空氣質(zhì)量問(wèn)題一直是交通系統(tǒng)、工業(yè)生產(chǎn)、民用建筑等各個(gè)工程領(lǐng)域的科學(xué)家和工程師們關(guān)注的焦點(diǎn)?？諝赓|(zhì)量監(jiān)測(cè)是大氣污染控制和預(yù)警的基礎(chǔ)。《Data Science in Air Quality Monitoring（空氣質(zhì)量監(jiān)測(cè)與數(shù)據(jù)科學(xué)）》從數(shù)據(jù)科學(xué)的角度介紹了各種工程環(huán)境中空氣質(zhì)量監(jiān)測(cè)的一系列*新方法。通過(guò)大量的實(shí)驗(yàn)?zāi)M，詳細(xì)闡述了空氣質(zhì)量監(jiān)測(cè)的預(yù)處理、分解、識(shí)別、聚類、預(yù)測(cè)和插值等數(shù)據(jù)驅(qū)動(dòng)的關(guān)鍵技術(shù)?！禗ata Science in Air Quality Monitoring（空氣質(zhì)量監(jiān)測(cè)與數(shù)據(jù)科學(xué)）》可為工程空氣質(zhì)量監(jiān)測(cè)數(shù)據(jù)科學(xué)技術(shù)的發(fā)展提供重要參考?！禗ata Science in Air Quality Monitoring（空氣質(zhì)量監(jiān)測(cè)與數(shù)據(jù)科學(xué)）》可供環(huán)境、大氣、城市氣候、民用建筑、交通和車輛等領(lǐng)域的學(xué)生、工程師、科學(xué)家和管理人員使用。
Contents 1 Introduction 1 1.1 Overview of Data Science in Air Quality Monitoring 2 1.1.1 Importance of Air Quality Monitoring 2 1.1.2 The Role of Data Science in Environmental Monitoring 6 1.1.3 Characteristics and Challenges of Air Quality Data 10 1.1.4 Current Application of Data Science and Technology in Air Quality Monitoring 13 1.2 Key Problems Data Science in Air Quality Monitoring 16 1.2.1 Data Processing 16 1.2.2 Data Decomposition 21 1.2.3 Data Identification 25 1.2.4 Data Clustering 27 1.2.5 Data Forecasting 33 1.2.6 Data Interpolation 36 1.3 Scope of the Book 42 References 44 2 Data Preprocessing in Air Quality Monitoring 49 2.1 Introduction 49 2.2 Data Acquisition 51 2.3 Characteristic Analysis of Air Quality Data 52 2.3.1 Temporal Characteristics 52 2.3.2 Spatial Characteristics 55 2.4 Missing Data Imputation of Air Quality Data 56 2.4.1 Missing Data Imputation Performance Evaluation 58 2.4.2 Univariate Missing Data Imputation Based on K-Nearest Neighbors 60 2.4.3 Multivariate Missing Data Imputation Based on Self-Organizing Map 61 2.5 Outlier Detection of Air Quality Data 65 2.5.1 Outlier Detection Performance Evaluation 66 2.5.2 Outlier Detection Based on Unsupervised Isolation Forest 67 2.5.3 Outlier Detection Based on Hampel Filter 70 2.5.4 Outlier Detection Based on Deep Learning Forecasting 75 2.6 Preprocessing Performance Comparison 78 2.6.1 Performance Comparison of Missing Data Imputation 78 2.6.2 Performance Comparison of Outlier Detection 81 2.7 Conclusions 82 References 83 3 Data Decomposition in Air Quality Monitoring 85 3.1 Introduction 85 3.1.1 Application of Wavelet Decomposition in Air Quality Data Analysis 86 3.1.2 Application of Modal Decomposition in Air Quality Data Analysis 86 3.1.3 Deficiencies and Challenges of Existing Research 87 3.1.4 Temporal Resolution 87 3.1.5 Frequency Resolution 87 3.1.6 Boundary Effect 88 3.1.7 Noise Reduction Effect 88 3.2 Wavelet Decomposition of Air Quality Data 90 3.2.1 Time-Frequency Localization Characteristics 90 3.2.2 Multi-resolution Analysis 90 3.2.3 Strong Sparse Representation Capability 91 3.2.4 Discrete Wavelet Transform 92 3.3 Top Layer: Approximation Coefficients 97 3.4 Detail Coefficients 97 3.4.1 Reconstruction Error 98 3.4.2 Signal-to-Noise Ratio (SNR) 98 3.4.3 Correlation Coefficient 99 3.4.4 Various Wavelet Basis Functions 100 3.4.5 Continuous Wavelet Transform 104 3.5 Mode Decomposition of Air Quality Data 106 3.5.1 Empirical Mode Decomposition 106 3.5.2 Variations and Improvements of the Traditional EMD Method 110 3.6 Decomposition Performance Comparison 113 3.6.1 Decomposition Accuracy 113 3.6.2 Computational Complexity 114 3.6.3 Boundary Effect 115 3.7 Conclusions 116 References 116 4 Data Identification in Air Quality Monitoring 119 4.1 Introduction 119 4.1.1 The Importance of Data Identification in Air Quality Monitoring 120 4.1.2 Methods for Data Identification in Air Quality Monitoring 121 4.2 Data Acquisition 122 4.3 Feature Selection of Air Quality Data 123 4.3.1 Feature Selection Performance Evaluation 123 4.3.2 Filter Methods 125 4.3.3 Wrapper Methods 126 4.4 Forward Selection 128 4.5 Backward Elimination 128 4.6 Recursive Feature Elimination (RFE) 129 4.6.1 Modeling Step 129 4.6.2 Embedded Methods 131 4.7 Feature Extraction of Air Quality Data 131 4.7.1 Feature Extraction Performance Evaluation 131 4.7.2 Statistical Feature Extraction 132 4.7.3 Time-Frequency Analysis 134 4.8 Identification Performance Comparison 137 4.8.1 Performance Comparison of Feature Selection 137 4.8.2 Performance Comparison of Feature Extraction 140 4.9 Conclusions 143 References 144 5 Data Preprocessing in Air Quality Monitoring 147 5.1 Introduction 147 5.2 Data Acquisition 148 5.3 Temporal Clustering of Air Quality Data 151 5.3.1 Definition and Role of Temporal Clustering 151 5.3.2 DBSCAN Temporal Clustering 152 5.3.3 AE-DBSCAN Temporal Clustering 154 5.3.4 CAE-DBSCAN Temporal Clustering 157 5.4 Spatial Clustering of Air Quality Data 159 5.4.1 K-Means Clustering 159 5.4.2 GMM 160 5.4.3 GAE -Kmeans 162 5.4.4 Modeling Step 164 5.5 Clustering Performance Comparison 165 5.5.1 Evaluation with Silhouette Score 165 5.5.2 Evaluation with Base Model 166 5.5.3 Comparison of Spatial Clustering 168 5.6 Conclusions 171 References 171 6 Data Forecasting in Air Quality Monitoring 173 6.1 Introduction 173 6.2 Data Acquisition 176 6.3 Deterministic Forecasting of Air Quality Data 178 6.3.1 Extreme Learning Machine 178 6.3.2 Gated Recurrent Unit 180 6.3.3 Bidirectional Long Short-term Memory 182 6.3.4 Deep Extreme Learning Machine 184 6.3.5 Transformer 185 6.4 Probabilistic Forecasting of Air Quality Da
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