0.13 | 223.10 | 0.00 |
pH | 2.11 ± 0.03 | 2.12 ± 0.04 | 2.11 ± 0.03 | 2.09 ± 0.02 | 37.60 | 0.00 |
COD (mg/L) | 1.44 ± 0.34 | 1.59 ± 0.33 | 1.45 ± 0.29 | 1.37 ± 0.18 | 22.68 | 0.00 |
TP (mg/L) | −2.54 ± 0.60 | −2.16 ± 0.75 | −2.41 ± 0.64 | −2.43 ± 0.50 | 6.47 | 0.01 |
TN (mg/L) | 0.72 ± 0.54 | 0.40 ± 0.57 | 0.30 ± 0.61 | 0.83 ± 0.53 | 165.20 | 0.00 |
WT (°C) | 3.12 ± 0.26 | 3.38 ± 0.08 | 2.70 ± 0.10 | 1.69 ± 0.35 | 469.20 | 0.00 |
Conductivity (μs/cm) | 6.17 ± 0.25 | 6.03 ± 0.27 | 5.93 ± 0.22 | 5.85 ± 0.20 | 2.12 | 0.15 |
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Wang, L.; Liu, S.; Ma, S.; Yang, Z.; Chen, Y.; Gao, W.; Liu, Q.; Zhang, Y. Quantile Regression Illuminates the Heterogeneous Effect of Water Quality on Phytoplankton in Lake Taihu, China. Water 2024, 16, 2570. https://doi.org/10.3390/w16182570
Wang L, Liu S, Ma S, Yang Z, Chen Y, Gao W, Liu Q, Zhang Y. Quantile Regression Illuminates the Heterogeneous Effect of Water Quality on Phytoplankton in Lake Taihu, China. Water. 2024; 16(18):2570. https://doi.org/10.3390/w16182570
Chicago/Turabian StyleWang, Lu, Shuo Liu, Shuqin Ma, Zhongwen Yang, Yan Chen, Wei Gao, Qingqing Liu, and Yuan Zhang. 2024. "Quantile Regression Illuminates the Heterogeneous Effect of Water Quality on Phytoplankton in Lake Taihu, China" Water 16, no. 18: 2570. https://doi.org/10.3390/w16182570