Development of land-use regression models to estimate particle mass and number concentrations in Taichung, Taiwan

Chang, Ta Yuan; Tsai, Ching Chih; Wu, Chang‐Fu; Chang, Li-Te; Chuang, Kai Jen; Chuang, Hsiao Chi; Young, Li Hao

doi:10.1016/j.atmosenv.2021.118303

Cited by 9 publications

(5 citation statements)

References 46 publications

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“…The results showed adjusted R 2 of 0.28-0.44 for the LUR models and R 2 of 0.27-0.41 for LOOCV. They indicated that, the smaller the difference between the LOOCV R 2 of model and R 2 , the better the model works [65]. The model performance of our PNC was similar to that of London, which used PNC data with a range of 10-1000 nm from 120 sites with an adjusted R 2 of 0.59, and the R 2 of HV was 0.44 [66].…”

Section: Comparison Of Model Performancementioning

confidence: 55%

Land Use Regression Models for Particle Number Concentration and Black Carbon in Lanzhou, Northwest of China

Fang,

Zhou,

Jin

et al. 2023

Sustainability

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It is necessary to predict the spatial variation in particle number concentration (PNC) and black carbon (BC) because they are considered air pollutants associated with traffic and many diseases. In this study, land use regression (LUR) models for PNC and BC were developed based on a mobile monitoring campaign in January 2020 in Lanzhou, and the performance of models was evaluated with hold-out validation (HV) and leave-one-out cross-validation (LOOCV). The results show that the adjusted R2 of the LUR models for PNC and BC are 0.51 and 0.53, respectively. The R2 of HV and LOOCV are 0.43 and 0.44, respectively, for the PNC model and 0.42 and 0.50, respectively, for the BC model. The performances of the LUR models are of a moderate level. The spatial distribution of the predicted PNC is related to the distance from water bodies. The high PNC is related to industrial pollution. The BC concentration decreases from south to north. High BC concentrations are associated with freight distribution centres and coal-fired power plants. The range of PNC particle sizes in this study is larger than in most studies. As one of few studies in Lanzhou to develop LUR models of air pollutants, it is important to accurately estimate pollutant concentrations to improve air quality and provide health benefits for residents.

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Section: Comparison Of Model Performancementioning

confidence: 55%

Land Use Regression Models for Particle Number Concentration and Black Carbon in Lanzhou, Northwest of China

Fang,

Zhou,

Jin

et al. 2023

Sustainability

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“…Compared with the previous research, the models built in this study have R 2 values of 0.57 and 0.58 for PM2.5 and PM10 concentrations, respectively, indicating that the model can moderately explain the variations in the concentrations. The R 2 values for PM2.5 of the previous research were in the ranges of 0.46 and 0.53 for the study areas of Bangkok [9] and Taichung, respectively, and 0.71 and 0.88 for the study areas in Xi-an and Shanghai, respectively [16,17,26,27].…”

Section: Discussionmentioning

confidence: 81%

“…Based on previous studies, the factors used in the LUR models were varied to suit the characteristics of each study area. These factors are commonly related to transportation (such as road area, traffic load, and road length), land use (such as residential, commercial, industrial, agriculture, number of populations, and number of households), topography (such as distance from sea and elevation), and meteorological factors (such as rainfall, wind speed, temperature, and humidity) [9,15,17,[26][27][28][29][30]. In this study, similar factors available within the study area, including traffic load, area of each land-use type, and meteorological data, were used.…”

Section: Discussionmentioning

confidence: 99%

“…The LUR model forecasts air pollutant concentrations in areas where monitoring stations are unavailable. These models use multiple linear regression analysis to explore the relationship between observed air pollutants and spatial factors such as traffic conditions, population, local pollution sources, land use, land cover, elevation, and distance of monitors to the sea [15][16][17][18]. Such models were developed for many cities.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Land Use and Meteorological Factors on PM2.5 and PM10 Concentrations in Bangkok, Thailand

et al. 2022

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Particulate matter (PM) is regarded a major problem worldwide because of the harm it causes to human health. Concentrations of PM with particle diameter less than 2.5 µm (PM2.5) and with particle diameter less than 10 µm (PM10) are based on various emission sources as well as meteorological factors. In Bangkok, where the PM2.5 and PM10 monitoring stations are few, the ability to estimate concentrations at any location based on its environment will benefit healthcare policymakers. This research aimed to study the influence of land use, traffic load, and meteorological factors on the PM2.5 and PM10 concentrations in Bangkok using a land-use regression (LUR) approach. The backward stepwise selection method was applied to select the significant variables to be included in the resultant models. Results showed that the adjusted coefficient of determination of the PM2.5 and PM10 LUR models were 0.58 and 0.57, respectively, which are in the same range as reported in the previous studies. The meteorological variables included in both models were rainfall and air pressure; wind speed contributed to only the PM2.5 LUR model. Further, the land-use types selected in the PM2.5 LUR model were industrial and transportation areas. The PM10 LUR model included residential, commercial, industrial, and agricultural areas. Traffic load was excluded from both models. The root mean squared error obtained by 10-fold cross validation was 9.77 and 16.95 for the PM2.5 and PM10 LUR models, respectively.

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“…Since then, many research groups have tried to monitor and model UFP (mainly PNC) not only in North American and European cities, [21][22][23][24][25][26][27][28][29][30][31] but also in other parts of the world in Australia, [32][33][34] India, 35 China, 36 or Taiwan. 37 To our knowledge, only one spatial model exists for UFP metric LDSA in Switzerland, 25 and no published research to date has develop models for mean particle size in the UFP range. More recently, mobile monitoring using Google Street View cars with intensive repeated day-time on-road measurements during weekdays has been used to develop novel models for UFP.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Ultrafine Particles in Copenhagen, Denmark

Amini

2023

Preprint

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Long-term mean ambient particle size (PS) in the ultrafine particle (UFP) range (< 100 nm) varies over space within cities, with locations near UFP sources having smaller PS. Spatial models for PS and lung deposited surface area (LDSA) within urban areas are limited. We collected particle number concentration (PNC), LDSA, and PS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 5 curbside monitors within the city. We developed 94 predictor variables (majority at 1 m spatial resolution (90%)), and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and PS. The annual mean PNC, LDSA, and PS were, respectively, 5,416 pt/cm3, 12.0 µm2/cm3, and 46.1 nm. The cross-validation R2 values (10-fold repeated 10-times) were 0.70, 0.67, and 0.60 for PNC, LDSA, and PS, respectively. Traffic-related variables, such as streets below/above specific speed-limits, and length of major roads within buffers of 100–150 m, amongst others, were strong predictors. External validation with high-quality data is warranted to ensure good performance of these models. These UFP predictions may assist urban planners, environmental justice studies, or epidemiologists conducting population-based studies.

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Development of land-use regression models to estimate particle mass and number concentrations in Taichung, Taiwan

Cited by 9 publications

References 46 publications

Land Use Regression Models for Particle Number Concentration and Black Carbon in Lanzhou, Northwest of China

Land Use Regression Models for Particle Number Concentration and Black Carbon in Lanzhou, Northwest of China

Influence of Land Use and Meteorological Factors on PM2.5 and PM10 Concentrations in Bangkok, Thailand

Machine Learning Models for Ultrafine Particles in Copenhagen, Denmark

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