Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Zhang, Xiangxue; Lin, Yan; Cheng, Changxiu; Li, Junming

doi:10.3390/ijerph18126261

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…The PM < 2.5 microns in aerodynamic diameter (PM2.5) concentration data from 2005–2018 analyzed in this study were collected from the Atmospheric Composition Analysis Group of Dalhousie University [ 10 ], which has great accuracy and reliability as it had been corrected with global station-based observation values based on the geographically weighted regression model [ 11 ], with an R 2 value of 0.817 and had been used in many studies [ 12 – 14 ]. The assessment of exposure to PM2.5 in this study was evaluated and the resulting annual mean PM2.5 concentrations were consistent with in-of-sample cross-validation observations ( R 2 = 0.722).…”

Section: Methodsmentioning

confidence: 99%

Lung cancer risk and exposure to air pollution: a multicenter North China case–control study involving 14604 subjects

Shi

Liang

et al. 2023

BMC Pulm Med

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Background For North Chinese lung cancer patients, there is limited study on the distribution of air pollution and smoking related features based on analyses of large-scale, high-quality population datasets. The aim of the study was to fully analyze risk factors for 14604 Subjects. Methods Participants and controls were recruited in 11 cities of North China. Participants’ basic information (sex, age, marital status, occupation, height, and weight), blood type, smoking history, alcohol consumption, history of lung-related diseases and family history of cancer were collected. PM2.5 concentration data for each year in each city of the study area from 2005 to 2018 were extracted based on geocoding of each person's residential address at the time of diagnosis. Demographic variables and risk factors were compared between cases and matched controls using a univariate conditional logistic regression model. Multivariate conditional logistic regression models were applied to estimate the odds ratio (OR) and 95% confidence interval (CI) for risk factors in univariate analysis. The nomogram model and the calibration curve were developed to predict lung cancer probability for the probability of lung cancer. Results There was a total of 14604 subjects, comprising 7124 lung cancer cases and 7480 healthy controls included in the study. Marital status of unmarried persons, people with a history of lung-related disease, corporate personnel and production /service personnel were protective factors for lung cancer. People younger than 50 years old, people who were smoking and quit smoking, people who had been drinking consistently, people with family history of cancer and PM2.5 exposure were proven to be a risk factor for lung cancer. The risk of lung cancer varied with sex, smoking status and air pollution. Consistent alcohol consumption, persistent smoking and smoking quit were risk factors for lung cancer in men. By smoking status, male was risk factor for lung cancer in never smokers. Consistent alcohol consumption added risk for lung cancer in never smokers. The combined effects of PM2.5 pollution exposure and ever smoking aggravated the incidence of lung cancer. According to air pollution, lung cancer risk factors are completely different in lightly and heavily polluted areas. In lightly polluted areas, a history of lung-related disease was a risk factor for lung cancer. In heavily polluted areas, male, consistent alcohol consumption, a family history of cancer, ever smokers and smoking quit were all risk factors for lung cancer. A nomogram was plotted and the results showed that PM2.5 was the main factor affecting the occurrence of lung cancer. Conclusions The large-scale accurate analysis of multiple risk factors in different air quality environments and various populations, provide clear directions and guidance for lung cancer prevention and precise treatment.

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Section: Methodsmentioning

confidence: 99%

Lung cancer risk and exposure to air pollution: a multicenter North China case–control study involving 14604 subjects

Shi

Liang

et al. 2023

BMC Pulm Med

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“…Among them, Q = q(X1)∩q(X2), X = q(X1) + q(X2), where q(X1) and q(X2) are the influencing factors of the spatial differentiation of sports tourism resources in China. Both Chi et al [22] and Zhang et al [17] used Geodetector to study the similarity between the independent variable and the dependent variable in the spatial distribution to understand whether different influencing factors have an interactive effect on the spatial distribution.…”

Section: Entropy Methodsmentioning

confidence: 99%

“…The value range of Moran's I is [−1, 1]: Moran's I > 0 indicates a positive spatial correlation phenomenon, Moran's I < 0 indicates a negative correlation phenomenon, and Moran's I = 0 indicates an independent random distribution [16]. Zuo et al [7] and Zhang et al [17] used global Moran's I to calculate the Moran's I value of the research elements on a continuous spatial scale to explore the strength of the spatial correlation of the research elements and their changes with the spatial scale.…”

Section: Kernel Densitymentioning

confidence: 99%

Spatial Distribution Pattern and Influencing Factors of Sports Tourism Resources in China

Zuo

Chen

Pan

et al. 2021

IJGI

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Sports tourism is an emerging tourism product. In the sports and tourism industry, resource mining is the foundation that provides positive significance for theoretical support. This study takes China’s sports tourism boutique projects as the study object, exploring its spatial distribution pattern through the average nearest neighbor index, kernel density, and spatial autocorrelation. On the strength of the wuli–shili–renli system approach, the entropy value method and geographic detector probe model are used to identify the driving factors affecting the spatial distribution pattern. Findings reveal the following: (1) From 2013 to 2014, the sports tourism resources in China present a distribution pattern with the Yangtze River Delta urban agglomeration as the high-density core area and the Guizhou–Guangxi border area and the western Hubei ecological circle as the sub-density core areas. (2) From 2014 to 2018, China’s sports tourism boutique projects increased by 381, and the regional differences among various provinces tended to converge. The high-density core area remained unchanged. The sub-density cores are now the Yunqian border area of the Karst Plateau, the Qinglong border area of the Qilian Mountains, and the Jinji border area of the Taihang Mountains, shaping the distribution trends of “depending on the city, near the scenery” and “large concentration, small dispersion”. (3) The proportion of provincial sports tourism development classified as being in the coordinated stage is 61.29%. (4) The explanatory power of the factors affecting the spatial layout in descending order is natural resource endowment, sports resource endowment, transportation capacity, industrial support and guidance, market cultivation and development, people’s living standards, software and hardware services, and economic benefit effects. The explanatory power of the interaction of two different factors is higher than that of the single factor.

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“…The PM <2.5 microns in aerodynamic diameter (PM2.5) concentration data from 2005-2018 analyzed in this study were collected from the Atmospheric Composition Analysis Group of Dalhousie University 10 , which has great accuracy and reliability and has been used in many studies [11][12][13] . Information on air pollution measures (PM2.5) concentration data for each year in each city of the study area from 2005 to 2018 were extracted based on geocoding of each person's residential address at the time of diagnosis.…”

Section: Data Collection and Measurementsmentioning

confidence: 99%

Lung Cancer Risk and Exposure to Air Pollution: A Multicenter North China Case-Control Study Involving 14604 Subjects

Shi

Liang

et al. 2023

Preprint

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Background:For North Chinese lung cancer patients, there is limited study on the distribution of air pollution and smoking related features based on analyses of large-scale, high-quality population datasets. The aim of the study was to fully analyze risk factors for 14604 Subjects. Methods: Participants and controls were recruited in 11 cities of North China. Participants’ basic information (sex, age, marital status, occupation, height, and weight), blood type, smoking history, alcohol consumption, history of lung-related diseases and family history of cancer were collected. PM2.5 concentration data for each year in each city of the study area from 2005 to 2018 were extracted based on geocoding of each person's residential address at the time of diagnosis. Demographic variables and risk factors were compared between cases and matched controls using a univariate conditional logistic regression model. Multivariate conditional logistic regression models were applied to estimate the odds ratio (OR) and 95% confidence interval (CI) for risk factors in univariate analysis. The nomogram model and the calibration curve were developed to predict lung cancer probability for the probability of lung cancer. Results: There was a total of 14604 subjects, comprising 7124 lung cancer cases and 7480 healthy controls included in the study. Marital status of unmarried persons, people with a history of lung-related disease, corporate personnel and production /service personnel were protective factors for lung cancer. People younger than 50 years old, people who were smoking and quit smoking, people who had been drinking consistently, people with family history of cancer and PM2.5 exposure were proven to be a risk factor for lung cancer. The risk of lung cancer varied with sex, smoking status and air pollution. Consistent alcohol consumption, persistent smoking and smoking quit were risk factors for lung cancer in men. By smoking status, male was risk factor for lung cancer in never smokers. Consistent alcohol consumption added risk for lung cancer in never smokers. The combined effects of PM2.5 pollution exposure and ever smoking aggravated the incidence of lung cancer. According to air pollution, lung cancer risk factors are completely different in lightly and heavily polluted areas. In lightly polluted areas, a history of lung-related disease was a risk factor for lung cancer. In heavily polluted areas, male, consistent alcohol consumption, a family history of cancer, ever smokers and smoking quit were all risk factors for lung cancer. A nomogram was plotted and the results showed that PM2.5 was the main factor affecting the occurrence of lung cancer. Conclusions:The large-scale accurate analysis of multiple risk factors in different air quality environments and various populations, provide clear directions and guidance for lung cancer prevention and precise treatment.

show abstract

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Cited by 9 publications

References 51 publications

Lung cancer risk and exposure to air pollution: a multicenter North China case–control study involving 14604 subjects

Lung cancer risk and exposure to air pollution: a multicenter North China case–control study involving 14604 subjects

Spatial Distribution Pattern and Influencing Factors of Sports Tourism Resources in China

Lung Cancer Risk and Exposure to Air Pollution: A Multicenter North China Case-Control Study Involving 14604 Subjects

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