An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species
Abstract
:1. Introduction
- Significant increases in OC, EC, and WSIS were observed during haze episodes.
- Crop residue burning and wildfires are the two main sources of PM2.5.
- Wet deposition is the main depletion mechanism for OC, EC, and WSIS.
- Ca2+ and Mg2+ are deeply connected with albedo.
2. Materials and Methodologies
2.1. Air Monitoring Locations
2.2. PM2.5 Sampling Procedure
2.3. Analysis of WSIS and Carbonaceous Aerosols
2.4. Statistical Analysis and Chemometrics Modelling
2.4.1. Principal Component Analysis (PCA)
2.4.2. Artificial Neural Networks (ANNs)
2.5. Back Trajectory Analysis and Meteorological Parameters
2.6. Computation of Secondary Organic Carbon (SOC)
3. Results and Discussion
3.1. Statistical Descriptions of PM2.5 Bounded Chemical Species
3.2. Spatial and Temporal Distribution of OC/EC Ratios and SOC
3.3. Pearson Correlation Coefficients (PCCs) and PCA
3.4. Hierarchical Cluster Analysis (HCA) and ANNs of Chemical Species in PM2.5
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Symbol | Description |
---|---|
αa,532 | The absorption coefficient at 532 nm |
ACE1 | The first aerosol characterisation experiment |
ANNs | Artificial Neural Networks |
ANOVA | Analysis of Variance |
AQM | Air Quality Model |
BOS | Bangkok Air Quality Observatory Site |
Ca2+ | Calcium ion |
CCNs | Cloud condensation nuclei, also known as cloud seeds, are small particles typically 0.2 µm, or 1/100 the size of a cloud droplet on which water vapor condenses. |
CCSA | Thailand’s Centre for COVID-19 Situation Administration |
Cl− | Chloride ion |
COS | Chiang-Mai Air Quality Observatory Site |
EC | Elemental Carbon |
ERI | Emission Reduction Impacts |
FNN | Feedforward Neural Network |
GFS | the Global Forecast System |
GLDAS | The goal of the Global Land Data Assimilation System |
HCA | Hierarchical Cluster Analysis |
HS-100 | Hotspots within radii of 100 km |
HS-200 | Hotspots within radii of 200 km |
HS-300 | Hotspots within radii of 300 km |
HYSPLIT | The Hybrid Single-Particle Lagrangian Integrated Trajectory |
INC | Incremental methods |
K+ | Potassium ion |
Mg2+ | Magnesium ion |
MLP | Multilayer Perceptron |
MLRA | Multiple Linear Regression Analysis |
MMLPL | the Mae Moh Lignite Power Plant |
MT | Mass-Transfer methods |
NARIT | National Astronomical Research Institute of Thailand |
NH4+ | Ammonium ion |
NO2 | Nitrogen dioxide |
NO3− | Nitrate ion |
NOAA-20 | NOAA-20, designated JPSS-1 prior to launch, is the first of the United States National Oceanic and Atmospheric Administration’s latest generation of U.S. polar-orbiting, non-geosynchronous, environmental satellites called the Joint Polar Satellite System. NOAA-20 was launched on 18 November 2017 and joined the Suomi National Polar-orbiting Partnership satellite in the same orbit. |
O3 | Ozone |
OC | Organic Carbon |
PCA | Principal Component Analysis |
PM2.5 | PM2.5 describes fine inhalable particles, with diameters that are generally 2.5 μm and smaller |
PMF | Positive Matrix Factorisation |
POS | Phuket Air Quality Observatory Site |
PSU | Prince of Songkla University |
PTEF | Polytetrafluoroethylene |
RH | Relative humidity |
RMs | Receptor-Oriented Models |
SSEs | Some squared errors |
SSRU | Suan Sunandha Rajabhat University |
SO2 | Sulphur dioxide |
SO42− | Sulphate ion |
SOC | Secondary Organic Carbon |
T | Ambient temperature |
TC | Total Carbon |
TCA | Total Carbonaceous Aerosol |
TPW | Total Precipitable Water |
US-EPA | United States Environmental Protection Agency |
WD | Wind direction |
uWind | u-component wind |
vWind | v-component wind |
WSIS | Water-Soluble Ionic Species |
Input Layer | Covariates | 1 | HS-100 |
---|---|---|---|
2 | HS-200 | ||
3 | HS-300 | ||
4 | Albedo | ||
5 | Temp | ||
6 | RH | ||
7 | u-Wind | ||
8 | v-Wind | ||
9 | TWP | ||
Number of Units a | 9 | ||
Rescaling Method for Covariates | Standardized | ||
Hidden Layer(s) | Number of Hidden Layers | 2 | |
Number of Hidden Layer 1 a | 7 | ||
Number of Hidden Layer 2 a | 5 | ||
Activation Function | Sigmoid | ||
Output Layer | Dependent Variables 1 | Chemical Species | |
Number of Units | 1 | ||
Rescaling Method for Scale Dependents | Normalized | ||
Activation Function | Sigmoid | ||
Error Function | Sum of Squares |
Model Summary | Ca2+ | Mg2+ | Cl− | K+ | Na+ | NH4+ | NO3− | NO2− | SO42− | TC | OC | EC | OC/EC | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Training | Sum of Squares Error | 0.728 | 0.415 | 0.507 | 0.83 | 0.342 | 0.996 | 0.613 | 0.704 | 0.564 | 0.949 | 0.854 | 0.749 | 0.513 |
Relative Error | 0.437 | 0.636 | 0.899 | 0.312 | 0.354 | 0.548 | 0.295 | 0.913 | 0.272 | 0.256 | 0.221 | 0.249 | 0.776 | |
Stopping Rule Used | 1 consecutive step(s) with no decrease in error a | |||||||||||||
Training Time | 0:00:00.02 | |||||||||||||
Testing | Sum of Squares Error | 0.265 | 0.152 | 0.038 | 0.182 | 0.174 | 0.31 | 1.31 | 0.608 | 0.352 | 0.852 | 0.87 | 0.369 | 0.102 |
Relative Error | 0.324 | 0.661 | 0.894 | 0.228 | 0.489 | 0.374 | 0.595 | 0.954 | 0.335 | 0.405 | 0.402 | 0.472 | 0.685 |
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COS (n = 82) | BOS (n = 48) | POS (n = 61) | BOS/COS | BOS/POS | ANOVA Test | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Aver | Stdev | Min | Max | Aver | Stdev | Min | Max | Aver | Stdev | Min | Max | (p < 0.001) | |||
TC | 57 | 42 | 5.4 | 208 | 97 | 52 | 21 | 234 | 13 | 7.6 | 5.1 | 49 | 1.7 | 7.2 | S |
OC | 43 | 31 | 4.9 | 154 | 71 | 39 | 18 | 164 | 11 | 6.0 | 5.1 | 41 | 1.6 | 6.2 | S |
OM | 69 | 50 | 8.0 | 246 | 113 | 62 | 28 | 262 | 18 | 10 | 8.1 | 66 | 1.6 | 6.2 | S |
EC | 14 | 12 | 0.45 | 55 | 26 | 15 | 3.3 | 71 | 2.0 | 1.8 | 0.064 | 8.3 | 1.9 | 13 | S |
TCA | 83 | 51 | 8.4 | 301 | 140 | 64 | 32 | 332 | 20 | 10 | 8.2 | 74 | 1.7 | 6.9 | S |
Cl− | 0.076 | 0.084 | 0.023 | 0.56 | 0.19 | 0.32 | 0.058 | 2.22 | 0.078 | 0.056 | 0.028 | 0.26 | 2.5 | 2.4 | S |
NO2− | 0.032 | 0.016 | N.D. | 0.10 | 0.048 | 0.037 | 0.014 | 0.19 | 0.030 | 0.022 | 0.011 | 0.12 | 1.5 | 1.6 | S |
NO3− | 0.22 | 0.15 | 0.068 | 0.71 | 1.1 | 0.68 | 0.25 | 4.5 | 0.10 | 0.050 | 0.059 | 0.29 | 5.0 | 11 | S |
SO42− | 0.92 | 1.0 | 0.075 | 5.6 | 2.2 | 1.1 | 0.36 | 6.4 | 0.25 | 0.25 | 0.090 | 1.7 | 2.4 | 9.0 | S |
Na+ | 0.49 | 0.18 | 0.17 | 1.1 | 0.85 | 0.25 | 0.49 | 2.3 | 0.37 | 0.055 | 0.29 | 0.53 | 1.7 | 2.3 | S |
NH4+ | 0.18 | 0.27 | 0.023 | 1.5 | 0.49 | 0.43 | 0.0069 | 1.9 | 0.020 | 0.041 | N.D. | 0.31 | 2.7 | 24 | S |
K+ | 0.14 | 0.15 | N.D. | 0.59 | 0.36 | 0.23 | 0.012 | 1.0 | 0.015 | 0.028 | N.D. | 0.17 | 2.5 | 23 | S |
Mg2+ | 0.084 | 0.045 | 0.021 | 0.35 | 0.078 | 0.023 | 0.042 | 0.16 | 0.042 | 0.016 | N.D. | 0.071 | 0.90 | 1.8 | S |
Ca2+ | 0.40 | 0.21 | N.D. | 1.2 | 0.43 | 0.11 | 0.21 | 0.71 | 0.13 | 0.057 | N.D. | 0.23 | 1.1 | 3.4 | S |
OC/EC | The Three Lowest OC/EC | SOC (μg m−3) | SOC (%) | |||||
---|---|---|---|---|---|---|---|---|
Aver | Stdev | Aver | Aver | Aver | Stdev | Aver | Stdev | |
COS | 3.49 | 1.17 | 2.24 | 0.142 | 12.4 | 8.92 | 31.4 15.2 | |
BOS | 2.86 | 0.832 | 1.76 | 0.127 | 25.0 | 18.2 | 34.3 16.1 | |
POS | 10.1 | 11.3 | 3.10 | 0.150 | 5.35 | 2.38 | 53.2 23.9 |
TC | OC | EC | Cl− | NO2− | NO3− | SO42− | Na+ | NH4+ | K+ | Mg2+ | Ca2+ | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Non-Haze * | Aver | 21 | 17 | 4.6 | 0.090 | 0.030 | 0.15 | 0.36 | 0.42 | 0.037 | 0.036 | 0.055 | 0.21 |
Stdev | 16 | 12 | 5.0 | 0.088 | 0.020 | 0.20 | 0.43 | 0.16 | 0.072 | 0.061 | 0.027 | 0.15 | |
Count | 103 | 103 | 103 | 103 | 103 | 103 | 103 | 103 | 103 | 103 | 103 | 103 | |
Haze ** | Aver | 91 | 68 | 23 | 0.12 | 0.04 | 0.69 | 1.8 | 0.69 | 0.41 | 0.30 | 0.086 | 0.45 |
Stdev | 49 | 36 | 14 | 0.24 | 0.03 | 0.65 | 1.2 | 0.27 | 0.40 | 0.21 | 0.041 | 0.19 | |
Count | 88 | 88 | 88 | 88 | 88 | 88 | 88 | 88 | 88 | 88 | 88 | 88 | |
Haze/Non-Haze | 4.3 | 4.0 | 5.1 | 1.4 | 1.4 | 4.5 | 5.0 | 1.6 | 11 | 8.2 | 1.6 | 2.1 | |
T-test (p < 0.05) | S | S | S | NS | S | S | S | S | S | S | S | S |
HS-100 | HS-200 | HS-300 | Albedo | Temp | RH | u-Wind | v-Wind | TWP | TC | OC | EC | Cl− | NO2− | NO3− | SO42− | Na+ | NH4+ | K+ | Mg2+ | Ca2+ | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
HS-100 | 1.00 | ||||||||||||||||||||
HS-200 | 0.95 | 1.00 | |||||||||||||||||||
HS-300 | 0.88 | 0.98 | 1.00 | ||||||||||||||||||
Albedo | −0.29 | −0.33 | −0.36 | 1.00 | |||||||||||||||||
Temp | −0.09 | −0.06 | −0.06 | 0.36 | 1.00 | ||||||||||||||||
RH | −0.56 | −0.63 | −0.65 | 0.39 | −0.30 | 1.00 | |||||||||||||||
u-Wind | 0.03 | 0.01 | −0.01 | 0.26 | 0.13 | 0.10 | 1.00 | ||||||||||||||
v-Wind | 0.06 | 0.12 | 0.18 | −0.36 | 0.34 | −0.25 | 0.17 | 1.00 | |||||||||||||
TWP | −0.47 | −0.54 | −0.57 | 0.63 | 0.50 | 0.57 | 0.28 | −0.03 | 1.00 | ||||||||||||
TC | 0.39 | 0.47 | 0.50 | −0.51 | −0.02 | −0.73 | −0.20 | 0.20 | −0.60 | 1.00 | |||||||||||
OC | 0.40 | 0.47 | 0.51 | −0.51 | −0.04 | −0.73 | −0.20 | 0.19 | −0.61 | 1.00 | 1.00 | ||||||||||
EC | 0.37 | 0.45 | 0.48 | −0.51 | 0.02 | −0.72 | −0.19 | 0.23 | −0.55 | 0.98 | 0.96 | 1.00 | |||||||||
Cl− | −0.04 | −0.03 | −0.02 | −0.02 | 0.20 | −0.07 | −0.03 | 0.27 | 0.05 | 0.16 | 0.19 | 0.09 | 1.00 | ||||||||
NO2− | 0.12 | 0.18 | 0.19 | −0.05 | 0.10 | −0.30 | −0.08 | 0.10 | −0.15 | 0.31 | 0.31 | 0.30 | 0.17 | 1.00 | |||||||
NO3− | 0.05 | 0.13 | 0.17 | −0.28 | 0.29 | −0.53 | −0.20 | 0.28 | −0.19 | 0.75 | 0.73 | 0.77 | 0.23 | 0.36 | 1.00 | ||||||
SO42− | 0.26 | 0.40 | 0.46 | −0.40 | 0.26 | −0.72 | −0.15 | 0.37 | −0.41 | 0.80 | 0.78 | 0.82 | 0.10 | 0.33 | 0.72 | 1.00 | |||||
Na+ | 0.05 | 0.12 | 0.17 | −0.34 | 0.38 | −0.52 | −0.15 | 0.48 | −0.15 | 0.60 | 0.60 | 0.58 | 0.64 | 0.32 | 0.66 | 0.67 | 1.00 | ||||
NH4+ | 0.27 | 0.40 | 0.46 | −0.34 | 0.17 | −0.66 | −0.14 | 0.25 | −0.41 | 0.82 | 0.80 | 0.85 | 0.04 | 0.34 | 0.77 | 0.94 | 0.55 | 1.00 | |||
K+ | 0.29 | 0.39 | 0.43 | −0.43 | 0.11 | −0.71 | −0.20 | 0.25 | −0.48 | 0.95 | 0.95 | 0.94 | 0.31 | 0.36 | 0.82 | 0.85 | 0.72 | 0.86 | 1.00 | ||
Mg2+ | 0.10 | 0.14 | 0.16 | −0.37 | −0.08 | −0.23 | −0.07 | 0.19 | −0.24 | 0.40 | 0.40 | 0.37 | 0.31 | 0.10 | 0.27 | 0.44 | 0.50 | 0.34 | 0.41 | 1.00 | |
Ca2+ | 0.17 | 0.25 | 0.30 | −0.55 | −0.05 | −0.44 | −0.13 | 0.33 | −0.40 | 0.61 | 0.61 | 0.60 | 0.20 | 0.18 | 0.44 | 0.66 | 0.55 | 0.55 | 0.59 | 0.82 | 1.00 |
Principal Component (PC) | |||||
---|---|---|---|---|---|
PC1 | PC2 | PC3 | PC4 | PC5 | |
HS−100 | 0.105 | 0.947 | 0.040 | 0.007 | −0.001 |
HS−200 | 0.209 | 0.959 | 0.072 | 0.008 | 0.023 |
HS−300 | 0.260 | 0.927 | 0.109 | 0.009 | 0.033 |
Albedo | −0.281 | −0.261 | −0.701 | 0.088 | 0.209 |
Temp | 0.274 | −0.125 | −0.383 | 0.309 | 0.697 |
RH | −0.673 | −0.549 | −0.072 | −0.105 | −0.109 |
u−Wind | −0.216 | 0.089 | −0.032 | −0.194 | 0.645 |
v−Wind | 0.184 | 0.072 | 0.398 | 0.190 | 0.622 |
TWP | −0.325 | −0.530 | −0.408 | 0.118 | 0.480 |
TC | 0.858 | 0.295 | 0.282 | 0.088 | −0.153 |
OC | 0.836 | 0.306 | 0.289 | 0.112 | −0.172 |
EC | 0.888 | 0.261 | 0.258 | 0.025 | −0.102 |
Cl− | 0.002 | −0.049 | 0.217 | 0.886 | 0.081 |
NO2− | 0.355 | 0.152 | −0.185 | 0.461 | −0.138 |
NO3− | 0.877 | −0.083 | 0.059 | 0.219 | 0.048 |
SO42− | 0.866 | 0.187 | 0.260 | 0.069 | 0.168 |
Na+ | 0.578 | −0.028 | 0.340 | 0.631 | 0.210 |
NH4+ | 0.907 | 0.186 | 0.139 | −0.013 | 0.062 |
K+ | 0.888 | 0.191 | 0.237 | 0.240 | −0.071 |
Mg2+ | 0.214 | −0.002 | 0.762 | 0.247 | 0.049 |
Ca2+ | 0.462 | 0.077 | 0.756 | 0.115 | 0.056 |
%Total of Variance | 45.5 | 14.1 | 9.10 | 7.34 | 5.04 |
Case Processing Summary | TC | OC | EC | OC/EC | Cl− | NO2− | NO3− | SO42− | Na+ | NH4+ | K+ | Mg2+ | Ca2+ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
% | % | % | % | % | % | % | % | % | % | % | % | % | |
ANNs-Training | 68.1 | 67.5 | 73.8 | 73.8 | 71.7 | 63.9 | 66.5 | 71.2 | 73.8 | 72.8 | 70.7 | 69.6 | 65.4 |
Measured Data-Testing | 31.9 | 32.5 | 26.2 | 26.2 | 28.3 | 36.1 | 33.5 | 28.8 | 26.2 | 27.2 | 29.3 | 30.4 | 34.6 |
Valid | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Model Summary | |||||||||||||
Training-Sum of Squares Error | 1.17 | 0.898 | 0.749 | 0.686 | 0.507 | 0.704 | 0.613 | 0.564 | 0.530 | 0.996 | 0.830 | 0.515 | 0.728 |
Training—Relative Error | 0.362 | 0.281 | 0.249 | 0.928 | 0.899 | 0.913 | 0.295 | 0.272 | 0.477 | 0.548 | 0.312 | 0.510 | 0.437 |
Testing—Sum of Squares Error | 0.372 | 0.335 | 0.369 | 0.052 | 0.038 | 0.608 | 1.31 | 0.352 | 0.092 | 0.310 | 0.182 | 0.704 | 0.265 |
Testing-Relative Error | 0.290 | 0.197 | 0.472 | 0.791 | 0.894 | 0.954 | 0.595 | 0.335 | 0.355 | 0.374 | 0.228 | 0.731 | 0.324 |
Independent Variable Importance | % | % | % | % | % | % | % | % | % | % | % | % | % |
HS-100 | 69.5 | 22.7 | 25.3 | 24.8 | 20.5 | 67.7 | 25.9 | 24.0 | 41.7 | 45.4 | 31.4 | 39.1 | 35.5 |
HS-200 | 23.5 | 66.1 | 67.9 | 33.9 | 14.4 | 49.8 | 21.6 | 56.6 | 22.0 | 28.8 | 42.9 | 34.6 | 13.9 |
HS-300 | 45.5 | 47.6 | 27.1 | 46.5 | 27.2 | 40.3 | 13.7 | 32.5 | 22.3 | 40.3 | 74.6 | 48.0 | 14.1 |
Albedo | 46.4 | 59.5 | 44.1 | 100 | 4.70 | 13.5 | 10.9 | 37.7 | 69.0 | 32.5 | 32.1 | 100 | 100 |
Temp | 10.4 | 11.3 | 24.2 | 51.0 | 38.5 | 41.4 | 24.7 | 41.7 | 78.8 | 35.7 | 16.1 | 56.5 | 37.4 |
RH | 100 | 100 | 100 | 38.9 | 38.9 | 100 | 100 | 100 | 100 | 100 | 100 | 67.7 | 58.3 |
u-Wind | 22.1 | 56.9 | 43.6 | 18.8 | 12.5 | 23.5 | 40.5 | 6.10 | 60.4 | 22.9 | 50.9 | 30.5 | 18.6 |
v-Wind | 4.70 | 26.8 | 30.8 | 84.9 | 100 | 16.7 | 8.10 | 14.9 | 92.1 | 10.2 | 13.0 | 23.6 | 38.0 |
TWP | 32.1 | 7.70 | 12.1 | 53.4 | 8.00 | 9.10 | 6.10 | 6.60 | 6.10 | 34.9 | 10.3 | 65.1 | 27.0 |
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Pongpiachan, S.; Wang, Q.; Apiratikul, R.; Tipmanee, D.; Li, Y.; Xing, L.; Li, G.; Han, Y.; Cao, J.; Macatangay, R.C.; et al. An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species. Atmosphere 2022, 13, 1042. https://doi.org/10.3390/atmos13071042
Pongpiachan S, Wang Q, Apiratikul R, Tipmanee D, Li Y, Xing L, Li G, Han Y, Cao J, Macatangay RC, et al. An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species. Atmosphere. 2022; 13(7):1042. https://doi.org/10.3390/atmos13071042
Chicago/Turabian StylePongpiachan, Siwatt, Qiyuan Wang, Ronbanchob Apiratikul, Danai Tipmanee, Yu Li, Li Xing, Guohui Li, Yongming Han, Junji Cao, Ronald C. Macatangay, and et al. 2022. "An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species" Atmosphere 13, no. 7: 1042. https://doi.org/10.3390/atmos13071042
APA StylePongpiachan, S., Wang, Q., Apiratikul, R., Tipmanee, D., Li, Y., Xing, L., Li, G., Han, Y., Cao, J., Macatangay, R. C., Poshyachinda, S., Aekakkararungroj, A., & Hashmi, M. Z. (2022). An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species. Atmosphere, 13(7), 1042. https://doi.org/10.3390/atmos13071042