Model-based dense air pollution maps from sparse sensing in multi-source scenarios

With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring.

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Effect of air pollution on lung cancer: a Poisson regression model based on vital statistics.

Environmental Health Perspectives ◽

10.1289/ehp.94102s841 ◽

1994 ◽

Vol 102 (suppl 8) ◽

pp. 41-45 ◽

Cited By ~ 14

Author(s):

T Tango

Keyword(s):

Lung Cancer ◽

Air Pollution ◽

Regression Model ◽

Poisson Regression ◽

Vital Statistics ◽

Poisson Regression Model ◽

Model Based

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Clustering the Concentrations of PM10 and O3: Application of Spatio-temporal Model-based Clustering

10.21203/rs.3.rs-264277/v1 ◽

2021 ◽

Author(s):

parisa saeipourdizaj ◽

saeed musavi ◽

Akbar Gholampour ◽

parvin sarbakhsh

Keyword(s):

Air Pollution ◽

Mixture Model ◽

Average Concentration ◽

Optimal Number ◽

Number Of Clusters ◽

Model Based Clustering ◽

Model Based ◽

Spatio Temporal ◽

Temporal Dimensions ◽

Optimal Number Of Clusters

Abstract Air pollution data are large-scale dataset which can be analyzed in low scales by clustering to recognize the pattern of pollution and have simpler and more comprehensible interpretation. So, this study aims to cluster the days of year 2017 according to the hourly O3 and PM10 amounts collected from four stations of Tabriz by using spatio-temporal mixture model-based clustering (STMC). Besides, mixture model-based clustering with temporal dimension (TMC) and mixture model-based clustering without considering spatio-temporal dimensions (MC) were utilized to compare with STMC. To evaluate the efficiency of these three models and obtain the optimal number of clusters in each model, BIC and ICL criteria were used. According to BIC and ICL, STMC outperforms TMC and MC. Three clusters for O3 and four clusters for PM10 were selected as the optimal number of clusters to fit STMC models. Regarding PM10, the average concentration was the highest in cluster 4. Regarding O3, all summer days were in cluster 3 and the average concentration of this cluster was the highest. Cluster 2 had the lowest concentration with a high difference from clusters 1 and 3 and its average temperature was the lowest. Autumn days make up about 84% of this cluster. The clustering of polluted and clean days into separate groups and observing the effect of meteorological factors on the amount of concentration in each cluster clearly prove the efficiency of the model. Results of STMC showed that efficiency of clustering in air pollution data increases by considering both spatio-temporal dimensions.

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Development and intercity transferability of land-use regression models for predicting ambient PM10, PM2.5, NO2 and O3 concentrations in northern Taiwan

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-5063-2021 ◽

2021 ◽

Vol 21 (6) ◽

pp. 5063-5078

Author(s):

Zhiyuan Li ◽

Kin-Fai Ho ◽

Hsiao-Chi Chuang ◽

Steve Hung Lam Yim

Keyword(s):

Land Use ◽

Air Pollution ◽

Air Pollutants ◽

Measurement Data ◽

Air Pollutant ◽

Population Exposure ◽

Land Use Regression ◽

Suspended Particulates ◽

Model Based ◽

Northern Taiwan

Abstract. To provide long-term air pollutant exposure estimates for epidemiological studies, it is essential to test the feasibility of developing land-use regression (LUR) models using only routine air quality measurement data and to evaluate the transferability of LUR models between nearby cities. In this study, we developed and evaluated the intercity transferability of annual-average LUR models for ambient respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in the Taipei–Keelung metropolitan area of northern Taiwan in 2019. Ambient PM10, PM2.5, NO2 and O3 measurements at 30 fixed-site stations were used as the dependent variables, and a total of 156 potential predictor variables in six categories (i.e., population density, road network, land-use type, normalized difference vegetation index, meteorology and elevation) were extracted using buffer spatial analysis. The LUR models were developed using the supervised forward linear regression approach. The LUR models for ambient PM10, PM2.5, NO2 and O3 achieved relatively high prediction performance, with R2 values of > 0.72 and leave-one-out cross-validation (LOOCV) R2 values of > 0.53. The intercity transferability of LUR models varied among the air pollutants, with transfer-predictive R2 values of > 0.62 for NO2 and < 0.56 for the other three pollutants. The LUR-model-based 500 m × 500 m spatial-distribution maps of these air pollutants illustrated pollution hot spots and the heterogeneity of population exposure, which provide valuable information for policymakers in designing effective air pollution control strategies. The LUR-model-based air pollution exposure estimates captured the spatial variability in exposure for participants in a cohort study. This study highlights that LUR models can be reasonably established upon a routine monitoring network, but there exist uncertainties when transferring LUR models between nearby cities. To the best of our knowledge, this study is the first to evaluate the intercity transferability of LUR models in Asia.

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