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Fu L, Wang Q, Li J, Jin H, Zhen Z, Wei Q. Spatiotemporal Heterogeneity and the Key Influencing Factors of PM 2.5 and PM 10 in Heilongjiang, China from 2014 to 2018. Int J Environ Res Public Health 2022; 19:ijerph191811627. [PMID: 36141911 PMCID: PMC9517409 DOI: 10.3390/ijerph191811627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 05/06/2023]
Abstract
Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial-temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014-2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.
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Affiliation(s)
- Longhui Fu
- School of Forestry, Northeast Forestry University, Harbin 150040, China
- Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China
| | - Qibang Wang
- School of Forestry, Northeast Forestry University, Harbin 150040, China
| | - Jianhui Li
- School of Forestry, Northeast Forestry University, Harbin 150040, China
| | - Huiran Jin
- School of Applied Engineering and Technology, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Zhen Zhen
- School of Forestry, Northeast Forestry University, Harbin 150040, China
- Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China
- Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
- Correspondence: (Z.Z.); (Q.W.)
| | - Qingbin Wei
- Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China
- School of Geographical Sciences, Harbin Normal University, Harbin 150025, China
- Correspondence: (Z.Z.); (Q.W.)
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Ding W, Qie X. Prediction of Air Pollutant Concentrations via RANDOM Forest Regressor Coupled with Uncertainty Analysis—A Case Study in Ningxia. Atmosphere 2022; 13:960. [DOI: 10.3390/atmos13060960] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Air pollution has not received much attention until recent years when people started to understand its dreadful impacts on human health. According to air pollution and the meteorological monitoring data from 1 January 2016 to 31 December 2017 in Ningxia, we analyzed the impact of ground surface temperature, air temperature, relative humidity and the power of wind on air pollutant concentrations. Meanwhile, we analyze the relationships between air pollutant concentrations and meteorological variables by using the mathematical model of decision tree regressor (DTR), feedforward artificial neural network with back-propagation algorithm (FFANN-BP) and random forest regressor (RFR) according to air-monitoring station data. For all pollutants, the RFR increases R2 of FFANN-BP and DTR by up to 0.53 and 0.42 respectively, reduces root mean square error (RMSE) by up to 68.7 and 41.2, and MAE by up to 25.2 and 17. The empirical results show that the proposed RFR displays the best forecasting performance and could provide local authorities with reliable and precise predictions of air pollutant concentrations. The RFR effectively establishes the relationships between the influential factors and air pollutant concentrations, and well suppresses the overfitting problem and improves the accuracy of prediction. Besides, the limitation of machine learning for single site prediction is also overcame.
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Jin Z, Ma Y, Chu L, Liu Y, Dubrow R, Chen K. Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model. Environ Res 2022; 204:111960. [PMID: 34464620 DOI: 10.1016/j.envres.2021.111960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1 × 1 km2. The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. Various spatial predictors were included as adjustment variables in all the base models, including land cover type, normalized difference vegetation index (NDVI), and elevation. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Although each base model performed well, the two machine learning models (RF [R2 = 0.97], XGBoost [R2 = 0.98]) had better performance than the two regression models (GAM [R2 = 0.95], GAMM [R2 = 0.96]). In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, NDVI, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment in future epidemiological studies.
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Affiliation(s)
- Zhihao Jin
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Yiqun Ma
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Lingzhi Chu
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Robert Dubrow
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA.
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Yu W, Li S, Ye T, Xu R, Song J, Guo Y. Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations. Environ Health Perspect 2022; 130:37004. [PMID: 35254864 PMCID: PMC8901043 DOI: 10.1289/ehp9752] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 02/14/2022] [Accepted: 02/14/2022] [Indexed: 05/29/2023]
Abstract
BACKGROUND Accurate estimation of historical PM2.5 (particle matter with an aerodynamic diameter of less than 2.5μm) is critical and essential for environmental health risk assessment. OBJECTIVES The aim of this study was to develop a multiple-level stacked ensemble machine learning framework for improving the estimation of the daily ground-level PM2.5 concentrations. METHODS An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily PM2.5 concentrations. The framework has a three-stage structure: At the first stage, four base models [gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)] were used to generate a new data set of PM2.5 concentrations for training the next-stage learners. At the second stage, three meta-models [RF, XGBoost, and Generalized Linear Model (GLM)] were used to estimate PM2.5 concentrations using a combination of the original data set and the predictions from the first-stage models. At the third stage, a nonnegative least squares (NNLS) algorithm was employed to obtain the optimal weights for PM2.5 estimation. We took the data from 133 monitoring stations in Italy as an example to implement the DEML to predict daily PM2.5 at each 1km×1km grid cell from 2015 to 2019 across Italy. We evaluated the model performance by performing 10-fold cross-validation (CV) and compared it with five benchmark algorithms [GBM, SVM, RF, XGBoost, and Super Learner (SL)]. RESULTS The results revealed that the PM2.5 prediction performance of DEML [coefficients of determination (R2)=0.87 and root mean square error (RMSE)=5.38μg/m3] was superior to any benchmark models (with R2 of 0.51, 0.76, 0.83, 0.70, and 0.83 for GBM, SVM, RF, XGBoost, and SL approach, respectively). DEML displayed reliable performance in capturing the spatiotemporal variations of PM2.5 in Italy. DISCUSSION The proposed DEML framework achieved an outstanding performance in PM2.5 estimation, which could be used as a tool for more accurate environmental exposure assessment. https://doi.org/10.1289/EHP9752.
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Affiliation(s)
- Wenhua Yu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Shanshan Li
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Tingting Ye
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Rongbin Xu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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Abstract
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.
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Rahman MM, Shafiullah M, Rahman SM, Khondaker AN, Amao A, Zahir MH. Soft Computing Applications in Air Quality Modeling: Past, Present, and Future. Sustainability 2020; 12:4045. [DOI: 10.3390/su12104045] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.
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Abstract
Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values.
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Shtein A, Kloog I, Schwartz J, Silibello C, Michelozzi P, Gariazzo C, Viegi G, Forastiere F, Karnieli A, Just AC, Stafoggia M. Estimating Daily PM 2.5 and PM 10 over Italy Using an Ensemble Model. Environ Sci Technol 2020; 54:120-128. [PMID: 31749355 DOI: 10.1021/acs.est.9b04279] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Spatiotemporally resolved particulate matter (PM) estimates are essential for reconstructing long and short-term exposures in epidemiological research. Improved estimates of PM2.5 and PM10 concentrations were produced over Italy for 2013-2015 using satellite remote-sensing data and an ensemble modeling approach. The following modeling stages were used: (1) missing values of the satellite-based aerosol optical depth (AOD) product were imputed using a spatiotemporal land-use random-forest (RF) model incorporating AOD data from atmospheric ensemble models; (2) daily PM estimations were produced using four modeling approaches: linear mixed effects, RF, extreme gradient boosting, and a chemical transport model, the flexible air quality regional model. The filled-in MAIAC AOD together with additional spatial and temporal predictors were used as inputs in the three first models; (3) a geographically weighted generalized additive model (GAM) ensemble model was used to fuse the estimations from the four models by allowing the weights of each model to vary over space and time. The GAM ensemble model outperformed the four separate models, decreasing the cross-validated root mean squared error by 1-42%, depending on the model. The spatiotemporally resolved PM estimations produced by the suggested model can be applied in future epidemiological studies across Italy.
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Affiliation(s)
- Alexandra Shtein
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
| | - Joel Schwartz
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston 02115, Massachusetts, United States
| | | | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Rome 00147, Italy
| | - Claudio Gariazzo
- Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers' Compensation Authority (INAIL), Monte Porzio Catone (RM) 00078, Italy
| | - Giovanni Viegi
- Institute for Biomedical Research and Innovation, National Research Council, Palermo 90146, Italy
| | - Francesco Forastiere
- Institute for Biomedical Research and Innovation, National Research Council, Palermo 90146, Italy
- Environmental Research Group, King's College, London SE1 9NH, U.K
| | - Arnon Karnieli
- Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus 84990, Israel
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Rome 00147, Italy
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden
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Wei Q, Zhang L, Duan W, Zhen Z. Global and Geographically and Temporally Weighted Regression Models for Modeling PM 2.5 in Heilongjiang, China from 2015 to 2018. Int J Environ Res Public Health 2019; 16:ijerph16245107. [PMID: 31847317 PMCID: PMC6950195 DOI: 10.3390/ijerph16245107] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/11/2019] [Accepted: 12/11/2019] [Indexed: 01/10/2023]
Abstract
Objective: This study investigated the relationships between PM2.5 and 5 criteria air pollutants (SO2, NO2, PM10, CO, and O3) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM2.5 and 5 air pollutants. Results: The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R2 and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R2 (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM2.5. Conclusions: The traditional OLS and GWR are inadequate for describing the non-stationarity of PM2.5. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial-temporal heterogeneity and possible solutions for modeling the relationships between PM2.5 and 5 criteria air pollutants for Heilongjiang province, China.
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Affiliation(s)
- Qingbin Wei
- School of Forestry, Northeast Forestry University, Harbin 150040, China; (Q.W.); (W.D.)
| | - Lianjun Zhang
- Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, New York, NY 13210, USA;
| | - Wenbiao Duan
- School of Forestry, Northeast Forestry University, Harbin 150040, China; (Q.W.); (W.D.)
| | - Zhen Zhen
- School of Forestry, Northeast Forestry University, Harbin 150040, China; (Q.W.); (W.D.)
- Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin 150040, China
- Correspondence: ; Tel.: +86-187-4568-7693
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Abstract
PURPOSE OF REVIEW Data science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper we review how data science can help advance environmental health research. RECENT FINDINGS We discuss the concepts computationally scalable handling of Big Data and the design of efficient research data platforms, and how data science can provide solutions for methodological challenges in environmental health research, such as high-dimensional outcomes and exposures, and prediction models. Finally, we discuss tools for reproducible research. SUMMARY In this paper we present opportunities to improve environmental research capabilities by embracing data science, and the pitfalls that environmental health researchers should avoid when employing data scientific approaches. Throughout the paper, we emphasize the need for environmental health researchers to collaborate more closely with biostatisticians and data scientists to ensure robust and interpretable results.
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Affiliation(s)
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
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Abstract
Air pollution has become a global environmental problem, because it has a great adverse impact on human health and the climate. One way to explore this problem is to monitor and predict air quality index in an economical way. Accurate monitoring and prediction of air quality index (AQI), e.g., PM2.5 concentration, is a challenging task. In order to accurately predict the PM2.5 time series, we propose a supplementary leaky integrator echo state network (SLI-ESN) in this paper. It adds the historical state term of the historical moment to the calculation of leaky integrator reservoir, which improves the influence of historical evolution state on the current state. Considering the redundancy and correlation between multivariable time series, minimum redundancy maximum relevance (mRMR) feature selection method is introduced to reduce redundant and irrelevant information, and increase computation speed. A variety of evaluation indicators are used to assess the overall performance of the proposed method. The effectiveness of the proposed model is verified by the experiment of Beijing PM2.5 time series prediction. The comparison of learning time also shows the efficiency of the algorithm.
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Lin J, Zhang A, Chen W, Lin M. Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model. Sustainability 2018; 10:2772. [DOI: 10.3390/su10082772] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively.
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