Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM(sub 2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R(2) = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R(2) = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R(2) = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R(2) > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models

Satellite-based PM2.5 Exposure Estimation and Health Impacts over China

Exposure to suspended fine particulate matter (PM2.5) has been proven to adversely impact public health through increased risk of cardiovascular and respiratory mortality. Assessing health impacts of PM2.5 and its long-term variations requires accurate estimates of large-scale exposure data. Such data include mass concentration and particle size, the latter of which may be an effect modifier on PM2.5 attributable health risks. The availability of these exposure data, however, is limited by sparse ground-level monitoring networks. In this dissertation, an optical-mass relationship was first developed based on aerosol microphysical characteristics for ground-level PM2.5 retrieval. This method quantifies PM2.5 mass concentrations with a theoretical basis, which can simultaneously estimate large-scale particle size. The results demonstrate the effectiveness and applicability of the proposed method and reveal the spatiotemporal distribution of PM2.5 over China. To explore the spatial variability and population exposure, particle radii of PM2.5 are then derived using the developed theoretical relationship along with a statistical model for a better performance. The findings reveal the prevalence of exposure to small particles (i.e. PM1), identify the need for in-situ measurements of particle size, and motivate further research to investigate the effects of particle size on health outcomes. Finally, the long-term impacts of PM2.5 on health and environmental inequality are assessed by using the satellite-retrieved PM2.5 estimates over China during 2005-2017. Premature mortality attributable to PM2.5 exposure increased by 31% from 2005 to 2017. For some causes of death, the burden fell disproportionately on provinces with low-to-middle GDP per capita. As a whole, this work contributes to bridging satellite remote sensing and long-term exposure studies and sheds light on an ongoing need to understand the effects of PM2.5, including both concentrations and other particle characteristics, on human health

머신러닝 모델을 사용한 2002~2020년 한국의 O3, NO2, CO 농도의 고해상도 추정

학위논문(석사) -- 서울대학교대학원 : 보건대학원 보건학과, 2023. 2. 김호.Backrgound : Long-term exposure to ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO) is known to cause various diseases and increase mortality. For that reason, estimating ground-level O3, NO2, and CO concentrations with a high spatial resolution is crucial for assessing the health effects associated with these air pollutants. However, related studies are limited in South Korea. This study aimed to develop machine learning-based models to predict the monthly O3 (average of daily 8-hour maximums), NO2, and CO at a spatial resolution of 1 km × 1 km across South Korea from 2002 to 2020. Methods : Approximately 80% of the monitoring stations were used to train the three machine learning models (random forest, light gradient boosting, and neural network) with a 10-fold cross-validation, and 20% of the monitoring stations were used to test the model performance. The author also applied ensemble models to integrate the variation in predictions among the models. Multiple predictors with satellite-based remote sensing data, inverse distance weighted ground-level air pollutants, land use variables, reanalysis datasets for meteorological variables, and regional socioeconmoic variables collected from various databases were included in the prediction model. Results : For O3, the overall R2 of the ensemble model was 0.841 during the entire study period. Urban areas showed a better model performance (R2 = 0.845) than rural areas (R2 = 0.762). For NO2, the highest overall R2 was 0.756, which best fit in autumn (R2 = 0.768). For CO, the overall R2 value was 0.506. This study provides high spatial resolution monthly average O3 and NO2 estimates with excellent performance (R2 > 0.75). Conclusion : The authors predictions can be used to analyze the spatial patterns in pollutants in relation to population characteristics and studies on the health effects of long-term exposure to air pollution using geocode-based health information and local health data.연구배경 : 오존(O3), 이산화질소(NO2), 일산화탄소(CO)에 장기간 노출되면 각종 질병을 유발하고 사망률을 높이는 것으로 알려져 있다. 그렇기에, 고해상도로 지표면 O3, NO2, CO 농도를 추정하는 것은 이러한 대기오염물질과 관련된 건강 영향을 평가하는 데 매우 중요하다. 하지만, 장기간에 걸쳐 고해상도로 가스상 대기오염물질(O3, NO2, CO)를 추정한 연구는 국내에서 아직 진행된 바가 없다. 따라서, 본 연구는 2002년부터 2020년까지 대한민국 전역에서 1km × 1km의 공간해상도로 월별 O3(일평균 8시간 최대치), NO2, CO를 머신러닝 기반 모델 및 그들의 앙상블 모형을 통해 예측하고자 한다. 연구방법 : 3가지 머신러닝 모델(랜덤 포레스트, 라이트 그래디언트 부스팅, 신경망)의 최적의 파라미터를 찾기 위해 모니터링 스테이션의 약 80%를 훈련 데이터로 사용하였고, 10-fold 교차검증을 통해 훈련 데이터 내에서 훈련/평가 단계를 거쳤으며, 나머지 모니터링 스테이션의 20%를 모델 평가에 사용하였다. 여기에 추가로 머신러닝 모델 간의 예측 변동을 통합하기 위해 앙상블 모델을 적용했다. 데이터에는 위성 기반 원격 감지 데이터, 역거리 가중치 기반 대기오염농도, 토지 이용 변수, 기상 재분석 자료, 다양한 데이터베이스에서 수집된 지역 사회경제적 변수 등이 포함되었다. 연구결과 : O3의 경우, 전체 연구 기간 동안 앙상블 모델의 R2가 0.841을 기록했으며, 도시 지역이 농촌 지역(R2 = 0.762)보다 우수한 예측 성능(R2 = 0.845)을 보였다. NO2의 경우, 앙상블(평균) 모델의 R2가 0.756으로 가장 높았으며, 계절로 보면 가을에 예측 성능이 가장 높았다(R2 = 0.768). CO의 경우, R2가 0.506 을 기록했다. 본 연구는 O3 및 NO2 에서 R2 > 0.75 으로 높은 예측력의 고해상도 월평균 추정치를 제공한다. 결론 : 본 연구에서 얻어진 대기오염 추정 결과는 인구 특성과 관련된 가스상 대기오염물질의 공간 패턴을 분석하거나, 위치 기반 건강 정보와 행정구역 단위 건강 데이터와 엮여서 장기간 대기오염 노출의 건강 영향을 평가하는 연구에 사용될 수 있을 것으로 기대된다.Chapter 1. Introduction 1 Chapter 2. Materials and Methods 6 2.1. Study area 6 2.2. Air pollution monitoring data 6 2.3. Satellite-based remote sensing data 7 2.3.1. Meteorological data 7 2.3.2. Land-use data 10 2.3.3. Surface reflectance 11 2.4. Regional socioeconomic predictors 12 2.5. Modeling procedures 13 2.5.1. Data Preprocessing 14 2.5.2. Machine learning-based model 15 2.5.3. Ensemble Model 16 2.5.4. Model Prediction 17 Chapter 3. Results 19 Chapter 4. Discussion 29 Chapter 5. Conclusion 34 Supplementary materials 47 국문 초록 82 Tables Table 1. Model performance for O3, NO2, and CO overall and in three- and four-year periods 21 Table S1. Detailed information about data sources 61 Table S2. Variables sorted by % missing values 65 Table S3. Results of parameter grid search using 10-fold cross-validation for O3, NO2 and CO 68 Table S4. Yearly ensemble (GAM) performance for O3, NO2, and CO 70 Table S5. Model performances for O3, NO2, and CO by season and urbanity 71 Table S6. Number of monitoring stations by year for O3, NO2 and CO in urban and rural areas 73 Figures Fig. 1. Flowchart of the modeling process. GEE: Google Earth Engine, SEDAC: Socioeconomic Data and Applications Center, RSD: Regional Socioeconomic Database from Korean Disease Control and Prevention Agency 18 Fig. 2. Density scatter plot for monthly averages of the monitored and predicted concentrations of O3, NO2, and CO 26 Fig. 3. Maps of monitored and predicted O3, NO2 and CO during 2002~2020 27 Fig. 4. Percentage decrease in R2 when excluding grouped variables from each machine learning model of O3, NO2, and CO. The closer the color is to red, the greater the effect of the variables on the model performance 28 Fig. S1. Urban/Rural and Metropolitan (Metro) area for entire contiguous regions of South Korea 74 Fig. S2. Distribution maps of predicted O3 (ppb) by year and season for contiguous South Korea 75 Fig. S3. Distribution maps of predicted NO2 (ppb) by year and season for contiguous South Korea 76 Fig. S4. Distribution maps of predicted CO (ppm) by year and season for contiguous South Korea 77 Fig. S5. Monthly fluctuations in the number of monitoring stations for O3, NO2, and CO between 2002 and 2020 78 Fig. S6. Density scatter plot for monthly averages of the monitored and predicted concentrations of O3, NO2, and CO with seasonal discrimination 79석

A comparison of statistical and machine-learning approaches for spatiotemporal modeling of nitrogen dioxide across Switzerland

Land use regression modeling has commonly been used to model ambient air pollutant concentrations in environmental epidemiological studies. Recently, other statistical and machine-learning methods have also been applied to model air pollution, but their relative strengths and limitations have not been extensively investigated. In this study, we developed and compared land-use statistical and machine-learning models at annual, monthly and daily scales estimating ground-level NO2 concentrations across Switzerland (at high spatial resolution 100 × 100 m). Our study showed that the best model type varies with context, particularly with temporal resolution and training data size. Linear-regression-based models were useful in predicting long-term (annual, monthly) spatial distribution of NO2 and outperformed machine-learning models. However, linear-regression-based models were limited in representing short-term temporal variation even when predictor variables with temporal variability were provided. Machine-learning models showed high capability in predicting short-term temporal variation and outperformed linear-regression-based models for modeling NO2 variation at high temporal resolution (daily). However, the best performing models, XGBoost and LightGBM, constantly overfit on training data and may result in erratic patterns in the model-estimated concentration surfaces. Therefore, the temporal and spatial scale of the study is an important factor on which the choice of the suitable model type should be based and validation is required whatever approach is used

Estimation of Respiratory Disease Burden Attributed to Particulate Matter from Biomass Burning in Northern Thailand Using 1-km Resolution MAIAC-AOD

The upper northern Thailand suffers from air pollution due to open burning, which has been known for a long time. It was also found that different respiratory diseases were attributed to air pollution, especially particulate matter. This study estimated the health impacts attributed to PM10 between 2014 and 2016 using the burden of disease in terms of the disability adjusted life year (DALYs). The spatial correlation was evaluated based on applicable remote sensing data using the geographically weighted regression (GWR) model. The average measured PM10 concentrations for the summer and annual periods between 2014 and 2016 were 73 and 89 µg m-3, respectively, exceeded the national standard (50 µg m-3). In the months of March and April, when PM10 concentrations were at their highest, the maximum values of the Multi-Angle Implementation of Atmospheric Correction (MAIAC-AOD), 2.70 and 3.48, were recorded.  There was a strong correlation between the MAIAC-AOD and the ground-based AOD measurements (AERONET stations), with R of 0.8468, 0.8396, and 0.8334 between 2014–2016. The correlation coefficients for the 3,208 co-located gridded of PM10 emissions vs. measured PM10, measured PM10 vs. MAIAC-AOD, and MAIAC-AOD vs. PM10 emissions were 0.6656, 0.6446, and 0.5580, respectively. The spatial correlation between the interpolated measured PM10 and 1-km MAIAC-AOD was 0.5979, 0.3741, and 0.7584 as an outcome of GWR. The total DALYs of chronic obstructive pulmonary disease (COPD) attributable to PM10 in 2014–2016 were 115,930 years per 100,000 population, with the relative risk of COPD related to PM10 at a 95% confidence interval of 1.2045–1.2107

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models

Working with the enemy? Social work education and men who use intimate partner violence

This article examines service user involvement in social work education. It discusses the challenges and ethical considerations of involving populations who may previously have been excluded from user involvement initiatives, raising questions about the benefits and challenges of their involvement. The article then provides discussion of an approach to service user involvement in social work education with one of these populations, men who use violence in their intimate relationships, and concludes by considering the implications of their involvement for the social work academy