기계학습과 수용모델을 이용한 초미세먼지 오염원 및 기여도의 시공간 분포 분석

학위논문(박사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2023. 2. 김재영.직경 2.5 µm 이하의 입자상 물질인 초미세먼지는 대기중에 존재하며, 건강에 미치는 악영향으로 인해 수십 년 동안 세계적으로 관심의 대상이 되고 있는 대기오염물질이다. 초미세먼지를 효과적으로 관리하기 위해서는 다양한 시간과 공간에 대해 초미세먼지의 오염원 유형을 파악하고, 각 유형별 기여도를 정량화하는 것이 중요하다. 따라서, 초미세먼지의 오염원 추정은 핵심 과제로 다뤄져 왔으며, 통계학적 방법론을 적용해 오염원을 추정하는 수용모델이 많이 활용되고 있다. 본 연구에서는 초미세먼지의 세부 특성을 파악하기 위해 오염원 추정과 추정된 오염원의 시공간 분석을 수행하였으며, 이를 통해 효과적인 초미세먼지 관리 방안 마련에 중요한 정보를 제공하는 것을 목적으로 하였다. 오염원 유형 추정 연구를 위해, 두 가지 모델링이 수행되었다. 첫번째는 양행렬 인자 분석(Positive matrix factorization, PMF) 모델링으로, 이는 한 장소에서 초미세먼지의 오염원 유형을 구체적으로 추정하기 위해 활용되었다. 두번째는 베이지안 다변량 수용 모델링(Bayesian spatial multivariate receptor modelingm, BSMRM)으로, 이는 다수의 측정 지점으로부터 넓은 범위의 면적에 대해 주요 오염원 유형을 추정하기 위해 활용되었다. 또한, 기계학습 모델들을 활용하여 초미세먼지 오염원 유형 추정에 가장 중요한 자료로 활용되는 초미세먼지 화학성분 농도를 예측하였다. 기계학습 모델을 초미세먼지 화학성분 자료에 대해 활용가능한지를 검토하였고, 이를 통해 초미세먼지 화학성분 자료의 무결성을 향상시키고자 하였다. PMF 모델링을 통해, 대한민국 시흥시의 초미세먼지 오염원 유형 10가지를 도출하였다. 이는 각각 2차 생성 질산염(24.3%), 2차 생성 황산염(18.8%), 이동 오염원(18.8%), 난방연소(12.6%), 생물체 연소(11.8%), 석탄 연소(3.6%), 중유 관련 산업 오염원(1.8%), 제련 관련 산업 오염원(4.0%), 해염 입자(2.7%), 토양(1.7%)였다. 도출된 오염원 유형별로, 초미세먼지 호흡에 따른 건강 영향을 평가하였다. 석탄 연소, 중유 관련 산업 오염원, 이동 오염원의 초미세먼지 기여도는 낮았지만, 이로 인한 발암 위해도는 10E-6 이상으로 나타났다. 따라서, 초미세먼지의 질량농도 감축 중심의 대응만이 아닌, 오염원별 건강영향 중심의 대응이 요구된다. 기계학습 모델의 초미세먼지 화학성분 예측 능력을 평가하기 위해 4가지 기계학습 모델에 대해 입력 자료 수준, 예측 대상 성분, 입력 자료 기간, 입력 자료의 결측 비율, 자료 대상 지역을 변화하며 예측 정확도를 비교 평가하였다. GAIN(Generative Adversarial Imputation Network), FCDNN(Fully Connected Deep Neural Network), Random forest(RF), kNN(k-nearest neighboring) 모델의 4가지 기계학습 모델을 한국의 3개 지역(서울, 울산, 백령)의 2016년부터 2018년까지의 초미세먼지 화학 성분 자료에 대해 적용하여 농도를 예측하였다. 예측값과 관측값 사이의 결정계수를 통해 정확도를 비교한 결과, 예측 정확도는 GAIN이 가장 높았고, FCDNN, RF 또는 kNN 순서로 나타났다. 입력 자료의 결측률이 20%에서 80%까지 증가함에 따라 예측 정확도는 모든 모델에서 감소하였으나, 비지도 기계학습 모델인 GAIN과 kNN에서 감소 폭이 더 크게 나타났다. 입력 자료의 기간이 길어질수록, 딥러닝 모델인 GAIN과 FCDNN이 다른 두 모델인 RF와 kNN보다 예측 정확도 증가 폭이 더 컸다. 예측 대상 지역별로는, 자체 배출원이 많은 울산의 경우가 예측 정확도가 가장 낮게 나타났고, 자체 배출원의 영향이 거의 없는 백령도의 경우 예측 정확도가 가장 높게 나타났다. 대상 성분별로는 이온 성분이 예측 정확도가 높게 나타났고, 미량원소 성분은 예측 정확도가 낮았다. 본 연구는 기계학습 모델의 예측 정확도를 다양한 실험 조건에 따라 평가하여 대기오염 분야에서의 기계학습 모델의 적용 가능성을 평가했다. 베이지안 다변량 수용 모델링(BSMRM)을 통해서는 8개의 관측 지점 자료를 통해 우리나라의 주요 초미세먼지 오염원 5가지를 도출하고, 각각 오염원 유형별 기여도를 우리나라 전체에 대한 공간 분포를 추정하였다. 5가지 오염원은 각각 2차 질산염, 2차 황산염, 자동차 배출, 산업 오염원, 해염 입자였다. 각 오염원 유형별 일평균 기여도 농도를 지도에 공간적으로 표현할 수 있었다. 또한, BSMRM을 통해 예측한 오염원 유형별 기여도의 타당성 검토를 위해 테스트 사이트(안산, 대전, 광주)의 자료는 각각 제외된 모델링을 수행하여 결과를 서로 비교하여 모델의 정확도를 확인하였다. 이처럼 공간적으로 추정된 오염원 유형 기여도는 초미세먼지 화학성분을 측정하지 않는 도시에서 초미세먼지 대응 방안을 수립하는데 큰 도움이 될 수 있다. 즉, 8개의 측정 자료만으로 우리나라 전체에 대해 예측한 결과를 통해, 측정 지점이 없는 모든 도시에 대해 추정이 가능하였으며, 이 결과는 건강 영향 평가와 같은 추가 연구에도 활용될 수 있다.Particulate matter less than 2.5 micrometers (PM2.5) has been a pollutant of interest globally for more than decades, owing to its adverse health effects. For developing effective PM2.5 management strategies, it is crucial to identify their sources and quantify how much they contribute to ambient PM2.5 concentrations in time and space. Source apportionment is the key to identifying the characteristics of PM2.5. Receptor modeling is widely used to identify PM2.5 sources as a statistical method of source apportionment. The chemical constituents of PM2.5 were used as input data for receptor modeling. Therefore, this study aimed to investigate the characteristics of PM2.5 using models of source apportionment and spatiotemporal analysis for effective management strategies. Two types of modeling were performed for the source apportionment study. The first is positive matrix factorization modeling, which identifies a specific source type and its contributions to PM2.5 from one site. The second is Bayesian spatial multivariate receptor modeling, which derives major sources and their contributions to PM2.5 from multiple monitoring sites. In addition, machine learning models were used to predict the concentrations of PM2.5, which are important data for receptor modeling. Machine learning models that can be used to increase data integrity and applicability to PM2.5 data were assessed. The sources of PM2.5 and their contributions in Siheung, South Korea, were identified using positive matrix factorization modeling. These 10 sources were secondary nitrate (24.3%), secondary sulfate (18.8%), traffic (18.8%), combustion for heating (12.6%), biomass burning (11.8%), coal combustion (3.6%), heavy oil industry (1.8%), smelting industry (4.0%), sea salt (2.7%), and soil (1.7%). Based on the derived sources, the carcinogenic and non-carcinogenic health risks due to PM2.5 inhalation were estimated. The contribution to PM2.5 mass concentration was low for coal combustion, heavy oil industry, and traffic sources but exceeded the benchmark carcinogenic health risk value (1E-06). Therefore, countermeasures on PM2.5 emission sources should be performed based on the PM2.5 mass concentration and health risks. The feature extraction capabilities of the four machine learning models to predict the chemical constituents of PM2.5 were assessed by comparing the prediction accuracy depending on input variables, target constituents for prediction, available period, missing ratios of input data, and study sites. The concentrations of PM2.5 constituents were predicted at three sites (Seoul, Ulsan, and Baengnyeong) in South Korea between 2016 and 2018, using four machine learning models: generative adversarial imputation network (GAIN), fully connected deep neural network (FCDNN), random forest (RF), and k-nearest neighbor (kNN). The prediction accuracy identified by the coefficient of determination (R2) between the prediction and observation was highest in GAIN, followed by FCDNN, RF, and kNN. As the missing ratios (20, 40, 60, and 80%) of the input data increased, the prediction accuracy decreased in the four models and was more noticeable in GAIN and kNN, which are unsupervised models. As the input data period increased, the two deep learning models, GAIN and DNN, had better applicability than the other models, RF and kNN. The study sites with more emission sources exhibited lower prediction accuracy, resulting in the highest R2 in the BR island and the lowest in Ulsan. Among the target constituent groups, ions and trace elements were predicted to have the highest and lowest R2, respectively. This study demonstrated that machine learning models can be extended for further air pollution studies depending on model features, required performance, and experimental conditions, such as data availability and time constraints. The spatial distributions of five PM2.5 sources in South Korea were estimated using Bayesian spatial multivariate receptor modeling. Secondary nitrate, secondary sulfate, motor vehicle emissions, industry, and sea salts were determined to be significant contributors to ambient PM2.5 concentrations in South Korea. The spatial surface of the daily average contribution for each source in South Korea was derived from measurement data from the eight monitoring sites. The source contributions predicted by the BSMRM were also validated using held-out data from a test site (such as Ansan, Daejeon, and Gwangju). These predicted source contributions can aid in developing effective PM2.5 control strategies in cities where no speciated PM2.5 monitoring stations are available. They can also be utilized as source-specific exposures in health effect studies, even in cities where no monitoring stations are available.CHAPTER 1. INTRODUCTION 1 1.1. Background 1 1.2. Objectives 4 1.3. Dissertation structure 5 References 7 CHAPTER 2. LITERATURE REVIEW 10 2.1. Source apportionment and receptor modeling of PM2.5 10 2.2. Toxicity and health risk of assessment PM2.5 21 2.3. Machine learning approaches in prediction of PM2.5 31 2.4. Bayesian approach in source apportionment 41 References 54 CHAPTER 3. SOURCE APPORTIONMENT OF PM2.5 USING PMF MODEL AND HEALTH RISK ASSESSMENT BY INHALATION 69 3.1. Introduction 69 3.2. Materials and methods 72 3.2.1 Study site, sampling, and analysis 72 3.2.2 Positive matrix factorization (PMF) modeling and combined analysis with meteorological data 76 3.2.3 Health risk assessment 80 3.3. Results and discussion 85 3.3.1 PM2.5 mass concentration and chemical speciation 85 3.3.2 Source apportionment of PM2.5 by PMF modeling 89 3.3.3 Carcinogenic and non-carcinogenic health risks 94 3.3.4 Probable source areas or directions 103 3.4. Summary 106 References 107 CHAPTER 4. FEATURE EXTRACTION AND PREDICTION OF PM2.5 CHEMICAL CONSTITUENTS USING MACHINE LEARNING MODELS 120 4.1. Introduction 120 4.2. Materials and methods 124 4.2.1. Study Sites and Data Collection 124 4.2.2. Machine Learning Models and Hyperparameter Optimization 127 4.2.3. Prediction Scenarios 131 4.2.4. Model Validation and Error Estimation 133 4.3. Results and discussion 134 4.3.1. Hyperparameter Optimization 134 4.3.2. Prediction Results for Scenario #1 135 4.3.3. Prediction Results for Scenario #2 157 4.3.4. Features and Performance of Four ML Models 164 4.4. Summary 166 Data Availability 167 Code Availability 167 References 168 CHAPTER 5. BAYESIAN SPATIAL MULTIVARIATE RECEPTOR MODELING FOR SPATIOTEMPORAL ANALYSIS OF PM2.5 SOURCES 175 5.1. Introduction 175 5.2. Materials and methods 180 5.2.1 Air pollution data 180 5.2.2 Bayesian spatial multivariate receptor modeling (BSMRM) 183 5.2.3 Application of BSMRM to Korea PM2.5 speciation data 185 5.3. Results and discussion 189 5.3.1 Bayesian spatial multivariate receptor modeling (BSMRM) results 189 5.3.2 Model validation 196 5.3.3 Spatial distribution of each source in South Korea 204 5.4. Summary 207 References 208 CHAPTER 6. CONCLUSIONS AND FUTURE WORK 214 6.1. Conclusions 214 6.2. Future work 218 국문 초록(ABSTRACT IN KOREAN) 219박

Current approaches used in epidemiologic studies to examine short-term multipollutant air pollution exposures

Air pollution epidemiology traditionally focuses on the relationship between individual air pollutants and health outcomes (e.g., mortality). To account for potential copollutant confounding, individual pollutant associations are often estimated by adjusting or controlling for other pollutants in the mixture. Recently, the need to characterize the relationship between health outcomes and the larger multipollutant mixture has been emphasized in an attempt to better protect public health and inform more sustainable air quality management decisions

Statistical Methods in Integrative Genomics

Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions

Multi-omics analysis of the ageing liver

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Bayesian Statistical Modeling of Spatially Resolved Transcriptomics Data

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Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

Book of Abstracts XVIII Congreso de Biometría CEBMADRID

Abstracts of the XVIII Congreso de Biometría CEBMADRID held from 25 to 27 May in MadridInteractive modelling and prediction of patient evolution via multistate models / Leire Garmendia Bergés, Jordi Cortés Martínez and Guadalupe Gómez Melis : This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019104830RBI00]; and the Generalitat de Catalunya (Spain) [2017SGR622 and 2020PANDE00148].Operating characteristics of a model-based approach to incorporate non-concurrent controls in platform trials / Pavla Krotka, Martin Posch, Marta Bofill Roig : EU-PEARL (EU Patient-cEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development non-profit organisation, Spring works Therapeutics Inc.Modeling COPD hospitalizations using variable domain functional regression / Pavel Hernández Amaro, María Durbán Reguera, María del Carmen Aguilera Morillo, Cristobal Esteban Gonzalez, Inma Arostegui : This work is supported by the grant ID2019-104901RB-I00 from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033.Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain / Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián / Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain : This work was partially supported by the Ministerio de Ciencia e Innovación under Grant PID2020-116873GB-I00; Gobierno de Aragón under Research Group E46_20R: Modelos Estocásticos; and JC-M was supported by Gobierno de Aragón under Doctoral Scholarship ORDEN CUS/581/2020.Estimation of the area under the ROC curve with complex survey data / Amaia Iparragirre, Irantzu Barrio, Inmaculada Arostegui : This work was financially supported in part by IT1294-19, PID2020-115882RB-I00, KK-2020/00049. The work of AI was supported by PIF18/213.INLAMSM: Adjusting multivariate lattice models with R and INLA / Francisco Palmí Perales, Virgilio Gómez Rubio and Miguel Ángel Martínez Beneito : This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, Spain) and FEDER, grant MTM2016-77501-P, funded by Ministerio de Economía y Competitividad (Spain), grant PID2019-106341GB-I00 from Ministerio de Ciencia e Innovación (Spain) and a grant to support research groups by the University of Castilla-La Mancha (Spain). F. Palmí-Perales has been supported by a Ph.D. scholarship awarded by the University of Castilla-La Mancha (Spain)

Inferential stability in systems biology

The modern biological sciences are fraught with statistical difficulties. Biomolecular stochasticity, experimental noise, and the “large p, small n” problem all contribute to the challenge of data analysis. Nevertheless, we routinely seek to draw robust, meaningful conclusions from observations. In this thesis, we explore methods for assessing the effects of data variability upon downstream inference, in an attempt to quantify and promote the stability of the inferences we make. We start with a review of existing methods for addressing this problem, focusing upon the bootstrap and similar methods. The key requirement for all such approaches is a statistical model that approximates the data generating process. We move on to consider biomarker discovery problems. We present a novel algorithm for proposing putative biomarkers on the strength of both their predictive ability and the stability with which they are selected. In a simulation study, we find our approach to perform favourably in comparison to strategies that select on the basis of predictive performance alone. We then consider the real problem of identifying protein peak biomarkers for HAM/TSP, an inflammatory condition of the central nervous system caused by HTLV-1 infection. We apply our algorithm to a set of SELDI mass spectral data, and identify a number of putative biomarkers. Additional experimental work, together with known results from the literature, provides corroborating evidence for the validity of these putative biomarkers. Having focused on static observations, we then make the natural progression to time course data sets. We propose a (Bayesian) bootstrap approach for such data, and then apply our method in the context of gene network inference and the estimation of parameters in ordinary differential equation models. We find that the inferred gene networks are relatively unstable, and demonstrate the importance of finding distributions of ODE parameter estimates, rather than single point estimates

Multivariate Models and Algorithms for Systems Biology

Rapid advances in high-throughput data acquisition technologies, such as microarraysand next-generation sequencing, have enabled the scientists to interrogate the expression levels of tens of thousands of genes simultaneously. However, challenges remain in developingeffective computational methods for analyzing data generated from such platforms. In thisdissertation, we address some of these challenges. We divide our work into two parts. Inthe first part, we present a suite of multivariate approaches for a reliable discovery of geneclusters, often interpreted as pathway components, from molecular profiling data with replicated measurements. We translate our goal into learning an optimal correlation structure from replicated complete and incomplete measurements. In the second part, we focus on thereconstruction of signal transduction mechanisms in the signaling pathway components. Wepropose gene set based approaches for inferring the structure of a signaling pathway.First, we present a constrained multivariate Gaussian model, referred to as the informed-case model, for estimating the correlation structure from replicated and complete molecular profiling data. Informed-case model generalizes previously known blind-case modelby accommodating prior knowledge of replication mechanisms. Second, we generalize theblind-case model by designing a two-component mixture model. Our idea is to strike anoptimal balance between a fully constrained correlation structure and an unconstrained one.Third, we develop an Expectation-Maximization algorithm to infer the underlying correlation structure from replicated molecular profiling data with missing (incomplete) measurements.We utilize our correlation estimators for clustering real-world replicated complete and incompletemolecular profiling data sets. The above three components constitute the first partof the dissertation. For the structural inference of signaling pathways, we hypothesize a directed signal pathway structure as an ensemble of overlapping and linear signal transduction events. We then propose two algorithms to reverse engineer the underlying signaling pathway structure using unordered gene sets corresponding to signal transduction events. Throughout we treat gene sets as variables and the associated gene orderings as random.The first algorithm has been developed under the Gibbs sampling framework and the secondalgorithm utilizes the framework of simulated annealing. Finally, we summarize our findingsand discuss possible future directions