Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations

Hsu, Cheng-Ying; Wu, Chih-Da; Hsiao, Ya Ping; Chen, Yu Cheng; Chen, Mu Jean; Lung, Shih-Chun Candice

doi:10.3390/rs10121971

Cited by 21 publications

(13 citation statements)

References 58 publications

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“…Yao et al [20] verify the effectiveness of recent highresolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. By incorporating land use data, AOD and meteorological data, some scholars build prediction model for daily PM2.5 using machine learning approaches [21], [22]. Goldberg et al [23] simulate PM2.5 in a computationally efficient manner that is constrained to ground monitors, satellite data, and chemical transport model output at high spatial resolution with little sacrifice of the temporal resolution (daily) or spatial coverage.…”

Section: Introductionmentioning

confidence: 99%

Short-Term PM2.5 Concentration Prediction by Combining GNSS and Meteorological Factors

Wen

Dang

Li³

2020

IEEE Access

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With the development of industrialization, fine particulate matter (PM2.5) severely chills the health of people. Studies have shown that the variation of PM2.5 concentration is related to the Global Navigation Satellite System (GNSS) tropospheric delay. Therefore, it is possible to use the widely distributed continuous operation reference station (CORS) to monitor and predict PM2.5 concentrations with high time resolution. In this paper, the zenith wet delay (ZWD) of five CORS located in Baoding, Hebei Province, China, from Sep 2014 to Feb 2015, is calculated firstly. Then, the correlation between PM2.5 and ZWD is investigated. Finally, the experimental data including PM2.5, ZWD, temperature, air pressure, and relative humidity data sampled in one hour are used to establish PM2.5 concentrations online prediction model based on support vector machine regression (SVMR) model with metabolic method. The experimental results show that in autumn and winter, the correlation coefficient between daily-mean PM2.5 and ZWD is mainly larger than 0.4, and the correlation coefficient between hourly mean PM2.5 and ZWD is mainly larger than 0.3. Meanwhile, in daily cycle analysis, air temperature, air pressure and relative humidity are related to PM2.5 concentration. Finally, Using SVMR model with metabolic method, by combining GNSS and meteorological factors, ideal short-term prediction accuracy is achieved, which shows that the use of GNSS and meteorological factors is potential in predicting PM2.5 concentration.

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Section: Introductionmentioning

confidence: 99%

Short-Term PM2.5 Concentration Prediction by Combining GNSS and Meteorological Factors

Wen

Dang

Li³

2020

IEEE Access

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“…The variables were concerned with multi-temporal variability data. The LUR model has been suitable method for estimating the concentration of pollutants in several areas, especially for PM 2.5 [9,11,12]. The model shows good predictive ability (R 2 = 56% for DKI Jakarta, and 84% for Taipei Metropolis), with spatial resolution of 50 × 50 meters.…”

Section: Discussionmentioning

confidence: 99%

“…The method is to develop statistical regression models based on GIS platforms. These can be used to estimate air pollutant levels in a particular site by establishing a statistical correlation between pollutant observations and potential prediction variables [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

PM2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan

Kusuma

Yu-Ting

et al. 2019

IJERPH

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Air pollution has emerged as a significant health, environmental, economic, and social problem all over the world. In this study, geospatial technologies coupled with a LUR (Land Use Regression) approach were applied to assess the spatial-temporal distribution of fine particulate (PM2.5). In-situ observations of air pollutants from ground monitoring stations from 2016–2018 were used as dependent variables, while the land-use/land cover, a NDVI (Normalized Difference Vegetation Index) from a MODIS sensors, and meteorology data allocations surrounding the monitoring stations from 0.25–5 km buffer ranges were collected as spatial predictors from GIS and remote sensing databases. A linear regression method was developed for the LUR model and 10-fold cross-validation was used to assess the model robustness. The R2 model obtained was 56% for DKI Jakarta, Indonesia, and 83% for Taipei Metropolis, Taiwan. According to the results of the PM2.5 model, the essential predictors for DKI Jakarta were influenced by temperature, NDVI, humidity, and residential area, while those for the Taipei Metropolis region were influenced by PM10, NO2, SO2, UV, rainfall, spring, main road, railroad, airport, proximity to airports, mining areas, and NDVI. The validation of the results of the estimated PM2.5 distribution use 10-cross validation with indicated R2 values of 0.62 for DKI Jakarta and 0.84 for Taipei Metropolis. The results of cross-validation show the strength of the model.

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“…The land-use inventory, the landmark database, the digital road network map, the Digital Terrain Model (DTM), Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) database, and the thermal power plant distribution dataset are used. Further details of land-use or land-cover related information for potential prediction variables ( Table S1 ) can be found in a previous study by the authors [ 17 , 26 , 27 ]. In this study, LUR models at monthly resolution were developed based on air pollutants measurements from 2015 to 2019.…”

Section: Methodsmentioning

confidence: 99%

Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration

Hsu

Zeng

Chen

et al. 2020

IJERPH

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This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

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Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations

Cited by 21 publications

References 58 publications

Short-Term PM2.5 Concentration Prediction by Combining GNSS and Meteorological Factors

Short-Term PM2.5 Concentration Prediction by Combining GNSS and Meteorological Factors

PM2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan

Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration

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